Abstract

A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem is the operating cost of conventional units, the cost of wind power generation, and the cost of overhauling units; the constraints considered mainly include system constraints and overhaul constraints. The solution method of combinatorial optimization is analyzed, and the genetic optimization algorithm adopted in this paper is selected and discussed. According to the characteristics of the system, various loads are modeled, and power supply constraints are considered. By establishing an effective objective function, the adjustable load scheduling problem is transformed into a solvable optimal control problem. Taking into account the uncertain factors in the system, the advantage of the real-time control system is that it can realize the dynamic update scheduling of the load, so it is more in line with the requirements of the actual system. The real-time algorithm proposed in the paper is based on a distributed control strategy, which can not only realize dynamic compensation for random fluctuations in renewable energy power generation but also satisfy the load curve optimization under the premise of making full use of power supply resources. In addition, simulation experiments compare the load dispatching capabilities of the proposed algorithm with the existing algorithms, thereby verifying the performance of the proposed method.

Highlights

  • A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. e centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids

  • In the actual environment, when a multiagent system is running, each agent will have a time delay due to the distance between each agent when receiving information sent by other agents [7]. e safe and stable operation of the power system needs to consider the influence of the communication system. e time delay between smart grid multiagent systems often affects the dynamic performance of the multiagent system, which may reduce the convergence speed of the system, and more seriously, it may make the system unstable [8]

  • Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. e objective function of the collaborative optimization problem is the operating cost of conventional units and the cost of wind power generation. e constraints considered mainly include two parts: system constraints and maintenance constraints

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Summary

Analysis of Smart Grid System Resilience

Smart grid covers a wide range, including basic systems for power transmission and distribution, and integrates many high-tech technologies such as artificial intelligence technology, digital technology, sensor technology, information technology, and communication technology. E power plants scattered over a large area generate electricity, which is boosted by high-voltage substations, and passed through high-voltage transmission lines, step-down substations, and smart grids to users. Smart dispatch is an important manifestation of the technical and application level of the smart grid. It is the key to quickly improve the power grid’s ability to accept clean energy and is the only way to build a smart grid.

Analysis of Smart Grid Failure Rate
Smart Grid Coordinated Dispatch Optimization Modeling
Simulation Experiment and Analysis
Conclusion
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