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This paper presents a customized local energy trading approach using a comprehensive set of performance indices (i.e. Community Savings, Self-Consumption, Self-Sufficiency, Fairness Index, Individual Household Savings) for evaluation of the trading practices. The simulations performed on historical data sets compared dispatch mechanisms based on Mixed Integer Linear Programming (MILP) and techniques originating from Auction-Based approaches. The intent was to use performance metrics in building Local Energy Market (LEM) trading solutions based on a given set of limited inputs and use them to optimize pricing or trading dispatch to get the best consumer returns. Modeling results indicate that the Auction-Based Model performed better than the MILP methods in terms of all performance indices and had potential to provide participants with higher levels of savings.

Over the past decade, the landscape of distribution network hosting capacity has been extensively explored through numerous studies. However, a notable gap remains in the absence of a standardised and universally accepted generic definition. Bridging this gap, this paper provides a comprehensive and unified definition of the hosting capacity concept for distributed energy resources (DERs). To illustrate the presented definition, a practical demonstration using a basic illustrative example is provided. Additionally, this paper aligns the most impactful papers in the field, as well as select literature articles, with the proposed definition. This comparative analysis showcases the versatility and applicability of the definition across a spectrum of research contributions. This paper seeks to establish a shared foundation for the determination of hosting capacity that can guide future research.

With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments.

Solar panels play a crucial role in converting solar energy into electricity, with PhotoVoltaic (PV) modules being their core components. To ensure solar panels function well, efficient and accurate defect detection of PV modules is essential. Visual-based deep learning detection methods, such as Transformer and Convolutional Neural Network (CNN) models, provide a cost-effective and adaptable solution. Although the Transformer-based detectors outperform CNN-based ones in detection tasks, their complicated model structures also lead to great computational cost and latency, limiting their application in resource-constrained environments or time-sensitive tasks. To improve the real-time performance of the DEtection TRansformer (DETR) model on these edge devices, a novel Lightweight Decoder-only DETR (LD-DETR) is proposed for defect detection of PV modules, which achieves high performance and low computational cost with an easy architecture. We remove the resource-intensive Transformer Encoder structure from the DETR baseline so that the Decoder can directly utilize the finely fused features produced by a specialized lightweight convolution module that includes an upsample operation to preprocess the features. Experiments demonstrate that the proposed LD-DETR achieves competitive performance on the PV cell dataset of ElectroLuminescence (EL) images with an accuracy of 87.4% and an inference time of 6.1ms on GPU as well as 1.1s on embedded CPU. Our source code is available at https://github.com/SeveralYang/LD-DETR.

This paper proposes a solar tracker design as both a solar energy harvester and a rainwater collector on the hybrid of photovoltaic-pumped hydro storage (PV-PHS) system design in small scale, called a solar tracker-rainfall collector (STRC). The system proposed in this work is designed for implementation in a high-rise building. Increasing the storage energy of the PHS system is the objective of the system design in this study. The STRC system consists of six PV panels. During the day, when the sun is shining, the solar tracker works to harvest solar energy by positioning the panel according to the position of the sun. Meanwhile, during rainy or nighttime hours, the solar tracker configuration will form a funnel to collect rainwater so that it flows into the upper tank of the PHS system. Simulation results to validate the system design show that the PHS system with STRC is able to store 24.78% more total energy than the PHS system without STRC in a two-day test, with the average stored energy of the PHS system with STRC being 24.84% higher than the PHS system without STRC. Meanwhile, for a one-month test in January 2020, it was found that the total energy stored in the PHS system with STRC was 25.10% greater than in the PHS system without STRC, with the average stored energy in the PHS system with STRC increasing by 25% compared to the PHS system without STRC.

Renewable energy systems that depend on photovoltaic energy are crucial in the shift towards sustainable energy. Detecting faults in photovoltaic components is highly valued. However, most research on fault detection relies on detailed electricity data for each panel of the photovoltaic station. In contrast, our study introduces a panel-contact-free algorithm for diagnosing faults using only whole station parameters. This algorithm enables the overall detection of panel faults, reducing the number of necessary sensors to only the irradiance detector while maintaining high accuracy in fault locating and classifying. Using fewer sensors minimizes the impact of power plant modifications and reduces overall transformation costs. Our study, which used actual PV power plant data for modeling, achieved a specific fault diagnosis accuracy of 93.18%, indicating strong potential for practical application and value.

The high proportion of distributed photovoltaic and electric vehicle access has a great impact on the distribution network. For hydropower-rich areas, the “hydropower, photovoltaic, storage and charging” collaborative planning method can effectively utilize and tap the flexible adjustment potential of small hydropower and energy storage to solve this problem. First, with the goal of optimizing economic cost, charging distance and system carbon emissions, a “hydropower, photovoltaic, storage and charging” distribution network collaborative optimization planning model is established. Secondly, the trip chain method is used to predict the charging demand of electric vehicles. The power output curve of a typical day is obtained using the K-means clustering algorithm and the hierarchical analysis method. The non-dominated sorting genetic algorithms II (NSGA-II) with elite strategy is used to solve the multi-objective model to obtain the Pareto solution set. The optimal ideal solution is used for screening to obtain the optimal planning scheme under different scenarios. Then, a comprehensive evaluation is carried out on the optimal planning schemes of different scenarios. Finally, a case analysis is carried out using the IEEE 33-bus system as an example. The simulation optimization results show that the proposed method can take into account the multi-dimensional evaluation indicators of the planning scheme, including economy, sociality, low carbon and reliability.

The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme, has made the PDS prone to various cyber threats, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven framework to detect FDI attacks against voltage regulation of PV-integrated PDS. Initially, an attack-free system is modeled along with its voltage regulation scheme, where the grid measurements are sent to a centralized controller and the control signals are transmitted back to PVs to be used by their local controllers. Then, a convolutional neural network (CNN) framework is proposed to detect FDI attacks. To train this framework—which should be able to distinguish between normal grid behaviors and attacks—a complete and realistic dataset is formed to cover all normal conditions and unpredictable changes of a PDS during a year. Since normal variations and fluctuations in power consumption lead to changes in the voltage profile, this dataset is enriched using features such as season, weekdays, weekends, load conditions, and PV generation power. The performance of the trained framework has been compared with other supervised Machine Learning-based and deep-learning techniques for FDI attacks against modified IEEE 33- and 141-bus PDSs. Simulation results demonstrate the superior performance of the proposed framework in detecting FDI attacks.

