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Carbon-aware Market Participation for Building Energy Management Systems

Young-ho Cho, Mohamad Chehade, Fatima Al-Janahi, Sol Lim, Javad Mohammadi, Hao Zhu

Abstract

Tackling climate change requires the rapid and deep decarbonization of electric power systems. While energy management systems (EMSs) play a central role in this transition, conventional EMSs focus mainly on economic efficiency and often overlook the environmental impact of operational decisions. To address this gap, this paper proposes a unified, real-time building-level carbon-aware EMS (CAEMS) capable of simultaneously co-optimizing grid imports, energy storage, and flexible demand within a single integrated framework. We formulate a mixed-integer linear program (MILP) model that directly integrates time-varying marginal carbon intensity signals into the EMS objective for coordinated participation in both day-ahead (DA) and real-time (RT) markets. To relax the unrealistic assumption of perfect foresight, we incorporate a model predictive control (MPC) extension driven by a Transformer-based forecaster that jointly predicts electricity prices and carbon intensity. The proposed CAEMS is validated using real-world data from the PJM electricity market. Simulation results demonstrate that modest carbon prices can achieve a significant 22.5% reduction in emissions with only a 1.7% increase in cost.

Carbon-aware Market Participation for Building Energy Management Systems

Abstract

Tackling climate change requires the rapid and deep decarbonization of electric power systems. While energy management systems (EMSs) play a central role in this transition, conventional EMSs focus mainly on economic efficiency and often overlook the environmental impact of operational decisions. To address this gap, this paper proposes a unified, real-time building-level carbon-aware EMS (CAEMS) capable of simultaneously co-optimizing grid imports, energy storage, and flexible demand within a single integrated framework. We formulate a mixed-integer linear program (MILP) model that directly integrates time-varying marginal carbon intensity signals into the EMS objective for coordinated participation in both day-ahead (DA) and real-time (RT) markets. To relax the unrealistic assumption of perfect foresight, we incorporate a model predictive control (MPC) extension driven by a Transformer-based forecaster that jointly predicts electricity prices and carbon intensity. The proposed CAEMS is validated using real-world data from the PJM electricity market. Simulation results demonstrate that modest carbon prices can achieve a significant 22.5% reduction in emissions with only a 1.7% increase in cost.
Paper Structure (8 sections, 17 equations, 4 figures, 1 table)

This paper contains 8 sections, 17 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Carbon-aware Energy Management System
  3. Model-Predictive Control (MPC)
  4. Transformer-Based Forecasting Estimator
  5. Simulations
  6. Forecasting model Validation
  7. Optimization results
  8. Conclusion

Figures (4)

  • Figure 1: PJM datasets of DA and RT markets: (a) Hourly electricity prices ($/MWh) (b) Hourly marginal emission rates (t CO$_2$/MWh).
  • Figure 2: Two-series, 24-hour-ahead forecasting setup. The model $f$ consumes the last 24 hours of real-time (RT) price $\mathbf{p}$ and carbon intensity $\mathbf{c}$ (aligned hourly; forward-filled if missing) and outputs 24-hour forecasts $\hat{\mathbf{p}},\hat{\mathbf{c}}$. The forecaster is implemented with a Transformer chosen by RMSE (Table \ref{['tab:forecasting_results']}).
  • Figure 3: Daily trade‑off between energy cost and carbon emissions under different carbon tax scenarios.
  • Figure 4: Intraday dynamics: (a) Hourly electricity prices in DA and RT markets for a single day. (b) Hourly emission rates in DA and RT markets for a single day. (c) Comparison of optimized net grid imports for the cost-only ($0/t) and carbon-aware ($30/t) strategies.