Efficient Integration of PV Sources in Distribution Networks to Reduce Annual Investment and Operating Costs Using the Modified Arithmetic Optimization Algorithm

Abstract

The optimal integration of photovoltaic generation systems is a challenge for distribution utilities since these devices have a direct impact on company finances due to the large amount of investment required at the beginning of the planning project. In this investigation, the problem regarding the optimal siting and sizing of photovoltaic resources in medium-voltage levels is addressed from an economical point of view, where the optimization model that represents said problem corresponds to a mixed-integer nonlinear programming model. The maximum allowed size for single photovoltaic units in the distribution network is set at 2400 kW. The investment costs, energy purchase costs and maintenance costs for photovoltaic units, are considered in the objective function. Typical constraints such as power balance, generation capacities, voltage regulation, among others, are considered in the mathematical formulation. The solution of the optimization model is addressed by implementing a modified version of the Arithmetic Optimization Algorithm, which includes a new exploration and exploitation characteristic based on the best current solution in iteration t, i.e., xbestt. This improvement is based on a Gaussian distribution operator that generates new candidate solutions with the center at xbestt, which are uniformly distributed. The main contribution of this research is the proposal of a new hybrid optimization algorithm to solve the exact optimization model, which is based on a combination of the Arithmetic Optimization algorithm with the Vortex Search algorithm and showed excellent numerical results in the IEEE 34-bus grid. The analysis of quantitative results allows us to conclude that the strategy proposed in this work has a greater effectiveness with respect to the General Algebraic Modeling System software solvers, as well as with metaheuristic optimizers such as Genetic Algorithms, the Newton–Metaheuristic Algorithm, and the original Arithmetic Optimization Algorithm. MATLAB was used as a simulation tool.

Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response

“…References [5][6][7] identified the optimal locations for PV and WT, but the shortcoming in these studies is the lack of consideration for uncertainty and DSR. 2.…”

An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation

“…Numerical results in the IEEE 33-and IEEE 69-bus networks demonstrated the superior performance of the DCVSA when compared to the BONMIN and the CBGA. Additional works that have addressed the problem regarding the optimal siting and sizing of PV sources in distribution networks include the Modified Arithmetic Optimization Algorithm (MAOA) [32], the Generalized Normal Distribution Optimization (GNDO) Algorithm [33], and the Tabu Search Algorithm [34], among others.…”

“…To compare the effectiveness and robustness of the proposed MIC model in determining the optimal PV sizes and locations, as well as their daily dispatch, multiple metaheuristic algorithms available in the current literature were used, namely (i) the Chu and Beasley Genetic Algorithm [26], (ii) the Newton Metaheuristic Algorithm (NMA) [27], (iii) the Modified Gradient-Based Metaheuristic Optimizer (MGbMO) [29], (iv) Generalized Normal Distribution Optimization (GNDO) [33], (v) the Modified Arithmetic Optimization Algorithm (MAOA) [32], and (vi) the Discrete-Continuous Vortex Search Algorithm (DCVSA) [31].…”

Research on optimization method of investment decision for medium and low voltage distribution network project based on genetic algorithm

“…In the past, the investment decision-making process of distribution network construction projects mostly took physical attributes such as region and voltage level as the decision-making basis, took a single project as the decision-making goal, and obtained the distribution network construction projects that met the requirements one by one through the repeated decision-making process. Although these methods differ in the specific decision-making process, their essence is a repeated decision-making process with a single project as the goal [6] . As there are many types of projects in the original power grid infrastructure project library, this section first combs the projects and establishes the project reserve library.…”