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The present study investigates the effect on methane production from waste paper when co-digested with macroalgal biomass. Both feedstocks were previously mechanically pretreated to reduce their particle size. The study was planned according two factors: the feedstock to inoculum (F/I) ratio and the waste paper to macroalgae (WP/MA) ratio. The F/I ratios checked were 0.2, 0.3 and 0.4 and the WP/MA ratios were 0:100, 25:75, 50:50, 75:25 and 100:0. The highest methane yield (386 L kg −1 VS added ) was achieved at an F/I ratio of 0.2 and a WP/MA ratio of 50:50. A biodegradability index of 0.87 obtained in this study indicates complete conversion of feedstock at an optimum C/N ratio of 26. Synergistic effect was found for WP/MA 25:75, 50:50 and 75:25 mixing ratios compared with the substrates mono-digestion. [ABSTRACT FROM AUTHOR]

This work reports an innovative simulation method for effectively utilizing paper-mill sludge based on the co-gasification approach for energy-enriched renewable fuel production. An Aspen plus-based co-gasification model was developed to simulate and examine the synergetic effects of biomass and paper-mill sludge co-gasification. Initially, the model was validated using experimental data from the reported literature and found good agreement regarding gasification and co-gasification approaches. Sensitivity analysis of biomass gasification revealed that the maximum H 2 content of 29.6% and CO (36.0%), LHV (7.8 MJ/Nm3), 72.2% (CCE) was obtained at optimal (850 °C: temperature; 1 bar: pressure; 0.2: ER) conditions. Further, the H 2 proportion enhanced, and CO declined to 38.8 and 30.7% at the same operating conditions, respectively, with a co-gasification ratio of 50% compared to 0% (H 2 : 29.6% and CO: 36.0%). However, with increasing the co-gasification ratio beyond 20%, the CO content started to follow upward trends; at the same CGR, the H 2 , LHV, and CCE observed were 36.0%, 7.76 MJ/Nm3, and 65.2%, respectively. Hence, the developed co-gasification model can provide vital information for large-scale gasifier design, operating decisions, and optimization using different biomass blends. • Biomass and PMS utilization to produce renewable fuel. • Co-gasification parametric effects evaluation on product yield. • Increasing the proportion of PMS in feedstock mixture increases H 2 content. • 20% PMS co-gasification provided consistency with reported literature. [ABSTRACT FROM AUTHOR]

The transition from conventional energy to clean renewable energy is becoming a global trend, and related issues are attracting widespread attention. Given the possible time-varying energy transition process, this paper proposes a dynamic signed network topology approach for empirical analysis, which is then applied to evaluate the transition from conventional energy to clean renewable energy in G7 countries. The research findings indicate a discrepancy between the positive and negative spillover effects of conventional and clean renewable energy consumption in G7 nations. The transition from conventional energy on clean renewable energy involves alternating positive and negative spillover effects. On average, oil exhibits a positive spillover effect on clean renewable energy consumption. However, the spillover effect of natural gas on clean renewable energy consumption presents an asymmetric relationship across the G7 countries. • This paper presents a symbolic network topology approach. • The approach is applied to the case of energy transitions in G7 countries. • The study employs a historical decomposition methodology. • The study examines the dynamic transition from traditional energy sources to clean and renewable energy. • Based on extracted policy implications led transitioning from dirty energy to clean energy dependency. [ABSTRACT FROM AUTHOR]

The energy sector prioritizes innovative clean energy production. This research explores a versatile hybrid solar-biomass trigeneration system, integrating organic Rankine cycle (ORC), a heat pump, and a proton exchange membrane electrolyser. Sustainable and adaptable, it produces electricity, heating, and hydrogen for Indian paper and pulp industries. The study assesses the system's thermodynamic, economic, and environmental feasibility. Initially, parametric analysis and first layer multi-objective optimization (MOO) are performed to select the efficient working fluid and operating temperatures. Benzene outperforms other fluids, achieving an energy utilization ratio (EUR) of 0.27 and exergy efficiency of 75 %. A typical heat requirement of 250 kW for processing 150 kg of paper necessitates collector areas of 3500 m2 and 4500 m2 in Aurangabad and Tiruchirappalli, respectively, and biomass waste of 0.05 kg/s. The combined system performance during day time and night time operation was studied by altering the critical solar radiation, ambient temperature and biomass waste flow rate. The second layer MOO revealed an optimum EUR of 0.25 and exergy efficiency of 9 % at the lowest cost of 33 $/h for biomass waste operation. In comparison to the conventional electricity-powered system, the proposed system would reduce CO 2 emissions by 90 kg/h to 270 kg/h. [Display omitted] • Solar and biomass waste powered systems proposed for paper and pulp applications. • ORC integrated heat pump system for heating, electricity, and green H 2 generation. • Collector area and biomass flow rate are designed for daily paper output of 300 kg. • Benzene is an efficient working fluid, produced 815 kW of heat and 3 kg/h of H 2. • The recommended system reduced CO 2 emissions by 90 kg/h to 270 kg/h. [ABSTRACT FROM AUTHOR]

Growing demand for electricity due to economic development contributes to increased greenhouse gas production, especially CO 2. However, emissions can be limited by enhancing the efficiency of primary energy conversion, such as integrating geothermal energy into coal-fired power plants. Therefore, this paper proposes replacing conventional feed-water heaters with geothermal preheaters to create a hybridized system. This study was based on a numerical model validated at a selected Polish power unit. The model was subsequently calibrated for off-design conditions to facilitate partial load analysis. The obtained characteristics outperformed those of the non-hybrid unit, generating over 18 MW of electric power output. Such an improvement could potentially boost the unit's net efficiency by more than 2.6 %. This enhancement is significant as power units typically operate under part load for approximately 90 % of the time, hence the need to evaluate the performance characteristics of hybridized units in those states. Furthermore, the research outlines the potential decrease in the plant's CO 2 emission factor, with reductions reaching up to 6.5 % under off-design conditions. Based on a gap analysis of the existing literature, this paper's comprehensive partial load evaluation serves as a new addition to research on hybridized systems. • An in-depth analysis of feed-water heaters operation is performed. • Turbine performance calculations for both design and off-design states is validated. • The comprehensive study of hybridized systems integrates three major factors. • Hybrid geothermal-fossil power cycle is vital for specific CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Decarbonising the heating sector is one of the key elements to realizing the ambitious dual carbon goals of China, which is the largest carbon emitter and energy consumer globally. Currently, district heating (DH) systems have penetrated approximately 88% of the urban heating areas in Northern China. Nevertheless, around 90% of the heating demand in China still relies on fossil fuels. A larger scale integration of renewable energy and waste heat sources into the DH systems is critical for decarbonising the entire heating sector in China. However, a deeper level of comprehension is required to harness its full potential. This paper provides a thorough investigation of the status, potential, and national policy schemes of renewable energy and waste heat recovery in the DH systems of China. Combined with a critical review of recent literature on relevant areas published in both international and Chinese domestic sources, the trends, challenges, and future perspectives are discussed from scientific research and practical implementation aspects. This paper highlights the synergy of the integration of renewable energy and waste heat sources in DH, the energy efficiency improvements as well as the use of thermal storage technologies through the implementation of 4th generation district heating and smart energy systems that could offer a more economically viable pathway forward. • Focus on renewable energy and waste heat (REWH) in the district heating (DH) systems in China. • A systematic review of REWH in Chinese DH systems from status, policy, and literature aspects. • Identification of research gaps based on the review of both international and Chinese journals. • Policy recommendations proposed to promote REWH in Chinese DH systems. • The introduction of international experiences on REWH in DH for analysis in the Chinese context. [ABSTRACT FROM AUTHOR]

