Optimal Allocation of Distributed Generation Using Lightning Search Algorithm for Profit Maximization in a Distribution System

This paper aims to address the best possible mathematical model for all components of an energy hub, consisting of major electrical appliances and combined gas as well as solar energy generation systems, boiler units, and electrical energy storage systems, as well as combined heat and power (CHP) systems. This model can make automated decisions to easily fit into smart grid decision technologies such as energy management and automation systems. This mathematical model can control all components of the energy hub in real time to meet the objectives of the problem. It is designed as an energy hub-based model to manage the internal production resources of the microgrid along with network-based carriers (electricity and natural gas) to provide optimal electrical and thermal power. In addition, besides the economic approach, this model also pays special attention to the production of environmental pollutants. In the introduced model, two efficient tools of responsive loads and electrical energy storage sources have been used to lessen the operating cost optimally. On the other side, the generating capacities of wind and solar units, which are a function of solar radiation and wind speed, respectively, are of an indefinite nature. Hence, a probabilistic model is proposed that aims to take into account the uncertainties caused by renewable production sources and responsive loads. Consequently, the microgrid can engage in the energy markets to increase its profitability and reserve and sell energy to end consumers, as well as make optimal use of these resources. A developed LSA (lightning search algorithm) is suggested to solve the issue, which uses a lightning-step leader propagation mechanism. In the first step, the projectile leaders establish the population’s beginning size. The projectiles suggest random solutions for solving the issue by LSA. The simulation outcomes of the suggested model display a rise in energy hub profits, a reduction in the cost of electricity purchased from the power grid, as well as a reduction in operating costs. The integration of renewable energy Resources (RERs) indicated substantial improvements, with a 1.76% increase in expected income, along with a 10.39% reduction in total energy hub costs and a 28.98% reduction in the cost of obtaining electricity from the power grid.