Table 3 Overview of the implemented functions for an Agent-based modeling with an urban energy system
ID | Market mechanism | Policies modelled | User behavior | Efficiency | Method | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Category: local heating transition—policies makers | |||||||||||
 Busch et al. (2017) |  | Three distinct instigators: local authorities, com-mercial developers, and community organizations | Twelve commercial instigators and thirty-two community instigators are included |  | Uses quantities data from different stakeholder; represent a large city in the UK | ||||||
 Wildt et al. (2021) |  | In 2015, a group of resi-dents launched the 'Warm in de Wijk' project to identify and implement a more ecological heating techno-logy than natural gas | Analyzing conflicts between different households in the heating sector |  | Empirical based: community driven heating initiative in ‘de Vruchtenbuurt’ | ||||||
 Nava-Guerrero et al. (2021) | Market conditions include electricity, natural gas, and retail heat prices of energy suppliers and contractors’ fees for conducting heating systems and insulation changes | Fiscal and disconnection are types of public policy interventions | Households could decide under the current market conditions, policies, and renewable energy in the heat transitions |  | Empirical based: community driven heating initiative in ‘de Vruchtenbuurt’ | ||||||
 Nava-Guerrero et al. (2022) |  | The taxation of electricity and gas, price regulation for net-work heating, and subsidies for insulation and heating are specified | A percentage of households in the neighborhood needs to be willing to join a project for the project to be realized |  | literature data were for persons and households, as well from dwellings; landscape model of Switzerland "swissTLM3D" | ||||||
Category: local heating transition—energy planer and energy multi-utilities | |||||||||||
 Pagani et al. (2020) | Expansion of the heat grid by a Demand-driven scenario and Predicted-demand driven scenario (cost optima) |  | Analyze Behaviors of the End-User |  | GIS-Data: St. Gallen, heating grid data | ||||||
 Guerrero et al. (2019) | They are analyzing the cost of the heat transition | Taxes and subsidies are considered | value orientation, social threshold, and ability to compare combined investments |  | Retail energy prices; technology parameters from the literature | ||||||
 Fouladvand et al. (2020) | Financial options for the households are an investment, payback time monthly payments |  | Energy plans for a household are maximizing renewable energy generation, maximizing the individuals' profit, or the best-mixed energy plan | Analyze thermal energy community systems. Four factors are relevant: neighbor-hood size, minimum member requirement, satisfaction factor, and drop-out factor | It uses empirical data from interviews | ||||||
 Fichera et al. (2021) | Self-consumption: analyzing rules for electricity exchan-ge in the neighborhood |  | building profiles are generated |  | theory-based | ||||||
Category: planning aquifer thermal energy storage | |||||||||||
 Beernink et al. (2022) |  | Analyzing the planning method for ATES |  | Analyze Aquifer Thermal Energy Storage with heat pump and building energy demand cooling and heating | GIS-Data-Utrecht, Dutch, heating and cooling de-mand from a Dutch data-base (RVO), climate data from the Royal Netherlands Meteorological Institute | ||||||
 Bloemendal et al. (2018) |  | Analyzing the planning method for ATES |  | Aquifer Thermal Energy Storage with heat pump and building energy de-mand cooling and heating | The Netherlands contained a dataset of the legal capacity of over 430 ATES systems from five provinces | ||||||
Category: microgrid only electricity—PV, battery, household, building, substation | |||||||||||
 Lovati et al. (2020) | (i) electricity from the communal PV is free available, (ii) at production cost (i.e., without profit), or (iii) the PV is owned by a single provider and set the price |  |  | Self-consumption for grid relief | GIS-Data-Swedish community for PV generator and load profile of forty-eight households; price data Eurostat 2007–2019 | ||||||
 Lovati et al. (2021) | Different designs of the local markets to analyzes prices and revenues |  | Effect of users who refuse the investment vs. investors who invest in the local energy system | Self-consumption for grid relief | GIS-Data for PV generator and load profile of forty-eight households; price data Eurostat 2007–2019 | ||||||
