Robust Transmission and Energy Storage Expansion Planning in Wind Farm-Integrated Power Systems Considering Transmission Switching

Dehghan, Shahab; Amjady, Nima

doi:10.1109/tste.2015.2497336

Cited by 179 publications

(122 citation statements)

References 29 publications

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“…Optimal planning of energy storage under different conditions and objectives have been studied in [10,[16][17][18][19][20][21][22]. In addition, [23][24][25][26][27] investigated joint optimal allocation and sizing of energy storage.…”

Section: Knowledge Gapmentioning

confidence: 99%

Optimal Investment Planning of Bulk Energy Storage Systems

2018

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Many countries have the ambition to increase the share of renewable sources in electricity generation. However, continuously varying renewable sources, such as wind power or solar energy, require that the power system can manage the variability and uncertainty of the power generation. One solution to increase flexibility of the system is to use various forms of energy storage, which can provide flexibility to the system at different time ranges and smooth the effect of variability of the renewable generation. In this paper, we investigate three questions connected to investment planning of energy storage systems. First, how the existing flexibility in the system will affect the need for energy storage investments. Second, how presence of energy storage will affect renewable generation expansion and affect electricity prices. Third, who should be responsible for energy storage investments planning. This paper proposes to assess these questions through two different mathematical models. The first model is designed for centralized investment planning and the second model deals with a decentralized investment approach where a single independent profit maximizing utility is responsible for energy storage investments. The models have been applied in various case studies with different generation mixes and flexibility levels. The results show that energy storage system is beneficial for power system operation. However, additional regulation should be considered to achieve optimal investment and allocation of energy storage.

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Section: Knowledge Gapmentioning

confidence: 99%

Optimal Investment Planning of Bulk Energy Storage Systems

2018

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“…Hence, energy storage–system modeling needs to be considered from both operational and economic perspectives to characterize its performance. Specifically, charging/discharging power limit combined with time‐coupling stat‐of‐energy (SoE) constraint have been adopted in the related optimization model . Moreover, the financial indicators based on life‐cycle analysis including upfront capital cost and operation cost can effectively support the decision‐making process .…”

Section: Introductionmentioning

confidence: 99%

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Wen

et al. 2019

Int Trans Electr Energ Syst

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To counterbalance the significant challenges imposed by renewable distributed generations penetration, this paper discusses the need of distributed energy storage system investment in distribution networks and proposes a robust optimization based storage investment model. The operational constraints of distribution network (e.g., voltage profile and substation capacity limitation) and storage device (e.g., state of energy and charging/discharging limit) are considered to guarantee the technical operation requirements. The proposed model is mathematically formulated as a two‐stage robust optimization with uncertainty of renewable distributed generator that is quantified by a polyhedral uncertainty set. The investment‐decision variables are optimized in the first stage, and the feasibility in the real‐time worst‐case scenario is checked in the second stage. A column‐and‐constraint generation (C&CG) algorithm and the big‐M linearization method are employed to solve the associated optimization problem. Numerical experiments on IEEE‐37‐node and IEEE‐123‐node distribution networks demonstrate the effectiveness of the proposed model.

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“…Recently, cost-benefit analysis of deploying ES in electricity systems has been carried out in [18], [19], [20], [21], [22], [23] under different assumptions. A Mixed Integer Linear Programming (MILP) deterministic static investment model has been proposed in [18] to reduce network investment cost.…”

Section: Introductionmentioning

confidence: 99%

“…The authors find the model becomes computationally demanding with even relatively few buses. A robust formulation of an integrated transmission and ES expansion planning problem is presented in [22]. The static model includes transmission switching and uncertainty in load demand and wind power productions through uncertainty sets.…”

Section: Introductionmentioning

confidence: 99%

Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

Falugi

Konstantelos

Štrbac

2018

IEEE Trans. Power Syst.

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Abstract-Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies such as energy storage can improve operational flexibility and assist with the costeffective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term costbenefit basis. However, such an analysis can result in large-scale Mixed Integer Linear Programming (MILP) problems which contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this manuscript we propose a novel, efficient and highly-generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of non-sequential investment state equations and sub-problem non-convexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under longterm uncertainty. The proposed method is shown to substantially outperform the current state-of-the-art. All the parameters involved in the optimization model can be represented by the vector ρ containing B n,g Bus-to-generation incidence matrix. I n,ℓ Bus-to-line incidence matrix. S n,s Bus-to-storage incidence matrix.

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Robust Transmission and Energy Storage Expansion Planning in Wind Farm-Integrated Power Systems Considering Transmission Switching

Cited by 179 publications

References 29 publications

Optimal Investment Planning of Bulk Energy Storage Systems

Optimal Investment Planning of Bulk Energy Storage Systems

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach

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