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Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

Under the dual pressure of energy consumption and environmental pollution, mankind hopes to develop clean and renewable alternative energy, and the rapid development of fuel cells meets people's demand for energy-efficient power systems. The emergence of portable micro energy systems represented by microfluidic fuel cells, such as paper-based microfluidic fuel cells, has greatly enriched the means of medical detection to better cope with the threat of disease transmission. In this work, the numerical simulation method is innovatively introduced to study the paper-based microfluidic fuel cells. Both transient and steady-state modes are employed to demonstrate the whole operation process of the paper-based microfluidic fuel cell. In addition, the different structural parameters, including electrode spacing, the distance between electrode and inlet, channel thickness, and electrode length, are also investigated their influence mechanisms on cell performance. Results show that the increase of most structural parameters decreases cell output power in different degrees. Even on the premise that increasing channel thickness has a positive impact on the output power, the fuel utilization still shows a downward trend. These conclusions provide theoretical support and reference for future optimization work and accelerate the development of microfluidic fuel cells. • Numerical simulation is employed to study paper-based microfluidic fuel cell. • The effects of structure parameters on cell performance are discussed. • Through fuel concentration distribution to reveal cell performance mechanism. • Fuel utilization is used to evaluate energy efficiency. [ABSTRACT FROM AUTHOR]

• A simple, large-scale fabrication of cooling paper via pulping and papermaking technology. • The cooling paper is facilely deconstructed and regenerated to architect the secondary cooler. • The cooling paper features a high solar reflectance of 94% and emissivity of 0.95. • The cooling paper shows the sub-ambient temperature drop of 6 ∼ 8.8 °C under directly solar radiation at daytime. Passive radiative cooling materials provide coldness without energy consumption for all-day thermal comfort by reflecting solar irradiation and emitting heat into the 3 K universe. However, the conventional radiative cooling materials involve complex multilayer structures and unsustainable polymers, showing the non-recyclable disadvantage, which induces resource waste and environmental issues. Herein, we reported a low-cost, scalable, and eco-friendly radiative cooling material (namely cooling paper), consisting of the delignified, fibrillated cellulose fibers and nano-sized hydroxyapatite (HA) via the mature pulping and papermaking process. The resulting cooled paper can be recycled, and it can be easily deconstructed to harvest cellulose fibers and HA, individually. The recovered source materials can be reused to prepare secondary products that show comparable performance to the original cooling paper, including a high solar reflectance of ∼94% and an infrared emissivity of ∼0.95 in the atmospheric transparency window. Such cooling paper enables superior cooling performance with the temperature drop of 6 ∼ 8.8 °C under directly solar radiation. Based on the EnergyPlus simulation, our cooling paper shows a average cooling energy saving of ∼29 % in buildings across China. The recyclable cooling paper with high cooling capability and scalability, as well as environmental friendliness, shows promising potential for sustainable energy-efficient buildings. [ABSTRACT FROM AUTHOR]

The paper addresses challenges arising from the unpredictable and probabilistic nature of wind speed and solar irradiance, leading to variable power production in renewable sources. Power imbalance issues in grids result from the difficulty in accurately estimating these variables. The study introduces an improved data-driven uncertainty set, utilizing a neural network trained with extensive historical data from a multi-microgrid system. This set efficiently identifies strong coupling and extracts characteristic scenarios, ensuring high reliability with a minimal number of scenes. The proposed probabilistic model, implemented in the coordination of pumped storage, wind, and solar power plants, incorporates energy limits and storage characteristics. A four-mode IEEE model for storage units allows quick response to system needs. Simulating storage unit states every hour, considering system load, output power, and energy limitations, optimizes power dispatch based on battery capacity. Additionally, the paper addresses the uncertainty in electric power demand in microgrids, proposing an improved data-driven uncertainty set obtained from a neural network. By eliminating unrealistic worst-case scenarios, the approach accurately captures system characteristics, maintaining high reliability while significantly reducing convergence time. The proposed uncertainty set is applied in a novel two-stage robust optimization dispatch model, transforming the optimization problem into a mixed-integer linear programming problem, and solving it using a column and constraint generation algorithm. Numerical case studies verify the feasibility and superiority of the proposed approach in terms of economy and convergence performance compared to existing robust optimization methods. [ABSTRACT FROM AUTHOR]

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]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

