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Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]

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

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling. • A self-adaptive working condition classification approach is proposed. • A power allocation strategy with optimal filter matching is proposed. • A reconstruction method for obtaining optimal solution set is proposed. [ABSTRACT FROM AUTHOR]

The recent international agreements signed by the majority of developed countries, such as those reached at the Climate Change Conferences in Paris'15 and Dubai'23, propose an increasingly rapid transition toward an energy ecosystem with a clear predominance of renewable energy in electrical power systems. The development of such ambitious energy programs should deal with the inherited stochasticity that renewable energy systems entail, which, when coupled with uncertainty about the capacity of the grid to maintain a power supply owing to increasing demands, decarbonization processes and the widespread closure of nuclear power plants, pose a serious threat to the normal functioning of energy systems. When combined with the escalation of armed conflicts that imply the loss of supply as a result of attacks or cyberattacks on power plants and distribution networks, it becomes clear that the current energy paradigm in which there is a centralized grid supplying numerous consumers, many of whom do not have their own generation capacity, must shift toward increasing the deployment of renewable-energy-based self-consumption facilities. The continuous advances toward a decentralized energy system of this nature will also lead to more cooperation, increasing the presence of energy communities with a great need to strengthen internal resilience as a sustaining factor. Considering the challenging framework of smart grids and energy transition, Microgrids would appear to be the key technology for the aggregation of generation, load and energy storage systems, and a cornerstone with which to provide the resilience and flexibility required for this new renewable-energy-based scenario. In addition to the complexity of the microgrid control problem, the issue of resilience energy management also has to be considered, which refers to the ability to adapt and supply loads during a specified period after a disruptive event with a loss of grid supply. This paper introduces an innovative method based on Model Predictive Control (MPC) techniques with the aim of enhancing resilience in microgrids by maximizing the energy surplus and reducing the aforementioned critical instants at which the capacity to feed loads is minimum in the case of power grid outages. The results obtained show that the proposed algorithm enhances the energy resilience in microgrids while the overall operational cost is optimized. A method with which to enhance the resilience of interconnected microgrids through cooperative optimization methods is also developed and validated. [Display omitted] • New techniques for resilience enhancement in microgrids/ networks of microgrids. • Optimizing energy management by enhancing surplus during critical resilience points. • Cooperative resilience management in networked microgrids with cost efficiency. [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]

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points. • A new game-based pricing mechanism for parking clusters is developed. • Two uncertainty-aware strategies are proposed and compared. • Vehicle-to-grid helps to improve the economy of the cluster notably. • The impact of uncertainties strongly depends on vehicle-to-grid availability. • The new proposal reduces the cost of users compared with other approaches. [ABSTRACT FROM AUTHOR]

Highlights • A novel quantitative life cycle sustainability assessment framework is developed. • Sustainable performance is assessed under normal condition and disruption scenarios. • Environmental, economic, and resilience aspects are quantified in monetary terms. • Innovative way of demonstrating results, named Sustainability Compass, is proposed. • The method is applied to IES of a university campus in support of decision analysis. Abstract There are two dimensions to "sustainability" as it applies to an engineered system: (i) it needs to be designed, operated, and managed such that its environmental impacts and costs are minimal, and, (ii) it ought to be designed and configured in such a way that it is robust to extreme disruptions and shocks posed by natural, manmade, or random events, i.e. be resilient under extreme events. These somewhat conflicting attributes call for a multi-criteria analysis framework. This paper proposes such an assessment framework for engineered systems involving three new location and circumstance-specific sustainability indices that monetize the economic, environmental and resiliency characteristics throughout the lifecycle of the system components. The proposed framework, thus, allows translating sustainability goals into engineering practice, and is applicable to: (i) the design of new systems, (ii) assessing performance of an existing system, (iii) day-to-day scheduling and operation of the system, and (iv) future growth planning. A novel presentation method, called "Sustainability Compass", is also proposed which allows decision-makers to visually track the direction and magnitude of changes in the individual sustainability indices of different scenarios; this would also allow easier communication with other stakeholders. The proposed methodology and the usefulness of the Sustainability Compass diagram have been illustrated using end-use monitored data for a whole year of operation of a university campus integrated energy system. The results of five alternative energy development scenarios involving a combined heat and power system and solar photovoltaic systems with different penetration levels have been investigated and are reported in this paper. [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]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

