Neuro-Fuzzy Logic Applications for Grid Energy Management

Cooper R. Wade^{1, 2,} , Daniel Kelly-B Danquah¹, Hossein Salehfar¹ and Olusegun S. Tomomewo¹

¹Institute for Energy Studies, University of North Dakota, Grand Forks, USA

²Grid Power, LLC, Little Rock, USA

Cite this paper:
Cooper R. Wade, Daniel Kelly-B Danquah, Hossein Salehfar and Olusegun S. Tomomewo. Neuro-Fuzzy Logic Applications for Grid Energy Management. American Journal of Systems and Software. 2023; 6(1):1-10. doi: 10.12691/ajss-6-1-1

Abstract

Grid management is becoming increasingly complex due to continuous growth in consumer demand and distributed energy resources (DERs). These two rapidly changing areas present a unique challenge when taking into consideration the mission of the United States Department of Energy being to ensure energy security through efficient, environmentally friendly, reliable, and affordable electric energy [1]. As consumer demand and DERs continue to saturate the electric grid, the problem that arises is data measurement and processing. The traditional electric grid had a one-way flow of power, whereas the modern electric grid can have bi-directional power flow via DERs and other load resources such as curtailment, load shifting programs, energy storage, to name a few. This new and rapidly developing electric grid has many more data points that will need to be processed due to the exponentially growing amount of energy resources throughout it. This means that more sophisticated management software is needed to help balance an increasingly complex electric grid. This paper will expand on traditional load management technology compared to the new concepts of neuro-fuzzy logic system managing the grid. A neuro fuzzy logic-based energy management system could be a new option to use for grid management and planning. This would allow grid operators to add additional decision processing capabilities. In this manner, the grid would be able to make complex decisions across the network rather than in specifically localized areas. This capability would allow grid operators the ability to make more efficient and reliable decisions since the system is able to process more data. This additional data could open options for the network to utilize complex responses to grid scenarios by utilizing more resources available to the network. The critical concept here is to allow more data to be processed, therefore opening more resources to be used creatively for complex grid response.

Keywords:
artificial intelligence neural network fuzzy logic controls energy management grid management grid modernization

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References:

[1]	U.S. Department of Energy. (n.d.). About Us: Department of Energy. https://www.energy.gov/about-us#:~:text=The%20mission%20of%20the%20Energy,Learn%20more.
[2]	Fathy A, Kassem AM. Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine. ISA Trans 2019; 87: 282-96 Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine – ScienceDirect.
[3]	Eshetu W, Sharma P, Sharma C. ANFIS based load frequency control an isolated micro grid. In: 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon; 2018. p. 1165-70.
[4]	Limitation of PID controllers https://www.academia.edu/37038423/LIMITATIONS_OF_PID_CONTROLLERS.
[5]	Prasanna Shrestha, Future Tech, August 27,2019The Advantages and disadvantages of Neural Networks, https://gkstuffs.com/future-tech/advantages-and-disadvantages-of-neural-networks/.
[6]	W. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulltin of Mathematical Biophysics, vol. 7, pp. 115-133, 1943.
[7]	Madhiarasan, M. & Louzazni, M. (April 18, 2022). Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications. Hindawi: Journal of Electrical and Computer Engineering. https://downloads.hindawi.com/journals/jece/2022/5416722.pdf.
[8]	L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.
[9]	D. O. Hebb, 1e Organization of Behavior, Wiley & Sons, New York, NY, USA, 1949.
[10]	F. Rosenblatt, “+e Perceptron — a perceiving and recognizing automaton,” Report 85–460–1, Cornell Aeronautical Laboratory, New York, NY, USA, 1957.
[11]	F. Rosenblatt, Principles of Neurodynamics, Spartan Books, Washington, DC, USA, 1962.
[12]	B. Widrow and M. E. Hoff, “Adaptive switching circuits,” 1960 IRE WESCON Convention Record, pp. 96-104, 1960.
[13]	B. Widrow, “Generalization and information storage in networks of Adaline’ neurons,” in Self-Organizing Systems 1962, M. C. Yovitz, G. T. Jacobi, and G. Goldstein, Eds., Article ID 435461, Spartan Books, Washington, DC, USA, 1962.
[14]	J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the National Academy of Sciences of the USA, vol. 79, no. 8, pp. 2554-2558, 1982.
[15]	D. E. Rumelhart, G. R. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition (V1 and V2), D. E. Rumulhart and J. L. McClelland, Eds., vol. 1, MIT Press, Cambridge, MA, USA.
[16]	L. O. Chua and L. Yang, “Cellular neural networks: theory,” IEEE Transactions on Circuits and Systems, vol. 35, pp. 1257-1272, 1988.
[17]	C. Cortes and V. N. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[18]	W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity”, Cambridge University Press, Cambridge, MA, 2002.
[19]	G. E. Hinton, “A practical guide to training restricted Boltzmann machines,” in Neural Networks: Tricks of the Trade, G. Montavon, G. B. Orr, and K. R. M¨uller, Eds., vol. 7700, Springer, Berlin, Germany, 2012.
[20]	Lithmee. (August 14, 2019). What is the Difference Between Fuzzy Logic and Neural Network. Peddia.com. https://pediaa.com/what-is-the-difference-between-fuzzy-logic-and-neural-network/#: ~:text=The%20main%20difference%20between%20fuzzy,human%20brain%20to%20perform%20computations.
[21]	Encyclopaedia Britannica. (n.d.). Physiology. https://www.britannica.com/science/information-theory/Physiology.
[22]	Dintsis, Danil. (2014). Re: What is the maximum number of variables that can be handled by fuzzy inference system? Retrieved from: https://www.researchgate.net/post/What_is_the_maximum_number_of_variables_that_can_be_ handled_by_fuzzy_inference_system/5461ea41cf57d72b058b461d/citation/download.
[23]	Comparison of fuzzy PID controller with conventional PID controller in controlling the speed of brushless DC Motor, S. sunsith, Lizi Joseph, M. Saritha http://www.ieejournal.com.
[24]	Goldthau, A. (2017). The handbook of global energy policy. Wiley Blackwell. https://www.quantamagazine.org/artificial-neural-nets-finally-yield-clues-to-how-brains-learn-20210218/.
[25]	Eibagi, A. (2014). Big Data Analysis Using Neuro-Fuzzy System. San Jose State University. https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1367&context=etd_projects.
[26]	Gilstrap, M., Amin, S., & Decorla-Souza, K. (July 2015). United States Electricity Industry Primer. U.S. Department of Energy: Office of Electricity Delivery and Energy Reliability. https://www.energy.gov/sites/prod/files/2015/12/f28/united-states-electricity-industry-primer.pdf.
[27]	Hansen, D. (July 24, 2008). IEEE 519 misapplications— Point of common coupling issues. IEEE. https://ieeexplore.ieee.org/document/4596868.
[28]	Richmond-Crosset, K. & Greene, Z. (September 30, 2022). How Distributed Energy Resources Can Lower Power Bills, Raise Revenue in US Communities. World Resources Institute. https://www.wri.org/insights/distributed-energy-resources-explained-us.
[29]	B. Ruprecht et al., “Possibilistic Clustering Enabled Neuro Fuzzy Logic,” 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, pp. 1-8.
[30]	Curry, Bruce and Peter H. Morgan. “Evaluating Kohonen's learning rule: An approach through genetic algorithms.” Eur. J. Oper. Res. 154 (2004): 191-205.
[31]	Center for Climate and Energy Solutions. (n.d.). Renewable Energy. https://www.c2es.org/content/renewable-energy/.
[32]	Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (January 28, 2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Elsevier: Computer Networks. https://www.sciencedirect.com/science/article/abs/pii/S1389128618307771.
[33]	Ananthaswamy, A. (February 18, 2021). Artificial Neural Nets Finally Yield Clues to How Brains Learn. Quanta Magazine.
[34]	British Broadcasting Corporation. (n.d.). Electric circuits – AQA. https://www.bbc.co.uk/bitesize/guides/zgvq4qt/revision/3#: ~:text=Potential%20difference%20is%20a%20measure,two%20points%20in%20a%20circuit.
[35]	Nazar AA, Jayabharath R, Udayakumar MD. An ANFIS based advanced MPPT control of a wind-solar hybrid power generation system. IREMOS 2014; 7: 638.
[36]	Real-life and business Application of Neural Networks. Real-Life Applications of Neural Networks | Smartsheet.