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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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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 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]

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]

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]

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]

Industry poses one of the biggest challenges in the renewable energy transition. In this paper, fossil fuels in the European industrial sector are replaced by renewable energy using a novel tool, IndustryPLAN, a planning tool for the assessment of national industrial sectors. In a bottom-up approach, each industry sub-sector is addressed with energy efficiency and fossil fuel replacement measures based on best available and innovative technologies, and in a top-down approach, the fuel and electricity consumption per country is analysed and decarbonised. The results indicate that: 1. Known technologies can decarbonise most of the industrial sector; 2. Costs and efficiencies are improved by energy savings and electrification; 3. Limiting bioenergy consumption is a critical challenge, emphasising the key role of energy savings and electrification, and the alternative of using hydrogen or hydrogen-based electrofuels will make the transition more expensive and induce energy losses. A full transition to renewable energy and a decarbonised industry sector may be possible before 2050, however, this requires that all investments are sustainable from 2030 onwards and that grid electricity is fully decarbonised. This paper presents several pathways toward 100% renewable energy supply in the European industrial sector and discusses the implications of the outlined scenarios. • Four tangible 100% renewable energy scenarios for industry in EU27 + UK are established. • 100% renewable energy in industry is feasible before 2050. • Energy efficiency improvements are essential to limit biomass consumption. • Industry energy transition needs to be prioritised in short- and long-term planning. [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]

This editorial gives an introduction to the important relationship between Smart Energy Systems and 4th Generation District Heating and presents a number of selected papers from the 1st International Conference on the topic. 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. The concept of Smart Energy Systems emphasizes the importance of being coherent and cross-sectoral when the best solutions are to be found and how this also calls for the active inclusion of the heating and cooling sectors. The concept of 4th Generation District Heating emphasizes that district heating and cooling are both important elements but also technologies that have to be developed further into a 4th generation version to be able to fulfil their roles in future sustainable energy systems. The conclusion is that further development of such theoretical scientific understanding is essential for the implementation of political goals related to future sustainable energy solutions. [ABSTRACT FROM AUTHOR]

In recent years, sustainability receives greater attention due to increasing demands and limited resources worldwide. The Indian state of Uttarakhand is rich in the availability of renewable energy resources such as solar, micro hydro, biomass, wind etc. Optimum utilization of these resources in stand-alone mode has been recognized as an economical and efficient option compared to grid extension for electrification of remotely located rural households. This paper presents a combined techno-economic optimization and energy management of a stand-alone solar-micro hydro-biomass-wind based IRES (Integrated Renewable Energy System) to meet the electrical energy demand of cluster of village hamlets of Uttarakhand state. A load shifting based DSM (demand side management) strategy has been suggested in the paper for energy management of the considered IRES. Further, optimization results without and with DSM strategy have been presented and compared. Finally, a sensitivity analysis has also been performed to evaluate the impact of different parameters on the considered system. [ABSTRACT FROM AUTHOR]

By 2020 Europe has to increase its energy efficiency and share of renewables in 20%. However, even accomplishing these challenging objectives Europe will be effectively wasting energy as we demonstrate in this paper. In our way towards a fully renewable scenario, we need at least to stop wasting energy in order to guarantee the energy supply needed for growth and comfort. We waste energy when we employ more primary energy than the final energy we ultimately use and this excess cannot be reutilized. In this paper we propose the WE (wasted energy) as a novel metric to measure the performance of energy systems and set up appropriate targets. The WE incorporates information about energy efficiency and renewable sources. Unlike European legislation, the WE considers them in an integrated way. This approach will help Member States to exploit their intrinsic capabilities and design their optimum strategy to reach their objectives. Using the information in Eurostat, we calculate the WE of Member States in EU-28 and their evolution. We also analyze illustrative examples to highlight strategies to reduce the WE, study the connection between economic development and WE, and provide a tool to diagnose the potential of improvement of an energy system. [ABSTRACT FROM AUTHOR]

