Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk

Khodabakhsh, Raheleh; Sirouspour, Shahin

doi:10.1109/tste.2016.2543024

Cited by 109 publications

(46 citation statements)

References 39 publications

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“…A multi-stage formulation for short-term trading with uncertain power from wind turbines and market prices is introduced in [43]. For grid-connected MGs a comparison between a scenario-based risk-averse stochastic and a worstcase optimisation approach is presented in [35].…”

Section: B Risk-based Approaches In Power Systemsmentioning

confidence: 99%

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Hans

Sopasakis

Raisch

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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In this paper we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenariobased optimisation problems which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst-case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadraticallyconstrained quadratic problem and its favourable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors.

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Section: B Risk-based Approaches In Power Systemsmentioning

confidence: 99%

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Hans

Sopasakis

Raisch

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…Formula (19) ensures that power supply satisfies the threshold of uncertain power deficiency (PDT). Constraint (20) makes operating time (load pickup time) t m within reasonable time interval. T min,m is taken into account to guarantee the availability of wind farm output and the suitability of system status for load pickup.…”

Section: A Construction Of the Utility-oriented Optimizationmentioning

confidence: 99%

“…Thirdly, a utility-oriented optimization is formulated to determine the optimal strategy with security constraints. Furthermore, by applying the optimization advantage of CPD [19]- [20] and the linear programming of AC power flow (LPAC) method [21], the utility-oriented optimization converts into a scenario-based linear programming (LP).…”

mentioning

confidence: 99%

Utility-Oriented Online Load Restoration Considering Wind Power Penetration

Zhao

Wang

Liu

et al. 2019

IEEE Trans. Sustain. Energy

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An efficient load recovery of a bulk system with wind power penetration requires careful consideration of uncertainties related to it, as well as on-line data gathered from measurement devices. From the perspective of uncertainties, a novel utility-based decision-making method is proposed in this paper. The method combines both risk and return of load restoration strategy to assist decision-making in uncertain states, and it also provides utility function to present preference of load restoration strategy. Furthermore, a utility-oriented optimization model is created to select the strategy with the largest utility value with a certain confidence level. To achieve efficient utilization of on-line data, as well as to ensure high computation efficiency, the utilityoriented optimization is transformed into a scenario-based linear programming model. The proposed method fills the gap between on-line data and the optimal load restoration strategy in uncertain condition. Besides, the optimal strategy is provided with adjustable robustness according to security requirements and data exactness. Therefore, it is particularly applicable for on-line load restoration with wind power penetration. The effectiveness of the proposed method is validated using IEEE-30 bus test system and an actual power system from the North-East of Shandong province, China.Index Terms-conditional value-at-risk, power system restoration, uncertain decision, wind uncertainty.

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“…where (X) + refers to the positive component of x . Equation (25) shows that minimizing CVaR is formulated as a simple linear programming problem, which makes it attractive in practical applications [29]. The robust nonlinear constraint in (23) can be represented using CVaR, which is transformed into a tractable formulation in (25).…”

Section: Robust Counterpart Formulationsmentioning

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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Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk

Cited by 109 publications

References 39 publications

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Utility-Oriented Online Load Restoration Considering Wind Power Penetration

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

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