Day-ahead forecasting of grid carbon intensity in support of heating, ventilation and air-conditioning plant demand response decision-making to reduce carbon emissions
Day-ahead forecasting of grid carbon intensity in support of heating, ventilation and air-conditioning plant demand response decision-making to reduce carbon emissions
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电网碳强度日前预测，支持供热、通风和空调工厂需求响应决策，减少碳排放
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发表时间：
2018
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通讯作者：
G. Lowry
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作者：
G. Lowry
建筑物中的电加热、通风和空调负荷是需求响应活动的合适选择。本文展示了一种方法，用于支持明确旨在减少碳排放的有计划的需求响应行动。需求响应通常被用于辅助电网运行，并可提高效率；在此，计划针对的是一天中发电碳强度较高的时段。一旦能够预见一天中节能的合适时间，供暖、通风和空调设备的操作人员以及空调空间的使用者就可以计划何时安排设备停机。研究表明，英国大陆电网的碳强度在一天中变化显著，但往往遵循每日和每周的季节性模式。为了能够对需求响应进行规划，开发了不依赖于收集多个外生数据集的电网碳强度24小时提前预测模型。在预测高碳强度的半小时时段时，可以使用线性自回归模型或非线性人工神经网络模型，但日季节性自回归模型在碳减排方面显示出20%的改进。实际应用：本文所展示的预测方法将使建筑运营商能够针对英国电网碳强度高的时段规划需求响应活动。由于所需的唯一数据是公开可用的，该方法将易于实施。
Electrical heating, ventilation and air-conditioning loads in buildings are suitable candidates for use in demand response activity. This paper demonstrates a method to support planned demand response actions intended explicitly to reduce carbon emissions. Demand response is conventionally adopted to aid the operation of electricity grids and can lead to greater efficiency; here it is planned to target times of day when electricity is generated with high carbon intensity. Operators of heating, ventilation and air-conditioning plant and occupants of conditioned spaces can plan when to arrange shutdown of plant once they can foresee the opportune time of day for carbon saving. It is shown that the carbon intensity of the mainland UK electricity grid varies markedly throughout the day, but that this tends to follow daily and weekly seasonal patterns. To enable planning of demand response, 24 h ahead forecast models of grid carbon intensity are developed that are not dependent on collecting multiple exogenous data sets. In forecasting half-hour periods of high carbon intensity either linear autoregressive or non-linear artificial neural network models can be used, but a daily seasonal autoregressive model is shown to provide a 20% improvement in carbon reduction. Practical application : The forecast method demonstrated in the paper would enable building operators to plan demand response activity to target times of high carbon intensity on the UK electricity grid. The method would be easy to implement as the only data required are publicly available.