Photovoltaic Microgrid Research Articles

Development of a PV/Battery Micro-Grid for a Data Center in Bangladesh: Resilience and Sustainability Analysis

Energy resiliency plays an important role in the proper functioning of data centers as they heavily rely on an uninterrupted power supply to ensure smooth operation. In the case of a power outage, the data center’s operation might be hampered, which results in system downtime, data, and economic loss. This issue is severe in developing countries where power supply infrastructures are inadequate and conventional. Microgrids can be an effective solution in this regard. Although several studies developed microgrids to observe the energy resilience benefit for some critical facilities, critical facilities like data centers are often overlooked. In addition, sustainability analysis of a microgrid is also scarce in the present literature. Therefore, one new resilience and sustainability indicator has been developed and implemented in this analysis to fill this gap. For this, new indicators, such as the resilience cost index (RCI) and renewable energy penetration (REP), were used in this study. This study used HOMER version 3.13.3 and REopt software to simulate a robust photovoltaic (PV) and battery microgrid for a hypothetical data center in Bangladesh. A random (48 h) outage was assigned to witness the adaptability of the modelled micro-grid. The suitable size of PV and battery was found to be 249,219 kW and 398,547 kWh, respectively. The system’s USD 18,079,948 net present value (NPV) demonstrates the economic potential of utilizing PV and battery microgrids for data centers. The RCI of the system is found to be 35%, while the REP is 87%. The energy consumption saving of the system is USD 21,822,076. The system emits 652% less CO2 than the grid. The result of this system is also compared with a diesel-based system. After comparison, it is found that the developed PV/battery microgrid provides better environmental and economical service than the diesel generator. During blackouts, the system keeps the data center powered up without interruption while improving energy resilience and lowering carbon emissions. The outcome of this current analysis can serve as a blueprint for other microgrid projects in Bangladesh and other developing countries. By integrating PV/battery microgrids, data centers can cut costs, reduce emissions, and optimize energy use. This will make data centers less reliant on grid services and more flexible to forthcoming development.

Nonsingular fast terminal sliding mode control for two-stage converters of AC island photovoltaic microgrid

Taking into account almost all kinds of variations and uncertainties to which AC island photovoltaic (PV) microgrid is often subjected, this paper proposes a new nonsingular fast terminal sliding mode control (NFTSMC) strategy for two-stage converters to enhance robustness against those disturbances and improve system dynamic performance. Firstly, to effectively decrease the adverse effect on the system from intermittent solar irradiation and uncertain environmental temperature, a NFTSMC cascaded with perturb and observe (P&O) scheme is put forward for fore-stage DC/DC converter, and PV output voltage is controlled to track its reference and thus maximum power point tracking (MPPT) performance is improved. Then, to significantly decrease the adverse effect on the system from variations of load and LC filter parameters, by incorporating an extended state observer (ESO) into NFTSMC, an ESO-based NFTSMC scheme is proposed for post-stage DC/AC converter, and the load voltage is regulated to track its reference and thus system robustness is strengthened, in which ESO is designed to replace load current sensor and thus reduce hardware cost. By using global terminal attractor without discontinuous switching function, the proposed NFTSMC strategy can not only strengthen system robustness but also improve system dynamic performance. Lastly, on account of the variations of PV generation and load demand, a conventional double-loop PI scheme is put into use for bidirectional DC/DC converter and thus system power balance is ensured. Comprehensive MATLAB simulation and StarSim/Hardware experiment results show that the proposed NFTSMC strategy for two-stage converters can overcome negative effects caused by various internal and external disturbances, and thus enable island PV microgrid to operate reliably and supply high-quality AC power.

