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Further research is needed on the capability of residential communities to achieve energy self-sufficiency under the constraints of current standards of land use, in particular for the Hot Summer and Cold Winter climate zone (HSCW) of China, where the majority of communities are dominated by high floor-area ratios, thus high-rise dwellings, namely less solar potential per unit floor area, while most residents adopt a "part-time, part-space" pattern of intermittent energy use behavior, thus using relatively low energy per unit floor area. This study examines 150 communities in Changsha to identify morphological indicators and develop a prototype model utilizing the Grasshopper platform. Community morphology is simulated and optimized by taking building location, orientation, and number of floors as independent variables and building energy consumption, solar PV generation, and energy self-sufficiency rate as dependent variables. The results reveal that the morphology optimization can achieve a 4.26% decrease in building energy consumption, a 45% increase in PV generation, and a 13.2% enhancement in energy self-sufficiency, with the optimal being 39%. It highlights that energy self-sufficiency cannot be achieved solely through morphology improvements. Moreover, the study underscores the crucial role of community orientation in maximizing energy self-sufficiency, with the south–north orientation identified as the most beneficial. Additionally, a layout characterized by a horizontally closed and staggered pattern and a vertically scattered arrangement emerges as favorable for enhancing energy self-sufficiency. These findings underscore the importance of considering morphological factors, particularly community orientation, in striving towards energy-self-sufficient high-rise residential communities within the HSCW climate zone of China. [ABSTRACT FROM AUTHOR]

Electrical Distribution Systems (EDS) are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time-series data of active power monitored at nodes of the electrical network. For these correlated networks, compute the network parameters such as clustering coefficient, assortative coefficient, average degree, and power law exponent for the anticipation; and percolation threshold for the determination of the network's withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavorable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using the simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system. [ABSTRACT FROM AUTHOR]

Further research is needed on the capability of residential communities to achieve energy self-sufficiency under the constraints of current standards of land use, in particular for the Hot Summer and Cold Winter climate zone (HSCW) of China, where the majority of communities are dominated by high floor-area ratios, thus high-rise dwellings, namely less solar potential per unit floor area, while most residents adopt a “part-time, part-space” pattern of intermittent energy use behavior, thus using relatively low energy per unit floor area. This study examines 150 communities in Changsha to identify morphological indicators and develop a prototype model utilizing the Grasshopper platform. Community morphology is simulated and optimized by taking building location, orientation, and number of floors as independent variables and building energy consumption, solar PV generation, and energy self-sufficiency rate as dependent variables. The results reveal that the morphology optimization can achieve a 4.26% decrease in building energy consumption, a 45% increase in PV generation, and a 13.2% enhancement in energy self-sufficiency, with the optimal being 39%. It highlights that energy self-sufficiency cannot be achieved solely through morphology improvements. Moreover, the study underscores the crucial role of community orientation in maximizing energy self-sufficiency, with the south–north orientation identified as the most beneficial. Additionally, a layout characterized by a horizontally closed and staggered pattern and a vertically scattered arrangement emerges as favorable for enhancing energy self-sufficiency. These findings underscore the importance of considering morphological factors, particularly community orientation, in striving towards energy-self-sufficient high-rise residential communities within the HSCW climate zone of China.

In the proposed work, a new control is used with inner loop as a current control and the outer loop as a voltage control of a solar photovoltaic (PV) array-fed single-phase grid interfaced system, using a voltage-source converter (VSC). It deals with the power quality mitigation, which includes power factor correction, harmonic mitigation in abnormal conditions such as grid voltage sag and swell and irradiation changes. The PI (Proportional–Integral) controller is used as an inner current loop control for reference and sensed DC link voltage balancing and to generate the command for the harmonics accurately, which generates the reference current. The PI controller is used for generating reference current and PR (Proportional-Resonant) is used for generating voltage reference, which, in turn, produces modulating signal within the limit of carrier wave. A comparison between reference voltage and carrier gives unipolar switching, attaining three levels for the VSC. Here, the maximum power point tracking (MPPT) is achieved by an incremental conductance (INC) method. Experimental performance is studied at dynamic abnormal conditions such as voltage sag and voltage swell in order to investigate the total harmonic distortion (THD) of the grid current and point of common coupling (PCC) voltage, which are observed within the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

