Cost Optimal Control of Microgrids Having Solar Power and Energy Storage

  fig. 1  - sampling-Based Model Predictive Optimization (SBMPO) diagram showing the major components and their interrelationship

fig. 1 - sampling-Based Model Predictive Optimization (SBMPO) diagram showing the major components and their interrelationship

Abstract

Solar power availability is intermittent and must be accompanied by an energy storage system (ESS). Hence, a strategy is needed to combine the use of grid power, solar power, and ESS power to minimize the total cost of energy. A solution to this problem is proposed here as Advanced Optimal Resource Allocation (AORA). This control scheme uses the prediction of solar power availability, the real-time price of energy, and levelized costs of energy for both solar power and ESS to optimally combine the use of the power sources. Simulation results are presented for a 24 h prediction window and compared with a baseline power usage scheme that is based on a fixed charging and discharging schedule for the ESS. The comparison shows that AORA results in a substantial cost savings over the baseline scheme.

Publication

N. Gupta, G. Francis, J. Ospina, A. Newaz, E. G. Collins, Jr., O. Faruque, R. Meeker and M. Harper, "Cost Optimal Control of Microgrids Having Solar Power and Energy Storage," submitted to IEEE Power & Energy Society Transmission & Distribution Conference & Exposition 2018 (IEEE PES T&D 2018]). [bib | pdf]