Sustainable energy planning for power generation with renewables requires a multidimensional approach since it directly impacts several sectors such as the national grid, the environment, and the economy. This paper suggests an intelligent decision based on a combination of the Analytical Hierarchy Process and Artificial Neural Networks to assist policymakers in evaluating future renewable energy investments for power generation. A case study of Bahrain's power system is used to illustrate the usefulness of the proposed planning approach and also to discuss its efficiency. The Analytical Hierarchy Process model outcomes revealed that wind turbines are the most appropriate technology for Bahrain with 32.5 % priority, followed by PV and CSP with 32.2 % and 16.3 %, respectively. Then, the Artificial Neural Networks model was structured based on the generated scenarios from the Analytical Hierarchy Process model, and it reached its best performance after 384 cycles. The integrated approach overcomes some of the Analytical Hierarchy Process's limitations and provides an intelligent tool for advanced assessment of a sustainable power system. • Planning addresses intermittent nature of renewable energies. • Sustainability study integrates diverse, context-sensitive perspectives. • Tool merges Analytical Hierarchy Process with Neural Networks. • Developed tool applied to Bahrain's renewable energy context. [ABSTRACT FROM AUTHOR]

In recent years, the construction of hydrogen refueling stations (HRSs) has been in full swing. However, irrational configuration and design can increase the operation and maintenance costs of HRSs and reduce their overall energy conversion efficiency. This paper introduces the configuration optimization of a hybrid PV/wind energy system for hydrogen refueling stations. Firstly, the distribution of hydrogen refueling demand of hydrogen fuel cell vehicles (HFCVs) in different time periods was simulated by the Monte Carlo method according to the driving rules of HFCVs. Then a model of renewable energy HRS was established in Homer, the purpose is to provide energy supply service for HFCVs through hydrogen production by electrolysis of water from renewable energy generation. Based on this, a load demand response model is developed. Finally, the best system configuration under different scenarios is obtained with the objective of minimizing the total net present cost (NPC). Through simulation calculations, the results show that the system configuration is more economically feasible after considering the demand response. The best system configuration was a grid-connected system containing a 548 kW PV, 1040 kW WT, 600 kW electrolyzer, and 600 kg hydrogen tanks This renewable energy system has the lowest NPC of $8,351,442 and produces 40,000 kg of green hydrogen annually. The sensitivity analysis also shows that with the advancement of hydrogen energy development and technology, the system will bring multiple benefits such as environmental protection and economic benefits in the future. • PV-wind-hydrogen-electrolyzer energy system for hydrogen refueling stations. • Predicting hydrogen refueling demand distribution for hydrogen fuel cell vehicles. • Demand response to guide load transfer to reduce system configuration cost. • Economic analysis in terms of net present cost and cost of electricity. [ABSTRACT FROM AUTHOR]

Renewable energy sources, such as wind and solar, can reduce environmental pollution and promote the transformation of the energy structure. However, the intermittency and uncertainty of new energy sources such as wind and photovoltaics pose challenges to large-scale grid integration. Effective utilization of intermittent energy sources requires large-scale energy storage facilities. Salt rock is recognized as an excellent medium for underground large-scale energy storage with a wide range of applications. This paper identifies the potential of salt caverns to be used for large-scale energy storage by analyzing the distribution of wind and solar energy resources in China, taking into account the grid-connected infrastructure of the Chinese power grid and the geographic location of salt mines. The overlapping areas are identified for utilization in compressed air energy storage and hydrogen storage. Through meticulous categorization and analysis of the factors influencing salt cavern underground storage site selection, the AHP-EWM method is introduced to establish an objective hierarchy model for a comprehensive evaluation system of salt cavern underground storage site selection. The comprehensive planning of China's 20 major salt mines is conducted, and the research findings hold significant reference value for facilitating the low-carbon energy transition in China. • The ways which salt mines can be used for renewable energy peaking are summarized. • Renewable energy religions combined with grid-connected infrastructure and salt mines in China were studied. • Objective hierarchical modeling of subsurface salt cavern siting was developed using a combinatorial assignment method. • Energy Storage Planning for 20 Major Salt Mines in China. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

A neural network-based design method for thermoacoustic Stirling engines (TASE) is presented in this work. The main goal of the article is to predict the design parameters (Length of resonator (stub) (L R), Length of inertance tube (L in), Mean pressure (P m)) of the thermoacoustic Stirling engine in such a way that the dynamic instability is guaranteed. In this regard, the governing equations of the engine are presented first. Next, with the help of the data extracted from the simulation of the governing equations of the engine, a neural network with the structure of 9–35-3 is trained. The effectiveness of the artificial neural network (ANN) structure is explored via regression and MSE (mean square error) analyses. Next, in order to evaluate and validate the neural network, the experimental data of the constructed thermoacoustic Stirling engine (SUTech-SR-4) is considered. It is important to note that the SUTech-SR-4 is introduced for the first time in this work. Investigations have shown that the neural network could estimate the engine design parameters well and with an acceptable approximation in such a way that the dynamic instability of the engine is also established. Besides, the value of the estimated design parameters for the engine made by the neural network was close to the experimental values. Following, the developed engine is introduced and its performance is discussed. The developed engine has been able to produce a power equivalent to 0.346 W at a pressure of 1 bar (with air working fluid) and a working frequency of 15.9 Hz. It should be noted that the method presented in this article can be used for other types of thermoacoustic Stirling engines. Moreover, the selection of design parameters for the presented method will depend on the physics of the engine and the available facilities. • This paper first introduces the governing mechanical equations of the engine. Then, the desired neural network's features and structure are selected. • After selecting the suitable neural network, it is trained through the simulation data obtained from the governing equations of the engine. • Finally, the constructed thermoacoustic Stirling engine (SUTech-SR-4) is introduced, and its performance is examined. [ABSTRACT FROM AUTHOR]

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network. • A stochastic model for coupled power-gas networks including refueling stations. • Effect of storage on operation of electric and gas networks is investigated. • Effect of electric and gas demand response on power-gas nexus is investigated. • Effect of contingencies on operation of electric and gas networks is investigated. • Effect of wind uncertainties on operation of power-gas nexus is assessed. [ABSTRACT FROM AUTHOR]