Considering photovoltaic systems’ sustainability and environmental friendliness, they have been widely used due to ease of installation as their cost reduces and their efficiency is improved. Analytical maximum power point tracking methods for photovoltaic system work effectively under uniform weather conditions. However, they may fall into local maximum power points due to partial shading conditions. Although numerous meta-heuristic methods can overcome these challenges, they can still be improved regarding the convergence time to the global maximum power point. This paper suggests an improved grey wolf optimization method to track global maximum power points, enhancing the convergence process and efficiency under various weather conditions. The proposed method has been verified experimentally under dynamic and real weather conditions, consisting of uniform and non-uniform weather conditions. The method provides better dynamic tracking speed and efficiency up to 82% and 1.4% compared to the basic grey wolf optimization. According to the daily performance evaluation, the IGWO reduces the runtime by up to 76% and improves energy harvesting up to 2.3% compared to basic grey wolf optimization. The obtained results validate the superiority of the method compared under partial shading conditions in terms of tracking time and accuracy.

Given the significant impact of the UN 2015 agenda on sustainable development, the search for eco-friendly and efficient energy solutions has intensified. The renewable based hybrid systems have emerged as feasible solution in this context. This paper explores the potential of utilizing a hybrid energy system (HES) from a distinctive perspective — addressing the challenges of load shedding at the distribution level. The HES configuration in this investigation includes photovoltaic (PV) array, wind turbines (WT), battery storage unit (BSU) and diesel generator system. The research involves simulations, optimization, and sensitivity analysis for a residential community in the southwestern part of Pakistan, which frequently experiences load shedding. Grasshopper optimization algorithm (GOA) is applied to simultaneously minimize the levelized electricity cost (LEC), payback period (PBP), and loss of power supply probability (LPSP). Simulations compare different HES configurations based on the estimation of local renewable energy potential and the load shedding schedule of the utility company. The optimal solution, with $N_{PV}=110$ , $N_{WT}=2$ , and $N_{BSU}=16$ , was selected, resulting in a minimum LEC of 6.64 cents/kWh and a PBP of 7.4 years. These results are validated using the particle swarm optimization algorithm (PSO). The performance of HES is benchmarked against conventional solutions, such as standalone diesel generators, battery-based uninterruptible power supplies (UPS), and combinations of generators and UPS. The findings demonstrate the significant superiority of HES over conventional solutions in terms of reduced LEC, shorter PBP and reduced carbon emissions. Finally, the sensitivity analysis examines the impact of varying component prices, feed-in tariff rates, meteorological conditions, and load demand variations on the LEC and PBP of the HES.

As photovoltaic (PV) systems have been successfully adopted worldwide, accurate power generation forecasting becomes increasingly essential to stable power grid operation and smart grid applications to cope with the variability of PV systems. Several data-driven models have recently been proposed for the more accurate prediction of PV power generation and have shown good performance. In particular, hybrid models that combine the characteristics of single-structure deep learning-based models have achieved better accuracies. To this end, a novel ultra-short term PV power generation forecasting model with a hybrid structure is proposed for instantaneous response to PV fluctuations. For higher forecasting accuracy, the proposed model decomposes the input feature data into trend and residual components and employs customized sub-models such as the linear, Transformer, and long short-term memory (LSTM). Furthermore, the proposed model is trained with data from the self-built PV site to implement a model suitable to real-world applications. Finally, the experimental results demonstrate that the proposed model has the best forecasting performance compared to conventional and state-of-the-art deep learning-based forecasting models with reasonable computational complexity.

The most versatile resource for storing energy is one that can rapidly charge or discharge while supporting the use of renewable energy. As renewable energy sources advance rapidly, batteries play a pivotal role in this progress. When integrating battery energy storage into a renewable energy system, it’s crucial to address the issue of battery degradation while implementing operational strategies. Furthermore, since solar irradiation varies due to changing cloud conditions, it can impact how batteries charge and discharge. This study focuses on investigating battery degradation and lifetime. Experimental work is being conducted with lead acid batteries connected to a solar photovoltaics system. The paper provides a detailed investigation of commonly used methods for predicting battery lifespan. It also analyzes aspects such as the effects of depth of discharge (DoD) and battery charge/discharge on temperature changes due to degradation. Using the coarse average approach, global battery aging, weighted Ah aging method, and RFC method, this paper estimates the DoD, temperature, life cycle loss (%), and lifespan and evaluates the extent of battery degradation. The battery lifespan is estimated using this method to be 8.42, 8.72, 8.33, and 8.93 years, respectively.

This research work proposed an optimal hybrid microgrid design for Riphah International University (RIU), Lahore, Pakistan, that ensures a continuous and affordable energy supply by harnessing reliable energy sources. The design factors include the various considerations such as energy resource availability, environmental sustainability, and financial viability. The proposed hybrid renewable energy system integrates the national grid, solar photovoltaic (PV) technology, a battery bank, and a converter to fulfill the energy requirements of RIU. In order to develop the most efficient design, various factors are taken into account, including electrical load, solar irradiation, ambient temperature on campus, lifespan and efficiency of PV modules, degradation factor, cost considerations, battery longevity, energy prices from the national grid, occurrences of load shedding, and the environmental impact in terms of toxic emissions. The net present cost (NPC) factor is also considered to make the design more cost effective. The simulations conducted using HOMER Pro software yielded 932 possible solutions, among which the proposed hybrid microgrid design emerged as the most viable with an NPC of ${\$}$ 0.483 million and 99.3% renewable energy utilization. The worst design, which utilized a national grid and diesel generator, resulted in an NPC of ${\$}$ 1.89 million with 0% renewable energy integration. The proposed hybrid microgrid design among 932 models established by HOMER Pro software using PV connected to a national grid, and a storage system represents the most optimal solution for RIU Lahore Campus by considering all the necessary factors and requirements.

