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The Sun is the main source of energy on planet Earth, and its activity is known to vary according to several cycles. Solar activity studied through sunspot variations led for the first time to the discovery of a widely accepted 11-year cycle that can be used to measure such variations, which feature pre-defined cycles over previous years, thus resulting in possible projections future ones. In this article, climate data were collected from meteorological stations dating back up to 59 years for each Brazilian state, totaling 27 units, with the aim of determining solar irradiation in recent years and, possibly, inferring the influence of solar cycles. As solar irradiation data only began to be collected in the 2000s in some locations in the country, solar insolation data were converted into solar irradiation using the Ångström–Prèscott formula. To have greater reliability in the prediction results, this formula was adjusted to the specific characteristics of each location using coefficients for all Brazilian states, as such information is not yet available in the literature. With these data, a predominant cycle was observed for each Brazilian state in relation to solar irradiation, lasting approximately 70 years, with durations whose solar maximum will occur in 2025. These results show that this is a very favorable situation for a growing scenario of implementation of new technologies aimed at photovoltaic generation of electrical energy. [ABSTRACT FROM AUTHOR]

In the case of resistance-inductance lines in PV station area, the problem of voltage overstep is easy to occur. This article proposes a reactive power compensation control method to improve the voltage stability in the photovoltaic power plant area, which addresses the problem of voltage at the point of common coupling (PCC) exceeding the upper limit due to resistance circuits and exceeding the lower limit due to relatively insufficient reactive power output when the output active power is high. The idea is to achieve dynamic adjustment of PCC voltage by paralleling a static reactive power generator (SVG) at the grid connection point and using a variable droop control method. In addition, a reactive power optimization method based on improved particle swarm optimization (IPSO) algorithm is proposed to address the changes in power flow caused by photovoltaic integration in the distribution network system. The proposed improvement method not only effectively reduces network losses but also significantly improves voltage stability. [ABSTRACT FROM AUTHOR]

The instability of the increasingly important photovoltaic (PV) electricity generation in Korea due to a dependence on meteorological conditions is an emerging problem. In this study, we examined the influence of various meteorological conditions on the prediction accuracy of PV generation and module temperature. The predictions were based on weather data acquired from the Korea Meteorological Administration and compared with actual PV generation data from the Dangjin coal yard PV power facility. The results were analyzed on the basis of meteorological conditions, insolation, ambient temperature, and wind velocity. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) are considered a crucial energy transformation to reduce carbon emissions, so more RESs are being integrated into contemporary power systems. Power electronic converters are extensively utilized to connect power grids with renewable generators to manage the fluctuations and unpredictability of these renewable energy sources. This paper introduces a multi-terminal multi-function inverter (MT-MF) designed for a battery energy storage system (BESS) to maintain the frequency stability of a hybrid microgrid (MG). The MG comprises a photovoltaic generation system, a diesel generator, BESS, and two loads: one constant load and the other variable, fed through a medium-voltage radial feeding system. An introduced approach involves utilizing a model predictive control controlled virtual synchronous generator (MPC-VSG) for BESS. This method offers inertia support during transient states and improves the dynamic characteristics of system frequency. In addition, it enables the connection of multiple batteries, provides individualized control for each, and supports the injection of reactive power into the MG. The required power from the BESS is shared between the two batteries using the low pass filter technique. The simulation outcomes affirm the proposed control strategy’s effectiveness and underscore the MT-MF inverter approach’s potential in integrating extensive RESs. This paper also explores how the proposed technique outperforms other methods in improving frequency stability.

In recent years, there has been a growing emphasis on diversifying and decarbonizing the energy matrix, paving the way for the emergence of new energy technologies as promising solutions for sustainable development. Among the emerging energies that have already achieved commercial maturity and deployment in Brazil are photovoltaic panels. This work proposes to analyze the potential impacts of on-load TAP-changers (OLTC) in transformers in industries located in distribution networks with a significant presence of photovoltaic distributed generation (PVDG). Thus, three industries are strategically allocated at different points of the IEEE 8500-node system using the OpenDSS program. The impacts are evaluated in several PVDG penetration scenarios in the network. The results show substantial fluctuations in the flow of energy generated by PVDG within the low-voltage network. This, in turn, induces frequent voltage changes in the medium-voltage network, leading to an increased operational frequency of OLTCs and a subsequent reduction in their lifetime. Moreover, this paper proposes alternative configurations for voltage regulator devices to enhance their lifetime in the face of intermittent voltage variations. The results suggest that adjusting the equipment's actuation delay and bandwidth of the OLTCs can decrease the OLTC switching frequency in a range of 65–85%, resulting in the preservation of the equipment's lifetime. [ABSTRACT FROM AUTHOR]