In order to minimize losses in the distribution network, integrating non-conventional energy sources such as wind, tidal, solar, and so on, into the grid has been proposed in many papers as a viable solution. Using electronic power equipment to control nonlinear loads impacts the quality of power. The unified power quality conditioner (UPQC) is a FACTS device with back-to-back converters that are coupled together with a DC-link capacitor. Conventional training algorithms used by ANNs, such as the Back Propagation and Levenberg–Marquardt algorithms, can become trapped in local optima, which motivates the use of ANNs trained by evolutionary algorithms. This work presents a hybrid controller, based on the soccer league algorithm, and trained by an artificial neural network controller (S-ANNC), for use in the shunt active power filter. This work also presents a fuzzy logic controller for use in the series active power filter of the UPQC that is associated with the solar photovoltaic system and battery storage system. The synchronization of phases is created using a self-tuning filter (STF), in association with the unit vector generation method (UVGM), for the superior performance of UPQC during unbalanced/distorted supply voltage conditions; therefore, the necessity of the phase-locked-loop, low-pass filters, and high-pass filters are totally eliminated. The STF is used for separating harmonic and fundamental components, in addition to generating the synchronization phases of series and shunt filters. The prime objective of the suggested S-ANNC is to minimize mean square error in order to achieve a fast action that will retain the DC-link voltage's constant value during load/irradiation variations, suppress current harmonics and power–factor enhancement, mitigate sagging/swelling/disturbances in the supply voltage, and provide appropriate compensation for unbalanced supply voltages. The performance analysis of S-ANNC, using five test cases for several combinations of loads/supply voltages, demonstrates the supremacy of the suggested S-ANNC. Comparative analysis was carried out using the GA, PSO, and GWO training methods, in addition to other methods that exist in the literature. The S-ANNC showed an extra-ordinary performance in terms of diminishing total harmonic distortion (THD); thus PF was improved and voltage distortions were reduced. [ABSTRACT FROM AUTHOR]

This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which binary variables define the nodes where the PV generators must be located, and continuous variables are related to the power flow solution and the optimal sizes of the PV sources. The implementation of a master–slave optimization approach is proposed in order to address the complexity of the MINLP formulation. In the master stage, the discrete-continuous generalized normal distribution optimizer (DCGNDO) is implemented to define the nodes for the PV sources along with their sizes. The slave stage corresponds to a specialized power flow approach for monopolar DC networks known as the successive approximation power flow method, which helps determine the total energy generation at the substation terminals and its expected operative costs in the planning period. Numerical results in the 33- and 69-bus grids demonstrate the effectiveness of the DCGNDO optimizer compared to the discrete-continuous versions of the Chu and Beasley genetic algorithm and the vortex search algorithm. [ABSTRACT FROM AUTHOR]

The optimal integration of photovoltaic generation systems is a challenge for distribution utilities since these devices have a direct impact on company finances due to the large amount of investment required at the beginning of the planning project. In this investigation, the problem regarding the optimal siting and sizing of photovoltaic resources in medium-voltage levels is addressed from an economical point of view, where the optimization model that represents said problem corresponds to a mixed-integer nonlinear programming model. The maximum allowed size for single photovoltaic units in the distribution network is set at 2400 kW. The investment costs, energy purchase costs and maintenance costs for photovoltaic units, are considered in the objective function. Typical constraints such as power balance, generation capacities, voltage regulation, among others, are considered in the mathematical formulation. The solution of the optimization model is addressed by implementing a modified version of the Arithmetic Optimization Algorithm, which includes a new exploration and exploitation characteristic based on the best current solution in iteration t, i.e., x best t . This improvement is based on a Gaussian distribution operator that generates new candidate solutions with the center at x best t , which are uniformly distributed. The main contribution of this research is the proposal of a new hybrid optimization algorithm to solve the exact optimization model, which is based on a combination of the Arithmetic Optimization algorithm with the Vortex Search algorithm and showed excellent numerical results in the IEEE 34-bus grid. The analysis of quantitative results allows us to conclude that the strategy proposed in this work has a greater effectiveness with respect to the General Algebraic Modeling System software solvers, as well as with metaheuristic optimizers such as Genetic Algorithms, the Newton–Metaheuristic Algorithm, and the original Arithmetic Optimization Algorithm. MATLAB was used as a simulation tool. [ABSTRACT FROM AUTHOR]