Increasing penetration of power electronic-based renewable energy generation resources, decreasing total rotational inertia, and bidirectional power flow have changed the concept of smart microgrid stability and operational requirements. The stability of low inertia and fast dynamic renewable smart microgrids is highly dependent on reliable and feasible interoperability between their components. Communication infrastructure is responsible for providing this interoperability within the specified time horizon, considering confidentiality, integrity, availability, and authenticity of the exchanged information. This makes smart microgrids vulnerable to cyber-attacks. Attackers attempt to disrupt microgrid operations by manipulating transmitted data or attacking the physical system. This paper presents a systematic review of standards, challenges, and solutions for renewable smart microgrid cyber resiliency, focusing on communication technologies, protocols, intelligent electrical devices, and renewable energy generation sources. It provides a detailed cyber threat investigation, including targeted components, methods of execution, provided solutions, and attack destructive results. Then, with deep practical and theoretical knowledge of renewable smart microgrid cyber-attack vulnerabilities, this review recommends several practical solutions for cyber resiliency and proposes potential future studies. • Renewable smart microgrid vulnerabilities to cyberattacks. • Communication, protocols, and governing standards for smart microgrids from cybersecurity perspective. • Cyber threats for intelligent electrical devices and renewable energy sources. • Solutions against cyberattacks in renewable smart microgrid. [ABSTRACT FROM AUTHOR]

The coupling with natural gas networks creates an excellent opportunity for renewable-rich power systems to facilitate the utilisation of renewable energy resources; on the other hand, with the increase in employment of fuel cell vehicles (FCVs), the number of hydrogen refuelling stations (HRSs) is increasing. The integration of HRSs in electric distribution systems may impact the operation of electric distribution systems. Through vehicle-to-infrastructure (V2I) technology, vehicles may exchange information with HRSs and know hydrogen prices. The operation of the coupled power and natural gas networks with integration of green HRSs, considering the model of FCVs has not been investigated in the literature, so, it has been set as the goal of this paper. To avoid the challenges of nonlinear gas flow model, they are linearized through piecewise linearisation. The case study is a 33-bus electric distribution system, coupled with a 14-node gas distribution network; each HRS includes a battery, a hydrogen tank, an electrolyzer, a PV and a wind turbine. MILP models have been used for FCVs, HRSs and power-gas networks. CPLEX solver is used for solving the developed MILP models. The results show that the total cost of the coupled electricity-gas network is $760.37 and the refuelling cost of each FCV is $17.30. The results indicate that both electric and hydrogen storage systems enhance the flexibility of HRSs, enabling efficient utilisation of PV modules and wind turbines, so, only in few hours, HRSs need to purchase electricity from electric distribution system; that is why each HRS makes a considerable profit of $519 per day. The achieved results also indicate that the pressure of all gas nodes and gas flow of all pipelines fall within their prespecified range. • A three-layer model for integration of HRSs in coupled power-gas networks. • FCVs schedule themselves using the prices received through V2I technology. • The nonlinear gas flow constraints have been linearized. • A comprehensive model has been used for HRSs. • Each HRS includes a battery, hydrogen tank, electrolyzer, PV and wind generator. [ABSTRACT FROM AUTHOR]

As the development and use of offshore islands increase, so do the diversity of their energy needs, the cost of utilising the mainland for resource replenishment, and the instability of island energy systems. To address this challenge, this paper introduces an innovative system integrating fossil and renewable energy tailored for these islands, considering seasonal variations and external energy fluctuations. Leveraging a P-graph, an optimisation framework is designed to assess different optimised solutions. A comprehensive analysis is also performed to compare the solutions, while sensitivity assessments evaluate factors including electricity and gas price fluctuations, renewable energy availability, and natural gas demand. Additionally, the carbon emissions resulting from different power generation scenarios are visually represented and thoroughly analysed. The results support two key findings: 1) the cost of combined energy supply is lowest when wind energy accounts for 59 % of the total energy, mainland grid connection accounts for 3 %, fossil fuel energy accounts for 28 %, and natural gas accounts for 10 % of the energy mix; and 2) the price of electricity and natural gas supplied to the islands and the variability in the energy sources of wind and natural gas can significantly affect the cost of the energy system. • Use the P-graph approach to provide optimal and near-optimal solutions. • Integrate electricity and gas systems in one energy network. • The integration enhances the stability of the island energy system. • Calculates carbon emissions of the island energy system. • Sensitivity analysis is used to support practical applications. [ABSTRACT FROM AUTHOR]

In light of the recent developments in the green hydrogen industry, it is essential to investigate its compatibility and integration with existing electrical grids. This task becomes more challenging when considering its implementation in off-grid sites that solely rely on renewable energy. In cases where off-grid sites should provide electricity and hydrogen to a variety of consumers, it is compulsory to have electrical and hydrogen storage devices with long-term storage capabilities. Consequently, a precise programming technique is necessary to manage the storage and consumption of hydrogen and electricity. To investigate such a model, this paper presents a connected electricity-hydrogen grid in which all the required energy is supplied by solar and hydroelectric units. This off-grid system supplies the necessary energy for industrial and residential loads, electric vehicle charging stations, fuelcell car refueling stations, and natural gas pipelines. The power-to-hydrogen (P2H) and hydrogen-to-power (H2P) systems are used to facilitate energy flow between the electrical and hydrogen sectors. A daily-seasonal hydrogen storage is integrated as well. The proposed method contributes by integrating and improving the modeling, management, optimization, and operation of the daily-seasonal hydrogen storage and electrical storage. Its primary objective is to supply electricity and hydrogen to various consumers using solely renewable energy, even in the face of renewable energy fluctuations and outages. The model is mathematically expressed as a mixed integer linear optimization, implemented in GAMS software, and solved by the CPLEX solver. It aims at maximizing profit from selling electricity and hydrogen to the users. Results demonstrate that P2H and H2P units have a complementary operation with the solar system. When there is abundant solar energy, the P2H converts excess clean energy to green hydrogen and H2P operates when solar energy is not available. Seasonal storage also stores hydrogen from spring to autumn and discharges it in winter because of higher hydrogen prices in this season. Such an operation increases the annual profit by about 9%. When the hydroelectricity unit faces an outage in hours 8 to 13, the fuelcell uses the stored hydrogen and comes into operation to compensate hydropower outage. The net present value of profit amounts to $275,179 per year. The total hydrogen required is approximately 49,870 kg per year, with a total electricity requirement of 1190 MWh per year. The levelized revenue from hydrogen is achieved at around $4 per kilogram, while the levelized revenue from electricity is about $0.058 per kilowatt-hour. It is concluded that the integration of seasonal storage effectively reduces costs, boosts profits, and efficiently manages renewable energy fluctuations across different seasons. • Standalone multi-carrier energy system including electricity-hydrogen-gas is developed. • Hybrid renewable generation comprising solar and hydroelectricity is integrated. • Power to hydrogen is manipulated for connection between electricity and hydrogen. • Hourly, daily, and seasonal hydrogen storage systems are modeled. • Electric vehicle charging station and fuel cell vehicle refueling station are integrated. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network. • Holistic energy hub engages in upstream electricity and heat markets, optimizing profit. • Empowered energy hub actively influences markets with optimized strategies. • Robust optimization ensures stable operations in uncertain renewable sources. • Advanced load management optimizes profiles, aligning consumption with pricing. • Particle swarm optimization algorithm enhances search for optimal solutions. [ABSTRACT FROM AUTHOR]

