Demand Response

One technique that is gaining recognition in the area of green energy and the more effective management of the Electricity Grid is Demand Response (DR). The goal of DR is to manage consumer consumption of electicity in response to supply conditions. An effective DR policy will result in consumers reducing their electricity consumption at critical times or in response to rising prices. DR differs from Dynamic Demand as the latter entails the use of devices which passively switch themselves off when the grid is overloaded. Devices with DR switches by contrast respond to explicit requests to switch off. DR can involve the reduction of power used and differs from energy efficiency schemes which implies the using of less energy to perform a particular task.

We know therefore that DR is a distinctive technique. The overriding rationale of DR would appear to be of societal benefit rather than of economic benefit for the utility companies. By managing consumer consumption, DR improves the reliability of the electricity grid and reduces peak electricity prices by decreasing peak demand. In essence, it is a set of actions that are taken to reduce load on the grid when congestion that threatens the supply-demand balance occurs or when market conditions raise the price of the electricity supply.

There are two types of DR. The first category of DR is triggered in response to emergencies and is referred to as interruptible DR. This DR category is activiated by utilities in cases of local system emeergices. The second category is prices responsive and is activated by utilties in the case of high wholesale electricity prices. DR is typically managed as a program consisting of participating (and volunteering) customers. Under the energy-only DR payment option, participants receive payment only for curtailment during a DR event. With capacity-payment DR, on the other hand, participants receive regular payment or a discount for being available for DR events.

Savings estimates for implementing DR for energy conservation and curtailment are in the billions of dollars. Customers benefit from availing of energy at cheaper prices. The question regarding DR is why utilty companies would implement it if it ostensibly reduces profits. Is DR something that will be regulatory driven rather than adopted voluntarily by utility companies?

If one looks beyond market price the benefits of DR can be seen for utility companies. Emergency DR programs are designed to relieve grid reliability issues. Extremely high demand for electricity, shortages of generation and significant transmission contraints can all be alleviated by DR. This ensures a smoother operation for utility companies and reduced maintenance issues and operational fire fighting.

Unsurprisingly, there is a high level of research activity in the area of demand response. The Pier Demand Response Research Center (http://drrc.lbl.gov/pier-drrc.html) at the Lawrence Berkeley National Laboratory in California are undertaking projects for the development of demand responsive technologies in buildings and the use of Critical Peak Pricing (CPP) as a form of price responsive demand response. The demand response and smart grid coalition (DRSG – see http://www.drsgcoalition.org/), a trade association for companies that provide products and services in the areas of demand response, smart meters and smart grid technologies, have developed policy recommendations advocating the use of DR as part of any solution to America’s environmental problems. DRSG count IBM, Oracle and the Zigbee Alliance among their many members so, clearly, DR has the attention of significant industry players.

DR is gaining credence among utility companies, particularly in the US. Pacific Gas and Electric Company (http://www.pge.com/demandresponse/) have a demand response program that offers incentive for business owners who curtail their facility’s energy use during times of peak demand while Puget Sound Energy (http://www.pse.com) in Washington state began a residential DR pilot program in October 2009 to evaluate how water heating and central air conditioning customers can voluntarily manage their electric demand during peak periods.

Demand Reponse, then, can be a key driver in the development of a green energy strategy for a country. As well as societal benefits both the consumer and the utility companies can benefit from the adoption of a DR policy.

Smart Economies, Smart Homes, Smart Cities & the Smart Planet

The adjective ’smart’ has been applied to many different entities in recent years. Terms such as smart home, smart grid and smart meter have now been joined by other, often vaguer, concepts such as the smart city, the smart economy and the smart planet. But what do these terms mean? And what role can technology play in enabling these ’smart’ concepts?

The term smart economy is frequently used by governments and can be a very loose term. In essence, a smart economy, like a real economy, has many components. It can be defined as using technology to deliver the infrastructure an economy needs. Initiatives that would be common in a ’smart economy’ include smart meters and grids to provide electricity in a ‘greener’ way, using Cloud Computing to deliver services and having environmentally friendly data centres that leverage virtualization and other cloud computing techniques. Allied to this is the development of communications infrastructure to support remote access to Cloud Computing services and Data Centres.