The power loss in the arrays due to non-uniform irradiances in the site is the most common scenario observed in a PV system. The irradiance non-uniformity occurs due to obstacles between actual irradiance and modules caused by dust and shadow of clouds, trees, and buildings in some portions of the array. This results in complications like mismatch, power reduction, deformed characteristics curves, failure of power tracking algorithms, and sometimes physical damage to the PV modules. Numerous solutions are present for mitigating the above complications among which the array reconfiguration gained a huge audience for ease of implementation, lower cost, and higher reliability but, each technique exhibits certain drawbacks. In this paper, a harmony search reconfiguration (HSR) algorithm for dynamic reconfiguration is proposed that has advantages in terms of simplicity, adaptability, convergence speed, reduced switch count, and arbitrary array application. The modeling and validation are done in the MATLAB platform using $5 \times 5$ , $9 \times 9$ , $9 \times 5$ , and $3 \times 3$ arrays under various shading cases and compared with the 22 existing conventional, static, and dynamic techniques. The depth investigation shows the higher performance of the HSR with 24.64% and 12.28% higher performance than conventional and static techniques with lowest actual power deviation of −1.10% and equivalent performance to that of existing techniques with reduced complexities.

Conventional perturb and observe (P&O) algorithm fails to track global maximum power point (GMPP) under complex partial shading conditions (PSC) in photovoltaic (PV) system. While many of the latest maximum power point tracking (MPPT) algorithms are designed to handle simpler PSCs with fewer peaks, their capability to handle highly complex PSCs remains uncertain. This study presented more practical, challenging, and complex PSCs that have over five peaks and extremely close peak values. A new deterministic peak hopping (PH)-based MPPT algorithm with simple mechanisms is proposed to address these complex PSCs. An agent is utilized to scan and hop between the lower and higher duty cycle regions of P-V curve with optimum step size, thereby effectively narrowing down the tracking region, moving towards the GMPP. Additionally, the proposed algorithm utilizes an adjustable sampling time during scanning and hopping process to accelerate the convergence. Extensive simulation studies have revealed the effectiveness of the proposed algorithm in tracking GMPP. Moreover, the proposed algorithm outperforms five of the latest MPPT algorithms. In experimental setup, the proposed algorithm is successfully implemented into real-time TI C2000 microcontroller and performed robustly using ITECH IT6012C-800-40 PV simulator, achieving tracking time shorter than 0.83s and tracking accuracy over 98.70%.

The escalating demand for environmentally sustainable energy sources in response to climate change necessitates the expansion of renewable power generation, particularly in the field of photovoltaic (PV) technology. Continuous advancements in PV power generation have substantially reduced production costs and chronic initial defects in PV modules. Effective PV power generation requires meticulous consideration of the installation structures and site selection processes. However, the identification of suitable power-generation locations often encounters challenges associated with civil complaints and environmental impediments. Floating Photovoltaic (FPV) systems positioned on water bodies have emerged as a promising solution for mitigating the environmental concerns associated with ground-based installations. This study endeavors to scrutinizes the reliability of PV modules within water-based FPV systems by leveraging climate data for analysis. Module deformation is forecasted through material simulations, and the output values are estimated. An empirical pressurization experiment validates the actual alterations in the output values during a defect, thereby explaining the potential defect types. Considering the escalating investments in FPV power generation facilities, this study anticipates and assesses the potential defects and problems arising from natural disasters. The objective is to provide a robust predictive model for FPV power generation, encompassing dynamic simulations based on material properties and the outcomes of mechanical design of experiments on PV modules. The proposed methodology aims to provide scholarly insights into the judicious selection of FPV installation structures and optimal sites in placid water conditions and in circumstances characterized by strong winds and high oceanic waves. The proactive analytical framework enhances the decision-making processes concerning the installation and operation of FPV systems.

This study examines the challenges encountered when using wireless technologies based on random multiple access for voltage monitoring in low voltage (LV) electrical grids. The introduction of photovoltaic modules on the roofs of buildings creates the need to monitor the dynamics of the node voltages of the grid. We consider two related objects – an LV electrical grid and a communication network with devices in the nodes of the grid. The communication network carries out one-way transmission of monitoring data from the devices to the LV grid operator and is based on LoRaWAN technology. Transmission is carried out via a common data transmission medium. We presume that each message contains information about the node voltage. The messages from each node in the grid are sent to the grid operator at random time intervals and independently of each other. If the airtimes of messages from two or more nodes overlap, a collision occurs and none of the messages reach the operator. The operator has to monitor the random voltage behavior over time to avoid exceeding a certain level (an upper voltage limit). Under the specifics of the voltage random process, we study the problem of choosing parameters of message transmission to uniformly minimize the probability of such excess among all nodes. The work precisely formulates the optimization problem and proposes an algorithm for its solution for the particular case.

A novel solar-fed quasi-resonant battery charger operating in the Discontinuous Voltage Mode (DVM) is proposed and designed to achieve a high efficiency on a wide range of operating powers. The converter design procedure ensures the achievement of the Zero Voltage Switching of its MOSFETs on a wide range of operating conditions, thus reducing the switching losses. An analytical model of the converter operation and its static characteristic in its two operating modes is provided. A step-by-step design procedure for the converter is proposed for given battery and PV panel specifications. Measurements on a 100 W, 12 V-output prototype validate the accuracy of the modelling and highlight a high efficiency always above 90.9% with the input voltage ranging from 26.5 V and 28.5 V and an input power in the wide 20 W–100 W range.

This article utilizes a Fractional Order Proportional-Integral-Derivative (FOPID) controller for voltage regulation of a Proton Exchange Membrane fuel cell (PEMFC) in DC Microgrids. The PEMFC is considered a promising candidate for integration into DC Microgrids. However, maintaining a stable and efficient operation requires precise voltage control, especially under varying load conditions and inherent nonlinearities. The FOPID controller tuned by artificial rabbits optimization algorithm (FOPID-ARO) is recommended to address this challenge, which extends the conventional PID controller by introducing two additional parameters: the fractional orders of the derivative and integral actions. This enhancement allows for a more flexible control strategy that is capable of handling the complex dynamics of PEMFCs more effectively than traditional PID controllers. The suggested controller effectiveness is assessed under different operational scenarios, such as load and solar irradiance variations, and compared with a PID controller tuned by the ARO algorithm (PID-ARO) and an FOPID controller tuned by the jellyfish search algorithm and grey wolf optimizer. Moreover, actual data on solar irradiance are considered. The findings indicate that the FOPID-ARO controller performs better than the PID-ARO controller in terms of dynamic response and minimizes steady-state error more effectively.

