A robust optimization approach for capacity configuration of solar towns considering supply-demand uncertainties

Abstract

The energy system of solar town is a comprehensive energy system with the photovoltaic as the main body, combined with other local energy sources. To fully utilize the solar energy and geothermal energy in such system, a two-stage robust optimization method considering uncertainties of both energy supply and demand is proposed. Uncertainty adjustment parameters are introduced to avoid sacrificing economic efficiency in order to ensure power supply reliability. A max-min-max two-stage robust optimization configuration model is established with the objective of minimizing the overall system cost. The optimization includes the allocation of photovoltaics, combined heat and power, ground source heat pumps and storage in the integrated energy system of the solar town. The uncertainty of photovoltaic and load data is described by a box-type uncertainty set independent of probability distribution. The upper and lower boundary interval as robust constraints represents the fluctuation range of photovoltaic and load. The column and constraint generation algorithm and strong duality transformation are employed to reduce the complexity level of solving the problem. A case study is conducted on a solar town located in northern China. By adjusting the uncertainty adjustment parameters, the conservatism of the configuration plan can be effectively controlled. The scheme has strong applicability, mainly embodied in not only ensuring the reliability of power supply, reducing the load power loss rate, but also reducing the configuration cost and improving the light rejection rate.

Keywords

• solar town
• integrated energy system
• robust optimization
• uncertainties
• capacity configuration
• column and constraint generation algorithm