Multiple PV Research Articles

Assessment of Breakwater as a Protection System against Aerodynamic Loads Acting on the Floating PV System

Offshore floating photovoltaic (FPV) systems are subjected to significant aerodynamic forces, especially during extreme wind conditions. Accurate estimation of these forces is crucial for the proper design of mooring lines and connection systems. In this study, detailed CFD simulations were performed for various PV panel configurations, and using these CFD simulation correlations were developed to estimate lift and drag forces as a function of the number of panels. These correlations provide valuable tools for designing large-scale FPV systems with multiple PV modules. Additionally, this study investigates the potential of using breakwaters to reduce aerodynamic forces on FPV systems. Breakwaters, typically used to mitigate wave impacts, can also serve as wind barriers, significantly reducing wind forces before they reach the FPV array. Aerodynamic simulations with and without a breakwater were conducted using CFD to assess this effect. The results show a substantial reduction in lift and drag coefficients, especially for angles of attack up to 10 degrees, demonstrating the effectiveness of the breakwater in protecting the FPV system. However, beyond this threshold, the effectiveness of the breakwater of 2 m reduces. These findings highlight the importance of strategic breakwater placement and heights and their role in enhancing FPV system resilience. The insights gained from this study are critical for optimizing breakwater design and placement, ensuring the structural integrity and performance of FPV systems in varying environmental conditions. The data generated will also contribute to future design improvements for floating PV systems.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Novel control of PV-wind-battery powered standalone power supply system based LSTM based ANN

Integrated wind-photovoltaic (PV) based standalone electric power supply systems are widely used for various applications. A battery storage system is needed to provide continuous power supply to loads despite changes in loads, wind speed, and solar irradiance. Power quality is crucial in these hybrid systems, as the battery needs to charge from surplus power when generation exceeds the load and discharge to meet load demand. A bidirectional DC to DC converter is used to connect the battery to the network, and maximum power point tracking devices with proper algorithms are incorporated for optimal utilization of PV and wind turbines. Multiple PV systems and wind turbines are considered for proper power supply system ratings. Long short-term memory (LSTM) based artificial neural network (ANN) controllers are implemented for various control units in the hybrid standalone power system. The proposed control techniques improve power quality under various situations. Results are presented using MATLAB/Simulink to evaluate the performance of the proposed method.

Integration of multiple distributed solar PV (DSP) into the grid: The case of the distribution network in Freetown, Sierra Leone

The optimal integration of distributed solar photovoltaic (DSP) is a transformative engineering method that is key in contributing to a sustainable and resilient energy future, especially when countries seek to increase the proportion of renewables in their energy mix to reduce carbon emissions. This paper presents a power flow-based approach that makes use of Newton Raphson's method in the ETAP tool to integrate multiple DSPs into both the existing and expanded Freetown distribution networks. The study determined the network's hosting capacities and optimal points of injection for the reduction of active power loss and improvement of bus voltage profiles. The study showed that the existing Freetown distribution network had a hosting capacity of 34.6 MW with an active power loss reduction of 0.967 MW while the expanded Freetown distribution network had a hosting capacity of 59.57 MW with an active power loss reduction of 5.12 MW. Before the injection of the DSPs into both networks, most of the bus voltages were not within acceptable limits. However, with the intervention of the injected DSPs, bus voltages considerably improve. The study showed that the expanded Freetown distribution network is better for DSPs integration compared to the existing Freetown distribution network. To evaluate the impacts of the injected DSPs and to validate the model used, four network scenarios were considered. The study used an analytical approach, considering future load growth and an evolving grid to integrate DSPs for long-term planning. The study will inform policymakers, utilities, etc., about the potential of integrating DSPs.

