Robust Operation of Energy Storage System with Uncertain Load Profiles

Abstract

Abstract:In this paper, we propose novel techniques to reduce total cost and peak load of factories from a customer point of view. We control energy storage system (ESS) to minimize the total electricity bill under the Korea commercial and industrial (KCI) tariff, which both considers peak load and time of use (ToU). Under the KCI tariff, the average peak load, which is the maximum among all average power consumptions measured every 15 min for the past 12 months, determines the monthly base cost, and thus peak load control is extremely critical. We aim to leverage ESS for both peak load reduction based on load prediction as well as energy arbitrage exploiting ToU. However, load prediction inevitably has uncertainty, which makes ESS operation challenging with KCI tariff. To tackle it, we apply robust optimization to minimize risk in a real environment. Our approach significantly reduces the peak load by 49.9% and the total cost by 10.8% compared to the case that does not consider load uncertainty. In doing this we also consider battery degradation cost and validate the practical use of the proposed techniques.

Practical Operation Strategies for Energy Storage System under Uncertainty

“…ESS is also beneficial from a power system point of view, since it contributes to stabilizing the power grid. Thus, there have been many studies to minimize total electricity cost using ESS [1][2][3][4][5][6][7][8].…”

“…In the literature, the ESS operation problem is formulated to have a linear objective function, and the solution can be obtainable using linear programming [2]. Please note that peak cut requires min-max operation, and linear programming can be used for this purpose [3]. In case operational constraints include binary decision variables, mixed-integer linear programming was used [4].…”

“…The works in [2,4,7], however, assumed that short-term load-forecasting is error-free. Indeed, peak cut using ESS is challenging because of the uncertainty of load profile, and it is known that peak cut may be vulnerable to load-forecasting error [3,9]; even if there is small load-forecasting error, the peak power originated both from the load and ESS operation can significantly vary. This is because battery scheduling for peak cut depends on the forecasted load profile [5], and small forecasting error may cause another unwanted peak power.…”

“…This is because battery scheduling for peak cut depends on the forecasted load profile [5], and small forecasting error may cause another unwanted peak power. Strategies for managing peak power may differ from countries to countries; for example, under Korea commercial and industrial (KCI) tariff, the peak power measured for 15 min determines the monthly base cost, and it may affect up to the next 12 months [3]. Thus, managing peak power is of prime importance under KCI tariff.…”

“…Since the charging and discharging scheduling of ESS is typically done day-ahead, under the presence of forecasting error, day-ahead scheduling can be problematic, specifically for managing peak load. To mitigate the adverse impact of forecasting error on peak cut, the authors in [3] use robust optimization and propose to reserve some portion of ESS. Robust optimization, however, can be too conservative because it is mainly focused on handling the worst case.…”