Analysis of key influencing factors for multi-mode solar heat pump systems

Abstract

To further improve the stability and renewable energy utilization of the compound heat source heat pump (CHSHP) system under complex operating conditions, a multi-mode solar heat pump (MMSHP) system was proposed and a mathematical model was established in this paper. The accuracy of the model is verified by experimental data, with the relative error within {plus minus}15%. The effects of the number of solar photovoltaic/thermal (PV/T) collectors (area), environmental parameters, and refrigerant flow rate on system performance are discussed separately for different models by the validated model. The results show that (1) PV/T-water source heat pump (PV/T-WSHP) mode: the number of PV/T collectors in parallel (area) increases to 3 (3.84 m2), and the system performance improves the most, subsequently weakening; (2) PV/T-water & air composite heat pump (PV/T-W&ASHP) model: When the refrigerant distribution ratio in the double-pipe heat exchanger1 is increased from 55% to 95%, the collector's heat collecting capacity, power generation efficiency, and system coefficient of performance (COP) increase by 8.99%, 1.22%, and 6.4%, respectively. Moreover, the increase in solar irradiance can promote more refrigerant flow into the double -pipe heat exchanger 1, and improve heat pump COP more than the effect of increased ambient temperature. The research results can provide an effective reference for further optimization of solar heat pump systems and full utilization of renewable energy.

Single‐Site Forecasts for 130 Photovoltaic Systems at Distribution System Operator Level, Using a Hybrid‐Physical Approach, to Improve Grid‐Integration and Enable Future Smart‐Grid Operation

“…Considering the aforementioned comparability issues, [11,22] a direct comparison of performance indicators across studies needs to be interpreted cautiously and shall not be overrated. But some articles using NWP models mentioned by Antonanzas et al [8] for the time frame "6 h to DA" for single sites could serve as a reference to compare our ID forecast results (0-24 h ahead).…”

“…Previously described benefits of NWP‐driven forecasting schemes and the requirements of the future DSOs as well as their data availability led to the choice of our forecast concept (see Section 3), which can be characterized as follows: as described in the study by Yang, [ 11 ] the authors should position their work with regards to the state of the art by classifying: 1) the methods used; 2) by characteristics. Regarding methods (1), we are using NWP data to drive a PV performance model to calculate single‐site PV power forecasts in 15 min resolution, from 0–72 h ahead.…”

“…[9] In recent years, well-recognized authors have recommended good practice to address the verification of forecast qualities of the models [22] and the related challenges in comparing results published in the research by Yang. [11] To evaluate: a) the performance of the proposed forecasting approach, b) its improvement by combining different methods, and c) to be comparable to other evaluations of forecasting approaches found in other articles, the following evaluation criteria have been chosen in analogy to the previous studies. [9][10][11]22,23] The forecast performance and the improvements by adding a data-driven approach to the method are mainly assessed by using the mean bias error (MBE), to identify systemic bias, and the root mean square error (RMSE) to evaluate the average deviation of the forecast from the measured value.…”

“…[11] To evaluate: a) the performance of the proposed forecasting approach, b) its improvement by combining different methods, and c) to be comparable to other evaluations of forecasting approaches found in other articles, the following evaluation criteria have been chosen in analogy to the previous studies. [9][10][11]22,23] The forecast performance and the improvements by adding a data-driven approach to the method are mainly assessed by using the mean bias error (MBE), to identify systemic bias, and the root mean square error (RMSE) to evaluate the average deviation of the forecast from the measured value. Mean absolute error (MAE) is not given here, since it provides a similar evaluation as the RMSE, but RMSE was considered more appropriate in view of our application to the power sector.…”

“…This underlines the problematics rising from different locations and climates, temporal resolutions, forecast horizons, and data sources apart from the diverse characteristics of the forecasting models. To reduce those influences, the skill score (RMSE based) [11,22] is often referred to as the most suitable metric for such comparison. Skill score "s" is defined as…”