Abstract
Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.
Highlights
The associate editor coordinating the review of this manuscript and approving it for publication was Behnam Mohammadi-Ivatloo
In order to meet the consumers’ needs, plays a more important role in almost every microgrid in the power system, and energy markets tempt agents that never get much worried about sales beyond their inner customers
This paper investigates and formulates an appropriate RCI method based peer to peer energy trading scheme for three microgrids connected in form of peer to peer structure
Summary
Is a software platform for achieving a consensus. Due to the changing grid structure in the past decade, lack of diagnosis of FDIA can damage the system the power energy trade is going up at a startling pace. In [34], researchers have formulated the online attack detection based on the reinforcement learning (RL) method In this investigation, the effective data in a consensus of P2P energy trading are sifted by the proposed algorithm it is broadcasted. This article investigates and proposes an effective P2P energy trading framework equipped by a security platform based on the IPS-RL method against malicious cyber-attacks. CYBER ATTACK DETECTION APPROACH BASED ON THE PROPOSED IPS-RL SCHEME The growing occurrence of malicious attacks in the cyberphysical systems (CPSs) is one of the main reasons to propose different detection methods In this regard, the CPSs need to develop their communications with the use of the detection technologies in order to preserve actual data against cyberattacks. Where index κ is described as the change-time for injecting false data in the system
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