The utility industry is undergoing a fundamental transformation with increased digitation and tighter coupling between IT and OT. Flutura outlines 7 ways by which utilities can monetize smartmeter data
The document discusses different definitions and components of a "smart grid". It describes technologies at the customer level like automated meter reading, time-of-use pricing, and load control. It also discusses distribution system automation which can isolate outages and enable selective load control during emergencies. The technologies range from basic to more advanced applications and realizing their full benefits may require a combination of smart meters, distribution automation, and changes to regulations.
The document provides an overview and market forecasts for the power and energy measurement industry from 2012 to 2017. It finds that the market is expected to grow from 25.3 million units in 2012 to 108.4 million units in 2017, driven by trends such as zero net energy and data center management. Applications like distributed power systems, metering, home automation, industrial automation, and building automation are analyzed and forecasts are provided for unit and revenue growth by application, wattage, and power supply over the period.
In many ways, the electricity industry makes an unlikely candidate for disruption. Not much changed between the 1880s, when Thomas Edison began building power stations, and the start of the 21st century. Top business leaders rarely had to think about electricity. They got their electricity from the power plant, or the local utility, or the government, and had little say in how it was produced, delivered, or managed. Utility executives, for their part, could make and execute long-term plans with a great deal of security. Demand tended to rise along with the economy; natural monopolies were the norm.
No longer. Several coincident, significant transformations are causing a revolution in the way electricity — the vital fuel of global commerce and human comfort — is produced, distributed, stored, and marketed. A top-down, centralized system is devolving into one that is much more distributed and interactive. The mix of generation is shifting from high carbon to lower carbon, and, often, to no carbon. In many regions, the electricity business is transforming from a monopoly to a highly competitive arena.
This paper proposes a novel approach to dependent demand response management in smart grids. It presents a hierarchical system model with three levels: power generation units, distribution algorithms, and residential/industrial consumers with automated metering infrastructure. A demand response management algorithm is used along with voltage/frequency controls to shift loads to weaker buses when they reach maximum capacity, improving the power system. The paper develops optimization models to determine the optimal demand response of consumers and power supply of utilities given a price signal. It analyzes performance using piecewise quadratic and logarithmic consumer utility functions. The approach integrates dynamic demand side management with smart grid technologies like demand response and automated metering to provide uninterrupted power supply even during load surges.
Evolving Distribution Grid article in Electric Perspectives magazine Jan-Feb 2015 edition. Article discusses emerging business opportunities for a utility Distribution Services Provider.
This document discusses rate design pathways for electricity providers to establish fair utility rates for solar PV customers in a distributed energy age. It proposes an integrated cost recovery approach for utilities based on three interrelated pricing approaches: 1) Allowing utilities to recover their minimum necessary customer-related fixed costs through a fixed charge. 2) Classifying utility costs as demand, energy, or customer-related and ensuring solar customers pay their fair share of these costs. 3) Considering utility rate cases like We Energies' proposal to increase fixed charges for solar customers cautiously to avoid over-recovery of costs or discouraging solar adoption.
This document summarizes a research report on advancing energy management through smart metering systems from an operational perspective. The report discusses the limitations of traditional electricity meters and how smart meters can help overcome issues by providing consumers with more information on their energy usage. It outlines the objectives of the study, which include analyzing currently deployed smart metering systems, suggesting improvements, and studying consumer energy usage behaviors. The report also reviews the evolution of electricity meters and discusses the costs and benefits of smart metering systems for consumers and electricity suppliers.
The document discusses different definitions and components of a "smart grid". It describes technologies at the customer level like automated meter reading, time-of-use pricing, and load control. It also discusses distribution system automation which can isolate outages and enable selective load control during emergencies. The technologies range from basic to more advanced applications and realizing their full benefits may require a combination of smart meters, distribution automation, and changes to regulations.
The document provides an overview and market forecasts for the power and energy measurement industry from 2012 to 2017. It finds that the market is expected to grow from 25.3 million units in 2012 to 108.4 million units in 2017, driven by trends such as zero net energy and data center management. Applications like distributed power systems, metering, home automation, industrial automation, and building automation are analyzed and forecasts are provided for unit and revenue growth by application, wattage, and power supply over the period.
In many ways, the electricity industry makes an unlikely candidate for disruption. Not much changed between the 1880s, when Thomas Edison began building power stations, and the start of the 21st century. Top business leaders rarely had to think about electricity. They got their electricity from the power plant, or the local utility, or the government, and had little say in how it was produced, delivered, or managed. Utility executives, for their part, could make and execute long-term plans with a great deal of security. Demand tended to rise along with the economy; natural monopolies were the norm.
No longer. Several coincident, significant transformations are causing a revolution in the way electricity — the vital fuel of global commerce and human comfort — is produced, distributed, stored, and marketed. A top-down, centralized system is devolving into one that is much more distributed and interactive. The mix of generation is shifting from high carbon to lower carbon, and, often, to no carbon. In many regions, the electricity business is transforming from a monopoly to a highly competitive arena.
