Search

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

The integration of 5G base station (5G BS) clusters and edge data services introduces novel digital loads (NDLs) into the distribution system (DS), significantly impacting the interactive coordination of 5G-DS. This paper proposes an expansion planning model of 5G and DS considering source-network-load-storage coordination. Here, renewable energy resources (RESs), 5G BS clusters, edge data centers (EDCs), and DS source-network-load-storage are coordinately planned. The dynamic service access of 5G BS cluster are formulated to capture the coupled nature and inherent flexibility of 5G services. Considering the flexible processing of EDCs, a detailed categorization is conducted based on data service type, leveraging the synergies between cloud and edge computing to achieve flexible data load distribution. The original expansion planning model is reformulated as a mixed integer linear programming (MILP) problem to facilitate its computational solution. Simulation results from case studies on Portugal 54 node network over individual expansion planning and 5G static-access are performed to validate the effective and superior performances of the proposed method. Simulations results show that the system cost can be reduced by at most 15% with a higher operational flexibility. [Display omitted] • An expansion planning model of 5G and DS considering source-network-load-storage coordination is proposed. • NDLs from 5G BS clusters and EDCs are introduced to accommodate the RESs. • Dynamic access services of 5G BS clusters are formulated to leverage their flexibility. • Synergies of cloud and edge computing in EDCs are captured to facilitate source-network-load- storage coordination. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [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]

As renewable energy becomes more prevalent in the power grid, energy storage systems (ESSs) are playing an ever-increasingly crucial role in mitigating short-term supply–demand imbalances. However, the operation and control of ESS are not straightforward, given the ever-changing electricity prices in the market environment and the stochastic and intermittent nature of renewable energy generations, which respond to real-time load variations. In this paper, we propose a deep reinforcement learning (DRL) approach to address the electricity arbitrage problem associated with optimal ESS management. First, we analyze the structure of the optimal offline ESS control problem using the mixed-integer linear programming (MILP) formulation. This formulation identifies optimal control actions to absorb excess renewable energy and perform price arbitrage strategies. To tackle the uncertainties inherent in the prediction data, we then recast the online ESS control problem into a Markov Decision Process (MDP) framework and develop the DRL approach, which involves integrating the optimal offline control solution obtained from the training data into the training process and introducing noise to the state transitions. Unlike typical offline DRL training over a long time interval, we employ the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) algorithms with smaller neural networks training over a short time interval. Numerical studies demonstrate the promising potential of the proposed DRL-enabled approach for achieving better online control performance than the model predictive control (MPC) method under different price errors. This highlights the sample efficiency and robustness of our DRL approaches in managing ESS for electricity arbitrage. • Incorporate optimal strategy as prior knowledge by revising the DRL reward. • Employ small neural networks to efficiently train samples and update models. • Utilize noise to enhance error tolerance and reduce distribution shifts' impact. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Periodic cleaning of photovoltaic (PV) panels, such as every three months, is a common industry practice. However, this fixed period may not be optimal for maximizing the profit of a PV power generation system, due to numerous time-variant influencing factors, such as weather, temperature, etc. To increase the overall profit of a solar farm, it is highly desirable to have a flexible cleaning schedule that considers time-variant influencing factors. For this requirement, a rolling-horizon cleaning recommendation system is presented in this paper. Within this cleaning recommendation system, a prediction model and profit model are proposed. The prediction model, called the ensemble long-term and nonlinear autoregressive, can provide a time-variant future horizon by analyzing and compressing the time-variant characteristics in historical information. The profit model based on mathematical constraints, can process time-variant future horizon output from prediction model to generate a flexible optimized recommendation for cleaning schedule. The effectiveness of the proposed system is validated in real farms and all data used in this paper is collected from real world. The two case studies in experiments show that the profit improvement can reach up to 6% and 30%, respectively. [Display omitted] • A rolling-horizon cleaning recommendation system for PV panels. • A novel long-term and nonlinear predictive method. • A concise profit model for easy deployment in industry. • Flexible cleaning schedule considering time-variant characteristics. • Improvements of 6% and 30% in two different real farms, respectively. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) systems offer significant potential as large-scale physical energy storage technologies. Given the increasing global emphasis on carbon reduction strategies and the rapid growth of renewable energy sources, CAES has garnered considerable attention. However, the optimal design of CAES systems presents challenges due to the intricate coupling of multiple parameters, complex physical processes, and highly variable operating conditions. This paper comprehensively reviews three key aspects related to CAES optimal design. Firstly, it examines system analysis and optimization methodologies. Secondly, it investigates the off-design characteristics of CAES systems. Lastly, it explores system design methodologies specifically addressing off-design operations. Notably, the corresponding-point methodology (CPM) is proposed as a means to provide clearer guidance for system improvements. Future research should aim to combine CPM with traditional methods to establish more efficient and intuitive system optimization approaches. Furthermore, the exploration of active off-design regulation strategies, including the integration of various adjustment methods and multistage combined regulation for CAES systems experiencing wide load fluctuations, requires further investigation. Novel CAES configurations that enable efficient off-design conditions should also be explored, despite the inherent complexities associated with regulation and control. Finally, a coupled design methodology based on off-design operation data and probabilistic-load factor analysis is presented. This paper provides valuable insights and guidance for the optimal design and study of CAES systems. • CPM combined with traditional methods is needed to improve CAES round-trip efficiency • Not high temperature/pressure but "corresponding" is key factor to high round-trip efficiency • Multiple regulations and new system structure are needed for wide-load operation • Design methodology addressing complex off-design operations needs further improvement [ABSTRACT FROM AUTHOR]

• Portable PCM-based energy harvesters can power sensors and unmanned systems. • Small-scale prototypes are developed and analyzed using a thermo-mechanical model. • Melting of phase change material (PCM) may cause high pressure and system failure. • Hydraulic accumulators can improve overall performance without increasing pressure. • PCM-based energy harvesters can outperform state-of-the-art thermoelectric modules. This paper presents the design and analysis of a scalable energy harvesting system that uses a solid/liquid phase change material (PCM), namely pentadecane (C 15 H 32), to harvest environmental thermal energy associated with relatively low temperature and small temperature variation. The basic idea is to utilize the volume expansion of the PCM in the phase transition process to do work and generate electricity, which may be particularly suitable for powering sensors and small-scale devices that are designed to carry out long-term missions in an environment that undergoes regular, cyclic temperature variation in space or time. In this paper, we first present the fabrication and testing of a small-scale prototype in the form of a tube system, as well as the development of a thermo-mechanical model that couples the thermodynamics of the involved PCM and fluid materials with the elastic deformation of the structure. Using both the prototype and the model, we show that achieving high performance requires maintaining a high pressure inside the system, which complicates structural design and leads to incomplete melting of PCM. Towards mitigating this critical issue, we propose the idea of using a hydraulic accumulator to regulate the internal pressure. To examine this approach, we add a piston-type hydraulic accumulator to the prototype, and modify the thermo-mechanical model accordingly. We show that the hydraulic accumulator leads to a onefold increase in both thermal efficiency and specific energy output. In particular, the thermal efficiency obtained by the prototype is comparable with state-of-the-art thermoelectric generators when operating between 1 and 20°C. Using the thermo-mechanical model, we also present a parametric study that shows the dependence of the system's performance on a few key design parameters. [ABSTRACT FROM AUTHOR]

