AUTHORS: Zaid Bari, Majid Ben Yakhlef

Download as PDF

ABSTRACT: In this work, a multi-agent system implementing a new incentive-based demand response model (MAS-IBDR) is designed to help the Grid Manager (GM) to find a balance between energy produced and demand during peak hours. The proposed approach adopts the negotiation model of the game theory, where a stackelberg game with two interaction loops is formulated to capture interactions between the actors of this hierarchical market (Generator, Grid Manager (GM), Charge Aggregators (CA) and end Users (Us)) having an oligopolistic structure in order to reduce costs required to compensate the resource deficiency. The Grid Manager launches an incentive offer to sell a demand reduction from the Charge Aggregators, which trigger a trading routine with their registered Users to encourage them to reduce their consumption and receive in return an award. From this negotiation process based on game theory, an optimal solution of stackelberg equilibrium is obtained. The simulation results confirm that the proposed approach is effective in offsetting the deficiency of system resources at minimum cost during peak hours.

KEYWORDS: - Multi Agent System; Game theory; Stackelberg duopoly; Oligopolistic Market; Demand Response; Incentive-Based Demand Response.

REFERENCES:

[1]. Zhong H, Xia Q, Xia Y, Kang C, Xie L, He W, et al. : ‘Integrated dispatch of generation and load: a pathway towards smart grids’. Electric Power Syst Res 2015;120:206–13.

[2]. K. H. S. V. S. Nunna and S. Doolla, “Responsive End-User-Based Demand Side Management in Multimicrogrid Environment,” IEEE Trans. Ind. Inform., vol. 10, no. 2, pp. 1262– 1272, May 2014.

[3]. Ghasemi A, Shayeghi H, Moradzadeh M, Nooshyar M. :’ A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management’. Appl Energy 2016;177:40–59.

[4]. Siano P, Sarno D. :’Assessing the benefits of residential demand response in a real time distribution energy market’. Appl Energy 2016;161:533–51.

[5]. D. T. Nguyen, M. Negnevitsky, and M. de Groot.: ‘Pool-Based Demand Response Exchange Concept and Modeling’,IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1677–1685, Aug. 2011.

[6]. Sarker MR, Ortega-Vazquez MA, Kirschen DS. :’ Optimal coordination and scheduling of demand response via monetary incentives’. IEEE Trans Smart Grid 2015; 6: 1341–52.

[7]. Aalami HA, Moghaddam MP, Yousefi GR. :’Demand response modeling considering Interruptible/Curtailable loads and capacity market programs’, Appl Energy 2010; 87: 243–50.

[8]. Haiwang Z, Le X, Qing X. :’ Coupon incentivebased demand response: theory and case study’, IEEE Trans Power Syst 2013; 28: 1266–76.

[9]. Fotouhi Ghazvini MA, Soares J, Horta N, Neves R, Castro R, Vale Z. : ’ A multiobjective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers’, Appl Energy 2015;151:102–18.

[10]. Ma J, Deng J, Song L, Han Z.: ‘ Incentive mechanism for demand side management in smart grid using auction’, IEEE Trans Smart Grid 2014;5:1379–88.

[11]. Meng F-L, Zeng X-J. : ‘A Stackelberg gametheoretic approach to optimal realtime pricing for the smart grid’, Soft Comput 2013; 17: 2365–80.

[12]. Maharjan S, Quanyan Z, Yan Z, Gjessing S, Basar T. : ‘ Dependable demand response management in the smart grid: a Stackelberg game approach’, IEEE Trans Smart Grid 2013; 4:120–32.

[13]. Shen J, Jiang C, Li B. : ‘ Controllable load management approaches in smart grids’, Energies 2015;8:11187–202.

[14].

[14]. “An Iterative On-Line Auction Mechanism for Aggregated Demand-Side Participation - IEEE Journals & Magazine.”

[Online]. Available: http://ieeexplore.ieee.org/document/7182785/.

[Accessed: 28-Mar-2018].

[15]. Adika CO, Lingfeng W. : ‘ Demand-side bidding strategy for residential energy management in a smart grid environment’, IEEE Trans Smart Grid 2014;5:1724–33.

[16]. M. Rahmani-andebili.: Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets’, Electr. Power Syst. Res., vol. 132, pp. 115–124, Mar. 2016.

[17]. “Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid - IEEE Journals & Magazine.”

[Online]. Available: https://ieeexplore.ieee.org/document/5628271/.

[Accessed: 22-May-2018].