Multi-objective Optimal Dispatch of Multi-energy Complementary Systems Considering Environmental Factors
In this paper, a day-ahead economic and environmental dispatching model for multi-energy complementary system composed of thermal power, wind and photovoltaic power, and pumped storage is established. The objective functions are the smallest net load fluctuation and the lowest comprehensive cost. In...
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| Vydané v: | Chinese Control Conference s. 1771 - 1775 |
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| Hlavní autori: | , , , |
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Technical Committee on Control Theory, Chinese Association of Automation
28.07.2024
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| ISSN: | 1934-1768 |
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| Abstract | In this paper, a day-ahead economic and environmental dispatching model for multi-energy complementary system composed of thermal power, wind and photovoltaic power, and pumped storage is established. The objective functions are the smallest net load fluctuation and the lowest comprehensive cost. In this model, the environmental cost is considered in comprehensive cost which includes the cost of \mathrm{SO}_{2}, \mathrm{NO}_{x} emission, the operation cost of desulfurization and denitrification, and carbon trading. An improved multi-objective particle swarm algorithm which can accelerate the convergence speed and also avoid the emergence of local optimums is presented to solve the dispatch model, and finally, by analyzing the dispatching results, it can effectively reduce the comprehensive cost of the system and decrease the net load fluctuation while reducing the pollutant emission, which verifies the effectiveness of the dispatch strategy and the improved algorithm. |
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| AbstractList | In this paper, a day-ahead economic and environmental dispatching model for multi-energy complementary system composed of thermal power, wind and photovoltaic power, and pumped storage is established. The objective functions are the smallest net load fluctuation and the lowest comprehensive cost. In this model, the environmental cost is considered in comprehensive cost which includes the cost of \mathrm{SO}_{2}, \mathrm{NO}_{x} emission, the operation cost of desulfurization and denitrification, and carbon trading. An improved multi-objective particle swarm algorithm which can accelerate the convergence speed and also avoid the emergence of local optimums is presented to solve the dispatch model, and finally, by analyzing the dispatching results, it can effectively reduce the comprehensive cost of the system and decrease the net load fluctuation while reducing the pollutant emission, which verifies the effectiveness of the dispatch strategy and the improved algorithm. |
| Author | Yao, Yubin Chen, Rui Yang, Fan Du, Jianwei |
| Author_xml | – sequence: 1 givenname: Rui surname: Chen fullname: Chen, Rui email: 18742519201@163.com organization: Dalian Maritime University,School of Marine Electrical Engineering,Dalian,116026 – sequence: 2 givenname: Yubin surname: Yao fullname: Yao, Yubin email: yaoyubin@dlmu.edu.cn organization: Dalian Maritime University,School of Marine Electrical Engineering,Dalian,116026 – sequence: 3 givenname: Jianwei surname: Du fullname: Du, Jianwei organization: Dalian Maritime University,School of Marine Electrical Engineering,Dalian,116026 – sequence: 4 givenname: Fan surname: Yang fullname: Yang, Fan |
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| Snippet | In this paper, a day-ahead economic and environmental dispatching model for multi-energy complementary system composed of thermal power, wind and photovoltaic... |
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| SubjectTerms | Carbon Trading Costs Desulfurization and Denitrification Dispatching Fluctuations Multi-objective Particle Swarm Algorithm Optimal Dispatch Particle swarm optimization Photovoltaic systems Power systems Simulation |
| Title | Multi-objective Optimal Dispatch of Multi-energy Complementary Systems Considering Environmental Factors |
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