考虑可变单位能量建设成本的储能规划方法

doi:10.11930/j.issn.1004-9649.202409040

摘要/Abstract

摘要：

储能是缓解新能源功率波动、支撑构建新型电力系统的关键元件。然而，现有储能规划未考虑其单位能量建设成本可能在规划周期内动态变化的特性，仅采用恒定的单位能量建设成本，不利于电力系统规划人员全面研判储能未来规划策略。因此，提出考虑可变单位能量建设成本的储能规划分析方法，旨在刻画储能单位能量建设成本与储能规划结果间的解析关系，提供更为全面和有效的研判信息。首先，针对储能规划模型中储能充放电互补约束引起的非线性特性，提出基于约束松弛技术的储能规划模型线性化构造方法，将原始非线性优化问题精确转化为线性优化问题。进而，依据线性优化问题形式的储能规划模型，将储能单位能量建设成本视作可变参数，基于多参数规划理论推导储能单位能量建设成本与储能规划结果的解析映射函数，从而实现储能单位能量建设成本与储能规划结果间的定量分析。通过IEEE 118节点测试系统验证所提方法，结果表明：所提方法可以较低计算负担精确求解原始非线性的储能规划问题，准确量化储能规划结果。

关键词: 储能规划, 可变单位能量建设成本, 充放电互补约束松弛, 多参数规划理论.

Abstract:

Energy storage is a key component in mitigating power fluctuations of renewable energy sources and supporting the construction of new power systems. However, existing energy storage planning methods do not consider the potential dynamic changes in unit energy construction costs during the planning period, but only rely on a constant unit energy construction cost, which is not conducive for power system planners to comprehensively assess energy storage planning strategies. Therefore, this paper proposes an analytical methodology for energy storage planning considering variable unit energy construction costs, aiming to analyze the relationship between the energy storage unit energy construction costs and the energy storage planning results so that power system planners can get more comprehensive and effective information. Firstly, to address the nonlinear characteristics of the energy storage planning model caused by energy storage charging/discharging complementary constraints, a constraint relaxation-based linearization framework for energy storage planning models was proposed, transforming the original nonlinear optimization problem into a precise linear optimization problem. Furthermore, based on the linear optimization formulation of the energy storage planning models, the unit energy construction cost was treated as a variable parameter. Using the multi-parameter planning theory, an analytical mapping function between the unit energy construction costs and the energy storage planning results was derived, enabling quantitative analysis of the relationship. The proposed method was verified through numerical experiments in the IEEE 118-bus test system. The results show that the proposed method can solve the original nonlinear energy storage planning problem with reduced computational burden while maintaining high precision, enabling accurate quantification of the energy storage planning results.

Key words: energy storage planning, variable unit energy construction cost, relaxation of charge/discharge complementarity constraints, multi-parametric programming theory

袁振华, 张丽娜, 张玉跃, 田鑫, 王鹏, 杜尔顺. 考虑可变单位能量建设成本的储能规划方法[J]. 中国电力, 2025, 58(7): 187-196.

YUAN Zhenhua, ZHANG Lina, ZHANG Yuyue, TIAN Xin, WANG Peng, DU Ershun. Methodology for Energy Storage Planning Considering Variable Unit Energy Construction Costs[J]. Electric Power, 2025, 58(7): 187-196.

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https://www.electricpower.com.cn/CN/Y2025/V58/I7/187

图/表 5

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储能类型	充放电效率	单位能量成本/(美元·(kW·h)^–1)		能量倍增率
储能类型	充放电效率	一阶段	二阶段	能量倍增率
Ⅰ类	0.70	400	350	10
Ⅱ类	0.95	500	450	10

储能类型	充放电效率	单位能量成本/(美元·(kW·h)^–1)		能量倍增率
储能类型	充放电效率	一阶段	二阶段	能量倍增率
Ⅰ类	0.70	400	350	10
Ⅱ类	0.95	500	450	10

