基于MPC的户用光-储系统容量配置及运行优化模型

doi:10.11930/j.issn.1004-9649.202404034

摘要/Abstract

摘要：

分布式光伏对于实现“双碳”目标和建设以新能源为主体的新型电力系统具有重要意义。研究了当前阶梯-峰谷电价体系下居民户用光-储系统的建设和运行问题。首先，介绍了户用光-储系统的结构和电价体系。其次，以户用光-储系统的投资成本和年运行维护费用最低为目标，建立了光伏和储能的最优容量配置模型，并在此基础上考虑阶梯-峰谷电价对用户长时间尺度用能策略的影响，提出了基于模型预测控制的户用光-储系统双层滚动优化运行算法。其中，上层模型为基于阶梯电价、以年综合收益最大为目标的年滚动优化，下层模型为基于峰谷电价、以日运行费用最低为目标的日滚动优化；下层模型中光伏和储能的运行方案以上层优化结果为参照，并修正由于不确定性因素导致的系统状态偏差。算例结果表明，所提算法可以推迟高阶梯电价的使用时刻，有效提高居民用户的整体收益。

关键词: 户用光-储系统, 阶梯-峰谷电价体系, 容量配置, 模型预测控制, 运行优化

Abstract:

Distributed photovoltaic (PV) is crucial to meeting the "double carbon" objective and establishing new power system with new energy sources as the main body. This research examines home PV-storage system construction and operation under the present stepped-peak-valley tariff system. Firstly, the household PV-energy storage system structure and tariff system are introduced. Secondly, the optimal capacity allocation model of PV and storage is established to minimize the investment cost and annual operation and maintenance cost of the household PV-storage system. Then, and a two-layer rolling optimization operation algorithm based on model predictive control is proposed considering the stepped-peak-valley tariff's effect on users' long-term energy use strategy. Among them, the upper model maximizes annual integrated return using a stepwise tariff-based annual rolling optimization, and the lower model is a daily rolling optimization based on a peak-valley tariff with the objective of minimizing daily operating costs. In the lower model, the operation scheme of PV-energy storage is based on the optimization results of the upper model, corrects the system state deviation caused by uncertainty factors. Finally, the algorithm's simulation findings suggest that it can delay high electricity tariff utilization and increase residential customer revenue.

Key words: household photovoltaic-energy storage system, stepped-peak-valley tariff system, capacity allocation, model predictive control, operation optimization

齐国民, 李天野, 于洪, 卢博伦, 马宝中, 张文欣, 吴恩同, 肖先瑶. 基于MPC的户用光-储系统容量配置及运行优化模型[J]. 中国电力, 2025, 58(1): 185-195.

Guomin QI, Tianye LI, Hong YU, Bolun LU, Baozhong MA, Wenxin ZHANG, Entong WU, Xianyao XIAO. Capacity Allocation and Operation Optimization Model of Household Photovoltaic-Storage System Based on MPC[J]. Electric Power, 2025, 58(1): 185-195.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202404034

https://www.electricpower.com.cn/CN/Y2025/V58/I1/185

图/表 14

图 1 户用光-储系统MPC双层滚动优化框架

Fig.1 MPC two-layer rolling optimization framework for household PV-storage system

图 2 双层滚动优化模型求解流程

Fig.2 Solution process of two-layer rolling optimization model

表 1 上海市居民电价

Table 1 Residential electricity price in Shanghai

分档	当年用电量/(kW·h)	电价/(元·(kW·h)^–1)
一级电价	0~3120	峰：0.617；谷：0.307
二级单价	3120~4800	峰：0.677；谷：0.337
三级电价	4800以上	峰：0.987；谷：0.667

表 2 中国部分城市的上网电价

Table 2 Feed-in tariffs in some cities in China

地区	光伏资源分区	光伏指导价/ (元·(kW·h)^–1)	燃煤基准价/ (元·(kW·h)^–1)
北京	Ⅱ	0.3582	0.3589
上海	Ⅲ	0.4146	0.4155
宁夏	Ⅰ	0.2584	0.2595
广东	Ⅲ	0.4529	0.4530

表 3 光伏储能经济参数

Table 3 Economic parameters of PV and energy storage

设备	投资成本	运维成本
光伏	4000 元/kW	0.3 元/kW
锂电池	1250 元/(kW·h)	100 元/(kW·年)
铅酸电池	800 元/(kW·h)	90 元/(kW·年)

图 3 用户负荷及光伏实际功率

Fig.3 Residential load and measured PV power

图 4 光伏出力和负荷预测

Fig.4 PV output and load forecasting

表 4 光伏储能容量规划结果

Table 4 Capacity planning results of PV and energy storage

方案	光伏/ kW	锂电池/ (kW, kW·h)	铅酸电池/ (kW, kW·h)	年最小成本/元
无储能	6			4380.33
光伏储能（锂电）	6	5.25, 10.5		3876.65
光伏储能（铅酸）	6		6.25, 12.5	4156.77

图 5 年滚动优化结果

Fig.5 Annual rolling optimization results

表 5 系统年滚动优化收益

Table 5 Earnings of system annual rolling optimization

年滚动优化起始时间/h		年收益/元
1848		3762.81
4056		4267.92
6264		4858.73
8448		5173.36

图 6 日内滚动优化结果

Fig.6 Daily rolling optimization results

表 6 用户收益对比

Table 6 Comparison of user earnings

方法	售电量/ (kW·h)	自用电量/ (kW·h)	售电收益/元	节省电费/元	总收益/ 元	投资回收期/年
余电上网	6221	886	2505.92	671.30	3177.22	5.66
传统规划	4217	3390	1728.97	3296.36	5024.97	5.88
滚动优化	3596	3851	1474.36	4105.02	5579.38	5.33

图 7 3种方法优化效果对比

Fig.7 Comparison of optimization effects of three methods

图 8 装备光-储系统的居民年用电量和收益分析

Fig.8 Analysis of annual electricity consumption and earnings of residents with PV-storage system

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