Clearly, the technology required to achieve any smart economy aspiration are many and varied. Smart Meters are clearly mandatory for any smart grid plans but, in addition, the data produced by smart grids needs to be converted into information that can be used by utility companies to better manage and monitor the energy being generated. Smart Grids are not limited of course to electricity as smart meters are available for measuring gas and water consumption. This more granular measurement of resource consumption is a fundamental tenet of any smart economy. Cloud Computing services such as Software As A Service (SaaS) will be supported by software companies. The evolution of Cloud Computing services could mean that we eventually come full circle back to the old Terminal-Host style architecture for computing where a typical user only needs a computer with an Internet connection to avail of application software. Infrastructure As A Service (IaaS) is currently being provided by services such as Amazon Elastic Compute Cloud (EC2) (See http://aws.amazon.com/ec2/) where you can effectively rent a server. From a social and economic point of view, Cloud Computing services could potentially result in more and more service enterprises being created to provide software, hardware and development platforms over the Internet while the hardware requirements of a typical user will potentially decrease so that only a PC and Internet connection is required to do the majority of work. This sharing of computer infrastructure and reduction of application installations is indeed ’smarter’ and, in the case of the former, ‘greener’.

Smart Homes is another term that can be very broadly defined. The vision of a smart home was first promulgated by the HomeRF working group. This group was disbanded in 2003 when Wi-Fi 802.11b became commonly available in the home and support for the competing Bluetooth protocol became commonplace in devices such as mobile phones (See http://en.wikipedia.org/wiki/HomeRF for further details about HomeRF). The growth of wireless networking protocols and their common availability has advanced the possibility of the smart home. Smart homes will be able to manage energy and, in conjunction with installed smart meters, can measure resource consumption (electricity, gas and water). In addition to energy management, smart homes can also be remotely managed. Security systems and home appliances can all be controlled remotely by the home owner inside and outside the home. Many major corporations are undertaking smart home projects. Ericsson Turkey (http://www.ericsson.com/developer/sub/articles/other_articles/090831_IMS) have developed a system in partnership with Done (http://www.donetr.com) which uses Ericsson’s Connected Home Gateway (HIGA) to turn equipment on and off. This system also allows more sophisticated levels of control such as automatically turning off the gas supply and electricity when fires are detected by video cameras installed in the home.

Nokia has also unveiled its Home Control Centre which combines technologies such as wireless networks, CCTV and mobile devices to facilitiate the remote control of household appliances through any computing device that has a web browser. The Home Control Centre, which is due to be released in 2010 and has been spun off to the independent There Corporation in May this year, will initially provide solution in the area of home security. Currently named ThereGate, the product is a technology independent open LINUX-based platform that supports the most common smart home technologies. All devices for monitoring energy and security are managed under one system that can be accessed from a mobile phone or web browser.  Further details are provided at There Corporation’s website (http://therecorporation.com/).

The question we must now ask is what technologies facilitiate the development of smart homes. The Z-Wave wireless communication proprietary standard (http://www.z-wave.com) for home appliances enables remote communication and control of these devices. The Zigbee set of communication standards (http://www.zigbee.org) is used for communication between low powered radio devices such as wireless sensors. Just like the aim of the Bluetooth protocol is to eliminate unnecessary cables the goal of Zigbee, from a home automation perspective, is to eliminate unnessary remote controls. Protocols like Zigbee and Z-Wave can be used  along with Wi-Fi and Bluetooth to enable the development of Home Area Networks (HANs). In addition to radio protocols the Internet Protocol (IP) can also play its part in the development of the smart home. IP can be used for security cameras and is also being used for wireless sensors. The Zigbee Alliance adopted Internet Protocol standards earlier this year and different manufacturers are developing sensors with embedded IP.  In 2008 Arch Rock (http://www.archrock.com/) introduced the first commercial implementation of the Internet Engineering Task Force’s (IETF) 6LoWPAN standard for IP version 6 communication over low power devices such as sensors.

The communication and networking standards are well established for the smart home but what about the hardware?Many components such as IP Cameras and CCTV have existed for a long time but the sensors and actuators required to control devices are continuously evolving. The market for these devices is a growing one. OnWorld predicts that the Wireless Sensor Network (WSN) market for smart homes will be US$6 billion in 2012. (See http://hiddenwires.co.uk/resourcesnews2009/news20090722-05.html). The key drivers here are energy and health management. Low-powered WSNs can be used in conjunction with smart grids to enable a utility company’s smart meter to communicate with a consumer’s network. In addition, WSNs can also be used to control lighting and heating in a home thus better managing energy consumption. WSNs along with Bluetooth are also being used in the area of home-based healthcare enabling the elderly and people with health problems to be remotely monitored by medical professionals. OnWorld also predicts that the aforementioned growth of IP-based WSNs will enable the home network to connect to the Cloud thus enabling the development of new products and services for the smart home. One possibility here would be an expert system that collects data from home appliances and issue recommendations regarding energy usage and possible savings.

