Interval Optimization for Integrated Demand Response in A Battery-Equipped Household Multi-Energy System

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI:10.1109/acait53529.2021.9731271

Diankun Hu, Yongxin Su, Mao Tan

{"title":"Interval Optimization for Integrated Demand Response in A Battery-Equipped Household Multi-Energy System","authors":"Diankun Hu, Yongxin Su, Mao Tan","doi":"10.1109/acait53529.2021.9731271","DOIUrl":null,"url":null,"abstract":"The promotion of gas-electric devices and electricity storage devices brings necessity to realize integrated demand response (IDR) for household multi-energy systems (HMES). This paper proposed an interval optimization method for load scheduling in HMES for IDR, which addresses uncertainties in the system model and considers dynamic energy-source switching among gas, electricity and storage. Controllable loads include washing machine, air conditioner and battery as electric devices, and water heater and kitchenware as gas-electric devices. For minimizing energy cost, an interval optimization model with tolerance degree is converted into a deterministic model based on the interval order relationship and interval probability. Then genetic algorithm is applied for solving the deterministic problem. Case studies show that the interval optimization with tolerance method is robust to HMES uncertainty. For a battery-equipped HMES, the energy cost savings are up to 14.7% compared with the traditional method without considering interval optimization and tolerance degree.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/acait53529.2021.9731271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

The promotion of gas-electric devices and electricity storage devices brings necessity to realize integrated demand response (IDR) for household multi-energy systems (HMES). This paper proposed an interval optimization method for load scheduling in HMES for IDR, which addresses uncertainties in the system model and considers dynamic energy-source switching among gas, electricity and storage. Controllable loads include washing machine, air conditioner and battery as electric devices, and water heater and kitchenware as gas-electric devices. For minimizing energy cost, an interval optimization model with tolerance degree is converted into a deterministic model based on the interval order relationship and interval probability. Then genetic algorithm is applied for solving the deterministic problem. Case studies show that the interval optimization with tolerance method is robust to HMES uncertainty. For a battery-equipped HMES, the energy cost savings are up to 14.7% compared with the traditional method without considering interval optimization and tolerance degree.

查看原文本刊更多论文

电池户用多能系统综合需求响应的区间优化

气电装置和蓄电装置的推广，为实现家庭多能源系统(HMES)的综合需求响应(IDR)提供了必要。本文提出了一种针对IDR的HMES负荷调度的区间优化方法，该方法解决了系统模型中的不确定性，并考虑了气、电、储三种能源的动态切换。可控负荷包括洗衣机、空调、电池等电气设备和热水器、厨具等燃气电设备。为使能源成本最小化，将具有容差度的区间优化模型转化为基于区间顺序关系和区间概率的确定性模型。然后应用遗传算法求解确定性问题。实例研究表明，公差区间优化方法对HMES不确定性具有较强的鲁棒性。对于配备电池的HMES，与不考虑区间优化和容差度的传统方法相比，可节省14.7%的能源成本。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)

自引率

0.00%

发文量