This paper proposes a new method for predicting the energy generated by Photovoltaic (PV) panels with coolant Calcium Chloride (CaCl2). The study seeks to address heat-related issues that can affect the performance of PV panels by reducing their operational temperature. High operational temperatures can decreased electricity production, potentially cause permanent damage to solar cells, and shorten the panel’s lifespan. Calcium Chloride compound possesses hygroscopic properties, meaning it can absorb water from the surrounding air, thus serving as a cooling material. Calcium Chloride compound is applied as crystalline granules placed in a container beneath the PV panel. This research seeks to evaluate the effect of Calcium Chloride usage on reducing the operational temperature of PV panels and its influence on increasing PV efficiency. Subsequently, using the temperature data of the PV panel, modeling is performed to predict the PV output power using three predictive models: Multiple Linear Regression (MLR), Temperature Coefficient of Power (TC-P), and Artificial Neural Network (ANN). These three models are then compared to test their effectiveness. The research results indicate that using Calcium Chloride as a coolant is highly effective in improving the performance and efficiency of PV panels. Additionally, the ANN model accuratelly predicts power output based on temperature condition variations, with a Coefficient of Determination ( $R^{2} =0.98$ ).

Under the condition of a small time scale (e.g. second), distributed photovoltaic (PV) power generation output has the problems of strongly fluctuating and difficult to accurately simulate. It affects the control strategy and operation mode of hybrid energy systems. To address this problem, a data-driven small-scale distributed PV plant power output model on a 1-second time scale is proposed for the generation of second-by-second PV power output scenarios in hybrid energy systems. Firstly, this work analyzes the characteristics of PV power output at the 1-second time scale based on the probability distribution of power output fluctuations. Secondly, an index system that characterizes the PV power output fluctuation characteristics at the 1-second time scale is constructed. Then, using the data-driven method, a BP neural network model is constructed to simulate the PV power output at the 1-second time scale. Finally, a simulation is performed using the measured data from the PV plant. The findings demonstrate that compared to PV power output models in seconds based on Pearson systematic random numbers: (1) The correlation coefficient (r) of the proposed model is more than 0.8, in a higher degree of fit; (2) The root mean square error (RMSE) of the proposed model achieves 0.005, generally representing a 37.12% reduction. Overall, both the time scale and model accuracy of this model have deep potential value in PV power output modeling and system regulation.

This paper presents an effective maximum power point tracking (MPPT) method for photovoltaic (PV) panels without any voltage sensor in a PV differential power processing (DPP) system. If a fault occurs in the voltage sensor, errors will occur in all control methods, including MPPT, thereby reducing power production efficiency. Therefore, this paper presents an effective duty MPPT method that enables normal MPPT, even without the voltage sensor of the DPP converter used in the PV power generation DPP systems. The proposed method performs MPPT by analyzing the power-loss elements of the DPP converter to recalculate the effective duty cycle used for actual power transmission and reduce the voltage calculation error. The effectiveness of the proposed method is verified in simulations and experiments, and the average accuracy compared with the general P&O MPPT is found to be 98.65[%] on average even without the use of a voltage sensor.

This article proposes a new high-gain converter to attain higher output voltage without coupled inductors. The operation of the proposed converter is examined under continuous conduction mode (CCM) and discontinuous conduction mode (DCM). Also, the mathematical expressions for the design of inductors, capacitors, voltage and current stress of semiconductor devices are provided. The proposed converter is simulated in MATLAB-Simulink and validated the effectiveness of the performance using a laboratory-based Prototype of 250 W at 50 kHz with a 60% duty cycle. The experimental results of the individual components are matched with the simulated results. Due to the continuous input current, the proposed converter is suitable for photovoltaic (PV) applications.

This paper presents a new soft switching (SS) multiphase interleaved boost converter (MIBC) consisting of three boost converters (BCs) that operate rapidly and efficiently to charge a lithium-ion battery for photovoltaic (PV) power system applications. This suggested converter can operate both active power switches at zero current switching (ZCS), and when switched off, it works at zero voltage switching (ZVS), which reduces switching losses and increases switching efficiency (SF). An interleaving structure is employed to decrease the conduction losses and reduce the input current ripple (ICR) and output voltage ripple (OVR) to increase the power rating. The suggested converter regulates the input and output power by modulating the phase shift in the pulse width modulation (PWM) technique using the maximum power point tracking (MPPT) algorithm via two closed feedback loops, one for a slower external loop and the other for a faster PWM control internal loop, similar to current mode control. The suggested converter has high voltage (HV) gain due to the coupled inductors (CIs), which act as a transformer. This magnetic design structure in both the steady-state and transient states also decreases the size and enhances the converter’s performance. Finally, the suggested converter was experimentally verified on a 285W prototype, and the maximum efficiency was found to be 98%, based on modeling and experimental data.

The integration of battery energy storage (BES) with photovoltaic (PV) systems is becoming economically attractive for residential customers. The conventional approach for the interconnection of PV and battery systems requires at least two separate power converters that results in multistage power conversion for some power flows. The dc-dc three port converters (TPCs) are an alternative solution. These converters have many topological variants and possess different operating principles, topological benefits and limitations, and complexities. This paper concentrates on the topological study of TPCs for integrated PV and BES systems applications in the power range from a few hundred watts to 350 kW. These are classified into three different categories based on their isolation features between the ports to establish a topological mapping of the reported TPCs. This provides a framework that systematically explores the full range of technical benefits and limitations of each TPC topology. This paper also examines the possible extension of the TPC topologies for grid-interactive PV-BES systems where bidirectional power flow capability is required between grid and BES systems. This extensive review will provide a useful framework and a strong point of reference for researchers for the selection of TPC topologies to meet the system requirements for PV and energy storage applications.

