In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes ...In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.展开更多
In the last few decades, in the world and also in the European Union, considerable resources had been invested in the rapid development of renewable energy sources and distributed generation in general. At the same ti...In the last few decades, in the world and also in the European Union, considerable resources had been invested in the rapid development of renewable energy sources and distributed generation in general. At the same time, power consumption is continuously increasing, and consumers are becoming more complex, which ultimately requires new investments in the distribution network. Concept of smart grids is generally accepted as a possible solution. Smart grid is a concept with many elements, where monitoring and control of every element in the chain of production, transmission, distribution and final consumption enable much more efficient delivery and use of electricity. One of the elements of smart grid efficiency is the ability of real-time demand-supply balancing. This balancing is carried out by monitoring of consumption and redistribution of electricity among individual end users, according to their needs. The aim of this paper is creating algorithm for real-time load management using power measurements. Algorithm for real-time load management at the ETFOS （Faculty of Electrical Engineering in Osijek）, Croatia is created based on measurements of photovoltaic power plant production, the power consumption of air conditioning system and the faculty building total electricity consumption. Expected result of real-time re-dispatching of air conditioners consumption, depending on the level of electricity production in photovoltaic power plant is decreasing peak demand of the faculty.展开更多
提出一种面向提高风电接纳能力的智慧建筑能量管理策略。首先,基于建筑热惯性,构建考虑建筑物内部不同制热区域的能耗预测模型;其次,基于支路潮流模型与二阶锥松弛方法,构建集成智慧建筑的主动配电网(active distribution network,ADN)...提出一种面向提高风电接纳能力的智慧建筑能量管理策略。首先,基于建筑热惯性,构建考虑建筑物内部不同制热区域的能耗预测模型;其次,基于支路潮流模型与二阶锥松弛方法,构建集成智慧建筑的主动配电网(active distribution network,ADN)统一数学模型。随后,基于模型预测控制方法,在保证用户舒适性前提下,对ADN进行能量管理;最后,基于冬季制热场景,通过多种暖通空调(heating,ventilation and air conditioning,HVAC)调控方案对智慧建筑参与ADN优化调度进行分析验证。算例表明,与未考虑ADN与智慧建筑集成的模型及方法相比,所提方法基于集成智慧建筑的ADN统一模型,充分利用了HVAC设备运行模式灵活性和居民舒适温度区间,在保障居民温度舒适性的同时,进一步提高电网的风电接纳能力,并保证ADN的经济安全运行。展开更多
基金supported by the National Science Foundation(NSF)grant ECCF 1936494.
文摘In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.
文摘In the last few decades, in the world and also in the European Union, considerable resources had been invested in the rapid development of renewable energy sources and distributed generation in general. At the same time, power consumption is continuously increasing, and consumers are becoming more complex, which ultimately requires new investments in the distribution network. Concept of smart grids is generally accepted as a possible solution. Smart grid is a concept with many elements, where monitoring and control of every element in the chain of production, transmission, distribution and final consumption enable much more efficient delivery and use of electricity. One of the elements of smart grid efficiency is the ability of real-time demand-supply balancing. This balancing is carried out by monitoring of consumption and redistribution of electricity among individual end users, according to their needs. The aim of this paper is creating algorithm for real-time load management using power measurements. Algorithm for real-time load management at the ETFOS （Faculty of Electrical Engineering in Osijek）, Croatia is created based on measurements of photovoltaic power plant production, the power consumption of air conditioning system and the faculty building total electricity consumption. Expected result of real-time re-dispatching of air conditioners consumption, depending on the level of electricity production in photovoltaic power plant is decreasing peak demand of the faculty.
文摘提出一种面向提高风电接纳能力的智慧建筑能量管理策略。首先,基于建筑热惯性,构建考虑建筑物内部不同制热区域的能耗预测模型;其次,基于支路潮流模型与二阶锥松弛方法,构建集成智慧建筑的主动配电网(active distribution network,ADN)统一数学模型。随后,基于模型预测控制方法,在保证用户舒适性前提下,对ADN进行能量管理;最后,基于冬季制热场景,通过多种暖通空调(heating,ventilation and air conditioning,HVAC)调控方案对智慧建筑参与ADN优化调度进行分析验证。算例表明,与未考虑ADN与智慧建筑集成的模型及方法相比,所提方法基于集成智慧建筑的ADN统一模型,充分利用了HVAC设备运行模式灵活性和居民舒适温度区间,在保障居民温度舒适性的同时,进一步提高电网的风电接纳能力,并保证ADN的经济安全运行。
