Price Of Solar Modules Research Articles

The impacts of DC/AC ratio, battery dispatch, and degradation on financial evaluation of bifacial PV+BESS systems

As the price of solar modules has decreased, oversizing PV system becomes a general practice. Without proper energy management, the oversized systems could lead to over-generation waste which cause a loss in revenue. Battery energy storage system (BESS) can be integrated to the PV system for utilizing the over-consumption energy and increasing the system’s financial benefits. This paper highlights the influence of technical and financial factors on the photovoltaic (PV) and PV with BESS design. To analyze the impacts of the factors on PV systems, thirty-six system configurations with a variety of geographic locations, tariff structures, billing methods, and PV system technology are simulated using System Advisor Model (SAM) as a tool with result validation from measurements on a real oversized residential grid-tied bifacial PV+BESS system. To study the financial criteria that affect the optimal system design, we conducted the sensitivity analysis of the DC/AC ratio, battery replacement threshold, and battery size to observe the optimal system design based on three financial outputs: levelized cost of energy (LCOE), net present value (NPV), and payback period. The findings show that the optimal DC/AC ratio for residential-scale PV systems lies between 1.1 - 1.3 and can go up to 1.6 with BESS. PV+BESS systems can reduce the payback period by 20% and boost NPV by 80% compared to the PV-only systems. The sensitivity study of the battery replacement shows that the battery can be degraded until 50% of its maximum capacity for the lowest LCOE. To achieve the optimal grid-tied PV system design, each economic parameter should be evaluated together.

Peer to Peer Distributed Solar Energy Trading

Solar module prices have dramatically dropped in recent years, which in turn has facilitated distributed solar energy resources (DSERs) in smart grids. To manage electricity real time supply and demand, the utilities has shown the strong interest in deploying virtual power plants(VPPs) which enable solar generated energy trading to reduce the intermittent DSERs impact in electric grid. Unfortunately, the current energy trading approaches in residential VPPs typically require a trusted third party to take on the role of the middleman, DSER users are not allowed to trade their surplus solar energy independently and simultaneously to maximize potential benefits. In addition, these approaches do not achieve "fair" for both VPPs and DSERs users. To address this issue, we build a multi-agent deep reinforcement learning based peer to peer (P2P) new solar energy trading system-SolarTrader+, which enables unsupervised, distributed, and long term fair solar energy trading in residential VPPs. We apply deep reinforcement learning with neural networks as Q-value function approximator.We implement SolarTrader+ and evaluate it using data from U.S. residential VPP communities that are comprised of - 119 residential DSERs. Our results show that SolarTrader+ can reduce the aggregated VPP energy consumption by 83.8% when compared against a non-trading approach.

SolarDiagnostics: Automatic damage detection on rooftop solar photovoltaic arrays

Homeowners are increasingly deploying rooftop solar photovoltaic (PV) arrays due to the rapid decline in solar module prices. However, homeowners may have to spend up to ∼$375 to diagnose their damaged rooftop solar PV system. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, recent approaches that leverage machine learning techniques have the limitation of distinguishing solar PV array damages from other solar degradation (e.g., shading, dust, snow). To address this problem, we design a new system—SolarDiagnostics that can automatically detect and profile damages on rooftop solar PV arrays using their rooftop images with a lower cost. In essence, SolarDiagnostics first leverages an K-Means algorithm to isolate rooftop objects to extract solar panel residing contours. Then, SolarDiagnostics employs a convolutional neural networks to accurately identify and characterize the damage on each solar panel residing contour. We evaluate SolarDiagnostics by building a lower cost prototype and using 60,000 damaged solar PV array images generated by deep convolutional generative adversarial networks. We find that SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews correlation coefficient (MCC) of 1.0. In addition, pre-trained SolarDiagnostics yields an MCC of 0.95, which is significantly better than other re-trained machine learning-based approaches and yields as the similar MCC as of re-trained SolarDiagnostics. We make the source code and datasets that we use to build and evaluate SolarDiagnostics publicly-available.

Optimum Sizing and Economic Assessment of Hybrid Microgrid for Domestic Load Under Various Scenario

Electricity price, various duties related to electricity bill and environmental concerns are increasing. Due to technological advancement and mass production, the price of solar modules and energy storage system are going down. Number of consumer are increasing having Solar Home System (SHS) so transform from consumer to prosumer. The aim to present assessment is to analyze the techno-economic feasibility of residential household at Karnal, India under three scenarios: grid connected net-metering system without feed in tariff, with feed-in-tariff and off-grid system so that the consumer can choose the best possible configuration as per their geographical location. The outcome of the study shows that grid connected system will require less investment and it will be more economical will feed-in tariff policy. Government support and environmental concern will help to motivate more peoples towards SHS so as to became less carbon free world.

India's e-reverse auctions (2017–2018) for allocating renewable energy capacity: An evaluation

In the renewable energy sector of India, the system of Feed-in-Tariff (FIT) is being rapidly replaced by the mechanism of e-reverse auctions to discover the lowest tariff. 23.375 GW of solar, wind and hybrid capacity has been auctioned by central and state agencies during the period 2017–2018. The auctions resulted in historical low bids, due to the expectation of low price of capital goods like solar modules. The highest winning bids in solar auctions were, on an average, 36.49% less than the ongoing solar FIT in the states that held the e-reverse auctions. Consequently, the FIT has been revised downward. While the auctions proved to be cost effective, commissioning of projects remains a suspect. Only 58.6% of solar capacity allocated through the auctions held in 2017 got commissioned by the end of 2018. Project developers face the winner's curse due to increased import price of solar modules. The bids were revised upward in 2018, due to expected cost escalation because of safeguard duty imposed on import of solar panels. 8 GW solar bids got cancelled in FY 2018–19. Deployment of projects got affected as the distribution companies attempted to force winners of auctions to match tariff with lowest bids in subsequent auctions. This article evaluates the e-reverse auctions on account of their impact on price, deployment and financing of projects.