Ever since 2002, when the first Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES) was held in Dubrovnik, the SDEWES conferences series has been providing a forum for world-wide scientists and those interested in learning about the sustainability of development, to present research progress and to discuss the state of the art, the future directions and priorities in the various areas of sustainable development. One of the main issues of the coming decades is to improve efficiencies by integrating various energy systems, using excess from one, as resource in another in the correct moment. Integrating electricity, heating, cooling, transport, water, buildings, waste, wastewater, industry, forestry and agriculture systems will be pivotal towards sustainable development. SDEWES has maintained high publishing standard as 2408 SDEWES papers were published in special issues of leading scientific journals including "Energy - The International Journal". In 2022, the 17th SDEWES Conference was held during November 6–10 in Paphos, Cyprus, the 5th SEE Conference took place in May 22–26, 2022 in Vlorë, Albania, the 3rd Latin American Conference was held through a virtual platform during July 24–28, 2022. The Energy journal has continued its cooperation with SDEWES launching a special issue dedicated to the 2022 Conferences. The special issue includes papers which traditionally cover a range of energy issues - higher renewables penetration and various technologies and fuels assessments at energy supply side, as well as, energy efficiency in various sectors, buildings, district heating, electric vehicles and demand modelling at energy demand side. [ABSTRACT FROM AUTHOR]

Power systems now face new issues due to incorporating electric vehicles (EVs) and renewable energy resources (RERs). This paper proposes a novel Enhanced Coati Optimization Algorithm (ECOA) for obtaining the optimal solution of the probabilistic optimal power flow (POPF) problems. The ECOA is a metaheuristic optimization algorithm that is robust and efficient for solving complex problems. It is used to tackle the OPF problem, which considers the stochastic characteristics of RERs. Moreover, EVs are included in the presented power systems in this paper. The novel approach is tested and verified on the IEEE-57 and IEEE-118 networks. The effectiveness of the proposed method is demonstrated by making a comparison with other metaheuristic-based methods. To obtain a practical study, real data of wind speed, solar irradiance, and electric vehicles profile are incorporated in the dynamic analyses. The simulation results show that the ECOA is robust and efficient for solving the OPF problem. It can also improve the performance of power systems with RESs and EVs. The findings of this research demonstrate that the suggested approach is promising for power system optimization problems, including RERs and EVs. • This paper presents a novel optimal energy management of smart grids. • The model contains renewables and electric vehicles uncertainties. • A practical study is presented including renewables and EVs real data. • A novel enhanced metaheuristic algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods [ABSTRACT FROM AUTHOR]

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving. • The optimal scheduling model for home energy management system of rural dwellings with flexible loads is proposed. • Influence of the proportion of flexible load and different time step sizes on the scheduling model are studied. • The operation cost by 26.62% of battery integration strategy based on load scheduling is further reduced. [ABSTRACT FROM AUTHOR]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [ABSTRACT FROM AUTHOR]

With the increase in renewable energy penetration and the widespread use of electric vehicles, the uncertainty of controllable resources adversely affects the economic scheduling of smart buildings. Meanwhile, the smart building can take advantage of its flexibility through multiple markets mechanism. Therefore, this paper proposes an optimal scheduling model for the smart commercial building, including electric vehicle charging, which partakes in the day-ahead energy reserve markets. Firstly, the behavior of electric vehicles is obtained through Monte Carlo simulation. This paper categorizes electric vehicles according to their behavioral characteristics and proposes corresponding scheduling strategies. Then, the joint energy and reserve markets mechanism is introduced, and the robust adaptive optimization (ARO) method is employed to deal with uncertainties. The proposed model is a three-level and two-stage model, which can be solved by the column and constraints generation algorithm. Finally, the economy and feasibility of the proposed model are validated through a case study. Results show that the ARO method saves 3%–4% of expenses more than the pure robust optimization method, and the proposed joint markets mechanism can increase profits by 1%–4%. • A three-level optimal model of SCBEMS is proposed through ARO method to reduce its conservativeness. • The joint energy and reserve markets are introduced to coordinate smart commercial buildings' flexibility. • The travel behaviors of EVs are processed by Monte Carlo simulation and different scheduling strategies are applied. [ABSTRACT FROM AUTHOR]

This paper is an Editorial for the special issue (SI) of Energy Reviews (RSER) dedicated to the 10th International Conference on Sustainable Energy and Environmental Protection SEEP, held in Bled-Slovenia. During the conference more than 300 papers were presented, around 25% of these papers were accepted for this special issue. Papers discussed different topics related to renewable energy, energy storage systems and some environmental issues related to transportation. During this conference Prof Olabi presented a keynote talk on the relation between circular economy and renewable energy, case studies about implementing this concept in Scotland were included in his presentation. This editorial will give a short description on the relation between circular economy and renewable energy, then it will be followed by addressing the accepted papers in this special issue. [ABSTRACT FROM AUTHOR]

Abstract Tidal energy is one of the most abundant renewable energy sources. Due to its intermittent nature, the integration of tidal energy within a Microgrid (MG) requires appropriate energy management strategies. Some important considerations are tidal energy generation prediction, energy storage requirements, load profile and demand within the Microgrid, and energy pricing structures. This paper considers an MG scenario that includes a small scale tidal farm near Darwin in north of Australia. A tidal power prediction model is introduced using tidal current speed data. Then, for a fixed-size energy storage system (ESS), an optimal scheduling strategy is devised using Particle Swarm Optimization (PSO) to achieve minimum operating costs in the MG. The work presented in this paper provides a framework for the planning of MGs that include tidal generation in their energy mix. Highlights • Visibility of tidal power in the North of Australia. • Tidal power prediction based on tidal current speed observation. • Optimal scheduling for a tidal Micro-grid using Particle Swarm Optimisation. [ABSTRACT FROM AUTHOR]

Abstract This paper provides a perspective on the development of future district heating systems and technologies and their role in future smart energy systems. The reviewed papers elaborate on or otherwise contribute to the theoretical scientific understanding of how we can design and implement a suitable and least-cost transformation into a sustainable energy future. Focus is on the important role of the next generation of district heating and cooling technologies. The status of the scientific contributions demonstrates a high level of understanding of how to deal with the technical aspects. The primary current challenge seems to be the understanding of the implementation of these. Highlights • A transformation into low-temperature becomes essential for future district heating. • High level of understanding of how to deal with the technical aspects. • Primary current challenge seems to be the understanding of the implementation. [ABSTRACT FROM AUTHOR]