Wind and solar energy systems are among the most promising renewable energy technologies for electric power generations. Hybrid renewable energy systems (HRES) enable the incorporation of more than one renewable technology, allowing increased reliability and efficiency. Nevertheless, the introduction of variable generation sources in concurrence with the existing system load demand necessitates maintaining the power balance between the components of the HRES. Additionally, the efficiency of the hybrid power supply system is drastically affected by the number of converters interfacing its components. Therefore, to improve the performance of the HRES, this paper proposes a robust sliding mode control strategy for both standalone and grid-connected operation. The control strategy achieves maximum power point tracking for both the renewable energy sources and stabilizes the DC-bus and load voltages irrespective of the disturbances, change in load demand, variations of irradiance level, temperature, and wind speed ensuring an efficient energy management. Furthermore, the solar PV system is directly linked to the DC-bus obviating the need for redundant interfacing boost converters with decreased costs and reduced system losses. Lyapunov candidate function is used to prove the asymptotic stability and the convergence of the entire system. The robustness of the proposed control strategy is tested and validated under various conditions of HRES, demonstrating its efficacy and performance under various conditions of the HRES. [ABSTRACT FROM AUTHOR]

This paper presents a framework to determine the feasible dispatch limits of solar photovoltaic (PV) generation in the unbalanced distribution network considering the interconnection of electric vehicles (EVs) and associated uncertainties. The proposed framework determines the lower and upper dispatch limits of PV generation considering the worst-case realization of a) the minimum and maximum storage capacity of EVs, b) the minimum and maximum power dispatch of EVs, c) the lower and upper bounds for the arrival and departure times, and d) the available energy at arrival and departure times. The unbalanced operation of the distribution network as well as the uncertainty in the maximum PV generation and demand forecasts were considered. The problem formulation and solution approach are validated using the modified IEEE 34-bus and IEEE 123-bus distribution systems. The impacts of the budget of uncertainty and the vehicle-to-grid operation mode of EV clusters were addressed in the case studies. It is shown that integrating EVs with charging capability will increase the lower dispatch limit or increase the upper dispatch limit of the PV generation. Moreover, the increase in the budget of uncertainty will reduce the difference between the upper and lower dispatch limits by increasing the lower dispatch or decreasing the upper dispatch limit of the PV generation. Finally, it is shown that the vehicle-to-grid capability will reduce the total lower dispatch limit or increase the total upper dispatch limit of the PV generation in the operation horizon. [ABSTRACT FROM AUTHOR]

In this article, the control of a grid-connected, single-phase, dual-stage photovoltaic (PV) residential system with electric vehicle (EV)-charging capability is being presented. A residential system faces a lot of dynamic conditions such as changing atmospheric condition or random patterns of load connection or disconnection as well as power failure. In this regard, this article proposes the optimization of proportional and integral gains selection of the bidirectional DC/DC converter (BDC) PI controller through golden eagle optimization (GEO) algorithm to improve the system performance. The system is subjected to various induced dynamic conditions simulated in MATLAB Simulink environment and the performance is compared with particle swarm optimization, genetic algorithm, and gains obtained through hit and trial. In comparison with other techniques, GEO algorithm provides optimum BDC PI gains, as a result, the DC-link voltage achieves 1.5 times shorter settling time. During the non-linear load changes and voltage swell condition, the settling time is 2 times shorter. Settling time of the EV battery power shows 1.7 times reduction. The transient response of the PV power also shows dramatic improvement with 3 times faster settling time during solar insolation changes. During the sudden non-linear load changes, the momentary transients die 1.5 times faster. Moreover, a seamless control is developed to achieve a seamless transition from islanded mode to grid connected mode and vice versa. The proposed system achieves unity power factor and complies with the IEEE -519 power quality standard. [ABSTRACT FROM AUTHOR]

