Addressing the Conditional and Correlated Wind Power Forecast Errors in Unit Commitment by Distributionally Robust Optimization

Zheng, Xiaodong; Qu, Kaiping; Lv, Jiaqing; Li, Zhengmao; Zeng, Bo

doi:10.1109/tste.2020.3026370

Cited by 50 publications

(27 citation statements)

References 30 publications

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“…Constraints ( 28) and ( 29) enforce minimum-up-time and -down-time restrictions, respectively. Constraints (30) define the values of s ti and h ti based on intertemporal changes in u ti . Constraints (31) and ( 32) impose integrality restrictions and ( 33) and ( 34) impose non-negativity.…”

Section: B Model Formulationmentioning

confidence: 99%

“…Alternatively, operational wind-integration costs can be reduced by modifying power-system operations. Such adjustment can be done using a stochastic, robust, or distributionally robust approach to modeling unit commitment [25] - [30]. Such approaches account explicitly for uncertain real-time wind availability in deciding unit commitment and dispatch.…”

Section: Introductionmentioning

confidence: 99%

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Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Mansouri

Sioshansi

2022

Journal of Modern Power Systems and Clean Energy

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Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational adjustments. We examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time dispatch. Using a simple stylized example and a case study that is based on ISO New England, we compare system-operation costs with and without interim recommitment. We find an important tradeoff-later recommitment provides better wind-availability forecasts, but the system has less flexibility due to operating constraints. Of the time windows that we examine, hour-20 recommitment provides the greatest operational-cost reduction.

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Section: B Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Mansouri

Sioshansi

2022

Journal of Modern Power Systems and Clean Energy

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“…Several approaches have been presented in the literature to model the uncertainties associated with the output generation of renewable-based generating units [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. However, some works have taken a deterministic approach when dealing with the output power of solar panels [27][28][29].…”

Section: Solar Panels Output Uncertainty Modelmentioning

confidence: 99%

“…Moreover, in [30,31] robust optimization method is used to consider the uncertainties, therefore the worst scenarios are characterized in these works. On the other hand, distributional robust optimization (DRO) are used in the literature which is a modelling approach that assumes only partial distributional information [32], whereas probabilistic optimization assumes complete distributional information. As the probability distribution of the solar radiation uncertainty is known, a probabilistic approach is preferred in this work.…”

Section: Solar Panels Output Uncertainty Modelmentioning

confidence: 99%

Flexibility provision of residential energy hubs with demand response applications

Ostovar

Moeini‐Aghtaie

Hadi

2022

IET Generation Trans & Dist

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Smart grids help local distribution companies (LDCs) facilitate the communication between grid operators and residential prosumers. While the prosumers are striving to optimize their daily load profile by home energy management systems (HEMSs) deployment, the LDCs attempt to enhance the operation of the grid in an efficient way and decrease network losses. This paper attempts to develop a new bi-level probabilistic optimization framework wherein an HEMS optimizes its respective daily load profile, and determines its flexibility provision, which is communicated to an LDC. In the proposed framework, a decentralized approach is used to achieve the flexibility product, which is the modulation of energy, through the incentive-based demand response (DR) programs. Finally, the LDC applies the prosumer's flexibility' offers to optimize its operational performance. The twopoint estimate method (2PEM) is employed to model the uncertainties. The applicability of the framework is demonstrated by applying it to a system with one residential feeder.

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“…Because of these advantages, distributionally robust optimization has been successfully applied in different fields of power systems, including unit commitment [13], optimal power flow [14], energy and reserve co-dispatch [15], and integrated energy systems [16]. Reference [17] studied a day-ahead unit commitment problem with stochastic wind power generations, where a distributionally robust optimization approach was employed to address wind power forecast errors. In this regard, the spatiotemporal correlation in wind power generations was captured appropriately.…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Ji¹,

Wang²,

Deng³

et al. 2022

Energy Engineering

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Virtual power plants can effectively integrate different types of distributed energy resources, which have become a new operation mode with substantial advantages such as high flexibility, adaptability, and economy. This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal dayahead dispatch under uncertainties of renewable energy sources. The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments. In this regard, the faults of stochastic optimization and traditional robust optimization can be overcome. Firstly, a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed, and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in dayahead electricity markets. Then, an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem, which improves the computational efficiency of the model. Finally, the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy. They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

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Addressing the Conditional and Correlated Wind Power Forecast Errors in Unit Commitment by Distributionally Robust Optimization

Cited by 50 publications

References 30 publications

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Flexibility provision of residential energy hubs with demand response applications

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

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