Facing the mounting pressures of meeting energy and environment needs and reducing its dependence for fossil fuels, China needs to make more effort to develop renewable energy. This paper attempts to use portfolio theory to optimize China's overall energy system with considering the learning curve effect of renewable energy cost and the characteristic of fossil energy cost increasing over time. It also takes into account additional factors such as environmental costs of coal consumption and various growth rates of the cumulative R&D (research and development) capacity for solar power. This research has found that the development of renewable energy in China has tremendous potential but it will not replace fossil energy in the next decades. The sensitivity analysis of this paper indicates that development of solar power is driven not only by the cumulative installed capacity but also by the cumulative R&D capacity. [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]

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]

Recently a great number of empirical research studies have been conducted on the relationship between certain indicators of environmental degradation and income. The EKC (Environmental Kuznets Curve) hypothesis has been tested for various types of environmental degradation. The EKC hypothesis states that the relationship between environmental degradation and income per capita takes the form of an inverted U shape. In this paper the EKC hypothesis was investigated with regards to the relationship between carbon emissions, income and energy consumption in 16 EU (European Union) countries. We conducted panel data analysis for the period of 1990–2008 by fixing the multicollinearity problem between the explanatory variables using their centered values. The main contribution of this paper is that the EKC hypothesis has been investigated by separating final energy consumption into renewable and fossil fuel energy consumption. Unfortunately, the inverted U-shape relationship (EKC) does not hold for carbon emissions in the 16 EU countries. The other important finding is that renewable energy consumption contributes around 1/2 less per unit of energy consumed than fossil energy consumption in terms of GHG (greenhouse gas) emissions in EU countries. This implies that a shift in energy consumption mix towards alternative renewable energy technologies might decrease the GHG emissions. [ABSTRACT FROM AUTHOR]

Abstract: The paper employs a management random parameters frontier stochastic frontier and a simple frontier stochastic model to benchmark European countries according to their management efficiency in growth and renewable energy development. The results come from an empirical application of a panel with 31 European countries over a 14 year old period using a translog type stochastic frontier production function. In particular the paper focuses on results from a management random coefficients model and compares results with the conventional stochastic frontier model with inputs such as renewable energy, fossil fuel energy, employment and capital. The results suggest that the interaction of renewable energy with management affects growth in Europe and that the technical efficiency estimated by the management model is by 6.05% higher than the one produced by the simple stochastic frontier model. [Copyright &y& Elsevier]

The optimization of distributed energy systems (DES) is challenging because of the diversity of the types of energies involved and the complexity of the structure. Mathematically, the optimization of DES is a mixed-integer non-linear programming problem (MINLP). The optimal tradeoff between precision and computational efficiency, to find the global optimal solution, is a core issue that needs to be solved. This present work proposes a two-stage superstructure model which is solved by the random walk algorithm with compulsive evolution (RWCE), to better approximate the global optimal solution of the MINLP. The paper is divided into two parts; the first focuses on the modeling methodology and model solving strategy. Moreover, to confirm the applicability and effectiveness of the recommended method, DESs for three case studies, i.e. , business park, residential building, and hotel, were optimized from system planning point of view. On comparison with literatures, it was found that the proposed method had positive effects on further improving the economy of the system at different scales and configurations. The resulting decrease in the total annual cost of the three systems was 12%, 36%, and 2%, respectively. Further research on system operation optimization will be published as the second part of this paper. • A two-stage superstructure model is established for the optimization of renewable energy integrated DES. • RWCE algorithm is used to solve the proposed model to better approximate the global optimal solution of the MINLP. • The applicability and effectiveness of the proposed method are proved by three case studies. • The economy of DESs at different scale and configurations has been improved. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a practical and simple method for determining the optimal size of renewable energy systems for building applications at the conceptual design stage. The method suggested in the paper is based on the use of the software tool RETScreen widely used for pre-feasibility or feasibility studies. The software is a convenient and relatively accurate tool for evaluating the feasibility of renewable systems. However, the tool does not provide with a method or function for optimal design. It seems there has been no publication or study to find optimal design parameters for renewable energy systems using the software. By using the features of this software, which can evaluate renewable technologies, this study provides a methodology to find the optimal design parameters for a renewable energy technology in the conceptual design of building energy systems. The paper describes a further development of the method for applications to integrated hybrid renewable energy resources in buildings. The paper also describes how to combine hybrid renewable heating systems, such as hybrid solar thermal and ground-source heat pump systems. Finally, this paper presents case studies on the optimal sizing of a single renewable system and an integrated renewable energy system for building applications. [Copyright &y& Elsevier]

