Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Abstract

A scenario reduction technique is used to reduce the computational burden of the model. The Pareto optimal solutions of the problem are found using a binary PSO algorithm and finally a fuzzy satisfying method is applied to select the optimal solution considering the desires of the planner. The effectiveness of the proposed model is demonstrated by applying it to a realistic 201-node distribution network. Index TermsDistributed generation, PSO, Dynamic planning, scenario reduction, Pareto optimal front. NOMENCLATURE Constants C ℓCost of reinforcement of feeder ℓ C

Risk-Averse Preventive Voltage Control of AC/DC Power Systems Including Wind Power Generation

“…It should be noted that finding the scenarios which describe all states of wind power generation depends on the wind farm location. Some techniques have been reported in the literature to find these states and the associated probabilities (which are based on the historical data and measurements) [34]. In other words, there is no general table for describing the wind power states (or scenarios).…”

Optimal Placement of Distributed Generation Using Bacterial foraging Optimization in an Electrical Distribution System

“…Several researchers have worked in this area [7][8][9][10][11][12][13].DGs are placed at optimal locations to reduce losses [7]. Some researchers have presented load flow algorithms to find the optimal size of DGs at each load bus [8,9].…”

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“…The objective was the minimization of the active losses of the feeder or the minimization of the total network supply costs, which includes generators operation and losses compensation or even the best utilization of the available generation capacity. A stochastic multi-objective model for placement of DG in electrical distribution networks is proposed in [13] with a binary particle swarm optimization (PSO) algorithm. The DS expansion planning strategy with DG systems with intermittent power generation is presented in [14] to obtain optimal solutions for system planners.…”

Optimal multi‐area generation schedule considering renewable resources mix: a real‐time approach

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