Quantitative tools to support the decision-making process for energy transition in residential and industrial sector

Abstract

In the context of the complex energy transition, this dissertation investigates methods to facilitate local energy transition in the residential and industrial sectors. On the one hand, the study examines local communities and Positive Energy Districts (PEDs). On the other hand, it analyzes the industrial sector’s challenges in contributing to an economically viable energy transition. This research explores, develops, and applies methods and tools to address key challenges in the energy transition across residential and industrial sectors. It integrates simulation approaches, high-resolution spatial data, and economic analysis to support decision-making processes. By providing quantitative insights, it highlights how stakeholders can design effective energy intervention, understand the right solar potential in urban areas, and foster industrial symbiosis through actions like industrial waste heat integration into local district heating networks. To address these topics, this dissertation develops the Multi Energy System Simulator (MESS), a modular, bottom-up, multi-node simulation tool. As a first step, this dissertation investigates how simulation compares to optimization approaches in modeling district-level energy systems. By simulating non-optimal solutions, the MESS model highlights the dynamic behavior of energy systems and their uncertainties, supporting decision-makers in designing effective energy transition interventions. Compared to optimization tools, the faster resolution time of MESS allows the investigation of multiple scenarios almost instantaneously answering the What happens if. . . ? question. On the contrary, an optimization approach is more indicated for investment planning models and macro energy systems analysis to understand what is needed to achieve a specific target. Consequently, combining optimization and simulation allows first to see the optimal solution and then investigate how non-optimal solutions, and often the reality is non-optimal, deviate from it. The study also explores the integration of spatial dimensions within energy system modeling. To do that, it exploits the value of high-resolution spatial data in assessing solar potential in complex urban environments such as the one of historic centers. The results show that in the historic center high-resolution spatial data are critical in improving solar irradiance estimates. In fact, considering them results in a 36% lower theoretical solar potential on a full year. These new insights are also critical to enable effective solar energy policy development and correct sizing of integrated systems. Finally, the dissertation integrates these technical insights with industrial project evaluation tools to investigate interventions that foster industrial symbios is among industries, districts, and district heating operators (DHO). The research in this last step focuses firstly on a scenario where the industry acts alone without the DHO and then on three scenarios that see the cooperation between industry and DHO. The first evaluates the implication of introducing a support scheme on heatprice, the second a financial incentive to cover the grid connection costs to be allocated to the DHO and finally a third scenario that combines the two. The findings reveal that the successful integration of industrial waste heat into district heating systems hinges on supportive policies and stakeholder coordination. By integrating simulation approaches, high-resolution spatialdata, and economic analysis, this dissertation demonstrates how tailored methodologies can address technical, spatial, and economic challenges. Consequently, the presented methods and tools can support stakeholder decisions to pave the road for a sustainable energy transition.