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Erschienen in: Neural Computing and Applications 6/2018

20.08.2016 | Original Article

A projection neural network for optimal demand response in smart grid environment

verfasst von: Yao Yao, Xing He, Tingwen Huang, Chaojie Li, Dawen Xia

Erschienen in: Neural Computing and Applications | Ausgabe 6/2018

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Abstract

This paper presents a smart grid model that fully considers the power consumption types of users and the features of electricity price. A satisfaction function is added into the bill function to balance the user experience of electricity usage and the consumption of load. For the purpose of minimizing the electricity bill of all users, a single-layer projection neural network (PNN) is used, which is proven to be global convergence. And the simulation results reveal that the effectiveness and peculiarities of the proposed PNN.

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Literatur
1.
Zurück zum Zitat Albadi MH, El-Saadany EF (2008) A summary of demand response in electricity markets. Electr Power Syst Res 78(11):1989–1996CrossRef Albadi MH, El-Saadany EF (2008) A summary of demand response in electricity markets. Electr Power Syst Res 78(11):1989–1996CrossRef
2.
Zurück zum Zitat Wang Z, Leng Y, Wu Q (2015) Review of smart grid demand response with clean energy access. Chin J Power Sources 39(8):1798–1800 Wang Z, Leng Y, Wu Q (2015) Review of smart grid demand response with clean energy access. Chin J Power Sources 39(8):1798–1800
3.
Zurück zum Zitat Aghaei J, Alizadeh MI (2013) Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sustain Energy Rev 18:64–72CrossRef Aghaei J, Alizadeh MI (2013) Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sustain Energy Rev 18:64–72CrossRef
4.
Zurück zum Zitat Aalami HA, Moghaddam MP, Yousefi GR (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87(1):243–250CrossRef Aalami HA, Moghaddam MP, Yousefi GR (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87(1):243–250CrossRef
5.
Zurück zum Zitat Rahimi F, Ipakchi A (2010) Demand response as a market resource under the smart grid paradigm. IEEE Trans Smart Grid 1(1):82–88CrossRef Rahimi F, Ipakchi A (2010) Demand response as a market resource under the smart grid paradigm. IEEE Trans Smart Grid 1(1):82–88CrossRef
6.
Zurück zum Zitat Wang J, Liu C, Ton D, Zhou Y, Kim J, Vyas A (2011) Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power. Energy Policy 39(7):4016–4021CrossRef Wang J, Liu C, Ton D, Zhou Y, Kim J, Vyas A (2011) Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power. Energy Policy 39(7):4016–4021CrossRef
7.
Zurück zum Zitat Wang H, Huang T, Liao X, Abu-Rub H, Chen G (2016) Reinforcement learning in energy trading game among smart microgrids. IEEE Transactions on Industrial Electronics. doi:10.1109/TIE.2016.2554079 Wang H, Huang T, Liao X, Abu-Rub H, Chen G (2016) Reinforcement learning in energy trading game among smart microgrids. IEEE Transactions on Industrial Electronics. doi:10.​1109/​TIE.​2016.​2554079
8.
Zurück zum Zitat Chen Z, Wu L, Fu Y (2012) Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans Smart Grid 3(4):1822–1831CrossRef Chen Z, Wu L, Fu Y (2012) Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans Smart Grid 3(4):1822–1831CrossRef
9.
Zurück zum Zitat Li C, Yu X, Yu W, Chen G, Wang J (2016) Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid 99:1–12 Li C, Yu X, Yu W, Chen G, Wang J (2016) Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid 99:1–12
10.
Zurück zum Zitat Wen S, Yu X, Zeng Z, Wang J (2016) Event-triggering load frequency control for multi-area power systems with communication delays. IEEE Trans Ind Electron 63(2):1308–1317CrossRef Wen S, Yu X, Zeng Z, Wang J (2016) Event-triggering load frequency control for multi-area power systems with communication delays. IEEE Trans Ind Electron 63(2):1308–1317CrossRef
11.
Zurück zum Zitat Maharjan S, Zhu Q, Zhang Y, Gjessing S, Basar T (2016) Demand response management in the smart grid in a large population regime. IEEE Trans Smart Grid 7(1):189–199CrossRef Maharjan S, Zhu Q, Zhang Y, Gjessing S, Basar T (2016) Demand response management in the smart grid in a large population regime. IEEE Trans Smart Grid 7(1):189–199CrossRef
