Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery

AlSkaif, Tarek; Luna, Adriana C.; Guerrero Zapata, Manel; Guerrero, Josep M.; Bellalta, Boris

doi:http://dx.doi.org/10.1016/j.enbuild.2016.12.050

Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery

Citació

AlSkaif T, Luna AC, Guerrero Zapata M, Guerrero JM, Bellalta B. Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery. Energy Build. 2017 Mar 1;138:228-39. DOI: 10.1016/j.enbuild.2016.12.050

Resum

Due to the decreasing revenues from the surplus renewable energy injected into the grid, mechanisms promoting self-consumption of this energy are becoming increasingly important. Demand response (DR) and local storage are among the widely used mechanisms for reaching higher self-consumption levels. Deploying a shared storage unit in a residential microgrid is an alternative scenario that allows households to store their surplus renewable energy for a later use. However, this creates some challenges in managing the battery and the available energy resource in a fair way. In this paper, a reputation-based centralized energy management system (EMS) is proposed to deal with these issues by considering households’ reputations in the reallocation of available energy in the shared storage unit. This framework is used in an optimization problem, in which the EMS jointly schedules households’ appliances power consumption and the energy that each household can receive from the storage unit. The scheduling problem is formulated as a Mixed Integer Linear Programming (MILP) with the objective of minimizing the amount and price of energy absorbed from the main grid. The MILP problem is coded in GAMS and solved using CPLEX. Numerical analysis is conducted using real data of renewable energy production and appliances’ demand profiles for different classes of households and different annual periods in Spain. Simulation results of the different scenarios show that by using the proposed framework higher cost savings can be achieved, in comparison with the classical scheduling scenario. The saving can reach up to 68% when different classes of households exist in the microgrid. The results also show that the fairness in energy allocation is guaranteed by the reputation-based policy, and that the total power absorbed from the main grid by the whole microgrid is significantly decreased.