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Residential prosumers with rooftop photovoltaic systems and behind-the-meter batteries are changing the operating conditions of low-voltage and medium-voltage distribution feeders. When these resources respond independently to retail tariffs, their aggregate behavior can intensify reverse power flows, transformer loading, and voltage excursions. This paper proposes a bilevel optimization model that coordinates residential demand response, rooftop photovoltaic generation, and battery storage under distribution-feeder congestion constraints. The objective is to align prosumer economic decisions with distribution system operator requirements for congestion relief and voltage compliance. The upper-level problem represents the distribution system operator and minimizes feeder congestion, voltage deviations, and coordination costs. The lower-level problem represents residential prosumers that minimize their individual energy costs through load shifting, photovoltaic self-consumption, and battery dispatch. The proposed formulation produces coordinated schedules that reduce peak feeder loading and voltage violations relative to uncoordinated operation. Battery arbitrage and flexible demand are redirected from purely price-driven operation toward feeder-aware congestion mitigation. The bilevel coordination framework provides an operational method for managing residential distributed energy resources without replacing prosumer autonomy. It enables distribution system operators to procure flexibility while maintaining economically rational prosumer behavior.

High penetration of rooftop photovoltaic generation introduces rapid voltage fluctuations in low-voltage distribution feeders. These fluctuations challenge conventional voltage regulation because inverter actions must respond locally while remaining coordinated across electrically coupled nodes. This article proposes a multi-agent reinforcement learning model for grid-edge voltage regulation under distributed PV variability and communication latency. The objective is to maintain feeder voltages within operational limits while reducing unnecessary reactive power support, curtailment, and communication burden. The voltage control task is formulated as a decentralized partially observable Markov decision process. Smart inverter and battery agents use local observations during execution, while centralized training uses feeder-wide voltage, PV, and control information to train coordinated policies. Simulation results show that the proposed MARL controller improves voltage compliance relative to local Volt/VAr control and approaches centralized optimal power flow performance. The controller remains stable under bounded latency and asynchronous agent updates. The proposed framework offers a robust decentralized control strategy for high-PV distribution feeders. It combines grid-edge autonomy with coordinated learning, making it suitable for communication-limited voltage regulation.

This study introduces a coordinated control approach to address the challenge of volt-var optimization (VVO), incorporating load tap-changing transformers, step-voltage regulators, and switched-capacitor banks, along with a photovoltaic (PV)-integrated microgrid. The harmony search (HS) metaheuristic algorithm was applied to efficiently compute the globally optimal operating parameters for these devices, ensuring robust performance across fluctuating operating conditions. The key aims of the volt-var control strategy were to achieve substantial reductions in active power losses, peak demand, and voltage deviations throughout the distribution system, while simultaneously maintaining bus voltages within acceptable regulatory limits under all possible load scenarios. The optimization problem is a mixed-integer nonlinear program with discrete decision variables, binary variables representing capacitor bank switching states (on/off), integer variables for voltage regulator tap positions, continuous variables for the microgrid’s injected current, and nonlinear network power-flow constraints. The proposed methodology was tested and validated on the standard IEEE 13-node distribution test feeder. Time-series simulations used realistic daily load profiles from the main grid, along with the microgrid’s PV output, both resolved at 15-minute intervals. All power flow computations were carried out with OpenDSS (version 9.5.1.1, released in 2022), an established open-source distribution system analysis tool. The developed control framework supports both real-time operation and long-term planning studies. Simulation results showed that the integrated volt-var coordination significantly reduced total active power losses, lowered peak demand, and effectively mitigated voltage variations across the network.

Over the past few years, integrating Distributed Energy Resources (DERs) with advanced communication infrastructures has created major challenges for the control and protection of electrical power systems. These difficulties stem mainly from the rapid evolution of smart grids and the growing threat of cyberattacks. As grid-tied solar Photovoltaic (PV) installations—equipped with smart inverters—become more widespread, their exposure to malicious cyber activities and the resulting risks to power network stability have become a pressing issue, especially given the inverter control logic. The present study examines the outcomes of targeted cyber intrusions targeting key PV system elements, with particular attention to the inverter and the Overcurrent Protection Relay (OCR). It further analyzes how these two components interact across a range of cyber-attack conditions. Simulations are conducted using a three-phase radial Electromagnetic Transients Program (EMTP) model to represent the grid and study transient responses during various cyber intrusions. Analysis results uncover the tight coupling between weaknesses in inverters and protection relays, demonstrating how disruption of one can severely affect the performance of the other. The work also quantifies how cyber incidents degrade the efficiency and operational reliability of PV systems connected to the grid. As a concrete illustration, when PV inverters were subjected to a brief 0.1 s pulse signal attack, the OCR failed to detect the abnormality and isolate the fault. This breakdown caused pronounced harmonic distortion together with considerable active power losses, stemming directly from the protection scheme’s inability to recognize and counteract the irregular disturbance. Overall, this investigation provides important new insights into cybersecurity challenges in grid-integrated solar PV installations. It underscores the urgent need to adopt stronger defensive strategies and build greater system resilience as power networks continue evolving into intelligent, interconnected smart grids.

