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This paper presented a novel mathematical programming model for the simultaneous optimization of heat integrated water allocation networks featuring parallel heat exchanger network (HEN) structure. In the HEN structure, both freshwater and wastewater can be split freely. This model was suitable for both uniform wastewater treatment and separate wastewater treatment cases. The proposed model was formulated as a MINLP (mixed-integer non-linear programming) problem, making it applicable to large scale problems. The main objective was to minimize the total annual cost. Three literature examples, including a large scale example, were illustrated to demonstrate the applicability of the model. It was shown that the proposed method was as accurate as the literature methods for small scale problems, but performed better for large scale problem applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel, simple method for reducing external operating condition datasets to be used in multi-generation system optimization models. The method, called the Characteristic Operating Pattern (CHOP) method, is a visually-based aggregation method that clusters reference data based on parameter values rather than time of occurrence, thereby preserving important information on short-term relations between the relevant operating parameters. This is opposed to commonly used methods where data are averaged over chronological periods (months or years), and extreme conditions are hidden in the averaged values. The CHOP method is tested in a case study where the operation of a fictive Danish combined heat and power plant is optimized over a historical 5-year period. The optimization model is solved using the full external operating condition dataset, a reduced dataset obtained using the CHOP method, a monthly-averaged dataset, a yearly-averaged dataset, and a seasonal peak/off-peak averaged dataset. The economic result obtained using the CHOP-reduced dataset is significantly more accurate than that obtained using any of the other reduced datasets, while the calculation time is similar to those obtained using the monthly averaged and seasonal peak/off-peak averaged datasets. The outcomes of the study suggest that the CHOP method is advantageous compared to chronology-averaging methods in reducing external operating condition datasets to be used in the design optimization models of flexible multi-generation systems. [ABSTRACT FROM AUTHOR]

Different control techniques together with intelligent building technology (Building Automation Systems) are used to improve energy efficiency of buildings. In almost all control projects, it is crucial to have building energy models with high computational efficiency in order to design and tune the controllers and simulate their performance. In this paper, a set of partial differential equations are formulated accounting for energy flow within the building space. These equations are then solved as conventional finite difference equations using Crank–Nicholson scheme. Such a model of a higher order is regarded as a benchmark model. An optimization algorithm has been developed, depicted through a flowchart, which minimizes the sum squared error between the step responses of the numerical and the optimal model. Optimal model of the construction element is nothing but a RC-network model with the values of Rs and Cs estimated using the non-linear time invariant constrained optimization routine. The model is validated with comparing the step responses with other two RC-network models whose parameter values are selected based on a certain criteria. Validations are showed for different types of building construction elements viz., low, medium and heavy thermal capacity elements. Simulation results show that the optimal model closely follow the step responses of the numerical model as compared to the responses of other two models. [ABSTRACT FROM AUTHOR]

The importance of household behavior in energy consumption is regularly raised in literature. Nevertheless energy-economy long-term planning models still often represent energy demand by a single mean household and thus fail to capture household behavior. This paper describes the original modeling approach developed within the TIMES model framework in order to take household behavior and heterogeneity into account: the TIMES-Households model. This model depicts both household daily energy consumption and equipment purchasing behavior and focuses on the French residential and transport sectors. We show the importance of taking household heterogeneity into account in long-term planning models within the comparison of two energy price scenarios applied to two different models. Finally, thanks to the highly disaggregated representation of households and their behavior in the model, we address the problem of unrealistic technology diffusion pathways often obtained when using an optimization bottom-up model. [ABSTRACT FROM AUTHOR]