This paper proposes a novel approach to dependent demand response management in smart grids. It presents a hierarchical system model with three levels: power generation units, distribution algorithms, and residential/industrial consumers with automated metering infrastructure. A demand response management algorithm is used along with voltage/frequency controls to shift loads to weaker buses when they reach maximum capacity, improving the power system. The paper develops optimization models to determine the optimal demand response of consumers and power supply of utilities given a price signal. It analyzes performance using piecewise quadratic and logarithmic consumer utility functions. The approach integrates dynamic demand side management with smart grid technologies like demand response and automated metering to provide uninterrupted power supply even during load surges.
Evolving Distribution Grid article in Electric Perspectives magazine Jan-Feb 2015 edition. Article discusses emerging business opportunities for a utility Distribution Services Provider.
This document discusses rate design pathways for electricity providers to establish fair utility rates for solar PV customers in a distributed energy age. It proposes an integrated cost recovery approach for utilities based on three interrelated pricing approaches: 1) Allowing utilities to recover their minimum necessary customer-related fixed costs through a fixed charge. 2) Classifying utility costs as demand, energy, or customer-related and ensuring solar customers pay their fair share of these costs. 3) Considering utility rate cases like We Energies' proposal to increase fixed charges for solar customers cautiously to avoid over-recovery of costs or discouraging solar adoption.
This document summarizes a research report on advancing energy management through smart metering systems from an operational perspective. The report discusses the limitations of traditional electricity meters and how smart meters can help overcome issues by providing consumers with more information on their energy usage. It outlines the objectives of the study, which include analyzing currently deployed smart metering systems, suggesting improvements, and studying consumer energy usage behaviors. The report also reviews the evolution of electricity meters and discusses the costs and benefits of smart metering systems for consumers and electricity suppliers.
The document analyzes economic welfare gains from demand response and real-time pricing for industrial processes. Demand response refers to electricity users reducing consumption during peak periods in exchange for incentives. Real-time pricing sets electricity rates based on current generation costs. The paper models industrial load-shifting as a demand response method for a customer on day-ahead real-time pricing, demonstrating the economic benefits for the power system. Upgrading the grid with smart technology, pricing signals, and policies promoting responsive behavior could capture surplus welfare by making all participants more responsive to price changes and grid events.
The document summarizes a report on the smart grid and consumers. It finds that while the smart grid aims to establish two-way communication between utilities and consumers, consumer applications have lagged behind. Early smart meter rollouts faced consumer resistance over costs and privacy issues. Surveys show consumers are open to energy management technologies but unwilling to pay much for them. The market for smart grid consumer applications is projected to grow to over $70 billion globally by 2014, but this depends on overcoming challenges in consumer acceptance and engagement.
The document discusses demand-side management (DSM) and its promise through the next generation of smart grid technologies. DSM allows customers to control the timing and amount of their electricity usage, which could save up to $59 billion by 2019 by helping utilities, commercial customers, and households. DSM programs encourage shifting usage from peak to off-peak periods through technologies that provide customers more information about their energy consumption.
This document discusses how commercial and industrial businesses can generate new revenue streams from distributed energy resources like solar, storage, and electric vehicle charging. It explains that assets like these, when paired with energy markets and incentive programs, can optimize savings and flexibility. The case study of the University of Massachusetts Boston is provided as an example of how combining solar, storage, EV charging, and demand response programs unlocked significant financial benefits. Expertise is needed to navigate the complex connections between assets, markets, and value streams to fully maximize earnings.
This document summarizes a presentation on smart grids given at a workshop in Barcelona, Spain in 2017. It discusses how the electricity system may look different in 20 years due to digitalization, with smart connected products changing competition. It also notes the potential for prosumers, or consumer-producers, to have significant market power if they collectively participate in energy markets. Finally, it discusses the case for transitioning to smart grids but also reasons why change may not fully materialize, such as certain large infrastructure investments undermining alternatives or business cases being too dependent on local conditions.
Microgrids provide numerous benefits to both customers and society. They improve electric reliability and resilience by continuing to supply power when the central grid fails. Microgrids also enhance cost savings and revenue opportunities for customers. Additionally, microgrids promote clean energy adoption, strengthen the central grid, bolster cybersecurity, provide local economic benefits, and improve community well-being during disasters. In conclusion, microgrids offer a flexible solution that provides reliable power while creating value for customers and society in many ways.
Smart Grid Data Centers Distributed & ICTs Sustainability on Generation Energ...IJMTST Journal
Smart grid has modernized the way electricity is generated, transported, distributed, and consumed by integrating advanced sensing, communications, and control in the day-to-day operation of the grid. Electricity is a core utility for the functioning of society and for the services provided by information and communication technologies(ICTs). Several concepts of the smart grid, such as dynamic pricing, distributed generation, and demand management, have significantly impacted the operation of ICT services, in particular, communication networks and data centers. Ongoing energy-efficiency and operational expenditures reduction efforts in communication networks and data center shave gained another dimension with those smart grid concepts. In this paper, we provide a comprehensive survey on the smart grid-driven approaches in energy-efficient communications and data centers, and the interaction between smart grid and information and communication infrastructures. Although the studies on smart grid, energy-efficient communications, and green data centers have been separately surveyed in previous studies, to this end, research that falls in the intersection of those fields has not been properly classified and surveyed yet. We start our survey by providing background information on the smart grid and continue with surveying smart grid-driven approaches in energy-efficient communication systems, followed by energy, cost and emission minimizing approaches in datacenters, and the corresponding cloud network infrastructure. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout.
