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

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

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

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

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

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

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

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

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

While Asia as a continent has enjoyed nearly 40% of the total installed wind energy capacity, the contribution of some countries in the region is less significant. Turkmenistan as an important oil and gas producing country is a major exporter of electricity in the Central Asia region. The country has an enormous potential for wind and solar energy development overshadowed by its wealth of oil and gas. This paper aims to assess the wind energy potential of Turkmenistan by taking into account the hourly wind speed data of eighteen different locations in a typical meteorological year. The widely utilized and validated two-parameter Weibull distribution function is utilized for calculating the power and energy density of the locations in the country. Fifteen commercially available wind turbines are also employed and compared against each other in terms of annual power output, energy output, and capacity factor for the considered locations. Finally, the economic metric indicator of the levelized cost of energy (LCOE) is employed to assess the feasibility of the system. The obtained results are expected to be a guideline for investors and policymakers regarding the wind energy development in the country while showing a major opportunity for Central Asia's energy market in the future. • Assessing the wind energy potential of 18 locations in Turkmenistan. • Kazandzhi, Turkmenbashi, Dashhowuz, and Gyzylarbat are the best sites for harvesting wind energy. • Providing insight into the best wind turbine option for implementation. • The results show a major opportunity for Central Asia's energy market in the future. • The cheap electricity generated from wind energy can be exported to other countries facing energy crises in the near future. [ABSTRACT FROM AUTHOR]

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

This research presents technical and cost-minimised design to decarbonise the heating network by using large-scale heat pump and thermal heat storage. In this paper, real hourly heat-consumption and heat-production cost data for the city of Aarhus, Denmark are used for calculating techno-economic feasibility of coupling the heating network with electrical grid. An optimum solution is suggested for the entire network with least amount of backup generation capacity, thermal heat storage capacity, natural gas boiler capacity and levelised cost of energy. Aarhus constitutes 5% of the Denmark's total heat demand and 4% of electrical load demand. This can be fulfilled with 160 MW of rated wind generation capacity, 35 MW of solar PV generation capacity, 45 MW of backup generation capacity, 221 MW of natural gas boiler capacity and 3.4 GWh of thermal heat storage capacity. The levelised cost of energy shows that, the coupling between the electrical grid and heating sector reduces the cost by more than 50% to 45  € /MWh. However, the cost-minimised design is possible with wind/solar mix of 85% and renewable energy penetration of 100%. Sensitivity analysis concedes that, the 100% decarbonisation of heating sector relies heavily upon the cost assumed for wind generation and solar PV generation, instead of the operation and maintenance cost for heat pump. Furthermore, the reduction in cost for wind generation and solar PV generation leads to the decrease in levelised cost of energy. Whereas, the reduction in cost for heat pump, thermal heat storage capacity and natural gas boiler capacity leads to an increase in renewable energy penetration. Sensitivity analysis further reveals that, increasing thermal heat storage capacity and the cost of selling excess renewable energy does not have major impact upon the levelised cost of energy and can be instrumental for the economic viability of fossil-free future. [ABSTRACT FROM AUTHOR]

Canada produces most of its electricity using renewables. However, in remote communities that are not connected to Southern Canada's main grid, the quasi-totality of microgrids rely on fossil fuels to ensure electricity supply. How much would it cost to decarbonize all of these microgrids? This paper uses a cost-based approach paired with a binary integer optimization model to find the least costly decarbonization solution for each off-grid settlement from now until 2050. By using wind speed and solar irradiance data together with future generation and storage cost estimates, our model determines whether solar or wind is more appropriate for a settlement and at which period it is best to undergo a transition from fossil fuel generation to renewables. Our results show that the cost of decarbonizing Canada's remote microgrids is not prohibitive and which technology and implementation period are cheapest for each settlement. We find that in 2020 wind turbines would be the cheapest option for most settlements, whereas in 2050 solar panels would be the cheapest option for most settlements. Settlements that currently use diesel of heavy fuel to produce electricity should consider undergoing decarbonization as soon as possible, while those that use natural gas could wait until production and storage technologies become cheaper. Larger settlements and fly-in communities could also be prioritized. • Most remote communities in Canada still use fossil fuels to produce electricity. • Decarbonizing Canada's remote microgrids would not be overly costly. • The choice of wind or solar depends on the year of decarbonization. • Larger settlements and fly-in settlements should be prioritized. [ABSTRACT FROM AUTHOR]