Optimal design of a renewable hydrogen production system by coordinating multiple PV arrays and multiple electrolyzers

In the design of a renewable hydrogen production system (RHPS), it is of great significance to coordinate the power generation and electrolyzers for efficient and stable hydrogen production. To investigate the coordination and complementary characteristics of multiple PV arrays and multiple electrolyzers in the optimal design of RHPS, a coordinated design approach based on mathematical optimization for the optimal RHPS with multiple PV arrays and multiple electrolyzers was proposed, which features comprehensive considerations of different characteristics of power generation and economy of different types of PV arrays, and the dynamic operating characteristics and economy of various types and capacity of electrolyzers to adapt to fluctuating hydrogen demand. The optimal configuration of the RHPS with minimal total cost can be obtained by combining multiple PV arrays with multiple electrolyzers. Furthermore, the impacts of power generation on the economy of the system are systematically explored, and electrolyzer types are optimally deployed. The influence of different PV array types on the economics of the system is analyzed. An example of the RHPS was employed to demonstrate the feasibility and effectiveness of the proposed method. Results show that for the RHPS, the selection of the type of electrolyzers is mainly affected by the characteristics of power generation. In the system with only PV arrays for power supply, the total cost of the system with proton exchange membrane electrolyzers (PEMEL) is 11.2% lower than that of the system with alkaline electrolyzers (AEL). In the system with simple wind turbines for power generation, the total cost of the system with AEL is lower than that of the system with PEMEL. The selections of type and capacity of the electrolyzers in the system are affected by the minimal power limit of the AEL. The total cost of the system can be effectively reduced by coordinating the power generation and electrolyzers and combining AEL and PEMEL in the system. The selection of PV types is related to the unit cost and the seasonal changes in the capacity factor of different PV arrays, which is also influenced by the hydrogen demand and the curtailment rate. When the variations of power generation align with hydrogen demands, the total cost can be reduced by lowering seasonal energy storage capacity.

Pattern-based integration of demand flexibility in a smart community network operation

The proliferation of utilizing smart devices and electric vehicles (EVs) has engendered new opportunities for local distribution operators (LDOs) to flexibly energize these loads, given customers’ preferences. In this context, this paper proposes an integrated scheduling model allowing an LDO to efficiently incorporate and optimally reschedule potential demand flexibilities. Given the full knowledge of the LDO over the grid conditions and availability of its owned assets, we argue that an LDO can better reshape the load profiles of the customers than they could do. The proposed model couples the branch flow-based AC power flow model in distribution network with potential demand flexibilities classified into four generic types. The LDO is assumed to operate multiple solar PV plants, energy storage systems (ESS), and EV parking lots with smart charging stations. The practicality of the proposed model is investigated through two case studies implemented on the modified IEEE 33-node distribution test system. The simulation results reveal that the total operation cost of the community can be reduced by ∼10% without sacrificing customers’ comfort if the potential demand flexibilities are optimally rescheduled.

Adaptive Voltage Control to Coordinate Multiple PV Inverters as a Cluster

Adaptive Voltage Control to Coordinate Multiple PV Inverters as a Cluster

Toward optimal voltage/VAR control with smart PVs in active distribution networks

Rapidly increasing penetration of distributed PVs brings great challenges to distribution networks. Driven by the revised IEEE 1547 standard, inverter-based smart PVs provide customized control and voltage regulation capabilities. This paper presents a comprehensive study investigating voltage/VAR control considering network reconfiguration, VAR optimization, and smart PVs. To do so, a second-order cone programming-based VAR optimization and network reconfiguration (VONR) model is formulated. The enhanced necessary radiality condition, and the extensions for multiple smart PV operation modes and multi-period operation are provided. A comprehensive case study considering various network and smart PV operation modes, and hourly VONR operation is conducted. The results show that implementing VONR with smart PV reactive power dispatching optimizes the scheduling plans, resulting in a reduction of network loss by 59.97 % and 30.37 % for the 33-bus and 123-bus test systems, respectively, compared to the no-control operation.

