A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

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 peerto-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 nearreality simulations., m Value of the average and variance. cThe injected malicious data.Attack time.

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

“…Figure 5 shows the single line schematic of IEEE 24-bus grid. The technical characteristics of smart grid and microgrid are available in [40][41][42]. Also, Table 2 expresses some parameters related to each bus that is a criterion for assessing the accuracy of results.…”

“…Step2: this stage is aimed to obtain the new KT (KT new r ) matrix. First, the W j the matrix needs to be updated by (40) using the w j matrix components. By considering the w 1 as the best option in the earlier iteration, W 2 initializes this step as stated in (19).…”

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges

“…Detection algorithm that requires less data transfer than most existing MAS-based load distribution studies to detect global microgrid information [ 122 ]. The rain flow counting algorithm is used to determine the number of cycles of the battery, but the optimization problem is solved using the particle mass optimization (PSO) algorithm [ 123 ]. MAS deals as a self-organized system that is very intelligent controlling multiple agents at a single time.…”

A Multi-Layer Security Scheme for Mitigating Smart Grid Vulnerability against Faults and Cyber-Attacks

“…As expressed in reference [31], considering the basic structure and approach of both the blockchain technology and the RL method, it is expected that the proposed framework can be acceptably able to cover attack types related to unauthorized access, interception, distributed denial of service (DDOS) and the balanced attack. To realize the challenges expressed in this paper, Table 1 shows the main differences between the recent investigations and the suggested approach in detail [7,26,[32][33][34]. As it can be seen, the authors in [31] have tried to provide a security platform for the smart grid without considering the network's vulnerable points.…”