Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains (1702.08065v3)

Abstract: 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.

• The paper presents a joint optimization framework that combines peak shaving and frequency regulation to deliver superlinear economic savings.
• It incorporates a linear degradation cost model to capture battery wear from frequent charge–discharge cycles, ensuring realistic operational cost estimates.
• A threshold-based real-time algorithm manages differing response times effectively, as simulations show up to a 12% reduction in electricity bills.

Joint Optimization of Battery Storage for Peak Shaving and Frequency Regulation

This paper investigates the use of battery energy storage systems (BESS) to simultaneously perform peak shaving and frequency regulation, proposing a joint optimization framework that captures battery degradation dynamics, operational constraints, and uncertainties inherent in customer load profiles and grid regulation signals. While individual applications of battery storage for either peak shaving or frequency regulation have been explored extensively, the integration of these services within a joint optimization framework offers novel insights into achieving enhanced economic benefits, demonstrating superlinear savings.

Strong Numerical Results

The numerical results presented in the paper are noteworthy, offering empirical evidence that supports the theoretical premise of superlinear gains. The authors report that joint optimization can lead to a reduction in electricity bills by up to 12% for commercial users. Intriguingly, the paper calculates economic savings under various operating scenarios and finds that the gain from joint optimization is often greater than the sum of optimal gains when using battery storage for the two services in isolation. For example, a Microsoft data center simulation revealed a total saving of $52,282 annually on its electricity bill due to joint service provision, with superlinear gains being prevalent across the scenarios tested.

Key Contributions

The primary contribution of the research lies in devising a joint optimization framework specifically accounting for battery degradation—a crucial consideration given the cycling demands associated with frequency regulation and peak shaving services. Degradation costs are incorporated into the model, where past approaches may have oversimplified or neglected this factor. The authors employ a linear degradation cost model capturing repeated charge/discharge cycles effects pertinent to Lithium Manganese Oxide batteries, thereby offering a more comprehensive understanding of operational costs.

Additionally, the paper proposes a decision-making algorithm for real-time battery operation. This algorithm addresses challenges stemming from the different timescales of peak shaving (hourly) and frequency regulation (second-level response), employing a threshold-based control policy that accommodates real-time conditions to maintain near-optimal performance. According to detailed simulations, such a mechanism closely matches the optimal solutions obtained through perfect foresight, emphasizing the sufficiency of threshold policies for real-time battery storage management.

Implications and Future Directions

The implications of this research extend to both theoretical advancements and practical applications in energy systems. The findings emphasize the importance of exploring hybrid service models for energy storage systems, potentially shifting economic perspectives among commercial users who perceive storage investments previously justified by single-use cases. Furthermore, the demonstrated superlinear gains may influence regulatory and market design to incentivize co-optimization frameworks, thereby enhancing grid resiliency leveraging distributed storage assets.

For future research directions, the integration of more complex stochastic models could refine predictions of market state variables, such as electricity prices and regulation signals, impacting decision-making processes. Additionally, extending the framework to incorporate other ancillary services or demand response initiatives could shed light on further economic optimization potentials. Novel applications in multi-energy systems and their interactions with renewable generation assets present another promising avenue for research expansion.

In summary, the paper delivers a well-substantiated model for joint optimization in battery storage usage, providing both theoretical insights and pragmatic considerations for implementation. The framework paves the way for subsequent explorations into broader service applications that could redefine the economic landscape of energy storage in power system environments.