摘要
鉴于大规模可再生能源系统新能源发电的不确定性和波动性特点非常突出,以电力平衡为目的的电网调度控制必须对此采取有针对性的应对措施;同时考虑到风电和光伏发电具有天然的出力互补特性,水电和抽水蓄能电站具有良好的出力调节能力,从日前调度与日内实时调度相互协调配合的角度给出含抽水储能电站的可再生综合能源系统的调度运行机制,分别建立日前调度和日内实时调度的系统优化模型。该模型以尽量减小火电机组煤耗成本和污染物排放惩罚成本为优化目标,以系统运行时满足功率实时平衡、各火电机组满足出力上下限制、各机组满足功率爬坡速率限制、系统旋转备用满足容量限制,以及风、光、水、储满足各自出力限制等为优化约束条件。针对该模型,提出一种增强自适应能力的改进粒子群优化算法,基于系统所预测的新能源出力数据和负荷数据,最终计算得到的综合优化调度方案能够在提高大规模新能源发电利用率的同时,充分利用抽水蓄能电站减少新能源出力不确定性对电网传统火电机组的影响。最后借助某实际电网的算例分析对所提方法的调度结果进行有效性验证说明。
In view of prominent uncertainties and volatility of energy generation in the large-scale renewable energy system, it is necessary to take pointed actions for realize power grid scheduling control aiming at electric power balance. Considering natural complementary characteristics of wind power and photovoltaic generation and favorable output adjustment capability of hydropower and pumped storage power stations, this paper gives a scheduling operation mechanism of the renewable integrated energy system with the pumped storage power station from the perspective of coordination of day-ahead scheduling and intraday real-time scheduling, and respectively builds the system optimization model. The model aims to reduce coal consumption cost and pollutant discharge penalty cost of the thermal power unit as much as possible, and is based on system satisfying real-time power balance in operation, the thermal power unit satisfying the upper and lower limits of output, the unit satisfying power ramp rate limit, the system rotation reserve satisfying capacity limit and the wind, light,water and storage satisfying output limits as optimized constraint conditions. Aiming at this model, this paper proposes an improve particle swarm optimization (PSO) algorithm with enhanced self-adaptive ability. Based on predicted new energy output data and load data, the final integrated optimization scheduling scheme can improve utilization rate of large-scale new energy generation and fully make use of the pumped storage power station to reduce impact of uncertainties of new energy on traditional thermal power units of the power grid. Finally, it verifies validity of the scheduling results of the proposed method by analysis of an actual power grid.
作者
区允杰
孙景涛
陈刚
OU Yunjie;SUN Jingtao;CHEN Gang(Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Foshan, Guangdong 528000, China)
出处
《广东电力》
2019年第10期79-88,共10页
Guangdong Electric Power
基金
广东电网有限责任公司科技项目(030600GS62190095)
关键词
可再生能源系统
抽水蓄能
优化调度
改进PSO算法
renewable energy system
pumped storage
optimized scheduling
improved particle swarm optimization algorithm
