Study on operation strategy and load forecasting for distributed energy system based on Chinese supply-side power grid reform

This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system(DES).Owing to the relevant Chinese policy of electrical transmission and distribution,combined cooling,heating,and power system(CCHP)and photovoltaic(PV)systems are not currently allowed.However,with the Chinese supply-side power grid reform,the permissions for connections between DESs and utilities are gradually evolving.By performing building simulation and using mixed integer linear programming(MILP),a real-time updated operation strategy of a DES is established.Then,considering the DES from Tianjin Eco-city as a case study,a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors:economic efficiency,energy consumption,and CO2 emission.The results show that the updated strategy can reduce 29.12%of electricity time-of-use cost,10.11%of total fuel consumption,and 18.40%of CO2 emission during the cooling season.Besides,a method of“rolling load forecasting”for DES by using Support vector regression machine(SVR)is proposed and discussed.The testing shows that the Mean Absolute Percentage Error(MAPE)is below 7.5%.And when the training sample is large,the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.

Deep learning analysis of smart meter data from a small sample of room air conditioners facilitates routine assessment of their operational efficiency

Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.