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

The rapid development of renewable energy sources (RESs) facilitates the coordinated operation of different energy sources to hedge against the uncertain and non-dispatchable nature of RESs. In this paper, we propose an effective approach for ultra-short-term optimal operation of a photovoltaic-energy storage hybrid generation system (PV-ES HGS) under forecast uncertainty. First, a generic approach for modelling forecast uncertainty is designed to capture PV output characteristics in the form of scenarios. Then, stochastic model predictive control (SMPC) is utilized in the coordinated operation of PV-ES HGS for load shifting under forecast uncertainty. Finally, a PV-ES power station located in Northeast China is selected as a case study to validate the feasibility and effectiveness of the proposed method in detail. The results show that: (1) The forecast uncertainty modelling method, which incorporates adaptive kernel density estimation (KDE) and two-layer nested copula, could capture the characteristics of PV output effectively. (2) The proposed sampling procedure ensures the representativeness and temporal dependence of scenarios extracted from the joint predictive density, leading to stability and efficiency in solving the stochastic optimization problems. (3) The SMPC-based coordinated operation strategy exhibits significant superiority in load shifting performance and resistance to risks. The standard deviation of residual load (SDORL) is reduced by 17.5% on average compared to the conventional scheme. Thus, this work provides operators with guidance in the ultra-short-term optimal operation of a PV-ES HGS under forecast uncertainty. [Display omitted] • Integration of adaptive KDE and two-layer copula to model PV forecast uncertainty. • Proposal of a representative scenario generation method incorporating LH and CD. • Utilization of scenario-based SMPC to optimize ultra-short-term PV-ES HGS operation. • Identification of trade-off between load shifting performance and operational risks. [ABSTRACT FROM AUTHOR]

• The method to effectively capture the key information of faults and improved model efficiency. • The method based on meta-learning represents an innovative path for the inverter output decision-making. • The method can quickly and accurately fit the mapping between features and decisions. With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources, the peer-to-peer (P2P) transactive energy market has emerged as an effective method for consuming the excess renewable energy on the end-user side. In the current research on transactive markets, distributed energy storages (DESs) have been widely applied in energy trading for transaction flexibility and system management. These small-scale energy storages have achieved a considerable momentum that, if properly harnessed, may unleash their potential for prosumers' utility maximization, reserves for renewable energy uncertainty, and transmission congestion relief. The present work reviews distributed energy storage in the transactive market, classifying and analyzing 120 papers according to their applications, algorithms, and adopted policies. This study first identifies DES functions in wholesale and transactive markets and then provides the mathematical models of DESs in various transactive market types in detail. This paper then focuses on the pricing mechanism for the DESs' contributions in the P2P transactive energy market. A transactive energy market mechanism for accommodating DESs is proposed based on the Vickrey–Clarke–Groves framework to meet incentive compatibility. The energy policies related to the promotion of DESs in electricity markets are listed and compared. Furthermore, this review highlights the possible questions and future development opportunities according to the analyzed studies. With the roles and relationships among aggregators, DES owners, and power grid determined, the benefit of participating P2P transactive market can be further improved. [Display omitted] • We analyze the application of distributed energy storage in electricity markets. • We focus on reviewing distributed energy storages in transactive energy market. • We propose a Vickrey–Clarke–Groves mechanism for accommodating energy storages. • The future development and possible questions are discussed in detail. [ABSTRACT FROM AUTHOR]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

• Site suitability assessments are at the heart of renewable energy planning. • A collaborative approach enables developing a fit for purpose decision-support tool. • Stakeholder-driven site suitability criteria supports transparent decisions. • The tool supports informed discussions and fosters consensus on deployment areas. • Data and technological limitations signal action areas for advancing practice. Competing land-use demands associated with planning for renewables is resulting in growing social and political challenges. Spatial decision-support tools are being developed and applied to tackle some of these challenges by facilitating systematic identification of suitable development sites based on technical, environmental and socio-economic criteria. Nevertheless, decision-support tools are often developed with limited input from stakeholders affecting participative and accountable planning and decision-making. This paper aims to address this shortcoming and foster greater transparency in the development of support tools for renewable energy planning, and thus in siting decisions. It present and discusses a stakeholder-driven approach to the co-creation of an online decision-support tool that attends to planning policy, technical considerations, environmental protection and social concerns influencing renewable energy planning decisions in Ireland. This bottom-up approach, easily transferable to other jurisdictions, fosters proactive expert input, awareness raising and greater comprehensibility and acceptance of siting decisions. It also enables developing an interface that responds to users' needs. The paper contributes to advancing renewable energy planning practice through effective and meaningful stakeholder engagement that supports transparent and accountable decisions from the outset. It also highlights ongoing data and technological shortcomings affecting the integration and operationalisation of stakeholders demands and needs, which signal priority areas of action to further advance the development of renewable energy decision-support tools. [ABSTRACT FROM AUTHOR]