Security and Privacy Challenges in SMART GRID.SaiSampath16
The document summarizes a technical seminar presentation on security and privacy challenges in smart grids. The presentation introduces smart grids as networks that monitor and manage energy usage. It describes how smart meters are used to collect usage data from customers and transmit it to utility companies. However, smart meters pose security and privacy risks as they can be hacked to manipulate energy costs or fabricate readings. Additionally, the detailed usage data collected from smart meters could expose customer habits and behaviors, violating privacy. The presentation calls for regulatory protections and more security testing to address these challenges in smart grids.
TM Forum- Management World Americas - Smart Grid SummaryShekhar Gupta
The document discusses the current electric grid and outlines the vision for a smart grid, which would implement a communications network overlay to measure, manage, and control the electric grid. It describes the various stakeholders in the smart grid ecosystem and outlines some of the standards and technologies needed to realize the smart grid vision, including advanced metering infrastructure (AMI) and a communications portal to enable two-way communication between utilities and customer energy devices.
This document summarizes several use cases for customer-sited distributed energy storage in California. It describes how energy storage could be used to manage customer bills, allow market participation, and provide utility control. Key uses include reducing peak demand and energy costs for customers and helping utilities manage intermittent renewable generation and distribution grid operations. Policy changes around incentives, rates, and demand response programs could encourage more coordinated use of energy storage by both customers and utilities.
Smart grids use two-way digital communications and computer processing to improve efficiency in electricity generation, transmission, distribution and usage. This allows for increased integration of renewable energy sources, energy storage, automated distribution and usage monitoring. Microgrids allow localized energy generation and distribution, improving reliability, reducing costs and facilitating renewable energy integration. Modeling frameworks like Modelica and EOOM are useful for designing and simulating large, complex smart grid systems.
The document describes a proposed intelligent load management system with renewable energy integration for smart homes. Some key points:
- It presents an evolutionary algorithm-based demand side management model for scheduling household appliances optimally based on time-of-use pricing while integrating renewable energy.
- The model aims to optimize appliance operation times to minimize electricity costs, reduce peak demand on the grid, and make use of generated renewable energy from sources like solar.
- It categorizes home energy users into traditional, smart, and smart prosumers (who also generate renewable energy) and develops models for each. The proposed system uses algorithms like binary particle swarm optimization to generate optimized appliance schedules.
- Key components include an advanced meter
This document provides an overview of traditional electricity grids and introduces smart grid systems. It discusses problems with traditional grids like blackouts and inefficiencies. The smart grid aims to address these through advanced infrastructure, metering, monitoring, management and communication technologies. This allows for two-way communication between energy producers and consumers, integrated renewable energy, automated maintenance and self-healing of outages. The document outlines India's plans to adopt smart grid technologies and analyzes barriers to implementation like high costs and security risks. Overall, smart grids are expected to improve energy efficiency, reliability and consumers' ability to manage their energy usage.
The document discusses smart grids and the business case for them in India. It begins with defining smart grids and comparing them to traditional grids. It then discusses the market definition and overview, including the various components of a smart grid and their advantages over traditional systems. Following this, the document outlines the market size and trends in India, including growth forecasts and government investment plans. It also discusses the various players involved in the different areas of the smart grid ecosystem and potential partners for collaboration.
This document discusses smart grids, which aim to make the electricity delivery system more efficient and sustainable. It describes how smart grids use two-way communication and automation to better manage generation, transmission and distribution of electricity. This allows for things like demand response, reduced carbon emissions and more reliable power. Key components of smart grids are discussed like smart meters, sensors, distributed generation and control centers. Challenges to implementing smart grids are also outlined.
Portland State University Smart Grid ClassMike Hoffman
The document discusses a class on smart grids at Portland State University that covered the history, politics, economics, and technology of smart grids. It focuses on enabling active consumer participation through distributed, small-scale smart grid technologies at the distribution level. This includes technologies that allow two-way communication and control of appliances and energy use. The key is developing solutions that benefit both utilities and consumers by reducing costs and increasing system reliability and resilience.
The document discusses the emerging transformation of the energy industry towards an "Energy Cloud" ecosystem enabled by new technologies and business models. Key points:
1) Nearly $3 trillion has been invested globally in grid modernization since 2000, with another $8 trillion needed over the next 25 years. New entrants see potential to disrupt the industry and capture an estimated $1.3 trillion in new annual revenue by 2030.
2) Triggers like policy shifts supporting renewables and distributed energy, changing customer demand, and declining technology costs are creating opportunities for disruption comparable to other industries like media and retail that have been upended.
3) The Energy Cloud will connect distributed energy resources through digital platforms, enabling new
Purchasing chassis with large numbers of HDDs is one way to configure your infrastructure, but such a solution can provide less than optimal performance, take up a large amount of prime data center space, and increase power and cooling costs. Configuring your servers with SSDs, on the other hand, can dramatically improve performance while simultaneously reducing the overall power consumption of your data center.
In our tests, replacing 24 HDDs in an external chassis with only six internal Kingston SSDs boosted total database performance by as much as 91.8 percent. Not only did the server with Kingston SSDs outperform the HDD solution, it also consumed 32.5 percent less power while active and 51.0 percent less power when idle. Such dramatic performance increases and reduction in power consumption can make Kingston SSDs a compelling choice for the database servers in your data center.
