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An Efficient Framework for Multi-DER Integration in Distribution Networks With Time-Series Demand Flexibility
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Efficient DER Voltage Control Using Ensemble Deep Reinforcement Learning.
Abstract not provided.
Increasing DER Hosting Capacity in Meshed Low-Voltage Grids With Modified Network Protector Relay Settings
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Deep Reinforcement Learning-Based Robust Protection in DER-Rich Distribution Grids
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Mitigation of fault related voltage swell on distribution feeders using DER-based advanced inverter controls
Unbalanced faults in distribution feeders can impact voltages on the non-faulted phases. This paper models a real-life feeder with a high penetration level of solar photovoltaic (PV) units in detail including the secondary circuits, and shows a large voltage swell in one of the non-faulted phases of the feeder during a single line to ground fault. The voltages seen during this swell are more than 1.2 p.u., which can have detrimental impacts on the connected equipment and can also lead to loss of connected generation. The reason for this voltage swell is found to be the equivalent impedance at the fault location resulting from the ratio of zero and positive sequence cable impedances. It is seen that the connected solar PV units with appropriate control are able to reduce the severity of the voltage swell, especially for higher penetration levels of solar PV considered, but not entirely eliminate it.
Autonomous Control Strategies for Interconnected DC Microgrid Applications with Multiple DER Resource Penetration.
Abstract not provided.
Field Demonstration of Residential DER Service-Oriented Load Participation
Amidst a concerning surge in power consumption during peak hours, coupled with heightened power grid instability, and driven by a growing demand for electricity, aggregations of Distributed Energy Resource are becoming a viable means for providing essential reliability services.Electric utility companies have proactively implemented Demand Response programs for decades. These programs employ Direct Load Control methods to enhance power grid stability, achieved by controlling customers’ Distributed Energy Resource during peak hours to reduce power consumption. However, a notable drawback of Direct Load Control has been high unenrollment rates of DR program participants due to customer discomfort.Therefore, the underlying issue of over-consumption persists. To address these concerns, this paper introduces a Service-Oriented Load Participation approach to providing grid services such as Demand Response. Leveraging a Service-Oriented Architecture, this method offers the advantage of efficient service management and provisioning within the system. The SOLP approach not only aims to reduce power consumption but also to maintain customer satisfaction by ensuring a comfortable grid service experience.
Safe Deep Reinforcement Learning for Active Distribution System Model Predictive Control with EVs and DERs
The temporal and spatial mismatch between PV generation and electric vehicle (EV) charging and discharging may cause voltage violations in active distribution networks. Despite the widespread use of deep reinforcement learning (DRL) in power system optimization and control, it lacks guarantees on constraint satisfaction during both training and deployment. This paper proposes a Lagrangian-based safe DRL approach for model predictive control (MPC) of active distribution systems with large-scale integration of PVs, EVs, and energy storage systems (ESSs). A Transformer-LSTM time-series model is proposed to forecast EV charging demand, which is then formulated as a constraint to ensure charging requirements are met. Using this prediction, a Lagrangian-based safe soft actor-critic (SAC) framework is developed for real-time control in a three-phase unbalanced distribution system, enforcing voltage safety constraints while optimizing the cumulative net reward. By integrating the forecasting model with multi-period constraints, the proposed framework jointly coordinates PV systems, EV charging and discharging, and ESS scheduling within the MPC horizon. Numerical experiments on a modified IEEE 123-bus system with real-world data show that, under a high PV penetration scenario, the proposed method increases the net reward by 30.74% and reduces average voltage violations from 0.0011 p.u. to 0.0002 p.u. compared with standard SAC. Compared with the optimal power flow (OPF) approach, it achieves similar voltage security while yielding lower line losses. It also maintains real-time control capability, reducing operation latency to 53.21 ms per 15-minute control interval. The proposed method remains effective under varying PV/EV penetrations and load conditions.
Fault Location in Active Distribution Networks Containing Distributed Energy Resources (DERs)
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Enhanced Situational Awareness for DER-Rich Distribution Systems Under Pre- and Post-Outage
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Distributed Optimization for Autonomous Restoration in DER-Rich Distribution System
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Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs
Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.
A new metal transfer process for van der Waals contacts to vertical Schottky-junction transition metal dichalcogenide photovoltaics
Two-dimensional transition metal dichalcogenides are promising candidates for ultrathin optoelectronic devices due to their high absorption coefficients and intrinsically passivated surfaces. To maintain these near-perfect surfaces, recent research has focused on fabricating contacts that limit Fermi-level pinning at the metal-semiconductor interface. Here, we develop a new, simple procedure for transferring metal contacts that does not require aligned lithography. Using this technique, we fabricate vertical Schottky-junction WS 2 solar cells, with Ag and Au as asymmetric work function contacts. Under laser illumination, we observe rectifying behavior and open-circuit voltage above 500 mV in devices with transferred contacts, in contrast to resistive behavior and open-circuit voltage below 15 mV in devices with evaporated contacts. One-sun measurements and device simulation results indicate that this metal transfer process could enable high specific power vertical Schottky-junction transition metal dichalcogenide photovoltaics, and we anticipate that this technique will lead to advances for two-dimensional devices more broadly.
Moiré potential impedes interlayer exciton diffusion in van der Waals heterostructures
Interlayer exciton diffusion in transition metal dichalcogenide heterostructures is controlled by the moiré potential.
Accessing new magnetic regimes by tuning the ligand spin-orbit coupling in van der Waals magnets
Previously unknown magnetic regimes and phenomena are revealed by tuning the competition between two types of spin-orbit coupling.