Differential evolution algorithm for static and multistage transmission expansion planning
ABSTRACT A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.
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ABSTRACT: One aspect of long-range planning of electric power systems involves the exploration of various designs for the bulk power transmission network. The use of linear programming for network analysis to determine where capacity shortages exist and, most importantly, where to add new circuits to relieve the shortages is presented. The new method of network estimation produces a feasible transmission network with near-minimum circuit miles using as input any existing network plus a load and generation schedule. An example is used to present the two steps of the method: 1) linear flow estimation and 2) new circuit selection. The method has become a fundamental part of computer programs for transmission network synthesis.IEEE Transactions on Power Apparatus and Systems 10/1970;
Conference Proceeding: A Differential Evolution Based Method for Power System Planning[show abstract] [hide abstract]
ABSTRACT: Power system planning is a complex multi-objective optimization problem. It aims at locating the minimum cost of additional transmission lines that must be installed to satisfy the forecasted load in a power system. A number of different methods for power system planning have been investigated over the past decades. In this paper, a differential evolution (DE) based approach is proposed as an optimization tool to solve the power system planning problem. A comparison between genetic algorithms, evolutionary strategy (ES), and five different DE schemes are carried out on two benchmark power systems. The results shown that, as a relatively new heuristic optimization method, DE is able to provide robust and efficient solution to power system planning problems.Evolutionary Computation, 2006. CEC 2006. IEEE Congress on;
Article: New Transmission Planning ModelIEEE Power Engineering Review 03/1989; 9(2):35-35.