In addition to the emerging technologies in the wireless arena it may be possible to use an existing technology that is already available in many homes – broadband. A recent GreentechMedia article ( http://www.greentechmedia.com/articles/read/the-smart-home-thats-tuned-to-the-weather/) has described how a Californian startup called EcoFactor uses a broadband gateway to control temperature in the home. Local weather forecasts are also analysed to determine an appropriate heating and cooling strategy over a 24 hour period. This demonstates how many homes will not have to wait to install wireless devices to become smart homes – they can potentially use their existing broadband connection.

The common thread thus far for the terms smart economy and smart home is that of energy management. Energy management also plays a key role in the Smart City. Many cities are implementing plans to become smart cities. One example is Amsterdam in Holland (See http://www.businessweek.com/globalbiz/content/jun2009/gb2009068_275981.htm). Energy management is the key driver for Amsterdam’s smart city initiative. Households are installing energy saving systems as well as solar panels and household wind turbines to enable them to sell energy back to the city. 300 power hookups to recharge electric cars have also been deployed around the city. The local utility Alliander is also in the process of developing a smart grid. Between now and 2012 up to €1.1 billion is expected to invested in this inital stage of making Amsterdam a smart city. The goal of the project is to cut emissions by 40% by 2025 as well as boosting the economy through private and public investment. More details are available at http://amsterdamsmartcity.com/.

Figure 1: Taipei is aiming to become a smart city.

Smart cities initiatives, then, can be viewed as energy management and emission reduction programs at a macro level while the micro implementation of such programs is carried out at the level of the smart home. In other words the technologies used to enable smart homes also enables smart cities.

Unsurprisingly, major industy players such as IBM and Cisco are involved in the development of smart city infrastructure. Currently, the emphasis for smart cities is on energy management but there are other initiatives in areas such as traffic management. Instelligent computing systems have been deployed in cities such as Singapore, Brisbane and Stockholm to reduce traffic congestion and pollution. One mechanism used to reduce congestion is a congestion tax. This tax has been applied in Stockholm, Oslo, London and Singapore. The aim of most congestion taxes is not just to reduce congestion but also to encourage ancilliary benefits such as alleviating environmental damage and improving public transport. In Stockholm, for example, proceeds from the congestion tax are to be used to build a ring road for the city.

To make a congestion charge system viable technology must play a role. In conjunction with IBM, Stockholm implemented a free-flow roadside system that uses laser and camera technology to detect, identify and charge vehicles. The results of Stockholm’s smart traffic system have been tangible and dramatic. Traffic has reduced by 25% and public transport timetables had to be rewritten as buses were getting to their destinations faster because of reduced traffic jams. More people are using the Stockholm public transport system and greenhouse gases such as carbon dioxide have reduced by 40% in the inner city. (See http://www.ibm.com/podcasts/howitworks/040207/index.shtml for more details.)

Another facet to smart traffic systems is enabling traffic signal controllers to act as a system. Usually, traffic lights are independent with limited coordination in sequencing. Using smart systems, real-time feedback can be provided so that, for example, a light can be turned from red to green for an approaching vehicle if an intersection has no other traffic. Smart systems can also enable the rereouting of traffic if an accident occurs or in the case of adverse weather conditions.

Smart cities aren’t just about energy and traffic management programs. They can be used to improve coordination across local government agencies as well. Services such as patient care can be dramatically improved if agencies use integrated information systems rather than multiple unintegrated IS. The US city of Albuquerque has reported a 2000% improvement in efficiency in sharing information across government agencies. The benefits have ranged from better and more timely information for citizens and improved services and public safety.

The scope of smart cities is an ambitious one. Cities from Incheon in Korea to Bordeaux in France have plans to become smart cities. Indeed, in some cases smart cities are being built from the ground up. For example, the city of Masdar in the United Arab Emirates has been designed as a smart city and relies entirely on renewable energy resources.

The prefix ’smart’ then principally implies the delivery of energy management programs using computing hardware and software systems. The principal hardware involved that will be used to achieve a smart entity be it a home, city or indeed economy are wireless sensors and smart meters. We are effectively considering better home development and urban planning when we consider the concept of ’smartness’.

Figure 2: The Smart Planet is a cornerstone of IBM’s strategy.

