The development of the World Wide Web, the emergence of social media and Big Data have led to a rising amount of data. Information and Communication Technologies (ICTs) affect the environment in various ways. Their energyconsumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness has led to discussions on sustainable development. The data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the ‘value’ of the data stored. In this paper, we study the possibility to methodically reduce the amount of stored data and records in organizations based on the ‘value’ of information, using the Green Archiving Model we have developed. Reducing the amount of data and records in organizations helps in allowing organizations to fight the data deluge and to realize the objectives of both Digital Archiving and Green IT. At the same time, methodically deleting data and records should reduce the consumption of electricity for data storage. As a consequence, the organizational cost for electricity use should be reduced. Our research showed that the model can be used to reduce [1] the amount of data (45 percent, using Archival Retention Levels and Retention Schedules) and [2] the electricity consumption for data storage (resulting in a cost reduction of 35 percent). Our research indicates that the Green Archiving Model is a viable model to reduce the amount of stored data and records and to curb electricity use for storage in organizations. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their ‘value’. Such an ICT will automatically reduce storage capacity and reduce electricity consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different formats, it will lower costs and it reduces the potential forliability.
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Information and Communication Technologies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sus- tainable computing. This can be defined as ‘designing, manufacturing, using, and disposing of com- puters, servers, and associated subsystems - such as monitors, printers, storage devices, and net- working and communications systems - efficiently and effectively with minimal or no impact on the en- vironment’. Nevertheless, the data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the value of the data stored. We explore the possibilities to use information and archival science to reduce the amount of stored data. In reducing this amount of stored data, it’s possible to curb unnecessary power consumption. The objectives of this paper are to develop a model (and test its viablility) to [1] increase awareness in organizations for the environ- mental aspects of data storage, [2] reduce the amount of stored data, and [3] reduce power consump- tion for data storage. This model integrates the theories of Green Computing, Information Value Chain (IVC) and Archival Retention Levels (ARLs). We call this combination ‘Green Archiving’. Our explora- tory research was a combination of desk research, qualitative interviews with information technology and information management experts, a focus group, and two exploratory case studies. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their value. Such an ICT will automatically reduce storage capacity and curb power consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different for- mats, it will lower costs and it reduces the potential for liability.
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Electric vehicles and renewable energy sources are collectively being developed as a synergetic implementation for smart grids. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are seen as a way forward to achieve economic, technical and environmental benefits. The implementation of these technologies requires the cooperation of the end-electricity user, the electric vehicle owner, the system operator and policy makers. These stakeholders pursue different and sometime conflicting objectives. In this paper, the concept of multi-objective-techno-economic-environmental optimisation is proposed for scheduling electric vehicle charging/discharging. End user energy cost, battery degradation, grid interaction and CO2 emissions in the home micro-grid context are modelled and concurrently optimised for the first time while providing frequency regulation. The results from three case studies show that the proposed method reduces the energy cost, battery degradation, CO2 emissions and grid utilisation by 88.2%, 67%, 34% and 90% respectively, when compared to uncontrolled electric vehicle charging. Furthermore, with multiple optimal solutions, in order to achieve a 41.8% improvement in grid utilisation, the system operator needs to compensate the end electricity user and the electric vehicle owner for their incurred benefit loss of 27.34% and 9.7% respectively, to stimulate participation in energy services.
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