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The integration of electric vehicles (EVs) into power systems worldwide will be challenged in many locations by grid constraints, such as load shedding in developing countries or active network management in developed countries. Nevertheless, an opportunity exists for EV fleets to alleviate financial and operational repercussions of these constraints while reducing carbon emissions through integrated photovoltaic-energy storage-charging station (PV-ES-CS) systems. However, the effectiveness of PV-ES-CS systems depends on the load they serve, and many commercial fleets may only partially electrify their vehicle stock, thus limiting their energy demand. Consequently, there is a need for methodologies that incorporate grid constraints into the design and techno-economic assessments of PV-ES-CS systems, alongside an understanding of the impact of varying levels of fleet EV penetration on system performance. We develop a novel methodology that incorporates grid constraints into a PV-ES capacity optimization model, and investigate the impacts of optimistic and conservative grid constraint scenarios and different degrees of fleet EV penetration on PV-ES-CS system performance through a case study of a paratransit fleet in Dobsonville, South Africa. To determine the most cost-effective design of the systems, the PV and ES components are optimized to maximize net present value (NPV). Three generalized insights emerge from the analysis: The first relates to the importance of timing PV-ES investments based on the planned fleet electrification strategy. The second sheds light on the sensitivity of PV-ES system performance to grid constraint frequency and forecasting, emphasizing the need for nuanced understanding and adaptive strategies in regions with pervasive grid constraints. The third explores the influence of trickle charging on system feasibility and economic performance, particularly at lower levels of EV penetration. Enhanced utilization of trickle charging emerges as a promising avenue to improve the economic viability of PV-ES systems, especially for fleets with morning and evening peak loads. Collectively, these insights contribute to advancing the understanding of renewable energy integration in commercial fleet operations in grid and capital-constrained environments. Moreover, the methodology provides a robust framework for fleet owners in these environments to optimally plan PV-ES investments, fostering informed decision-making and propelling the transition towards sustainable and economically efficient transportation. • Novel method for integrating grid constraints in solar-battery capacity optimization. • Analyzes impact of varying levels of energy supply via fleet EV penetration on system performance. • Investigates effect of intelligent battery management on system performance. • Investigates effects of feed-in-tariff and uncertain grid forecasting. • First techno-economic assessment of solar-battery charging station for paratransit. [ABSTRACT FROM AUTHOR]

Reducing energy consumption from buildings' operations is a sine-qua-non for achieving a future with net-zero emissions. Could urban trees play a role? Several observational and simulation studies demonstrate the effect of vegetation on reduced energy consumption and this study confirms it, this time looking at densely populated areas in diverse climate zones. A comprehensive multi-year monthly electricity consumption data set representative of all of China, together with high-resolution NDVI (Normalized Difference Vegetation Index) data, reveal the relationship between an urban area's greenness and its residents' domestic electricity consumption. Findings show that for every 0.1 increase in the cities' NDVI, urban per-capita residential electricity consumption decreases by 1.76% (95% CI: 1.11–2.42%). This cut in electricity consumption results in life-cycle power-sector CO 2 annual emissions reductions of 23.05–45.52 Mt., equivalent to 2.85–5.63% of China's total residential sector CO 2 emissions in 2020, depending on assumptions on urban population's size and electricity sector's carbon intensity. Moreover, these cities' trees can store 247–284 Mt. CO 2 over 30 years, so the present value of the net benefits, equal to monetized electricity savings plus carbon emissions' reductions and carbon storage minus tree planting and maintenance costs, is positive for 29 Chinese provinces (except Chongqing and Xizang) ranging between US$-0.84 and US$21,000 per tree (−6–156,485 yuan/tree). If not planted exclusively in urban areas but all over China, a carbon price of 267–1339 yuan/ton is needed to ensure the benefits cover the costs of planting and maintaining the trees. • Trees reduce residential electricity consumption and store carbon as they grow. • A 0.1 increase in NDVI saves 1.11%- 2.46% residential electricity consumption. • We quantify trees' costs, CO 2 emissions reductions, and trees' CO 2 storage. • Benefits outweigh city tree planting/maintenance costs in all but two provinces. • Increasing China's NDVI (not just in cities) is cost-effective with low carbon tax. [ABSTRACT FROM AUTHOR]

Hybrid photovoltaic (PV) - wind power plants (HyPPs), i.e., where the PV and wind systems are co-located and share the Point of Interconnection (POI) with the grid, have recently attracted more attention. This trend is driven by the expected reduced capital and operational expenditures achieved through, e.g., shared land and POI infrastructure for HyPPs compared to two individual PV and wind installations. However, if the POI is underdimensioned relative to the PV and wind capacities, the generation from either or both of the assets must at times be curtailed, unless mitigated by solutions like energy storage. This curtailment might lead to income losses. Moreover, as HyPPs typically are designed using wind and PV generation data on hourly resolution, the actual curtailment losses can be underestimated. This might in turn lead to HyPP designs which are techno-economically sub-optimal. In this study, a comparative analysis is conducted to analyze how the curtailment and income loss estimations for HyPPs, as well as the techno-economic optimal HyPP topologies, are impacted by varying the input data time resolution. One year of site measured PV and wind power generation data on 5 s resolution from an existing HyPP located in Eastern Germany are used as basis for the study. For a HyPP topology with an undersized POI, where the installed capacities of the POI, PV, and wind systems are all equal, the curtailment losses are estimated to be 1.45%, 1.43% and 1.09% using the 5 s, 1 min and 1 h resolution datasets, respectively. Moreover, using the 1 h instead of the 1 min dataset leads to a 1.86% overestimation of the total Net Present Value (NPV) for this HyPP topology. As the shares of the PV and wind systems increase relative to the POI capacity, the differences in the estimation of the curtailment losses and NPVs between the high- and low-resolution datasets become more significant. [Display omitted] • High-resolution time series data from an existing PV–wind hybrid plant is analyzed. • The effects of data resolution on curtailment losses are demonstrated. • Hourly data underestimates losses for systems oversized relative to grid connection. • The data resolution is not found to greatly affect the optimal system topology. • Hourly data overestimates the net present values of the optimized topologies. [ABSTRACT FROM AUTHOR]

Food waste is a significant issue in many developed and emerging countries and has significant environmental, social, and economic impacts. As China's urban population increases and restaurants expand, so does food wastage, resulting in many environmental and social issues. Therefore, this study investigates how the economic feasibility and environmental benefits of energy generation using biogas from food waste in Jiangsu Province can help achieve sustainable development goals and China's 14th Five-Year Plan. This study used Buswell's formulation, mathematical formulation, and economic metrics methods to analyze data from 2004 to 2020. The findings of the study are as follows: 1. The results show that 109.1 Mt. of food waste was produced in Jiangsu Province. 2. The total amount of electricity production potential from biogas yield during the 17 years is 16,126.0 GWh. 3. The study revealed that 3090.32 million tons (3.43% of the coal employed for energy production during the period) of coal combustion could be saved. 4. This prevented the release of 7262.26 million kilograms of carbon dioxide, 149.49 million kilograms of nitrous oxide, and 996.63 million kilograms of methane emissions. 5. The avoided coal consumption also helped reduce global warming by 76,727.33 kt of CO 2 equivalent. 6. The project is economically beneficial with a shorter net present value (NPV) for 1 year, a positive NPV (US$34.7 Million), an internal rate of return (23%), a return on investment (33.0%), and a lower levelized cost of energy (US$ 0.039/kWh). Policy recommendations are also discussed. • Electricity production potential from food waste in Jiangsu is analyzed. • The economic feasibility and environmental impacts were assessed. • Biogas derived from food waste has a high electricity generation potential. • Food waste to energy project is economically viable. • Food waste-to-energy projects can save coal combustion. [ABSTRACT FROM AUTHOR]

Solar photovoltaic generation has become the dominant global method of producing renewable electricity around the globe. However, solar PV farms require a considerable amount of land. Agrivoltaics has been a promising field of interest recently as this system maximizes the land's utilization by producing crops beneath the photovoltaics panels. This paper proposes a new agrivoltaics system that simultaneously produces crops and electrical power by installing PV panels on agricultural land in such a way that the production of regular crops does not get curtailed and can still grow around and beneath the panels, avoiding any reduction in crop yield. The architectural design of the proposed mounting structure and the installation method are discussed, ensuring full utilization of the land area under the panel with no crop limitations. Bangladesh is considered a case study location as its economy is mostly dependent on agriculture. The country started allocating enormous amounts of farmland for solar photovoltaic farms to mitigate the energy crisis. After a preliminary survey, an agrivoltaics system was designed, developed and installed in the Chuadanga District of Bangladesh. Then a detailed techno-economic analysis was performed to evaluate the feasibility and economic viability of the implemented agrivoltaics project. A comparative analysis of seven different scenarios is demonstrated in terms of equity payback, internal rate of return, modified internal rate of return, net present value, annual life cycle savings, and benefit-cost ratio to determine the optimum agrivoltaics approach as well as to showcase the superiority of the proposed system. The results demonstrate that the proposed agrivoltaics system achieves full land utilization, by producing crops along with electricity generation with the lowest payback period, highest profit margin, and highest benefit-cost ratio over the project lifetime. • An innovative Agrivoltaic (AV) approach for simultaneous food and energy production on the same land. • A technique to minimise the shading effect on the crop yield ensures full farmland utilization. • A quantitative techno-economic analysis for evaluating profit margin and energy balance. • Reduced payback period (3 years) compared to typical PV system (5–6 Years). [ABSTRACT FROM AUTHOR]