Task Resource Consumption Prediction for Scientific Applications and WorkflowsRafael Ferreira da Silva
Presentation held at the Algorithms and Scheduling Techniques to Manage Resilience and Power Consumption in Distributed Systems 2015 Seminar - Dagstuhl
Estimates of task runtime, disk space usage, and memory consumption, are commonly used by scheduling and resource provisioning algorithms to support efficient and reliable scientific application executions. Such algorithms often assume that accurate estimates are available, but such estimates are difficult to generate in practice. In this work, we first profile real scientific applications and workflows, collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize task requirements based on these profiles. Our method estimates task runtime, disk space, and peak memory consumption. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets using the statistical recursive partitioning method and conditional inference trees to identify patterns that characterize particular behaviors of the workload. We then propose an estimation process to predict task characteristics of scientific applications based on the collected data. For scientific workflows, we propose an online estimation process based on the MAPE-K loop, where task executions are monitored and estimates are updated as more information becomes available. Experimental results show that our online estimation process results in much more accurate predictions than an offline approach, where all task requirements are estimated prior to workflow execution.
The document analyzes economic welfare gains from demand response and real-time pricing for industrial processes. Demand response refers to electricity users reducing consumption during peak periods in exchange for incentives. Real-time pricing sets electricity rates based on current generation costs. The paper models industrial load-shifting as a demand response method for a customer on day-ahead real-time pricing, demonstrating the economic benefits for the power system. Upgrading the grid with smart technology, pricing signals, and policies promoting responsive behavior could capture surplus welfare by making all participants more responsive to price changes and grid events.
The document summarizes a report on the smart grid and consumers. It finds that while the smart grid aims to establish two-way communication between utilities and consumers, consumer applications have lagged behind. Early smart meter rollouts faced consumer resistance over costs and privacy issues. Surveys show consumers are open to energy management technologies but unwilling to pay much for them. The market for smart grid consumer applications is projected to grow to over $70 billion globally by 2014, but this depends on overcoming challenges in consumer acceptance and engagement.
The document discusses demand-side management (DSM) and its promise through the next generation of smart grid technologies. DSM allows customers to control the timing and amount of their electricity usage, which could save up to $59 billion by 2019 by helping utilities, commercial customers, and households. DSM programs encourage shifting usage from peak to off-peak periods through technologies that provide customers more information about their energy consumption.
This document discusses how commercial and industrial businesses can generate new revenue streams from distributed energy resources like solar, storage, and electric vehicle charging. It explains that assets like these, when paired with energy markets and incentive programs, can optimize savings and flexibility. The case study of the University of Massachusetts Boston is provided as an example of how combining solar, storage, EV charging, and demand response programs unlocked significant financial benefits. Expertise is needed to navigate the complex connections between assets, markets, and value streams to fully maximize earnings.
This document summarizes a presentation on smart grids given at a workshop in Barcelona, Spain in 2017. It discusses how the electricity system may look different in 20 years due to digitalization, with smart connected products changing competition. It also notes the potential for prosumers, or consumer-producers, to have significant market power if they collectively participate in energy markets. Finally, it discusses the case for transitioning to smart grids but also reasons why change may not fully materialize, such as certain large infrastructure investments undermining alternatives or business cases being too dependent on local conditions.
Microgrids provide numerous benefits to both customers and society. They improve electric reliability and resilience by continuing to supply power when the central grid fails. Microgrids also enhance cost savings and revenue opportunities for customers. Additionally, microgrids promote clean energy adoption, strengthen the central grid, bolster cybersecurity, provide local economic benefits, and improve community well-being during disasters. In conclusion, microgrids offer a flexible solution that provides reliable power while creating value for customers and society in many ways.
Smart Grid Data Centers Distributed & ICTs Sustainability on Generation Energ...IJMTST Journal
Smart grid has modernized the way electricity is generated, transported, distributed, and consumed by integrating advanced sensing, communications, and control in the day-to-day operation of the grid. Electricity is a core utility for the functioning of society and for the services provided by information and communication technologies(ICTs). Several concepts of the smart grid, such as dynamic pricing, distributed generation, and demand management, have significantly impacted the operation of ICT services, in particular, communication networks and data centers. Ongoing energy-efficiency and operational expenditures reduction efforts in communication networks and data center shave gained another dimension with those smart grid concepts. In this paper, we provide a comprehensive survey on the smart grid-driven approaches in energy-efficient communications and data centers, and the interaction between smart grid and information and communication infrastructures. Although the studies on smart grid, energy-efficient communications, and green data centers have been separately surveyed in previous studies, to this end, research that falls in the intersection of those fields has not been properly classified and surveyed yet. We start our survey by providing background information on the smart grid and continue with surveying smart grid-driven approaches in energy-efficient communication systems, followed by energy, cost and emission minimizing approaches in datacenters, and the corresponding cloud network infrastructure. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout.
Security and Privacy Challenges in SMART GRID.SaiSampath16
The document summarizes a technical seminar presentation on security and privacy challenges in smart grids. The presentation introduces smart grids as networks that monitor and manage energy usage. It describes how smart meters are used to collect usage data from customers and transmit it to utility companies. However, smart meters pose security and privacy risks as they can be hacked to manipulate energy costs or fabricate readings. Additionally, the detailed usage data collected from smart meters could expose customer habits and behaviors, violating privacy. The presentation calls for regulatory protections and more security testing to address these challenges in smart grids.
