Data-Driven Risk-Averse Two-Stage Optimal Stochastic Scheduling of Energy and Reserve With Correlated Wind Power

Xu, Xiaoyuan; Yan, Zheng; Shahidehpour, Mohammad; Li, Zhiyi; Kong, Xiangrui

doi:10.1109/tste.2019.2894693

Cited by 97 publications

(41 citation statements)

References 28 publications

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“…Kernel density estimation is a widely used method in nonparametric estimation. In [38], Kernel density estimation is applied to construct an ellipsoidal uncertainty set and shows well performance. But the disadvantage is that the influence of boundary points should be considered in Kernel density estimation.…”

Section: Ambiguity Set Constructionmentioning

confidence: 99%

“…Problem (38) can be solved with the existing commercial solver. The procedure to solve the energy and reserve dispatch problem is summarized in Algorithm 1 and the flow chart of the algorithm is described in Figure 1.…”

Section: Solution Strategymentioning

confidence: 99%

“…IEEE 118-bus system contains 19 generators, 35 synchronous condensers, 177 lines, 9 transformers, and 91 loads with total load demand 5500 MW. Detail data can be obtained from online at http://labs.ece.uw.edu/pstca/pf118/ieee118psp.txt [38]. The same hypothetical historical data of Figure 5 depicts that with the increases of the ambiguity index η, the objective value increases.…”

Section: -Bus Systemmentioning

confidence: 99%

“…Energy and Reserve Dispatch. Collect historical data regarding variable uncertainties; Construct the uncertainty set using KL divergence method;3: Design an empirical distribution using(20) and(21); Reformulate the energy and reserve dispatch problem into(33); Decompose the optimization problem into (34) and(35); Use the SAA method to approximate problem(36); Solve problem(38).…”

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confidence: 99%

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Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

et al. 2019

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This paper proposes a distance-based distributionally robust energy and reserve (DB-DRER) dispatch model via Kullback-Leibler (KL) divergence, considering the volatile of renewable energy generation. Firstly, a two-stage optimization model is formulated to minimize the expected total cost of energy and reserve (ER) dispatch. Then, KL divergence is adopted to establish the ambiguity set. Distinguished from conventional robust optimization methodology, the volatile output of renewable power generation is assumed to follow the unknown probability distribution that is restricted in the ambiguity set. DB-DRER aims at minimizing the expected total cost in the worst-case probability distributions of renewables. Combining with the designed empirical distribution function, the proposed DB-DRER model can be reformulated into a mixed integer nonlinear programming (MINLP) problem. Furthermore, using the generalized Benders decomposition, a decomposition method is proposed and sample average approximation (SAA) method is applied to solve this problem. Finally, simulation result of the proposed method is compared with those of stochastic optimization and conventional robust optimization methods on the 6-bus system and IEEE 118-bus system, which demonstrates the effectiveness and advantages of the method proposed.

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Section: Ambiguity Set Constructionmentioning

confidence: 99%

Section: Solution Strategymentioning

confidence: 99%

Section: -Bus Systemmentioning

confidence: 99%

mentioning

confidence: 99%

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Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

et al. 2019

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“…Recently, data-driven robust optimization has been proposed to overcome the deficiency of solution conservation by considering the information knowledge available in the statistical data. Compared to many existing data-driven methods [22][23][24], a new method of data analysis is developed for addressing the characteristics of the uncertainty, from the perspective of hypothesis testing. Moreover, in our work, the proposed uncertainty model is adaptive and flexible, so we can incorporate statistical information into the uncertainty model when such variable information is available.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Han

Yang

Sun

et al. 2019

IEEJ Transactions Elec Engng

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To facilitate energy system integration, it is imperative that a multienergy system produce and deliver in a coordinated way the energy in its different component forms. In particular, a small-scale integrated energy system must accommodate renewable energy resources, flexible loads, and energy coupling technologies, which creates new challenges to the interactions between the energy vectors. Hence, an energy management model for a microenergy system in grid-connected mode under uncertainties is proposed to perform the decision-making for shifting energy modes for all energy sources and end-use applications with the aim of optimally scheduling controllable energy resources in the system and minimizing the net management cost under uncertainty. To address the stochastic nature of the price of electricity, a data-driven robust optimization approach is introduced. It uses the available sample data to design the appropriate uncertainty set using statistical hypothesis tests, and a combination of conditional value-at-risk and a worst-case optimization problem is used to formulate the energy management problem under uncertainty. We explore a computationally tractable robust counterpart of the original optimization problem. The optimal energy scheduling solution obtained from the proposed approach is immune against any worst-case realizations. Numerical results demonstrate the effectiveness of the proposed approach and its performance of less conservation.

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Overview of TCLs in Smart Grids

Meng

Gao

2021

Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy

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Data-Driven Risk-Averse Two-Stage Optimal Stochastic Scheduling of Energy and Reserve With Correlated Wind Power

Cited by 97 publications

References 28 publications

Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Overview of TCLs in Smart Grids

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