Abstract

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees’ cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.

Introduction

The steep growth of anthropogenic greenhouse gas (GHG) emissions related to the use of fossil resources requires fast and challenging solutions; transportation and electricity production sectors are responsible for around two thirds of global CO2 emissions. According to the numbers published by the U.S. Energy Information Administration (EIA) in its monthly energy review of August 2022 [1], the total consumption of primary energy in the U.S.A. in the year 2021 was 28.5 Million terawatt-hours (TWh) or 2.4546 Billion tonnes of oil equivalent (toe), 7.9 Million TWh (0.6782 Billion toe) of which are used by the transportation sector and 10.8 Million TWh (0.9267 Billion toe) by the electric power sector. 94% of the total energy used for transportation came from petroleum and natural gas and 6% from biofuels and electricity [2]. In 2021, according to EIA estimates [3], cars, light trucks, and motorcycles accounted for the largest shares of total U.S. transportation sector energy consumption, with light-duty vehicles (cars, small trucks, vans, sport utility vehicles, and motorcycles) accounting for 54.2%, commercial and freight trucks 4.5%, jets, planes, and other aircraft 8.7%, boat, ships, and other watercraft 4.6%, trains and buses 2.6%, the military sector 2.0%, pipelines 2.8%, lubricants 0.5%. Globally, carbon dioxide emissions associated with transportation accounted in 2021 for 7620 Mt; 600 Mt less than pre pandemic level by 2019 [4].

Since the onset of the industrial revolution, ever increasing amounts of GHG have been released into the atmosphere. While the energy mix and corresponding supply chains shifted from coal to oil and more recently towards natural gas, the prosperity of humankind still relies on fossil fuels and energy conversion systems based on fuels combustion. Even today, transportation and electricity production are heavily reliant on CO2 emitting technologies. But there is a silver lining, as technological improvements coupled with a strong cost reduction enabled the fast growth of the total installed energy capacity of renewable energy sources for electricity production (RES-E). Together with powerful battery energy storage systems (BESSs), they will impose drastic changes in these two historically conservative sectors. Today, the fast growth of electric vehicles (EVs) related technologies, as well as their forecasted market penetration, reaching the record of 6.6 million vehicles sold in 2021 [5], generates new scenarios impacting the electric energy production and transportation market. Based on existing climate-focused policy pledges and announcements, the International Energy Agency predicts for 2030 EVs to represent more than 30% of vehicles sold globally across all modes (excluding two- and three-wheelers) and the global electricity demand for EVs to reach 1100 TWh; then about 4% of the total final electricity demand [5].

Conclusions

This paper presented the SPEM IoT platform, capable of simulating, designing and real time managing the smart charging of EV fleets in a RES-E driven parking lot.

A deterministic rule-based algorithm was introduced for the fine-grained management of the energy fluxes between EVs and the different electric components of a solar parking lot. Since the algorithm does not utilize any assumption on the future mobility and energy demand it can handle unforeseen changes avoiding critical situations.

As it was shown, intra-day deferral serves to shift EV charging demand towards the time of maximum production, guided by MPC, constantly analyzing the momentary situation of 1) intermittent solar production, 2) stored energy and energy storage availability, 3) charging station occupancy and 4) solar production forecast.

An extensive case study was conducted, where the design mode of SPEM was applied to the historic situation of an Italian Research Center in 2019, assuming the substitution of all employee’s cars by EVs. As a benchmark for the validation of SPEM, HOMER® Grid, a commercial software for the modeling, design and simulation of electrical infrastructure, was used.

Abstract

Keywords

1. Introduction

2. Related work

3. Application research of Internet of Things in solar photovoltaic power station monitoring and management system

4. Development of integrated computer-aided design system for solar photovoltaic buildings

5. Conclusion

Declaration of Competing Interest

References

Abstract

At the same time of economic development, the increasing scarcity of energy has had a great impact on people’s development. People’s production and life demand for electricity is also increasing rapidly, and solar power technology has received more and more attention. As a new energy source, solar energy has the advantages of environmental protection and sustainability, and it has no regional restrictions, can be used on-site, and designed to scale. Solar power generation is an important way to use solar energy. As the main component of the grid-connected power generation system, solar grid-connected inverters complete the tracking problem of the maximum power point in the photovoltaic array and transmit electrical energy to the grid through a set of control algorithms. The electrical energy is transmitted to the grid through the inverter, consistent with solar energy. In order to solve the problems of low integration, low energy efficiency, low reliability, high power consumption and lack of effective monitoring measures for solar energy devices. This article starts with the design of the solar cell integrated system, and through detailed analysis of the solar production system and building integrated planning, establishes the shadow radiant energy model of the solar cell system building electrical and solar cell system based on the Internet of Things, and designs an object-based Networked comprehensive auxiliary platform.

