Optimal thermal storage operation strategies with heat pumps and solar collector

Abstract

Energy consumption inside of building plays a key role occupying as 30-40% by the data of United Nations Statics Division (UNSD). Energy efficient building with light, medium and massive type discusses for designing the high efficient system component inside of building model. Control strategy between two tanks and solar collector completes the task by the Matlab codes and building simulation software. Result compares with the Pareto Efficiency curve for achieving the energy saving component and low cost operation.

In this thesis, to achieve the goal of energy strategy and get the higher efficiencies of building energy, most common building type of single family house (light, medium and massive type) is suggested to renovate the energy system of the house. The domestic hot water consumption and space heating heat demand is the main target to satisfy the energy need in the house, two geothermal heat pumps and two thermal tanks with one solar collector studies for the respond of the energy requirement.

Simulation software, IDA ICE (version 4.7.1) employs for the energy-utilized data set and Matlab studies for the control and the optimization result. IDA ICE can generate one tank model with one heat pump and solar collector, in the scope of the two tanks and two heat pumps model, one tank model is made separately only for the usage of domestic hot water consumption and the other is made only for the space heating. After producing two tanks model separately named as high temperature tank and low temperature tank, both combine with the Matlab software for the control strategy and optimization.

To make the result after the control of the system components, energy balance equation and Artificial neural network (ANN) introduces. ANN is required for making the structure of the heat demand of solar collector and heat pump. Multi objective optimization presents and non-dominant sorting genetic algorithm (NSGA II) shows the Pareto Front of the result. Pareto Front is the optimal selection of the tank size and solar collector area by using the two different objective functions. One is annual heat pump energy usage and the other is operating cost of components considering Life Cycle Cost (LCC).

Validation conducts with one tank model. One tank model in medium type of building is chosen for validation and comparing the result with the IDA and MOBO (Multi-Objective Building Performance Optimization) together. MOBO is optimization software possible to find suitable decision variables in the huge number of possible combinations, which let achieve defined conflicting objective functions and satisfy specified constraint functions. Validation turns out tank model with Matlab and ANN with NSGA II generates same pace of IDA ICE and MOBO combination later on.