A stochastic unit commitment model for integrating renewable supply and demand response

Papavasiliou, Anthony; Oren, Shmuel S.

doi:10.1109/pesgm.2012.6344858

Cited by 31 publications

(20 citation statements)

References 34 publications

(44 reference statements)

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“…The main parameters of units and DR providers can be obtained from Tables 1 and 2, respectively. Two kinds of wind power profiles, shown in Figure 3b are considered [33][34][35]. The wind power profiles were created using data from the National Renewable Energy Laboratory (NREL) Western Wind Integration Study based on two Southern California locations [36].…”

Section: Case Studymentioning

confidence: 99%

“…The wind power profiles were created using data from the National Renewable Energy Laboratory (NREL) Western Wind Integration Study based on two Southern California locations [36]. Combining the two kinds of 24 h original wind output profiles with the peak-normalized aggregate load benchmark in Two kinds of wind power profiles, shown in Figure 3b are considered [33][34][35]. The wind power profiles were created using data from the National Renewable Energy Laboratory (NREL) Western Wind Integration Study based on two Southern California locations [36].…”

Section: Case Studymentioning

confidence: 99%

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

et al. 2015

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Abstract:In this paper, a two-stage stochastic unit commitment (UC) model considering flexible scheduling of demand response (DR) is proposed. In the proposed UC model, the DR resources can be scheduled: (1) in the first stage, as resources on a day-ahead basis to integrate the predicted wind fluctuation with lower uncertainty; (2) in the second stage, as resources on an intra-day basis to compensate for the deviation among multiple wind power scenarios considering the coupling relationship of DR on available time and capacity. Simulation results on the Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and IEEE 118-bus system indicate that the proposed model can maximize the DR value with lower cost. Moreover, different types of DR resources may vary in the contract costs (capacity costs), the responsive costs (energy costs), the time of advance notice, and the minimum on-site hours. The responsive cost is considered as the most important factor affecting DR scheduling. In addition, the first-stage DR is dispatched more frequently when transmission constraints congestion occurs.

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Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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

et al. 2015

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“…Among the various residential load types, thermostatic loads such as air conditioner (AC) and water heater (WH) loads, and deferrable loads such as electric vehicle (EV) loads are placed at the top of the merit list of DR appliances due to their greater load shifting potential and larger daily energy demand share [14][15][16]. Recently, the load management benefits of controlled appliances for the increased deployment of intermittent renewable generation have been investigated [17][18][19][20][21]. A stochastic unit commitment model is presented for assessing the reserve requirements resulting from the integration of renewable sources in [17], where alternative DR paradigms for assessing the benefits of demand-side flexibility on absorbing the variability and uncertainty of renewable supply are investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the load management benefits of controlled appliances for the increased deployment of intermittent renewable generation have been investigated [17][18][19][20][21]. A stochastic unit commitment model is presented for assessing the reserve requirements resulting from the integration of renewable sources in [17], where alternative DR paradigms for assessing the benefits of demand-side flexibility on absorbing the variability and uncertainty of renewable supply are investigated. Aghaei et al [18] considered flexible loads as a storage device in a hybrid power plant of a wind farm to compensate for wind power imbalances and proposed a procedure to derive the strategic offer for a hybrid power plant selling energy in the pool-based market.…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

2017

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Abstract:The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system's imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers' comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

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“…In contrast, stochastic unit commitment procedures [16,24,25] assume the availability of a number of forecast scenarios, each representing a distinct potential time series of the forecasted quantities. Throughout, we use the term scenario in a narrow sense, representing a full specification of all random data required to instantiate a unit commitment problem, with associated probability of occurrence.…”

Section: Introductionmentioning

confidence: 99%

Toward scalable stochastic unit commitment. Part 1: load scenario generation

et al. 2015

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Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra-ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce-narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi-tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method-ology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit com-mitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark. Abstract Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessitates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation methodology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit commitment solvers, as described in the companion paper. The scenarios generated Disciplines Industrial Engineering | Systems Engineering

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A stochastic unit commitment model for integrating renewable supply and demand response

Cited by 31 publications

References 34 publications

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

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

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

Toward scalable stochastic unit commitment. Part 1: load scenario generation

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