Towards a sustainable edge computing framework for condition monitoring in decentralized photovoltaic systems

In recent times, the rapid advancements in technology have led to a digital revolution in urban areas, and new computing frameworks are emerging to address the current issues in monitoring and fault detection, particularly in the context of the growing renewable decentralized energy systems. This research proposes a novel framework for monitoring the condition of decentralized photovoltaic systems within a smart city infrastructure. The approach uses edge computing to overcome the challenges associated with costly processing through remote cloud servers. By processing data at the edge of the network, this concept allows for significant gains in speed and bandwidth consumption, making it suitable for a sustainable city environment. In the proposed edge-learning scheme, several machine learning models are compared to find the best suitable model achieving both high accuracy and low latency in detecting photovoltaic faults. Four light and rapid machine learning models, namely, CBLOF, LOF, KNN, ANN, are selected as best performers and trained locally in decentralized edge nodes. The overall approach is deployed in a smart solar campus with multiple distributed PV units located in the R&D platform Green & Smart Building Park. Several experiments were conducted on different anomaly scenarios, and the models were evaluated based on their supervision method, f1-score, inference time, RAM usage, and model size. The paper also investigates the impact of the type of supervision and the class of the model on the anomaly detection performance. The findings indicated that the supervised artificial neural network (ANN) had superior performance compared to other models, obtaining an f1-score of 80 % even in the most unfavorable conditions. The findings also showed that KNN was the most suitable unsupervised model for the investigated experiments achieving good f1-scores (100 %, 95 % and 92 %) in 3 out of 4 scenarios making it a good candidate for similar anomaly detection tasks.

Intelligent IoT Platform for Multiple PV Plant Monitoring.

Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day's power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.

Integrated Energy Management System for Microgrid based on Renewable Energy Sources

An effective energy management strategy is crucial to ensure highest system reliability, stability, operation efficiency and cost-effective operation of renewable energy sources based standalone microgrid. This paper presents an efficient energy management system for microgrid incorporated with Photovoltaic system, PMSG based wind turbine and energy storages including battery, fuel cell-Electrolyzer. Implemented hybrid modified invasive weed optimization with perturbed and observed method for PV systems to harvest maximum energy during partial shading condition. A sliding mode controller is implemented for boost converter to work as maximum power point tracker for wind turbine. Three solar plants and three wind farms are considered in this paper to establish 1 MW microgrid. Each wind farm is established with multiple wind turbines and similarly each solar plant having multiple PV modules. Each wind turbine and solar plant has their own inverter to synchronize at point of common coupling (PCC). Effective controllers are proposed to supply quality power at PCC under linear status and nonlinear status of single and three phase loads. Small size battery is considered to work under transient time and electrolyzer-fuel cell set will be working under steady state condition to reduce the cost of the system. TS-Fuzzy based controllers are designed for all the converters and implemented hardware-in-loop on a Real Time Simulator (RTS) by using OPAL RT technology/modules. The results unveiled that the RTS precisely emulate the dynamics of the microgrid with proposed controllers.

Control Strategy for Multiple Residential Solar PV System in Distribution Network With Improved Power Quality

This paper presents a modified power control (MPC) for a grid-interfaced autonomous microgrid connecting multiple solar photovoltaic inverter (SPI) units. An MPC provides the automatic transition capability between grid-tied mode (GTM) and an islanding mode (IAM) for a microgrid consisting of multiple parallel-operated SPIs. In GTM, SPIs extract PV power to fulfil the load demand, and the rest power is fed to the grid at the unity power factor (UPF) by working in the current control mode (CCM). Upon grid failure, the main SPI unit inherently changes from CCM in GTM to voltage control mode (VCM) in the IAM state to achieve an uninterruptable power supply to loads. When the grid restores, the main SPI unit near the point of common coupling (PCC) re-synchronizes the PCC voltage with grid voltage in the microgrid and control shifts back to GTM. A seamless mode transition in the microgrid is achieved without rearrangement of control formation, and power distribution between the parallel SPI units is achieved without communication setup. This reduces the cost of remote communication setup for resynchronization and saves the complexity. Moreover, MPC accurately regulates the reference grid current of SPIs when the load demand fluctuates. The MPC method combines the adaptive integrator filter (AIF), which attenuates the harmonics from the input signal to strictly control the power factor at PCC and maintains the total harmonics distortion (THD) of grid current and PCC voltage according to the IEEE stand. 519. The performance of the presented MPC method is evaluated via a simulation and test results.

