Pub Date : 2022-12-12DOI: 10.1109/SASG57022.2022.10199644
Motab A. Almerab
This paper deals with the management and Technical Study of the Smart Grid Services’ future options and priorities of applications in software automation process integration with a centralized center. Utilizing Substation’s standalone subsystems to get online monitoring and communication capabilities as a part of a comprehensive substation remote data monitoring solutions. In addition, it further discusses the application of accumulated data value for National Grid (NG) Saudi Arabia (SA) data operation with Artificial intelligence analysis. This paper will highlight the most effective solutions as Smart Grid Services and their prospects impact on the network stability and reliability. Finally, the study will recommend a roadmap of solutions integration of People, Process, Data, and Technology.
{"title":"Smart Grid Services for Energy Utilities : Insights on Options and Priorities and Main Application Enabler in National Grid SA","authors":"Motab A. Almerab","doi":"10.1109/SASG57022.2022.10199644","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199644","url":null,"abstract":"This paper deals with the management and Technical Study of the Smart Grid Services’ future options and priorities of applications in software automation process integration with a centralized center. Utilizing Substation’s standalone subsystems to get online monitoring and communication capabilities as a part of a comprehensive substation remote data monitoring solutions. In addition, it further discusses the application of accumulated data value for National Grid (NG) Saudi Arabia (SA) data operation with Artificial intelligence analysis. This paper will highlight the most effective solutions as Smart Grid Services and their prospects impact on the network stability and reliability. Finally, the study will recommend a roadmap of solutions integration of People, Process, Data, and Technology.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133553858","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200128
Muaiz Ali, Omar Alkadi, A. Alotaibi, Alawi Almajed, Hussain Albesher, M. Khalid
Saudi Arabian decision-makers are seeking innovative and cost-effective solutions to meet the country’s power needs, understanding that it is impractical to continue in the existing scenario of high demand rise and low energy consciousness. One pretty powerful collection of technologies in the forthcoming inventory of electricity generation is demand side management (DSM), which comprises demand response (DR), energy efficiency (EE), and load management. Many electric companies have implemented DSM to flatten the load profile by altering the rate during the day to encourage citizens to minimize their electricity consumption during peak hours. In this project, DSM technologies are implemented. Based on pricing signals forecasted by a recurrent neural network model, the DR system adjusts loads from peak to off-peak hours. It was able to minimize the electricity bill for the householder. Furthermore, the EE system consists of an air conditioning (AC) system and a lighting system was implemented to reduce power consumption. The energy saved from the AC system is around 7.8% for a typical year for a typical villa in Al-Ahsa in the case of over insulation. Also, the lighting system saved around 25.1% of the energy consumption.
{"title":"Smart Home Energy Scheduling Using Demand Side Management Programs","authors":"Muaiz Ali, Omar Alkadi, A. Alotaibi, Alawi Almajed, Hussain Albesher, M. Khalid","doi":"10.1109/SASG57022.2022.10200128","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200128","url":null,"abstract":"Saudi Arabian decision-makers are seeking innovative and cost-effective solutions to meet the country’s power needs, understanding that it is impractical to continue in the existing scenario of high demand rise and low energy consciousness. One pretty powerful collection of technologies in the forthcoming inventory of electricity generation is demand side management (DSM), which comprises demand response (DR), energy efficiency (EE), and load management. Many electric companies have implemented DSM to flatten the load profile by altering the rate during the day to encourage citizens to minimize their electricity consumption during peak hours. In this project, DSM technologies are implemented. Based on pricing signals forecasted by a recurrent neural network model, the DR system adjusts loads from peak to off-peak hours. It was able to minimize the electricity bill for the householder. Furthermore, the EE system consists of an air conditioning (AC) system and a lighting system was implemented to reduce power consumption. The energy saved from the AC system is around 7.8% for a typical year for a typical villa in Al-Ahsa in the case of over insulation. Also, the lighting system saved around 25.1% of the energy consumption.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132354553","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200630
T. Azzouni, H. Bentarzi
Generally, the State of Charge (SoC) of a battery is used as an important parameter in the management energy system. Since the electrochemical process of such a device is complex and it is impossible to be accessed, its parameters such as SoC or energy storage capacity cannot directly be measured by any sensor. Besides, its dynamic energy storage does not depend only on its internal characteristics, but it also depends on its operating conditions. A method of the SoC determination may use certain electrical parameters such as the open circuit voltage or the internal impedance of the battery. For example, the Open Circuit Voltage OCV method is often described by a lookup table. This type of method is simple to implement and provides a good estimation of the SoC. However, an accurate estimate of SoC can only be obtained with an accurate measurement of OCV. This is due to an equilibrium voltage can only be measured after very long period of battery relaxation. In this work, we will study the different methods of SoC estimation for different batteries using real-time characterization data.