Abstract: The main aim of this paper is to review of the current state of energy systems in the Western Balkans countries. The paper gives a review of the individual parameters relating to energy development, defines the current state of the achieved level of sustainable energy development as a whole and defines possible strategic directions for energy development in the region. The paper also examines the possibility for implementation of the adopted EU target of 20% RES energy production. The main characteristics in the region are high energy consumption, high values of carbon emission, fossil fuels import dependency, constant production and electric energy supply, and high potential but minimal RES energy production. Further development of regulation framework and market liberalization is are basic preconditions of demanding energy reform in all countries. Increasing energy the second energy development priority. The third long term strategic goal is increasing the amount of the energy obtained from hydro and transition to clean coal technologies. Intensifying RES production is currently the fourth strategic priority. The analysis shows that production of energy from wind is priority for the region, but currently it is constrained by too great investment requirements for developing countries. [Copyright &y& Elsevier]

Integrated Assessment Models (IAMs) are omnipresent in energy policy analysis. Even though IAMs can successfully handle uncertainty pertinent to energy planning problems, they render multiple variables as outputs of the modelling. Therefore, policy makers are faced with multiple energy development scenarios and goals. Specifically, technical, environmental, and economic aspects are represented by multiple criteria, which, in turn, are related to conflicting objectives. Preferences of decision makers need to be taken into account in order to facilitate effective energy planning. Multi-criteria decision making (MCDM) tools are relevant in aggregating diverse information and thus comparing alternative energy planning options. The paper aims at ranking European Union (EU) energy development scenarios based on several IAMs with respect to multiple criteria. By doing so, we account for uncertainty surrounding policy priorities outside the IAM. In order to follow a sustainable approach, the ranking of policy options is based on EU energy policy priorities: energy efficiency improvements, increased use of renewables, reduction in and low mitigations costs of GHG emission. The ranking of scenarios is based on the estimates rendered by the two advanced IAMs relying on different approaches, namely TIAM and WITCH. The data are fed into the three MCDM techniques: the method of weighted aggregated sum/product assessment (WASPAS), the Additive Ratio Assessment (ARAS) method, and technique for order preference by similarity to ideal solution (TOPSIS). As MCDM techniques allow assigning different importance to objectives, a sensitivity analysis is carried out to check the impact of perturbations in weights upon the final ranking. The rankings provided for the scenarios by different MCDM techniques diverge, first of all, due to the underlying assumptions of IAMs. Results of the analysis provide valuable insights in integrated application of both IAMs and MCDM models for developing energy policy scenarios and decision making in energy sector. [ABSTRACT FROM AUTHOR]

With the increasing negative effects of fossil fuel combustion, many countries have paid more attention on supporting environmentally friendly energy generation, particularly renewable and sustainable energy sources (RES). However, renewables still cannot economically compete with fossil-fuels and are facing new challenges. Appropriate electricity pricing mechanism plays a vital role on mitigating the stress of limited fossil fuels and can promote renewable energy consumption. As one major type of renewable energy, wind power has been globally promoted. In order to promote the application of wind power, appropriate wind power prices (WPP) should be established by considering the local conditions. This paper targets such a field and conducts a bibliometric and network analysis based on the data from Scopus. The results show that the numbers of total related publications are gradually increasing, with the US as the leading country. European countries also have outstanding achievements. Moreover, both the most cited articles and keywords distribution offer future research directions. In general, this study provides valuable insights to both wind power researchers and practitioners. [ABSTRACT FROM AUTHOR]

