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

TL;DR

This paper introduces a stochastic optimization method for real-time, cost-effective 24/7 carbon-free energy operations, incorporating renewable sources, storage, and forecasting to meet flexible CFE targets.

Contribution

It presents a novel two-stage stochastic optimization framework that explicitly models CFE compliance and integrates deep learning forecasts for near real-time energy management.

Findings

Effective in minimizing costs while ensuring CFE targets

Handles uncertainties in load and solar generation

Supports flexible CFE compliance requirements

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.