Effective energy management plays a vital role in improving greenhouse (GH) performance and advancing sustainability. This study introduces an innovative multi-objective optimization strategy specifically developed for GH energy management, aiming to reduce reliance on grid energy while maximizing the battery state of charge (SOC) over a defined time period. The optimization framework incorporates decision variables, including network power, battery power, and battery energy, while respecting constraints on battery capacity, initial energy levels, and the minimum and maximum energy limits of the battery storage system. The performance of a smart energy management system (EMS) is assessed by comparing it against conventional optimization techniques. Critical hyperparameters for the optimization process—such as plateau time, prediction time, and optimization time—are identified using the ellipse optimization technique. By modeling the GH as a microgrid, the study examines relevant energy management metrics and load profiles. Simulations performed in MATLAB Simulink (R2021b) reveal substantial improvements, with the smart EMS reducing the objective function by more than 50% relative to traditional EMS approaches. Furthermore, the proposed EMS demonstrates strong robustness under varying load power and irradiation patterns. Even in partial-shading scenarios, the system remains adaptable, with a maximum increase in the objective function of only 0.35553%. Matching the photovoltaic (PV) system output power to realistic field conditions further confirms the EMS’s practical effectiveness. These results highlight the smart EMS’s capability to optimize energy use in GH settings, paving the way for more sustainable energy management solutions. This work advances energy optimization techniques in agricultural contexts, supporting greater resource efficiency and environmental responsibility.

In this study, an observer-based fuzzy controller design approach is presented to enhance the efficiency and performance of a photovoltaic (PV) system. The targeted controller is developed by integrating linear matrix inequalities (LMIs). The system comprises a photovoltaic generator (PVG) linked to a boost converter, with a battery connected to the converter to store surplus energy for later utilization. A fuzzy controller incorporating a Takagi–Sugeno (T–S) fuzzy observer is proposed, ensuring predefined performance levels while achieving maximum power point tracking (MPPT) under varying weather conditions. To maintain maximum power operation, the system must follow an optimal trajectory. For this purpose, a dedicated reference fuzzy model is introduced to generate the desired trajectories. This technique enables accurate replication of the system dynamics across a wide operating range. Overall, the T–S fuzzy framework proposed in this paper aims to maximize energy recovery during battery charging under partially shaded conditions, thereby achieving higher system efficiency. The suggested approach is implemented and simulated in MATLAB/SIMULINK, with realistic reference trajectories applied while accounting for climatic variations. Simulation analysis, along with comparisons with two widely adopted methods, demonstrates that the proposed strategy successfully reduces tracking time and eliminates the oscillations typically observed around the maximum power point (MPP).

Rooftop solar photovoltaic (PV) systems are playing an increasingly important role in the worldwide shift toward sustainable energy. This development creates a need for precise, high-resolution geospatial assessments of rooftop PV’s technical potential for policy formulation and power system planning. To tackle this issue, we introduce a comprehensive framework that integrates various satellite imagery sources with deep learning techniques to estimate rooftop PV electricity generation. The framework employs a deep learning-based inversion to estimate hourly solar radiation from geostationary satellite imagery. It uses an automated segmentation model to identify building footprints from high-resolution satellite imagery. This enables a detailed assessment of available rooftop areas and hourly simulation of power output at a 100 m spatial resolution. A case study conducted in Jiangsu Province shows that the framework works effectively over large regions and can be scaled from specific sites to broader areas across different time scales. Results indicate that the province’s rooftop resources offer a potential installed capacity of 245.17 GW, which could generate 290.66 TWh of electricity annually. These findings underscore the substantial opportunity for reducing carbon emissions by expanding rooftop PV deployment.