District cooling systems offer a promising alternative to conventional cooling, thanks to overall larger efficiencies and reduced carbon footprint. On the other hand they are characterized by large capital and operational costs that impede their diffusion. Optimizing the design and operation of these systems is therefore fundamental to fully exploit their potential. This paper proposes a novel optimization framework for the simultaneous optimization of design and operation of these systems. The model integrates a genetic algorithm at a master level with Mixed Integer Linear Programming models at a lower level. This approach enables the optimization of various aspects, including network topology, plant locations, supply temperatures and the selection of thermal storage technology. The study showed that in the specific case of Singapore, where the cost for space occupancy is significant, latent heat thermal storages are preferred. In addition, the results highlighted the cost benefits of thermal storage, as district cooling networks lacking it can be from 6.2% to 20.8% more expensive, depending on the scenario. Furthermore, the study demonstrated that the utilization of waste heat through absorption chillers enhances the economic feasibility of district cooling, lowering the payback time by up to 5 years. Lastly, it was observed that 3 °C increase of the indoor set point temperature could reduce the payback time by 3 years and increase the final net present value by 43%, as larger network supply temperatures allow chillers to operate with better performances. The developed model allows to estimate various crucial outcomes with few input parameters, representing a useful tool for both research and planning. • A hierarchical optimization model has been implemented. • NPV can be increased by 43% with an increase of 3 °C of indoor set point temperature. • When waste heat is available, payback time can decrease by up to 5 years. • District cooling without thermal storage is up to 20.8% more expensive. • Electricity cost influences the network topology and storage strategy. [ABSTRACT FROM AUTHOR]

This work provides a comprehensive techno-economic assessment of high-temperature direct air capture (HTDAC) integrated with Fischer–Tropsch synthesis (FTS) for producing renewable jet fuel on a scale of approximately 25 t/h. Specifically, the HTDAC is based on calcium looping employing calcination in a hydrogen-based atmosphere to enable simultaneous RWGS reaction. A detailed description of the Aspen Plus model for HTDAC, alkaline and solid oxide electrolysis systems, RWGS reactor and the FTS is provided for future reference. Five system configurations are analysed, comparing the energy efficiency effects of hydrogen oxy-fuel combustion and electric calcination in a hydrogen-based atmosphere. In this regard, the potential omission of the RWGS reactor is examined, considering that 62 to 78 % of the CO 2 is already converted into CO in the calciner, with the highest conversion rates achieved through electric calcination. Moreover, the study investigates the impact of alkaline electrolysis and solid oxide electrolysis on the system's energy efficiency. Overall carbon efficiencies of more than 50 % in the jet fuel are achieved, with system energy efficiencies ranging from 30.6 to 39.0 % corresponding to fuel-specific energy consumption in the range of 122 to 156 MJ/kg Jet fuel. Net present value (NPV) analysis reveals a projected minimum fuel selling price (MFSP) in the range of 4.45 to 6.32 €/L, which is significantly higher than previous literature estimates based on more optimistic cost assumptions. Uncertainties in cost parameters are addressed through a Monte Carlo analysis which shows the potential of a significant decrease in the MFSP provided that some of the cost estimates will be cheaper than the conservative initial estimate. Conclusively, this study highlights the significance of developing a financially feasible electrically heated calciner that can effectively handle high-temperature hydrogen. Furthermore, maximising the yield of jet fuel emerges as a critical factor for unlocking the future potential of the process, as it substantially reduces the cost of fuel production. Lastly, the reduction of hydrogen production costs is identified as the most crucial aspect in enhancing the economic viability of the process, as it serves as the biggest contributor to the high minimum fuel selling price. • Comparison of 5 integrated direct air capture, Fischer–Tropsch, and electrolysis systems. • Potential integration of H 2 -fired calciner and RWGS in single component is discussed. • Jet fuel energy efficiency up to 39 %; potential for 47 % with lights and waxes. • Minimum jet fuel selling price: 3.5–6.5 €/L with H 2 as main cost contributor. • Electric calcination in H 2 reduces the minimum jet fuel selling price with ∼ 0.6 €/L. [ABSTRACT FROM AUTHOR]

Retrofitting adjacent hydropower plants with pumping stations to construct hybrid pumped storage hydropower (HPSH) plants is an important attempt to promote hydropower flexibility and renewable energy consumption. However, the operation mode and optimal configuration for HPSH and photovoltaic (PV) power plants remain unclear. In this study, based on the evaluation of different energy storage operation modes for HPSH-PV systems, a seasonal joint operation method considering the long-term energy storage of pumping stations for large HPSH plants is developed. The PV plant size thresholds corresponding to different pumping station sizes are evaluated by comparing technical performances. Finally, the potential of hydropower retrofitting to improve PV consumption is analyzed, and the optimal system capacity is determined through techno-economic analysis. A case study on the Wujiang River in China, revealed following results: (1) HPSH plants can effectively reduce energy curtailment and increase power generation benefits. For HPSH formed by retrofitting large cascade hydropower plants, the seasonal energy storage characteristics of pumping stations should be considered to improve the long-term regulation capability; (2) adding a pumping station increases the optimal size and net present value of PV plants. In this case study, with a pumping station to upstream hydropower size ratio of 0.08–1, the optimal PV plant size can be increased by 12.9 % to 50.3 % compared with the conventional hydropower-PV system; (3) the optimal PV plant size is most affected by initial investment costs and electricity price, while the discount rate and electricity price influence pumping station feasibility. • A seasonal storage operation model for cascade hydropower retrofitting is proposed. • The potential of hydropower retrofitting to improve PV consumption is investigated. • The sensitivity of the optimal configuration of pumping stations and PV plants is analyzed. [ABSTRACT FROM AUTHOR]

One approach to reducing atmospheric carbon dioxide (CO 2) emissions is to adopt carbon capture utilization and storage (CCUS) strategies. This is particularly crucial for countries to achieve their net-zero goal. In this study, we investigate the techno-economic viability of a proposed CCUS process that utilizes geologically stored CO 2 , associated with hydrogen (H 2) production from fossil fuels, as a working fluid to extract geothermal energy from deep, large-scale, sedimentary storage formations. The process ensures permanent geological storage of all injected CO 2 while generating adaptable geothermal power through supercritical CO 2 turbine expansion, thus, bolstering revenue and reducing the final cost of blue hydrogen production. We simulate subsurface-wellbore fluid flow and heat transport for a representative 4-way closed anticlinal Arabian reservoir and optimize system power output, including a comprehensive and integrated economic analysis. We find that CO 2 captured from an equivalent blue H 2 production of ∼4.1 Mt./year can be injected at an annual rate of 0.92–1 million metric tons (Mt) per well, resulting in a cumulative sequestration of ∼1.15 gigatons (Gt) of CO 2 over 11.5 years. To mitigate the risks of reaching the formation parting pressure at the crest of the anticline and ensuring sufficient CO 2 saturation at the production wells, phased drilling schedules and pressure-controlled injection and production are essential. With horizontal production wells, our simulations generate an average geothermal net electricity of 164 MW. The base-case economic analysis reveals a Levelized Cost of Electricity (LCOE) of 77 $/MWh and a Net Present Value (NPV) of 480 million USD over 50 years. In contrast, vertical production wells double LCOE, and the project remains unprofitable throughout its life (negative NPV). Our economic sensitivity analysis further emphasizes how the capacity factor, electricity selling price, and drilling costs govern LCOE and NPV. On the other hand, discount rates and Opex fraction are influential yet uncertain parameters affecting the system's techno-economic outlook. Therefore, our study provides valuable insights into the benefits of geothermal energy production from over 1 Gt of stored CO 2 and the associated economic landscape. • A thorough techno-economic analysis of large-scale CO2-based geothermal systems is presented. • Phased drilling schedules and pressure-controlled development strategies offer power generation and economic advantages. • Projected LCOE is 77 $/MWh utilizing horizontal production wells, while vertical production wells double LCOE (infeasible). • A sensitivity analysis on the impact of economic parameters on LCOE and NPV is presented. • We emphasize a synergistic CCUS approach to achieve decarbonization goals, along with its limitations. [ABSTRACT FROM AUTHOR]

This research addresses advancements towards third-generation concentrated solar power (CSP) systems, highlighting the critical need for improved system efficiency through advanced power cycles and optimized waste heat utilisation. By integrating a direct parabolic trough solar collector (PTSC) with a supercritical CO₂ (sCO₂) Brayton cycle and direct contact membrane distillation (DCMD), coupled with a bottoming organic Rankine cycle (ORC), this study proposed a novel solution for the co-generation of power and clean water. Innovative design and optimisation methodologies for the integrated system were introduced, and a comprehensive mathematical model was developed and validated, facilitating a comparative analysis of various ORC fluids' techno-economic performance. The investigation revealed that under baseline conditions, neopentane and isobutane demonstrated slightly better cycle's net-work, whereas toluene showed significantly higher water production due to the higher mass flow rate of seawater needed, resulting from the lower condenser inlet temperature. Furthermore, parametric analysis revealed that varying ambient temperatures resulted in different optimal fluids, with cyclohexane, n-octane, n-nonane, and n-heptane achieving the highest thermal efficiency of around 36.52% at 10 ° C. Additionally, this study highlighted substantial exergy destruction in the DCMD desalination process, accounting for 63.12% of the exergy destruction in the bottoming cycle, predominantly at the membrane where approximately 67% occurred. Moreover, the second compression and expansion stages, especially at higher Direct Normal Irradiance (DNI) levels, contributed the most to exergetic destruction in the topping cycle. Significantly, the double cycle showed exergetic efficiency improvements between 0.12% and 0.35% across different DNI levels while in contrast, single cycles demonstrated marginally superior water production capacity. Economic analyses using bare module costing to assess the levelized cost of electricity and water, along with net present value calculations, revealed that varying ambient temperatures resulted in different optimised fluids. Notably, n-octane achieved the highest net present value, approximately 4.53% above baseline conditions, at an ambient temperature of 10 ° C. Finally, recommendations for organic fluids for each ambient temperature based on techno-economic optimisation were detailed. • Introduces an innovative CSP system integration for power and water co-generation. • Innovative design and optimisation methodologies are introduced. • Comprehensive techno-economic evaluation and optimisation were conducted. • Ambient temperature significantly impacts power and water techno-economic performance. • Recommendation for organic fluids across ambient temperatures is shown [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for strategically placing EV charging stations (EVCS) and capacitors within electrical distribution networks (EDN) to mitigate issues arising from the widespread adoption of Electric Vehicles (EVs). The goal is to maximize the net present value (NPV) by reducing energy losses and minimizing system interruption costs while ensuring reliability. To achieve this, the paper introduces a hybrid optimization technique called HGP_CS, which combines Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search optimization methods. By employing various compensation coefficients to assess failure rates and identify strategies for enhancing NPV, the approach aims to optimize the placement of EVCS and capacitors within the EDN. Validation is carried out in IEEE 33-bus and 118-bus systems to demonstrate the performance of the proposed approach. It is found that the proposed approach, compared to the base case, reduced the energy loss cost by 34.19% and 31.57% and also the Expected Interruption Costs (EIC) by 17.26% and 13.41%, for IEEE 33-bus and 118-bus system, respectively. Additionally, the proposed method is economically beneficial as it can attain higher profits. In IEEE 33- bus and 118 bus systems, the annual net profit using the proposed approach is 36,805 $/year and 271,670.72 $/year, respectively. Thus, the proposed technique enhances the overall system performance and significantly boosts profitability, making it a compelling choice for optimizing the placement of charging stations and reactive power sources in networks. • Investigates EV charging impact on EDNs, focusing on voltage profile and power losses. • Proposes an NPV-based objective function, balancing energy loss, EIC, and installation costs. • Aims to maximize NPV while ensuring network reliability with optimal EVCS/capacitor placement. • Introduces HGP_CS: a novel hybrid algorithm combining GWO, PSO, and Cuckoo Search. • Solves optimal placement and sizing of EVCS and capacitors in the IEEE 33 and 118 bus system. • The proposed HGP_CS consistently outperformed other methods in achieving objectives. [ABSTRACT FROM AUTHOR]

