The United Energy Smart Meter Project
At the request of the Victorian Government United Energy is currently engaging in “best efforts” to replace the analogue meters on its network with smart meters. Smart meters are an advanced energy meter that measures the energy consumption of a consumer (Depuru, Wang and Devabhaktuni 2011), they allow for demand response energy distribution based on on-peak and off-peak consumption. The benefits of Smart meters are well recognised by many counties with world-wide deployment of digital meters expected to reach 212 million units by 2014, perceived benefits include; energy efficiency, environmental benefits, more consumer choice, better services and greater market competition. (Gerwen, Jaarsma & Wilhite R 2009, Filippini, Hrovatin & Zoric 2004, Victorian Auditor General 2009)

About United Energy
United Energy is an energy distributor responsible for the infrastructure required to distribute energy from the power station to the consumer. There are currently 16 major distribution networks in Australia with each energy distributor maintaining a monopoly over its designated area. (AEC 2009) Energy distributors own, operate and maintain the network of infrastructure that transports electricity from the generator to the consumer. They do not work directly with the consumer and instead work with energy retailers who are responsible for managing the relationship with the consumer. Energy retailers will bulk purchase energy from an energy generator and will then work with the distributor to ensure the reliable, safe, and efficient transportation of that energy to the consumer. (United Energy & Multi Gas 2014)

As a natural monopoly United Energy does not compete for market share, instead it competes in the energy market by improving the efficiency and effectiveness of its distribution network and operational costs. Under the current regulatory framework it is able to retain revenue from any efficiency gains for five years after the gain is made (AEC 2009). This allows United Energy to profit from the energy tariffs it charges consumers, which make up approximately 45% of the average residential electricity bill (Energy Watch 2014).

Outcomes of the Smart Meter Project
To realise the benefits of smart meters it is understood that United Energy must do more than simply install the advanced meters, United energy must collect the data, create systems and processes that analyse the data, and feed the information to recipients both internal and external who will make decisions that will ultimately lead to more productive and efficient energy production. (Gjukaj and Bualot 2011, Marr, Schiuma and Neely 2004, Martin 2008) This application of advances in communication and information technology to building, expanding and improving electricity infrastructure is commonly known as “Smart Grid” (IEEE Coordinating Committee 2011)

Traditionally electricity was delivered without much administration or control on consumption (IEEE Standards Coordinating Committee 2011) however rapid technological advances and increasing demand have challenged this model of operation. (Niyato and Wang 2012) Smart Grid technology will allow United Energy to meet these challenges and move beyond the traditional function of delivering electricity from supplier to consumer.

The detailed data provided by smart meters on electrical measurement expressed in terms of physical measurements; voltage, current, harmonics etc. is of little use by itself. To deliver the benefits of a Smart Grid this data must be analysed and transformed into information and understanding and delivered to the respective decision maker. (Schneider Electric 2008)

Key information that can be derived from electrical measurement data includes:

* Consumer Behaviour – which can show energy costs and energy demand at the individual consumer level, this information provided to the consumer will provide insight on their electricity costs, consumers will then be able then modify their behaviour and manage their energy usage more effectively and efficiently. (Hadjsaid and Sabonnadiere 2013) * Electrical Demand – demand response energy distribution is depend on understanding electrical demand and supply; electricity demand on the grid is far from uniform with fluctuations in supply and demand of electricity occurring across the grid. Information on electrical demand allows operators to better allocate resources to where they are needed for example diverting an oversupply of electricity in one area to an area with increased demand. (Momoh 2012, IEEE Standards Coordinating Committee 2011, Niyato and Wang 2012) * Operational Information – can be analysed to provide benefits throughout United Energy. Operational information provides the intelligence required for more effective decision making on electricity flows, allow benchmarking between grid zones which can increase field staff efficiency, increase productivity of the grid, and reduce grid down time. (Schneider Electric 2008)

Such information will not only provide the immediate benefits specified but over time will become ingrained in operational routines, skills and knowhow of the organisation. (Martin, B 2008) As United Energy ingrains many of the benefits of the smart grid into its daily operations it will move closer to developing the organisational knowledge required to meet the demands of a 21st century energy distributor. (Marr, Schiuma and Neely 2004)

