A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services

Abstract

Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a management system for a residential virtual power plant that includes household loads, photovoltaic generation, energy storage systems, and electric vehicles. With the proposed management system, the virtual power plant is economically optimized (as in commercial virtual power plants) while providing ancillary services (as in technical virtual power plants) to the distribution grid. A genetic algorithm with appropriate constraints is designed and tested to manage the energy storage system and the charge/discharge of electric vehicles, with several economic and technical objectives. Single-objective optimization techniques are compared to multi-objective ones to show that the former perform better in the studied scenarios. A deterministic gradient-based optimization method is also used to validate the performance of the genetic algorithm. The results show that these technical targets (usually reserved for larger virtual power plants) and economic targets can be easily managed in restricted-sized virtual power plants.

3D Vase Design Based on Interactive Genetic Algorithm and Enhanced XGBoost Model

“…If fitness is better than the global best Then (10) Update the global best hyperparameters (11) end ( 12) end ( 13) end (14) For particle in population do (15) Update particle velocity and position using the PSO formula ( 16) end (17) end (18)…”

“…If generation > interactive_generation && is_adjust() Then (7) adjust(population[generation]) (8) end (9) evaluate(population[generation]) (10) chromosomes = encode(population[generation]) (11) While(population[generation+1].size() < population_size) do (12) pairs = select(chromosomes) (13) If rand() < crossover_rate Then (14) pairs = crossover(pairs) (15) end…”

“…The GA involves the optimization of target systems, models, and performance in multiple fields. In recent years, the GA has been applied to effective feature selection for IoT botnet attack detection [6], active disturbance rejection control of bearingless permanent magnet synchronous motors [7], gesture recognition CAPTCHA [8], state-ofcharge estimation of lithium-ion batteries [9], and residential virtual power plants [10]. The application of the GA in these areas has become increasingly widespread, with more and more researchers applying the GA to solve various practical problems in their own fields.…”