A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Proactive Monitoring, Anomaly Detection, and Forecasting of Solar Photovoltaic Systems Using Artificial Neural Networks

The world of energy sustainability landscape is witnessing high proliferation of smartgrids and microgrids, it has become significant to use intelligent tools to design, operate and maintain such crucial systems in our lives. Solar energy is an intermittent source and purely Photovoltaic (PV) based, or PV and storage based smartgrids require characterization and modelling of PV resources for an effective planning and effective operations. This dissertation familiarizes briefly the existing tools for design, monitoring, forecasting and operation of a solar system in smart electric grids infrastructure and proposes a unique application-based infrastructure to monitor, operate, forecast and troubleshoot a working PV of a smartgrid. A resilient smartgrid communication is proposed which enables monitoring and control of different elements in any PV system. This communication architecture is used to facilitate a feedback-oriented monitoring of different elements in a microgrid ecosystem and investigated thoroughly. This integrated architecture which is a combination of sensors, network elements, database and computation elements is designed specifically for solar photovoltaic (PV) powered grids on modular basis. Apart from this, the network resilience and redundancy for smooth and loss less communication is another characteristic factor in this research work. Subsequently, a deep neural network algorithm is developed to diagnose the underperformance in the generation of a PV system connected to a smartgrid. As PV generation is predominantly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing performance of the system at any given instance. To address this challenge, this deep neural network architecture is presented for instantaneous performance diagnosis. The proposed architecture enabled modeling and diagnose of soiling and partial shade conditions prevalent with an accuracy of 90+%. Features of monitoring and regulating the generation and demand side of the grid were integrated through network along with feedback-based measures for effective performance in the PV system of a smartgrid or microgrid using the same network. The novelty in this work lies in real-time calculation of ideal performance and comparison for diagnosing critical performance issues of solar power generation like soiling and partial shading. Furthermore, long-short term memory (LSTM), which is a recurrent neural network model, is created for forecasting the PV solar resources, in which can assist in quantifying PV generation in various time intervals (hourly, daily, weekly). PV based smartgrids often experience expensive or inaccurate resources planning due to the lack of accurate forecasting tools where the projected methodology would eliminate such losses. This research work in its whole provides a different proposition of vertical integration which can transform into a new concept called Internet of Microgrid (IoMG). Planning, monitoring and operation form the core of smartgrids administration and if intelligent tools intertwined with network are being used as integral part in each of these aspects, then it forms a holistic view of smartgrids

Deep learning approach to forecasting hourly solar irradiance

Abstract: In this dissertation, six artificial intelligence (AI) based methods for forecasting solar irradiance are presented. Solar energy is a clean renewable energy source (RES) which is free and abundant in nature. But despite the environmental impacts of fossil energy, global dependence on it is yet to drop appreciably in favor of solar energy for power generation purposes. Although the latest improvements on the technologies of photovoltaic (PV) cells have led to a significant drop in the cost of solar panels, solar power is still unattractive to some consumers due to its unpredictability. Consequently, accurate prediction of solar irradiance for stable solar power production continues to be a critical need both in the field of physical simulations or artificial intelligence. The performance of various methods in use for prediction of solar irradiance depends on the diversity of dataset, time step, experimental setup, performance evaluators, and forecasting horizon. In this study, historical meteorological data for the city of Johannesburg were used as training data for the solar irradiance forecast. Data collected for this work spanned from 1984 to 2019. Only ten years (2009 to 2018) of data was used. Tools used are Jupyter notebook and Computer with Nvidia GPU...M.Ing. (Electrical and Electronic Engineering Management

A Review of Considered Factors to Penetrate Renewable Energy Resources in Electrical Power System

As an increasing of load demand, scarcity of fossil fuel and penetration of greenhouse gasses (GHG) effect, utilization of renewable energy resources (RER) such as wind, solar, biomass and tidal are rising drastically. Distributed generation (DG) is a technology giving opportunity to integrate RER into power system network. These integrations are needed optimal long term planning. Those planning, hopefully, can increase reliability of electrical power system network while saving environment from GHG with minimum infestation, operation and maintenance cost. The aim of this paper is reviewing factors should be consider when preparing, operating and evaluating electrical power system with integration of RER. By this planning, it is expected that its integration is effective and efficient in a lifetime of project. Finally, this review can help government, researcher, engineer and private sector to make policies to preparing hybrid power system-DGs.   Keywords: Penetration of renewable energy resources, electrical power system, long term planning, distributed generation, policies &nbsp

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Decarbonization cost of Bangladesh's energy sector: Influence of corruption

