Electric Power ›› 2025, Vol. 58 ›› Issue (7): 168-176.DOI: 10.11930/j.issn.1004-9649.202410100
• New Energy and Energy Storage • Previous Articles Next Articles
					
													ZHAO Lin1(
), GUO Shangmin1(
), SHANG Wenying1(
), DONG Jian1,2(
), WANG Wei3(
)
												  
						
						
						
					
				
Received:2024-10-31
															
							
															
							
															
							
																	Online:2025-07-30
															
							
							
																	Published:2025-07-28
															
							
						Supported by:ZHAO Lin, GUO Shangmin, SHANG Wenying, DONG Jian, WANG Wei. Configuration Optimization of Renewable Energy Systems Based on FDOA[J]. Electric Power, 2025, 58(7): 168-176.
| 场景 | 并网配置 | 关闭的开关 | 打开的开关 | |||
| 1 | 光伏系统 | S1, S4, S6 | S2, S3, S5 | |||
| 2 | 风电系统 | S2, S4, S6 | S1, S3, S5 | |||
| 3 | 混合光伏与风电 | S1, S2, S4, S6 | S3, S5 | |||
| 4 | 光伏与抽水蓄能系统 | S1, S3, S5 | S2, S4, S6 | |||
| 5 | 风电与抽水蓄能系统 | S2, S3, S5 | S1, S4, S6 | |||
| 6 | 混合光伏、风电与抽水蓄能系统 | S1, S2, S3, S5 | S4, S6 | 
Table 1 Switch status of six grid-connected scenarios in the optimization model
| 场景 | 并网配置 | 关闭的开关 | 打开的开关 | |||
| 1 | 光伏系统 | S1, S4, S6 | S2, S3, S5 | |||
| 2 | 风电系统 | S2, S4, S6 | S1, S3, S5 | |||
| 3 | 混合光伏与风电 | S1, S2, S4, S6 | S3, S5 | |||
| 4 | 光伏与抽水蓄能系统 | S1, S3, S5 | S2, S4, S6 | |||
| 5 | 风电与抽水蓄能系统 | S2, S3, S5 | S1, S4, S6 | |||
| 6 | 混合光伏、风电与抽水蓄能系统 | S1, S2, S3, S5 | S4, S6 | 
| 场景 | 并网配置 | GUF/ %  | 光伏板 数量  | 风力涡轮 机数量  | 抽水蓄 能水位 高度/m  | |||||
| 1 | 仅光伏系统 | 47.06 | ||||||||
| 2 | 仅风能系统 | 41.58 | 65 | |||||||
| 3 | 光伏与风能混合系统 | 33.55 | 54 | |||||||
| 4 | 光伏与抽水蓄能系统 | 1.62 | 105.40 | |||||||
| 5 | 风能与抽水蓄能系统 | 1.96 | 47 | 102.05 | ||||||
| 6 | 光伏、风能与抽水蓄 能混合系统  | 0.59 | 28 | 106.98 | 
Table 2 Optimal values of GUF and decision variables for each grid-connected scenario
| 场景 | 并网配置 | GUF/ %  | 光伏板 数量  | 风力涡轮 机数量  | 抽水蓄 能水位 高度/m  | |||||
| 1 | 仅光伏系统 | 47.06 | ||||||||
| 2 | 仅风能系统 | 41.58 | 65 | |||||||
| 3 | 光伏与风能混合系统 | 33.55 | 54 | |||||||
| 4 | 光伏与抽水蓄能系统 | 1.62 | 105.40 | |||||||
| 5 | 风能与抽水蓄能系统 | 1.96 | 47 | 102.05 | ||||||
| 6 | 光伏、风能与抽水蓄 能混合系统  | 0.59 | 28 | 106.98 | 
| 场景 | LCOE(元·(kW·h)−1) | 场景 | LCOE(元·(kW·h)−1) | |||
| 1 | 0.240 | 4 | 0.098 | |||
| 2 | 0.170 | 5 | 0.130 | |||
| 3 | 0.168 | 6 | 0.042 | 
Table 3 LCOE assessment for each scenario
| 场景 | LCOE(元·(kW·h)−1) | 场景 | LCOE(元·(kW·h)−1) | |||
| 1 | 0.240 | 4 | 0.098 | |||
| 2 | 0.170 | 5 | 0.130 | |||
| 3 | 0.168 | 6 | 0.042 | 
| 方案 | 应用场景 | 储能类型 | 优化方法 | 其他指标 | ||||
| 本文 | 光伏、风能与抽水蓄能系统的并网优化 | 抽水蓄能为主 | FDOA | 二氧化碳排放量、负荷需求、电力成本 | ||||
| 文献[ | 孤岛型可再生能源大规模制氢系统 | 电解水 制氢  | 深度确定性策略梯度算法 | 经济性、安全性 | ||||
| 文献[ | 微电网、区域综合能源系统 | 全钒液流电池、先进绝热压缩空气 储能  | 遗传粒子群算法、多目标麻雀搜索算法、混合整数线性规划方法等 | 投资和运营总成本、碳减排效益、供电质量、能源节约率、污染物排放量 | ||||
| 文献[ | 微电网 | 未明确 提及  | 简化粒子群优化算法 | 微电网运营成本 | ||||
| 文献[ | 含风力、光伏、火电、储电单元、电解制氢、燃料电池的多能互补供电 系统  | 储电单元 | 量子粒子群算法 | 系统运行累计净现值、系统运行稳定性、负荷满足率 | ||||
| 文献[ | 风光制氢系统 | 蓄电池 | 精英非支配排序遗传算法、多目标粒子群优化 | 系统安装成本、系统功率波动性、电解槽效率及输入功率波动性 | 
Table 4 Comparative study of different renewable energy system optimization schemes
| 方案 | 应用场景 | 储能类型 | 优化方法 | 其他指标 | ||||
| 本文 | 光伏、风能与抽水蓄能系统的并网优化 | 抽水蓄能为主 | FDOA | 二氧化碳排放量、负荷需求、电力成本 | ||||
| 文献[ | 孤岛型可再生能源大规模制氢系统 | 电解水 制氢  | 深度确定性策略梯度算法 | 经济性、安全性 | ||||
| 文献[ | 微电网、区域综合能源系统 | 全钒液流电池、先进绝热压缩空气 储能  | 遗传粒子群算法、多目标麻雀搜索算法、混合整数线性规划方法等 | 投资和运营总成本、碳减排效益、供电质量、能源节约率、污染物排放量 | ||||
| 文献[ | 微电网 | 未明确 提及  | 简化粒子群优化算法 | 微电网运营成本 | ||||
| 文献[ | 含风力、光伏、火电、储电单元、电解制氢、燃料电池的多能互补供电 系统  | 储电单元 | 量子粒子群算法 | 系统运行累计净现值、系统运行稳定性、负荷满足率 | ||||
| 文献[ | 风光制氢系统 | 蓄电池 | 精英非支配排序遗传算法、多目标粒子群优化 | 系统安装成本、系统功率波动性、电解槽效率及输入功率波动性 | 
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