Market and behavior driven predictive energy management for residential buildings
Market and behavior driven predictive energy management for residential buildings
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DOI：
10.1016/j.scs.2018.01.030
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发表时间：
2018-04
影响因子：
11.7
通讯作者：
Amin Mirakhorli;B. Dong
中科院分区：
文献类型：
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作者：
Amin Mirakhorli;B. Dong
随着智能家居和电网的发展，电网的连接更加紧密，运行更加高效。将建筑物作为最大的电力消费者纳入这种智能操作是实现交互式电网系统的关键一步。本文介绍了一种综合考虑电价和人的行为，对单户住宅的主要用电户进行控制的建筑能源管理系统。在配备光伏（PV）的建筑物中控制空调、热水器、电动车辆和电池存储器。考虑系统模型、电价和人的行为模式，设计了一种模型预测控制，以最小化运行成本。提出了MPC在建筑能源管理中的集中式和独立式配置，并对分时电价、小时电价和5分钟电价进行了对比。仿真结果表明，在真实的5分钟定价中，与传统的基于规则的控制相比，这些方法可以在不同的电器上节省20%-30%的成本，并且在加入电池优化控制的情况下，可以节省42%的总电费。成本节约和调峰结果证明了引入的基于价格和行为的控制的能力。
With the advancement of smart home and grid, a more connected and efficient operation of the grid is achievable. Involving buildings as the largest consumer of electricity in such a smart operation is a critical step in achieving an interactive grid system. In this paper, a building energy management system is introduced considering electricity price and people behavior, controlling major consumers of electricity in a single family residential building. An air conditioner, water heater, electric vehicle, and battery storages are controlled in a photovoltaic (PV) equipped building. A model predictive control is designed to minimize the operation cost considering system model, electricity price and people behavior patterns in each device control. Centralized and stand-alone configuration of MPC for building energy management is formulated and were put in contrast for time of use pricing (TOU), hourly pricing and five minutes pricing. Simulation results show that in real time five minutes pricing these methods can achieve 20%–30% cost savings in different appliances, and 42% savings in overall electricity cost adding battery optimal control compared to traditional rule based control. Cost savings and peak shaving results demonstrate the capabilities of introduced price and behavior based control.