Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598407
Harivina Gunnaasankaraan, A. Viswanath
In deregulated electricity markets, optimal transmission expansion achieved by a centralized planner with social welfare objective is higher than that achieved by investors with profit maximization objective. From a system operators perspective it is desirable to have social optimal transmission expansion. To achieve this in a profit driven framework, however, a monetary incentive mechanism is needed to encourage profitable transmission expansion to social optimal levels. This paper proposes capacity Premiums that could assure social optimal transmission capacity based on network users willingness to pay and which ensures that investors get adequate incentives for installing additional transmission capacity. Incentives for optimal transmission expansion are based on realistic market signals.
{"title":"Capacity Premium Model to Assure Social Optimal Transmission Expansion in a Profit Driven Framework","authors":"Harivina Gunnaasankaraan, A. Viswanath","doi":"10.1109/EPEC.2018.8598407","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598407","url":null,"abstract":"In deregulated electricity markets, optimal transmission expansion achieved by a centralized planner with social welfare objective is higher than that achieved by investors with profit maximization objective. From a system operators perspective it is desirable to have social optimal transmission expansion. To achieve this in a profit driven framework, however, a monetary incentive mechanism is needed to encourage profitable transmission expansion to social optimal levels. This paper proposes capacity Premiums that could assure social optimal transmission capacity based on network users willingness to pay and which ensures that investors get adequate incentives for installing additional transmission capacity. Incentives for optimal transmission expansion are based on realistic market signals.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125427424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598459
R. Babazadeh, Ataollah Gogani Khiabani
This paper investigates a novel nonlinear observer design approach based on the State-Dependent Riccati Equation (SDRE) technique for estimation of the state of charge (SOC) and state of health (SOH) parameters of nonlinear RC battery model. Due to practical restrictions on direct measurement of SOC, we try to introduce effective and accurate observing scheme which excel estimation results. The estimation of SOC and SOH has a crucial role in applications involving rechargeable batteries. SDRE observer is an extended form of Kalman Filter (KF) estimator for nonlinear systems. In this paper, the SDRE-based observer has been proposed for nonlinear RC battery model which is widely used. The resulting observer has several advantages including a faster convergence, better accuracy, and simpler structure in comparison with most existing methods. The simulation results show the merits of SDRE filter in estimating of SOC and SOH.
{"title":"Nonlinear Observer Design for RC Battery Model for Estimating State of Charge & State of Health Based on State-Dependent Riccati Equation","authors":"R. Babazadeh, Ataollah Gogani Khiabani","doi":"10.1109/EPEC.2018.8598459","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598459","url":null,"abstract":"This paper investigates a novel nonlinear observer design approach based on the State-Dependent Riccati Equation (SDRE) technique for estimation of the state of charge (SOC) and state of health (SOH) parameters of nonlinear RC battery model. Due to practical restrictions on direct measurement of SOC, we try to introduce effective and accurate observing scheme which excel estimation results. The estimation of SOC and SOH has a crucial role in applications involving rechargeable batteries. SDRE observer is an extended form of Kalman Filter (KF) estimator for nonlinear systems. In this paper, the SDRE-based observer has been proposed for nonlinear RC battery model which is widely used. The resulting observer has several advantages including a faster convergence, better accuracy, and simpler structure in comparison with most existing methods. The simulation results show the merits of SDRE filter in estimating of SOC and SOH.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122073318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598427
N. Chothani, M. Raichura, D. Patel, K. Mistry
Due to deregulation of the power system, grid reliability becomes more complicated. Even, day by day small-scale and renewable power generation is inserted in a distribution system effectively due to people awareness. A power transformer is one of the most important equipment in the grid to reliably and efficiently transmit power to the consumers. Asset management and protection are the best concepts for enlargement of transformer lifespan as well as to increase grid reliability. This article reflects on electrical and other parameter based power transformer asset management. Voltage, current, and power based inrush detection and Power Differential Protection (PDP) are applied to protect the transformer. Various data such as load history, losses and temperature will be monitored in real time to enhance the working capability of the transformer. The proposed method adopts various electrical and nonelectrical parameters for condition monitoring. The proposed scheme is successfully tested on SkV A laboratory transformer using Arm CORTEX-M4 processor. A fitness function estimated from the collected data in the processor will reflect the condition of the transformer. The hardware result confirms the effectiveness of the scheme for monitoring and protection of transformer.
