Assessing the modelling approach and datasets required for fault detection in photovoltaic systems

Reliable monitoring for photovoltaic assets (PVs) is essential to ensuring uptake, long term performance, and maximum return on investment of renewable systems. To this end this paper investigates the input data and machine learning techniques required for day-behind predictions of PV generation, within the scope of conducting informed maintenance of these systems. Five years of PV generation data at hourly intervals were retrieved from four commercial building-mounted PV installations in the UK, as well as weather data retrieved from MIDAS. A support vector machine, random forest and artificial neural network were trained to predict PV power generation. Random forest performed best, achieving an average mean relative error of 2.7%. Irradiance, previous generation and solar position were found to be the most important variables. Overall, this work shows how low-cost data driven analysis of PV systems can be used to support the effective management of such assets

Comprehensive Methodology for Sustainable Power Supply in Emerging Countries

[EN] Electricity has become one of the main driving forces for development, especially in remote areas where the lack of energy is linked to poverty. Traditionally, in these areas power is supplied by grid extension projects, which are expensive, or stand-alone systems based on fossil fuels. An actual alternative to these solutions is community micro-grid projects based on distributed renewable energy sources. However, these solutions need to introduce a holistic approach in order to be successfully implemented in real cases. The main purpose of this research work is the definition and development of a comprehensive methodology to encourage the use of decentralized renewable power systems to provide power supply to non-electrified areas. The methodology follows a top-down approach. Its main novelty is that it interlinks a macro and micro analysis dimension, considering not only the energy context of the country where the area under study is located and its development towards a sustainable scenario; but also the potential of renewable power generation, the demand side management opportunities and the socio-economic aspects involved in the final decision on what renewable energy solution would be the most appropriate for the considered location. The implementation of this methodology provides isolated areas a tool for sustainable energy development based on an environmentally friendly and socially participatory approach. Results of implementing the methodology in a case study showed the importance of introducing a holistic approach in supplying power energy to isolated areas, stating the need for involving all the different stakeholders in the decision-making process. Despite final raking on sustainable power supply solutions may vary from one area to another, the implementation of the methodology follows the same procedure, which makes it an inestimable tool for governments, private investors and local communities.This research was funded by Universitat Politecnica de Valencia and Generalitat Valenciana, grant references SP20180248 and GV/2017/023, respectively.Peñalvo-López, E.; Pérez-Navarro, Á.; Hurtado-Perez, E.; Cárcel Carrasco, FJ. (2019). Comprehensive Methodology for Sustainable Power Supply in Emerging Countries. Sustainability. 