Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Abstract

An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

“…These platforms are used for collecting the data from distributed or remote meters along with sensors through the network. Via injecting false meter data as a cyberattack, the DMS or EMS functions at the control center will be deceived through the state estimators that can make power dispatch, inaccurate decisions on contingency analysis, and even billing transactions [55], [56].…”

“…Tricking a utility company's billing system [40], [56], [61], [62], [88], [96], [122] Tampering the smart meters to reduce the electricity bill…”

“…Reducing the utility bill to deceive the utility [40], [56], [61], [96], [122] Losing the financial The physical threats aim at the power system components, e.g., transmission lines, generators, and transformers, that can change the power system's topology or trigger cascading failures [114]- [116]. As the transmission lines can spread over a large area, it is easy to be attacked than the substations.…”

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

“…Such an approach is rather important because previous studies have primarily dealt with improving the performance of single systems separately (eg, ESS or PV) which is likely suboptimal. [26][27][28][29][30][31][32][33][34] The system working principle is made of two modules: a forecasting module and a scheduling module. The forecasting module uses real-time weather information and is composed of two deep neural networks (DNN), forecasting models for EHP energy consumption and PV energy generation.…”

An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

“…In this research, the authors used GA, FPA, and the combination (hybrid) of these two techniques, the genetic-flower pollination algorithm (GFPA). Similarly, HEMS focuses on scheduling home appliances in such a way to reduce the peak load, as a result, it reduces electricity cost, PAR, optimizes user comfort as well as the time of execution [23]. The time of use (TOU) concept was used to obtain the required results; moreover, the proposed techniques are GA, biogeography-based optimization (BBO).…”