{"title":"Real-Time Monitoring & Protection of Power Transformer to Enhance Smart Grid Reliability","authors":"N. Chothani, M. Raichura, D. Patel, K. Mistry","doi":"10.1109/EPEC.2018.8598427","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598427","url":null,"abstract":"Due to deregulation of the power system, grid reliability becomes more complicated. Even, day by day small-scale and renewable power generation is inserted in a distribution system effectively due to people awareness. A power transformer is one of the most important equipment in the grid to reliably and efficiently transmit power to the consumers. Asset management and protection are the best concepts for enlargement of transformer lifespan as well as to increase grid reliability. This article reflects on electrical and other parameter based power transformer asset management. Voltage, current, and power based inrush detection and Power Differential Protection (PDP) are applied to protect the transformer. Various data such as load history, losses and temperature will be monitored in real time to enhance the working capability of the transformer. The proposed method adopts various electrical and nonelectrical parameters for condition monitoring. The proposed scheme is successfully tested on SkV A laboratory transformer using Arm CORTEX-M4 processor. A fitness function estimated from the collected data in the processor will reflect the condition of the transformer. The hardware result confirms the effectiveness of the scheme for monitoring and protection of transformer.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598352
Shao-ren Wang, Jinqiu Li, A. Yazdani
In this paper a new methodology for dynamic service restoration (DSR) of distribution grids is proposed and tested. The approach utilizes the dynamic load curves of the case study grid. The Time variable load profile is applied to design an effective service restoration plan. This new method proposes an optimization algorithm to find candidate networks for reducing frequency and duration of customer interruptions. During the service restoration period the network configuration is altered in each hour to minimize frequency and duration of outages. Service restoration plan with global dynamic characteristics is one of the major contributions of the study. A constrained multi-objective mathematical model is forming the DSR methodology. The approach is tested on a 70-node distribution system. Comparing the simulation results to the existing literature show a great deal of advancement in providing a fast, secure and reliable service restoration plan in distribution feeders.
{"title":"An Approach to Distribution Systems Dynamic Service Restoration Utilizing Load Curves","authors":"Shao-ren Wang, Jinqiu Li, A. Yazdani","doi":"10.1109/EPEC.2018.8598352","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598352","url":null,"abstract":"In this paper a new methodology for dynamic service restoration (DSR) of distribution grids is proposed and tested. The approach utilizes the dynamic load curves of the case study grid. The Time variable load profile is applied to design an effective service restoration plan. This new method proposes an optimization algorithm to find candidate networks for reducing frequency and duration of customer interruptions. During the service restoration period the network configuration is altered in each hour to minimize frequency and duration of outages. Service restoration plan with global dynamic characteristics is one of the major contributions of the study. A constrained multi-objective mathematical model is forming the DSR methodology. The approach is tested on a 70-node distribution system. Comparing the simulation results to the existing literature show a great deal of advancement in providing a fast, secure and reliable service restoration plan in distribution feeders.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128105058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598448
Raghad Alhusari, M. Fadel, F. Omar
Heating/Cooling demands account for more than half of energy consumption in residential, agricultural and industrial fields. Ground Heat-Exchanger is an environmentally friendly solution used for heating/cooling purposes which is based on seasonal temperature difference between the ground and the ambient. A fuzzy-based controller is developed to utilize the ground heat and the weather conditions for maintaining the temperature in a thermally insulated greenhouse system. The greenhouse system is equipped with actuated windows, fans, sunlight collector and environmental sensors. Results showed the heat exchanger can be used for pre-cooling in summer and heating in winter in hot and imbalanced climate zones like UAE. The proposed controller was able to maintain the greenhouse temperature within the acceptable range on most days of the year with significantly less operational cost compared to the ON/OFF controller.
