Overview

Version:

1.1.6

Last update:

Mar 24, 2025

Authors:

Dorsan Lepour, Joseph Loustau, Cédric Terrier

Short summary:

Renewable Energy Hub Optimizer (REHO) - A Decision Support Tool for Renewable Energy Communities

The transition to sustainable energy systems in the face of growing renewable energy adoption and electrification is a complex and critical challenge. Renewable Energy Hub Optimizer (REHO) emerges as a powerful decision support tool designed to investigate the deployment of energy conversion and storage technologies in this evolving landscape. This documentation introduces REHO and highlights its key features and contributions to the field of sustainable energy system planning.

Model foundations

MILP Framework: REHO leverages a Mixed-Integer Linear Programming (MILP) framework to simultaneously address the optimal design and operation timing of capacities. This approach ensures a comprehensive analysis, capturing the dynamic interplay of technology deployment and energy demand.

Multi-Objective Optimization: REHO’s versatility extends to multi-objective optimization, accommodating objectives related to economic (capital and operational costs), environmental (life cycle analysis and global warming potential), and efficiency criteria. Epsilon constraints provide fine-grained control, enabling decision-makers to explore trade-offs and identify Pareto fronts.

Multi-Scale Capabilities: REHO’s flexibility spans various scales, from individual buildings to entire districts. The district-scale optimization feature capitalizes on synergies between buildings, allowing them to function as an energy community and enabling energy and financial flows between buildings. In addition, the district-scale approach opens up the possibility of deploying larger capacities, such as centralized equipment and heat distribution infrastructures.

Multi-Service Consideration: REHO encompasses a wide range of end-use demands, including thermal comfort (heating and cooling needs), domestic hot water, domestic electricity, mobility, and even information and communication technologies (ICT) end-use demand.

Multi-Energy Integration: REHO incorporates various energy sources and networks, such as electricity, fossil fuels, biomass, biofuels, hydrogen, and district heating and cooling networks. This holistic approach ensures a comprehensive representation of the energy landscape.

Open-Source and Interoperability: REHO is fully open-source, supported by comprehensive documentation. It utilizes AMPL for the core optimization model, encompassing objective functions, modeling equations, and constraints (e.g., energy and mass balances, heating cascade). Python is employed for efficient data management, boasting the capability to interface and exchange information with other tools.

Features

Building stock database

Each building to be optimized needs to be characterized to estimate its energy demand, its renewable potential, and its sector coupling potential. REHO embeds a coupling with QBuildings, a GIS database characterizing the entire Swiss building stock from an energy point of view (end-use demand, buildings morphology, endogenous resources). This database is built by gathering different public databases and combining with norms and standard energy requirements as defined by the Société suisse des Ingénieurs et des Architectes (SIA). REHO therefore contains default values for characterizing the building stock and generating their end-use demand profiles. Furthermore all these parameters can be overwritten with available measurements.

For the optimization of buildings that are not part of the QBuildings database, REHO can also simply read out the relevant information from CSV files.

Typical days

The task of optimally designing and scheduling energy systems with a high share of renewable energies is complex and computationally demanding. REHO includes machine learning techniques to cluster yearly input data to typical periods. The model operates in the conventional way with typical periods of 24 hours, but can be freely adapted to a finer or coarser granularity as required.

Energy communities

An energy community allows buildings to exchange energy flows with each other and share mutualized equipment. They improve the self-consumption of local resources by coupling distributed energy sources, and enhance the energy efficiency by supplying multiple services to the consumers. They foster a high penetration of renewable energy in urban systems, while mitigating the need for equipment and infrastructure reinforcements. As energy communities are central to the energy transition, it is crucial to assess their technical advantages, and to quantify their financial and environmental benefits.

REHO allows to conduct the optimization of a specified territory at the building-scale or the district-scale. A building-scale optimization treats all the considered buildings independently (i.e. each of them is optimized regardless of the presence of the others). In contrast, a district-scale optimization considers the whole building stock as being able to interact, and exploits the synergies of the overall system defined by the buildings to be optimized. Buildings are optimized inter-dependently, by applying the Dantzig-Wolfe decomposition algorithm to break down the problem into a master problem and subproblems. The master problem adopts the transformer perspective, while a subproblem is defined for each building connected to this transformer.

PV integration

Given the pivotal role of photovoltaic (PV) systems in the energy transition, their optimal deployment is of paramount importance and must take into account the specific characteristics of the building morphology, the local solar irradiance, and the existing electrical grid. REHO integrates the deployment of solar panels on roofs and facades, with the possibility to take into consideration the orientation of surfaces (i.e., tilt and azimuth of PV panels as decision variables).

Electric mobility

The electrification of mobility is at the heart of the energy transition in urban environments. REHO enables the integration of electric vehicles into neighborhoods, including the possibility of intelligent unidirectional or bidirectional charging. The fleet of electric vehicles can thus be used to provide an energy storage service.

Transformer and lines constraints

As the electrification of heating and mobility sectors gains momentum, the demands placed on the electricity grid are expected to further escalate. The existing electrical grid, originally designed for centralized power generation and unidirectional energy flow, now faces new demands and complexities. These challenges encompass managing the variability in electricity supply resulting from renewable energy integration and addressing the increasing electrification of diverse sectors. REHO allows for the consideration of the local low-voltage grid characteristics, through hard (maximum line capacity, maximum transformer capacity) or soft constraints (power peaks mitigation, grid usage penalty).

District heating and cooling

District heating and cooling (DHC) systems have a large potential to support the decarbonization of the heating and cooling needs. REHO enables the deployment of DHC, with consideration of several heat transfer fluids and distribution temperatures. Infrastructure costs are also incorporated, based on the topology of the considered neighbourhood.