Abstract
In a Smart Grid (SG) scenario, domestic consumers can gain cost reduction benefit by scheduling their Appliance Activation Time (AAT) towards the slots of low charge. Minimization in cost is essential in Home Energy Management Systems (HEMS) to induce consumers acceptance for power scheduling to accommodate for a Demand Response (DR) at peak hours. Despite the fact that many algorithms address the power scheduling for HEMS, community based optimization has not been the focus. This paper presents an algorithm that targets the minimization of energy costs of whole community while keeping a low Peak to Average Ratio (PAR) and smooth Power Usage Pattern (PUP). Objective of cost reduction is accomplished by finding most favorable AAT by Particle Swarm Optimization (PSO) in conjunction with Inclined Block Rate (IBR) approach and Circular Price Shift (CPS). Simulated numerical results demonstrate the effectiveness of CPS to assist the merger of PSO & IBR to enhance the reduction/stability of PAR and cost reduction.
Highlights
Implementation of demand response (DR) has been the prime focus of researchers targeting optimization of smart grids (SG) from the last five years
Some of them rely on resident habits using load prediction models [3, 6, 7] to adjust scheduling, whereas others rely on pricing schemes and penalty terms [8, 9]
This section is dedicated for simulation results to prove the capability of Community based HEMS (CHEMS) to produce better results for large populations
Summary
Implementation of demand response (DR) has been the prime focus of researchers targeting optimization of smart grids (SG) from the last five years. Surges in power requirements at peak hours are inevitable due to evolution in domestic electrical appliance industry despite advances in renewable energy alternatives. Electricity supply companies (ESC) only run their base power production units and secondary units are operated if the power usage pattern (PUP) of the whole grid crosses some threshold. Operation of sub units for a short span of time caused by frequent peaks in PUP is a technical hassle for ESC, smooth PUP and reduced PAR is primary goal in demand side management (DSM). Home energy management system (HEMS) is a principal solution for domestic DSM, which involves automated decisions for load optimization [2, 3]. A time-based electricity pricing scheme is an actively used tool for both cost and load curtailment [4, 5]. Some of them rely on resident habits using load prediction models [3, 6, 7] to adjust scheduling, whereas others rely on pricing schemes and penalty terms [8, 9]
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