The final term we will consider is that of the Smart Planet. This vision is principally being driven by IBM (See http://www.ibm.com/ibm/ideasfromibm/us/smartplanet/index.shtml?sa_campaign=message/ideas/leadspace/all/planetflash). The vision of a smart planet is one where natural resources such as water are managed using software and sensors and computing driven solutions are available across entire ecosystems such as supply chains, healthcare networks and cities. The smart planet strategy is much more than a marketing gimmick.  The big idea behind the strategy is that many of the physical systems in the world – electrical grids, transportation systems, buildings, factories and rivers – can be managed more efficiently if they’re monitored by sensors.  The performance of these systems can then be analysed and improved. Sensors, RFID and GPS (Global Positioning System) can all be used to enable the management of these systems. IBM cites diverse examples of its strategy in action. Volkswagen is using IBM software and RFID technology to better manage its manufacturing operations while the San Francisco Public Utilities commission is using IBM software to reduce water pollution. (More details can be found at http://www.businessweek.com/globalbiz/blog/globespotting/archives/2009/07/ibms_smart_plan.html). Other examples include the use of IBM software and sensors by the Irish environmental protection agency to collect data across beaches and lakes to measure water quality and the use of sensors in Galway Bay in Ireland to collect data for the evaluation of weather and environmental conditions for the local fishing industry.

There are other terms prefixed as ’smart’ such as smart infrastructure, smart business etc. The fundamental point is that anything that is classified as ’smart’ is enabled by computer hardware and software technology, sensor in particular being common to ’smart’ ecosystems.

The final question we will consider is where Vertoda fits into the concept of a smart home, smart city and indeed smart planet. Vertoda can capture data from both wireless sensors and smart meters and so is ideal for translating this new pool of data into meaningful information that users can act upon. For smart homes, Vertoda can be used to capture data from household appliances and can provide information about energy usage and securing the home. Vertoda is an open source system and so can be used with any vendor hardware and can aggregate all the data emanating form the smart appliances in a household. Similarly, Vertoda can capture and secure data from smart meters and supply meaningful information on energy consumption and revenue to a utility company’s Enterprise Systems and Databases. Vertoda can also secure and translate the information required for building, security and energy management programs that make up a smart city. As Vertoda can be used with any type of sensor or smart meter it can used in the many diverse ecosystems envisaged by IBM’s smart planet strategy.

Blogs on Wireless Sensor Networks

For information on Wireless Sensor Networks there are a number of good blogs available on the Internet including http://www.wsnblog.com, http://freaklabs.org http://www.blogcatalog.com/blog/sensor-networks and http://www.blogcatalog.com/blog/wireless-network-and-sensor-networks.

Complex Event Processing & Green IT

Complex event processing (CEP) is the analysis of event data in real-time to detect patterns and respond to these event patterns. The area of CEP is an emerging one but it is growing – so much so that Forrester Research has recently conducted a report on the industry (See http://www.destinationcrm.com/Articles/CRM-News/Daily-News/Forrester-Gives-a-Welcoming-Wave-to-Complex-Event-Processing-55492.aspx). It cites hedge-fund trading and algorithmic trading as common applications for CEP. Other CEP applications include credit card fraud detection, business activity monitoring, and security monitoring. The principles of CEP were first defined by Dr David Luckham in his book “The Power of Events”.

A complex event is inferred from a series of simple events. Within an organisation, events occur all the time at every level of the firm. The goal of CEP is to discover information contained in these events and analyse their collective impact. CEP will then respond to the occurrence of a particular complex event.

The website complexevents.com (http://complexevents.com) describes CEP as an emerging technology for building and managing information systems including Enterprise Application Integration, Network and Business Security, Regulatory Compliance, Activity Monitoring and Event-Driven Architectures. CEP enables organisations to keep track and react to the information being produced by their systems. The overriding goal of CEP is to make the information contained in events occurring within the organisation’s Information Systems available as well as to detect its impact on the organisation and act upon this information in real time. complexevents.com specifically refers to RFID as a technology that can provide information for CEP. CEP is enabled using techniques such as event stream processing, event relationship detection and complex pattern detection. Using CEP, event data can be filtered, aggregated, correlated and analysed. CEP is enabled using the Rapide programming language. Rapide is an event driven system simulation language and analysis toolset. It uses complex event patterns and event processing agents (EPAs) to model dynamic, hierarchical systems.  It is free to download at http://complexevents.com/stanford/rapide/.

