Abstract
A cloud-fog computing-based event-triggered distributed energy optimization management method based on predictive attack compensation is proposed to address the problem of denial of service (DoS) attack, the complexity of computation, and the bandwidth constraint on the communication network in microgrids. Firstly, in order to optimize the energy supply of microgrid and maximize the profit, the minimum cost function of maintaining the balance of supply and demand is given considering the power loss of microgrid. Secondly, considering the problem of bandwidth-constrained communication, a distributed event-triggered consensus algorithm is proposed based on fog computing. Thirdly, a model predictive compensation algorithm based on cloud computing is proposed, which uses the mismatched power between supply and demand at the historical time before the attack to predict and compensate the missing data of the agent power at the current time and many times after attack. Finally, the effectiveness of the proposed method is verified by simulation results.
Highlights
IntroductionDue to the energy structure adjustment caused by the slowly draining away of traditional energy, microgrids that integrate traditional energy (electric generator), renewable energy (wind energy and photovoltaic power), energy storage device (battery), and load and control equipment into a compositive grid system have become an important way to improve energy efficiency and reduce energy consumption
Due to the energy structure adjustment caused by the slowly draining away of traditional energy, microgrids that integrate traditional energy, renewable energy, energy storage device, and load and control equipment into a compositive grid system have become an important way to improve energy efficiency and reduce energy consumption
To address the microgrid’s energy optimization management problem in the case of denial of service (DoS) attack, this paper proposes a cloud-fog computing based distributed eventtriggered consensus energy optimization management method based on predictive attack compensation. e main work is as follows: (1) is paper proposes a distributed event-triggered consensus predictive compensation algorithm. e communication bandwidth of the system is reduced by the event-triggered method, and the missing data of microgrid is predicted by the prediction algorithm
Summary
Due to the energy structure adjustment caused by the slowly draining away of traditional energy, microgrids that integrate traditional energy (electric generator), renewable energy (wind energy and photovoltaic power), energy storage device (battery), and load and control equipment into a compositive grid system have become an important way to improve energy efficiency and reduce energy consumption. To solve the problem of energy management in the microgrid, a distributed economic dispatch algorithm with communication delay is proposed in [3], and the coupling equality constraint is considered. Most cloud computing models use centralized management methods to transmit and deal with data, which makes them unable to meet the requirements of the system boundary area for the minimum delay in real-time and semi-real-time applications and the model is limited by the bandwidth of communication. These methods have the problems of large energy consumption and insufficient use of computing capability. (2) is paper proposes a three-layered cloud-fog-object smart grid data processing architecture, which uses the cloud computing model to calculate the prediction data of the smart grid and the fog computing model to calculate the consistency of the smart grid to obtain the energy supply-demand balance of the smart grid as well as the minimum cost of optimization
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