A Two-stage Rolling Scheduling Strategy for Battery Energy Storage in Multi-periods Electricity Market

Yin, Wenqian; Liu, Haoming; Ni, Xiangsheng; Zhou, Hui; Hou, Yunhe

doi:10.1109/pesgm.2018.8586254

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…In contrast with the approaches mentioned above, some studies also explicitly address several electricity markets, where electricity is traded at different sequential closing times for a specific fulfillment date. Most prominently, the day-ahead and intraday markets are analyzed by Yin et al [22], Xu et al [23], Ji et al [24], and Abdeltawab et al [25], but Nguyen Duc and Nguyen Hong [26] also address balance capacity. Again, most authors focus on electrical systems, and only a few analyze sector-coupled energy systems.…”

Section: Introductionmentioning

confidence: 99%

Planning of Reserve Storage to Compensate for Forecast Errors

Koch,

Bensmann,

Eckert

et al. 2024

Energies

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Forecasts and their corresponding optimized operation plans for energy plants never match perfectly, especially if they have a horizon of several days. In this paper, we suggest a concept to cope with uncertain load forecasts by reserving a share of the energy storage system for short-term balancing. Depending on the amount of uncertainty in the load forecasts, we schedule the energy system with a specific reduced storage capacity at the day-ahead market. For the day of delivery, we examine the optimal thresholds when the remaining capacity should be used to balance differences between forecast and reality at the intraday market. With the help of a case study for a simple sector-coupled energy system with a demand for cooling, it is shown that the energy costs could be reduced by up to 10% using the optimal reserve share. The optimal reserve share depends on the forecast quality and the time series of loads and prices. Generally, the trends and qualitative results can be transferred to other systems. However, of course, an individual evaluation before the realization is recommended.

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Section: Introductionmentioning

confidence: 99%

Planning of Reserve Storage to Compensate for Forecast Errors

Koch,

Bensmann,

Eckert

et al. 2024

Energies

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“…Hence, Xu et al (2018) chose to formulate an optimal intraday rolling operation model of energy storage with the prediction data during the intraday. Yin et al, (2018) adopted model predictive control (MPC) to realize the intraday rolling optimal scheduling of ESSs. However, the guidance of day-ahead schedules to the intraday operation was ignored in the above works.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach

2021

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There is a growing tendency for industrial consumers to invest in both photovoltaic (PV) and energy storage systems (ESSs) to meet their electricity requirements. However, the uncertainty of load demand and PV output brings great challenges for ESS operation. In this paper, a stochastic model predictive control (MPC) approach-based energy management strategy for ESSs is proposed. A non-parametric probabilistic prediction method embedded in time series correlation is adopted to describe the uncertainty of load demand and PV output. Then, a two-stage energy management model is proposed aiming at minimizing the total operation cost. The upper stage can generate an hourly operation strategy for ESSs, while the lower stage focuses on a more detailed minute-level operation strategy. The hourly operation strategy is also used as a basis to guide the ESS operation in the lower stage. Besides, a chance constraint was introduced to achieve a win–win solution between PV power consumption and electricity tariff, while the terminal value constraint of the capacity of ESSs to better cope with the uncertainty beyond the prediction time window. Finally, the numerical results showed that the proposed method can achieve an effective ESS energy management strategy.

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A Two-stage Rolling Scheduling Strategy for Battery Energy Storage in Multi-periods Electricity Market

Cited by 2 publications

References 9 publications

Planning of Reserve Storage to Compensate for Forecast Errors

Planning of Reserve Storage to Compensate for Forecast Errors

Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach

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