Voltage regulation in photovoltaic-integrated distribution feeders is normally achieved through tap-changing regulators, capacitor banks, inverter reactive-power support, and coordinated Volt/Var control. This paper presents an exploratory OpenDSS-based assessment of an equivalent magnetizing-current surrogate and quantifies its influence on daily voltage and loss behavior. In this study, the OpenDSS %imag parameter is used as an equivalent modeling surrogate for an additional magnetic or reactive degree of freedom, rather than as a directly dispatchable control input for conventional regulators. A MATLAB–OpenDSS quasi-static time-series framework is formulated for multiple control locations and scheduling segments and evaluated on the unbalanced IEEE 123-bus feeder over 24 h at 15-min resolution. Fixed reference taps and capacitor states are used to isolate the surrogate effect, while a standard OpenDSS regulator-and-capacitor case provides a practical benchmark. Uniform sensitivity analysis and DE-based shared and regulator-specific six-segment scheduling are first examined, followed by a five-algorithm benchmark of the regulator-specific formulation. Increasing the surrogate reduces overvoltage exposure but increases active-loss energy and, more strongly, reactive-loss energy. Regulator-specific scheduling provides a better voltage–loss balance than shared scheduling. Across 10 independent benchmark runs, archive-assisted adaptive differential evolution achieves the lowest mean objective value, reducing the mean objective value, mean active-loss energy, and mean reactive-loss energy by approximately 4.9%, 2.2%, and 16.8%, respectively, relative to baseline differential evolution, while maintaining zero mean undervoltage duration. Standard OpenDSS controls achieve better voltage-limit compliance, confirming that the proposed framework is an exploratory assessment rather than a replacement for established voltage-control devices.

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band-a range around the setpoint where no action is taken-to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to gen erate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (R2) of 95.75 %.

This paper presents an integrated approach to enhancing the cybersecurity resilience of electrical power distribution systems by bridging Open-Source Intelligence insights with artificial intelligence-based protection strategies. Leveraging Open-Source Intelligence-driven vulnerability identification, advanced anomaly detection methodologies, and Federated Learning-based frameworks, our methodology addresses the evolving threat landscape in Supervisory Control and Data Acquisition and Industrial Control System environments. By combining network scanning, intrusion detection, and distributed intelligence, we demonstrate improved threat awareness and timely detection of malicious activities. Our results underscore the potential of data-driven, privacy-preserving solutions to secure critical energy infrastructures against sophisticated cyberattacks.

This paper addresses overvoltage issues in low-voltage (LV) AC distribution networks using the volt-VAr (Q(V)) control function of smart inverters (SIs) connected to photovoltaic (PV) systems and battery energy storage systems (BESSs). Two optimization frameworks are proposed: (i) a rule-based approach and (ii) an optimal power flow (OPF)-based approach for selecting the most effective droop slope parameter. The rule-based method performs successive power flow simulations to identify the slope minimizing voltage deviations, while the OPF-based method formulates the problem as a mixed-integer linear program (MILP) using linearized power flow equations and a piecewise Q(V) droop representation. Both methods are implemented and validated on a real LV microgrid testbed at the Ricerca sul Sistema Energetico (RSE) facility, modeled in OpenDSS and verified against real measurements under dynamic load variations. Results show that both approaches achieve effective voltage control, with the OPF-based method providing superior regulation and computational efficiency.

In this paper, we address the challenge of reducing energy losses in distribution systems through reactive-power compensation by utilizing single phase capacitors. We develop a minute-resolved scheduling approach for single-phase capacitor banks and apply it to a real 0.4 kV network in Mangystau, Kazakhstan. Our approach uses a recently developed heuristic algorithm: Sine-Cosine Algorithm (SCA). The optimization model aims to find the on/off switching positions of 24 capacitors over a 24 hour time horizon with 1 minute time intervals. We use OpenDSS software to perform power flow simulations required in SCA based optimization model, and consider constraints on switching frequency of bank capacitors to limit wear. From the simulation results we observed that with the use of the proposed model the energy losses decrease with a better voltage profile.

As is known, batteries have started to be used increasingly in both power distribution and transmission networks. This study develops a near-optimal approach for ancillary services in power networks from the perspective of the battery owner. We first model the optimization algorithm for the battery owner, then utilize a grey wolf optimization approach, where near-optimal actions are selected daily from available services. We use real data of frequency, voltage magnitude, combined home and Photovoltaic system, and transformer load to perform the simulations. The simulation results show that battery owners may profit from these services and help the system operators solve the issues such as over-voltage, under-voltage, frequency, and similar.

The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. The power flow calculation, based on Python, is performed using the pandapower library, leveraging the actual distribution network structure of the Hillingdon area by incorporating recent smart meter data from a distribution system operator alongside historical weather data from the past decade. Based on the outcome of power flow calculation, the transformer loadings and voltage levels were assessed for existing and projected heat pump and EV adoption rates, in line with national policy targets. Findings highlight that varied consumer density and diverse usage patterns significantly influence upgrade requirements.

