A Benders' decomposition approach to the multi-objective distribution planning problem

Kagan, Nelson; Adams, Randee

doi:10.1016/0142-0615(93)90047-q

Cited by 42 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the second stage, the optimum site and size for the DG units for the networks obtained in the first stage are determined by another multi-objective optimization, i.e., minimization of DG penetration level and power loss. The simultaneous optimization of the multiple objectives can be done in many ways, for example, aggregating approach [3][4][5][6][7][8], Pareto-dominance based approach [9][10][11][12][13][14][15], lexicographic ordering [35,42], and non-Pareto based approach [42]. Most of the multi-objective optimization algorithms are based on the Pareto-dominance principle [31,35,42] which is used to yield a set of non-dominated solutions.…”

Section: I S (C M S )mentioning

confidence: 99%

See 1 more Smart Citation

A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation

2010

View full text Add to dashboard Cite

This paper presents a novel particle swarm optimization (PSO) based multi-objective planning approach for electrical distribution systems incorporating distributed generation (DG). The proposed strategy can be used for planning of both radial and meshed networks incorporating DG. The DG plays an important role in the distribution system planning due to its increasing use motivated by reduction of power loss, voltage profile improvement, meeting future load demand, and optimizing the use of non-conventional energy sources etc. The overall approach consists of two multi-objective planning stages. In the first stage, a contingency-based multiobjective planning is used to optimize the number of feeders and their routes, and the number and location of the sectionalizing switches. In the second stage, the optimum siting and sizing of the DG units is determined for the networks obtained in the first stage by another multi-objective optimization. The multiple objectives of the first planning stage are: (i) minimization of the total installation and operational cost, and (ii) maximization of network reliability. The reliability of the distribution network is evaluated by a reliability index, i.e., contingency-load-loss index (CLLI), defined as the ratio of the average non-delivered load due to failure of all branches, taken one at a time, to the total load. The objectives for the second stage optimization are the DG penetration level and the total power loss. A set of non-dominated solutions/networks is obtained by simultaneous minimization of the conflicting objectives (at each stage) using the Pareto-optimality principle based trade-off analysis. A novel multi-objective PSO (MOPSO) is proposed for solving these optimization problems using a technique for selection and assignment of leaders/guides for efficient search of the non-dominated solutions. The selection of the leaders makes use of the available non-dominated and dominated solutions. The proposed planning algorithm is tested for the static and expansion planning of typical 100-node and 21-node distribution systems, respectively. The computer simulation results are critically 292 S. Ganguly et al.evaluated. The performance of the algorithm is compared with that of the popular Strength Pareto Evolutionary Algorithm-2 (SPEA2)-based PSO and few other existing MOPSO techniques by means of statistical tests to highlight the efficacy of the proposed scheme.

show abstract

Section: I S (C M S )mentioning

confidence: 99%

“…Normally, the numerical optimization tools such as nonlinear programming (NLP) [3,4], dynamic programming (DP) [5,6], and benders' decomposition [9] have been used. There are some disadvantages with these analytical approaches, i.e., curse of dimensionality, non-differentiability, discontinuous objective space etc.…”

Section: I S (C M S )mentioning

confidence: 99%

A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation

2010

View full text Add to dashboard Cite

show abstract

“…Two approaches have been used for optimizing the cost and reliability. In the first approach [6][7][8][9][10][11][12][13][14][15][16], both objectives are aggregated to obtain a single solution, while the second approach [17][18][19][20][21][22][23][24][25][26][27][28][29] takes the conflicting natures of the cost and reliability into account by simultaneous optimization of the two objectives to obtain a set of non-dominated solutions, called Pareto solutions [19,20] and a decision maker or the planning engineer selects one solution for implementation.…”

Section: Introductionmentioning

confidence: 99%

“…The heuristics-based algorithm can produce an acceptable solution to a problem in many practical scenarios, but there is no formal proof of its optimality. The deterministic algorithms that have been used for this problem are: nonlinear mixed integer programming [7], dynamic programming [6,8], nonlinear programming [9,10], Benders' decomposition [17,18], etc. Most of the heuristics-based algortithms applied to this problem are based on the evolutionary algorithms (EAs), such as genetic algorithm (GA) [12][13][14][15][16][19][20][21][22][23][24], tabu search (TS) [25,26], artificial immune system (AIS) [27], particle swarm optimization (PSO) [28,29], and honey bee mating optimization [30].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective planning of electrical distribution systems using dynamic programming

Ganguly

Sahoo

Das

2013

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…Solution approaches based on mathematical programming can be found in Refs. [1][2][3]. Some new algorithms based on artificial intelligence, such as simulated annealing [4] , genetic algorithm [5,6] , evolutionary technique [7−9] , ant colony algorithm [10] and tabu search [11] also have been proposed to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Distribution system optimization planning based on plant growth simulation algorithm

Chun

Cheng

et al. 2008

J. Shanghai Jiaotong Univ. (Sci.)

View full text Add to dashboard Cite

An approach for the integrated optimization of the construction/expansion capacity of high-voltage/ medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.

show abstract

A Benders' decomposition approach to the multi-objective distribution planning problem

Cited by 42 publications

References 10 publications

A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation

A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation

Multi-objective planning of electrical distribution systems using dynamic programming

Distribution system optimization planning based on plant growth simulation algorithm

Contact Info

Product

Resources

About