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Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

Farnaz Fallahi, Murat Yildirim, Jeremy Lin, Caisheng Wang

TL;DR

The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting and provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience.

Abstract

Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers, and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience.

Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

TL;DR

The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting and provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience.

Abstract

Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers, and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience.

Paper Structure

This paper contains 20 sections, 1 theorem, 28 equations, 4 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Methodology
  3. Sensor-Driven Degradation Analytics
  4. Adaptive Predictive Operations and Maintenance of MMG
  5. Maintenance Coordination
  6. Maintenance & Operations Coupling
  7. Load Management
  8. Power Transactions
  9. Storage Operation
  10. Power Balance
  11. Non-Renewable DER Operation
  12. Two-Stage Stochastic Reformulation
  13. Experiments
  14. Experimental Framework and Convergence Analysis
  15. Experimental Results and Discussions
  16. ...and 5 more sections

Key Result

Lemma 1

Let $\bm{\pi}^{1,r}_{t,\omega}$ and $\bm{\pi}^{2,r}_{t,\omega}$ denote the dual multipliers associated with the optimal solution of the sub-problem for week $t$ and scenario $\omega$ at iteration $r$. Then: Constraints eq:cutW represents the optimality cuts in the per week decomposition method. where $\alpha_t$ and $\boldsymbol{\beta}_t$ are defined as : Constraint eq:cutWS represents the optima

Figures (4)

  • Figure 1: Flowchart of the proposed algorithms for SD-IOM.
  • Figure 2: MMG resilience performance in the (top) locally-connected and (bottom) islanded Mode -2MW storage capacity
  • Figure : Per-week Algorithm
  • Figure : Cost Recovery Algorithm (CRA)

Theorems & Definitions (1)

  • Lemma 1