Summary

High-capacity hybrid thermoacoustic electric generators (HTAEGs) are ideal in different small- and micro-scale energy systems, especially in space nuclear power systems. In this work, an HTAEG with a post-positioned gas spring is proposed. To demonstrate the superiority of the gas-spring-post-positioned design on high-capacity HTAEGs, an HTAEG prototype is modeled, built, and tested from the perspective of thermoacoustics accordingly. Experimental results demonstrate an output electric power of 15.0 kW. Furthermore, it could achieve the highest efficiency of 39.2% with an output electric power of 11.1 kW. Given that a 15-kW power output is an ultra-high level on a single-piston HTAEG of this type to date, and the achieved efficiency on the prototype is also encouraging, this work marks an important milestone in the development of high-capacity HTAEGs. It also demonstrates that the gas-spring-post-positioned design has significant advantages and enormous potential for application.

Introduction

The thermoacoustic electric generator (TAEG) emerges as a promising dynamic conversion technology due to its exceptional reliability, extremely long lifespan, versatility in heat source utilization, and high intrinsic efficiency.1 These attributes render it particularly well suited for space and submarine thermoelectric applications.2,3 Within the TAEG family, a hybrid one (traditionally called the free-piston Stirling engine,4 currently recognized as a generalized thermoacoustic engine5) that incorporates a solid mass to tune the internal acoustic field transcends the conventional gas-tuning systems in the aspects of power density and thermal-to-electrical efficiency.6

Most recently, dramatic progress, e.g., 16-year non-stop operation and over 39% thermal-to-electrical efficiency,7 has stoked up the enthusiasm for the hybrid thermoacoustic electric generator (HTAEG) in space application. However, the current achievements are all confined in a rather small power level (i.e., around 100 W), not sufficient to cover various application scenarios. Scaling up to tens of kilowatt power while maintaining high reliability simultaneously is formidably challenging, and so far, there is no public literature reported on it. The main obstacle is the spring force mechanism that is imperative to tune the acoustic field. For low- and medium-power HTAEGs (ranging from tens of watts to kilowatts), planar springs are commonly employed as the spring force mechanism for their excellent compactness and radial rigidity.8 These planar springs can be positioned either at the bounce space or compression space, resulting in two distinct designs for HTAEG. Meanwhile, both designs have been extensively utilized in numerous HTAEG prototypes, with their reliability well demonstrated through ground tests.7

Results and discussion

System configuration and numerical study
Figure 1 presents the schematics of an MGS-HTAEG and a PGS-HTAEG. The PGS-HTAEG is primarily a helium-filled pressure vessel that houses a hybrid thermoacoustic engine (HTAE) and a linear alternator (LA). The HTAE comprises an ambient heat exchanger (AHX), a regenerator, a hot heat exchanger (HHX), and a displacer. Meanwhile, the LA consists of an electromagnetic conversion circuit consisting of outer and inner stators, moving magnets, and coils, along with a power piston supported by gas bearings. Within the HTAEG, a combination of the AHX, the regenerator, and the HHX constitutes its thermoacoustic core, designed to transform external thermal energy into acoustic energy. By heating the HHX while rejecting heat from the AHX, helium within the regenerator undergoes oscillatory flow due to the thermoacoustic effect. Consequently, thermal energy is converted into acoustic energy. The displacer plays a pivotal role in modulating the acoustic field and facilitating the transfer of acoustic power. When the acoustic pressure fluctuation generated by the thermoacoustic core pushes the power piston to move reciprocating in the cylinder, the moving magnets mounted on the power piston move in a stationary coil accordingly, and the acoustic power is converted to electric power through electromagnetic induction.

Abstract

A novel time–current-rate-based inverse characteristic curve for relays in a DC microgrid is proposed in this paper. Line current rise rate is used as actuating quantity, ensuring quick line fault clearing (relay operating time is in order of a few . The advantage of using line current rise rate as actuating quantity is that for a line short-circuit fault, it does not vary significantly for grid-connected and islanded modes of operation and varying network topologies of DC microgrid. Consequently, using the proposed characteristic, a single set of optimal relay settings is obtained using an optimization solver in MATLAB. The obtained settings ensure reliable and selective coordination between primary and backup relays for various pole-to-ground and pole-to-pole line faults under different operating conditions with multiple sources in two different 4-bus 400V low voltage DC microgrids. The maximum relay operating time for a high resistance fault with the proposed curve is for the first considered low voltage DC microgrid. Simulations in the Real Time Digital Simulator and comparisons with previous schemes (one comparison on the second considered DC microgrid), conventional standard inverse, and extremely inverse curves for DC microgrid protection indicate the effectiveness of the proposed scheme.

