EnCortex: A General, Extensible and Scalable Framework for Decision Management in New-age Energy Systems

TL;DR

EnCortex is a general, extensible framework for decision management in modern energy systems that integrates standard energy entities, contracts, and decision units within a three-layer architecture (Abstraction, Environment, Optimizer). It supports multiple optimizers (SA, MILP, DQN-RL) and enables scalable, data-driven what-if analysis, forecasting integration, and MLOps workflows. Through three real-world scenarios—Energy Arbitrage, Microgrid optimization, and Market Bidding optimization—the paper demonstrates composability, extensibility, and tangible cost and carbon savings, while highlighting latency benchmarks and forecast-uncertainty considerations. The work advances the field by providing a reusable, production-grade platform that can be extended with new entities, markets, and optimization strategies to meet evolving energy-market needs.

Abstract

With increased global warming, there has been a significant emphasis to replace fossil fuel-dependent energy sources with clean, renewable sources. These new-age energy systems are becoming more complex with an increasing proportion of renewable energy sources (like solar and wind), energy storage systems (like batteries), and demand side control in the mix. Most new-age sources being highly dependent on weather and climate conditions bring about high variability and uncertainty. Energy operators rely on such uncertain data to make different planning and operations decisions periodically, and sometimes in real-time, to maintain the grid stability and optimize their objectives (cost savings, carbon footprint, etc.). Hitherto, operators mostly rely on domain knowledge, heuristics, or solve point problems to take decisions. These approaches fall short because of their specific assumptions and limitations. Further, there is a lack of a unified framework for both research and production environments at scale. In this paper, we propose EnCortex to address these challenges. EnCortex provides a general, easy-to-use, extensible, and scalable energy decision framework that enables operators to plan, build and execute their real-world scenarios efficiently. We show that using EnCortex, we can define and compose complex new-age scenarios, owing to industry-standard abstractions of energy entities and the modularity of the framework. EnCortex provides a foundational structure to support several state-of-the-art optimizers with minimal effort. EnCortex supports both quick developments for research prototypes and scaling the solutions to production environments. We demonstrate the utility of EnCortex with three complex new-age real-world scenarios and show that significant cost and carbon footprint savings can be achieved.

Figures (17)

• Figure 1: Illustration of an energy producer with the complexity involved in the decision-making.
• Figure 2: Solar entity
• Figure 3: Battery entity
• Figure 5: Overview of EnCortex architecture.
• Figure 6: Overview of Energy Arbitrage Scenario.