Development of Energy System Smart Grids Based on Multi-Criteria Decision Making

Abstract

An energy management system is a set of interconnected or interactive elements used to establish energy policy and objectives and to accomplish those objectives. Such a system is established on various levels: organization, local community, and the state. Energy management system on the level of an organization is defined by ISO 50001. The standard specifies the requirements for establishing, implementing, maintaining, and improving the energy management system, which allows the organization to continually improve energy performance and energy efficiency and to preserve energy. The development of telecommunication and information technologies caused a rapid development of transportable and electric distribution grids. The concept of smart grids pertains to all components of the production, transfer, and distribution of electricity. Therefore, it is also necessary to define the integration of different management systems into a single complex system using the principles of interoperability aimed at: improved functioning (connection improvement) of all system elements and technologies; enabling end-users to participate in system operation optimization; giving end-users more information on system operation and the choice of a provider; significantly reducing the impact of the environment on the entire electricity supply system; and significantly increasing the degree of supply reliability and security. In addition, traditional distribution grids are faced with increased demands to use new technologies and to extend functionality. A smart grid is usually defined as an electrical grid that intelligently integrates the actions of all users connected within it – producers, consumers/end users, and those who are both, with the purpose of efficiently producing electricity and delivering it sustainably, economically, and safely. So far, several terms have been used to denote this step forward toward new grids: advanced distribution grid, smart grid, intelligent grid, or adaptive grid. In the EU, the concept of smart grids was adopted in 2005, as an official document of the European Commission through the European Technology Platform Smart Grids. In 2007, it was more precisely defined through the Strategic Research Agenda. In April 2010, the Strategic Deployment Document for Europe Electricity Networks of the Future. In early April 2011, the European Commission issued a statement reiterating the need to improve the existing grids, listing the following as the main objectives: increased use of renewable electricity sources, grid security, energy conservation and energy efficiency, and deregulated energy market. Unfortunately, unlike Europe, Serbia has not defined a complete strategy regarding the adaptation to new requirements and technologies. After the pioneering project by the Electric Power Industry of Serbia “Vučje – Smart Grid City”, smart grid projects remained only at the level of certain, less important, aspects of remote electric meter reading. A significant step forward in this direction was made only with the projects financed by the Serbian Ministry of Education, Science and Technological Development. Only through a thus defined rigid formal framework is it possible to unite the regrettably divergent and solitary cases of smart grid development in our country. Energy system development must be in keeping with the strategy for sustainable, competitive, and safe energy, which primarily implies: competitiveness, use of different energy sources, sustainability, innovation, and technological improvement. The result of energy system development is reflected in energy performance. Energy performance refers to quantifiable results pertaining to energy (e.g. energy efficiency, energy intensity, or specific energy consumption). Energy performance indicators are quantitative indexes of energy performance. The key energy performance indicators were defined in 2005 as a result of cooperation between several international organizations – global leaders in energy and environmental statistics and analysis: International Atomic Energy Agency (IAEA), United Nations Department of Economic and Social Affairs (DESA), International Energy Agency (IEA), European Environment Agency (EEA), and the Directorate-General of the European Commission for statistics – Eurostat. The key energy performance indicators include a set of 30 indicators: 4 social indicators, 16 economic indicators, and 10 environmental indicators. The American Energy Institute combines 37 metrics within four sub-indexes that define the four major areas of energy security: geopolitical, economic, reliability, and environmental. These are subsequently used to determine the aggregate index of energy security and are weighted as follows: geopolitical 30%, economic 30%, reliability 20%, and environmental 20%. The values of the U.S. Energy Security Risk Index were determined based on the data for the period between 1970 and 2010, and predicted for the period between 2011 and 2035. The indicator values do not merely represent data but the basis for communication between stakeholders regarding sustainable energy use. Each set of indicators (social, economic, or environmental) expresses specific aspects or impacts of energy production and use. From the standpoint of manufacturers of intelligent power grid, the most important standards are those dealing with the technical details of information security applicable to the design of the device. To IEC 62 443 are in the field of SCADA devices, IEC 62 351 in the field of intelligent reading, and NIST 800-53 in validation, testing and documentation of computer security. On the other hand, for the comprehensive information security policy most companies are ISO/IEC 27000, CPNI recommendations, and the NERC CIP standards. For electric power companies in Europe the selection of IT security standards is clear - ISO / IEC 27000. It must be the basis for risk assessment, security policies and plans to control and overcome the risks. In practice, the problem arises with the implementation of this standard because it is too generic. It is therefore recommended to use other standards, particularly NERC CIP and CPNI recommendations where necessary to develop specific procedures and plans to overcome the risks. The choice of adequate activity for planning smart grid development is a complex and difficult task. This is due to three main reasons: (1) presence of various alternatives; (2) existence of multiple criteria (economic, technical, environmental, etc.) to be met simultaneously, although they are often incommensurable and incomparable; (3) renewable energy sources in a distribution grid must be optimized based on operational needs and on algorithms used for the optimization. Consequently, it is necessary to perform a multicriteria analysis in the process of smart grid development. This paper proposes a new algorithm, which uses the fuzzy max-min and AHP methods for multi-criteria decision making. Based on dynamic fuzzy matching of alternatives, the method determines the activity timetable for distribution system planning. The method is illustrated on the example of equipment replacement in 35/10 kV distribution substations. We proved that the method is highly successful in the evaluation of alternatives in the presence of heterogeneous criteria.