Since a renewable energy is connected to a high-altitude integrated energy system (HAIES), challenges arise for system operation. Shared energy storage as a jointly operated energy hub for multi-integrated energy system (IES) can effectively improve the economy and flexibility of the system. This paper proposes a joint day-ahead and intra-day scheduling strategy for a HAIES considering a shared composite energy storage operator (SCESO) and profit clearing scheme. First, a HAIES structure suitable for residential, industrial, and commercial applications is built based on combined oxygen supply and integrated demand response (IDR). An SCESO is then proposed to provide multiple energy sharing services for HAIES cluster, including electricity–oxygen–hydrogen. Accordingly, HAIESs and SCESOs are aggregated to create a HAIES cluster energy sharing framework. Second, during the intra-day scheduling stage, which is based on real-time forecast data that considers multiple source–load–charge uncertainties, the flexible adjustment capabilities of each unit of the framework are used to achieve intra-day energy balance and ensure the safe operation of the framework. Furthermore, a profit clearing scheme is proposed, introducing different energy contribution quantification methods for different energy cooperation modes. Finally, day-ahead and intra-day simulation results under multiple scenarios are analyzed. The simulation results demonstrate that the framework is optimal in terms of economy, adaptability to different RE outputs and RE uncertainties, and energy storage utilization efficiency. • It proposes a new HAIES and SCESO energy cooperation framework. • An intra-day scheduling strategy for the framework is proposed. • A profit clearing scheme is constructed. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are being deployed all around the World as a technically feasible solution to decreasing users' dependence on fossil fuels. Several demonstration facilities have shown their potential to provide final consumers with clean energy and all associated environmental benefits. However, the economic evaluation of these systems as a whole set is more complex than evaluating generation technologies individually, which can be considered a barrier, and it may be more complicated to calculate its uncertainty with precision. This paper deals with this challenge and adapts a model for the evaluation of the global Levelized Cost of Electricity (LCOE) of a polygeneration microgrid to the characteristics of a typical REC, allowing the assessment of the distribution of the LCOE depending on the uncertainty of the input parameters. Thanks to its simple analytical formulation, the proposed model, that can be used for any combination of technologies (both renewable and conventional), provides relevant information on uncertainty propagation in a symbolic way that avoids the need to run numerical simulations or make assumptions on the distribution of the random input parameters. A case study has been presented, considering a typical small electrical REC with photovoltaic plants and micro wind turbines. Although the model can be defined to any market, as a representative example, it has been evaluated according to the current Spanish and Italian regulations, which are analyzed in depth with reference to the scientific literature. Results show that uncertainties in parameter estimates give rise to a very large scatter in the LCOE, pointing out a set of quantities whose role is crucial for a reliable estimate, among which electricity purchase and selling prices, yearly power load, and self-consumption / virtually-shared energy rates stand out. [Display omitted] • The global LCOE for a typical REC has been defined. • The uncertainty propagation in the LCOE formulation has been studied. • The proposed model has been applied to an electrical polygeneration REC. • As an example, the Spanish and Italian regulatory frameworks have been considered. • The LCOE of RECs is mainly sensitive to the electricity price and the power load. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features. • A novel VAEGAN model with controllable interpretable features is proposed. • External universal meteorological features are used to generate scenarios with unknown patterns. • Internal interpretable latent features are designed to generate scenarios with specific features. • The results show a better performance of the proposed method on controllable scenario generation. [ABSTRACT FROM AUTHOR]

• Targeting United States residential HVAC electricity load using wind energy. • Partial load targeting concept using renewable energy source. • Capacity factor and estimation analysis of wind energy potential production. • Offshore versus onshore capacity factor estimated production performance. • Heating and cooling load curtailment methodology and analysis. This paper explores the concept of partial-load targeting through presenting a technical analysis for modelling the electrical energy demand of residential HVAC systems spanning the U.S. using onshore and offshore wind turbine technology. The proposed modeling technique uses DOE backed survey data and both known and theoretical capacity factor (CF) levels to determine the amount of wind turbines required to sustain the electrical load necessary for the heating and cooling components of residential housing units within all major U.S. climate regions. To perform the analysis, annual load data sourced from the energy information administration (EIA) surveys utilizing National Renewable Energy Laboratory (NREL) benchmark simulation house parameters are calculated and compared to wind turbine CF data to determine an equivalent range of turbines required to satisfy the target load. Two production profiles based on the CF range of each region are created using onshore and offshore scenarios. These profiles are then fitted to the corresponding average annual household load for the particular climate region. An analysis on the results is then conducted to determine overall potential load reduction as well as observe characteristics between the offshore and onshore data. [ABSTRACT FROM AUTHOR]

• This paper proposes a concept that utilizes historical time-series data from transmission system operators (TSOs) to estimate the optimal day-ahead energy-mix proportion for a power system dominated by converters. • This paper employs a long short-term memory (LSTM) network as a data-driven model to forecast the day-ahead values of key parameters. • To ensure secure system functioning, an energy-mix operation and reserve scheduling model is developed. • This paper emphasizes the importance of power system security in incorporating inverter based resources (IBRs). Advancements in various scientific fields have encouraged the development of novel tools, techniques, components, methodologies, and innovations aimed at addressing the challenges encountered in modern power systems dominated by inverter-based resources (IBRs). This paper focuses on a concept that leverages historical time-series data obtained from transmission system operators (TSOs) to enhance the secure management and operation of power systems. By employing a data-driven model, the day-ahead values of power generation and load consumption are estimated and integrated with a dynamic model of the power system for further analysis. To optimize energy generation and ensure grid stability, an energy-mix operation and reserve scheduling model is utilized. This model optimally combines different power-generating technologies, including synchronous generators (SGs), grid-following converters (GFLs), and grid-forming converters (GFMs), to meet the energy demands of the day while enhancing the overall system strength. The findings are supported by quantitative analysis utilizing variables such as frequency, power production, terminal voltages, and system non-synchronous penetration (SNSP). Simulation results demonstrate that implementing the proposed concept enables the power system under consideration to operate securely, even in the face of a 38% increase in immediate load, with a maximum SNSP ratio of 59%. These findings highlight the effectiveness of the proposed approach in addressing the reliability, system dynamics, stability, control efficiency, and security challenges posed by IBR-dominated power systems. Furthermore, it is believed that this research contributes to the ongoing efforts in decarbonization, renewable energy integration, and combating global warming by facilitating the secure and optimized operation of renewable energy-dominated power systems. [ABSTRACT FROM AUTHOR]

