Three ways are being explored to make the new transformation to the SG:
2.5.1 Increasing penetration level of renewable energy sources
The rst journey is to gradually increase the penetration level of renewable energy sources to signi cantly decarbonise the electricity industry. Both wind and solar power have been taking a signi cant share in the total renewable generation around the world. With new generation capacity installed in the European Union and United States in the recent years, wind energy seems to be the largest share holder of the generation industry [24]. One major problem of the RESs is their unreliability in continuous energy production in real time. Wind power depends on wind speed and solar power depends on solar insolation rate at a certain period of time. As both wind speed and solar insolation rate directly depend on the meteorological conditions of the generating site, energy production is highly variable from these renewable sources and the power system containing these sources suer from unreliability of continuous supply. This intermittency in power production can lead to a substantial variation in power supply even within few minutes. Such frequent changes in wind and solar power production
are a major challenge for the ISOs. Precise forecasting methods are extremely essential to cope up with this situation. Literature [25] shows that for a day-ahead forecasting, wind forecast errors are more than 10% of the capacity while for an hour-ahead forecasting, the error goes down to as less as 5% at the current stage. If more renewable energy sources are deployed to balance the distribution system, the system will be more likely to be unreliable in the real time. The U.S. Department of Energy has asserted in a literature [26] that with invariable system operating conditions, the grid operation will face no signi cant disturbances until renewable energy penetration, mainly wind, exceeds 20% of the total generation. Current pace of renewable deployment may exceed this limit within a short period of time. A de nite example is the case of California, USA that targets to serve 33% of their retail load from renewable by 2020 [27]. Other such examples are easy to nd as well. Therefore, limiting the penetration level will not serve the purpose of embracing SG technologies, rather than nding solutions for disturbances caused by these renewable sources.
2.5.2 Enabling demand response and use of information and commu-
nication technology
The second step towards SG encompasses customer interaction through advanced information and communication technologies. In the traditional power system, load demands are always passive elements and users do not have any in uence on the load demand or pricing. In a sense, the end users cannot see the other side of the power system where the whole system is managed. On the other hand, the utility and the central management system cannot see the real-time energy usage and thus cannot take any measure against any event from the distribution side. The communication between the generation side and the distribution, and similarly consumer side is more or less blind in nature. The SG is meant to improve this information and commu- nication system through advanced technologies. The vision is to accurately monitor, analyse, optimise and control the whole system from central utility locations to the transmission and distribution grids. The envisioned system is meant to work as a distributed automation system and is expected to deal with the interoperability of data exchanges and integration of existing and future devices, systems and applications [28].
Smart metering and demand response is one of the most important mechanisms used in the SG environment. Both the consumers and the utility can be bene ted from using the smart meters as the meters gives real-time measure of the power usage. The end user can control their energy usage according to the real-time pricing and the utility can control the behaviour
Figure 2.3: An example of smart metering structure [17].
of consumer appliances so as to reduce energy costs and unreliability of the entire system. Using AMI, two-way communication has been made possible, which led to the availability of real-time and on-demand information for improved operational e ciency and customer satisfaction. A smart meter can record energy consumption in intervals of as short as minutes and send this information to the central management system to assist with the billing and monitoring systems [29]. In addition, a smart meter can connect or disconnect a particular user appliance to control the energy usage of the user and to help stabilize the utility load demand [19]. One such metering structure is shown in Figure 2.3 [19] where the smart meter collects energy usage data from the appliances, and passes the command to control the appliances' energy usage if necessary. The data aggregator collects usage and/or control data from dierent buildings which can be further transmitted to the utility or the distribution substation. The smart meter can be communicated from the utility as well to control or manage the user load demand.
Demand response is a mechanism where the electricity users can manipulate and control their energy usage themselves in a response to real time energy pricing information available from the smart meters. End users can choose what appliances are to be kept on or o at a certain period of time as determined by the energy pricing at that particular period. Demand response has been made possible by deploying the smart meters and the two-way communi- cation system. Increasing level of customer participation has been enabled by adopting these techniques, which are the basic elements of the SG. This advanced metering structure and com- munication technology is thus bringing about major changes in the structure of power system operation.
2.5.3 Using su cient cost-eective and sustainable storage systems
The third step involves the use of storage energy systems to balance for the intermittency of the renewable energy sources in continuous energy production. Modeling the renewable energy sources is a di cult problem in integrating them in the SG with the other conventional generators. The variable nature of generation has made the forecasting system and resource scheduling a more complex problem. Understanding the long-term and short-term patterns of energy output from the renewables and their likely behaviour has become a new dimension of research [30]. One of the main elds of research in the SG deployment is the e cient and economic resource scheduling problem. With the renewable sources considered as compulsory sources, as they are meant for in the SG, a well analysed forecasting method may even fail to model the real-time energy production from the renewables leading to a failure of the e cient resource scheduling method. If at any time, resource scheduling fails to supply the load with required energy demand, the whole power system may be aected and incur a huge loss of money and reputation. Reliability from this perspective is thus of utmost importance. To address the possibility of potential blackouts or mismatch between the source and the load, dierent technologies have been proposed, such as e cient forecasting tools, demand control, fast start- up units and storage devices of any form [31]. Dierent electric energy storage technologies are available nowadays, such as ywheels, capacitors, compressed air, pumped hydro and batteries that can be used to balance the grid [32][33][34]. Costs and size of the storage devices dictate the choice of energy storage types. Among them, batteries have been proposed by many researchers as viable solution in the context of SG environment. Batteries are rather costly and the space requirement for the batteries is a real concern. Alternate solution to this energy storage problem is to use the Electric Vehicles (EVs) and the Plug-in Hybrid Electric Vehicles (PHEVs), with the capacity to charge/discharge from/to the utility grid, recently de ned as `Gridable Vehicles' by some research [2], as portable storage devices to instantly respond to the utility grid's need.