Power grids have become a key component in the energy transition and decarbonization of industries due to the increasing electrification of different loads, such as heating. This paper presents the overview and validation of a new method for electricity retailers or Virtual Power Plant operators to match their production portfolio dominated by Renewable Energy Resources (RES) with electrical demand of thermal loads (heat pumps) via a price control mechanism. We propose a novel three-step framework to exploit the flexibility of Thermostatically controlled loads (TCLs) by (1) simulating a pool of households, (2) estimating the governing physical parameters of the aggregated households, and (3) controlling the heat pump electric power via a dynamic price policy; for parameter estimation and price policy a Differential Evolution (DE) optimization algorithm is used. The proposed method performs well for a large number of parameters and reduced training data (errors around 0.4% and 0.55% on the average load power and standard deviation) and effectively controls the loads through a dynamic price policy that reduces the total price for the household owner or customer compared to a tariff without demand response (DR) (reduction of up to 53.63% on average per house), respecting the technical constraints of the grid.