• Ei tuloksia

7.6 Distributed energy production

In the smart grid the used electricity does not come only from a classical nuclear or coal plants or from a bit modern centralized wind mill or solar power farms. The electricity comes from every house, factory, plant or farm that is connected to the energy grid. This kind of totally distributed energy production show what can be achieved using the smart grids, although the system itself is difficult to produce. Still there are some research reports that take the possibility to sell the extra power produced to the global energy grid into consideration [20, 23] and there is also Finnish researchers trying to find out a functional solution to this problem [2, 3, 4].

In this work an approach of the two directional energy flows was not really considered more than the level of a possible idea mainly because the energy production at home is not that usual, at least at the moment, and because it would have required creating the agents for more complex tasks than they were designed. Still, when considering the next version of this system a distributed energy production is one important part that should be taken into consideration as it takes the whole energy market to a whole new level where anyone can be a producer.

Of course, the problem itself is quite complex, as it is not enough just to measure the power consumption and the production of a house, for example, but the system should be able to transfer the extra power from the production facilities to the global grid and to measure the amount of the transferred power so it can be billed properly. At the same time the system should switch of incoming connection from the global grid and use only the locally created power monitoring the power levels all the time, so no blackouts would occur.

44 7.7 Client connection

The client connection was ruled out from this work, but when considering the future of the system it is important to think also this aspect of the system. Currently people have access to a huge amount of information from where ever they are, but still a proper application for monitoring the personal energy consumption is missing in most cases. In Finland power companies provide the temporal information about the power consumption for clients, but they do not provide the current consumption, the forecast or the changes in the earlier data. This kind of data is valuable for customer who wants to change the structure of daily consumption or to shift the peak consumption.

For these reasons it would be reasonable to create a client application that could interact with the main system and get the client specific data out of the system. All necessary information is located in the main database so only the interface is missing. The client application could follow the scheme presented in Figure 17.

Figure 17. Customer connections.

45

8 CONCLUSION

In this thesis a structure for a smart grid control system using a multi-agent approach was presented. In the thesis was also considered the functionality of the control system and how to achieve this functionality. For the functionality some theory and existing implementations were presented to see how the selected methods support the goal of the thesis.

As the control system was not finished at the same time with the thesis in Chapter 7 the future of the system was considered. In this chapter was discussed what will be done and why. How to achieve the ultimate goal of the fully functional system set by the Russian university SPBSTU. These subjects were well considered in Chapter 7 and also some ideas outside the given goal were presented to improve the smart grid solution and to achieve the full potential of the system.

In all, the thesis was finished on time and the content was what it was planned to be, although the time table was tight and some small changes happened on the way.

Nevertheless this part of the work was finished and the presented multi-agent system can also be used in many different kinds of systems and not only in the presented smart gird.

46

REFERENCES

[1] Intelligent information system for monitoring and optimization of the energy resource consumption for housing and public utilities, Research Report.

[2] Sarvaranta, A., Development of smart grids in the European Union and in Finland, Aalto University, Research work, 2010, [www] Available:

http://www.energia.fi/sites/default/files/alykkaat_sahkoverkot_2010_diplomityo_anni_sarv aranta.pdf.

[3] Yrjölä, S., Smart Grids and Energy Markets (SGEM) program building Finnish Smart Grid 2.0, ETSI Smart Grid Workshop, 2011, [www] Available:

http://docbox.etsi.org/workshop/2011/201104_smartgrids/07_pilotprojects/sgemprogrambu ildingfinnish_yrjola.pdf.

[4] Kronman, D., Smart Grids and Energy Markets, 2009, [www] Available:

http://webhotel2.tut.fi/units/set/raportteja/dg/SGEM/SGEM_introduction.pdf.

[5] Lee, S. J. and Siau, K., A review of data mining techniques, Industrial Management &

Data Systems, Vol. 101, No. 1, 2001, Pp. 41-46.