Abstract: This paper discusses the opportunities and challenges of implementing District Heating (DH) in Canada, with focus on the network design and operation. We selected for case study an urban area in Ottawa. First, we proved that the medium-temperature district heating (MTDH) (70°C≤ T supply ≤90°C) had better energy delivery performance than high-temperature district heating (HTDH) (T supply >100°C), decreasing the heat loss by approximately 40%. The low-temperature networks (T supply <60°C) achieved even lower heat losses, but they required additional capital investment. The implementation of low-temperature district heating (LTDH) should be considered, thanks to the capability of including more renewable energy and excess industrial waste heat. Next, the simulations show that DH can be implemented to supply present heating loads with medium temperature DH, and operate in the future at low temperature, after energy saving measures have been implemented in the buildings. Areas having linear heat densities greater than 3MWh/(myr) could economically be supplied by DH. Areas with linear heat density below 1.5MWh/(myr) are considered not practically feasible with the current energy market situation in Canada. The paper discusses critical issues and quantifies the performance of design concepts for DH supply to low heat density areas. DH is a fundamental energy infrastructure and is part of the solution for sustainable energy planning in Canadian communities. [Copyright &y& Elsevier]

Abstract: Pinch Analysis is a well-established methodology of Process Integration for designing optimal networks for recovery and conservation of resources such as heat, mass, water, carbon, gas, properties and solid materials for more than four decades. However its application to power systems analysis still needs development. This paper extends the Pinch Analysis concept used in Process Integration to determine the minimum electricity targets for systems comprising hybrid renewable energy sources. PoPA (Power Pinch Analysis) tools described in this paper include graphical techniques to determine the minimum target for outsourced electricity and the amount of excess electricity for storage during start up and normal operations. The PoPA tools can be used by energy managers, electrical and power engineers and decision makers involved in the design of hybrid power systems. [Copyright &y& Elsevier]

Abstract: This paper will summarise the European Strategy for Energy and Climate Change. In current international negotiations Europe has proposed a 20% reduction in GHG (greenhouse gases) in the developed countries by 2020 or 30% should there be an international agreement in the domain. However it is important to define measures to achieve the targets. One of the principal tools is to improve energy efficiency under the energy efficiency action plan, which will help to achieve a 20% energy saving by 2020. On the other hand, the amount of energy from renewable sources consumed in Europe will have to rise from its current level of 8.5%–20% by 2020. These are ambitious but achievable targets. Nonetheless, these can only be achieved through strong investment in areas of the knowledge triangle which strengthens research and innovation in the energy sector in Europe. The paper covers European Energy and Climate Change Policy, the European Strategic Energy Technology plan, the consequences of the Lisbon Treaty, European and national Road maps to a low carbon economy, the Energy Efficiency Plan for 2011 and finishes with a brief consideration of the EU’s energy infrastructure priorities. [Copyright &y& Elsevier]

Abstract: The subject addressed in this paper is the analytical study of the transition of an energy generation system for a real MV/LV distribution system from one that is “fuel-based” to a distributed and smart “renewables-based” system. The paper outlines both the technical and the economic issues related to such a transition from one type of system to the other. The study is carried out for a real islanded network located in the Island of Pantelleria (Mediterranean Sea). The results obtained are presented and discussed, putting into evidence the technical, environmental and economic benefits of using smart technologies and renewable energy sources. [Copyright &y& Elsevier]

