Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure

Abstract

This paper proposes a central energy management system (EMS) in smart buildings. It is based on the coalition method for optimal energy sharing between smart buildings. Game theory is applied to obtain an optimal allocation of the building's surplus energy on the deficient energy buildings using the Shapley value, which enables the unequal energy distribution based on the energy demand. The main objective is reducing energy waste while preserving the generation/demand balance. The fog platform with memory storage is applied, which handles all the measured data from the smart buildings through Wi-Fi-based communication protocol and performs the EMS program. The smart meter links the smart buildings with the fog-based EMS central unit. Two scenarios are implemented based on the difference between total deficient and surplus energy. Coalition game theory is applied for optimal surplus energy allocation on deficient buildings when the total energy surplus is lower than the total energy deficient. Also, there is a one-to-one relationship between the surplus and deficient building; if the surplus energy is larger than the deficit, the extra surplus energy is stored for further usage. The proposed EMS is applied and tested using a smart city with 10 buildings in the MATLAB program. A comparison between the result obtained with and without applying the proposed method is performed. The performance of the fog platform is introduced based on the run and delay time and the memory size usage. The results show the effectiveness of the proposed EMS in a smart building.

Self-sufficient energy communities using cooperative games over blockchain and smart contracts

“…In literature the management of peer-to-peer energy trading among energy entities of local energy systems with games theory is mostly addressed using non-cooperative or coalitionbased games [13], [16], [19].…”

“…A stochastic programming approach with Conditional Value at Risk is used to address the uncertainty of renewable energy. In their work, Saeed et al [19] propose a central energy management system for buildings that aims to optimize energy sharing, reduce energy waste, and maintain the balance between generation and demand. The system employs coalition game theory, utilizing the Shapley value to allocate surplus energy to deficient buildings.…”

“…The Build Coalition contract is deployed using its constructor (lines [11][12][13][14][15][16][17][18][19][20]. The selling offer is stored in the contract state, and the coalition state is set to ACCEPTING_BIDS.…”

“…The smart contract determines the additional flexibility for the bids with prosumers that have energy deficit, the tokens are increased proportional, and the token distribution function for the coalition is updated (lines 11-13) and must remain positive. If the constraints are met the buy order is accepted in the coalition (lines [15][16][17][18][19]. If the prosumer is in a self-sufficient state, flexibility is used to increase its consumption to complete the remaining energy surplus (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36).…”

“…Most approaches in the literature address the creation of coalitions of prosumers on-demand by linking them to energy transactions in a profit-driven manner [17], [18], [19]. Only a few of them consider the blockchain as an overlay for information sharing and peer-to-peer energy trading implementation.…”