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To address the uncertainty of renewable energy in unit commitment, an adjustable uncertainty set of renewable energy is introduced and unit commitment is scheduled to guarantee the safe operation of the power system in this set. However, the operational risk arises when renewable energy falls out of the adjustable uncertainty set and this risk should be evaluated to determine the adjustable uncertainty set. In this paper, a distributionally robust optimization approach is proposed to calculate the risks of load shedding and renewable energy curtailment at the lower and upper bounds of the adjustable uncertainty set. After the evaluation of the operational risk, the day-ahead unit commitment with the adjustable uncertainty set is determined together with the demand response reserve in the reduction of the operational risk. In real time, the dynamic demand response is proposed to further reduce the operational risk. The proposed distributionally robust method with the dynamic demand response for dealing with the uncertainty of renewable energy is verified on the IEEE 6-bus, 30-bus, and 118-bus systems. Simulation results show that the proposed method reduces the cost of the unit commitment and the operational risk. • The adjustable uncertainty set and demand response are used to tackle the uncertainty. • A distributionally robust optimization approach is proposed to evaluate the risk. • A dynamic demand response scheduling is proposed to reduce the operational risk. [ABSTRACT FROM AUTHOR]

Hydrogen is seen as the future energy that will help decarbonise the emissions of global energy use. Hydrogen-related technologies have recently attracted considerable attention due to their relatively low emissions and high energy yield. Even then, little attention was given to hydrogen's use in energy distribution networks. A renewable-based multi-energy system (RMES) considers power, cooling, heating, and hydrogen energy as utility systems for integrated urban and industrial areas to achieve urban-industrial symbiosis. This paper formulates the RMES as a multi-period mixed-integer nonlinear programming (MINLP) model to optimise the RMES, which minimises the financial implications and environmental impacts. Renewable solar energy is provided to the system using the photovoltaic solar system for electrical generation and the solar thermal collector for heat generation. Thermal, battery and hydrogen energy storages are integrated into the RMES to mitigate the energy supply and demand fluctuations and intermittency. A comparative analysis is conducted to individually identify the performance of different energy storage systems for economic and environmental objective functions. The comparison findings indicate that ESS performs better with 45% usage increases under objective environmental functions. The multi-objective optimisation using the ϵ-constraint method obtains the Pareto optimal solutions to the proposed multi-objective problem, which the 4th solution (ATC: 782,500 USD/y; ACE: 2,777.03 kg CO 2 -eq/y) appears to be the most viable. The solution maintains a high-profit level without sacrificing many opportunities for carbon emissions reduction while satisfying both objective functions simultaneously to a degree of satisfaction of 0.75. Overall, the proposed RMES is proven economical and environmentally friendly for implementation; however, the model is needed to optimise the system based on the specific situation. This study provides the optimisation model for energy recovery and technology optimisation in the multi-energy system for urban-industrial symbiosis, which minimises carbon emission and energy cost. This could lead the energy sector to achieve the Sustainable Development Goals, considering the economically and environmentally viable. • Renewable-based multi-energy system for urban-industrial symbiosis. • Heating, electricity, cooling and hydrogen energy demands are considered. • Multi-period optimisation based on economic and environmental factors. • Thermochemical, battery and hydrogen storages are compared. • Multi-objective optimisation for obtaining the non-dominating Pareto optimal. [ABSTRACT FROM AUTHOR]

With the support of incentive policies, the diffusion of wind power has achieved tremendous achievements in China. However, behind this rapid expansion, it is raised a question: what are the driving forces behind the diffusion of wind power? And which policies are more efficient to improve the diffusion rate of wind power? To scientifically answer this question, this paper combines system dynamics and the two-factor learning curve to investigate the dynamic diffusion process of wind power. The results show that: (1) The demand-pull policy through feed-in tariff (FIT) policy can promote the diffusion of wind power; (2) The technology-push policy through R&D policy also promotes the diffusion of wind power; (3) A higher level of FIT is more efficient to expand the installed capacity of wind power, in addition, a higher level of R&D intensity is more efficient for cost reduction; (4) The coefficient of learning by doing of wind power is 0.0274 and the coefficient of learning by research of wind power is 0.2540, which indicates China's wind power industry is transitioning from price subsidies to technology innovation policies. The research results can enrich the research on the diffusion of wind power and provide a reference for other regions to study the diffusion of renewable energy. • Both demand-pull and technology-push can promote the diffusion of wind power. • The coefficient of learning-by-doing and learning-by-researching is 0.0274, 0.2540 • The reduction in wind power costs is driven more by innovation policies. • A higher level of FIT policy is more efficient to expand the installed capacity. • A higher level of R&D intensity is more efficient for cost reduction. [ABSTRACT FROM AUTHOR]

