High Photovoltaic Penetration Research Articles

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

Study of distribution network loss allocation with high photovoltaic penetration ratio

With the rapid development of renewable energy, the penetration ratio of photovoltaic (PV) in the distribution network is getting higher and higher. The fair and reasonable allocation of high loss caused by high PV penetration in the distribution network is of great significance to the healthy development of distribution systems. In order to study the loss allocation in distribution networks with high PV penetration, based on the cooperative game theory, combined with the power flow algorithm, the power flow and loss of the 72-node active distribution network in agricultural and pastoral areas were analyzed. Three scenarios were included: no PV, low PV penetration, and high PV penetration. The allocated loss of the distribution network with high PV penetration and multiple distributed generators was computed. The results show that the loss allocation method, which takes into account the power as well as the network topology, is applicable to distribution networks with multiple distributed generators. In distribution networks with high PV penetration, distributed generators are the main cause of high loss. If the network contains the extreme reverse power flow concerned in this paper, the loss allocated to DGs accounts for more than 95% of the total network power loss.

Malta's low carbon transition towards sustainability

AbstractThe transition to low‐carbon energy and energy independence of a country play an important role in the sustainable development of its energy sector. Another important issue of sustainable energy development is the cost competitiveness in the generation process; with new renewable energy technologies, a sustainable energy transition to carbon‐neutral society is possible. In this article, we present a view of sustainable energy transformation based on a case study of Malta. We have created a simulation of a Maltese electricity system with projected growth and dominance of photovoltaic energy in the electricity market. The study results suggest that a system with a high penetration of photovoltaics has significant advantage over a conventional system using fossil fuels. In particular, in the simulated Maltese system, the total annual cost of energy was reduced threefold, the CO2 emissions were reduced by 40%, and the energy independence of Malta increased by 60%. In the end, the article gives a recommendation for further research into the Maltese energy system.

Advanced Control Strategies for Resilient Voltage and Frequency Regulation in Smart Grids

This study discusses advanced control strategies for voltage and frequency regulation in smart grids, particularly in the integration of renewable energy sources and electrification. These strategies, including Model Predictive Control (MPC), adaptive control, optimal control, robust control, and distributed control, aim to optimize control actions while adhering to system constraints. Case studies show their effectiveness in high photovoltaic penetration, wind power integration, and microgrid operation. However, challenges persist, such as managing uncertainties and coordinating multiple controllers in decentralized power systems. The study acknowledges ongoing research and development in this field, emphasizing the potential for enhancing voltage and frequency regulation in smart grids.

Coordinated Q‐V/P‐f Control Strategy Using Virtual Power Factor for Autonomous AC Microgrid with PV Smart Inverters

Given the increasing interests in carbon neutrality due to climate change, photovoltaic (PV)‐based microgrid has become an important research topic worldwide. Typically, high PV penetration without proper control schemes causes power quality issues such as voltage or frequency violation, resulting in the limited PV hosting capacity. Reactive and active power support from the PV is effective to solve the issues, however, it is a critical challenge to efficiently contribute to the stabilizing controls under the inverter capacity constraint. Recent studies have applied smart inverters, equipped with fixed active and reactive power control reserves, for dynamic voltage and frequency support. In this paper, we proposed a control strategy in which the control reserves for voltage and frequency support are flexibly adjusted using virtual power factor (VPF) to improve the power quality with reducing active power curtailment. The effectiveness of the proposed Q‐V/P‐f control is verified through numerical simulations using a microgrid model developed in MATLAB/Simulink platform. It was observed through the simulation that the proposed method contributed to mitigating voltage and frequency fluctuation with satisfying the inverter capacity constraint. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Robust microgrids for distribution systems with high solar photovoltaic penetration

Robust microgrids for distribution systems with high solar photovoltaic penetration

Analysis of fast frequency control using battery energy storage systems in mitigating impact of photovoltaic penetration in Ethiopia–Kenya HVDC link

