Decentralised coordination of power system operation using dualevolutionary programming

This paper proposes a decentralised coordination framework for operating power systems with dispersed generation and energy storage. It then develops a novel decentralised approach for calculating the system optimal operation of resources with intertemporal links. The method combines elements of dynamic programming with evolutionary programming. Each power system resource evolves a 'future benefit' function that describes the impact of its own possible decisions on future power system operation. This 'dual evolutionary programming' approach can handle complex resource models and objective functions. It is shown to be computationally faster than discrete dynamic programming for a number of power system problems with multiple storage resources