A number of vendors offer CEP solutions. Oracle (http://www.oracle.com/technologies/soa/complex-event-processing.html) offers a CEP solution that uses real-time pattern matching to define and identify complex event patterns. It has been voted number 1 CEP provider by Waters Ranking. Microsoft announced their entry into the CEP market in May 2009 (See http://geekswithblogs.net/cyoung/archive/2009/05/11/microsoft_does_wombles-again.aspx) while IBM released their CEP software in May (http://searchdatamanagement.techtarget.com/news/article/0,289142,sid91_gci1356275,00.html). There are also some smaller vendors in the CEP space such as Aleri (http://www.aleri.com/) and Coral8 (http://www.coral8.com), both of whom recently merged.

CEP vendors are mostly focussing on delivering solutions for financial markets. There have also been some applications in Customer Relationship Management (CRM) and solutions that leverage RFID and sensors for industries such as Telecommunications and Utilities. The question we are going to address here is the role CEP could potentially play in the management and securing of devices such as sensors, smart meters and wind turbines.

Coral8’s platform offer solutions for the utilities industry so that is a good starting point for assessing the role CEP can play in Green IT. As Coral8 point out, their platform can be used to highlight problems in the delivery of service to consumers. CEP can play an even greater role in the smart grids being developed by electricity utilities as the whole aim of smart meters is to better assess consumption and spread demand for electricity. As a richer level of detail is being provided to utility companies, CEP can be used to analyse this data and identify critical patterns in electricity usage. In addition, the event correlation functionality of CEP can be used to manage the network as device utilisation can be monitored and alert conditions can be flagged when they occur. This equipment monitoring also facilitates predictive maintenance of equipment. 

CEP also gives real-time visibility of events that impact smart grids. Key Performance Indicators for smart grid performance can be detected as they occur and the flow of data across a utility company’s IT infrastructure – SCADA systems, ERP systems, Billing systems etc. – can be monitored as they occur. Early trials of smart meters are also presenting problems that CEP can assist. Data can be unreliable and intermittent and CEP can quickly detect the lack of data quality and/or integrity. The functionality of CEP gives a finer level of control over operations as well an enhancing business decision making.

Wind Farms can also benefit from using CEP in the management of their turbines. An individual wind turbine produces a wealth of data and events. From a maintenance perspective, single individual events that occur could represent a pattern that is in fact an alert condition or maintenance requirement. CEP can be used to analyse this stream of events that take place within an individual wind turbine. Furthermore, while data is made available using a SCADA  system and in many cases is accessible from an OPC (OLE Process Control) server, this data is relevant for other systems and software within the organisation. The amount of energy a wind turbine produces, for example, ultimately represents the revenue a company is making. CEP can trace the flow of this data across the Information Systems of a Wind Farm. Availability is another key metric for wind turbines. The time that a wind turbine is unavailable and consequently the amount of revenue being lost can be analysed using CEP and persistent unavailability patterns can be detected and analysed. This is a cardinal point as many Wind Farms have Service Level Agreements with their suppliers where the latter are penalised if a wind turbine is unavailable for more than an agreed period.

Sensors, and Wireless Sensor Network (WSN) technology in particular, can also leverage CEP to detect patterns in the vast amounts of data that can be potentially produced. RFID has already being cited as a technology that can enable CEP and WSNs can play a similar role. WSNs enable a context aware environment for equipment, environments and buildings and produce data that can analysed and transformed into meaningful information using CEP. This is not just applicable to Green IT but also for building, security and supply chain management applications among many others.

CEP then has great potential in many areas including Green IT. Given the rich data and events pools these new technologies provide, CEP should play a major role in analysing these data and events in the coming years.

Data Grids & Green IT

(Note: Data Grids in computing can also refer to a UI Component. We will not be dealing with these types of Data Grids in this Blog.)

Data Grids are highly concurrent distributed data structures. They typically allow you to address a large amount of memory and store data in a way that is quick to access. They also tend to feature low latency retrieval and maintain adequate copies across a network to provide resilience to server failure. It is in essence a data structure where data can be evenly distributed across the network. As you add servers (or nodes) to the network you are adding storage space. Load Balancing policies are not needed.

Data Grids and Grid Computing are concepts that are distinct from Cloud Computing even though the terms frequently go hand in hand. Grid Computing in turn is also distinct from a Data Grid. Cloud Computing is effectively an evolution of Grid Computing wherehardware and software resources and services are provided in the Cloud (Internet). Grid Computing divides pieces of a program into several thousand computers.

A Data Grid, on the other hand, is essentially Grid Computing that is concerned with data. Unlike other types of Grid Computing where, if one piece of a program fails pieces on other resources will also fail, a good Data Grid will have backup of data across the network.  This distributed data will be shared and managed within the Cloud.

