Multi‐objective energy management approach in distribution grid integrated with energy storage units considering the demand response program

This paper presents a multiple hybrid methods combining the lambda iteration and simulated annealing methods (MHLSA) to solve an economic dispatch (ED) problem with smooth cost function characteristics. The constraints of the economic dispatch were load demand and transmission loss. The proposed MHLSA algorithm is a hybrid of the lambda iteration and simulated annealing methods and increases efficiency by adding multiple search mechanisms. It is a sequential execution of an individual hybrid algorithm of the lambda iteration and simulated annealing algorithm (HLSA) using only one personal microcomputer. The MHLSA algorithm introduces additional techniques for improvement of the search processes, such as the hybrid method, scope for multiple adaptive searches, and multiple searches. To show its effectiveness, the MHLSA is applied to two systems consisting of three and six power generating units. The optimized results from MHLSA are compared with those from HLSA and from conventional approaches, such as bee colony optimization (BCO), hybrid particle swarm optimization (HPSO) and simulated annealing (SA). Results confirm that the proposed MHLSA approach is capable of obtaining the greater speed of convergence and a higher quality solution efficiently. Thus, it has great potential to be implemented in different types of power system optimization problem.

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Multiple of Hybrid Lambda Iteration and Simulated Annealing Algorithm to Solve Economic Dispatch Problem with Ramp Rate Limit and Prohibited Operating Zones

Algorithm of inclusion and interchange of variables for capacitors placement

Distributed generation units (DGUs) as auxiliary sources of power generation can play an effective role in meeting the load consumption of the distribution network, also have positive effects such as reducing loss and improving voltage Moreover, capacitors by reactive power compensation produce positive effects similar to DGUs in the distribution networks The idea of joint operation of DGUs and shunt capacitors (SCs) in the presence of demand response program (DRP) to derive maximum benefits from their installation is proposed in this paper The time of use (TOU) mechanism is used as one of the demand response programs (DRPs) to alter the consumption pattern of subscribers and improve the performance of the distribution system Objective functions include minimization of energy loss, operational cost, and energy not supplied (ENS) In general, the problem of determining the optimal capacity of DGUs and SCs is complex due to the demand variation Also, considering the effect of uncertainty sources complicate the optimization problem Hence, a modified shuffled frog leaping algorithm (MSFLA) is proposed to overcome the complexities of this problem The proposed approach is tested on two 95, and 136-node test networks, and the results are compared with other evolutionary algorithms According to the obtained results, after using the proposed approach in determining the optimal capacity of DGUs and SCs in the first system, the amount of energy loss, operational cost and ENS dropped by 11, 255 and 5% compared to baseline values After applying the TOU mechanism in allocation of DGUs and SCs simultaneously in the second system by proposed method, the values obtained for the mentioned objectives reduced by 29, 65 and 7% compared to initial values

Optimal sizing of distributed generation units and shunt capacitors in the distribution system considering uncertainty resources by the modified evolutionary algorithm

This paper proposes an application of a recent nature inspired optimization technique namely Moth-Flame Optimization (MFO) algorithm in solving the Economic Dispatch (ED) problem. In this paper, the practical constraints will be included in determining the minimum cost of power generation such as ramp rate limits, prohibited operating zones and generators operating limits. To show the effectiveness of proposed algorithm, two case systems are used: 6-units and 15-units systems and then the performance of MFO is compared with other techniques from literature. The results show that MFO is able to obtain less total cost than those other algorithms.

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Economic dispatch solution using moth-flame optimization algorithm

Locate and determine the optimal capacity shunt capacitors in to reduce power losses and improve voltage profile and use the maximum capacity of transmission lines, one of the common problems in the design and control of power systems. Shunt capacitors are used to power loss reduction and improving voltage profile in order to utilize maximum capacity of transfer lines. This paper proposed a novel method, Improved Particle Swarm Optimization (IPSO), to placement capacitor in radial distribution systems. This approach has used a combination of shuffled Frog-leaping algorithm (SFLA) and particle swarm algorithm (PSO). The objective function is combination of the power losses cost and the cost of installing capacitors, to determine the location and optimal size of capacitors and constraint including minimum and maximum voltage limitations. The simulation applied on a 34-bus distribution network, and compared with existing methods. The effectiveness of the suggested approach is in power losses and total cost reduction as well.

Optimal capacitor placement and sizing in radial distribution system using an improved Particle Swarm Optimization algorithm

Generally the generation units in the traditional structure of the electricity industry try to minimize their costs. However, in a deregulated environment, generation units are looking to maximize their profits in a competitive power market. Optimum generation planning in a such structure is urgent. This paper presents a new method of solving economic dispatch in the competitive electricity market with the aim of maximizing the total contribution profit of power generation. In this regard, with a combination of two intelligent optimization, a new efficient algorithm which called improved particle swarm optimization algorithm is suggested. The simulation of the new approach and conventional PSO algorithm were performed on two case study systems, 10-units and 15-units. According to the results, the suggested method not only resolves the convergence problem, but it also makes more efficient response.

Ali Dadpour

Papers

Optimal capacitor placement and sizing in radial distribution system using an improved Particle Swarm Optimization algorithm

Solving economic dispatch in competitive power market using improved particle swarm optimization algorithm