Operation control, energy management, and power quality enhancement for a cluster of isolated microgrids

Assessment of charging technologies, infrastructure and charging station recommendation schemes of electric vehicles: A review

This paper proposes an economical-environmental-technical dispatch (EETD) model for adjusted IEEE 30-bus and IEEE 57-bus systems, including thermal and high penetration of renewable energy sources (RESs). Total fuel costs, emissions level, power losses, voltage deviation, and voltage stability are the five objectives addressed in this work. A large set of equality and inequality constraints are included in the problem formulation. Metaheuristic optimization approaches-Coronavirus herd immunity optimizer (CHIO), salp swarm algorithm (SSA), and ant lion optimizer (ALO)-are used to identify the optimal cost of generation, emissions, voltage deviation, losses, and voltage stability solutions. Several scenarios are reviewed to validate the problem-solving competency of the defined optimisation model. Numerous scenarios are studied to verify the proficiency of the optimisation model in problem-solving. The multi-objective problem is converted into a normalized one-objective issue through a weighted sum-approach utilizing the analytical hierarchy process (AHP). Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) is presented for identifying the optimal value of Pareto alternatives. Ultimately, the results achieved reveal that the proposed CHIO performs the other approaches in the EETD problem-solving.

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Economical-Environmental-Technical Operation of Power Networks with High Penetration of Renewable Energy Systems Using Multi-Objective Coronavirus Herd Immunity Algorithm

Consensus-Based Dispatch Optimization of a Microgrid Considering Meta-Heuristic-Based Demand Response Scheduling and Network Packet Loss Characterization

Temperature prediction for electric vehicles of permanent magnet synchronous motor using robust machine learning tools

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Energy management of a microgrid using demand response strategy including renewable uncertainties

Demand Side Load Management by Using Priority Based Load Shedding Algorithm with and without Renewable Energy Generation

Optimal Sizing and Energy Management of a Microgrid Using Single and Multi-Objective Particle Swarm Optimization under Autonomous and Grid Connected Mode

Rajarajeswari Rathinam

Papers

A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Energy management of a microgrid using demand response strategy including renewable uncertainties

Demand Side Load Management by Using Priority Based Load Shedding Algorithm with and without Renewable Energy Generation

Optimal Sizing and Energy Management of a Microgrid Using Single and Multi-Objective Particle Swarm Optimization under Autonomous and Grid Connected Mode