1. Introduction

Today’s society is an information society centered on the Internet. Digitization, informationization and world economics are the hallmarks of the current era. With the continuous rapid development of information technology, the process of world economics has been continuously strengthened. The use of computer technology, communication data and the Internet to complete e-logistics and Internet commerce has been widely used in society. The continuous improvement of information technology and information storage is just the prelude to the transformation of the IT industry. The cost of information dissemination is getting less and less, and it is bound to usher in the era of the Internet of Things dominating. As a post-IP product, the Internet of Things is developing rapidly and will become popular in the future. This is a major plan to realize the intelligentization of the world.

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

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

     Energy efficiency is one of the most important issues of today. The limited reserves of fossil fuels make the use of renewable energy sources more attractive. Photovoltaic panels are one of the ways to utilize solar energy, which is one of the renewable energy sources.

     The instantaneous efficiency of photovoltaic panels is related to the cell temperature of the panels. In the current studies in the literature, there are empirical expressions that give the photovoltaic panel cell temperature, but the results obtained from these expressions do not overlap with each other. In order to obtain the instantaneous efficiencies of photovoltaic panels and, accordingly, the production values theoretically, there is a need for a global expression that gives the photovoltaic panel cell temperature. In this study, a global expression was developed that gives the photovoltaic panel cell temperature depending on the ambient temperature, solar radiation and wind speed. In addition, using the meteorological data of Kütahya for many years, expressions giving ambient temperature, solar radiation and wind speed were created.

Introduction

     The limited reserves of fossil fuels make renewable energy sources more attractive day by day. While the world primary energy consumption was 581.51 exajoules in 2019, this value was 557.1 exajoules in 2020 (BP, 2021). The amount of renewable energy sources in primary energy consumption in 2019 was 28.82 exajoules (5 %), and in 2020, primary energy consumption from renewable energy sources was 31.71 exajoules (5.7 %) (BP, 2021). From renewable energy sources, 2,789.2 TWh of electricity was produced in 2019 and 3,147.0 TWh in 2020 in the world. While total solar energy production was 707.9 TWh (25.4 %) in 2019, total solar energy production was 855.7 TWh (27.2 %) in 2020 (BP, 2021).

     Table 1 and Table 2 show the solar energy installed power and solar energy production values for 10 years between 2011 and 2020 (BP, 2021). When the tables are examined; It is seen that the installed PV power in the world has increased by 882 % in 10 years. Likewise, electrical energy production from PV systems has increased by 1,212 % over the 10-year period.

     Fig. 1 shows Turkey’s solar energy situation. While the total installed power was 7 MW as of the end of 2011, this value increased by 95,143 % and reached 6,667 MW at the end of 2020. Likewise, while electricity generation from solar energy was 2.86 GWh in 2011, this value increased by 378,732 % in 10 years and reached 10,834.6 GWh (BP, 2021).

Evaluations and suggestions

     In the current studies in the literature, there are empirical expressions that give the photovoltaic panel cell temperature, but the results obtained from these expressions do not overlap with each other. In order to obtain the instantaneous efficiencies of photovoltaic panels and, accordingly, the production values theoretically, there is a need for a global expression that gives the photovoltaic panel cell temperature.

     The instantaneous efficiency of the photovoltaic panel and the generation of electrical energy depending on the efficiency are related to the cell temperature of the photovoltaic panel. Within the scope of this study, a global expression that gives the photovoltaic panel cell temperature was created by using real photovoltaic plant data with the 7 most popular expressions in the literature that give the photovoltaic panel cell temperature. With the obtained expression, instantaneous photovoltaic panel temperature can be obtained depending on the ambient temperature, solar radiation and wind speed.