The impacts of climate change, largely fuelled by the consumption of fossil fuels and an unhealthy consuming lifestyle, are encouraging a wide and far ranging adoption of renewable energy sources. This is critical, and well received, for countries who are on the frontline of climate change, in particular Small Island Developing States (SIDS) and those with fragile, unique and complex natural ecosystems. While there has been research on the importance of renewable energy and on the increasing of its efficiency for bettering its adoption and resulting economic gain, there is however little literature on the design and its implementation in respect to the rich and fragile biodiversity of areas; which contribute greatly to local ecosystems, and even to liveability dimensions of fauna, flora as well as humans, regions and cities. To ensure optimal energy generation while preserving fragile ecosystems, a method for numerical Genetic Algorithm (GA) is proposed for wind farming optimization to determine both the sitting of wind turbines and the Levelised Cost of Energy (LCOE). Optimal sites were identified in the small island of Mauritius and the model was applied on a complex terrain around an airport to identify optimal sites for the sitting of wind turbines while analysing the energy offset in terms of demand and supply of the area to encourage decentralised and more stable energy networks. Based on a set of parameters including local regulations and guidelines, 3 scenarios with a layout of 12, 24 and 36 wind farming sitting arrangements were proposed with a minimal impact on the fauna and flora; including the pathways of migratory birds. This paper is aimed at energy, urban planners and policy makers looking at sustainable energy planning. • Areas of fragile and complex ecosystems are often disregarded in energy planning. • A mathematical method is designed for assessing and optimizing wind turbine placement from a biodiversity perspective. • A case study is proposed and nature reserves, park areas and Important Bird Areas are included in the analysis. • The design method aims to maximize energy output and minimize cost for given number of turbines. [ABSTRACT FROM AUTHOR]

Abstract Providing electricity generation is a crucial indicator for development of a country's progress index. Renewable energy resources are rapidly being recognized as important alternatives for clean production of electricity. The present paper aimed to assess the wind and solar data from selected metrological stations in the province of Sistan and Baluchistan in Iran in order to produce hydrogen as a clean fuel. For using wind and solar energies, a detailed techno-economical analysis was presented. At first, the solar energy potential was assessed; indicating that the most part within the province possesses a high radiation intensity of more than 500 W/m2. Secondly, wind resources were examined, revealing the high power density of about 500 W/m2 in Lutak station. According to evaluation of hydrogen production from several small wind turbines, it was shown that the maximum amount of 39.7 ton-H 2 can be produced every year. It was observed that in the best conditions, the cost of 1 kWh electrical energy generated by wind turbine was 2 cents cheaper than the market energy unit price while this value is 8 cents cheaper by employing both wind and solar energies. GIS maps of hydrogen production, wind speed, wind power and global horizontal radiation were produced. Highlights • Wind/solar potentials in the province of Sistan & Baluchistan in Iran are assessed. • A detailed techno-economical analysis is presented using wind/solar energies. • Economical solar energy above 500 W/m2 is observed in several stations. • High hydrogen production of 39.7 ton-H 2 is observed in Lutak station. • The GIS maps of solar/wind/hydrogen are reported. [ABSTRACT FROM AUTHOR]

Abstract Hybrid power systems (HPSs) are a variant of distributed generation utilising two or more complementary energy sources for power generation, and are thus more efficient, reliable and cost-effective than single-source systems. HPSs can be used in urban, rural and remote areas. HPS research has focused on sizing and optimisation, which requires efficient and effective methodologies to ensure reliable power supply and a cost-effective system. This paper presents a mathematical programming technique for the design of off-grid and grid-connected HPSs, taking into account uncertainties in renewable energy resources and load demands. The basic model formulation is based on a comprehensive superstructure that includes all possible connections for power allocation. Chance-constrained programming is applied to determine the optimal capacities of power generation and energy storage units with a specified minimum system reliability level. Furthermore, fuzzy optimisation is adopted to account for the trade-off between conflicting economic and environmental goals, as well as parametric uncertainties in HPS design. Two case studies are presented to demonstrate the application of the proposed approach. Highlights • A generic modelling framework is developed for hybrid power system optimisation. • Chance-constrained programming is applied for resource and demand uncertainties. • Fuzzy optimisation is used for multiple objectives and parametric uncertainties. • Compromise solutions are found with conflicting economic and environmental goals. • The incorporation of uncertainties into design gives more conservative solutions. [ABSTRACT FROM AUTHOR]