Introduction

The ease of integration of solar Photo-Voltaic (PV) and Battery Energy Storage Systems (BESSs) based renewable energy resources in a DC microgrid, compared to an AC microgrid, has led to the increased expansion and research interest of DC microgrids over AC microgrids [1], [2]. Also, DC microgrid can be a great way to power remote towns, shipboards, spaceships, and grids with sensitive loads, where the quality of power is crucial [3], [4]. However, the rate of rise of current is very high in a DC microgrid during a line fault due to the rapid discharge of the DC link capacitor and the associated low line impedance [5]. Hence, in general, the DC microgrid protection schemes follow a three-time frame based protection methodology in which the protection of the line, feeder, and the source should be done within a few milliseconds, and seconds, respectively [6]. Several DC Circuit Breakers (CBs) have been developed to support the quick isolation of lines during a fault in a DC microgrid, among which the solid-state CBs have a response time in the range of a few

Conclusion

This paper proposes a novel inverse time relay characteristic for the protection of DC microgrids against line faults using line current rise rate as an actuating quantity. It can coordinate relays in a DC microgrid with non-unit fault detection schemes or act as a backup scheme for relays with communication-based primary method. The relay coordination using the proposed characteristic is tested on a V LVDC microgrid. A single set of optimal relay settings ( and ) and a common set of values of and in (8) are obtained, considering all possible modes of operation, network configurations, and worst-case PG, PP LRFs and PG HRFs. Consequently, the proposed approach does not require an online update of and with a change in operating condition of DC microgrid. The efficacy of the proposed curve is validated with the obtained optimal unique relay settings for various PG, PP LRFs, PG HRFs for GC, islanded modes of operation, ring, radial network configurations, and intermittency of generation from renewable energy sources. Zero relay miscoordinations with a maximum primary relay operating time with the proposed curve are and for LRF and HRF faults, respectively, proving the effectiveness of the proposed technique in LVDC microgrid.

Further, the issues of using SI and EI curves to coordinate relays in a LVDC microgrid using line current and line current rise rate as actuating quantity and issues with using line current as actuating quantity in proposed scheme are discussed. It is observed that the conventional EI curve with line current rise rate may be used for relay coordination in DC microgrid. However, the high relay operating time (maximum primary relay operating time being ) may lead to prolonged line fault isolation. Additionally, the proposed scheme is fast and reliable, compared to EI curves, since the sum of primary relay operating times, considering all fault scenarios, is with the proposed curve, compared to the EI curve, which gives . The comparison with [16], [17] reveals the efficacy of the proposed scheme with the relay operating times in the proposed scheme being in the range of a few for line faults in LVDC microgrid.

Abstract

Solar energy forecasting has seen tremendous growth by using weather and photovoltaic (PV) parameters. This study presents new approach that predicts solar energy production by using the scheduled, unscheduled maintenance activities and weather data. The dataset is obtained from the 1MW solar power plant of PDEU (our university), which has 12 structured columns and 1 unstructured column with manual text entries about different scheduled and unscheduled maintenance activities, and weather conditions on the daily basis. The unstructured column is used to create new features by using Hash-Map, flag words and stop words. The solar power generation forecasting is formulated as a vector auto regression (VAR) optimization problem and total power generation forecasting is presented with the results of four different cases. The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting for effective and efficient management of solar power plants. The power generation decay is different for all the PV sets which show the variations in the impacts of weather, aging and maintenance on the solar power plant. This research work has proven that the peaks of total power generation forecasting and prediction can be tracked in a better way by using daily unscheduled, scheduled maintenance activities and weather conditions.

Introduction

Solar energy forecasting is an emerging area over the last decade by using historical time series data collected through a weather station (such as weather variables wind speed and direction, solar irradiance, and temperature). The scheduled and unscheduled maintenance activities are carried out frequently for efficient and effective management of solar power plant. Another important parameter that contributes slowly but effectively is the age of the Solar PV module. The degradation of PV modules is further impacted by the conditions such as High temperature, solar cell shedding, discoloration, and delamination, etc. India has been endowed with non-depleting renewable energy resources like solar, hydro, geothermal, and wind and it is currently ranked no. 3rd globally in the renewable energy market. According to MNRE sources, about 5,000 trillion kWh of energy is falling on India’s land mass annually, with the majority receiving between 4 and 7 kWh per square meter per day. This is going to be one of the fastest-developing industries to boost rural electrification as well as the economy. Currently, there are more than 35 solar power plants in India. These plants are managed and operated by various government organizations. The operational expenditure of MW to GW solar photovoltaic power plants imposes a huge cost. Artificial Intelligence based solutions such as forecasting and recommendation-based systems can provide fruitful insights to reduce such expenditure.