TM Forum- Management World Americas - Smart Grid SummaryShekhar Gupta
The document discusses the current electric grid and outlines the vision for a smart grid, which would implement a communications network overlay to measure, manage, and control the electric grid. It describes the various stakeholders in the smart grid ecosystem and outlines some of the standards and technologies needed to realize the smart grid vision, including advanced metering infrastructure (AMI) and a communications portal to enable two-way communication between utilities and customer energy devices.
This document summarizes several use cases for customer-sited distributed energy storage in California. It describes how energy storage could be used to manage customer bills, allow market participation, and provide utility control. Key uses include reducing peak demand and energy costs for customers and helping utilities manage intermittent renewable generation and distribution grid operations. Policy changes around incentives, rates, and demand response programs could encourage more coordinated use of energy storage by both customers and utilities.
Smart grids use two-way digital communications and computer processing to improve efficiency in electricity generation, transmission, distribution and usage. This allows for increased integration of renewable energy sources, energy storage, automated distribution and usage monitoring. Microgrids allow localized energy generation and distribution, improving reliability, reducing costs and facilitating renewable energy integration. Modeling frameworks like Modelica and EOOM are useful for designing and simulating large, complex smart grid systems.
The document describes a proposed intelligent load management system with renewable energy integration for smart homes. Some key points:
- It presents an evolutionary algorithm-based demand side management model for scheduling household appliances optimally based on time-of-use pricing while integrating renewable energy.
- The model aims to optimize appliance operation times to minimize electricity costs, reduce peak demand on the grid, and make use of generated renewable energy from sources like solar.
- It categorizes home energy users into traditional, smart, and smart prosumers (who also generate renewable energy) and develops models for each. The proposed system uses algorithms like binary particle swarm optimization to generate optimized appliance schedules.
- Key components include an advanced meter
This document provides an overview of traditional electricity grids and introduces smart grid systems. It discusses problems with traditional grids like blackouts and inefficiencies. The smart grid aims to address these through advanced infrastructure, metering, monitoring, management and communication technologies. This allows for two-way communication between energy producers and consumers, integrated renewable energy, automated maintenance and self-healing of outages. The document outlines India's plans to adopt smart grid technologies and analyzes barriers to implementation like high costs and security risks. Overall, smart grids are expected to improve energy efficiency, reliability and consumers' ability to manage their energy usage.
The document discusses smart grids and the business case for them in India. It begins with defining smart grids and comparing them to traditional grids. It then discusses the market definition and overview, including the various components of a smart grid and their advantages over traditional systems. Following this, the document outlines the market size and trends in India, including growth forecasts and government investment plans. It also discusses the various players involved in the different areas of the smart grid ecosystem and potential partners for collaboration.
This document discusses smart grids, which aim to make the electricity delivery system more efficient and sustainable. It describes how smart grids use two-way communication and automation to better manage generation, transmission and distribution of electricity. This allows for things like demand response, reduced carbon emissions and more reliable power. Key components of smart grids are discussed like smart meters, sensors, distributed generation and control centers. Challenges to implementing smart grids are also outlined.
Portland State University Smart Grid ClassMike Hoffman
The document discusses a class on smart grids at Portland State University that covered the history, politics, economics, and technology of smart grids. It focuses on enabling active consumer participation through distributed, small-scale smart grid technologies at the distribution level. This includes technologies that allow two-way communication and control of appliances and energy use. The key is developing solutions that benefit both utilities and consumers by reducing costs and increasing system reliability and resilience.
The document discusses the emerging transformation of the energy industry towards an "Energy Cloud" ecosystem enabled by new technologies and business models. Key points:
1) Nearly $3 trillion has been invested globally in grid modernization since 2000, with another $8 trillion needed over the next 25 years. New entrants see potential to disrupt the industry and capture an estimated $1.3 trillion in new annual revenue by 2030.
2) Triggers like policy shifts supporting renewables and distributed energy, changing customer demand, and declining technology costs are creating opportunities for disruption comparable to other industries like media and retail that have been upended.
3) The Energy Cloud will connect distributed energy resources through digital platforms, enabling new
Purchasing chassis with large numbers of HDDs is one way to configure your infrastructure, but such a solution can provide less than optimal performance, take up a large amount of prime data center space, and increase power and cooling costs. Configuring your servers with SSDs, on the other hand, can dramatically improve performance while simultaneously reducing the overall power consumption of your data center.
In our tests, replacing 24 HDDs in an external chassis with only six internal Kingston SSDs boosted total database performance by as much as 91.8 percent. Not only did the server with Kingston SSDs outperform the HDD solution, it also consumed 32.5 percent less power while active and 51.0 percent less power when idle. Such dramatic performance increases and reduction in power consumption can make Kingston SSDs a compelling choice for the database servers in your data center.
