Energy Communities’ Archetypes and Operational Scenarios Using Agents and Embedding Heating and Transportation

Abstract Energy Communities (ECs) in ex-communist countries face cultural and technical challenges that have historically hindered their adoption. This paper examines various urban and rural archetypes and operational scenarios for ECs in these regions. We propose a methodology for selecting the best scenario based on factors such as self-sufficiency, investment payback period, cost savings, fair value share allocation, and CO 2 emissions. EC’s members are modeled as agents using Mesa and PyMarket Python libraries. Our analysis indicates that integrating heating and transportation within ECs significantly enhances efficiency, leading to substantial benefits for residents. Cost savings range from 20% when replacing gas heating with heat pumps to 59% with electric transportation, increasing EC attractiveness. Thus, we demonstrate that these integrated scenarios are feasible and viable, with payback periods between 1 and 9 years for medium solar photovoltaic (PV) systems (up to 200 kWp). Furthermore, environmental benefits are notable, with CO 2 emissions reduced by 25% when replacing gas heating and 60% with transportation. This paper provides evidence of the practicality of these scenarios, highlighting their potential to improve energy management and community satisfaction.