Modeling and simulation of photovoltaics help to reduce development costs, design turnaround time and facilitates better techno-economic decisions. However, there is a current need to generate new theories, algorithms, applications and software in order to increase the contribution of solar energy to the global energy supply. For future advancements in the field of photovoltaics, robust techniques for PV modeling, simulation, visualisation and design are required to overcome the limitations of the current approaches. This study proposes the Code-Based Modeling (CBM) approach as a potent approach to facilitate the study of PV technologies. Experimental data were synthesised and used for coding and training of the code-based (CB) model; followed by a validation of the trained model using commercial PV modules. Results clearly show that the model can repeatedly and reliably predict the short circuit current, maximum power point, open circuit voltage with 0%, < 2% and < 10% deviations, respectively. Furthermore, instances of the applicability of the CBM approach in the study of the thermodynamics of PV, solar cell materials characterisation, PV systems design and power monitoring were presented. Above all, CBM approach accepts user-defined functions and therefore presents new opportunities for scientists and engineers to advance model-based investigations of the photovoltaics beyond the current state-of-the-art.

Introduction

Research is actively being carried out on how photovoltaics can be applied as a clean source of energy because it does not emit greenhouse gases during operation (Bukar and Tan, 2019; Ogbonnaya et al., 2019a). The Renewables 2019 Global Status Report (GSR) (REN21, 2018) indicates that solar photovoltaic (PV) constitutes around 100 GW out of 2378 GW global renewable power capacity installed in 2018; which represents 55% of the renewable capacity additions in the year, followed by wind power (28%) and hydropower (11%). Apart from direct power generation with PV systems, PV could also become a primary power subsystem in integrated systems in the future. For instance, PV modules have been studied for integration with electrolysers, fuel cells and batteries for reliable power generation (Lehman and Chamberlin, 1991; Meurer et al., 1999; Özgirgin et al., 2015). Also, PV modules have been integrated with thermal absorbers to create photovoltaic-thermal (PV/T) systems to supply electricity and hot water (Avezov et al., 2011; Michael et al., 2015). The present trends in integrating PV as a power source; or as a subsystem of a hybrid system, suggest that the demand for software including PV models would increase in the future. As an example, the possibility of integrating a maximum power point tracking (MPPT) algorithm with an automotivebased software to optimise solar energy harvesting was designed and verified by (Cheddadi et al., 2018).

1. Introduction

2. Methods

3. Results

4. Limitations

5. Conclusion

Acknowledgments

Appendix A. Supplementary data

References

Abstract

This paper discusses the impact of bulk electric storage on the production from dispatchable power plants for rising variable renewable electricity shares. Two complementary optimization frameworks are used to represent power systems with a varying degree of complexity. The corresponding models approximate the wholesale electricity market, combined with the rational retirement of dispatchable capacity. Two different generic storage technologies are introduced exogenously to assess their impact on the system. The analysis covers two countries: France, where the power supply’s large nuclear share allows for the discussion of storage impact on a single generator type; and Germany, whose diverse power supply structure enables storage interactions with multiple electricity generators. In the most general case, additional storage capacity increases dispatchable power production (e.g. nuclear, coal) for small wind and solar shares, i.e. it compensates the replacement induced by renewable energies. For larger variable renewable electricity volumes, it actively contributes to dispatchable power replacement. In a diverse power system, this results in storage-induced sequential mutual replacements of power generation from different plant types, as wind and solar capacities are increased.

This mechanism is strongly dependent on the technical parameters of the storage assets. As a result, the impact of different storage types can have opposite signs under certain circumstances. The influence of CO2 emission prices, wind and solar profile shapes, and power plant ramping costs is discussed.

Introduction

Todecarbonize the electricity sector, windandphotovoltaic (PV) power are likely to cover increasing shares of future electricity production (Creutzig et al., 2017; Luderer et al., 2017). To cope with the inherently variable nature of these renewable resources, their integration requires additional system flexibility (International Energy Agency, 2018). This flexibility is necessary in order to follow steeper loadramps (Huber et al., 2014),tomanage short-termpowerfluctuations (International Energy Agency, 2018), and to counteract the market value erosion of resources with inflexible profiles (Hirth, 2013). Ultimately, very high shares of variable renewable electricity (VRE) require the system to absorb otherwise curtailed peak generation (Denholm and Hand, 2011; Després et al., 2017).

The future needs for additional flexibility of supply and demand can be satisfied in a multitude of ways, relying both on improvements of the legacy system (such as power plant upgrades (International Energy Agency, 2018)) and on dedicated measures, including storage installation and cross-sector coupling (Lund et al., 2015). Among these options, electricity-to-electricity storage stands out as especially versatile due its ability to provide a multitude of (e.g. ancillary) services and due to its lack of scalability constraints (with the notable exception of pumped hydro storage—PHS) (Fitzgerald et al., 2015; Palizban and Kauhaniemi, 2016). This is in addition to several more technology-specific advantages like estimated low capital costs per energy capacity (e.g. compressed air energy storage (Lazard, 2016)), pronounced anticipated cost decreases (e.g. battery technologies (Schmidt et al., 2017)), very fast deployment time, modularity, and increasing maturity due to rapidly accelerating worldwide use (batteries (REN21, 2018; Tortora, 2014)).

