Power System Dynamic Control and Performance Improvement Based on Reinforcement Learning

This dissertation investigates the feasibility and effectiveness of using Reinforcement Learning (RL) techniques for power system dynamic control, particularly voltage and frequency control. The conventional control strategies used in power systems are complex and time-consuming due to the complicated high-order nonlinearities of the system. RL, which is a type of neural network-based technique, has shown promise in solving these complex problems by fitting any nonlinear system with the proper network structure. The proposed RL algorithm, called Guided Surrogate Gradient-based Evolution Strategy (GSES) determines the weights of the policy (which generates the action for our control reference signal) without back-propagation process for gradient update using a simultaneous perturbation stochastic approximation approach comparing to many other RL algorithms, thus it achieves a much faster and more robust learning convergence. It is introduced and implemented in three different power system scenarios: High Voltage Direct Current (HVDC) based inter-area oscillation damping system, Doubly-fed Induction Generator (DFIG) based Fault-Ride-Through (FRT) system, and modified IEEE-39 Bus based frequency regulation system. In the case of the HVDC-based system, the proposed GSES-based power oscillation damping control approach overcomes the challenges of setting optimal controller parameters of the HVDC under various system transient events. This approach is also shown to be superior to conventional power oscillation damping methods. Further, the GSES algorithm is found to be effective in controlling the DFIG power and capacitor DC-link voltage, which helps prevent the rotor of DFIG from over-current risk and maintain the grid-connected operation. Finally, the proposed RL-based solution for frequency response in wind farms is tested on a modified IEEE-39 bus system and is found to reliably support the frequency of the power system and prevent unnecessary load shedding. Overall, this dissertation shows the potential of RL-based techniques in power system dynamic control, particularly frequency control, and provides evidence for the effectiveness of the GSES algorithm in various power system scenarios. The use of RL in power systems could lead to more efficient and effective control strategies during contingencies, which is crucial in maintaining the stability of today’s large, high-order nonlinear dynamic power systems

Improving Grid Hosting Capacity and Inertia Response with High Penetration of Renewable Generation

To achieve a more sustainable supply of electricity, utilizing renewable energy resources is a promising solution. However, the inclusion of intermittent renewable energy resources in electric power systems, if not appropriately managed and controlled, will raise a new set of technical challenges in both voltage and frequency control and jeopardizes the reliability and stability of the power system, as one of the most critical infrastructures in the today’s world. This dissertation aims to answer how to achieve high penetration of renewable generations in the entire power system without jeopardizing its security and reliability. First, we tackle the data insufficiency in testing new methods and concepts in renewable generation integration and develop a toolkit to generate any number of synthetic power grids feathering the same properties of real power grids. Next, we focus on small-scale PV systems as the most growing renewable generation in distribution networks and develop a detailed impact assessment framework to examine its impacts on the system and provide installation scheme recommendations to improve the hosting capacity of PV systems in the distribution networks. Following, we examine smart homes with rooftop PV systems and propose a new demand side management algorithm to make the best use of distributed renewable energy. Finally, the findings in the aforementioned three parts have been incorporated to solve the challenge of inertia response and hosting capacity of renewables in transmission network

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

Game theory-based optimal deloading control of wind turbines under scalable structures of wind farm

This study addresses the problem of how to harvest as much kinetic energy as possible during deloading control of variable speed wind turbines (VSWTs) by distributedly adjusting rotor speeds under scalable wind farm topology. To that end, a game theory-based distributed control framework is investigated to enable the optimal rotor speed setting of VSWTs such that maximal kinetic energy can be stored in rotating masses of VSWTs for further system support, and meanwhile, the power dispatch objective for VSWTs can be fulfilled. It is shown that through distributedly detecting the changing network topologies within wind farm, the proposed methodology enables individual VSWT to adaptively increase its stored kinetic energy while requiring only local information sharing. It is important both theoretically and practically that the design has autonomously guaranteed maximal kinetic energy storing for all VSWTs in wind farm of time varying topologies and provides robustness, scalability and efficiency in the absence of global information sharing. Simulation results and case studies are included to demonstrate the effectiveness of the proposed controls both in Matlab and DIgSILENT/PowerFactory