模型	规划期间最优总成本	一阶段规划期间成本	二阶段规划期间成本
M1	2.1208×10⁸	1.1622×10⁸	9.586×10⁷
M2	2.1208×10⁸	1.1622×10⁸	9.586×10⁷

模型	规划期间最优总成本	一阶段规划期间成本	二阶段规划期间成本
M1	2.1208×10⁸	1.1622×10⁸	9.586×10⁷
M2	2.1208×10⁸	1.1622×10⁸	9.586×10⁷

成本变化范围		解析表达式
$ \begin{aligned} &\left[ {\begin{array}{{20}{c}} 0&0&{0.707\;1}&{0.707\;1} \\ { - 1}&0&0&0 \\ 0&{ - 1}&0&0 \\ 0&0&{ - 1}&0 \\ 0&0&0&{ - 1} \\ 1&0&0&0 \\ 0&1&0&0 \end{array}} \right]\cdot\\&\left[ {\begin{array}{{20}{c}} {{C_{{{1,{{Ⅰ}}}}}}} \\ {{C_{{{2,{{Ⅰ}}}}}}} \\ {{C_{{{1,{{Ⅱ}}}}}}} \\ {{C_{{{2,{{Ⅱ}}}}}}} \end{array}} \right] \leqslant\left[ {\begin{array}{*{20}{c}} {565.685\;4} \\ { - 250} \\ { - 250} \\ { - 350} \\ { - 350} \\ {350} \\ {350} \end{array}} \right]\end{aligned} $		$\begin{aligned} &{C_{{{\rm total}}}} =\\&{\left[ {\begin{array}{{20}{c}} {168.484\;2} \\ {168.484\;2} \\ {64.792\;3} \\ {64.792\;3} \end{array}} \right]^{{\rm T}}}\left[ {\begin{array}{{20}{c}} {{C_{{{1,{{Ⅰ}}}}}}} \\ {{C_{{{2,{{Ⅰ}}}}}}} \\ {{C_{{{1,{{Ⅱ}}}}}}} \\ {{C_{{{2,{{Ⅱ}}}}}}} \end{array}} \right]\end{aligned}$
$ \begin{aligned} &\left[ {\begin{array}{{20}{c}} 0&0&{ - 0.707\;1}&{ - 0.707\;1} \\ { - 1}&0&0&0 \\ 0&{ - 1}&0&0 \\ 0&0&{ - 1}&0 \\ 0&0&0&{ - 1} \\ 1&0&0&0 \\ 0&1&0&0 \\ 0&0&1&0 \\ 0&0&0&1 \end{array}} \right]\cdot\\&\left[ {\begin{array}{{20}{c}} {{C_{{{1,{{Ⅰ}}}}}}} \\ {{C_{{{2,{{Ⅰ}}}}}}} \\ {{C_{{{1,{{Ⅱ}}}}}}} \\ {{C_{{{2,{{Ⅱ}}}}}}} \end{array}} \right] \leqslant \left[ {\begin{array}{*{20}{c}} { - 565.685\;4} \\ { - 250} \\ { - 250} \\ { - 350} \\ { - 350} \\ {350} \\ {350} \\ {450} \\ {450} \end{array}} \right]\end{aligned} $		$\begin{aligned} &{C_{{{\rm total}}}} =\\&{\left[ {\begin{array}{{20}{c}} {168.484\;2} \\ {168.484\;2} \\ {63.964\;3} \\ {64.964\;3} \end{array}} \right]^{{\rm T}}}\left[ {\begin{array}{{20}{c}} {{C_{{{1,{{Ⅰ}}}}}}} \\ {{C_{{{2,{{Ⅰ}}}}}}} \\ {{C_{{{1,{{Ⅱ}}}}}}} \\ {{C_{{{2,{{Ⅱ}}}}}}} \end{array}} \right]\end{aligned}$