arXiv subjects

Kyung-bin Kwon

Publications and source records attributed to Kyung-bin Kwon.

8 recordsLinked to original sources

arXiv2026

Stochastic Multi-Segment Scheduling of Variable-Speed Pumped Storage Hydropower for Energy and Ancillary Services Provision

Variable-speed pumped storage hydropower (VS-PSH) offers long-duration energy storage alongside ancillary services in competitive electricity markets. However, its operation and scheduling are challenged by head-dependent nonlinearities, discrete mode transitions, and energy-continuity constraints. This study proposes a stochastic framework for VS-PSH that employs a multi-segment bidding structure to generate market-consistent energy and synchronized reserve offers in compliance with market rules. The framework explicitly incorporates physical constraints, including head-dependent capability limits, discrete pumping and generating modes, as well as state-of-charge (SoC) and head dynamics, within a stochastic mixed-integer linear programming (MILP) formulation. Price uncertainty is represented through a scenario-based modeling approach that scales base-case prices and allows variations in (dis)charging incentives. The stochastic MILP produces optimal energy and mode schedules that maintain feasible SoC trajectories across scenarios and ensure physically feasible operating strategies. Case studies under different levels of price variability demonstrate the operational feasibility and market applicability of the proposed framework, showing effective coordination between energy arbitrage and reserve provision under uncertainty. These results highlight the operational and economic value of VS-PSH as a grid-scale energy storage resource.

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arXiv2026

Degradation-Aware Pumping Control of Variable-Speed Pumped Storage via Residual Reinforcement Learning

Variable-speed pumped storage hydropower (VS-PSH) must honor short-block dispatch commitments while limiting the operational degradation that intensified regulation duty inflicts on its components. When a single controller pursues both aims at once, every tracking gain is paid for in degradation, a conflict that persists even under full model knowledge and look-ahead. This paper proposes a two-layer control architecture that separates the guaranteed commitment from the bounded learning. A deterministic feedforward-PI gate controller, auditable and certifiable for grid-connected operation, secures average power delivery over each five-minute block, while a residual reinforcement learning policy adjusts only the rotor speed within a fixed bound the gate loop can always absorb, so the worst-case command is bounded by construction. The speed policy tracks a demand-dependent best-efficiency-point reference and is trained against an operation-degradation index that combines off-best-efficiency hydraulic loss with power and actuation variation into one physically interpretable signal. Across normal and stressed dispatch, the proposed policy lowers best-efficiency-point tracking error by roughly 96\% relative to a fixed-speed baseline and cuts total degradation by up to about 56\% under the most demanding dispatch. It matches or slightly exceeds a full-information model-based optimizer in efficiency while preserving substantially tighter block tracking.

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arXiv2025

Communication-aware Wide-Area Damping Control using Risk-Constrained Reinforcement Learning

Non-ideal communication links, especially delays, critically affect fast networked controls in power systems, such as the wide-area damping control (WADC). Traditionally, a delay estimation and compensation approach is adopted to address this cyber-physical coupling, but it demands very high accuracy for the fast WADC and cannot handle other cyber concerns like link failures or {cyber perturbations}. Hence, we propose a new risk-constrained framework that can target the communication delays, yet amenable to general uncertainty under the cyber-physical couplings. Our WADC model includes the synchronous generators (SGs), and also voltage source converters (VSCs) for additional damping capabilities. To mitigate uncertainty, a mean-variance risk constraint is introduced to the classical optimal control cost of the linear quadratic regulator (LQR). Unlike estimating delays, our approach can effectively mitigate large communication delays by improving the worst-case performance. A reinforcement learning (RL)-based algorithm, namely, stochastic gradient-descent with max-oracle (SGDmax), is developed to solve the risk-constrained problem. We further show its guaranteed convergence to stationarity at a high probability, even using the simple zero-order policy gradient (ZOPG). Numerical tests on the IEEE 68-bus system not only verify SGDmax's convergence and VSCs' damping capabilities, but also demonstrate that our approach outperforms conventional delay compensator-based methods under estimation error. While focusing on performance improvement under large delays, our proposed risk-constrained design can effectively mitigate the worst-case oscillations, making it equally effective for addressing other communication issues and cyber perturbations.

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arXiv2023

Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls

This paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.

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arXiv2022

Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control

Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to distributed paradigm, LFC needs to consider the peer-to-peer (P2P) based scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.

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arXiv2022

Reinforcement Learning Based Optimal Battery Control Under Cycle-based Degradation Cost

Battery energy storage systems are providing increasing level of benefits to power grid operations by decreasing the resource uncertainty and supporting frequency regulation. Thus, it is crucial to obtain the optimal policy for battery to efficiently provide these grid-services while accounting for its degradation cost. To solve the optimal battery control (OBC) problem using the powerful reinforcement learning (RL) algorithms, this paper aims to develop a new representation of the cycle-based battery degradation model according to the rainflow algorithm. As the latter depends on the full trajectory, existing work has to rely on linearized approximation for converting it into instantaneous terms for the Markov Decision Process (MDP) based formulation. We propose a new MDP form by introducing additional state variables that can easily keep track of past switching points for determining the cycle depth. The proposed degradation model allows to adopt the powerful deep Q-Network (DQN) based RL algorithm to efficiently search for the OBC policy. Numerical tests using real market data have demonstrated the performance improvements of the proposed cycle-based degradation model in enhancing the battery operations while mitigating its degradation, as compared to earlier work using the linearized approximation.

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arXiv2022

Model-free Learning for Risk-constrained Linear Quadratic Regulator with Structured Feedback in Networked Systems

We develop a model-free learning algorithm for the infinite-horizon linear quadratic regulator (LQR) problem. Specifically, (risk) constraints and structured feedback are considered, in order to reduce the state deviation while allowing for a sparse communication graph in practice. By reformulating the dual problem as a nonconvex-concave minimax problem, we adopt the gradient descent max-oracle (GDmax), and for modelfree setting, the stochastic (S)GDmax using zero-order policy gradient. By bounding the Lipschitz and smoothness constants of the LQR cost using specifically defined sublevel sets, we can design the stepsize and related parameters to establish convergence to a stationary point (at a high probability). Numerical tests in a networked microgrid control problem have validated the convergence of our proposed SGDmax algorithm while demonstrating the effectiveness of risk constraints. The SGDmax algorithm has attained a satisfactory optimality gap compared to the classical LQR control, especially for the full feedback case.

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arXiv2022

Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning

Effectively operating electrical vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces scales with the number of EVs and is thus very large and time-varying. This dimensionality issue affects the efficiency and convergence properties of generic RL algorithms. We develop aggregation schemes that are based on the emergency of EV charging, namely the laxity value. A least-laxity first (LLF) rule is adopted to consider only the total charging power of the EVCS which ensures the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Based on the proposed representation, policy gradient method is used to find the best parameters for the linear Gaussian policy . Numerical results have validated the performance improvement of the proposed representation approaches in attaining higher rewards and more effective policies as compared to existing approximation based approach.

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