In recent years, in order to improve the fuel efficiency of electric vehicles, a lot of research has been conducted to adopt photovoltaic (PV) systems to electric vehicles. In addition, various Differential Power Processing (DPP) converters that can mitigate power imbalance among submodules in PV systems are being studied and developed. However, most conventional DPP converters suffer from large volume, low-price competitiveness, and low efficiency. Therefore, in this paper proposes a novel DPP converter. The proposed DPP converter can significantly reduce the volume of converter by integrating multiple transformers into single transformer. Moreover, because of the active clamp structure, it is possible to eliminate secondary side circuits. Furthermore, more than 95% efficiency under 10-60% load and maximum 96.654% efficiency about 20% load were achieved. Thus, the proposed DPP converter can achieve high power density, high price competitiveness, as well as high efficiency. To support the validity of the proposed converter, a prototype of the proposed DPP converter was built, and experiments were proceeded with 8V input and 60W/30V output specifications.

A modified two-channel LLC resonant converter with optimal efficiency for wide input voltage applications. The converter has the same voltage gain and power handing ability as conventional full-bridge (FB) resonant converter, but the hardware cost is lower than conventional FB resonant converter, and the operational frequency range is narrower than conventional resonant converter for the same working condition. It has the characteristics of low input impedance at low input voltage, which is used to provide high voltage gain, and high input impedance at high input voltage, which is used to provide low voltage gain, the above characteristics are analyzed and optimized to improve the efficiency for photovoltaic applications where wide voltage gain is require. A 1 kW prototype is developed, the input voltage ranges from 80 V to 200 V, the output voltage and output power are 400 V and 1 kW, the experimental result shows that the full-load efficiency can reach 96.5% over the entire input voltage range, and the peak efficiency can reach 96.9% at full-load state.

The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.

A rooftop photovoltaic (PV) system is a significant solution of building-integrated centralized generation in the low-voltage (LV) DC grid. The drilling-free rooftop PV-inductive wireless power transfer (PV-IWPT) system for the LVDC grid can reduce the installation and post-maintenance costs, with the elimination of physical cable connections and preventing breaking of thermal bridge and rain penetration between the rooftop and the inside of the building. Moreover, it has inherent isolation properties between the input and the output terminals. However, it is challenging for the PV-IWPT system to simultaneously achieve the maximum power point tracking (MPPT) for the PV panels and the maximum efficiency point tracking (MEPT) for the system even under diverse irradiance conditions. In this paper, we propose an efficient PV-IWPT system, which consists of a series-series IWPT converter, driven by the PV panels and loaded by the LVDC grid directly. Moreover, we apply in the proposed system a particle swarm optimization with an auxiliary Perturb and Observe (P&O) algorithm and a load matching method without wireless feedback communication to realize MPPT and MEPT in the overall PV-IWPT system simultaneously. Finally, we analyze theoretically and verify experimentally the proposed PV-IWPT system in a 500-W experimental platform under different irradiance conditions.

The objective of reducing the size and cost of the grid-connected photovoltaic system has led to advancements in the field of transformerless grid-connected inverters and gained high popularity in recent years. However, in such systems, the major limitation lies in realizing maximum power from individual modules. In this regard, this paper proposes a modular transformerless grid-connected photovoltaic multilevel inverter that realizes the individual maximum power point (MPP) of each module under different operating scenarios. The presented configuration is simple and modular, providing flexibility to increase the number of inputs with less component count. A single-phase synchronous reference frame PI (SRF-PI) controller has been designed and realized for the proposed system. The systematic procedure for designing the controller has been detailed. The converter and controller allow the system to realize individual MPP of the modules and simultaneously achieve the load demand maintaining the desired grid voltage. This has been verified under different operating scenarios in the MATLAB/Simulink environment. The same has been validated on a 300W laboratory prototype under source and load intermittencies. The proposed configuration has been compared with the similar works reported in the literature and it has been observed that it employs only 18 components. Also, the efficiency of the converter has been observed in the range of 89-95%. Further, the common-mode voltage and the leakage current have been measured to verify their suitability for grid-connected systems as per German VDE 0126-1-1 standards.

In recent decades, there has been a growing global focus on solar power as a renewable energy source (RES) to supply local energy demands and reduce greenhouse gas emissions. Rooftop solar photovoltaic (PV) system provides a small-scale utilization of solar energy on the roofs of apartment buildings. Investment in this system and its profitability depends on several factors, including geographic conditions, electricity price, and local load profiles. However, in Finland, the maritime and continental climates and electrically heated residential buildings present unique challenges to the investment and utilization of rooftop PV systems. Common solutions to incentivize the investment of grid-connected PV in apartments are battery energy storage systems (BESSs), demand side management (DSM), and power-to-x (P2X) approaches. Nevertheless, the value of these solutions is limited in Finland due to the seasonal variation of solar PV generation and customers’ energy consumption. This paper presents a novel and practical control and hedging mechanism to encourage investments in rooftop solar PV-BESS systems by investing in cryptocurrency mining devices (CMDs) as dispatchable and flexible loads, which facilitate the use of excess renewable energy for producing cryptocurrency, such as bitcoin (BTC). This mechanism can optimally switch the output of excessive renewable energy between exporting to the main grid and mining cryptocurrency. The proposed mechanism is studied using a dataset obtained from a residential apartment building in Helsinki, Finland, and its effectiveness is demonstrated through several practical scenarios. The results of a case study employed in this work demonstrate that the proposed hedging mechanism can provide sufficient encouragement for investors to invest in a PV system, with a return on investment equal to 57.7%. This mechanism also reduces the annual cost of residential apartments by 68.1%.

Photovoltaic (PV) systems can reduce greenhouse gas emissions while providing rapid reactive power support to the electric grid. At the distribution grid level, the PV inverters are controlled to reduce the system’s active power loss and to address problems caused by the PV systems themselves. For example, the distribution grid may face overvoltages due to high PV generation during off-peak hours. In this paper, a reactive power control approach for PV inverters is proposed to control the injection/absorption of reactive power to reduce the active power loss of the system while solving the overvoltage problem. To achieve this, the proposed controller periodically dispatches the reactive power setpoints and applies a real-time volt/var algorithm. The proposed method uses probabilistic distributions to account for the uncertainties in PV generation and load demand. The controller is implemented at the lateral level which simplifies the required communication platform and reduces the computational cost. The real-time volt/var control coordinates the operation of the different inverters during overvoltage conditions so that the voltage rise is limited using as little reactive power as possible by the inverters. Accordingly, the active power loss due to reactive power flow in the system is reduced. Two distribution systems are simulated using Open Distribution System Simulator (OpenDSS) and used to evaluate the proposed controller and compare with two other methods. A daily time series simulation is performed to test different operating conditions. The simulation results show that the proposed controller is able to reduce the active power loss in general and solve the overvoltage problem with a lower reactive power requirement than the other volt/var methods.

