Practical Operation Strategies for Energy Storage System under Uncertainty

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.

Cloud Energy Storage System Operation with Capacity P2P Transaction

“…, the charging efficiency u c , and the discharging efficiency u d as in (7). Since scheduling is done daily, net energy transfer is zero during one day as in (8). The constraint (9) is used to formulate peak minimization in the form of linear programming; η i plays a role of target peak load on the scheduling day.…”

“…From another aspect, energy cost is the main concern of customers. Specifically, large industrial customers have huge electricity costs, so they actively seek a way to reduce electricity costs, and using ESS becomes promising by taking advantage of energy buffering [5][6][7][8][9]. The basic operation of ESS is to charge when the electricity price low and discharge when the electricity price is high [9].…”

“…The basic operation of ESS is to charge when the electricity price low and discharge when the electricity price is high [9]. Considering that electricity cost is highly dependent on the peak load, ESS can be also used to shave peak loads [5,8,9]. In doing this, the uncertainty of renewable generation and/or user loads should be taken into account.…”

“…Specifically, to minimize the peak load under uncertainty, robust optimization has been studied [5]. Another approach is to compensate the load forecasting error by introducing a concept of marginal power using machine learning [8].…”

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

“…e degradation of battery energy storage system (BESS) depends on many factors, such as external temperature, internal resistance, depth of discharge (DOD), and a number of cycles [31]. e optimal scheduling of distributed energy resources in a microgrid is the proposed model considering system constraints.…”