To alleviate the issues of climate warming and fossil resources depletion, this paper proposes a wind-photovoltaic-thermal energy storage-electric heater cogeneration system, which adopts the structure of concentrated solar power plant but replaces the expensive collector field and tower with wind and photovoltaic farms and electric heater that converts the excess electricity as thermal energy source. The cogeneration system can effectively regulate the fluctuating wind and photovoltaic output to satisfy electricity load by electric heater based on resistive heating of molten salt, two-tank molten salt thermal energy storage and power block based on steam Rankine cycle, and supply heat load directly from the molten salt heat transfer fluid and heat exchanger. This paper considers minimizing levelized cost of energy, loss of power and heat supply probability, energy reduction rate and lifecycle equivalent CO 2 emissions as optimized objectives, and investigates the many-objective (having four or more objectives) capacity optimization problem by Nondominated Sorting Genetic Algorithm-Ⅲ coupled with Principal Component Analysis and Technique for order preference by similarity to ideal solution. Moreover, residual analysis is applied to consider the uncertainty of wind power prediction. The detailed optimization process and performance comparison of many-objective algorithms, the all-year energy flow and seasonal output characteristics analysis of the optimal-compromise solution are presented. Finally, the proposed cogeneration system is compared to the power-only system with the same installed capacity and equivalent load demand, and the comparison results show that the proposed cogeneration system performs better in all evaluation metrics. Therefore, the proposed renewable energy cogeneration system is feasible and cost-effective for practical applications. • A novel wind-photovoltaic-thermal energy storage cogeneration system is proposed. • A many-objective capacity optimization model with uncertainty is developed. • Economy, reliability, energy efficiency and emissions are considered as objectives. • Dimensional reduction and decision-making method are coupled with NSGA-Ⅲ. • The cogeneration system is feasible and cost-effective for practical applications. [ABSTRACT FROM AUTHOR]

Several influential energy systems models indicate that renewable energy must provide a significant share of the world's electricity to limit global temperature rises to below 2 °C this century. To better represent the costs and other implications of this shift, it is important that these models realistically characterise the technical and economic potential of renewable energy technologies. Towards this goal, this paper presents the first temporally-explicit Geospatial Information System (GIS) methodology to characterise the global onshore wind energy potential with respect to topographical features, land use and environmental constraints. The approach combines the hourly NASA MERRA-2 global wind speed data set with the spatially-resolved DTU Global Wind Atlas. This yields high resolution global capacity factors for onshore wind, binned into seasonal and diurnal time-slices to capture the important temporal variability. For each country, the wind power generation capacity available for various capacity factor ranges is produced, and made freely available to the community. This data set can be used to assess the economically viable wind energy potential on a global or per-country basis, and as an input to various energy systems models. [ABSTRACT FROM AUTHOR]

Some benefits of spot electricity markets integration include the optimization of renewable power, increasing transmission grid security and the decreasing need for internal generation reserves. The high penetration of wind power is known to have a clear influence on price convergence between electricity markets joined by market splitting. However, in multiple interconnected markets, cross-border flows can also play a role in the market splitting behaviour. Denmark, with a high penetration of wind power, is clearly the ideal case study. This paper aims to assess the influence of high penetration of wind power on the market splitting behaviour between West and East Denmark, taking into account cross-border electricity flows. This is modelled through logit and non-parametric models, estimating the probability of market splitting occurrence between both Danish bidding areas. Market splitting probability is found to be sensitive to wind power, nevertheless with distinct behaviour according to interconnection congestion configuration. The highest availability of wind power in West Denmark, which can reach a generation share of 1.5 times the demand, requires strong cross-border interconnections to allow the export of the excess generation. Policies governing a joint assessment of the requirements for additional interconnection and wind power expansion plans, should be developed. [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 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]

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]

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]

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]

Microgrids with renewable generation can improve environmental impact on remote islands. This paper presents a case study of a plan for a microgrid that addresses the inconsistency of renewable energy with governor control and the inertial force of a diesel generator. This paper aims to design of a microgrid without a battery storage system. The case study was modeled to determine the maximum amount of renewable generation that can be introduced while maintaining stable frequency and voltage within the transmission grid. Data from Japan's Teuri and Yagishiri islands were used. The findings of our study revealed that wind and photovoltaic power can account for about 20% of the generation in such a microgrid while maintaining frequency stability. Adjustment of the moment of inertia of diesel generators effectively compensates for the fluctuating output of renewable sources at small remote islands. The cost of such a microgrid could be recovered within 16–17 years. • Design method for a small-scale microgrid with renewable energy in remote islands. • The microgrid addresses the electricity with governor control and the inertial force. • Case study was modeled to determine the maximum amount of renewable generation. • Suitable setting of transformers can be installed to ensure a stable voltage supply. • Cost of the proposed microgrid would be recovered in about 16–17 years. [ABSTRACT FROM AUTHOR]