For the purpose of effectively providing protection and utilization of historical earthen sites, the fully-enclosed exhibition halls with electricity driven air-conditioning have been successfully adopted to provide a suitable preservation air environment. However, its electricity consumption and carbon emissions are considerable due to the continuous operation of environmental control, the energy consumption of air cooling in exhibition halls can reach more than 10 times that of office buildings. To further reduce energy consumption, this current study drew on the concept of NBS (Nature Based Solution) and proposed a low-carbon environmental control system for an enclosed exhibition hall for storage of earthen relics by applying natural energy (solar photovoltaic and earth-air-cooling) and space optimization. Firstly, a numerical model was established to simulate the preservation environment in exhibition halls and the electricity consumption of each energy subsystem. Then the model was applied to optimize the design parameters (e.g. earth-air tunnel and airflow pattern) for the environmental control system. Numerical results showed that the electricity consumption was reduced by 58% by applying earth-air-cooling and solar photovoltaic. After optimization of the exhibition hall height, electricity consumption was approximately reduced by 65%. This research is of great value to mitigate energy consumption problems encountered in earthen relics museums and other similar environments. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electric power systems with a high penetration of photovoltaic generation and a relevant fleet of electric vehicles face significant stability challenges, particularly in mountainous areas where the variability of photovoltaic resources is pronounced. This study presents a novel methodology to strategically place electric vehicle aggregators along a feeder. This approach considers electrical variables and the dynamics of cloud movements within the study area. This innovative methodology reduces the substation's power load demand and significantly improves the end user's voltage levels. The improvements in voltage regulation and reduced demand on the substation provide clear benefits, including increased system resilience, better integration of renewable energy sources, and enhanced overall efficiency of the electric grid. These advantages are particularly critical in regions with high levels of photovoltaic generation and are important in promoting sustainable electric vehicle charging infrastructure. When analyzing different load scenarios for the IEEE European Low Voltage Test Feeder system, the consideration of distributed aggregators based on cloud movements decreased the power required at the substation by 21.25%, and the voltage drop in loads was reduced from 6.9% to 4.29%. This research underscores the critical need to consider both the variability and geographical distribution of PV resources in the planning and operation of electrical systems with extensive PV generation. [ABSTRACT FROM AUTHOR]

In the case of resistance-inductance lines in PV station area, the problem of voltage overstep is easy to occur. This article proposes a reactive power compensation control method to improve the voltage stability in the photovoltaic power plant area, which addresses the problem of voltage at the point of common coupling (PCC) exceeding the upper limit due to resistance circuits and exceeding the lower limit due to relatively insufficient reactive power output when the output active power is high. The idea is to achieve dynamic adjustment of PCC voltage by paralleling a static reactive power generator (SVG) at the grid connection point and using a variable droop control method. In addition, a reactive power optimization method based on improved particle swarm optimization (IPSO) algorithm is proposed to address the changes in power flow caused by photovoltaic integration in the distribution network system. The proposed improvement method not only effectively reduces network losses but also significantly improves voltage stability.

This paper proposes a new and simplified Nearest Vector Control (NVC) modulation technique for a grid-connected photovoltaic (PV) system using a Modular Multilevel Converter (MMC). Compared to the Nearest Level Control (NLC) technique, which defines three independent states for the three phases of medium to large four-wire multilevel converters, NVC offers a more coordinated behavior for three-wire converters. The proposed scheme is easy to implement, and it simplifies the understanding of using vectors when detecting the vector of the converter nearest to a given reference. Because it uses natural coordinates, namely, ab, bc and ca, the proposed method is easier to understand and more useful for further developments. Compared with earlier NVC methods, this approach offers full independence of the number of levels at the converter and it can readily accommodate changes in the number of levels, with no need for lookup tables or artificial coordinate transformations. The proposed NVC method was implemented on a 16-cell MMC used for PV generation and then it was compared to NLC, leading to a smaller and more consistent low-order harmonic distortion, requiring about the same complexity of implementation. Furthermore, in comparison to NLC, when applying the proposed NVC modulation, a behavior more insensitive to changes in the grid voltage was found, the most hazardous odd harmonics from the 5th to the 19th were reduced, and a consistent reduction of about 25 dB was achieved on the 5th and 7th harmonics. The newly proposed method is supported by simulations and experimental results with constant and sharply changing solar irradiance, leaving or removing the 100 Hz component of the MMC circulating currents. [ABSTRACT FROM AUTHOR]

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems. [ABSTRACT FROM AUTHOR]

