A couple of years ago, another regulation was introduced by the German legislation. A law which states that the installation of Smart Meters for newly built houses is mandatory. This seems smart, since they give a detailed breakdown on energy consumption and allow for more incentive-based tariff models. They also save energy by analyzing and learning from usage patterns.
Smart Meters are recording the energy consumption and then send it to the utility companies for billing, monitoring and reporting. They can track the consumption behavior of households or industrial consumers, which provides the foundation for usage patterns and, therefore, enables the utility company to make forecasts about future demand. The devices are usually capable of bidirectional communication. That means, they are not only tracking the consumption but also if energy is generated (by households e.g. photovoltaic installations) that is fed into the grid.
One of the overall goals for the upcoming future is to follow a resource driven approach for providing power to consumers including the possibility to match individual preferences about the kind of energy they would like to use. An intersecting point between Smart Grid and Smart Home is to equip household devices like washing machines, tumble driers or dish washers with actuators, being switched on automatically if for instance the energy is cheaper during the nighttime. Since, those devices normally need more energy than others in a typical household, this approach can clearly show how cost savings can be achieved by using this kind of Machine-to-Machine Communication.
Those examples are not far away from where we are at the moment. But there are still a couple of shortcomings that have to be properly addressed. One of the biggest challenges is the intermediation platform. Currently, in order to determine the demand of energy, utility companies are making a forecast based on consumption data from the past. The procurement of energy is done on a market, where amounts of that non-storable commodity are traded. Energy is bought in advance on different markets depending on the timeframe.
Mainly three markets for trading energy are differentiated: the futures market (long-term), the spot market (short-term, one day ahead) and the intraday market (same day as consumption). The trading tries to create cost-efficiency in the supply and demand process of energy. If just looking at conventional energy generating methods like coal or nuclear power plants, supply is to some extent controllable but only in advance. The fact that renewables are preferably treated when feeding into the grid creates higher uncertainty about the amount, the location and the point of time energy is available or deliverable, since their generation partly depends on weather conditions, which can change quickly. Therefore, the timeframe is rather short to integrate them. In sum, the biggest problem on the energy market is uncertainty about demand, while also conventional energy sources cannot adjust quick enough in order to meet the optimal output level. In addition, the energy production by renewables can only be considered in a short time frame. So the biggest challenge seems to be time.
The Social Sensor Cloud (abbreviated SSC), a R&D project carried out by azeti Networks with research support of the Technical University of Berlin, can address the challenges in the energy market and increases not only the efficiency of matching supply and demand in an even shorter term than currently possible. It also allows to integrate Smart Home devices (with actuators) like washing machines and tumble driers to be automatically triggered when preferences of the household are matched (e.g. green energy usage or low price of energy).
The cloud-based platform, where sensors and actuators can interact on a global scale, helps to broker near real-time information between utility companies, energy producers, households and industrial consumers. All the collected information is grouped and analyzed in order to provide the most efficient allocation with regard to energy supply of all different available kinds and the demand including the preferences. Especially utility companies, which have to buy energy in advance are now enabled to optimize their energy portfolio, due to shorter time windows for reacting on specific demands or shortages in supply. This makes the integration of renewable sources of energy production more efficient in terms of quantity and overall costs.
With the SSC, it is possible to build virtual sensors based on topics. On the consumer side, households or industrial customers preferences can be assigned to virtual sensors like demand for renewable energy in a region or households with heavy nighttime consumers and a preference for energy generated from coal-fired power plants for instance. On the supply side, the SSC can collect information from conventional power plants as well as from the various small energy producers, whose amount of generated power can be aggregated in a virtual power plant, which can contribute to the optimization of the energy supply.
In summary, the specific structure and characteristics of the energy market, namely the time-constraint together with the lack of short-term information, can be addressed by using the Social Sensor Cloud. In terms of the energy market, the SSC represents an intermediation platform, where near-real time information of both, the supply and the demand side is brokered in order to achieve the optimal allocation with regard to quantity produced, quantity demanded (taking into account the various kinds of energy and preferences over it) and the price. With this technology, the logical continuation of Smart Meter and Smart Grid can be provided, which is Smart Power.