Microgrids with renewable generation can improve environmental impact on remote islands. This paper presents a case study of a plan for a microgrid that addresses the inconsistency of renewable energy with governor control and the inertial force of a diesel generator. This paper aims to design of a microgrid without a battery storage system. The case study was modeled to determine the maximum amount of renewable generation that can be introduced while maintaining stable frequency and voltage within the transmission grid. Data from Japan's Teuri and Yagishiri islands were used. The findings of our study revealed that wind and photovoltaic power can account for about 20% of the generation in such a microgrid while maintaining frequency stability. Adjustment of the moment of inertia of diesel generators effectively compensates for the fluctuating output of renewable sources at small remote islands. The cost of such a microgrid could be recovered within 16–17 years. • Design method for a small-scale microgrid with renewable energy in remote islands. • The microgrid addresses the electricity with governor control and the inertial force. • Case study was modeled to determine the maximum amount of renewable generation. • Suitable setting of transformers can be installed to ensure a stable voltage supply. • Cost of the proposed microgrid would be recovered in about 16–17 years. [ABSTRACT FROM AUTHOR]

Reliable wind energy forecasting is crucial for the stable operation of power grids. This paper proposes a regional wind power probabilistic forecasting model comprising an improved kernel density estimation (IKDE), regular vine copulas, and ensemble learning. The IKDE is firstly used to generate the margin probability density function (PDF) of each wind farm and the KDE bandwidth is optimized via the golden-section search algorithm to obtain the best possible prediction. Then, several dependence structures are formulated by building different regular vine copulas based on multiple criteria, and all the dependence structures work together with marginal PDF to generate respective joint distribution functions. Finally, ensemble learning is applied to combine all the joint distribution functions and establish an ultimate distribution function. Furthermore, a novel multi-distribution mega-trend-diffusion (MD-MTD) with parametric optimization is proposed to improve the prediction when the data are insufficient. The results of comparative evaluations conducted on datasets from eight wind farms indicate that the proposed model outperforms existing models in wind power generation prediction. Specifically, the proposed model can reliably forecast power generation for an entire region for the next 24 h with only three months of historical data. In contrast, most benchmark models require a year of data. • A regional wind power probabilistic forecasting model is proposed. • Improved kernel density estimation/regular vine copulas/ensemble learning are joined. • Multi-distribution mega-trend-diffusion (MD-MTD) with optimization is also proposed. • MD-MTD improves the prediction when the data are insufficient. • The proposed model outperforms existing models in wind power generation prediction. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents important technical details for the design, construction and operation of seawater pumped storage systems (S-PSS). S-PSS co-operating with wind parks, features as the most promising technology towards maximizing Renewable Energy penetration in insular systems with low annual rainfalls. This article summarises the fundamental points of integrated studies of S-PSS, from the feasibility study to the precise positioning of the systems' components and the selection of the main equipment. Special issues regarding the use of seawater from the PSS (pumped storage system), such as the use of materials for the construction of the penstock, the construction of the upper reservoir, placing the pump station and the hydro power plant on the coast and the selection of pump and hydro-turbine models are presented thoroughly. Indicative results are presented from two S-PSS of small and medium size. The study proves that current technology enables the secure use of seawater in PSS. The electricity surplus from Wind Powered Pumped Storage Systems (WP-PSS) can also be exploited in reverse osmosis desalination plants for producing potable water. Seawater can be pumped directly from the sea, thus construction of a lower reservoir is avoided, compensating higher costs arising from the use of corrosion-resistant materials for certain components. [Copyright &y& Elsevier]

Abstract: The intermittent nature of wind energy poses questions on how electricity production can match the demand. The present paper analyses possible ways of achieving this goal when a large share of the energy is provided by wind. The study considers a certain share of wind energy as a target to be reached, and analyses different avenues for reaching such a target. The possible role of electricity energy storage, wind curtailment and transmission grid reinforcement is highlighted and discussed. The results show that above a certain percentage of electricity produced by wind, electricity storage becomes an economical option for integrating wind energy by reducing wind curtailments. Also, the synergy of storage and grid interconnections is highlighted. [Copyright &y& Elsevier]