Abstract The transition to a 100% renewable energy system based on variable renewable energy raises technical but also institutional questions. The smart energy system concept integrates variable renewable energy by addressing the technical challenges through the integration of different energy sectors, but integration of variable renewable energy also entails a change in the cost structures, especially related to electricity. The effect of this change in cost structures on market prices is investigated. This is done through simulation of a 100% renewable energy system that utilises a large degree of cross-sector integration but maintaining the current electricity market structure. The paper uses a 100% renewable energy system scenario for a 2050 Danish energy system. This is reflected in the use of wind energy as the primary renewable energy source. It is concluded that the current electricity market structure is not able to financially sustain the amounts of wind power necessary for the transition to a 100% renewable energy system. Since earlier research shows that neither electricity production costs nor the total system costs is higher for the renewable path than the fossil-based alternatives, the conclusion in this paper points towards a need for reshaping the institutional structure of electricity trade. Highlights • Calculates electricity prices in a renewable energy system with current market design. • Calculates private profitability of wind power investments within such system. • The market design cannot financially sustain wind power in a renewable energy system. [ABSTRACT FROM AUTHOR]

This paper deals with extremes and resource assessment of wind and waves in central Mediterranean Sea. The extreme value analysis is performed following Equivalent Storm Models (ESMs) approach, commonly employed in the context of significant wave height return values estimation. In this work, it is extended to wind storm events thanks to some analogies between wave and wind processes at short and long-term scale. The Equivalent Exponential Storm (EES) Model is selected for this study because of its realistic description of actual events time evolution. Energy estimations are based on both real storm data and EES model. The input data used for this analysis comes from the re-analysis of atmospheric and wave conditions performed by MeteOcean group at DICCA of Genoa University and cover the period from January 1979 to December 2020. Sixteen points have been selected including the south and west coast of the Italian peninsula, the Sicily and Sardinia Islands. The study is focused on return values estimation and energy assessment, mainly related on the storm concept; it aims to elucidate analogies and/or differences of wind and wave at a given site as well as how the two resources are distributed along the examined locations, identifying the potential working time over the year and which are the storm events providing the prevalent energetic contribution to harvestable energy. Results show that the location characterized by the severest wind storm and wave storm events do not coincide and not always the location where the severest storm occur is also the most energetic site. Further, the variability of return values with different levels of return period for wind speeds is lower than for significant wave heights: this aspect is motivated by a shape parameter of Weibull distribution that is systematically higher for the wind speed with respect to the significant wave height. The extremes of significant wave height are more variable from one location to another than those of wind speed ones and the same behavior is exhibited by energy resources, elucidating that the wind resource in the investigated sites is more homogeneous. • The paper deals with wind and wave extremes and resources assessment in central Mediterranean Sea. • A storm based approach is proposed, including the Equivalent Exponential Storm (EES) model. • The EES model is new in the context of wind extremes and energy related estimations. • An atlas for site selection and preliminary design of wind and wave energy devices is provided. [ABSTRACT FROM AUTHOR]

Accurate and robust wind power forecasting (WPF) is great significance to ensure the safe and stable operation of the power system and promote the transformation of low-carbon energy. However, the high randomness and intermittency of wind power bring great challenges when designing reliable forecasting models. In this paper, a novel spatial-temporal attention graph convolutional network model is proposed. Firstly, the spatial attention mechanism is used to aggregate and extract the spatial correlations of the raw wind power data. Secondly, the temporal attention mechanism is applied to capture the temporal correlations. Then, the extracted spatial-temporal correlations were put into the temporal convolution network and the spatial convolution network to further obtain the temporal and spatial dependencies. Finally, the wind power forecasting results is output through the full connection layer. The proposed method is verified by using wind power data from real wind farm in China. The experimental results reveal that the proposed depth spatiotemporal prediction model has more significant advantages than other advanced models in terms of prediction accuracy and stability. • The spatiotemporal attention mechanism is used to learn the dynamic spatiotemporal correlations. • The spatiotemporal convolution mechanism is applied to enhance feature extraction of wind power. • The proposed model can improve the accuracy of wind power prediction by experiments. • Real datasets from wind farm in China is employed for the modeling study. [ABSTRACT FROM AUTHOR]

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50% of the flexible loads, which is possible due to the use of the stationary energy storage. • Real-time load coordination strategy for optimizing electrical loads in smart homes. • Inclusion of renewable energy sources and stationary batteries in the coordination. • Definition of generalizable metrics for the evaluation of load shifting strategy. • Improvements in improved energy efficiency and reduced electricity costs. • Comprehensive analysis that considers diverse home characteristics such as location. [ABSTRACT FROM AUTHOR]

This paper examines renewable and technological prices' asymmetric multifractality and efficiency in international and Chinese marketplaces. The asymmetric multifractal detrended fluctuation analytics (A-MFDFA), multifractal detrended fluctuation analytics (MFDFA), and fractal dimension techniques are employed for multifractality and herding behavior. In addition, market deficiency measures (MDM) and hurst exponents are used to construct the inefficiency index (MLM- Magnitude of long-memory) during and before Covid-19. The empirical outcomes supported the existence of asymmetric multifractality across all renewable and technological marketplaces. This multifractality has been observed in up-and-down trends. Moreover, during the COVID-19 outbreak, inefficiency in CELS and SPGlobal's green energy prices increased, which is more apparent in the descending trends. Fractal dimension outcomes suggest a herding behavior in these markets during pandemic. The SPTSX green energy pricing statistics demonstrate that its upward multifractality is larger than the downward in both phases, signifying the strong efficiency position in the market. The SPIC-SH green energy pricing displayed considerable asymmetric multifractality, higher levels of efficiency, and even low levels of market uncertainty during COVID-19. The findings imply that all financial market players should prioritize various green energy investments depending on its asymmetric efficiency and predictability. It will help in their decision-making and reduces herding behavior in the market. • Asymmetric multifractal behavior is tested in global renewable and technological prices. • A-MFDFA and MFDFA and fractal dimension are used to analyze efficiency, scaling and herding behavior during and before COVID. • CELS and SPGlobal's green energy prices showed herding behavior before and inefficacy during COVID. • Green Technology and Chinese green energy prices were stable during COVID. • All green and technological prices were more stable before COVID-19. [ABSTRACT FROM AUTHOR]

Abstract As the smart grid paradigm is capable to encourage the active consumers for efficacious participation in increasing system efficiency, demand response programs (DRPs) have attracted much interest in the worldwide recently, especially in optimization of smart microgrids (MGs). Under this context, this paper proposes an integrated method relies on cleverly cooperation of time rate-based DRP and heterogeneous distributed energy resources (DERs) deployment with aim to reliability-oriented planning of multiple MGs. To do this, a novel two-stage decision making model is exploited in which at the first stage the MGs construction is formed by optimal dynamic planning of hybrid DERs simultaneously with section switch allocation considering a reliability criterion for MGs as loss of load expectation (LOLE) constraint. Subsequently, at the next stage the critical energy peak pricing-based program accomplishes in order to flatten the load profile as well as diminishing the investment costs of MGs. Besides, owing to the unpredictable nature pertaining to renewable power production, the uncertainty modeling is inevitable where in this paper, a novel pragmatic robust optimization approach has been employed to deal with intense uncertainty of the problem. Numerical results obtained from an illustrative case study elucidate how the proposed MGs planning and utilized DRP pairing significantly increases the expected profit of system and ameliorates the reliability of end-users. Highlights • An innovative two-stage framework is developed for DRP and MGs planning pairing. • A time-rate based DRP is employed to reduce and postpone the investment costs of MGs. • Dynamic model is used to design of multiple MGs considering reliability criterion. • A pragmatic robust optimization is proposed to cope with uncertainty of the problem. [ABSTRACT FROM AUTHOR]

