The effect of contract methods on the lead time of a two-level photovoltaic supply chain: revenue-sharing vs: cost-sharing
تأثیر روش های قرارداد بر زمان سربازی یک زنجیره تأمین فتوولتائیک دو سطح: تقسیم درآمد در مقابل تقسیم هزینه-2021
In the photovoltaic industry, a large number of photovoltaic power plants are not delivered according to construction schedules, resulting in considerable impacts on various stakeholders. Lead time has been identiﬁed as one of the key issues that urgently needs to be resolved. In this paper, we study a two-level photovoltaic supply chain consisting of the customer, the assembler, and the module manufacturer. The basic model, revenue-sharing model, and cost-sharing model are established to analyze the lead time of the module manufacturer and the assembler considering the decline of government subsidies. The results indicate that the cost-sharing contract can effectively control the lead time of the module manufacturer, but the revenue-sharing contract cannot exert this control. Furthermore, if the government subsidy drops from 0.0553 USD/kWh to 0.01195 USD/kWh, the production capacity will be reduced by approximately 37% due to the reduction in installed capacity, and the lead time will decrease by about 27%. For the module manufacturer, when the non-production capacity costs drop by 20%, the proﬁt increases by about 58%. However, when the production capacity costs are reduced by 20%, the proﬁt increases by only about 6%.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Photovoltaic industry | Lead time | Supply chain | Revenue-sharing | Cost-sharing
Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation
تشخیص و تعیین کمبود در تصاویر الکترولومینسانس ماژول های PV خورشیدی با استفاده از تقسیم بندی معنایی U-net-2021
Electroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules that are otherwise invisible to the naked eye, much the same way an x-ray enables a doctor to detect cracks and fractures in bones. The prevalence of multiple defects, e.g. micro cracks, inactive regions, gridline defects, and material defects, in PV module can be quantiﬁed with an EL image. Modern, deep learning tech- niques for computer vision can be applied to extract the useful information contained in the images on entire batches of PV modules. Defect detection and quantiﬁcation in EL images can improve the efﬁciency and the reliability of PV modules both at the factory by identifying potential process issues and at the PV plant by identifying and reducing the number of faulty modules installed. In this work, we train and test a semantic segmentation model based on the u-net architecture for EL image analysis of PV modules made from mono-crystalline and multi-crystalline silicon wafer-based solar cells. This work is focused on developing and testing a deep learning method for computer vision that is independent of the equipment used to generate the EL images, independent of the wafer-based module design, and independent of the image quality.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Electroluminescence | EL | PV | U-net | Semantic segmentation | Machine learning
Research on the policy route of China’s distributed photovoltaic power generation
تحقیق در مورد مسیر سیاست تولید انرژی فتوولتائیک و توزیع شده در چین-2020
The distributed photovoltaic power generation is an important way to make use of solar energy in cities. China issues a series of policies to support the development of distributed photovoltaics in law, electricity price, grid connection standard, project management, financial support and so on. However, there are still some defects in policies and market mechanism. China creates a competitive market with a significant number of projects of distributed photovoltaic power through the reform of the electricity market, yet substantial drawbacks of the corresponding investment subsidies prevent distributed photovoltaic power from rapidly developing. This paper summarizes the status quo of China’s distributed photovoltaic power development, given its long-term plan, presents excellences and shortcomings of the existing policy system, and looks into the supporting policies and implementation paths for China’s distributed photovoltaic power in different stages. Innovative business models and financial support models are conducive to the development of distributed photovoltaic power. Financial innovation methods such as crowd funding and asset securitization should be encouraged to develop a sound risk assessment mechanism for projects, involve insurance institutions, and establish a risk sharing mechanism. In the context of a series of supporting policies, the distributed photovoltaic power in China will move towards market-oriented standardization for a healthier and more stable development.
Keywords: Distributed photovoltaic power | Electricity price | Policy route | Development strategy
Modified deep learning and reinforcement learning for an incentive-based demand response model
یادگیری عمیق اصلاح شده و یادگیری تقویتی برای یک مدل پاسخ تقاضای مبتنی بر انگیزه-2020
Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security.
