Distribution network planning using a constructive heuristic algorithm

Lavorato, Marina; Rider, Marcos J.; Garcia, A.V.; Romero, Rubén

doi:10.1109/pes.2009.5275860

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Thus, a great variety of models and solution techniques exist [5]- [17]. For instance, [5] uses a modified dynamic programming to solve the expansion problem, but without taking reliability into account.…”

Section: D) Continuous Variablesmentioning

confidence: 99%

Distribution System Planning With Reliability

Lotero¹,

Contreras

2011

IEEE Trans. Power Delivery

152

123

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This paper presents a model for solving the multistage planning problem of a distribution network. The objective function to be minimized is the net present value of the investment cost to add, reinforce or replace feeders and substations, losses cost, and operation and maintenance cost. The model considers three levels of load in each node and two investment alternatives for each resource to be added, reinforced or replaced. The nonlinear objective function is approximated by a piecewise linear function, resulting in a mixed integer linear model that is solved using standard mathematical programming. The model allows us to find multiple solutions in addition to the optimal one, helping the decision maker to analyze and choose from a pool of solutions. In addition to the optimization problem, reliability indexes and associated costs are computed for each solution, based on the regulation model used in Brazil. Numerical results and discussion are presented for an illustrative 27-node test network.

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Section: D) Continuous Variablesmentioning

confidence: 99%

Distribution System Planning With Reliability

Lotero¹,

Contreras

2011

IEEE Trans. Power Delivery

152

123

View full text Add to dashboard Cite

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“…In order to go beyond the prior research that is currently available in the literature, we provide an easily implementable optimization technique to create rural distribution grids that is composed of three steps and that can be used to single-phase or three-phase distribution grids [70]. The first step handles the route selection problem by articulating the problem of least spanning tree (linear integer programming model) to reduce the total length of the conductors [71,72].…”

Section: Application Of Heuristic Methods On Ac MV Gridsmentioning

confidence: 99%

Modern Techniques for the Optimal Power Flow Problem: State of the Art

2022

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Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the state of the art (according to the authors, the most pertinent studies). The presented optimization techniques are grouped according to their sources of inspiration, including human-inspired algorithms (harmony search, teaching learning-based optimization, tabu search, etc.), evolutionary-inspired algorithms (differential evolution, genetic algorithms, etc.), and physics-inspired methods (particle swarm optimization, cuckoo search algorithm, firefly algorithm, ant colony optimization algorithm, etc.).

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“…Thus the voltage magnitude of the node is fixed to the network nominal operating voltage V 0 ; otherwise, x n ≤ 0, that is x n = 0, and the voltage magnitude is bounded byV n andV n . The positive integer M is any upper bound on the sum in (11). The optimal choice of M in the absence of any other knowledge of the planning problem may be computed by a knapsack problem [22, Chap.…”

Section: Slack Bus Reference Valuesmentioning

confidence: 99%

“…There is a good fit since the design variables are naturally integer-valued. While distribution expansion planning by MIP techniques has been used extensively right up to the present [8][9][10][11], recent work has formulated similar problems as a continuous optimal placement and sizing problem and solved with a sequential quadratic programming approach [12] or particle swarm optimization [13], and recent related research in the literature has focused on heuristic methods from evolutionary computation [14,15]. In fact, the DGPP first appeared as one aspect of a multi-criteria optimization problem, solved using a genetic algorithm [7,16].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Mixed-Integer Programming and Genetic Algorithm Methods for Distributed Generation Planning

Foster

Berry

Boland

et al. 2014

IEEE Trans. Power Syst.

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This paper applies recently developed mixed-integer programming (MIP) tools to the problem of optimal siting and sizing of distributed generators in a distribution network. We investigate the merits of three MIP approaches for finding good installation plans: a full AC power flow approach, a linear DC power flow approximation, and a nonlinear DC power flow approximation with quadratic loss terms, each augmented with integer generator placement variables. A genetic algorithm based approach serves as a baseline for the comparison. A simple knapsack problem method involving generator selection is presented for determining lower bounds on the optimal design objective. Solution methods are outlined, and computational results show that the MIP methods, while lacking the speed of the genetic algorithm, can find improved solutions within conservative time requirements and provide useful information on optimality.

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Distribution network planning using a constructive heuristic algorithm

Cited by 5 publications

References 20 publications

Distribution System Planning With Reliability

Distribution System Planning With Reliability

Modern Techniques for the Optimal Power Flow Problem: State of the Art

Comparison of Mixed-Integer Programming and Genetic Algorithm Methods for Distributed Generation Planning

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