• BRICS countries exhibit expansionary strategies in solar and wind power. • DE countries exhibit conservative strategies in solar and wind power. • The solar and wind power markets in BRICS countries are capital-oriented. • BRICS countries should prevent the new energy technology dilemma. Carbon neutrality is proposed by many countries for responding to climate change and energy transformation. Developing new energy is crucial to achieving carbon neutrality goals. In the context of carbon neutrality, this paper analyzes the factor structure and market orientation of the new energy market in BRICS countries (including the Federative Republic of Brazil, Russian Federation, the Republic of India, People's Republic of China, and the Republic of South Africa) and developed European (DE) countries in recent years. The primary conclusions are as follows: (1) In wind and solar energy markets, the BRICS countries exhibit an elastic substitution effect in production, indicating they have chosen the expansionary strategy with unbalanced factors. Through the contribution analysis, we find the development of wind and solar energy in BRICS countries is capital-oriented. However, the DE countries have opted for a conservative strategy with balanced factors, in which the wind and solar energy markets need promotional measures for capital investment and technological progress. (2) In the biomass energy market, both the BRICS countries and DE countries adopt the expansionary strategy. Besides, the development of the biomass energy market is technology-oriented except for India. (3) For the nuclear energy market, the BRICS countries and DE countries choose the conservative strategy and expansionary strategy, respectively. However, due to safety concerns, the expansion of nuclear energy in all countries has slowed. (4) Although a capital-oriented strategy can quickly promote the development of the new energy market, this practice compromises the local welfare and the benefits of the new energy technology progress to varying extents. The core insight of this paper is that BRICS countries should avoid the new energy capital trap and prevent the new energy technology dilemma. For the BRICS countries, policymakers should further evaluate the balance between capital-oriented interest and technology-oriented development. Also, achieving carbon neutrality is more difficult for the BRICS countries than DE countries. Hence, the BRICS countries should further refine their energy strategies for the long term. [ABSTRACT FROM AUTHOR]

• Solved the profit distribution and optimal scheduling problem of multi-operator VPP. • A bi-level multi-time scale scheduling method based on bidding is proposed. • Proposed bidding and price formation methods considering fluctuation cost. • Established a multi-time scale scheduling model including default penalty mechanism. With the development of energy internet and power market, the operation regulation and pricing mechanism of traditional virtual power plants are improved to adapt to the new environment. In this paper, a bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant is proposed to provide a framework for solving the interest distribution between operators and optimal scheduling problems of multiple-operator virtual power plant. An operator power allocation and internal electricity price formation method based on bidding equilibrium is proposed in the upper level, which introduces the fluctuation cost coefficient to express the influence of the uncertainty of renewable energy power generation on the bidding process. A multi-time scale optimal scheduling method combining scheduling model and adjustment strategy is established in the lower level. A default penalty mechanism in the scheduling model is used to ensure that operators provide the electricity allocated from the bidding process and considering the influence of demand response based on internal electricity price on adjustment strategy. Simulation results show that the proposed method can realize the optimal distribution of operators' power generation and form the internal electricity price that reflects the internal supply and demand level of virtual power plant. Besides, it can reduce the impact of uncertainty on dispatching results and improve the application range of virtual power plant to enhance the competitiveness of virtual power plant in market transactions. [ABSTRACT FROM AUTHOR]

• Study of microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV system. • Development of energy management system based on model predictive control (MPC-EMS). • Results compared with proportional sharing algorithm based on SOC level (SOC-EMS). • Best results obtained from MPC-EMS. This paper presents a new energy management system (EMS) based on model predictive control (MPC) for a microgrid with solar photovoltaic (PV) power plants and a quasi-Z-source cascaded H-bridge multilevel inverter that integrates an energy storage system (ES-qZS-CHBMLI). The system comprises three modules, each with a PV power plant, quasi-impedance network, battery energy storage system (BESS), and voltage source inverter (VSI). Traditional EMS methods focus on distributing the power among the BESSs to balance their state of charge (SOC), operating in charging or discharging mode. The proposed MPC-EMS carries out a multi-objective control for an ES-qZS-CHBMLI topology, which allows an optimized BESS power distribution while meeting the system operator requirements. It prioritizes the charge of the BESS with the lowest SOC and the discharge of the BESS with the highest SOC. Thus, both modes can coexist simultaneously, while ensuring decoupled power control. The MPC-EMS proposed herein is compared with a proportional sharing algorithm based on SOC (SOC-EMS) that pursues the same objectives. The simulation results show an improvement in the control of the power delivered to the grid. The Integral Time Absolute Error, ITAE, achieved with the MPC-EMS for the active and reactive power is 20 % and 4 %, respectively, lower than that obtained with the SOC-EMS. A 1,3 % higher charge for the BESS with the lowest SOC is also registered. Furthermore, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is implemented to validate the simulation results. [ABSTRACT FROM AUTHOR]