• A realistic PHS model is proposed for energy management applications. • Flow rate, water level, evaporation and precipitation affect model performance. • The proposed PHS model is validated with experiments in different operating points. • The error of the estimated stored water is reduced from 13.17% to 0.74%. This paper proposes a comprehensive pumped hydro storage model with applications in microgrids and smart grids. Existing models within current literature produce high error in calculating stored energy since some critical parameters are ignored. Thus, they are not suitable choices for energy management applications. Accordingly, the main objective of this study is to provide a more realistic model by estimating all the essential parameters in the system. First, all the losses due to the pump, pipes, and fittings are modelled. Next, a water balance approach is used to calculate the volume of water in the upper reservoir considering inflow, outflow, precipitation, and evaporation. Finally, the turbine power is calculated as a function of the water level in the reservoirs, considering the hydraulic losses of the turbine, pipes and fittings. The proposed model is validated using the experimental results of a physical system. The accuracy of the model is compared with other established models. The results demonstrate that the proposed model decreases the error of the estimated stored energy from 13.17% to 0.74%. Moreover, this study shows the capability of the model to simulate different configurations. The model provided in this paper assists researchers in the field and is of benefit to engineers in designing, sizing, and managing pumped hydro storage systems. [ABSTRACT FROM AUTHOR]

This paper proposes an aggregated flexibility estimation method considering the distributed electricity-hydrogen (H 2) interactions for virtual power plants (VPPs) to enhance the economic benefits from the peak-regulation market (PRM) while facilitating the accommodation of renewable generation. Firstly, various distributed energy resources (DERs) such as electric vehicles (EVs), air conditioning systems (ACs), and electricity-H 2 coupled hydrogen refueling stations (HRSs) are modeled as virtual storage (VS) with characterized charging/discharging response parameters. Besides, the temporal coupling power profile of VS can be abstracted as a polytope from the geometric perspective, and the aggregated high-dimensional flexibility region of VPP is formulated by the Minkowski sum for these polytopes. Furthermore, a day-ahead peak-regulation optimal scheduling model considering the conditional value at risk (CVaR) is developed for the electricity-H 2 integrated VPP participating the PRM, and the dimensionality of the aggregated polytope is reduced by the inscribed hyperbox approximation to provide a compact and concise scheduling region. Comparative studies have validated the aggregated power range and net income can be improved by 13.05% and 15.11% with the mechanism of electricity and hydrogen integrations. • The aggregated flexibility of distributed electricity and H 2 resources is unlocked. • Different types of virtual storage models are designed for EVs, ACs, and HRSs. • An inscribed hyperbox based aggregated flexibility projection method is proposed. • A concise day-ahead peak-regulation optimal scheduling model is developed for VPPs. [ABSTRACT FROM AUTHOR]

In order to facilitate the carbon emission reduction to mitigate climate change, this paper proposes a coordinated multi-stage planning strategy for the transmission-level integrated electricity-gas-heating system. The strategy coordinates installations of gas-fired generation plants, renewable energy plants, gas wells, power-to-gas plants, transmission lines and pipelines, considering retirements of coal-fired generation plants. It can help facilitate carbon emission reduction of the electricity system as well as the integrated energy system. The objective is to minimize the total present value of the investment and the operation economic cost, with consideration of constraints from each sub-system and carbon emission restriction. In the case studies, the proposed planning strategy is verified to be able to obtain the installation plan of the related facilities. With which the system can reduce the proportion of fossil fuel energy utilization and increase renewable energy utilization. Hence, carbon emission reduction is achieved. In addition, it has demonstrated that integrating the multi-energy systems can help reduce the economic cost in the planning result. • Multi-stage planning strategy of the integrated energy system is proposed. • Energy conversions among different sub-systems are considered. • An aggregated model of district heating system is adopted. • Capability of carbon emission reduction is verified. • Contributions from flexibility of gas and heating systems are demonstrated. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present options to make low-carbon footprint large-scale desalination a reality on arid islands with weak electrical grids. Through these options, the goal is to reconfigure on-grid wind energy/desalination systems for large- and medium-scale water production. In this context, it is proposed to use lithium-ion batteries for stationary energy storage together with management strategies aimed at avoiding the wind energy/desalination systems having to consume energy from the conventional grid they are connected to. The control strategy is based on ensuring that the power provided by the wind farm and batteries remains in synchrony with the power demand of the desalination plant throughout the system's useful life. The interannual variation of wind energy is considered when sizing the renewable energy system and processes for its estimation are proposed. The case study is centred on the Canary Archipelago, a region that is especially vulnerable to the impacts of climate change, but which enjoys exceptional characteristics for the exploitation of wind energy. The results obtained show the optimal wind farm and energy storage system capacities of the analysed configurations. The approach presented allows a low-carbon operational footprint. If the control strategy were to be put into practice today, the current grid restrictions and a life cycle assessment of the system carried out in a societal context that continues to be fossil fuel dependent indicate a potential reduction of 77.4% of the footprint. However, the remaining 22.6% could be eliminated in the future when the manufacturing processes of wind turbines, batteries and desalination plants receive the benefits of carbon-neutral societies. [Display omitted] • A method for achieving large-scale, low-carbon desalination is presented. • Optimal wind farm and storage capacities for the system are determined. • The method offers substantial CO2 emission reduction potential. • Using climate ERA5 reanalysis data, enhances robustness by addressing wind variability. • Proposed adjustments enhance adaptability to technological advances and future scenarios. [ABSTRACT FROM AUTHOR]

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations. • Detailed power and water operations are included in a hydrothermal scheduling model. • It incorporates short-term storage, reserve requirements, and irrigation effects. • The uncertain hydro inflows are represented by vector autoregressive models. • A calibration method captures the main effects induced by operational constraints. • Extensive experiments are analyzed on a detailed Chilean hydroelectric representation. [ABSTRACT FROM AUTHOR]

