Robust Transmission Expansion Planning Representing Long- and Short-Term Uncertainty

Zhang, Xuan; Conejo, Antonio J.

doi:10.1109/tpwrs.2017.2717944

Cited by 124 publications

(77 citation statements)

References 18 publications

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“…Moreover, the RP-TM&CI model could be extended to a stochastic model to consider uncertainty in renewable energy production or hydro inflows for long-term storage. Therefore, the main challenge in this topic is the representation at the same time of long-and short-term uncertainties, such as in [26].…”

Section: Discussionmentioning

confidence: 99%

Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis

Tejada-Arango

Domeshek

Wogrin

et al. 2018

IEEE Trans. Power Syst.

102

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This paper analyzes different models for evaluating investments in energy storage systems (ESS) in power systems with high penetration of renewable energy sources. First of all, two methodologies proposed in the literature are extended to consider ESS investment: a unit commitment model that uses the "system states" (SS) method of representing time; and another one that uses a "representative periods" (RP) method. Besides, this paper proposes two new models that improve the previous ones without a significant increase of computation time. The enhanced models are the "system states reduced frequency matrix" model which addresses short-term energy storage more approximately than the SS method to reduce the number of constraints in the problem, and the "representative periods with transition matrix and cluster indices" (RP-TM&CI) model which guarantees some continuity between representative periods, e.g., days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as benchmark. While both system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The RP-TM&CI model, on the other hand, succeeds in approximating both shortand long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.Index Terms-Energy storage systems, power system planning, power system modeling, system states, representative days.

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Section: Discussionmentioning

confidence: 99%

Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis

Tejada-Arango

Domeshek

Wogrin

et al. 2018

IEEE Trans. Power Syst.

102

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“…To obtain more optimal decisions, the uncertainty sources in an EH (eg, wind generations and energy prices) have been modeled through stochastic programing (SP) approaches in Najafi et al and Pazouki et al However, SP faces two main challenges including the lack of tractable methodology and requiring full distributional knowledge of uncertain parameters, which may not be easily available in practice . To overcome these challenges, robust optimization (RO) technique as a tractable approach, eliminating the need for having full distributional knowledge of uncertain parameters, has been recently developed and applied to different applications in power systems, such as day‐ahead market participation and transmission expansion planning . In Parisio et al, a single‐stage RO model is developed for EH optimal operation, considering the uncertainty of converters' efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…24 To overcome these challenges, robust optimization (RO) technique as a tractable approach, eliminating the need for having full distributional knowledge of uncertain parameters, has been recently developed and applied to different applications in power systems, such as day-ahead market participation 25 and transmission expansion planning. 26 In Parisio et al, 27 a single-stage RO model is developed for EH optimal operation, considering the uncertainty of converters' efficiency. However, the uncertainties of load and electricity price as well as recourse decisions have not been considered in Parisio et al 27 According to the background of the EH concept, especially in facing uncertainties, further studies are needed to be undertaken in order to provide more realistic and promising models for EH operation.…”

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confidence: 99%

A linearized energy hub operation model at the presence of uncertainties: An adaptive robust solution approach

Aghamohamadi

Amjady

Attarha

2019

Int Trans Electr Energ Syst

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Summary This paper presents a new adaptive robust operation optimization approach for energy hub (EH) to identify the optimal decisions on purchased energy carriers, upstream network interactions, and storing/conversion of the energy resources considering uncertainties. In this regard, a linearized framework for EH operation is first introduced. The proposed model is used to develop an EH including electrical energy, natural gas, and direct heat as inputs and electricity and heat demands as outputs. The electrical input energy is provided considering both purchased energy from upstream market and a photovoltaic (PV) generation, operated by the EH operator (EHO). The proposed approach characterizes the uncertain nature of loads, energy prices, and PV generations through polyhedral uncertainty sets, while the robustness of the proposed model can be controlled using the budget of uncertainty. The proposed adaptive robust model is formulated as a min‐max‐min optimization problem, which cannot be solved directly through an off‐the‐shelf optimization package. Thus, a new method, consisting decomposition + primal cutting plane + duality theory + exact linearization + post‐optimization analysis, is introduced to determine the EH optimal solution. The performance of the proposed approach is evaluated through a comprehensive case study.

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“…Taking into account the load demand uncertainty, the stochastic problem has been solved in Alaee et al using the shuffled frog leaping algorithm where the reliability cost has been added to the conventional objective function. Short‐term uncertainties like the renewable power generation and the long‐term uncertainties such as the demand growth and the variations of the production capacity have been addressed in the context of adaptive robust TEP in Zhang and Conejo . The TEP problem under uncertainty was handled in Majidi‐Qadikolai and Baldick using a scalable and configurable decomposition method, and Haghighat and Zeng proposed a bilevel ACOPF‐based TEP model.…”

Section: Introductionmentioning

confidence: 99%

“…Short-term uncertainties like the renewable power generation and the long-term uncertainties such as the demand growth and the variations of the production capacity have been addressed in the context of adaptive robust TEP in Zhang and Conejo. 28 The TEP problem under uncertainty was handled in Majidi-Qadikolai and Baldick 29 using a scalable and configurable decomposition method, and Haghighat and Zeng30 proposed a bilevel ACOPF-based TEP model. Comprehensive reviews on the TEP and the challenges of the TEP problem have been carried out in Hemmati et al and Lumbreras and Ramos, 31,32 respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective, multi‐year dynamic generation and transmission expansion planning‐ renewable energy sources integration for Iran's National Power Grid

Javadi

Nezhad

2018

Int Trans Electr Energ Syst

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Summary The paper presents a multi‐year, multi‐objective framework for integrating Renewable Energy Sources (RESs) into the high voltage transmission network of Iran's National Power Grid (INPG). The objective functions in this study are the total cost, including the investment cost and operating cost for the planning horizon, and the system reliability. The first objective function is stated from the economic point of view, while the second objective function is considered as a security index in the expansion planning issue. The main purpose of this paper is to increase the RES penetration into the generation mix of INPG. Since the mentioned 230 to 400‐kV INPG is a large‐scale power system, the problem formulation is investigated in a mixed‐integer programming, and then, the developed multi‐objective problem has been solved using the augmented epsilon‐constraint optimization method. In order to select the executive plan for installation, the fuzzy satisfying decision‐making procedure is adopted in this study.

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Robust Transmission Expansion Planning Representing Long- and Short-Term Uncertainty

Cited by 124 publications

References 18 publications

Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis

Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis

A linearized energy hub operation model at the presence of uncertainties: An adaptive robust solution approach

Multi‐objective, multi‐year dynamic generation and transmission expansion planning‐ renewable energy sources integration for Iran's National Power Grid

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