High Photovoltaic Penetration Research Articles

Mitigation of High Photovoltaic Penetration Effects in Electrical Grid Systems Using a Hybrid Particle Swarm and Grey Wolf Optimization Approach

The rising integration of photovoltaic (PV) systems into electrical grids has introduced significant challenges allied with voltage stability, power quality, and grid reliability, particularly beneath high PV penetration scenarios. This work presents a hybrid optimization approach combining particle swarm optimization (PSO) and grey wolf optimization (GWO) to mitigate these challenges effectively. The hybrid PSO–GWO method is not only applied to optimize the position and sizing of PV systems, but also the configuration of grid‐supportive devices like static synchronous compensators (STATCOMs), in Institute of Electrical and Electronics Institute (IEEE) 14‐test bus system. The proposed hybrid system led to a 42.2% power loss reduction, from 18.5 to 10.7 MW effectively enhancing efficiency of the grid, improves voltage profiles, all bus voltages maintained with in the acceptable range of 0.95–1.05 p.u. The study also identified optimal locations for PV units, with placements at bus 3, 6, and 9, where PV sizes were adjusted to 10, 12, and 8 MW, respectively. Additionally, the optimal placement of STATCOMs at bus 4, 7, and 10 provided reactive power support of up to 9 MVAR, further enhancing system stability. The findings suggest that the hybrid PSO–GWO optimization technique is a promising tool for managing the complexities of high PV penetration in modern electrical grids, ensuring proficient and reliable grid operation.

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.

Harmonic Distortion and Hosting Capacity in Electrical Distribution Systems with High Photovoltaic Penetration: The Impact of Electric Vehicles

Electric vehicles and the charging stations and their operation require a thorough examination to evaluate the effects on the electrical network. This becomes particularly challenging in the case of high photovoltaic penetration, due to the variability of the solar resource and vehicle connection patterns, which cater to individual user preferences. The current study investigates the impact of harmonics generated by charging stations and electric vehicles on different photovoltaic penetration scenarios within an electrical distribution system. DC and AC charging stations are analyzed. The findings reveal a third harmonic magnitude increase exceeding 300% compared to other cases. Furthermore, this study demonstrates the effects of current and voltage variations on end-users and substation transformers. The impact of harmonics on the hosting capacity of the network is also analyzed, resulting in a 37.5% reduction in the number of vehicles.

A bi-level optimization method for voltage control in distribution networks using batteries and smart inverters with high wind and photovoltaic penetrations

In electrical distribution systems, the voltage quality problem considers the most restrictive issue that hinders high photovoltaic (PV) and wind integration. Therefore, this study aims at providing a reliable control method for the voltage problem in distribution networks embedded with high PV and wind sources. The proposed voltage management strategy consists of two controlling stages. The first stage uses battery energy storage systems (BESS), while the second stage employs the smart PV inverters' reactive power injection capability. For BESS operation, a bi-level optimization method based on metaheuristic optimization algorithms (MOA) is developed to regulate the voltage levels by governing the batteries charging/discharging rates. The bi-level optimization problem aims to maximize PV and wind energy exploitation while improving the voltage profile. In this study, a detailed comparison is conducted between three MOA: Social Spider Optimization, Particle Swarm Optimization, and Cuckoo Search Optimization. In addition, considering the uncertainty associated with PV system generation, a PV generation forecast is embedded in the voltage control strategy to reinforce the decision-making process of BESS operation. The efficacy of the proposed method is deployed on a modified IEEE-34 bus test feeder with real-world load and PV and wind power generation data. Experimental results show the effectiveness of the proposed method and indicate that proper coordination between the BESS and smart PV inverters is beneficial for distribution system operations that seamlessly integrate PV and wind energy. Overall, the proposed voltage management strategy can be used to control the voltage to any desired range at different locations.

Safety-Aware Semi-End-to-End Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to bridge the gap from the downstream voltage regulation to the upstream multiple prediction models in a coordinated differential way. The semi-end-to-end model maps the input features to the optimal var decisions via prediction, decision-making, and decision-evaluating layers. It leverages the neural network and the second-order cone program (SOCP) to formulate the stochastic PV/load predictions and the var decision-making/evaluating separately. Then the var decision quality is evaluated via the weighted sum of the power loss for economy and the voltage violation penalty for safety, denoted by regulation loss. Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality. Case studies verify the effectiveness of the proposed model with lower power loss for economy and lower voltage violation rate for safety awareness.

Coordinated Use of Smart Inverters With Legacy Voltage Regulating Devices in Distribution Systems With High Distributed PV Penetration—Increase CVR Energy Savings

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review

The increasing demand for electricity and the need for environmentally friendly transportation systems has resulted in the proliferation of solar photovoltaic (PV) generators and electric vehicle (EV) charging within the low voltage (LV) distribution network. This high penetration of PV and EV charging can cause power quality challenges, hence the need for hosting capacity (HC) studies to estimate the maximum allowable connections. Although studies and reviews are abundant on the HC of PV and EV charging available in the literature, there is a lack of reviews on HC studies that cover both PV and EVs together. This paper fills this research gap by providing a detailed review of five commonly used methods for quantifying HC including deterministic, time series, stochastic, optimization, and streamlined methods. This paper comprehensively reviews the HC concept, methods, and tools, covering both PV and EV charging based on a survey of state-of-the-art literature published within the last five years (2017–2022). Voltage magnitude, thermal limit, and loading of lines, cables, and transformers are the main performance indices considered in most HC studies.