A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch

Stochastic Hopfield artificial neural network for unit commitment and economic power dispatch

This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.

The economic power dispatch (EPD) is an integral part of the power system, and the major roles and purpose of its use are for achieving a reliable and efficient operation out of power system generation networks, and this operation should be obtained by minimizing the generator fuel cost. Getting optimal solutions to EPD problem requires efficient optimization algorithms. Novel Bat Algorithm (NBA) is one of the most recent methods and it has already proven its efficiency and reliability for solving the EPD problem. This paper proposes Novel Bat Algorithm (NBA) in order to solve the EPD problem based on the large scale power system. The NBA has proved its efficiency and it gave a perfect performance for small-scale systems compared with the original Bat algorithm (BA), because of considering the Doppler Effect and assumed that bat can move between various habitats. The study of the EPD for large-scale power systems is more important than study it for small ones, because all power systems in countries are considered large networks. This is why all modern studies focus on the study of EPD for large systems. To test the performance of NBA during small and large scale power system, we have applied it to many systems, including 3-thermal units, 6-thermal units, 31-Iraqi thermal units and IEEE 40-thermal units respectively, with transmission losses and generator limits, and we have made a comparison of the obtained results of NBA with other optimization methods. The Iraqi thermal units have been utilized as a new data.

This paper presents a Particle Swarm Optimization (PSO) algorithm for Economic Load Dispatch (ELD) problem. The PSO, which is based on the analogy of swarms of birds, is a very robust and efficient algorithm. A swarm of particles are initiated and fly through the solution space, to find the optimal solution. The PSO algorithm belongs to Metaheuristic Optimization techniques. To show its efficiency and effectiveness, the proposed Particle Swarm Optimization (PSO) algorithm is applied to sample ELD problems composed of 4 generators.

The paper presents a stochastic Hopfield artificial neural network for unit commitment and economic power dispatch. Because of uncertainties in both the system load demand and unit availability, the unit commitment and economic power dispatch problem is stochastic, in this paper we model forced unit outages as independent Markov processes, and load demand as a normal Gaussian random variable. The (0,1) unit commitment-status variables and the hourly unit loading are modelled as sample functions of appropriate random processes. They are solutions of appropriately derived stochastic differential equations which describe the dynamics of a stochastic system for which the operating cost function is a stochastic Lyapunov function. Once the unit commitment and economic power dispatch have been done, the corresponding production costs are computed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Combined heat and power economic dispatch by mesh adaptive direct search algorithm

Economical dispatch of power is a significant real time optimization problem in the operation of power system, which allocates the loads on the committed generating units to reduce the generation cost, while meeting all the inequality and equality constraints. Traditionally, economic load dispatch problems have been formulated as convex optimization problem and various classical methods such as gradient methods, base-point participation factor method, etc. have been used for solving them. The traditional formulation is a quadratic input-output curve or piece wise linear cost curve, which are continuous and convex, however, real generating units have valve loading effects, which make the cost curve discontinuous and non-convex and hence the economic dispatch problem is no longer simple to handle. Thus, the classical methods cannot be employed for non-convex problems. The non-convex, non-linear and discontinuous economic dispatch (ED) problem can be better solved by metaheuristic approaches. Such approaches often require tuning of their respective evolutionary parameters. Although, solution to such type of problem have already been solved by different metaheuristic optimization techniques in previous studies as well, however, the algorithm employed in this paper to solve Power Economic Load Dispatch (PELD) problem is adaptive real coded quantum-inspired evolutionary algorithm, which does not require tuning of evolutionary parameters and performs better than some of the existing techniques on standard 6-bus 3 machine system and IEEE 14 bus 5 unit system.

Multiobjective multi-verse optimization algorithm to solve combined economic, heat and power emission dispatch problems

Combined heat and power economic dispatch problem using gravitational search algorithm

Neighborhood-based differential evolution algorithm with direction induced strategy for the large-scale combined heat and power economic dispatch problem

Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm

Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy

This article presents differential evolution to solve the combined heat and power economic dispatch problem. Differential evolution, an improved version of the genetic algorithm and evolutionary programming, is a very simple, fast, and robust global optimization technique. The proposed algorithm is illustrated for a test system, and the test results are compared with those obtained from particle swarm optimization and evolutionary programming.

Improved particle swarm optimization algorithm with harmony search (IHPSO) is proposed in this paper. This algorithm takes particle swarm search direction estimation mechanism and harmony search (HS) approach to particle swarm optimization (PSO) algorithm, which increases the search capability of PSO algorithm considerably. The proposed algorithm initializes a new search with harmony pitch adjusting or random selection when PSO search direction is estimated incorrectly. This can provide further opportunities of finding better solutions for the particle swarm by guiding the entire particle swarm to promising new regions of the search space and accelerating the search. PSO, HPSO and IHPSO, as well as other advanced PSO procedures from the literature were compared on several benchmark test functions extensively. Statistical analyses of the experimental results indicate that the performance of IHPSO is better than the performance of PSO and HPSO.

Adaptive equalization mitigates the distortions caused by radio channels. The least mean square (LMS) and the recursive least squares (RLS) algorithms are used for such purpose. Recently, particle swarm optimization (PSO) algorithms such as PSO using a linear time decreasing inertia weight (PSO-W) and the PSO using constant constriction factor (PSO-CCF) were shown to be very effective in handling systems having nonlinear behavior. However, these algorithms can be trapped in local minima. This paper presents a new PSO-based algorithm called the hybrid PSO (HPSO) that is capable to handle such problems. The HPSO includes the randomization of particles to improve the search capacity of the swarm, which in turn reduces the probability of being trapped in some local minima. It also adapts the inertia weight assignment to the particles. Extensive simulation results are conducted to confirm the consistency in the performance of the HPSO algorithm in different scenarios. The proposed HPSO secures the minimum steady-state error as compared to LMS and other PSO-based algorithms in both nonlinear and linear channels. Finally, the proposed HPSO algorithm shows a great improvements in Bit Error Rate and convergence rate.