Reachability analysis for the verification of adaptive protection setting selection logic

The testing of adaptive protection schemes is a problem that remains largely unaddressed. These schemes can be characterized by uncertainty in behavior due to the dynamic changes in their configuration to suit prevailing network conditions. This paper proposes a novel approach to formalizing this behavior using hybrid systems modeling. This unlocks the ability to verify the safety performance of the schemes using reachability analysis. In this paper, an adaptive setting selection logic for distance protection is verified for its safety, using reachability analysis, during changes in network conditions

Anticipating and Coordinating Voltage Control for Interconnected Power Systems

This paper deals with the application of an anticipating and coordinating feedback control scheme in order to mitigate the long-term voltage instability of multi-area power systems. Each local area is uniquely controlled by a control agent (CA) selecting control values based on model predictive control (MPC) and is possibly operated by an independent transmission system operator (TSO). Each MPC-based CA only knows a detailed local hybrid system model of its own area, employing reduced-order quasi steady-state (QSS) hybrid models of its neighboring areas and even simpler PV models for remote areas, to anticipate (and then optimize) the future behavior of its own area. Moreover, the neighboring CAs agree on communicating their planned future control input sequence in order to coordinate their own control actions. The feasibility of the proposed method for real-time applications is explained, and some practical implementation issues are also discussed. The performance of the method, using time-domain simulation of the Nordic32 test system, is compared with the uncoordinated decentralized MPC (no information exchange among CAs), demonstrating the improved behavior achieved by combining anticipation and coordination. The robustness of the control scheme against modeling uncertainties is also illustrated

Control Architecture Modeling for Future Power Systems

Uncontrollable power generation, distributed energy resources, controllable demand, etc. are fundamental aspects of energy systems largely based on renewable energy supply. These technologies have in common that they contradict the conventional categories of electric power system operation. As their introduction has proceeded incrementally in the past, operation strategies of the power system could be adapted. For example much more wind power could be integrated than originally anticipated, largely due to the flexibility reserves already present in the power system, and the possibility of interregional electricity exchange. However, at the same time, it seems that the overall system design cannot keep up by simply adapting in response to changes, but that also new strategies have to be designed in anticipation. Changes to the electricity markets have been suggested to adapt to the limited predictability of wind power, and several new control strategies have been proposed, in particular to enable the control of distributed energy resources, including for example, distributed generation or electric vehicles. Market designs addressing the procurement of balancing resources are highly dependent on the operation strategies specifying the resource requirements. How should one decide which control strategy and market configuration is best for a future power system? Most research up to this point has addressed single isolated aspects of this design problem. Those of the ideas that fit with current markets and operation concepts are lucky; they can be evaluated on the present design. But how could they be evaluated on a potential future power system? Approaches are required that support the design and evaluation of power system operation and control in context of future energy scenarios. This work addresses this challenge, not by providing a universal solution, but by providing basic modeling methodology that enables better problem formulation and by suggesting an approach to addressing the general chicken/egg problem of planning and re-design of system operation and control. The dissertation first focuses on the development of models, diagrams, that support the conceptual design of control and operation strategies, where a central theme is the focus on modeling system goals and functions rather than system structure. The perspective is then shifted toward long-term energy scenarios and adaptation of power system operation, considering the integration of energy scenario models with the re-design of operation strategies. The main contributions in the first part are, firstly, by adaptation of an existing functional modeling approach called Multilevel Flow Modeling (MFM) to the power systems domain, identifying the means-ends composition of control levels and development of principles for the consistent modeling of control structures, a formalization of control-as-a-service; secondly, the formal mapping of fluctuating and controllable resources to a multi-scale and multi-stage representation of control and operation structures; and finally the application to some concrete study cases, including a present system balancing, and proposed control structures such as Microgrids and Cells. In the second part, the main contributions are the outline of a formation strategy, integrating the design and model-based evaluation of future power system operation concepts with iterative energy scenario development. Finally, a new modeling framework for development and evaluation of power system operation in context of energy-storage based power system balancing is introduced.<br/

On the analysis of stochastic timed systems

The formal methods approach to develop reliable and efficient safety- or performance-critical systems is to construct mathematically precise models of such systems on which properties of interest, such as safety guarantees or performance requirements, can be verified automatically. In this thesis, we present techniques that extend the reach of exhaustive and statistical model checking to verify reachability and reward-based properties of compositional behavioural models that support quantitative aspects such as real time and randomised decisions. We present two techniques that allow sound statistical model checking for the nondeterministic-randomised model of Markov decision processes. We investigate the relationship between two different definitions of the model of probabilistic timed automata, as well as potential ways to apply statistical model checking. Stochastic timed automata allow nondeterministic choices as well as nondeterministic and stochastic delays, and we present the first exhaustive model checking algorithm that allows their analysis. All the approaches introduced in this thesis are implemented as part of the Modest Toolset, which supports the construction and verification of models specified in the formal modelling language Modest. We conclude by applying this language and toolset to study novel distributed control strategies for photovoltaic microgenerators

Voltage coordination in multi-area power systems via distributed model predictive control