Conclusions and Future Work

The maintenance activities like “grid failure”, “inverter failure”, “module cleaning” and the weather parameter “cloudy” have huge impact on the solar power generation forecasting and prediction. The optimum value of lag p=12 results in lowest RMSPE, RMSE and MAE for different cases. The error is reduced due to inclusion of duration for maintenance activities in this model for case 2. The VAR model used in this work is capable of forecasting all the four features as well as power generation target variable simultaneously. The predicted power generation is able to follow the trends of original power generation when the thresholding is applied to the predicted values of maintenance features (activities). The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values for all cases. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting which result in effective and efficient management of solar power plant. The power generation decay is different for all the PV sets which show the variation in the impacts of weather, aging and maintenance on the solar power plant. The effects of aging, dust and other environmental factors can be considered in the model as a future scope to improve the solar power generation forecasting and prediction.

Abstract

     The modern active distribution systems necessitate integrating storage systems, thereby facilitating the large-scale proliferation of photovoltaic (PV) energy resources. This further calls for the optimal planning of energy storage systems, satisfying all the operational and economic constraints. This article describes an exhaustive storage integration method, deeming the life cycle of the battery energy storage, the uncertainty of load and PV output, and the islanded mode of operation of the system. A two-stage mixed-integer linear programming problem is formulated that determines the capacity and the number of discharge cycles of the batteries in the first stage. The lifetime of the battery based on the partial depth of discharge is analyzed in the second stage. Furthermore, the uncertainty and variability of PV and demand are taken into account through probabilistic analysis and time-period clustering. The method is validated on a standard 33-bus radial distribution network for the allocation of distributed lithium-ion batteries. Also, the method’s scalability is validated on a practical Indian distribution network and a 141-bus distribution network of metropolitan area of Caracas with distributed PV installations on various nodes.

Introduction

     S MART grid is a gratuity to the consumers and the stakeholders involved in the electric grid’s generation, transmission, and distribution sections. It includes the complete modernization of the power system with smart meters, automation devices, intelligent monitoring, demand-side management, and self-healing capability schemes. Along with these activities, generation units are set up in the distribution section, i.e., nearer to the consumer premises, to reduce the transmission losses. This shift of the electric grid from a passive network to a bidirectional active network is due to the small generators being set up in the distribution grid known as distributed/decentralized generation. These distributed generators (DGs) mainly generate energy using renewable energy resources, such as photovoltaic (PV), wind, biomass, tidal, etc., as they are clean sources of energy and promote sustainable development. However, the intermittent nature and excess penetration of these resources pose problems, such as reverse power flow, voltage fluctuations, increase in losses, harmonic distortion, and instantaneous matching of demand and supply [1], [2]. Although integrating storage systems in the distribution network can help alleviate the aforementioned problems if allocated optimally. Also, India’s target of 40-GW PV rooftop installations by the year 2022 and the declining cost of batteries will provide a fillip to the installation of energy storage systems (ESSs) [3]. Therefore, an ESS can be seen as a game changer in the ever-changing power system paradigm and can serve multiple ranges of benefits. To achieve these benefits, methods for optimal sizing, allocation, and operation of ESSs are required

Conclusion

     The integration of renewable energy resources necessitates the allocation of distributed energy storage to mitigate voltage deviation and defer distribution upgrades. This article illustrated a two-stage procedure for integrating lithium-ion batteries in a PVpenetrated active distribution network. The planning methodology considered an ac-OPF model with the uncertainty of PV and demand to determine the batteries’ accurate size. Moreover, the reverse power flow due to excess PV penetration was utilized and the voltage was within limits after the integration of batteries in the system. The battery’s life cycle was examined in the second stage with the partial cycle of discharge and complete discharge cycles. For lithium-ion batteries, partial cycle discharging was fine as there was no memory effect, and the battery does not need full discharges periodically. From the results, it was illustrated that with the partial cycle of discharge, the battery will last longer. Moreover, the method used here was the mathematical programming, which provides a robust solution, but may require significant computation time for large and complex problems. Also, the maximum discharging capacity of the battery gradually declined with its cycles, which can be kept as a future research direction. Furthermore, to enhance the stability and cost-effectiveness, a smart charging and discharging operation can be added to the problem. In addition, different types of DGs, such as wind, micro-turbines, and fuel cells, can be considered an extension to the OPF formulation.

