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Enhanced Battery Degradation-Aware Scheduling for Distribution Network with Electric Vehicle Load

Vijay Babu Pamshetti, Wei Zhang, Andy Man-Fai Ng, Qingyu Yan, Kuan Tak Tan

TL;DR

This work tackles battery degradation in distribution networks with PV and EV load by formulating a bi objective optimization that jointly minimizes monetary cost and network performance impacts. The authors introduce Bach, a degradation aware scheduling framework that solves the problem with two methods: an ε constraint approach to generate a Pareto front and a fuzzy decision making method to select a single balanced solution. The study on the IEEE 33 bus network demonstrates that Case 3 achieves competitive cost and performance, while Case 2 offers best network quality and Case 1 minimizes cost, illustrating clear trade offs between degradation, cost, losses, and voltage deviation. The findings highlight the practical potential of degradation aware scheduling to improve sustainability and resilience in modern power systems through tunable objective weighting and decision support mechanisms.

Abstract

Batteries play a key role in today's power grid. In this paper, we investigate the impact of battery degradation on the distribution network. We formulate a multi-objective framework for optimizing battery scheduling with the goals of minimizing monetary costs and improving network performance. Our framework incorporates energy purchase and battery degradation into the costs and measures the network performance through energy losses and voltage deviation. We propose Bach for battery degradation-aware cheduling based on e-constraint and fuzzy logic methods. Bach is implemented for the IEEE 33-bus network for an experimental study. The results show the effectiveness of Bach in optimizing costs and performance simultaneously with battery degradation awareness and demonstrate the flexibility of further customization.

Enhanced Battery Degradation-Aware Scheduling for Distribution Network with Electric Vehicle Load

TL;DR

This work tackles battery degradation in distribution networks with PV and EV load by formulating a bi objective optimization that jointly minimizes monetary cost and network performance impacts. The authors introduce Bach, a degradation aware scheduling framework that solves the problem with two methods: an ε constraint approach to generate a Pareto front and a fuzzy decision making method to select a single balanced solution. The study on the IEEE 33 bus network demonstrates that Case 3 achieves competitive cost and performance, while Case 2 offers best network quality and Case 1 minimizes cost, illustrating clear trade offs between degradation, cost, losses, and voltage deviation. The findings highlight the practical potential of degradation aware scheduling to improve sustainability and resilience in modern power systems through tunable objective weighting and decision support mechanisms.

Abstract

Batteries play a key role in today's power grid. In this paper, we investigate the impact of battery degradation on the distribution network. We formulate a multi-objective framework for optimizing battery scheduling with the goals of minimizing monetary costs and improving network performance. Our framework incorporates energy purchase and battery degradation into the costs and measures the network performance through energy losses and voltage deviation. We propose Bach for battery degradation-aware cheduling based on e-constraint and fuzzy logic methods. Bach is implemented for the IEEE 33-bus network for an experimental study. The results show the effectiveness of Bach in optimizing costs and performance simultaneously with battery degradation awareness and demonstrate the flexibility of further customization.
Paper Structure (16 sections, 13 equations, 3 figures, 2 tables)

This paper contains 16 sections, 13 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem formulation
  3. Objective 1: Monetary Cost
  4. Objective 2: Distribution Network Performance
  5. Constraints
  6. Methodology
  7. $\varepsilon$-Constraint Method
  8. Fuzzy Decision-Making Method
  9. Experimental Study
  10. Experimental Setup
  11. Results and Discussions
  12. Case 1
  13. Case 2
  14. Case 3
  15. Parameter Sensitivity Analysis
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Sample daily data for PV generation, electricity price, and EV load, with 150 EVs for each level.
  • Figure 2: Case 3 with multiple optimal solutions for both objectives for cost minimization and network performance as a Pareto front. The solution based on our fuzzy method for decision-making is annotated and highlighted in red.
  • Figure 3: The parameter sensitivity analysis results with varying $\lambda_1$ for the cost objective and $\lambda_2$ for the network performance objective. Large $\lambda_1$ emphasizes the importance of battery degradation and voltage deviation becomes more important with large $\lambda_2$.