Due to photovoltaic (PV) technology advantages as a clean, secure, and pollution-free energy source, PV power plants installation have shown an essential role in the energy sector. Nevertheless, the PV power plant cost of energy must be competitive when compared to traditional energy sources. Therefore, numerous studies are continuously being conducted aiming to optimize PV power plants, including components arrangements within the installation site, the inverter topology, cables, PV modules and inverters numbers, PV module tilt angle and shading effect. For selecting the most suitable combinations for system parameters, this study seeks to systematically analyze and synthesize the design of the PV power plant optimization from the current literature. The study also examines component sizing for PV power plants, involving PV modules tilt angle, inverter, transformer, and cables. Moreover, it provides an overview of the main components employed to install the PV power plant, which includes PV modules, inverter, transformer and wiring. It examines the different inverter topologies used in PV power plants along with a comparison between these topologies.

Access to reliable electricity is crucial for rural development and improving the quality of life in remote areas. Standalone photovoltaic PV systems and hybrid power systems HPS are promising solutions for rural electrification. However, frequency stabilization is a critical challenge in such systems, and conventional control techniques are inadequate. This work proposes a novel approach to optimize the PID controller gains for the standalone PV-based isolated HPS (IHPS). The student psychology-based optimization algorithm (SPBOA) and quasi-oppositional-based whale optimization algorithm (QOWOA) are employed to enhance the performance of the IHPS models separately. The objective function considered is the integral of time absolute error (ITAE), and the frequency response profile is studied in the presence of the proposed SPBOA and QOWOA-based PID controllers. The results show that the proposed SPBOA outperforms the QOWOA in terms of reducing the ITAE by nearly 5% and minimizing the peak and settling time of the frequency response by 5%-7% in different scenarios. The transient responses of the systems to different input conditions are also analyzed, indicating that both the IHPS-I and IHPS-II models are feasible for remote rural electrification. Overall, the proposed approach offers an efficient solution to optimize the PID controller gains for frequency stabilization in standalone PV and HPS. The results demonstrate the benefits of using the SPBOA and QOWOA algorithms to enhance the performance of the systems, making them suitable for remote rural electrification.

The main aim of this paper is to design a local controller for DC/DC converter in a battery energy storage system (BESS) and a controller based on a virtual synchronous generator (VSG) for photovoltaic (PV) converter connected to AC grid. In the DC/DC converter design, the state feedback method is used so that the voltage and current control loops are combined, leading to higher flexibility and improved damping. This flexibility, which maintains the state of charge (SOC) of BESS, is supported by the design of virtual resistance and virtual capacitor statically and dynamically. In this design method, the BESS can be independently connected to each DC bus node and detect disturbances through local measurements. In addition, the improved Pre-Parallelism (PREP) method has been used to improve the transient performance caused by the disconnection and connection of additional loads, the existence of faults, and the inherent inertia difference in parallel operation between VSG and synchronous generator (SG). In this method, the problem of phase jump caused by transient disturbances is solved by considering a cosine function in VSG design. Also, to solve the problem of inertia difference between the units, a small signal model has been presented, in which, by considering the capacity ratio of the units on the AC side, the necessary inertia for VSG can be included in the design. The proposed method is simulated by considering different scenarios in MATLAB software, so the results demonstrate the superiority of the proposed controller compared to other existing methods.

In the past decade, substantial investments have been made in researching and developing concepts and technologies to support the smart grid, renewable integration, and grid-interactive buildings. Adaptation of integrated solar photovoltaics with energy storage is increasing in residential buildings as consumers and utilities are becoming aware of their economic benefits and resilience benefits. Effective integration and control of these systems with other building loads is critical for providing load flexibility to improve building energy efficiency, reduce carbon footprint, and support grid resiliency. In recent years vendors are shifting towards device-level optimization and defining more sophisticated operational modes for controlling energy storage systems rather than charge and discharge power. As a result, optimization techniques must encompass the characteristics of these modes and their interactions with other system disruptions and attributes. This complexity gives rise to a nonlinear optimization problem that cannot be effectively addressed by an open-source solver and is impractical to implement in real-world scenarios. In this paper, we designed and evaluated a linear multi-objective model-predictive control optimization strategy for integrated photovoltaic and energy storage systems in residential buildings by using manufacturer-defined operational modes. The optimization goal is to minimize the power-purchasing cost from the grid and maximize the power selling cost to the grid. We developed a generalized method to keep the optimization linearized, even with operational modes consideration while coupling the modes with the overall system charging and discharging power. Our simulation results were aligned with real-world measurements and validated the linearized optimization formulation for each operational mode and for the economic use-case. The optimization results for the economic use-case demonstrated that the power associated to grid charge is mostly larger than the grid discharge power which means the optimization tried to maximize the power selling to the grid when the price is high and avoid power purchasing from the grid during high price.

Accurate net load forecasting is a cost-effective technique, crucial for the planning, stability, reliability, and integration of variable solar photovoltaic (PV) systems in modern power systems. This work presents a direct short-term net load forecasting (STNLF) methodology for solar-integrated microgrids by leveraging machine learning (ML) principles. The proposed data-driven method comprises of an initial input feature engineering and filtering step, construction of forecasting model using Bayesian neural networks, and an optimization stage. The performance of the proposed model was validated on historical net load data obtained from a university campus solar-powered microgrid. The results demonstrated the effectiveness of the model for providing accurate and robust STNLF. Specifically, the optimally constructed model yielded a normalized root mean square error of 3.98% when benchmarked using a 1-year historical microgrid data. The $k$ -fold cross-validation method was then used and proved the stability of the forecasting model. Finally, the obtained ML-based forecasts demonstrated improvements of 17.77% when compared against forecasts of a baseline naïve persistence model. To this end, this work provides insights on how to construct high-performance STNLF models for solar-integrated microgrids. Such insights on the development of accurate STNLF architectures can have positive implications in actual microgrid decision-making by utilities/operators.

