序优化理论在大规模机组组合求解中的应用
Application of ordinal optimization theory to solve large-scale unit commitment problem

序优化理论以满足工程实际需要为目的, 能够简化优化问题复杂程度, 节省大量计算时间, 保证以足够高 的概率求得足够好的解. 文中将煤耗费用、机组启动成本、购电费用、SO2排放费用作为目标函数, 考虑了带时间耦 合关系的系统运行约束、机组特性约束、一次能源约束, 建立了考虑火电、水电、核电、生物质、燃气多种类型电源 的96时段机组组合动态优化模型, 并引入序优化理论予以求解. 最后, 分别对10100机24时段标准火电测试系统 和128机96时段某省级实际电力系统进行算例仿真, 并与其他优化算法的求解结果进行了详细的对比分析, 进一步 验证了采用序优化理论解决电力系统大规模机组组合问题的可行性和实用性.
Complex optimization problems can be simplified by using ordinal optimization theory to reduce computing time and raise probability to obtain good enough solutions. The weighted sum of coal consumption cost, unit start-up cost, power purchase cost and emissions cost is proposed as the objective function of unit commitment subject to time-coupled system operating constraints, unit features constraints and primary energy constraints. The dynamic unit commitment optimization model for the large-scale power system during the daily 96 periods is built by considering all kinds of power generation units such as thermal, hydro, nuclear, biomass and gas units. Then, the ordinal optimization theory is applied to optimize this large-scale unit commitment problem. Simulations have been carried out respectively on 10-100 units 24 periods standard test examples and 128 units 96 periods test example of some real provincial power generation systems. The feasibility and practicality of ordinal optimization theory in solving such large-scale unit commitment problem are validated by comparing results from ordinal optimization theory with results from other optimization algorithms.