Grid-Tied Solar Power Sharing with V2G and G2V Power Exchange with Dual Bridge Integrated Electrical Vehicle

An increasing demand of electrical vehicle technology, renewable power is recommended for the charging of vehicle batteries. The proposed electric vehicle battery charging module has the capability of exchanging power between the grid and the vehicle. For this exchange of power, a DAFB circuit is integrated between the battery and the grid. The grid is induced with a solar-powered plant with multiple PV panels generating high power. This renewable power is either utilized by DAFB charging circuit or injected into the grid. The electric vehicle module V2G and G2V conditions are controlled with the PSM technique applied on DAFB with modeling done in MATLAB Simulink environment. The graphs are plotted with time as a reference for different operating conditions. The outcome of this paper is to analyze the DAFB circuit with different operating conditions shifting from charging and discharging modes of the vehicle battery.

Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults

Investigation of power output from PV arrays under different fault conditions is an essential task to enhance performance of a photovoltaic system under all operating conditions. Significant reduction in power output can occur during various PV faults such as module disconnection, bypass diode failure, bridge fault, and short circuit fault under non-uniform shading conditions. These PV faults may cause several peaks in the characteristics curve of PV arrays, which can lead to failure of the MPPT control strategy. In fact, impact of a fault can differ depending on the type of PV array, and it can make the control of the system more complex. Therefore, consideration of suitable PV arrays with an effective control design is necessary for maximum power output from a PV system. For this purpose, the proposed study presents a comparative study of two intelligent control schemes, i.e., fuzzy logic (FL) and particle swarm optimization (PSO), with a conventional control scheme known as perturb and observe (P&O) for power extraction from a PV system. The comparative analysis is based on the performance of the control strategies under several faults and the types of PV modules, i.e., monocrystalline and thin-film PV arrays. In this study, numerical analysis for complex fault scenarios like multiple faults under partial shading have also been performed. Different from the previous literature, this study will reveal the performance of FL-, PSO-, and P&O-based MPPT strategies to track maximum peak power during multiple severe fault conditions while considering the accuracy and fast-tracking efficiencies of the control techniques. A thorough analysis along with in-depth quantitative data are presented, confirming the superiority of intelligent control techniques under multiple faults and different PV types.

Simultaneous Placement of Multiple Rooftop Solar PV Integrated Electric Vehicle Charging Stations for Reliability Benefits

Simultaneous Placement of Multiple Rooftop Solar PV Integrated Electric Vehicle Charging Stations for Reliability Benefits

Grid integration of multiple PV inverters with reduced number of interfacing transformers— A dedicated controller for elimination of DC current injection

The injection of dc current offset into ac networks may impacted the lifespan of the distribution system equipment including isolation transformers and measurement devices and in serious event may cause equipment’s malfunction. This paper intents to eliminate dc current offsets in the output currents using a combinational of proportional-integral (PI) and proportional resonance (PR) controls embedded in one inverter unit. Resultant output currents of this method are sinusoidal and clean from dc offset before entering the point of common coupling. This method gives advantages for transformerless option for group of interfacing inverters in the medium-scale solar farm or in arrangement of inverters restricted in a small locale. Moreover, the use of expensive and high-accuracy measurement sensor nor complex transformer can be omitted, whilst indirectly give positive impact to the operational cost of the farm. The simulation verifications proved the usefulness of the proposed method by introducing varying unknown dc offset levels in the phase currents, and a dedicated dc offset suppressor inverter able to successfully eliminate the dc offset to zero. The validity of the proposed method is demonstrated in simulation using MATLAB/Simulink.