The high penetration of photovoltaic (PV) in power grids typically leads to the displacement of traditional synchronous generators (SGs). However, with a high penetration of PV, fewer SGs are running, and the sharing of responsibility to control the system frequency is reduced and easily exacerbates the problem of reduced inertia response in the power system. This can increase frequency deviation when there is a load-generation imbalance or unexpected disturbances. The limited amount of inertial response from the PV generation means that it cannot provide the same frequency support as SGs. Therefore, this paper suggests a fast frequency control (FFC) technique for the battery energy storage system (BESS) to reduce the instantaneous frequency deviation (IFD) in the Ethiopian grid. The authors specifically provide knowledge of the modeling of droop-type controlled BESS, which can provide additional damping, enhance the inertial ability of the system, and reduce IFD. The simulation results indicate the effectiveness of BESS in mitigating the adverse inertial impact of PV and meeting the necessary grid requirement. The proposed method was tested on the Ethiopian power system model through simulations by using DIgSILENT simulation software. To validate the model, transient frequency simulation studies are performed for various fault conditions, and comparisons are made with and without BESS for different levels of PV penetration.

Pilot Protection of a Distribution Network with Distributed Generators Based on 5G and Dynamic Time Warping Considering Cosine Transform

The application of 5G-based communication for pilot protection in a distribution network with distributed generators is becoming increasingly widespread, but the existence of a 5G communication transmission data delay adversely affects the rapidity and reliability of the pilot protection based on the principle of the traditional dynamic time warping distance (DTW) algorithm. Therefore, to address this problem, and according to the difference in fault currents between distributed generators and synchronous machines, a new scheme of pilot protection based on the principle of an improved DTW is proposed. The scheme firstly performs cosine transform on the fault current sequence, and then it normalizes the DTW value. Finally, the proposed scheme is verified via simulation. The simulation results show that, compared with the traditional DTW, the proposed algorithm has better anti-delay characteristics and a stronger anti-interference ability, and the scheme can quickly and reliably identify in-zone and out-of-area faults with strong noise resistance. Further, the action times for a single-phase ground fault, two-phase ground fault, two-phase-to-phase fault, and three-phase short-circuit fault were reduced by 2.9 ms, 4.54 ms, 5.81 ms, and 5.89 ms, respectively. In addition, it is also sui for a distribution network with a high wind and photovoltaic penetration rate.

RELAY COORDINATION PLANNING FOR HIGH PHOTOVOLTAIC PENETRATION USING PRESET-TIME METHOD

In light of the advancement and technological development, electricity has become the main component of all areas of life. Photovoltaic (PV) systems are widely used within distribution networks. Despite all the advantages that PV possesses, it has some effects on the protection system. One of the most essential effects caused by PV is an increase in the relay operating time (ROT) of the backup protection relay during faults. Many studies have focused on improving the coordination of protection relays at high PV penetration, either by using current limiters or modifying the characteristics of the protection curves. However, they require a high cost and advanced relay. Therefore, the Preset-Time method is introduced in this study to shorten the ROT of the backup protection relay within the radial distribution networks which contributes to accelerating the removal of faults. The proposed method is tested on the Iraqi distribution network and is simulated using ETAP 19 software. The performance of the Preset-Time method is evaluated during the change in penetration levels. To validate the preset time method, its results are compared to the conventional method and the Max PSM method. From the results obtained by the Preset-Time method, the highest percentage of reduction in ROT was 33.20% and 29.23% compared to the conventional and the Max PSM methods, respectively.

Low Carbon Economic Dispatch of Rural Distribution Network under High Photovoltaic Penetration

To promote the low-carbon development of the power grid, the penetration rate of distribution photovoltaics in power systems has greatly increased. Some distribution networks may switch roles between energy producers and energy consumers multiple times a day. Especially in rural areas with low loads and abundant land resources, a large number of distributed photovoltaics may increase the energy balancing burden of the transmission system. To address this, the optimal operation of local resources is the key to the local accommodation of photovoltaics. In rural distribution networks, the light industrial load and agricultural load in rural distribution networks have similar electricity consumption behavior, and are suitable for centralized load management. This article proposes a low-carbon economic dispatch model of rural distribution networks under high photovoltaic penetration.. In specific, the light industrial load and the agricultural load are modeled considering their production requirements. Carbon emission costs and photovoltaic consumption costs are incorporated into the dispatch model of the distribution network. The case study is conducted based on a rural distribution network. The results of the case study indicated that the proposed model could increase the photovoltaic absorption rate by 10.96% and reduce carbon emissions by 3.4% for the test system.