After end of decades of loadshedding, the power system of Nepal is moving towards the scenario of self-sufficiency. The Government of Nepal has formulated a policy to add 20 GW of hydropower in the next ten years, making a ten-fold increase compared to the current capacity. Therefore, there is a high need for the assessment of the power system stability of the current Nepal grid to mitigate any potential issues that can occur due to the addition of this huge generation. This paper conducts a comprehensive assessment of the power system stability namely voltage (static), angle (transient and small signal), and frequency stability. The proposed assessment methodology consists of three stages: the first stage constitutes the selection and modeling of the components (generator, load, line, excitation system, governor, renewable energy resources) of the power system of Nepal. The second stage then uses the stability assessment tools: time domain simulations for frequency and transient stability, modal (eigenvalue) analysis for small signal stability and voltage stability, and sensitivity analysis for identification of critical areas prone to instability. The result shows that the current grid of Nepal has a low margin of the small signal stability. The critical clearing time of the system is approximately 0.51s. The result also show that there are five potential voltage instability areas in Nepal but is robust in terms of the frequency stability. Additionally, the integration of photovoltaic generation to the Nepal grid has also been studied in this paper. The study shows that the margin of the low frequency oscillatory stability decreases with the penetration of photovoltaic generation of the power grid of Nepal.

The present paper proposes a new methodology to solve a problem in the literature in which a consumer becomes eligible for reactive power excess charging (RPEC) after installing a photovoltaic (PV) system, even if its reactive demand has not changed. This problem mainly affects this kind of consumer, which is known as PV prosumers, in countries where the power factor at the point of common coupling (PCC) is used as the main parameter in calculating reactive power excess charges instead of directly pricing the surplus reactive energy delivered from the distribution network. The classical approaches to reducing or eliminating RPEC are based on providing reactive power within the consumer’s installations from their PV inverters and their capacitor banks. However, the solutions presented require optimization studies since the costs of producing reactive energy can be even higher than the penalties involved. In this context, we developed a new hourly-based optimization model for the RPEC problem based on the variable neighborhood search meta-heuristic and the Fibonacci search method. In addition, we apply the proposed methodology to two medium voltage prosumers in Santo André, Brazil. The results showed the proposal’s effectiveness, mitigating RPEC and minimizing the prosumers’ electricity bills without installing new equipment to supply their reactive demand. The best results were found when reactive power was delivered by the capacitor bank and PV inverters simultaneously rather than applying these solutions separately. The proposed hourly-based correction method can help in day-ahead energy management, avoiding premature wear from switching the consumer’s reactive power compensation equipment switches. Also, the methodology proposal improved the power factor at the utility substation, which benefits the power distribution systems.

With the increasing integration of distributed photovoltaic (PV) generation into distribution networks, challenges such as power reverse flow and high line losses have emerged, leading to greater uncertainty in power systems. To address these issues, this paper presents an analytical model for calculating line losses in low-voltage distribution networks with PV generation, utilizing power flow calculations. A simulation model of a 15 node low-voltage network is developed using SIMULINK to validate the accuracy of the analytical model under the scenario of uniform load distribution (ULD). Additionally, a line loss optimization algorithm based on quantum genetic algorithms (QGA) is proposed for low-voltage distribution networks with distributed PV generation, along with an optimization model. The objective function of the optimization model is based on the reduction in line losses resulting from the integration of the PV system. The example results demonstrate the consistency between the line loss optimization using QGA and the analytical results, highlighting the significant advantages of QGA in terms of speed and accuracy. This research provides valuable insights for line loss optimization in low-voltage distribution networks with distributed PV generation and serves as a theoretical reference for future studies in this field.

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract's availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior. [ABSTRACT FROM AUTHOR]

In Brazil, most photovoltaic generation systems are owned by small consumers connected to the distribution network. These consumers are, in fact, prosumers, since they have the right to be financially compensated for providing power to the grid. This paper describes a stochastic bilevel optimal power flow model that can be used to analyze the impact of independent actions of such prosumers on power system steady-state performance. Simulations are conducted using real feeders of a Brazilian distribution company. [ABSTRACT FROM AUTHOR]

Uncertainties of solar PhotoVoltaic (solar PV) generation and Electric Vehicle (EV) demand are major issues for Optimal Energy Management (OEM) tasks in MicroGrid (MG), especially regarding power system stability and increased overall demand. A Probabilistic Load Flow (PLF) with a Battery Energy Storage System (BESS) controlled by an Energy Management System (EMS) can deal with these issues. However, the PLF and optimization algorithm based on the iterative method leads to a high computation burden. Therefore, this paper proposes a model-free data-driven approach assisted Deep Reinforcement Learning (DRL) to decrease the computation burden of PLF and problem-solving. Deep Neural Networks (DNNs) are developed as a model-free data-driven to estimate the power flow parameters of MG instead of PLF. Moreover, the DRL named a Deep Deterministic Policy Gradient (DDPG) is deployed as the optimization algorithm to find the optimal solution in the OEM task. In addition, finding appropriate parameters of the DDPG is proposed in this paper. To showcase the efficacy of the proposed method, a low-voltage distribution network is developed as the MG. Objective functions including exchanged energy, carbon emission, and BESS degradation costs are considered in this work. Simulation results indicate that the proposed method is capable of reducing the computation burden by 88.47% in comparison to the Differential Evolution (DE) algorithm that uses 10 populations. Moreover, the best agent model obtained from finding suitable parameters of DDPG can provide a total cost of 115.63 USD/day, which is the lowest cost compared with the cost obtained by the DE.