12.
Zurück zum Zitat Li C, Yu X, Yu W, Huang T, Liu Z (2015) Distributed event-triggered scheme for economic dispatch in smart grids. IEEE Trans Ind Inform 68:1–11 Li C, Yu X, Yu W, Huang T, Liu Z (2015) Distributed event-triggered scheme for economic dispatch in smart grids. IEEE Trans Ind Inform 68:1–11
13.
Zurück zum Zitat Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renew Sustain Energy Rev 59:166–178CrossRef Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renew Sustain Energy Rev 59:166–178CrossRef
14.
Zurück zum Zitat Nosratabadi SM, Hooshmand RA, Gholipour E (2016) Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy. Appl Energy 59:590–606CrossRef Nosratabadi SM, Hooshmand RA, Gholipour E (2016) Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy. Appl Energy 59:590–606CrossRef
15.
Zurück zum Zitat Siano Pierluigi (2014) Demand response and smart grids—a survey. Renew Sustain Energy Rev 30:461–478CrossRef Siano Pierluigi (2014) Demand response and smart grids—a survey. Renew Sustain Energy Rev 30:461–478CrossRef
16.
Zurück zum Zitat Li N, Chen L, Low SH (2011) Optimal demand response based on utility maximization in power networks. 2011 IEEE Power and Energy Society General Meeting, pp 1–8 Li N, Chen L, Low SH (2011) Optimal demand response based on utility maximization in power networks. 2011 IEEE Power and Energy Society General Meeting, pp 1–8
17.
Zurück zum Zitat Atwa YM, El-Saadany EF, Salama MMA, Seethapathy R (2010) Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 25(1):360–370CrossRef Atwa YM, El-Saadany EF, Salama MMA, Seethapathy R (2010) Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 25(1):360–370CrossRef
18.
Zurück zum Zitat Urias MEG, Sanchez EN, Ricalde LJ (2015) Electrical microgrid optimization via a new recurrent neural network. IEEE Syst J 9(3):945–953CrossRef Urias MEG, Sanchez EN, Ricalde LJ (2015) Electrical microgrid optimization via a new recurrent neural network. IEEE Syst J 9(3):945–953CrossRef
19.
Zurück zum Zitat Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52(3):141–152MATH Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52(3):141–152MATH
20.
Zurück zum Zitat Tank DW, Hopfield JJ (1986) Simple neural optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans Circuits Syst 33(5):533–541CrossRef Tank DW, Hopfield JJ (1986) Simple neural optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans Circuits Syst 33(5):533–541CrossRef
21.
Zurück zum Zitat Li H, Liao X, Chen G, Hill DJ, Dong Z, Huang T (2015) Event-triggered asynchronous intermittent communication strategy for synchronization in complex dynamical networks. Neural Netw 66:1–10CrossRef Li H, Liao X, Chen G, Hill DJ, Dong Z, Huang T (2015) Event-triggered asynchronous intermittent communication strategy for synchronization in complex dynamical networks. Neural Netw 66:1–10CrossRef
22.
Zurück zum Zitat He X, Yu J, Huang T, Li CD, Li CJ (2014) Neural networks for solving Nash equilibrium problem in application of multiuser power control. Neural Netw 57(9):73–78CrossRefMATH He X, Yu J, Huang T, Li CD, Li CJ (2014) Neural networks for solving Nash equilibrium problem in application of multiuser power control. Neural Netw 57(9):73–78CrossRefMATH
23.
Zurück zum Zitat Liu Q, Guo Z, Wang J (2012) A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization. Neural Netw 26:99–109CrossRefMATH Liu Q, Guo Z, Wang J (2012) A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization. Neural Netw 26:99–109CrossRefMATH
24.
Zurück zum Zitat Xia Y, Wang J (2004) A general projection neural network for solving monotone variational inequalities and related optimization problems. IEEE Trans Neural Netw 15(2):318–328CrossRef Xia Y, Wang J (2004) A general projection neural network for solving monotone variational inequalities and related optimization problems. IEEE Trans Neural Netw 15(2):318–328CrossRef
25.