Development of control strategy for community battery energy storage system in grid-connected microgrid of high photovoltaic penetration level

The focus of this paper is to develop a control strategy for a community battery bank in a grid-connected microgrid in which a significant level of photovoltaic generation is embedded. In order to minimize the capacity of the community battery, the power transfer capacity of the interconnection link between the microgrid and the external grid system is utilized to safe maximum levels. Through Empirical Mode Decomposition analysis of the net power flows of the microgrid, the daily and seasonal modes of the net power oscillations are identified as the two dominant low-frequency components. Whence a rule-based operational strategy is developed to control the power flows of the community battery via a novel dynamic referencing scheme for the state-of-charge of the battery bank. The battery control scheme counteracts the dominant daily and seasonal modes of oscillations of the net power. The numerical calculations performed for a case study shows that the proposed scheme leads to an approximately 16% decrease in the required battery capacity for particular growth rate of solid-electrolyte interphase film in the battery bank. The scheme does not require the forecasting of the net power, and thus, it has an increased degree of robustness when the community battery undertakes the power buffering task.

Performance Analysis of Bus Voltage in Distribution Network with High Penetration of PV Controlled via Data-Driven Approach

Integration of large-scale distributed photovoltaic (PV) generation resources can lead to technical challenges, particularly voltage rise caused by PVs power injection at the time of high solar radiation profile and low load demand. Reactive power control of PV inverters can help mitigate the voltage rise, which arises just for a short duration due to high incident solar radiation. There is a possibility to control functions on these PV inverters based on the rating of the PV inverter. For one-second resolution, local data-driven voltage sensitivity estimation is applied to update the control functions such as volt-var and volt-watt on the PV inverters so as to regulate the bus voltage. Different forms of solar radiation profiles, transient varying, smooth varying, and worst operating scenario corresponding to the minimum load at the time of high PV generation, are included. The study is demonstrated for different PV penetration levels. The voltage analysis on buses is performed using power hardware (PV emulator) in the loop simulation, at one of the buses in the network, while the remaining PVs are being controlled using volt-var control (VVC)/volt-watt control (VWC) methods. The applied approach confirms to maintaining bus voltage within limits even against a very short transient time instant of solar radiation profile.

Development of PV hosting-capacity prediction method based on Markov Chain for high PV penetration with utility-scale battery storage on low-voltage grid

ABSTRACT The previous stochastic hosting capacity prediction method using the Monte Carlo method for high photovoltaic (PV) penetration with a battery energy storage system (BESS) required a large number of computations to achieve the expected accuracy. The problem of high computational load must be addressed so that the electrical distribution planner can practically use the PV hosting capacity prediction in actual situations. Therefore, this study developed a Markov-chain-based PV hosting capacity prediction method for high PV penetration using BESS. The proposed method is described in detail, followed by case and validation studies. The obtained hosting capacity was 123.58 kW, which increased to 3676.4 kW after the utility-scale BESS implementation. The results demonstrate that the proposed Markov-chain-based PV hosting capacity prediction method outperforms the Monte Carlo method, which is the most popular stochastic hosting capacity method, in terms of accuracy and computational cost.

Resilience of hydrogen fuel station-integrated power systems with high penetration of photovoltaics

