Smart Photovoltaic Research Articles

Deep Learning-Based Image Regression for Short-Term Solar Irradiance Forecasting on the Edge

Photovoltaic (PV) power production is characterized by high variability due to short-term meteorological effects such as cloud movements. These effects have a significant impact on the incident solar irradiance in PV parks. In order to control PV park performance, researchers have focused on Computer Vision and Deep Learning approaches to perform short-term irradiance forecasting using sky images. Motivated by the task of improving PV park control, the current work introduces the Image Regression Module, which produces irradiance values from sky images using image processing methods and Convolutional Neural Networks (CNNs). With the objective of enhancing the performance of CNN models on the task of irradiance estimation and forecasting, we propose an image processing method based on sun localization. Our findings show that the proposed method can consistently improve the accuracy of irradiance values produced by all the CNN models of our study, reducing the Root Mean Square Error by up to 10.44 W/m2 for the MobileNetV2 model. These findings indicate that future applications which utilize CNNs for irradiance forecasting should identify the position of the sun in the image in order to produce more accurate irradiance values. Moreover, the integration of the proposed models on an edge-oriented Field-Programmable Gate Array (FPGA) towards a smart PV park for the real-time control of PV production emphasizes their advantages.

Smart Photovoltaic Energy Systems for a Sustainable Future

This book is the result of a concerted effort to shed scientific light on the timely theme of “Smart Photovoltaic Energy Systems for a Sustainable Future” [...]

Bi-Level Volt/VAR Optimization in Distribution Networks With Smart PV Inverters

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach.

Multi-objective Stochastic Planning of Electric Vehicle Charging Stations in Unbalanced Distribution Networks Supported by Smart Photovoltaic Inverters

Due to the environmental concerns, getting deteriorated ongoing, it is becoming essential to encourage people to use sustainable energies. One of the most effective alternatives to mitigate pollution is to promote green mobility via electric vehicles. However, the lack of electric charging stations may decrease individuals’ satisfaction to use electric vehicles in daily life. To bridge the gaps, this paper aims to simultaneously allocate electric vehicle charging stations and smart photovoltaic inverters in distribution networks to optimize three important objective functions, including power loss, voltage deviation, and voltage unbalance factor. To solve such a multi-objective optimization problem, a novel hybrid fuzzy Pareto dominance concept with differential evolution algorithm is proposed. A scenario-based framework is also used to capture the uncertainties of the model comprising loads, PVs’ generation and the demand of electric vehicle charging stations. The effectiveness of the stochastic multi-objective approach is then examined and verified on an unbalanced 37-bus network under different case studies. Attained results illustrate that if smart photovoltaic inverters integrate into the network with charging stations, the network performance is significantly improved, such as keeping voltage unbalance factor under standard value accounting for two percent.

A State-of-the-Art Self-Cleaning System Using Thermomechanical Effect in Shape Memory Alloy for Smart Photovoltaic Applications

This research aims to present a state-of-the-art cleaning technology solution that effectively overcomes the dust accumulation issue for conventional photovoltaic systems. Although continuous innovations and advanced developments within renewable energy technologies have shown steady improvements over the past years, the dust accumulation issue remains one of the main factors hindering their efficiency and degradation rate. By harvesting abundant solar thermal energy, the presented self-cleaning system uses a unique thermomechanical property of Shape Memory Alloys to operate a solar-based thermomechanical actuator. Therefore, this study carries out different numerical and experimental validation tests to highlight the promising practicability of the developed self-cleaning system from thermal and mechanical perspectives. The results showed that the system has a life expectancy of over 20 years, which is closely equivalent to the life expectancy of conventional photovoltaic modules while operating under actual weather conditions in Dammam city. Additionally, the thermal to mechanical energy conversion efficiency reached 19.15% while providing average cleaning effectiveness of about 95%. The presented outcomes of this study add to the body of knowledge an innovative methodology for a unique solar-based self-cleaning system aimed toward smart and modern photovoltaic applications.

PV Active Power Curtailment for Distribution Network Management

Two of the main operation issues in medium voltage (MV) feeders are the voltage and thermal limits and become more relevant when a significant amount of distributed generation is integrated. This paper proposes a control strategy based on PV active power curtailment for distribution network management in order to reduce voltage and thermal issues in MV feeders under high photovoltaic (PV) penetration in order to postpone or avoid feeder reinforcement costs, when usually this kind of analysis is carried out in low voltage feeders. The strategy estimates the hosting capacity (HC) of the feeders based on decreasing the active power generated by the PV systems as required. The proposed approach uses Monte-Carlo simulations to estimate the HC in the feeder. In order to get more realistic scenarios, stochastic simulations are used to determine the random location of the PV systems, and by using statistics, several PV capacities are proposed. Smart PV inverters are employed to reduce their active power output based on the derived control signals. As an example, Mexican standards are used to estimate the HC and to test the control strategy. The results show an increase of HC in the feeder and benefits as better voltage regulation and thermal feeder profile.

