基于改进人工蜂鸟算法的VRV空调需求响应功率削减策略

Demand response power reduction strategy of VRV air conditioning based on improved artificial hummingbird algorithm

摘要:

针对夏季用电高峰时期用户对空调设定温度随意调节造成能源浪费以及需求侧对电网控制指令响应不够精确的问题，提出了一种基于功率削减的空调温度分档需求响应调控策略；以某办公建筑VRV空调为研究对象，分别建立该办公建筑空调物理仿真模型以及功耗数学模型，并对模型的准确性进行验证；提出基于不同舒适度和激励电价的VRV空调温度控制档位，构建室内机温度分档调控多目标优化模型，优化目标为调控时期空调实际功率与调控目标值的平均偏差以及负荷聚合商对用户的激励补偿费用同时最小；选取人工蜂鸟算法作为优化算法，针对该算法存在搜索速度慢、寻优精度低、易早熟收敛等缺点，在种群初始化阶段采用Hammersley序列生成更加均匀的初始种群以提高算法的收敛速度与精度，在搜索阶段采用高斯变异算子对蜂鸟位置进行扰动以进一步提升算法的探索能力。运用改进人工蜂鸟算法对模型进行求解，并与人工蜂鸟算法、粒子群算法、灰狼优化算法和鲸鱼优化算法的求解结果进行对比，以证明所提策略的有效性；实验结果表明，应用改进人工蜂鸟算法求解后的结果在保证用户舒适度的条件下最多可将功率调控精度提高83.1%并且将激励费用减小8.36%。

Abstract:

A power reduction-based demand response regulation strategy for air conditioning temperature staging is proposed to address the problems of wasteful energy use caused by users' arbitrary adjustment of air conditioning temperature setpoints and imprecise response to grid control commands on the demand side during the summer peak electricity consumption. Taking an office building VRV air conditioning as the research object, a physical simulation model of office building VRV and a mathematical model of power consumption are established and validated. A optimization model is proposed for VRV air conditioning indoor units temperature staging control based on different comfort levels and incentive tariffs, with the optimization objectives is to minimize the average deviation between the actual power and the target value of the air conditioner during the regulation period and to minimize the incentive compensation cost of the load aggregator to the user. The artificial hummingbird algorithm is selected as the optimization algorithm. To address the shortcomings of the algorithm, such as slow search speed, low accuracy of the search and easy premature convergence, the Hammersley sequence is used in the population initialization stage to generate a more uniform initial population to improve the convergence speed and accuracy of the algorithm, and the Gaussian variational operator is used in the search The Gaussian variation operator is used to perturb the hummingbird positions in the search phase to further enhance the exploration capability of the algorithm. The improved artificial hummingbird algorithm was used to solve the model and compared with the results of the artificial hummingbird algorithm, the particle swarm algorithm, the grey wolf optimization algorithm and the whale optimization algorithm, to demonstrate the effectiveness of the proposed strategy; The model is solved using Improved Artificial Hummingbird Algorithm and compared with the optimization results of four optimization algorithms, namely artificial hummingbird algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm and whale optimization algorithm, to demonstrate the effectiveness of the proposed strategy. The experimental results show that the improved artificial hummingbird algorithm can improve the power regulation accuracy by up to 83.1% and reduce the incentive cost by 8.36% while ensuring user comfort.