Abstract
Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method.
Highlights
IntroductionRecent developments of battery technologies have made battery prices drop sharply, and individual customers actively deploy the energy storage system (ESS) to minimize electricity cost considering time-varying electricity price and renewable generation such as solar and wind power
Recent developments of battery technologies have made battery prices drop sharply, and individual customers actively deploy the energy storage system (ESS) to minimize electricity cost considering time-varying electricity price and renewable generation such as solar and wind power.ESS is beneficial from a power system point of view, since it contributes to stabilizing the power grid
We have discussed the day-ahead optimization based on the forecasted load profile e
Summary
Recent developments of battery technologies have made battery prices drop sharply, and individual customers actively deploy the energy storage system (ESS) to minimize electricity cost considering time-varying electricity price and renewable generation such as solar and wind power. In minimizing total cost using ESS, energy management system needs to consider several factors such as load profiles, peak power, country-specific tariff, and time-of-use (TOU) pricing. To combat against load-forecasting error, we propose a real-time control mechanism on top of the day-ahead optimization. We propose a learning method to estimate the marginal power from the observed forecasting errors from historical dataset In this way we can schedule the ESS in a nearly optimal way to reduce peak power even in the presence of forecasting error while, at the same time, minimizing battery degradation and electricity cost. Our experimental results verify that the proposed method reduces peak load by about 30% and improves the profit by almost four times compared to the case of using only day-ahead optimization.
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