Modern Methods of Urban Energy System Planning
https://doi.org/10.21122/1029-7448-2019-62-2-377-387
Abstract
Knowledge of the nature of the energy load alterations not only in time, but also in space will allow achieving the optimal structure of energy sources in the city and thereby reducing unproductive energy costs of energy resources and increasing energy efficiency. Changing the paradigm of power supply systems development towards the development of small distributed power production, intellectualization and demand management requires a more accurate understanding of the planned local loads in the city. At present it is still difficult to obtain such data; it requires analysis of many sources and, consequently, takes a lot of time. The article presents a possible algorithm of formation of the space-time profile of energy resources consumption. At the heart of the load disaggregation there is a spatial distribution of consumers in the city, estimated by the density of the distribution area of buildings of energy consumer groups. The dimension of the model is not limited in both temporal and spatial resolution: the model is flexible and can be adapted to different cases and local conditions. The proposed algorithm has been applied to the modeling of the profile of electricity consumption in St. Petersburg. The profile is based on an annual graph of electricity consumption by hour (8760 values). The spatial resolution of the model ranges from hundreds of meters to several kilometers and depends only on the availability of initial data. In the example, the division of the city territory into administrative districts (18 districts of St. Petersburg) is used as a spatial unit. The obtained results showed their logicality and compliance with empirical observations.
About the Authors
Т. M. Bugaeva
Peter the Great St. Petersburg Polytechnic University
Russian Federation
Russian Federation
Address for correspondence: Bugaeva Tatiana М. – Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya str., 195251, Saint Petersburg, Russian Federation. Tel.: +7 812 297-09-72 bugaeva@spbstu.ru
O. V. Novikova
Peter the Great St. Petersburg Polytechnic University
Russian Federation
Russian Federation
References
1. Keirstead J., Jennings M., Sivakumar A. (2012) A Review of Urban Energy System Models: Approaches, Challenges and Opportunities. Renewable and Sustainable Energy Reviews, 16 (6), 3847–3866. https://doi.org/10.1016/j.rser.2012.02.047.
2. Bushuev V. V., Livinskii P. A. (2015) Energy-Efficient Megapolis – Smart City “New Moscow”. Moscow, Energiya Publ. 75 (in Russian).
3. Makarov A. A., Veselov F. V., Volkova E. A., Makarova A. S. (2007) Methodical Bases of Development of Elaboration of Prospects of Electric Power Industry Development. ?oscow, Energy Research Institute of the Russian Academy of Sciences. 103 (in Russian).
4. Mirakyan A., De Guio R. (2013) Integrated Energy Planning in Cities and Territories: a Review of Methods and Tools. Renewable and Sustainable Energy Reviews, 22, 289–297. https://doi.org/10.1016/j.rser.2013.01.033.
5. Howard B., Parshall L., Thompson J., Hammer S., Dickinson J., Modi V. (2012) Spatial Distribution of Urban Building Energy Consumption by End Use. Energy and Buildings, 45, 141–151. https://doi.org/10.1016/j.enbuild.2011.10.061.
6. Mikkola J., Lund P. D. (2014) Models for Generating Place and Time Dependent Urban Energy Demand Profiles. Applied Energy, 130, 256–264. https://doi.org/10.1016/j.apenergy. 2014.05.039.
7. Rules of Determination of Cost of the Electric Energy (Power) Delivered in the Retail Market at the Regulated Prices (Rates), Payment of Deviations of the Actual Volumes of Consumption from Contractual, and also Compensation of Expenses in Relation with Alteration of Contractual Volume of Consumption of Electric Energy: Appendix to the Order of FTS of Russia of 21.08.2007 N? 166-?/1 (in the Wording of FTS of Russia Orders of 03.07.2008 N 249-?, ?f 29.05.2009 N? 103-?/1). Accessed from the Reference Retrieval System “Konsul'tantPlyus” Russia. Moscow, 2009 (in Russian).
8. Chicco G. (2012) Overview and Performance Assessment of the Clustering Methods for Electrical Load Pattern Grouping. Energy, 42 (1), 68–80. https://doi.org/10.1016/j.energy. 2011.12.031.
9. Stoimenov L., Davidovic N., Stanimirovic A., Bogdanovic M., Nikolic D. (2016) Enterprise Integration Solution for Power Supply Company Based on GeoNis Interoperabi-Lity Framework. Data & Knowledge Engineering, 105, 23–38 https://doi.org/10.1016/j.datak.2015.10.001.
10. Fonseca J. A., Schlueter A. (2015) Integrated Model for Characterization of Spatiotemporal Building Energy Consumption Patterns in Neighborhoods and City Districts. Applied Energy, 142, 247–265. https://doi.org/10.1016/j.apenergy.2014.12.068.
11. Andersen F. M., Larsen H. V., Boomsma T. K. (2013) Long-Term Forecasting of Hourly Electricity Load: Identification of Consumption Profiles and Segmentation of Customers. Energy Conversion and Management, 68, 244–252. https://doi.org/10.1016/j.enconman.2013.01.018.
12. Andersen F. M., Larsen H. V., Juul N., Gaardestrup R. B. (2014) Differentiated Long Term Projections of the Hourly Electricity Consumption in Local Areas. The Case of Denmark West. Applied Energy, 135, 523–538. https://doi.org/10.1016/j.apenergy.2014.08.075.
13. Turkina O. V. (2011) Development of Methods to Improve the Validity and Reliability of the Calculation and Analysis of Actual Losses and Balances of Electrical Energy in Electrical Networks. Moscow, “STC of Electric Power Industry” JSC. 27 (in Russian).
14. St. Petersburg in 2016. (2017) St. Petersburg, Petrostat Publ. 206 (in Russian).
15. BDEW. Standardlastprofile_2016. Available at: https://www.bdew.de/energie/standardlastprofile-strom/. (Accessed 10 November 2017).
Review
For citations:
Bugaeva Т.M., Novikova O.V. Modern Methods of Urban Energy System Planning. ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations. 2019;62(4):377-387. (In Russ.) https://doi.org/10.21122/1029-7448-2019-62-2-377-387
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