Review and Outlook of Autonomous Unit Planning of Distribution Network

doi:10.11930/j.issn.1004-9649.202304096

Abstract

Abstract:

The development of distributed power generation, new energy storage, and adjustable load on the distribution network side increased the difficulty of ensuring secure and reliable power supply. Rational planning of autonomous units can enhance flexibility, power supply security, and ability to integrate energy at the minimum economic cost. However, existing planning methods mostly consider constraints of power supply reliability and economic efficiency, without considering resource relevance, control modes, and electricity market factors, resulting in redundant planning results and resource waste. This paper reviewed and analyzed the physical scope of autonomous units, influencing factors of capacity planning, modeling methods and solving algorithms, clarifies the physical meaning of autonomous units, analyzes the influence of source-load random uncertainty and correlation, regulation mode, electricity market policy and other factors on autonomous unit capacity planning, discusses the autonomous unit modeling method considering multiple factors, and compares the applicable conditions of different solving algorithms. Finally, the shortcomings of the current autonomous unit capacity planning were summarized, and corresponding suggestions and prospects were proposed.

Key words: autonomous unit planning, uncertainty models, regulation mode, electricity market, solving algorithms

Can CHEN, Jing WANG, Zongmin YU, Yuan MA, Yi WANG, Liangchenxi FAN, Ran DING, Hongquan LI. Review and Outlook of Autonomous Unit Planning of Distribution Network[J]. Electric Power, 2023, 56(12): 9-19.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202304096

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References 70

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资源类型		物理边界	资产属性		运营方
资源类型		物理边界	电源	网架	运营方
自治台区		单台配变的供电范围	用户	供电企业	供电企业
并网型微电网	并网自用型微电网	由微电网内部的电气接线网络结构、相应负荷和分布式电源所在微电网的节点位置决定	用户	用户	用户/ 第三方
并网型微电网	并网公用型微电网	由微电网内部的电气接线网络结构、相应负荷和分布式电源所在微电网的节点位置决定	第三方	供电企业/ 第三方	供电企业/ 第三方
乡镇供电网格		乡镇网格行政边界	用户	供电企业	供电企业

资源类型		物理边界	资产属性		运营方
资源类型		物理边界	电源	网架	运营方
自治台区		单台配变的供电范围	用户	供电企业	供电企业
并网型微电网	并网自用型微电网	由微电网内部的电气接线网络结构、相应负荷和分布式电源所在微电网的节点位置决定	用户	用户	用户/ 第三方
并网型微电网	并网公用型微电网	由微电网内部的电气接线网络结构、相应负荷和分布式电源所在微电网的节点位置决定	第三方	供电企业/ 第三方	供电企业/ 第三方
乡镇供电网格		乡镇网格行政边界	用户	供电企业	供电企业

不确定性种类	处理方法	建模方法	参考文献
风光发电及负荷的不确定性	鲁棒优化	采用不确定变量区间鲁棒优化	[13-15]
	鲁棒优化	区间线性规划	[16]
	基于概率模型	中值拉丁超立方抽样	[17]
		拉丁超立方抽样	[18]
		基于Copula方法的光伏发电概率模型	[19]
	场景分析法	基于数据的季节行为进行分类生成各不确定变量场景	[20]
	场景分析法	基于K-means聚类进行典型场景分析	[21]
	不确定集合	基于数据驱动压缩不确定性集	[22]
		引入0-1变量建立源荷不确定区间集合	[23]
		引入0-1变量建立风光不确定区间集合	[24]
		改进的不确定多面体集合	[25]
		采用包络模型和盒式不确定集合	[26]
		采用不确定度矩阵处理	[27]
		同时最小化风电出力原始场景和典型场景的空间距离和矩距离	[28]
	机会约束	建立基于机会约束的多时段规划模型，协调长期不确定性和短期不确定性	[29]
	决策树辅助	基于决策树辅助的自治单元规划方法	[30]
价格的不确定性	随机优化	处理能源价格波动的长期不确定性	[14]
	鲁棒优化	采用鲁棒优化方法的市场价格区间代替随机优化的概率分布函数	[15]
	信息差距决策	基于信息差距决策的多能源微网多时段规划模型	[14]
异常情况的不确定性	极端灾害场景	极端灾害场景被处理为灵活约束	[31]
异常情况的不确定性	故障事件	采用故障事件约束的方法	[32]
光伏出力相关性	点估计方法	基于Nataf变换的点估计方法	[4]
光伏和负荷相关性	标准正态随机分布	基于等概率转换原则和Cholesky分解技术，生成存在相关性的光伏功率、负荷随机样本	[33]
光伏和负荷的时序相关性	动态规划	基于动态弯曲距离构造光-荷联合时序场景	[33-34]