A number of Data Grid Platforms have been created for storing and managing data across computer networks. JBoss, for example, have recently launched the Infinispan (http://www.jboss.org/infinispan) open source Data Grid platform which is written in Java.  Infinispan gives a good overview of not just its own platform but also of Data Grids generally. As Infinispan point out the grid creates new copies of lost data if a server fails and puts this copy on other servers. Data Lookups are no longer directed to a single database server which greatly alleviates a major bottleneck for most Enterprise Applications. Gigaspaces (http://www.gigaspaces.com/xap) also offer an In-Memory Data Grid in their eXtreme Application Platform (XAP) that partitions data and distributes the data across large numbers of servers.

In the context of Green IT what role can Data Grids play? With respect to Wireless Sensor Networks (WSNs) , it is predicted there will be millions of sensors deployed in mesh networks in years to come in diverse applications including security, agriculture and energy management. All of these devices emit data readings that need to be stored and translated into information that can be used for decision making which can result in millions of data sets being stored every minute. Given the potential dispersed deployment of sensors thoughout a wide area there is an issue regarding the capturing and storing of this data. Data Grids can provide a solution here whereby sensor data readers or the sensors themselves can relay data to the nearest server on a Data Grid. A Data Grid would also avoid bottlenecks in analysing this data and retrieving it for Business Intelligence purposes.

Smart Grids and the meters therein could also benefit from the provision of a Data Grid. In current field trials of Smart Meters, data is relayed back to a central server every half an hour. Smart Meters will ultimately be deployed in every household and business premises in the vast majority of industrialised countries so the issues relating to data volumes and system bottlenecks applicable to WSNs are also relevant here. The issue of data management for smart grids is further exacerbated when we consider that smart meters will also be deployed for water and natural gas consumption as well as electricity. The other element we need to consider is the securing of smart meters and the data they emit. The encryption or signing of data will be an additional load that could be dealt with within a Data Grid rather than on a centralised server.

In the case of Wind Turbines the case for Data Grids initially seems less clear. Wind Farms can certainly benefit from Cloud Computing – for example, the optimisation of a Wind Farm using simulation services in the Cloud was recently demonstrated (See http://www.computerweekly.com/Articles/2009/07/21/236976/cloud-based-simulation-cuts-engineers-design-costs.htm).  However, when we consider that different Wind Turbine manufacturers have different ways of transmitting data and often have different raw data for what is essentially the same metric it is clear that this data will need to be captured and processed before it becomes meaningful information. Given the processing load, one possible solution could be to distribute data emitted from Wind Farms on a per farm or per manufacturer basis throughout a Data Grid. Furthermore, given that the energy and availability metrics determine a Wind Farm’s revenue this data will need to be retrieved in a timely fashion. This will be easier to do with a Data Grid than a centralised server.

Vertoda is examining Data Grids and their use in our framework. We are researching the development of a Cloud Edition of our Framework and given the volume of data the networked devices we manage can emit a Data Grid will certainly play a major role.

Cloud Computing & Green IT

Cloud Computing

An area of computing that’s gaining traction in recent years in Cloud Computing. Essentially, Cloud Computing refers to the provision of services over the Internet. Users of these services will need no detailed technical knowledge to avail of them. There are three recognised types of Cloud Computing – Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The term ‘Cloud’ is an analogy for the Internet as the Internet is usually depicted as a Cloud in network diagrams.

IaaS is where an organisation outsources equipment such as Computer Servers, Storage Devices and Network Components and users access this equipment over the Internet. Common examples of IaaS include Web Server Hosting and Platform Virtualization. Platform Virtualization is where a computing system that has been partitioned at every level (applications, operating system, processors etc.)  runs on a single platform. This means that different users can access what are effectively distinct subsystems over the Cloud. In summary, IaaS offers computer infrastructure that can be accessed remotely over the Cloud.

PaaS is the delivery of a platform over the Internet. A platform here can be defined as a computing workspace to enable users to develop and build web applications without the user installing any development tools on their own computer. Examples of PaaS include LongJump (http://www.longjump.com)  which enables you to build Web Applications by remotely accessing development tools via your web browser and WaveMaker Cloud Edition for developing AJAX applications (http://www.wavemaker.com).

Originally Cloud Computing and SaaS were effectively interchangeable terms. Essentially, SaaS is a form of software deployment whereby, instead of installing software, users would access it over the Internet. The most well known company in the SaaS space is probably Salesforce.com (http://www.salesforce.com) where subscribers can use Customer Relationship Management (CRM) software over the Internet.