Faced with international climate obligations and domestic pressure, Singapore has recently raised its climate goals significantly. The government had affirmed its intention for carbon emissions to peak before 2030 and for Singapore to attain net zero greenhouse gas emissions by 2050. However, as Singapore transitions to green energy, there remains much uncertainty if this transformation, coupled with rapidly evolving geopolitics, would present vulnerabilities to energy security. Hence, this paper is an attempt to investigate Singapore's possible paths to achieve these goals while enhancing energy security in view of present and future energy plans. Key parameters of energy security in Singapore's context are identified to determine how different policy decisions would influence them. The effects of various current and potential policies can thus be evaluated using a systems thinking and system dynamics approach to propose new energy solutions to improve energy security. System dynamics modelling reveals that new energy supply solutions would have to be considered in Singapore's Energy 2050 Plan while furthering efforts in reducing energy demand. Findings reveal that Singapore should have a target of >80% renewable energy by 2050, pursue a central strategy towards sustainable diversification of its energy sources into hydrogen, electricity imports and solar energy and reduce energy consumption. Accordingly, Singapore's energy security can then be improved in terms of the economic, energy supply chain and environmental dimensions, and towards attaining net-zero emissions. [Display omitted] • Systems approach to Singapore Green Plan 2030 and simulation of policies towards energy security and net zero. • Systems thinking and system dynamics modeling to evaluate key assumptions and energy security parameters. • Model validation, both structural and behavioral, through base model simulations as well as behavioral tests. • Effect of government policies on hydrogen energy, low carbon electricity imports and energy saving on energy security. • Simulations suggest policies for higher renewable energy targets to attain net zero carbon emissions and energy security. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy in power grids introduces higher levels of uncertainty, while current decision-making models typically favour either a risk-averse or risk-neural strategy, and the research works related to windfall profit-aware risk-seeking strategies are quite limited. In this paper, a novel concept of integrated risk-seeking/averse preference is proposed, and a windfall profit-aware stochastic scheduling model for an industrial virtual power plant (IVPP) is developed based on this type of risk preference, which can realize the joint management of potential high profits and extreme losses. Firstly, the potential best- and worst-case results are incorporated into the holistic decision-making framework, which are quantified by the value at best (VaB) and the conditional value at risk (CVaR) measures, respectively. Next, the windfall profit-aware stochastic scheduling model is developed for the optimal operation of IVPP in day-ahead and real-time electricity markets, where multi-type flexible resources, such as energy conversion and storage devices, industrial production workstations, material storages and financial instruments, are utilized to improve expected profits and minimize the potential risks. Specifically, two types of credit-based virtual transactions, increment and decrement bids, are employed by the IVPP to increase its trading flexibility in electricity markets. Finally, simulation studies are conducted for a multi-energy IVPP to validate the proposed windfall profit-aware framework and model, showcasing that the risk-seeking and risk-averse preferences of decision-makers can be fully satisfied simultaneously under actual market environment. Moreover, risk parameters can be adjusted accordingly to manage windfall profits, expected profits, and extreme losses flexibly in electricity markets. • A concept of integrated risk-seeking/averse preference is proposed. • A holistic decision framework considering the best and worst scenarios is proposed. • A windfall profit-aware stochastic model is developed for virtual power plant. • Various flexible resources are used to improve expected profits and manage risks. [ABSTRACT FROM AUTHOR]

Large-scale renewable integration presents an effective way to decarbonize power grids, but carries increased risk of supply shortfalls owing to its volatility and uncertainty. Storage is a promising option to improve the generation adequacy of renewable. Thus, capacity credit assessment of renewable and storage is crucial in ensuring adequate generation capacity to meet loads. However, efficiently and accurately assessing capacity credit of these resources is challenging due to temporal dependencies in the operational time series (net loads and conventional generation units) and the need for extensive operation simulations. This paper develops a comprehensive multi-time-scale assessment framework integrating analytical and simulation methods to calculate the capacity credit of renewable and storage, thus capturing temporal features of these time series. Then, an interval-based strategy is proposed to simulate system operations, incorporating demand response in key scenarios. Furthermore, partitioning around medoids clustering and parallel computing techniques are employed to greatly accelerate the numerous operations for capacity credit assessment. The proposed method is validated using the RTS-79 system and a provincial real-world power grid in China. The results indicate that the developed framework can achieve efficient and refined capacity credit assessment and thus evaluate the impact of storage on the capacity credit. • Propose a novel method to quantify temporal features of operational time series for capacity credit (CC) assessment. • Develop a multi-time-scale CC assessment framework with an interval-based strategy to capture temporal features. • Utilize partitioning around medoids clustering and parallel computing to greatly enhance calculation efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a solution to the energy planning problem in buildings by implementing the net Zero Energy cost Building (nZEcB), a new concept that refers to buildings with zero or almost zero annual energy costs, using Artificial Intelligence (AI) techniques such as bidirectional long short-term memory, ordinary least squares linear regression, K-means, Pearson's correlation, decision tree, and binary gravitational search algorithm. AI techniques are used to design the optimal structure of a distributed generation system. The system includes wind and photovoltaic renewable energy sources, a battery bank, and an automated capacitor bank for power factor compensation. A case study was conducted in a real public building with an annual consumption of 1.748 GWh, which resulted in the specification of a distributed generation system with a generation of 2.805 GWh per year. This system meets approximately 160.5% of the building's electrical demand and 99.999% of annual energy costs, i.e., attending the nZEcB concept. The excess production of approximately 60% of the energy is necessary because the exported energy (feed-in tariff) has a lower value than that imported from the network, having to deduct costs with demand, in addition to having to compensate for losses in the battery banks. The project has a payback period of 6.79 years. This novel study demonstrates the feasibility and effectiveness of using AI techniques to achieve nZEcBs more efficiently, economically and sustainably. • Efficient integration of AI techniques in nZEcB design. • BiLSTM employed to forecast weather variables of subsequent years. • Load demand prediction using OLS Regression for forthcoming years. • K-means pre-selection of nZEcB equipment sizes based on generation and consumption. • AI outcomes integration in a BGS Algorithm to optimize annual net energy and payback. [ABSTRACT FROM AUTHOR]

• Short-term scenarios of aggregated RES production are generated by a copula. • Forecasting by energy source is compared to direct forecasting of the aggregation. • The value of scenarios is evaluated on practical power system applications. This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind-PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion and to the contribution of each source in the aggregation. [ABSTRACT FROM AUTHOR]

Highlights • Conduct comprehensive literature review of U.S. microgrid development in the recent decade. • Discuss U.S. progress on microgrid policies, demonstration projects, control methods, and software tools. • Summarize key successful experience of U.S. microgrid development. Abstract Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool’s core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries. [ABSTRACT FROM AUTHOR]