[6] Venkatadri, M., Lokanatha, C., A Review on Data mining from Past and the Future, Internation Journal of Computer Applications, Vol. 15, No. 7, 2011, Pp.19-22

[7] Berkhin, P., Survey of Clustering Data Mining Techniques, 2002, [www] Available:

http://www.cs.iastate.edu/~honavar/clustering-survey.pdf.

[8] Inniss, T., Seasonal clustering technique for time series data, European Journal of Operational Research, Vol. 175, No. 1, 2006, Pp. 376-384.

47

[9] Cao, Q., Ewing, B., Thompson M., Forecasting wind speed with recurrent neural network, European Journal of Operational Research, Vol. 221, No. 1, 2012, Pp. 148-154.

[10] Cubiles-de-la-Vega, M.-D., Pino-Mejías, R., Pascual-Acosta, A. ,Muñoz-García, J., Building neural network forecasting models from time series ARIMA models: A procedure and a comparative analysis, Intelligent Data Analysis, Vol. 6, No. 1, 2002, Pp. 17-35.

[11] Heyd, G., Khotanzad, A., Farahbakhshian, N., A Method For The Forecasting of the Probability

Density Function of Power System Loads, IEEE Transactions on Power Apparatus and Systems, Vol. 100, No. 12, 1981, Pp. 5002-5010.

[12] Charytoniuk, W., Chen, M.S., Kotas, P., Van Olinda, P., Demand Forecasting in Power Distribution System Using Nonparametric Probability Density estimation, IEEE Transactions on Power Systems, Vol. 14, No. 4, 1999.

[13] Taylor, J., McSharry, P. and Buizza, R., Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models, IEEE Transactions on Energy Conversion, Vol. 24, No. 3, 2009.

[14] Kozine, I., Survey of decision-making theories, Final Report, [www] Available:

http://ew.eea.europa.eu/research/drivers/policy/uncertanties-_presentation_metropolis_.pdf.

[15] Zhang, W., Zhu, J., Research on Multi-Attribute Decision-Making Method Based on Fuzzy Set Theory, Second International Symposium on Intelligent Information Technology Application, 2008, Vol. 1, Pp. 482-285.

[16] Fan, Z., Huang, M., Fuzzy Rule Set Based Engine Fault Diagnosis, Power and Energy Engineering Conference, 2009, Pp. 1-5.

48

[17] Shkodurev, V. P., Project: Software prototypes for the technical condition monitoring of industrial systems, Final Report, 2008.

[18] Weert, P. V. ,Efficient Lazy Evaluation of Rule-Based Programs, IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 11, 2010.

[19] Zhang, Z-g., Cai, J-f., Research on Multiple Attribute Decision Making

under Uncertainty based on Grey Theory, The 1st International Conference on Information Science and Engineering, 2009, Pp. 4462 – 4466.

[20] Forgy, C., Rete: A fast algorithm for the many pattern/many object pattern match problem, Artificial Intelligence, Vol. 19, No. 1, 1982, Pp. 17-37.

[21] Logenthiran, T., Srinivasan, D., Khambadkone, A.M., Multi-Agent System for Energy Resource Scheduling of Integrated Microgrids in a Distributed System, Electric Power System Research, Vol. 81, No. 1, 2011, Pp. 138-148.

[22] Ueda, Y., Takeshi, Nagata. Consideration of Smart Grid Operations by Multi-Agent.

Energy Procedia, Vol. 14, 2012, Pp. 738-743.

[23] Hatziargyriou, N., Asano, H., Iravani, R., Marnay, C., Microgrids, IEEE power &

energy magazine, Vol.5, No. 4, 2007, Pp. 78-94.

[24] Logenthiran, T., Srinivasan, D., Multi-Agent System for Market Based Microgrid

Operation in Smart grid Environment, [www] Available:

http://www.smartgridcontest.com/idea.php?id=63