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

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.

Combined heat and power economic dispatch problem using gravitational search algorithm

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 (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.

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

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

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.

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

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

Combined heat and power economic dispatch (CHPED) is one of the foremost subjects in the operation of power systems. In this article, a new variant of cuckoo search (CS) algorithm, elitist CS (yECS), is advanced to tackle CHPED. During the optimisation process of the original CS as well as lots of its variants, the guidance of search directions relies merely upon the best individual, causing the loss of other beneficial information and further influencing their performance potentials. Therefore, in yECS, an elitist mechanism is developed to fully utilise the beneficial information of other elite individuals. Specifically, three new iterative strategies are developed, one for the global search phase and the other two for the local search phase. Further, a coordinated mechanism is put forward to effectively integrate the local iterative strategies, thus helping yECS in maintaining an appropriate balance between exploitation and exploration. The superior performance of yECS is firstly substantiated via CEC 2017 test suite and two engineering design problems and then it is utilised to address CHPED problems. All optimal dispatch results acquired by yECS are feasible and in most cases display remarkable improvements over the results determined by other CS variants and some recently-published literature results.

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 optimal generation scheduling of combined heat and power units aims to minimize total cost of generation units satisfying power and heat demands.The combined heat and power economic dispatch problem should be solved considering a series of electrical and operational constraints, which will be more complicated considering valve-point effects of power-only units and nonconvex feasible operating regions of cogeneration units. In such a case, the problem has nonconvex and nondifferential characteristic. In this article, a two-stage model is proposed to handle the nonconvexity and nondifferentiability of valve-point effects in cost functions of power-only units. The proposed model obtains a convex feasible operating region in the first stage using a linear approximation model. In addition, an equivalent formulation is proposed in the second stage to handle the nondifferentiable term of valve-point effects using the obtained convex feasible operating regions. The proposed model is implemented on two case studies to evaluate performance of the proposed model.

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.