The prime facets for the control of grid integrated voltage source converters (VSC) during abnormal grid variations are the control of voltage as well as power quality. In this article, a unique control strategy is presented for the control of solar photovoltaic (PV) system interfaced to the grid utilizing an interweaved generalized integrator. A single-stage three-phase topology is considered. The primary purpose of control is to deliver the PV power to the grid even during various abnormal grid variations. During normal operation, the system delivers power at unity power factor. However, during variations in the grid voltage, the profile of the PCC voltage is maintained within prescribed limits by reactive power injection. Moreover, LVRT operation is undertaken during severe voltage sags. The utilization of the system is increased in the absence of PV generation during night, the VSC and dc link capacitor act as a distribution static compensator. Contrary to traditional control techniques, power quality of the system is not compromised. The achievements of the control are demonstrated through simulation as well as with hardware implementation. Furthermore, a comparative analysis, with the state-of-the-art techniques, is highlighted, which shows the efficacy of the presented control. [ABSTRACT FROM AUTHOR]

This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which binary variables define the nodes where the PV generators must be located, and continuous variables are related to the power flow solution and the optimal sizes of the PV sources. The implementation of a master–slave optimization approach is proposed in order to address the complexity of the MINLP formulation. In the master stage, the discrete-continuous generalized normal distribution optimizer (DCGNDO) is implemented to define the nodes for the PV sources along with their sizes. The slave stage corresponds to a specialized power flow approach for monopolar DC networks known as the successive approximation power flow method, which helps determine the total energy generation at the substation terminals and its expected operative costs in the planning period. Numerical results in the 33- and 69-bus grids demonstrate the effectiveness of the DCGNDO optimizer compared to the discrete-continuous versions of the Chu and Beasley genetic algorithm and the vortex search algorithm.

In this article, a methodology for implementation of an automated transition of a solar PV array and battery integrated unified power quality conditioner (PV-B-UPQC) between standalone and grid connected modes of operation is presented and analyzed. This system consists of a shunt and series active filters connected back to back with a common dc-link. The system addresses the issue of the integrating power quality improvement along with the generation of clean energy. Moreover, due to the automated transition, the critical loads have continuous power supply irrespective of grid availability. The key challenges addressed are implementation of automated transition in a PV-B-UPQC system with minimal disturbance to the local loads. The system operation is validated through experimentation under a number of dynamic conditions such as automated transition, supply voltage variations, unavailability of the grid, variation in solar power generation, load variation, etc., which are typically encountered in a modern distribution network. [ABSTRACT FROM AUTHOR]

This work deals with a harmonic control strategy for a transformerless double stage solar energy conversion system (SECS) to alleviate the leakage current in the presence of nonlinear loads enabling harmonics suppression capability. In this SECS, the PV module stray capacitance can elicit leakage current, which causes electromagnetic interferences and potential safety problems. To address these issues, the model predictive controller based adaptive harmonic compensation strategy is presented herein, which not only provides leakage current suppression but it also serves manifold ancillary power quality improvement features. The revised IEEE-519-2014 standard is incorporated in adaptive harmonic compensation strategy to achieve harmonics within recommended limits. The comparative response with a conventional control is exhibited to verify the capabilities of harmonic control strategy. Furthermore, a comparative harmonic suppression capability is analyzed herein to validate the competence of voltage controller. Various real-time simulated results are discussed to manifest capabilities of this control. The leakage current is restricted below 300 mA as per desirable safety requirements against leakage current of VDE-00126-01 and IEC-62109-2 standards. The total harmonic distortion (THD) of grid current and voltage, are achieved within permissible limits in compliance with the revised IEEE-519-2014 code. [ABSTRACT FROM AUTHOR]

The intermittent nature of renewable energy sources results in an interruptible and inadequate power. The integration of the battery energy storage to renewable energy sources provides reliable and continuous power to the loads. Further, the high penetration of renewable energy sources raises the power quality problems in the grid. This work deals with performance improvement of PV‐battery energy storage based energy conversion system achieved through implementing a multi‐structural adaptive circular noise estimation control for its grid connected mode. The control is developed for the grid side voltage source converter, which provides the power quality improvement and automated transition from grid connected mode to standalone mode or vice‐versa. It provides multi‐functional features such as harmonics extraction, DC offset elimination, and power quality improvement in PV‐battery energy storage based energy conversion system even at nonlinear loading condition in grid connected mode. In standalone mode, the amplitude and waveform of load voltage are controlled sinusoidal by the voltage control. Performance of PV‐battery energy storage based energy conversion system is studied to validate the acceptability of these controls according to the IEEE‐519 standard. [ABSTRACT FROM AUTHOR]