Active disturbance rejection control of photovoltaic microgrids based on disturbance compensation and feed-forward correction strategy

Photovoltaic microgrid (PV) is a promising direction of new energy generation technology. However, due to the performance of low-voltage side interface affected by complex disturbance, PV microgrid has the problem of output voltage fluctuation, which will make the power quality not guaranteed. In order to stabilize the low-voltage side load supply, the active disturbance rejection control strategy with disturbance compensation and feed-forward correction (ADRC-DCFC) is proposed. By expanding the conventional state observer (ESO) dimension, a feed-forward correction link for disturbance tracking compensation is added. In contrast, the internal deviations extracted from the system model are actively added to the disturbance compensation link. In addition, a theoretical analysis of its disturbance observation performance and noise suppression performance is presented. Experimental results on a 300-watt test platform verified the accuracy and effectiveness of the ADRC-DCFC.

Modelling and control of grid connected microgrid with hybrid energy storage system

<p class="JESAbstract"><span lang="EN-US">This paper presents a photovoltaic (PV) microgrid with battery and super capacitor hybrid energy storage systems. The proposed microgrid system is designed for both grid connected and standalone mode with coordinated control-based energy management system, which controls DC link voltage, voltage and frequency balance at point of common coupling. DC link voltage control is implemented using dual loop PI controller-based voltage controller and inverter control is based on D-Q reference frame technique. The microgrid system is demonstrated in MATLAB/Simulink. The</span><span lang="EN-US"> presentati</span><span lang="EN-US">on</span><span lang="EN-US"> of the planned energy supervision system is analyzed for varying generation and load condition. In the proposed microgrid the battery energy storage system is utilized to provide long term energy during average power requirement and supercapacitor energy storage system is utilized to provide short term power requirements during sudden load variation, generation variation and during transition of modes. Designed energy management system performs effectively in grid connected mode, standalone mode with smooth transition between the modes. And it maintains dc bus voltage of the microgrid constant irrespective of load and generation variations and also during mode changing conditions.</span></p>

Optimization Method of Photovoltaic Microgrid Energy Storage System Based on Price-based DR

Extreme events lead to an increasing number of power outages in the distribution network. It is currently the most effective method to restore power supply after distribution network failure to connect distributed photovoltaic to the distribution network in the form of microgrids. However, the randomness of distributed PV output and load is the biggest obstacle limiting its development. Therefore, an optimization method of photovoltaic microgrid energy storage system (ESS) based on price-based demand response (DR) is proposed in this paper. Firstly, based on the influence of the uncertainty of the time of use (TOU) and load on the price-based DR, a price-based DR model is built. Then, taking the minimum difference between photovoltaic output and load demand as the optimization objective, the optimal operation model of price-based DR based on the fuzzy chance constrained program (FCCP) is established, and an optimization model of photovoltaic microgrid ESS based on FCCP is obtained. Finally, the fuzzy chance optimization problem is converted into a deterministic optimization problem through constraint equivalent treatment. The proposed method is verified by numerical simulations.

Multi-operational solar photovoltaic microgrid with synchronization, backup and power quality enhancement ability

The proposed three phase solar photovoltaic microgrid (SPV-MG) works as a multi-mode operational system. It operates under different modes of operations: 1) Daytime, in presence of sunlight, it transfers real power to three-phase loads and grid, also mitigates power quality issues and provides power balance, 2) During grid absence, it operates in standalone mode, gives power backup to local demand and stores excess solar generation into the battery storage (BS). 3) At night, in the absence of sunlight, the SPV-MG has zero power generation so the VSI (Voltage Source Inverter) acts as a Distribution Static Compensator (DSTATCOM). It is capable of providing reactive power compensation, power factor correction (PFC), harmonics elimination, voltage regulation etc. which in turn improves the utilization factor of devices by nearly double-fold. 4) It has re-synchronization and isolation capability with respect to grid availability. A battery storage (BS) is attached at the DC bus of VSI to support the SPV-MG during peak demands and during grid and solar uncertainties. A laboratory prototype is developed to validate the performance with the experimental results.

