An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

Modified effective butterfly optimizer for solving optimal power flow problem

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies

This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.

A Hybrid Firefly–JAYA Algorithm for the Optimal Power Flow Problem Considering Wind and Solar Power Generations

Optimal power flow (OPF) is widely used in power systems. This problem involves adjusting variables such as online capacity, generator output, power stability, and bus voltage to reduce production costs. This paper presents HFAJAYA, a combined evolution method using the Firefly and JAYA algorithms to solve the OPF problem effectively and efficiently. While considering renewable energy, including solar energy and wind energy systems, the problem is regarded as a single-objective and multi-objective function. It considers power losses, emissions, emissions taxes, the total cost of fuel, and voltage deviation as objective functions of the problem. I have successfully implemented all simulations with different scenarios on a standard 30-bus IEEE network. A comparison of the results obtained from the HFAJAYA simulation with results from other well-known works has been undertaken to confirm the efficiency of the recommended HFAJAYA method.

Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis

A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorporated. The results generated by the SpDEA are investigated and compared to standard multi-objective differential evolution (MODE) to prove their viability. The best answer is chosen carefully among trade-off Pareto points by using the technique of fuzzy Pareto solution. Two power system networks such as IEEE 30-bus and 118-bus systems as large-scale optimization problems with 129 design control variables are utilized to point out the effectiveness of the SpDEA. The realized results among many independent runs indicate the robustness of the SpDEA-based approach on OPF methodology in optimizing the defined objectives simultaneously.

Optimal Allocation of Static Var Compensators in Electric Power Systems

In the current age, power systems contain many modern elements, one example being Flexible AC Transmission System (FACTS) devices, which play an important role in enhancing the static and dynamic performance of the systems. However, due to the high costs of FACTS devices, the location, type, and value of the reactive power of these devices must be optimized to maximize their resulting benefits. In this paper, the problem of optimal power flow for the minimization of power losses is considered for a power system with or without a FACTS controller, such as a Static Var Compensator (SVC) device The impact of location and SVC reactive power values on power system losses are considered in power systems with and without the presence of wind power. Furthermore, constant and variable load are considered. The mentioned investigation is realized on both IEEE 9 and IEEE 30 test bus systems. Optimal SVC allocation are performed in program GAMS using CONOPT solver. For constant load data, the obtained results of an optimal SVC allocation and the minimal value of power losses are compared with known solutions from the literature. It is shown that the CONOPT solver is useful for finding the optimal location of SVC devices in a power system with or without the presence of wind energy. The comparison of results obtained using CONOPT solver and four metaheuristic method for minimization of power system losses are also investigated and presented.

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Known as a multi-objective, large-scale, and complicated optimization problem, the multi-objective optimal power flow (MOOPF) problem tends to be introduced with many constraints. In this paper, compared with the frequently-used penalty function-based method (PFA), a novel constraint processing approach named the constraints-prior Pareto-domination approach (CPA) is proposed for ensuring non-violation of various inequality constraints on dependent variables by introducing the Pareto-domination principle based on the sum of constraint violations. Moreover, for solving the constrained MOOPF problem, the multi-objective firefly algorithm with CPA (MOFA-CPA) is proposed and some optimization strategies, such as the crowding distance calculation and non-dominated sorting based on the presented CPA, are utilized to sustain well-distributed Pareto front (PF). Finally, in order to demonstrate the feasible and effective improvement of MOFA-CPA, a comparison study between MOFA-CPA and MOFA-PFA is performed on two test systems, including three bi-objective optimization cases and three tri-objective optimization cases. The simulation results demonstrate the capability of the MOFA-CPA for obtaining PF with good distribution and superiority of the proposed CPA for dealing with inequality constraints on dependent variables. In addition, some quality indicators are used to evaluate the convergence, distribution, and uniformity of the PFs found by the MOFA-CPA and MOFA-PFA.