Power System Economic Load Dispatch Research Articles

Optimizing ELD in power systems applying GWO: A Practical Approach

Addressing the Economic load Dispatch (ELD) Problem in power systems is crucial for minimizing generation cost and transmission losses while meeting the load demand. This research explores the application of Grey Wolf Optimization (GWO) to solve the ELD problem, leveraging ‘GWO’s inspiration from grey wolf social behavior. Through simulation, ‘GWO’s superior convergence speed and solution quality compared to traditional techniques is demonstrated. The finding highlights ‘GWO’s effectiveness in enhancing the economic and operational efficiency of power systems, offering promising avenues for sustainable energy management strategies.

Golden jackal optimization for economic load dispatch problems with complex constraints

This research paper uses the golden jackal optimization (GJO), a novel meta-heuristic algorithm, to address power system economic load dispatch (ELD) problems. The GJO emulates the hunting behavior of golden jackals. GJO algorithm uses the cooperative attacking behavior of golden jackals to tackle complicated optimization problems efficaciously. The objective of ELD problem is to distribute power system load requirement to the different generators with a minimum total fuel cost of generation. ELD problems are highly complex, non-linear, and non-convex optimization problems while considering constraints namely valve point loading effect (VPL) and prohibited operating zones (POZs). The proposed GJO algorithm is applied to solve complex, non-linear, and non-convex ELD problems. Six different test systems having 6, 10, 13, 40, and 140 generators with various constraints are used to validate the usefulness of the suggested GJO method. Simulation outcomes of the test system are compared with various algorithms reported in the algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and backtracking search algorithm (BSA). Results show that the proposed GJO algorithm produces minimal fuel cost and has good convergence in solving ELD problems of power system engineering.

A behavior-selection based Rao algorithm and its applications to power system economic load dispatch problems

A behavior-selection based Rao algorithm and its applications to power system economic load dispatch problems

A multigroup marine predator algorithm and its application for the power system economic load dispatch

AbstractMarine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.

An Efficient New Hybrid ICA-PSO Approach for Solving Large Scale Non-convex Multi Area Economic Dispatch Problems

Multi-area economic dispatch (MAED) is one of the vital problems in economic operation of interconnected power systems. This paper proposes a novel hybrid approach based on combined imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) methods in order to determine the feasible optimal solution of the non-convex economic dispatch (ED) problem considering valve loading effects. In the proposed algorithm we have defined new type of countries in ICA algorithm, namely independent countries. These types of countries improve their position using a PSO based search strategy. The proposed method benefits from the advantage of the both algorithms. The proposed hybrid approach based on ICA-PSO is applied on different test systems and compared with most of the recent methodologies. Also, a large scale multi-area economic dispatch (MAED) problem is solved using the proposed hybrid approach to minimize total fuel cost in all areas while satisfying power balance constraints, generating limits and tie-line capacity constraints. The results show the effectiveness of the proposed approach and prove that ICA-PSO is applicable for solving the power system economic load dispatch problem, especially in large scale power systems.

A review on application of various heuristic techniques to combined economic and emission dispatch in a modern power system scenario

In current scenario electricity market is like a commodity market, the electric energy must be economic as well as reliable. For the economic operation in power system economic load dispatch plays an important factor. As most of the generation is based on fossil fuel, it emits the oxides of Nitrogen (NOx), Sulphur (SOx) and Carbon (CO2). To improve the economic operation and minimize the adverse environmental impact of the fossil fuel based systems, combined economic and emission dispatch has been suggested by many authors. This paper provides review of various authors around the world suggesting different technique applied to the combined economic operation and environmental impact as a multi-objective optimization problem. Various optimization techniques have been proposed to apply such as Genetic algorithm (GA), Evolutionary Programming (EP), particle swarm optimization (PSO), Artificial Bee Colony Algorithm (ABC), Differential evolution (DE), Backtracking Search Optimization (BSO), Flower Pollination Algorithm (FPA) and etc.

Proposed Differential Evolution Technique for Solving the Power System Economic Load Dispatch Problem

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. In This paper presents an application of the Differential Evolution optimization algorithm to power system economic load dispatch problem with ramp rate limit for 15 unit test case system. Power system Economic load dispatch problems are applied and compared its solution quality and computation efficiency to Genetic algorithm (GA) and Differential Evolution optimization algorithm.

Harmony Search Algorithm with Opposition-Based Learning for Power System Economic Load Dispatch

A harmony search algorithm with opposition-based learning techniques (HS-OBL) to solve power system economic load dispatch has been presented. The proposed algorithm integrates the opposition-based learning operation with the improvisation process to prevent the HS-OBL algorithm from being trapped into the local optimum effectively. Besides, a new adjusting strategy is designed to dynamic adjust pitch adjusting rate (PAR) and harmony memory consideration rate (HMCR), which is to further improve the performance of algorithm. The HS-OBL is employed to solve 6 units and 13 units power system, the numerical results indicate that the HS-OBL has perform much better than harmony search(HS) algorithm and other improved algorithms that reported in recent literature.