Abstract Recent electricity management systems such as Smart Grids and Virtual Power Plants help to better integrate distributed generation renewable resources (RDG), such as photovoltaic, small hydro or micro wind in electricity markets. In this context, an important increase of small-scale power generation technologies is expected in Spain by 2020, according to the Spanish Action Plan for Renewable Energy. This paper analyses the environmental and economic impacts of the massive integration of RDG in Spain for 2020 by using an Input-Output approach. The induced production and employment effects in every sector due to new RDG project investments as well as the CO 2 reductions attributed to a decline in conventional electricity consumption are estimated. The results indicate that investments in RDG based on photovoltaic and small-hydro have the greatest impact on employment generation. The RDG investments will lead to a decrease in emissions of 21.67% in 2020 compared with 2013. The RDG technologies become an alternative to reach a sustainable economic growth in Spain. In future research, a multi-regional input-output model and the study of the links between energy consumption, standard of living and consumption patterns will aid to delve deeper into the relationship between economic growth and the environmental impacts of small-scale renewable technologies. Highlights • RDG will be over half of total installed electricity generation capacity in 2020. • The impact of RDG deployment is quantified on Spanish economy in 2020. • The IO methodology is used to study the environmental and economic impacts. • Photovoltaic and small-hydro have the highest impact on employment generation. • RDG becomes an alternative to reach a sustainable economic growth in Spain. [ABSTRACT FROM AUTHOR]

Abstract Although renewables in the resource-rich countries of the Middle East and North Africa (MENA) are inconsequential contributors to regional total primary energy supply, recent project developments and overt support from a range of influential regional actors suggest a general trend towards a more environmentally sustainable electricity supply. This trend is driven just as much by economics as other factors, as rapidly falling renewable energy capital costs are complementing favourable policy environments, technical suitability, and concerns around the impacts of anthropogenic climate change and local pollution. Finance is an especially important consideration in this transition, yet it receives insufficient coverage. This paper seeks to remedy this deficiency of academic inquiry by highlighting the case of the Gulf Cooperation Council (GCC) to draw out broader implications for the region. We outline the factors that affect the financeability of projects, review the latest developments in renewable energy finance in the region, and present policy recommendations going forward. Graphical abstract Image Highlights • Renewable energy finance plays a critical role in reducing GCC renewable energy costs. • An overview of critical drivers of renewable energy finance is presented. • Three case studies in the GCC are used to draw out implications for MENA as a whole. • We provide recommendations for encouraging regional renewables finance. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with modeling possible future paths for Lebanon’s electricity future and evaluating them. The baseline scenario (BS) reflects the business-as-usual state of affairs and thus describes the most likely evolution of the power sector in the absence of any climate change-related or other policies. Two alternative scenarios are examined in contrast to the BS; the renewable energy scenario (RES) and the natural gas scenario (NGS). Using the Long range Energy Alternatives Planning System (LEAP) software we conduct a full-fledged scenario analysis and examine the technical, economic, and environmental implications of all scenarios. From an economic standpoint as well as from an environmental perspective both alternative scenarios are superior to the baseline. Hence, the results of the simulation show that the alternative scenarios are more environmentally and economically attractive than the BS. They would help Lebanon meet its social, environmental, and economic development goals, while at the same time providing other unquantifiable benefits that are discussed further in the paper. Anticipated barriers to the shift in energy mix from conventional sources to renewable energy sources are also presented and discussed [Copyright &y& Elsevier]

Abstract Energy system integration can bring several benefits to energy systems, notably to those that are in transition to high shares of renewable energy. Strategies are needed to realize the theoretical benefits of this approach in practice. Therefore, this paper proposes the organization and mathematical formulation of a multi-carrier day-ahead market in which electricity, gas and heat are traded simultaneously. This market set-up is applied to a conceptual test case to identify how - compared to a reference set-up mimicking the current practice - the multi-carrier market is able to unlock the benefits of energy system integration. It is quantitatively shown that the multi-carrier market (1) eliminates the need for forecasts of prices on subsequent markets and the consequences of the related errors, (2) allows to use the flexibility available in one carrier to facilitate the balancing in another, e.g. using the flexibility of a heat system to help balance the electricity system, and (3) enables specific market outcomes, unachievable in a sequential set-up, which increase the optimality of the market outcome. Highlights • Consistent multi energy market design solves many energy transition challenges. • Simultaneous clearing of different markets results in an increased social welfare. • Simultaneous clearing of different markets reduces the need for price forecasting. • A policy to facilitate integration of heat and electricity market is to be devised. • Measures to increase the liquidity on DA gas and heat markets should be brought. [ABSTRACT FROM AUTHOR]