{"title":"A Battery State of Charge Estimation Using Real-Time Characterization Data","authors":"T. Azzouni, H. Bentarzi","doi":"10.1109/SASG57022.2022.10200630","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200630","url":null,"abstract":"Generally, the State of Charge (SoC) of a battery is used as an important parameter in the management energy system. Since the electrochemical process of such a device is complex and it is impossible to be accessed, its parameters such as SoC or energy storage capacity cannot directly be measured by any sensor. Besides, its dynamic energy storage does not depend only on its internal characteristics, but it also depends on its operating conditions. A method of the SoC determination may use certain electrical parameters such as the open circuit voltage or the internal impedance of the battery. For example, the Open Circuit Voltage OCV method is often described by a lookup table. This type of method is simple to implement and provides a good estimation of the SoC. However, an accurate estimate of SoC can only be obtained with an accurate measurement of OCV. This is due to an equilibrium voltage can only be measured after very long period of battery relaxation. In this work, we will study the different methods of SoC estimation for different batteries using real-time characterization data.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318924","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200754
Hassan A. Alsobaie, A. Al-Awami
Control of power generation is significant to a power system. Reaching a steady-state response for frequency generation in a shorter time will make generation more stable in power supply than load distribution. The automatic generation control (AGC) controls the electrical generation frequency and transfers power to another area as required from economic dispatch (ED) with minimum disturbance. This work illustrates the classical and integrated economic dispatch with automatic generation control (ED-AGC) in one area. The classical ED frequently defines every generator’s base point power from optimization problems for AGC scheduling during generation. On the other hand, the integrated ED is the control feedback representing the base point power of every generator for AGC during load power variance. This work will also design the two areas of integrated ED-AGC and shows the different results for using classical and integrated ED-AGC in two areas system. The simulation program will generate a better frequency regulation response with better power generated from the integrated controller during load disturbance. It will provide more optimally result in the cost of generating power.
{"title":"Integrated Economic Dispatch and Automatic Generation Control for Two-Area","authors":"Hassan A. Alsobaie, A. Al-Awami","doi":"10.1109/SASG57022.2022.10200754","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200754","url":null,"abstract":"Control of power generation is significant to a power system. Reaching a steady-state response for frequency generation in a shorter time will make generation more stable in power supply than load distribution. The automatic generation control (AGC) controls the electrical generation frequency and transfers power to another area as required from economic dispatch (ED) with minimum disturbance. This work illustrates the classical and integrated economic dispatch with automatic generation control (ED-AGC) in one area. The classical ED frequently defines every generator’s base point power from optimization problems for AGC scheduling during generation. On the other hand, the integrated ED is the control feedback representing the base point power of every generator for AGC during load power variance. This work will also design the two areas of integrated ED-AGC and shows the different results for using classical and integrated ED-AGC in two areas system. The simulation program will generate a better frequency regulation response with better power generated from the integrated controller during load disturbance. It will provide more optimally result in the cost of generating power.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061439","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200292
Ahmed M. Alrashed, Mohammed Y. Alnassar, Alassane Ndour
This paper assesses the economic feasibility of integrating small scale photovoltaic (PV) plant (30MW) as distributed generation (DG) coupled with a demand center in the form of a virtual power plant (VPP) in the western region of Saudi Arabia. The operation of the PV-based VPP was simulated in Plexos for a one-year time horizon on an hourly basis considering a typical residential demand profile as its main demand center. The simulation was performed at the premise of the characteristics of the western operational area’s (WOA) existing electrical system and the cost associated with the PV system. The results revealed a high contribution of the VPP’s PV generation to meeting the regional VPP demand under international fuel prices regime, which indicates an economic feasibility of building a PV system to meet a residential VPP.
{"title":"Economic Benefits of PV-Based Virtual Power Plant in the Western Region of Saudi Arabia : Submitted for Poster Session","authors":"Ahmed M. Alrashed, Mohammed Y. Alnassar, Alassane Ndour","doi":"10.1109/SASG57022.2022.10200292","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200292","url":null,"abstract":"This paper assesses the economic feasibility of integrating small scale photovoltaic (PV) plant (30MW) as distributed generation (DG) coupled with a demand center in the form of a virtual power plant (VPP) in the western region of Saudi Arabia. The operation of the PV-based VPP was simulated in Plexos for a one-year time horizon on an hourly basis considering a typical residential demand profile as its main demand center. The simulation was performed at the premise of the characteristics of the western operational area’s (WOA) existing electrical system and the cost associated with the PV system. The results revealed a high contribution of the VPP’s PV generation to meeting the regional VPP demand under international fuel prices regime, which indicates an economic feasibility of building a PV system to meet a residential VPP.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123787597","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10201192
A. Alsuhaibani, G. Fotiou
the depreciation of the service interruption rate, the stability and quick response over transient faults, and the reliability and quality of the electrical power are key design elements of the transmission and distribution networks for the Oil and Gas sector. In critical offshore platforms, utilizing a STATCOM system can address the above aspects, enhance the power quality indexes and extend the fault ride-through capability. The assessment of the more than six years of in-service experience of such a system in an offshore platform highlights this technology’s benefits and pitfalls.