 Monroe et al. (2020) | Eighteen trial participants had access to an online trading platform that allowed them to set and adjust their buying and selling prices at any time (local market) |  |  |  | Empirical data from the P2P energy trading Renewable Energy and Water Nexus in Perth, Australia, was used between August 2018 and June 2019. In addition, electricity consumption and PV generation data were collected from fifty households | ||||||
 Fichera et al. (2020a) | Prosumers increase the degree of self-sufficiency | EU Directive 2019/944: Neighborhoods to consume, store, and sell self-generated electricity |  |  | GIS Data- Catania, Italy for 370 buildings (ca. 0,7 km2); GIS-Data Tool: energy demand, specific technology (production and storage) | ||||||
 Hoffmann et al. (2020) |  | Self-organization: All actors make decisions independently; soft control: The DSO sends feedback and incentives to the end-user; strong control: By contract, the DSO is allowed to access flexibilities automatically |  | Electrification scenario: gas vs. Electrification, Building type conversion scenario: change building types due to economic or social reasons, Energy-efficient scenario: A shift in energy use intensities for different building types | Empirical Data from End-User and DSO | ||||||
 Sun et al. (2018) |  | Weights of each energy source can be set, denoting different policies | Electrification, change of building types, and improvement of energy efficiency |  | GIS-Data- London, UK: electricity demand and electricity generation | ||||||
 Kuznetsova et al. (2015) |  |  |  | Increase the performance of the microgrid by using uncertainties in generation and demand | theoretical approach | ||||||
 Schiera et al. (2019) |  | Incentives include capital grants, tax reliefs, or other supporting schemes (e.g., Net-Billing, Feed-in Tariff, Feed-in-Premium) and mass media advertising actions | Social influence and interaction between consumers with economical and technical interests |  | Three Layer: Environmental, Socio-Cultural, and Techno-Economic Layer used different empirical data from other databases | ||||||
 Bellekom et al. (2016) | increase self-sufficiency for a community by using peer-to-peer |  |  |  | The ‘Icare’ demo will be used to generate data on power demand and a web-based tool for PV data | ||||||
 Fichera et al. (2020b) | Self-consumption, A microgrid can exchange electric energy if their mutual spatial distance is (i) lower than 50 m or (ii) lower than 200 m |  |  |  | GIS-Data—urban community in Italy. Public databases of the National Statistical Institute and Data from the Italian National Agency for New Technolo-gies, Energy and Sustain-able Economic Development (ENEA) | ||||||
Category: microgrid electricity and heat | |||||||||||
 Haque et al. (2017) | Local market to coordinate flexibility of PV and heat pump |  |  | The network assets' thermal overload and voltage limit violations at the connection points should be avoided | theory-based: 400 Dutch residential consumers and solar irradiation and the outdoor temperature from the Royal Dutch Meteorological Institute | ||||||
 Shen et al. (2021) | By finding leaks, the opera-tional cost of the heating grid should be reduced |  |  | Optimizing the operation and maintenance of district heating networks | Data collected from end-users’ pressure and flow of information | ||||||
Kremers 2020) | The price signal is composed of a local and an external component to strive for a contribution to overall energy efficiency | Â | Â | Improving the local and overall energy efficiency | An actual demonstration is implemented | ||||||
 Hall and Geissler (2020) | Self-consumption: (i) all PV production offers are accepted, (ii) substation’s limits: remaining offers from the buildings are ranked, whereby fixed amounts like heat pumps or electrical DHW boilers come first, and battery offers are last |  |  |  | Three typical building clusters in Basel, Switzerland; Climate data of the year 2015 is used | ||||||
 Loose et al. (2020) | Market-oriented operating mode for a wastewater heat pump and CHP |  |  | Efficient operation of a heating network using a wastewater heat pump and a combined heat and power (CHP) system | Empirical data from municipal utilities of Lemgo | ||||||
 Khalil and Fatmi (2022) |  |  | Bottom-up energy consumption model that takes end-users in-home and out-home activities into account |  | Empirical data from End-user for the in-home and out-home activities | ||||||