Towards the global endeavor of clean energy transition, there is a rapid development of distributed energy resources installed in the premises of residential or commercial users, enabling them to act as flexible energy prosumers. Empowering prosumers is envisioned as a catalytic development for modern energy economies, with recent research, as well as innovation and policy actions, pointing to the promising direction of decentralized energy markets, where active energy prosumers exchange energy in a decentralized fashion. Despite the vast amount of recent research on prosumer-centric peer-to-peer (p2p) energy markets, only a small subset of studies accounts for managing the inherent uncertainty of prosumers' flexible demands. In this paper, we consider the problem of controlling the decisions of energy prosumers' within a p2p exchange network. The multi-bilateral economic dispatch is formulated as an optimal control problem. The proposed solution is based on a direct lookahead policy, effectively addressing the issues of dimensionality and local constraint satisfaction. Experimental simulations demonstrate the method's efficiency and the system's behavior. The proposed formulation and method is shown to effectively address the operation of p2p markets under uncertainty, closely tracking the performance of the (full information) optimal-in-hindsight benchmark. • A p2p economic dispatch problem is considered, taking into account the system's uncertainty. • The problem is formulated as a multi-agent optimal control problem. • A machine learning algorithm is used to predict the system's optimal dual variables. • The ADMM algorithm is combined with the machine-learning predicted duals to obtain an efficient control policy. • The proposed policy respects the prosumers' local constraints while achieving a near-optimal dispatch. [ABSTRACT FROM AUTHOR]

Meeting the growing electricity demand in many developing countries is a major challenge as there is a shortage in power generation. This paper proposes a novel strategy for mitigating the effect of weather intermittency on the scheduling power interruption of residential houses. The proposed method involves optimal scheduling strategy for the electricity supply of these houses in a distribution grid and weather temperature prediction using a probability paper plot is deployed. Data training is achieved through the Monte-Carlo Simulation, and the prediction model is validated through error analysis. Using this method, novel power interruption schedules, based on maximizing houses’ accessibility to electricity and considering consumers’ fairness of accessing electricity services on hourly basis, have been designed. The design considers two optimization problems, scheduling electricity supply and validating the schedule in the power distribution grid by ensuring no violations of grid operational requirements. Moreover, a sensitivity analysis has been conducted to investigate the effect of scaling up the demand from the house context on enabling electricity access to such houses. The effect of conservative voltage reduction schemes on energy consumption reduction has also been explored. The studied results demonstrate the effectiveness of the proposed method in enabling electricity access of new regions accommodated in the grid by up to 47.6% which increases the profit of the power utility from residential electricity bill payments. [ABSTRACT FROM AUTHOR]

• A co-simulation platform for Hybrid Electric Vehicles is developed for virtual assessment of Energy Management, performance, CO 2 & pollutant emissions. • CO 2 & pollutant emissions models are based on convolutional neural network for accurate transient state prediction. • A continuous deep reinforcement learning based EMS of HEV is proposed. • DDQL is trained with random generated driving cycles based on Markov Chain approach from real recordings. • Cumulative d error for predicted pollutants emissions is below 8.5% while HEV simulation is running up to 10 times faster than real time in a standard laptop. The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO 2 and pollutant emissions (CO, NO x and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution. The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO 2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions. [ABSTRACT FROM AUTHOR]

Concerns about the reliability of electricity supplies have motivated researches to investigate the possibility of electrical consumers to take a more active role in the operations of the power system. The work in this paper looks into the potential of an industrial chemical plant to provide support to the electricity grid by means of demand-side response (DR) programs. To do so, this paper proposes a method to assess the flexibility of the plant to provide electrical power reserves while ensuring that the production demand is satisfied, as well as an economic analysis of the plant operations incorporating DR programs to quantify the incentives the plant should receive in order to participate in these programs. Therefore, the current study presents a novel optimization framework which integrates production scheduling with DR programs, with the aim to determine optimal decisions for the operating conditions within the plant while safely providing services to the electricity grid. [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]

With the proposed concept of energy blockchain, peer-to-peer (P2P) energy trading between prosumers is regarded as a potential way for future power market development. However, existing P2P energy trading often has problems such as low trading efficiency and significant communication losses. This paper presents an energy trading strategy in the blockchain environment to solve the problems of P2P energy trading. First, the type of market is determined based on energy supply and demand. Then, a Stackelberg game is used to establish a two-layer model of the leader (upper) and follower (lower) to determine the price. In addition, we propose an energy trading method based on feasible regions. The trading strategy considers the supply and demand of the market, provides an accurate energy transfer signal, and can help energy blockchain achieve better "self-sufficiency". A case study proves the effectiveness of the strategy. Compared with the existing methods, the strategy proposed in this paper considers the market type and can promote the coordination and complementation of energy in the microgrid, and improve trading efficiency and reduce communication loss without sacrificing privacy. This study demonstrates that P2P power trading has brought benefits to prosumers, has promoted the development of the energy market, and has shown the massive potential of energy blockchain. • A P2P energy trading model and a consensus algorithm in the blockchain are proposed. • An interactive pricing-and-matching P2P energy trading strategy is proposed. • A novel P2P energy trading pricing model based on game theory is proposed. • A P2P energy trading matching method based on the feasible region is proposed. • Results show that the strategy proposed in this paper can optimize the microgrid. [ABSTRACT FROM AUTHOR]

