[Paper Review] Arbitrage with Power Factor Correction using Energy Storage

Md Umar Hashmi, Deepjyoti Deka+3 more|arXiv (Cornell University)|Mar 14, 2019

Smart Grid Energy Management27 references4 citations

TL;DR

This paper proposes a co-optimization framework for energy storage systems to simultaneously perform energy arbitrage and local power factor correction (PFC) in distribution networks with high inverter-interfaced distributed generation. Using McCormick relaxation and penalty-based methods, the non-convex joint optimization is efficiently solved, showing that PFC can be achieved without reducing arbitrage profits, and that reactive power control is largely decoupled from active power arbitrage due to converter size constraints.

ABSTRACT

The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries.

Motivation & Objective

• Address the growing challenge of low power factor in distribution networks due to widespread inverter-interfaced distributed generation, especially solar PV.
• Develop a joint optimization framework for energy storage to perform both energy arbitrage and local power factor correction (PFC) without compromising profit.
• Investigate the impact of forecast uncertainty on arbitrage performance and PFC compliance in real-time control settings.
• Analyze the sensitivity of arbitrage profit to battery ramp rate and converter sizing under uncertainty.
• Provide a model predictive control (MPC) policy using ARIMA forecasts to enable real-time implementation with robust PFC and arbitrage outcomes.

Proposed method

• Formulate a non-convex joint optimization problem for active and reactive power dispatch of storage systems to maximize arbitrage profit while maintaining power factor within regulatory limits.
• Apply McCormick relaxation to approximate the non-convex problem and use penalty-based schemes to enforce power factor constraints.
• Implement a model predictive control (MPC) framework with auto-regressive (ARIMA) forecasts for electricity prices, net load, and renewable generation to handle real-time uncertainty.
• Use a time-discretized dynamic model where battery state of charge evolves via $ b^i = b^{i-1} + x^i $, with charging/discharging efficiency $ \eta_{\text{ch}}, \eta_{\text{dis}} $.
• Define apparent power limits via $ S_B^{\max} $, constraining the converter capacity, which governs reactive power capability and thus PFC performance.
• Compare deterministic optimal control (full knowledge) with real-time MPC using forecasted data to evaluate profit loss and PFC violations under uncertainty.

Experimental results

Research questions

• RQ1 Can energy storage simultaneously achieve high arbitrage profit and effective local power factor correction without compromising either objective?
• RQ2 How does forecast uncertainty in electricity prices and net load affect the performance of arbitrage and PFC in real-time MPC control?
• RQ3 To what extent is power factor correction dependent on converter size versus future load and price predictions?
• RQ4 How does the ramp rate of the battery influence sensitivity to forecast uncertainty in arbitrage performance?
• RQ5 What is the trade-off between increasing converter size and maintaining high power factor while preserving arbitrage profit?

Key findings

• Energy storage can perform power factor correction locally without reducing arbitrage profit, with the joint optimization achieving 97.04% mean power factor for a 0.25C-0.25C battery under real-time MPC.
• Arbitrage profit loss due to forecast uncertainty is significantly higher for fast-ramping batteries (e.g., 35.8% loss for 2C-2C battery) compared to slow-ramping ones (3% loss for 0.25C-0.25C battery).
• Power factor correction is primarily governed by converter size, not future forecasts; thus, look-ahead in time has minimal impact on PFC performance.
• For the same battery, increasing converter size improves mean power factor (e.g., from 0.9375 to 0.9765) without reducing arbitrage profit.
• PFC violations remain comparable between deterministic and real-time MPC settings, indicating robustness of PFC to uncertainty when converter size is sufficient.
• The optimal control action for high-price scenarios is to discharge at maximum rate for fast-ramping batteries, but this is constrained by capacity, making them more sensitive to forecast errors.