Real-time implementation of sliding mode controller for standalone microgrid systems for voltage and frequency stabilization

This paper presents for stabilizing the output voltage and frequency of the hybrid source microgrid system (HSMG) and standalone solar photovoltaic microgrid system (SAPV), under varying source and loading conditions. An HSMG system consists of a solar, wind and battery system incorporated with a solar boost converter, wind rectifier/boost converter and bidirectional converter, respectively. These different dc level outputs are applied to the multiple input dc–dc converter for obtaining the constant dc level and then converted into three-phase power with suitable inverters. The source-side converters are designed to obtain the maximum output irrespective of the input conditions with PI controllers. The multiple input dc–dc converter controls the dc link voltage with the well-designed proportional, proportional integral, proportional integral derivative controller. The three-phase inverter is premeditated to obtain the constant voltage and frequency even under varying load patterns. The tuning of the PID controller is incorporated with Ziegler Nichols’s technique to reduce the frequency deviation and steady-state error. A SAPV microgrid system is developed with a high-capacity solar system incorporated with two-stage power conversion. Stage 1 and 2 are constant dc/constant ac obtained using suitable converters incorporated with an adaptive controller. The sliding mode controller (SMC) is an adaptive controller that performs better in dynamic operations. In this work, SMC maximizes the solar output and stabilizes the DC voltage using an appropriate PWM pulse to the boost converters. Also, the output voltage and frequency are maintained constant during the different operating situations. All simulations are carried out in MATLAB. PID and SMC controllers are implemented to the HSMG and SAPV microgrid systems in the OPAL-RT environment to validate the results in real-time applications respectively. The different operating conditions are tested for both the systems and the results prove that the chosen controllers are more suitable for these applications.

Decarbonizing a solar PV and gas turbine microgrid with hydrogen and batteries

Challenges associated with incorporating the technical potential of photovoltaic (PV) power generation into an existing university microgrid system are investigated with the goal of achieving complete decarbonization. Three energy storage integration pathways are established: sending excess solar PV electricity to 1) battery energy storage systems (BESS) for daily energy shifting, 2) an electrolyzer to produce hydrogen for conversion in a gas turbine power plant (P2G-GT), and 3) an electrolyzer to produce hydrogen for conversion in a fuel cell system (P2G-FC). Installing the maximum geospatially limited amount of solar PV results in directly meeting 20% of the electric load. 58% of PV electric production is curtailed unless energy storage is implemented. The P2G-FC and BESS strategies result in similarly high 64% renewable energy supply (RES) despite having different roundtrip efficiencies. BESS are found to be better than hydrogen storage and fuel cell systems for continuous run times of a day or less. Somewhere between one to three days of continuous power, the hydrogen storage plus fuel cell option is better. To fully decarbonize the microgrid, a significant portion of the RES percentage must come from procured biogas, or from an off-campus resource.

Optimization of Solar Photovoltaic-Based Microgrid for Sustainable Energy Planning in Bajoe Port, Bone Regency, South Sulawesi: A Case Study

Electrification in rural areas is still inadequate due to the limitations of the main power grid. Therefore, harnessing available renewable energy sources, such as solar energy, is an appropriate solution. The solar photovoltaic (PV) system can be utilized either in connection with the main power grid or in standalone mode (islanded) and can be supported by other power generation sources through a microgrid approach to enhance reliability. This research focuses on the design of a power system backed by a single renewable energy source, based on Photovoltaic (PV) technology, for the administrative building and passenger waiting area of Bajoe Port in Bone Regency. The microgrid technology is applied to improve the reliability and continuity of the electricity supply. The limited power supply at the site will be addressed using a microgrid system that combines power from the main grid, PV modules, and emergency generators. The system modeling takes into account the sensitivity of global climate conditions and local load requirements. The HOMER Pro software is utilized as a simulator to optimize the designed system. The PV module, with an array area of 186.92 m2, will be installed in the parking area of Bajoe Port, which covers an area of 3,264.3 m2. The best result for the PV system design within the microgrid system, with a capacity of 41.5 kWp, yields an electricity output of 180,451 kWh per year based on the local average solar irradiation of 5.05 kWh/m2/day. The economic evaluation, expressed in terms of Net Present Cost (NPC) amounts to Rp. 421,251,000, with a Cost of Energy (COE) of 126.04/kWh. Moreover, the system can contribute 110,305 kWh/year to the main grid operated by the state-owned utility, Perusahaan Listrik Negara (PLN). Simulation results indicate that the designed PV microgrid system can contribute to the local daily electrical load, with a daily power requirement of 117.78 kWh and excess energy being sold to the PLN grid.

