Distributed optimization for economic power dispatch with event‐triggered communication

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.

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

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

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>

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

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.

The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties of generators such as valve point effects (VPE), prohibited operating zones (POZ), ramp rate limits, multiple fuel options, and transmission loss. The WMA algorithm is a novel metaheuristic algorithm inspired by the mating behavior of woodpeckers and sound intensity (a physical quantity). The WMA is implemented on six test systems of different operational dimensions and characteristics to show its capacity for solving the ED problem. The results are compared with the latest and most efficient methods introduced in the literature. Proving the efficiency of the WMA to solve the ED problem, simulation results are promising and offer the optimal fuel cost of production.

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.

Synergic predator-prey optimization for economic thermal power dispatch problem

Solving non-convex economic load dispatch problem via artificial cooperative search algorithm