This paper demonstrates the optimum design and techno-economic assessment of hybrid renewable energy system (HRES) for rural electrification in the remote district of Korkadu, India. The renewable energy resources namely solar photovoltaic, wind turbine and bio generators are considered as the main sources because of its high potential in Korkadu district. This paper estimates the load forecasting for the selected district, which is a mixture of different load patterns namely residential, commercial, institutional and agricultural demands. The desired HRES has to meet the forecasted load demand for reliable electrification. The optimum design and techno-economic analysis of proposed HRES system is carried using powerful tool HOMER. This study also compares HRES’s operational behavior with different operational strategies such as load following strategy, cycle charging strategy and combined strategy of the system. The outcome of the proposed dispatched strategy expressed the contribution of solar power as 86.8%, wind as 12.7% and bio gen power as 0.5% to meet out developed rural load pattern with 6.8% of battery bank losses and 1.78% of converter losses. This research work also illustrate the HRES based power generation can be a cost effective sustainable power alternative to the conventional grid extension system.

Introduction

Total power generation capacity of India was 340 GW as on March 2018, a major portion of which 65% was from fossil fuels such as coal, oil etc. whole the renewable energy, natural gases based energy generation contributed to 19.2% [1]. The research says that still 240 millions of India are living in dark without electricity and most of the electrified villages have poor quality of electricity especially during the peak demand period i.e., during summer [1,2]. Government of India has initiated several schemes, namely, Remote Village Electrification Program (RVEP), Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY), Jawaharlal Nehru National Solar mission (JNNSM) etc., for addressing this issue and for ensuring electrification of the rural areas in India [1,3]. The main objective of these schemes is to ensure clean source of electricity generation for the electrification of un-electrified villages in India. Various researchers have proposed an attractive solution called the Renewable Energy based Hybrid Rural Electrification System (REHRES). But the main challenges in this proposed solution relate to system reliability, power management and economical cost of energy. In addition, wide variations are seen in the renewable energy availability and its potential depending on their geographical location. Hence the optimum sizing is considered as an important parameter for the development of an efficient and economical REHRES. In recent years, various researchers have proposed the solution for REHRES worldwide such as Hydrogen fuel system based hybrid energy system has been deployed in Newfoundland [4], Solar/wind/hydrogen based HRE [5], solar/wind powered reversed osmosis system [6], PV/ wind/battery based isolated island system [7] these study clearly expressed the issues related with HRE system sizing and necessity of performance optimization of the system.

To develop solar energy as a primary source of electricity supply in China, it is imperative to also develop an overall and complete solar energy potential analysis. Such an analysis technique would be a substantial contribution to solar power generation development both nationally and regionally. This study analyzes the spatial and temporal distribution of solar energy in China and estimates the solar energy potential from three aspects: geography, technology, and economy. The results of this research showed that the solar energy resource in China is substantially rich and stable, but also has notable spatial heterogeneity. A potential estimation indicated that Xinjiang Province was the most optimal site for large-scale photovoltaic station construction, displaying the highest values for all three potentials. It was also found that solar energy potential in western China is greater, while the eastern region is less suitable for solar photovoltaic development. These results can provide support for the large-scale development and utilization of solar energy resources in the future.

Introduction

In the light of ensuring a sustainable future and addressing the increasingly serious impacts of climate change, especially global warming, developing countries are urgently seeking to switch from traditional energy to renewable energy [1–5]. Solar energy is abundant, free, and non-polluting; hence, it is considered one of the most competitive choices of all the renewable energy choices [4,6]. The global solar PV market has rapidly grown by 50% over the past decade [7]. The International Energy Agency (IEA) expects that the share of global electricity from photovoltaic (PV) systems will reach 16% by 2050 (IEA, 2010). In particular, China is playing an increasingly immense role in the PV electricity supply. Due to the guidance of the 13th Five-Year Plan in China, more than 110 million kilowatts of solar power is planned to be installed by 2020 [9]. It has also been estimated that nearly 40% of the global installed PV capacity will be held by China by 2023 [10]. According to the CHINESE RENEWABLE ENERGY DEVELOPMENT REPORT (2018) [11], solar energy and wind power remain the two primary pillars of electricity generation in China. A critical step in the process of utilizing this tremendous solar energy resource is to identify and prioritize optimal sites for PV power stations [12,13]. Furthermore, to find suitable places for solar energy exploitation, it is imperative to first estimate the actual solar energy potential on the ground [3]. The estimation of solar energy potential depends on multi-dimensional indicators. These include but are not limited to local solar energy resources, land cover, technological development, the economics of solar products, and the governmental policies. All of these factors exert significant impacts on the development of the solar energy market [14].