Reactive Power Compensation for Preventing Voltage Instabilities in Distribution Lines Enriched with PV Power Plants

The efficient voltage regulation method by capacitive reactive power usage is represented in the article. The stochastic electricity generation of multiple PV stations placed in local distribution lines induces voltage instabilities. These voltage fluctuations may overcome the allowable limits and cause technical problems and economic losses. The method of applying reactive power generated by a capacitor bank at the load is proposed. Primarily, a strict analytical solution of nonlinear equations describing voltage deviations in the distribution line connected with photovoltaic systems was obtained. The control system can fast select optimal capacitors based on the analytical solution and then connect them smoothly to a load. The developed experimental setup verified and approved the correctness and accuracy of theoretical analysis. Experimental measurements confirmed the proposed method’s ability to improve voltage stability in distribution grids. The proposed method is supposed to be used together with the transformer tap-changers in electrical substations. The capacitive power is applied in a time of inappropriate voltage deviations only and is disconnected as the tapchanger corrected the transformation ratio.

A Modular Multiport Converter to Integrate Multiple Solar Photo-Voltaic (PV) Modules With a Battery Storage System and a DC Microgrid

A modular multiport converter (MMPC), which is capable of interfacing multiple solar Photo-Voltaic (PV) modules, a battery bank, and a dc microgrid, is proposed in this article. The number of solar PV modules that can be interfaced with the proposed MMPC is equal to the number of converter modules present in the system. Furthermore, all these solar PV modules can operate at their respective maximum power point, thus increasing the power extraction capability. A high-frequency transformer facilitates the interfacing of low-voltage solar PV modules and the battery bank with a 380-V microgrid. However, the power from the solar PV modules to the battery bank does not flow through this transformer. The variation in the transformer utilization with changes in operating voltages of the solar PV modules is analyzed. Detailed simulation studies are carried out on a system having five solar PV modules to verify the performance of the proposed MMPC. A laboratory prototype of the proposed MMPC consisting of four numbers of 400-W converter modules is fabricated. The performance of the MMPC is analyzed and the experimental results are presented.

Enhancing expansion of rooftop PV systems through Mixed Integer Linear Programming and Public Tender Procedures

Concerning the growth rate in Renewable Energy share, especially in the EU, it is fact that Rooftop PV systems have a significant role to play. Nevertheless, it is also accepted that with current policies, schemes and scientific methods, today's Renewable Energy growth rate is still far from the desired one. Based on that, this study proposes a novel integration of a Mixed-Integer Linear Programming based model in a Public Tender Procedure, which enables the bulk installation and thus, the rapid expansion of Rooftop PV systems, in a least-cost manner. The main objective and contribution of this paper is to provide an economic solution to interested Public Bodies for utilizing the rapid growth of rooftop PV systems through novel mechanisms and hence, to better achieve the desired Renewable Energy targets. To further highlight the capabilities of the proposed method, the results from a real-life project, showed that the bulk installation of PV systems, in 404 public schools with a total PV capacity of 4.8 MWp, can be realized at an optimum cost. The main achievements are: (i) high-level competition, due to the ability of the proposed model to select multiple PV Installers, (ii) a 35% lower project cost compared to the initial estimated budget and (iii) a significantly lower unit cost of about 0.7 million €/MWp, compared to about 0.91 and 1.54 million €/MWp obtained from other similar projects. Finally, the outcomes of the proposed MILP model are validated by those obtained from an exhaustive optimal solution search procedure.

Priority-based market clearing model for off-grid P2P energy trading

Standalone solar PV power system is being used as an option for electrification in remote areas around the world providing basic electricity needs. However, the approach suffers from power mismatch and energy efficiency issues. This paper proposes market-clearing model peer-to-peer energy trading (P2PET) based on multiple standalone solar power system design specification in rural Sarawak, Malaysia. The proposed system combined multiple standalone solar PV system within the community through P2PET trading concept. P2PET creates the platform for energy transaction between each system and even support business such as a workshop to operate high-power electrical appliances. As energy generation is constrained in an off-grid system, the proposed market-clearing model prioritizes the energy trading between the seller and business buyer who bring more benefit to the community. Subsequently, participants in energy trading have a selection of strategies to maximize personal benefits such as profit earning or energy sufficiency. Simulation studies are applied to verify the performance of the proposed model which increases energy efficiency, improves the local economy, and maximizes the community’s welfare from electrification.