This article deals with a dual second-order generalized integrator phase-locked loop (DSOGI-PLL) with in-loop filter-based control approach for a single-stage, three-phase three-wire solar-grid-interfaced system under abnormal grid voltage conditions, unbalanced load conditions, and varying solar insolation levels. The DSOGI with in-loop filter algorithm with enhanced filtering capability, employed for both voltages and currents, helps to attenuate the harmonics, eliminates dc offset, and estimates the sequence components. This algorithm elicits the fundamental component of highly nonlinear load current required for calculating the reference magnitude of the grid currents. Even during unbalanced load conditions, these fundamental components of currents are free from the dc offset and dominant harmonics of double frequency. In order to maintain the sinusoidal and balanced grid currents, the positive sequence voltages are estimated to get the accurate unit templates during the unbalanced and abnormal grid voltages conditions. Moreover, these positive sequence voltages are used by PLL to compute the phase required for the magnitude and the angle calculation of the currents and voltages. The dc-link voltage is maintained at maximum power point by an incremental conductance based technique. This system is simulated in MATLAB/Simulink environment. Test results on a developed laboratory prototype are observed in accordance with the standard of the IEEE-519. [ABSTRACT FROM AUTHOR]

More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this paper proposes the combination of a two-step neural network bi directional long short-term memory (BD-LSTM) model with an artificial neural network (ANN) model using exponential moving average (EMA) preprocessing. In this study, four types of historical input data are used: hourly PV generation for one week (168 h) ahead, hourly horizontal radiation, hourly ambient temperature, and hourly device (surface) temperature, downloaded from the Korea Open Data Portal. The first strategy is employed using the LSTM prediction model, which forecasts the SPVG of the desired time through the data from the previous week, which is preprocessed to smooth the dynamic SPVG using the EMA approach. The SPVG was predicted using the LSTM model according to the trend of the previous time-series data. However, slight errors still occur because the weather condition of the time is not reflected at the desired time. Therefore, we proposed a second strategy of an ANN model for more accurate estimation to compensate for this slight error using the four inputs predicted by the LSTM model. As a result, the LSTM prediction model with the ANN estimation model using EMA preprocessing exhibited higher accuracy in performance than other options for SPVG. [ABSTRACT FROM AUTHOR]

This article deals with the multimode operation of a photovoltaic (PV) array, a battery, the grid and the diesel generator (DG) set-based charging station (CS) for providing the continuous charging and uninterruptible supply to the household loads. In this CS, a single voltage source converter operates the CS in an islanded mode, the grid connected mode and the DG set connected mode (DGM) and performs various tasks, such as power management among different energy sources and charging the electric vehicles (EVs), extraction of maximum power from the PV array, the regulation of voltage and frequency of the generator, harmonics current compensation of nonlinear loads and intentional reactive power compensation. The control of charging station (CS) is designed such that it primarily takes power from the PV array and a storage battery. In the absence of these two sources, the charging station takes power from the grid, and at last, it utilizes a squirrel cage induction generator-based DG set. However, the DG set is operated such that it generates up to 33% more power than its rated capacity without exceeding the rated current in windings, therefore, the size of the DG is reduced. Moreover, the voltage and frequency of the generator are regulated at its rated values without a mechanical speed governor. In all operating modes, the CS complies with the IEEE 1547 standard and the total harmonic distortion of voltage and current, is achieved less than 5%. [ABSTRACT FROM AUTHOR]

The hybrid renewable energy conversion systems and their increased penetration into the utility grid are intensifying the power quality (PQ) issues especially in the form of increased total harmonic distortion of voltages and currents at point of common coupling. The objective of the proposed grid-tied wind–solar photovoltaic (PV) energy conversion system is to analyze PQ issues and to mitigate them by utilizing the unbiased circular leakage centered (UCLC) adaptive filtering control. An implementation of UCLC adaptive control improves the PQ indices and system performance by overcoming the intermittency issues associated with solar and wind energies. UCLC adaptive control effectively extracts the fundamental load current component and mitigates the grid current harmonics. It has simple structure, enhanced convergence rate, and better performance with noise corrupted input and output signals. It effectively resolves the weight drift problem depicted by the conventional least mean square (LMS) control and leaky LMS control algorithm by avoiding biased estimates. The averaging theory and the deterministic stability analysis provide the relying facts of the performance of UCLC adaptive control. The extraction of maximum power from solar PV array energy and wind generation is carried out by perturb and observe scheme. A prototype is made in the laboratory and verified for environmental variations of solar insolation level, wind speed, and perturbing load demand. The PQ issues are effectively alleviated. Test results confirm the effective performance of the proposed system. [ABSTRACT FROM AUTHOR]

Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.

Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data.

More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this paper proposes the combination of a two-step neural network bi directional long short-term memory (BD-LSTM) model with an artificial neural network (ANN) model using exponential moving average (EMA) preprocessing. In this study, four types of historical input data are used: hourly PV generation for one week (168 h) ahead, hourly horizontal radiation, hourly ambient temperature, and hourly device (surface) temperature, downloaded from the Korea Open Data Portal. The first strategy is employed using the LSTM prediction model, which forecasts the SPVG of the desired time through the data from the previous week, which is preprocessed to smooth the dynamic SPVG using the EMA approach. The SPVG was predicted using the LSTM model according to the trend of the previous time-series data. However, slight errors still occur because the weather condition of the time is not reflected at the desired time. Therefore, we proposed a second strategy of an ANN model for more accurate estimation to compensate for this slight error using the four inputs predicted by the LSTM model. As a result, the LSTM prediction model with the ANN estimation model using EMA preprocessing exhibited higher accuracy in performance than other options for SPVG.

Energy crisis and energy cost are always embedded with the educational institutions which significantly influences the scope for the sustainable growth of the organisation. Moreover, the increasing gap between energy demand and energy supply has become a global issue. The electrical power generation from the solar photovoltaic (PV) is the area of interest of many researchers in the recent past. However, the cost of energy is always a constraint for distributed renewable generation from solar PV. Based on the addressed issues, this study proposes a hybrid scheme consists of utility and solar PV for institutional buildings in a cost‐effective manner. This study identifies the scope for appropriate augmentation of solar PV with the existing power utility for minimum overall energy cost. Moreover, the proposed scheme is environmental friendly and can be extended to other utility based load centres with similar type of power demand characteristics. [ABSTRACT FROM AUTHOR]

• The extended proportional complex filter (EPCF) control allows a fast estimation of the quadrature and in-phase fundamental load current components with reduced harmonics within the prescribed limit. • The EPCF-based control has extracted the fundamental component of load current with high rate of convergence and better system response. The variations on load, utility, and solar array radiation are well established in simulation and test results. The PQ is enhanced at unbalancing of load, change in solar insolation and grid adverse conditions of voltages unbalances. The performance of the system is found satisfactory and its response is enhanced at adverse grid conditions. Experimental results have demonstrated that the THD are as recommended by the IEEE-519 standard even during weak grid conditions. • A comparative analysis is included, which validates the superior filtering capability and improved power quality during weak grid conditions. In addition a tabular comparative analysis is included to support its effectiveness while addressing abnormalities of the grid. • The EPCF-PLL grid synchronization and phase angle estimator scheme has provides fast mode transition, frequency tracking and uninterrupted supply to the load during non availability of the grid. This has given precise phase angle estimation. Conventional PLL controllers are non-adaptive towards large frequency. EPCF-PLL has provided effective frequency adaptability. This makes the synchronization process immune from frequency variation and voltage unbalance. • The proportional resonant controller with harmonic compensation (PRHC) controller provides satisfactory performance under all the intermittent conditions overcoming the tradeoff between improved steady-state and transient performance. This paper presents a solar photovoltaic (PV) based energy conversion system with seamless transfer to the grid. To provide satisfactory performance in grid connected mode, a control based on extended proportional complex filter (EPCF) is used. The EPCF is used to estimate the fundamental current component (LCFC) of local loads At the linkage point (LP), the power quality (maintaining IEEE-519 standard) is improved using this control. The EPCF maintains balanced grid currents whether PV array generation is available or not. It provides effective mitigation of harmonics and monitors the flow of active power, which improves the grid current quality.This control provides versatile actions such as extraction of LCFC, harmonics elimination, DC offset removal, robust performance during voltages unbalance etc. The same control technique (EPCF) along with phase locked loop is used for smooth synchronization; it reduces the use of another algorithm, which improves the dynamic response of the system. This control estimates exact phase and frequency variation for the seamless mode switching. It provides frequency adaptation during mode shifting. The comparative results validate that the control based on EPCF has a faster detection of grid in comparison with existing controls. During the grid-outage, the proportional resonant (PR) controller with harmonics compensation (HC) based control algorithm enhances performance of the microgrid. This provides successful solution for harmonics compensation and minimizing steady state errors. Simulated and experimental results, in various scenarios, have verified the effectiveness of the controllers and demonstrate the favourable solution in controlling the unfavorable grid conditions. [ABSTRACT FROM AUTHOR]

