Energy Commitment for a Power System Supplied by Multiple Energy Carriers System using Following Optimization Algorithm

Abstract

In today’s world, the development and continuation of life require energy. Supplying this energy demand requires careful and scientific planning of the energy provided by a variety of products, such as oil, gas, coal, electricity, etc. A new study on the operation of energy carriers called Energy Commitment (EC) is proposed. The purpose of the EC is to set a pattern for the use of energy carriers to supply energy demand, considering technical and economic constraints. EC is a constrained optimization problem that can be solved by using optimization methods. This study suggests the Following Optimization Algorithm (FOA) to solve the EC problem to achieve technical and economic benefits. Minimizing energy supply costs for the total study period is considered as an objective function. The FOA simulates social relationships among the community members who try to improve their community by following each other. Simulation is carried out on a 10-unit energy system supplied by various types of energy carriers that includes transportation, agriculture, industrial, residential, commercial, and public sectors. The results show that the optimal energy supply for a grid with 0.15447 Millions of Barrels of Oil Equivalent (MBOE) of energy demand costs 9.0922 millions dollar for a 24-h study period. However, if the energy supply is not optimal, the costs of operating energy carriers will increase and move away from the optimal economic situation. The economic distribution of electrical demand between 10 power plants and the amount of production units per hour of the study period is determined. The EC outputs are presented, which include an appropriate pattern of energy carrier utilization, energy demand supply costs, appropriate combination of units, and power plant production. The behavior and process of achieving the answer in the convergence curve for the implementation of FOA on EC indicates the exploration and exploitation capacity of FOA. Based on the simulated results, EC provides more information than Unit Commitment (UC) and analyzes the network more efficiently and deeply.

MLBO: Mixed Leader Based Optimizer for Solving Optimization Problems

“…In this regard, many population-based optimization algorithms are developed by researchers with the aim of more appropriate quasi-optimal solutions and closer to the global optimal. In this regard, optimization algorithms have been applied by scientists in various fields such as operation of the electricity network [6,7], energy [8,9], energy commitment [10,11], protection [12], placement of electrical equipment [13][14][15], and energy carriers [16,17] to achieve the optimal solution.…”

RSLBO: Random Selected Leader Based Optimizer

“…This has led to the introduction of various optimization algorithms by researchers in order to provide quasi-optimal solutions. In addition, optimization algorithms have been applied in various fields in the literature such as energy [8][9][10][11], protection [12], electrical engineering [13][14][15][16][17][18], and energy carriers [19,20] to achieve the optimal solution. Innovation and scientific contribution of this paper in the design of a new optimizer called Random Selected Leader Based Optimizer (RSLBO).…”

GBUO: “The Good, the Bad, and the Ugly” Optimizer

“…The main goal is to maximize the efficiency, profit, and performance of the problem. In this regard, different optimization algorithms have been applied in various fields such as energy [1,2], protection [3], energy commitment [4], electrical engineering [5][6][7][8][9], and energy carriers [10,11] to achieve the optimal solution.…”