1- INTRODUCTION

2- SYSTEM LAYOUT

3- CONTROL SCHEME

4- CONCLUSION

REFERENCES

Abstract

In this paper, we are proposing currentcontrolled hill-climbing strategy used in Maximum power point tracking (MPPT) of the solar photovoltaic array to Charge OFF Board Electric vehicle lithium-ion batteries within the safety limits and supported rating of batteries so that EV battery does not get damaged and can also increase the life of the battery. Here, a current sensor and potential difference sensor-based electric vehicle off-board battery charging by hill-climbing strategy is tested on MATLAB simulation. The satisfactory steady-state battery is charging within the rated charging limit, under variable solar irradiance.

INTRODUCTION

The transportation sector has seen a major shift towards electric vehicles as it not only stops our dependence on fossil fuels and stop pollution from these vehicles that vehicles release. Hence, we are moving towards cleaner sources of energy. Also, photovoltaic resources are increasing in demand as they are the source of clean energy and don’t emit carbon dioxide

Two types of charging method are there ON Board charging and OFF Board charging. ON Board charging is when Battery is kept in the Electric vehicle and charging circuit is not on the vehicle itself but near the charging station. And the battery is charged directly through the DC supply.

This paper presents the benefits of the solar photovoltaic technology and the operation challenges corresponding to the large-scale integration of this technology in the distribution networks. A voltage control algorithm is proposed to mitigate the adverse effects of PV generation on the voltage profile of the distribution network. An operation planning framework is proposed that captures the heterogeneous objectives of the generation and demand entities and features limited information sharing among these entities.

Introduction

The Solar photovoltaic (PV) generation is a modular electricity generation asset that converts solar energy into electricity. Solar PV units can be manufactured for various applications in different capacities. As the price of solar PV panels decreases with the advancement in technology and the economies of scale, the installed capacity of PV is increased in the transmission and distribution networks. The global PV generation capacity reached 398 GW in 2017 to produce over 460 TWh electricity and to hold about 2% of the global electricity generation (IEA, 2018). In the U.S., 1.7 GW of solar PV capacity is installed in the third quarter of 2018 to increase the installed capacity to 60 GW. The total installed capacity of PV generation is expected to be doubled over the next five years; and by 2023, it is estimated that over 14 GW of PV generation capacity will be installed annually (SEIA, 2018). Several challenges in the energy sector were addressed by improving the penetration level of PV generation technology. The greenhouse gas emission due to burning the fossil fuels, the high cost of energy, lack of electricity supply in remote and underdeveloped regions as well as the high transmission and distribution network losses in urban and developed regions are among the challenges that could be addressed by PV generation in the modern global electricity industry. Here, we briefly present these challenges and evaluate the impact of PV generation technology to address them. Greenhouse gas emission is the main driver of climate change. A considerable portion of the emission worldwide is produced in China as it holds the largest carbon footprint in the world since 2004. China is accountable for 27.6% of global carbon dioxide production in 2017 (CSIS, 2018) and in order to reduce the carbon footprint, the government is heavily invested in renewable energy resources. Since 2015, China has had the largest installed capacity and power generation of PV and by 2017, 130 GW of solar PV generation is installed with the total capacity projected to be 400 GW by 2030. This effort is further pushed by the National Development & Reform Commission agency in China to increase in the share of renewable energy resources from 20% to 35% by 2030 (Lowder, 2018).

Nomenclature

1. Introduction

2. Experimental section

3. Result and discussion

4. Conclusions

Acknowledgement

References

Abstract

The chemical looping process on honeycomb reactor for solar syngas is experimentally studied in this work, which is the key reaction of the liquid sunshine production process. The honeycomb reactor realizes the integration of oxygen carrier and reaction chamber. NiO is placed in the reactor as oxygen carrier and methane is introduced as fuel gas. The results show that, with the development of process, the major reaction in the reactor gradually changed from methane complete oxidation to methane partial oxidation. During the process, the methane conversion and outlet syngas concentration is affected by the methane flow and fractional oxidation. Under the optimal operating conditions, the methane conversion can be maintained more than 95% and the concentration of outlet syngas can be around 90%. Compared with non-honeycomb fixed bed reactor, the methane conversion increases by more than 20 percent point and the concentration of outlet syngas increases by about 10 percent point. In addition, oxygen carrier in honeycomb reactor shows excellent cyclic stability in 30 times experiments.