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value. In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M ± 1.09M USD and 104.99M ± 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems. [Display omitted] • Synthetic time series model using Wasserstein metric is proposed. • Proposed model is capable of capturing the underlying statistics of energy price. • A 30-year case study is performed through bi-level capacity and dispatch optimization. • Intra-day price fluctuations prove crucial in maximizing project profitability. • Variable cost of Thermal Energy Storage is critical in determination of system NPV. [ABSTRACT FROM AUTHOR]

The optimal placement of electric vehicle charging infrastructure (EVCI) is critical for meeting the charging demand of battery electric vehicles (BEVs). However, the questions of how to coordinate the stochastic charging demand and placement of charging infrastructure in an economically effective way and how to solve such a problem on a large real-world scale remain unanswered. This study addresses these questions by developing an agent-based charging station placement model (ACPM) that incorporates demand uncertainty by simulating the behaviors of BEV users. A solution algorithm based on random embedding Bayesian optimization (REMBO) is proposed and shown to significantly improve computational efficiency and solve the ACPM on a large real-world scale. The model and algorithm are applied to a case study of the Atlanta metropolitan area. In the study, the algorithm finds an optimal placement using only 2% of the runtime of existing benchmark methods. This enables the use of the algorithm on a more complex, real-world scale. Results from the case study indicate that the optimal placement of EVCI uses level-2 and direct-current fast charging (DCFC) roughly equally and distributes these relatively evenly throughout the region. This optimal EVCI placement has significant economic benefits because it can improve the expected net present value (NPV) by 50% to 100% relative to common benchmark charging placement practices, such as placing the EVCI uniform randomly or weighted by the number of parked vehicles. Finally, a sensitivity analysis demonstrates that factors such as the size of the BEV market, charging preferences, and charging price are shown to have significant impacts on the optimal placement and profitability of an EVCI project. • A novel agent-based charging station placement model. • A highly efficient solution algorithm by random embedding Bayesian optimization. • Simulate individual behaviors and solve charging station placement problems on a large real-world scale. • The profitability of charging stations is sensitive to the placement, market size, and charging behaviors. • Level-2 and direct-current fast charging are equally essential for the success of public charging. [ABSTRACT FROM AUTHOR]

A thermal power plant employing amine-based post-combustion CO 2 capture (PCC) stands as a key technology for low-carbon, stable power sources aiming to economically achieve net zero CO 2 emissions in the electric power sector. However, the high cost associated with amine-based PCC has prevented its swift implementation. This study proposes a novel power-to-heat amine-based PCC system featuring solvent storage tanks. This system provides CO 2 absorption during the operation of the thermal power plant and facilitates CO 2 desorption through a heat pump driven by exceptionally inexpensive electricity procured during peak power generation periods from the grid. To demonstrate this concept, we evaluated the profitability of the proposed system using mathematical modelling and the calculation of the capital expenditure (CAPEX) and fixed operating expenses. The mathematical model optimizes the operational modes of the system facilities based on actual annual electricity prices. The results demonstrated that the novel PCC system decreased the average cost for energy consumption during the CO 2 stripping and compression process per tonne of CO 2 by approximately 30% compared with the conventional, flexible PCC system with venting. Consequently, it increased the net present value by approximately 15%. This showcases the feasibility of the power-to-heat amine-based PCC system in electricity markets prone to extreme fluctuations in electricity prices, where prices plummet during periods of surplus and escalate during peak demand. Notably, results also revealed that despite the low-capacity factor of the power plant (<30%) in such markets, retrofitting an amine-based PCC system into fully depreciated existing natural gas combined cycles yielded positive net present values even without economic incentives like credits for captured and stored CO 2 or grant funding for CAPEX. This suggests potential economic sustainability of the system, even in the imminent rise of inexpensive variable renewable energy. However, the results also indicated that such a low-capacity factor and high PCC CAPEX require high CO 2 price (350–400 USD/t-CO 2) to incentivize carbon capture and storage (CCS) adoption in a power plant in the market. Therefore, reduction in PCC CAPEX should be emphasized as a future challenge in the scenario of swiftly employing thermal power plants equipped with CCS as a dispatchable firm power source. Finally, implementing the power-to-heat amine-based PCC system will bolster power supply reliability and alleviate the curtailment of power generation caused by variable renewable energy sources. [Display omitted] • A power-to-heat amine-based CO 2 capture system with solvent storage is proposed. • Unit commitment was optimized via a numerical model with annual electricity prices. • An Aspen Plus model is employed to simulate the amine-based CO 2 capture process. • The proposed system has a higher net present value than the conventional one. • Thermal power can remain profitable even for CO 2 capture with a low capacity factor. [ABSTRACT FROM AUTHOR]

As building energy consumption and greenhouse gas emissions rise globally, retrofitting existing structures offers a pathway toward more sustainable construction practices. However, balancing improvements to technical performance with financial constraints poses challenges. Specifically, whether coordinated implementation of energy retrofits and on-site photovoltaic generation provides synergistic benefits worthy of initial capital investment remains mostly unclear. To address this, the present study evaluates the economic and energy performance impacts of integrated retrofit and renewable energy integration strategies for a university building in Recife, Brazil. In this study, a calibrated building energy model simulated conservation measures and photovoltaic sizing under varying optimization objectives and sensitivities. The most significant contribution lies in its holistic, location-specific approach to achieving near-zero energy status within the educational facility through optimized retrofit strategies. This includes incorporating real-world metered energy data to calibrate the model and inform cost assessments, enhancing practical relevance. Tabu search optimization identified packages minimizing electricity usage intensity and life cycle costs over 25 years. Results indicate that integrating thermal modeling, economic analysis, and optimization identifies synergistic retrofits and photovoltaic sizing configurations reducing consumption by over 45 MWh/yr while improving net present value by more than $170,000 relative to baseline performance without increased investment. Notably, Case 5 achieved 48.6 kWh/m2.yr intensity and $412,978 net present value, reductions of 11% and a 4% increase, respectively, versus the baseline. Occupancy and HVAC emerged as impactful factors highlighting operational adjustment benefits. Findings support deep retrofits coordinated with on-site renewables as a pathway to cost-effectively decarbonize building stocks. • A optimization model to search energy retrofit with a photovoltaic system was developed. • Passive and active solutions to an educational building were considered and analyzed. • Constructive, climatic, operational and financial aspects were considering in the model. • Sensitivity analysis to find the most sensitive variables was conducted. • An increasing of 2% of the profitability of it using the same initial was observed. [ABSTRACT FROM AUTHOR]

This paper presents a techno-economic model based on experimental and market data able to evaluate the profitability of vanadium flow batteries, which are emerging as a promising technology for specific stationary energy services. Models like this are very informative on the present and perspective competitivity of industrial flow batteries in operating specific services, but they have not yet been developed to an accurate grade. This model uses technical parameters which are taken from large-area multi-cell stacks, rather than from small single cell experiments, to better characterize the behavior of real industrial reactors, and from real financial market and economic patterns of some major manufacturers. The model yields economic performance indicators as the capital cost, the operative cost, the levelized cost of storage and the net present value. A prudential present-state and a perspective analysis are elaborated to show where the economic indicators are heading, and which parameters affect more the investment profitability, thus tracking a possible roadmap for system optimization. Perspective estimations indicate that technological and market evolutions are heading to much more competitive systems, with capital costs down to 260 € kWh−1 at a energy/power duration of 10 h, to be compared with a break-even point in the net present value of 400 € kWh−1, which suggests that flow batteries may play a major role in some expanding markets, notably the long duration energy storage. [Display omitted] • A techno-economic model for vanadium redox flow battery is presented. • The method uses experimental data from a kW-kWh-class pilot plant. • A market analysis is developed to determine economic parameters. • Capital cost and profitability of different battery sizes are assessed. • The results of prudential and perspective analyses are presented. [ABSTRACT FROM AUTHOR]

In a context of massive renovation of residential buildings, stakeholders need decision-support models based on knowledge of the current building stock and accurate simulation of energy demand. This paper presents a new strategy for reducing energy consumption in the building sector, a key factor in combating climate change and promoting sustainability. We introduce an approach to (1) plan retrofits at community level, with a building resolution, for different years of an optimization period and (2) assist local authorities in selecting effective measures to improve the environmental performance of their building stock. The focus is on creating trajectory retrofit plans creation for a building stock with three main retrofit options: improving insulation, heating systems and hot water systems. We adapt a complex but linear approach, a type of problem-solving structure known as a multidimensional multiple-choice knapsack problem, which manages to handle a large number of possible retrofit combinations without becoming unwieldy. The planning process is streamlined as a single-objective optimization task that aims to reduce the total cost of retrofits by reducing their net present value. The efficiency of the model is demonstrated by simulating retrofit scenarios for 4,000 buildings in a French region to prove its ability to tackle large problems. France's targets for decarbonizing the residential sector are taken into account, with a target of reducing GHG emissions by a factor of 10 and a building stock consuming 80kWhEP/m2/year. The results show that these plans are feasible, but that they will require 50% of all buildings to undergo major renovation with abatement costs of around €200/tGES. Our practical application to an actual community demonstrates the model's ability to identify appropriate retrofitting measures and compile building data. • Development of an energy optimization framework integrating a linear simulation model. • Multi-stage optimization generalizing the knapsack problem for building renovation dynamics. • Bridging the gap in energy retrofit strategies by comparing decision-maker and model proposals. [ABSTRACT FROM AUTHOR]

