Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics

Abstract

This paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed multi-objective optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF) model for the optimal operation. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant savings in electricity cost with network operational constraints satisfied.

Linearised three‐phase optimal power flow for coordinated optimisation of residential solid‐state power substations

“…Under these circumstances, the objective function ( 1) is to minimise a virtual cost, reflecting the system operation cost and performance. In specific, the first line is the cost of active and reactive power purchasing; the second line is the cost of battery degradation; the third and fourth lines are the costs of reactive power generation/consumption by the GI converter and the DCSI of PV, respectively; and the fifth and sixth lines are the voltage deviations, which could be recast into a linear format [36]. In general, the degradation cost of a battery can be expressed as a function of the actual battery cycle life [37].…”