Abstract

Introduction

Methodology

PV efficienc

Data collectio

Experimental setup

Results and discussion

Effect of temperature on the power output of PV panels and TEGs

Conclusions

Nomenclature

References

Abstract

     Many factors affect the efficiency of photovoltaic panels (PV), which convert solar energy directly into electrical energy. Among these factors, temperature is one of the most important one. While some of the radiation from the sun is converted into electrical energy, part of it emerges as heat energy. This causes the photovoltaic cells to heat up and reduce their electrical efficiency. Different methods are used in the literature to reduce the temperature in PV panels. In this study, in order to reduce the adverse effects caused by the high temperature in the PV panels, 30 Thermoelectric Generators (TEG) were applied to the back surface of the PV panel to increase the PV panel output power and to produce additional electrical energy. Energy and exergy analysis made on the data obtained from both PV panels in the climatic conditions of the installation site showed that the temperature of the PV panel is reduced, and the energy and exergy efficiency is increased with the TEG application. At the end of July, August, and September, when the experiments were carried out, an average of 8.4% more electrical energy was obtained from a single PV panel with TEG, compared to the standard PV panel. Our results suggests that combination of TEG with PV panels could significantly increase the electrical energy, especially when a series of PV panels are used together.

Introduction

     Recently, investments in electricity generation from renewable energy sources, especially solar and wind energy, have been increasing. However, when the performance of photovoltaic cells in the natural field environment is compared with the version of the standard test conditions, the efficiency of the PV panels decreases with the increase in temperature due to radiation (Bel Hadj Brahim Kechiche, Hamza, and Sammouda 2016). Since the energy that cannot be converted into electricity increases the photovoltaic surface temperature, it causes damage to the PV panel module cells and leads to reduced service life of the PV panels (Enescu and Spertino 2017). When the studies in the literature about reducing the heat losses in photovoltaic panels (PV) are examined; A water-cooled nozzle spray system has been developed that significantly reduces the module cell temperature (Benato and Stoppato 2019). The factors affecting the performance of PV panels and the applications that can be made to increase the panel power are explained in detail (Fouad, Shihata, and Morgan 2017).To obtain higher efficiency by cooling the PV panel surface, the panel output power and the cell’s lifetime are increased with different cooling methods (Siecker, Kusakana, and Numbi 2017). To prevent the decrease in PV panel power output with temperature increase, new models are proposed on the bottom surface. In experiments carried out under natural conditions, it has been determined that the panel temperature can be reduced by 10°C with the evaporative cooling model and the power improvement is 5% (Haidar, Orfi, and Kaneesamkand 2021).

Conclusions

     In this study, the losses in energy production due to the increase in temperature in the PV panels, especially in the summer months, were reduced by TEG application. An increase in the PV Panel output power was achieved. According to the results obtained;

● More electrical energy was produced in the PV panel with a thermoelectric generator in July, August, and September when the experiments were carried out.

● With the application of a thermoelectric generator in the PV panel, an average of 8.4% more electrical energy was obtained per month. This indicates that when the number of PV panels combined with TEG is increased, the total energy could increase significantly

● The output power of TEGs also increases depending on the rise in air temperature and PV panel surface temperature

● Since the average temperature was higher in July, maximum energy was produced in the thermoelectric generator panel. Compared to the standard panel, the PV panel, in which a thermoelectric generator is applied, had 10.29% more electricity in July, 7.15% in August, and 7.09% more in September, respectively.

Abstract

INTRODUCTION

COMPUTATIONAL METHODS

DPD Method

Drift-diffusion Model

RESULTS AND DISCUSSION

One-step Quench

Two-step Quench

Comparison with Existing Experimental Observations

CONCLUSIONS

REFERENCES

Abstract

     The mixing morphology control plays a crucial role in photovoltaic power generation, yet this specific effect on device performances remains elusive. Here, we employed computational approaches to delineate the photovoltaic properties of layered heterojunction polymer solar cells with tunable mixing morphologies. One-step quench and two-step quench strategies were proposed to adjust the mixing morphology by thermodynamic and kinetic effects. The computation for the one-step quench revealed that modulating interfacial widths and interfacial roughness could significantly promote the photovoltaic performance of layered heterojunction polymer solar cells. The two-step quench can provide a buffer at a lower temperature before the kinetic quenching, leading to the formation of small-length-scale islands connected to the interface and a further increase in photovoltaic performance. Our discoveries are supported by recent experimental evidence and are anticipated to guide the design of photovoltaic materials with optimal performance.

