Tools and Methods of Information Support for the Development of Renewable Energy

Tools and Methods of Information Support for the Development of Renewable Energy

Ravil I. Mukhamediyev (ISMA University, Latvia), Sophia Kiseleva (Lomonosov Moscow State University, Russia), Kirill Yakunin (Institute of Information and Computational Technologies, Kazakhstan), Renat Mustakayev (Institute of Information and Computational Technologies, Kazakhstan) and Jelena Muhamedijeva (Institute of Information and Computational Technologies, Kazakhstan)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-9179-5.ch002
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The chapter considers information systems that provide analysis of the RES resource base and support decision making. RES resources in the Republic of Kazakhstan are briefly analyzed. The methods and systems for evaluation of theoretical, technical, and economic potential of renewable energy sources are considered. Based on this overview, the authors propose six steps of estimation that should be realized in intellectual GIS. One of the major steps is the identification the factors that affect using of renewable energy sources. These factors can contribute to impeding the development of green energy. The simple taxonomy of these factors is proposed. The second important step is to aggregate estimations of the factors. As a result, the authors propose the aggregation method based on Bayes rule. Compared to other methods, this approach allows us to obtain two estimates: probabilities of positive and negative hypotheses. The methods discussed are at the heart of the information system supporting the development of renewable energy sources in the Republic of Kazakhstan.
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Use of renewable energy sources (RES) is a modern powerful trend in energy development. “Green energy” technologies (technologies of gathering energy from renewable sources) are actively developed and will allow reduce significantly the use of fossil resources (oil, gas, coal, peat), reduce the ecological impact of energy plants, improve the ecology around populated areas, reduce the cost of energy generation, increase the autonomy of life support systems and energy security of the country (International Energy Agency 2014, Environmental Consulting, 2010, Mustafina, 2010) in the future.

According to experts, there is no reasonable alternative to development of renewable energy sources and energy efficiency, as decarbonisation of the energy sector will allow avoid the catastrophic consequences of global warming. Under the best case scenario of development it is planned to reduce the demand for oil (30%), coal and gas with overall growth of energy supply by 60%. By the year 2050 the share of fossi00l fuels will be slightly more than 40% of total energy.

The prospects of RES (primarily solar, wind, bio-sources and geothermal plants) use in Kazakhstan are very high (Antonov, 2014). The part of renewable energy (production) in Kazakhstan should be 50% by 2050 according to the “The Concept of transition to green economy” accepted in 2013.

Evaluation of technical and economic efficiency of new technologies requires special approaches (Sysoeva, Pahamov, 2011) and those must be adapted to the conditions of Kazakhstan.

The chapter considers of the following topics

  • Information systems applying in the field of renewable energy (RE);

  • Kazakhstan's renewable energy resources;

  • Inhibitors and accelerators of renewable energy utilization;

  • Methods of heterogeneous factors aggregation;

  • In conclusion, we summarize the information from the presented parts of the paper.



Despite large potential of renewable energy sources (RES) it might be economically indefensible to harness them in full. Consequently it is necessary to select the locations in the territory where use of RES would be most useful. Although such a work has been performed in a range of projects mentioned below, purely engineering considerations are not sufficient for detailed analysis of the specified territories, as deployment of such facilities is influenced by a variety of different factors, which should be evaluated and consolidated in a generalized estimate. Such factors encompass geographical (environmental, geomorphological, location (Arán, Espín, Aznar, 2008), ecological, technical, economical, social factors. Particularly, recent research show that there should be taken into account the problem of generator recycling (Bodrova, Solomin, 2016) (Gorbunova, Gorbunov, 2016) landscape and aesthetic limiting criteria, emerging in recreational area locations (Gorbunova, Gorbunov, 2016) etc.

RES potential evaluation approaches are considered in different research papers. The paper (Angelis-Dimakis, 2011) defines basic stages of that process (Figure 1). It shows that the total scope of existing potential of a certain renewable (theoretical RES potential) is evaluated at the first stage. Technological potential, depending on parameters of the environment, generator effectiveness, service lines, etc., is evaluated at the second stage. Economic strength is evaluated at the third stage, based on greatest possible number of factors. As soon as RES depend mostly on geographic conditions, the paper (Solovjev, 2016) suggests an additional step – evaluation of geographic RES potential. Geographic potential is determined as a part of technological potential, being geographically available and necessary in a certain region.

Key Terms in this Chapter

MCDM: Multi-criteria decision making algorithms, used for solving multi-criteria aggregation problems. Includes AHP, multiplication method, PROMETHEE, and others.

RES Potential Evaluation: Multi-stage process to assess territories’ suitability for renewable energy source generators installation.

Bayesian Aggregation Model: Method of generalization based on Bayes law, proposed in the work.

Decarbonization: Process of excluding carbon-based energy sources and using green, RES energy instead.

Aggregation Methods: Method of heterogeneous data generalization to obtain single value that assess the suitability of territory.

Inhibitors and Accelerators: Negative and positive factors respectively, affecting the target function.

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