Reliable and inexpensive electricity provision is one of the significant research objectives since decades. Various Economic Dispatch (ED) methods have been developed in order to address the challenge of continuous and sustainable electricity provision at optimized cost. Rapid escalation of fuel prices, depletion of fossil fuel reserves and environmental concerns have compelled us to incorporate the Renewable Energy (RE) resources in the energy mix. This paper presents Combined Emission Economic Dispatch (CEED) models developed for a system consisting of multiple Photo Voltaic (PV) plants and thermal units. Based on the nature of decision variables, our proposed model is essentially a Mixed Integer Optimization Problem (MIOP). Particle Swarm Optimization (PSO) is used to solve the optimization problem for a scenario involving six conventional and thirteen PV plants. Two test cases, Combined Static Emission Economic Dispatch (SCEED) and Combined Dynamic Emission Economic Dispatch (DCEED), have been considered. SCEED is performed for full solar radiation level as well as for reduced radiation level due to clouds effect. Simulation results have proved the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• Deterministic approach for evaluation of solar PV hosting capacity subjected to over-voltage curtailment in LV networks. • Solar PV hosting capacity limits in an LV feeder or network based on deterministic studies. • Nomographic tool for feeder based solar PV hosting capacity assessment in LV networks. • Three stage solar PV connection criteria for LV distribution networks. Proliferation of solar photovoltaic (PV) generation in low voltage (LV) distribution networks has imposed a set of challenges in network operation and control. Voltage rise is currently the main constraint that limits solar PV capacity increase in LV networks. Together with this, there is a growing need for a generalised and versatile tool which utilities can use to deal with customer requests for new solar PV connections. This paper proposes a generalised approach to assess solar PV hosting capacity (HC) subjected to over-voltage curtailment based on a Nomogram representation, which facilitates reasonable modeling insights for HC assessment in LV networks. In addition, solar PV connection criteria are further developed using the Nomogram representation of HC evaluation. The proposed Nomogram based approach for HC assessment and connection criteria will contribute to further improvement of available guidelines on solar PV connections in LV networks. [ABSTRACT FROM AUTHOR]

Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model. [ABSTRACT FROM AUTHOR]

Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data. [ABSTRACT FROM AUTHOR]

This paper presents the results from a real residential field test in which one of the objectives was to maximize the instantaneous self-consumption of the local photovoltaic production. The field test was part of the REnnovates project and was conducted in different phases on houses in several residential districts located in Soesterberg, Heerhugowaard, Woerden and Soest, the Netherlands. To maximize self-consumption, buffered heat pump installations for domestic hot water and stationary residential battery systems were chosen due to their respective thermal and electrical storage capacities. The algorithm used to tackle the associated sequential decision-making problem was model-based reinforcement learning. The proposed algorithm learns the stochastic occupant behavior, uses predictions of local photovoltaic production and considers the dynamics of the system. The results show that this algorithm increased the average self-consumption percentage of the local PV generation (used instantaneously in situ) on average by 14%, even if only buffered heat pump installations for domestic hot water were used. This increase was achieved without causing any perceived discomfort to the residential end users. The average energy shifted per day from the solar production period to the night by the 2 kW/3.6 kWh batteries was 1.5 kWh. The main contribution of this work was therefore the real field implementation of the proposed algorithm. The results demonstrate that it is possible to improve even further the integration of local production using flexible loads. [ABSTRACT FROM AUTHOR]