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements. • An improved interval optimization method is proposed to deal with the uncertainty of inflow runoff and PV power output. • A MILP model for the medium- and long-term operation of a cascade hydro-PV complementary system based on extreme scenarios is established. • The resulting scheduling scheme achieves balance between economy and robustness. • The influence of penalty factor and ecological flow on the dispatching results is not obvious. [ABSTRACT FROM AUTHOR]

• It is proposed a topology consisting of an input step-up stage, followed by a step-down stage and an unfolding inverter. • A decentralized control strategy of the DC-DC stage allows maximizing the renewable energy harvest and synthesizing an output current to be injected into the grid with low harmonic distortion. • The grid current controller proposed in this paper consists of a reduced number of resonant stages meeting the energy quality required by standards. • The article introduces a novel extension of a formerly presented design strategy for Proportional + Resonant controllers, to include harmonics compensation. • A 500 W prototype converter was implemented, and the experimental results show a fast transient response to step changes in both the input current and the phase angle reference. Also, the multi-resonant controller design presented in this article resulted in an injected current with a THD of 3.37%, compliant with the IEEE Std. 1547–2018. This paper presents a two-stage current-source DC-AC converter for grid-connected PV applications which is composed of an input step-up stage, followed by a step-down stage and an unfolding inverter. A decentralized control strategy of the DC-DC stage allows maximizing the renewable energy harvest using an Incremental Conductance MPPT algorithm and synthesizing an output current to be injected into the grid with low harmonic distortion. Double-loop PI controllers are used for the boost stage. The DC bus voltage of the buck stage is regulated using a PI controller, and an inner Proportional-Resonant (PR) controller tracks a sinusoidal reference. The PR controller proposed in this paper, includes a reduced number of resonant stages meeting the energy quality required by standards, which results in good stability margins. Finally, a SOGI-FLL algorithm synchronizes the inverter operation with the grid. Experimental results show an excellent dynamic response of the system, and the injected current complies with the IEEE Std. 1547–2018 specifications regarding harmonic content using a control law with a low computational burden. [ABSTRACT FROM AUTHOR]

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project. • A sharing energy storage mechanism in multi-energy systems is presented in this paper. • The hydrogen trading methods based on time-of-use hydrogen prices are considered in this paper. • A hierarchical optimal scheduling method based on the Stackelberg game is designed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity. Previous studies have demonstrated the potential for P2G technology to optimize the integration of PV into the grid. However, the proposed method considers the PV's acceptance rate and provides system planners with necessary information on RES and P2G facility capacity. The method proposed in this paper can be extended to include other RES. • Novel Index for PV-P2G integration (PRPI). • Enhancing Acceptance rate of PV applying PRPI • Reduced optimization complexity. • Adaptable to various renewables. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [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]

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as "shoeboxes" using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning. • Optimization framework combines urban building energy modeling and local planning of renewables. • Urban neighborhood buildings are modeled in an abstracted manner as "shoeboxes". • Considering local wind and solar resources along with energy storage and grid supply. • PV has the most prominent role in supplying local renewables to the neighborhood. • Substantial reduction in CO2 emissions (75%) can be achieved with limited increase in energy costs (15%). [ABSTRACT FROM AUTHOR]

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility. This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model's results with similar studies and found consistency in terms of technico-economic estimations. • Linear formulation of an optimisation model to represent a whole energy system transition pathway with an hourly resolution. • Short computational time, 15 min on a personal laptop • Quantify the pillars of the transition: efficiency, renewable energies and sector coupling. • Offers a new and incremental tool to support decision-makers in the energy transition. • Comparison of different approach to represent the transition. [ABSTRACT FROM AUTHOR]

The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system. • A multi-regional model is built to find the most convenient energy transition path. • General guidelines for Western countries are derived from the Italian case study. • Replacement of boilers with heat pumps allows increasing RES share from 20 to 30%. • For RES share >60%, PtG is the low-carbon strategy for high temperature heat demand. • Transportation is the last energy sector that should be decarbonized. [ABSTRACT FROM AUTHOR]

Energy is the key driver of life. It is required for the growth, exaltation, modernization, and financial development of any country. The world is turning away from fossil fuels in favor of renewable energy sources in order to meet the rising demand for energy and protect the environment from pollution. Solar energy is becoming a significant source of energy among these new energy sources. The primary solar energy-using appliances are solar air heaters. However, the creation of a laminar sub-layer on the absorbing plate is the cause of its poor thermal efficiency, which is a cause for concern. This paper describes various techniques through which the efficiency of solar air heaters (SAH) is enhanced by disturbing the laminar sub-layer. This study is concentrated on four techniques artificial roughness, jet impingement, piezo-electric fan, and phase change material (PCM), which are explained in four sections. The first section gives a detailed description of artificial rib roughness, turbulators, and protrusions. The second section reviews the use of jet impingement in SAH. The third section introduces a new technique; a piezo-electric fan and describes its mechanism, working parameters, and possibilities for its use in SAH through the previous studies. The fourth section reviews the use of PCM material in SAH by the previous studies. The maximum thermal hydraulic performance factor (THPP) for non-circular ribs, ribs with a rectangular cross section, ribs with an equilateral cross section, and jet impinged SAH, respectively, has been observed to be 1.44–1.29, 2.19, 2.11, and 3.66. [ABSTRACT FROM AUTHOR]

