Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

Identification of sand and dust storm source areas in Iran

Sand and dust storms （SDS） are common phenomena in arid and semi-arid areas. In recent years, SDS frequencies and intensities have increased significantly in Iran. A research on SDS sources is important for understanding the mechanisms of dust generation and assessing its socio-economic and environmental impacts. In this paper, we developed a new approach to identify SDS source areas in Iran using a combination of nine related datasets, namely drought events, temperature, precipitation, location of sandy soils, SDS frequency, hu- man-induced soil degradation （HISD）, human influence index （HII）, rain use efficiency （RUE） and net primary pro- ductivity （NPP） loss. To identify SDS source areas, we firstly normalized these datasets under uniform criteria in- cluding layer reprojection using Lambert conformal conic projection, data conversion from shapefile to raster, Min-Max Normalization with data range from 0 to 1, and data interpolation by Kriging and images resampling （resolution of 1 km）. After that, a score map for the possibility of SDS sources was generated through overlaying multiple datasets under average weight allocation criterion, in which each item obtained weight equally. In the score map, the higher the score, the more possible a specific area could be regarded as SDS source area. Exceptions mostly came from large cities, like Tehran and Isfahan. As a result, final SDS source areas were mapped out, and Al-Howizeh/Al-Azim marshes and Sistan Basin were identified as main SDS source areas in Iran. The SDS source area in Al-Howizeh/Al-Azim marshes still keeps expanding. In addition, Al-Howizeh/Al-Azim marshes are now suf- fering rapid land degradation due to natural and human-induced factors and might totally vanish in the near future. Sistan Basin also demonstrates the impacts of soil degradation and wind erosion. With appropriate intensity, dura- tion, wind speed and altitude of the dust storms, sand particles uplifting from this area might have developed into extreme dust storms, especially during the summer.