Highlights • Yield per hectare of sweet sorghum are influence by temperature and precipitation. • Xinjiang and Inner Mongolia are suitable for planting sweet sorghum. • The 2020 and 2050 biofuel goal can be satisfied by certain scenarios in this paper. • Climate change and land-use change are critical to energy crop development plan. Abstract Biofuel from energy crops will play an important role in supplying carbon-neutral fuel for the transport sector. However, there are large uncertainties regarding energy crop development and the energy crop potential and distribution need to be evaluated more comprehensively and systematically. In this study, we propose an integrated approach using the AquaCrop model, a geographic information system approach, and a scenario analysis and we conduct a land availability assessment with multiple factors including climate change, land use, soil, topography, regional crop management, and technological improvement to shed light on the potential production, distribution, and temporal-spatial variation of land suitable for sweet sorghum production in China. We evaluate a full range of spatial scales from 1-km resolution to a national assessment in four representative concentration pathways from 2010 to 2100. The results show that most provinces can plant sweet sorghum. The most suitable provinces for sweet sorghum production are Xinjiang, Heilongjiang, Jilin, Inner Mongolia, Yunnan, and Sichuan. The biofuel potential of sweet sorghum in high-slope farmland (0.30–0.73 EJ) and marginal land (11.98–15.18 EJ) can satisfy the biofuel goal in 2020 (0.12 EJ) and 2050 (6.70–8.60 EJ). Climate change and land-use change are critical factors influencing for the current implementation and long-term planning of energy crops in China. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Switching control and sliding mode control are combined to control a buck converter. • The novel design of the switching sliding surface can address the singularity problem. • Multi-type restrictive condition can guarantee the stability of the SSMC system. • The SSMC strategy can simplify the implementation of exponential circuit. Abstract In this paper, a switching sliding-mode control strategy is proposed for the voltage control of a buck converter in auxiliary energy source. The aim of this study is to solve the singularity problem of the terminal sliding-mode control strategy and simplify the implementation of the exponential operation of the non-singular terminal sliding-mode control strategy in practical applications. To achieve this goal, the state-space model of the buck converter in the auxiliary energy source is established. Furthermore, a novel switching sliding surface is defined based on the non-singular terminal sliding-mode control strategy. To ensure the stability of the switching sliding-mode control system, a multi-type restrictive condition, including non-singular type and reachable type, is designed based on equivalent control law and Lyapunov stability criterion, respectively. Simulations and experiments are presented to verify the effectiveness of the proposed switching sliding-mode control strategy. The results show that both the non-singular terminal sliding-mode control and the switching sliding-mode control can effectively solve the singularity problem. Compared with the terminal sliding-mode control strategy, the non-singular terminal sliding-mode control and the switching sliding-mode control strategies have over 18% and 34.4% settling-time improvement of voltage adjustment to deal with the large load variation, respectively. The proposed switching sliding-mode control strategy can not only ensure the robust voltage control of the buck converter in the auxiliary energy source, but also simplify the implementation of exponential circuit in practical applications. [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]

IETB framework for green clean hydrogen energy development in the low-carbon transition. [Display omitted] • Most of the countries agree to consider renewable energy-based hydrogen generation as their long-term strategy. • Primary driving reasons for the growth of hydrogen energy in BRICS-US are economic, institutional and behavioural factors. • Key aspects of hydrogen are reviewed i.e. production cost, application, production and delivery in BRICS-US. • This study focuses on the qualitative inflection point through IETB framework in the development of hydrogen energy. Carbon dioxide (CO 2) levels in the atmosphere have grown rapidly (by 50% since the 1750 industrial revolution) due to natural processes, the use of fossil fuels, deforestation, and other factors. Carbon dioxide contributes to global warming, which causes climate change and extreme weather events. Because of technological advancement and use, the world's energy consumption has skyrocketed. The novelty of the study as it focuses on the present state of carbon emissions (in terms of sectors and fuel) as a result of current hydrogen production projects, applications, cost, and delivery, as well as government policy and structural changes in the BRICS-US (Brazil, Russia, India, China, South Africa, and the United States) relationship throughout time. A conceptual model has been established for multi-dimensional debate for logical comparison analysis in institution-economic-technology-behavior (IETB). This paper has three main findings. First, in terms of horizontal comparison, each country has its advantages but distinct underlying driving causes. Second, in terms of vertical comparison, each country's hydrogen energy development is still in the quantitative stage, with the qualitative inflection point still to be reached. Third, in terms of a strategic goal, producing hydrogen energy benefits the energy transition from a primary mission aspect. However, studies have revealed that China is the leader in CO 2 emissions, followed by the US. China is the largest producer and consumer of hydrogen (24 million tonnes). Even though the world's largest green hydrogen plant is being developed in California (United States) (11,000 kg of green hydrogen per day and 3.8 million kilograms per year). This study highlights potential future development despite existing challenges and opens doors for enlightening the core researchers for establishing programs in transition (emerging and frontier) economies to strengthen state energy, efficiency, and conservation policies. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the development of a simple hybrid desalination system of a Multi-Effect Distillation (MED) and an adsorption (AD) cycle operating at sub-atmospheric pressures and temperatures. By hybridizing the conventional MED with an AD cycle, there is a symbiotic enhancement of performances of both cycles. The performance enhancement is attributed to (i) the cascade of adsorbent’s regeneration temperature and this extended the usage of thermal energy emanating from the brine heater and (ii) the vapor extraction from the last MED stage by AD cycle which provides the effect of lowering saturation temperatures of all MED stages to the extent of 5°C, resulting in scavenging of heat leaks into the MED stages from the ambient. The combined effects of the hybrid cycles increase the water production capacity of the desalination plant by nearly twofolds. In this paper, we demonstrate a hybrid cycle by simulating an 8-stage MED cycle which is coupled to an adsorption cycle for direct vapor extraction from the last MED stage. The sorption properties of silica gel is utilized (acting as a mechanical vapor compressor) to reduce the saturation temperatures of MED stages. The modeling utilizes the adsorption isotherms and kinetics of the adsorbent+adsorbate (silica-gel+water) pair along with the governing equations of mass, energy and concentration. For a 8-stage MED and AD cycles operating at assorted temperatures of 65–90°C, the results show that the water production rate increases from 60% to twofolds when compared to the MED alone. The performance ratio (PR) and gain output ratio (GOR) also improve significantly. [Copyright &y& Elsevier]

