- Nigeria
- PhD candidate, solar PV renewable energy researcheredit
This paper presents the design of an energy-efficient and self-sufficient residential building for the typical weather in Nigeria and by extension sub-Saharan Africa (SSA), considering aerogel material and photovoltaic power generation.... more
This paper presents the design of an energy-efficient and self-sufficient residential building for the typical weather in Nigeria and by extension sub-Saharan Africa (SSA), considering aerogel material and photovoltaic power generation. Firstly, a typical residential building design of the selected region is modelled using aerogel material to minimize the energy consumption of the building while also improving its thermal comfort. Then, an isolated photovoltaic system, with an optimally sized battery energy storage system (BESS), is incorporated to make the building energy self-sufficient. The proposal aims at tackling the extreme weather condition and the recurrent power outage of the region. The outcome of the designed system shows that the use of aerogel insulation material attained a 6% reduction in the recorded mean air and operative temperatures while also maintaining acceptable range of humidity. The use of aerogel also reduces energy consumption in the building by 11.7%. Also, incorporating a PV system with an optimally sized BESS tackles power shortage issue effectively, making the building energy self-sufficient.
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Generation of energy from renewable energy sources has been gaining momentum and popularity in recent years. This is owing to the global commitment to decelerate global warming, reduce greenhouse gas emission and avoid environmental... more
Generation of energy from renewable energy sources has been gaining momentum and popularity in recent years. This is owing to the global commitment to decelerate global warming, reduce greenhouse gas emission and avoid environmental pollution by cutting down fossil fuel-based generation to the barest minimum. Solar energy generation as an alternative is one of the most common renewable energy generation technologies in the world today. The technology however, has its setbacks and requires constant maintenance in order to maintain high generation efficiency and avoid faults and generation downtime. This study reviews the strategies and methods for mitigating the various faults associated with solar photovoltaic systems. It also attempts examining the effects of these strategies on the overall performance of photovoltaic systems.
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This paper presents the analysis of two BESS sizing methodology in solar PV systems, a proposed methodology considering predicted hourly solar radiation data and the methodology recommended by panel manufacturers. In the proposed method,... more
This paper presents the analysis of two BESS sizing methodology in solar PV systems, a proposed methodology considering predicted hourly solar radiation data and the methodology recommended by panel manufacturers. In the proposed method, Solar radiation behavior is predicted by studying and processing historical hourly solar radiation data of a location in Brazil using Box-Jenkins method and autoregressive (AR) and time series models are used to generate hourly synthetic series. The generated series combined with hourly load demand and battery storage capacity are used in simulating a PV system and the BESS is sized considering energy deficit and supply interruption outcomes. Comparison is made between the result of the proposed methodology and that of panel manufacturers' methodology considering two case studies. Results of the analysis showed that the proposed methodology is more adequate for BESS sizing. Probability analysis is also performed using multiple radiation scenarios of synthetic solar radiation data.
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The objective of this work is to evaluate the potential availability of primary energy from wind and sun contained in an air Basin aiming at generating electricity by conventional technologies. Methodologically, the systematic behavior of... more
The objective of this work is to evaluate the potential availability of primary energy from wind and sun contained in an air Basin aiming at generating electricity by conventional technologies. Methodologically, the systematic behavior of the wind and the incidence of the Sun is set, through anemometric and solarimetric data in loco using two Anemometric Station measuring 100 and 150 m and one Solarimetric Station. The results show that the local wind profile varies: (i) throughout the year, with the highest velocities and, in turn, potential energy available during the winter months; and (ii) throughout the day with mean velocities greater during night periods than daytime, 5.4 and 6.3 m/s, respectively. The solar resource also features interannual variation and its conversion into energy depends on the type of technology employed considering that the thin-film technology offers better performance during the months of spring and summer and monocrystalline technology (m-Si) in the winter. Another observation is that the solar and wind conversion technologies present daily and annual complementarity. Therefore, we may conclude that there is a need to specify a system with multiple technologies of generation to allow interannual, daily and nightly complementarity of different resources and thus to allow greater integration of generation and security of supply
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This paper presents a two-step cost-based method of optimally sizing and selecting BESS in standalone solar PV system applications considering predicted solar radiation data and economic performance (BESS cost analysis). The methodology... more
This paper presents a two-step cost-based method of optimally sizing and selecting BESS in standalone solar PV system applications considering predicted solar radiation data and economic performance (BESS cost analysis). The methodology is basically divided into two distinct parts; the first part is the sizing process and the second part is the selection process. In the first part, several BESS sizes suitable for a particular standalone PV system are determined using energy deficit and supply interruption outcomes of a PV system simulation with predicted hourly solar radiation series, hourly load demand and battery storage capacity as simulation parameters. In the second step, the economic performance of the determined BESS sizes is evaluated through a cost analysis process where two financial metrics; net present value (NPV) and payback period (PBP), are utilized. This step is necessary in order to ascertain the investment risks and benefits of the BESS sizes. To test its adequacy, the methodology was applied to two case studies; a residential load and a commercial load, and the results obtained for both case studies suggests that combining BESS sizing using predicted solar radiation data and BESS selection considering economic performance is an adequate process of incorporating BESS in standalone PV system applications.