As a rapidly developing lower-middle income country, Bangladesh has been maintaining a steady growth of +5% in the gross domestic product (GDP) annually since 2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle- income and developed by 2021 and 2041 respectively, which translates to an annual GDP growth rate of 7.58% during this period. The bulk of this growth is expected to come from the manufacturing sector, the significant shift towards which started at the turn of this century. Energy intensity of manufacturing-based growth is higher, the evidence of which can be seen in the 3.17 times increase in national energy consumption between 2001 and 2014. Also, Bangladesh aims to achieve 100% electrification rate by 2021 against an annual population growth rate of 1.08%. With the increasing per capita income, there is now a growing middle class fuelling the growth in demand for convenient forms of energy. Considering the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher energy demand than that of 2010 by 2050. The government and private sector have started a substantial amount of investments in the energy sector to meet the signi ficant future demand. Approximately US$104 billion would be invested in the power sector of Bangladesh for establishing 33 GW installed capacity by 2030, the majority of which would be financed by national and international loans. However, Bangladesh is one of the most corrupted country in the world which may influence the energy planning development. The current policies of Bangladesh power sector paved the future direction towards predominantly coal-based energy mix which would augment the greenhouse gas (GHG) emissions five times (117.5 MtCO2e) in 2030 than that of 2010. By increasing GHG emissions, the country would undermine the worldwide effort of keeping global temperature rise in 21st century below 2°C, as per the Paris agreement and COP21. V The objective of this research was to develop a framework to explore the cost of decarbonizing the Bangladesh's energy sector by 2050. For the study, six emissions scenarios business as usual (BAU), current policy (CPS), high-carbon (HCS), medium-carbon (MCS), low-carbon (LCS) and zero-carbon scenarios (ZCS), and three economic conditions high, average and low costwere considered. The combination of emissions and economic scenarios rendered 18 different emissionseconomic scenarios for the research. The results showed that Bangladesh would emit 343 MtCO2e by 2050 without any emissions reduction strategies under HCS. However, Bangladesh can reduce 23 of HCS by adopting decarbonization strategies such as energy mix change towards renewable and nuclear. On the optimistic side, the emissions can be reduced 73 by 2050 under ZCS than that of HCS. The study demonstrated that a zero carbon future is not yet feasible for Bangladesh by 2050 because the operational fossil fuel based plants would be operational. Therefore, the GHG emissions are going to rise even if Bangladesh adopts renewables and nuclear dominating energy mix. However, it will be possible to keep the GHG emissions approximately 2 tCO2e/capita threshold if the country adopts LCS. On the other hand, only MCS and LCS can meet the projected energy demand by 2050. The energy sector can meet the projected demand under ZCS only if the electricity consumption is reduced 26 2050. In terms total cost, the MCS was found to be 3.9 LCS by 2050. LCS would have a higher cost than that of MCS up to 2030, due to the high capital cost of renewable technologies. The total cost under LCS would start to be lower than of MCS after 2035 for the fossil fuel cost. Accumulated fuel cost would reach$250 billion in 2050 under HCS, which can be reduced 23% under ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh is under rapid development, the accumulated capital would be comparatively high by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which would also ensure lower emissions. The study suggested that energy mix change, technological maturity, corruption and demand reduction can influence the cost of decarbonization. However, the most significant influencer for the decarbonization of Bangladeshi energy sector would be the corruption. Results showed that if Bangladesh can minimize the effect of corruption on the energy sector, it can reduce the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS

Control System for Electrical Power Grids with Renewables using Artificial Intelligence Methods

Modern electrical and electronic devices are very sensitive to the power supply and require steady and stable electric power. Factories may also need electric power within a specific standard range of voltage, frequency, and current to avoid defects in the production. For these reasons electric power utilities must produce an electric power of a specific standard of power quality parameters [EN50160]. Nowadays, renewable energy sources, such as wind energy and solar energy are used to generate electric power as free and clean power sources as well to reduce fuel consumption and environmental pollution as much as possible. Renewable energy, e.g. wind speed or solar irradiance, are not stable or not constant energies over the time. Therefore smart control systems (SCSs) are needed for operate the power system in optimal way which help for producing a power with good quality from renewable sources. The forecasting and prediction models play a main role in these issues and contribute as the important part of the smart control system (SCS). The main task of the SCS is to keep the generated power equal to the consumed power as well as to consider standard levels of power quality parameters as much as possible. Some of previous studies have focused on forecasting power quality parameters, power load, wind speed and solar irradiance using machine learning models as neural networks, support vector machines, fuzzy sets, and neuro fuzzy. This thesis proposes designing forecasting systems using machine learning techniques in order to be use in control and operate an electrical power system. In this study, design and tested forecasting systems related to the power and renewable energies. These systems include wind speed forecasting, power load forecasting and power quality parameters forecasting. The main part of this thesis is focus in power quality parameters forecasting in short-term, these parameters are: power frequency, magnitude of the supply voltage, total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), and short term flicker severity (Pst) according to the definition in [EN50160]. The output of the forecasting models of power quality parameters can be used in shifting the load to run in switch time which will help for correct and optimize the quality of the power.Modern electrical and electronic devices are very sensitive to the power supply and require steady and stable electric power. Factories may also need electric power within a specific standard range of voltage, frequency, and current to avoid defects in the production. For these reasons electric power utilities must produce an electric power of a specific standard of power quality parameters [EN50160]. Nowadays, renewable energy sources, such as wind energy and solar energy are used to generate electric power as free and clean power sources as well to reduce fuel consumption and environmental pollution as much as possible. Renewable energy, e.g. wind speed or solar irradiance, are not stable or not constant energies over the time. Therefore smart control systems (SCSs) are needed for operate the power system in optimal way which help for producing a power with good quality from renewable sources. The forecasting and prediction models play a main role in these issues and contribute as the important part of the smart control system (SCS). The main task of the SCS is to keep the generated power equal to the consumed power as well as to consider standard levels of power quality parameters as much as possible. Some of previous studies have focused on forecasting power quality parameters, power load, wind speed and solar irradiance using machine learning models as neural networks, support vector machines, fuzzy sets, and neuro fuzzy. This thesis proposes designing forecasting systems using machine learning techniques in order to be use in control and operate an electrical power system. In this study, design and tested forecasting systems related to the power and renewable energies. These systems include wind speed forecasting, power load forecasting and power quality parameters forecasting. The main part of this thesis is focus in power quality parameters forecasting in short-term, these parameters are: power frequency, magnitude of the supply voltage, total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), and short term flicker severity (Pst) according to the definition in [EN50160]. The output of the forecasting models of power quality parameters can be used in shifting the load to run in switch time which will help for correct and optimize the quality of the power.410 - Katedra elektroenergetikyvyhově