The multi energy system (MES) is promising in the process of carbon neutrality, such that multi energy resources are utilized comprehensively to reduce the operation cost. Another way is to promote carbon neutrality by increasing the penetration of renewable energy. Hence, in this paper, we study the energy management of a typical MES under the challenges of stochastic renewable supplies and energy demands. To address the challenges, a stochastic optimization problem is established as a Markov decision process (MDP). An improved deep reinforcement learning (DRL) method is then developed to achieve the dynamic optimal energy dispatch. In particular, the comfort experience of users and complex coupling are both considered in the MES. In this framework, we propose an improved soft actor critic (SAC) algorithm based on maximum entropy to improve exploration ability, together with a long short-term memory (LSTM) network to extract temporal features efficiently. Meanwhile, we add the prioritized experience replay (PER) to increase the training efficiency to speed up the convergence of the algorithm. Finally, the case study demonstrates that the proposed algorithm can converge rapidly and greatly reduce the operation cost. In addition, the effectiveness and robustness of the improved method are verified. • A stochastic optimization model is mapped to a Markov decision process. • Uncertainties of renewable supplies and energy demands are considered. • An improved deep reinforcement learning algorithm based on PLSAC is applied. • The effectiveness of the optimal dispatch in the multi energy system is validated. [ABSTRACT FROM AUTHOR]

Among the numerous types of renewable energy technologies, wave energy technologies have been considered by many experts as one of the most promising for Taiwan. However, the amount of energy presently provided by wave energy technologies constitutes a minor percentage of Taiwan's total energy production. This paper presents experimental results using a laboratory water chamber, wherein the fundamental parameters of an oscillating water column (OWC) system's geometrical design are individually investigated and optimized for maximum amplification factor of wave energy to mechanically useful energy. The effects of back plate angle, fence plate and open wide parameters of chamber in a view of wave energy catching capability are analyzed. Three experimental models are evaluated to include a discussion of the back plate, fence plate, and open wide. The OWC's operation qualitatively differs from that predicted by linear theory, identify to critical flow characteristic; front wall down-wash in the water column. The surveys were then expanded to offer general arrangement or a planning material for the geometry optimization of the chamber that could potentially achieve the largest amplification factor of an oscillating water column (OWC) system. The amplification factor of the change in back angle of the OWC is significant. [ABSTRACT FROM AUTHOR]

Increasing the use of variable renewable electricity (VRE) such as solar and wind power will impose major challenges to existing energy systems. The increased variability needs to be considered, but also how to optimally operate the remaining power system. In this paper, we present a fast and easy-to-use optimization model to find cost-optimal ways to manage the energy system with large-scale VRE. This is done by minimizing the running costs and maximizing revenues of the energy production. The model accounts for the energy system dynamics. It can handle both electric and thermal loads at different scales (building, city) and allows incorporating different flexibility strategies. The model is applied to analyzing a very large wind power scheme for Helsinki city (Finland), coupled to the existing CHP-dominated district heating schemes through power-to-heat conversion for more optimal system integration leading to major CO 2 emission reductions. The model is well suited to studying integration of renewable energy, to design better policies for introducing VRE and to find solutions to increase energy system flexibility, among others. [ABSTRACT FROM AUTHOR]

It is imperative to explore the full carbon dioxide-saving potential for plug-in hybrid electric vehicles. This paper seeks to examine the role of renewable energy and powertrain optimization in minimizing daily carbon emissions of plug-in hybrid electric vehicles. A spectrum of influencing factors are investigated, including charging protocol, timing, on-road power management strategy, battery size, and carbon-emission intensity of the grid. A high-efficiency convex programming framework is harnessed to optimize a plug-in hybrid powertrain. Two originally important contributions evidently distinguish this work from existing efforts. First, diverse heuristic scenarios and concomitant weaknesses are elucidated, and the carbon reductions arising from renewable energy integration and the convex programming framework are quantified. The great importance of their synergy is accentuated, i.e., swiftly adapting charging/power management controls to wind intermittency. Second, battery health implication is explored for the optimal and heuristic scenarios, via a dynamic battery State-of-Health model. [ABSTRACT FROM AUTHOR]