Keywords: Demand response | Modified deep learning | Reinforcement learning | Smart grid
Sustainable groundwater management in arid regions considering climate change impacts in Moghra region, Egypt
مدیریت پایدار آبهای زیرزمینی در مناطق خشک با توجه به تأثیر تغییرات آب و هوایی در منطقه مقرا ، مصر-2020
Egypt is one of the most water-scarce countries of the Middle East and North Africa region and is highly vulnerable to climatic changes. In the Egyptian deserts, new land reclamation projects depend mainly on groundwater as the main source of water. Also, solar energy is the most promising renewable source of energy for pumping and transport of water. Moghra region is one of the well-known “1.5 Million Acres Reclamation Projects” areas in the Western Desert. In this paper, a groundwater model was constructed and used to investigate the sustainable groundwater management scenarios in Moghra region taking into consideration impacts of the expected climate changes. Using MODFLOW/GMS software, Moghra model was prepared and calibrated based on the region’s topographic, climatic, geologic and hydrologeolgic conditions. The model was used to explore the impacts of climate changes; Sea Level Rise (SLR) by 1.0 m and temperature increase by 2�0C and 40�C on the management scenarios. In addition, the required power for water management after 5, 10, 50 and 100 years were determined. It was concluded that the best management scenario is to use 1000 wells to extract 1.2 Mm3/d of water for serving a total area of 85,714 acres (360 km2). This scenario satisfies the project criteria that permits a maximum drawdown less than 1 m/year. It was also concluded that SLR has mild effects on groundwater levels due to the vast aquifer dimensions. Additionally, the increase in evapotranspiration due to temperature increase will lead to a significant increase in the consumptive use. The power needed to extract water will continuously increase due to the expected increase in drawdown. The required area for Photovoltaic (PV) solar plant was determined and its value increased by 6% and 12% due to temperature increase of 2�C and 4�C, respectively.
Keywords: ArcGIS | Climate change | Groundwater management | MODFLOW/GMS | Moghra aquifer | Solar energy
Optimal planning of distributed photovoltaic generation for the traction power supply system of high-speed railway
برنامه ریزی بهینه از تولید فتوولتائیک توزیع شده برای سیستم منبع تغذیه کششی راه آهن با سرعت بالا-2020
The ever-increasing electricity price and energy consumption in high-speed railway industry push railway companies to seek a promising way to realize their sustainable developments. Making full use of the solar resource along with high-speed railways can be a potential solution to cut the electricity bill, bring more profit to railway companies and realize the decarbonization of high-speed railway industry. This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative method is proposed to study the time and space characteristics of photovoltaic generation and electricity demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy management strategy of DPVG and ESS, a mathematical programming model with the objective of minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed railway in China is used to illustrate the capability and characteristics of the proposed model. The computational results show that DPVG is able to supply 32:5% electricity demand of high-speed trains. The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing- Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS in high-speed railway industry.
Keywords: High-speed railway | Photovoltaic generation | Energy storage system | Traction power supply system
Using reinforcement learning for maximizing residential self-consumption - Results from a field test
استفاده از یادگیری تقویتی برای به حداکثر رساندن خود مصرفی مسکونی - نتایج یک آزمون میدانی-2020
This paper presents the results from a real residential field test in which one of the objectives was to maximize the instantaneous self-consumption of the local photovoltaic production. The field test was part of the REnnovates project and was conducted in different phases on houses in several residential districts located in Soesterberg, Heerhugowaard, Woerden and Soest, the Netherlands. To maximize self- consumption, buffered heat pump installations for domestic hot water and stationary residential battery systems were chosen due to their respective thermal and electrical storage capacities. The algorithm used to tackle the associated sequential decision-making problem was model-based reinforcement learning. The proposed algorithm learns the stochastic occupant behavior, uses predictions of local photovoltaic production and considers the dynamics of the system. The results show that this algorithm increased the average self-consumption percentage of the local PV generation (used instantaneously in situ ) on average by 14%, even if only buffered heat pump installations for domestic hot water were used. This increase was achieved without causing any perceived discomfort to the residential end users. The average energy shifted per day from the solar production period to the night by the 2 kW/3.6 kWh batteries was 1.5 kWh. The main contribution of this work was therefore the real field implementation of the proposed algorithm. The results demonstrate that it is possible to improve even further the integration of local production using flexible loads.