Abstract: In March 2011, China’s central government set a new challenging target of reducing its energy intensity by 16% during 2011–2015, after it had achieved a reduction of 19.1% during 2006–2010. And this new target was assigned to provincial authorities in August 2011. However, China’s provincial energy-economic developments are unbalanced and different provinces have different key sectors for energy conservation. Most previous studies focused on provincial energy efficiency at the aggregate level, or the three-industry level (or one-digit level). However, whether for policy decision or academic research, it is necessary to further subdivide the sectors. In this paper, we use three indicators (Gini Coefficient, energy consumption share and energy intensity) to compare provincial energy conservation potentials at the two-digit sector level. To our knowledge, this paper is the first one to identify the keys for energy conversation across the 31 provinces×65 sectors. And the results are shown in visualized maps and matrix tables to help identify the key province×sectors for energy conservation easier. This also helps the central and provincial governments to distinguish key sectors when they monitor the energy conservation progress. [Copyright &y& Elsevier]

Abstract: HCCI combustion can substantially reduce NOx and soot emissions of diesel engines. However it is hard for diesel HCCI to obtain the same level of fuel economy as traditional diesel engines. This paper presents a way to improve the fuel economy of diesel HCCI by adopting Integrated Starter Generator (ISG). A set of diesel parallel hybrid power system (in this article, we name it “hybrid engine”) is constructed by installing an ISG motor on a diesel engine, which applies HCCI–CI combined combustion. Based on a lot of investigation on diesel HCCI, this paper is focus on improving HCCI engine performance, especially the fuel economy, by adopting ISG motor assist control. The coordination control strategies of engine and ISG motor in HCCI transient states, HCCI–CI transition, engine quick start and braking energy regeneration are developed. The results of equivalent New European Driving Cycle (NEDC) tests show that, by using ISG assistance, the fuel consumption of the diesel HCCI engine is greatly reduced. Meanwhile the NOx and soot emission are also improved. [Copyright &y& Elsevier]

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]

Range-extended electric vehicles (REEVs) are becoming a development trend of new vehicles. Energy management is one of the core problems in REEVs. The structure and control method of the auxiliary power unit (APU) is determined based on the configuration analysis in this paper. An energy management optimization problem is proposed to solve the power distributions of APUs and batteries in the charge-sustaining (CS) stage of REEVs, which are determined by dynamic programming and pseudo-spectral optimal control, respectively. The results show that different limits of the APU power changing rate significantly influence fuel consumption. To obtain the power changing rate of APUs and to evaluate the energy management optimization method of REEVs, a model of the APU control system is built and verified by a platform test; the dynamic response characteristics and control parameters of the APU are obtained by step-changing conditions. Two types of strategies for tracking APU power are proposed for different power changing rates, and the fuel consumption of REEVs is analyzed in four types of driving cycles. The effect on fuel consumption caused by the power changing rate of the APU is verified. [ABSTRACT FROM AUTHOR]

Rapid vehicle and powertrain development has become essential to for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer while keeping pace with reduced development cycle and more frequent product releases. Recently, advances in large-scale additive manufacturing have provided the means to bridge hardware-in-the-loop (HIL) experimentation and preproduction mule chassis evaluation. This paper details the accelerated development of a printed range-extended electric vehicle (REEV) by Oak Ridge National Laboratory, by paralleling hardware-in-the-loop development of the powertrain with rapid chassis prototyping using big area additive manufacturing (BAAM). BAAM’s ability to accelerate the mule vehicle development from computer-aided design to vehicle build is explored. The use of a hardware-in-the-loop laboratory is described as it is applied to the design of a range-extended electric powertrain to be installed in a printed prototype vehicle. The integration of the powertrain and the opportunities and challenges it presents are described in this work. A comparison of offline simulation, HIL and chassis rolls results is presented to validate the development process. Chassis dynamometer results for battery electric and range extender operation are analyzed to show the benefits of the architecture. [ABSTRACT FROM AUTHOR]