The common process in all applications of a salinity-gradient solar pond (SGSP) is the energy extraction process using single-phase mode heat transfer with some limitations such as pumping the large amount of mass flow rate, and need for big size of heat exchanger. In every respect, two-phase mode heat transfer can be selected as an advantage due to its passive case of operation and comparatively high heat transfer capacity with rational system size. In this paper, an enhanced design of a large scale SGSP power plant using some two-phase closed thermosyphons has been simulated and compared with the single-phase mode heat transfer. The simulation results showed that the overall thermal efficiency of the solar pond power plant was the highest using both thermosyphons and heat exchangers. [ABSTRACT FROM AUTHOR]

This paper presents a computational fluid dynamics study on a solar-chimney power plant with an inclined collector roof. A three-dimensional model using the RNG (Re-Normalisation Group) k−ε turbulence closure is simulated. The discrete ordinates non-gray radiation model is used to implement the radiative-transfer equation. To simulate solar irradiation, the solar ray-tracing algorithm is employed. A parametric study is performed to illustrate the effect of the collector configuration on the performance of the Manzanares power plant. In this approach, the roof inclination is adjusted by increasing the outlet collector height, while the inlet collector height is fixed to the height of the collector of the Manzanares solar chimney pilot plant. The results showed that changes in the collector-roof inclination affect the convection pattern through the collector, which results in an increase in the mass flow rate of the system. [ABSTRACT FROM AUTHOR]

Solar pond has been a reliable supply of heat source for industrial process that requires heat at the temperature <100 °C. In this paper, the prospect of solar pond in generating electricity has been explored with the aid of using TEGs (thermoelectric generators) for converting the heat available at LCZ (lower convective zone) into electricity. By using the transient heat transfer model developed and the testing result of a thermoelectric module, this study has covered the potential of generating electricity for the solar pond operates in the climate of Group A, B, and C under Köppen climate classification. The effect of heat extraction, climatic variation, temperature polarisation, and the conversion efficiency of TEG on the thermal performance and electrical performance of the system has been discussed. The solar pond operates in Riyadh (Group B of Köppen climate classification) possesses the highest potential in generating electricity, which is about 4.834 kWh/year-m 2 at heat extraction of 15% of year average horizontal solar radiation (i.e. solar pond yearly efficiency of 15%) while having average LCZ temperature of 80 °C throughout the year. Overall, the thermal-electrical conversion efficiency, η t of this system is in the range of 1%–1.5% from the heat extracted. [ABSTRACT FROM AUTHOR]

The energy requirements of the planetary exploration spacecrafts constrain the lifetime of the missions, their mobility and capabilities, and the number of instruments onboard. They are limiting factors in planetary exploration. Several missions to the surface of Mars have proven the feasibility and success of solar panels as energy source. The analysis of the exergy efficiency of the solar radiation has been carried out successfully on Earth, however, to date, there is not an extensive research regarding the thermodynamic exergy efficiency of in-situ renewable energy sources on Mars. In this paper, we analyse the obtainable energy (exergy) from solar radiation under Martian conditions. For this analysis we have used the surface environmental variables on Mars measured in-situ by the Rover Environmental Monitoring Station onboard the Curiosity rover and from satellite by the Thermal Emission Spectrometer instrument onboard the Mars Global Surveyor satellite mission. We evaluate the exergy efficiency from solar radiation on a global spatial scale using orbital data for a Martian year; and in a one single location in Mars (the Gale crater) but with an appreciable temporal resolution (1 h). Also, we analyse the wind energy as an alternative source of energy for Mars exploration and compare the results with those obtained on Earth. We study the viability of solar and wind energy station for the future exploration of Mars, showing that a small square solar cell of 0.30 m length could maintain a meteorological station on Mars. We conclude that the low density of the atmosphere of Mars is responsible of the low thermal exergy efficiency of solar panels. It also makes the use of wind energy uneffective. Finally, we provide insights for the development of new solar cells on Mars. [ABSTRACT FROM AUTHOR]

Combining technological solutions with investment profitability is a critical aspect in designing both traditional and innovative renewable power plants. Often, the introduction of new advanced-design solutions, although technically interesting, does not generate adequate revenue to justify their utilization. In this study, an innovative methodology is developed that aims to satisfy both targets. On the one hand, considering all of the feasible plant configurations, it allows the analysis of the investment in a stochastic regime using the Monte Carlo method. On the other hand, the impact of every technical solution on the economic performance indicators can be measured by using regression meta-models built according to the theory of Response Surface Methodology. This approach enables the design of a plant configuration that generates the best economic return over the entire life cycle of the plant. This paper illustrates an application of the proposed methodology to the evaluation of design solutions using an innovative linear Fresnel Concentrated Solar Power system. [ABSTRACT FROM AUTHOR]

Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic Time Warping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verification measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of California, USA reaching its renewable electricity target of 33%, excluding large hydro, by 2020, which is set out in the state's RPS (Renewable Portfolio Standard). The emerging renewable electricity mix in California and surrounding states which form the WECC (Western Electricity Coordination Council) is analysed using the CEPA (Carbon Emission Pinch Analysis) and EROI (Energy Return on Energy Invested) methodologies. The reduction in emissions with increased renewables is illustrated and the challenge of maintaining high EROI levels for renewable generation is examined for low and high electricity demand growth. Results demonstrate that wind and solar PV collectively form an integral part of California reaching the 33% renewables target by 2020. Government interventions of tax rebates and subsidies, net electricity metering and a four tiered electricity price have accelerated the uptake of electricity generation from wind and solar PV. Residential uptake of solar PV is also reducing overall California electricity grid demand. Emphasis on new renewable generation is stimulating development of affordable wind and solar technology in California which has the added benefit of enhancing social sustainability through improved employment opportunities at a variety of technical levels. [ABSTRACT FROM AUTHOR]

Jordan is a country faced with several environmental and energy related issues. It is the Worlds' fourth most water deprived country with a water consumption of only 145 m 3 per capita annually, less than a third of the established severe water poverty line. Jordan is also a country rich in wind and solar potential but practically no utilization with 99% of the produced electricity coming from imported fossil fuels resulting in high CO 2 emissions and a potential security of supply issue. The utilization of reverse osmosis desalination in a combination with brine operated pump storage units and wind and (PV) photovoltaic plants can tackle both issues. The desalination plants can produce the much needed water and act as a flexible demand to increase the penetration of intermittent renewables supported by the brine operated pump storage units. This paper presents six scenarios for the development of the Jordanian energy system until the year 2050. The results have shown that the demonstrated configuration can increase the share of intermittent renewables in the production of electricity up to 76% resulting in a high reduction of fuel consumption, CO 2 emissions and costs. These analyses have been performed using the EnergyPLAN advanced energy system analyses tool. [ABSTRACT FROM AUTHOR]

There has been an intense debate on the quantification of the employment effects of renewable electricity deployment in the European Union. However, most studies carried out in the past have focused on specific countries and those with a European-wide scope have not provided disaggregated results per country. Furthermore, differences between importing and exporting countries have not been considered. This paper aims to cover those gaps. It presents a novel methodology which integrates the aforementioned considerations in order to calculate the employment effects of renewable energy deployment. The methodology is useful to calculate dynamic employment factors (considering technology learning effects) and the specific capacity (based on trade effects) to which these factors should be applied. It is applied to three renewable energy technologies – photovoltaics, wind on-shore and wind off-shore- in the European Union Member States in the 2008–2012 period. The results using this novel methodology broadly confirm the figures provided in other contributions in the literature. The proposed methodology can be instrumental in assessing the socio-economic effects of policies and investment programmes targeting the deployment of renewable energy technologies. [ABSTRACT FROM AUTHOR]

This article analyses energy trends in Cambodia and Laos and constructs future scenarios. The target is (i) to compare different scenario methods, (ii) to construct scenarios with these methods, (iii) to analyse the future energy demand and the related CO 2 emissions and (iv) to analyse the potential role of renewable energy sources and their impact on CO 2 emissions. Decomposition scenarios have been constructed based on the trends of the drivers of the energy consumption and CO 2 emissions. More detailed scenarios have been constructed using combined LEAP/LINDA models to take into account the structural socio-economic and technological changes in the energy system. The paper provides a novel combination of bottom-up modelling and macroeconomic modelling. The constructed scenarios indicate rapid increase in energy consumption and related CO 2 emissions mainly due to the fast economic growth and industrialization. Increase in fossil fuel use in electricity production in the future increases the emissions considerably. The potential of different renewable agricultural residues is analysed to evaluate the possibilities to reduce the CO 2 emissions and fossil fuel import dependence. The reduction potential is considerably large in both countries. [ABSTRACT FROM AUTHOR]

