Abstract
Many combined heat and power (CHP) units have been installed in domestic buildings to increase energy efficiency and reduce energy costs. However, inappropriate sizing of a CHP may actually increase energy costs and reduce energy efficiency. Moreover, the high manufacturing cost of batteries makes batteries less affordable. Therefore, this paper will attempt to size the capacity of CHP and optimise daily energy costs for a domestic building with only CHP installed. In this paper, electricity and heat loads are firstly used as sizing criteria in finding the best capacities of different types of CHP with the help of the maximum rectangle (MR) method. Subsequently, the genetic algorithm (GA) will be used to optimise the daily energy costs of the different cases. Then, heat and electricity loads are jointly considered for sizing different types of CHP and for optimising the daily energy costs through the GA method. The optimisation results show that the GA sizing method gives a higher average daily energy cost saving, which is 13% reduction compared to a building without installing CHP. However, to achieve this, there will be about 3% energy efficiency reduction and 7% input power to rated power ratio reduction compared to using the MR method and heat demand in sizing CHP.
Highlights
Combined heat and power (CHP) units are regarded as one of the most promising low carbon technologies in solving energy-related problems, because they have many advantages when compared with other energy generation technologies [1]
Considering the fact that the linear programming (LP) method needs to linearise all constraints which can lead to loss of accuracy in the optimisation results, and the fact that nonlinear programming (NLP) has trouble in distinguishing between the local minima and the global minimum, the maximum rectangle (MR), in this paper, will be used to optimise the size of a gas engine and fuel cell combined heat and power (CHP) for a domestic house, based firstly on the daily heat load and secondly on the electricity load curves
Output efficiencies are formulated to functions which are only related to input power and this gives a more accurate optimisation result compared to using constant CHP output efficiencies as optimisation criteria; (3) Different types of CHP and loads are considered as sizing criteria, and the optimisation results will give suggestions for engineers on how to choose and size CHP
Summary
Combined heat and power (CHP) units are regarded as one of the most promising low carbon technologies in solving energy-related problems, because they have many advantages when compared with other energy generation technologies [1]. Considering the fact that the LP method needs to linearise all constraints which can lead to loss of accuracy in the optimisation results, and the fact that NLP has trouble in distinguishing between the local minima and the global minimum, the MR, in this paper, will be used to optimise the size of a gas engine and fuel cell CHP for a domestic house, based firstly on the daily heat load and secondly on the electricity load curves. This is because the MR can cover an ‘average’. The computation time is significantly reduced by using the MR method and the optimal costs are lower when using the GA method; (2) The CHP output efficiencies are formulated to functions which are only related to input power and this gives a more accurate optimisation result compared to using constant CHP output efficiencies as optimisation criteria; (3) Different types of CHP and loads are considered as sizing criteria, and the optimisation results will give suggestions for engineers on how to choose and size CHP
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