To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model based on quarter division and self-organizing map (SOM) neural network algorithm is proposed. In the model, the clustering feature vector is selected as the input vector of the SOM algorithm to perform DSR clustering analysis to get the different scenarios. In addition, to obtain the resource aggregation (RA) with good load characteristics, response characteristics and distributed generation (DG) consumption, a multi-scenario objective optimization aggregation model of DSR based on scenario partition is established, and an the model is solved by an improved niche evolutionary multi-objective immune algorithm. Finally, the case studies are given to verify the validity of the model.

To solve the problem of solar abandoning, which is accompanied by the rapid development of photovoltaic (PV) power generation, a demonstration of a photovoltaic-battery energy storage system (PV-BESS) power plant has been constructed in Qinghai province in China. However, it is difficult for the PV-BESS power plant to survive and develop with the current electricity price mechanism and subsidy policy. In this paper, a three-part electricity price mechanism is proposed based on a deep analysis of the construction and operation costs and economic income. The on-grid electricity price is divided into three parts: the capacity price, graded electricity price, and ancillary service price. First, to ensure that the investment of the PV-BESS power plant would achieve the industry benchmark income, the capacity price and benchmark electricity price are calculated using the discounted cash flow method. Then, the graded electricity price is calculated according to the grade of the quality of grid-connected power. Finally, the ancillary service price is calculated based on the graded electricity price and ancillary service compensation. The case studies verify the validity of the three-part electricity price mechanism. The verification shows that the three-part electricity price mechanism can help PV-BESS power plants to obtain good economic returns, which can promote the development of PV-BESS power plants.

Load aggregator (LA) needs to fully tap the demand response potential of the resources and aggregates the users with high response capacity and low electricity default rate. This can make LA to improve the market competitiveness and gain greater income. Firstly, the load data of resident users are analysed and compared. The summer typical daily response potential curves of users participating in time‐of‐use price are obtained. Secondly, the 24 h response values of the user's typical response curve are used as the data set of the spectral clustering based on dual‐scale similarities of distance and curve shape to classify the users, and the user's response levels are divided according to the clustering centre of each class. Next, the user optimisation aggregation model of LA considering risk is established. Finally, a practical case study is employed to verify the correctness and validity of the model.