Optimal Operation of Power Systems With Energy Storage Under Uncertainty: A Scenario-Based Method With Strategic Sampling

Hu, Ren; Li, Qifeng

doi:10.1109/tsg.2021.3127922

Cited by 11 publications

(22 citation statements)

References 52 publications

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“…However, the CCP is generally NPhard. With the deluge of PSO data and the success of big data techniques, data-driven optimization methods based on scenario optimization (SO) [8]- [12] provide a viable way to achieve an approximate solution for CCP problems, such as chance-constrained reserve scheduling [13], [14], economic dispatch [15], [16], OPF [17], and multiperiod OPF-ESDUU [18]. These SO-based DDO methods are PD-free, driven by the uncertainty samples of UREPL, not depending on any prior knowledge of the PD of UREPL, and guarantee that the solution satisfies the constraints with a high confidence level.…”

Section: Introductionmentioning

confidence: 99%

“…These SO-based DDO methods are PD-free, driven by the uncertainty samples of UREPL, not depending on any prior knowledge of the PD of UREPL, and guarantee that the solution satisfies the constraints with a high confidence level. As the number of uncertainty samples fed into SO-based DDO methods has a critical influence on their computational efficiency, Reference [18] proposed strategic sampling (SS) using metric learning and reinforcement learning techniques to reduce the uncertainty samples needed to solve the multiperiod OPF-ESDUU problem, which is an effective alternative to the conventional SO-based DDO methods in [8]- [17] using random sampling. However, the existing SS ignores the correlation information between the uncertainty samples from a global perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [18] converted the original non-convex multi period OPF-ESDUU problem into a convex optimization problem using the least absolute shrinkage and selection operator (LASSO) to learn the convex approximation of ACPF (CAACPF). However, the existing learning methods [18], [22] only consider the information within each individual bus, called the within-bus variability, and do not incorporate the spatial correlations between different buses, called between-bus correlations. The within-bus information includes physical quantities, such as the active and reactive power, the real and imaginary components of the voltage at each individual bus.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty

2023

IEEE Access

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This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under uncertainty (OPF-ESDUU) of uncertain renewable energy and power loads (UREPL). This DDO method depends only on the uncertainty samples to yield an optimal solution that satisfies a specific confidence level, which is effective because of two resounding learning algorithms: Bayesian hierarchical modeling (BHM) and determinantal point process (DPP). Considering both the local bus information and spatial correlations over all buses, BHM learns the convex approximation of AC power flow (CAACPF) more accurately than the existing learning methods, converting the originally non-convex OPF-ESDUU to a convex optimization problem. DPP considers the correlations between samples to find a small set of significant samples by measuring the relative weight of each sample using the random matrix theory, significantly decreasing the data samples required by the existing SS. The experimental analysis in IEEE test cases shows that after considering data correlations, 1) BHM learns CAACPF better with 13-90% accuracy improvement on average, compared with the existing learning methods, and 2) the proposed DDO performs more efficiently than the existing DDO as DPP-based SS boosts the sampling efficiency by 50% at least. INDEX TERMSBayesian hierarchical modeling, determinantal point process, power flow, strategic sampling. NOMENCLATURE A. SETS AND INDICES n Number of buses (nodes) in the system. 𝑖, 𝑗 Index for the buses, 𝑖, 𝑗 =1, 2, …, n. 𝑇 ′ Index set of the time periods. T Superscript to denote the transpose operation. 𝑡 Index for any time-period, 𝑡 ∈ 𝑇′. 𝑔 Superscript to denote generators. 𝑛𝑒𝑡 Superscript to denote the net power injection. 𝑀 Scenario (sample) set. 𝑠 Superscript for the 𝑠-th sample, 𝑠 ∈ 𝑀. 𝑖 ′ 𝑗 ′ Index for the i'-th and j'-th samples. 𝒫 A point process on a finite set 𝒴. 𝒜 Any subset of 𝒴. B. PARAMETERS 𝜔 𝑝𝑖 (𝜔 𝑞𝑖 ) Participation factors of the active (reactive) power of the generator or energy storage. 𝑃 𝑗,𝑡(𝑓𝑟𝑡) 𝑛𝑒𝑡 Forecast values of the net active power injection of UREPL. 𝑄 𝑗,𝑡(𝑓𝑟𝑡) 𝑛𝑒𝑡 Forecast values of the net reactive power injection of UREPL. ∆𝑝 𝑗 , ∆𝑞 𝑗 Corresponding forecast errors of net active and reactive power injections. 𝛼 Probability level. 𝜖 Violation probability level. 𝛽 Confidence level. 𝑑 Number of decision variables. 𝑁 𝑢 , 𝑁 3 Minimum sample size (MSS) considered in strategic sampling-based DDO methods. 𝑁 1 , 𝑁 2 MSSs by the conventional scenario optimization (SO)-based methods. 𝑁 𝑑𝑝𝑝 MSS by DPP-based strategic sampling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty

2023

IEEE Access

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“…To cope with the intermittency of RESs' outputs and achieve a higher level of system flexibility, power system investors and planners have tried to suggest short-and long-term solutions [3,6]. Amongst, expanding the transmission capacity, called transmission expansion planning (TEP) [7] and employing energy storage systems (ESSs) [8,9] have been introduced as the pivotal solutions. Due to the required considerable capital investment cost in the expansion of TEP and ESSs, developing a proper optimization procedure to determine the optimal capacity/location of trans-mission lines and ESSs modules would be essential considering the adequate uncertainties [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [14] try to introduce a robust ESSs framework to alleviate the fluctuating generation of RESs based on a non‐convex non‐linear AC optimal power flow method, while reference [15] investigates an optimal transmission network reconfiguration model considering non‐linear AC power flow. In [8], a multi‐period optimal power system operation is presented considering the non‐linear ESSs and AC power flow models. Authors in [16] develop a tri‐level model to simultaneously determine the optimal sizing and siting of merchant BESS and TEP to maximize the BESS owners’ profit and minimize the cost of TEP decisions.…”

Section: Introductionmentioning

confidence: 99%

Harnessing power system flexibility under multiple uncertainties

Mazaheri

Saber

Fattaheian‐Dehkordi

et al. 2022

IET Generation Trans & Dist

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Increasing the intermittent outputs of renewable energy sources (RESs) has forced planners to define a new concept named flexibility. In this regard, some short‐ and long‐term solutions, such as transmission expansion planning (TEP) and energy storage systems (ESSs) have been suggested to improve the flexibility amount. A proper optimization procedure is required to choose an optimal solution to improve flexibility. Therefore, a mixed‐integer linear programming (MILP) direct‐optimization TEP versus ESSs co‐planning model is presented in this paper to enhance power system flexibility. In doing so, a novel RES‐BESS‐based grid‐scale system flexibility metric is proposed to investigate the improvement of flexibility amount via ESSs modules in the numerical structure. In this paper, a novel repetitive fast offline method has been proposed to quickly reach the desired amount of flexibility by defining an engineering price/benefit trade‐off to finally find the best investment plan. Also, multiple uncertainties associated with wind farms and demanded loads and a practical module‐type battery energy storage system (BESS) structure for each node are defined. The proposed model is applied to the modified IEEE 73‐bus test system including wind farms, where the numerical results prove the model efficiency as BESS impacts on flexibility, investment plans and power system economics.

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Security constrained optimal power flow solution of hybrid storage integrated cleaner power systems

Pattnaik

Dauda

Padhee

et al. 2023

Applied Thermal Engineering

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Optimal Operation of Power Systems With Energy Storage Under Uncertainty: A Scenario-Based Method With Strategic Sampling

Cited by 11 publications

References 52 publications

A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty

A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty

Harnessing power system flexibility under multiple uncertainties

Security constrained optimal power flow solution of hybrid storage integrated cleaner power systems

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