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This paper presents a methodology for optimal Battery Energy Storage System (BESS) sizing in photovoltaic (PV) systems. The method is based on an established mathematical relationship between generated energy, demand and storage, allowing... more
This paper presents a methodology for optimal Battery Energy Storage System (BESS) sizing in photovoltaic (PV) systems. The method is based on an established mathematical relationship between generated energy, demand and storage, allowing one to determine energy deficit and supply interruption periods. Autoregressive and time series models were used to generate daily synthetic series data from a set of hourly estimated global irradiance for the city of Petrolina in Brazil. The proposed method facilitates the assessment of the performance of several possible BESS, indicate the risk of energy deficit and the possible frequency of energy interruption.
Research Interests: Renewable Energy, Electric Energy Storage, Energy Storage (in Engineering), Renewable energy resources, Renewable Energy Systems, and 7 moreSolar Energy, Energy storage systems, Li-ion battery modeling and control design in electrified vehicles, Energy Storage, Solar Energy and Thermal Energy Storage Systems, Solar Photovoltaic, Distributed generation & energy storage systems, and Battery Energy Storage
This study presents a methodology for the sizing of Battery Energy Storage Systems (BESS) in isolated Photovoltaic Plants (PV) using predicted hourly solar radiation data. The method is based on a mathematical relationship that was... more
This study presents a methodology for the sizing of Battery Energy Storage Systems (BESS) in isolated Photovoltaic Plants (PV) using predicted hourly solar radiation data. The method is based on a mathematical relationship that was established between PV generated energy, hourly load demand and storage capacity, allowing one to determine energy deficit and supply interruption periods. To achieve this, solar radiation behavior must be predicted through acquisition and processing sets of historical hourly solar radiation data so that autoregressive (AR) and time series models are used to generate hourly synthetic series. The generated series, combined with available hourly load demand, are used as inputs in the simulation. The sizing of BESS is considered by adjusting the variables in the simulation to determine the corresponding power output and energy capacity until acceptable percentages of energy deficit and supply interruptions are attained. Probability analysis is also carried out using multiple radiation scenarios of synthetic solar radiation data, represented using probability and cumulative distribution curves. The proposed method is applied to a location in Northeastern Brazil using the Box Jenkins AR method in order to facilitate the assessment of the performance of several possible BESS, indicate the risk of energy deficit and the possible frequency of energy interruption. A cost analysis is carried out to analyze the risks and benefits of investing in battery energy storage system installation in PV plants and shows the contributions of the proposed approach. The methodology was applied to self-sufficient non-utility scale case studies for purposes of energy harvesting and curtailment, results were presented, discussed and conclusions were drawn.
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Solar energy as a source of clean and renewable energy generation has gained traction over the years as an alternative to conventional fossil fuels. This is as a result of the search for permanent and effective solutions to the... more
Solar energy as a source of clean and renewable energy generation has gained traction over the years as an alternative to conventional fossil fuels. This is as a result of the search for permanent and effective solutions to the environmental issues such as environmental pollution, global warming and greenhouse gas emission affecting our planet. Solar photovoltaic sys‐ tem is one of the technologies developed to harness solar energy which is in abundance across the globe. This technology however, has operational and maintenance setbacks and requires close and constant monitoring to maintain highly effective generation of energy. Engineers, researchers and other stakeholders in the field have over the years proposed and developed various operation and maintenance strategies designed to help solar photovoltaic systems maintain high generation efficien‐ cies. The current study is an elaborate review of various strategies and methods proposed in literature and the effects of these strategies on overall system performance. It examines common solar photovoltaic system faults and the strategies or methods proposed by experts to mitigate these faults. The reviewed methods are organized in groups based on their functionality and the manner in which they detect faults in solar photovoltaic system operations
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Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced... more
Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman Neural Network (ENN), Boosted Tree Algorithms (BTA), Multi-layer Perceptron (MLP), and Gaussian Processes Regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages their strengths for accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction, the fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out, the first scenario is done for PV array fault detection with DC power (DCP) as output. The second scenario is done for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified reliably among all the models with MAPE=0.0393, and MAE=0.002 for inverter fault detection and MAPE=0.091, and MAE=0.000 for PV array fault detection.
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