Distributed Constraint Optimization Applications in Power Networks
Adrian Petcu, Boi V. Faltings · 2008
In recent years the electricity sector has undergone a number of changes that all point in the direction of liberalization and decentralization of control. However, a number of technological challenges have to be addressed before the desired degree of de- centralization is obtained. Currently, most of the decisions about the operation of a power system are still made in control centers in a centralized fashion. We present in this paper two applications in this context, and show how they benefit from the Distributed Artificial Intelligence techniques we have developed. The first application is a distributed scheduling application, where several power plant operators schedule preventive maintenance on their generation units in a distributed fashion. This method does not require the centralization of private data of the power plant operators (like available capacities, internal maintenance schedules, maintenance and operation costs, etc). Fur- thermore, the method guarantees globally optimal schedules while observing the power generation demand at all times. The second application is the distributed (re)configuration of power networks, where one seeks to optimally configure the network to e.g. minimize line losses, or restore power to a maximal number of customers after a line failure. The problems are modeled as distributed constraint optimization problems (DCOP). DCOP is a powerful paradigm for solving nu- merous tasks in distributed AI, like planning, scheduling, resource allocation. The algorithm that we use is a complete method for distributed constraint optimization, based on dynamic programming. It requires a linear number of messages , whose maximal size depends on a parameter of the constraint graph, called induced width. This makes our algorithm well suited for large but loose problems. We present a number of interesting extensions of the basic method that show promise for applications in power systems well beyond the two examples we present here.