Empirical analysis of factors influencing the price of solar modules

PurposeOne of the key drivers behind the recent growth in the global solar energy market is the decline in solar module prices. Many empirical analyses have been carried out to identify the mechanism behind this price reduction. However, studies on the price reduction mechanism of solar modules over the years have focused purely on the technological aspect of manufacturing. The purpose of this study is to consider the influence of economic and monetary factors such as the interest rate and exchange rate on solar module pricing in addition to other factors that considered in earlier studies including technology, wage rate and other energy prices.Design/methodology/approachIn this paper, an oligopolistic model and econometric method are used to determine the economic factors that have an influence on solar module prices. The paper constructs a solar module pricing model and conducts a fully modified ordinary least squares analysis to estimate the influence of each factor. Analysis is conducted for the top five solar module producing countries in the world from 1997 to 2015. The five countries are the People’s Republic of China, Germany, Japan, the Republic of Korea and the USA.FindingsEmpirical analysis provides several findings concerning the solar module pricing mechanism. These vary for each country. However, generally the interest rate has a positive correlation with solar module prices, while the exchange rate, knowledge stock and oil price have a negative correlation with solar module prices.Practical implicationsFirst, the government must expand channels for renewable energy funding. As renewable industries are high-tech, the influence that capital cost has on technology price is significant. Government efforts to provide industries with low-interest finance will accelerate renewable business. There have been many attempts to lower interest rates for renewable energy technology to accelerate growth in the green technology market. Second, the government must expand research and development (R&D) expenditures focused on renewable energy technology. The technological advancements acquired through R&D enhance module performance efficiency, thereby reducing costs. Therefore, government policies aimed at increasing targeted R&D expenditure will be an effective means of expanding the installation of renewable energies.Originality/valueStudies on the price reduction mechanism of solar modules over the years have focused purely on the technological aspect of the manufacturing. This is the first research to bring economic, monetary and technological factors of solar module pricing together.

Studi Kasus Kelayakan Penerapan Sistem Hybrid Building Applied Photovoltaics (BAPV)-PLN pada Atap Gedung Politeknik Aceh

With the undeveloped BAPV(Building Applied Photovoltaics) at Office Building and Public Facilities, Caused the high price of solar modules and the lack the economic study on the use of solar modules that housed in the Banda Aceh region. Furthermore, the price of solar modules is expensive, so people think it will cost so much funds to building a BAPV’s system. These problems could be overcomed if the existing technical studies and economic studies of the application of the BAPV’s system. This study aims to assess the application of the BAPV’s system on institutions building in terms of technical and economic value, in this case the building that is used as the study object is the Polytechnic Aceh’s Building. The method that used in the technical studies are theoretical calculations and simulations using helioscope software, while the methods used for economic studies is using the methods of cost-benefit analysis ( cost benefit analysis). The method used to find the NPV (Net Present Value), PP (Payback Period), IRR (Internal Rate of Return), and BCR (Benefit Cost Ratio). If the average value of solar radiation reaching 4.79 kWh / m2 / day and the average daily energy requirement is 592 kWh, the energy generated from BAPV-PLN hybrid system on the roof of the object building will reach the amount of 237 MWh/year with the capacity charge controller used is 7490 A and the capacity of the battery used is 64.487 Ah. Panel tilt angle used is 25 o and the type of panel used is Monocrystalline manifold. From the economic value will obtained NPV value of Rp. 20.022.106.937, PP during 5,2 years, IRR of 36% and 3,49 of BCR. Based on the evaluation results of the feasibility study, the project of hybrid BAPV-PLN’s system on the roof of the Polytechnic Aceh’s building can be realized, because its already meet the criteria of the feasibility study to make the systems get established in real term.

Impact of inverter loading ratio on solar photovoltaic system performance

Due to decreasing solar module prices, some solar developers are increasing their projects’ inverter loading ratio (ILR), defined as the ratio of DC module capacity to AC inverter capacity. In this study, we examine the operational impacts of this trend. Using minute-level solar data, we examine the relationship between inverter induced clipping losses and AC generation. We find minimal clipping losses at an ILR of 1.25; at an ILR of 2.0, we observe that 16% of potential annual generation is lost. Minute-level data prove to be essential in determining the generation lost to clipping, as hourly data mask key clipping and ramping events. Higher ILRs lead to a greater frequency of time spent at maximum generation, but also a greater frequency and magnitude of large solar ramping events. Module degradation can attenuate the impacts of inverter clipping over time. We observe that the effective degradation rate (net of any changes to inverter clipping losses) can be as little as half the actual degradation rate for projects with high ILRs. The diurnal and seasonal trends in clipping correspond with solar insolation patterns, with the highest clipping occurring around noon. For fixed tilt installations with tilt angles at latitude, we observe the highest clipping near the autumnal and vernal equinoxes. Increasing the tilt angle leads to more winter clipping, while lower tilt angles shifts the clipping to summer months. Shifting from fixed tilt to north–south single axis tracking increases the generation lost to clipping significantly. At an ILR of 1.25, annual clipping approximately doubles to 1% compared to fixed tilt at latitude, while clipping under an ILR of 2.0 increases to 22%, compared to 16% for the fixed configuration. As expected, more clipping occurs during the hours preceding and following noon when using single axis tracking.