Abstract This paper presents a techno-economic model of two interconnected hybrid microgrids (MGs) whose electricity and thermal dispatch strategy are managed with Sequential Least Squares Programming (SLSQP) optimization technique. MGs combine multiple thermal and electric power generation, transmission, and distribution systems as a whole, to gain a tight integration of weather-dependent distributed renewable generators with multiple stochastic load profiles. Moreover MGs allow to achieve an improvement in the return of investment and better cost of energy. The first part of the work deals with a method to obtain an accurate prediction of climate variables. This method makes use of Fast Fourier Transform (FFT) and polynomial regression to manipulate climate datasets issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). The second part of the work is focused on the optimization of interconnected MGs operations through the SLSQP algorithm. The objective is to obtain the best financial performance (IRR) when clean distributed energy resources (DERs) are exchanging both thermal and electric energy. SLSQP optimizes the energy flows by balancing their contribution with their nominal Levelized Cost of Energy (LCOE). The proposed algorithm is used to simulate innovative business scenarios where revenue streams are generated via sales of energy to end users, sell backs and deliveries of demand response services to the other grids. A business case dealing with two MGs providing clean thermal and electric energies to household communities nearby the city of Bremen (Germany) is examined in the last part of the work. This business case with a payback in two years, an internal rate of return (IRR) at 65% and a LCOE at 0.14 €/kWh, demonstrates how the interconnection of multiple hybrid MGs with SLSQP optimization techniques, makes renewable and DERs outcompeting and could strand investments in fossil fuel generation, shaping the future of clean energy markets.