文章摘要
陈厚合,李泽宁,姜涛,等.基于模型预测控制的智能楼宇用能灵活性调控策略[J].电力系统自动化,2019,43(16):116-124. DOI: 10.7500/AEPS20180728001.
CHEN Houhe,LI Zening,JIANG Tao, et al.Flexible Energy Scheduling Strategy in Smart Buildings Based on Model Predictive Control[J].Automation of Electric Power Systems,2019,43(16):116-124. DOI: 10.7500/AEPS20180728001.
基于模型预测控制的智能楼宇用能灵活性调控策略
Flexible Energy Scheduling Strategy in Smart Buildings Based on Model Predictive Control
DOI:10.7500/AEPS20180728001
关键词: 暖通空调系统  楼宇蓄热特性  热阻热容网络模型  模型预测控制  可再生能源
KeyWords: heating, ventilation and air conditioning(HVAC)system  heat storage characteristics of building  resistor-capacitor network model  model predictive control(MPC)  renewable energy
上网日期:2019-05-31
基金项目:国家自然科学基金资助项目(51677022);国家重点研发计划资助项目(2017YFB0903400);吉林省综合能源系统创新团队资助项目(20180519015JH)
作者单位E-mail
陈厚合 东北电力大学电气工程学院, 吉林省吉林市 132012  
李泽宁 东北电力大学电气工程学院, 吉林省吉林市 132012  
姜涛 东北电力大学电气工程学院, 吉林省吉林市 132012 t.jiang@aliyun.com 
李雪 东北电力大学电气工程学院, 吉林省吉林市 132012  
张儒峰 东北电力大学电气工程学院, 吉林省吉林市 132012  
李国庆 东北电力大学电气工程学院, 吉林省吉林市 132012  
摘要:
      提出一种基于模型预测控制的智能楼宇用能灵活性调控策略。首先,根据楼宇蓄热特性,构建考虑楼宇内部不同制热区域的智能楼宇能耗预测模型,并将楼宇系统作为灵活可控单元集成到配电网中;然后基于模型预测控制方法,通过楼宇内部暖通空调系统在温度舒适度范围内对室温进行优化调节,实现楼宇系统的能耗灵活管理,降低楼宇运行成本;最后,在冬季制热场景下,对不同暖通空调控制方法下的楼宇集群进行优化调度分析,并对比分析了楼宇集群优化调度对于配电网运行状态的影响。结果表明,所提方法在保证温度舒适度的前提下可充分发掘智能楼宇的需求响应潜力,降低楼宇运行成本,同时可有效解决由可再生能源出力预测数据误差而导致的楼宇日前调控方案与实际运行场景偏差较大的问题,在预测不确定性环境下具有较强的鲁棒性。
Abstract:
      An flexible energy scheduling strategy in smart buildings based on model predictive control is proposed. Firstly, based on the heat storage characteristics of building, this paper develops an energy consumption prediction model for smart building with different heating zones, and the building system is integrated into the distribution network as a flexible and controllable unit. Then, based on the model predictive control method, the room temperature is adjusted within the suitable temperature comfort range through the heating, ventilation and air conditioning(HVAC)system in the building. The flexible energy consumption management of building system is achieved, while the operation cost of building is reduced. Finally, this paper analyzes the optimal scheduling results of the aggregation of smart buildings with different HVAC control strategies in the winter heating scenario, and evaluates the effects of optimal schedules of the aggregation of smart buildings on the operation of distribution network. Results show that the proposed optimal scheduling method can make full use of the demand response potential of smart buildings to reduce the building operation cost on the premise of ensuring temperature comfort. Meanwhile, the method can effectively solve the problem of large deviation between the day-ahead building scheduling strategy and actual building operation scenario caused by the inaccuracy prediction data of renewable energy output, and it has a strong robustness in prediction uncertainty environment.
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