Pub Date : 2022-12-01DOI: 10.1109/MELE.2022.3211109
C. Chin, Anurag Sharma, Dhivya Sampath Kumar, Sumith Madampath
Being a small, resource-constrained country, Singapore has been dependent on the import of energy products, such as coal, petroleum, natural gas, etc., since its independence. Specifically, it has relied on natural gas, which is the cleanest form of fossil fuel, for several years. In the past, the natural gas supply came from the neighboring countries of Indonesia and Malaysia through pipelines. However, the construction of the Singapore Liquefied Natural Gas Terminal in 2013 allowed Singapore to import and store liquefied natural gas from many different countries and sources, thus broadening its natural gas supply options. A four-switch approach (see Figure 1) was presented with the aim of having a future where energy is sustainable, reliable, affordable, and produced and consumed efficiently. The four switches mentioned are as follows:
{"title":"Singapore’s Sustainable Energy Story: Low-carbon energy deployment strategies and challenges","authors":"C. Chin, Anurag Sharma, Dhivya Sampath Kumar, Sumith Madampath","doi":"10.1109/MELE.2022.3211109","DOIUrl":"https://doi.org/10.1109/MELE.2022.3211109","url":null,"abstract":"Being a small, resource-constrained country, Singapore has been dependent on the import of energy products, such as coal, petroleum, natural gas, etc., since its independence. Specifically, it has relied on natural gas, which is the cleanest form of fossil fuel, for several years. In the past, the natural gas supply came from the neighboring countries of Indonesia and Malaysia through pipelines. However, the construction of the Singapore Liquefied Natural Gas Terminal in 2013 allowed Singapore to import and store liquefied natural gas from many different countries and sources, thus broadening its natural gas supply options. A four-switch approach (see Figure 1) was presented with the aim of having a future where energy is sustainable, reliable, affordable, and produced and consumed efficiently. The four switches mentioned are as follows:","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"10 1","pages":"84-89"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87324297","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 : 2022-12-01DOI: 10.1109/mele.2022.3211126
F. Li
{"title":"Successful Applications and Future Challenges of Machine Learning for Power Systems: A Summary of Recent Activities by the IEEE WG on Machine Learning for Power Systems [What’s Popular]","authors":"F. Li","doi":"10.1109/mele.2022.3211126","DOIUrl":"https://doi.org/10.1109/mele.2022.3211126","url":null,"abstract":"","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"17 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83547650","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 : 2022-12-01DOI: 10.1109/MELE.2022.3211017
F. Ding, Weijia Liu, Utkarsh Kumar, Yiyun Yao
The power system landscape is presently undergoing a dramatic change with the rapid integration of numerous distributed energy resources (DERs) at the grid edge. Broadly, we consider grid-edge DERs as any electricity source, storage, and demand response program connected to the medium-to-low-voltage distribution system.
{"title":"Unleash Values From Grid-Edge Flexibility: An overview, experience, and vision for leveraging grid-edge distributed energy resources to improve grid operations","authors":"F. Ding, Weijia Liu, Utkarsh Kumar, Yiyun Yao","doi":"10.1109/MELE.2022.3211017","DOIUrl":"https://doi.org/10.1109/MELE.2022.3211017","url":null,"abstract":"The power system landscape is presently undergoing a dramatic change with the rapid integration of numerous distributed energy resources (DERs) at the grid edge. Broadly, we consider grid-edge DERs as any electricity source, storage, and demand response program connected to the medium-to-low-voltage distribution system.","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"31 1","pages":"29-37"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85549278","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 : 2022-12-01DOI: 10.1109/MELE.2022.3211018
S. Katipamula, Robert Lutes, Sen Huang, Roshan L. Kini
To mitigate the impacts of climate change, significant reductions in emissions from all sectors of the economy are needed. The electricity generation sector has embarked on an ambitious plan to include renewable generation as part of its decarbonization efforts, and many cities and states are mandating all-electric buildings. While renewable resources will reduce emissions, they are not dispatchable, they vary temporally, and their generation is uncertain. Under these conditions, traditional approaches to managing grid reliability, where supply follows demand, will not be efficient and may not be cost-effective. There is a more efficient alternative for balancing the supply–demand imbalance and for absorbing variability and uncertainty of renewable energy using distributed energy resources (DERs) as opposed to reserve generation. Because buildings consume more than 75% of total U.S. annual electricity consumption, behind-the-meter (BTM) DERs have a load flexibility of 77 GW of power and 90 GWh of virtual energy storage capacity nationwide (Kalsi, 2017). Therefore, some portion of the supply–demand imbalance can be met by these DERs at a lower cost compared to business-as-usual solutions.
{"title":"Building Energy Systems as Behind-the-Meter Resources for Grid Services: Intelligent load control and transactive control and coordination","authors":"S. Katipamula, Robert Lutes, Sen Huang, Roshan L. Kini","doi":"10.1109/MELE.2022.3211018","DOIUrl":"https://doi.org/10.1109/MELE.2022.3211018","url":null,"abstract":"To mitigate the impacts of climate change, significant reductions in emissions from all sectors of the economy are needed. The electricity generation sector has embarked on an ambitious plan to include renewable generation as part of its decarbonization efforts, and many cities and states are mandating all-electric buildings. While renewable resources will reduce emissions, they are not dispatchable, they vary temporally, and their generation is uncertain. Under these conditions, traditional approaches to managing grid reliability, where supply follows demand, will not be efficient and may not be cost-effective. There is a more efficient alternative for balancing the supply–demand imbalance and for absorbing variability and uncertainty of renewable energy using distributed energy resources (DERs) as opposed to reserve generation. Because buildings consume more than 75% of total U.S. annual electricity consumption, behind-the-meter (BTM) DERs have a load flexibility of 77 GW of power and 90 GWh of virtual energy storage capacity nationwide (Kalsi, 2017). Therefore, some portion of the supply–demand imbalance can be met by these DERs at a lower cost compared to business-as-usual solutions.","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"16 1","pages":"38-49"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84251407","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 : 2022-12-01DOI: 10.1109/MELE.2022.3210778
Y. Zhang, Marc Spieler
As the demand for energy increases along with the requirements for decarbonization, there is more complexity in which the world produces, moves, and consumes energy. The way we supply and consume energy is becoming increasingly complex and requires us to leverage technology and learn from the success of other industries. From the fuel that powers cars and planes, to the gas used for stove top cooking, to the electricity that keeps the lights on in homes and businesses, energy powers our daily lives. Oil, gas, and electricity are mature commodity markets, but as the mix changes at a rate never seen before, the industry must adapt at a similar pace. This includes leveraging artificial intelligence (AI), real-time analytics, and machine learning to create autonomous energy systems that can increase reliability and resiliency in a more complicated world. However, AI alone is not enough to transform the processes used to produce, transport, and deliver these resources.
