A Stochastic Resource-Planning Scheme for PHEV Charging Station Considering Energy Portfolio Optimization and Price-Responsive Demand

2018 ◽  
Vol 54 (6) ◽  
pp. 5590-5598 ◽  
Author(s):  
Zhaohao Ding ◽  
Ying Lu ◽  
Lizi Zhang ◽  
Wei-Jen Lee ◽  
Dayu Chen
Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 83
Author(s):  
Yingxin Liu ◽  
Houqi Dong ◽  
Shengyan Wang ◽  
Mengxin Lan ◽  
Ming Zeng ◽  
...  

Based on the comprehensive utilization of energy storage, photovoltaic power generation, and intelligent charging piles, photovoltaic (PV)-storage charging stations can provide green energy for electric vehicles (EVs), which can significantly improve the green level of the transportation industry. However, there are many challenges in the PV-storage charging station planning process, making it theoretically and practically significant to study approaches to planning. This paper promotes a bi-level optimization planning approach for PV-storage charging stations. First, taking PV-storage charging stations and EV users as the upper- and lower-level problems, respectively, during the planning process, a bi-level optimization model for PV-storage charging stations considering user utility is established for capacity allocation and user behavior-based electricity pricing. Second, the model is converted into a single-level mixed-integer linear programming model using the piecewise linear utility function, Karush–Kuhn–Tucker (KKT) conditions, and linearization methods. Finally, to verify the validity of the proposed model and the solution algorithm, a commercial solver is used to solve the optimization model and obtain the planning scheme. The results show that the proposed bi-level optimization model can provide a more economical and reasonable planning scheme than the single-level model, and can reduce the investment cost by 8.84%, operation and maintenance cost by 13.23%, and increase net revenue by 5.11%.


2017 ◽  
Vol 8 (4) ◽  
pp. 1898-1910 ◽  
Author(s):  
Mahdi Ghamkhari ◽  
Adam Wierman ◽  
Hamed Mohsenian-Rad

2012 ◽  
Vol 524-527 ◽  
pp. 3027-3035
Author(s):  
Zai Bin Liu

China’s energy planning problem was concluded to the energy portfolio optimization problem which was solved by multi-attribute utility theory and genetic algorithm methods. A fundamental objectives hierarchy was established to structure the energy technology alternatives. Based on this hierarchy model, weights and utilities of energy resources were calculated by the multi-attribute utility theory. The Evolver decision tool was used to find an optimal energy portfolio by the genetic algorithm. The results indicated China should reduce coal and nuclear power in the future energy portfolio.


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