Analysis of the Impact of Urban Microclimate on Air Conditioning Load Control

Due to the presence of urban heat island effect (UHIE), high humidity and other urban microclimate, temperature of city central area rises. This causes that the actual air-conditioning energy consumption (ACEC) in the urban central area is much higher than that in the suburbs. Load control of air-conditioners (ACs) is considered to be equivalent to a power plant of the same capacity, and it can greatly reduce the system pressure to peak load shift. In this paper, a simplified second order transfer function control model of ACs is presented, and its parameters will be influenced by the ambient temperature and urban microclimate. The temperature is obtained by using the temperature inversion algorithm of the heat island effect. Then, the heat index is calculated by combining temperature and humidity. The ambient temperature index of urban central area is modified based on the above microclimate, and the second order linear time invariant model of aggregated ACs is upgraded to the linear time varying model. Furthermore, the consequent parameter changes of the second order transfer function model are studied and the influence of urban microclimate on AC load control is analyzed. The proposed method is verified on numerical example

Analysis of the Impact of Urban Microclimate on Air Conditioning Load Control

Due to the presence of urban heat island effect (UHIE), high humidity and other urban microclimate, temperature of city central area rises. This causes that the actual air-conditioning energy consumption (ACEC) in the urban central area is much higher than that in the suburbs. Load control of air-conditioners (ACs) is considered to be equivalent to a power plant of the same capacity, and it can greatly reduce the system pressure to peak load shift. In this paper, a simplified second order transfer function control model of ACs is presented, and its parameters will be influenced by the ambient temperature and urban microclimate. The temperature is obtained by using the temperature inversion algorithm of the heat island effect. Then, the heat index is calculated by combining temperature and humidity. The ambient temperature index of urban central area is modified based on the above microclimate, and the second order linear time invariant model of aggregated ACs is upgraded to the linear time varying model. Furthermore, the consequent parameter changes of the second order transfer function model are studied and the influence of urban microclimate on AC load control is analyzed. The proposed method is verified on numerical example

Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models

Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages