Adaptive Virtual Inertia Control for Efficient Power Management Scheme of DC Microgrids

Industrialization and technological advancement have raised the increase in power demand all around the world. Due to limited reserve of fossil fuels, it is urgent to investigate alternative energy resources and develop new methodology for harvesting energy from renewable sources. Different renewable sources, such as solar and wind, are integrated in system as distributed generation (DG) units. In recent years, due to wide utilization of DC power sources, such as solar photovoltaic (PV), fuel cell, different DC loads as well as high-level integration of different energy storage systems such as battery, supercapacitor, DC microgrids are gaining more importance. Furthermore, unlike conventional AC system, the DC microgrids do not have the issues such as synchronization, harmonics, reactive power control, and frequency control. The main areas of application of DC microgrids are renewable energy system, electric aircraft, data centers, electric ships, and electric vehicles. The DC microgrids have the low inertia problem due to the presence of large number of converters and electronic loads. Thus, the system faces serious instability issues such as DC link voltage fluctuation and imbalanced power flow. The DC microgrids in islanded mode face more difficulties in controlling DC link voltage and balancing active power flow. In this mode, the energy storage systems (ESSs) are under high peak charge/discharge to meet the load power demand. Thus, it is imperative to design new control strategy to reduce the voltage fluctuation and the high-peak charge and discharge of ESS in order to increase their life cycles. In this project, a new adaptive virtual inertia control strategy is proposed for ESS integrated DC microgrids in both the grid connected mode and islanded mode. The DC link voltage is controlled by the grid converter in the grid connected mode, while one of ESSs converters controls the DC link voltage during islanded mode. Since the constant power load (CPL) has much detrimental impacts on DC microgrid due to negative incremental impedance, this project considers the CPL with the proposed virtual inertia control strategy. The stable power flow among the sources and loads are maintained with the power management scheme while the high-peak charge/discharge of ESS are also reduced to increase its life cycle. The proposed control strategy requires communication among the converters; however, the number of signals to be communicated are reduced which in turn reduces the overall implementation cost of the proposed scheme. A state of charge (SOC) management scheme is adopted for the ESS to protect from overcharge and high depth of discharge (DoD). The work in this project entails four main phases. In the first phase, a comprehensive literature review on DC microgrids and the latest control strategy with ESS and CPL will be conducted. In the second phase, the DC microgrids mathematical modelling with ESS and CPL will be developed. The third phase of the project will focus on developing the proposed control strategy and implementing it through developing simulation codes and examining the system performance under different scenarios such as islanded mode, grid connected mode, load change, ESS outage and so on. The fourth phase of the project will focus on experimental validation of the proposed controller with real time hardware in loop (RTHIL) setup based on real-time digital simulator (RTDS) and dSPACE controller board. Such setup will help also in building the research capacity for further utilization in KFUPM. The last phase will be dedicated to comparisons, discussions, and conclusions. In addition, the developed prototype system will help in building research capacity in the area of renewable energy and DC microgrids, which is of major importance in the Kingdom of Saudi Arabia 2030 vision in view of the Ministry of Energy ambitious plan of integrating about 40 GW of PV systems by 2030. The project will be completed within 30 calendar months at a total cost of 75,000 USD, approximately.