首页> 外文会议>7th international green energy conference amp; 1st DNL conference on clean energy >SOLVING POWER SYSTEM UNIT COMMITMENT WITH WIND FARMS USING MULTI-OBJECTIVE QUANTUM-INSPIRED BINARY PARTICLE SWARM OPTIMIZATION
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SOLVING POWER SYSTEM UNIT COMMITMENT WITH WIND FARMS USING MULTI-OBJECTIVE QUANTUM-INSPIRED BINARY PARTICLE SWARM OPTIMIZATION

机译:多目标量子启发式二进制粒子群优化算法解决风电系统机组故障

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摘要

To reduce air pollutant emissions and consider the randomness and uncontrollability of wind power, power system dispatching with consideration of the forecasted wind generation of certain probabilistic confidence interval and air pollutant emissions will be a good choice. This paper proposes a day-ahead unit commitment model with the constraints based on the probabilistic confidence interval of the forecasted wind generation and a multi-objective function considering both costs and emissions; simultaneously the paper presents a new method to solve it. The proposed method uses a new Multiobjective Quantum-inspired Binary PSO for the unit on/off problem and the primal-dual interior point method for load economic dispatch problem. Based on the QBPSO, the article introduces the Pareto optimal concept and the external archive to it. The paper also adopts the heuristic adjusted regulations to ensure the whole algorithm to search the optimal particle in the feasible region. The proposed method is applied to power systems which are composed of 10units with 24-h demand horizon and a certain proportion of wind farms. The simulation results prove the validity of the model and algorithm.
机译:为了减少空气污染物的排放并考虑风能的随机性和不可控性,考虑一定概率置信区间的预测风力发电量和空气污染物排放的电力系统调度将是一个不错的选择。提出了一种基于预报风力发电的概率置信区间和考虑成本和排放的多目标函数约束的日前机组承诺模型。同时,本文提出了一种新的解决方法。所提出的方法使用了一种新的多目标量子启发式二进制PSO来解决单位开/关问题,并使用了原始对偶内点法来解决负荷经济调度问题。在QBPSO的基础上,本文介绍了Pareto最优概念和外部归档。本文还采用启发式调整规则,以确保整个算法在可行区域内搜索最优粒子。该方法适用于由10个机组组成的电力系统,其需求范围为24小时,并具有一定比例的风电场。仿真结果证明了该模型和算法的有效性。

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