Shipping is a major contributor to global CO 2 emissions. Various operational and technical measures have been proposed to reduce ship emissions. However, these emission reduction measures may not be all economically feasible to implement. Therefore, it is important to rank all these measures and select the most cost-effective measures for emissions reduction. Moreover, there are various uncertainties in evaluating emission reduction measures, such as uncertainties of implementation cost, fuel consumption, abatement potential and fuel price. These uncertainties may significantly influence the ranking of the emission reduction measures, which further result in an inappropriate selection of the measures for implementation. In this paper, a ranking algorithm with a new criterion is proposed to rank all the emission reduction measures by considering the preference between cost and abatement. Furthermore, a ranking under uncertainty method is developed which takes into account various uncertainties of the impact factors. This method can support policy makers in ranking and selecting emission reduction measures more appropriately by better quantifying and reflecting the uncertainties. [ABSTRACT FROM AUTHOR]

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]

Abstract: The paper presents an investigation on the development of energy security level assessment technology. The system of energy security indicators, covering technical, economic and socio-political aspects, is introduced. The security indicator is a special index which gives numerical values to important issues for security of energy sector. In the paper, each indicator is described by presenting the title, comments, factual and threshold, pre-critical and critical state values. The integral characteristics of these indicators show the level of energy security and in order to identify it, a point system assessment scale is used. The methodology developed in this paper is applied for the assessment of the Lithuanian energy security level in different scenarios. At first, indicator groups are constructed and group weights are determined. The weights of indicators within each group are established in two ways: when all weights are equal and one indicator is dominating. Taking into consideration the assessment of indicators by points, their weights in groups and group weights, the Lithuanian energy security level was determined according to separate indicator blocks. The security level of each indicator and each indicator block, and total security level are presented as the results. The indicators that have the highest impact on the security level increase or decrease are determined as well. [Copyright &y& Elsevier]

The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters ( e . g . length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one. [ABSTRACT FROM AUTHOR]

Envisioning a sustainable biorefinery requires reliable information on the sustainable availability of biomass, optimal plant location and delivered cost. In this paper, we have developed an integrated Geographic Information System (GIS) based sustainable biomass assessment, site optimization and supply logistics cost model to assess the spatial and temporal availability of crop residues, to identify optimal plant sites and to calculate the delivered cost. The grid-level (30 × 30 m) assessment model was developed for crop residues using three primary sustainability indicators: (1) Soil Erosion (SE), (2) Soil Conditioning Index (SCI) and (3) Crop residue yield ⩾2.5 dry Mg/ha. The Artificial Neural Networks (ANNs) prediction models for each indicator were developed and implemented in the GIS platform to assess sustainably available crop residues. A multi-criteria geospatial analysis was used to identify suitable plant sites. GIS-based location-allocation model was used to site biorefineries/plants at optimal locations and generate feedstock supply curves. The developed model was demonstrated with the sustainable assessment of cotton stalk (CS) to produce fuel pellets in the study region (Georgia, USA). The model has estimated that about 1.6 million dry Mg of CS is available annually to support seven pellet plants with an average annual plant capacity of 200,000 dry Mg. The average delivered cost of CS ranged between 68 and 75 $/dry Mg delivered as large rectangular bales with the transport radii ranged from 31 to 60 km. The spatial and temporal variations in the topology and crop yield directly influenced the sustainable availability of CS, the optimal plant location and its capacity and the delivered cost. However, the changes in the optimal plant location and delivered cost were minimal for large capacity plants (>400,000 dry Mg). The developed model can be used to assess multiple crop residues, to manage and control feedstock supply risks and delivered cost variations for a sustainable biorefinery. [ABSTRACT FROM AUTHOR]

Solar aided (coal fired) power generation (SAPG) is the synergy of solar thermal energy and fossil power plant technologies by using solar thermal energy to replace extraction steam to preheat feed water. SAPG combines environmental benefits of the former and large capacity, high efficiency and reliability of the latter. However, there are no appropriate criteria for evaluating the thermal performance of the system now. In this paper, a new evaluation method of SAPG system is proposed based on the exergy analysis and the characteristics of exergy flows crossing the boundary of the system. In the established evaluation method, a new ideal cycle is constructed in the perspective of external heat sources, and the difference of solar proportion among the exergy input, work output and exergy output in the same subsystem is considered. With the evaluation method, the contribution proportion of solar energy in a SAPG system can be determined. To illustrate applications of the new method, a parabolic trough solar field aided 600 MW coal-fired power generation system is discussed as a case study. [ABSTRACT FROM AUTHOR]

