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There is growing world-wide and Australian interest in the greater potential role of distributed generation and demand-side resources within the electricity industry. These distributed resources can offer promising economic and environmental benefits for power system operation. There are considerable challenges, however, in developing modelling tools that can explore the operational value of such resources within restructured electricity industries. This paper describes a dual evolutionary programming approach where software agents for power system resources co-evolve optimal operational behaviours over repeated power system simulations. The tool is applied to a simple case study exploring the potential operational synergies between significant PV penetrations and distributed energy storage options including controllable loads. The case study demonstrates this tool's capabilities in modelling the potentially complex operational behaviours of these distributed resources including stochastic PV outputs and loads with varying daily demand profiles, thermal energy storage, charging and discharging constraints and self-leakage.