考虑精细化充放电与碳效益的配电网储能多目标双层规划模型

doi:10.11930/j.issn.1004-9649.202506048

摘要/Abstract

摘要：

高比例新能源发展愿景下，为有效缩短储能回报周期、提升新能源消纳以及降低配电网碳排放，提出一种考虑精细化充放电与碳效益的配电网储能多目标双层规划模型。首先，基于Wasserstein距离和梯度惩罚的改进生成对抗网络（Wasserstein generative adversarial network with gradient penalty，WGAN-GP）以及K-中心聚类算法（K-medoids）生成光伏典型场景。其次，建立储能系统的充放电精细化模型，并基于储能降碳量和全生命周期碳排放量构建碳效益模型。然后，构建考虑精细化充放电与碳效益的双层配电网储能规划运行模型，以日总成本最小为上层目标，对储能进行优化配置；以运行成本最小、电压偏移量最小和储能碳效益最大为下层目标，实现配电网的优化运行。再次，利用跨层关联变量建模将双层模型转化为单层多目标模型，并采用归一化法向约束法（normalized normal constraint，NNC）求解多目标问题，采用熵权-逼近理想解排序法（technique for order preference by similarity to ideal solution，TOPSIS）选取最优折中解。最后，基于IEEE 33节点系统进行算例仿真，验证模型有效性。

关键词: 光储, 碳效益, 改进生成对抗网络, 精细化充放电, 调峰填谷辅助服务, 配电网

Abstract:

Under the vision of high-proportion renewable energy development, in order to effectively shorten the energy storage payback period, enhance renewable energy accommodation, and reduce distribution network carbon emissions, this paper proposes a multi-objective bilevel planning model for energy storage systems (ESSs) in distribution networks that considers refined charging/discharging strategies and carbon benefits. Firstly, typical photovoltaic scenarios are generated using an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and the K-medoids clustering algorithm. Secondly, a refined charging/discharging model for ESS is established, and a carbon benefit model is constructed based on both the carbon emission reduction enabled by ESS and its lifecycle carbon emissions. And then, a bilevel ESS planning and operation model for distribution network is constructed considering the refined charging/discharging strategies and carbon benefits. The upper-level model aims to minimize the total daily cost for optimal energy storage configuration, while the lower-level model pursues the minimization of operational costs and voltage deviation, as well as the maximization of carbon benefits from energy storage, to achieve optimized distribution network operation. Subsequently, the bilevel model is transformed into a single-layer multi-objective model by modeling the inter-layer coupling variables. The multi-objective model is then solved using the normalized normal constraint (NNC) method, and the optimal compromise solution is selected via the entropy-weighted TOPSIS method. Finally, the effectiveness of the proposed model is verified through numerical case studies based on the IEEE 33-node system.

Key words: photovoltaic-storage, carbon benefits, improved generative adversarial networks, refined charging and discharging, peak shaving and valley filling auxiliary services, distribution network

齐桓若, 陈晨, 郭放, 薛文杰, 闫向阳, 康祎龙, 刘俊成, 马思源. 考虑精细化充放电与碳效益的配电网储能多目标双层规划模型[J]. 中国电力, 2025, 58(10): 121-135.

QI Huanruo, CHEN Chen, GUO Fang, XUE Wenjie, YAN Xiangyang, KANG Yilong, LIU Juncheng, MA Siyuan. Multi-objective Bi-level Planning Model for Distribution Network Energy Storage Considering Refined Charging/Discharging and Carbon Benefits[J]. Electric Power, 2025, 58(10): 121-135.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202506048

https://www.electricpower.com.cn/CN/Y2025/V58/I10/121

图/表 13

参考文献 34

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电价	价格/(元·(kW·h)^–1)	时间段
谷值电价	0.282	22:00—次日08:00
平值电价	0.540	08:00—10:00、19:00—22:00
峰值电价	0.920	10:00—19:00

电价	价格/(元·(kW·h)^–1)	时间段
谷值电价	0.282	22:00—次日08:00
平值电价	0.540	08:00—10:00、19:00—22:00
峰值电价	0.920	10:00—19:00

参数		取值
梯度惩罚系数 $ \lambda $		10
时间尺度 $ {{\Delta }}{t_j^*} $/h		1
储能充电效率 $ \eta _{\rm c}^{\rm ESS} $		0.95
储能放电效率 $ \eta _{\text{d}}^{\rm ESS} $		0.95
运输碳排放 $ {k_{{\rm ct}}} $/(kg∙(t∙km)^–1)		0.1553
调峰折算火电出力系数 $ {k_{\rm fi}} $		0.588
火力发电碳排放因子 $ k_{\rm ffp}^{\rm {CO_2}} $/(t∙(MW∙h)^–1)		0.8953
调峰辅助价格 ${M_{{\rm zb} }}$/(元∙(MW∙h)^–1)		0.8
生产单位功率储能碳排放 $ k_{\rm pm}^{\rm p} $/(t∙MW^–1)		$ 2.536 \times 1{0^{ - 5}} $
生产单位容量储能碳排放 $ k_{\rm pm}^{\rm S} $/(t∙(MW∙h)^–1)		0.0913
设备运行天数 $ \rho $		365
折算利率 $ r $		0.08
设备生命周期 $ T $/年		20
ESS运行成本 $ M_{\rm ESS}^{\rm on,cost} $/(元∙kW^–1)		0.08
弃光惩罚 $ M_{\rm PV}^{\rm cf} $/(元∙(kW∙h)^–1)		0.8
网损价格 $ M_{{\rm loss}}^{{\rm jg}} $/(元∙kW^–1)		0.6

参数		取值
梯度惩罚系数 $ \lambda $		10
时间尺度 $ {{\Delta }}{t_j^*} $/h		1
储能充电效率 $ \eta _{\rm c}^{\rm ESS} $		0.95
储能放电效率 $ \eta _{\text{d}}^{\rm ESS} $		0.95
运输碳排放 $ {k_{{\rm ct}}} $/(kg∙(t∙km)^–1)		0.1553
调峰折算火电出力系数 $ {k_{\rm fi}} $		0.588
火力发电碳排放因子 $ k_{\rm ffp}^{\rm {CO_2}} $/(t∙(MW∙h)^–1)		0.8953
调峰辅助价格 ${M_{{\rm zb} }}$/(元∙(MW∙h)^–1)		0.8
生产单位功率储能碳排放 $ k_{\rm pm}^{\rm p} $/(t∙MW^–1)		$ 2.536 \times 1{0^{ - 5}} $
生产单位容量储能碳排放 $ k_{\rm pm}^{\rm S} $/(t∙(MW∙h)^–1)		0.0913
设备运行天数 $ \rho $		365
折算利率 $ r $		0.08
设备生命周期 $ T $/年		20
ESS运行成本 $ M_{\rm ESS}^{\rm on,cost} $/(元∙kW^–1)		0.08
弃光惩罚 $ M_{\rm PV}^{\rm cf} $/(元∙(kW∙h)^–1)		0.8
网损价格 $ M_{{\rm loss}}^{{\rm jg}} $/(元∙kW^–1)		0.6

序号	优化目标	日均综合成本/元	电压日总偏移量 (p.u.)	储能日均碳效益/ kgCO₂	储能参与调峰填谷辅助服务日收益/元
1	日均综合成本最小	42776.85	44.6956	5.7680	4200.74
2	电压日总偏移量最小	50286.74	15.4672	6.6394	4831.20
3	储能日均碳效益最大	31044.57	39.5132	22.6144	14904.99
4	最优权衡解	28108.49	28.9570	20.9082	14735.07