With increasing deployment of offshore wind farms, energy storage systems (ESS) are necessary to balance the renewable energy's intermittency. Instead of independently sizing ESS for an existing renewable energy system (RES), this research highlights the importance of simultaneously sizing all subsystems of an RES. With specific electricity markets in mind and the offshore transmission cable power capacity as a constraint, a model predictive control algorithm is used to maximize the revenue streams for the studied RES. Results demonstrate how the choice of financial metric plays a key role in the outcome: using the levelized cost of energy yields a differently sized RES compared to using the net present value (NPV). In terms of the latter, including a sized ESS in an existing RES increases the NPV significantly. Simultaneously sizing all subsystems of the RES, on the other hand, shows that a 50% increase in NPV can be achieved by deploying a smaller generation subsystem with a larger power capacity ESS, compared to the case in which only the ESS is sized. The introduced sizing approach can be utilized before deploying RES to take their context of operation into account, thereby avoiding the design of suboptimal solutions. • Context and application must be included when sizing an RES to maximize its value. • Sizing the generation and storage systems leads to a fivefold increase in NPV. • The value of storage for an RES depends on proper sizing based on its application. [ABSTRACT FROM AUTHOR]

Enhanced geothermal system (EGS) demonstrates low carbon intensity for supplying electricity but it has not been widely developed commercially. Although the engineering feasibility of different operational strategies has been illustrated, the energy industry is still ambiguous in economic aspects; in particular, incorporating carbon policies into the decision-making process is necessary. In this study, an evaluation framework from technical and economic perspectives considering the impact of carbon credit is introduced for the first time to compare six EGS working schemes including two well configurations and three flow strategies. Afterwards, sensitivity analyses are performed with respect to six technical parameters and four economic constraints. Key findings in this study prove that mixed CO 2 -water horizontal-well-EGS achieves the most profitability with an NPV of $35.3 million. CO 2 -EGS can produce the greatest geothermal power and carbon storage, but the high CO 2 consumption is a major obstacle to achieving profitability. Compared to water-EGS, mixed CO 2 -water EGS shows a greater profit gained the advantage from carbon storage even with less geothermal energy harvesting. Regarding various well types, the horizontal well system can earn more profits than the vertical well system if the thermal breakthrough is not serious. The sensitivity analyses suggest that the parameters that dominate fluid injection amount and flow rate shall be assigned the highest priority. The electricity market price is another critical factor, and a mixed CO 2 -water EGS project is recommended to start according to the rising trend of the electricity sale price. This study provides an informative decision-making strategy for EGS stakeholders. • CO 2 -EGSs show the best performance in power generation but potential negative NPV. • Mixed CO 2 -water horizontal-well-EGS demonstrates the best economic performance. • Fluid injectivity is a critical concern for EGS technically and economically. • Uncertain electricity retail price also affects the profitability of EGS. [ABSTRACT FROM AUTHOR]

Energy sharing between multiple zero-emission buildings is an important energy management strategy that can reduce the negative effects of energy mismatch. This study proposed a cross-harbour nearshore and offshore ocean renewable energy system connected by submarine cables to support two large-scale zero-emission harbour-to-harbour buildings in the Greater Bay Area. Two scenarios named were proposed based on the absence or presence of intermediate submarine cables and the energy sharing between the two regions. Eight representative renewable energy combinations consisting of wind turbines, tidal stream generators, and floating photovoltaic systems were designed to demonstrate the impact of different renewable energy systems on the overall performance. Two integration policies were designed based on the different renewable energy policies of the two regions, and their characteristics were tested. The impact of the submarine cable capacity on the system performance, the possible installation of batteries at offshore sites to replace some of the submarine cable capacity, and the impact of realistic land prices in Hong Kong and Macau on different renewable energy combinations were also investigated. The results showed that the combination with only the nearshore wind turbine system achieved the best economic performance, with relative net present values of 25.63 × 108 and 30.78 × 108 HKD in the two scenarios when the nearshore and offshore sites had equally cheap land prices. When the true land price was introduced, only the combinations with the all-offshore system had positive relative net present values, while the combinations with nearshore systems all had negative relative net present values. Meanwhile, batteries could not replace the role of submarine cables in this system. Installing intermediate submarine cables and sharing energy between the two regions could significantly improve the energy matching by 8 to 21% and economic performance by 4.44 × 108 to 5.30 × 108 HKD of the system. When all submarine cables were maintained at 40% of their maximum capacity, the energy usage ratio could be guaranteed to be 100% without dumped energy. • Nearshore and offshore ocean-energy-supported zero-emission public buildings. • Co-ordinations of energy sharing between cross-harbour zero emission system. • Energy exchange and sharing supported by submarine cables. • Integrated feed-in tariff policy in HK and MO to support energy exchange. • Impact of the realistic land prices on the performance of zero-emission system. [ABSTRACT FROM AUTHOR]

To recover boil-off gas (BOG) and liquefied natural gas (LNG) cold energy, this paper proposes three systems integrating light hydrocarbon separation (LHS), BOG combustion, organic Rankine cycle (ORC) with different structures and natural gas (NG) direct expansion. Both single and multi-objective optimizations are implemented to find the best system performance. Meanwhile, two multi-objective decision methods are also used to refine this work. Single objective optimization depicts that the system with parallel ORC has the highest net electric power (NEP), exergy efficiency, and net present value (NPV) of 6.85 MW, 29.07%, and 31.08 × 107 $ respectively. Multi-objective optimization results indicate the NEP, exergy efficiency, and NPV of the system with parallel ORC still outperform the other two systems. From the energy analysis, it is found that the system with parallel ORC achieves 81.07% utilization of LNG cold energy, which is the highest among the three systems. The exergy analysis indicates that the combustion chamber and heat exchanger (HX) with the highest exergy destruction in any system. Moreover, economic analysis demonstrates that the three systems are more sensitive to fluctuations in interest rates than in electricity prices. • Three systems integrating light hydrocarbon separation, LNG cold energy power generation, BOG combustion are proposed. • Energy, exergy and economic analysis are applied to the systems. • The effects of operating parameters on the system performances are investigated. • Two optimizations are performed using NSGA-II and MOPSO respectively. • The systems are compared from viewpoint of thermodynamic and economic. [ABSTRACT FROM AUTHOR]

Blue hydrogen is viewed as an important energy vector in a decarbonised global economy, but its large-scale and capital-intensive production displays economic performance vulnerabities in the face of increased market and regulatory uncertainty. This study analyses flexible (modular) blue hydrogen production plant designs and evaluates their effectiveness to enhance economic performance under uncertainty. The novelty of this work lies in the development of a comprehensive techno-economic evaluation framework that considers flexible centralised and decentralised blue hydrogen plant design alternatives in the presence of irreducible uncertainty, whilst explicitly considering the time value of money, economies of scale and learning effects. A case study of centralised and decentralised blue hydrogen production for the transport sector in the San Francisco area is developed to highlight the underlying value of flexibility. The proposed methodological framework considers various blue hydrogen plant designs (fixed, phased, and flexible) and compares them using relevant economic indicators (net present value (NPV), capex, value-at-risk/gain, etc.) through a detailed Monte Carlo simulation framework. Results indicate that flexible centralised hydrogen production yields greater economic value than alternative designs, despite the associated cost-premium of modularity. It is also shown that the value of flexibility increases under greater uncertainty, higher learning rates and weaker economies of scale. Moreover, sensitivity analysis reveals that flexible design remains the preferred investment option over a wide range of market and regulatory conditions except for high initial hydrogen demand. Finally, this study demonstrates that major regulatory and market uncertainties surrounding blue hydrogen production can be effectively managed through the application of flexible engineering system design that protects the investment from major downside risks whilst allowing access to favourable upside opportunities. • Techno-economic study of flexible blue H 2 production plants under uncertainty. • Use cases of centralised/decentralised plants for transport around San Francisco. • Fixed, phased, flexible designs analysed using NPV, capex, value-at-risk/gain. • Flexible centralised designs yield better value, despite modularity cost-premium. • Flexibility mitigates market & regulatory uncertainty around global H 2 economy. [ABSTRACT FROM AUTHOR]

A key strategy for the transition towards a low-carbon economy is the electrification of industrial heat. Heat pumps can recover and upgrade excess or waste heat. They present a highly efficient component to decarbonize process heating. In Pinch Analysis, most approaches to design the heat recovery system as well as the utility system are based on a single operating point or a couple of operating point. In the past, this was due to the lack of temporally detailed process data. However, the available process data is expected to increase drastically by the use of transient process simulation models. Thus, a method is needed which interprets the data correctly and assists with design choices. This study proposes a methodology for the design and sizing of a heat pump based on the simulated annual process data of an industrial process. Three approaches are explored: (1) the conventional approach for heat pump integration by application of the Time Average Model (TAM), (2) an approach that investigates the optimal heat pump parameters for each data point by the principles of Pinch Analysis and mathematical optimization and (3) an optimization method, which considers the entire annual process data as well as thermo-economic objectives such as net present value (NPV) and internal rate of return (IRR). The new methodology compared to the conventional TAM approach is able to design a 33 % smaller heat pump, which reduces the annual operating cost by an additional 2.2 %. The NPV and IRR are more than tripled. • Industrial heat pump integration for non-continuous processes. • Dynamic Pinch Analysis. • Heat pump optimization based on simulated, annual process data. • Techno-economic optimization objective for heat pump design. • Large-scale heat pump cost functions. [ABSTRACT FROM AUTHOR]

