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Real-time Measurement-based Optimization for Distribution System Operation Considering Battery Voltage and Thermal Constraints

Sen Zhan, Lingkang Jin, Haoyang Zhang, Nikolaos G. Paterakis

TL;DR

The paper addresses secure distribution-system operation under high DER penetration by proposing a forecast-free, measurement-based control policy for battery storage using Lyapunov optimization. It develops linear and convex quadratic models for battery electrochemical and thermal dynamics from real-time data and replaces nonlinear power flows with measurement-based linearized network sensitivities to enable real-time decisions. The approach employs a drift-plus-penalty objective with virtual queues to balance performance and SoC stability, solved via a centralized projected gradient descent algorithm with explicit voltage and thermal constraints. Numerical validation on a nonlinear 97-bus grid with 54 PV and 36 storage units demonstrates that the method maintains voltage and transformer loading within limits, respects cell voltage and temperature bounds, and provides robust performance across parameter settings, all without requiring load forecasts.

Abstract

The secure operation of power distribution systems is challenged by the growing integration of distributed energy resources. Leveraging the flexibility of battery storage offers a cost-effective alternative to measures like generation curtailment, which results in energy losses. However, developing an effective operational model for battery storage is hindered by inaccurate grid models, unavailability of load data, nonlinear relationship between power injections and network states, intertemporal constraints, and complex electrochemical and thermal dynamics. To address these challenges, this paper proposes a data-driven operational control scheme for battery storage in distribution systems. Linear and convex quadratic operational constraints are constructed based on real-time distribution system and battery storage measurements. Lyapunov optimization decouples multi-period battery operation, enabling a real-time, forecast-free control strategy with low computational complexity. Numerical studies using nonlinear distribution system and battery storage simulators validate the effectiveness of the approach in ensuring secure distribution system operation and satisfaction of voltage and thermal constraints of battery storage.

Real-time Measurement-based Optimization for Distribution System Operation Considering Battery Voltage and Thermal Constraints

TL;DR

The paper addresses secure distribution-system operation under high DER penetration by proposing a forecast-free, measurement-based control policy for battery storage using Lyapunov optimization. It develops linear and convex quadratic models for battery electrochemical and thermal dynamics from real-time data and replaces nonlinear power flows with measurement-based linearized network sensitivities to enable real-time decisions. The approach employs a drift-plus-penalty objective with virtual queues to balance performance and SoC stability, solved via a centralized projected gradient descent algorithm with explicit voltage and thermal constraints. Numerical validation on a nonlinear 97-bus grid with 54 PV and 36 storage units demonstrates that the method maintains voltage and transformer loading within limits, respects cell voltage and temperature bounds, and provides robust performance across parameter settings, all without requiring load forecasts.

Abstract

The secure operation of power distribution systems is challenged by the growing integration of distributed energy resources. Leveraging the flexibility of battery storage offers a cost-effective alternative to measures like generation curtailment, which results in energy losses. However, developing an effective operational model for battery storage is hindered by inaccurate grid models, unavailability of load data, nonlinear relationship between power injections and network states, intertemporal constraints, and complex electrochemical and thermal dynamics. To address these challenges, this paper proposes a data-driven operational control scheme for battery storage in distribution systems. Linear and convex quadratic operational constraints are constructed based on real-time distribution system and battery storage measurements. Lyapunov optimization decouples multi-period battery operation, enabling a real-time, forecast-free control strategy with low computational complexity. Numerical studies using nonlinear distribution system and battery storage simulators validate the effectiveness of the approach in ensuring secure distribution system operation and satisfaction of voltage and thermal constraints of battery storage.
Paper Structure (17 sections, 10 equations, 6 figures, 1 table)

This paper contains 17 sections, 10 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Notation
  4. Lyapunov optimization framework
  5. Distribution network model
  6. Battery storage model
  7. Solution process
  8. Convergence analysis
  9. Sensitivity matrices
  10. Numerical Validation
  11. Case description
  12. Result
  13. Cell temperature dynamics regression analysis
  14. Incorporating battery dynamics
  15. Network constraint satisfaction
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Voltage and temperature profiles based on simulated operational data for a 6.8 kW (0.5C) lithium-ion NMC battery using the PyBaMM simulator pybamm. The battery is cycled between low and high SoC limits, with charging and discharging power setpoints randomly selected at each time step. Data points are collected every 5 minutes.
  • Figure 2: A 97-bus test grid adopted from SimbenchMeinecke2020SimBench.
  • Figure 3: A 3D representation of the linear regression model for the cell temperature dynamics of a 6.8 kW battery unit.
  • Figure 4: Cell voltage and temperature profiles with/without measurement-based constraints.
  • Figure 5: Network voltage and transformer loading with and without control and aggregated power profiles.
  • ...and 1 more figures

Theorems & Definitions (1)

  • Remark