{"title":"Bringing Artificial Intelligence to the Grid Edge [Technology Leaders]","authors":"Y. Zhang, Marc Spieler","doi":"10.1109/MELE.2022.3210778","DOIUrl":"https://doi.org/10.1109/MELE.2022.3210778","url":null,"abstract":"As the demand for energy increases along with the requirements for decarbonization, there is more complexity in which the world produces, moves, and consumes energy. The way we supply and consume energy is becoming increasingly complex and requires us to leverage technology and learn from the success of other industries. From the fuel that powers cars and planes, to the gas used for stove top cooking, to the electricity that keeps the lights on in homes and businesses, energy powers our daily lives. Oil, gas, and electricity are mature commodity markets, but as the mix changes at a rate never seen before, the industry must adapt at a similar pace. This includes leveraging artificial intelligence (AI), real-time analytics, and machine learning to create autonomous energy systems that can increase reliability and resiliency in a more complicated world. However, AI alone is not enough to transform the processes used to produce, transport, and deliver these resources.","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"15 1","pages":"6-9"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72731836","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 : 2022-12-01DOI: 10.1109/MELE.2022.3211019
Arnie de Castro, Ashley Mui, Garrett Frere
In this article, the authors discuss the applications of big data analytics with behind-the-meter (BTM) distributed energy resources (DERs) to manage the abundance of data, provide better customer care, improve operations, and reduce costs in the increasingly digitized grid.
{"title":"Turning Data into Knowledge: Big data analytics with behind-the-meter distributed energy resources in the digital utility","authors":"Arnie de Castro, Ashley Mui, Garrett Frere","doi":"10.1109/MELE.2022.3211019","DOIUrl":"https://doi.org/10.1109/MELE.2022.3211019","url":null,"abstract":"In this article, the authors discuss the applications of big data analytics with behind-the-meter (BTM) distributed energy resources (DERs) to manage the abundance of data, provide better customer care, improve operations, and reduce costs in the increasingly digitized grid.","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"12 1","pages":"50-57"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73062398","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 : 2022-12-01DOI: 10.1109/MELE.2022.3211016
N. Yu, Wenyu Wang, Raymond Johnson
Behind-The-Meter (BTM) resources are distributed energy resources (DERs), such as rooftop solar photovoltaics (PVs), electric vehicles, and battery storage systems, located on the customer side of smart meters. Driven by monetary incentives, declining costs, and increasing electricity service interruptions, the penetration of BTM resources has been increasing exponentially in the past few years. For example, the small-scale BTM solar PV capacity in the United States has quickly increased from 7,642 MWac in September 2015 to 34,029 MWac in February 2022.
{"title":"Behind-the-Meter Resources: Data-driven modeling, monitoring, and control","authors":"N. Yu, Wenyu Wang, Raymond Johnson","doi":"10.1109/MELE.2022.3211016","DOIUrl":"https://doi.org/10.1109/MELE.2022.3211016","url":null,"abstract":"Behind-The-Meter (BTM) resources are distributed energy resources (DERs), such as rooftop solar photovoltaics (PVs), electric vehicles, and battery storage systems, located on the customer side of smart meters. Driven by monetary incentives, declining costs, and increasing electricity service interruptions, the penetration of BTM resources has been increasing exponentially in the past few years. For example, the small-scale BTM solar PV capacity in the United States has quickly increased from 7,642 MWac in September 2015 to 34,029 MWac in February 2022.","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"39 1","pages":"20-28"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89130661","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 : 2022-12-01DOI: 10.1109/mele.2022.3210777
Y. Zhang, Rui Yang
{"title":"Welcome to the Special Issue on Grid-Edge Computing With Behind-the-Meter Resources [Guest Editorial]","authors":"Y. Zhang, Rui Yang","doi":"10.1109/mele.2022.3210777","DOIUrl":"https://doi.org/10.1109/mele.2022.3210777","url":null,"abstract":"","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"377 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84948330","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 : 2022-12-01DOI: 10.1109/MELE.2022.3211102
Killian McKenna, P. Gotseff, Meredith Chee, Earle Ifuku
In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter (Figure 1). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.
{"title":"Advanced Metering Infrastructure for Distribution Planning and Operation: Closing the loop on grid-edge visibility","authors":"Killian McKenna, P. Gotseff, Meredith Chee, Earle Ifuku","doi":"10.1109/MELE.2022.3211102","DOIUrl":"https://doi.org/10.1109/MELE.2022.3211102","url":null,"abstract":"In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter (Figure 1). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.","PeriodicalId":45277,"journal":{"name":"IEEE Electrification Magazine","volume":"14 1","pages":"58-65"},"PeriodicalIF":3.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79859388","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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