Search

With the rapid increase in solar photovoltaic (PV) installation capacity, the strain on grid transmission burden has intensified. A house energy management system is recognized as an effective solution to mitigate this grid burden. However, existing research has not fully explored the potential of battery utilization and the forecasting of uncertainties. In this paper, a novel multi-objective optimization framework based on the genetic algorithm-based method for the house energy management system is proposed, to enhance renewable self-consumption, improve on-site renewable self-sufficiency, and optimize economic benefits for users. The framework integrates an artificial neural network for predictions of meteorological data and user load at a 5-minute temporal resolution, enabling the simulation and optimization of the PV-battery-flexible load system. Emphasizing deferrable loads, constant-temperature control loads, and batteries, the proposed framework devises optimal strategies for distributed PV battery systems in residential. It harnesses load flexibility and battery storage capabilities while incorporating comfort assessment metrics. This approach significantly improves the system's economic and technical performance metrics, with system self-consumption rate, self-sufficiency rate, and cost reduction ratio improved by 13.5%, 11.3%, and 6.2%, respectively, compared to the basic strategy. Additionally, the optimization of the air conditioning system enhances alignment with the photovoltaic generation, resulting in a 9.8% reduction in energy consumption and a 9.4% decrease in electricity costs, while maintaining user comfort at an acceptable level. The proposed framework promotes the practical application of renewable management systems, highlighting renewable energy efficient utilization, grid dependency reduction, and user economic benefit increase. [ABSTRACT FROM AUTHOR]

The secondary solar heat gain, defined as the heat flows from glazing to indoor environment through longwave radiation and convection, grows with the increasing of glazing absorption. With the rapid development and application of spectrally selective glazing, the secondary solar heat gain becomes the main way of glazing heat transfer and biggest proportion, and indicates it should not be simplified calculated conventionally. Therefore, a dynamic secondary solar heat gain model is developed with electrochromic glazing system in this study, taking into account with three key aspects, namely, optical model, heat transfer model, and outdoor radiation spectrum. Compared with the traditional K-Sc model, this new model is verified by on-site experimental measurements with dynamic outdoor spectrum and temperature. The verification results show that the root mean square errors of the interior and exterior glass surface temperature are 3.25 °C and 3.33 °C, respectively, and the relative error is less than 10.37%. The root mean square error of the secondary heat gain is 13.15 W/m2, and the dynamic maximum relative error is only 13.2%. The simulated and measured results have a good agreement. In addition, the new model is further extended to reveal the variation characteristics of secondary solar heat gain under different application conditions (including orientations, locations, EC film thicknesses and weather conditions). In summary, based on the outdoor spectrum and window spectral characteristics, the new model can accurately calculate the increasing secondary solar heat gain in real time, caused by spectrally selective windows, and will provide a computational basis for the evaluation and development of spectrally selective glazing materials. [ABSTRACT FROM AUTHOR]

Self-powered photovoltaic windows, which integrate photovoltaic with electrochromic devices, have attracted widespread attention of scholars since they can generate electricity in situ and reduce building energy consumption by modulating the transmitted solar radiation. However, previous studies mainly focused on the material development and performance characterization, lack of comfort assessment and energy saving potential of its application to buildings. To address this issue, an adjustable semi-transparent photovoltaic (ATPV) window which integrates CdTe-based photovoltaic and WO3-based electrochromic, was taken as the research object, and a novel rule-based control strategy taking the beam solar radiation luminous efficacy (CtrlEff) as decision variable was proposed for the first time. The ATPV window model was established in WINDOW software based on the measured data, and then it was exported to integrated with a medium office building model in EnergyPlus for performance evaluation including the visual comfort, thermal comfort, net energy consumption, and net-zero energy ratio. The results of a case study in Changsha (E 112°, N 28°) indicated that the ATPV window under the CtrlEff strategy can effectively reduce the southward and westward intolerable glare by 86.9% and 94.9%, respectively, and increase the thermal comfort hours by 5% and 2%, compared to the Low-E window. Furthermore, the net-zero energy consumption can be decreased by 58.7%, 65.7%, 64.1%, and 53.8% for south, west, east, and north orientations, and the corresponding net-zero energy ratios are 65.1%, 54.6%, 62.7%, and 61.6%, respectively. The findings of this study provide new strategies for the control and optimization of the adjustable window. [ABSTRACT FROM AUTHOR]

A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics, energy-related occupant behavior, and the benefits of different stakeholders. A pilot test was conducted for a typical household. According to the monitored appliance-level data, operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods. Moreover, a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data. Occupant behavior of appliance usage, such as daily turn-on times, turn-on moment, duration of each operation, preference of temperature setting, and flexibility window, were determined by the developed data-mining approach. Based on the established flexible load models and the identified occupant energy usage behavior, a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs, occupants' dissatisfaction, CO2 emissions, and the average ramping index of household power profiles. The model was solved with the assistance of the NSGA-III and TOPSIS methods. Results indicate that the proposed framework can effectively optimize the operation of household flexible loads. Compared with the benchmark, the daily electricity costs, CO2 emissions, and average ramping index of household power profiles of the optimal plan were reduced by 7.3%, 6.5%, and 14.4%, respectively, under the TOU tariff, while those were decreased by 9.5%, 8.8%, and 23.8%, respectively, under the dynamic price tariff. The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice. [ABSTRACT FROM AUTHOR]