{"title":"Static Synchronous Compensator (STATCOM) Effects on Power Supply Reliability of Distribution Network Utilizing Long-Distance Submarine Cables","authors":"A. Alsuhaibani, G. Fotiou","doi":"10.1109/SASG57022.2022.10201192","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10201192","url":null,"abstract":"the depreciation of the service interruption rate, the stability and quick response over transient faults, and the reliability and quality of the electrical power are key design elements of the transmission and distribution networks for the Oil and Gas sector. In critical offshore platforms, utilizing a STATCOM system can address the above aspects, enhance the power quality indexes and extend the fault ride-through capability. The assessment of the more than six years of in-service experience of such a system in an offshore platform highlights this technology’s benefits and pitfalls.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125005683","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10199879
Y. Kamarianakis, Yannis Pantazis, E. Kalligiannaki, T. Katsaounis, K. Kotsovos, I. Gereige, Marwan Abdullah, A. Jamal, A. Tzavaras
Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.
{"title":"Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods","authors":"Y. Kamarianakis, Yannis Pantazis, E. Kalligiannaki, T. Katsaounis, K. Kotsovos, I. Gereige, Marwan Abdullah, A. Jamal, A. Tzavaras","doi":"10.1109/SASG57022.2022.10199879","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199879","url":null,"abstract":"Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126449630","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10201059
M. Hossain, M. Shafiullah, Md. Shafiul Alam, M. A. Abido
Modular multilevel converter (MMC) plays the dominant role in integrating renewable energy from a remote location via a high-voltage DC transmission line. This work develops the MMC-based multi-terminal HVDC network, where the wind energy is integrated through the doubly fed induction generator (DFIG). The MMC’s arm circulating current and submodule capacitor voltage balancing controls are taken into account to present the actual dynamics of MMC. Instead of using an equivalent current source for the representation of renewable energy, this article considers the full dynamics of the DFIG and associated converters. It then scales one entire unit’s dynamics to form the wind farm. It optimally tracks the maximum wind energy during the wind speed variation via field-oriented control. The high voltage AC side is established for wind energy integration by employing feed-forward control. The controller for MMC supports reactive power during symmetrical and unsymmetrical low voltage faults at the point of common coupling (PCC) of the AC grid in line with the grid code. The proposed strategy is simulated in a real-time digital simulator (RTDS) machine. The results verify the fault ride-through (FRT) capability improvement of the MMC-HVDC network during the low voltage faults at the PCC of the AC grid. Moreover, the control proposed strategy successfully extracted the optimum wind energy under wind speed variation.
{"title":"Multi-terminal MMC-HVDC Transmission Network Connected DFIG Based Wind Energy","authors":"M. Hossain, M. Shafiullah, Md. Shafiul Alam, M. A. Abido","doi":"10.1109/SASG57022.2022.10201059","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10201059","url":null,"abstract":"Modular multilevel converter (MMC) plays the dominant role in integrating renewable energy from a remote location via a high-voltage DC transmission line. This work develops the MMC-based multi-terminal HVDC network, where the wind energy is integrated through the doubly fed induction generator (DFIG). The MMC’s arm circulating current and submodule capacitor voltage balancing controls are taken into account to present the actual dynamics of MMC. Instead of using an equivalent current source for the representation of renewable energy, this article considers the full dynamics of the DFIG and associated converters. It then scales one entire unit’s dynamics to form the wind farm. It optimally tracks the maximum wind energy during the wind speed variation via field-oriented control. The high voltage AC side is established for wind energy integration by employing feed-forward control. The controller for MMC supports reactive power during symmetrical and unsymmetrical low voltage faults at the point of common coupling (PCC) of the AC grid in line with the grid code. The proposed strategy is simulated in a real-time digital simulator (RTDS) machine. The results verify the fault ride-through (FRT) capability improvement of the MMC-HVDC network during the low voltage faults at the PCC of the AC grid. Moreover, the control proposed strategy successfully extracted the optimum wind energy under wind speed variation.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122488723","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200117
Ariel B. Suan, Bandar Al-Amer, Ibraheem A. Assiri
Aggressive increase in demand in Saudi Arabia is a major concern for National Grid Network planning engineers for over a decade. Using sophisticated commercial software such as SPSS, SAS and even excel-based forecasting had been delivering results by planning engineers preparing for the future of the kingdom. Neural Network has been so powerful in today’s digital transformation, and it is known as useful in forecasting. This paper demonstrates and uses a different Neural Network structure called Recurrent Neural Network (RNN) the Long-Short Term memory (LSTM), to capture and predict substation demand behavior. Temperature, temperature dewpoint, and historical demand are the features used to predict the short-term demand of high-voltage substations located in Jeddah. A high-dimensional, preprocessed with a year-long hourly historical substation demand data is utilized. Using a sophisticated anomaly detection algorithm, Isolation Forest to track outliers of the preprocessed data. The MSE result of preprocessed and sanitized significantly reduced from 4.257 to 3.959 respectively. RNN-LSTM structure has a week-long (168 data points) timesteps with 3 input layers or features, 3 hidden layer neurons coupled with 20% dropouts in each layer densely connected to produce a month-long demand forecast. Consideration for the selection of activation functions would also ease the requirement of computing time which is reduced with an average of 5 seconds per epoch in this model when using RELU activation function.