{"title":"Temperature Control of MIMO System by Utilizing Ground Temperature and Weather Conditions","authors":"Raghad Alhusari, M. Fadel, F. Omar","doi":"10.1109/EPEC.2018.8598448","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598448","url":null,"abstract":"Heating/Cooling demands account for more than half of energy consumption in residential, agricultural and industrial fields. Ground Heat-Exchanger is an environmentally friendly solution used for heating/cooling purposes which is based on seasonal temperature difference between the ground and the ambient. A fuzzy-based controller is developed to utilize the ground heat and the weather conditions for maintaining the temperature in a thermally insulated greenhouse system. The greenhouse system is equipped with actuated windows, fans, sunlight collector and environmental sensors. Results showed the heat exchanger can be used for pre-cooling in summer and heating in winter in hot and imbalanced climate zones like UAE. The proposed controller was able to maintain the greenhouse temperature within the acceptable range on most days of the year with significantly less operational cost compared to the ON/OFF controller.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130764166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598345
M. Abarzadeh, K. Al-haddad
In this paper, an improved finite-control-set model predictive controller (FCS-MPC) is proposed for five level active-neutral-point-clamped (5L-ANPC) converter. The dc-link capacitors and flying capacitor (FC) voltages, and the output current are controlled simultaneously in one control loop by employing the proposed improved FCS-MPC. In addition, the neutral point current is remarkably reduced by utilizing the proposed controller. Moreover, the cost function of the proposed improved FCS-MPC only consists of the neutral point and FC voltages, and the output current. Three decoupled pseudo functions are defined to predict the dc-capacitors and FC voltages by using only the output current. Hence, the proposed control method does not need to measure the dc-link and FC currents to predict the dc-link capacitors and FC voltages. The performance and feasibility of the proposed improved FCS-MPC for 5L-ANPC converter are verified by the simulation results.
{"title":"An Improved Model Predictive Controller for Five-Level Active-Neutral-Point-Clamped Converter","authors":"M. Abarzadeh, K. Al-haddad","doi":"10.1109/EPEC.2018.8598345","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598345","url":null,"abstract":"In this paper, an improved finite-control-set model predictive controller (FCS-MPC) is proposed for five level active-neutral-point-clamped (5L-ANPC) converter. The dc-link capacitors and flying capacitor (FC) voltages, and the output current are controlled simultaneously in one control loop by employing the proposed improved FCS-MPC. In addition, the neutral point current is remarkably reduced by utilizing the proposed controller. Moreover, the cost function of the proposed improved FCS-MPC only consists of the neutral point and FC voltages, and the output current. Three decoupled pseudo functions are defined to predict the dc-capacitors and FC voltages by using only the output current. Hence, the proposed control method does not need to measure the dc-link and FC currents to predict the dc-link capacitors and FC voltages. The performance and feasibility of the proposed improved FCS-MPC for 5L-ANPC converter are verified by the simulation results.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128578286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598330
Shady A. El-Battawy, B. Basta, W. Morsi
In this paper, the impact of plug-in electric vehicles charging demand on the distribution transformer's insulation aging in the presence of rooftop solar photovoltaic is probabilistically quantified. The monthly/seasonal variations in ambient temperature are incorporated into the thermal model used to estimate the loss-of-life of distribution transformers. Markov Chain Monte Carlo is used to probabilistically estimate the hourly loading on the transformers and hence emulating different scenarios of plug-in electric vehicles charging according to time-of-use and considering different penetrations of rooftop solar photovoltaic. The results quantifying the impact on the transformer's insulation aging due to variations in ambient temperature and plug-in electric vehicles charging according to time-of-use are discussed and conclusions are drawn.