Application of System-Based Solar Photovoltaic Microgrid for Residential Real Estate

This research explores the techno-economic potential for a predominantly renewable electricity-based microgrid serving Ethiopian residential real estate buildings, the fastest-growing sector. A stand-alone photovoltaic (PV)-Battery energy storage system (BESS)-Genset (PV-BESS-Genset) connected microgrid model, utilizing measured solar irradiation data, real-time manufacturer data for technology components, case study area daily energy consumption data, and a bottom-up approach to model demand response. The modeled system results in a $ 0.298 cost of energy (COE), reduces the 2000.34 kg/yr amount of CO2 released into the environment, and yields 1,470 kWh/yr of excess energy, which indicates that the system is the most cost-effective, ecologically friendly, and reliable, respectively. Moreover, solar PV production potential is very high onsite and can meet the onsite demand with a renewable fraction of 99.3%. However, BESS and generator production potential is substantial and provides a more balanced supply that can supply electricity when solar PV production is insufficient. These results can help to develop rules for residential real estate villages to generate their own electricity needs, distribute residential real estate's current grid energy share to other underserved areas, and lessen the issue of power outages. In an original test case, HOMER software was used to build a microgrid system based on renewable energy (RE), with the single objective of minimizing the net present cost (NPC), and MATLAB/Simulink for energy management. Thus, the system could be a benchmark for new roof-mounted solar-based technology for residential real estate buildings in Ethiopia.

Control of Multiple SPV Integrated Parallel Inverters for Microgrid Applications

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This work presents a hybrid control method (HCM) for inverters in a single-phase AC grid-interactive photovoltaic (PV) microgrid connecting multiple PV inverter (PVI) units. The HCM is incorporated into a single control method employing three cascaded loops -grid current loop, voltage control loop and improved third order adaptive integrator-quadrature signal generator (ITOAI-QSG). The HCM is used for mode transitions between the grid integrated mode (GIM) to islanding mode (IAM) and vice-versa, and this confirms the seamless transfer between the modes without rearranging the control structure. In GIM, the presented controller regulates the grid current and provides dynamic grid support by the reactive power compensation. In IAM, the controller changes from the grid current control to the voltage control to adjust the PV inverter's output voltage. The control method is presented for a parallel-operated single-stage where PV panels are interfaced with the inverters with independent maximum power point tracking (MPPT) to harvest maximum power. The battery energy storage (BES) is interfaced with the main PVI unit to support the load demand in peak demand periods and maintains the power balance in IAM. The charging and discharging profiles of BES are achieved by a DC-DC bidirectional converter (DBC). Test results verify the performance of the developed control.</b>

Research on the optimal allocation method of PV micro-grid energy storage capacity based on empirical modal decomposition

To address the problem of high battery usage throughout the year, an empirical modal decomposition-based optimal allocation method for PV microgrid energy storage capacity is designed. This paper describes the basic structure of the photovoltaic equivalent circuit to obtain the output power of the photovoltaic microgrid panel. We use the energy storage battery as a secondary power source to smooth the photovoltaic power output and calculate the wasted power. In this paper, an energy management model based on empirical mode decomposition is constructed, and the photovoltaic power generation unit and energy storage unit are regarded as optimally configured whole. Based on the above conditions, an optimal allocation method of energy storage capacity is designed. Experimental results: the mean value of annual battery usage for the PV microgrid capacity optimization allocation method in the paper is: 53.74%, indicating that the designed PV microgrid capacity optimization allocation method is more feasible after combining the empirical modal decomposition algorithm.

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.

