Assessing renewables-to-electricity systems: a fuzzy expert system model

Abstract The assessment of Renewables-to-Electricity Systems is a complex, time-consuming task and requires skilled, experienced engineers. This paper describes the on-going research effort that takes place in the development of a new Intelligent Approach, an efficient decision making tool in this problem based on the employment of the Expert Systems and Fuzzy Logic techniques. As far as expert knowledge representation is concerned, the proposed approach is based on Expert System techniques (rule-based methodology). Moreover, trying to assess a Renewables-to-Electricity project or several alternative ones, the analysis has to face, in general, a series of uncertainties. To handle effectively these uncertainties, a new methodology is proposed (by use of Fuzzy Sets Theory and Fuzzy Logic Techniques). The proposed Fuzzy Project Priority Index for each Renewables-to-Electricity System is very useful especially in decision-makers. In order to demonstrate the proposed intelligent fuzzy analysis-based approach a simple case study is provided, supposing that a legal entity is to assess and finally select/propose an electricity production system, which uses RES (wind energy OR solar energy to photovoltaic OR small hydro). As fuzzy variables are concerned the Life Cycle Analysis (versus equipment production, plant preparation, operation and decommissioning) and the Development Cost (versus firm capabilities, spillover effects and potential downside damage).

[1]  G. S. Hope,et al.  Expert Systems in Electric Power Systems a Bibliographical Survey , 1989, IEEE Power Engineering Review.

[2]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[3]  Ralph E.H. Sims,et al.  Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation , 2003 .

[4]  J. Kaldellis,et al.  Evaluation of the wind–hydro energy solution for remote islands , 2001 .

[5]  Michio Sugeno,et al.  Fuzzy systems theory and its applications , 1991 .

[6]  J. Kaldellis,et al.  Techno-economic evaluation of small hydro power plants in Greece: a complete sensitivity analysis , 2005 .

[7]  A. Kaufman,et al.  Introduction to the Theory of Fuzzy Subsets. , 1977 .

[8]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[9]  Theocharis Tsoutsos,et al.  An analysis of the Greek photovoltaic market , 2004 .

[10]  B. C. Papadias,et al.  An intelligent tool for distribution substations troubleshooting and maintenance scheduling , 1991 .

[11]  J. Kaldellis Social attitude towards wind energy applications in Greece , 2005 .

[12]  T. Tsoutsos,et al.  Environmental impacts from the solar energy technologies , 2005 .

[13]  Yannis A. Phillis,et al.  Sustainability: an ill-defined concept and its assessment using fuzzy logic , 2001 .

[14]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[15]  John L. Souflis,et al.  Application of a deep level knowledge model to dynamic behavior analysis of power systems , 1990, IEEE Trans. Syst. Man Cybern..

[16]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[17]  Theocharis Tsoutsos,et al.  Renewable energy sources project appraisal under uncertainty: the case of wind energy exploitation within a changing energy market environment , 2002 .

[18]  E. G. Koukios,et al.  Selection of a reaction system for cellulose saccharification : a hierarchical approach , 1991 .