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A Methodology to Evaluate Reactive Power Reserves Scarcity During the Energy Transition

The lack of reserves for reactive power production and absorption is an envisioned, still basically unexplored, threat to the voltage profile adequacy and thereby secure operation of transmission grids during the energy transition toward renewable-dominated power production. This paper proposes a novel, generic, comprehensive, and realistic methodology to identify when this issue of reactive power reserves (RPRs) scarcity during plausible scenarios of the energy transition would become severe. The computational core of the proposed methodology comprises four different AC security-constrained optimal power flow (SCOPF) problems: one conventional, two tailored ones that assess the RPRs scarcity in production and absorption modes, respectively, and an optimal reactive power dispatch. The methodology is versatile, offering the possibility to assess RPRs in different timescales, ranging from day-ahead short-term operation to years-ahead long-term operation, and considering appropriate renewable energy production forecasts and day-dependent load profiles. The proposed methodology can serve as a decision support tool for the transmission system operator (TSO), allowing to plug and play different plausible energy transition scenarios (e.g., differing in terms of sequence and timing of: phased out power plants as well as location, type, and size of renewable energy sources deployed) and ultimately informing the TSO about the timing where RPRs become insufficient to maintain security. Without loss of generality, the value of the proposed methodology is extensively demonstrated in a 60-bus Nordic32 system, considering 52 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-1$</tex-math></inline-formula> line and generator contingencies, while the tractability of AC SCOPF problems is assessed in a 1,203-bus system.

Small-Signal Synchronous Stability of a New-Generation Power System With 100% Renewable Energy

The traditional power system dominated by synchronous generators (SGs) is now evolving into a new-generation power system dominated by voltage source converter (VSC)-based renewable energy, causing substantial changes in the dynamical behavior. This paper investigates the small-signal synchronous stability of a new-generation power system composed solely of grid-following and/or grid-forming power electronics devices in the absence of an infinitely strong bus. The phase-lock-loop (PLL)-based VSC is taken as a representative of grid-following devices and treated as a controlled-current source whose phase is driven by the power factor angle. In sharp contrast, the virtual synchronous generator (VSG)-based VSC is taken as a representative of grid-forming devices and treated as a controlled-voltage source whose phase is driven by the active power. Small-signal synchronization stability models are established for multiconverter power systems within the framework of the classical Phillips–Heffron model for traditional power systems. Explicit expressions for equivalent inertia, damping, and synchronization coefficients are obtained. We find that both PLL-based and VSG-based VSCs show similar synchronization principles and contribute to the inertia and damping of the system. All these findings are well supported and verified by our modal analysis and time-domain simulations.

Real-Time Energy Management for Marine Applications Using Markov Approximation

All-electric and hybrid-electric ships have become the centerpiece for reducing greenhouse gas emissions and improving fuel efficiency in the maritime industry. Real-time power management of multiple power sources in both design and operation becomes critical due to the uncertainty and randomness of the vessel's power demand. Furthermore, the vessel operators may have additional requirements for operational flexibility, such as fully recharging the battery before the next trip and limiting engine switch on/off to reduce wear-and-tear, which lead to complex power management systems requiring domain-expert knowledge. In this paper, a hybrid solution for real-time power management is proposed. The model-based approach encapsulates the power system, and the data-driven approach (a machine learning technique called Markov approximation) handles the uncertainty. First, an offline deterministic optimization problem for power management is formulated. Second, the deterministic problem is transformed into a maximum weighted independent set (MWIS) problem. Next, the Markov approximation framework is applied to the transformed problem to utilize the machine learning techniques widely used in wireless communications and computing industries. Finally, a reinforcement learning-based real-time solution is proposed. Extensive simulations are performed on a ferry case study, and the results are within the proven theoretical bounds of machine learning techniques.

Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading

This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning. Further, a parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations. Without these modifications to the training procedure, the RL agent failed for most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL learning. The agent was tested by RTE for the 2019 learning to run the power network challenge and was awarded the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> place in accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1^{st}$</tex-math></inline-formula> place in speed. The developed code is open-sourced for public use. Analysis of a simple system proves the enhancement in training RL-agents using the curriculum.

Open Access
Adaptive Event-Triggered Model Predictive Load Frequency Control for Power Systems

This paper investigates the adaptive event-triggered (AET) output feedback model predictive control (MPC) method for the load frequency control (LFC) problem of hybrid power system involving the thermal power and the photovoltaic power. First, an AET communication mechanism, which possesses the ability to dynamically adjust the trigger threshold parameter according to an adaptive law, is addressed to reducing the communication overhead, while keeping the required control performance; Second, the output feedback model predictive controller which is composed of an off-line designed state observer and an on-line solved optimization problem, is proposed using the techniques of quadratic boundedness and S-procedure. The state observer gain is obtained by off-line maximizing the trace of a matrix which is closely related to minimal positively invariant set; While the output feedback MPC strategy is acquired by expressing the infinite horizon control moves as a free control move and a linear feedback law based on the estimated state; Third, the recursive feasibility of the on-line optimization problem and the stability of the closed-loop system are analyzed by the fact that the estimated state and estimation error will converge to the presented minimal positively invariant set. Finally, MATLAB and its linear matrix inequality (LMI) toolbox are used to solve the optimization problem, and the LFC system with load disturbance is simulated and analyzed. The simulation results show that the proposed method is feasible and effective.