This study examines the national landscape of hybridized fossil energy (FE) power plants with energy storage (ES) technologies ("ES-FE") and presents the compilation of an ES-FE dataset, which includes over 65 ES-FE projects and concepts in the United States, comprising approximately 500 MWh of co-located ES capacity with FE power plants. This study also estimates the economic feasibility of adding ES to existing FE power plants by characterizing the potential revenues that can be generated by the ES component through flexibility and capacity value. The analysis focuses on ES technologies with 2- to 10-h. durations located in four U.S. independent system operators (ISOs): Midcontinent ISO (MISO), Electric Reliability Council of Texas (ERCOT), PJM Interconnection (PJM), and California ISO (CAISO), which have +70,000 MW of combined FE power capacity that could add ES. Annual revenues are estimated for the ES component using a what-if-analysis approach, for capacity value, price arbitrage, or ancillary services provision. The results show that annual revenues depend on the end-use storage service, wholesale electricity and capacity market prices, and ES technology operation parameters such as discharging duration and cycling frequency. When performing a sensitivity analysis, ES accrues $7–178/kW-yr. via price arbitrage and ancillary services provision in the four ISOs, and $13–92/kW-yr. when providing capacity value only in MISO and PJM. A cash flow analysis is performed to estimate the net present value (NPV) of the ES addition using a range of ES costs. The study finds that for most ES technologies considered, these revenues alone are insufficient to achieve economic feasibility. Of the 1645 total runs analyzed, 115 had positive NPVs (7%). Therefore, other revenue streams or monetizable benefits are necessary to achieve the break-even point. • There are at least 65 ES-FE projects and concepts in the U.S., as of end of 2022. • MISO, ERCOT, PJM, and CAISO have +70,000 MW of FE power units, which could add ES. • ES capacity provision yields $13–92/kW-yr. in PJM and MISO (2019$). • ES stacking services yields $7–178/kW-yr. in MISO, ERCOT, PJM, CAISO (2019$). • Only 115 out of 1645 cases analyzed have positive NPVs. [ABSTRACT FROM AUTHOR]

• A thermodynamic model representative of the GoBiGas process has been built. • One 71 MW SNG bio-SNG plant is the most competitive configuration, LCOE = 86.3 €/MWh. • A two-plant system is the most profitable configuration, NPV = 165.8 M€. • For the optimal configuration, the cost of the bio-SNG varies from 59 to 98 €/MWh. • Incentives from 22 to 66 €/MWh are necessary to break-even the investment. This article presents a novel method for the optimal integration of large-scale bio-SNG (biomass-derived synthetic natural gas) production system into the existing natural gas network. A thermodynamic model representative of a commercial bio-SNG process fed by forestry residues has been built and validated with experimental data. The variation of the capital and operational expenditures as functions of the power output is calculated, identifying the economies of scale of the process. A spatially-explicit model, based on a modified location-allocation algorithm, has been built to find the site and size of the plant(s) that minimise the levelised cost of the energy produced (LCOE). The combined effects of the capital, operational and transport costs resulted in an optimal LCOE of 86.3 €/MWh for a single 71 MW SNG plant. On the other hand, the most profitable configuration has been found for a two-plant configuration, which has a net present value of 165.8 M€ and LCOE of 89.5 €/MWh. The resulting optimal configurations are shown in maps, including the resource harvesting sites and the selected bio-SNG and grid injection points. The mean distance from harvesting sites to the one-plant injection point is approximately 95 km, which means transportation costs, while small are not negligible. A scenario analysis shows the impact of the resource cost and incentives for bio-SNG injection, showing that a low cost of the resource (45 €/t DB) would reduce the one-plant LCOE to 59 €/MWh, which is currently above the cost of natural gas for household consumers, 52 €/MWh. [ABSTRACT FROM AUTHOR]

• A generalizable sorbent-focused economic model of direct air capture. • Estimating the maximum allowable price for sorbents described in the literature. • Sorbent cycle duration and sorbent degradation strongly affect cost of air capture. • Developed a tool for cost-optimization in a multi-parameter system. Direct air capture, the removal of carbon dioxide from air, requires special sorbents, with high capture capacity, fast kinetics and long lifetime. Beyond that they also need to be affordable. However, since air capture is still in an early development stage, costs are still uncertain. We present a techno-economic model to value a sorbent based on the CO 2 market price and the most important sorbent characteristics: cycle time, loading capacity, and rate of degradation. The model gives a net present value equation for any air capture sorbent, whether it uses a moisture swing, pressure swing or thermal swing for regeneration and makes it possible to estimate the maximum allowable budget for any air capture sorbent, i.e., the sorbent value. The analysis aims to focus the rapidly growing field of air capture sorbent development onto the most important parameters. The value of a sorbent is dramatically affected by its longevity. To be economically viable, the typical sorbent must survive tens if not hundreds of thousands of loading and unloading cycles. The model can also be used to investigate the interactions between different sorbent parameters. For example, the correlation between loading capacity and cycle time makes it possible to determine the cycle time that optimizes the economics of an air capture device. Our analysis highlights the significance of some neglected parameters in air capture cost analysis such as cycle duration and stability of the sorbent. Finally, the analysis can also be adapted to post-combustion flue gas sorbents. [ABSTRACT FROM AUTHOR]

• A conceptual framework was designed for a bioenergy system for rural India. • Agro waste potential was estimated through survey. • Techno-economic analysis of an integrated system was conducted. • Uncertainty was assessed through Monte-Carlo simulation. This paper presents a holistic approach to promote bioenergy in India by designing a conceptual framework that combines resource, technology and market. The proposed concept is an attempt to integrate pelletization and gasification technology for bioenergy system development through an end-to-end approach. The potential of bioenergy resource (i.e. agro waste) was estimated based on survey. The study further assessed the economic feasibility of agro waste pelletization. The economic evaluation was made using indicators such as net present value (NPV), internal rate of return (IRR), discounted payback period (DPBP) etc. Pellet plant capacity of 0.5 ton h−1 showed acceptable economics and the NPV , IRR and DPBP were ₹9.35 million ($0.13 million), 41% and 2.8 years, respectively. Moreover, the larger capacity plants (>2 ton h−1) were subjected to more risk under low pellet prices (< ₹5 kg−1 or $71.4 ton−1). The cash flow statement showed a strong debt paying ability for the project. Pellet price was the most sensitive factor followed by annual operating days on pellet plant economics. Monte Carlo simulation predicted an average NPV of ₹9.3 ± 2.0 million ($133.2 ± 29.1 thousand). The economics of fuel pellets utilization in a gasifier for energy applications was also evaluated. The pellet fed gasifier system appeared to be cost competitive with commercial liquefied petroleum gas (LPG) and wood at a pellet price range of ₹6.3–8.8 kg−1 ($90–126 ton−1) in a select scenario. Overall, the designed framework appears to reduce over-dependency on wood or fossil sources, and facilitate bioenergy promotion in rural areas. [ABSTRACT FROM AUTHOR]

• Developed an integrated CO 2 -EOR evaluation model based on historical data. • Add the possible carbon tax into the economic performance assessment. • Conduct six scenarios to present the performance results with the model. • Identify the impact of critical parameters to the economic performance. Carbon dioxide enhanced oil recovery (EOR) has been proved to effectively reduce CO 2 emissions by storing industrial CO 2 underground while increasing oil production. An integrated assessment model was developed to evaluate the technical and economic performance of CO 2 -EOR based on data from more than 40 historical field projects. Unlike previous models, the model comprehensively assesses economic potential and greenhouse gases (GHG) emission of CO 2 -EOR together and is based on a large amount of field data. The technical model estimates incremental oil production based on CO 2 injection. The model also estimates the net CO 2 emissions by considering the emissions from oil production and CO 2 utilization. The economic model simulated the net present value (NPV) of six scenarios under different design requirements. The breakeven oil price for small scale CO 2 pipeline project was $46.4/barrel. The porosity, net pay, CO 2 injection, depth, oil formation factor, and oil saturation are the critical parameters affecting the economic performance based on the sensitivity analysis. The breakeven price of carbon tax scenario was $64.10/barrel, of which CO 2 injection and carbon tax contributed 52% of the NPV variations. The study provides operators, investors, and even policymakers the sufficient and important information to fully understand CO 2 -EOR developments with various external and internal parameters before making the right decisions. [ABSTRACT FROM AUTHOR]

• A surrogate is developed using supervised and transfer learning. • Surrogate model is coupled with the optimization algorithm to design energy systems. • Model adaptation allows the tool to be used even with notable changes in demand and generation. • The computational time can be reduced up to 84%. This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model (AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently, a hybrid optimization algorithm (HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model (trained using supervised learning technique) for different scenarios where solar energy potential, wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10% (with reasonable differences in the decision space variables). HOA can reach to Pareto solutions (similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning (SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions. Therefore, STML can be used along with the HOA, which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale. [ABSTRACT FROM AUTHOR]

• Two-step optimization tool for design and off-design of binary geothermal ORCs. • Brine temperature and electricity price are most-influencing parameters. • Net power output varies from 1.95 MW to 4.74 MW for T env = 29.07 °C to −4.28 °C. • Data reduction and splines reduce off-design calculation time by a factor of 88. • Optimal ORC design for a given location, including off-design performance. In this paper, a two-step optimization methodology for the design and off-design optimization of low-temperature (110–150 ° C) geothermal organic Rankine cycles (ORCs) is proposed. For the investigated conditions—which are based on the Belgian situation—we have found that the optimal ORC design is obtained for design parameter values for the environment temperature and for the electricity price which are both higher than the respective yearly-averaged values. However, the net present value is negative (−12.62 MEUR) which indicates that the low-temperature (130 ° C) geothermal electric power plant is not economically attractive for the investigated case. Nevertheless, and demonstrated by the results of a detailed sensitivity analysis, a low-temperature geothermal power plant might be economically feasible for geological sites with a higher brine temperature or in a country with a more favorable economic situation; e.g., with higher electricity prices (∼ 70 EUR/MWh). The novelty of our paper is the development of a thermoeconomic design optimization strategy for low-temperature geothermal ORCs, accounting for the off-design behavior already in the design stage. The generic methodology is valid for low-temperature geothermal ORCs (with MW scale power output) and includes detailed thermodynamic and geometric component models, is based on hourly data rather than monthly-averaged data and accounts for economics. [ABSTRACT FROM AUTHOR]

