Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration

Wang, Beibei; Liu, Xiaocong; Zhu, Fu Rong; Hu, Xin; Ji, Wenlu; Shuzi, Yang; Wang, Ke; Feng, Shuhai

doi:10.3390/en81212390

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…are the upward reserve and the downward reserve of the thermal unit, respectively. Based on the mathematical transformation in the Section 2.2, the reserve shortage can be rationally modelled by CCGP by the Equation (19), which refers to the deviation between the actual reserve capacity and the required reserve to cover the given risk level. In the terms of risk measure, the reserve shortage reflects the VaR of the scheduling under the given risk level:…”

Section: Fscvar Based On System Reserve Shortagementioning

confidence: 99%

“…Integrated into the power system as dispatchable resources, demand response (DR) programs have the potential to accommodate the uncertainty of wind power, and the traditional DR is dispatched as deterministic consumer behaviors [19]. However, due to the change in consumption behaviors, lack of policy attention, and other arbitrary factors, the actual response under DR is uncertain [20].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

Yin

Zhao

2018

Energies

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With the increasing penetration of wind power and demand response integrated into the grid, the combined uncertainties from wind power and demand response have been a challenging concern for system operators. It is necessary to develop an approach to accommodate the combined uncertainties in the source side and load side. In this paper, the fuzzy stochastic conditional value-at-risk criterions are proposed as the risk measure of the combination of both wind power uncertainty and demand response uncertainty. To improve the computational tractability without sacrificing the accuracy, the fuzzy stochastic chance-constrained goal programming is proposed to transfer the fuzzy stochastic conditional value-at-risk to a deterministic equivalent. The operational risk of forecast error under fuzzy stochastic conditional value-at-risk assessment is represented by the shortage of reserve resource, which can be further divided into the load-shedding risk and the wind curtailment risk. To identify different priority levels for the different objective functions, the three-stage day-ahead unit commitment model is proposed through preemptive goal programming, in which the reliability requirement has the priority over the economic operation. Finally, a case simulation is performed on the IEEE 39-bus system to verify the effectiveness and efficiency of the proposed model.

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Section: Fscvar Based On System Reserve Shortagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

Yin

Zhao

2018

Energies

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“…Existing studies on the flexibility problem can be categorized into two types: those that discuss the application of flexible resources, such as energy storage systems, and demand-side resources (DSRs) to mitigate the variability and uncertainty [3,[17][18][19][20]; and those that investigate operational methods such as the unit commitment (UC) and power flow to effectively respond to increased variability and uncertainty [21][22][23][24][25][26][27][28]. In the latter category, the effects of variability and uncertainty are restricted using ramp-up/down constraints (collectively known as ramp constraints) in stochastic and deterministic UC formulations [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Impact of Variability and Uncertainty on Power System Flexibility

Min

2019

Applied Sciences

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Featured Application: This study presents a method for separating the variability and uncertainty in a power system, and determining which is more influential in terms of flexibility, based on the flexibility index, named the ramping capability shortage probability (RSP). A process of scenario generation and sensitivity analysis is also proposed, and applied to a modified IEEE-RTS-96. The proposed method can evaluate the individual effect of variability and uncertainty and effectively provide the system operator with information that will enable more efficient operation and planning of a power system. Abstract:This study investigates the impact of variability and uncertainty on the flexibility of a power system. The variability and uncertainty make it harder to maintain the balance between load and generation. However, most existing studies on flexibility evaluation have not distinguished between the effects of variability and uncertainty. The countermeasures to address variability and uncertainty differ; thus, applying strategies individually tailored to variability and uncertainty is helpful for more efficient operation and planning of a power system. The first contribution of this study is in separating the variability and uncertainty, and determining which is more influential in terms of flexibility in specific system situations. A flexibility index, named the ramping capability shortage probability (RSP), is used to quantify the extent to which the variability and uncertainty affect the flexibility. The second contribution is to generate various scenarios for variability and uncertainty based on a modified IEEE-RTS-96, to evaluate the flexibility. The penetration level of renewable energy resources is kept the same in each scenario. The results of a sensitivity analysis show that variability is more effective than uncertainty for high and medium net loads.

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“…Loads can be effective in mitigating congestion issues that occur when the capacity of some distribution lines are overloaded [10]. This type of contingency may happen far from the typically centralized grid operator controls.…”

Section: Introductionmentioning

confidence: 99%

Electrolyzers Enhancing Flexibility in Electric Grids

et al. 2017

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This paper presents a real-time simulation with a hardware-in-the-loop (HIL)-based approach for verifying the performance of electrolyzer systems in providing grid support. Hydrogen refueling stations may use electrolyzer systems to generate hydrogen and are proposed to have the potential of becoming smarter loads that can proactively provide grid services. On the basis of experimental findings, electrolyzer systems with balance of plant are observed to have a high level of controllability and hence can add flexibility to the grid from the demand side. A generic front end controller (FEC) is proposed, which enables an optimal operation of the load on the basis of market and grid conditions. This controller has been simulated and tested in a real-time environment with electrolyzer hardware for a performance assessment. It can optimize the operation of electrolyzer systems on the basis of the information collected by a communication module. Real-time simulation tests are performed to verify the performance of the FEC-driven electrolyzers to provide grid support that enables flexibility, greater economic revenue, and grid support for hydrogen producers under dynamic conditions. The FEC proposed in this paper is tested with electrolyzers, however, it is proposed as a generic control topology that is applicable to any load.

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Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration

Cited by 16 publications

References 29 publications

Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

Analyzing the Impact of Variability and Uncertainty on Power System Flexibility

Electrolyzers Enhancing Flexibility in Electric Grids

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