Highlights • Procurement auctions ignoring grid development may lead to sub-optimal deployment. • Presented auction integrates dynamic transmission planning & renewables procurement. • Full integration of both modelling frameworks reduces investor rent-seeking. • A more efficient deployment path is demonstrated under this policy framework. Abstract Competitive renewable energy procurement auctions are becoming increasingly prevalent. In a pay-as-bid auction, investors bid the price support required and receive that price if successful. Bidding strategy may be influenced by factors external to the auction, such as transmission expansion planning decisions. This may increase costs. In this paper, we show that integrating a pay-as-bid auction with transmission expansion planning may allow for closer total system cost minimisation over many time periods. This paper develops an auction mechanism and associated modelling framework to carry this out. The contributions of this framework are verified using a numerical example. Our results show that ignoring generation costs in transmission expansion planning can have economic consequences, while traditional pay-as-bid auctions can benefit from incorporating features associated with transmission expansion planning, such as multi-period optimisation. Full integration of both modelling frameworks can lead to efficiency improvements, both in terms of reduced investor rent-seeking and a more efficient deployment path. [ABSTRACT FROM AUTHOR]

Highlights • Power-to-gas can provide energy storage for renewable energy storage. • PtG can play different roles in different energy systems. • PtG can support the transition of energy system toward sustainability. • PtG can be an important source of renewable gases such as hydrogen, methane or SNG. Abstract This paper presents an overview of power-to-gas opportunities. Power-to-gas technology is gaining more and more popularity. It can provide large and long-term storage for increasing share of renewable sources in the energy system. In this paper, we would like to review the role of the power-to-gas system in different energy system architectures. Authors have analyzed the literature in the context of the problems, which could be solved by power-to-gas technology. Additionally, different assessment such as techno-economic, Life Cycle Assessment and Multi-criteria decision analysis for power-to-gas were revised. [ABSTRACT FROM AUTHOR]

The deployment of renewable technologies as part of climate mitigation strategies have provoked a range of responses from various actors, bringing public acceptance to the forefront of energy debates. A key example is the reaction of communities when renewable projects are proposed in their local areas. This paper analyses the effect that community acceptance has had on planning applications for onshore wind and solar farms in Great Britain between 1990 and 2017. It does this by compiling a set of indicators for community acceptance and testing their association with planning outcomes using binomial logistic regression. It identifies 12 variables with statistically significant effects: 4 for onshore wind, 4 for solar farms, and 4 spanning both. For both technologies, the visibility of a project, its installed capacity, the social deprivation of the area, and the year of the application are significant. The paper draws conclusions from these results for community acceptance and energy justice, and discusses the implications for energy decision-making. [ABSTRACT FROM AUTHOR]

Transitioning electricity generation sectors to renewable energy is a necessary element of climate change mitigation but a challenging one with years of technical and social lock-in creating considerable inertia and a pace of change insufficient to meet the task. Transition management has emerged as a promising approach to proactively manage and accelerate transitions in sectors like energy and water and has the potential to do so in manner that would help secure energy justice. However, the approach has been criticised for ignoring the political dynamics of transitions, risking procedural and recognition injustice for traditional energy production regions while increasing opposition to a transition. This paper uses a qualitative case study approach to understand how a transition to renewable energy generation could be achieved in a way that secures energy justice for traditional energy production regions like those of Gladstone in the Australian state of Queensland. The paper augments the transition management approach with the “just transitions” concept, which has emerged from labour movements as a means to mitigate negative impacts on workers and communities in traditional energy production regions, to create a “Just Transition Management” framework that is applied as a diagnostic tool to the case study. While results suggest a transition is imminent within Queensland, a lack of: consistent, supportive federal policy; long-term visions, and a clear process leaves it at risk of being captured by the powerful incumbent resources sector utilising an “environment vs. jobs” narrative. Yet, the results also suggest that adopting a just transition management approach has the potential to successfully resist the influence of the resources sector, by providing cheap, secure renewable energy and supporting affected workers into roles in the renewable energy sector using a reflexive, representative network governance approach. Data analysis reveals how the application of just transition principles has already engaged communities and unions that were previously sceptical of renewable energy. Overall, this study suggests that the just transition management framework assists in identifying the political barriers to transitions and energy justice more broadly, while also providing a management approach which creates powerful niche actor-networks to counter the narratives and influence of the incumbent resource sector. Thus it could support successful transitions that achieve the distributional, recognition and procedural justice needed for energy justice globally. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

This paper deals with development of a versatile and compact control strategy for voltage source converters in grid-connected and islanded microgrids using synchronous current converters technology. The key feature is its new integrated high-order controller/synchronizer with applicability to both operational modes without strategy rearrangement. Using high-order controllers, on the other hand, results in rather complex analysis and design process, therefore this paper aims at providing a general and simple theme for the design and parameter selection. The controller also provides adaptive and automated current-based grid synchronization. Moreover, the controller realizes a power-source current-controlled microgrid with minimum control loops, as compared to widely adopted voltage controlled microgrids in the literature, with advantages such as fault-ride-through and inherent droop-less power sharing capabilities. Adaptive current-based synchronization and smooth switching to islanding mode provides high flexibility, reliability and only-plug operation capability. Extensive simulation and experimental results are presented to demonstrate performance of the proposed control and management strategy. [ABSTRACT FROM AUTHOR]

The high variability and uncertainty introduced into modern electrical distribution systems due to decentralized renewable energy generators requires new solutions for grid management and power quality assurance. One of these possible solutions includes grid integrated energy storage. The appropriate size and placement of decentralized storage is highly dependent on purpose of the battery system and expected operational strategy. However, battery operational strategies are difficult to simulate simultaneously during a sizing and placement planning calculation. The motivation of this paper is to propose an algorithm that is capable of integrating sizing, placement and operational strategies of batteries into an Optimal Power Flow (OPF) distribution grid planning tool. The choice of the OPF approach permits to account for grid constraints which is more adapted for grid-connected storage devices compared to other approaches in the state of the art that are based only on an email balance analysis. This paper presents an alternating current (AC) multi-temporal OPF algorithm that uses a convex relaxation of the power flow equations to guarantee exact and optimal solutions with high algorithmic performance. The algorithm is unique and innovative due to the fact that it combines the simultaneous optimization of placement and sizing of storage devices taking into account load curves, photovoltaic (PV) production profiles, and distribution grid power quality constraints. The choice to invest in battery capacity is highly sensitive to the price of battery systems. The investment in battery systems solely for reducing losses an operational costs was proven not to be cost effective, however when battery systems are allowed to buy and sell electricity based on variable market prices they become cost effective. The assumptions used for this study shows that current battery system prices are too high to be cost effective even when allowing battery system market participation. [ABSTRACT FROM AUTHOR]