• Techno-economic analysis of battery storage systems providing frequency control. • A stochastic, data-driven optimisation algorithm to optimise the battery controller. • Use of frequency data and detailed, yet computationally efficient battery models. • Case study of Germany shows the highest margins for a battery rated at 1.6 MW/1.6 MWh. • Calendar ageing drives battery degradation, while cycle ageing has less impact. Optimal investment in battery energy storage systems, taking into account degradation, sizing and control, is crucial for the deployment of battery storage, of which providing frequency control is one of the major applications. In this paper, we present a holistic, data-driven framework to determine the optimal investment, size and controller of a battery storage system providing frequency control. We optimised the controller towards minimum degradation and electricity costs over its lifetime, while ensuring the delivery of frequency control services compliant with regulatory requirements. We adopted a detailed battery model, considering the dynamics and degradation when exposed to actual frequency data. Further, we used a stochastic optimisation objective while constraining the probability on unavailability to deliver the frequency control service. Through a thorough analysis, we were able to decrease the amount of data needed and thereby decrease the execution time while keeping the approximation error within limits. Using the proposed framework, we performed a techno-economic analysis of a battery providing 1 MW capacity in the German primary frequency control market. Results showed that a battery rated at 1.6 MW, 1.6 MWh has the highest net present value, yet this configuration is only profitable if costs are low enough or in case future frequency control prices do not decline too much. It transpires that calendar ageing drives battery degradation, whereas cycle ageing has less impact. [ABSTRACT FROM AUTHOR]

Highlights • Assessment of co-digestion of milk whey and potato stem was performed for the first time. • Raw material cost was the main economic parameter to define the codigestion viability. • Sensitivity analyses were performed to assess the influence of raw material cost. • Heat and electricity generation through co-digestion was simulated with Aspen Plus. • Digestate valorization increases the viability of heat and electricity generation. Abstract Energy conversion strategies based on lignocellulosic and industrial waste streams is considered a challenge in many countries producing huge quantities of biomass. The production of biogas as an energy vector has been gaining attention in the industry sector due to the energy policies for wastes managements or the feasibility of using the biogas for electricity and steam generation. An interesting feedstock alternative for the biogas production is milk whey, one of the main residues of the dairy industry. Additionally the potato stem generated in the harvest stage can be an attractive raw material for biogas production. Co-digestion is the combination of biodegradable raw materials to improve the balance of nutrients in anaerobic digestion. In this context, the characteristics of milk whey and potato stem are not enough to consider it as a good single substrate. However, the synergetic use of these two residues can represent an improvement in biogas production. The biogas production was calculated in Aspen Plus software using stoichiometric and kinetic models based on the experimental characterization of both materials. Through seven different scenarios: potato stem digestion, milk whey digestion and five co-digestion relations of both materials. Heat and electricity generation using biogas was analyzed. Then the generation of heat and electricity was simulated, where the economic profit was evaluated in terms of the production cost, capital cost, revenues and net present value. In terms of biogas production, the scenarios that involved high organic load were the best. For the economic assessment the raw material cost had the most influence over the total processing cost (80% approximately). However, even if energy is produced it is necessary to include the valorization of the digestate as biofertilizer in order that the different scenarios present economic viability. [ABSTRACT FROM AUTHOR]

• An analytical power-rating method based on techno-economic criterion is proposed. • Optimal power-rated motors work with less energy losses and life-cycle cost. • Techno-economic optimal power-rated motors are 1–2 steps larger than the RMS load. • The proposed method can be used even when the list price is not available. Electric motors are valuable, long-life industrial assets, whose lifespan is often measured in decades. A period of unplanned motor downtime in an industrial plant frequently incurs an expenditure of thousands of euros per hour. As a consequence, plant engineers often focus their decisions on reliability rather than on operating cost (energy bill). Unfortunately, the high price of energy and its growing trend mean that operating cost has an increasingly negative impact on production cost and company competitiveness. The problem of selecting the rated power of a line-operated single-speed electric motor to drive a time-variable mechanical load has traditionally been solved, based on the well-established root mean square (RMS) value of the time-power profile of the mechanical load. This conventional method of rating is strictly technical, since the rated power of the motor is determined, based only on the power profile of the load. No other factors are considered, such as the energy consumption throughout the whole in-service life of the motor, or, even better, the whole life cycle cost (LCC), which is defined as the net present value of purchasing, installing, operating (energy), maintaining and decommissioning the motor throughout its life. As a consequence, conventional rating often leads to the selection of motors with a power rating that is technically sufficient to drive the mechanical load, but insufficient to do so with the lowest possible energy (losses) consumption, or even better, with the lowest possible life cycle cost. A new analytical method to determine the techno-economic optimum power rating of a line-operated single-speed electric motor to drive a time-variable mechanical load will be introduced in this paper. The proposed method takes into account not only the technical restrictions due to the time-power profile of the mechanical load, but also the whole life cycle cost. Based on the list of electric motors offered in the manufacturer's catalogue, the new methodology enables the optimum techno-economic rated power of the motor to be calculated, which minimizes the energy consumption (energy loss) throughout its in-service life or its total life cycle cost. The results show that the optimum techno-economic rating is one or two rated-power levels above the conventional rating based solely on the RMS load. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Method is proposed to evaluate batteries combining applications for consumers. • Electricity demand input data have 1 min resolution across 100 dwellings. • The lithium-ion battery model includes ageing throughout the battery lifetime. • Couple of storage applications are classified as complementary or substitutive. • Results show the importance of the electricity demand for combining applications. Abstract Batteries are expected to play an important role in the transition to decarbonised energy systems by enabling the further penetration of renewable energy technologies while assuring grid stability. However, their hitherto high capital costs is a key barrier for their further deployment. In order to improve their economic viability, batteries could provide several applications offering revenues. The techno-economic evaluation of batteries simultaneously serving several applications has proven to be challenging due to the trade-offs between energy and power applications. Focusing on residential batteries, we develop an optimisation method for designing optimal value propositions and we test it for four different applications both individually and jointly: PV self-consumption, demand load-shifting, avoidance of PV curtailment and demand peak shaving. Our results show that the combination of all applications currently helps batteries to get closer to profitability, from a net present value (NPV) per unit of capital expenditure (CAPEX) of −0.63 ± 0.04 for PV self-consumption only to −0.36 ± 0.10, with the combination of demand peak-shaving and PV self-consumption adding most value (0.21 ± 0.04). We also find that the annual household's electricity consumption determines the value of energy storage applications. The proposed method allow us to classify storage applications as complementary and substitutive depending on whether their combined application increases their economic attractiveness or not. These results thus offer valuable insight for stakeholders interested in the deployment of energy storage in combination with energy efficiency, heat pumps and electric vehicles such as consumers, utility companies and policy makers. [ABSTRACT FROM AUTHOR]

Highlights • Battery provides pseudo-flexibility by not affecting production processes. • Optimize battery size for peak shaving, arbitrage trading, primary balancing power. • Application to load profiles of 50 German SMEs. • Profitably of revenue streams: not in isolation, but combined for some companies. • Developed load indicators provide general profitability insights. Abstract This paper evaluates the economic potential of energy flexibility in 50 different German small and medium sized enterprises (SMEs) through the installation of a battery storage system (BSS). The central innovation lies in the possibility of pursuing multiple revenue streams simultaneously: peak shaving, provision of primary control reserve (PCR) and energy-arbitrage-trading through intraday and day-ahead markets. The energy system of an industrial manufacturing plant is modelled as a linear program (LP) with a 15-min resolution. The model offers the option to invest in BSSs with different capacities, with the objective of minimizing the overall cost and identifying the optimal size of the BSS. The results show that none of these three revenue streams individually is economically attractive, but when combined, all three together can achieve profitability for some companies, whereby the majority of the cash flow comes from peak shaving and PCR. With a fixed BSS capacity of 500 kWh, the Net Present Value (NPV) varies from a minimum of −350,000 € for just arbitrage up to about 200,000 € for all three use cases in parallel. In the case of a variable BSS capacity, the capacity varies up to 1200 kWh and the Profitability Index (the ratio of investment to NPV) varies from 0.06 to 0.31. Under current German market conditions, arbitrage trading contributes only marginally to the profitability, as the price spreads are too small to justify stronger battery degradation. The paper also identifies various load indicators from the analysis of the demand profile that support the evaluation of a BSS in industry. A stepwise linear regression reveals a moderate dependency of the BSS profitability on two newly developed load indicators. Future work should focus on a more detailed depiction of the battery's technical behaviour and increasing the sample size to improve the statistical significance of the results. [ABSTRACT FROM AUTHOR]

In complex optimization processes such as CO 2 -water alternating gas (CO 2 -WAG), several iterative steps are needed before finding an optimal or sub-optimal set of solutions. This leads to time-consuming operations, especially when the simulation is run with a compositional simulator. Proxy models have been used to tackle this issue as they can replicate efficiently and accurately reservoir simulators in specific studies. However, the construction of such a proxy model, its basic database in particular, differs according to the designer and the objective function(s). In this study, a hybrid algorithm was designed that combined Artificial Neural Networks (ANN) and NSGA-II to find optimum solutions for a CO 2 -WAG injection process. The proxy model was developed based on a specific sampling method that considered the total injection time of a WAG cycle equal to one year and characterized by a new parameter, gas water injection time ratio (GWITR). The Latin Hypercube Design (LHD) is used to sample the inputs (bottom-hole flowing pressure of the producers, and water and gas injection rates) and the outputs are the cumulative oil recovery factor (FOE) and Net Present Value (NPV). After the proxy model was assessed, it was combined with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find the Pareto Front of the optimum solutions. The developed ANN model was able to predict the two outputs simultaneously with an R2 higher than 0.999 and an MSE of 6.5E-5. The found Pareto front, with good diversity and convergence, provided the operator with various solutions for decision-making. • Hybrid algorithm using ANN and NSGA-II for CO2-WAG optimization • Proxy model developed using LHD sampling for inputs and outputs • Accurate ANN model predicts cumulative oil recovery factor and Net Present Value • Pareto Front development to ease for CO2-WAG process decision-making [ABSTRACT FROM AUTHOR]

