Increased penetration of renewable energy leads to increased challenges for the transmission system operators (TSOs) to operate their system in a secure way. Through the limited means available to the TSOs to manage power flows in the system, reliability is jeopardized. Reliability must be managed differently, replacing the current ways of reliability management which fails as uncertainty increases. In this paper, a novel risk-based approach of system operation is proposed that can be helpful for the TSOs to assess the confidence of system operation day-ahead, that is, the probability of the forecasted system to end up in an insecure state is calculated. This paper only focuses on violation of power flow constraints in the system. It also demonstrates the increase of this operation confidence using already installed power flow controlling devices. It is shown that these devices aid in enhancing confidence of system operation by shifting power flows to a more optimal one in the light of generation uncertainty. The main emphasis is laid on preventive action. The proposed approach is demonstrated on test systems.

The transmission grid in Europe is interconnected to guarantee the security of supply and to facilitate the competition among different market players, thereby making the system highly meshed. It is a challenging task for the transmission system operators (TSOs) to manage the power flows in their system, especially in the light of integration of renewable energy generation sources into the transmission system. The intermittent nature of such generation sources creates variable power flows and loop flows, in turn, questing for installation of controllable devices to manage these flows. The TSOs are currently installing such devices to cope with the situation. A proper coordination is needed for the operation of these devices, since they can lead to adverse effects on power flows in a meshed system. Coordination among TSOs in Central Western Europe (CWE) is performed, however, not towards a full system-wide objective, since there is no regulatory framework that exists for such coordination. This paper focuses on the potential of coordination among TSOs with respect to operation of the controllable devices. Two aspects are investigated: management of constraints in the system in the dayahead scheduling process and wind in-feed optimization. Both approaches are implemented at the Regional Security Center and tested on a high-stress situation in the CWE region. Furthermore, a case study at the coordination center is performed using actual data for the month of January 2013 to assess the usefulness on a longer time period. Index Terms-Coordination, high-voltage direct current (HVDC), phase-shifting transformer (PST), transmission system operations, uncertainty management. NOMENCLATURE l Line index. t Time index. b Node index. N h Number of hours. c Contingency index. j PST index. N P ST Number of PSTs. N b Number of nodes.

The fault response of a 100% converter-based system can be significantly different to that of a synchronous generator-based system, considering the lower capacity headroom, but flexible control capability, of power electronic converters. The system response is investigated for an Irish grid under balanced three-phase faults comprising of 100% converterbased generation: grid-forming (GF) and grid-following (GL). Electro-magnetic transient (EMT) simulations show that a system consisting only of GF converters (all droop control, all dispatchable virtual oscillator control, or a mix of both) is robust against three-phase faults, with little variation in performance, despite the fault location or choice of GF control methods. However, the rating and location of GF converters are critical to operating the grid securely in the presence of both GF and GL converters. Assuming that individual converter bus nodes are either GF or GL, a minimum GF requirement (by capacity) is found to be 37-40%, with these GF converters located close to the major load centres. Assuming instead that individual generation nodes consist of a mix of GF and GL converters, it is found that the GF requirement can be relaxed by 8-10%.

We develop new optimization methodology for planning installation of Flexible Alternating Current Transmission System (FACTS) devices of the parallel and shunt types into large power transmission systems, which allows to delay or avoid installations of generally much more expensive power lines. Our methodology takes as an input projected economic development, expressed through a paced growth of the system loads, as well as uncertainties, expressed through multiple scenarios of the growth. We price new devices according to their capacities. Installation cost contributes to the optimization objective in combination with the cost of operations integrated over time and averaged over the scenarios. The multi-stage (-time-frame) optimization aims to achieve a gradual distribution of new resources in space and time. Constraints on the investment budget, or equivalently constraint on building capacity, is introduced at each time frame. Our approach adjusts operationally not only newly installed FACTS devices but also other already existing flexible degrees of freedom. This complex optimization problem is stated using the most general AC Power Flows. Non-linear, non-convex, multiple-scenario and multi-time-frame optimization is resolved via efficient heuristics, consisting of a sequence of alternating Linear Programmings or Quadratic Programmings (depending on the operational cost dependence on the power injected by the generators) and AC-PF solution steps designed to maintain operational feasibility for all scenarios. Computational scalability and other benefits of the newly developed approach are illustrated on the example of the 2736-nodes large Polish system. One most important advantage of the framework is that the optimal capacity of FACTS is build up gradually at each time frame in a limited number of locations, thus allowing to prepare the system better for possible congestion due to future economic and other uncertainties. NOMENCLATURE Parameters: N l Number of power lines in operation N b Number of buses in the system M Number of loading scenarios representing given time frame N Number of scenarios representing planning horizon T Number of time frames representing horizon t = 1..T Index of a decision point a = 1..M Index of a scenario at time frame t P r t,aOccurrence probability of a scenario a at time frame t x 0 ∈ R N l Vector of initial line inductancesVector of line apparent power limitsVector of maximum (minimum) allowed voltages C SC ∈ R Cost per Ohm of a series FACTS device C SV C ∈ R Cost per MVAr of a shunt FACTS device N years ∈ R Planning horizon Optimization variables (operational, scenario dependent): V ∈ R N b Vector of bus voltage magnitudes θ ∈ R N b Vector of bus voltage angles P G ∈ R N b Vector of generator active power injections Q G ∈ R N b Vector of generator reactive power injections x ∈ R N l Vector of line inductances modified by SC devices ∆x ∈ R N l Vector of series FACTS settings ∆Q ∈ R N b Vector of shunt FACTS settings Optimization variables (investment, scenario independent): ∆x t...