Legacy software, databases and systems

While the benefits of Smart Grid technology are clear, one of the clear challenges United Energy faces is how to achieve Smart Grid status within schedule, at minimum cost and without jeopardising existing critical services being provided. (Farhangi 2010) Legacy systems may be inefficient if not capable of meeting the demanding requirements needed to achieve the full benefits of Smart Grid technology, (Khan and Khan 2012) however building completely new dedicated technologies and systems are unlikely to be very cost effective. (MacDonald 2007) In view of this challenge United Energy will need to find a middle ground in which design of its Smart Grid architecture accommodates its legacy systems.

This poses the question of which legacy systems to maintain, modify, replace, remove and when. To assist senior management in answering this question a cost benefit analysis of each system should be used to determine the business value of each system as well as demonstrate the cost associated with modifying, upgrading or replacing the system to meet Smart Grid technical requirements. (Summerville 2000)

One of the key challenges of piggy-backing off legacy systems is creating a channel of communication between old world legacy systems and new world Smart Grid systems.
Energy production and distribution systems historically operate in silos, with information in each department not accessible by applications and users in another department. Additionally legacy systems can typically operate under historical data models and standards, have a lack of current documentation and specifications, and often have become overly complex through constant modification over time. (Summerville 2000, Roncero 2008) To achieve integration across networks United Energy may employ a number of strategies including software re-engineering, refactoring and deploying middleware to allow for the communication between legacy systems and new world Smart Grid technology (Farhangi 2010, Meng, Qu and Guo 2013 )

IT and Data Governance
As outlined in the Victorian Auditor General Report 2009 the implementation of smart meters is a technically ambitious project that will require the implementation and use of leading edge technologies on a mass scale at significant cost and risk. While there is no universal approach to the implementation of smart meters, what can be agreed upon is that implementation, installation, operation and management of the smart meter system will require methodical and rigorous planning and assessment to reduce risk and uncertainty. (Gerwen, Jaarsma & Wilhite R 2009, Victorian Auditor General 2009)

Technical projects with limited resources, a high level of complexity and inherent risks maintain a tendency to deliver over budget, behind schedule and not meet key business requirements. (Neimat 2005) To combat this tendency an IT governance structure should be put in place to maintain alignment between project objectives and business strategy throughout the project. (Luftman 2003) There are a number of frameworks available including ISO/IEC 38500 and COBIT to assist in enabling the business to direct and manage the effective and efficient use of IT resources. These frameworks outline a set of standards, and directives for which senior management should work toward enabling a structure approach to the investment of IT resources. (Chaudhuri 2011)

In order to move toward Smart Grid capability United Energy will be collecting, storing and analysing large quantities of data (Zhou, Hu and Qian 2012), this makes the quality, security and integration of that data within United Energies backend systems critical to the success of the Smart Meter Project. (Otto 2011) To achieve this United Energy should implement a data governance structure, the development of a set of rules, policies, guidelines and standards for managing data. A data governance structure formalises the accountability of managing data, which stakeholders within the organisation and outside the organisation will be accountable for data quality and security. (David Plotkin 2013) It will outline the value of data and calculate the probability of risk, which can then be factored into an overall data governance strategy. This strategy can ensure that risks associated with security and data quality are controlled and monitoring processes are put in place that will ensure the value of data is aligned with business requirements. (Adler 2007)

Stakeholder Analysis
Having been requested by the Victorian Government to implement Smart Meters and operating within a highly regulated industry, government and government bodies represent key stakeholders to the project. Regulatory bodies Australian Energy Regulator (AER), Australian Energy Market Commission (AEMC) and Ministerial Council on Energy (MCE) which govern the national regulatory framework will be monitoring the project to ensure it is compliant, additionally the Victorian government will be looking to publicise the success of the project in justification of the mandate for Smart Meter implementation. (AEC 2009, Victorian Auditor General 2009)

There are a growing number of consumer groups opposed to the Smart Meter rollout. Groups such as “Stop Smart Meters Australia” point to concerns regarding the privacy of information, the passed on costs associated with meter implementation and possible health issues that may arise from meter usage. (McHenry 2013, Victorian Auditor General 2009)