The configuration of the hybrid generation systems (HGSs) is critical for maximizing the synergy between different power sources. However, accurately simulating the operation of HGSs over their lifespan is challenging due to the need to balance multiple objectives covering different timeframes. For example, long-term water management and short-term peak-shaving can conflict. To tackle this problem, we propose a comprehensive evaluation framework that optimizes the size of a renewable power plant integrated with an existing hydropower station. This framework combines a long-term and short-term operation model with a techno-economic analysis. Unlike traditional models, our short-term operation model prioritizes power supply quality and quantity while maintaining satisfactory peak-shaving performance for the HGS. We generated numerous daily generation plans using this model to derive the multidimensional response functions, which describe the relationship between long-term hydropower decision, renewable power output and short-term performance indicators. These response functions were then incorporated into a refined long-term operation model to simulate the HGS's extended operation. Finally, we employed a holistic evaluation framework comprising various techno-economic indicators to determine the cost-effective size of the renewable power plant. We applied this framework to the Igong Zangpo River Basin in China, a tributary of the Yarlung Zangbo River. The results revealed the following: (1) Increasing the size of the photovoltaic (PV) component resulted in a decline in technical performance, including energy use efficiency and peak-shaving capabilities; (2) Given sufficient transmission capacity, a PV size threshold of 2400 MW was identified, while the optimal PV size with the highest net present value was 1950 MW, twice the capacity of the hydropower installation (1030 MW); and (3) The optimal PV size varied from 0 to 2400 MW based on electricity price and initial investment factors. Thus, our proposed techno-economic framework effectively guides the capacity configuration of the hydro-based HGSs. • Short-term complementary operation with multi-priority objectives was modelled. • Improved response functions were proposed to bridge long- and short-term operations. • Impact of the new energy consumption on hydropower production was quantified. • Proposes a holistic techno-economic evaluation framework for capacity configuration. [ABSTRACT FROM AUTHOR]

• Solar-assisted DAC system combining condensation heat recovery is initially proposed. • The effects of water on techno-economic performance are conducted. • A optimal relative humidity value occurs for techno-economic assessment. • Techno-economic potential of the proposed system is proved for practical applications. Direct air capture (DAC) has emerged as a promising tool to address the sizeable non-point sources of CO 2 emissions. However, energy penalty of its regeneration process is extremely high due to the low CO 2 concentration in the atmosphere. This paper aims to explore the potential of a temperature vacuum swing adsorption (TVSA) system for DAC which utilizes solar energy and condensation heat recovered from air conditioners. The effect of indoor environmental parameters especially for relative humidity (RH) is targeted for carbon removal processes. It demonstrates that 40% RH is the optimal value with regard to the minimum heat consumption of 12.16 MJ/kg. Then four representative cases with different energy supply methods for the regeneration processes, i.e., solar-assisted condensation heat recovery, solar energy, condensation heat, and electricity, are compared and analyzed in terms of techno-economic aspects to seek the best system configuration. The effects of various indoor environmental conditions, e.g., temperature, RH, and CO 2 concentration are also counted. The results demonstrate that the levelized cost of DAC (LCOD) and the cost calculated via net present value (NPV) increase with the increased temperatures and the reduced CO 2 partial pressure. Due to a tradeoff between the increased adsorption capacity and regeneration heat in the presence of water, a minimum value of economic performance could be obtained at 30% RH. It reveals a novel and cost-effective TVSA combined system for DAC, which may bring more merits to approach carbon mitigation shortly. [ABSTRACT FROM AUTHOR]

• With optimal market strategy and DTR, the annual revenue may be 204% of a base case. • Ideal power forecast may not correspond to the best commitment strategy. • Depending on market prices, NPV may be between −441 million € and +9.5 billion €. • Economic benefits of DTR are estimated between 0.7 and 1 billion euros. • Overplanting and DTR may reduce LCOE by 1 €/MWh. The overplanting of an offshore wind farm (OWF) with dynamic thermal ratings (DTR) is a promising solution for enhancing OWF performance. As the overplanted OWF generates an additional energy and DTR ensures its better transfer, the research question is if final OWF profits would be higher than associated costs. While there has been growing attention on this subject in recent years, there is still no research investigating the commitment strategies of overplanted OWFs with DTR. This paper investigates how commitment strategies may affect the economic performance of an overplanted OWF with DTR. The results show that, depending on the day-ahead commitment strategy, the annual revenue of OWFs may theoretically increase by up to 21% even without overplanting nor DTR, and by up to 204 % with overplanting and DTR. However, although commitment strategies, overplanting and DTR may significantly increase annual revenues of an OWF, its net present value (NPV) still heavily depends on market prices. In the presence of low market prices as in 2018, overplanting actually reduces the NPV of an OWF and, under the conditions considered here, keeps its NPV always negative. On the contrary, in the presence of high market prices as in 2022 or feed-in tariffs, the overplanting increases the OWF NPV. In the latter case, the NPV of the overplanted OWF is estimated between 1.5 billion and 9.5 billion euros while the discounted payback period is 3–12 years. The economic benefits of DTR for the overplanted OWF are estimated between 0.7 and 1 billion euros. The paper also shows that the optimal overplanting rate is very different whether the NPV or LCOE are considered. Finally, the paper shows that committing to the actual OWF power production (i.e. assuming a perfect power forecast) does not necessarily result in the highest revenue. [ABSTRACT FROM AUTHOR]

Farm operations in the USA and Europe have seen a radical change in the last decades: small sized farms are disappearing, and farm size and total livestock on larger farms are increasing. The resulting spatial density of animals causes several environmental impacts. Anaerobic digestion is one promising technical solution to alleviate most of these impacts while simultaneously providing a regional energy source. This analysis assesses the economic viability of using dairy-cow manure for either (i) the on-farm production and use of biogas to generate electricity and heat or (ii) the upgrading biogas to biomethane, a natural-gas substitute. A non-linear optimization model was developed to optimize plant capacity for anaerobic digestion and maximize the net present value for each option by farm size. In this study, we used Idaho‘s dairy farms as a case study. The analysis implies that at least 3000 cows per farm are required for an economically viable anaerobic-digestion plant operation. For farms with up to 3600 animals, the highest net present value was achieved for the on-farm use of biogas. Farms larger than that achieved their best economic results via the production of biomethane. In total about 45% of Idaho’s dairy manure could be utilized by economically feasible biogas and biomethane plants. A higher manure utilization rate could be achieved through joint, cooperative anaerobic digestion plants and manure transportation. The results can be transferred to other regions and countries, respectively, to reduce the negative impact of intensive livestock farming. [ABSTRACT FROM AUTHOR]

A new solution for harvesting energy simultaneously from two different sources of energy by combining geothermal energy production and thermal enhanced heavy oil recovery is introduced. Numerical simulations are employed to evaluate the feasibility of generating energy from geothermal resources, both for thermally enhanced oil recovery from a heavy oil reservoir and for direct heating purposes. A single phase non-isothermal fluid flow modeling for geothermal doublet system and a two-phase non-isothermal fluid flow modelling for water flooding in an oil reservoir are utilised. Sensitivity and feasibility analyses of the synergy potential of thermally-enhanced oil recovery and geothermal energy production are performed. A series of simulations are carried out to examine the effects of reservoir properties on energy consumption and oil recovery for different injection rates and injection temperature. Our results show that total oil production strongly depends on the shape of heat plume which can be affected by porosity, permeability, injection temperature, well spacing and injection rate in the oil reservoir. The favourable oil recovery obtains at high amount of (a) injection rate, (b) injection temperature, (c) porosity and (d) low amount of oil reservoir permeability respectively. Furthermore, our study indicates the wellbore spacing plays an important role in oil recovery and an optimum wellbore spacing can be established. The analyses suggest that the extra amount of oil produced by utilising the geothermal energy could make the geothermal business case independent and may be a viable option to reduce the overall project cost. Furthermore, the results display that the enhance oil productions are able to reduce the required subsidy for a single doublet geothermal project up to 50%. [ABSTRACT FROM AUTHOR]

In 2011 China initiated policies to promote the adoption of solar photovoltaic (PV) using feed-in tariff (FIT) policies. Since then the PV domestic market expanded substantially. In the past six years, the FIT policies were updated (adjustment of tariff levels, division of three FIT regions, setting of installation quotas) to address emerging problems such as PV waste, explosive installation, unbalanced spatial distribution. This paper aims to investigate the historical development and implementation of FIT policies in China from 2011 to 2016. The tools of net present value (NPV)/internal rate of return (IRR), learning curve and the system dynamics are employed to show the degree of economic incentives of FIT policies, to understand the learning rate of centralized PV systems, and to study the dynamic mechanism of the FIT system. We conclude that in the near term the tariff levels should be adjusted more frequently to keep IRR values in the range of 8–12%, and a tight quota combined with the deployment of ultra-high voltage (UHV) lines should be continued for the provinces with severe PV waste. [ABSTRACT FROM AUTHOR]

The mobilized thermal energy storage (M-TES) system is a promising alternative to conventional heating systems to meet the heat demand for distributed users. This paper provided a techno-economic assessment of the M-TES system based on a case study in China. According to the analysis of the design specifications of the heating system, the suitability of matching the M-TES with existing heating systems was analyzed. The results show that the M-TES is appropriate for use with heating systems with a fan-coil unit and under-floor pipe. Containers and operating strategies for the M-TES with different transportation schemes were also designed. The maximum allowed load of the M-TES container is 39 t according to the discussion of transportation regulations on the road. The cost and income of the M-TES in the study case were estimated, and the net present value (NPV) and payback period (PBP) were also calculated. The best operating strategy is the use of 2 containers and 4 cycles of container transportation per day, with a PBP of approximately 10 years. The M-TES is applicable for middle and small-scale heat users in China. [ABSTRACT FROM AUTHOR]

