摘要
建立了含风电场的电力系统多目标动态优化调度模型,以总燃料耗量、污染气体排放量及购电费用最小为优化目标,针对风电场出力随机性的特点,采用极限场景代表风电场出力偏离预测场景的各种可能方向,考虑了风电场出力极限场景下,各常规机组备用动作对应的网络安全、机组爬坡等约束。将极限场景与预测场景联立求解,得出机组出力计划及各极限场景下的备用动作方案。采用规格化法平面约束法（normalized normal constraint,NNC）和GAMS/CONOPT求解器求解三目标优化问题,以获得均匀分布的Pareto最优解集;并提出通过确定乌托邦面位于目标函数空间中可行域内部的完整部分,以求解完整Pareto前沿的方法。最后以IEEE 39节点系统和某省级电网为例,分析结果表明,所提出模型和算法能够为调度人员提供更为完整的决策信息,并得到在风电场出力波动条件下,能够满足系统各种运行约束的备用动作方案。
A multi-objective optimal dynamic dispatch model of power system with wind farms is established, taking fuel consumption, pollutant emission and power purchase cost as objectives. Aimed at randomness of wind farm output, extreme scenarios are used to represent various possible directions of wind farm output deviating from predicted scenario, and reserve action constraints of conventional units under extreme scenarios were considered, including network security constraints and unit ramp rate constraints, et al. By solving the optimal dispatch model with constraints of forecasting scenario and extreme scenarios, the unit power output schedule and reserve action scheme in each extreme scenario is obtained. The evenly distributed Pareto optimal sets were obtained using normalized normal constraint（NNC） method and GAMS/CONOPT solver to solve the threeobjective optimization problem. A method of obtaining complete Pareto frontier by determining the complete part of the Utopia plane within feasible region in the objective function space was proposed. Test results on IEEE 39-buses system and a real provincial power system demonstrate that the proposed model and algorithm can provide more complete decision information to dispatchers, and obtain the reserve action scheme under condition of fluctuating output of wind farms meeting various operating constraints of the system.
出处
《电网技术》
EI
CSCD
北大核心
2018年第2期479-486,共8页
Power System Technology
基金
国家重点基础研究发展计划项目(973项目)(2013CB228205)
广东省自然科学基金资助项目(2015A030313233)
中央高校基本科研业务费资助项目(2015ZM106)~~
