ECE PhD Proposal Review: Ya Guo

PhD Proposal Review: Power Optimization and Management of PV Grid-Connected Microgrid in Energy Market

Ya Guo

Location: Zoom Link

Abstract: Microgrids can integrate renewable energy resources (RES), such as photovoltaics (PV) and wind energy generation, with the main power grid to provide reliable, secure and affordable energy. Fortunately, the electricity markets have evolved to facilitate RES participation. One major challenge lies in how to manage power and energy flow within grid-connected microgrid system, to optimize financial gains while maintaining high reliability. This becomes challenging since electricity trading policies and tariffs vary by utility companies from area to area. Furthermore, RES are mostly intermittent sources. Adding additional energy storage systems (ESS) into microgrids becomes a vital solution to mitigating the energy production intermittency, as well as providing energy backup in emergency. Battery ESS (BESS) are deployed on a large scale in grid-connected installations worldwide. Optimal operation of the energy storage system also becomes important for microgrid end-users to ensure that they will at least recover BESS operating cost. Moreover, there always exist uncertainties in RES power generation, load power consumption, and even dynamic electricity pricing. It is vital to deal with the forecasted errors in real-time. Developing proper uncertainty characterization can better facilitate the whole system power management to limit the negative influences of these uncertainties.
In this research, dynamic programming (DP) algorithm is proposed to forecast the global optimal solution to power flow dispatch of PV grid-connected microgrid. Various electricity pricing structures, including fixed pricing, time-of-use (TOU) pricing and real-time pricing (RTP) are explored for customers in different areas. The battery nonlinear charging/discharging degradation model is also exploited for system power optimization. The objective is to achieve the minimum microgrid system operation cost, in other words, the maximum economic benefits for end-users. Besides, this research proposes power control methods to implement forecasted optimal power schedule, as well as dealing with errors among forecast and real-time PV, load and RTP. Rule-based (RB) algorithm is also studied as a baseline for comparison. Moreover, uncertainty characterization for PV, load and dynamic pricing will be developed using Monte Carlo Simulation (MCS), and stochastic optimization approach will be explored in cooperation with these uncertainties.