| Summary: | Dynamic real-time power systems like the national power grid operate in continuously changing environments such as adverse weather conditions, power line malfunctions, device failures, etc. These disruptions can lead to different fault conditions in the power system, ranging from a local outage to a cascading failure of global proportions. It is vital to be able to guarantee that all consumers with critical loads won’t be seriously affected when these outages occur, and to also be able to detect potential faults early on, to prevent them from spreading and creating a generalized failure. In order to achieve this, the power grid must be able to perform intelligent behavior to adapt to ever changing conditions and also to self-heal itself in the event that a fault condition occurs.
The Smart Grid must continuously monitor its own status and if an abnormal state is detected, it must automatically perform corrective actions to restore the grid to a healthy state. Due to the large scale and complexity of the Smart Grid, anticipating all possible scenarios that lead to performance lapses is difficult [2]. There is a high degree of uncertainty in accurately estimating the impact of disruptions on the reliability, availability and efficiency of the power delivery system. The use of simulation models can promote trust in Smart Grid solutions in safe and cost effective ways.
In this work, we first present an innovative framework that can be used as a design basis when implementing agent based simulations of the smart grid. The framework is based on two primary concepts. First, the electrical grid system is separated into semi-autonomous units or micro-grids, each with their own set of hierarchically organized agents. Second, models for automating decision-making in the grid during crisis situations are independently supported, allowing simulations that can test how agents respond to the various scenarios that can occur in the smart grid using different decision models. Advantages of this framework are scalability, modularity, coordinated local and global decision making, and the ability to easily implement and test a large variety of decision models.
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