Assessing system costs for power generation is essential for evaluating the economical aspect of energy resources. This paper examines traditional and renewable energy resources under uncertainty and variability of input variables. The levelized cost of electricity (LCOE) of each technology is computed using a global sensitivity analysis. A Monte Carlo approach is utilized to study the thermoeconomics of a variety of power generation methods in the United States: fossil fuel-based, nuclear, developed renewable, and emerging renewable energy resources. The results of this study demonstrate how uncertainties in input data can significantly influence the LCOE values. Power generation from well-developed energy technologies exhibit less variability in LCOE due to established capital costs, operating and maintenance costs, and power generation. On the contrary, emerging renewable energy technologies are subject to high uncertainties in both technical and economic performance, as expected for technologies in early stages of development. A scenario with carbon pricing in power generation is also carried out in the paper. The presence of carbon pricing significantly increases the LCOEs of fossil fuel technologies, and LCOEs of other technologies also experience significant changes when life-cycle carbon assessments are considered. Several cost reduction opportunities are discussed to guide the development of future energy conversion, especially from emerging renewable energy resources. [ABSTRACT FROM AUTHOR]

Development of electric mobility and sustainable energy result in new technologies such as contactless electric vehicle charging and roadway energy harvesting methods, but also self-healing asphalt roads. By combining these technologies a new concept of Future Sustainable Roads for Electric Mobility is created and presented in the paper. This paper bridges the gap created by these unilateral technology developments using a multi-disciplinary approach including placing cautions when necessary and suggesting viable alternatives for optimal utilization of these energy transfer and conversion techniques. Through theoretical analysis, simulations, and tests on lab-scale experimental prototypes, the impact of our proposal is showcased. Thermal and loss models are developed for self-healing asphalt. Also, integration study of solar roads and contactless charging is performed. Applying the insight gained from the results, it is discussed how some challenges also pave a way towards interesting opportunities, for instance, infrastructure sharing for material use optimization and efficient mosaic integration. Finally, an economic viability case study is presented for a future Dutch highway with such newly emerging components. [ABSTRACT FROM AUTHOR]

The rapid development of distributed generation in different forms and capacities is transforming the conventional planning of distribution networks. Despite the benefits offered by renewable distributed generation technologies, several economic and technical challenges can result from the inappropriate integration of distributed generation in existing distribution networks. Therefore, the optimal planning of distributed generation is of paramount importance to ensure that the performance of distribution network can meet the expected power quality, voltage stability, power loss reduction, reliability and profitability. In this paper, we firstly discuss several conventional and metaheuristic methodologies to address the optimal distributed generation planning problem. Metaheuristic algorithms are often used as they offer more flexibility, particularly for multi-objective planning problems without the pursuit of globally optimized solution. Analytical techniques are considered suitable for modeling power system mechanisms and validating numerical methods. Then, this paper conducts a comprehensive review and critical discussion of state-of-the-art analytical techniques for optimal planning of renewable distributed generation. The analytical techniques are discussed in detail in six categories, i.e. exact loss formula, loss sensitivity factor, branch current loss formula, branch power flow loss formula, equivalent current injection and phasor feeder current injection. In addition, a comparative analysis of analytical techniques is presented to show their suitability for distributed generation planning in terms of various optimization criteria. Finally, we present conclusive remarks along with a set of recommendations and future challenges for optimal planning of distributed generation in modern power distribution networks. [ABSTRACT FROM AUTHOR]

Urban areas consist of a mix of households and services, such as offices, shops and schools. Yet most urban energy models only consider household load profiles, omitting the service sector. Realistic assessment of the potential for renewable resource integration in cities requires models that include detailed demand and generation profiles. Detailed generation profiles are available for many resources. Detailed demand profiles, however, are currently only available for households and not for the service sector. This paper addresses this gap. The paper (1) proposes a novel approach to devise synthetic service sector demand profiles based on a combination of a large number of different data sources, and (2) uses these profiles to study the impact of the service sector on the potential for renewable resource integration in urban energy systems, using the Netherlands as a case study. The importance of the service sector is addressed in a broad range of solar and wind generation scenarios, and in specific time and weather conditions (in a single scenario). Results show that including the service sector leads to statistically significantly better estimations of the potential of renewable resource integration in urban areas. In specific time and weather conditions, including the service sector results in estimations that are up to 33% higher than if only households are considered. The results can be used by researchers to improve urban energy systems models, and by decision-makers and practitioners for grid planning, operation and management. [ABSTRACT FROM AUTHOR]

• Off-grid Power-to-Fuel systems fit demands for implementing at early market stages. • Excellent wind park sites yield methanol costs of 1.28–1.73 €/l Gasoline Equivalent. • Future electrolyzer costs have high impact on costs, but are still highly uncertain. • Suitable regulatory framework can promote implementing Power-to-Fuel technology. In this paper the integration of systems for the production of electrofuels for a market entrance scenario from a short-term perspective is examined. A model was built to optimize the design parameters and simulate the yearlong operation of an off-grid power-to-fuel system, consisting of a wind park, electrolyzer, hydrogen storage, a CO 2 source and synthesis plant. In this manuscript, the regional focus is Germany; however, the results as well as the methodology can be applied to other regions. Successively, the production costs for the designed system were calculated on the basis of the model results. Various cases for different operational modes and sites have been analyzed. In addition, a sensitivity analysis was made to test the influence of single economic assumptions. Calculations demonstrate that the proposed off-grid systems are a viable option for implementation in a short-term scenario. The results include net production costs of 1.73 €/l GE (GE = gasoline equivalent) for methanol production in the reference case. For optimal wind farm sites, the calculated production costs can drop to 1.32 €/l GE. [ABSTRACT FROM AUTHOR]