Electric power systems with a high penetration of photovoltaic generation and a relevant fleet of electric vehicles face significant stability challenges, particularly in mountainous areas where the variability of photovoltaic resources is pronounced. This study presents a novel methodology to strategically place electric vehicle aggregators along a feeder. This approach considers electrical variables and the dynamics of cloud movements within the study area. This innovative methodology reduces the substation’s power load demand and significantly improves the end user’s voltage levels. The improvements in voltage regulation and reduced demand on the substation provide clear benefits, including increased system resilience, better integration of renewable energy sources, and enhanced overall efficiency of the electric grid. These advantages are particularly critical in regions with high levels of photovoltaic generation and are important in promoting sustainable electric vehicle charging infrastructure. When analyzing different load scenarios for the IEEE European Low Voltage Test Feeder system, the consideration of distributed aggregators based on cloud movements decreased the power required at the substation by 21.25%, and the voltage drop in loads was reduced from 6.9% to 4.29%. This research underscores the critical need to consider both the variability and geographical distribution of PV resources in the planning and operation of electrical systems with extensive PV generation.

For a solar photovoltaic power system on a university campus, the electricity generated by the system meets the campus load, and the extra electricity is delivered to the grid. Generally, the price of the photovoltaic system is cheaper than that of the utility power system. The full use of solar electricity can reduce the electricity cost of the school. The deep belief network is used to predict solar photovoltaic generation and electricity load, and the gap is found. According to the gap, the power loads on the campus are adjusted to improve the utilization rate of solar power generation. Through the practical application of Changqing Campus of Qilu University of Technology in China, it is found that the utilization rate of solar photovoltaic power generation effectively improved from 91.24% in 2017 to 98.16% in 2019, and the annual electricity is saved by 68 610 yuan (in 2019).

This paper presents the topology and control of a photovoltaic inverter with an internal battery storage system in conjunction with droop control designed to perform ancillary services such as frequency and reactive power support (voltage regulation), active power dispatch through a proposal to control the charging and discharging of batteries and harmonic current compensation (active filter) in a microgrid connected to an IEEE 13-bus system. The converter in question consists of two stages, a 3-level NPC DC/AC converter and Boost and Buck-Boost DC/DC converters. The system in question is capable of helping to dampen frequency oscillations, as well as regulating the PCC charging curve by charging or discharging the battery bank. In addition, the proposed system is capable of supplying reactive power to the grid on a permanent basis, thus helping with voltage control. It is also capable of compensating for harmonic currents caused by non-linear loads connected to the PCC. In this context, we can see the multifunctionality of the photovoltaic inverter in helping to mitigate disturbances associated with the power quality, with the differential of charge and discharge control to preserve the useful life of the energy storage system. For this study, Matlab/Simulink software was used to implement and validate the proposed topology and control systems. In the computer simulations carried out, satisfactory results were obtained in relation to the execution of the ancillary services mentioned above, thus demonstrating the viability of the proposed strategy. [ABSTRACT FROM AUTHOR]

In rural regions with limited access to the power grid, self-reliance for electricity generation is paramount. This study focuses on enhancing the design of stand-alone photovoltaic installations (SAPV) to replace conventional fuel generators thanks to the decreasing costs of PV modules and batteries. This study presents a particle swarm optimization (PSO) method for the reliable and cost-effective sizing of SAPV systems. The proposed method considers the variability of PV generation and domestic demand and optimizes the system design to minimize the total cost of ownership while ensuring a high level of reliability. The results show that for the PSO method with 500 iterations, the error is around 2%, and the simulation time is approximately 2.25 s. Moreover, the PSO method allows a much lower number of iterations to be used in the Monte Carlo simulation, with a total of 100 iterations used to obtain the averaged results. The optimization results, encompassing installed power, battery capacity, reliability, and annual costs, reveal the effectiveness of our approach. Notably, our discretized PSO algorithm converges, yielding specific parameters like 9900 W of installed power and a battery configuration of five 3550 Wh units for the case study under consideration. In summary, our work presents an efficient SAPV system design methodology supported by concrete numerical outcomes, considering supply reliability and installation and operational costs. [ABSTRACT FROM AUTHOR]