This editorial introduces the content of selected papers from the 2nd International Conference on Smart Energy Systems and 4th Generation District Heating. All of the papers elaborate on or otherwise contribute to the theoretical scientific understanding on how we can design and implement a suitable and least-cost transformation into a sustainable energy future with a focus on the important role of district heating and cooling technologies. [ABSTRACT FROM AUTHOR]

This paper compares different strategies to transform the heating sector into a future 100% renewable energy solution. It focuses on the consequences for infrastructures in terms of grids and storage across the electricity, gas and heating sectors. The hypothesis is that these consequences are rarely taken into proper consideration, even though the costs are significant and differ substantially between the alternative pathways. While the smart grid scenarios are based on electricity as an energy carrier, the “smart energy systems” approach is based on a cross-sectoral use of all grids. Using Denmark as a case, this paper shows how the current gas and district heating grids each have twice the capacity of the electricity distribution grid. Moreover, the existing gas and thermal storage capacities are substantially higher and the additional future capacities are more affordable than within the electricity sector. The conclusion is that the “smart grid” pathway requires a 2–4 times expansion of the electricity grid and significant investments in electricity storage capacities, while the “smart energy systems” pathway can be implemented with relatively few investments in affordable minor expansions of existing grids and storage capacities. [ABSTRACT FROM AUTHOR]

The role of energy transition amidst the energy crisis and how policymakers can drive down emissions while focusing on energy security are critical. Given the geo-political situation, energy crisis volatility, energy shortage and climate change all affect the green transition and the short-term priorities for energy companies and policymakers. Energy security is not an isolated issue but has widespread implications as various sectors depend on energy supply to function properly. Governments around the world are faced with this trilemma, how to balance energy security with energy sustainability while also considering energy affordability. Sustainability has been in focus for about a decade. However, energy security is suddenly becoming one of the most important priorities that policymakers need to consider. Unfortunately, the renewable energy infrastructure is not yet ready to replace the growing volume of energy demand from hydrocarbon, which the world has been dependent on. This means, for now, a surge in energy generation through hydrocarbon to meet the existing energy demand deficit. However, it is important not to lose focus on the challenge of energy sustainability and climate change adaption and mitigation. Where trends like carbon capture and storage; solar, wind, hydro, green hydrogen, etc.; renewable energy infrastructure and integrations, with supply chain and engineering services consideration [in aspect for the growing market in this space] need better attention with regards to investment and full-scale implementation. This paper aims to analyze this 1st energy crisis of green transition with a priori on energy poverty with consideration of major influences and associated impacts. Furthermore, it proposes a specific framework for inclusive investigations, which considers the entire energy ecosystem with consideration of major influences, to enable the policymakers to better drive the green transition. This involves formulating energy policies that are not entirely conservative towards renewable energy sources but instead promote investments in both green and relatively more environmentally benign energy sources compared to high emission hydrocarbons. In this regard, this paper renders exhaustive prospects and recommendations. • The nexus between energy transition, energy poverty and their impacts are investigated. • Influence of climate change, COVID-19, geo-politics, and energy infrastructure on energy transition. • Exhaustive prospects and recommendations are rendered. • Requirement for inclusive investigations and recognition of energy poverty. [ABSTRACT FROM AUTHOR]

Due to the increase in electrical and cooling demands, the use of renewable resources for supplying these demands are subjected to more attention in recent years. This is while in the hot months of the year, due to the weather conditions, the need for electricity and cooling is increased. In this paper, a hybrid solar/wind system is proposed to satisfy the electrical and cooling demands, and hot water consuming of a building. Cooling demand is covered by combining absorption and compression chillers; so that, the primary energy of the compression chiller is supplied by a wind turbine and photovoltaic (PV) panels and the required thermal energy of the absorption chiller is supplied through evacuated tube collectors (ETCs). Considering the space limitation for installing solar systems, the main issue of this paper is focused on allocating the best space share for each of the PV panels and ETCs (in a case study building, 100 m2 of area is considered as a free space for solar systems). Three cases are assumed for the share of installation space as: 75% of area for ETC and 25% for PV (case 1), half share for both (case 2), 25% of area for ETC and 75% for PV (case 3). All of the defined cases are investigated by developing a comprehensive computational code based on energy, exergy, and economic analyses. The results show that although the first case with energy and exergy efficiencies of 50.60% and 18.5% has the best performance, the third case, with the highest net present value (NPV) has a payback period of nearly 7 years, is the eco-feasible case. Finally, by applying 7 different weights for the defined objective functions (energy efficiency, exergy efficiency, and NPV), a multi-objective optimization is applied to select the best system operation depending on the design requirements. Considering equal weight coefficients, the optimization results reports the case 3 as the optimum configuration. • A multi-generation system for providing electricity, cooling and hot water consuming is proposed. • The wind turbine, ETC and PV collectors and absorption and compression chillers are used. • Three defined cases are assumed for the share of collector installation space. • Energy and exergy efficiencies for first case are obtained 50.60% and 18.5%, respectively. • TOPSIS multi-objective optimization method is applied for selecting the best case. [ABSTRACT FROM AUTHOR]

Energy poverty and greenhouse gas emissions (GHGs) are two major challenges on the road to sustainable development goals. The main goal of the global policy is to combat GHGs and energy poverty through the transition from fossil fuels to clean and renewable sources. Empirical research with a global or regional focus can offer practical implications for the development of global energy and environmental policies. To this end, this paper estimates the impact of electricity from different sources on energy poverty and GHGs in low-, middle- and high-income countries with annual data. The paper firstly performs the generalized method of moments and the sequential estimation of a linear panel-data model. According to the findings, (i) electricity from coal contributes to the increase in GHGs in the upper-middle income countries but does not have a significant effect on energy poverty. (ii) The effect of electricity from natural gas and nuclear energy on GHGs and energy poverty is mixed. Electricity from nuclear power is confirmed to have a reducing effect on energy poverty in middle and low-income countries. However, it does not have a significant effect in high-income countries. (iii) Electricity from renewable and hydro sources makes a significant contribution to reducing GHGs and energy poverty. (iv) Finally, heterogeneous estimates for robustness check greatly support baseline findings. • Examines the impact of electricity from renewable and non-renewable sources on energy poverty and GHGs in 92 countries. • GMM and SELPDM dynamic panel data estimators are employed. • Electricity from coal is an unsustainable option in tackling energy poverty and GHGs. • The effects of electricity from nuclear and natural gas are controversial. • Electricity from renewable sources is an option that supports Win-Win status. [ABSTRACT FROM AUTHOR]

