Renewable Energy System Design for Residential Customers
نویسنده
چکیده
This report proposed a genetic algorithm (GA) based stochastic optimization method for gridconnected residential renewable energy (solar/wind) system layout design and module selection. With the customer input of housing location, electricity demand, default system input of location-specified historical weather data, PV/inverter/turbine module database, forecast model for electricity retail/wholesale rate, the optimization algorithm is expected to automatically provide different investment plans to meet different customer preferences. The hourly renewable energy supply model is developed with SAM Simulation Core (SSC) Software Development Kit (SDK) API. The cost function considered stochastic factors for monthly electricity demand and retail/wholesale rate variations. Different grid-connected electricity policies can also be embedded into the model. The preliminary test on solar energy system design for a typical family in Phoenix demonstrated the feasibility and robustness of the model and optimization algorithm. The expectation on proposing several sub-optimal strategies to meet different customer investment preferences is achieved and the results are practically realistic according to the common sense.
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