Task Resource Consumption Prediction for Scientific Applications and WorkflowsRafael Ferreira da Silva
Presentation held at the Algorithms and Scheduling Techniques to Manage Resilience and Power Consumption in Distributed Systems 2015 Seminar - Dagstuhl
Estimates of task runtime, disk space usage, and memory consumption, are commonly used by scheduling and resource provisioning algorithms to support efficient and reliable scientific application executions. Such algorithms often assume that accurate estimates are available, but such estimates are difficult to generate in practice. In this work, we first profile real scientific applications and workflows, collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize task requirements based on these profiles. Our method estimates task runtime, disk space, and peak memory consumption. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets using the statistical recursive partitioning method and conditional inference trees to identify patterns that characterize particular behaviors of the workload. We then propose an estimation process to predict task characteristics of scientific applications based on the collected data. For scientific workflows, we propose an online estimation process based on the MAPE-K loop, where task executions are monitored and estimates are updated as more information becomes available. Experimental results show that our online estimation process results in much more accurate predictions than an offline approach, where all task requirements are estimated prior to workflow execution.
The document discusses the history and future of smart home systems. It describes how early home systems have evolved from basic controls to integrated wireless technologies that allow remote monitoring and control of devices. The future will bring more connectivity through technologies like cloud computing and mobile apps. This will enable new capabilities around energy management, security, and automated control to improve convenience, savings and sustainability. Schneider Electric provides an example product that allows users to remotely monitor and control home energy use through a wireless system and online dashboard.
How to Lower Android Power Consumption Without Affecting Performancerickschwar
The document discusses various ways mobile app developers can lower the power consumption of their apps without affecting performance. It begins by explaining that most apps do not efficiently use system resources like the processor, cellular radio, and display, wasting power and reducing battery life. It then provides tips for optimizing specific areas of power consumption, such as using the cellular radio efficiently by bundling network traffic, offloading tasks to hardware accelerators like the DSP to reduce CPU usage, and managing the display to minimize brightness. The document stresses that measuring power consumption is key, and provides tools developers can use to profile and optimize the power impact of their apps.
• What is a web log?
• Where do they come from?
• Why are they relevant?
• How can we analyze them?
• What about Clickstream?
Facing these questions I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. This presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Check out this white paper from eInfochips which showcases how energy and utility providers can unlock potential service opportunities using our predictive analytics solution across all stages of the business cycle. Major utility players are set to roll out millions of smart meters with the aim of generating actionable insights even though as per the industry’s own admission, any serious effort toward monetization is being offset by a lack of core IT capabilities, especially in big data technology. Capturing proactive intelligence on consumer behavior is the way to go. In this white paper, eInfochips demonstrates how utility players can predict demand response, generation response and create new revenue models around coincidental peak demands, smart expenditure modeling and other forms of end user data.
power consumption of household equipments in indiaAlbi Thomas
This document provides examples to calculate the electricity consumed, in kilowatt-hours (kWh), by various household appliances and electronics. It lists the wattage of common devices like lights, fans, refrigerators, washing machines, and more. It then shows sample calculations of kWh used for each appliance running for a given time period. The examples demonstrate how to find daily or yearly kWh by multiplying the wattage by hours used and dividing by 1000. The document aims to educate people on estimating electricity consumption of appliances and identifying ways to save energy.
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
This talk given at the Hadoop Summit in San Jose on June 28, 2016, analyzes a few major trends in Big Data analytics.
These are a few takeaways from this talk:
- Adopt Apache Beam for easier development and portability between Big Data Execution Engines.
- Adopt stream analytics for faster time to insight, competitive advantages and operational efficiency.
- Accelerate your Big Data applications with In-Memory open source tools.
- Adopt Rapid Application Development of Big Data applications: APIs, Notebooks, GUIs, Microservices…
- Have Machine Learning part of your strategy or passively watch your industry completely transformed!
- How to advance your strategy for hybrid integration between cloud and on-premise deployments?
The document provides an overview of IBM's big data and analytics capabilities. It discusses what big data is, the characteristics of big data including volume, velocity, variety and veracity. It then covers IBM's big data platform which includes products like InfoSphere Data Explorer, InfoSphere BigInsights, IBM PureData Systems and InfoSphere Streams. Example use cases of big data are also presented.
Storage provides capacity for files and information through devices like hard disks, while memory provides working space through RAM. Primary storage includes RAM and cache for running the computer, while secondary storage is long-term storage like hard disks. RAM is volatile memory used for running programs, coming in static RAM and dynamic RAM forms. ROM is read-only memory storing basic instructions. Cache memory improves performance by storing frequently used data and instructions. Optical storage includes CDs, DVDs, and Blu-rays, while magnetic storage encompasses floppy disks and hard disks. Flash memory offers portable options like USB drives and solid-state drives.
MODERN SMART GRIDS AND LEVERAGING SMART METER DATA.pptxJasmeet939104
The document discusses leveraging smart meter data to recognize appliances. It proposes a scheme to analyze recorded consumption information from smart meters to provide an appliance-specific breakdown of energy use. It describes simulating smart meter data for resistive, inductive and capacitive loads. Changes in power characteristics when appliances are switched on/off could be detected and compared to signature databases to identify individual appliances. However, challenges include smaller appliances being masked by larger household activity and continuously variable appliances being difficult to detect.
People are talking about the smart grid to television commercials on this topic, we have a plethora of activities around the world where engineers, policy makers, entrepreneurs, and businesses have shown a keen interest in various aspects of this technology. There are smart-grid-related funding opportunities, projects, seminars, conferences, and training programs going on in Europe, the United States, Japan, India and China to name a few. With all this hope and expectation about the smart grid, the question needs to be asked— what it will take to make it real. For the smart grid to be practical and beneficial to society, the following are some of the expectation from the civic society.
