Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

Abstract

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

Generating Hourly Electricity Demand Data for Large-Scale Single-Family Buildings by a Decomposition–Recombination Method

“…Within in such P2P energy trading framework, the household prosumers (i.e., households with renewable energy systems installed which can thus also produce renewable power as well as consume) can trade their surplus power with the households that have a shortage of renewable production (due to the large power demands), and thus the renewable energy local utilization and grid interaction performances at the aggregated level will be improved [24] . Despite a number of business models for P2P trading developed, most of these business models are based on hourly household electricity demand/supply data [25][26][27] . For instance, Lovati et al analyzed the P2P energy trading economic performances under different scenarios of PV ownerships and pricing strategies based on the 48 h household electricity demand and PV power production data [25] .…”

“…Despite a number of business models for P2P trading developed, most of these business models are based on hourly household electricity demand/supply data [25][26][27] . For instance, Lovati et al analyzed the P2P energy trading economic performances under different scenarios of PV ownerships and pricing strategies based on the 48 h household electricity demand and PV power production data [25] . A flocking-based decentralized double auction method was developed for P2P trading based on hourly electricity demand [27] .…”

Employer Expectations Regarding the Competencies of Employees on the Energy Market in Poland

“…Therefore, the energy sector is focused on the transition from fossil fuels, which are limited and negatively affect the state of the natural environment, to clean energy [70,71]. Recently, many technologies have been discovered or improved that can significantly reduce greenhouse gas emissions, such as renewable or low-carbon energy generation installations, advanced energy storage systems, or carbon capture devices [72]. Applying more renewables reducing carbon dioxide emissions, and increasing energy efficiency as well as introducing smart grids create new opportunities for energy companies to operate and develop [73,74].…”

Holistic fuzzy logic methodology to assess positive energy district (PathPED)

“…For example, ABM has been used for decisionmaking processes (Chappin et al, 2017) or for energy demand estimation (Hansen et al, 2019). ABM has been used to simulate human behaviour in smart homes (Kamara-Esteban et al, 2016), the effect of energy trading in a local network (Lovati et al, 2020) or specific applications of microgrids (Lovati et al, 2021) where for example, various price schemes (Mohandes et al, 2019) are considered for the adoption of PV infrastructure. In general, ABM are used to assess decision making processes and their impact on complex issues: investments in residential sector (Sachs et al, 2019), configuration of energy grids (Fichera et al, Jul.…”