Abstract: This paper presents the ability of the existing Chinese energy system to integrate wind power and explores how the Chinese energy system needs to prepare itself in order to integrate more fluctuating renewable energy in the future. With this purpose in mind, a model of the Chinese energy system has been constructed by using EnergyPLAN based on the year 2007, which has then been used for investigating three issues. Firstly, the accuracy of the model itself has been examined and then the maximum feasible wind power penetration in the existing energy system has been identified. Finally, barriers have been discussed and suggestions proposed for the Chinese energy system to integrate large-scale renewable energy in the future. It is concluded that the model constructed by the use of EnergyPLAN can accurately simulate the Chinese energy system. Based on current regulations to secure grid stability, the maximum feasible wind power penetration in the existing Chinese energy system is approximately 26% from both technical and economic points of view. A fuel efficiency decrease occurred when increasing wind power penetration in the system, due to its rigid power supply structure and the task of securing grid stability, was left primarily to large coal-fired power plants. There are at least three possible solutions for the Chinese energy system to integrate large-scale fluctuating renewable energy in the long term: Redesigning the regulations to secure grid stability by means of diversifying the participants, such as including hydropower and CHP plants; integrating large-scale heat pumps combined with heat storage devices to satisfy district heat demands and developing electric vehicles to promote off peak electricity utilisation. [Copyright &y& Elsevier]

Abstract: The purpose of this study is twofold: first, it is aimed at determining the best renewable energy alternative for Istanbul by using an integrated VIKOR-AHP methodology. Second, a selection among alternative energy production sites in this city is made using the same approach. In the proposed VIKOR-AHP methodology, the weights of the selection criteria are determined by pairwise comparison matrices of AHP. In energy decision making problems, the judgments of decision makers are usually vague. As it is relatively difficult for decision makers to provide exact values for the criteria, the evaluation data for the alternative energy policies should be expressed in linguistic terms. In order to model this kind of uncertainty in human preferences, fuzzy logic is applied very successfully. Thus, both classical VIKOR and classical AHP procedures are performed under fuzzy environment. The originality of the paper comes from the application of the proposed integrated VIKOR-AHP methodology to the selection of the best energy policy and production site. It is found that wind energy is the most appropriate renewable energy option and Çatalca district is the best area among the alternatives for establishing wind turbines in Istanbul. [Copyright &y& Elsevier]

Power-to-gas (P2G) using excess renewable sources is an effective method to reduce renewable curtailment issues in microgrid system. The produced hydrogen is versatile green fuel for different energy sectors, such as electricity, heat and mobility. In recent researches, fuel cell-based system is considered as a promising technology to consume hydrogen (H 2) generated from P2G due to high efficiency and cleanness. However, its economic and thermodynamic adaptability when coupled with intermittent renewable sources remains an open question to be addressed carefully. One of the major challenges in optimizing such system is to simultaneously capture the intraday and seasonal variation of renewable sources & load, as well as the internal thermodynamic process of critical components in appropriate modeling detail. This paper presents a multi-energy system for microgrid in which a wind-powered P2G is coupled with a detailed thermoeconomic model of solid oxide fuel cell/gas turbine (SOFC/GT) hybrid system. A two-level multi-objective optimization of planning and operation together is proposed. For system planning, the optimal balance between the least wind curtailment rate and total life cycle cost (LCC) is determined. To facilitate the coordinate operation of system components, a power management strategy is proposed in response to fluctuations of wind power and electricity load with considerations of multiple thermodynamic safety criteria. Results show that in the selected case, the multi-energy system operates with low wind curtailment rate of 0.63% and high renewable penetration level of 90.1%. The optimized LCC of multi-energy system is £2,468,093 with wind power accounting for 68.35% of total capital investment. With the power management strategy applied, the SOFC/GT could operate under the maximum electrical efficiency of 67.1% with safety constraints satisfied, making up only half investment cost of P2G. To capture seasonal variations, both winter and summer scenarios are detailly analyzed, sensitivity analysis is also carried out to evaluate the interaction between capacities of MES components. • Designing an integrated multi-energy system of wind-powered P2G, storage and SOFC/GT. • Multi-objective optimization method coordinately integrating both design and operation levels by genetic algorithm. • Proposing a novel power management strategy to avoid subsystem unbalance problem. • Wind energy penetration of 90.1% is achieved with low curtailment rate of 0.63%. [ABSTRACT FROM AUTHOR]