Power systems are nowadays becoming more and more interconnected, and controlled by several TSOs (Transmission System Operators), in order to ensure a reliable and economical supply and distribution of electric power. These (interconnected) electrical power networks are often considered as the most complex man-made dynamical systems ever. For example, according to the dataset provided by the ENTSO-E (European Network of Transmission System Operators for Electricity) for static studies (calculation of the AC load flow), the European interconnected power grid consists of approximately 4300 buses, 6300 lines and 1100 transformers together with their loads, distribution systems and generations in-feeds (in different voltage levels of 380 kV, 220 kV and 150 kV). The proper control of such a large-scale interconnected power system is a very challenging problem due to the various continuous and discrete dynamics evolving in the system and their complicated interactions. Each local control agent (CA), corresponding to an area operated by one TSO, tries to achieve local improvement. However, it happens frequently that a local initiating disturbance in one area triggers some local control actions in its own area which in turn triggers further disturbances in the neighboring areas causing some undesirable control actions by their neighbors, and eventually a cascade of possibly wrong control actions lead the overall system to a final collapse. One important class of power system instability is voltage instability, which actually arises from the inability of combined generation-transmission systems to deliver the power requested by (dynamical recovery) voltage-dependent loads. Such a voltage instability, if not corrected properly, due to a cascade of events, can eventually lead to voltage collapse (abnormally low voltages in a major portion of the system) often resulting in blackouts or separation of the system into separate unsynchronized islands. The societal impacts and financial costs/losses caused by blackouts are significantly huge. The voltage in electrical power systems is, in nature, a ``local" variable unlike frequency being a ``global" variable. This means that, in multi-area power systems, only areas that are electrically close together interact with each other for voltage, and there is no need to involve distant areas with negligible common interest in solving a local optimization problem. The latter promotes the decomposition approaches for voltage control, where the voltage control still remains a prerogative of the local utilities. This thesis focuses on long-term voltage instability - in the order of several minutes after a major disturbance. The driving force of such instability, following a disturbance, is the process of load restoration, where the dynamics of recovering loads directly as well as some control mechanism such as LTCs (Load Tap Changing transformers) indirectly (by restoring the distribution-side voltages of the corresponding voltage-dependent loads), try to locally restore the load powers to the pre-disturbance values. The long-term voltage instability often occurs when LTCs try to restore the distribution side voltages of the connected buses, while the maximum power that the transmission system can provide to loads is reduced by the reactive power capability limits of generators, mainly enforced by OXLs (Over eXcitation Limiters). It seems rather intuitive, then, to seek some way of anticipating what will be the future behavior of a power system, by employing controllers which can look ahead in time. The long-term voltage control becomes even a more complex and harder problem in large-scale multi-area power system, each controlled by an independent TSO. The reason is that, for example, an arbitrary LTC move in one area can trigger undesirable LTC move(s), OXL activations in other areas, and such complicated global interactions may eventually lead to a blackout in the form of a voltage collapse. In order to avoid such a collapse in large-scale multi-area power systems, the local control actions taken by each CA, must be coordinated with those of (adjacent) neighbors. This coordination requires communications between neighboring CAs. This thesis proposes an efficient distributed Model Predictive Control (MPC) paradigm which combines two concepts of ``looking-ahead" and ``coordination". The proposed MPC-based control scheme relies on the communication of planned local control actions among neighboring CAs, each possibly operated by an independent TSO. Modelica, a free of charge object-oriented language, is used to develop a much-faster-than-real-time simulator, providing an hybrid framework for effectively modeling and simulating power systems. Modelica facilitates the development of tools to generate very efficient codes for modeling of compositional physical systems such as electrical power networks, by relaxing the causality constraint of components, and focusing only on the topology of the overall system. In this thesis, the dynamic models for anticipation, are derived by considering each area as a hybrid dynamical system, using DAEs to describe piecewise continuous dynamics, and the set of events of hybrid automata representing the discrete logical controllers. This hybrid modeling framework captures the complex interactions between continuous and discrete dynamics. The ``looking-ahead" voltage controller can anticipate, within the prediction horizon window, for example, the activation of OXLs, moving towards reaching the maximum physical tap limits for LTCs, and deviating too much from the prescribed voltage bounds for buses. The controller will then efficiently use these anticipations, by selecting a control sequence that does not cause the above-mentioned constraint violations. The first input of the best control sequence selected by each local MPC, at each discrete time instant, will be applied to the local system until the next time instant, where the local optimization repeats again selecting the new best control action. Each CA, knowing a local model of its own area, as well as a reduced-order Quasi Steady-State (QSS) models of its immediate neighboring areas, and assuming a simpler equivalent PV model for the distant areas, performs a greedy local optimization over a finite window in time, communicating its planned control input sequence to its immediate neighbors only. The ``communicating" voltage controller enables each CA to coordinate its own local action with what its immediate neighbors are planning to do, assuming a QSS model for predicting how control actions of neighbors influence the interacting variables. The good performance of the proposed real-time model-based feedback coordinating controller, following major disturbances, is illustrated using time-domain simulation of the well-known realistic Nordic32 test system, assuming worst-case conditions. Robustness of the proposed method against measurement inaccuracies, modeling errors as well as the uncertainty of the load behavior has also been illustrated. This thesis considers two cases where, in the first reasonably sized network, a local CA, knows the complete model of the overall system, while, in the second realistic sized system, it employs reduced-order QSS models for immediate neighbors, and assumes a simpler equivalent PV model for the distant areas. Simulation results illustrates the significant achievements obtained when the proposed model-based coordinating control is applied to different systems under some severe disturbances. This thesis compares the above-mentioned simulation results with scenarios where a purely decentralized uncoordinated deadband control, as the current practice for LTCs, is applied, or where a decentralized uncoordinated MPC approach with no communication is applied. In this way it becomes possible to identify the two afore-mentioned distinct contributions of the proposed model-based coordinating approach namely ``looking-ahead" and ``communication", since the decentralized deadband approach lacks both anticipation and coordination, and the decentralized MPC approach ignores the communications with neighbors