Stochastic EMS for Optimal 24/7 Carbon-Free Energy Operations

TL;DR

This work tackles the problem of achieving $24/7$ carbon-free energy (CFE) for a microgrid by integrating a two-stage stochastic MILP with a 15-minute operational cadence. The approach combines deep-learning–based forecasts with a rolling-horizon model to jointly optimize battery usage and multi-source energy procurement while selecting CF days within a planning horizon, and it explicitly accounts for forecast uncertainty through a scenario set ${\mathcal S}$. Key contributions include a Flexible-CFE formulation that permits CF-day selection, a multi-source green-budget constraint, and a rolling model-predictive control scheme that updates daily based on new forecasts. Empirical results show that the stochastic formulation reduces real-time adjustments and lowers net costs by roughly 6.4–7.2% compared with a deterministic baseline across CF targets, while still meeting CF-day requirements; the framework provides a practical operational pathway for deploying 24/7 CFE in emerging markets like Thailand. The study also highlights a limitation: in rolling EMS, only the first-day decisions are executed before re-planning, which can cause deviations from planned CF status as information updates arrive.

Abstract

This paper proposes a two-stage stochastic optimization formulation to determine optimal operation and procurement plans for achieving a 24/7 carbon-free energy (CFE) compliance at minimized cost. The system in consideration follows primary energy technologies in Thailand including solar power, battery storage, and a diverse portfolio of renewable and carbon-based energy procurement sources. Unlike existing literature focused on long-term planning, this study addresses near real-time operations using a 15-minute resolution. A novel feature of the formulation is the explicit treatment of CFE compliance as a model parameter, enabling flexible targets such as a minimum percentage of hourly matching or a required number of carbon-free days within a multi-day horizon. The mixed-integer linear programming formulation accounts for uncertainties in load and solar generation by integrating deep learning-based forecasting within a receding horizon framework. By optimizing battery profiles and multi-source procurement simultaneously, the proposed system provides a feasible pathway for transitioning to carbon-free operations in emerging energy markets.

Figures (12)

• Figure 1: 1 day-ahead load forecasts.
• Figure 2: 1 day-ahead solar irradiance forecasts.
• Figure 3: Optimal battery and purchased power planning to achieve the targeted number of CF days.
• Figure 4: Purchased power from different sellers.
• Figure 5: Monthly net cost comparison among different EMS formulations under 24/7 CF operation.