The global shift to renewable energy is conducive to carbon reduction, but its impact on the economy is still uncertain. Unlike existing research focusing on the effect of domestic renewable energy (RE), this paper considers the global RE connection among countries and examines the impact of global RE development on economic growth (EG) using China as a sample. In addition, we apply a novel wavelet-based quantile-on-quantile method, which can estimate the local influence of the α-quantile of global RE on the β-quantile of the EG in different periods. The finding shows that the upper quantiles of RE promote EG , and this effect is greater at the upper quantiles of EG in the medium- and long-term. This is mainly because China plays a leading role in RE development and has an advantage in exporting RE -related products with the development of global RE. The finding proves the growth effect, indicating that RE is a blessing for China's EG. However, in the short term, a higher level of global RE development impedes China's EG due to high upfront costs of RE production, fierce competition from other countries, and insufficient energy supply when significant investment shifts from non-renewable energy to RE. The empirical results provide important policy implications to balance RE 's short-term inhibitory effect and long-term promoting effect on EG in the process of the global energy transition. • The global RE connection and the impact of global RE development on economic EG. • Applying wavelet-based quantile-on-quantile method can estimate the local influence of global RE on EG in different periods. • Upper quantiles of RE promote EG is greater at the upper quantiles of EG in the medium- and long-term. • The growth effect indicating that RE is a blessing for China's EG. • Balance RE's short-term inhibitory effect and long-term promoting effect on EG in the process of energy transition. [ABSTRACT FROM AUTHOR]

China has set a goal of carbon neutrality by 2060 and is determined to increase wind and solar power to 1200 GW by 2030, even though it already has the world's largest installed capacity of renewables. However, the lack of flexibility in the power system will limit the development of a higher proportion of renewable energy. This paper evaluates the applicability of different types of flexible power sources in China for the first time. Above all, it describes the current situation of China's power industry and the necessity of increasing flexible power sources, establishes an evaluation system from the perspectives of benefit, opportunity, cost & risk, then determines 19 evaluation criteria. The BOCR-ANP-TOPSIS method was used to evaluate the system, where the ANP method determines the weights and the TOPSIS method ranks the alternatives. The result shows that pumped storage hydropower has the highest overall ranking, followed by the flexibility transformation of coal-fired power. Coupled with sensitivity and comparative analysis, valuable findings that will help future energy policymakers are reported. • Evaluating the applicability of different flexible power sources in China for the first time. • The identification of criteria for the selection of flexible power sources was given. • Making recommendations for China's flexible power source development policy. • The BOCR-ANP-TOPSIS method adopted can be extended to select flexible power sources in different regions meticulously. [ABSTRACT FROM AUTHOR]

Building a multi-energy complementary power generation system is a viable way to encourage the use of renewable energy and decarbonize power generation. However, the intermittent nature of renewable power generation, such as photovoltaic and wind power, has prompted concerns regarding power grid stability. To balance such fluctuations, energy storage systems or other flexible power generation technologies should be integrated. In this paper, the peak regulation ability of integrated solar combined-cycle has been enhanced via employing a gas/oil exchanger between the top and bottom cycle. When integrating high penetration intermittent renewable energy, an appropriate operational strategy towards high-quality steady power output regulation is proposed. Dynamic performance analysis of the system, coupled characteristic of heat and mass transfer between subsystems have been highlighted. The case study demonstrates that fluctuations of the multi-energy complementary system power output can be controlled below 0.3 MW without renewable energy curtailment, even though wind-PV power generation fluctuates from 0.3 MW to 26.1 MW with variations per second reaching −2.8/3.7 MW. Furthermore, the system's levelized cost of electricity is down to 0.0512 $/kWh, which is cost-competitive with conventional power generation technologies. • Combination of integrated solar combined cycle with wind and PV power plant. • Power stability of system with high penetration intermittent energy. • Novel system configuration and operation strategy. • Deviations from target power output below 0.3 MW per second. [ABSTRACT FROM AUTHOR]

Increasingly complex global geopolitical situations are causing severe impacts on energy markets and making renewable energy (RE) the best energy option. Against fierce geopolitical conflicts, this investigation attempts to probe the bidirectional impact between geopolitical risk (GPR) and RE. By applying the rolling-window Granger causality test, we find that GPR positively and negatively impacts RE. The positive influence confirms that geopolitical competition increases the traditional energy usage cost and incentivises investment in the RE sector. This suggests GPR can be perceived as a winner in driving the energy transition. However, the rise in GPR reduces RE projects' attractiveness and inhibits the energy transition process's smooth progress. The adverse impact of RE on GPR indicates that RE is an effective channel for increasing energy diversity and reducing geopolitical events. However, the expansion of RE is vulnerable to national policy changes and critical resource competition, which induces new geopolitical conflicts. This finding is supported by the classic production model, emphasising that GPR and RE have mutual connections. To this end, governments should provide incentives for deploying RE projects to ensure energy security and promote energy transition. • This paper proves that there is a mutual correlation between GPR and RE. • The rolling-window Granger causality test is used for analysis. • Findings suggest that GPR has positive and negative effects on RE. • RE has positive and negative effects on GBR. • Policymakers should provide incentives for deploying RE projects to ensure energy security and promote energy transition. [ABSTRACT FROM AUTHOR]

We present a study on the influence of the turbulence generated by a cylinder on the performance of a free horizontal-axis turbine. The analysis focuses on the power coefficient, flow characteristics, and Turbulence Kinetic Energy (TKE) spectral density distribution. The mean velocity, turbulence intensity, and TKE are evaluated in the upstream and downstream planes of the turbine for configurations with and without the upstream cylinder. The results show that the power coefficient exhibits a peak when the rotor rotation frequency coincides with the frequency of vortex shedding from the cylinder, indicating a mechanism of conversion of TKE carried by large-scale structures into mechanical power. It was also observed that the turbine generates downstream turbulence in the absence of the cylinder but reduces the downstream turbulence when the cylinder is present. Spectral analysis of TKE confirms the influence of the frequency of vortex shedding on the velocity recovery of the turbine wake. The main contribution of this paper is the identification of a new mechanism of conversion of the TKE contained in large coherent turbulent structures into mechanical power. This study provides a further understanding of the complex phenomena involved in the interaction between the turbine and the upstream turbulent flow. • Horizontal turbine power assessed under high turbulence. • Peak power occurs if the turbine rotates at the large eddies' frequency. • New TKE-to-mechanical power conversion mechanism found. • Shorter wake recovery in high incoming turbulence. • Rotor reduces wake TKE at high turbulence. [ABSTRACT FROM AUTHOR]

