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Efficient MPC-Based Energy Management System for Secure and Cost-Effective Microgrid Operations

Hanyang He, John Harlim, Daning Huang, Yan Li

TL;DR

This work tackles the challenge of secure, cost-efficient microgrid operation under high renewable penetration by formulating an MPC-based EMS that explicitly includes branch-flow constraints via a convex SOCP relaxation. It integrates a forecast pipeline with empirical-dictionary–assisted KRR for solar and a DR-aware, linearized load model to preserve convexity, enabling online optimization. The framework reduces grid losses, enhances security margins, and achieves lower operating costs across 10-, 18-, and 33-bus microgrids, with demonstrable peak-shaving benefits from DR. The approach offers scalable online applicability and a practical pathway to secure, low-cost microgrid operation in distribution networks with significant DR and DER integration.

Abstract

Model predictive control (MPC)-based energy management systems (EMS) are essential for ensuring optimal, secure, and stable operation in microgrids with high penetrations of distributed energy resources. However, due to the high computational cost for the decision-making, the conventional MPC-based EMS typically adopts a simplified integrated-bus power balance model. While this simplification is effective for small networks, large-scale systems require a more detailed branch flow model to account for the increased impact of grid power losses and security constraints. This work proposes an efficient and reliable MPC-based EMS that incorporates power-loss effects and grid-security constraints. %, while adaptively shaping the battery power profile in response to online renewable inputs, achieving reduced operational costs. It enhances system reliability, reduces operational costs, and shows strong potential for online implementation due to its reduced computational effort. Specifically, a second-order cone program (SOCP) branch flow relaxation is integrated into the constraint set, yielding a convex formulation that guarantees globally optimal solutions with high computational efficiency. Owing to the radial topology of the microgrid, this relaxation is practically tight, ensuring equivalence to the original problem. Building on this foundation, an online demand response (DR) module is designed to further reduce the operation cost through peak shaving. To the best of our knowledge, no prior MPC-EMS framework has simultaneously modeled losses and security constraints while coordinating flexible loads within a unified architecture. The developed framework enables secure operation with effective peak shaving and reduced total cost. The effectiveness of the proposed method is validated on 10-bus, 18-bus, and 33-bus systems.

Efficient MPC-Based Energy Management System for Secure and Cost-Effective Microgrid Operations

TL;DR

This work tackles the challenge of secure, cost-efficient microgrid operation under high renewable penetration by formulating an MPC-based EMS that explicitly includes branch-flow constraints via a convex SOCP relaxation. It integrates a forecast pipeline with empirical-dictionary–assisted KRR for solar and a DR-aware, linearized load model to preserve convexity, enabling online optimization. The framework reduces grid losses, enhances security margins, and achieves lower operating costs across 10-, 18-, and 33-bus microgrids, with demonstrable peak-shaving benefits from DR. The approach offers scalable online applicability and a practical pathway to secure, low-cost microgrid operation in distribution networks with significant DR and DER integration.

Abstract

Model predictive control (MPC)-based energy management systems (EMS) are essential for ensuring optimal, secure, and stable operation in microgrids with high penetrations of distributed energy resources. However, due to the high computational cost for the decision-making, the conventional MPC-based EMS typically adopts a simplified integrated-bus power balance model. While this simplification is effective for small networks, large-scale systems require a more detailed branch flow model to account for the increased impact of grid power losses and security constraints. This work proposes an efficient and reliable MPC-based EMS that incorporates power-loss effects and grid-security constraints. %, while adaptively shaping the battery power profile in response to online renewable inputs, achieving reduced operational costs. It enhances system reliability, reduces operational costs, and shows strong potential for online implementation due to its reduced computational effort. Specifically, a second-order cone program (SOCP) branch flow relaxation is integrated into the constraint set, yielding a convex formulation that guarantees globally optimal solutions with high computational efficiency. Owing to the radial topology of the microgrid, this relaxation is practically tight, ensuring equivalence to the original problem. Building on this foundation, an online demand response (DR) module is designed to further reduce the operation cost through peak shaving. To the best of our knowledge, no prior MPC-EMS framework has simultaneously modeled losses and security constraints while coordinating flexible loads within a unified architecture. The developed framework enables secure operation with effective peak shaving and reduced total cost. The effectiveness of the proposed method is validated on 10-bus, 18-bus, and 33-bus systems.

Paper Structure

This paper contains 52 sections, 42 equations, 22 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. General Framework of MPC
  4. Overview of the Proposed MPC-based EMS
  5. $C_{\mathrm{dyn}}$: Dynamic constraints
  6. $C_{\text{alg}}$: Algebraic constraints
  7. $C_{\text{ieq}}$: Inequality constraints
  8. Forecasting Model
  9. Empirical-Dictionary-Assisted KRR Solar Forecasting
  10. Load Forecasting Considering Demand Response
  11. Branch Flow Constrained MPC-based EMS
  12. SOCP Branch Flow Relaxed Model
  13. Conic-Program Form for the MPC-Based EMS Problem
  14. Computational Complexity
  15. Iteration complexity
  16. ...and 37 more sections

Figures (22)

  • Figure 1: Overview of the MPC-based EMS algorithm.
  • Figure 2: Demonstration of the diverse solar power profiles under varying weather conditions with different irradiation levels.
  • Figure 3: Radial network power flow model.
  • Figure 4: Solar forecasting results initialized at different starting times $t$. (a) $t=0$h; (b) $t=5$h; (c) $t=6$h; (d) $t=7$h.
  • Figure 5: Topology of the CIRED 18-bus grid.
  • ...and 17 more figures