Introduction

     Polymer solar cells (PSCs) are a kind of potential clean-energy technology, which holds promise for manufacturing lightweight and highly flexible devices such as portable electronic products and building-integrated photovoltaics.[1−5] Although the power conversion efficiency was dramatically improved recently, it has not yet broken through the requirements of commercial markets due to the limitation on the mechanism of charge carrier generation and transport.[6,7] To date, a considerable effort has been devoted to creating novel π-conjugated polymers to improve photovoltaic performance. In contrast, understanding the morphology effect on PSC performance and formulating basic rules that guide morphology optimization need to be further enhanced.

     Optimizing the morphology is indispensable for the successful preparation of PSCs with outstanding performance.[8−12] The PSC performance can be quantitatively correlated with phase purity and Flory-Huggins parameters.[13] Insufficient phase separation in PSCs can lead to performance deteriorations. However, a larger repulsion between donors and acceptors can lead to over-purification of mixed domains and decreased PSC performance.[14] For example, Ade et al. observed that the average power conversion efficiency shows a substantial drop as the composition of the amorphous mixed domains is below the percolation threshold.[15] Ye et al. recently found that such a problem can be resolved by kinetically quenching the mixed domains to an optimal composition close to the percolation threshold.[16] Despite this, fundamental guidelines are still required to optimize PSC performances with optimal mixing morphologies. Combining thermodynamic effects and kinetic controls can assist the design of heterojunctions with varied mixing morphologies.[16] The thermodynamics can drive the phase separation of donors and acceptors in PSCs, and the quench by kinetic control can “lock-in” instantaneous phase-separated morphologies. Recent attention has been paid to the stability of such mixing morphologies.[17−20] However, little is known about the kinetic route to control mixing morphologies and the influence of mixing morphologies on PSC performances. Quantifying the impact of mixing morphology on device performance by developing kinetic control rules is the key to optimizing the heterojunction structure and promoting power conversion efficiency.

Conclusion

     The DPD method, coupled with the drift-diffusion model, was employed to study the mixing morphology effect on the photovoltaic performance of layered heterojunction PSCs. We performed an in silico layer inter-diffusion experiment and designed two quench approaches to regulating the mixing morphology of layered heterojunctions, that is, one-step quench and two-step quench. In the one-step quench, the layered heterojunction with intermediate interfacial width exhibits an optimal photovoltaic performance. We further enhanced the power conversion efficiency of the layered heterojunction by modulating the mixing morphology with a two-step quench method. We discovered that the interfacial width, interfacial roughness, and small-length-scale island structures formed within acceptor- and donor-rich domains combinedly affect the power conversion efficiency. Our work delineates the effect of quenching processes on photovoltaic performance, which could be beneficial to the design and quantitative optimization of active layers.

Abstract

1. Introduction

2. PV cell model and design parameters

3. Fuzzy Distribution of PV cell parameters

4. Global sensitivity analysis of PV cell parameters

5. Analysis and discussion

6. Conclusion

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgments

References

Abstract

     Photovoltaic cells can directly convert solar energy into electrical energy, which is environmentally friendly and has a wide range of applications. In practical engineering, the uncertainty of photovoltaic cell parameters has an important impact on cell performance. In order to study the influence of parameter uncertainty on the output performance of photovoltaic cells, a photovoltaic cell model is established and five parameters are selected: irradiation intensity, photovoltaic cell surface temperature, temperature coefficient, equivalent series resistance and equivalent parallel resistance. The influence of these parameters on the output power and conversion efficiency of photovoltaic cells is studied by using the global sensitivity analysis method based on fuzzy theory. The principal and total global sensitivity indexes describing the influence of each parameter on the output performance were therefore calculated and ranked. The key parameters influencing the performance of photovoltaic cells were determined to be related to cell surface temperature and equivalent parallel resistance. In actual production and life, the output performance of photovoltaic cells can be improved by controlling these two factors. The results of this study can help to guide the performance analysis and parameter optimization of photovoltaic cells, accelerating the development and improving the adoption of photovoltaic systems.