Hybrid imperialist competitive-sequential quadratic programming (HIC-SQP) algorithm for solving economic load dispatch with incorporating stochastic wind power: A comparative study on heuristic optimization techniques

The paper presents a new method based on a hybrid algorithm consisting of imperialist competitive algorithm (ICA) and sequential quadratic programming (SQP) technique to solve the power system economic load dispatch (ELD) problem. ICA could be taken into account as a powerful technique. Nevertheless, it may be trapped in local optima especially when numbers of imperialists increase. To alleviate this drawback, SQP is used to fine-tune the results of ICA to increase confidence in the solution. Renewable sources and wind energy especially have recently been getting more interest because of various environmental and economical considerations. So, wind power is included in the problem formulation. The incomplete gamma function (IGF) is used to characterize the impact of wind power. The hybrid imperialist competitive algorithm (ICA) and sequential quadratic programming (SQP) technique (HIC-SQP) is applied to solve economic load dispatch with incorporating stochastic wind power. To evaluate its effectiveness, the proposed method is tested on various power systems with 6, 13, 15, and 40 power plants with and without wind power. Simulation results of proposed method are compared with state-of-the-art heuristic optimization methods. It can be clearly seen that the proposed method improves the solution of ELD problem.

Opposition-Based Modified Differential Evolution Algorithm for Power System Economic Load Dispatch

Opposition-based modified differential evolution algorithm (OMDE) is proposed for solving power System economic load dispatch in this paper. This algorithm integrates the opposition-based learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on 6 units and 13 units power system experiment simulations, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other improved algorithms that reported in recent literature.

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.

A SOLUTION TO AN ECONOMIC DISPATCH PROBLEM BY A FUZZY ADAPTIVE GENETIC ALGORITHM

In practice, obtaining the global optimum for the economic dis- patch (ED) problem with ramp rate limits and prohibited operating zones is presents diculties. This paper presents a new and ecient method for solving the economic dispatch problem with non-smooth cost functions using a Fuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm deals with the issue of controlling the exploration and exploitation capabilities of a heuristic search algorithm in which the real version of Genetic Algorithm (RGA) is equipped with a Fuzzy Logic Controller (FLC) which can eciently explore and exploit optimum solutions. To validate the results obtained by the proposed FAGA, it is compared with a Real Genetic Algorithm (RGA). Moreover, the results obtained by FAGA and RGA are also compared with those obtained by other approaches reported in the literature. It was observed that the FAGA outperforms the other methods in solving the power system economic load dispatch problem in terms of quality, as well as convergence and success rates.

Solution of Different Types of Economic Load Dispatch Problems Using a Pattern Search Method

Direct search (DS) methods are evolutionary algorithms used to solve constrained optimization problems. DS methods do not require information about the gradient of the objective function when searching for an optimum solution. One such method is a pattern search (PS) algorithm. This study presents a new approach based on a constrained PS algorithm to solve various types of power system economic load dispatch (ELD) problems. These problems include economic dispatch with valve point (EDVP) effects, multi-area economic load dispatch (MAED), companied economic-environmental dispatch (CEED), and cubic cost function economic dispatch (QCFED). For illustrative purposes, the proposed PS technique has been applied to each of the above dispatch problems to validate its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method has been assessed and investigated through comparison with results reported in literature. The outcome is very encouraging and suggests that PS methods may be very efficient when solving power system ELD problems.

Application of pattern search method to power system valve-point economic load dispatch

Direct search (DS) methods are evolutionary algorithms used to solve constrained optimization problems. DS methods do not require any information about the gradient of the objective function at hand, while searching for an optimum solution. One of such methods is pattern search (PS) algorithm. This study presents a new approach based on a constrained pattern search algorithm to solve well-known power system economic load dispatch problem (ELD) with valve-point effect. For illustrative purposes, the proposed PS technique has been applied to various test systems to validate its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method has been assessed and investigated through comparison with results reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving power system economic load dispatch problem.

An enhanced Lagrangian neural network for the ELD problems with piecewise quadratic cost functions and nonlinear constraints

This paper presents a new Lagrangian artificial neural network (ANN) and its application to the power system economic load dispatch (ELD) problems with piecewise quadratic cost functions (PQCFs) and nonlinear constraints. By restructuring the dynamics of the modified Lagrangian ANN [IEEE ICNN, 1 (1996) 537], stable convergence characteristics are obtained even with the nonlinear constraints. The convergence speeds are enhanced by employing the momentum technique and providing a criteria for choosing the learning rate parameters. Instead of having one convex cost function for each unit, which is normally the case in typical ELD problem formulations, more realistic multiple quadratic cost functions are used to reflect the effects of valve point loadings and possible fuel changes. In addition, the B matrix approach is employed for more accurate estimation of the transmission losses than treating them as a constant, which necessitate the inclusion of a nonlinear equality constraint. The effectiveness of the proposed ANN applied to the ELD problem is demonstrated through extensive simulation tests.

Economic emission load dispatch with line flow constraints using a classical technique

An attempt is made to explore the feasibility of developing an approach to solve the power system economic emission load dispatch (EELD) problem with line flow constraints using a classical technique based on co-ordination equations. The inability of the classical technique to handle the line flow constraints so far is circumvented innovatively by expressing the line flows in terms of active power generations through distribution factors. These distribution factors are elegantly developed from existing load flow information using a perturbation technique. The proposed model based on the classical technique for EELD is tested on IEEE 14- and 30-bus test systems and the results are compared with those obtained by quadratic programming, the Hessian method, the Ricochet gradient method and the linear programming method.