NCCR Automation
The National Centre of Competence in Research «Dependable, ubiquitous automation», NCCR Automation for short, investigates new approaches to the control of complex automated systems and implements them in concrete applications in practice. Through networked research, the development of new technologies and education, the NCCR aims to strengthen Switzerland's leading role in automation and control technology. More information can be found on external pagenccr-automation.ch.
I conduct research in the intersection of modeling, control and optimization in relation
to energy systems. See Figure 1.
Based on the concept of temporal control hierarchies, one can improve the overall system
efficiency: Through addressing and overcoming modeling and control challenges. In this
regard I develop dynamical system models either from physical insights (white), from data
(black) or by combining both information sources (grey). I consider the uncertainty of
the system or its drivers through probabilistic modeling principles. One may control a
system directly (Direct Control) or indirectly (Indirect Control), price-based control being
an example for the latter. One may incentivize a desired behaviour of prosumers in a
system through price-based control. Using price-based control, I work towards prosumer
response activation schemes in microgrids and virtual power plants that improve the
system performance. I integrate these approaches in a programmatic way by combining
them with software engineering principles coming from the field of computer science.
Frederik’s research is part of NCCR Automation, see external pagehttps://nccr-automation.ch/.
Contact: Frederik Banis
The growing shares of renewable production in power systems have led to decreased levels of system inertia, which impacts system stability and increases the need for fast-responding frequency control services. In this context, the type of ancillary services and how they are being compensated likely needs to be adjusted from the current practice.
In this project, we would like to tackle the following questions: what types of services are needed in a highly decentralized system with large shares of renewable resources? How to facilitate the participation of ancillary services providers in wholesale markets and empower them to offer their full flexibility? And, in a fully renewable power system with a negligible fuel cost component, how should these services be remunerated in order to reflect the cost of variability and uncertainty?
This project will add the missing link of the design of ancillary service product and market trading floors, and the pricing of such.
In particular, we will first develop a frequency control-aware inertia commitment model which anticipates the impact of system inertia levels on primary frequency control (PFC) markets using hierarchical optimization. In this inertia-dependent PFC market formulation, the requirements and quality of different PFC services will be modelled as inertia-dependent functions.
This work will open the way to investigate the value and pricing of inertia and build demand curves for inertia and other fast-responding frequency-control services which reflect the cost of variability and uncertainty in real time.
In parallel, we will investigate how different providers, such as active distribution grids, can optimally participate in ancillary services and energy markets using hierarchical optimization and reinforcement learning methods.
This work will open the way to design new ancillary services products and bid formats which accurately represent the full flexibility of these providers and facilitates their participation in wholesale markets.
This project is part of the NCCR Automation:
external pagehttps://nccr-automation.ch/nccr-automation
Contact: Dr. Lesia Mitridati
In recent years, electric power systems have been experiencing a shift in paradigm from conventional energy sources to renewable energy sources (RES). Additionally, new loads are being introduced, such as electric vehicles, heat pumps, etc. High penetration of renewables, alongside their intermittency, renders power flow patterns less predictable and system dynamics significantly faster. Therefore, the focus of the power system community is moving from static state estimation (SSE) towards dynamic state estimation (DSE).
DSE uses dynamic equations given in a generic way by
x ̇=f(x(t),y(t),u(t)), (1)
0=g(x(t),y(t),u(t)). (2)
Equations (1)–(2) represent a set of nonlinear, differential-algebraic equations that govern the dynamic behavior of the whole system. We are interested in estimating system states, at each time step, in a recursive fashion. Kalman filter and its nonlinear variants are standard tools used in literature to perform this task. In particular, we try to make these estimators robust against topology errors, parameter uncertainty, and cyber-attacks. To achieve this goal, least-absolute value (LAV), H_∞ or distributionally robust estimators can be employed. Besides robustness, other aspects, such as scalability and computational tractability, have to be addressed to enable the application to high-dimensional systems in practice.
Like many other cyber-physical systems, power systems are extremely safety-critical. Therefore, secure operation and intrusion detection are of paramount importance in everyday operation. As part of the NCCR Automation project, we look into enhancing power systems' security by detecting inconsistent patterns in the measurements collected over time. One way to achieve this goal relies on exploiting the information from previous time steps propagated through the system dynamics. Another way could be using model-free, data-driven methods.
Contact: Milos Katanic
The current trends in large-scale integration of Renewable Energy Resources (RES) and decommissioning of conventional power plants impose new challenges to power system operation. Safe operation of the transmission system is challenged by the decreasing amounts of rotational inertia and damping, leading to faster frequency dynamics and larger frequency deviations. Consequently, novel frequency services, such as Fast Frequency Control (FFC), acting at signi cantly shorter timescales have emerged to improve system resilience. The most suitable units for providing FFC are grid-forming Voltage Source Converters (VSCs) with associated energy storage due to their fast response times. This project aims at developing novel control strategies, based on model predictive control and reinforcement learning, for FFC provision that can be incorporated as a supervisory layer to the already existing VSC control techniques, as shown in Fig. 1.
Nevertheless, only a smaller share of the total RES capacity is connected to the transmission system and the majority of fexible units in the form of Distributed Energy Resources (DERs) are installed at low- and medium-voltage levels of Distribution Networks (DNs). Although DERs are presently used only to support the local operation of DNs, focusing on problems such as voltage rise and thermal overloading, a coordinated aggregation of a large number of fexible units in DNs o ers great potential for ancillary service provision. The project will thus focus on developing control schemes that will allow DERs to collectively provide frequency and voltage support to the transmission system, as presented in Fig. 2.
The project will be conducted within the scope of NCCR Automation: external pagehttps://nccr-automation.ch
Contact: Ognjen Stanojev