{"title":"Multi-Variate, Recurrent Neural Network in a Short-Term Time-Series Substation Demand Forecasting","authors":"Ariel B. Suan, Bandar Al-Amer, Ibraheem A. Assiri","doi":"10.1109/SASG57022.2022.10200117","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200117","url":null,"abstract":"Aggressive increase in demand in Saudi Arabia is a major concern for National Grid Network planning engineers for over a decade. Using sophisticated commercial software such as SPSS, SAS and even excel-based forecasting had been delivering results by planning engineers preparing for the future of the kingdom. Neural Network has been so powerful in today’s digital transformation, and it is known as useful in forecasting. This paper demonstrates and uses a different Neural Network structure called Recurrent Neural Network (RNN) the Long-Short Term memory (LSTM), to capture and predict substation demand behavior. Temperature, temperature dewpoint, and historical demand are the features used to predict the short-term demand of high-voltage substations located in Jeddah. A high-dimensional, preprocessed with a year-long hourly historical substation demand data is utilized. Using a sophisticated anomaly detection algorithm, Isolation Forest to track outliers of the preprocessed data. The MSE result of preprocessed and sanitized significantly reduced from 4.257 to 3.959 respectively. RNN-LSTM structure has a week-long (168 data points) timesteps with 3 input layers or features, 3 hidden layer neurons coupled with 20% dropouts in each layer densely connected to produce a month-long demand forecast. Consideration for the selection of activation functions would also ease the requirement of computing time which is reduced with an average of 5 seconds per epoch in this model when using RELU activation function.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398531","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10199186
MA. Saafi
Fighting climate change has been an increasingly important task worldwide. Since signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. The United Sates, being one of the biggest greenhouse gas global emitters, has set a long-term strategy to reach Net-Zero Emissions by 2050. Moreover, there is a growing interest in using hydrogen (H2) as a long-duration energy storage resource for an electric grid dominated by renewable energy generation. In this work, we focus on the Midwest region of the United States, as potential key region to lead the energy transition pathway thanks to its outstanding wind and hydro resources. Here, we develop a generalized framework for the Midwest under a range of technology cost and carbon emission targets to ensure a long-term economic dispatch. Given operational and emission constraints, our model determines a combination of electricity generation outputs to meet the hourly demand at the lowest cost up to 2050. This study investigates the long-term grid decarbonization feasibility in the Midwest and quantifies the impact of H2, including electrolysis and steam methane reforming (SMR), on electricity generation. The simulation results show that H2 through SMR could be increasingly involved in electricity generation, while electrolytic H2 could have a major role to help with renewable energy intermittency.
{"title":"Economic Dispatch method to quantify H2 impact on electricity generation in the Midwest region","authors":"MA. Saafi","doi":"10.1109/SASG57022.2022.10199186","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199186","url":null,"abstract":"Fighting climate change has been an increasingly important task worldwide. Since signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. The United Sates, being one of the biggest greenhouse gas global emitters, has set a long-term strategy to reach Net-Zero Emissions by 2050. Moreover, there is a growing interest in using hydrogen (H2) as a long-duration energy storage resource for an electric grid dominated by renewable energy generation. In this work, we focus on the Midwest region of the United States, as potential key region to lead the energy transition pathway thanks to its outstanding wind and hydro resources. Here, we develop a generalized framework for the Midwest under a range of technology cost and carbon emission targets to ensure a long-term economic dispatch. Given operational and emission constraints, our model determines a combination of electricity generation outputs to meet the hourly demand at the lowest cost up to 2050. This study investigates the long-term grid decarbonization feasibility in the Midwest and quantifies the impact of H2, including electrolysis and steam methane reforming (SMR), on electricity generation. The simulation results show that H2 through SMR could be increasingly involved in electricity generation, while electrolytic H2 could have a major role to help with renewable energy intermittency.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133183121","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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