Risk-Based Distributionally Robust Real-Time Dispatch Considering Voltage Security

Li, Peng; Wang, Chengfu; Wu, Qiuwei; Yang, Ming

doi:10.1109/tste.2020.2964949

Cited by 25 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the CSP power station, the relationship between the input and output energy of the turbine is shown in (12):…”

Section: Operation Mechanism Of Concentrating Solar Powermentioning

confidence: 99%

“…In addition, the distance of the known distribution function [10], data-driven methods [11], and how to accurately construct the ambiguity set and dock it with the traditional robust scheduling model is the key. There have been many studies on the application of the distributionally robust optimization method to solve the uncertain optimal scheduling problem [12][13][14][15], but there are still gaps in solving the uncertain problem of N-k security criteria with the distributionally robust optimization method. Therefore, it is very important to extract the statistical information of transmission line faults from the existing data, incorporate the N-k safety criterion into the economic scheduling, and make economic scheduling decisions that take both economy and security into account.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributionally Robust Unit Commitment with N-k Security Criterion and Operational Flexibility of CSP

Pei

Han

et al. 2022

Energies

View full text Add to dashboard Cite

In order to reduce the conservatism of the robust optimization method and the complexity of the stochastic optimization method and to enhance the ability of power systems to deal with occasional line fault disturbance, this paper proposes a distributionally robust unit commitment (DRUC) model with concentrating solar power (CSP) operational flexibility and N-k safety criterion under distributed uncertainty. According to the limited historical sample data, under the condition of satisfying a certain confidence level, based on the imprecise Dirichlet model (IDM), an ambiguity set is constructed to describe the uncertainty of transmission line fault probability. Through the identification of the worst probability distribution in the ambiguity set, the adaptive robust optimal scheduling problem is transformed into a two-stage robust optimization decision model under the condition of deterministic probability distribution. The CSP flexibility column and constraint generation (C&CG) algorithm is used to process the model and the main problem and subproblem are solved by using the Big-M method, linearization technique, and duality principle. Then, a mixed integer linear programming problem (MILP) model is obtained, which effectively reduces the difficulty of solving the model. Finally, case studies on the IEEE 14 bus system and the IEEE 118 bus system demonstrate the efficiency of the proposed method, such as enhancing the ability of power systems to cope with occasional line fault disturbances and reducing the conservatism of the robust optimization method.

show abstract

“…In the CSP power station, the relationship between the input and output energy of the turbine is shown in (12):…”

Section: Operation Mechanism Of Concentrating Solar Powermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Unit Commitment with N-k Security Criterion and Operational Flexibility of CSP

Pei

Han

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…In recent years, the distributionally robust optimization (DRO) has been considered a more effective way to handle uncertainty in multiple power systems applications, such as economic dispatch [9]- [11], power dispatch [12]- [16], unit commitment [17]- [19], and voltage regulation and reactive power control [20], [21]. The DRO techniques construct an ambiguity set of probability distributions based on historical data, including all possible uncertainty distributions.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the ambiguity set of DRO can be constructed by the momentbased method and metric-based ambiguity set [15]. For example, the first two moments (e.g., mean and variance) are used to build moment-based ambiguity set in [9], [10], [12], [13], [20]. Another type of ambiguity set is constructed by the metricbased method [11], [16]- [19], [21], where the possible distributions are centered at the reference distribution with a certain distance metric based on the training samples.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, the moment-based DRO methods can construct the ambiguity set of multiple uncertainties with a reasonable assumption of distribution [22]. Most of the DRO methods based on metric-based ambiguity set are computational intractability and need relaxation transformation to obtain trackable models [23], such as KL-diverge and Wasserstein metric ball, which may lead to an infeasible solution [20]. The computational burden of the metric-based DRO model grows heavily with the number of employed data [24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

Zhang

Guo³

et al. 2024

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable generation not only brings clean power but also challenges the voltage regulation and energysaving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.

show abstract

Virtual power plant formation strategy based on Stackelberg game: A three-step data-driven voltage regulation coordination scheme

Esfahani,

Alizadeh,

Cao

et al. 2025

Applied Energy

View full text Add to dashboard Cite

Risk-Based Distributionally Robust Real-Time Dispatch Considering Voltage Security

Abstract: His research interests are operation and control of power systems with high penetration of renewables, including wind power modeling and control, active distribution networks, and operation of integrated energy systems. He

Cited by 25 publications

References 42 publications

Distributionally Robust Unit Commitment with N-k Security Criterion and Operational Flexibility of CSP

Distributionally Robust Unit Commitment with N-k Security Criterion and Operational Flexibility of CSP

Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

Virtual power plant formation strategy based on Stackelberg game: A three-step data-driven voltage regulation coordination scheme

Contact Info

Product

Resources

About