Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm
•Multi-objective, multi-constrain optimization model of load dispatch for microgrid.•Modified gravitational search algorithm and particle swarm optimization algorithm to solve load dispatch.•Ordered charging-discharging strategy reducing cost by 13.4%, load variance by 78.8% With the increasing prop...
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| Published in: | Applied energy Vol. 306; p. 118018 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
15.01.2022
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| Subjects: | |
| ISSN: | 0306-2619, 1872-9118 |
| Online Access: | Get full text |
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| Abstract | •Multi-objective, multi-constrain optimization model of load dispatch for microgrid.•Modified gravitational search algorithm and particle swarm optimization algorithm to solve load dispatch.•Ordered charging-discharging strategy reducing cost by 13.4%, load variance by 78.8%
With the increasing proportion of electric vehicles in the automobile market, the negative impact of vehicle’s charging on the power system is gradually increasing. The charging-discharging model of vehicles and the multi-objective optimization model of the load dispatch for the microgrid are established. By combining gravitational search algorithm (GSA) and particle swarm optimization (PSO) algorithm, a hybrid modified GSA-PSO (MGSA-PSO) scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles. To improve the global search performance of the GSA algorithm, the proposed scheme introduces the global memory capacity of the PSO into the GSA. At the same time, the hybrid algorithm is improved by designing adaptive inertia vector, learning factor and chaotic initialization population. The load dispatch optimization are implemented and analyzed, including the unordered charging strategy, the ordered charging-discharging strategy, and the ordered charging-discharging strategy with distributed generations. The optimization results show that, under the same weight factor, the ordered charging-discharging strategy can reduce 13.38% of the total cost, 78.77% of the microgrid load variance and improve the safety and economy of the grid. In addition, reasonable scheduling of distributed power output power can further reduce the total cost by 14.06% and the load variance by 22.36%. Further, the effectiveness of the proposed scheme is proved by analyzing the influences of different numbers of electric vehicles and different charging models. |
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| AbstractList | With the increasing proportion of electric vehicles in the automobile market, the negative impact of vehicle’s charging on the power system is gradually increasing. The charging-discharging model of vehicles and the multi-objective optimization model of the load dispatch for the microgrid are established. By combining gravitational search algorithm (GSA) and particle swarm optimization (PSO) algorithm, a hybrid modified GSA-PSO (MGSA-PSO) scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles. To improve the global search performance of the GSA algorithm, the proposed scheme introduces the global memory capacity of the PSO into the GSA. At the same time, the hybrid algorithm is improved by designing adaptive inertia vector, learning factor and chaotic initialization population. The load dispatch optimization are implemented and analyzed, including the unordered charging strategy, the ordered charging-discharging strategy, and the ordered charging-discharging strategy with distributed generations. The optimization results show that, under the same weight factor, the ordered charging-discharging strategy can reduce 13.38% of the total cost, 78.77% of the microgrid load variance and improve the safety and economy of the grid. In addition, reasonable scheduling of distributed power output power can further reduce the total cost by 14.06% and the load variance by 22.36%. Further, the effectiveness of the proposed scheme is proved by analyzing the influences of different numbers of electric vehicles and different charging models. •Multi-objective, multi-constrain optimization model of load dispatch for microgrid.•Modified gravitational search algorithm and particle swarm optimization algorithm to solve load dispatch.•Ordered charging-discharging strategy reducing cost by 13.4%, load variance by 78.8% With the increasing proportion of electric vehicles in the automobile market, the negative impact of vehicle’s charging on the power system is gradually increasing. The charging-discharging model of vehicles and the multi-objective optimization model of the load dispatch for the microgrid are established. By combining gravitational search algorithm (GSA) and particle swarm optimization (PSO) algorithm, a hybrid modified GSA-PSO (MGSA-PSO) scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles. To improve the global search performance of the GSA algorithm, the proposed scheme introduces the global memory capacity of the PSO into the GSA. At the same time, the hybrid algorithm is improved by designing adaptive inertia vector, learning factor and chaotic initialization population. The load dispatch optimization are implemented and analyzed, including the unordered charging strategy, the ordered charging-discharging strategy, and the ordered charging-discharging strategy with distributed generations. The optimization results show that, under the same weight factor, the ordered charging-discharging strategy can reduce 13.38% of the total cost, 78.77% of the microgrid load variance and improve the safety and economy of the grid. In addition, reasonable scheduling of distributed power output power can further reduce the total cost by 14.06% and the load variance by 22.36%. Further, the effectiveness of the proposed scheme is proved by analyzing the influences of different numbers of electric vehicles and different charging models. |
| ArticleNumber | 118018 |
| Author | Zhang, Xizheng Lu, Zhangyu Wang, Zeyu |
| Author_xml | – sequence: 1 givenname: Xizheng surname: Zhang fullname: Zhang, Xizheng email: z_x_z2000@163.com organization: The Innovative Center of Wind Equipments and Energy Conversion, Hunan Institute of Engineering, Xiangtan 411104, China – sequence: 2 givenname: Zeyu surname: Wang fullname: Wang, Zeyu email: ze_yu2020@163.com organization: The Innovative Center of Wind Equipments and Energy Conversion, Hunan Institute of Engineering, Xiangtan 411104, China – sequence: 3 givenname: Zhangyu surname: Lu fullname: Lu, Zhangyu email: lzy@hnie.edu.cn organization: The Innovative Center of Wind Equipments and Energy Conversion, Hunan Institute of Engineering, Xiangtan 411104, China |
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