考虑需求响应资源和储能容量价值的新型电力系统电源规划方法
收稿日期: 2021-12-01
修回日期: 2022-05-13
录用日期: 2022-05-16
网络出版日期: 2023-01-11
基金资助
国家自然科学基金项目(51907066);中国南方电网公司科技项目(GDKJXM20200187)
Power System Planning Considering Demand Response Resources and Capacity Value of Energy Storage
Received date: 2021-12-01
Revised date: 2022-05-13
Accepted date: 2022-05-16
Online published: 2023-01-11
高比例可再生能源的接入对电力系统容量充裕性带来了新的挑战,系统必须具备充足的置信容量应对可再生能源的出力波动性和随机性.由于储能置信容量与电源规划结果的非线性关系,传统电源规划方法难以准确计算储能置信容量并建立系统置信容量充裕度约束.通过综合考虑火电、可再生能源、储能以及需求侧响应建立了电源规划模型,内嵌全年8 760时段生产运行模拟以确保系统具有充足灵活性,同时改进容量充裕性约束以考虑需求响应资源和储能的容量价值.针对储能置信容量的非线性问题,设计了迭代算法进行求解,并用某区域电力系统验证了模型的有效性.结果表明,高比例可再生能源系统中,影响系统成本的主要因素是灵活性约束,引入少量需求侧响应资源可大幅降低系统成本,为未来高比例可再生资源电力系统规划问题提供了新的思路.
黄远明, 张玉欣, 夏赞阳, 王浩浩, 吴明兴, 王宁, 陈青, 朱涛, 陈新宇 . 考虑需求响应资源和储能容量价值的新型电力系统电源规划方法[J]. 上海交通大学学报, 2023 , 57(4) : 432 -441 . DOI: 10.16183/j.cnki.jsjtu.2021.477
The access of a high proportion of renewable energy has posed new challenges to the supply reliability of the power system. The system must have sufficient capacity credit to cope with the output fluctuation and randomness of renewable energy. Due to the nonlinear relationship between energy storage capacity credit and power planning results, it is difficult to establish accurate capacity adequacy constraints for traditional power planning methods. Therefore, a generation expansion model is established, in which thermal power, renewable energy, energy storage, and demand response resources are incorporated, with the full-year hourly production simulation to ensure adequate operation flexibility and improved capacity adequacy constraint to incorporate the capacity value of energy storage and demand response resources. An iterative algorithm is designed to solve the nonlinear problem of energy storage capacity credit, and the validity of the model is verified by some regional grid in China. The results show that in the high-proportion renewable energy system, the system capacity is surplus, and the main factor affecting the system cost is the flexibility constraint. The introduction of a small amount of demand response resources can greatly reduce the system cost, which provides new ideas for power system planning at a high proportion of renewable energy.
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