Constrained Optimal Power Flow Problem Research Articles

Application of Covariance Matrix Adaptation‐Evolutionary Strategy for Modified Constrained Optimal Power Flow Problem Incorporating Valve Point and Emission Effect

A prevailing problem in power and energy subsystems is the smooth operation of electric energy systems. This work presents recent, efficient, and reliable evolutionary algorithm for solving the optimal power flow (OPF) analysis. All various practical complex equality and inequality constraints, namely, bus voltages, real powers of the generator buses, tap settings of the transformers and the reactive power generations, shunt compensation, and emission, are considered for the real‐world scenario. Primary feature in a gas power plant that raises a lot of computational shortcomings with nonlinear structure in fuel cost is valve point effect. The existing research works have not factored the valve point effect and lack the accuracy in the fuel cost minimization and do not reflect the various practical complexities such as valve point and emission effects in the OPF problem formulation. This paper, for the first time, introduces modified OPF problem formulation incorporating valve point effect and applies covariance matrix adaptation‐evolution strategy (CMA‐ES) for solving the modified OPF problem. The algorithm is scrutinised and tested on a modified IEEE‐30‐bus platform for various OPF objectives such as cost minimization, transmission loss, and total voltage deviation, subjected to practical constraints. Load flow analysis has been carried out using the Newton–Raphson method. This work aims to lay the foundation in such a way that it can be applicable in a real‐world scenario for any number of buses.

Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.

Application of Sunflower Optimization Algorithm for Solving the Security Constrained Optimal Power Flow Problem

Finding the Optimal Power Flow (OPF) which minimizes total generator cost is the answer to one of the most important problems in the electricity market operation. Independent System Operators (ISO) face many challenges while operating the system in order to obtain economic benefits and security. The solution to this problem is known as Security Constrained Optimal Power Flow (SCOPF). SCOPF is a very large-scale and nonlinear optimization problem with many complex constraints. This paper proposes the Sunflower Optimization (SFO) algorithm for solving the SCOPF problem. The proposed method is tested on the IEEE 30-bus and the IEEE 118-bus systems for both normal and outage cases. The result comparison with other known methods showed that the proposed SFO algorithm is an effective method for solving the SCOPF problem in the electricity market.

Application of Sunflower Optimization Algorithm for Solving the Security Constrained Optimal Power Flow Problem

Application of Sunflower Optimization Algorithm for Solving the Security Constrained Optimal Power Flow Problem

Multi-objective Optimal Power Flow Using Biogeography-based Optimization

This article presents a novel biogeography-based optimization algorithm for solving constrained optimal power flow problems in power systems, considering valve point non-linearities of generators. In this article, the feasibility of the proposed algorithm is demonstrated for 9-bus, 26-bus, and IEEE 118-bus systems with three different objective functions, and it is compared to other well-established population-based optimization techniques. A comparison of simulation results reveals better solution quality and computational efficiency of the proposed algorithm over evolutionary programming, genetic algorithm, and mixed-integer particle swarm optimization for the global optimization of multi-objective constrained optimal power flow problems.

Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function

This paper presents biogeography based optimization (BBO) technique for solving constrained optimal power flow problems in power systems, considering valve point nonlinearities of generators. In this paper, the proposed algorithm has been tested in 9-bus and IEEE 30-bus systems under various simulated conditions. A comparison of simulation results reveals optimization efficacy of the proposed scheme over evolutionary programming (EP), genetic algorithm (GA), particle swarm optimization (PSO), mixed-integer particle swarm optimization (MIPSO) and sequential quadratic programming (SQP) used in MATPOWER for the global optimization of multi-constraint OPF problems.

Optimal power flow using biogeography based optimisation

This paper presents a novel biogeography based optimisation (BBO) algorithm for solving constrained optimal power flow (OPF) problems in power system. In this paper, the feasibility of the proposed algorithm is demonstrated for IEEE 30-bus and IEEE 118-bus systems with three different objective functions and it is compared to other population based optimisation techniques. A comparison of simulation results reveals better solution quality and computation efficiency of the proposed algorithm over hybrid particle swarm optimisation with constriction factor approach (PSOCFA), hybrid particle swarm optimisation with inertia weight approach (PSOIWA), real coded genetic algorithm (RGA) and differential evolution (DE) for the global optimisation of constrained OPF problems.

Improved genetic algorithm for voltage security constrained optimal power flow problem

Voltage stability has become an important issue in the planning and operation of many power systems. Contingencies such as unexpected line outages in a stressed system may often result in voltage instability which may lead to voltage collapse. This paper presents a Genetic Algorithm (GA) approach for solving the Voltage Security Constrained Optimal Power Flow (VSC-OPF) problem. Base-case generator power output, voltage magnitude of generator buses, transformer tap position and reactive power generation of capacitor banks are taken as the control variables. Maximum L-index of load buses is used to specify the voltage stability level of the system. An improved GA which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimisation problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point numbers and integers are used. The proposed approach has been evaluated on the IEEE 30-bus test system. Simulation results show the effectiveness of the proposed approach for improving the voltage security of the system.