Wind Integration in Power Systems: Operational Challenges and Possible Solutions

Xie, Le; Carvalho, Pedro; Ferreira, L.A.F.M.; Liu, Juhua; Krogh, Bruce H.; Popli, Nipun; Ilić, Marija

doi:10.1109/jproc.2010.2070051

Cited by 341 publications

(37 citation statements)

References 51 publications

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“…Current levels of wind capacity do not pose a large problem for grid operators in most regions, but future growth will require operational changes in order to ensure a reliable grid (Xie et al 2011). Since wind power is not dispatchable, wind forecasts are used along with load forecasts to make operational decisions ranging from minutes to days in advance (Botterud et al 2010).…”

Section: Introductionmentioning

confidence: 99%

What day-ahead reserves are needed in electric grids with high levels of wind power?

Mauch

Apt

Carvalho

et al. 2013

Environ. Res. Lett.

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Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16-0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07-0.13 MW of dispatchable capacity for each MW of additional wind capacity.

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Section: Introductionmentioning

confidence: 99%

What day-ahead reserves are needed in electric grids with high levels of wind power?

Mauch

Apt

Carvalho

et al. 2013

Environ. Res. Lett.

Self Cite

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“…Number of time interval related to the day-ahead collaborative scheduling N τ Number of time interval related to the hour-ahead optimization ∆t Length of a single time interval of the day-ahead collaborative scheduling ∆τ Length of a single time interval of the hour-ahead optimization Weighting factor of battery swapping demand curtailment ρ 2 Weighting factor of wind curtailment ρ 3 Weighting factor of output power deviation P t,τ wn Wind power at time τ within the hour t P t,τ wq Wind curtailment power at time τ within the hour t P…”

Section: N Tmentioning

confidence: 99%

“…With the rapid deployment of wind power, the impact of its randomness and uncontrollability on the power grid is also becoming increasingly challenging [2][3][4][5]. Thus, the penetration level of wind power is restricted.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

Jiang

Han

et al. 2016

Energies

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Abstract:In order to deal with the uncertainties of wind power, wind farm and electric vehicle (EV) battery switch station (BSS) were proposed to work together as an integrated system. In this paper, the collaborative scheduling problems of such a system were studied. Considering the features of the integrated system, three indices, which include battery swapping demand curtailment of BSS, wind curtailment of wind farm, and generation schedule tracking of the integrated system are proposed. In addition, a two-stage multi-objective collaborative scheduling model was designed. In the first stage, a day-ahead model was built based on the theory of dependent chance programming. With the aim of maximizing the realization probabilities of these three operating indices, random fluctuations of wind power and battery switch demand were taken into account simultaneously. In order to explore the capability of BSS as reserve, the readjustment process of the BSS within each hour was considered in this stage. In addition, the stored energy rather than the charging/discharging power of BSS during each period was optimized, which will provide basis for hour-ahead further correction of BSS. In the second stage, an hour-ahead model was established. In order to cope with the randomness of wind power and battery swapping demand, the proposed hour-ahead model utilized ultra-short term prediction of the wind power and the battery switch demand to schedule the charging/discharging power of BSS in a rolling manner. Finally, the effectiveness of the proposed models was validated by case studies. The simulation results indicated that the proposed model could realize complement between wind farm and BSS, reduce the dependence on power grid, and facilitate the accommodation of wind power.

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“…A look-ahead economic dispatch approach is proposed in [15] with the objective of minimizing the total production cost. For each sub-period, based on the latest forecast information for a prespecified time horizon, the look-ahead dispatch model formulates a dispatch plan in a rolling manner [16], and only the results of the first one or several periods are actually executed.…”

Section: Introductionmentioning

confidence: 99%

Optimization Scheduling Method for Power Systems Considering Optimal Wind Power Intervals

2018

Energies

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Wind power intervals with different confidence levels have an impact on both the economic cost and risk of dispatch plans for power systems with wind power integration. The higher the confidence level, the greater the bandwidth of corresponding intervals. Thus, more reserves are needed, resulting in higher economic cost but less risk. In order to balance the economic cost and risk, a unit commitment model based on the optimal wind power confidence level is proposed. There are definite integral terms in the objective function of the model, and both the integrand function and integral upper/lower bound contain decision variables, which makes it difficult to solve this problem. The objective function is linearized and solved by discretizing the wind power probability density function and using auxiliary variables. On the basis, a rolling dispatching model considering the dynamic regulation costs among multiple rolling plans is established. In addition to balancing economic cost and risk, it can help to avoid repeated regulations among different rolling plans. Simulations are carried on a 10-units system and a 118-bus system to verify the effectiveness of the proposed models.

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Wind Integration in Power Systems: Operational Challenges and Possible Solutions

Cited by 341 publications

References 51 publications

What day-ahead reserves are needed in electric grids with high levels of wind power?

What day-ahead reserves are needed in electric grids with high levels of wind power?

Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

Optimization Scheduling Method for Power Systems Considering Optimal Wind Power Intervals

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