An energy cooperative system concept of DC grid distribution and PV system for supplying multiple regional AC smart grid connected houses

This paper proposed a suitable control strategy for power sharing management for hybrid AC/DC residential PV systems. This control method is used as an Energy Cooperative Approach (ECA) to manage the power flow between a PV and battery-fed smart grid within a DC micro-grid, supplying some connected houses. In fact, using an ECA which is based on photovoltaic (PV) power transfer between three houses, connected to the AC network; through a DC micro-grid, the photovoltaic energy can be effectively used to satisfy the load demand. Such a process ensures a continuous demand satisfaction, lengthens the storage system life time by minimizing the resort of the battery, and alleviates the stress of the main grid minimizing the consumption of electricity fed by the AC grid. In fact, this strategy increases the system efficiency and the power supply quality. To achieve such purposes, a control algorithm for distributed flexible network residential PV systems was properly developed. The decision criterion for the developed supervisor is based on the instantaneous PV power, the required power, and the battery state of charge in order to manage the power flow under six operating modes. The control strategy was proposed also to regulate DC bus voltage and maintain the power balance within the micro-grid. The simulation results validated the effectiveness and feasibility of the proposed control strategy under various conditions. To highlight this strategy benefits, a comparative study between strategies without and with ECA was performed. The results of this study showed that using the ECA on a typical day, the energy PV contribution to satisfy the loads (Fpv) grows by a rate that varies from 15.5% to 23.45%, the battery contribution part (Fbat) recorded a decrease that varied between 8.77% and 18.42%, while the main grid contribution (Fgrid) was reduced by a rate that varied between 2.41% and 13.1%.

Online PV Smart Inverter Coordination using Deep Deterministic Policy Gradient

Fast and frequent solar power variations present new challenges to modern power grid operation with increasing adoption of photovoltaic (PV) energy. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating active and/or reactive power at the connection point. In this paper, a deep reinforcement learning (DRL) based algorithm is proposed to coordinate multiple SIs. A reward scheme is designed to balance voltage regulation and SI reactive power utilization. The proposed DRL agent for voltage control learns its policy through massive offline simulations and adapts to load and solar variations. The DRL agent results are compared against autonomous Volt-Var control and optimal power flow (OPF) on the IEEE 37 bus feeder and IEEE 123 bus feeder for 8760 different scenarios. The results demonstrate that a properly trained DRL agent can intelligently coordinate different SIs to satisfy grid voltage limits despite large solar and load variations. The DRL agent achieves nearly the optimal performance of OPF by mitigating all voltage violations, while reducing PV production curtailment by 88% compared to the autonomous Volt-Var scheme. Contrary to OPF, the DRL agent can provide a coordination signal in milliseconds without load and solar forecasts and without explicit knowledge of the distribution network model.

Analysis of Voltage and Reactive Power Algorithms in Low Voltage Networks

The rapid development of renewable energy sources and electricity storage technologies is further driving the change and evolution of traditional energy systems. The aim is to interconnect the different electricity systems between and within countries to ensure greater reliability and flexibility. However, challenges are faced in reaching it, such as the power grid complexity, the system control, voltage fluctuations due to the reverse power flow, equipment overloads, resonance, incorrect island setting, and the diversity of user needs. The electricity grid digitalization in the market also requires the installation of smart devices to enable real-time information exchange between the generator and the user. Inverter-based distributed generation (DG) may be used to control the grid voltage. Smart PV inverters have the capability to supply both inductive and capacitive reactive power to control the voltage at the point of interconnection with the grid, and only technical parameters of smart PV inverters limit this capability. Reactive power control is related to ensuring the quality of voltage in the electricity distribution network and compensating reactive power flows, which is a technical–economic aspect. The goal of this research is to present an analysis of controllers that supply reactive power to the electrical grid via PV systems. This research analyzes recent research on local, centralized, distributed, and decentralized voltage control models in distribution networks. The article compares various approaches and highlights their advantages and disadvantages. The voltage control strategies and methodologies mentioned in the article can serve as a theoretical foundation and provide practical benefits for PV system development in distribution networks. The results of the research show that the local voltage control approach, as well as linear and intelligent controllers, has great potential.

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

Transparent Wood with Self-Cleaning Properties for Next-Generation Smart Photovoltaic Panels

Transparent Wood with Self-Cleaning Properties for Next-Generation Smart Photovoltaic Panels

Efficient Reactive Power Management in Low Voltage Distribution Networks Using Pv Smart Inverters and Fc with Cost-Benefit Analysis

Efficient Reactive Power Management in Low Voltage Distribution Networks Using Pv Smart Inverters and Fc with Cost-Benefit Analysis

Evaluation of Data-Enhanced Hierarchical Control for Distribution Feeders With High PV Penetration

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.

