A multi-agent system based coordinated multi-objective optimal load scheduling strategy using marginal emission factors for building cluster demand response

Building cluster load management to harness energy flexibility and reduce both electricity cost and carbon emissions is an important but inadequately addressed issue in the context of carbon neutrality. This study develops a multi-agent system (MAS) based coordinated optimal load scheduling strategy for building cluster load management in response to dynamic electricity price and marginal emission factor (MEF) simultaneously. The strategy effectively solves the multi-objective optimization problem of conflicts, i.e., minimizing the electricity cost, carbon emissions and peak load while maintaining a good level of users’ satisfaction with electricity use quantified by a utility function. Case study on a campus building cluster is carried out to test the strategy developed. Three demand response (DR) schemes are designed for the building cluster, i.e., price-based DR, MEF-based DR, and the price and MEF hybrid-based DR which implements the optimal scheduling strategy developed. In addition, two real scenarios with opposite correlations between dynamic electricity price and MEF, i.e., positively correlated (scenario 1) and negatively correlated (scenario 2), are extracted from German electricity market. The electricity costs, carbon emissions, peak loads, and utility of the three DR schemes in the two scenarios are critically compared. The results show that the price-based DR may result in the rise of carbon emissions, and the MEF-based DR may lead to higher electricity cost, depending on the correlation between dynamic electricity price and MEF. The optimal strategy developed can achieve a compromise between the conflicting objectives in both scenarios. Under the extremely disadvantageous situation like scenario 2, where the trends of the price and MEF are completely opposite, the price-based DR results in an increase of carbon emission of 2.78%, and the MEF-based DR leads to an increase of electricity cost of 2.63%. The hybrid-based DR can reduce the peak power by 5.54% without increasing electricity cost and carbon emissions in scenario 2. This research provides an effective optimal load scheduling strategy as well as the application guideline for building cluster DR management towards decarbonization and economic benefit.