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7 Ways to unlock value from Smartmeter Big Data
1. WAYS TO UNLOCK VALUE FROM
SMART METER BIG DATA7Jobil Louis, Allen & Swapnil
Flutura Decision Sciences & Analytics
2. Technology shifts occur in history periodically and change the rules of the game. It is Flutura's belief that
Machine 2 Machine (M2M) & Big Data Analytics are two such phenomenons which are profoundly disrupting
business models globally. M2M + Big Data Analytics offer revolutionary opportunities by harvesting behavioural
patterns which were previously not visible and provide breakthrough answers to powerful questions.
The Utility Sector for example, is ripe for unlocking energy efficiencies. This can be done by understanding the
energy consumption habit patterns at a level of granularity which was previously not possible – neighbourhood
& consumer level. Furthermore, it also enables you to reduce technical and commercial losses along the
complete grid value chain. Flutura would like to present an outline of a 7 point framework to unlock value
embedded within ‘Smart Grid’ data.
Outage Predictor
Distribution
Transformer
Interventions
Consumer Energy
Habit Gamification
TOU (Time of Use)
Dynamic Pricing Models
Guzzler
Micro Segmentation
Extract Device
Signatures
Bottom up Energy
Demand Forecast
1
2
3
4
5
6
7
3. The last three years have seen a paradigm shift in the increase of data points (by using more instruments)
resulting in a sudden data avalanche for the Utility sector. This has been driven by two waves and Utility
companies need to make sense of it. In the first wave, as Smart meters proliferate, Utilities have to process data
at 15 minute intervals which is a 3000 fold increase in daily data processing. In the second wave, as the number
of SCADA devices which are metering energy flow throughout the grid (like substations, transformers and other
elements of the distribution systems) increases there will be a next level of data explosion. The massive release
of data from Utility grids has profound implications for the industry as it opens up a huge set of possibilities to
monetize these massive grid data pools.
ROI on money already invested
xxx dollars are spent on smart grid investments. One of the challenges is to demonstrate the value unlocked from
smart meter data.
Questions Utility Companies are asking
• How can we monetize the massive smart grid data which has been collected
from millions of customers spanning billions of events at intra hour level?
• How can we see energy consumption patterns not seen before and drive last mile changes?
Smart grid analytics is a graceful blend of art and science and success is possible if one
harmonizes these two dimensions.
How can one unlock value from massive Smart meter investments using analytics?
1
Energy economics from Guzzler segmentation
DATA
1 Energy Habits data
2 Consumer profiles
3 Location data
TARGETED ACTIONS
3
1 Targeted Energy Audit
2 RT SMS behaviour alerts
3 Neighborhood Gamification
4 Bill“what if” energy calculators
4
2 GUZZLER MICRO SEGMENTATION CEREBRA APP
MONETIZATION – IMPACT QUANTIFICATION
4. Context:
Peak power demand is a frequent source
of concern for utility. Before smart meters,
the meter reading frequency was once a month.
As a result one could not specifically pinpoint
consumers who were responsible for ‘guzzling’
power. Now with Smart grid data one can have
granular energy consumption patterns in an
hourly on 15 min interval time frame. Now, it’s
even possible to micro segment consumers based
on the amount of power they consume, their
deviation from baseline consumption, consumer
type and location.
Unanswered questions
• Are the numbers of ‘guzzlers’ increasing or decreasing with time?
• Which segment type experienced maximum surge in ‘guzzler’ migration?
• What is the change in habit between peak time ‘guzzlers’ and night time ‘guzzlers’?
Actions triggered
Energy audits can be offered to ‘guzzlers’ to encourage them to optimize their equipment. For example, in the
industrial segment, factories may have a large number of inefficient motor or pumps which have been habitually
‘guzzling’ a lot of power.
Smart meter value-1
Pinpoint grassroots level neighborhood guzzlers
Blind spots during peak power when grid gets taxed
5. Price is an untapped lever in utility
Today, two large sectors like the Airlines and Retail have dynamic pricing. Why not consider the same in the
Utility sector? What’s more, in order to sensitize people towards energy consumption, it imay be relevant
to have ‘time-of-use’ pricing. For example, peak power tariff for industrial units would be different from peak
power tariff for hospitals and government entities. Another opportunity is to increase the tariff for individual
households who have two-sigma-variance compared to neighborhood baselines.
• Which households are responding to peak power price?
• Do we need to have more pricing slabs?
• How much should we recalibrate pricing to optimize energy usage?
• What is the tipping price at which consumers become sensitive to energy usage?
Smart meter value-2
Time of use pricing
6. Smart meter value-3
‘Gamification’ of consumer energy habits
‘Gamification’ is about integrating gaming frameworks to alter
energy habits of specific high value guzzlers and engaging them.
For example, if in a neighborhood there are 2000 individual
households and in the bill if one puts a big bold statement
saying, "You are in the top 100 energy consumers in your
neighborhood" or "Congrats you have altered your habits
to climb-down from the top 100 list" or "Your change in energy
habits has earned you 100 points which you can redeem at the
local store". Consumers are creatures of habit and if their
change of habit is benchmarked in the neighborhood and
rewarded, their sensitivity towards peak power usage is highly
likely to change. In order to put this into action Utility
companies have to delineate target energy behaviors – say
households not consuming above a certain average threshold
during peak power and create activity loops when that pattern
is detected.