Hydrogen fuel stations (HFSs) may be connected to power systems; to absorb electricity, produce hydrogen and supply hydrogen demands. To the best of the authors' knowledge, the resilience of HFS-integrated power systems has not been addressed in the literature. In this research, a novel strategy is proposed for resilience enhancement of PV-rich HFS-integrated power systems, considering the uncertainties. In the proposed strategy, fuel cells (FCs) and hydrogen tanks are added to HFSs and mobile batteries are added to the power system. A two-stage stochastic model is developed in which the location of mobile batteries are the first-stage here-and-now decision variables and other variables are second-stage wait-and-see decision variables. The model is formulated as a multi-objective problem as the ability of the system to supply both electricity and hydrogen demands are considered. Expected load not served (ELNS) is used as resilience metric. Case study is a modified IEEE 24-bus power system with 5 HFSs. The results confirm that with the proposed resilience enhancement strategy, hurricane does not result in any shed for hydrogen demands. As per results, mobile batteries are the most efficient tools in resilience enhancement of the studied system; they cause 84 % improvement in ELNS of electricity and 43 % improvement in ELNS of hydrogen. The results also show that besides mobile batteries, hydrogen storage tanks and FCs are the most efficient components in resilience enhancement of the system. Hydrogen tanks improve ELNS of electricity by 1.8 % and remove the need of system operator to shed hydrogen demands. A sensitivity analysis is done to see how the results of the developed model are sensitive to its parameters.

Small-Sample Solar Power Interval Prediction Based on Instance-Based Transfer Learning

In the context of high photovoltaic (PV) penetration, high-quality solar power interval prediction is important for grid system operation. However, in some cases, sufficient amount of data are not available to train a reliable prediction model, especially for new installed PV stations. To tackle this problem, this paper proposes a novel Small-sample Interval Prediction model with improved TrAdaBoost (SIPTAB) method for solar power prediction. Sample weight is designed to select the important samples from other data sources. First, Extreme Learning Machine (ELM) with Direct Quantile Regression (DQR) is employed as the base predictor to construct interval boundaries. Second, an improved TrAdaBoost algorithm is proposed to iteratively construct a boosting ensemble interval predictor to enhance the prediction performance with limited amount of data. Third, a two-stage model training strategy is introduced in the architecture to optimize the boosting ensemble interval predictor and further improve prediction quality. Comprehensive experiments based on realistic solar power data are conducted to confirm the superiority of proposed model.

Net Load Forecasting With Disaggregated Behind-the-Meter PV Generation

As worldwide use of residential photovoltaic (PV) systems grows, system operators and utilities will need to transition from forecasting pure demand to forecasting net load with behind-the-meter (BTM) PV generation. However, PV generation can be difficult to predict and the measurements of PV generation from BTM residential systems are often invisible behind a measurement of the net load, making net load forecasting challenging. This paper proposes a novel two-stage framework for net load forecasting in areas with limited observability and high BTM PV generation. First, the profiles of observable customers are used to disaggregate the net load measurements into the pure load and PV generation. Then, separate models are used to forecast the PV generation and pure load individually, and the results are combined for a net load forecast. This paper also proposes a compensator for correcting the error of the net load forecast, using historical forecast errors of the PV generation, pure load, and net load. The proposed framework is tested through two case studies for areas with high BTM PV penetration and less than 10% observable customers. The two-stage forecasting model is compared to two benchmark methods – a time series forecasting model, and a model that forecasts the net load directly using historical net load measurements. Results show that the proposed disaggregation-forecasting framework reduces the error of the net load forecast compared to both benchmark models. In addition, when the net load forecast error is periodic, the compensator can correct the error to improve the forecast accuracy.

Optimal Placement of PV Smart Inverters With Volt-VAr Control in Electric Distribution Systems

The high R/X ratio of typical distribution systems makes the system voltage vulnerable to the active power injection from distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to the voltage control. This article proposes a two-stage stochastic optimization strategy to optimally place the photovoltaic (PV) smart inverters with Volt-VAr capability for distribution systems with high PV penetration to mitigate voltage violation issues. The proposed optimization strategy enables a planning-stage guide for upgrading the existing PV inverters while considering the operation-stage characteristics of the Volt-VAr control. One advantage of this planning strategy is that it utilizes the local control capability of the smart inverter that requires no communication, thus avoiding issues related to communication delays and failures. Another advantage is that the Volt-VAr control characteristic is internally integrated into the optimization model as a set of constraints, making placement decisions more accurate. The objective of the optimization is to minimize the upgrading cost and the number of the smart inverters required while maintaining the voltage profile within the acceptable range. Case studies on an actual 12.47 kV, 9-km-long Arizona utility feeder have been conducted using OpenDSS to validate the effectiveness of the proposed placement strategy in both static and dynamic simulations.

