Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains

We consider using a battery storage system simultaneously for peak shaving and frequency regulation through a joint optimization framework which captures battery degradation, operational constraints and uncertainties in customer load and regulation signals. Under this framework, using real data we show the electricity bill of users can be reduced by up to 15\%. Furthermore, we demonstrate that the saving from joint optimization is often larger than the sum of the optimal savings when the battery is used for the two individual applications. A simple threshold real-time algorithm is proposed and achieves this super-linear gain. Compared to prior works that focused on using battery storage systems for single applications, our results suggest that batteries can achieve much larger economic benefits than previously thought if they jointly provide multiple services.Comment: To Appear in IEEE Transaction on Power System

Optimal Regulation Response of Batteries Under Cycle Aging Mechanisms

When providing frequency regulation in a pay-for-performance market, batteries need to carefully balance the trade-off between following regulation signals and their degradation costs in real-time. Existing battery control strategies either do not consider mismatch penalties in pay-for-performance markets, or cannot accurately account for battery cycle aging mechanism during operation. This paper derives an online control policy that minimizes a battery owner's operating cost for providing frequency regulation in a pay-for-performance market. The proposed policy considers an accurate electrochemical battery cycle aging model, and is applicable to most types of battery cells. It has a threshold structure, and achieves near-optimal performance with respect to an offline controller that has complete future information. We explicitly characterize this gap and show it is independent of the duration of operation. Simulation results with both synthetic and real regulation traces are conducted to illustrate the theoretical results

How do Aspects of Chain Restaurants Affect the Overall Rating: Trip-Advisor Multi-dimensional Rating System Analysis

In this study, we analyze the aspect ratings and overall ratings of chain restaurants retrieved from the TripAdvisor multi-dimensional rating system. We gain aspect ratings including food aspect rating, value aspect rating, service aspect rating, and atmosphere aspect rating and their corresponding overall restaurant rating from individual reviews and each restaurant. We build three econometric models to examine how overall rating is affected and found that the food aspect has a significant positive impact on the overall rating. Another interesting finding of our analysis is that the service aspect negatively affects the overall rating. This is explainable under the chain restaurant domain because when these restaurants share the standard menu, close price range, and other similar features, service becomes the most diverse aspect for restaurants under the same brand name. When receiving good service, customers would expect other aspects of this restaurant perceived are of the same quality as the service. However, this is hard to achieve because of the similar evaluation of other aspects from the same chain. Therefore, the overall rating would decrease. In addition, we understand and interpret the value aspect of the chain restaurants. Service, food, and atmosphere ratings influence the value aspect ratings significantly and positively