Smart Inverters (SIs), which are power-electronics based devices, have capability to effectively regulate the voltage on distribution feeders with better time granularity due to their faster response compared to legacy devices such as on load tap changers (OLTCs) and capacitor banks. In this study, we propose an approach to predict the droop settings of SIs that dynamically adjust based on network conditions as observed through the voltage measurements. This work adopts Long Short Term Memory (LSTM) based Neural Network (NN) approach for predicting dynamic droops for SIs as the network condition changes. We test the effectiveness of the dynamic droops on a large (IEEE 8500-node) distribution network. The case studies demonstrate that the voltage performance on distribution feeders can be improved with dynamic droop settings.

This paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve overvoltage problem in power distribution networks. The approach aims to minimize the voltage deviations and to keep voltage magnitudes in the allowed ranges. The numerical simulations are performed on a modified unbalanced 123 node network. The modified test system includes a total number of 34 single phase Photovoltaics (200 kVA) connected to three phases. We modified the base case load profile based on real-world daily variations obtained from EPIAS. The PV generation profile was modeled according to a typical sunny day. Using OpenDSS and Python, we implemented PPO-based RL to optimize the setpoints of smart inverters and voltage regulators. The model was trained with load and solar profiles at 09:00, 12:00, and 16:00 to derive optimal voltage regulation strategies for these time points. From the simulation results, we observed that the proposed PPO-based RL approach significantly reduces voltage deviations across all phases, which may help efficient operation of the distribution networks.

Fast restoration following long outages is a challenge in the smart city management process. It is necessary to accurately characterize the real operating conditions of the system for optimal restoration. This study focuses on two key factors of a practical distribution system restoration. The first factor is cold load pickup (CLPU), which commonly occurs after an outage and is caused by thermostatically controlled loads. A time-dependent CLPU is modeled to accurately describe the restored load behaviors. The second factor is the effect of the distributed generators (DG), energy storage systems (ESSs), and load priority factors on the system's restoration process. To address this challenge, a robust optimization model is proposed that fully considers the effect of DG, and ESS units and uncertainty of CLPU. The proposed models are tested on the IEEE 33-node and 69-node test systems using the Advanced Grey Wolf Algorithm (AGWO). The simulation scenarios are designed to uncover optimal scheduling strategies for the restoration process corresponding to each Pareto solution of a previous study. The results are discussed for several distinct initial conditions. Moreover, a comparative evaluation is done, contrasting the outcomes achieved through the AGWO algorithm with those stemming from alternative heuristic methods.

Accurate solar irradiance prediction is important in optimizing photovoltaic power generation, improving grid reliability, and promoting the integration of renewable energy. This study proposes a physics-informed machine learning approach that combines domain-specific knowledge — such as clear sky modelling and meteorological feature engineering— with targeted temporal windowing to improve short-term solar irradiance prediction. We evaluated several machine learning algorithms with different data pre-processing and balancing strategies, including Random Forest, Artificial Neural Network, k-Nearest Neighbours, Linear Regression, XGBoost, and LSTM. Our experimental results showed that the XGBoost model, when trained on a reduced, physically informed feature set with a temporarily focused training window achieved the lowest RMSE among the models tested. The results emphasize the benefits of integrating physical knowledge with machine learning to improve prediction performance and provide a scalable approach suitable for operational renewable energy forecasting systems.

Integrating a significant amount of solar energy into the power grid may cause a net load with steep ramps and deep midday valleys called the Duck Curve. In this work, an optimization-based approach is used for optimal scheduling of Battery Energy Storage Systems (BESS) in a 33-bus distribution network to lower the net load slope and reduce the Duck-curve effect. A metaheuristic optimization algorithm is employed to determine the optimal hourly dispatch and the initial state of charge (SoC) to flatten the Duck Curve. The suggested strategy decreased the slope of the net load by 78% and provided a scalable and practical solution for distribution network operators to improve network flexibility and stability in the face of increasing penetration of renewable energy.

Due to their technical, economical, and environmental advantages, active distribution networks implement renewable energy resources (RERs) such as photovoltaic (PV) units in distribution networks DNs. However, some drawbacks may arise due to the intermittent nature of RERs, such as voltage fluctuations and increased system losses. This paper presents an optimization problem that is solved by sequential linear programming (SLP) to improve the voltage profile of the unbalanced distribution network. A probabilistic approach was applied to both the load profile and the active power generation of the PV units. SLP is applied to the modified IEEE 34 Bus Test system. The method optimizes the voltage deviations by changing the taps of the voltage regulators and the reactive power injected by the inverters of the PV systems and, in some cases, by switching a shunt capacitor. MATLAB simulations are done at different times of the day with different loads and PV outputs to compare base case and optimal case voltage profiles. The results show better voltage profiles after applying the presented approach.

