引入可调度空间约束的电力系统优化调度模型

Power system optimal scheduling model with schedulable space constraints

中文关键词: 电力系统调度  可调度空间  负荷增量比  混合粒子群算法  净负荷

英文关键词: optimize scheduling in power system  schedulable space  load increment ratio  hybrid particle swarm optimization  net load

中文摘要:

      传统电力系统优化模型通常以火电机组固有技术出力作为该机组的出力约束，而实际运行中，机组调度出力范围还受净负荷值及机组上一时段出力的影响，出力约束存在较大的优化空间。为有效缩减机组出力变量的可行域范围，提出净负荷增量指标，应用该指标及机组上一时段的出力状态优化各机组可调度空间范围。进一步以可调度空间范围作为约束，建立改进电力系统优化调度模型。运用标准粒子群与模拟退火算法相结合的混合粒子群算法求解。算例结果表明，混合粒子群算法能够有效改进标准粒子群算法陷入局部最优的缺点，提高模型求解精度；此外，引入可调度空间值约束的电力系统优化调度模型与传统优化调度模型相比，在确保求解精度的同时大大减少了计算量，且不易陷入局部最优。该改进的思路与方法也可应用于其它能源系统的优化调度模型中。

英文摘要:

      The traditional power system optimization model usually use the inherent technical output of the thermal power unitas the output constraint of the unit, in actual operation, the unit’sdis patching output range is also affected by the net load and theunit’s output during the previous period, there is a large optimization space for output constraints. In order to effectively reduce the feasible range of unit output variables, a net load incremental indexis proposed, and the index and the output state of the unit in the previous period are used to determine the dispatch able space of eachunit. With the scope of schedulable space as a constraint, an improved dispatching model for improved power systems is established. A hybrid particle swarm optimization algorithm combining standard particle swarm optimization and simulated annealing algorithm is used. The example results show that the hybrid particles warm optimization algorithm can effectively improve the shortcomings of the standard particle swarm optimization algorithm to fall into the local optimum, and improve the accuracy of the model solution. In addition, compared with the traditional optimization scheduling model, the improved optimization scheduling model of power system that introduces schedulable space constraints, while ensuring the accuracy of the solution, the calculation amount is greatly reduced, and it is not easy to fall into a local optimum. the improved ideas and methods can also be applied to the optimization scheduling model of other energy systems.