A Dynamic Strategic Plan for Transition to Campus-Scale Clean Electricity Using Multi-Stage Stochastic Programming

TL;DR

This paper develops a multi-stage stochastic programming framework to guide campus-scale clean electricity transitions, optimizing investments and operations under uncertainty to achieve sustainability goals.

Contribution

It introduces a novel dynamic planning model that jointly considers technology investments, storage, and grid interactions with explicit uncertainty modeling.

Findings

Accounting for uncertainty improves economic viability.

Temporal detail enhances operational feasibility.

The framework supports strategic decision-making for sustainable campuses.

Abstract

The decarbonization of energy systems at energy-intensive sites is an essential component of global climate mitigation, yet such transitions involve substantial capital requirements, ongoing technological progress, and the operational complexities of renewable integration. This study presents a dynamic strategic planning framework that applies multi-stage stochastic programming to guide clean electricity transitions at the campus level. The model jointly addresses technology investment, storage operation, and grid interaction decisions while explicitly incorporating uncertainties in future technology cost trajectories and efficiency improvements. By enabling adaptive, stage-wise decision-making, the framework provides a structured approach for large electricity consumers seeking to achieve self-sufficient and sustainable energy systems. The approach is demonstrated through a case study of Middle East Technical University (Ankara, Turkey), which has committed to achieving carbon-neutral electricity by 2040. Through the integration of solar photovoltaics, wind power, and lithium-ion batteries, the model links long-term investment planning with operational-level dynamics by incorporating high-resolution demand and meteorological data. Our findings from the case study, sensitivity analyses, and comparisons with simplified models indicate that accounting for uncertainty and temporal detail is crucial for both the economic viability and operational feasibility of campus-scale clean electricity transitions.