Environmental degradation has become one of the major challenges facing the global community. This paper aims to explore the more effective options to combat environmental degradation by investigating the linearity and nonlinearity (trade openness set as a threshold variable) of energy efficiency, natural gas, renewable energy and ecological footprint. To this end, linear and non-linear panel models were constructed using panel data from 60 countries. The results of the linear analysis show that improving energy efficiency, and increasing the consumption of natural gas and renewable energy are conducive to inhibiting environmental degradation. Energy efficiency has the most significant negative correlation with ecological footprint, followed by renewable energy, and then natural gas, implying that improving energy efficiency and developing renewable energy is a more effective strategy to combat environmental degradation. The results of the nonlinear analysis show that there are threshold effects of trade openness between energy efficiency and ecological footprint, and between renewable energy and ecological footprint. The negative impact of both renewable energy and energy efficiency on the ecological footprint increases when trade openness exceeds the threshold. This suggests that, in terms of curbing environmental degradation, enhancing trade openness may further increase the negative impacts of energy efficiency and renewable energy on ecological footprint. At the same time, this confirms the positive role of trade openness in environmental protection. [Display omitted] • Exploring more effective option to combat environmental degradation. • Energy efficiency, renewable energy, natural gas can reduce ecological footprint. • Trade openness help energy efficiency, renewables lower ecological footprint. • The most effective strategy is energy efficiency, followed by renewables and natural gas. [ABSTRACT FROM AUTHOR]

In 100% renewable energy systems, the requirements for flexibility will be greater than for traditional carbon-based energy systems. New technologies and system setups are needed to provide flexibility for balancing the system. Implementing electricity storages in the energy system could provide parts of the required flexible demand and production, though most of these storage solutions have been shown to have relatively high costs. So-called Carnot batteries have been shown to have a relatively lower cost than traditional batteries, but at a reduced electric efficiency. This paper investigates to what extent large-scale integration of Carnot batteries has a role in the transition to and the operation of 100% renewable energy systems. By implementing Carnot batteries in a 100% renewable energy scenario for Denmark, the energy system effects are identified. The results indicate that the potential economic benefit could be as high as 60.5–66.2 EUR/MWh e discharged, not including costs related to investment as well as operation and maintenance of the Carnot batteries. Thus, large-scale integration of Carnot batteries must perform below this economic threshold to be economic relevant. Existing concepts for stand-alone Carnot batteries are not able to achieve these costs today, therefore solutions for cost reductions should be investigated. • Electricity storages could provide parts of future required system flexibility. • Most of these storage solutions have been shown to have relatively high costs. • Carnot batteries have a relatively lower costs but at reduced electric efficiency. • Large-scale integration of Carnot batteries is tested in a renewable energy system. • Carnot battery concepts should aim for a cost lower than 60.5–66.2 EUR/MWh e. [ABSTRACT FROM AUTHOR]

This paper assesses the relationship between energy security and renewable energy in the context of geopolitical risk through the quantile-on-quantile method. The finding suggests that energy security positively impacts renewable energy in the upper to middle quantiles, meaning that renewable energy inclusion in the energy mix significantly affects conflicts and lessens energy security. Moreover, energy security positively impacts renewable energy across all quantiles. On the other hand, the results recommend that renewable energy positively impacts energy security in the higher to medium quantiles. Finally, the results show that renewable energy consistently positively impacts energy security across the board except in the medium run. The increased reliance on fossil fuels implies that energy markets are not set up to promote renewables. Therefore, the transition to renewable energy must be accelerated immediately. • Examine energy security and renewable energy in the context of geopolitical risk. • Energy security positively impacts renewable energy in the upper to middle quantiles. • Energy security positively impacts renewable energy across all quantiles. • Renewable energy positively impacts energy security in the higher to medium quantiles. • Renewable energy consistently positively impacts energy security. [ABSTRACT FROM AUTHOR]

Lack of reliable data often prevents new private investments in renewable energy and government programs in this sphere. The paper offers a novel renewable energy resources system of potentials: energy, resource saving and environmental; better designed and more accurate methods for calculating the future exploitable technical potentials of wind and solar PV energy for a wide range of generating equipment in centralized and distributed power systems, taking into account existing social and environmental limitations. The paper also presents the latest assessments of these potentials for Russia and its 85 regions (in terms of fossil fuel saving and substitution, power and heat production and environmental effects), offering solutions to overcome methodological limitations of previous country-level assessments, such as incomplete or unreliable sources, absence of hourly, daily and monthly assessments, etc. Given the limitations of existing network of national meteorological stations and satellite monitoring systems it was decided to use NASA SSE database as a source of meteorological information. The methodology was validated through a comparative analysis of the data obtained in the course of this study and the data of wind monitoring campaign carried out by a German engineering company in one Russia's region for 18 months using 70 m masts. [ABSTRACT FROM AUTHOR]

Fulfilling heating requirements in cold regions using fossil fuels can create problems in terms of excessive greenhouse gas emissions, making the replacement of conventional heating systems with electric heat pumps powered by renewable energy sources a desirable goal. To help address this issue, this paper examines the introduction of independent microgrids consisting of natural gas combined cycle and large-scale photovoltaic (L-PV) generating sources accompanied by electric heat pump capacity. These results help to clarify the optimal output rate of L-PV to the overall output of such a microgrid. Furthermore, this paper examine a dynamic-characteristics model of a CO 2 split cycle heat pump in terms of the error of the supply-and-demand balance. This paper show that introduction of the proposed microgrid can significantly reduce the environmental and economic impact. This study clarified the environmental capability and economical efficiency of the microgrid for cold regions under management of electric power quality. When an electric power unit price and CO 2 emissions of the proposal microgrid are referred to the domestic price based on official announcements of the Japanese government, the amount of CO 2 reduction and electric power unit price of the proposal system can expect 48% and 30% reduction in Sapporo, respectively. [ABSTRACT FROM AUTHOR]

In the present context, energy access to remote rural households through local energy generating systems has been recognized as a cost effective and extent efficient option. With increased emphasis on eco-friendly technologies and high fuel cost associated with conventional energy generation, the use of renewable energy resources (small hydro, biomass, solar, wind energy etc.) are being explored. The present paper is focused on the optimal sizing of a stand-alone Integrated Renewable Energy System (IRES) which comprises the resources of micro hydro power (MHP), biogas, biomass, solar and wind energy. Initially, a demand response (DR) strategy based on energy consumption scheduling of appliances has been suggested and modelled in the paper. This strategy aims to minimize the peak hourly energy consumption of the study area. Further, different combinations of system components without and with DR strategy were considered and optimized for power reliability criteria of 0% and 5% unmet load. It has been found that significant amount of savings in system sizes and costs are obtained with DR strategy compared to system without DR. [ABSTRACT FROM AUTHOR]

This paper discusses the article " Assessing temporal complementarity between three variable energy sources through correlation and compromise programming." The discussed paper proposes a novel method of assessing complementarity between three energy sources using correlation, compromise programming, and normalization. The method is then used to calculate a complementarity index which is applied to a case study in Poland. However, upon inspection, the normalization of the index overestimates the complementarity potential. This issue is discussed in detail in this paper, and an alternative way of calculating the index is proposed, eliminating the issue of overestimating complementarity. [ABSTRACT FROM AUTHOR]