|Viktoriia Brazovskaia||Peter the Great St. Petersburg Polytechnic University, Politehnicheskaya Street 29, Saint Petersburg, 195251, Russia|
|Svetlana Gutman||Peter the Great St. Petersburg Polytechnic University, Politehnicheskaya Street 29, Saint Petersburg, 195251, Russia|
transition from fossil fuels to “green” energy involves increasing energy
efficiency from existing energy systems and reducing harmful emissions into the
atmosphere. Currently, renewable energy sources (RESs) are a priority way of
generating energy for Smart Grid systems that meet the requirements of
efficiency and safety. Classification of regions by similar climatic
characteristics helps as an effective tool for avoiding various risks when
implementing a Smart Grid based on RES. In this paper, the clustering method is
considered by the authors as a tool to achieve the goal of the study: the
division of regions into clusters with similar climatic characteristics, which
will allow to choose the most efficient RES for each specific region. The
authors considered the selected climatic characteristics based on certain
factors after a review of existing sources, for example, the level of solar
insolation, average annual wind speed, average annual temperature, and average
annual precipitation for 84 subjects of the Russian Federation. As a result,
five clusters were identified by the k-means
method using the Stata software. For each cluster, the characteristics and the
most preferred type of RES for the implementation of Smart Grid are described.
Cluster analysis based on climatic characteristics is the first stage of a
comprehensive methodology for selecting the most favorable regions for the
development of both a particular type of RES and Smart Grid systems in general,
as proposed by the authors.
Climate-related risks; Clustering analysis; Efficiency; Renewable energy; Smart grid
The energy sector is one of the key sectors in the economy of any country. The energy sector ensures the uninterrupted operations of the entire industrial complex and enables the population to use electricity and heat for household needs every day; therefore, its development is an important task of public administration. In recent years, the topic of introducing “smart” technologies into the energy system has been widely discussed. These technologies would not only reduce the costs of electricity production but also help to control consumption, reduce losses in networks, and therefore reduce the risks of implementing Smart Grid projects. In addition, global trends indicate the need to switch from traditional energy to cleaner energy sources to reduce harmful emissions (Kusrini et al., 2017; Tishkov et al., 2020).
The relevance of the work is due to the growing interest in the issue of intellectualization of the electric power industry as well as the development of digital technologies in general. In addition, the requirements for reliability, quality, and environmental friendliness of the electricity produced are constantly growing, which is the driving force behind the development of modern technologies in this industry and the implementation of renewable energy sources (RES) into the country’s energy system.
The purpose of this work is to develop a map of the use of renewable energy in the regions of the Russian Federation when implementing Smart Grid in order to reduce climate risks. In this regard, it is necessary to determine for which regions it is advisable to introduce such a system as well as to determine the risks of implementing a Smart Grid based on RES.
The United States and the European Union (EU) countries were the first to introduce the Smart Grid concept. They initiated the introduction of smart grids as one of the objectives of the energy and innovation development strategy. Currently, China is the leader in the investment and implementation of Smart Grids, which will become an effective tool to increase the volume of wind and solar energy there to cover the growing energy consumption, especially for remote areas (Lui and Van, 2019).
When analyzing publicly available sources, six main advantages of Smart Grid were identified: (1) reliability, which had made it possible to reduce the cost of power outages and quality violations, in addition to reducing the likelihood of occurrence and consequences of widespread outages (Dorofeeva et al., 2019); (2) saving—lower electricity prices compared to classical networks as well as the creation of new jobs; (3) efficiency—with the integration of renewable and alternative energy sources, it becomes possible to reduce the cost of production, delivery, and consumption of electricity; (4) environmental friendliness—global climate change encourages the use of RESs. This will reduce emissions compared to public energy networks and increase the efficiency of energy production, delivery, and consumption (Khalil et al., 2019); (5) protection, which is achieved by reducing the likelihood and consequences of man-made accidents and natural disasters (Bugaeva et al., 2020); (6) safety, which is achieved by reducing the risks inherent in the electrical system as well as reducing the exposure time to these hazards (Lukyanchenko, 2017).
The concept of using Smart Grids together with RESs is more often considered these days (Prakesh et al., 2017) and the use of solar panels (on photovoltaic cells) and wind generators is becoming more widespread every year. Power electronics, according to the authors, is the most important element of modern Smart Grids and renewable energy systems. This paper also discusses modern power semiconductor devices and the use of power electronics in energy saving, electric vehicles, renewable energy systems and grid energy storage, and Smart Grid elements.
RESs together with energy storage systems represent the solution to the main problems of the modern energy system, which are related to the need to improve reliability, environmental friendliness, etc. Such systems embedded in Smart Grids are able to solve the problem of power outages (Baza et al., 2019). Moreover, much attention is being paid to the modernization of storage systems to improve the properties of reliability and continuity, and, therefore, the quality of energy supply (Quan et al., 2019). A partial transition to RESs will help to stop the growth of harmful emissions into the atmosphere and increase the environmental friendliness of the energy produced (Jebli et al., 2020).