The flexibility of power systems is critical to cope with the variability and uncertainty caused by the integration of renewable energy resources. Incorporating district cooling systems (DCSs) into power systems is an emerging approach to provide flexibility for renewable energy utilization. However, the existing models of DCS components, such as ice-storage cooling sources, cooling pipelines, and cold warehouses, are simplified, failing to fully utilize that flexibility, which brings difficulties for renewable energy utilization. Therefore, this paper proposed a coordinated operational strategy for an integrated power and cooling system combining detailed operating characteristics of DCSs to exploit the potential flexibility of DCSs for better integration of renewable energy. The primary cooling source components, including the chiller, ice storage tank, pump, and cooling tower, are established to model a detailed process of cooling generation and storage. The thermal inertial of district cooling networks (DCNs) is formulated as a quasi-dynamic model by considering dynamical temperature characteristics and transmission delay to exploit the virtual energy storage capacity of the cooling water pipeline. Moreover, the thermal inertia of the demand side, such as buildings and cold warehouses, is considered to utilize thermal storage capacity. The objective function of the proposed optimization model is to minimize the overall operating cost of the integrated power and cooling system. Numerical simulations show that the proposed method could reduce the operating cost by 15.4% and the wind curtailment rate by 86.2%, indicating better performance of the proposed method in improving economic benefits and reducing renewable energy curtailment. • We proposed a coordinated operational strategy combining flexible resources of DCSs. • We established a detailed model of cooling source including chiller, ice storage tank, pump, and cooling tower. • The thermal inertial of district cooling networks is formulated as a quasi-dynamic model. • The combined cooling and power system can reduce the costs and wind curtailment. [ABSTRACT FROM AUTHOR]

Given the significant energy consumption and carbon emissions associated with buildings, there is increasing interest in improving sustainability of building operations to reduce impact on climate change. A common goal is to operate buildings as "net-zero" energy users in which all energy consumed by the building is balanced against renewable energy purchased from the grid or produced on site. To achieve net-zero status, many buildings will require significant retrofit so as to both reduce energy consumption in the absolute sense and provide the remainder without consuming fossil fuels. Thus, multi-year planning is required to ensure that goals can be met on time. In addition, due to the inherent uncertainty associated with energy consumption and generation, actually achieving net-zero energy use may require discretionary curtailment actions to be taken, and deciding whether such actions are necessary can be challenging. To address these challenges, we propose in this paper a design optimization and operational planning strategy to make the decisions needed to achieve sustainability goals in buildings. The strategy can be applied to schedule design changes over a long horizon to meet annual targets, and it can also be applied in closed loop on a shorter horizon to determine whether curtailment is needed to stay on track. We discuss the formulation of the optimization problem, solution methods, and modeling approaches for key parameters. Application of the strategy is illustrated via examples. Overall, this approach will help automate planning that is often done manually, allowing buildings to take a significant leap forward toward achieving their sustainability goals. • Achieving sustainability goals in buildings requires multi-year planning. • The required design and operational decisions can be made via optimization. • Key problem parameters can be obtained from data-driven modeling. • The strategy allows sustainability goals to be met at minimum cost. [ABSTRACT FROM AUTHOR]

• Peer-to-peer energy trading problem of multi-energy microgrids are investigated. • The concept of energy trading consistency is firstly proposed. • The off-design performance model of the energy conversion device is considered. • The decision-making problem is solved by multi-agent soft actor-critic approach. Multi-energy microgrid technology is an essential for addressing the diversification of energy demand and local consumption of renewable energy sources. Peer-to-peer energy trading has emerged as a promising paradigm for the design of a decentralized trading framework. Therefore, this paper investigated the external peer-to-peer energy trading problem and internal energy conversion problem of interconnected multi-energy microgrids. The concept of energy trading consistency to avoid unreasonable energy trading behavior is first proposed and an off-design performance model of the energy conversion device is considered to more accurately reflect the operating status of the device. The complex decision-making problem with significantly large high-dimensional data is formulated as a partially observable Markov decision process and solved using the proposed multi-agent deep reinforcement learning approach combining the centralized training decentralized execution framework and soft actor-critic algorithm. Finally, the effectiveness of the proposed method was verified through a case simulation. The simulation results showed that the proposed method can reduce the total cost compared with the rule-based method. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated. • Short-term solar power generation prediction model established. • An influential factors analysis is designed to obtain the optimal combination of the influential factors. • A novel decomposition-ensemble framework is proposed to mine the intrinsic modes of the solar power generation sequence. • An attention-based Bayesian Seq2Seq method is presented. [ABSTRACT FROM AUTHOR]

