| 引用本文: | 许汉平,李姚旺,苗世洪,罗纯坚,徐秋实.考虑可再生能源消纳效益的电力系统“源—荷—储”协调互动优化调度策略[J].电力系统保护与控制,2017,45(17):18-25.[点击复制] |
| XU Hanping,LI Yaowang,MIAO Shihong,LUO Chunjian,XU Qiushi.Optimization dispatch strategy considering renewable energy consumptive benefits based on “source-load-energy storage” coordination in power system[J].Power System Protection and Control,2017,45(17):18-25[点击复制] |
|
| 摘要: |
| 为了提升可再生能源消纳率，降低电力系统运行成本，考虑以“源—荷—储”协调互动作为提升系统运行经济性与消纳可再生能源的手段。将常规机组、储能装置与负荷侧调度资源同时进行优化调度，综合考虑系统常规机组运行成本、负荷侧调度成本和可再生能源消纳效益，建立了基于“源—荷—储”协调互动的电力系统优化调度模型。根据决策变量的不同特性，提出了一种两级优化方法。第一阶段采用离散粒子群算法优化常规机组启停成本、弃风弃光成本和负荷调度成本；第二阶段采用双层连续粒子群算法优化常规机组燃料成本。通过算例仿真验证了该优化调度模型的有效性。 |
| 关键词: “源—荷—储”协调互动 需求响应 粒子群算法 优化调度 |
| DOI：10.7667/PSPC161442 |
| 投稿时间：2016-09-03修订日期：2016-11-25 |
| 基金项目:国网湖北省电力公司科技项目(52153815000D) |
|
| Optimization dispatch strategy considering renewable energy consumptive benefits based on “source-load-energy storage” coordination in power system |
| XU Hanping,LI Yaowang,MIAO Shihong,LUO Chunjian,XU Qiushi |
| (State Grid HBEPC Economic & Technology Research Institute, Wuhan 430077, China;State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China) |
| Abstract: |
| In order to increase the accommodation rate of renewable energy and cut the system operation cost, this paper takes “source-load-energy storage” coordination as an important mean to improve the system operation economy and the accommodation rate of renewable energy. Then, an optimization dispatch model based on “source-load-energy storage” coordination which considers thermal units, energy storage device and demand response as dispatching resources is established. The objective of this model including the operation costs of thermal units, the costs of demand response and the benefits from renewable energy consumption. Based on the different characteristics of the decision variables, a two-stage optimal algorithm is proposed. The first stage algorithm uses binary particle swarm optimization algorithm to minimize the start costs of thermal units, costs of wind power and photovoltaic curtailment and costs of demand response; the second stage uses double layer continuous particle swarm optimization algorithm to minimize the fuel costs of thermal units. The simulation results verify the validity of the proposed optimization dispatch strategy. |
| Key words: “source-load-energy storage” coordination demand response particle swarm optimization algorithm optimization dispatch |
