An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid

Abstract

The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO 2 ) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO 2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO 2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO 2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO 2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO 2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO 2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.INDEX TERMS Smart grids, advanced metering infrastructure, demand side management, energy management controller, price-based demand response program, heuristic algorithms, multi-layer perceptron, forecasting, carbon reduction.The associate editor coordinating the review of this manuscript and approving it for publication was Arash Asrari .

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

“…Nevertheless, user comfort has not considered. In [12], authors have proposed an intelligent approach for demand side management with forecasting and net-metering structure. Their objectives were to reduce cost, PAR and carbon emission.…”

“…In this work, the appliances are classified in two kinds: smart schedulable appliances (SSA). SSA can operate themselves such as washing machines, dish washer, air conditioners, refrigerator and manually-operated [12] while fixed loads does not incur in bill or PAR reduction. Consider that the SH has two major sets of appliances i.e.…”

Review of a Disruptive Vision of Future Power Grids: A New Path Based on Hybrid AC/DC Grids and Solid-State Transformers

“…In terms of technologies, at the top of the list and with special emphasis, are renewables (mainly solar PV), energy storage systems (mainly batteries and ultra-capacitors), and all aspects of electric mobility that require the power grid for charging purposes. Specifically, contemplating these technologies in future power grids, it is important to establish advanced metering infrastructure and demand-side management strategies (e.g., to deal with the uncertainties of power production from renewables, the consumption profiles of loads in different sectors, and cooperation of variable production of renewables with energy storage [44]), including the participation of buildings, the residential sector, and even data centers, as well as the impact of prediction errors in scenarios of demand-side management [45][46][47][48][49][50]. Specifically, the contextualization of a High Variable Renewable Energy Penetration in hybrid AC/DC grids is presented in [51].…”

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

“…However, the PAR was not addressed. In [21], an efficient heuristic algorithm-based EMC utilizing RESs, and TOUP and Incline Block Rate (IBR) tariffs was developed for DSM. The DSM problem formulated was mapped to reduce the electricity cost, user discomfort, and PAR.…”