Optimal placement of hydrogen fuel stations in power systems with high photovoltaic penetration and responsive electric demands in presence of local hydrogen markets

In this research, a computationally-inexpensive stochastic MILP model is proposed for the optimal placement of hydrogen fuel stations (HFSs) in power systems with high penetration of renewables, while power system operator sells its extra hydrogen in a day-ahead local hydrogen market. In the developed model, a linearisation strategy is proposed to transform the nonlinear binary terms into linear terms and the uncertainties of electricity and hydrogen demands, photovoltaic (PV) generation and hydrogen market prices are modeled as scenarios. Any available HFS includes an electrolyzer and a hydrogen storage system. As the penetration of renewables in the studied power system is high, most of the produced hydrogen is green. According to the results, system operator uses the potential of hydrogen storage systems and responsive electricity demands to sell more hydrogen to the local hydrogen market and increase its profit. In times with higher hydrogen prices, system operator commands both shift-down in electricity demands and discharge mode for batteries to be able to sell more electricity and make more profit; on the other hand, in times with lower hydrogen prices, system operator commands shift-up in electricity demands and charge mode for batteries. The results show that demand response program increases expected profit of system by 4.7%. The results confirm that addition of HFSs strongly decreases PV curtailment. The impact of the participation in hydrogen market on system profit is assessed. The sensitivity of HFS profit to the number of HFSs, size of electrolyzers and demand response participation factor is assessed.

Design of Grid-Connected rooftop Photovoltaic system for leakage current reduction using optimization algorithms

Rooftop solar power systems refer to the organization of photovoltaic (PV) panels on the rooftop of a building. They are a feasible substitute for land-based solar arrays, and they are being used in different Asian countries. Thus regulations, grid connectivity, off-take, and land acquisition are the main obstacles that renewable energy plant owners face. At high penetration of rooftop PV, this voltage variability reduces the stability of the grid due to transient imbalance in load and generation and causes voltage and frequency to exceed set limits if not countered by power controls. To reduce this power loss in the transmission cable, a Manta Ray Foraging Optimization (MRFO) process is presented in this research. This method also enhances the PV system's performance. A Non-isolated Buck-Boost Converter is connected between the PV and the grid. For renewable energy applications, a new non-isolated Buck-Boost converter with high voltage gain and positive output voltage has been developed. This converter receives the voltage of input from the PV and gives the output as boosting or lowering the voltage. To improve the current tracking with high power conversion efficiency, a Finite Control Set Model Predictive Controller (FCS-MPC) is introduced for this converter. The proposed optimization algorithm is utilized to tune the controller and improves the dynamic process of the power plant system connected to the grid. This algorithm is simulated using the Matlab Simulink software. The voltage and power generated by the proposed rooftop PV are 560 V and 495.25 kW/sec. The voltage and electricity produced by the ground-mounted are very low compared to the proposed method, therefore, utilizing this rooftop solar PV model, that study generates more power than the ground-mounted PV. This study contributes to the field by improving the energy production of roof-top solar PV systems based on roof design.

Cluster Partition-Based Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks

Considering the possible overvoltage caused by high-penetration photovoltaics (PVs) connected to the distribution networks (DNs), a cluster partition-based voltage control combined day-ahead scheduling and real-time control for distribution networks is proposed. Firstly, a community detection algorithm utilizing a coupling quality function is introduced to divide the PVs into clusters. Based on the cluster partition, day-ahead scheduling (DAS) is proposed with the objective of minimizing the operating costs of PVs, as well as the on-load tap changer (OLTC). In the real-time control, a second-order cone programming (SOCP) model-based real-time voltage control (RTVC) strategy is drawn up in each cluster to regulate the PV inverters, and this strategy can correct the day-ahead scheduling by modifications. The proposed strategy realizes the combination of day-ahead scheduling and real-time voltage control, and the optimization of voltage control can be greatly simplified. Finally, the proposed method is applied to a practical 10 kV feeder to verify its effectiveness.