Introduction

When the consequences of the overuse of fossil fuels come, are we prepared to deal with them? Since the first industrial revolution, fossil fuels have been the primary source of global energy. Behind the exponential progress powered by fossil fuels are the potential of energy crisis and the environmental deterioration [1,2]. Nowadays, annual carbon dioxide emissions are about twice as much as that can be absorbed by nature [3]. If we continue to rely on fossil fuels, the resulting climate change and air pollution will threaten our life [4]. Sunlight is the most abundant source of energy on earth, and it can provide about 885 million TWh of energy in a year, surpassing other kinds of renewable energy by hundreds of times [5]. But it is hard to use it as easily as you flip a switch due to the decentralization and discontinuity of solar [6]. If we want to capture, store sunlight and supply it as energy source, the key process is to convert it into a stable, storable, highenergy-density chemical fuel.

The liquid sunshine vision is increasingly appealing to researchers [7]. Liquid fuels are not difficult to transport and store, and it can be widely used with some improvements to existing infrastructures. Liquid sunlight is designed to convert sunlight into liquid fuels such as methanol. Methanol is an attractive candidate because of its wide application. There are no C-C bonds in methanol which can effectively reduce the greenhouse gas emission due to its low carbon to hydrogen ratio [7]. As the raw material of methanol production, the preparation of solar syngas is a key process in current researches [8–11].

In this paper, we have utilized finite difference time domain (FDTD) method to investigate the solar energy absorption of hybrid solar cells (HSCs) with trapezoid-pyramidal structure (TPs) based PEDOT:PSS/c-Ge. We have changed some parameters of TPs (different heights and different ratios of top to bottom) to study its effects on the solar energy absorption for HSCs. The optimization of geometric parameters is based on the maximum solar energy absorption efficiency. The optical absorption of the HSCs with TPs is basically above 90% from ~300 nm to ~1300 nm and the average solar energy absorption is 93.8% under AM 1.5 solar spectrum (from 300 nm to 1500 nm). Simultaneously, we have calculated the short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), maximum power (Pmax) and photoelectric conversion efficiency (PCE) respectively through DEVICE software. The results of electrical simulation reveal that the maximum Jsc is 43.47 mA/cm2 , it is 45.73% higher than planar PEDOT:PSS/c-Ge HSCs. Moreover, to further explain the mechanism of solar energy absorption of HSCs with TPs, the logarithmic figures of electric field intensity for trapezoid-pyramidal PEDOT:PSS/c-Ge HSCs and planar PEDOT:PSS/c-Ge HSCs at different wavelengths are analyzed. The result shows that TPs array has shown excellent light-trapping effect. This work reveals the huge potential of c-Ge HSCs in solar absorption, and at the same time has certain guiding significance for the structural design of Ge SCs. It is believed that through further exploration, the conversion efficiency of c-Ge HSCs will be further improved.

Introduction

In recent years, with the development of society, energy and environmental issues have attracted much more attention. People pay more and more attention to research clean and renewable energy, such as wind energy, geothermal energy, tidal energy, etc. (Orrego et al., 2017; Sun et al., 2018; Zhang et al., 2020). However, due to the infinite energy provided by the sun, the photovoltaic devices which can directly convert solar energy into electricity have caused extensive research (Yi et al., 2019; Wu et al., 2020; Gao et al., 2019). As we know, researchers have been working to improve the efficiency of solar cells (SCs) (Li et al., 2019; Iman et al., 2020; Van der Heide et al., 2009). The photoactive layer realizes the conversion from light energy to electricity by absorbing photons to excite electrons from the valence band to the conduction band, and then separates the generated electron-hole pairs through a PN junction to form photocurrent. The more light energy is absorbed, the more electron-hole pairs are generated, the larger the photocurrent is generated, and the efficiency of the SCs will be improved accordingly (Li et al., 2019). In order to improve the light absorption efficiency, various microstructures are fabricated on the active layer to improve the solar energy absorption (Liu et al., 2017).