The milk processing and preservation is a fast growing business in developing countries and it is facing problems due to high energy cost and environmental concerns in using conventional energy sources. The energy tapped from renewable energy sources through the technological innovations would be one of the best options to implement the milk preservation strategies at village level. In rural areas, bioenergy is one of the most versatile energy-generating options. Because of the diversity in feed stock and conversion technologies, suitable study is needed to implement renewable energy base technologies to provide a continuous flow of energy services. In this paper, the use of locally available renewable energy sources, in various combinations, to operate a milk chilling plant at village level has been analysed using the Matlab software. The effect of variations in the combination of renewable energy sources on the overall system COP has been studied. The study predicts that the best possible overall system COPs in hilly, rubber cultivation, paddy and seashore regions are 0.26, 0.25, 0.235 and 0.24 respectively. Moreover, suitable combinations identified in the aforementioned regions are Biomass/Gobar gas (0.7:0.3), Biomass/Biogas/Gobar gas (0.7:0.1:0.2), Biogas/Biomass/Gobar gas (0.6:0.15:0.25) and Biomass/Gobar gas/Biogas/Solar (0.5:0.25:0.125:0.125) respectively. [ABSTRACT FROM AUTHOR]

Hybrid energy investment projects help to increase the efficiency in renewable energy production. This situation can be a solution to the high installation cost problem of the clean energy investments. While examining similar studies in the literature, it is determined that there is a need for a new study to identify the optimal combinations of the renewable energy alternatives to increase the effectiveness of the hybrid energy investments. This study aims to identify coalition-oriented strategic selection of renewable energy system alternatives. Several combinations are weighted by q-rung orthopair fuzzy (q-ROF) decision making trial and evaluation laboratory (DEMATEL) with golden cut and coalition game theory. Intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs) are also used to check the validity of the findings. Moreover, multi stepwise weight assessment ratio analysis (M-SWARA) methodology is also considered to measure the consistency of the analysis results. It is defined that solar is the most efficient energy alternative for the hybrid energy investments. Solar energy should be prioritized in hybrid investment projects where at least two of the renewable energy alternatives are evaluated together. The usage area of solar energy is wide because it is not necessary to be close to the water source or the city network to install solar panels. Additionally, solar energy systems generally do not require much maintenance that provides a cost advantage compared to others. Owing to these issues, solar energy projects provide the highest contribution in all investment combinations. The novelty of this paper is to provide a unique model by combining the methodology of q-ROF and golden cut properly with the aim of proposing a set of renewable energy system combinations in terms of coalition game theory. • Coalition-oriented strategic selection of clean energy alternatives is defined. • Several combinations are weighted by q-ROF DEMATEL. • M-SWARA is also considered to measure the consistency of the findings. • IFSs and PFSs are also used to check the validity of the results. • Solar is the most efficient energy alternative for the hybrid energy system. [ABSTRACT FROM AUTHOR]

Financial events in global energy markets could trigger extreme volatility spillovers and even become financial crises without effective risk management. To analyze extreme volatility spillovers in energy markets and demonstrate how risks are spread under extreme market conditions, this paper first measures the extreme volatility spillovers in the main energy markets from 2011 to 2019 using the vine Copula model then constructs the extreme volatility spillover networks based on their tail dependence correlations. Furthermore, the extreme risk spread is investigated by applying clique analysis. In consideration of the recent shock namely the COVID-19 pandemic, we further analyze the energy markets' extreme volatility spillovers in 2020. We find that extreme volatility spillover effects are stronger in renewable energy markets, especially during market booms and it also reflects an asymmetry feature of the energy markets' extreme volatility spillovers. The water energy market is influential in the extreme volatility spillover networks. More significantly, there are strong volatility spillover effects among renewable energy markets, while risks are less likely to spread among non-renewable energy markets under extreme circumstances. We make contributions to reveal the volatility spillover under extreme conditions, then comprehensively analyze the risk spread in the extreme volatility spillover networks. By comparing how risks spread before and during the COVID-19, and between renewable and non-renewable energy markets, our findings could be of great help for policymakers and other stakeholders to cope with the uncertainties, especially in the extreme environment. • Extreme volatility spillover networks of main energy markets are constructed. • Extreme risk spread among energy markets are revealed by using clique analysis. • We investigate extreme volatility spillovers and risk spread in the COVID-19 shock. • We conclude that extreme risks are easily spread among renewable energy markets. [ABSTRACT FROM AUTHOR]