There are two aspects in any discussion of Cloud Computing and Green IT. The first perspective is that of energy savings which there is some debate about (See http://greenmonk.net/how-green-is-cloud-computing/ and http://blogs.sun.com/marchamilton/entry/how_green_is_cloud_computing). the perspective that we will examine is how Cloud Computing can enable and enhance the operations of ‘Green’ Energy networked devices such as smart meters and wind turbines.

Smart Meters have a number of issues that could be alleviated by Cloud Computing. The two major problems utility companies are encountering with Smart Meters are a lack of standardisation from vendors and security. We have discussed the issue of security and smart meters in a previous blog but the lack of standardisation means that organisations who source their smart meters from multiple suppliers need to have software processes in place to format and translate the data from each vendor. Such processes can be expensive in terms of processing power and might be better outsourced to an IaaS organisation which will provide the infrastructure requirements to manage and store smart meter data and transform it into information for business decision making.

The proliferation of smart meter vendors and the lack of standardisation leads to extra work for IT departments in developing software to capture smart meter data. Vertoda is currently working on a development kit to enable software engineers to write programs to capture data from any smart meter and integrate the program with the Vertoda Framework. This in turn provides a pool of information for the rest of the organisation’s software and systems. In addition to installing this kit on a PC, users will also be able to access the kit online as a PaaS.

Smart Meters provide what is for many organisations a new pool of data. Water, Gas and Electricity Meters are being installed by organisations of all sizes and many of these organisations will not want to install software to manage this data. Cloud Computing then can provide solutions to manage smart grid infastructure, develop software solutions using a remote platform or offer SaaS to manage and capture data from smart meters.

Wind Turbines are another category of equipment that could derive benefits from Cloud Computing. The data generated by Wind Turbines is comprehensive but two areas we will deal with here are energy and maintenance data. The energy being generated by a Wind Farm is of interest to both the Wind Farm and their Utility customer. This data should be available in a timely fashion to both parties. Currently, as both organisations have separate IT infrastructures we are dealing with what are effectively unintegrated Information Systems, sometimes resulting in inconsistent data between the organisations – a problem as this is effectively how Wind Farms measure revenue. One solution Cloud Computing can offer a Wind Farm and its utility customer is in IaaS where energy data is stored on a central shared server on the Internet for both organisations.

Maintenance data is also key to the operation of a Wind Farm. Availability is a key metric for organisations and turbine manufacturers are subject to contractual penalties if availability exceeds a certain threshold. For this reason, a shared system using IaaS might be appropriate here too. Of course, there are political and confidentiality reasons why a Wind Farm might not want to interact with its suppliers and customers in this way but you can partition your system on IaaS so that suppliers and customers see only the data that is relevant to them. Platform Virtualization would be an appropriate solution here.

The transformation of Wind Turbine data into information that the company can use for assessing revenue or equipment performance is a complex task. Again, different manufacturers have different formats for their data and for making their data available. A centralised SaaS for capturing and managing this data would be useful here.

Vertoda is examining the role Cloud Computing can play in its solution offerings. We plan to release a Cloud Edition of our development kit and are planning to offer the system as a subscription service. Vertoda can also enable an individual organisation’s IaaS solutions by securing data transferred from Smart Meters and Wind Turbines and by offering a central middleware system that will capture and translate data into meaningful and timely information for organisations.

Social Networking & Green Energy

To learn more about Green Energy there are many useful groups on social networking sites such as Facebook and LinkedIn. Examples on Facebook include the Green Energy Group (http://tinyurl.com/y9flp6j). Other groups exist for smart meters, wind turbines and sensors. LinkedIn has similar groups in the Green Energy area.

Smart Meter Security

Smart Grid Security has been cited as one reason for the creation of the new ‘cyberczar’ position by the White House. A recent article by GreentechMedia (See http://tinyurl.com/l9sfvz) has highlighted the vulnerability of smart meters to a cyber attack. Indeed, it has been claimed that the vast majority of smart grid systems have no encryption or authentication process that would prevent the malicious upload of software or indeed the turning on or off of smart meters en masse.

Critical operations are carried out on Smart Meters including the running of software updates and the disconnecting of users from the smart grid. Indeed, the recent Black Hat Security conference demonstrated the vulnerability of smart meter security when a researcher developed a worm virus that could take control of all the meters in the grid. In many cases, the researchers were able to put their own firmware onto the endpoint device and could then hop from one meter to the next updating the firmware.

