Demand Side Management (DSM) is one of the ways to create interaction between the MicroGrids (MGs) and increase consumer participation in management schemes. Different algorithms and strategies have been used to execute consumption management programs which often cover a limited number of loads in several specific types. In this paper, first, the load shift method, as an optimization problem to reduce system demand peak and subscriber’s bills for various loads in smart MGs, is solved by Hybrid Particle Swarm Optimization algorithm with Sinusoidal and Cosine Acceleration Coefficient (H-PSO-SCAC). Then the study is aimed at measuring the effect of the proposed program on the generation and presence of MGs in the market for improving the level of social welfare. The results are performed on a Smart Grid (SG) consisting of three residential, commercial and industrial MGs which include different types of controllable loads. The results show that the highest percentages of peak load reduction after the implementation of the DSM program by (H-PSO-SCAC) algorithm for the three MGs are 23%, 19% and 19%, respectively. Also, the highest percentages of reduction in subscriber’s bill for the three MGs are 16.8%, 19.2% and 20.5%, respectively. The proposed algorithm has performed much better in reducing bills and peak loads than most other methods such as Logarithmic Function (LF), Multi Agent (MA), Evolutionary Algorithm (EA), and Symbiotic Organisms Search (SOS). The findings show that the proposed program can reduce peak load, reduce subscriber’s bills, save production costs, help balance the supply and demand, and improve the level of social welfare from the perspective of the distribution system operator.

需求方管理（DSM）是在微电网（MG）之间创建交互并增加消费者参与管理方案的方式之一。已经使用了不同的算法和策略来执行消耗管理程序，这些程序通常涵盖几种特定类型的有限数量的负载。在本文中，首先，通过采用正弦和余弦加速度系数（H-PSO-SCAC）的混合粒子群优化算法，解决了负荷转移方法，作为降低系统需求高峰和智能MG中各种负荷的用户账单的优化问题。 ）。然后，该研究旨在衡量拟议计划对市场中MG的产生和存在的影响，以提高社会福利水平。结果是在由三个住宅，包括不同类型可控负载的商业和工业MG。结果表明，通过（H-PSO-SCAC）算法对三个MG执行DSM程序后，峰值负荷降低的最高百分比分别为23％，19％和19％。此外，三个MG减少订户费用的最高百分比分别为16.8％，19.2％和20.5％。与对数函数（LF），多代理（MA），进化算法（EA）和共生生物搜索（SOS）等大多数其他方法相比，所提出的算法在减少费用和减少峰值负荷方面表现更好。研究结果表明，拟议的计划可以减少高峰负荷，减少订户的账单，节省生产成本，帮助平衡供需，