• Metal hydride tanks were investigated experimentally. • Levelized cost of hydrogen was determined. • Alkaline and PEM- based generators were compared. • Avoided energy consumption comparing to compression was shown. • Universal methods for economic analysis were developed. This study presents methodology for the evaluation of appropriateness of a hydrogen generator for gas production in multiple distributed plants based on renewable energy sources. The general idea is to form hydrogen clusters integrated with storage and transportation. The paper focuses on the financial viability of the plants, presenting the results of economic evaluation together with sensitivity analysis for various economic factors. The analyzed case study proves that over a wide range of parameters alkaline electrolyzers show favorable economic characteristics, however, a PEM-based plant is more resilient to changes in the price of electricity, which is the main cost component in hydrogen generation. The study is enriched with an experimental investigation of low-pressure storage methods, based on porous metal hydride tanks. The effectiveness of the tanks (β) compared to pressurized hydrogen tanks, in the same volume and pressure is equal to β = 10.2. A solution is proposed whereby these can be used in a distributed hydrogen generation concept due to their safe and simple operation, without additional costly equipment, e.g., compressors. A method for evaluation of the avoided energy consumption as a function of the effectiveness of the tanks is developed. Avoided energy consumption resulting from implementing MH tanks equals 1.33 – 1.37 kWh per kilogram of hydrogen depending on the number of stages of a compressor. The methods proposed in this paper are universal and can be used for various green hydrogen facilities. [ABSTRACT FROM AUTHOR]

Highlights • A methodology to obtain urban-energy diagnoses of different city areas is developed. • The methodology identifies critical urban-energy aspects in different city areas. • Energy improvement measures can be addressed specifically to each city area. • The current methodology allows building long-term scenarios in future works. Abstract The promotion of energy efficiency (EES) and renewable energy (RES) programs has a long history in developed countries; however, in the rest of the world such initiatives are incipient. Meanwhile, cities are the main recipients of this type of programs; therefore, the development of methodologies for the elaboration of prospective urban-energy studies is really important to efficiently use the available resources. Nevertheless, cities have substantial differences among themselves in terms of structure, conformation and growth, along different regions in the world. Hence, it is necessary that methodologies can respond to different realities that each case presents. In Argentina, as in many developing countries, cities are heterogeneous within their own urban structure, manifesting large socio-economic, morphological and infrastructure inequalities. This situation forces us to board the territory in a particular way and, therefore, to develop a proper methodology for the construction of prospective studies for our context. Thus, this paper proposes an innovative methodology that disaggregates the city in urban areas with similar internal behaviors [homogeneous areas (HA)]; which allows us to assess specific EES/RES measures for each of them, such as the application of regulations, programs or incentives for: the improvement of new and existing building's envelopes; the insertion of solar energy systems for space heating, sanitary hot water or PV; the replacement of energy-intensive appliances; among other options. This analysis considers socio-demographic, urban-morphology, building and energy consumption variables/indicators, which enable us to identify critical aspects in each homogenous area. This approach is unusual in most prospective urban-energy studies, where the study areas are addressed as a single element or, alternatively, a small urban sector is used as an example. In this context, this paper presents the methodology for the urban-energy diagnosis stage, obtaining as results the construction of the "base year" model into the LEAP software and the energy guidelines for each homogenous area. The continuation of this research, with the assessment of "alternative scenarios", will be presented in future publications. [ABSTRACT FROM AUTHOR]

Abstract This paper provides a comprehensive review of 48 studies on the Life Cycle Assessment (LCA) of biogas production from 2006 to 2018 (13 years). The scope of the study, type of feedstock and impact categories used in the reviewed studies were discussed. Although various studies have been carried out to assess the efficiency and potential of wastes for biogas production, however, little attention has been paid particularly in the developing countries to conduct comprehensive analysis related to the environmental performance of biogas generation based on the LCA methodology. In Malaysia, the utilization of palm oil mill effluent (POME) as the source of energy has recently been expanded due to its abundant production at the palm oil mills. Therefore, this paper also highlights and discusses the feasibility of LCA approach on biogas production from POME as well as the opportunities and challenges from the Malaysian perspective. Highlights • Utilization of biogas production from organic waste to provide a clean energy. • Environmental performance of biogas as a renewable energy using an LCA methodology. • The trends of LCA approach in biogas production was reviewed worldwide. • The opportunities of biogas production from POME in Malaysian was discussed. • The feasibility of LCA approach on biogas production in Malaysian was discussed. [ABSTRACT FROM AUTHOR]

Highlights • Review of the application of reinforcement learning (RL) for demand response (DR) • DR is relevant for integrating renewable energy source into the smart grid. • Considering RL/DR from energy generation and storage, to supply and user satisfaction. • Typical algorithms and open research directions are discussed. • Most articles focus on single-agent systems in stationary environments. Abstract Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth. [ABSTRACT FROM AUTHOR]

Highlights • This paper broadens current conceptualizations of energy justice for smart grids. • The study explores values in the public debates on smart grids in two countries. • Value conflicts show the importance of distributive and procedural justice. • It is debated if the systems lead to more equity or reinforce injustices. • Energy justice needs to be broadened to include data privacy and security issues. Abstract Smart grid systems are considered as key enablers in the transition to more sustainable energy systems. However, debates reflect concerns that they affect social and moral values such as privacy and justice. The energy justice framework has been proposed as a lens to evaluate social and moral aspects of changes in energy systems. This paper seeks to investigate this proposition for smart grid systems by exploring the public debates in the Netherlands and the United Kingdom. Findings show that smart grids have the potential to effectively address justice issues, for example by facilitating small-scale electricity generation and transparent and reliable billing. It is a matter of debate, however, whether current smart grid designs contribute to cost and energy savings, advance a more equitable and democratic energy system, or reinforce distributive and procedural injustices. The increased use of information and communication technology raises value conflicts on privacy and cyber security, which are related to energy justice. This research contributes by conceptualizing energy justice in the context of smart grids for the first time. The energy justice framework is broadened by including values and value conflicts that pertain directly to the increased use of information and communication technology. For policy makers and designers of smart grids, the paper provides guidance for considering interconnected social and moral values in the design of policies and smart grid technologies. [ABSTRACT FROM AUTHOR]

