In order to effectively improve the utilization rate of solar energy resources and to develop sustainable urban efficiency, an integrated system of electric vehicle charging station (EVCS), small-scale photovoltaic (PV) system, and battery energy storage system (BESS) has been proposed and implemented in many cities around the world. This paper proposes an optimization model for grid-connected photovoltaic/battery energy storage/electric vehicle charging station (PBES) to size PV, BESS, and determine the charging/discharging pattern of BESS. The multi-agent particle swarm optimization (MAPSO) algorithm solves this model is solved, which combines multi-agent system (MAS) and the mechanism of particle swarm optimization (PSO). In this model, a load simulation model is presented to simulate EV charging patterns and to calculate the EV charging demand at each time interval. Finally, a case in Shanghai, China is conducted and three scenarios are analyzed to prove the effectiveness of the proposed model. A comparative analysis is also performed to show the superiority of MAPSO algorithm.

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Recently, an increasing number of photovoltaic/battery energy storage/electric vehicle charging stations (PBES) have been established in many cities around the world. This paper proposes a PBES portfolio optimization model with a sustainability perspective. First, various decision-making criteria are identified from perspectives of economy, society, and environment. Secondly, the performance of alternatives with respect to each criterion is evaluated in the form of trapezoidal intuitionistic fuzzy numbers (TrIFN). Thirdly, the alternatives are ranked based on cumulative prospect theory. Then, a multi-objective optimization model is built and solved by multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal PBES portfolio. Finally, a case in South China is studied and a scenario analysis is conducted to verify the effectiveness of the proposed model.

Sustainable energy development has gained worldwide attention, in part thanks to the wind power industry value chain that focuses on overall value creation and innovation, especially in China. This paper aims to construct a wind power industry value chain model and comprehensively analyze factors that have significant influences on it using a modified diamond model, which has remained nebulous. Focused on the value-adding effect of constructed value chains, we offer key ideas from different angles. A factor condition lays the foundation of the value chain, and shows that China is experiencing energy structure adjustment in which wind power will play a key role; its resource potential is huge, but with mismatched distribution. Demand conditions reveal an increasing demand for wind but serious wind rejection as well; this is where the value-adding probability exists. Related and support departments collaborate to determine the overall value creation process. Firm strategy, structure, and rivalry are terms that describe possible value-adding subjects considering the wind industry as a whole. Government and opportunity provide robust prices and non-price policies to support value integration, and Technology is an effective factor in cost reduction and value creation as a high value-adding sector. Furthermore, a comparison of wind power industry value chains in China and Japan is conducted. Our findings underscore that a gap exists between actual performance and the expected wind power industry value chain, and corresponding measurements to promote the performance are discussed, including encouraging diversified business models, enhancing R&D and independent innovation, professional cultivation, effectively reducing wind rejection rate, and the full range of government support.

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