Combined heat and power economic dispatch problem using gravitational search algorithm

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

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.

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

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

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

The paper presents a new, integrated genetic tabu search algorithm based technique to solve the combined heat and power economic dispatch (CHPED) problem. The proposed method is developed in such a way that a simple GA acts as a base level search. to direct the search towards the optimal region and local searches synergistically combined with tabu search is next employed to fine tune the search to reach the optimal solution. The constraint satisfaction technique has been applied to reduce the computation time and improve the quality of the solution. The proposed method is fast, efficient and applicable to all types of non-linear and discontinuous objective functions. In order to prove the validity and effectiveness of the proposed algorithm, a test system with four units is considered. Among the four units, the first unit is a conventional power unit, the second and third units are cogeneration units and the fourth is a heat only unit. When the results are compared with those of other methods, it is clear that the proposed method is practical and valid for real time applications.

Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm

A new algorithm such as biogeography based optimization is proposed to solve combined heat and power economic dispatch (CHPED) problem by considering some linearities as well as non-linearities constraints. The main purpose of solution of CHPED is optimal power and heat allocation satisfying load demand within operating limits such that the combined cost is minimum. This algorithm is based on genetic biological allocation of species. The proposed algorithm has been applied on a seven unit test systems. The convergence characteristics are checked with other techniques which prove the superioty properties of the proposed BBO algorithms.

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

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.

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.

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

Chaotic whale optimization algorithm for optimal solution of combined heat and power economic dispatch problem incorporating wind

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

Combined heat and power economic dispatch (CHPED) is an energy management problem that minimizes the operation cost of power and heat generation while a vast variety of operational constraints of the system should be met. The CHPED is a complicated, non-convex and non-linear problem. In this study, a new real-coded genetic algorithm with random walk-based mutation (RCGA-CRWM) is under study, which is effective in solving large-scale CHPED problem with minimum operation cost. In the presented optimization method, a simple approach is introduced to combine the positive features of different probabilistic distributions for the step size of random walk. Using the presented approach, while the genetic algorithm is speeded up, the premature convergence is also avoided. After verifying the performance of the presented method on the benchmark functions, two large-scale and two medium-scale case studies are used for determining the algorithm strength in solving the CHPED problem. Despite the fact that the complexity of the CHPED rises dramatically by increasing its dimensionality, the algorithm has solved the problems accurately. The application of RCGA-CRWM method improves the results of the CHPED problem in terms of both operation cost and convergence speed in comparison with other optimization methods.