• Proposed an energy sharing model of MMGs considering the multiple uncertainties and the cheating behaviors. • Using robust optimization to deal with uncertainties brought by grid tariffs and renewable energy. • Cheating behaviors in energy transaction are analyzed and a cheating equilibrium mechanism based on ITM is proposed. Multi-microgrid system (MMGs) has drawn extensive attention recently because of its high energy efficiency. However, MMGs' operational efficiency can be affected by market price fluctuations and intermittent renewable energy. This paper proposes an energy-sharing model based on the Nash bargaining game between multi-microgrids. The proposed model provides a robust energy trading schedule to deal with uncertainties brought by grid tariffs and renewable energy. To ensure the model is tractable, the original game problem is equivalently converted into a system benefit maximization subproblem and an additional profit distribution subproblem to get optimal energy sharing power and prices. In addition, microgrid has the motivation to cheat for maximizing its benefits which may lead to the breakdown of cooperation. Furthermore, cheating behaviors in energy transaction are analyzed; the energy sharing scheme based on cheating equilibrium is derived by proposing an intermediary transaction mode. Finally, the alternating direction method of multipliers (ADMM) is used to protect the players' privacies in a distributed way. Simulation results show that the proposed model can realize stable cooperation, effectively reduce operating costs and immunize against multiple uncertainties and cheating behaviors. [ABSTRACT FROM AUTHOR]

• Renewables are variable and require operation flexibility. • Colombia expects more than 10 GW of solar and wind power capacity by 2030. • We quantify the flexibility requirements for the net load and ramps. • Conventional reserves must be doubled at noon. • Conventional generators must provide 7%–10% of additional ramps. Multiple factors drive the energy transition toward power systems based on renewable energies. The use of renewable energies brings new challenges for operators since they are difficult to predict. Power system flexibility is the ability to handle differences between supply and load and can be quantified to measure the effects of renewable energies on power systems. Colombia expects to triple the current solar and wind power capacity by 2030; therefore, it is essential to evaluate the flexibility of the Colombian power. This paper presents a pioneering analysis in the local context, filling a local research gap, by quantifying the flexibility requirements of the Colombian power system based on changes in the net load and its ramps and considering wind and solar generation scenarios and load projections. This analysis has not been conducted locally and can be used by policymakers to determine the country's energy transition path. The results show that renewable power impacts conventional generators. Hydro generation handles high ramp deviations well under normal conditions but faces challenges during low hydrology periods. The Colombian power system is projected to be flexible in 2030 but less so in 2050 due to overproduction and higher net load ramps. [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]

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management. • Impact of energy consumption and production on GHG emissions were analyzed for 122 countries in 1990–2020. • Quantile and Quantile-on-Quantile regression were used to catch the specifics of energy consumption and production patterns. • Two temporal breakpoints (1999/2000 and 2009/2010) were introduced to catch shifts in variables dependencies. • The influence of fossil fuels on GHG emissions is not homogeneous as it usually considered, the same is fare for renewables. • A promising option to reduce GHG emissions is carbon capture, utilization and storage (CCUS). [ABSTRACT FROM AUTHOR]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive overview of advanced water desalination systems powered by renewable energy sources, specifically focusing on developments in the past decade. Solar energy is particularly emphasized as a pivotal resource for powering diverse desalination methods, including membrane distillation, reverse osmosis, humidification-dehumidification, forward osmosis, and hybrid systems. The primary objectives of this study encompass evaluating current advancements in renewable energy systems, especially solar energy, for driving water desalination processes. Furthermore, the study aims to identify gaps in research, challenges, and recommendations within these processes, and to assess the performance and refinement potential of hybrid desalination systems. Through a comprehensive analysis of various investigations, integrated effectiveness and cost-efficiency are examined, focusing on practical energy utilization. Notably, the analysis reveals the potential of hybrid desalination systems to reduce energy consumption. For instance, the hybridization of forward osmosis with membrane distillation showcases a significant energy ratio decrease from 0.89 to 0.64. Moreover, the combination of humidification-dehumidification with reverse osmosis is shown to reduce energy usage while enhancing freshwater output by up to 38% compared to standalone reverse osmosis. However, challenges persist, such as the need for improved integration of hybrid systems with concentrated solar collectors like photovoltaic thermal and concentrated photovoltaic thermal technologies. • A comprehensive review of water desalination systems powered by renewable energy. • The importance of combination of solar energy with hybrid desalination systems. • Renewable energy integration with different desalination techniques. • A comparative analysis of integration of renewable energy with hybrid desalination. • Challenges and future recommendations for successful integration of renewable energy with desalination. [ABSTRACT FROM AUTHOR]