This paper explores the impacts of active suspension with energy regeneration capability on the fuel economy of a power split Hybrid Electric Vehicle (HEV). Models of HEV powertrain and suspension are established and, based on their interactions, unified. The impacts of control parameters on the suspension performance are synthetically analyzed. The analyses form a basis for the design of suspension controller. High-level supervisory controller and low-level sub-controllers for HEV powertrain and suspension are designed. Low-level sub-controllers can ensure the performance of each system, whose control parameters are updated by the supervisory controller. The equivalent consumption minimization strategy (ECMS), superior in maintaining charge sustainability, is improved for better application in the proposed system. Simulation results under different conditions demonstrate that both HEV fuel economy and ride comfort are improved. Comparative analyses of various scenarios validate the positive effect of suspension energy regeneration on the energy conservation performance of power split HEV. Meanwhile, the improvement of HEV fuel economy can be highlighted by applying ECMS. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable. [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]

In this paper a data-validated power-efficiency model of a diesel-powered fuel-cell-based auxiliary power unit (APU) system is used to investigate the various sizes of the power unit and the battery and to evaluate the optimal choices for specified load profiles. The challenge comes from the FCGEN (Fuel Cell-based power GENeration) EU FP7 project, where such an APU was developed. The system consists of a fuel processor, a PEM stack, and a battery providing power for the startup, shutdown, and for covering load transients; however, the developed prototype system is not optimised. Before redesigning it for mass production, the optimal size of the main components needs to be identified to enable the best possible exploitation of the technology. In this work a case-specific load profile was used and a mesh grid of scenario simulations has been performed using various sizes of the fuel cell with fuel processor as a power unit and the batteries of various capacities as an energy storage unit. For this purpose a scalable APU model, including the BoP component consumption, has been developed. Upon the analysis results, the relation for optimal combinations in terms of efficiency and degradation is proposed and the confronted tradeoffs are discussed. [ABSTRACT FROM AUTHOR]

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically identify and assess critical factors, which foster or hinder the development of bottom-up initiatives in the diffusion of photovoltaics. Bottom-up initiatives are social innovations, which entail civil engagement in energy transition at a local or regional level, and are expected to play a growing role in the governance of local energy systems in Europe. A mixed design methodology is used to identify critical factors and assess their importance. This involves combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process. The findings indicate that successful initiatives are those which are able to draw upon substantial local public interest and trust in the new technology, and which manage to combine financial attractiveness with environmental concerns. The results make clear that the political context is also an extremely important success factor. Given the appropriate circumstances, such initiatives may make a significant contribution in the transition to a sustainable energy system, and thus prove useful in reaching European energy targets. [ABSTRACT FROM AUTHOR]

Energy storage is a cutting edge front for renewable and sustainable energy research. In fact, a massive exploitation of intermittent renewable sources, such as wind and sun, requires the introduction of effective mechanical energy storage systems. In this paper we introduce the concept of a trigenerative energy storage based on a compressed air system. The plant in study is a simplified design of the adiabatic compressed air energy storage and accumulates mechanical and thermal (both hot and cold) energy at the same time. We envisage the possibility to realize a relatively small size trigenerative compressed air energy storage to be placed close to the energy demand, according to the distributed generation paradigm. Here, we describe the plant concept and we identify all the relevant parameters influencing its thermodynamic behavior. Their effects are dissected through an accurate thermodynamic model. The most relevant technological issues, such as the guidelines for a proper choice of the compressor, expander and heat exchangers are also addressed. Our results show that T-CAES may have an interesting potential as a distributed system that combines electricity storage with heat and cooling energy production. We also show that the performances are significantly influenced by some operating and design parameters, whose feasibility in real applications must be considered. [ABSTRACT FROM AUTHOR]

This paper presents a maximum power point tracking (MPPT) of a scale-up pressure retarded osmosis (PRO) based osmotic power generator. Inspired by the well-known MPPT in photovoltaic (PV) array, two algorithms, perturb & observe (P&O) and incremental mass-resistance (IMR) method, are investigated. Using a series of simulations, both the algorithms are demonstrated to be capable of tracking the maximum power point (MPP) and capturing the transitions between varied MPPs due to the fluctuations of operating temperature. However, in both cases the trade-off between the rise time and the oscillation is found requiring further consideration on the selection of the step-size for perturbation pressure or incremental pressure. In order to improve the performance of the MPPT, furthermore, an optimum model-based controller (OMC) is used to estimate the initial optimum pressure for the MPPT in a scale-up PRO process. It is found that with OMC, the performance of the MPPT is improved significantly. Finally, a strategy to operate and coordinate the MPPT and OMC to deal with the rapid variations of the salinities are proposed and evaluated in terms of individual variation of the concentration or flow rate and co-variation of the both. The simulations demonstrate the preferred performance of the proposed strategy to adjust the operation subject to the rapid changes of the salinities. [ABSTRACT FROM AUTHOR]