• Liquid Air Energy Storage is flexibly coupled with LNG cold energy based on cold storage. • A high liquid air yield of ∼87% is obtained due to the contribution of LNG cold energy. • The round trip efficiency of the proposed hybrid LAES is ∼88%. • Exergy efficiency of the standalone LAES is improved by 28%. • The year-round performance of the hybrid LAES is evaluated. Liquid Air Energy Storage (LAES) stands out among other large-scale energy storage technologies in terms of high energy density, no geographical constraints, low maintenance costs, etc. However, the LAES has a relatively lower round trip efficiency, 50–60%, which is a big disadvantage. One of the main reasons is the lower liquid air yield, ∼70%, which is far from 100% due to the lack of cold energy during air liquefaction. Thus, in this paper, cold energy released during liquified natural gas (LNG) regasification is recovered and stored with pressurized propane, which is used to help air liquefaction in the LAES (denoted as LAES-LNG-CS). The LNG regasification process works independently of the LAES thanks to cold storage. Effects of various working conditions on the LAES-LNG-CS system are studied and three operation periods (off-peak, peak and full hours) of the LNG regasification process are considered. Comparisons are made between the LAES-LNG-CS and standalone LAES systems. The results show that the LAES-LNG-CS system could achieve a liquid air yield up to ∼89% and the power consumption per unit mass of liquid air is reduced by ∼32%, compared with the standalone LAES system. What's more, the system exergy efficiency of the standalone LAES is improved by ∼28% as the air charging pressure is at 8 MPa under studied conditions. Year-round performance study indicates that the round trip efficiency of the LAES-LNG-CS is in the range of 78–89%. Therefore, the proposed LAES-LNG-CS offers a good option for the future development of the LAES system. [ABSTRACT FROM AUTHOR]

• FuturES framework optimises electricity scenarios with high renewables penetration. • Reservoir and concentrating solar power modelled based on storage value. • Feasible scenarios created with 81–100% renewables by 2050. • Long-term energy storage helps to achieve low electricity costs. Although electricity supply is still dominated by fossil fuels, it is expected that renewable sources will have a much larger contribution in the future due to the need to mitigate climate change. Therefore, this paper presents a new framework for developing Future Electricity Scenarios (FuturES) with high penetration of renewables. A multi-period linear programming model has been created for power-system expansion planning. This has been coupled with an economic dispatch model, PowerGAMA, to evaluate the technical and economic feasibility of the developed scenarios while matching supply and demand. Application of FuturES is demonstrated through the case of Chile which has ambitious plans to supply electricity using only renewable sources. Four cost-optimal scenarios have been developed for the year 2050 using FuturES: two Business as usual (BAU) and two Renewable electricity (RE) scenarios. The BAU scenarios are unconstrained in terms of the technology type and can include all 11 options considered. The RE scenarios aim to have only renewables in the mix, including storage. The results show that both BAU scenarios have a levelised cost of electricity (LCOE) lower than, or equal to, today's costs ($72.7–77.3 vs $77.6/MWh) and include 81–90% of renewables. The RE scenarios are slightly more expensive than today's costs ($81–87/MWh). The cumulative investment for the BAU scenarios is $123-$145 bn, compared to $147-$157 bn for the RE. The annual investment across the scenarios is estimated at $4.0 ± 0.4 bn. Both RE scenarios show sufficient flexibility in matching supply and demand, despite solar photovoltaics and wind power contributing around half of the total supply. Therefore, the FuturES framework is a powerful tool for aiding the design of cost-efficient power systems with high penetration of renewables. [ABSTRACT FROM AUTHOR]

• Simulating wind power systems at 10 min resolution results in remarkable errors. • The total self-sufficiency is overestimated with an error range of 0.06–3.6%. • The battery utilization is underestimated with an error range of 7.5–44.7%. • The higher the battery capacity, the lower the total self-sufficiency error. • The C rate limit is the most decisive factor impacting the battery utilization error. In simulating renewable energy systems (RES), researchers tend to focus on the modeling methodology, but less on the impact of data on the obtained results and associated conclusions. Yet, results may be particularly dependent on the data resolution. RES studies on that impact of data resolution mostly consider households with PV systems, leaving industrial sites and wind power systems out of scope. In this paper, we consider a system comprising a medium-sized wind turbine, a high power battery and an industrial load, connected to a distribution grid. Wind and load power were measured concurrently in real time at 1 s resolution for 2 months in summer 2017, at two locations in Belgium. We performed two simulations, one using the real time data at 1 s resolution and the other using the same power data averaged over 10 min intervals. We compared both simulations to calculate three metrics: total self-sufficiency error, battery utilization error and instantaneous self-sufficiency error. We repeated the analysis using different settings in terms of battery capacity, battery C rate limit and load ratio. We conclude that: (i) total self-sufficiency, in all cases, is overestimated, ranging between 0.06 and 3.6%, with errors decreasing for higher battery capacity, (ii) battery utilization is always underestimated with errors ranging between 7.5 and 44.7%, primarily depending on the C rate limit, and (iii) there is a clear correlation between instantaneous self-sufficiency error and instantaneous ratio of the averaged load and wind power, with errors exceeding 100% when the ratio ranges in 0.5–2. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO 2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community. [Display omitted] • An IES with multiple renewable energy and energy storage subsystems is proposed. • A two-layer co-optimization model of IES design and operation is developed. • IES optimization results for building-level and community-level are compared. • Impact of roof area limitation for PV on IES optimization results is investigated. [ABSTRACT FROM AUTHOR]

• Examines renewable energy as a moderated, moderating, and mediating variable in affecting carbon emissions. • Introduces two base moderated mediation models to investigate these three functions of renewable energy. • Introduces multiple wavelet coherence as a robustness test for moderated mediation analysis. Previous research has extensively explored the direct impact of renewable energy on carbon emissions. This paper investigates three points against this backdrop. The first point is whether renewable energy operates as a moderated, moderating, or mediating variable in influencing the direct or indirect effects of growth on carbon emissions. The second point is which models should be used to estimate these three functions of renewable energy. The third point is whether these three functions should be estimated using aggregate or disaggregate data. The study draws two major findings. First, the three functions of renewable energy in influencing the impact of growth on carbon emissions should be investigated using moderated mediation models in which nonrenewable energy serves as a moderating or mediating variable and financial development serves as a moderating variable. Second, these models should be estimated using both aggregate and disaggregate proxies for renewable energy, economic growth, and financial development. Based on its empirical findings, the study presents pertinent policy suggestions for assessing the three roles of renewable energy. [ABSTRACT FROM AUTHOR]

