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Coalition Formation with Limited Information Sharing for Local Energy Management

Luke Rickard, Paola Falugi, Eric C. Kerrigan

Abstract

Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to cooperatively reduce costs; however, existing bottom-up coalition-formation methods typically require full information sharing, raising privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition-formation algorithm that requires only limited aggregate information exchange among agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real-world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full-information approaches.

Coalition Formation with Limited Information Sharing for Local Energy Management

Abstract

Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to cooperatively reduce costs; however, existing bottom-up coalition-formation methods typically require full information sharing, raising privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition-formation algorithm that requires only limited aggregate information exchange among agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real-world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full-information approaches.

Paper Structure

This paper contains 20 sections, 3 theorems, 25 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Notation
  3. Background
  4. Building Model
  5. Open-loop Optimisation
  6. Decentralised
  7. Coalitions
  8. Coalition Formation
  9. Optimal (Size-Limited) Coalition
  10. Bottom-Up Coalition Formation DBLP:conf/amcc/FeleMC15
  11. Limited Information Coalition Formation
  12. A Practical Bound
  13. Within Coalition Optimisation
  14. Model Predictive Control
  15. Numerical Results
  16. ...and 5 more sections

Key Result

Theorem 1

Consider Algorithm alg:bottom_up, with an upper bound $\overline{V}(\mathcal{C}_j)$ (satisfying eq:priv_UB) on the joint cost in place of solving the optimisation problem in Line ln:opt. Then the algorithm will satisfy the following properties

Figures (4)

  • Figure 2: Overview of the energy system with coalitions. Buildings may trade with the grid, or with other buildings within their coalition (but not those outside the coalition). Our limited-information approach shares only planned grid trades, whereas full-information methods require sharing all optimisation variables and constraints.
  • Figure 3: Number of ADMM iterations vs Number of buildings for different schemes
  • Figure 4: Grid trades (in kWh, positive buy, negative sell) for a day under decentralised and limited information coalitions.
  • Figure 5: Maximum coalition size affect on number of ADMM iterations, and average cost.

Theorems & Definitions (7)

  • Definition 1: Coalition Structure
  • Theorem 1: Limited Information Algorithm Properties
  • proof
  • Corollary 1
  • proof
  • Theorem 2: MPC with Coalition Formation
  • proof