Adaptive Demand Response Management System using Polymorphic Bayesian Inference Supported Multilayer Analytics

Abstract

The most significant limitation of stand-alone microgrid systems is the challenge of meeting unexpected additional demands. If demand exceeds the capacity of a stand-alone system, the system may be unable to satisfy demand. This issue is alleviated in grid-connected technology since the utility system will provide more power if it is demanded. As a result, load scheduling is an integral element of the demand response of a standalone system. There are two components to this problem. If the capacity of a battery-supported power system is restricted, for the period of time that the source is available, it will not be able to meet the entire demand. Appropriately the demand is dispersed across a period of time until the next charge becomes available. Some demands may be disregarded in order to accomplish peak load trimming or if the system is incapable of meeting demand without compromising other important technical and consumer objectives. This is a challenging assignment. This article aims to develop an Adaptive Demand Response Management System (ADRMS) capable of load scheduling and load shedding using an interwoven multidimensional Bayesian inference supported by multiple mathematical models. A two-stage hardware architecture is being developed, with the first hardware measuring demand and source capacity before sending the data to the second hardware via LPWAN for mathematical analysis. In the first phase, two approaches are used to forecast demand: Gaussian Naive Bayes Model (GNBM) and Bayesian Structural Time Series analysis. GNBM is rapid but fails to properly forecast the output when there is zero frequency error whereas BSTS can offer more precise results than GNBM but is slower. Hence two approaches are employed in tandem. The next stage is to assign demand source integration. This is accomplished using Bayesian Reinforcement Learning (BRL), which is based on a number of incentives, including anomaly, cost factors, usefulness, reliability, and size. All Bayesian models are subjected to much of the common Bayes rule, resulting in the formulation of a blended polymorphism model that reduces computing time and memory allocation, and improves processing reliability. The Isolation Forest (IF) method is used to identify and avoid vulnerable loads by determining demand anomalies. The last step employs a Dynamic Preemptive Priority Round Robin (DPPRR) algorithm for preemptive priority based load scheduling based on forecasted data to allocate the next loads to be added.