| 引用本文: | 魏 文,姜 飞,戴双凤,陈长青,陈 磊.计及需求侧储能事故备用风险与火电机组深度调峰的
经济优化研究[J].电力系统保护与控制,2022,50(10):154-163.[点击复制] |
| WEI Wen,JIANG Fei,DAI Shuangfeng,CHEN Changqing,CHEN Lei.Economic optimization of deep peak regulation of thermal power units taking intoaccount the risk of emergency storage on the demand side[J].Power System Protection and Control,2022,50(10):154-163[点击复制] |
|
| 摘要: |
| 针对电网调峰过程中需求侧储能(Energy Storage, ES)事故备用闲置时间过长，以及传统火电机组深度调峰(Depth Peak Regulation, DPR)成本较高的问题，提出了一种考虑需求侧储能事故备用风险与火电机组深度调峰的经济优化方法。首先，通过分析天气状态、负载率水平以及故障风险类型等影响因素，构建了考虑需求侧的ES风险量化模型、ES事故备用调峰经济模型。其次，结合火电机组进行深度调峰时对机组损伤及燃料需求分析，构建了考虑火电机组投油成本的深度调峰经济模型。最后，提出了一种基于需求侧风险量化的ES事故备用辅助火电机组DPR的经济优化模型，采用粒子群优化算法在修正后的IEEE30节点系统中进行算例验证。结果表明ES事故备用参与DPR能有效提高电网调峰能力和ES事故备用利用率。 |
| 关键词: 储能 调峰 风险量化 粒子群算法 |
| DOI：DOI: 10.19783/j.cnki.pspc.210992 |
| 投稿时间：2021-07-31修订日期：2021-09-06 |
| 基金项目:国家自然科学基金青年基金项目资助(51707014)“串联接入电网的电压源型变流器暂态特性与故障穿越研究”；湖南省社科基金基地项目资助“基于需求侧响应的绿色电力认购交易定价机制研究”(18JD02) |
|
| Economic optimization of deep peak regulation of thermal power units taking intoaccount the risk of emergency storage on the demand side |
| WEI Wen,JIANG Fei,DAI Shuangfeng,CHEN Changqing,CHEN Lei |
| (1. School of Economics and Management, Changsha University of Science and Technology, Changsha 410076, China;
2. School of Economics and Management, Hunan University of Science and Technology, Yueyang 414000, China;
3. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410076, China;
4. School of Mechanical and Electrical Engineering, Hunan City University, Yiyang 413000, China) |
| Abstract: |
| There are problems of long idle time of demand-side energy storage accident backup and high cost of traditional thermal power units. Thus an economic optimization method considering standby risk of demand-side energy storage accident and thermal power unit deep peak shaving is proposed. First, a demand side ES risk quantification model and a standby peak adjustment economic model of ES accident are constructed by analyzing the weather state, load rate level and failure risk type. Secondly, the DPR economic model of fuel injection cost of a thermal power unit is built based on the analysis of unit damage and fuel demand during DPR. Finally, an economic optimization model of an ES accident standby auxiliary thermal power unit DPR based on demand side risk quantification is proposed. The particle swarm optimization algorithm is used to verify the calculation example in the modified IEEE 30-node system. The results show that the participation of ES accident standby in DPR can effectively improve the peak load adjustment capability and the utilization rate of ES accident standby.
This work is supported by the Youth Fund of National Natural Science Foundation of China (No. 51707014) “Research on Transient Characteristics and Fault Traverse of Voltage Source Converter Connected in Series to Power Grid”. |
| Key words: energy storage peak shaving risk quantification particle swarm optimization |