United Energy regularly works alongside with energy generators and energy retailers to distribute energy from generator to consumer; the success of the Smart Meter Project is therefore within their interests to ensure the successful continuation of this partnership. (Victorian Auditor General 2009)

United Energy does not have all the capabilities required for Smart Grid implementation end to end. Numerous third party providers of technological services providers and suppliers such as smart meter manufacturers and application vendors will be brought on to assist with the project. (MacDonald 2007, Victorian Auditor General 2009)

Consumers are the ultimate end user of services provided by United Energy, ultimately gains from Smart Grid implementation will be passed on to the consumer in the form of lower energy prices and a more reliable and efficient network. Consumers also bear the ultimate risks within the project, if concerns over security, cost and health are not adequately addressed within the project it is the consumer who may ultimately lose out. (Victorian Auditor General 2009)

Meter Data Management System

Despite the variability and lack of consensus on smart grid architecture there are a number of standards that have arisen, such as the IEEE 2030, 2011 and 1547. (Basso and DeBlasio 2012) IEEE 2030 standards specify a logical architecture for Smart Grid Communications comprising of three sub-networks. The Neighbourhood Area Network or Home Area Network which connects the customer’s premise with the utility control centre, allowing for intelligent metering tasks such as consumption metering, power quality monitoring and optionally perform load control activities. (Khan and Khan 2012) The Wider Area Network which facilitates data exchange between grid substations, external networks, and last mile networks to central meter data management system. (IEEE Coordinating Committee 2011) And the Workforce Mobile Network which will be used by United Energy technicians in the provision of dispatch, maintenance, and day to day operational requirements. (Khan and Khan 2012)

These three networks makeup the suggested ‘distributed communication architecture’ many of the existing standards mention in their framework. Collection of data from the smart meter to a centralised area poses significant challenges on performance and scale as bottlenecks are likely to slow performance as large amounts of data are processed through a centralised server. Whereas a distributed architecture in which processing units are placed across different locations and linked via telecommunication equipment allowing for each network to process only the data relevant to its localised area; increasing performance and removing bottlenecks. (Zhou, Hu and Qian 2012) The centralised data management system can then take the form of a distributed database with data from each Neighbourhood Area Network or Wider Area Network being fed to a centralised control point. (Stair and Reynolds 2012)

Fig. 1. A fully distributed communication architecture in smart grid. (Zhou, Hu and Qian 2012)

Within the Neighbourhood Area Network and the Wider Area Network there are a plethora of possible applications of smart grid technology with varying requirements. These include demand response energy distribution (DR) (Momoh 2012), automatic meter reading (AMR), substation automation, (Khan and Khan 2012) self-healing integrated distributed energy resource, improved grid security, interoperability (Niyato and Wang 2012) and support of energy diversification (Hadjsaid and Sabonnadiere 2013). The centralised meter data management system provides the benefit of operating as a data warehouse a semantically consistent data store which can store and aggregate data from the many networks throughout the Smart Grid. Data warehouses support online transactional processing and online analytical processing applications which provide the means for modern business intelligence and support of organisational decision making. (Reddy et al 2010)

Future Technology Considerations
Early adoption of new and future technologies should be considered in the United Energy Smart Grid Project. Cloud based technology is a new trending technology that promises virtually limitless hardware and software resources available on-demand and through self-service. There are many benefits cloud computing can provide in Smart Grid implementation and operation including; * Ability to capture grid data reliably, accurately and synchronously * Deliver grid state data quickly and reliably over a wide area * Rapidly process large quantities data and redirect information to appropriate applications

However before United Energy moves to take advantage of this new technology there are a number of issues that need to be addresses, as with all new technologies there is an element of risk involved in implementation. Under a cloud based model data will be warehoused and processed externally with potentially a 3rd party provider, under this model United Energy must ensure that data is held securely and it is compliant with National Electricity and Privacy Law. A cloud based model may also pose substantial costs in both implementation and operation of Smart Grid, for this reason a cost benefit analysis as well as a feasibility study should be undertaken to support senior management in the decision to take up cloud technology. (Bakken and Iniewski 2014)

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