A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid

Abstract

Recently, multi-agent systems (MASs) have received attention due to the consideration of distributed optimization and control in blockchain (BC) based energy trading applications. However, due to the dynamic behavior of uncertain and random variables, the coordination and control of MAS are still challenging to attain resilience and dynamicity. Traditional trust systems, which rely on access control and cryptography, cannot deal with dynamic behavior of agents. Furthermore, they are inefficient in addressing the computational overhead of cryptographic primitives. To overcome these limitations, this work proposes a BC-based trusted suite (TS) for MAS to handle privacy and anonymity issues during energy trading in a smart grid (SG). In this work, three objectives are simultaneously achieved: trust, cooperation, and confidentiality. Firstly, the proposed trust system is employed to perform trust credibility of agents based on trust deformation, coherence, and stability. The credibility evaluation is used to determine the dynamic behavior of agents and to detect dishonest agents in the system. Secondly, a tri-tit-for-tat (TTFT) repeated game approach is used to improve the cooperation among agents. The proposed strategy is more forgiving than the existing Di-TFT (DTFT) and TFT techniques. It motivates scammers and deceptive agents to regain their trust by cooperating in three consecutive rounds of a game. Furthermore a proof-of-cooperation (PoC) consensus mechanism is introduced to facilitate agent cooperation in block creation and validation. Thirdly, the publicly verifiable secret sharing (PVSS) technique is introduced to preserve the privacy of the agents. Unlike VSS, PVSS provides the immunity against different types of security threats. Where, the dealer agent maintained the trust, while the verification of the dealer and combiner is maintained within agent-to-agent cooperation. Simulation results show that the devised BTS model is superior to the existing benchmark model such as fuzzy logic trust (FLT) in terms of detecting the cheating and deceptive behavior of agents in the system. Besides, the devised TTFT allows cheating agents to effectively regain the trust if they cooperate thrice in a row as compared to the existing DTFT and TFT strategies. Furthermore, This study analyzes two trust-related attacks: bad-mouthing and on-off. Analysis shows that the proposed system is protected from trust related attacks.