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less. • Multiscale simulations reveal complex interactions between IESs and energy markets. • Standard price taker formulations are not predictive in realistic design scenarios. • Neural network surrogates accurately predict IES/market interactions. • Optimization with market surrogates is tractable and accurate. • Computational design framework is built entirely from open-source tools. [Display omitted] [ABSTRACT FROM AUTHOR]

Ocean wave energy is a kind of renewable energy with huge reserves, which can play an important role in the future construction of smart oceans. The low capture efficiency and narrow bandwidth of traditional wave energy capture devices seriously restrict the large-scale application of wave energy. To address these issues, a rack-pinion-lever-spring (RPLS) adaptive bistable Power Take-Off (PTO) mechanism with a time-varying potential barrier is proposed and applied to a point absorber wave energy converter (WEC). In the proposed nonlinear PTO system, a rack-pinion-lever (RPL) mechanism, as a displacement amplifier, has an effect on enhancing the poor amplitude sensitivity issue of the negative stiffness mechanism. Based on linear wave theory, a special nonlinear one-and-half-degree of freedom dynamic model is developed to describe the adaptive bistable wave energy converter (AB WEC). In comparison with the traditional bistable WEC (TB WEC) and equivalent linear WEC, the numerical results show that the AB WEC can greatly enhance the capture efficiency of lower frequency energy, especially at small wave excitations. The energy capture efficiency of the AB WEC is close to that of the TB WEC even at large wave amplitudes and is higher than that of the equivalent linear WEC. Finally, the wave energy capture characteristics of the three kinds of systems are studied based on the wave distribution diagram of a certain sea area in the South China Sea. The results show that the AB WEC has a large wave height adaptability in the low frequency region. The AB PTO mechanism with a displacement amplifier proposed in this paper can provide a technical reserve for the highly efficient and wide bandwidth capture of low frequency ocean wave energy. [ABSTRACT FROM AUTHOR]

The increase in energy demand makes it urgent to explore green energy sources. And wind energy is one of the most promising renewable energy sources. In this paper, a rotating hybrid nanogenerator with a combination of a direct-current triboelectric nanogenerator (DC-TENG) and an electromagnetic generator (EMG) is designed; the design concept is to harvest wind energy. First, an optimized ternary DC-TENG is designed based on the ternary dielectric triboelectrification effect. A soft contact structure is designed, which can effectively increase the frictional area as well as reduce the frictional resistance. Then, by introducing the EMG unit, the output power of the device and the speed of charging the capacitor are increased. The high output voltage of the DC-TENG and the high output current of the EMG complement each other to produce excellent power output. At 180 rpm/min, the output power of DC-TENG and EMG can reach 1.78 mW and 215 mW, respectively. With a simple power management circuit design, the device is successfully implemented to power small electronic devices. By charging a suitable capacitor, the device can be used as a power supply to power the sensor. Together with the sensor and power management circuit, it forms a self-powered sensing system that can drive the sensor for continuous operation. This work provides a simple, convenient, and reliable design that offers new insights into energy harvesting in nature. • A ternary dielectric triboelectrification effect triboelectric nanogenerator. • A soft contact structure is designed to minimize frictional resistance. • Introduction of an electromagnetic generator to reduce the capacitor charging time. • Energy supply for the continuous operation of the self-powered sensing systems. [ABSTRACT FROM AUTHOR]

The development of renewable energy is the key to tackling climate changes and ensuring energy security. Meanwhile, the process is usually associated with a huge investment and financing gap, especially for transitional countries like China. Hence, developing and innovating the traditional financial system is urgent. However, existing studies have mainly focused on traditional financial perspectives, and few papers have critically examined how digital financial inclusion affects the growth of renewable energy industries. To this end, using Chinese provincial data from 2011 to 2020 and applying Feasible Generalized Least Squares (FGLS) method, this study explores the effect of financial inclusion on renewable energy and its underlying mechanisms from the production and consumption perspectives. It is found that the impact of financial inclusion on the production side of the renewable energy industry is significantly better than on the consumption side, and there is heterogeneity effect in different energy resource varieties, different regional locations, and different financial regulation intensities. The financial inclusion on renewable energy has a significant pulling effect in photovoltaic and wind provinces, but not in large hydropower provinces. Then, its driving effect is particularly pronounced in the northern region. Nonetheless, it is not significant for renewable energy consumption in the southern region. Besides, this positive effect cannot be achieved without stronger financial regulation. The mechanism of action assessment finds that optimizes the consumption pattern, increase the competition of commercial banks, and improve technological innovation all contribute to the positive effect whereas, FDI shows the negative effect. This study provides new evidence on China's achievement of the energy transition, suggesting the need to further improve incentives for FDI firms and to strengthen consumption-side guidance. [ABSTRACT FROM AUTHOR]