Huangzangsi Reservoir is the first comprehensive utilization reservoir in the upper reaches of the Heihe River. Supported by the new energy policy of Qinghai Province, if the Huangzangsi Reservoir is equipped with photovoltaic power stations, a complex complementary system of hydropower and photovoltaic power generation in the upper reaches of the Heihe River will be formed. This study used PVsyst software to complete the design of a 100 MW photovoltaic plant, and proposed a daily output clustering method based on the number of output curve fluctuations, maximum output change and output proportion as indicators. A daily optimal operation model for Huangzangsi Reservoir was established with the goal of stabilizing the output of the Hydro-PV complementary system and improving the power generation revenue of cascade hydropower stations and solved by using the NSGA-Ⅱ. The results indicate that cloudy weather, peak shaving power generation by cascade hydropower stations, installed capacity of Huangzangsi Reservoir and water volume limitations that can be regulated are the main factors affecting the stability of the output of the Hydro-PV complementary system; When the Huangzangsi Reservoir compensates for photovoltaic output, the outgoing flow has a small fluctuation, this fluctuation is absorbed by the Baopinghe Hydropower Station; When the Huangzangsi Reservoir compensates for the cascade hydropower station peaking and power generation, the upstream produces a large flow of incoming water, Longshou-Ⅱ Hydropower Station ensures that the Yingluoxia section has a stable flow throughout the day; The Hydro-PV complementary mode has a small impact on the hydropower benefits of Huangzangsi Reservoir and has a significant effect on stabilizing the fluctuation of photovoltaic output, however, the capacity configuration of photovoltaic power stations should be further optimized. [ABSTRACT FROM AUTHOR]

This article, besides offering data of great value for any designer of high-power short-circuits of special three-winding design, illustrates the correlation with the corresponding FRA measurements, validating this type of measurement. The frequency response measurements can provide data about the transformer's status after it is put into service, its vulnerability in incipient states, and, particularly for this type of transformer, its insulation, which is the subject of high dielectric stress due to its invertor working regime. This article presents the behavior of a three-phase 8400 kVA medium-voltage step-up transformer (corrugated hermetic tank) specially designed for photovoltaic applications during short-circuit tests. This transformer, fed by two inverters, has two secondaries with elliptical windings (non-circular aluminum foil for LV windings and an aluminum conductor for HV windings). Various experiments were performed, including measurements of winding resistance, measurements of voltage ratio, measurements of short-circuit impedance and load loss on three tappings, measurements of no-load loss and current, a frequency response analysis, and short-circuiting. These experiments were performed to study the behavior of the transformer, which, in real life, is powered by photovoltaic inverters on the LV side that feed into the MV grid on the HV side, making it the interface between the photovoltaic inverter and the MV grid. An auxiliary supply transformer may be connected to the LV side. Given these elements, concerning both the importance and the particularities of the problem studied, we can say that this article represents a niche study on the guarantee of good functioning and safety in operation given the passing of the test to withstand the dynamic effects of short-circuiting. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a Multi-Period Optimal Power Flow (MOPF) modeling applied to the minimization of energy losses in Distribution Networks (DNs) considering the reactive power control of Photovoltaic Generation (PVG) that can be applied to both short-term and long-term operation planning. Depending on the PV Power Factor ( P V p f ) limitations, PVG may provide both active and reactive power. The optimal power factor control on the buses with PVG contributes to an economical and safe operation, minimizing losses and improving the voltage profile of the DN. The proposed MOPF was modeled in order to minimize active energy losses subject to grid constraints and P V p f limitations. The variations of loads and PVG were discretized hour by hour, composing a time horizon of 24 h for day-ahead planning; nonetheless, the methodology can be applied to any other time period, such as a month, year, etc., by simply having generation and load forecasts. To demonstrate the effectiveness and applicability of the proposed approach, various tests were carried out on 33-bus and 69-bus distribution test systems. The analyses considered the DN operating with PVG in four different cases: (a) P V p f fixed at 1.0; (b) P V p f fixed at 0.9 capacitive; (c) hourly P V p f optimization; and (d) optimization of P V p f for a single value. The results show that a single optimal adjustment of P V p f minimizes losses, improves voltage profile, and promotes safe operation, avoiding multiple P V p f adjustments during the operating time horizon. The algorithm is extremely fast, taking around 2 s to reach a solution. [ABSTRACT FROM AUTHOR]

This paper proposes a new and simplified Nearest Vector Control (NVC) modulation technique for a grid-connected photovoltaic (PV) system using a Modular Multilevel Converter (MMC). Compared to the Nearest Level Control (NLC) technique, which defines three independent states for the three phases of medium to large four-wire multilevel converters, NVC offers a more coordinated behavior for three-wire converters. The proposed scheme is easy to implement, and it simplifies the understanding of using vectors when detecting the vector of the converter nearest to a given reference. Because it uses natural coordinates, namely, ab, bc and ca, the proposed method is easier to understand and more useful for further developments. Compared with earlier NVC methods, this approach offers full independence of the number of levels at the converter and it can readily accommodate changes in the number of levels, with no need for lookup tables or artificial coordinate transformations. The proposed NVC method was implemented on a 16-cell MMC used for PV generation and then it was compared to NLC, leading to a smaller and more consistent low-order harmonic distortion, requiring about the same complexity of implementation. Furthermore, in comparison to NLC, when applying the proposed NVC modulation, a behavior more insensitive to changes in the grid voltage was found, the most hazardous odd harmonics from the 5th to the 19th were reduced, and a consistent reduction of about 25 dB was achieved on the 5th and 7th harmonics. The newly proposed method is supported by simulations and experimental results with constant and sharply changing solar irradiance, leaving or removing the 100 Hz component of the MMC circulating currents.