• Imported green e-H 2 is more expensive than e-H 2 produced in Germany and Finland. • Local supply of e-H 2 found to be more economical across all scenarios and years. • Transportation costs constitute 29–63% of the total imported e-H 2 cost. • Pipeline transmission may be cheaper than shipping for distances of 2000–2500 km. Widely available and low-cost solar photovoltaics and wind power can enable production of renewable electricity-based hydrogen at many locations throughout the world. Hydrogen is expected to emerge as an important energy carrier constituting some of the final energy demand; however, its most important role will be as feedstock for further processing to e-fuels, e-chemicals, and e-steel. Apart from meeting their own hydrogen demand, countries may have opportunities to export hydrogen to countries with area limitations or higher production costs. This paper assesses the feasibility of e-hydrogen imports to Germany and Finland from two case regions with a high availability of low-cost renewable electricity, Chile and Morocco, in comparison to domestic supply. Special attention is paid to the transport infrastructure, which has a crucial impact on the economic viability of imports via two routes, shipping and pipelines. This study has found that despite lower e-hydrogen production costs in Morocco and Chile compared to Germany and Finland, additional transportation costs make imports of e-hydrogen economically unattractive. In early 2020s, imported fuel costs are 39–79% and 34–100% higher than e-hydrogen produced in Germany and Finland, respectively. In 2050, imported e-hydrogen is projected to be 39–70% more expensive than locally produced e-hydrogen in Germany and 43–54% in the case of Finland. e-Hydrogen may become a fuel that is mostly produced domestically and may be feasible for imports only in specific locations. Local e-hydrogen production may also lower dependence on imports, enhance energy security, and add jobs. [ABSTRACT FROM AUTHOR]

With the rising threat of climate change, green hydrogen is increasingly seen as the high-capacity energy storage and transport medium of the future. This creates an opportunity for low- and middle-income countries to leverage their high renewable energy potential to produce, use, and export low-cost green hydrogen, creating environmental and economic development benefits. While identifying ideal locations for green hydrogen production is critical for countries when defining their green hydrogen strategies, there has been a paucity of adequate geospatial planning approaches suitable to low- and middle-income countries. It is essential for these countries to identify green hydrogen production sites which match demand to expected use cases such that their strategies are economically sustainable. This paper therefore develops a novel geospatial cost modelling method to optimize the location of green hydrogen production across different use cases, with a focus on suitability to low- and middle-income countries. This method is applied in Kenya to investigate the potential hydrogen supply chain for three use cases: ammonia-based fertilizer, freight transport, and export. We find hydrogen production costs of €3.7–9.9/kg H 2 are currently achievable across Kenya, depending on the production location chosen. The cheapest production locations are identified to the south and south-east of Lake Turkana. We show that ammonia produced in Kenya can be cost-competitive given the current energy crisis and that Kenya could export hydrogen to Rotterdam with costs of €7/kg H 2 , undercutting current market prices regardless of the carrier medium. With expected techno-economic improvements, hydrogen production costs across Kenya could drop to €1.8–3.0/kg H 2 by 2030. [Display omitted] • We develop a novel GIS method to identify least-cost green hydrogen production locations. • This is applied in Kenya for ammonia, freight transport, and export use cases. • Hydrogen production costs of €3.7–9.9/kg H 2 are currently achievable in Kenya. • The cheapest production locations are to the south/south-east of Lake Turkana. • Kenya could export hydrogen and undercut current market prices with any carrier. [ABSTRACT FROM AUTHOR]

This paper presents a novel long-term model for the joint expansion planning of power and hydrogen systems with short-term operational considerations. We propose linear reserve constraints with adjustable parameters within the planning model to address the reliability requirements from significant levels of variable renewable generation. These adjustable parameters are calibrated using an iterative training methodology that connects the long-term planning model with a short-term unit commitment model. A crucial aspect of our methodology is the iterative improvement of investment decisions through the feedback obtained by evaluating the operational performance of the investment decisions in different scenarios computed under the unit commitment model. Computational experiments in a case study of a large-scale Chilean power-hydrogen system show the effects of the proposed methodology on capacity expansion recommendations, where a correct balance between variable and flexible technologies is obtained. Moreover, when simulating the operation of the system for a full year, we observe that the proposed methodology generates an investment plan that achieves a lower total cost compared to other methodologies commonly used in the literature. Additional experiments in 100% renewable scenarios show how conventional methodologies reaches levels of not-supplied-energy between 2%–7%, while the proposed methodology achieves less than 1% in the same metric. • We develop a new capacity expansion planning model for power and hydrogen systems. • We connect the planning model to a unit commitment to assess renewable variability. • The proposed scheme is compared to conventional planning models using simulation. • The model achieves the best operational performance at the lowest total cost. • Conventional models present high levels of energy shedding in 100% renewable case. [ABSTRACT FROM AUTHOR]

The research on photovoltaic conversion is continuously overtaking technological challenges and modern PV cells can nowadays be efficiently combined with solar concentrators. In this paper a new photovoltaic solar concentrator model based on non-imaging optics and embedding high efficiency multi-junctions cells is presented. The concentrator has been optimized to maximize the electricity production but it is thought to work in cogeneration to allow also for thermal energy by recovering the residual heat, since an active cooling system for the cells is necessary. The efficiency enhancement is obtained thanks to the high matching between the collected solar irradiance and the receiver electrical features. The aim of the paper is the definition of the number of residential utilities connected to the system in order to minimize the exchange of energy with the network and also the use of auxiliary boiler. Also the optimal size of electric and thermal storage to be integrated to the concentrator, has been determined as function of the number of utilities. To achieve these goals a parametric analysis has been carried out by the use of an in-house-developed calculation code considering a whole year of operation in order to estimate the energy fluxes and the economic performance. Furthermore, the maximum capital cost of the system has been estimated in order to achieve a return on the investment in ten or twenty years. According to the Authors’ knowledge, the novelty of this study regards the implementation of this kind of photovoltaic solar concentrator – for combined heat and power production – in a residential application. [ABSTRACT FROM AUTHOR]

Internal electrical networks of large wind farms constitute complex and dispersed grid structures. Wind turbines are scattered over vast areas and the total length of cabling infrastructure might reach several dozens of kilometers. Outlays related to cable laying significantly contribute to the entire project budget. Therefore the design process should minimize these expenses considering also operation and maintenance costs calculated over the project lifetime on condition of fulfilment of all technical requirements. An analysis presented in this paper demonstrates that an independent optimization of the twofold problem dealing both with investment and operation costs does not result in the cheapest solution. The analysis confirmed also reliability and effectiveness of application of Mixed Integer Linear Programming method (MILP) to solve this kind of optimization problem. The paper shows that the developed integrated optimization algorithm is efficient and delivers an absolute optimal solution (GAP = 0) in a reasonable computation time. The results obtained for a real wind farm project confirm that the optimal design of a wind farm network can’t be determined a priori and the final outcome strongly depends on the configuration of wind turbines (e.g., number of feeders, number of turbines connected to a single feeder, etc.) and technical parameters of cables. Spread over time, discounted costs of energy losses are an integral part of the objective function. The study proves that cost of energy losses impacts on the overall financial results and shouldn’t be neglected. The related expenses are roughly at the same level as expenditure linked to cable laying and they heavily influence the final design of the internal network. The results show the possibility of practical use of the proposed algorithm in the wind farm design process. [ABSTRACT FROM AUTHOR]