In this study, the game theory approach has been used to perform Time-Of-Use (TOU) pricing for renewable and conventional energy supply chains with government intervention to achieve sustainable development goals. Also, a Demand Response Program (DRP) based on TOU pricing has been implemented to improve the profits of power producers and the energy consumption pattern of end customers. Decision variables included the price of conventional and renewable energy during low- and high-load periods, tax rate, and subsidies. These variables are determined in three scenarios with the goals of maximizing government revenue, maximizing social welfare, minimizing environmental impacts, and under two-game structures of cooperative and Nash between the producers. The equilibrium solutions of each game for the three scenarios were obtained by backward induction. The results showed that decisions related to energy prices and tariffs play a major role in achieving the goals of sustainable development, profits of supply chain members, and success in meeting consumer demand. For all three scenarios, the government revenue function and the social welfare functions earn higher values in the Nash game than in the cooperative game, but the environmental impacts and the producers' profit function earn respectively lower and higher values in the cooperative game. • TOU pricing is provided in renewable and conventional energy supply chains. • The game theory approach is used to model competition between energy supply chains. • Government intervention to achieve sustainable development goals are considered. • The paper provides some managerial insights for energy producers and governments. [ABSTRACT FROM AUTHOR]

In this paper, the synthetic inertia need of the small island of Pantelleria in the Mediterranean Sea is assessed. Firstly, the optimal renewable energy mix able to minimize the Levelized Cost of Energy for the generation system of the island is evaluated, considering the yearly load demand and the characteristics of the local natural resources. The optimal energy mix introduces in the island a quota of power generation with static power converters interfaced to the grid that could jeopardize the security of the system if not suitably controlled. Therefore, the inherent inertial response of the diesel generators of the system is calculated hour by hour for each month of one year, considering the new schedule of the generators for various cases characterized by different percentages of production from the optimum renewable energy mix. Finally, the rated power and capacity of a battery energy storage system is calculated in order to compensate the reduction of the inertial response in the power system thanks to a suitable synthetic inertia control. The calculation is done by considering the maximum allowed frequency nadir in case of a disturbance. For each scenario, the maximum disturbance is also assessed with and without synthetic inertia. The results are compared with those obtained in a previous study on the same island where batteries were used for different purposes. • Sizing of a renewable energy mix to cover a share of energy demand in small island. • The best energy mix is given by the minimization of the Levelized Cost of Energy. • Three daily contributions from unprogrammable renewable source are assessed. • The corresponding inertia of the power system is calculated in all scenarios. • A controlled battery energy storage system providing synthetic inertia is needed. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. • A novel stochastic-interval model for robust scheduling of PVaCSs is developed. • A comprehensive stochastic model for multi-mode EV charging is presented. • A multi-stage solution procedure for the developed model is described. • The new proposal allows to consider optimistic and pessimistic strategies. • A case study validates the developed formulation and modelling. [ABSTRACT FROM AUTHOR]

Energy security assessment quantifies the energy supply to a population and the likelihood, or risk, of an energy disruption or shortage and represents an important aspect of national security, economic stability and prosperity. The quantification of the state of energy supply is context-dependent and involves multiple perspectives: infrastructural, technological, environmental, market, social and geopolitical. Among all the different and relevant aspects involved, diversity and dependence of the energy fuel mix are two of the main energy security dimensions. The present paper investigates the diversification of the energy supply in Europe, by analysing import dependence, market concentration and renewable energy resource deployment in the European Union over the last decade. The analysis utilises a set of indicators aimed at measuring the fuel mix diversity, market concentration, geopolitical stability, renewable energy share and stochasticity - both at single country and at aggregated European levels. Results show a stable evolution of the diversity of the fuel mix and a relatively low market concentration of the period examined. However, the import dependency reduces the energy security by approximately 30% due to the high proportion of imports from a limited number of countries. Moreover, an increasing trend in renewable electricity production share is evident over the last decade, albeit with differences between member states, as a result of the decarbonisation policies implemented by the European Union. • Europe shows a stable evolution of the fuel mix diversity at EU28 aggregated level. • European import dependency reduces the energy security by 30%. • The implementation of the RES policies across the EU is not homogenous. • Diversification of the fuel mix and support for domestic fuels need to be fostered. • Policies need to merge decarbonisation targets with energy security constraints. [ABSTRACT FROM AUTHOR]