Characterization of Voltage Stabilization Functions of Residential PV Inverters in a Power Hardware-in-the-Loop Environment

The exponential growth of Photovoltaic (PV) technology is creating concerns for electric grid operators. As PV penetration increases, overvoltage in the distribution network can occur due to a mismatch between PV generation and load demand. However, PV smart inverters can be part of the solution to stabilize grid voltage. By providing reactive power and other grid supporting functions, PV inverters in a distribution network can mitigate this problem and enable a higher integration of renewable energy. To accomplish this, characterization and testing of advanced functions must be performed at a small scale before deploying these strategies in a field. In this work, we described in detail the components and communication interfaces of a Hardware-in-the-Loop testbed that includes two 3.8 kW PV inverters from different manufacturers. We conducted efficiency tests on the inverters and characterized the grid supporting functions for grid voltage stabilization, specifically constant power factor, volt-var, and volt-watt. We identified some abnormalities in the operation of the volt-var-watt control in one of the inverters and presented a method to overcome the limitation in remote control of another inverter using Modbus communication. Identifying, understanding, and overcoming shortcomings on the operation of PV smart inverters that provide grid supporting functions is key for the quick adoption of this technology and can help regulatory agencies to determine what is the appropriate control mode that will facilitate higher PV capacity. Additionally, we discuss the economic and technical implications of operating the inverter in active or reactive power grid control.

Design and Development of an Online Smart Monitoring and Diagnosis System for Photovoltaic Distributed Generation

In photovoltaic power plants, fault diagnosis tools are essential for ensuring a high energy yield. These tools should be capable of accurately identifying and quantifying the factors behind the various fault mechanisms commonly found in photovoltaic plants. Considering the aforementioned factors, this article proposes an online smart PV monitoring solution, which is capable of detecting malfunctions that arise from accidental and/or technical causes through the analysis of I-V curves, however, without the necessity to interrupt the operation of the system, thus reducing the maintenance cost. Accidental causes can lead to the reduction of energy productivity due to the excessive accumulation of dirt on the photovoltaic modules, partial shading and eventual errors that occur during its installation. On the other hand, technical causes can be attributed to faults found on the photovoltaic modules, which lead to gradual losses in their electric and material characteristics. Therefore, by using the electric characteristics supplied by the manufacturer of the installed modules, the I-V and P-V curves of the operational photovoltaic strings were obtained in real time, compared to the respective theoretical curves obtained through mathematical modeling. In order to validate the proposed online monitoring system and its potential for predictive maintenance application, a field experimentation was mounted in a 93.8 kWp photovoltaic system.

Performance of Multifunctional Smart PV-Based Domestic Distributed Generator in Dual-Mode Operation

This article briefs about a smart multifunctional single-phase inverter control for a domestic solar photo voltaic (PV)-based distributed generation that can work in both a grid-connected mode and an islanded mode by making the inverter mimic the operation of a synchronous generator. The control objectives were threefold: to provide the required active and reactive power for normal operating conditions and under varying operating conditions, to maintain the rated voltage and the rated frequency for the islanded mode, and to switch between the two modes of operation with the least amount of disturbance for the system while behaving as a virtual synchronous generator (VSG). The control structure is divided into three major loops: the outermost loop responsible for power control, the middle loop responsible for voltage control, and the innermost loop responsible for current control. The proposed control methodology incorporates the functionalities of the grid-connected and the islanded-mode control into a single complex structure and thus provides support to the grid under abnormal conditions while providing good-quality power to consumers under grid failure. The efficacy of the system is good. The operation under various modes were simulated in MATLAB Simulink, and the proportional integral (PI) controllers used for current controllers were tuned using particle swarm optimization (PSO). It can be concluded that the control structure becoming complex is benefitted by the added advantages of the smart PV system. The smart domestic PV system helps the prosumer to actively provide frequency support and voltage support, adding frequency support to the existing multifunctional PV systems.

The potency of Electricity Bill Saving and GHG Emission Reduction of A Middle-Class House in Indonesia by Using Solar- PV Rooftop and Smart Home System

The potency of Electricity Bill Saving and GHG Emission Reduction of A Middle-Class House in Indonesia by Using Solar- PV Rooftop and Smart Home System

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

PV-Statcom based Smart Inverter in Distribution System

Shrewd PV Inverter can be used to give voltage control during basic framework needs on an every minute of every day premise. In the evening, the whole inverter limit is used for Smart PV Inverter activity. During a basic framework unsettling influence in the daytime, the shrewd inverter suspends its genuine force age work incidentally, and discharges its whole inverter limit with regards to Smart PV Inverter activity. When the aggravation is cleared and the requirement for network voltage control is satisfied, the sun oriented homestead gets back to its pre-unsettling influence genuine force creation. The Low Voltage Ride Through (LVRT) execution of the Smart PV Inverter is shown through both MATLAB reenactments and research center execution utilizing d-SPACE control. This proposed Smart PV Inverter with a reaction season of 1-2 cycles, can offer a comparable support as a genuine Smart PV Inverter in a given application and conceivably look for incomes for offering this assistance.

Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.