{"title":"Impact of Integrating Electric Vehicles and Rooftop Solar Photovoltaic on Transformer's Aging Considering the Effect of Ambient Temperature","authors":"Shady A. El-Battawy, B. Basta, W. Morsi","doi":"10.1109/EPEC.2018.8598330","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598330","url":null,"abstract":"In this paper, the impact of plug-in electric vehicles charging demand on the distribution transformer's insulation aging in the presence of rooftop solar photovoltaic is probabilistically quantified. The monthly/seasonal variations in ambient temperature are incorporated into the thermal model used to estimate the loss-of-life of distribution transformers. Markov Chain Monte Carlo is used to probabilistically estimate the hourly loading on the transformers and hence emulating different scenarios of plug-in electric vehicles charging according to time-of-use and considering different penetrations of rooftop solar photovoltaic. The results quantifying the impact on the transformer's insulation aging due to variations in ambient temperature and plug-in electric vehicles charging according to time-of-use are discussed and conclusions are drawn.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598381
P. Memari, Saleh Mohammadi, S. Ghaderi
According to high electrical energy consumption and rising energy costs, accurate model factory with a high performance is necessary to discover energy consumption patterns and forecast future demands. Factory sectors have a large share in global energy consumption; therefore, consuming energy in this section should be controlled and managed. In this study, a smart decision support system (SDSS) framework is applied in a cloud environment. It includes three main stages. The first stage collects data from a smart grid system and stores them in cloud databases. The second stage, which analyzes energy consumption data, is an analytic system including Autoregressive Integrated Moving Average (ARIMA) and Sensor Data Regularity-Tree (SDR-Tree) methods. The third stage is a web-based portal for user communication and displays the results on charts. Cloud computing technology presents services for a grid system infrastructure and software, which raises the speed and quality of processes and reduces the costs of storage devices. In the last stage, for speeding up the operations and reducing time response, a Load Balancing Decision Algorithm (LBDA) mechanism is applied in the cloud environment. The main aim of this study is to propose a model combined with two ARIMA and SDR-Tree methods in order to increase the accuracy of the results and solve the problems of both single models. Implementation of this hybrid model is suitable for the electrical energy efficiency improvement and smart factories development.
{"title":"Data mining model for evaluating and forecasting energy consumption by cloud computing","authors":"P. Memari, Saleh Mohammadi, S. Ghaderi","doi":"10.1109/EPEC.2018.8598381","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598381","url":null,"abstract":"According to high electrical energy consumption and rising energy costs, accurate model factory with a high performance is necessary to discover energy consumption patterns and forecast future demands. Factory sectors have a large share in global energy consumption; therefore, consuming energy in this section should be controlled and managed. In this study, a smart decision support system (SDSS) framework is applied in a cloud environment. It includes three main stages. The first stage collects data from a smart grid system and stores them in cloud databases. The second stage, which analyzes energy consumption data, is an analytic system including Autoregressive Integrated Moving Average (ARIMA) and Sensor Data Regularity-Tree (SDR-Tree) methods. The third stage is a web-based portal for user communication and displays the results on charts. Cloud computing technology presents services for a grid system infrastructure and software, which raises the speed and quality of processes and reduces the costs of storage devices. In the last stage, for speeding up the operations and reducing time response, a Load Balancing Decision Algorithm (LBDA) mechanism is applied in the cloud environment. The main aim of this study is to propose a model combined with two ARIMA and SDR-Tree methods in order to increase the accuracy of the results and solve the problems of both single models. Implementation of this hybrid model is suitable for the electrical energy efficiency improvement and smart factories development.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598451
Aisha M. Pasha, Hebatallah M. Ibrahim, S. R. Hasan, R. Belkacemi, F. Awwad, O. Hasan
With the increase in renewable energy integration in the electrical power systems along with increase in the time-varying energy consumption by the users, it is imperative to regulate the load profile through pragmatic economical Demand-Side Management. Thus, the study carried out in this paper presents a real-time algorithm for cost optimization to achieve Demand-Side Management of a Renewable Energy Source integrated microgrid. The algorithm aims to achieve utility maximization and cost reduction for an optimal power scheduling in the presence of variable loads. The proposed approach mitigates the continuous changes in the variable loads that emulates the load profile found in residential, commercial and industrial users. The particular focus of this work is on developing a decentralized control scheme and a utility-oriented energy community, which provides user satisfaction based on energy management system, production units and load demand. Moreover, the paper presents utility maximization solutions on the combined energy profile of the microgrid targeting two main objectives, i.e., (1) minimizing the aggregate energy cost and (2) maximizing the provider's and user's satisfaction. Minimizing the aggregate energy cost aims to reduce the peak to average ratio of the aggregate energy profile of the microgrid using the cost function for energy cost minimization. The proposed technique is tested on microgrid which is coordinated in master-slave control topology. The implemented algorithm ensures a stable and efficient operation of the microgrid while minimizing the total cost of production.
