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Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems

Xiaotong Cheng, Ioannis Tsetis, Setareh Maghsudi

TL;DR

This work tackles distributed energy-sharing among networked DERs under non-stationary renewable generation using bandit convex optimization with long-term constraints. It proposes DRS, a gradient-free distributed algorithm based on a regularized Lagrangian, and proves dynamic regret and constraint-violation bounds, along with an adjustment mechanism to enforce feasibility. To handle non-stationarity, it introduces MA-NSDRS, a meta-algorithm that ensembles multiple experts to track changing optima, with rigorous dynamic/static regret guarantees. Empirical results on the NY_DERs dataset show that the proposed methods reduce energy waste and improve equitable energy distribution compared to BanSaP and other baselines, highlighting practical impact for privacy-preserving, real-time DER coordination in dynamic grids.

Abstract

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.

Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems

TL;DR

This work tackles distributed energy-sharing among networked DERs under non-stationary renewable generation using bandit convex optimization with long-term constraints. It proposes DRS, a gradient-free distributed algorithm based on a regularized Lagrangian, and proves dynamic regret and constraint-violation bounds, along with an adjustment mechanism to enforce feasibility. To handle non-stationarity, it introduces MA-NSDRS, a meta-algorithm that ensembles multiple experts to track changing optima, with rigorous dynamic/static regret guarantees. Empirical results on the NY_DERs dataset show that the proposed methods reduce energy waste and improve equitable energy distribution compared to BanSaP and other baselines, highlighting practical impact for privacy-preserving, real-time DER coordination in dynamic grids.

Abstract

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.
Paper Structure (21 sections, 14 theorems, 65 equations, 8 figures, 2 tables, 2 algorithms)

This paper contains 21 sections, 14 theorems, 65 equations, 8 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. Problem Formulation
  5. Problem Formulation
  6. Proposed Solution
  7. Distributed Resource-sharing Algorithm
  8. Performance Guarantees
  9. Meta Algorithm for Non-stationary DRS
  10. Expert Algorithm
  11. Meta Algorithm
  12. Performance Guarantees
  13. Numerical Results
  14. Experiment I
  15. Experiment II
  16. ...and 6 more sections

Key Result

Proposition 1

The loss function (eq:loss_f) is convex and Lipschitz continuous with the lipschitz constant $L = \frac{1}{\min_{j \in \mathcal{N}} l_j}$.

Figures (8)

  • Figure 1: System Model.
  • Figure 2: Map of DERs facilities.
  • Figure 3: Setting of Experiment I.
  • Figure 4: Result of Experiment I.
  • Figure 5: Resource distribution of each node.
  • ...and 3 more figures

Theorems & Definitions (26)

  • Proposition 1
  • proof
  • Remark 1
  • Remark 2
  • Lemma 1: Flaxman2004BCO
  • Theorem 1
  • proof
  • Corollary 1
  • Corollary 2
  • proof
  • ...and 16 more