Cost-Optimal Operational Security in Transmission Grids with Embedded HVDC Systems and Energy Storage

The future transmission grid for electrical power will face challenges on an unprecedented scale as the transformation of the energy system progresses. The massive integration of renewable energy sources will require new methods and additional equipment to maintain the system secure and cost-efficient. This doctoral thesis presents an approach to securely operate a transmission grid based on optimal power flow. Optimal control of phase shifting transformers, overlaying HVDC grids and large-scale energy storage lead to reduced operating costs. Furthermore, this work discusses efficient approaches to optimally coordinate multiple inter-connected control areas, if one central controller is undesirable for political or technical reasons

Optimisation and Integration of Variable Renewable Energy Sources in Electricity Networks

The growing penetration of renewable energy sources (RESs) into the electricity power grid is profitable from a sustainable point of view and provides economic benefit for long-term operation. Nevertheless, balancing production and consumption is and will always be a crucial requirement for power system operation. However, the trend towards increasing RESs penetration has raised concerns about the stability, reliability and security of future electricity grids. The clearest observation in this regard is the intermittent nature of RESs. Moreover, the location of renewable generation tends to be heavily defined by meteorological and geographical conditions, which makes the generation sites distant from load centres. These facts make the analysis of electricity grid operation under both dynamic and the steady state more difficult, posing challenges in effectively integrating variable RESs into electricity networks. The thesis reports on studies that were conducted to design efficient tools and algorithms for system operators, especially transmission system operators for reliable short-term system operation that accounts for intermittency and security requirements. Initially, the impact of renewable generation on the steady state is studied in the operation stage. Then, based on the first study, more sophisticated modeling on the electricity network are investigated in the third and fourth chapters. Extending the previous studies, the fourth chapter explores the potential of using multiple microgrids to support the main grid’s security control. Finally, the questions regarding the computational efficiency and convergence analysis are addressed in chapter 5 and a DSM model in a real-time pricing environment is introduced. This model presents an alternative way of using flexibility on the demand side to compensate for the uncertainties on the generation side

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

Cooperative Predictive Control to enhance Power System Security

The thesis proposes a distributed model predictive control (dMPC) method with has one control unit for each controllable device (Generators, FACTS, HVDC) and coordinates their behavior after a fault. Further, a cooperative multi-area optimization strategy is presented which enables transmission system operators (TSOs) to dispatch/redispatch interconnected networks securely

Grid Capacity Issues with Distributed Generation

Climate change is more evident than ever as reflected in The European union's environmental directives which say that the carbon emission must be reduced by 20% and 20% of the used energy must come from renewable energy sources until 2020. In Germany political decision has been made that the nuclear power will be replaced by renewable energy in long term. The purpose of the Master Thesis is to investigate how high penetration of photovoltaic affects the electrical grid on a distribution level concerning active power and map the potential for different renewable energy sources in Germany. Using a simulation model and grid data received from E.ON the goal is to map what problems that may occur and evaluate different measures for solving the problems. The data and information collection have been done by interviews and literature studies. The simulation program that has been used is DIGSILENT Power Factory where all the simulations have been static ones. Different load profiles for households have been handed by an internal source in E.ON and evaluated before inserted in the simulations. The studied measures for balancing the active power are battery storages of different technologies, load shifting and biomass power plants. The investigated battery technologies were Li-ion batteries, Lead-acid batteries and Vanadium Redox flow batteries. The main purpose of evaluating three different technologies is the costs for each technology. Battery storages and load shifting have been used for all load profiles, the biomass power plants have been used while the PV output has been low. The results showed that Germany is able to increase its wind and PV output in the future. Implementation of battery storage and load shifting will balance the grid and less power will be taken from the transmission grid. Load shifting is very hard to analyze and utilize but assumed to have low capital costs. Load shifting in households is also a very immature technology. Storing energy is the most effective measure for balancing the active power because of the valuable property to store energy and use when it is needed. But the costs of battery storages are high even if no costs of power electronics were included. Implementation of a 10 MW biomass power plant will balance the active power while low production of PV and high demand

Resilience Enhancement Strategies for Modern Power Systems

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. Also, the massive integration of information and communication technology to power systems has evolved the power systems into what is known as cyber-physical power systems (CPPSs). Although advanced technologies in the cyber layer improve the operation and control of power systems, they introduce additional vulnerabilities to power system performance. This has motivated studying power system resilience evaluation and enhancements methods. Power system resilience can be defined as ``The ability of a system to prepare for, absorb, adapt to, and recover from disruptive events''. Assessing resilience enhancement strategies requires further and deeper investigation because of several reasons. First, enhancing the operational and planning resilience is a mathematically involved problem accompanied with many challenges related to modeling and computation methods. The complexities of the problem increases in CPPSs due to the large number and diverse behavior of system components. Second, a few studies have given attention to the stochastic behavior of extreme events and their accompanied impacts on the system resilience level yielding less realistic modeling and higher resilience level. Also, the correlation between both cyber and physical layers within the context of resilience enhancement require leveraging sophisticated modeling approaches which is still under investigation. Besides, the role of distributed energy resources in planning-based and operational-based resilience enhancements require further investigation. This calls for developing enhancement strategies to improve resilience of power grids against extreme events. This dissertation is divided into four parts as follows. Part I: Proactive strategies: utilizing the available system assets to prepare the power system prior to the occurrence of an extreme event to maintain an acceptable resilience level during a severe event. Various system generation and transmission constraints as well as the spatiotemporal behavior of extreme events should be properly modeled for a feasible proactive enhancement plan. In this part, two proactive strategies are proposed against weather-related extreme events and cyber-induced failure events. First, a generation redispatch strategy is formulated to reduce the amount of load curtailments in transmission systems against hurricanes and wildfires. Also, a defensive islanding strategy is studied to isolate vulnerable system components to cyber failures in distribution systems. Part II: Corrective strategies: remedial actions during an extreme event for improved performance. The negative impacts of extreme weather events can be mitigated, reduced, or even eliminated through corrective strategies. However, the high stochastic nature of resilience-based problem induces further complexities in modeling and providing feasible solutions. In this part, reinforcement learning approaches are leveraged to develop a control-based environment for improved resilience. Three corrective strategies are studied including distribution network reconfiguration, allocating and sizing of distributed energy resources, and dispatching reactive shunt compensators. Part III: Restorative strategies: retain the power service to curtailed loads in a fast and efficient means after a diverse event. In this part, a resilience enhancement strategy is formulated based on dispatching distributed generators for minimal load curtailments and improved restorative behavior. Part IV: Uncertainty quantification: Impacts of uncertainties on modeling and solution accuracy. Though there exist several sources of stochasticity in power systems, this part focuses on random behavior of extreme weather events and the associated impacts on system component failures. First, an assessment framework is studied to evaluate the impacts of ice storms on transmission systems and an evaluation method is developed to quantify the hurricane uncertainties for improved resilience. Additionally, the role of unavailable renewable energy resources on improved system resilience during extreme hurricane events is studied. The methodologies and results provided in this dissertation can be useful for system operators, utilities, and regulators towards enhancing resilience of CPPSs against weather-related and cyber-related extreme events. The work presented in this dissertation also provides potential pathways to leverage existing system assets and resources integrated with recent advanced computational technologies to achieve resilient CPPSs