Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm – Monte Carlo Hybrid Optimization Method

DOI: 10.1007/978-3-642-13250-6_1 In book: Wind Power Systems, pp.1-23


The increasing amount of wind power integrated to power systems presents a number of challenges to the system operation. One
issue related to wind power integration concerns the location and capacities of the wind turbines (WTs) in the network. Although
the location of wind turbines is mainly determined by the wind resource and geographic conditions, the location of wind turbines
in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern
WTs with power-electronic interface have the capability of controlling reactive power output, which can enhance the power
system security and improve the system steady-state performance by reducing network losses. This chapter presents a hybrid
optimization method that minimizes the annual system power losses. The optimization considers a 95%-probability of fulfilling
the voltage and current limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear
optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities
of WTs. The gradient-based optimization finds the optimal power factor setting of WTs. The sequential MCS takes into account
the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an
11 kV 69-bus distribution system.

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    • "In [10] and [11], the authors proposed a GA-based method to determine the optimal sizes and locations of multiple DGs in order to minimize the network losses considering network constraints. In [12], the authors proposed a hybrid optimization method to minimize the annual system power losses to find optimal locations and capacities of WTs. The method combines GA, gradient-based constrained nonlinear optimization algorithm, and sequential Monte Carlo simulation. "
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    ABSTRACT: This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.
    IEEE Systems Journal 06/2015; 9(2). DOI:10.1109/JSYST.2013.2279733 · 1.98 Impact Factor
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    • "The integration of WTs in distribution network needs to consider not only local information such as wind speed and allocated area but also the characteristic of WTs [2]. In addition to all these, the operation and structure of network and economic parameters should bring into study [3]. "
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    ABSTRACT: This paper presents the result of development wind farm as unpredictable renewable energy resource in radial distribution network. Wind farm consists of pitched regulated fixed-speed wind turbine which is developed in 24-hour forward-backward sweep load flow analysis. Through load flow analysis, some parameters of network such as voltage profiles, total real power fed of substation and total power loss are calculated for each internal period hour of day. Procedure for capacity allocation of wind farm is determined by both network and wind turbine characteristics for one hourly wind speed for one day. The proposed method is developed for one case-study using MATLAB® software. The results are shown and compared in an abbreviated manner.
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    • "The problem is formulated as mixed integer non-linear programming (MINLP), with an objective function for the minimization of the annual energy losses. In [18], the authors have proposed a hybrid optimization method to minimize the annual system power losses. The method combines GAs, gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo Simulation (MCS). "
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    ABSTRACT: One of the most important topics associated to the integration of wind turbines (WTs) into distribution systems is their siting and sizing. Inappropriate allocation of WTs in distribution systems not only leads to threaten the system operation but also to decrease consumers' benefits. In this paper, a new methodology is proposed to allocate WTs in distribution networks. An optimal power flow (OPF) is used to determine the optimal placement of WTs that maximizes the system Social Welfare (SW). The method is solved by using Step-Controlled Primal Dual Interior Point Method (SCPDIPM) considering network constraints and its effectiveness has been verified on an 83-bus 11.4 kV radial distribution system. By yielding location-specific WTs capacity settlement both in terms of cost reduction and consumers' benefits is consistent with distribution network topology and limitations and can help resource developers to better allocate WTs.
    IEEE 11th International Electrical Power Quality and Utilization Conference (EPQU 2011); 01/2011
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