This work presents a library of microgrid (MG) component models integrated in a complete university campus MG model in the Simulink/MATLAB environment. The model allows simulations on widely varying time scales and evaluation of the electrical, economic, and environmental performance of the MG. The models include photovoltaic (PV) generation (with MPPT control), battery storage (including charge/discharge control), loads (campus buildings and outdoor lighting), electric vehicle charging stations, and interconnections. The campus MG model is shown to be useful for (i) week-long simulations throughout the year, to analyze seasonal differences; (ii) year-long simulations for the estimation of overall economic and environmental impacts; (iii) MG islanded-mode simulations. The model is an agile tool for planning the deployment and penetration of photovoltaic plants and battery storage, with consideration of economic and environmental (carbon footprint) aspects. [ABSTRACT FROM AUTHOR]

The key objective of this paper is to develop a photovoltaic (PV) maximum power point tracking (MPPT) algorithm based on particle swarm optimization–butterfly optimization algorithm (PSO-BOA) that is adapted for partial shading conditions (PSCs). Generally, conventional MPPT techniques are often unable to accurately locate the global maximum power point (GMPP) generated by partial shading in PV systems. As a result, a significant decrease in power output occurs. The traditional particle swarm optimization (PSO) algorithm traps the local maxima point easily, while the butterfly optimization algorithm (BOA) has slow convergence speed and large oscillations during its use in research. To address the limitations of the aforementioned PSO and BOA algorithms, the MPPT strategy of PV systems combining PSO-BOA is presented, which can ameliorate the efficiency and accuracy in PSCs. In this paper, the control parameter of sensory modality in the BOA can be acquired based on logistic mapping, and the self-adaptive adjustment of the inertial weight of the PSO algorithm is designed. According to the simulation findings, the suggested method is more suitable than PSO and BOA with respect to intricate shading-induced variations in irradiance and changes in external temperatures. The average tracking time is less than 0.5 s, and the tracking accuracy is not less than 99.94%. Especially under sudden variations in irradiance and temperature conditions, the tracking time of the PSO-BOA algorithm is only 49.70% of that of the PSO algorithm and 55.63% of that of the BOA. Therefore, the MPPT method presented has the ability to improve the oscillations and result in less convergence speed, which in turn accurately tracks the GMPP. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy is increasingly positioned as an excellent renewable energy source to be implemented in energy communities. However, studies on photovoltaic potential are still required in order to enhance the clean energy transition in energy dependent systems, such as oceanic islands. This work is a study of photovoltaic potential of public building roofs in the World Heritage city of San Cristóbal de La Laguna in Tenerife (Canary Islands). The buildings selected for the study are sports centres, educational centres and civic centres, considering optimum and maximum production scenarios. The results showed the electricity generation capacity of each building, with 8811 and 861 MWh capacity for the maximum and optimum scenarios, respectively. In the optimal scenario, 56% of energy would be generated, of which 31% would be consumed on site, and the remaining 25% would be sent to the grid. The main difference is that in the case of optimal photovoltaic systems, economic profitability criteria take precedence, while in the case of maximum photovoltaic systems, the environmental benefits in terms of CO 2 emissions into the atmosphere increase considerably. In the latter case, CO 2 emissions are reduced by 771%, compared to 75% in the case of optimal photovoltaic systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid development of photovoltaic generation has brought great challenges to operation and planning of power systems. Based on actual operation data of several photovoltaic power stations, this paper analyzes the fluctuation of photovoltaic generation output first. Then a correlation coefficient based quantitative calculation method for coupling characteristics among multiple photovoltaic power stations is proposed, and the load-following level under various photovoltaic generation capacities is given at last. This paper illustrates that the output of photovoltaic generation presents complementary coupling characteristics, which reduces the fluctuation of the overall output and can deteriorate the load-following level. The results of this paper can provide a theoretical basis for the planning of power systems with a high penetration of renewable energy such as photovoltaic generation.