With the consuming of fossil fuels, sustainable alternative sources are needed for energy production. Renewable energy is an important source in the fight against climate change and provides economic benefits by increasing energy security. Solar energy is one of the popular renewable energy sources. The photovoltaic system is a technology that produces electrical energy from solar radiation. These systems, which are clean, renewable, and environmentally friendly, are important in terms of meeting our future energy needs. Photovoltaic system power forecasting is an important tool for both energy planning and energy management, as it increases the predictability of bulk power system. In addition, power estimates also allow improving the system efficiency and planning the maintenance date. In this study, due to its high accuracy, fast computation, and easy applicability, Extreme Learning Machine (ELM) method was used to estimate the daily average energy production of a solar power plant located in Elazig/Turkey. To improve the performance of the model, ELM’s hyperparameters were optimized using the Modified Golden Sine Algorithm (GoldSA-II). To show the power generation change that occurs in parallel with the seasonal weather change, graphs of the real power of different months from the four seasons and the power estimated by ELM are presented in the study. The radiation, temperature, wind speed, real power, and ELM predicted power values for the ten-month period in which the operating data were obtained are given in the table. By examining this table, the effect of weather conditions on production can be observed more clearly. The R and RMSE values of the ELM model, which are calculated separately for each month, are presented in the form of a radar chart. The average daily R and RMSE values calculated for the ten-month period were calculated as 0.95 and 0.0716, respectively, and the performance of the ELM model used in the study was proven by the calculated R and RMSE values.

About 11 million tons of retired batteries are expected to be produced globally by 2030, of which a huge percentage will proceed from individual owners who charge their vehicles at home. To overcome this issue, multiple studies focused on the viability of reusing mobility batteries for different applications. However, this kind of analysis in individual dwellings are still scarce. This paper aims at filling this gap by developing a novel optimization methodology for design of photovoltaic arrays in domestic installations considering second-purpose batteries from mobility. In particular, we aim at analyzing the effect of using batteries from vehicles when they are no longer valid for mobility. Thus, we consider the problem in which these batteries are used for stationary applications at home, observing their effects on the photovoltaic design as well as other aspects like total project cost and self-consumption rates. To this end, a planning strategy is developed including suitable models of photovoltaic units, flexible loads and electrical vehicle. The developed methodology is tested on a benchmark prosumer environment and different scenarios are profusely studied. The results obtained demonstrate that the use of second-purpose batteries seem profitable in smart homes, reducing the total cost by 15% and increasing the self-consumption rate by 20%. Moreover, other aspects like its effect on the final scheduling and the behavior under limited photovoltaic capacity are commented. Lastly, the importance of the developed methodology for future similar studies is highlighted.

This paper proposes a heuristic method for calculating the capacity of a set of residential photovoltaic-battery systems in providing upward flexibility services to the grid in an energy communities framework. The proposed method has been designed to calculate the upward service capacity in a few minutes, assuming a scenario where the grid operator urgently needs an upward service in a specific area. The proposed method calculates the service capacity by exploiting the PV overgeneration and the state of charge of batteries, adopting a distributed approach. If the service capacity varies relevantly over time, a centralized approach is considered allowing the service capacity to remain constant over time. An algorithm is provided that implements the proposed heuristic method that can be easily translated into a software code and solved even in the absence of specific skills and expensive high-level computational tools, i.e. using cost-effective single-board computers. The main benefits and advantages of the proposed method are due to its applicability in real-time problems and to its simplicity which makes it easy to be translated into software code and solved even in the absence of specific skills and high-level computational tools. Therefore, it is a simple and advantageous solution, especially for small energy communities. The numerical results demonstrate the effectiveness of the proposed method and algorithm, studying a set of four residential photovoltaic-battery systems and real input data. For this test case, the algorithm returns a flat service capacity of approximately 8 kW which remains perfectly constant for 1-hour. Lastly, the performance of the proposed heuristic method is compared with the solution of two optimization problems aiming at the same scope.

In the medium voltage cascaded converter, the power imbalance problem among the converter modules will result in certain converter modules over-modulated. Reactive power control (RPC) scheme is an effective way to balance the cascaded converters. Many efforts, such as reactive power sharing control and apparent power sharing control have been used to solve the overmodulation issue. This paper investigates an optimization RPC scheme to solve the overmodulation problem by minimizing the total required reactive power. An improved minimum reactive power (IMRP) RPC scheme is presented, the feasible area for the active power imbalance is evaluated and compared with the reactive power sharing control scheme and the apparent power sharing control scheme. Finally, the control scheme is verified on a three-module scaled-down cascaded inverter.

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.

We report experimental fabrication and characterization of photoMOSFETs with self-aligned gate-stacking heterostructures of indium-tin-oxide (ITO)/Ge nanospheres/SiO2-shell/Si1-xGex-nanosheets. Array of Ge-nanosphere/SiO2-shell/SiGe-nanosheet heterostructures was created in a self-organized, CMOS approach using the thermal oxidation of lithographically-patterned poly-Si0.85Ge0.15 nanopillars over buffer layers of Si3N4 on top of SOI substrates. With a polysilicon dummy-gate, source and drain self-align with the transparent ITO gate using a replacement-metal-gate process. Very high photocurrent gain, large photoresponsivity, as well as improved input capacitance and 3dB frequency were experimentally achievable in our photoMOSFETs. The pivotal roles of Ge-optical gate and SiGe-channel for large photoresponsivity and current gains were analyzed via numerical simulation.

Electric vehicles (EVs) are widely deployed throughout the world, and photovoltaic (PV) charging stations have emerged for satisfying the charging demands of EV users. This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system (CBSCS), in order to enhance the economic efficiency while reducing its adverse effects on power grid. The proposed method involves a multi-objective optimization scheduling model, which minimizes the total operation cost and smoothes load fluctuations, simultaneously. Afterwards, we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III (NSGA-III) for solving this scheduling problem. Finally, simulation studies verify the effectiveness of the proposed multi-objective operation method.