Keywords: Reinforcement learning | Q-Learning | Field test | Solar PV generation | Thermal storage | Thermostatically controlled loads | Electrical storage | Battery | Residential loads
Techno-economic evaluation of PV based institutional smart microgrid under energy pricing dynamics
ارزیابی فنی و اقتصادی میکروگرید هوشمند نهادی مبتنی بر PV تحت پویایی قیمت گذاری انرژی-2020
The solar photovoltaic (PV) system with battery energy storage have a lot of potential to provide reliable and cost-effective electricity and to contribute in micro-grid operation. However, the operational performance of such type of micro-grid system depends on many factors (e.g. techno-economic sizing, energy management among the sources, market energy prices dynamics, energy dispatch strategies, etc.). In this paper, a typical Indian institutional energy system has considered for techno-economic performance evaluation for operating as a smart micro-grid under market energy pricing dynamics. The institutional energy system has integrated PV, battery storage and DG for operating as a smart microgrid. An operational energy dispatch strategy for micro-grid has proposed and evaluated for maximizing the local energy resources utilization with contemplation of peak demand and grid outage conditions under market energy pricing dynamics. With techno-economic sizing of PV, battery and DG of considered system; the peak demand has reduced by 10%, DG contribution by 92% and annual energy savings by 45% compare to operation of base system. With proposed energy management strategy, the annual battery energy throughput has increased from 0.4% to 10%, and the DG’s contribution has decreased from 7% to 5% with 10% reduction in levelized cost of energy (CoE) compare to case with techno-economic sizing of PV, battery and DG for considered system. With inclusion of electrical energy pricing dynamics scenario, it has observed that the CoE has increased by 89% with change in time-of-use (ToU) tariff from 100% to 200% and considering energy-selling price to the grid at 100%. However, 8% reduction in the CoE has observed, when the energy-selling price to grid has increased from 100% to 200% at ToU of 100%. The results from this work are going to be useful for developing electrical tariff policies for promoting the PV based institutional micro-grid system under market energy pricing dynamics.
Keywords: Smart micro-grid | Market energy pricing dynamics | Techno-economics | Solar photovoltaic | Energy management strategy
Renewable energy powered membrane technology: Energy buffering control system for improved resilience to periodic fluctuations of solar irradiance
فن آوری غشایی با انرژی قابل تجدید: سیستم کنترل بافر انرژی برای بهبود مقاومت در برابر نوسانات دوره ای تابش خورشیدی-2020
Energy management is required to enable autonomous photovoltaic-powered membrane (PV-membrane) desalination systems to make the optimal use of solar energy. In this paper, a novel charge controller based on pre-set voltage sensing thresholds was designed to optimise the energy from PV panels and supercapacitors (SCs). The control algorithms were established from the data derivations with high-temporal-resolution (1s) solar irradiance (SI) source, allowing for resilient system operation under variable conditions. The impacts of ramp rates, in both SI and PV output voltage (VPV) on the system, were systematically investigated. Under a worst-case scenario, with a rapid ramp rate of DVPV ¼ 2 V/s, the charge controller enabled the SCs to bridge the power gap to 6 min 20 s, permitting an additional 10 L of permeate water produced. The state-of-charge of the SCs varied from 11 to 86%, regardless of the magnitude of the ramp rate. The combination of the voltage thresholds (Vpump_on ¼ 160 V and Vpump_off ¼ 90 V) was determined to result in optimum system performance, realising a high permeate production at low specific energy consumption. It is concluded that the proposed charge controller is an effective method to enhance system resilience under worst-case solar conditions.
Keywords: Ramp rates | Charge controller | Supercapacitors | Photovoltaic | Reverse osmosis | Energy fluctuation
System-level Power Integrity Optimization Based on High-Density Capacitors for enabling HPC/AI applications
بهینه سازی یکپارچگی قدرت در سطح سیستم مبتنی بر خازن های با چگالی بالا برای فعال کردن برنامه های HPC / AI-2020
In this work, we introduce platform-level power integrity (PI) solutions to enable high-power core IPs and highbandwidth memory (HBM) interface for HPC/AI applications. High-complexity design methodology becomes more significant to enable high-power operations of CPU/GPU/NPU that preforms iteratively tremendous computing processes. In order to achieve high-power performance at larger than 200W class, system-level PI analysis and design guide at early design stage is required to prevent drastic voltage variations at the bump under comprehensive environments including SoC, interposer, package and board characteristics. PI solutions based on highdensity on-die capacitors are suitable for mitigating voltage fluctuations by supplying quickly stored charges to silicon devices. In adopting 2-/3-plate metal-insulator-metal (MIM) capacitor with approximately 20nF/mm2 and 40nF/mm2, and integrated stacked capacitor (ISC) with approximately 300nF/mm2, it is demonstrated that voltage properties (drop and ripple) are able to be improved by system-level design optimization such as power delivery network (PDN) design and RTL-architecture manipulation. Consequently, system-level PI solutions based on high-density capacitor are anticipated to contribute to improving target performance of high-power products in response to customer’s expectation for HPC/AI applications.
Keywords: HPC/AI | high-power applications | power integrity | power delivery network | decoupling capacitor | systemlevel design optimization