• Green hydrogen generation devices were studied. • Methods for annual operation evaluation were proposed. • Experimental study on transient states was conducted. • Case study for 10 MW renewable energy plant was considered. This study presents a comprehensive methodology to evaluate plants that integrate renewable energy sources and hydrogen generation devices. The paper focuses on presenting the methods for devices' operation assessment taking into account the annual operation. Multiple effectiveness indices have been presented. On the basis of experimental investigation with the hydrogen generator, the methods for assessing its operation during start-up phase and sudden change in the supply current were proposed. The results of the experiments and the provided mathematical models show that dynamics of the hydrogen generator should be taken into account when selecting the suitable device for cooperation with variable renewable energy. It is especially important for multiple start-ups throughout the day due to significant differences in the amount of hydrogen produced by devices characterized by the same efficiency, yet various time constants. Methodology for selecting the optimal nominal power for hydrogen generator to cooperate with given renewable sources was developed. It was proven the optimal power depends on the type of the renewable source and minimal load of the hydrogen generator. Several case studies, including the integration of wind and solar energy farms to yield a 10 MW renewable energy farm were considered and the minimal load of the hydrogen generator impacts the annual operation of the device has been presented. The paper provides a set of tools to contribute to the development of sustainable energy plants. The methods proposed in this paper are universal and can be used for various renewable energy sources. [ABSTRACT FROM AUTHOR]

This paper discusses the application of distributed energy resources (DERs) at water resource recovery facilities (WRRF). The DERs considered include solar photovoltaic (PV) systems, biomass electricity generation, and energy storage systems (ESS). The financial and demand management implications of the deployment of various DER combinations are analyzed. The load profile of a single plant is studied in order to draw conclusions regarding the optimal size for energy storage solutions based on the goals of the WRRF. A case study of a Medium sized WRRF, located in a large metropolitan area, is used to compare different financial and technical implementation strategies. An ideal combination of DERs is determined based on analysis of various scenarios for nine distinct use cases. The overarching goal of this paper is to understand the potential of various combinations of DERs for managing peak demand and resilience at WRRFs. Elements in the financial models are compared to determine the combination that creates a financially viable project while providing tangible benefits in resilience and/or demand management to the WRRF. • Energy efficiency is key for urban regions to combat global warming. • Distributed energy resources (DERs) improve resiliency during power failures. • Energy efficiency is possible through deployment of DERs and better use of biogases • Distributed energy resources (DERs) improve resiliency during power failures. [ABSTRACT FROM AUTHOR]

• A new wake model is proposed to predict the turbine wake in yawed conditions. • The wake model can be applied for both wind turbines and hydrokinetic turbines. • The skew distribution is employed to express the spanwise velocity asymmetry. • The wake model is validated by the wind tunnel and open channel flow. • measurements. Predicting the spatial evolution of horizontal-axis turbine wakes is a key factor to enhance the performance of wind and hydrokinetic power plants. The yaw angle misalignment is not only important to account for the uncertainty of the wind direction, but also a control strategy to improve the energy production averaged over the turbine array. In this paper, a new wake model, based on the skew-normal distribution and conservation of mass and momentum, is proposed to predict the turbine wake velocity distribution for given yaw angles. The new model is experimentally validated through wake measurements of yawed miniature wind and hydrokinetic turbines, in wind tunnel and open channel flows, respectively. Predictive capabilities extend from the spatial distribution of the maximum velocity deficit at hub height, to the spanwise asymmetry of the velocity distribution about the wake center. [ABSTRACT FROM AUTHOR]

• Two innovative small-scale CCHP solar ORC systems are modelled in TRNSYS/MATLAB. • The two systems are powered by two different CSP technologies: CPC and LFR. • The performance is analysed varying location and composition of global irradiance. • The DNI/GHI ratio has an influence on the system performance. • Useful hints for the selection of the best CSP technology with DNI are provided. In this paper, two innovative small-scale solar Organic Rankine Cycle (ORC) trigeneration plants are investigated and compared using a simulation analysis. In particular, the first plant (Plant 1) consists of a 146 m2 Compound Parabolic Collectors (CPC) solar field, a 3 m3 diathermic oil storage tank, a 3.5 kWe ORC plant and a 17 kWc absorption chiller, while the second plant (Plant 2) consists of a Linear Fresnel Reflectors (LFR) solar field of equal reflecting area, a phase change material storage tank equipped with reversible heat pipes, a 3.2 kWe ORC unit and the same 17 kWc absorption chiller as the former. The dynamic performance of the considered plants has been assessed for two Italian locations representative of the European Mediterranean area, Napoli and Messina, having a similar global radiation but a significantly different ratio of direct normal irradiance to diffuse irradiance. The comparison between the two different solar ORC trigeneration systems has revealed the great influence of the solar radiation on the effectiveness of such systems even for locations at similar latitudes. The energy production has been analysed both on a monthly and daily basis. Results have shown that while the performance of Plant 1 is not so sensitive to location and radiation conditions, Plant 2 is greatly affected by these parameters. Moreover, the higher condensing temperatures necessary in summer to supply the absorption chiller significantly limit the electrical efficiency of the solar CPC ORC. On the contrary, the LFR technology allows the achievement of higher temperatures and conversion efficiencies in summer, thus resulting especially suitable for solar cooling applications. In conclusion, this study has highlighted the importance of adequate technology selection with different radiation conditions in order to better exploit the potential of trigenerative solar ORC systems. [ABSTRACT FROM AUTHOR]

• A multi-criteria framework is proposed for bioethanol facility location. • The best worst method is used to calculate the weights of the criteria. • The provinces of Iran are evaluated as alternatives in this study. • A questionnaire answered by 41 experts from Iran is used for the BWM. • Province of Khuzestan (followed by Tehran) is selected for bioethanol production. One of the major factors in the success of renewable energy is finding a proper location for production facilities. At a national level, different parts of a country (e.g. provinces) can be seen as alternatives that can be assessed based on a set of criteria, and ranking them to identify the best location. The focus in this paper is on identifying the best location for the production of bioethanol. After a comprehensive literature review, an evaluation framework is proposed based on the three dimensions of sustainability (economic, environmental and social). Using data provided by a sample of experts in a developing country – Iran – and applying the best-worst method (BWM), a number of decision-making criteria are evaluated. Performance data involving the various provinces of Iran are collected from different sources. The performance data and the weights identified through BWM are used to calculate an overall score for each province, which is then used to rank the provinces, with the province of Khuzestan (closely followed by Tehran) being identified as the most suitable province for bioethanol production in Iran. [ABSTRACT FROM AUTHOR]