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Dr. Omid Rahbari: Integration of EVs and RESs into Power Systems: From Battery Pack Design Phase to Optimal Power Flow Analysis

PhD Omid Rahbari

Energy Storage Systems (ESSs) are recognized as key technologies for both electric power systems and electric vehicle (EV) applications. Indeed, ESSs provide great opportunities for the former application to regulate frequency deviation, voltage deviation, and to respond to the demand, namely ancillary services. In addition, ESSs are considered a distinct possibility for de-cabornization of urban areas and greening of the transportation sector, which could be only sustainable if the power is generated from environmentally friendly power sources. Therefore, generating power from renewable resources could play a prominent role in the power sector de-cabornization as well. Renewable Energy sources (RESs) assume increasing importance due to relatively low generation costs as well as high generation potential. Due to the temporal fluctuation in renewable power generation units, ESSs can reduce the uncertainty and intermittence of such generation. In this regard, the integration of RESs and ESSs into the electric power system has been proposed as an alternative solution to the mentioned challenges. 

Nevertheless, this integration can be criticized by development of electric vehicles as a dis-patchable source; in addition, stationary energy storage systems add additional cost to the existing infrastructure, which led scientist and engineers to provide a solution to fulfill EVs' charging requirements and add a new concept (vehicle to grid) to satisfy power system requirements as well. It has been predicted that 100 million light-duty EVs will be sold by 2050, greatly exceeding the 1.2 million EVs purchased from automotive industries in 2015. As a result, the integration of electric vehicles, and RESs into the electric power system ha been attracting much attention in both academia and industry.

Electricity demand to serve EVs is predicted to increase significantly. It is predicted that electricity demand reaches almost 640 TWh in 2030, which leads the integration to pose technical challenges to the grid, such as thermal transmission capability (e.g., lines, transformers), cables (overloading), and so forth if EVs are charged and discharged uncoordinatedly. For better understanding, 640 TWh is equivalent to the combined total electricity consumption of France and Spain in 2016. Therefore, the present thesis tackles the optimal size and site of smart parking lots of electric vehicles, requiring an adaptive intelligent control strategy with V2G and G2V capabilities, to increase EVs' participation through EV aggregators. In this regard, an IEEE tandard grid structure is employed to test the proposed solution and compare it with the literature.

Furthermore, this study suggests that the aforementioned grid could employ a complex versatile control unit able to optimize the operating point, scheduling charging and discharging for a large number of electric vehicles while considering the techinal aspects (total active power loss and voltage deviation). Nevertheless, the computational effort and response time for severe operating conditions remained challenging with such strategies. The mentioned gap has been addressed by the double-layer control scheme, "a predictive optimal power flow." The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid. Moreover, vehicle-to-grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation. The Nickel-Manganese-Cobalt Oxide (NMC) with 20 Ah capacity has been used to create a battery pack in the Simulink framework. Please note that in other chapters 3 and 4, the battery NMC 40 Ah from Kokam Company has been used.

In addition, the successful coordination of electric vehicles and renewable energy sources into the electric power system requires bi-directional communication enabling the power sytem to adapt to different power source structures and improves the acceptability of intermittent renewable energy generation units. The flexibility of electric vehicles in vehicle-to-grid connections is entirely dependent on the maximum practical capacity and state of charge of each vehicle. Therefore, RLS-EKF is developed to estimate the SoC and capacity of the battery. Due to the drawbacks of coulomb-counting-based capacity estimation and to reduce computational efforts and complexity, two artificial intelligence algorithms are developed to estimate SoH in the charging phase, which is more reasonably accurate than other techniques. In addition, key enablers in battery cost reduction are developments in innovative design architecture: right size batteries, appropriate cooling systems, in which chapter 3 addresses the right size of batteries in the hybrid electric application.