A MBNLP Method for Centralized Energy Pricing and Scheduling in Local Energy Community

This paper proposes a new optimization model for pricing and demand scheduling in a grid-connected local energy community (LEC), including residential homes (consumers and prosumers), and combined heat and power (CHP) generation units. It is assumed that some homes have photovoltaic (PV) generation, electric vehicles (EVs), or both. A central entity is responsible for managing the EVs' charging and discharging process, and for scheduling energy exchanges among homes. Moreover, the central entity is responsible for setting the LEC-price for the energy exchanged between CHP producers and homes, and among homes themselves. We model and solve the problem with a mixed binary non-linear programming (MBNLP) approach with the objective to maximize the total benefit of consumers/prosumers. The LEC-price is set based on demand and supply. The proposed approach is applied to a case study with 100 homes and 5 CHP generation units, and the results are reported for three different electricity grid tariffs A, B, and C. The results suggest 12%, 10% and 23% cost reduction in comparison with not considering the LEC respectively for tariffs A, B, and C.