Human beings are
creatures of habit
7. Smart meter value-4
Signature extraction & Habit design
There are individual devices within a commercial organisation or household which typically consume more
energy than others – for example heaters, dish washers, etc. These energy intensive appliances can be put
on a watch list and their consumption signatures detected. This consists of analyzing changes in the voltage
and current going into a house from the smart meter time series data and inferring the specific individual
energy consumption of appliances. The appliance signatures can be decoded by identifying patterns in the
variation in measured power change each time an appliance is switched ON or OFF. Once these appliance
signatures are detected, utility companies can provide tailored energy feedback in their bills to influence
their habits.
• What are the devices in the appliance watch list?
• What are the energy consumption signatures of these appliances in the watch list?
• How do we direct the consumer’s attention to appliances or actions that have high energy saving potential?
8. Smart meter value-5
Predictive models for preventative outage hotspots
Smart meter value-6
Next best Distribution transformers interventions
The harmonic distortion of current is increasing with the enhanced use of nonlinear loads from solid state
devices. Examples of nonlinear loads are personal computer, laptop, laser printer, fax machine, television set
(TV), fluorescent tube with electronic ballast, compact fluorescent lamp, battery charger, adjustable speed
drives, uninterrupted power supply (UPS) and any other equipment powered by switched-mode power supply
(SMPS) unit.
Strategic customers like hospitals, military establishments and
political establishments are affected to a greater extent when an
outage event happens. Now with machine learning algorithms one
can decode patterns leading up to an outage event – brownout
frequencies, transient voltage, step change in energyconsumption.
Earlier when an outage occurred one could not learn from the
patterns leading to it. For example:
CUSTOMER DATA
DTR DATA
OUTAGE EVENTS
METERING DATA
GRID NETWORK
STRUCTURE
TAMPER EVENTS
8 0 6 5 4 7 3
E L E C T R I C M E T E R
SMART METER INFORMATION LANDSCAPE
9. Smart meter value-7
Reduce spot buying through bottom up forecasting
Load forecasting is currently a top down process which looks at
historical patterns to predict future demand. It's a complex problem
to be solved since energy is a "perishable" item. As a result over
forecasted excess power procured cannot be "stored". At the same
time under forecasting would result in last minute procurement of
power which is extremely expensive. With the availability of granular
data, neighborhood level energy profiles can be created based on
individual smart meter data and then used to triangulate on the
amount of power to be procured resulting in enhanced value.
These nonlinear loads draw more current than the fundamental current and cause overloading of the Distribution
Transformers (DTR). This leads to higher losses, reduces the strength of insulation and subsequently leads
to reduction of useful life of the transformer. Aging of transformer increases due to overheating caused by
overloading. Current harmonics from Smart meter data can be used to identify aging of transformer caused by
harmonics due to non linear loads. It's also compounded by the fact that many of the distribution transformers
in the grid responsible for the last mile distribution of power have not been changed for many years. One can
look at power harmonics data, brownouts, blackouts and transient event data to rank order and prioritize DTRs
in specific neighborhoods where it needs to be replaced. In order to do this DTR master and event data must be
collected and stored in a central smart grid event repository DTR 360:
• Which are the neighborhoods where the last mile DTR performance needs to be analyzed?
• Which segment of customers – industrial, household, strategic needs to be prioritized?
• Which events must be brought into Central Smart Grid Event Repository (CSGER)?
• What is the DTR scoring process we must deploy?
Nonlinear Loads Impact Aging Distribution Transformers
10. So can Utilities get started?
Build Foundational Smart Grid Data Model
Bottom up
demand response
Strategic Outage Hotspots
Outage Frequent
Sequence Analyzer
Time of Use (TOU)
Dynamic Pricing
Distrb Transformer DTR 360
Device Signatures
Outage Events
Customer Data
DTR Data
Tamper Events
Grid Network Structure
Metering Data
100+ Energy Vectors
Signal Detectors
12 Core Energy Markers
Scoring Models
VEEDA
Apriori
Advanced Visualisation
Machine Learning Algorithms
Tame Big data ref arch
Guzzler Micro
Segmentation
Cerebra Smart Meter Nano Apps
Cerebra Signal Studio
Conclusion
The Utility industry is facing an inflection point where technology is shifting to Machine 2 Machine (M2M) & Big
Data Analytics and profoundly disrupting business models. Utilities must act and move quickly to respond to
changes and leverage the advantages. M2M and Big data analytics offer immense opportunities for monetizing
from investments in Smart meter infrastructure.
11. About Flutura
Flutura is a niche big data analytics solutions company with a vision to help contain massive risk exposures for
organizations and radically unlock operational efficiencies. It does this by extracting meaningful signals from data
using Big Data Analytics. The name Flutura stands for butterfly; inspired by nature's greatest transformation from
a caterpillar to a butterfly. We are obsessed with Trust and Transformation and align our daily lives to these core
principles. Flutura is funded by Silicon Valley’s leading venture capital firm The Hive which primarily invests in big
data companies worldwide. Flutura at a very early stage has been identified as among the Top 10 most promising
big data companies by CIO Review, a leading analyst magazine.