Introduction

     With the aggravation of the global energy crisis and increasing environmental pollution, the development of solar energy occupies a critical position in the energy structure (Wang et al., 2021b, Xiong et al., 2022). As photovoltaic (PV) cells directly convert solar energy into electric energy, they represent an increasingly popular source of renewable energy. The number of deployed PV power generation systems has significantly increased worldwide (Wang et al., 2021a, Wu et al., 2022), and many countries have introduced grid pricing policies to accelerate investment in PV power generation. Therefore, research into the performance of PV cells is of great significance for ensuring their continued adoption (Sharma et al., 2021, Sadan and Dwivedi, 2020). To date, many experts and scholars have studied the parameters of PV cells (Chehreh Ghadikolaei, 2021, Chen et al., 2020, Chandran et al., 2021, Ganesh Pardhu and Kota, 2021, Ji et al., 2021). For example, Chen et al. (2020) used variables reduction and improved shark optimization technique to extract photovoltaic cells parameters, Chandran et al. (2021) proposed an improved distribution estimation algorithm to estimate the optimal parameters of fuel cells and solar cells, and Ganesh Pardhu and Kota (2021) proposed a new random radial shift optimization algorithm based on a group algorithm to extract the unknown parameters of solar PV cells. The confirmation and extraction of parameters of photovoltaic cells and their components is of great significance for evaluating the performance of photovoltaic cell systems and preventing aging and failure of systems and components.

Conclusion

     To address the instability of PV cell output performance according to the inherent fluctuation of design parameters, this study applied the global sensitivity index based on fuzzy theory to measure the influence of inherent design parameter fluctuation on the stability of PV cell output power  and conversion efficiency . The Monte Carlo method was used to calculate the principal and global sensitivity indexes of the design variables based on the credibility variance. The results show that compared with the cell surface temperature  and equivalent parallel resistance , the total global sensitivity index values of the short-circuit current temperature coefficient , irradiation intensity , and equivalent series resistance  are minimal for both the output power  and conversion efficiency , and the effects of their variation can therefore be ignored. Therefore, the key design parameters of PV cells are related to the working environment () and material (). In actual production and application, the output performance of PV cells can therefore be improved by controlling material selection(ensure a consistent equivalent parallel resistance ) and expected working environment temperature . The results of this study can provide guidance for the design and optimization of future PV cells, and further improve their application and utility to increase adoption.

Abstract

1. Introduction

2. Methods

3. Results

4. Conclusions

Code availability

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgements

References

Abstract

     Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.

Introduction

     Cities have become a determining factor in climate change, as they are the place where much energy is consumed (64% of global primary energy use) and high levels of greenhouse gases emitted (70% of the global total), due to the use of fossil fuels as energy sources (International Energy Agency, 2016). There is a great opportunity for citizens to reduce these emissions. Replacing polluting energy sources by renewable energies that respect the environment and do not compromise future generations is one of the essential requirements to achieve energy-sustainable cities and favour the fight against climate change. Additionally, switching to renewable energy sources as a detriment to polluting energies could improve health and quality of life. Precisely, one of the goals proposed in the 2030 Agenda for Sustainable Development by UN is making cities inclusive, safe, resilient and sustainable (United Nations, 2015).

     Solar energy has seen a large increase among renewable energies. According to the International Energy Agency (IEA), there was a 22% growth up to 720 TWh (representing 3% of global electricity generation) in 2019 (International Energy Agency, 2020). Although large photovoltaic infrastructures are away from cities, there has been an exponential rise in distributed installations in buildings, industry and houses in Europe, the United States and Japan (International Energy Agency, 2020). This is very important since local production reduces transportation losses and enhance citizen’s responsibility because of inspiration for searching for energy self-sufficiency. In recent years, a new type of building, based on that type of installation, has been proposed as an evolution of Zero Energy Building (ZEB): the Positive Energy Building (PEB) (Magrini et al., 2020).

Conclusions

     An open-source tool, URSUS-PV, has been built to estimate potential electricity that can be generated in photovoltaic (PV) facilities in the short-term (one-day-ahead) and in the long-term (daily average) in an urban area of interest (neighbourhoods, streets, complex buildings). It could be potentially useful for multiple types of users, including municipalities, public administrations, companies in the photovoltaic sector, cooperatives or neighbourhood communities.

     One of the tools’ most significant benefits is the automation of the complex process. It initially had to be performed manually to obtain global results in urban areas of interest to produce short-term or long-term photovoltaic energy estimations. After conducting a data mining process, such manual processing has now been computerized. CRISP-DM methodology has supported the process. We have executed all the steps from the business understanding to the final deployment, which includes models discovered in intermediate phases. Therefore, following such methodology, once the data sources (meteorological and geographical data) have been identified, its acquisition and integration are automated. Transformations done in the preprocessing stage are also programmed, meaning that segmentation of the roofs available in urban areas of interest and extraction of their features can be easily computed. The calculating photovoltaic energy potential phase has also been fully automated using meteorological data of the city and the configuration of PV facility that could be integrated into each roof.