Abstract The Brazilian electricity system is an important example of a large country relying on a high renewable energy matrix with a major focus on hydropower, which has historically allowed for low carbon electricity production. However, the increase in the demand and climate change impacts on the availability of these renewable resources represent important challenges for long-term power planning. The contribution of this paper is twofold: Firstly, a first attempt to use the EnergyPLAN model for the analysis of the Brazilian electricity sector and in particular to study future scenarios is presented. Secondly, the possibility of achieving a 100% RES system is also addressed. The 100% RES scenario is found to be theoretically possible but a substantial increase in the overall installed capacity would be required, to support the grid mainly during the spring and summer season. The results underline the importance of seasonal complementarity of hydro and wind power and reveal how an increase in RES would add exportation potential, reducing also the Brazilian external energy dependency. The study identifies risk factors for these high RES scenarios and outlines several avenues for future research to address cost, environmental and technical uncertainties of the system. Highlights • The Brazilian power sector is analysed with EnergyPLAN computer model. • Renewable scenarios are compared for technical, cost, emissions and risk parameters. • The importance of seasonal complementarity of hydro and wind power is outlined. • The 100% RES scenario results in full decarbonization of the system at higher cost. • The 100% RES scenario results in higher exportation and lower importation potential. [ABSTRACT FROM AUTHOR]

Abstract The intermittency of renewable energy makes it difficult to be accommodated by the power grid. Energy storage technology can alleviate the power fluctuation and help meet peak demands. Therefore, it plays a role in assisting the renewable energy integration. In this paper, the improvement of renewable energy penetration through energy storage system in complementary and stand-alone renewable energy scenarios was investigated. As a case, two energy infrastructures of Chongming (China) in 2016 and 2040 were modeled by EnergyPLAN. The decrease of coal consumption through renewable energy integration and the enhancement of renewable energy utilization efficiency through energy storage technologies in renewable scenarios were researched by organizing the outputs of EnergyPLAN. Results showed that the coal consumption dropped by 34.62%, 38.46% and 50% when the renewable energy penetration was 100% in "wind", "photovoltaic" and "wind-photovoltaic" scenarios for Chongming, 2016 respectively. The utilization efficiency of renewable energy can be improved up to 62.94%, 55.95%, and 60.14% respectively through energy storage system in these renewable scenarios for Chongming 2040. The most critical excess electricity production and electricity import can be decreased by the heat pump for Chongming, 2040 compared with compressed air energy storage, vehicle to grid and electricity to gas. Highlights • The predicted energy structure for Chongming in 2040 is built with EnergyPLAN model. • Impact of complementary and stand-alone renewable energy sources supply on coal consumption is investigated. • Optimal scale of energy storage system in three renewable energy scenarios for regional energy system is researched. • Four energy storage technologies are compared. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy is the energy generated from natural resources such as sunlight, wind, rain, tides and geothermal heat which are renewable. Energy resources are very important in perspective of economics and politics for all countries. Hence, the selection of the best alternative for any country takes an important role for energy investments. Among decision-making methodologies, axiomatic design (AD) and analytic hierarchy process (AHP) are often used in the literature. The fuzzy set theory is a powerful tool to treat the uncertainty in case of incomplete or vague information. In this paper, fuzzy multicriteria decision- making methodologies are suggested for the selection among renewable energy alternatives. The first methodology is based on the AHP which allows the evaluation scores from experts to be linguistic expressions, crisp, or fuzzy numbers, while the second is based on AD principles under fuzziness which evaluates the alternatives under objective or subjective criteria with respect to the functional requirements obtained from experts. The originality of the paper comes from the fuzzy AD application to the selection of the best renewable energy alternative and the comparison with fuzzy AHP. In the application of the proposed methodologies the most appropriate renewable energy alternative is determined for Turkey. [Copyright &y& Elsevier]