Phase unbalance is a significant issue for power distribution networks. It can lead to increased energy losses and voltage instability, undermining the electrical grid's reliability and efficiency. We propose an approach to minimize voltage unbalance through reactive power management from PV instal-lations and the optimization of charging/discharging of energy storage devices utilizing a control algorithm based on Ant-Lion Optimizer. We tested the approach on the IEEE 123-Bus Test System, incorporating PV generations by daily simulations. From the results, the combined operation of reactive power support from PVs and Storage Units with the help of the ALO algorithm offers a promising solution to the phase unbalance problem.

Power distribution networks may need to be switched from one radial configuration to another radial structure, providing better technical and economic benefits. Or, they may also need to switch from a radial configuration to a meshed one and vice-versa due to operational purposes. Thus the detection of the structure of the grid is important as this detection will improve the operational efficiency, provide technical benefits, and optimize economic performance. Accurate detection of the grid structure is needed for effective load flow analysis, which becomes increasingly computationally expensive as the network size increases. To perform a proper load flow analysis, one has to build the distribution load flow (DLF) matrix from scratch cost of which is unavoidable with the growing size of the network. This will considerably increase the computation time when the system size increases, compromising applicability in online implementations. In this study we introduce a novel graph-based model designed to rapidly detect transitions between radial and weakly meshed systems. By leveraging the characteristic properties of Sparse Matrix-Vector product (SpMV) operations, we accelerate power flow calculations without necessitating the complete reconstruction of the DLF matrix. With this approach we aim to reduce the computational costs and to improve the feasibility of near-online implementations.

Efficient and grid-aware management of home-scale heating, ventilation, and air conditioning (HVAC) systems is one of the key enablers of demand-side management (DSM) and associated grid services in the residential sector. HVACs regulate the indoor temperature around a set point through a thermostat operating within a closed-loop control scheme. Conventional thermostats typically have a built-in temperature dead band or differential where the thermostat is idle, and HVAC stays at the most recent state (On/Off). The temperature dead band is an important control parameter that can help save energy as well as preventing frequent On/Off switching cycles leading to excessive wear and tear on the equipment. However, strategic and dynamic adjustment of the dead band can be a challenging task for an occupant. This paper proposes a mixed-integer linear program (MILP)-based tuning scheme to optimally determine the dead band. The novelty in this formulation is the inclusion of thermostat hysteresis curve modeled by piecewise techniques for tuning the dead band accurately. The proposed formulation is solved as a receding horizon manner for normal as well as under a demand response (DR) event and has been found it can achieve up to 10% reduction in energy consumption without degrading the regulation performance significantly.

This paper deals with the generation of synthetic data, which plays an important role in the Non-Intrusive Load Monitoring (NILM) problem. We introduce the NILM problem and then explain its crucial role in improving energy efficiency and supporting smart grid functions. The paper explains the stages of the NILM problem, including data acquisition, feature extraction, event detection, and appliance classification. We also explain two methods for generating synthetic data: AMBAL (Appliance Model Based Algorithm for Load monitoring) and SmartSim. Then, we propose a synthetic data generation method based on Markov chains, which is designed to generate labeled data useful for training supervised machine learning models. The proposed method utilizes the probabilistic transitions between different operational states of appliances, and captures the stochastic nature of real-world appliance usage. Thus, the generated synthetic data not only reflects realistic usage patterns, but also contains labels indicating the state of each appliance at a given time. The simulations are then run by generating synthetic data for typical office equipment such as laptops and televisions. The generated data sets provide detailed and accurate usage profiles, which are important for the effective training and validation of NILM algorithms. Since the generated data also includes the labeled data, this method will improve the ability of NILM systems to accurately identify and monitor individual appliances in a complex load environment.

Batteries may provide ancillary services to the power network and thus can be used as a energy storage tool. In this paper, we examine the income of the battery owner, the amount of energy drawn from the battery, and the amount of change in the state of the charge (SOC) by considering different cases. For this aim, our study uses real data of frequency, voltage, load, energy market prices and house power production and consumption profiles. From the simulation results obtained we observe that the profit that battery owners can make as a result of using these services is of great importance for the energy market of the future and the battery to grid system is crucial solution for minimizing problems caused by abnormal frequency, voltage, load, etc. in the network.

This study presents an off-line optimization-guided machine learning approach for coordinating the local control rules of on-load tap changers (OLTCs) and step-voltage regula-tors (SVRs). Based on a bang-bang control rule, these legacy devices autonomously regulate the feeder voltage around the nominal level by varying the tap position in the lower or raise direction. The characterizing parameter of the local control rule is the dead band, which affects the number of tap switching in operation and is directly related to the economical use life of the equipment. The bandwidth is typically set within a standard voltage range and is generally kept constant in daily operation. However, adjusting the bandwidth dynamically can prevent excessive tap switching while maintaining satisfactory voltage regulation for varying loading and distributed generation conditions. Our approach aims to set the bandwidth parameter systematically and efficiently through a machine learning-based scheme, which is trained with a dataset formed by solving the distribution network optimal power flow (DOPF) problem. The performance of learning the bandwidth parameter is demonstrated on the modified 33-node feeder, which is promising for integrated voltage control schemes.