Abstract

Introduction

Methodology

Data collecti

Performance of ANN forecaster

Performance of hybrid-ANN forecaster

Performance of hybrid-ANN forecaster-S

Conclusion

Nomenclature

Acknowledgments

Disclosure statement

References

Abstract

     The integration of solar photovoltaic (SPV) system to the grid has introduced a new source of intermittency in the grid, and the grid has to react smartly to the changes that occur in the penetration of SPV power. Accurate modeling of weather-dependent SPV power will be helpful in forecasting the penetration of SPV power into the grid. An SPV power output forecasting model has been developed based on artificial neural network (ANN) approach. Two forecasters, namely ANN forecaster and two-stage hybrid-ANN forecaster, are developed with operational and weather parameters. The historical data of SPV power (P), hours of operation of SPV system (to), daily global solar radiation (H), and ambient temperature(T) are used as modeling parameters. The combination of modeling parameters {P, H, T, to} is identified as the best combination that influences the forecasting of day-ahead power output. A relative root mean square error (RRMSE) of 5.74% was obtained with the combination of {P, H, T, to}. An RRMSE of 6.04% was observed with the combination of {P, H, T} as inputs, and the hours of operation of the SPV plant could be ignored in the model. The historical power data of the SPV plant is identified as the crucial parameter in the SPV power forecast model and has given an RRMSE of 7.25%.

Introduction

In recent times, the consumption of energy from conventional sources has been increased, which causes the rapid depletion of fossil fuel-based resources. The emission of greenhouse gases has increased because of the utilization of conventional sources over the past few decades (Fikru and Gautier 2015). In addition to that, the energy demand has been increasing enormously in both developed and developing countries due to the increase in population, urbanization, and industrialization. The portion of this energy demand can be met by renewable energy sources (RES) such as wind and sun. Solar energy can be captured by installing solar collectors, solar ponds, solar chimneys, and solar photovoltaics (SPVs). Out of all these installations, the SPV installation growth rate has increased enormously. The generation of electricity using SPV is widely accepted (Larson, Nonnenmacher, and Coimbra 2016). PV power generation is ecofriendly because it produces no toxic emission, of low maintenance, of noiseless operation, and it is abundantly available (Yan et al. 2021). Up to now, several thousands of SPV systems of 1 kW to several hundreds of megawatts have been installed and integrated into the grid worldwide (Tina, Scavo, and Gagliano 2020). The integration of SPV systems to the grid has introduced a new resource of intermittency in the grid (Sepasi et al. 2017). This is due to the sensitivity of the SPV system to local weather conditions and variation in solar radiation availability. The SPV power output is uncertain since the parameters like irradiance level, ambient temperature, surface temperature of the panel, dust deposits, wind speed, cloud cover, and relative humidity etc., will have an impact on the SPV power generation. Accurate modeling of SPV power is required by the utilities to control the high instabilities of the electric grid due to unpredictable PV power penetrations (Wan et al. 2015).

Conclusion
A day ahead SPV power forecast models are developed by using ANN approach. Daily GSR (Hm, Hp), ambient temperature, hours of operation of SPV system, and historical data of SPV power are used as input parameters in the modeling of the day ahead forecast models. The trial and error method has revealed that the ANN model with three neurons in the hidden layer with linear-log-sigmoid transfer function has shown the best performance.

     The effect of each parameter and the combination of parameters on the model accuracy is identified by developing various forecast models, and a total of 21 forecast models are developed. Out of the 21 developed forecast models, ANN forecaster-4 with the combination of inputs {P, H, T, to} has shown the best performance. The combination has shown a strong influence on the SPV power output. The MAPE of 4.18% and the RRMSE of 5.74% are derived with 4-3-1 ANN architecture. Historical data of measured power is the best suitable modeling parameter to predict the SPV power. Historical power measurements will not be available before the installation of the SPV system, and in such cases, ANN forecaster-2 with {Hm, T} could be used as a forecast model.

Abstract

I. INTRODUCTION

II. EXPERIMENTAL DETAILS

III. RESULTS

IV. DISCUSSION

V. CONCLUSION

REFERENCES

Abstract

     Nanocomposites have been developed as the third-generation insulating materials. This article introduces the crosslinked polyethylene (XLPE) nanocomposite dielectric doped with a small amount of nano-hexagonal boron nitride ( h -BN), which exhibits excellent insulation properties under high dc voltage. The test results show that XLPE/ h -BN nanocomposites possess significant improvements in dc breakdown strength and 0.1 wt% nano h -BN doping can actually increase the dc breakdown strength by 39%. The space charge properties of the XLPE/ h -BN obtained by the pulsed electro-acoustic (PEA) measurements and the direct current integrated charge [DCIC- Q(t) ] measurements show that the positive homocharge injection under dc voltage is obviously suppressed, and the electrical conductivity under relatively high electric field decreases. The thermally stimulated current results show that the nano h -BN doping introduces only deep charge traps (above 1 eV) to the XLPE matrix. The trap energy is deeper than that of the original traps, and this is the main factor leading to the improvement of the dc insulation properties. Besides, the doping of h -BN does not introduce impurities or change the crystallinity of XLPE. This article presents the novel XLPE/ h -BN nanocomposites with excellent dc insulation properties and indicates the large potential of the nano h -BN-doped insulation materials.