Clearly, then, there is a need for cryptography and signature services in a smart grid. However, as we cited previously (See http://tinyurl.com/neduse) traditional security services are not feasible for constrained devices such as smart meters. Because the Vertoda Framework uses more lightweight protocols suitable for diverse environments, Vertoda offers a solution whereby smart meter data could be encrypted or verified before critical operations take place without impairing data transmission performance.

For further discussions on Smart Meter Security see:

• http://tinyurl.com/lhfha2
• http://tinyurl.com/lce5xp
• http://tinyurl.com/lgjrk6

Challenges for Wind Farms

Wind Farms consist of a number of wind turbines that are used to generate electric power. An individual wind turbine is a complex piece of machinery that needs to be managed by their owners for two key reasons: their operational efficiency and the measurement of the energy and hence revenue being generated by the equipment. There are a number of challenges that Wind Farm operators must meet to achieve these goals.

An individual wind farm may consist of a number of wind turbines but, in general, the turbines in a given wind farm come from the same manufacturers. If an operator has more than one wind farm, however, they may find themselves in the situation where they have to manage turbines from different manufacturers. Individual wind farms are managed by the turbine manufacturers’ SCADA system offering (SCADA stands for Supoervisory Control And  Data Acquisition). SCADA systems are powerful systems that supervise the operation of the wind turbines and the wind farm as a whole. Through the use of the SCADA system, operators can assess how much energy a farm is producing and hence how much revenue they are generating.

This situation is complicated where there are wind farms with multiple manufacturers as operators as they then have a situation where they have a SCADA system for each manufacturer. In a worst case sencario, operators may have to manually add up the output of each SCADA system to ascertain the revenue figures. Some SuperSCADA systems have been developed where turbines from multiple manufacturers and hence multiple wind farms can be managed. One such system has been developed by the Technical University of Denmark – see http://tinyurl.com/no2ezs and http://tinyurl.com/mu8269 for more detailsfor more details.

SuperSCADA systems offer one solution to the problem of managing several wind farms with multiple wind turbine manufacturers but Vertoda can offer another solution. Vertoda can aggregate the data produced by different vendors’ SCADA systems and can present maintenance, energy production and revenue information in a unified fashion. Vertoda provides a central data repository for the data generated by wind farms and makes this information available in a timely fashion to the rest of the Wind Farm operator’s IT systems and software. Users can then view this data from both fixed and mobile devices.

A key metric of wind turbine performance is its availability. Unfortunately for operators, different vendors have different ways of calculating availability. Unsurprisingly, given their complexity, wind turbines can be temperamental. A typical service agreement would be 97% availability for 50 weeks of the year with 2 weeks set aside for maintenance, 1 week every 6 months is standard. This is key data regarding wind turbine performance but can be difficult to obtain. Using Vertoda enables a wind farm operator to assess the availability performance of their wind farms at a high level and at an individual turbine level. Revenue is lost when a turbine is unavailable so without this data accountable losses are not accrued correctly and operators can’t quantify how manufacturers are performing.

In addition to availability, the other key metrics for assessing wind turbine performance are production and wind speed. These metrics can be viewed as interdependent. For example, if a wind turbine is unavailable during a period of high wind speed operators lose more revenue than during a time of low wind speed. Metrics therefore need to correlated and Business Intelligence is required for assessing operational performance using the Vertoda Framework.

Typically, a wind turbine is exposed through a firewall and has an IP Address and port that can be accessed. To access a wind turbine, its OPC interface needs to be opened by the manufacturer, usually for an extra fee (OPC stands for OLE for Process Control, OLE in turn stands for Object Linking and Embedding.). We previously discussed how Vertoda can read output from OPC Servers. OPC hardware exists at the hardware level. Without access to an OPC interface, the only route to accessing a wind turbine’s data is through its manufacturer’s SCADA system. Vertoda can make wind turbine data available by accessing either the SCADA system or the OPC interface.

Access to the OPC interface determines the realtime availability of data. If an operator elects not to open the OPC interfaces for their turbines, realtime data is unavailable. It can take between 6 and 24 hours for performance data to be made available without access using OPC as data is collected for SCADA systems only once a day.

There is a potential alternative here as some wind turbines can send email and SMS text messages. The Vertoda Framework can access, centralise and parse these messages and publish them on Desktop, Web and Mobile User Interfaces. This would be a richer presentation than is possible with an email or SMS client and enables realtime data without an OPC interface.

Alpha Version Released

Vertoda Community Edition (Alpha Version) has been released. You can also download the prerequisities for the software in our Download Area.