The most successful peer-to-peer networks are based on the concept of supernode, which is an operating point of the network that provides services and advanced functionalities to other nodes. Inspired by this idea, this paper proposes to integrate nodes that provide intelligent advanced services in the future architecture of the electrical grid. Besides facilitating the access to data services such as demand estimations and weather forecasts, these nodes are especially meant to hold virtual environments in which software agents, after being contracted, negotiate on behalf of users in energy markets. This architecture is designed to be compatible with the Energy Interoperation OASIS standard. The capabilities and feasibility of the proposal is demonstrated through realistic experiments based on OpenADR programs, in which users exchange energy by using parallel auction markets. In addition, in order to have the roles of buyer and seller in demand-response programs, thus allowing the creation of markets, a conceptual model based on negative loads and critical loads is provided. The experiments have proven that the proposed architecture facilitates the implementation of advanced distributed management systems in order that smart metering infrastructures, in contrast with traditional agent-based solutions, are released to perform negotiation tasks and access data services, while users gain both autonomy and decision-making capacity. [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]

In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy ( E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the importance of an accurate E RDE method in real-world applications. [ABSTRACT FROM AUTHOR]

Environmental performance (EP) uncertainties span a number of energy technology options, and pose planning risk when the energy system is subject to environmental constraints. This paper presents two approaches to integrating EP uncertainty into the long-term energy planning framework. The methodologies consider stochastic EP metrics across multiple energy technology options, and produce a development strategy that hedges against the risk of exceeding environmental targets. Both methods are compared within a case study of emission-constrained electricity generation planning in British Columbia, Canada. The analysis provides important insight into model formulation and the interactions with concurrent environmental policy uncertainties. EP risk is found to be particularly important in situations where environmental constraints become increasingly stringent. Model results indicate allocation of a modest risk premium in these situations can provide valuable hedging against EP risk. [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]

[Display omitted] • A directly solar-driven power-to-methane system is proposed. • The detailed distributed parameter method is used to model the system. • A comprehensive thermodynamic and economic analysis is performed. • Energy and exergy efficiencies are 9.88% and 11.08% with 914.51 MWh/y SNG yield. • Unit cost is 186.57 $/MWh in 2020 and will be reduced to 91.51 $/MWh in 2050. The transformation of solar energy into easily transportable and storable fuel brings a prospective solution to the issue caused by its intermittency and non-dispatching. Relying on well-established infrastructures and mature technologies for application, methane can be an attractive energy carrier for improving the penetration of solar energy in the future energy structure. To evaluate the feasibility of solar-to-methane from technical and economic views, this paper proposes a directly solar-driven power-to-methane system integrating photovoltaic plant, solid oxide electrolysis cell, methanation reactor, and membrane module. The detailed distributed parameter method is employed for thermodynamically modeling the core components of the system to consider the profiles of current density, temperatures, and composition concentrations. A comprehensive thermodynamic and economic analysis is further conducted for the system, and the results indicate that the power-to-methane efficiency, total energy efficiency, and total exergy efficiency of the system can reach 68.41%, 9.88%, and 11.08%, respectively. With the annual average solar radiation of 557.43 W/m2, the system achieves an annual synthetic natural gas yield of 914.51 MWh/y. A high total product unit cost of 186.57 $/MWh is obtained in 2020, whereas it is predicted to decrease to 91.51 $/MWh (about 51% reduction) in a future scenario with a payback period of 4.66 years due to the significant decrement of the photovoltaic investment cost. The present work reveals the engineering realizability and economic viability of solar-to-methane and provides a competitive option for the development of solar energy. [ABSTRACT FROM AUTHOR]

• Spatial pattern and evolution trends of four kinds of residential energy consumption in China are investigated. • Spatial difference in the distribution of gravity centers of residential energy consumption is depicted. • Standard deviational ellipse demonstrates the dynamic evolution patterns of residential energy consumption. • Coupling coordination degree between residential energy consumption and the economic indicator is investigated. • Electricity should be taken into further consideration in the development of residential energy improvement. The residential energy consumption in China shows diversity and complexity in space. This paper aims to investigate the spatial pattern and evolution trends of four kinds of residential energy consumption (electricity, coal, oil, and natural gas) through gravity center modeling and standard deviational ellipse modeling. Further, their relations to economic growth are probed by using the coupling and coordination degree model. Panel data at the provincial level in China are collected from 2000 to 2019. Our results show that the most drastic movement trend of gravity centers is coal, natural gas, followed by oil, whereas electricity moved slightly in space. Further, the standard deviational ellipse of electricity exhibits relatively stable patterns than the other three residential energy consumption during the study period. In addition, the coupling coordination degree between residential energy consumption and the economic growth is examined, and the results indicate that the total residential electricity consumption shows the highest coupling coordination degree, followed by residential gas, oil, and coal. It demonstrates lower coupling coordination degrees in rural areas than in urban areas in the total and sectoral residential energy consumption. In sum, several policy implications can be demonstrated based on our findings, i.e., the development of electricity consumption in the residential energy structure may improve the energy stability in space in China, and ensuring access to electricity should be taken into further consideration in the development of residential energy improvement to achieve a better coordination level to economic development. [ABSTRACT FROM AUTHOR]