Multicriteria Decision-Making Approach for Optimum Site Selection for Off-Grid Solar Photovoltaic Microgrids in Mozambique

Given the constraints associated with grid expansion costs, limited access to reliable electricity, and priorities in addressing the climate agenda and Sustainable Development Goals in low-income countries, microgrids and off-grid solar projects represent a viable solution for rural electrification. This type of solution has the advantage of being less expensive than conventional technologies, is rapidly scalable, affordable, environmentally sustainable, and can play a critical role in empowering rural communities. In this context, this study proposed a spatial framework for off-grid solar energy planning based on a Geographical Information System and Boolean logic, Fuzzy logic, and Analytic Hierarchy Process Multicriteria Decision-Making methods. The results of the applied methodology show that the selection of optimal locations for off-grid solar photovoltaic microgrid projects in Mozambique is significantly influenced by the following order of criteria: climatology, orography, technical and location, social, and institutional criteria. Geographically, about 49% or 344,664.36 km2 of the total study area is initially suitable for an off-grid solar photovoltaic microgrid project; 4% is low suitable, 14% is moderately suitable, 18% is suitable, and 13% is highly suitable. However, 51% of the ranked areas fall into the not feasible and restricted areas, mainly in conservation areas, protected areas, and areas at high risk of flooding and cyclones, covering a total of 387,005.5 km2 within the study area. In general, the approach helps to reduce uncertainty and increase flexibility to identify appropriate sites and strengthen indicators of sustainable development impacts of decentralized rural electrification.

The Use of Evolutionary Algorithms in the Modelling of Diffuse Radiation in Terms of Simulating the Energy Efficiency of Photovoltaic Systems

In light of the rapidly growing number of photovoltaic micro-grids, the modelling of their short-term power yields based on meteorological measurements is increasing in significance. This requires the knowledge of total and diffuse instantaneous solar radiation; however, most meteorological stations conduct actinometric measurements only with regard to total solar radiation, especially on a minute scale. This paper contains an analysis of the currently used PV cell mathematical model and suggests its modification aimed at calculating PV cell power with satisfactory accuracy, without the knowledge of diffuse solar radiation. Three function families were proposed to approximate the relationship between diffuse irradiance and the total and theoretical total irradiance variance for a cloudless sky. A program has been implemented to identify functions from the aforementioned function families. It leverages an evolution strategy algorithm and a fitness function based on the least-squares point method. It was employed to calculate the desired functions based on actual measurement data. The outcome was the sought-after dependence that enables predicting diffuse irradiance based on more frequently available measurement data.

Design of Fuzzy PI Controller for Voltage Regulation in DC Micro-Grid

Efficiency, standard and security of a micro-grid can be tested by measuring the voltage regulation in the DC bus. Photovoltaic micro-grid are prone to time invariant and non-linearity due to variation in solar irradiance. Energy storage device is incorporated to overcome such variations. Traditionally using PI controllers, we can control the charging and dis-charging of battery which only regulates voltage in the bus but it fails to control the fluctuations in the DC bus. Therefore, Fuzzy PI controller is proposed in this paper where it employs the intelligent controlling method thereby reducing the fluctuations in the DC bus. Keywords—Fuzzy, PI, Fuzzy-PI, DC-micro-grid, closed loop control, voltage regulation.

Current and future prospective for battery controllers of solar PV integrated battery energy storage systems

Solar photovoltaic (PV) microgrids have gained popularity in recent years as a way to improve the stability of intermittent renewable energy generation in systems, both off-grid and on-grid, and to meet the needs of emergency settings during natural catastrophes. Over the last several decades, researchers have been interested in improving the efficiency of photovoltaic (PV) systems. Solar-battery charge controllers based on various algorithms are continuously and intensively employed to improve energy transfer efficiency and reduce charging time. This paper presents state-of-the-art solar photovoltaic (PV) integrated battery energy storage systems (BESS). An overview of and motivations for PV-battery systems is initially introduced, followed by the survey methodology and its contributions. In addition, this study classifies residential solar PV systems and battery charge controllers with their corresponding references in the review structure, which also provides details on battery charger topologies. Subsequently, an analytical review of the PV-Battery charge controller and the failure probability of such systems is discussed to determine the system components that mostly fail and their importance in the system. Finally, recommendation amendments to the existing charge controller that potentially contribute to increasing the system efficiency, reducing the failure probabilities, and reducing the cost are presented as future design concepts for the entire system.

