Ensuring energy balance for sudden demand changes in smart grids

Abstract

With the increasing diversity in the use of electrical energy and the inclusion of renewable electricity power plants in the system, more efficient and more controllable electrical power systems are provided by smart grid technologies. Load balance based on pricing in electrical power systems; It is a strategy that takes into account customer demands, enables customers to use electrical energy at the most attractive price, and provides suppliers with the desired profit, that is, in summary, it coordinates the efficient operation of energy systems. In this study; While load balance is created with smart grid technology, demand-supply-price balance is achieved by using an optimum Fuzzy Logic PI controller within a closed-loop electric power control system model. Within the scope of the study, the simulation results obtained by applying 6 different demand scenarios to the power system model consisting of different types of generation resources were given graphically and analyzed. The different demand scenarios applied include difficult demand conditions such as possible sudden increase or decreases in demand in the electrical power system.

Keywords

• Smart grids
• Load balancing
• Electric energy pricing
• Fuzzy logic PI controller

References

1. [1] U.S. Department of Energy, Grid 2030: A national vision for electricity’s second 100 years, Department of Energy Technical Report, 2003.
2. [2] Hadjsaid N, Sabonnadiere JC. Smart Grids, Wiley-ISTE, New Jersey, 2012.
3. [3] He J, Zhao C, Cai L, Cheng P, Shi L. Practical closed-loop dynamic pricing in smart grid for supply and demand balancing. Automatica 2018; 89: 92-102.
4. [4] Kakran S, Chanana S. Smart operations of smart grids integrated with distributed generation: A review. Renewable and Sustainable Energy Reviews 2018; 81(1): 524-535.
5. [5] Eissa MM. First time real time incentive demand response program in smart grid with “i-energy” management system with different resources. Applied Energy 2018; 212: 607-621.
6. [6] Alagoz BB, Kaygusuz A, Akcin M, Alagoz S. A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market. Energy 2013; 59: 95-104.
7. [7] Alagoz BB, Kaygusuz A. Dynamic energy pricing by closed-loop fractional-order PI control system and energy balancing in smart grid energy markets. Transactions of the Institute of Measurement and Control 2016; 38(5): 565–578.
8. [8] Alquthami T, Ghrayeb A, Milyani AH, Awais M, Rasheed MB. An incentive based dynamic pricing in smart grid. Sustainability 2021; 13: 6066.

9. [9] Albogamy FR, Zakria M, Khan TA, Murawwat S, Hafeez G, Khan I. An optimal adaptive control strategy for energy balancing in smart microgrid using dynamic pricing. IEEE Access 2022; 10: 37396 – 37411.
10. [10] Tsao Y, Thanh V, Lu J. Multiobjective robust fuzzy stochastic approach for sustainable smart grid design. Energy 2019; 176: 929-939.
11. [11] Al-Mousaa A, Fazab A. A fuzzy-based customer response prediction model for a day-ahead dynamic pricing system. Sustainable Cities and Society 2019; 44: 265-274.
12. [12] Alagoz BB, Kaygusuz A. Kapalı çevrim kesir dereceli PI kontrolör ile dinamik enerji fiyatı kontrolü ve akıllı şebekelerde otomatik enerji arz-talep dengelemesi uygulaması (in Turkish), TOK Proceedings, Kocaeli/Turkey, 11-13 September 2014.
13. [13] Alagoz BB. Değişken üretim ve değişken talep koşullarında akıllı şebekelerde enerji dengeleme (in Turkish), PhD Thesis, Inonu University, Graduate School of Natural and Applied Science, 2015.