不确定性种类	处理方法	建模方法	参考文献
风光发电及负荷的不确定性	鲁棒优化	采用不确定变量区间鲁棒优化	[13-15]
	鲁棒优化	区间线性规划	[16]
	基于概率模型	中值拉丁超立方抽样	[17]
		拉丁超立方抽样	[18]
		基于Copula方法的光伏发电概率模型	[19]
	场景分析法	基于数据的季节行为进行分类生成各不确定变量场景	[20]
	场景分析法	基于K-means聚类进行典型场景分析	[21]
	不确定集合	基于数据驱动压缩不确定性集	[22]
		引入0-1变量建立源荷不确定区间集合	[23]
		引入0-1变量建立风光不确定区间集合	[24]
		改进的不确定多面体集合	[25]
		采用包络模型和盒式不确定集合	[26]
		采用不确定度矩阵处理	[27]
		同时最小化风电出力原始场景和典型场景的空间距离和矩距离	[28]
	机会约束	建立基于机会约束的多时段规划模型，协调长期不确定性和短期不确定性	[29]
	决策树辅助	基于决策树辅助的自治单元规划方法	[30]
价格的不确定性	随机优化	处理能源价格波动的长期不确定性	[14]
	鲁棒优化	采用鲁棒优化方法的市场价格区间代替随机优化的概率分布函数	[15]
	信息差距决策	基于信息差距决策的多能源微网多时段规划模型	[14]
异常情况的不确定性	极端灾害场景	极端灾害场景被处理为灵活约束	[31]
异常情况的不确定性	故障事件	采用故障事件约束的方法	[32]
光伏出力相关性	点估计方法	基于Nataf变换的点估计方法	[4]
光伏和负荷相关性	标准正态随机分布	基于等概率转换原则和Cholesky分解技术，生成存在相关性的光伏功率、负荷随机样本	[33]
光伏和负荷的时序相关性	动态规划	基于动态弯曲距离构造光-荷联合时序场景	[33-34]

综合措施类型	物理模型	约束条件	数学建模方法	参考文献
储能	铅酸蓄电池	放电深度和循环次数约束	清晰等价处理模糊机会优化约束	[35]
	锂离子电池	放电深度和循环次数约束	直流微电网优化配置模型	[36]
	压缩空气储能	备用约束	双层优化配置模型	[37]
	电氢耦合的混合储能	电解槽的最大输入功率与燃料电池的最大输出功率约束	NLP混合储能优化配置模型	[38]
	锂电池和超级电容器	能量偏差约束	NLP交直流混合储能优化配置模型	[39]
	氢储能和蓄电池混合	通用约束	MINLP混合储能优化配置模型	[40]
冷热电耦合	冷热电联供	通用约束	NLP冷热电联供优化配置模型	[41]
	冷热电联供	供热管网传递最大热能约束	MILP多微网优化配置模型	[42]
	电-储热系统	通用约束	MILP微电网优化配置模型	[43]
	热电联供	储存热能容量约束、储热系统的充放电速率约束	MILP微电网优化配置模型	[44]
		CO₂热循环平衡、热泵平衡	MILP微电网优化配置模型	[45]
		CHP换热器平衡	线性处理交流方程	[46]
备用需求	随机故障	事故概率	提出考虑成本收益比的备用容量分配方法	[47]
		将机会约束最优潮流作为概率约束	提出一种自治单元随机备用容量优化模型	[48]
		自治单元孤岛概率	提出考虑孤岛惩罚项的有功-频率控制的最优备用容量配置方法	[49]
	预测误差	风光电预测误差	基于多场景概率旋转备用模型	[50]
			采用拉丁超立方抽样	[51]
			提出一种发电机备用容量计算方法	[52]
			提出了考虑运行时有功调度策略的备用需求模型	[53]
			min-max两阶段博弈模型	[54]
			建立了考虑风电备用与需求侧备用的两阶段鲁棒备用容量优化模型	[55]
		负荷预测误差	建立一种考虑备用共享的备用容量分配模型	[47]