A Novel Dynamic Load Scheduling and Peak Shaving Control Scheme in Community Home Energy Management System Based Microgrids

Abstract

Load scheduling and peak demand shaving are two critical aspects of the utility grid operation that help both the grid operators as well as end-users. This paper proposes a two-stage community home energy management system for microgrids. The first stage deals with the dynamic clustered community load scheduling scheme. Comparatively flatter power demand was attained using particle swarm optimization (PSO) incorporating user-defined constraints. The new arising or remaining peaks as a consequence of consumer constraints are catered to in the second stage. The second stage proposes a rule-based peak shaving management method for the photovoltaic (PV) systems that are connected to the grid and the battery energy storage systems. The proposed technique determines the dynamic demand and feed-in limits based on the estimations of the upcoming day's load demand and PV power profiles. Also, the study presents an optimal rule-based management technique for peak shaving of utility grid power that sets the charge/discharge day ahead schedules of the battery. For peak energy minimization, PSO is used to calculate the optimal inputs needed for implementing the appropriate rulebased management strategy. MATLAB is used to test the proposed method for different PV power and load demand patterns, thus, achieving an average improvement of 8.5%.

Artificial Rabbits Optimized Neural Network-Based Energy Management System for PV, Battery, and Supercapacitor Based Isolated DC Microgrid System

“…Two essential elements of microgrid operation that benefit both the grid and consumers are load planning and peak shaving. [23] offers an ideal rule-based management method for reducing grid power's peak shaving demand, which establishes the battery's charge/discharge plans for the day ahead, and PSO is utilized to determine the ideal inputs required for putting the proper rule-based management plan into action for peak energy reduction. A precise strategy for achieving demand response is presented in [24], which continuously tracks power use and relies on automated tech-nology to modify energy use in response to shifts in supply and demand.…”

Methodology for Energy Management in a Smart Microgrid Based on the Efficiency of Dispatchable Renewable Generation Sources and Distributed Storage Systems

“…This application is the exact one implemented in EMSs, where cost of energy and operating cost have been minimised [85][86][87]. Again, the technology that plays the role of ESS to counteract the peaks are batteries, being implemented in practically all the case studies [88][89][90][91][92][93].…”

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

“…Rule-based optimization approaches are based on pre-defined rules, attempting to achieve an optimized solution to a mathematical problem [26]. Rule-based optimization approaches include Action Dependent Heuristic Dynamic Programming (ADHDP) [27], [38], [39], static and dynamic rule-based approaches [28]- [31], Particle Swarm Optimization (PSO) [35]- [37], and Fuzzy Logic [32], [33]. The primary objectives of rule-based optimization approaches are minimizing the energy costs, reducing energy consumption from the electric grid and emission, optimizing the energy consumption and flow, and maximizing the comforts of consumers [27]- [33], [35]- [38].…”