The use of hybrid energy storage systems (HESS) in renewable energy sources (RES) of photovoltaic (PV) power generation provides many advantages. These include increased balance between generation and demand, improvement in power quality, flattening PV intermittence, frequency, and voltage regulation in Microgrid (MG) operation. Ideally, HESS has one storage is dedicated for high energy storage (HES) and another storage for high power storage (HPS) purpose. HES is used to fulfill long-term energy demand, while HPS is used to handle power transients and fast load fluctuations. This paper examines HESS comprehensively for PV power generation and focuses on its ability to combine two storage technologies. The two storage technologies include high energy and high power. This paper also analyzes the important aspects of advance HESS in PV power generation in the context of capacity sizing, power converter topology and strategies management energy. Several capacity sizing methods were critically reviewed and tabulated. Power converter (PC) topologies are classified and briefly discussed regarding their advantages and disadvantages. Furthermore, energy management strategies with various control techniques are critically classified and evaluated for better future direction. In addition, the implementation of HESS on PV power generation in current real projects is presented and evaluated. Finally, this paper can be considered as useful guide for the use of HESS in PV power generation including features, limitations, and real applications.

As photovoltaic energy increasingly penetrates in power systems, transmission system operators have started to request its participation in providing ancillary services. One of the demanded services is the power ramp-rate control (PRRC), which attempts to limit the power ramps produced by intermittent irradiance conditions. In order to achieve the desired objective, solutions based on storage systems or modifying the maximum power point tracking (MPPT) in perturb and observe (P&O) algorithms are commonly adopted. The starting point in PRRC is the determination of the instantaneous power ramp-rate, and different methods have been proposed in the literature for its calculation. However, the accuracy and computational speed of existing procedures can be improved, which may be critical in situations with rapid irradiance fluctuations. In this paper, a decoupled photovoltaic power ramp-rate calculation method is presented, in which the effect of variable irradiance and the P&O algorithm are computed separately. The proposed method has been theoretically demonstrated and tested through simulation and experimental tests. Simulation results show that it can improve the previous methods in terms of accuracy and computation time. Experimental validation with hardware-in-the-loop demonstrates the suitability of the proposed method for real-time applications, even in presence of noisy measurements.

A high step-up PWM non-isolated dc–dc converter with soft switching is proposed in this paper. The converter has minimum auxiliary elements to achieve high power density and high voltage gain. The ZVS operation of the main power switch results in negligible capacitive turn-on losses. Since the duty cycle of the auxiliary switch is narrow and its operation is under ZCS condition, the losses that the auxiliary circuit imposes are not significant. Also, all the diodes are operated under soft-switching conditions solving the reverse recovery problem. All the inductors are coupled on only one magnetic core, reducing size and conduction losses. Compared to the hard switched counterpart, the electromagnetic interference of the proposed converter is reduced. Furthermore, the output voltage of the proposed converter is controlled by an integral sliding mode control strategy during the load variations. Also, the proposed integral sliding mode control strategy has been compared with the PI control strategy, improving the transient response and the robustness under load variations while the switching frequency is constant. The effectiveness and accuracy of the proposed converter are verified by practical laboratory results which are obtained from a 250W prototype.

The availability of reliable photovoltaic (PV) power forecasting tools is an important factor for the dissemination of this technology. This is true not only for the integration of these difficult to predict sources in large power grids but also for small grids or standalone applications. The concept of edge computing, through the use of small, low power and inexpensive devices can help to make predictions more localized and feasible also in small size applications. In this article prediction methods based on Artificial Neural Networks (ANNs) models are considered and compared, along with the possibility of reducing their cost in terms of memory and computational power requirements possibly without increasing prediction error. It is shown that quantization and pruning methods, implemented in the AI libraries of a common platform for Microcontroller programming, is a viable solution of this problem. Solar panel aging effects are also considered, and it is shown how the same system used for the prediction can be an indicator of reduced plant efficiency.

The use of electric vehicles (EVs) and photovoltaics (PV) is increasing worldwide. Transportation networks require the effective use of renewable energy (RE) for EVs, whereas power networks require local consumption of PV energy, mainly at the initiative of local governments. Although many previous studies have addressed these requirements using private EVs as mobile storages, their uncertainty and uncontrollability remain highly problematic. Therefore, our previous studies focused on electric buses because of their high controllability and certainty. These studies involved the development and evaluation of two independent minimization problems—kilowatts (KW) and kilowatt-hours (KWH) of surplus RE—as a charging schedule optimization method using mixed integer linear programming. However, the feasibility of simultaneously minimizing KW and KWH still presented technical problems. This study aims to extend and generalize the method to the simultaneous minimization of KW and KWH of the PV-derived reverse power flow (RPF). With this multiobjective optimization, the KW peak-cut of the RPF improves the hosting capacity and increases the availability of connectable RE resources, whereas the minimization of KWH promotes the local consumption of RE and decarbonization of public transportation. Two simulations using detailed data of actual bus operations and actual power flow data for a real city confirmed the feasibility of simultaneously optimizing KW and KWH and the relationship between these indicators and number of EV chargers. The optimized charging of the 17 electric buses achieved a maximum of 211.4 kW peak-cut and 1318.4 kWh RE-RPF absorption, reducing CO2 emissions by 495.7 kg/day.

Many Electric Power Systems (EPS) already include geographically dispersed photovoltaic (PV) systems. These PV systems may not be co-located with highest-priority loads and, thus, easily integrated into a microgrid; rather PV systems and priority loads may be far away from one another. Furthermore, because of the existing EPS configuration, non-critical loads between the distant PV and critical load(s) cannot be selectively disconnected. To achieve this, the proposed approach finds ideal switch locations by first defining the path between the critical load and a large PV system, then identifies all potential new switch locations along this path, and finally discovers switch locations for a particular budget by finding the ones the produce the lowest Loss of Load Probability (LOLP), which is when load exceed generation. Discovery of the switches with the lowest LOLP involves a Particle Swarm Optimization (PSO) implementation. The objective of the PSO is to minimize the microgird’s LOLP. The approach assumes dynamic microgrid operations, where both the critical and non-critical loads are powered during the day and only the critical load at night. To evaluate the approach, this paper includes a case study that uses the topology and Advanced Metering Infrastructure (AMI) data from an actual EPS. For this example, the assessment found new switch locations that reduced the LOLP by up to 50% for two distant PV location scenarios.