The concentrating solar power (CSP) plant with the thermal energy storage (TES) is one of the most effective methods to solve the intermittent characteristics of solar energy. CSP plants combined with wind farms could provide continuous, stable power generation and reduce the uncertainty of the wind power. In this paper, a look-ahead technique is proposed to optimize the bidding strategy for a joint system of CSP plants with wind farms considering both the day-ahead and the following day. It is assumed that the CSP plant participates in the ancillary service (AS) market bidding while providing the reserve capacity for the wind farm to counteract the output fluctuations. As the price-taker, the joint system has different levels of attention to the market price and renewable energy output. Therefore, the scenario set and the chance-constrained programming (CCP) is used to describe its uncertainty, respectively. Besides, in order to further enhance the flexibility and maximize the economic revenues of the system, an electric heater (EH) is added to the model. The joint operator can adjust the final heat storage level to balance profit opportunities during the current market window against potential opportunities in the subsequent market window. Finally, we compare the results of different bidding models to demonstrate the effectiveness of the proposed model and the advantages of the joint system. • We propose a look-ahead bidding strategy for CSP plants with wind farms. • The CSP plant is assumed to provide reserve capacity to counteract fluctuations. • An EH is added to enhance flexibility and maximize revenues. • We balance current profit opportunities against subsequent potential opportunities. [ABSTRACT FROM AUTHOR]

Finland has recently adopted a high profile in climate change mitigation. Finland has declared a national target of achieving carbon neutrality by 2035. As a part of this, the use of coal for energy purposes has been banned from year 2029 onwards. The Finnish electricity system is already very low-carbon, and more wind and nuclear power is being constructed. However, District heating (DH) is a backbone of the Finnish energy system, and it is still quite reliant on fossil fuels and domestic high-emission fuel peat, their share being 51% of DH fuels in 2018. This paper models the impacts of this transition on the electricity markets and DH systems and develops scenarios with a large-scale transition to wind and nuclear power and heat pumps in DH systems. The study finds that large-scale introduction of heat pumps would be profitable in cities Helsinki, Espoo, Turku and Vantaa, especially with the planned decrease of electricity tax. The study indicates that the impacts on Winter time capacity adequacy could be managed, but this requires considerable increases in nuclear and wind capacity. • Finland abandons hard coal use by 2029 as part of achieving carbon neutrality. • Increasing wind and nuclear power mitigates electricity price increase and capacity deficit. • Heat pumps can provide alternative to coal-CHP. • Electricity distribution fee and tax limit economic potential of heat pumps. [ABSTRACT FROM AUTHOR]

Between 2009 and 2017 the share of wind and solar energy sources in the GB electricity generation mix increased from 2.5% to 17%. Due to the variable nature of these renewable sources, large thermal power stations designed for constant base-load operation have been required to operate more flexibly to compensate for fluctuations in renewable generation. This flexible operation results in increased thermal stress and reduced efficiency causing increased operation, maintenance and fuel costs for these assets. In this paper we present the results of what is, to the best of our knowledge, the first empirical study on the impact of renewables generation on startups, ramping and part-loading (collectively, 'cycling') of base-load generators. We develop regression models using half-hourly generation data from 2009 to 2017 that capture the impact of increased renewable penetration while taking into account confounding factors including seasonality and demand. We find that with 2009-levels of renewable generation, cycling in 2017 would have been less severe, with 20% fewer startups. We also present estimates for cycling under National Grid Future Energy Scenarios to 2030 with implications for investment in generation assets. Additionally, the dataset derived in this research is made available and comprises the first open-access dataset on cycling. • A dataset for base-load cycling in the GB power system from 2009 to 2017 is presented. • Base-load cycling is shown to have become stronger during the period 2009–2017. • Regression models for cycling variables are estimated using power system variables. • The models are used to predict cycling under Future Energy Scenarios to 2030. [ABSTRACT FROM AUTHOR]

With the rapid development of electric power industry, the problem of dispatching renewable energy resources attracts worldwide attention. This paper presents a stochastic scheduling model to study the day-ahead coordination of a multi-source power system. The proposed stochastic scheduling model aims to find a base-case solution with relatively stable operation cost in the presence of uncertain renewable generation. Wind and photovoltaic generation uncertainties are modeled as scenarios using the Monte Carlo simulation method. Considering that wind and photovoltaic generations present complementary characteristics, scenarios are generated with correlations between wind and photovoltaic generations by Copula theory. To better reflect the characteristics of historical wind data, the fluctuation of wind power is also considered when scenarios are generated. The fast scenario reduction method is applied as a tradeoff between accuracy and computational speed. Numerical simulations indicate the effectiveness of the proposed approach in the coordinated scheduling of Thermal-Hydro-Wind- Photovoltaic systems. • The coordinated scheduling of multi-source power systems is modeled with thermal-hydro-wind-photovoltaic generations. • The proposed model derive relatively stable operation cost in the presence of uncertainties. • Copula theory is effective in modeling the complementary characteristics of wind and photovoltaic generations. • Stronger correlations between wind-photovoltaic generations result in less system operation cost. [ABSTRACT FROM AUTHOR]