A Sequential Linear Programming algorithm for economic optimization of Hybrid Renewable Energy Systems

Combining renewable energy sources, as photovoltaic arrays (PV), wind turbine (WT), biomass fuel generators (BM), with back-up units to form a Hybrid Renewable Energy System (HRES) can provide a more economic and reliable energy supply architecture compared to the separate usage of such units. In this work an optimization tool for a general HRES is developed: it generates an operating plan over a specified time horizon of the setpoints of each device to meet all electrical and thermal load requirements with possibly minimum operating costs. A large number of devices, such as conventional and renewable source generators, mandatory and deferrable/adjustable electrical loads, batteries, combined heat and power configurations are modeled with high fidelity. The optimization tool is based on a Sequential Linear Programming (SLP) algorithm, equipped with trust region, which is able to efficiently solve a general nonlinear program. A case study of a real HRES in Tuscany is presented to test the major functionalities of the developed optimization tool.