An Integrated Distributed Generation Optimization Model for Distribution System Planning

El-Khattam, Walid; Hegazy, Yasser G.; Salama, M.M.A.

doi:10.1109/tpwrs.2005.846114

Cited by 441 publications

(111 citation statements)

References 15 publications

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“…V min and V max are the the upper and lower limits of voltage amplitude; S ij and S ijmax Energies 2015, 8,[13641][13642][13643][13644][13645][13646][13647][13648][13649][13650][13651][13652][13653][13654][13655][13656][13657][13658][13659] are the power flow and the permitted maximal power flow limit in the feeder between node i and node j, respectively.…”

Section: Chance Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Peng

Zheng

et al. 2015

Energies

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Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem.

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Section: Chance Constraintsmentioning

confidence: 99%

“…In [8], an optimization model was proposed for optimizing DG allocation problem, but it mainly focused on total planning cost minimization. In [9], four indices including real and reactive power loss, voltage profile and feeder capacity were considered for the ODGA problem in distribution systems with different load models.…”

Section: Introductionmentioning

confidence: 99%

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Peng

Zheng

et al. 2015

Energies

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“…This issue is such a constraint on the power grid which should also be considered in solving the load shedding problems. This constraint is written as Equations (5) and (6) [15].…”

Section: Active and Reactive Power (C 1 )mentioning

confidence: 99%

Multi Objective Load Shedding Framework

Delavari¹,

Nemati²,

Moradi³

2015

SGRE

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In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems; the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator's opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator; in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator's opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator's opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.

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“…For example, a model for allocating and sizing DG that intends to minimize the total investment, operating and substation and feeder upgrade costs was proposed in [14]. The work in [1] proposed allocating DG in deregulated energy markets based on the locational marginal price of the network nodes.…”

Section: Introductionmentioning

confidence: 99%

Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids

Atia

Yamada

2016

Energies

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This paper considers the contribution of independent owners (IOs) operating within microgrids (MGs) toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG) and a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for demand response (DR), DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential for IO/MG investments and their strategies.

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An Integrated Distributed Generation Optimization Model for Distribution System Planning

Cited by 441 publications

References 15 publications

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Multi Objective Load Shedding Framework

Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids

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