Energy sectors of all the countries around the world are faced with a big challenge: the energy transition under the imperative of continuous reduction of carbon dioxide emissions. The energy transition and the sustainability constraints pose a significant challenge for engineers, researcher and scientists developing new and clean energy-efficient technologies. Energy production and transformations, along with its storage, distribution and consumption, are achieved by utilising new and clean energy technologies, with the goal of the continuous increase of energy efficiency, the growth of renewable energy sources utilisation, the uninterrupted switch from fossil fuels to alternative energy sources, and the continued reduction of CO2 emissions. This editorial paper addresses the latest progress and the findings in research and developments in the sustainable energy technologies, energy and environment systems from selected papers of the conference on Sustainable Development of Energy, Water and Environment Systems held in 2019. [ABSTRACT FROM AUTHOR]

As a response to climate change and the desire to gain independence from imported fossil fuels, there is a pressure to increase the proportion of electricity from renewable sources which is one of the reasons why electricity grids are currently being turned into Smart Grids. In this paper, we focus on private consumers’ acceptance of having Smart Grid technology installed in their home. We analyse acceptance in a combined framework of the Technology Acceptance Model and the Norm Activation Model. We propose that individuals are only likely to accept Smart Grid technology if they assess usefulness in terms of a positive impact for society and the environment. Therefore, we expect that Smart Grid technology acceptance can be better explained when the well-known technology acceptance parameters included in the Technology Acceptance Model are supplemented by moral norms as suggested by the Norm Activation Model. We tested this proposition by means of an online survey of Danish ( N = 323), Norwegian ( N = 303) and Swiss ( N = 324) private consumers. The study confirms that adding personal norms to the independent variables of the Technology Acceptance Model leads to a significant increase in the explained variance in consumer acceptance of Smart Grid technology in all three countries. [ABSTRACT FROM AUTHOR]

• The data envelopment analysis is introduced to renewable energy development. • The method has two stages: comprehensive evaluation and structure optimization. • Indexes from different aspects are quantified via intuitionistic fuzzy numbers. • The comprehensive performances are determined based on the relative efficiencies. • The proportions of renewable energy are optimized based on the whole efficiency. Different kinds of renewable energy resources have developed rapidly. For renewable energy planning, it is meaningful to assess the comprehensive performances of different schemes and then to determine the optimal design of the energy structure. This paper presents a two-stage method for the comprehensive evaluation and structure optimization of renewable energy plans. First, in the evaluation stage, multiple indexes from different aspects are taken into consideration, of which each qualitative index will be converted quantitatively, using the intuitionistic fuzzy number to describe the fuzziness and ambiguity in a qualitative index. Then, the superefficiency data envelopment analysis model is used to determine the comprehensive performances of different plans based on the concept of relative efficiency. Next, in the optimization stage, an optimal model combining multiple renewable energy resources is established based on the relative efficiency results of the evaluation process. This model aims at the maximum efficiency as a whole and can be used to optimize the proportions of different renewable energy resources. Finally, a real case of renewable energy development from a province in China is given to demonstrate the feasibility and effectiveness of the proposed method in this paper. The results show that it can overcome the shortcomings of the traditional basic model, obtain more objective evaluation results and provide beneficial references for the strategy making of renewable energy development. [ABSTRACT FROM AUTHOR]