The use of anthropogenic CO 2 for enhancing oil recovery from mature oil fields in the North Sea has several potential benefits, and a number of assessments have been conducted. It remains, however, difficult to realistically simulate the economic circumstances and decisions, while including the economic uncertainties that surround the relevant markets and policies, and the geological and technological uncertainties that are inherent to dealing with reservoirs and novel technologies in a challenging environment. A new method is proposed here introducing a unique combination of innovations, that include true limited foresight, project flexibility, and the consideration of realistic investment risk. The value of project is here expressed as the Net Present Value (NPV). These elements are combined in the PSS IV simulator. This is a techno-economic simulator for CO 2 -enhanced oil recovery (CO 2 -EOR), which applies limited foresight and Real Options Analysis to make realistic investment decisions on projects with significant uncertainties and thus risk. Consecutive project decisions are taken based on a decision tree. Multiple oil fields can be approached as a single cluster project, which can provide a lower investment hurdle. In a first test case for PSS IV, the Claymore and Scott oil fields are assessed, and it is shown that economic simulations where EOR projects are regarded as a sum of the individual field assessments will undervalue projects. Simulation results show that results are in a realistic range compared to published numbers, with individual project values for the Claymore field on average of 15.8 €/barrel (bbl; standard deviation SD = 8.3) and for the Scott field of 14.3 €/bbl (SD = 8.6). Due to the inclusion of uncertainties and the application of limited foresight, results range from −6 €/bbl (loss) to over 30 €/bbl. In a cluster configuration 5 €/bbl of additional value is created. [ABSTRACT FROM AUTHOR]

The use of a solar cooling system has the potential to reduce the amount of energy required for cooling buildings. One of the most important methods of improving energy efficiency in buildings is by carefully designing building façades. A façade integrated evacuated tube collector (ETC)-organic Rankine cycle (ORC)-vapour compression cycle (VCC) was applied in this study. To optimise the design parameters of ORC, a steady-state semi-empirical model was developed in Engineering Equation Solver (EES). The optimum number of plates in each heat exchanger is obtained by maximising the net present value (NPV) of electricity savings. The financial performance of the optimised system was assessed through a unit cooling cost (UCC) analysis. It was found that the UCC of the optimised facade integrated ETC-ORC-VCC system is $0.24 per kWh r of cooling effect. [ABSTRACT FROM AUTHOR]

Over the last decade, Microbial Fuel Cells (MFCs) have experienced significant scientific and technological development, to the point of becoming close to commercialization. One key assessment that clearly establishes whether one technology can fully enter the market is the profitability demonstration. For this demonstration, classical evaluation criteria for investment decisions such as the Net Present Value and the Internal Rate of Return can be applied to a given proposal. This paper presents an economic assessment of a microbial fuel cell in a juice processing plant. Three different scenarios, optimistic, pessimistic and most likely scenarios based on the maximum power density of the cell on two basic MFC cases (cathodes with and without Pt, respectively), were studied and compared to the conventional activated sludge process. The results show that under most of the scenarios under consideration, including the pessimistic one, MFC is a more attractive option. Furthermore, a sensitivity analysis was performed with respect to the electrode area, and the annual growth rate of the electricity pricing has revealed that the electrode area parameter is the most influential, reducing the MFC profitability for larger electrode areas, whereas the higher the annual growth rates of the electricity price, the higher the MFC profits. In summary, the results of this study show that the implementation of MFC is a promising alternative to the use of classical aerated activated sludge, and it has potential economic benefits. [ABSTRACT FROM AUTHOR]

• Verifying Egypt's capacity for green hydrogen generation utilizing the RERs. • Constructing a robust model of an islanded system for producing green hydrogen. • Determining the system's optimal component sizes to decrease COE, NPV, and LCOH. • Quantifying the reduction in GHG emissions from RERs with green hydrogen. • Studying operational reserve and inflation rate impact on system size and pricing. Global greenhouse gas (GHG) emissions, including carbon dioxide (CO 2), continue to rise yearly, mainly driven by increases in fuel and industrial emissions. Africa has a lot of sustainable energy resources to help satisfy the continent's electricity needs, which is crucial for economic growth and achieving world CO 2 reduction goals. There are many promising energy technologies, but green hydrogen stands out as a frontrunner. Green hydrogen capacity was assessed in this paper as a potentially new medium for storing Egypt's renewable electrical energy and fuel. Egypt represented nations with similar renewable energy resources and climates. The lowest net present value (NPV), levelized cost of hydrogen (LCOH), carbon emissions, and cost of energy (COE) were achieved by implementing five different configurations in three different sites. Using solar panels, an electrolyzer, a hydrogen fuel cell, and a hydrogen storage tank, the initial configuration (A) was carried out in Benban-Aswan. The second configuration (B), in Ras Ghareb, used the identical parts as the first but substituted wind turbines for solar panels to generate electricity. Wind and solar energy were combined in a third configuration (C) tested in Edko. In Ras Ghareb, but this time with a diesel engine and the wind turbine, the fourth configuration (D) was implemented. Excluding the hydrogen fuel cell from the prior setup, the final configuration (E) was identical. The HOMER software measured the GHG emissions from diesel generators and renewable energy sources. A sensitivity analysis of the operating reserve amount and discount rate variance was performed. According to the findings, the second configuration (B) carried out in Ras Ghareb had the lowest NPV at 1.81 M $, the lowest COE at 0.3085 $ / k W h , and the lowest LCOH at 3.94 $ / k g. Regarding greenhouse gas emissions, Configuration (D) produced 7,664 kg / y e a r of CO 2. Configuration (E) has a CO 2 output of 87,021 kg / y e a r. [ABSTRACT FROM AUTHOR]

• This paper evaluates a wind farm that feeds a electrlolyzer for producing hydrogen. • Both the spot power price and the wind capacity factor follow stochastic processes. • These processes show distinctive seasonalities along with mean reversion and jumps. • The paper uses Spanish data for parameter estimation, simulation and optimization. • Green hydrogen turns economically viable above 3 €/kg. Sensitivity analyses follow. This paper evaluates the economic viability of a combined wind-based green-hydrogen facility from an investor's viewpoint. The paper introduces a theoretical model and demonstrates it by example. The valuation model assumes that both the spot price of electricity and wind capacity factor evolve stochastically over time; these state variables can in principle be correlated. Besides, it explicitly considers the possibility to use curtailed wind energy for producing hydrogen. The model derives the investment project's net present value (NPV) as a function of hydrogen price and conversion capacity. Thus, the NPV is computed for a given price and a range of capacities. The one that leads to the maximum NPV is the 'optimal' capacity (for the given price). Next, the authors estimate the parameters underlying the two stochastic processes from Spanish hourly data. These numerical estimates allow simulate hourly paths of both variables over the facility's expected useful lifetime (30 years). According to the results, green hydrogen production starts becoming economically viable above 3 €/kg. Besides, it takes a hydrogen price of 4.7 €/kg to reach an optimal conversion capacity half the capacity of the wind park. The authors develop sensitivity analyses with respect to wind capacity factor, curtailment rate, and discount rate. [ABSTRACT FROM AUTHOR]

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner's NGCC configuration as representative of existing NGCC units and Svante's rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. The results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process. • Workflow for assessing carbon capture systems in the context of electricity markets • Multiperiod formulation for simultaneous optimization of design and operation • Assess NPV for retrofitting existing NGCC in future electricity market scenarios • Analysis for grid with high percentage of variable renewable energy generation [ABSTRACT FROM AUTHOR]

• It enhances the fault-current limiting capability of the PDGs, using the FCL-HTSCs , as a desirable alternative with low space requirements upon installation and high capacity. • It is a cost-minimization, mixed-integer nonlinear DEP framework for jointly reinforcing and/or installing substations , feeders , PL-/FCL-HTSCs , and DGs . • It models the stochastic nature of load demand, power production of the DGs, and capital expenditure budget using a potent MH-FIGDT . • It handles complexities via a powerful ICMSA empowered with a multi-computational-stage multi-dimensional multiple-homogeneous structure . In this paper, the authors present a new stochastic multiyear framework for distribution expansion planning (DEP) that integrates fault current-limiting high-temperature superconducting cables (FCL-HTSCs) and distributed generations (DGs). The proposed framework, from a new perspective, is capable of analyzing and comparing the efficiency of the FCL-HTSCs with other, more generic cables under different short-circuit conditions. The objective function to be minimized is the net present value of the total investment costs for reinforcing and/or installing substations, feeders, FCL-HTSCs, and DGs, as well as operation and maintenance costs. The framework also considers Kirchhoff's current and voltage laws, operational limits on equipment capacities, voltage limits, as well as financial and discrete logical constraints. As a comprehensive overview, the authors have developed: (i) a graph-based bi-conditional scheme to ensure radiality requirements and (ii) a multi-horizon fractional information-gap decision theory (MH-FIGDT) to address the stochastic nature of anticipated load demand, power production of the DGs, and a capital expenditure budget. The resulting mixed-integer nonlinear optimization problem is solved by an integer-coded melody search algorithm (ICMSA) empowered with a multi-computational-stage multi-dimensional multiple-homogeneous structure that offers better performance in diversification and exploration. Case studies using the standard 27-node and realistic, large-scale 104-node distribution grids are provided here in order to show the effectiveness of the proposed framework. Based on simulation results, several conclusions can be drawn: (i) the MH-FIGDT critical cost and/or MH-FIGDT critical deviation coefficient chosen by the distribution grid operator play a key role in achieving robust expansion plans against the given uncertainty set; (ii) the incorporation of the FCL-HTSCs into the DEP problem offers a satisfactory compromise between the required amount and capacity of the equipment and total investment costs; (iii) the functionality of the FCL-HTSCs outperforms the generic cables in mitigating feeder congestion and excessive fault currents under normal and short-circuit conditions, respectively; and, (iv) the variations in fault currents caused by rising DG penetration are keep below the nominal interrupt rating of the circuit breakers, which speaks to the suitability of the proposed framework. [ABSTRACT FROM AUTHOR]