Zurück zum Zitat Hu X, Zhang B (2009) An alternative recurrent neural network for solving variational inequalities and related optimization problems. IEEE Trans Syst Man Cybern Part B (Cybern) 39(6):1640–1645CrossRef Hu X, Zhang B (2009) An alternative recurrent neural network for solving variational inequalities and related optimization problems. IEEE Trans Syst Man Cybern Part B (Cybern) 39(6):1640–1645CrossRef
26.
Zurück zum Zitat He X, Li CD, Huang T, Li CJ, Huang J (2014) A recurrent neural network for solving bilevel linear programming problem. IEEE Trans Neural Netw Learn Syst 25(4):824–830CrossRef He X, Li CD, Huang T, Li CJ, Huang J (2014) A recurrent neural network for solving bilevel linear programming problem. IEEE Trans Neural Netw Learn Syst 25(4):824–830CrossRef
27.
Zurück zum Zitat Li C, Yu X, Huang T, Chen G, He X (2016) A generalized Hopfield network for nonsmooth constrained convex optimization: Lie derivative approach. IEEE Trans Neural Netw Learn Syst 27(2):308–321MathSciNetCrossRef Li C, Yu X, Huang T, Chen G, He X (2016) A generalized Hopfield network for nonsmooth constrained convex optimization: Lie derivative approach. IEEE Trans Neural Netw Learn Syst 27(2):308–321MathSciNetCrossRef
28.
Zurück zum Zitat He X, Huang T, Li C, Che H, Dong Z (2015) A recurrent neural network for optimal real-time price in smart grid. Neurocomputing 149:608–612CrossRef He X, Huang T, Li C, Che H, Dong Z (2015) A recurrent neural network for optimal real-time price in smart grid. Neurocomputing 149:608–612CrossRef
29.
Zurück zum Zitat Zeng Z, Cichocki A, Cheng L, Xia Y, Hu X (2012) Guest editorial special issue on neurodynamic systems for optimization and applications. IEEE Transactions on Neural Networks and Learning Systems 27(2):210–213MathSciNetCrossRef Zeng Z, Cichocki A, Cheng L, Xia Y, Hu X (2012) Guest editorial special issue on neurodynamic systems for optimization and applications. IEEE Transactions on Neural Networks and Learning Systems 27(2):210–213MathSciNetCrossRef
30.
Zurück zum Zitat He X, Li CD, Huang T, Li CJ (2014) Neural network for solving convex quadratic bilevel programming. Neural Netw 51(3):17–25MATH He X, Li CD, Huang T, Li CJ (2014) Neural network for solving convex quadratic bilevel programming. Neural Netw 51(3):17–25MATH
31.
Zurück zum Zitat Cheng L, Hou ZG, Lin Y, Tan M, Zhang WC, Wu FX (2011) Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans Neural Netw 22(5):714–726CrossRef Cheng L, Hou ZG, Lin Y, Tan M, Zhang WC, Wu FX (2011) Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans Neural Netw 22(5):714–726CrossRef
33.
Zurück zum Zitat Xia Y, Leung H, Wang J (2002) A projection neural network and its application to constrained optimization problems. IEEE Trans Circuits Syst I Fundam Theory Appl 49(4):447–458MathSciNetCrossRefMATH Xia Y, Leung H, Wang J (2002) A projection neural network and its application to constrained optimization problems. IEEE Trans Circuits Syst I Fundam Theory Appl 49(4):447–458MathSciNetCrossRefMATH
34.
Zurück zum Zitat Fu W, Jing Z, Luo Z, Huang X, Wu Y (2015) A time-of-use pricing based control scheme for intelligent household appliances. Power Syst Technol 39(3):717–723 Fu W, Jing Z, Luo Z, Huang X, Wu Y (2015) A time-of-use pricing based control scheme for intelligent household appliances. Power Syst Technol 39(3):717–723
35.
Zurück zum Zitat Yang P, Tang G, Nehorai A (2013) A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Trans Power Syst 28(2):884–892CrossRef Yang P, Tang G, Nehorai A (2013) A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Trans Power Syst 28(2):884–892CrossRef
36.
37.
38.
Zurück zum Zitat Friesz TL, Bernstein D, Mehta NJ, Tobin RL, Ganjalizadeh S (1994) Day-to-day dynamic network disequilibria and idealized traveler information systems. Oper Res 42(6):1120–1136MathSciNetCrossRefMATH Friesz TL, Bernstein D, Mehta NJ, Tobin RL, Ganjalizadeh S (1994) Day-to-day dynamic network disequilibria and idealized traveler information systems. Oper Res 42(6):1120–1136MathSciNetCrossRefMATH
Metadaten
Titel
A projection neural network for optimal demand response in smart grid environment
verfasst von
Yao Yao
Xing He
Tingwen Huang
Chaojie Li
Dawen Xia
Publikationsdatum
20.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2532-0

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