When compiling these projects, you should pay attention not only to the financial payback but also to how the selected systems are suitable in each case. Each project is individual, hence it is necessary to take into account the peculiarities of the climate and consumer preferences. In addition, there is an increase in the stability of the power system with more implementation of such projects, that is, an increase in the ability of the system to reduce the likelihood of power outages (Khalid and Javaid, 2020).
Managing such projects always comes with a lot of risks. Despite the possibility of reducing energy consumption costs when implementing energy efficiency requirements in projects, the cost of the project itself increases significantly, which affects the payback of such projects (Setiawan and Asvial, 2016; Mansouri et al., 2021). It is also noted that the digitalization of home systems puts personal data at risk. In addition, there are concerns that it will make it the collection of personal data easier that may get to third parties, thanks to the introduction of intelligent networks (Attia, 2019). The risks associated with the scale of such projects as well as organizational and financial risks are separately noted (Gasho et al., 2020; Olifirov et al., 2019).
The implementation of new technologies, such as Smart Grid, is accompanied by technological risks. First, the entire power system must be prepared, which means that modernization of all fixed assets, replacement of outdated equipment, and comprehensive IT equipment is required. It is also necessary to establish the production of modern equipment, reequipment of production lines, etc., in order to reduce risks when importing equipment from other countries (Ourahou et al., 2020).
In order to achieve the maximum environmental effect in reducing harmful emissions into the atmosphere from the introduction of Smart Grid, RESs are integrated into this technology as energy generators. RES and energy storage systems are the key technologies for smart grids and provide ample opportunities for decarbonization of urban areas, frequency regulation, voltage deviations, and rapid response to peak ratings when the demand exceeds production (Guenther, 2018; Worighi et al., 2019).
The implementation of renewable energy requires not only reforming the electricity market but also taking into account the peculiarities of climatic characteristics (dependence of output on external environmental conditions) in order to reduce climate risks. The efficiency of solar panels is associated with the level of solar insolation, and energy sources based on wind turbines are associated with the average annual wind speed (Pereverzeva et al., 2020). Finding renewable energy-based stations in an open space creates the need to keep in mind the amount of precipitation (Abdelkareem et al., 2018). In their project, Kalogirou et al. (2013) considered the maximum and minimum average annual temperature as climatic characteristics. In order to enhance the service life of such stations as well as the efficiency of generation and reduce the payback period of Smart Grid projects, it is necessary to competently approach the analysis of each specific territory according to these climatic characteristics. Therefore, it was decided to consider the cluster analysis method as the basis for making a decision on choosing the type of renewable energy for each individual region.
The introduction of smart grids based on RESs needs to be developed in our country to improve the quality of the energy system. When reviewing the works of other authors, it was revealed that with an increase in the share of renewable sources in the energy balance of the country, the overall energy efficiency increases. Also, due to the introduction of Smart Grid, the growth rate of energy consumption decreases, caused by the growth of the economy and the increasing needs of the population.
The introduction of smart grids based on RESs is accompanied by a number of climatic, technological, economic, and other risks. For the most effective digitalization and intellectualization of the energy system, it is necessary to work on identifying and reducing these risks, that is, managing them.
The concept proposed by the authors for determining the most favorable regions for the introduction of intelligent grid systems based on RESs can become part of a comprehensive analysis of the regions before the implementation of such projects.
The variability of renewable energy generation is primarily associated with weather conditions, and therefore with climatic characteristics. Cluster analysis of climatic characteristics selected based on the analysis of existing studies will help to reduce climate risks at the initial stage of developing projects in the field of RESs, since the type of source will be selected most effectively by taking into account the environmental conditions.
research is partially funded by the Ministry of Science and Higher Education of
the Russian Federation under the strategic academic leadership program
'Priority 2030' (Agreement 075-15-2021-1333 dated 30.09.2021).