11(19):1-22. https://doi.org/10.3390/su11195398S1221119LOKEN, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595. doi:10.1016/j.rser.2005.11.005Cherni, J. A., Dyner, I., Henao, F., Jaramillo, P., Smith, R., & Font, R. O. (2007). Energy supply for sustainable rural livelihoods. A multi-criteria decision-support system. Energy Policy, 35(3), 1493-1504. doi:10.1016/j.enpol.2006.03.026Gabaldón-Estevan, D., Peñalvo-López, E., & Alfonso Solar, D. (2018). The Spanish Turn against Renewable Energy Development. Sustainability, 10(4), 1208. doi:10.3390/su10041208Ouyang, W., Cheng, H., Zhang, X., & Yao, L. (2010). Distribution network planning method considering distributed generation for peak cutting. Energy Conversion and Management, 51(12), 2394-2401. doi:10.1016/j.enconman.2010.05.003Chaurey, A., Ranganathan, M., & Mohanty, P. (2004). Electricity access for geographically disadvantaged rural communities—technology and policy insights. Energy Policy, 32(15), 1693-1705. doi:10.1016/s0301-4215(03)00160-5CARCEL CARRASCO, F. J., PEÑALVO LOPEZ, E., & DE MURGA, G. (2018). OFICINAS AUTO-SOSTENIBLES PARA LAS AGENCIAS DE AYUDA INTERNACIONAL EN ZONAS GEOGRÁFICAS REMOTAS. DYNA INGENIERIA E INDUSTRIA, 94(1), 272-277. doi:10.6036/8507Erdinc, O., & Uzunoglu, M. (2012). Optimum design of hybrid renewable energy systems: Overview of different approaches. Renewable and Sustainable Energy Reviews, 16(3), 1412-1425. doi:10.1016/j.rser.2011.11.011Al-falahi Monaaf D.A., Jayasinghe, S. D. G., & Enshaei, H. (2017). A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Conversion and Management, 143, 252-274. doi:10.1016/j.enconman.2017.04.019Bajpai, P., & Dash, V. (2012). Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renewable and Sustainable Energy Reviews, 16(5), 2926-2939. doi:10.1016/j.rser.2012.02.009Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.030Al-Alawi, A., & Islam, S. . (2004). Demand side management for remote area power supply systems incorporating solar irradiance model. Renewable Energy, 29(13), 2027-2036. doi:10.1016/j.renene.2004.03.006Ardakani, F. J., & Ardehali, M. M. (2014). Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting. Energy Conversion and Management, 78, 745-752. doi:10.1016/j.enconman.2013.11.019Kavrakoǧlu, I., & Kiziltan, G. (1983). Multiobjective strategies in power systems planning. European Journal of Operational Research, 12(2), 159-170. doi:10.1016/0377-2217(83)90219-9Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—A review. Renewable and Sustainable Energy Reviews, 8(4), 365-381. doi:10.1016/j.rser.2003.12.007Kabak, M., & Dağdeviren, M. (2014). Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Conversion and Management, 79, 25-33. doi:10.1016/j.enconman.2013.11.036Peñalvo-López, E., Cárcel-Carrasco, F., Devece, C., & Morcillo, A. (2017). A Methodology for Analysing Sustainability in Energy Scenarios. Sustainability, 9(9), 1590. doi:10.3390/su9091590HOMER Pro® Microgrid Software, the Micro-Power Optimization Model; HOMER Pro 3.13, HOMER Energyhttps://www.homerenergy.com/products/pro/index.htmlSuper Decisions Softwarehttps://www.superdecisions.com/ENRGYPLAN Advanced Energy System Analysishttp://www.energyplan.eu/LEAP Code Energy Analysishttps://www.energycommunity.org/default.asp?action=introductionRodríguez-García, Ribó-Pérez, Álvarez-Bel, & Peñalvo-López. (2019). Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy. Energies, 12(13), 2605. doi:10.3390/en12132605Huld, T., Müller, R., & Gambardella, A. (2012). A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy, 86(6), 1803-1815. doi:10.1016/j.solener.2012.03.006Fischer, G., & Schrattenholzer, L. (2001). Global bioenergy potentials through 2050. Biomass and Bioenergy, 20(3), 151-159. doi:10.1016/s0961-9534(00)00074-xHurtado, E., Peñalvo-López, E., Pérez-Navarro, Á., Vargas, C., & Alfonso, D. (2015). Optimization of a hybrid renewable system for high feasibility application in non-connected zones. Applied Energy, 155, 308-314. doi:10.1016/j.apenergy.2015.05.09

Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability

[EN] In rural areas or in isolated communities in developing countries it is increasingly common to install micro-renewable sources, such as photovoltaic (PV) systems, by residential consumers without access to the utility distribution network. The reliability of the supply provided by these stand-alone generators is a key issue when designing the PV system. The proper system sizing for a minimum level of reliability avoids unacceptable continuity of supply (undersized system) and unnecessary costs (oversized system). This paper presents a method for the accurate sizing of stand-alone photovoltaic (SAPV) residential generation systems for a pre-established reliability level. The proposed method is based on the application of a sequential random Monte Carlo simulation to the system model. Uncertainties of solar radiation, energy demand, and component failures are simultaneously considered. The results of the case study facilitate the sizing of the main energy elements (solar panels and battery) depending on the required level of reliability, taking into account the uncertainties that affect this type of facility. The analysis carried out demonstrates that deterministic designs of SAPV systems based on average demand and radiation values or the average number of consecutive cloudy days can lead to inadequate levels of continuity of supply.This work has been supported by research funds of the Universitat Politecnica de Valencia.Quiles Cucarella, E.; Roldán-Blay, C.; Escrivá-Escrivá, G.; Roldán-Porta, C. (2020). Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability. Sustainability. 12(3):1-18. https://doi.org/10.3390/su12031274S118123Twaha, S., & Ramli, M. A. M. (2018). A review of optimization approaches for hybrid distributed energy generation systems: Off-grid and grid-connected systems. Sustainable Cities and Society, 41, 320-331. doi:10.1016/j.scs.2018.05.027Mandelli, S., Barbieri, J., Mereu, R., & Colombo, E. (2016). Off-grid systems for rural electrification in developing countries: Definitions, classification and a comprehensive literature review. Renewable and Sustainable Energy Reviews, 58, 1621-1646. doi:10.1016/j.rser.2015.12.338Luthander, R., Widén, J., Nilsson, D., & Palm, J. (2015). Photovoltaic self-consumption in buildings: A review. Applied Energy, 142, 80-94. doi:10.1016/j.apenergy.2014.12.028Evans, A., Strezov, V., & Evans, T. J. (2012). Assessment of utility energy storage options for increased renewable energy penetration. Renewable and Sustainable Energy Reviews, 16(6), 4141-4147. doi:10.1016/j.rser.2012.03.048https://www.boe.es/diario_boe/txt.php?id=BOE-A-2019-5089Bugała, A., Zaborowicz, M., Boniecki, P., Janczak, D., Koszela, K., Czekała, W., & Lewicki, A. (2018). Short-term forecast of generation of electric energy in photovoltaic systems. Renewable and Sustainable Energy Reviews, 81, 306-312. doi:10.1016/j.rser.2017.07.032Abuagreb, M., Allehyani, M., & Johnson, B. K. (2019). Design and Test of a Combined PV and Battery System Under Multiple Load and Irradiation Conditions. 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). doi:10.1109/isgt.2019.8791565Moharil, R. M., & Kulkarni, P. S. (2010). Reliability analysis of solar photovoltaic system using hourly mean solar radiation data. Solar Energy, 84(4), 691-702. doi:10.1016/j.solener.2010.01.022Dissawa, D. M. L. H., Godaliyadda, G. M. R. I., Ekanayake, M. P. B., Ekanayake, J. B., & Agalgaonkar, A. P. (2017). Cross-correlation based cloud motion estimation for short-term solar irradiation predictions. 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). doi:10.1109/iciinfs.2017.8300338Kaplani, E., & Kaplanis, S. (2012). A stochastic simulation model for reliable PV system sizing providing for solar radiation fluctuations. Applied Energy, 97, 970-981. doi:10.1016/j.apenergy.2011.12.016Benmouiza, K., Tadj, M., & Cheknane, A. (2016). Classification of hourly solar radiation using fuzzy c-means algorithm for optimal stand-alone PV system sizing. International Journal of Electrical Power & Energy Systems, 82, 233-241. doi:10.1016/j.ijepes.2016.03.019Ozoegwu, C. G. (2019). Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number. Journal of Cleaner Production, 216, 1-13. doi:10.1016/j.jclepro.2019.01.096Palensky, P., & Dietrich, D. (2011). Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Transactions on Industrial Informatics, 7(3), 381-388. doi:10.1109/tii.2011.2158841Roldán-Blay, C., Escrivá-Escrivá, G., & Roldán-Porta, C. (2019). Improving the benefits of demand response participation in facilities with distributed energy resources. Energy, 169, 710-718. doi:10.1016/j.energy.2018.12.102Roldán-Porta, Roldán-Blay, Escrivá-Escrivá, & Quiles. (2019). Improving the Sustainability of Self-Consumption with Cooperative DC Microgrids. Sustainability, 11(19), 5472. doi:10.3390/su11195472Huang, Y., Yang, L., Liu, S., & Wang, G. (2018). Cooperation between Two Micro-Grids Considering Power Exchange: An Optimal Sizing Approach Based on Collaborative Operation. Sustainability, 10(11), 4198. doi:10.3390/su10114198Goel, S., & Sharma, R. (2017). Performance evaluation of stand alone, grid connected and hybrid renewable energy systems for rural application: A comparative review. Renewable and Sustainable Energy Reviews, 78, 1378-1389. doi:10.1016/j.rser.2017.05.200Weniger, J., Tjaden, T., & Quaschning, V. (2014). Sizing of Residential PV Battery Systems. Energy Procedia, 46, 78-87. doi:10.1016/j.egypro.2014.01.160Maleki, A., Rosen, M., & Pourfayaz, F. (2017). Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications. Sustainability, 9(8), 1314. doi:10.3390/su9081314Cao, S., Hasan, A., & Sirén, K. (2014). Matching analysis for on-site hybrid renewable energy systems of office buildings with extended indices. Applied Energy, 113, 230-247. doi:10.1016/j.apenergy.2013.07.031Ren, H., Wu, Q., Gao, W., & Zhou, W. (2016). Optimal operation of a grid-connected hybrid PV/fuel cell/battery energy system for residential applications. Energy, 113, 702-712. doi:10.1016/j.energy.2016.07.091Ghafoor, A., & Munir, A. (2015). Design and economics analysis of an off-grid PV system for household electrification. Renewable and Sustainable Energy Reviews, 42, 496-502. doi:10.1016/j.rser.2014.10.012Maleki, A., Hajinezhad, A., & Rosen, M. A. (2016). Modeling and optimal design of an off-grid hybrid system for electricity generation using various biodiesel fuels: a case study for Davarzan, Iran. Biofuels, 7(6), 699-712. doi:10.1080/17597269.2016.1192443Castillo-Cagigal, M., Caamaño-Martín, E., Matallanas, E., Masa-Bote, D., Gutiérrez, A., Monasterio-Huelin, F., & Jiménez-Leube, J. (2011). PV self-consumption optimization with storage and Active DSM for the residential sector. Solar Energy, 85(9), 2338-2348. doi:10.1016/j.solener.2011.06.028Zhou, W., Lou, C., Li, Z., Lu, L., & Yang, H. (2010). Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems. Applied Energy, 87(2), 380-389. doi:10.1016/j.apenergy.2009.08.012Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772-781. doi:10.1016/j.rser.2013.08.055Roldán-Blay, C., Escrivá-Escrivá, G., Roldán-Porta, C., & Álvarez-Bel, C. (2017). An optimisation algorithm for distributed energy resources management in micro-scale energy hubs. Energy, 132, 126-135. doi:10.1016/j.energy.2017.05.038Hoevenaars, E. J., & Crawford, C. A. (2012). Implications of temporal resolution for modeling renewables-based power systems. Renewable Energy, 41, 285-293. doi:10.1016/j.renene.2011.11.013Cao, S., & Sirén, K. (2014). Impact of simulation time-resolution on the matching of PV production and household electric demand. Applied Energy, 128, 192-208. doi:10.1016/j.apenergy.2014.04.075Cucchiella, F., D’Adamo, I., Gastaldi, M., & Stornelli, V. (2018). Solar Photovoltaic Panels Combined with Energy Storage in a Residential Building: An Economic Analysis. Sustainability, 10(9), 3117. doi:10.3390/su10093117Kosmadakis, I., Elmasides, C., Eleftheriou, D., & Tsagarakis, K. (2019). A Techno-Economic Analysis of a PV-Battery System in Greece. Energies, 12(7), 1357. doi:10.3390/en12071357Faza, A. (2018). A probabilistic model for estimating the effects of photovoltaic sources on the power systems reliability. Reliability Engineering & System Safety, 171, 67-77. doi:10.1016/j.ress.2017.11.008Borges, C. L. T. (2012). An overview of reliability models and methods for distribution systems with renewable energy distributed generation. Renewable and Sustainable Energy Reviews, 16(6), 4008-4015. doi:10.1016/j.rser.2012.03.055Roldán-Blay, C., Roldán-Porta, C., Peñalvo-López, E., & Escrivá-Escrivá, G. (2017). Optimal Energy Management of an Academic Building with Distributed Generation and Energy Storage Systems. IOP Conference Series: Earth and Environmental Science, 78, 012018. doi:10.1088/1755-1315/78/1/012018Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). 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An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Reducing Voltage Volatility with Step Voltage Regulators: A Life-Cycle Cost Analysis of Korean Solar Photovoltaic Distributed Generation

To meet the United Nation’s sustainable development energy goal, the Korean Ministry of Commerce announced they would increase renewable energy generation to 5.3% by 2029. These energy sources are often produced in small-scale power plants located close to the end users, known as distributed generation (DG). The use of DG is an excellent way to reduce greenhouse gases but has also been found to reduce power quality and safety reliability through an increase in voltage volatility. This paper performs a life-cycle cost analysis on the use of step voltage regulators (SVR) to reduce said volatility, simulating the impact they have on existing Korean solar photovoltaic (PV) DG. From the data collected on a Korean Electrical Power Corporation 30 km/8.2 megawatts (MW) feeder system, SVRs were found to increase earnings by one million USD. SVR volatile voltage mitigation increased expected earnings by increasing the estimated allowable PV power generation by 2.7 MW. While this study is based on Korean PV power generation, its findings are applicable to any DG sources worldwide.11Nsciescopu

Challenges to the Integration of Renewable Resources at High System Penetration

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.