基于混沌多目标粒子群算法的综合能源调度

目的 针对当前综合能源系统中资源协同优化效率不足、微网运行经济性和环保性差的问题，提出了一种计及风电储能及不稳定因素的微网优化调度方法。方法 该方法在微网负荷侧需求响应对新能源消纳影响的基础上，以消纳新能源和削峰填谷为目的，提出了优化负荷曲线的方案；然后，考虑微网调度侧风电出力的不稳定性以及微网内部设备的耦合，进行优化调度以降低微网运行成本、减少环境惩罚费用并提高风电消纳平稳性；最后，采用混沌多目标粒子群算法对优化问题进行求解，并在风电不稳定度占比0%、5%、10%和15%时进行了算例仿真分析。结果 当风电不稳定度为10%和加入风电储能，系统运行成本和环境治理费用最少，比方案1和无风电储能少6 919.4元，风电平稳量也提高38 kWh。在电热冷网中，负荷侧加入需求响应后，系统得到稳定运行和能源合理利用，可以很好地满足负荷侧用能需求。从算法对比中，混沌多目标粒子群算法加入自适应权重和变异率后，具有较强的全局搜索能力和更好的准确性。结论 该方法通过合理设置风电不稳定度能够有效降低运行成本和环境惩罚费用，提高风电稳定性，其次，负荷侧的需求响应可以一定程度地削峰填谷和消纳新能源。

Comprehensive Energy Scheduling Based on Chaotic Multi-objective Particle Swarm Algorithm

Objective Aiming at the current problems of insufficient efficiency of resource synergistic optimization and poor operation economy and environmental protection of microgrid in the integrated energy system a microgrid optimization scheduling method was proposed to take into account the wind power storage and instability factors. Methods Based on the influence of microgrid load-side demand response on new energy consumption this method proposed a scheme to optimize the load curve to absorb new energy shave peaks and fill valleys. Then considering the instability of the wind power output on the dispatch side of the microgrid and the coupling of the equipment inside the microgrid the optimal scheduling was performed to reduce the microgrid operation cost reduce the environmental penalty cost and improve the smoothness of wind power consumption. Finally the chaotic multi-objective particle swarm algorithm was used to solve the optimization problem and the simulation analysis was carried out at 0% 5% 10% and 15% of wind power instability.Results When the wind power instability was 10% and the wind power storage was added the system operation cost and environmental management cost were the least which was RMB 6 919. 4 less than that of Option 1 and no wind power storage and the wind power smoothing amount was also improved by 38 kWh. In the electric heating and cooling network after the demand response was added on the load side the system was running stably and the energy was rationally utilized which can well meet the energy demand of the load side. From the comparison of algorithms the chaotic multiobjective particle swarm algorithm has strong global search capability and better accuracy after adding adaptive weights and variation rate. Conclusion The experimental conclusion shows that the proposed method can effectively reduce the operating cost and environmental penalty cost and improve the stability of wind power by reasonably setting the wind power instability degree. Moreover the demand response on the load side can cut the peak and fill the valley to a certain extent and consume new energy.