This paper proposes a novel synergistic balancing control scheme for low-inertia power systems with high photovoltaic (PV) penetration in Tibet power grid (TPG). The mechanism and dispatching principle of choosing the suitable area control error (ACE) control mode for TPG are analyzed., An adaptive control mode switching method is proposed to meet the different control demands for the dynamically changing topologies. Then, we propose a new coordinated control scheme to dispatch hydropower generators and PV power stations. A set of optimum control objectives considering balancing control performance, regulation mileage of conventional generators, and power curtailment of PV are first presented. The control objectives are decomposed and simplified by step-by-step approximation and linearization, which are easily extended to be applied to real-world automatic generation control (AGC) systems. The simulation results show the effectiveness of the proposed solutions for synergistic balancing control by using the equivalent TPG test system. The equivalent system includes 63 generators with total power equal to 1618.5 MW and 51 PV power stations over 1100 MW. In addition, the actual operation cases present that the coordinated control between conventional and renewable generation dispatched by AGC has the basic conditions in the actual operation of the power grids, which will play a more significant role with the rapid increase of renewable generation sources in the future.

The problem regarding the optimal siting and sizing of photovoltaic (PV) generation units in electrical distribution networks with monopolar direct current (DC) operation technology was addressed in this research by proposing a two-stage convex optimization (TSCO) approach. In the first stage, the exact mixed-integer nonlinear programming (MINLP) formulation was relaxed via mixed-integer linear programming, defining the nodes where the PV generation units must be placed. In the second stage, the optimal power flow problem associated with PV sizing was solved by approximating the exact nonlinear component of the MINLP model into a second-order cone programming equivalent. The main contribution of this research is the use of two approximations to efficiently solve the studied problem, by taking advantage of convex optimization models. The numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed approach when compared to multiple combinatorial optimization methods. Two evaluations were conducted, to confirm the efficiency of the proposed optimization model. The first evaluation considered the IEEE 33-bus grid without current limitations in all distribution branches, to later compare it to different metaheuristic approaches (discrete versions of the Chu and Beasley genetic algorithm, the vortex search algorithm, and the generalized normal distribution optimizer); the second simulation included the thermal current limits in the model's optimization. The numerical results showed that when the maximum point power tracking was not regarded as a decision-making criterion, the expected annual investment and operating costs exhibited better performances, i.e., additional reductions of about USD 100,000 in the simulation cases compared to the scenarios involving maximum power point tracking. [ABSTRACT FROM AUTHOR]

With more and more micro-generation being connected to the low-voltage network, it becomes increasingly difficult for operators to anticipate the loading condition of their networks. Several facts concur with such difficulty, one being the frequent lack of knowledge about the generation component of the measured net load. The generation component is typically more volatile than the natural load and is impossible to predict without reliable information on the grid's renewable installed capacity. In this context, the objective of this paper is to propose a non-intrusive methodology to estimate distributed generation' installed capacity. Relying upon net-load historical data obtained at the secondary substation level and on historical meteorological data, the paper presents algorithms that proved effective in determining an approximation of the renewable embedded capacity. The accuracy and effectiveness of the proposed algorithms are illustrated in the paper with real data, for real use cases in the distribution systems. • New non-intrusive methodology to estimate PV capacity installed in LV distribution grids. • Disaggregation of net load profiles into demand and PV generation. • Use of disaggregated profiles for improved LV net-load forecast. [ABSTRACT FROM AUTHOR]

This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time. [ABSTRACT FROM AUTHOR]

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the grid connected and off grid switching control of grid connected inverters, an ac bus-voltage control method based on load impedance characteristics is proposed for single-phase photovoltaic grid-connected inverters with batteries in this paper. The proposed control method includes a detection algorithm for the load impedance angle and an AC bus-voltage control algorithm. Thus, grid-connected inverters can output active and reactive current in proportion to the load impedance characteristics without a special reactive power calculation module to support the AC bus voltage. At the same time, the proposed control method can avoid the switching action of the control algorithm when the grid-connected inverter is switched from grid-connected mode to island mode. Finally, simulation and experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Unmanned systems are increasingly adopted in various fields, becoming an indispensable technology in daily life. Power systems are the lifeblood of unmanned systems, and affect the working time and task complexity. However, traditional power systems, such as batteries and fuels have a fixed capacity. Therefore, once the power supply is exhausted and cannot be replenished in time, the unmanned systems will stop working. Hence, researchers have increasingly begun paying attention to renewable energy generation technologies. The principles, advantages, and limitations of renewable energy generation technologies are different, and their application effects in different unmanned systems are also uneven. This paper presents a comprehensive study of the application and development status of photovoltaic, thermoelectric, and magnetoelectric generation technologies in four kinds of unmanned systems, including space, aviation, ground, and water, and then summarizes the adaptability and limitations of the three technologies to different systems. Moreover, future development directions are predicted to enhance the reliability of renewable energy generation technologies in unmanned systems. This is the first study to conduct a comprehensive and detailed study of renewable energy generation technologies applied in unmanned systems. The present work is critical for the development of renewable energy generation technologies and power systems for unmanned systems. [ABSTRACT FROM AUTHOR]

P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract’s availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior.