A hybrid renewable energy system (HRES) utilizes the coaction of diverse energy to enhance energy efficiency while improving economic benefit. Under the paradigm of the electricity market, the HRES decomposes bilateral contract (BC) signed in the medium to long-term market and bids in the spot market are a promising way to get more revenue while ensuring basic profits by exploiting the synergy of different markets. However, the scheme of BC decomposition and the uncertainties of renewable energy affect the revenue of HRES scheduling. This paper proposes an optimal scheduling model for HRES to perform the BC flexible decomposition while bidding in the day-ahead energy market to maximize total revenue. The HRES is a real system located in Southwest China, and its constituent units are a pumped storage, two hydropower, and two solar stations. The Wasserstein metric ambiguity set is adopted to portray the uncertainty of available solar power and the forecasted market price error. Then, the proposed model is formulated as a distributionally robust chance-constrained scheduling model. Finally, the conditional value-at-risk approximation and strong duality are employed to reformulate this model into a tractable problem. The numerical results show that the proposed model can increase the total profit of HRES by 2.26 % (i.e., 2.56 × 104 DKK) compared to the case of fixed BC decomposition and bidding. Moreover, the impact analysis related to the ambiguity set and risk tolerance of uncertainty on total revenue of the proposed model is also presented, which provides the measure for the HRES to trade-off the robustness and economy of the solution. • The dispatch of the hybrid renewable energy system in the power market is studied. • A distributionally robust chance-constrained model is proposed accordingly. • The bilateral contract flexible decomposition is more profitable for the system. • The model can balance the economy and conservativeness of the solution. [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]

The large-scale integration of non-dispatchable renewable energy must be carefully planned, especially in poorly robust electrical energy systems. Tenerife is the most populated of the islands in the Canary Archipelago (Spain) and has an isolated electrical system. It is envisaged for the horizon of 2040 that the island will have a high contribution of non-dispatchable renewable energies (fundamentally wind and solar photovoltaic). Tenerife also has exploitable high enthalpy geothermal resources. An energy planning model is implemented in this paper based on which different scenarios with high non-dispatchable renewable energy penetration in the island's electrical system are simulated. Geothermal energy is also incorporated in the simulations as a dispatchable renewable energy source, with the power required sized according to the hypotheses of each scenario. The results are compared according to different technical and economic parameters. An evaluation is also made of the influence that the capacity of the electrical system to take on higher non-dispatchable renewable power and the presence of energy accumulation systems might have on the minimization of losses in the non-dispatchable renewable energy capacity factor. The results of the simulated scenarios show that geothermal energy could meet up to 28.8% of the island's electrical energy demand, increasing the stability, flexibility and supply guarantee of system. A 100% renewable system could be possible through the incorporation of this energy source and the planning of energy accumulation systems. In the case study, the economic saving for Spain's electrical system could exceed 400 €million per year. • Sustainable electricity generation in poorly robust insular electrical systems. • Large-scale integration management of non-dispatchable renewable energies (NDREs). • Exploitation of geothermal energy in 100% renewable electrical systems. • Importance of energy accumulation systems in NDRE integration. • LCOE decrease through exploitation of renewable energies. [ABSTRACT FROM AUTHOR]

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. • A data-driven scheduling-correction framework is proposed to provide real-time actions with a global vision, making better use of the seasonal hydrogen storage. In the scheduling module, ex-post optimal SoC sequences of the seasonal storage are generated using historical data. In the correction module, the real-time operation relies on a bi-objective rolling-horizon optimization problem which minimizes the operating cost while following the SoC reference of the seasonal storage, which is sequentially updated along with time. • The kernel regression technique is established to sequentially update the SoC reference of the seasonal hydrogen storage. This technique calibrates a conditional probability distribution based on the newly observed data; the resulting distribution (weight coefficients) interprets how likely the current year is similar to each of the past years. The reference is updated in each period according to the weighted sum of the ex-post optimal SoC sequences. • Systematic tests are conducted to compare the cost, load shedding, and renewable energy curtailment achieved by different methods. Numerical results show that the scheduling-correction framework can fully exploit the seasonal storage. The proposed method outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. [ABSTRACT FROM AUTHOR]

Increasing low-grade and renewable energy sources in the HVAC systems has become necessary to reduce heating energy consumption. Hence, seeking a new air conditioning system that can be combined with renewable energy is significant to maintain a healthy and comfortable indoor environment with a low cost. This paper proposes a novel air source heat pumps assisted solar hot water with interactive cascade ventilation system (shortened as SWAP-ICV). Taking the traditional air source heat pumps with mixing ventilation system (AP-MV) as a reference, the virus control performance and energy efficiency of SWAP-ICV is investigated through CFD and TRNSYS simulations. The results demonstrate that interactive cascade ventilation under full-fresh air operation mode and normal-epidemic linkage mode can make the annual infection risk reduce by 41.16% and 9.33% compared with mixing ventilation under full-fresh air operation mode. Meanwhile, the proposed system can reduce energy consumption by 59.92% and 69.8% during the normal period and epidemic period by introducing solar energy. The average system COP during the heating season indicates that SWAP-ICV can improve the system efficiency by 1.9–2.58 times than AP-MV system. The conclusions obtained from this research can provide new ideas for the design and operation of air conditioning systems in the post-epidemic era. • A novel interactive cascade ventilation (SWAP-ICV) system was established. • Normal-epidemic linkage operation mode of SWAP-ICV was proposed. • The SWAP-ICV system can make the annual infection risk reduce by 41.16% and 9.33%. • The new operation mode can reduce energy consumption by 69.8%. • The SWAP-ICV system can improve COP by 1.9–2.58 times during the heating season. [ABSTRACT FROM AUTHOR]