Multi-Objective Optimisation for Energy Scheduling in Smart Grids using Peer-to-Peer Trading

Efficient scheduling of the sources within a community is essential to reduce the electricity-related cost as well as the carbon emissions from the community. A novel energy management strategy for community grids is introduced in this research, leveraging peer-to-peer trading and the multi-objective optimisation of the cost and carbon emissions in scheduling the diverse energy sources and battery storage systems within the community. The grid, photovoltaic farms, Combined Heat and Power plants, and battery energy storage are considered in this paper, and our approach, underpinned by real-life data analysis, is used to find effective schedules for each source. The model is implemented on MATLAB and solved using the YALMIP optimisation toolbox to obtain optimal scheduling of the sources. An operation cost savings of up to $\mathbf{6 2. 5 \%}$ is achieved in a range of scenarios, highlighting the importance of optimal source scheduling in smart grids.