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Real-time price-based demand response model for combined heat and power systems

机译:基于实时价格的热电联产需求响应模型

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Real-time electricity pricing could promote the adaptation of demand response programs in the presence of price volatility in smart grids. This paper proposes a real-time price-based demand response management model for heat and power consumers. In the proposed demand response program (DRP) the responsive electrical load can vary in different time intervals. In addition, total power and heat demands of the consumer are met, without any curtailment. The price uncertainty is envisaged through robust optimization for minimizing the worst-case electricity and heat demand procurement cost while flexibly adjusting the solution robustness. The proposed model can be easily embedded in the energy management system of the customer equipped with combined heat and power systems (CHPs), a power-only unit, a boiler unit and a heat buffer tank (HBT) and makes it possible to achieve a minimum cost. In this paper, the dual dependency characteristic of the heat and power in different types of CHPs has been taken into account. Furthermore, technical constraints, i.e. minimizing number of start-ups and shutdowns, ramp rate limits and minimum up/down-time limits of generation facilities are satisfied. Numerical simulations confirming the applicability and effectiveness of the proposed model are provided. According to the simulation results, there is a significant increase in the daily cost of the consumer without smart grid technology in comparison with the consumer employing the proposed real-time model. (C) 2018 Elsevier Ltd. All rights reserved.
机译:在智能电网价格波动的情况下,实时电价可以促进对需求响应程序的适应。本文提出了一种基于价格的实时供热和电力用户需求管理模型。在提出的需求响应程序(DRP)中,响应的电负载可以在不同的时间间隔内变化。另外,满足了消费者的总功率和热量需求,而没有任何限制。可以通过鲁棒性优化来设想价格不确定性,以最小化最坏情况下的电力和热需求采购成本,同时灵活地调整解决方案的鲁棒性。所提出的模型可以轻松地嵌入到客户的能源管理系统中,该系统配备了热电联产系统(CHP),仅功率单元,锅炉单元和热缓冲罐(HBT),并有可能实现最低费用。在本文中,已经考虑了不同类型的热电联产中热量和功率的双重依赖性特征。此外,满足了技术限制,即最小化启动和关闭的次数,斜坡速率限制以及发电设施的最小上/下时间限制。数值模拟证实了该模型的适用性和有效性。根据仿真结果,与采用建议的实时模型的消费者相比,没有智能电网技术的消费者的日常成本将显着增加。 (C)2018 Elsevier Ltd.保留所有权利。

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