Year: 2024
Type: article
Abstract: This paper investigates optimising battery energy storage systems (BESSs) to enhance the business models of Local Energy Markets (LEMs). LEMs are decentralised energy ecosystems facilitating peer-to-peer energy trading among consumers, producers, and prosumers. By incentivising local energy exchange and balancing supply and demand, LEMs contribute to grid resilience and sustainability. This study proposes a novel approach to BESS optimisation, utilising advanced artificial intelligence techniques, such as multilayer perceptron neural networks and extreme gradient boosting regressors. These models accurately forecast energy consumption and optimise BESS reserve allocation within the LEM framework. The findings demonstrate the potential of these AI-driven strategies to improve the BESS reserve capacity setting. This optimal setting will target meeting Energy Community site owners’ needs and avoiding fines from the distribution system operator for not meeting contract conditions.
Source: Sustainability
DOI: 10.3390/su16188017
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4402519493