Daily consumption monitoring method of photovoltaic microgrid based on genetic wavelet neural network

Abstract In order to comprehensively monitor the daily consumption of photovoltaic power and power generation of photovoltaic microgrid, a daily consumption monitoring method of photovoltaic microgrid based on genetic wavelet neural network is proposed to reduce the relative error of daily consumption monitoring. Considering the power generation forms of various units such as wind power, thermal power, hydropower and photovoltaic power generation, the upper and lower limits of daily consumption of different units and the constraints of consumption calculation are analyzed to obtain the daily consumption of photovoltaic microgrid. On this basis, the daily consumption monitoring model of photovoltaic microgrid including multiple inputs and outputs is constructed by using Morlet wavelet function, and the power generation is calculated by wavelet neural network. The genetic algorithm is used to optimize the individual fitness of wavelet neural network through the training of the number of wavelet bases and related thresholds and weights, and to normalize the optimal individual fitness to realize the daily consumption monitoring of photovoltaic microgrid. The experiment shows that this method can monitor the actual photovoltaic power in sunny weather, and after 12 o’clock, the photovoltaic power gradually drops below 30 kW. In cloudy weather, the actual photovoltaic power reaches its peak at around 12 o’clock, ~45–50 kW, and drops to about 10 kW at 17 o’clock. And the power generation in cloudy days is relatively low, and the power generation in rainy days is the lowest. When the relative humidity is 30%, the power generation increases rapidly and keeps at 8 kWh. When the relative humidity is 50%, the power generation gradually drops to 2 kWh. When the temperature is 20°C, the maximum radiation intensity is about 0.6 kW m2. When the temperature is 30°C, the maximum radiation intensity is greater than 0.8 kW m2. At 11:00 and 12:00, the power generation error is 0.02 kWh. In order to improve the monitoring accuracy of photovoltaic power and daily power generation of photovoltaic microgrid in different environments.

Collaborative Planning Model of PV-Battery Storage System for Microgrid Considering Demand Response

The configuration of energy storage with reasonable capacity in the photovoltaic microgrid is a powerful way to promote the local consumption of distributed photovoltaic and improve the efficiency of photovoltaic system. DSM can effectively reduce grid investment and become an important regulation means for efficient operation of power system. The main innovation of this research is to consider the utilization of demand side resources in microgrid planning, which is rarely deployed in previous studies. Firstly, the models of renewable energy output characteristics and energy storage SOC characteristics are established, and the demand response mechanism is analysed; Secondly, taking the minimization of the comprehensive cost of the system as the objective function and the energy balance, equipment characteristics as constraints, a collaborative planning model of PV-Battery storage system for microgrid considering demand response is constructed. Finally, the rationality and effectiveness of the proposed optimization model are verified by the actual data of a microgrid in Fuzhou.

Industry 4.0 Based Efficient Energy Management in Microgrid

Industry 4.0 which includes new technologies such as artificial intelligence, machine learning, and the internet of things etc. has brought the revolution in the field of energy management of a microgrid. Energy management is the backbone of a microgrid that needs to be controlled efficiently for a low system failure. There are a lot of issues, such as the intermittent nature of generation, proper voltage distribution, and harmonics, which may arise while implementing an energy management for a microgrid. Machine learning establishes the core of industry 4.0 and is one of the best-suited methods to mitigate such challenges in the current industry 4.0 scenario. In this paper, a Back Propagation Neural Network (BPNN) based machine learning approach is applied for forecasting of a photovoltaic (PV) generation in a microgrid to deal with its intermittent nature for efficient energy management. Further, a firefly optimization technique is utilized to mitigate the harmonics in the voltage. This model is implemented on a real dataset of a solar power plant in Delhi, India. The proposed approach achieves the results of high precision, recall, and accuracy, which shows the efficiency of the system to monitor and regulate uncertainties in the PV microgrid systems.