Introduction

     NANOCOMPOSITES have excellent dielectric properties and have become the third-generation insulating materials [1]. Nano-doping can improve corona resistance and electrical tree characteristics as well as regulate dielectric properties of the polymer matrix. More importantly, the dc insulation properties can be enhanced to various degrees by inorganic nanoparticle doping, such as the suppression of the charge injection and transportation, as well as the increase of the breakdown strength. With the construction of high-voltage direct current (HVDC) transmission line in a large scale, the power industries have proposed stricter requirements on the dc insulation performance of insulating materials. Therefore,nanocomposite dielectrics may be more suitable for dc insulation systems in the future, and nanocomposite materials with higher insulation performance need to be continuously developed. For example, crosslinked polyethylene (XLPE)- based nanocomposites are expected to be used in the new generation of HVDC cable.

Conclusion

     The XLPE/h-BN nanocomposite with high dc breakdown strength and suppressed charge injection features were introduced in this study. It is useful to reach the conclusions as follows.

     1) XLPE/h-BN nanocomposite samples with loading content ranging from 0.1 to 3 wt% were prepared successfully. The dc Eb of the nanocomposite sample shows at most a 39% increase under 0.1 wt% loading.

     2) The effect of h-BN doping on the charge trap characteristics of XLPE is a significant increase in the density of deep traps, and this is the main cause for the improvement of dc insulation properties.

Abstract

1. Introduction

2. Microgrid Control

3. Overview of AI framework for microgrid control

4. AI in Hierarchical control – review and possible improvement

5. Networked microgrid

6. Discussion and future scopes

7. Conclusion

Acknowledgement

References

Abstract

     Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and forming essential consumer/prosumer centric integrated energy systems. Integration, coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task. Classical control techniques are not enough to support dynamic microgrid environments. Implementation of Artificial Intelligence (AI) techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks. Therefore, this paper briefly reviews the control architectures, existing conventional controlling techniques, their drawbacks, the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers. This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments. It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.

Introduction

     Microgrids can be distinguished from any distribution network containing DERs by two distinct features. First, their capabilities to operate in an islanded mode confirms the resiliency and reliability of the network. Second, to appear as controlled and coordinated units viewing from the upstream network [1]. Microgrids provide noteworthy benefits to consumers as well as utilities, the majority of which include; higher reliability by incorporating flexibility at the community layer distribution network, improved power quality by managing flexible loads, reduced carbon emission by the diverse DERs, lowering transmission & distribution losses, cheap energy supply utilising more renewable resources, and the possibility of active participation in the energy markets [2], and must fully satisfy the network and load requirements at islanded mode [3].

     Taking into consideration the large share of stochastic natured DERs comes with lots of uncertainty and a range of instances where effective controlling of the microgrid network becomes very critical. Interconnecting multiple microgrids as a network of microgrids can be an effective solution to accommodate and improve the operation quality of the large number of DERs. It has also been recognised that when multiple microgrids, geographically close to each other, are tied together through the distribution line to form a networked microgrid, the reliability and resilience of the interconnected microgrid can be significantly increased [4, 5].

Conclusion

     This paper provides an overall review of AI-based control in microgrid environments. An overview of existing traditional control methods, their drawbacks, the need for AI techniques and their implementation at the different levels have been reviewed and future scopes have been presented. Despite system model complexities and challenges, it is found that AI can certainly be an important tool to enable the seamless integration and control of DERs at the local and networked levels. NN-based models are getting more focus on all levels. Most of the implemented AI techniques are physical model-based, whereas data-driven techniques should also gain more interest. Since a data-driven model doesn’t require extensive physical system information, the design becomes less complex. However, in some specific applications like inertia estimation in the primary control, there is a lack of data available at the low voltage distribution network level. As ESS is becoming a core part of decarbonising the smart and microgrid networks, the datasets for ESS SoC levels which are accessible and open to the public require extensive preprocessing to improve the data quality and better predictability.