{"title":"A Utility Maximized Demand-Side Management for Autonomous Microgrid","authors":"Aisha M. Pasha, Hebatallah M. Ibrahim, S. R. Hasan, R. Belkacemi, F. Awwad, O. Hasan","doi":"10.1109/EPEC.2018.8598451","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598451","url":null,"abstract":"With the increase in renewable energy integration in the electrical power systems along with increase in the time-varying energy consumption by the users, it is imperative to regulate the load profile through pragmatic economical Demand-Side Management. Thus, the study carried out in this paper presents a real-time algorithm for cost optimization to achieve Demand-Side Management of a Renewable Energy Source integrated microgrid. The algorithm aims to achieve utility maximization and cost reduction for an optimal power scheduling in the presence of variable loads. The proposed approach mitigates the continuous changes in the variable loads that emulates the load profile found in residential, commercial and industrial users. The particular focus of this work is on developing a decentralized control scheme and a utility-oriented energy community, which provides user satisfaction based on energy management system, production units and load demand. Moreover, the paper presents utility maximization solutions on the combined energy profile of the microgrid targeting two main objectives, i.e., (1) minimizing the aggregate energy cost and (2) maximizing the provider's and user's satisfaction. Minimizing the aggregate energy cost aims to reduce the peak to average ratio of the aggregate energy profile of the microgrid using the cost function for energy cost minimization. The proposed technique is tested on microgrid which is coordinated in master-slave control topology. The implemented algorithm ensures a stable and efficient operation of the microgrid while minimizing the total cost of production.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131427211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598442
A. Gagné, N. Ninad, John Adeyemo, D. Turcotte, Steven Wong
The irradiance at ground level mostly fluctuates due to cloud coverage. As clouds are moving toward a certain direction, the cardinal orientation of photovoitaic arrays affects the variability of the output power, and thus the impact on the electric power grid. This paper presents a new methodology with a circular layout for irradiance monitoring units to assess the solar variability in different directions of any site based on cloud speed-direction trend and directional variability reduction. The proposed methodology is used to assess the directional variability for a site at Varennes, QC, Canada using 1 year of measured data. The cloud speed direction is studied in order to observe any trend from a month-to-month and from an hour-to-hour. Overall the cloud direction has a trend of West to East direction, especially during the winter months. The variability reduction for each axis is estimated using the variability index (VI). The largest VI reduction is observed close to the cloud direction axis.
{"title":"Directional Solar Variability Analysis","authors":"A. Gagné, N. Ninad, John Adeyemo, D. Turcotte, Steven Wong","doi":"10.1109/EPEC.2018.8598442","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598442","url":null,"abstract":"The irradiance at ground level mostly fluctuates due to cloud coverage. As clouds are moving toward a certain direction, the cardinal orientation of photovoitaic arrays affects the variability of the output power, and thus the impact on the electric power grid. This paper presents a new methodology with a circular layout for irradiance monitoring units to assess the solar variability in different directions of any site based on cloud speed-direction trend and directional variability reduction. The proposed methodology is used to assess the directional variability for a site at Varennes, QC, Canada using 1 year of measured data. The cloud speed direction is studied in order to observe any trend from a month-to-month and from an hour-to-hour. Overall the cloud direction has a trend of West to East direction, especially during the winter months. The variability reduction for each axis is estimated using the variability index (VI). The largest VI reduction is observed close to the cloud direction axis.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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