Wind farm optimal connection to transmission systems considering network reinforcement using cost‐reliability analysis

Taherkhani, Morteza; Hosseini, Seyed Hamid

doi:10.1049/iet-rpg.2013.0086

Cited by 22 publications

(27 citation statements)

References 25 publications

(44 reference statements)

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“…Thus the cost C DR of load adjustment of customers is modeled by a quadratic form in (18) [12]. Equation (19) states that negative P DR means load increment; while positive P DR means load decrement. Equation (20) states the maximum DR ratio ρ and P D denotes demand.…”

Section: Incentive-based Demand Response Modelmentioning

confidence: 99%

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

2017

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This paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR) costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver's six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

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Section: Incentive-based Demand Response Modelmentioning

confidence: 99%

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

2017

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“…As pointed out in Subsection 2.3 and shown in Figure , in this paper, the capacity of each WFL is determined based on the interaction between the WFL investment cost and the WF reliability cost using the cost‐reliability analysis . Thus, wind system cost ( WSC ) objective function based on the cost‐reliability analysis for each stage is defined as follows:

WSC = \sum_{italict = 1}^{T} \sum_{italicw = 1}^{W} {italicICL}_{t} \times WL {italicC}_{w, t} \times {italicd}_{italicw} + \sum_{italict = 1}^{T} \sum_{italicw = 1}^{W} Δ LOE {italicE}_{w, t} \times IEAR

…”

Section: System Modelingmentioning

confidence: 99%

IGDT-based multi-stage transmission expansion planning model incorporating optimal wind farm integration

Taherkhani

Hosseini

2014

Int. Trans. Electr. Energ. Syst.

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Summary In this paper, a new transmission expansion planning (TEP) model considering wind farms (WFs) optimal integration to power systems is proposed based on the information‐gap decision theory (IGDT). The uncertainties of WFs output power and forecasted demand are considered in the problem, and IGDT is used to control the investment risk as well as to reduce the effects of these uncertainties on the investors' strategies. The TEP model is formulated for the risk‐averse and risk‐seeker investors through the robustness and opportunity models, respectively. Moreover, this TEP model allows WF lines and network lines to be added at multiple time points during a multi‐stage time horizon. A genetic algorithm approach is employed to solve the bi‐level IGDT‐based optimization problem. Finally, this IGDT‐based model is applied to the simplified Iranian 400‐kV system, and the results are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

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“…In [22], a chance constrained programming-based wind power capacity planning problem was proposed, aiming at minimizing the system social cost while satisfying the reliability criteria and operational constraints. Transmission cost was considerable as it was a significant proportion of capital investment [17,23,24]. The interconnection cost was also included in the overall cost [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal wind capacity integration considering the possibilistic uncertainty of wind resources

Sun

Xie

Bie

et al. 2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Due to the energy crisis, environmental contamination and other problems related with conventional generations, it is essential to consider the integration of renewable energy, especially wind power. This paper, therefore, outlines the design and study of an interval optimization model to determine optimal wind capacity in an existing power grid. Interval wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources. This model aims at minimizing the overall cost including the variable cost of coal-fired generation, start-up/shutdown cost of generation units, capital investment of wind farm, reserve cost, penalty cost of wind curtailment and fixed operational cost of wind power. The effectiveness of the proposed model is validated by its application in a real-life power grid. Compared with previous research, this method can aid power system planners to make decisions about wind capacity considering the possibilistic uncertainty of wind resource.

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Wind farm optimal connection to transmission systems considering network reinforcement using cost‐reliability analysis

Cited by 22 publications

References 25 publications

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

IGDT-based multi-stage transmission expansion planning model incorporating optimal wind farm integration

Optimal wind capacity integration considering the possibilistic uncertainty of wind resources

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