The smart control of the biogas-hydrogen engine is needed to improve the overall energy efficiency of the hybrid renewable energy system. The paper presents some simulation results of the optimal control parameters of the engine aiming to achieve the compromise between performance and pollutant emissions of the biogas-hydrogen engine. In neat biogas fueling mode, the optimal equivalence ratio changes from 1.05 to 1.01 as the CH 4 composition in biogas increases from 60% to 80%. By adding 20% hydrogen into biogas, the optimal equivalence ratio practically reaches the stoichiometric value, despite the variation of CH 4 concentration. At the same operating condition and hydrogen content, an increase of 10% CH 4 in biogas leads to a decrease of 2°CA in the optimal advanced ignition angle. However, at a given engine speed and biogas composition, the optimal advanced ignition angle decreased by 3°CA when adding 10% hydrogen into biogas. The optimal ignition angle is independent of the load regime. Under optimal operating conditions, the addition of 20% hydrogen content into biogas is found to improve the indicated engine cycle work by 6%, to reduce CO and HC emissions by 5–10 times; however, it increases NO x emission by 10–15% compared to neat biogas fueling mode. [Display omitted] • A blend of 20% hydrogen and 80% biogas is an optimal ratio. • Optimal equivalence ratio of poor biogas and biogas/hydrogen blend are 1.05 and 1. • Advanced ignition angle decreased by 2°CA and 3°CA with adding 10% CH 4 and 10% H 2. • Indicative cycle work increased by 6% as adding 20% H 2 to biogas in optimal condition. • HC and CO reduced by 5–10 times, but NO x increased by 10–15% in optimal condition. [ABSTRACT FROM AUTHOR]

Ever-increasing price of conventional energy resources and related environmental concern enforced to explore alternative energy sources. Inherent uncertainty of power generation and demand being strongly influenced by the electricity market has posed severe challenges for DRPs (Demand Response Programs). Definitely, the success of such uncertain energy systems under new market structures is critically decided by the advancement of innovative technical and financial tools. Recent exponential growth of DG (distributed generations) demanded both the grid reliability and financial cost–benefits analysis for deregulated electricity market stakeholders. Based on the SGT (signaling game theory), the paper presents a novel user-aware demand-management approach where the price are colligated with grid condition uncertainties to manage the peak residential loads. The degree of information disturbances are considered as a key factor for evaluating electricity bidding mechanisms in the presence of independent multi-generation resources and price-elastic demand. A correlation between the cost–benefit price and variable reliability of grid is established under uncertain generation and demand conditions. Impacts of the strategies on load shape, benefit of customers and the reduction of energy consumption are inspected and compared with Time-of-Used based DRPs. Simulation results show that the proposed DRP can significantly reduce or even eliminate peak-hour energy consumption, leading to a substantial raise of revenues with 18% increase in the load reduction and a considerable improvement in system reliability is evidenced. [ABSTRACT FROM AUTHOR]

Economic models are needed to analyze the impact of policies adopted for controlling carbon emissions and increasing distributed renewable generation in microgrids (green penetration). The impacts are manifested in performance measures like emissions, electricity prices, and electricity consumption. This paper presents an economic model comprising bi-level optimization and Pareto analysis. In the bi-level framework, the upper level models the operation of the microgrids and the lower level deals with electricity dispatch in the grid. The economic model is applied on a sample network in two steps. In step1, the bi-level model yields operational strategies for the microgrids and the corresponding values of the grid performance measures. In step2, a statistical analysis of variance combined with Pareto optimization attains guidelines for setting policies for emissions reduction and green penetration without adversely impacting electricity prices and demand. We conclude that renewable generation from microgrids can significantly reduce the negative impacts of the policies. Our economic model is novel as it 1) integrates operational strategies of microgrids and the grid under an emissions control regime, 2) explicitly considers social cost of carbon in the electricity dispatch, and 3) balances multiple objectives of emissions reduction, green penetration, and electricity consumption using a Pareto analysis. [ABSTRACT FROM AUTHOR]

This paper examines the impact of location and load shape selection on microgrid optimal design. 96 unique combinations of location and load shape are considered to provide a broader scope than any previous work in microgrid design sensitivity analysis. In addition, the level of autonomy from the macrogrid is considered as a tunable parameter in the optimization. A generic system is considered consisting of photovoltaics, wind turbine, microturbines, electric and natural gas boilers, thermal storage, and a battery bank. The microgrid is grid-connected and designed to supply both heat and power. A mixed integer linear program is used to minimize the expected cost of energy supply over a 20 year horizon. Trends in the design results are discussed and important input parameters that depend on the location and load shape are identified. Finally, a procedure to quantify these trends and predict optimal design results in new locations is proposed. [ABSTRACT FROM AUTHOR]

Worldwide, renewable electricity projects are granted production support to ensure competitiveness. Depending on the design of these support schemes, the cash inflows to investment projects will be more or less exposed to fluctuations in electricity and/or subsidy prices. Furthermore, as renewable electricity technologies mature, there is a possibility that the current support scheme will be terminated or revised in ways that make it less generous or more in line with market mechanism. Using a real options approach, we examine how investors in power projects respond to such market and policy risks. We show that: (1) due to price diversification, the differences in market risk between support schemes like tradeable green certificates, feed-in premiums and feed-in tariffs are less than commonly believed; (2) the prospects of termination will slow down investments if it is retroactively applied, but speed up investments if it is not; and, (3) this policy uncertainty may add a substantial risk to investments, especially in the first case where investors expect future curtailment of subsidies to affect new and old installations alike. We conclude the paper by discussing the division of risk between investor and government. [ABSTRACT FROM AUTHOR]

