Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid

Aqdas Naz, Nadeem Javaid, Muhammad Babar Rasheed, Abdul Haseeb, Musaed Alhussein and Khursheed Aurangzeb
Additional contact information
Aqdas Naz: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Muhammad Babar Rasheed: Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
Abdul Haseeb: Department of Electrical Engineering, Institute of Space Technology (IST), Islamabad 44000, Pakistan
Musaed Alhussein: Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Khursheed Aurangzeb: Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Sustainability, 2019, vol. 11, issue 10, 1-22

Abstract: In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques.

Keywords: forecasting; solar generation; storage capacity; game theory; nash equilibrium; distributed energy management algorithm; micro grid; meta heuristic techniques (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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