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

ABSTRACT 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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    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.
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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.
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    ABSTRACT: A method for solving the optimal power flow (OPF) problem including HVDC connected offshore wind farms is presented in this paper. Different factors have been considered in the proposed method namely, voltage source converter (VSC-HVDC) and line-commutated converter high-voltage DC (LCC-HVDC) link constraints, doubly fed induction generators’ (DFIGs) capability curve as well as the uncertainties of wind power generation. Information gap decision theory (IGDT) is utilized for handling the uncertainties associated with the volatility of wind power generation. It is computationally efficient and does not require the probability density function of wind speed. The proposed decision making framework finds the optimal decision variables in a way that they remain robust against the considered uncertainties. To illustrate the effectiveness of the proposed approach, it is applied on the IEEE 118-bus system. The obtained results validate the applicability of the proposed IGDT-based OPF model for optimal operation of AC/DC power systems with high penetration of offshore wind farms
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