This paper describes preliminary work on the generation of synthesis gas from water electrolysis using graphite electrodes without the separation of the generated gases. This is an innovative process, that has no similar work been done earlier. Preliminary tests allowed to establish correlations between the applied current to the electrolyser and flow rate and composition of the generated syngas, as well as a characterisation of generated carbon nanoparticles. The obtained syngas can further be used to produce synthetic liquid fuels, for example, methane, methanol or DME (dimethyl ether) in a catalytic reactor, in further stages of a present ongoing project, using the ELECTROFUEL ® concept. The main competitive advantage of this project lies in the built-in of an innovative technology product, from RE (renewable energy) power in remote locations, for example, islands, villages in mountains as an alternative for energy storage for mobility constraints. [ABSTRACT FROM AUTHOR]

Electric vehicle charging by renewable energy can help reduce greenhouse gas emissions. This paper presents a data-intensive techno-economic model to estimate the cost of charging an electric vehicle with a battery capacity of 16 kW h for an average travel distance of 65 km from small-scale renewable electricity in various jurisdictions in Canada. Six scenarios were developed that encompass scale of operation, charging time, and type of renewable energy system. The costs of charging an electric vehicle from an off-grid wind energy system at a charging time of 8 h is 56.8–58.5 cents/km in Montreal, Quebec, and 58.5–60.0 cents/km in Ottawa, Ontario. However, on integration with a small-scale hydro, the charging costs are 9.4–11.2 cents/km in Montreal, 9.5–11.1 cents/km in Ottawa and 10.2–12.2 cents/km in Vancouver, British Columbia. The results show that electric vehicle charging from small-scale hydro energy integration is cost competitive compared charging from conventional grid electricity in all the chosen jurisdictions. Furthermore, when the electric vehicle charging time decreases from 8 to 4 h, the cost of charging increases by 83% and 11% from wind and hydro energy systems, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we reported the current status of the energy sector and its future challenges in Morocco. Recent strategies deployed by the government for sustainable development were reviewed and discussed. These strategies put the transition to renewable alternatives on the top of the national policy implications for energy security and low-carbon economy. We have also suggested a methodological tool to prioritize energy alternatives for power generation. It was found that wind energy is the most appropriate power generation alternative in Morocco at the current level. [ABSTRACT FROM AUTHOR]

Water resources are a crucial problem for a number of arid countries. One possible solution for this issue is the implementation of desalination but its utilization can greatly increase the demand for electricity. An important concept for a wide scale implementation of desalination is the integration of water and energy resources. Jordan is currently one of the most water deprived countries in the world. It is also a country very rich in renewable energy sources but almost no utilization of that potential. A combination of desalination, pump storage that utilize the produced brine and renewable energy sources could solve both issues. The desalination plants could produce enough water to ensure the supply for Jordan’s ever growing population while the use of wind and solar power could provide much needed electricity and reduce the need for imported fossil fuels as well as CO 2 emissions. The goal of this paper is to evaluate the impact of desalination in a combination with pump storage that utilizes the produced brine on the penetration of intermittent renewable energy sources in an energy system. The analysis has been conducted on a case study for the country of Jordan using the EnergyPLAN advanced energy system analyses tool. [ABSTRACT FROM AUTHOR]

This paper provides a critical review of the palm biodiesel landscape in Malaysia. The palm industry is a key source of revenue for the country, where in 2009 the industry was responsible for generating 8% of the gross national income per capita. The interest in palm-based biodiesel can be traced back to the early 1980s however it was only in 2006 that Malaysia officially formulated its first National Biofuel Policy as a strategic government intervention to drive development and implementation of palm biodiesel as substitute to regular fossil-based diesel. As at January 2013, the implementation of the biodiesel mandate (at 5% concentration) has been limited to the Klang Valley central region only. But there are plans for a much wider roll-out and at higher concentration blends in 2014. The policy in its current form assumes that all biofuels are sustainable and therefore fails to provide assurances that the fossil diesel will be replaced by a more sustainable energy source. Here it has been argued that a market-based policy approach would be better than a technology-forcing mechanism. [ABSTRACT FROM AUTHOR]