Abdelkareem, M.A., El Haj Assad, M., Sayed, E.T., Soudan, B., 2018. Recent Progress in the Use of Renewable Energy Sources to Power Water Desalination Plants. Desalination, Volume 435, pp. 97–113
Attia, T.M., 2019. The Challenges and Risks facing ICT in the Management and Operation of the Smart Grid. Renewable Energy and Sustainable Development, Volume 5(1), pp. 3–14
Baza, M., Nabil, M., Ismail, M., Mahmoud, M., Serpedin, E., Ashiqur Rahman, M., 2019. Blockchain-Based Charging Coordination Mechanism for Smart Grid Energy Storage Units. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp. 504–509
Bugaeva, T., Filippova, I., Tribunskii, N., 2020. Applying the Analytic Hierarchy Process to Decision Making on the Development of Urban Energy System. In: Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy: SPBPU IDE-2020. pp. 1–6
Dorofeeva, L., Rodionov, D., Velichenkova, D., 2019. Infrastructure Potential of Creating “Smart Cities”. In: Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy. ACM, New York, NY, USA, pp. 1–7
Engeland, K., Borga, M., Creutin, J.-D., François, B., Ramos, M.-H., Vidal, J.-P., 2017. Space–Time Variability of Climate Variables and Intermittent Renewable Electricity Production—A Review. Renewable and Sustainable Energy Reviews, Volume 79, pp. 600–617
Gasho, E.G., Kiseleva, A.I., Romanov, G.A., 2020. Hybrid Energy Systems based on Renewable Energy Sources in Various Climate Zones. Journal of Physics: Conference Series, Volume 1683, pp. 1–5
Guenther, M., 2018. Challenges of a 100% Renewable Energy Supply in the Java-Bali Grid. International Journal of Technology, Volume 9(2), pp. 257–266
Jebli, M.B., Farhani, S., Guesmi, K., 2020. Renewable Energy, CO2 Emissions and Value Added: Empirical Evidence from Countries with Different Income Levels. Structure Change and Economic Dynamics, Volume 53, pp. 402–410
Kalogirou, S., Mpiska, A., Giaoutzi, M., 2013. The ESPON 2013 Programme, p. 91
Khalid, R., Javaid, N., 2020. A Survey on Hyperparameters Optimization Algorithms of Forecasting Models in Smart Grid. Sustainable Cities and Society, Volume 61, 102275
Khalil, M., Berawi, M.A., Heryanto, R., Rizalie, A., 2019. Waste to Energy Technology: The Potential of Sustainable Biogas Production from Animal Waste in Indonesia. Renewable and Sustainable Energy Reviews, Volume 105, pp. 323–331
Kusrini, E., Kartohardjono, S., Sofyan, N., Yuwono, A.H., 2017. Innovation of Renewable Energy, CO2 Capture and Storage Materials for Better Applications. International Journal of Technology, Volume 8(8), pp. 1371–1375
Lui, U., Van, S., 2019. Innovative Transformation of Energy Networks (Smart Grid) in China: Problems and Prospects. New Economy, Business and Society, pp. 359–365
Lukyanchenko, S.?., 2017. Management Problems in Smart Grid Systems. Bulletins of Donetsk National Technical University, pp. 38–42
Mansouri, S.A., Ahmarinejad, A., Nematbakhsh, E., Javadi, M.S., Jordehi, A.R., Catalão, J.P.S., 2021. Energy Management in Microgrids Including Smart Homes: A Multi-Objective Approach. Sustainable Cities and Society, Volume 69, 102852
Nasledov, A., 2013. IBM SPSS Statistics 20 and AMOS: Professional Statistical Data Analysis. ?????
Olifirov, A.V., Makoveichuk, K.A., Petrenko, S.A., 2019. Integration of Cyber Security into the Smart Grid Operational Risk Management System. In: CEUR Workshop Proceedings. pp. 132–144
Ourahou, M., Ayrir, W., EL Hassouni, B., Haddi, A., 2020. Review on Smart Grid Control and Reliability in Presence of Renewable Energies: Challenges and Prospects. Mathematics and Computers in Simulation, Volume 167, pp. 19–31
Pereverzeva, K., Gutman, S., Petrov, K., Rytova, E., Shmatko, A., 2020. On Technology Readiness to Develop Wind Energy Sector in Russia. In: Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy: SPBPU IDE-2020. ACM, New York, NY, USA, pp. 1–8
Prakesh, S., Sherine, S., BIST, B., 2017. Forecasting Methodologies of Solar Resource and PV Power for Smart Grid Energy Management. International Journal of Pure and Applied Mathematics, Volume 116, pp. 313–318
Quan, H., Teo, J.K., Trivedi, A., Srinivasan, D., 2019. Optimal Energy Management of Vanadium Redox Flow Batteries Energy Storage System for Frequency Regulation and Peak Shaving in an Islanded Microgrid. In: 2019 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia). IEEE, pp. 4053–4058
Roques, F., Hiroux, C., Saguan, M., 2010. Optimal Wind Power Deployment in Europe—A Portfolio Approach. Energy Policy, Volume 38(7), pp. 3245–3256
Setiawan, E.A., Asvial, M., 2016. Renewable Energy’s Role in a Changing World. International Journal of Technology, Volume 7(8), pp. 1280–1282
Tishkov, S., Shcherbak, A., Karginova-Gubinova, V., Volkov, A., Tleppayev, A., Pakhomova, A., 2020. Assessment the Role of Renewable Energy in Socio-Economic Development of Rural and Arctic Regions. Entrepreneurship and Sustainability Issues, Volume 7, pp. 3354–3368
Worighi, I., Maach, A., Hafid, A., Hegazy, O., Van Mierlo, J., 2019. Integrating Renewable Energy in Smart Grid System: Architecture, Virtualization and Analysis. Sustainable Energy, Grids and Networks, Volume 18, 100226