• A metaheuristic-based model is proposed to optimally size a hydrogen-based microgrid. • The microgrid system is equipped with an innovative hydrogen refuelling station. • The performances of eight metaheuristics are studied in terms of accuracy and speed. • The moth-flame optimizer is found to outperform the popular algorithms in this area. • The system's levelized costs of electricity and hydrogen are $0.09/kWh and $4.61/kg. There has been growing interest in the development of sustainable energy systems using the potential of renewable energy sources. However, due to the intermittent nature of renewable energy sources, they must be accompanied by appropriate storage devices to be optimally integrated into so-called smart micro-grid systems. The optimal design problem of sustainable micro-grids is associated with several nonlinearities and non-convexities, and therefore is not amenable to exact methods of optimization. Accordingly, this paper proposes a metaheuristic-based approach for optimizing the size and typology of the components of an off-grid hydrogen-based micro-grid, backed up with super-capacitors and stationary fuel cell systems, in the presence of a hydrogen refuelling station. The paper also compares the performance of six recent metaheuristics. The simulations are carried out for the climatic conditions of the Feilding area, New Zealand using MATLAB. Based on the comparative results, the moth-flame optimization algorithm is found to result in a significant reduction in the total net present cost of the system in comparison with other investigated algorithms. Notably, it outperforms the genetic algorithm and the particle swarm optimization in terms of solution quality by ~2.1% and ~3.2% (equating to cost savings of $123,910 and $188,129 from the target system). The results also demonstrate both the technical feasibility and cost-effectiveness of the proposed stand-alone micro-grid architecture. Particularly, the levelized costs of electricity and hydrogen of the conceptualized system are found to be $0.09/kWh and $4.61/kg, respectively, which are well below the current tariffs in New Zealand. [ABSTRACT FROM AUTHOR]

Abstract: China has set ambitious goals to increase the use of renewable energy. Developing renewables in rural areas is also one of the most important energy strategies. This paper examines rural social acceptance of renewable energy deployment taking Shandong as a case study via a field questionnaire survey. Theory of planned behavior is adopted to establish an analytical framework, and a logit model is used to examine possible determinants of local social acceptance. The results show that rural residents are generally supportive renewable energy development given its positive impacts on environment. A stated willingness to pay more for renewable electricity is taken as a variable representing an individual’s behavioral intention. The probability of occurrence of positive intention is found to increase with household income, individual knowledge level and belief about costs of renewable energy use but decrease with individual age. Residents with higher level of income are more likely to be willing to pay more for green electricity, so are the younger people. Enhancive knowledge and understanding about renewable energy (for instance, the cost) would be conducive to win public acceptance of renewable energy deployment. [Copyright &y& Elsevier]

Abstract: Thermal energy storage (TES) is widely recognized as a means to integrate renewable energies into the electricity production mix on the generation side, but its applicability to the demand side is also possible. In recent decades, TES systems have demonstrated a capability to shift electrical loads from high-peak to off-peak hours, so they have the potential to become a powerful instrument in demand-side management programs (DSM). Thermal storage is a technology that ensures energy security, efficiency and environmental quality. Of particular interest are applications where TES systems help manage the mismatch between availability of renewable electricity and the demand for electricity in buildings where hot water, heating and cooling are delivered by heat pumps and air conditioning for example. Thus this paper demonstrates the state of the art of present applications of thermal storage for demand-side management. A particular focus of this work is the attention paid to the characteristics of DSM and their relationship to different thermal storage systems. If TES effectiveness for the abovementioned applications is demonstrated, TES devices have a small percentage of the potential market. Therefore challenges and guidelines for a development plan are suggested. [Copyright &y& Elsevier]

• Different Energy Management Sytesm (EMS) modeling strategies are compared. • A graphic representation of the operational microgrid configurations is proposed. • Neural Networks, Fuzzy Logic and Support Vectors EMS based model are implemented. • A conventional Rolling Time Horizon EMS is used as benchmark solution. • EMSs operational time has been verified on a Raspberry Pi microcontroller. With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm. Such strategy, known as Rolling Time Horizon (RTH), demonstrated very effective when the supporting prediction system performs well. However, it is featured by high operational times. In this work, different lightweight EMS models synthesized through machine learning algorithms have been compared considering six different simulation scenarios. Results shows that an RTH-based EMS owns the best overall performances. However, in some case studies, also other EMSs show competitive results, especially those based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) trained by clustering, which in one case outperform RTH EMSs, and in other 3 cases (out of 6) yields performances close to RTH EMSs within 5 %. A second contribution concerns the RTH EMS implementation on a small micro-controller, highlighting the high computational effort which can range in the order of minutes. Conversely, the ANFIS EMS shows always almost negligible computational costs (less than one second) and therefore can be used in realistic scenarios on cheap devices at run time. The paper also proposed a novel graphic tool to better represent, observe and analyze microgrid energy flows in each time slot or along the overall considered dataset. [ABSTRACT FROM AUTHOR]