Battery Integration to the Power Grid and Frequency Regulation

The growing interest in battery energy storage systems (BESSs) at both small-scale and large-scale levels in power grids highlights their significant roles in future power grids. The future grid in the presence of renewable resources such as hydro-power, wind, and solar energy face two major technical challenges; location of potential renewable sources and uncertainty, which can cause serious issues such as blackouts in power systems. However, in both cases, BESSs is one of the promising solutions. While small-scale battery energy storage systems can decrease the need for long-distance heavy load transportation in the power system, which is one of the primary reasons for the blackouts, large-scale BESSs can provide load frequency control to their fast response. A well-managed large-scale battery integration to the power grid reduces load flow deviation in the tie-lines and frequency oscillations caused by small load disturbances. In general, the battery’s small time-constants, fast response, and high energy density creates a large spectrum of potential applications for BESSs in power systems. This thesis focuses on the battery integration to the power system in both distribution and transmission level to evaluate its potential impact on power grid; then, it focuses on the frequency regulation by taking the advantage of the small-scale and large-scale batteries. The first part of this research investigates the small-scale battery integration to the power system in the distribution level and its potential effects on the transmission level\u27s frequency deviation. It is shown that the higher penetration level of the renewables can cause serious issues such as overvoltage, thermal, and frequency deviation issues in the distribution and transmission levels under current tariffs. The load profile\u27s sensitivity to the battery characteristics and its efficiency, and electricity tariffs are studied. Then, tariff modification as one of the promising tools for load profile adjustment is introduced to modify the customers\u27 load profile and mitigate the frequency deviation. The results under modified tariffs are compared to the frequency control results in a small microgrid using model predictive control. In the second chapter, the effect of those new loads on the power flow and inter-area oscillation modes are studied. Then a servomechanism controller is designed to damp the inter-area oscillations. Considering the small time constant of the large-scale battery, we model a large-scale battery integration to the power system to study the effect of its integration on the power system\u27s stability. Finally, centralized and decentralized hybrid controls are designed on the inverter\u27s firing angle to manage the large-scale battery\u27s active and reactive power to damp the oscillations. Results show a notable improvement on frequency deviations

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)