考虑基荷的联合电力系统多目标分层优化调度
摘要：为充分发挥水电的灵活调节作用来应对高比例风电的随机性、间歇性对电力系统调度带来的影响，以促进风电全额消纳、火电运行费用最低、火电机组出力波动量最小作为优化目标，建立风−水−火电力系统多目标优化调度模型，并且综合考虑火电机组启停与低负荷运行、风电有无弃风等多种场景，提出水电利用率、火电运行成本、火电波动量等量化指标，用以评价调度结果的优劣。为保证模型求解效率，设计分层优化求解策略：第一层优化水电机组出力，促进水电调节能力的充分发挥，保证火电机组总波动量最小；第二层优化各台火电机组出力，使得系统运行费用最低；采用粒子群算法对每层优化问题进行求解。通过不同应用场景下的算例测试，进行优化前后的调度结果对比、考虑机组启停的调度结果对比、不同风电装机容量下的调度结果对比，验证了所建模型和分层优化策略的有效性。
Abstract:To overcome the disadvantages of wind power on dispatching such as the randomness and intermittentness, and exert the flexible adjustment ability of high-proportion hydropower. The optimization goal is set to promote full consumption of wind power, lowest operating cost of thermal power and minimum output fluctuation, then a multi-objective optimal dispatch model for wind-hydro-thermal power systems is established to evaluate the results of dispatch. We considered a variety of scenarios such as start-stop and low-load operation of thermal power generator and whether wind power has abandoned wind, some quantitative indicators such as hydropower utilization rate and thermal power fluctuations are proposed. And hierarchical optimization strategy is designed to ensure the efficiency in solving model, first layer optimizes output of hydropower generators, ensures minimum total fluctuation of thermal power, and promotes full use of hydropower adjustment ability, second layer optimizes output of each thermal power generator to make the system operation cost the lowest, and uses particle swarm optimization (PSO) algorithm to solve each layer optimization problem. Through experimental test, the scheduling results before and after optimization, the scheduling results considering the start-stop of power unit, and the scheduling results under different wind power installed capacity are compared, and the effectiveness of the proposed model and hierarchical optimization strategy is verified.
标题：考虑基荷的联合电力系统多目标分层优化调度
英文标题：Multi-objective Hierarchical Optimal Scheduling of Combined Power Systems Considering Base Loads
作者：赵宁波, 王开艳, 李沛航, 吕挺, 贾宏涛, 王争冕,
关键词：风力发电, 水力发电, 多目标优化, 经济调度, 粒子群算法,
发表日期：2024-02-19
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