Extreme weather conditions and natural disasters (ND) are the main causes of power outages in the electric grid. It is necessary to strengthen the electrical power system's resilience during these catastrophic occurrences, and microgrids may be seen as the best way to achieve this goal. In this paper, two different energy system scenarios were proposed for increasing the resiliency of the electric power system during random outages. In the first scenario, a diesel generator (DG) was used to deliver energy to key loads during grid disruptions, in conjunction with a utility electric grid (UEG) and local electric load (ELL). A grid-connected ad hoc microgrid (MG) with a photovoltaic (PV) system, a battery energy storage (BES) system, and local electric loads made up the second scenario. The PV system and the BES system were used to supply the key loads with electricity during the outage. The major aim of this research was to compare the two resilient-based systems from the perspectives of technology, economics, and the environment. Given that it requires greater resilience than the other loads during severe weather, a hospital load on Indonesia's Lombok Island was chosen as the critical load. The objective function considers the system's predefined constraints to reduce the overall net present cost (NPC) and the cost of energy in order to maximize the system resilience (COE). The Optimization of Multiple Energy Resources (HOMER) Grid simulated a 3-day outage in August 2021, and the results demonstrated that the resiliency enhancement for both scenarios was nearly identical. The first scenario resulted in fewer CO2 emissions; however, the second scenario delivered lower operating costs and COE. The simulation's findings showed that system 1 created an annual emission of 216.902 kg/yr while system 2 only produced an emission of 63.292 kg/yr. This study shows that since RES-based MGs don't burn fossil fuels to generate power, they are more environmentally friendly resources. [ABSTRACT FROM AUTHOR]

The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead dispatch operation scenario. A convex approximation is used which is based on linearization via Taylor's series expansion to the hyperbolic relations between voltages and powers in the demand nodes. A recursive solution methodology is introduced via sequential convex programming to minimize the errors introduced by the linear approximation in the power balance constraints. Numerical results in the DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed convex model when compared to different combinatorial optimization methods, with the main advantage that the optimal global solution is found thanks to the convexity of the solution space and the reduction of the error via an iterative solution approach. Different objective functions are analyzed to validate the effectiveness of the proposed iterative convex methodology (ICM), which corresponds to technical (energy losses reduction), economic (energy purchasing and maintenance costs), and environmental (equivalent emissions of CO 2 to the atmosphere in conventional sources) factors. The proposed ICM finds reductions of about 43.9754 % in daily energy losses, 26.9957 % in energy purchasing and operating costs, and 27.3771 % in CO 2 emissions when compared to the benchmark case in the DC version of the IEEE 33-bus grid. All numerical validations were carried out in the MATLAB programming environment using the SEDUMI and SDPT3 tools for convex programming and our own scripts for metaheuristic methods. [ABSTRACT FROM AUTHOR]

As one of the most prominent renewable energy resources, Photovoltaic (PV) generation has been growing dramatically over the last years and its applications are classified into residential, commercial, and power plant class, based on its power capacity. Especially, a typical residential PV system has been configured with serially connected PV modules. For serially connected PV modules, shading is a critical factor to reduce the whole string PV output power. This paper presents study on performance analysis of a residential PV string under partial shading. This analysis contains how many maximum power points (MPPs) occur and how much power would be lost while a PV string is under partial shading with different shading positions and different shading intensities. PSIM simulation tool is used to verify the performance analysis. As a result, the number of MPPs is directly related with the number of shading intensities, regardless of the shading position. In this paper, it was verified that 8 maximum power points were generated for 8 solar intensities regardless of the location of the shadows under extreme conditions. These results can be utilized for fault diagnosis in the PV string. [ABSTRACT FROM AUTHOR]

In recent years there is the tendency to charge Electric Vehicles (EVs) with clean sources of energy. Due to this tendency, scientific literature focused on problems of locating and sizing Photovoltaic (PV) Charging Stations (CSs). In this paper, we studied the case of the localization and sizing of PV powered CSs in the region of Valle de Aburrá, Colombia, which is the region of the country with the fastest growth of EV adoption. Looking to utilize the largest amount of PV power, we considered level 1 (low-power) PV-grid CSs. The use of other PV charging systems, like level 3 (high-power) charging, would require covering bigger roof areas with PV panels, more than the available on an urban landscape. Considering that this problem takes into account low-power CSs, candidate CS locations are places where people go for extended periods of time, with large roof areas and parking availability. In this problem, the decision variables are the location of the CSs, the amount of charging spots at each selected location, and the size of the PV system. These decisions aim to minimize the added annualized cost of the PV systems, the annualized cost of the charging spots in the selected CSs, and the cost of energy purchased from the grid in a year. For calculating the amount of power that the PV systems are capable of generating, we obtained the typical irradiation curves for each candidate CS location. For solving the studied problem, we propose a Mixed Integer Linear Programming (MILP) formulation. [ABSTRACT FROM AUTHOR]