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

Taherkhani, Morteza; Hosseini, Seyed Hamid

doi:10.1002/etep.1965

Cited by 20 publications

(13 citation statements)

References 37 publications

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“…The offering price of wind power producers is investigated in References [38,39]. A multistage transmission expansion planning under wind power uncertainties is obtained using the IGDT method in References [40][41][42][43]. A security constrained unit commitment (SCUC) with undispatchable resources with a high penetration of wind power for retaining the load-generation balance is proposed in References [44,45], and the frequency management is presented in References [46,47].…”

Section: Literature Reviewmentioning

confidence: 99%

Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

et al. 2019

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Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

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Section: Literature Reviewmentioning

confidence: 99%

Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

et al. 2019

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“…In this regard, IGDT needs even less information about uncertainty structure [19], which makes it adaptive for the problems under unstructured uncertainties. Given its modeling capacity against uncertainties, we observe that IGDT has been applied to the expansion planning of generation system [20] and transmission network [21], but is rarely mentioned in microgrid planning.…”

Section: Introductionmentioning

confidence: 99%

A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning

Cao

Wang

Zeng

2018

IEEE Trans. Power Syst.

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This paper presents a chance constrained information gap decision model for multi-period microgrid expansion planning (MMEP) considering two categories of uncertainties, namely random and non-random uncertainties. The main task of MMEP is to determine the optimal sizing, type selection, and installation time of distributed energy resources (DER) in microgrid. In the proposed formulation, information gap decision theory (IGDT) is applied to hedge against non-random uncertainties of long-term demand growth. Then, chance constraints are imposed in the operational stage to address the random uncertainties of hourly renewable energy generation and load variation. The objective of chance constrained information gap decision model is to maximize the robustness level of DER investment meanwhile satisfying a set of operational constraints with a high probability. The integration of IGDT and chance constrained program, however, makes it very challenging to compute. To address this challenge, we propose and implement a strengthened bilinear Benders decomposition method. Finally, the effectiveness of proposed planning model is verified through the numerical studies on both the simple and practical complex microgrid. Also, our new computational method demonstrates a superior solution capacity and scalability. Compared to directly using a professional mixed integer programming solver, it could reduce the computational time by orders of magnitude.

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“…This method models the gap between what is known (or predicted) and what may happen in reality of the uncertain parameter [13]. Recently, IGDT has been applied in different part of power system studies such as optimal demand-side scheduling [14][15][16][17][18], bidding strategy problems [19][20][21][22][23][24], and TEP [25,26]. In [25], a novel multi-year TEP has been proposed based on mixed integer linear programming model.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the augmented ɛ‐constraint method has been used to solve the obtained multi‐objective optimization problem. In , a new TEP model considering wind farms (WFs) optimal integration to power systems has been proposed in which the IGDT method has been used to model the uncertainty of WFs output power and forecasted demand where the delivered wind generation to the network has been considered as negative load. Thus, according to this linear correlation, two uncertainties have been converted to one uncertain parameter.…”

Section: Introductionmentioning

confidence: 99%

IGDT-based robust decision making applied to merchant-based transmission expansion planning

Ranjbar

Hosseini

2016

Int. Trans. Electr. Energ. Syst.

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Summary Deregulation in power systems has created new uncertainties and increased the previous ones. The presence of these uncertainties causes the transmission network to remain monopoly and the private investors not being interested in investing in this section. This paper presents a new merchant‐based transmission expansion planning (TEP) formulation from the viewpoint of private investors. The information‐gap decision theory (IGDT) is used to model the inherent uncertainties associated with the estimated investment cost of candidate lines and the forecasted system load and NSGAII is utilized to solve the multi‐objective optimization problem. This algorithm helps private investors to select the best lines for investment in the presence of uncertainties. In order to verify the effectiveness of the proposed method, it has been applied to the IEEE RTS 24‐bus system and the simplified Iranian 400‐kV transmission system. Copyright © 2016 John Wiley & Sons, Ltd.

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

Cited by 20 publications

References 37 publications

Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning

IGDT-based robust decision making applied to merchant-based transmission expansion planning

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