The optimization of polygeneration systems considering hourly periods throughout one year is a computationally demanding task, and, therefore, methods for the selection of representative days are employed to reproduce reasonably the entire year. However, the suitability of a method strongly depends on the variability of the time series involved in the system. This work compares the methods Averaging, k-Medoids and OPT for the selection of representative days by carrying out the optimization of grid-connected and standalone polygeneration systems for a building in two different locations. The suitability of the representative days obtained with each method were assessed regarding the optimization of the polygeneration systems. Sizing errors under 5% were achieved by using 14 representative days, and the computational time, with respect to the entire year data, was reduced from hours to a few seconds. The results demonstrated that the Averaging method is suitable when there is low variability in the time series data; but, when the time series presents high stochastic variability (e.g., consideration of wind energy), the OPT method presented better performance. Also, a new method has been developed for the selection of representative days by combining the k-Medoids and OPT methods, although its implementation requires additional computational effort.
Polygeneration systems enable natural resources to be exploited efficiently, decreasing CO 2 emissions and achieving economic savings relative to the conventional separate production. However, their economic feasibility depends on the legal framework. Preliminary design of polygeneration systems for the residential sector based on the last Spanish self-consumption regulations RD 900/2015 and RD 244/2019 was carried out in Zaragoza, Spain. Both regulations were applied to individual and collective installations. Several technologies, appropriate for the energy supply to residential buildings, for example, photovoltaics, wind turbines, solar thermal collectors, microcogeneration engines, heat pump, gas boiler, absorption chiller, and thermal and electric energy storage were considered candidate technologies for the polygeneration system. A mixed integer linear programming model was developed to minimize the total annual cost of polygeneration systems. Scenarios with and without electricity sale were considered. CO 2 emissions were also calculated to estimate the environmental impact. Results show that RD 900/2015 discourages the investment in self-consumption systems whereas the RD 244/2019 encourages them, especially in renewable energy technologies. Moreover, in economic terms, it is more profitable to invest in collective self-consumption installations over individual installations. However, this does not necessarily represent a significant reduction of CO 2 emissions with respect to individual installations since the natural gas consumption tends to increase as its unit price decreases because of the increase of its consumption level. Thus, more appropriate pricing of natural gas in residential sector, in which its cost would not be reduced when increasing its consumption, would be required to achieve significant CO 2 emissions reduction. In all cases, the photovoltaic panels (PV) are competitive and profitable without subsidies in self-consumption schemes and the reversible heat pump (HP) played an important role for the CO 2 emissions reduction. In a horizon to achieve zero CO 2 emissions, the net metering scheme could be an interesting and profitable alternative to be considered.
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