Hoang Tien Nguyen

A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs a limited dataset to learn the power-voltage relationship of an unbalanced three-phase distribution system. The proposed surrogate model is designed using a fixed-point load-flow equation, and the stochastic gradient descent method with an automatic differentiation technique is employed to update the parameters of the surrogate model using complex power and voltage samples. Numerical examples in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed surrogate model can outperform surrogate models based on the deep neural network and Gaussian process regarding prediction accuracy and sample efficiency.

Operational constraint violations may occur when deep reinforcement learning (DRL) agents interact with real-world active distribution systems to learn their optimal policies during training. This letter presents a universal distributionally robust safety filter (DRSF) using which any DRL agent can reduce the constraint violations of distribution systems significantly during training while maintaining near-optimal solutions. The DRSF is formulated as a distributionally robust optimization problem with chance constraints of operational limits. This problem aims to compute near-optimal actions that are minimally modified from the optimal actions of DRL-based Volt/VAr control by leveraging the distribution system model, thereby providing constraint satisfaction guarantee with a probability level under the model uncertainty. The performance of the proposed DRSF is verified using the IEEE 33-bus and 123-bus systems.

In coupled power distribution and transportation (CPT) system, a joint scheduling framework for mobile energy storage systems (MESSs) and Volt/VAR control (VVC) ensures reliable power distribution grid operations while supporting electric vehicle loads at electric vehicle charging stations (EVCSs). However, conventional model-based optimization methods for MESS scheduling and VVC may yield suboptimal solutions and greater computation times because of MESS operation and VVC in uncertain environment of CPT systems. To resolve this issue, this study proposes a model-free deep reinforcement learning (DRL) framework. In this framework, smart inverters of MESSs and solar photovoltaic (PV) systems cooperate to minimize the real power loss and mitigate the violations of both MESSs’ state of charge (SOC) and voltage in the power distribution network, while MESSs travel via the transportation network to satisfy EV loads at EVCSs. A MESS routing algorithm based on Dijkstra’s algorithm is developed to determine the optimal destinations of the MESSs. In addition, two safety modules are developed to ensure that neither SOC nor voltage violations occur by adjusting real and/or reactive power of MESSs and PV systems during the training process. The developed MESS routing algorithm and safety modules are integrated into the proposed DRL framework, wherein the DRL agent performs the desired MESS scheduling and VVC through safe exploration during the training procedure. The proposed approach is tested in coupled IEEE 33-bus power distribution and 15-node transportation systems and coupled IEEE 57-bus power distribution and 42-node transportation systems. Numerical examples demonstrate the advantages of the proposed approach in terms of training convergence, real power loss, and SOC/voltage violation.

This paper proposes a distributionally robust model predictive control (DRMPC) for energy management of a vehicle-to-grid (V2G)/vehicle-to-vehicle (V2V)-enabled smart electric vehicle charging station (EVCS) with a photovoltaic (PV) system and an energy storage system. The proposed DRMPC method aims to reduce the operational cost of the EVCS while ensuring the desired charging demands of electric vehicle (EV) users under uncertainties in electricity buying/selling prices, PV generation outputs, and future EV charging demands. To cope with these uncertainties, the proposed method includes the following three features: i) tractable reformulation of the worst-case expected buying cost and selling revenue using a Wasserstein metric and duality theory, ii) determination of a distributionally robust bound on the random PV generation output using its support information, and iii) a scenario-based approach to predicting the future EV charging demand. To improve computational efficiency, a penalty method is proposed to relax the complementarity constraints, while still ensuring nonsimultaneous charging and discharging of EVs under the derived sufficient conditions. Numerical examples using a real-world operational dataset of the EVCS are provided to demonstrate the effectiveness of the proposed DRMPC method under uncertain environments in terms of the EVCS cost saving via V2G/V2V capability, data utilization, and computational complexity.

Coordination between the distribution system operator (DSO) and charging station operators (CSOs) is crucial for ensuring stable and economical operation of distribution grids and electric vehicle charging stations (EVCSs). We propose a computationally efficient and privacy-preserving decentralized DSO-CSO coordination framework for robust operation of distribution grids and EVCSs under uncertainties in active unbalanced distribution systems. A centralized model predictive control (MPC) is developed based on distributionally robust optimization (DRO), which characterizes the uncertainties in photovoltaic generation outputs, loads, and electricity prices using the Wasserstein metric. Two scalable optimization approaches are presented to address the high computation time requirement of the proposed centralized DRO-based MPC method through the reduction of constraints: i) a robust optimization method including a distributionally robust bound on the uncertainties, and ii) a bi-level optimization method that determines critical buses associated with voltage violations. Furthermore, to preserve data privacy of electric vehicles, the centralized DRO-based MPC problem is transformed through an alternating direction multiplier method into a decentralized DRO-based MPC problem comprising the DSO and CSO problems. The efficiency and scalability of the proposed DSO-CSO coordination framework are analyzed using the IEEE 37-bus and IEEE 123-bus systems.

This paper presents a three-stage inverter-based peak shaving and Volt-VAR control (VVC) framework in active distribution systems using the online safe deep reinforcement learning (DRL) method. The proposed framework aims to reduce the peak load, voltage violations, and real power loss by coordinating three stages with different control timescales. In the first stage, a day-ahead charging/discharging scheduling of energy storage systems (ESSs) with a 30 min resolution is performed via their inverters for peak shaving. In the second stage, the discharging power of ESSs is adjusted through measurements with a 1 min resolution to completely shave peak loads. A model-free DRL algorithm integrated with a safety module is also implemented in the second stage. Using this algorithm, the reactive powers of photovoltaic (PV) systems and ESSs are controlled by the DRL agent to reduce the voltage violation and real power loss, whereas no voltage violation occurs during the online training process. In the third stage, a proportional-integral controller with real-power compensation is integrated into inverters of PV systems and ESSs to rapidly mitigate local voltage violations with a 0.1 s resolution. The high efficiency and safety of the proposed method were validated on the IEEE 33-bus and IEEE 123-bus systems.

This study develops a new method for economic dispatch together with nodal price calculations which includes transient stability constraints and, at the same time, optimises the reference inputs to the flexible AC transmission system (FACTS) devices for enhancing system stability and reducing nodal prices. The method draws on the sensitivity analysis of time-domain transient stability simulation results to derive a set of linearised stability constraints expressed in terms of generator active-powers and FACTS devices input references. The optimal power flow (OPF) formulation based on the Newton's algorithm augmented with the set of stability inequality constraints is applied for forming economic dispatch solution and nodal prices. The solutions obtained also include the Lagrange multipliers associated with all of the constraints. Based on the Lagrange multipliers, individual components, including those associated with stability constraints, of the nodal prices are directly identified for assisting the market participants to make investment decisions. The sensitivity technique for forming the set of transient stability constraints lends itself to parallel-computing implementation. Therefore the additional computing-time requirement is minimal, given that the transient stability simulations can be performed independently of one another and outside the OPF loop. Representative studies and results are presented here.

Recently, a massive focus has been made on demand response (DR) programs, aimed for electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market performance. The paper develops a method for optimally selecting the locations of DR programs in the network together with their capacities to achieve the maximum technical benefits derived from the programs. The method is based on the constrained optimization of multi-objective function formed in terms of the EENS (expected energy not supplied), active power loss, ATC (available transmission capacity) and total DR programs capacity. This multi-objective function is then optimized based on NSGA II (non-dominated sorting genetic algorithm) to find the amount and locations of DR programs. The IEEE 30 bus test system is used to demonstrate the effectiveness of the proposed method.