Aggregation refers to the process of collecting and summarizing data or information from multiple sources into a single, coherent, and often more concise representation. It is commonly used in various fields, including data analysis, statistics, and databases, to extract meaningful insights or to simplify complex datasets. Aggregation can involve various operations, such as calculating averages, sums, counts, or other statistical measures to provide a higher-level view of the data. This aggregated data can make it easier to analyze trends, patterns, or characteristics that might not be apparent when examining individual data points. Aggregation is a fundamental concept in data processing and reporting, and it plays a crucial role in decision-making and understanding large datasets.


Stochastic Decision-Making Model for Aggregation of Residential Units with PV-Systems and Storages

Many residential energy consumers have installed photovoltaic (PV) panels and energy storage systems. These residential users can aggregate and participate in the energy markets. A stochastic decision making model for an aggregation of these residential units for participation in two-settlement markets is proposed in this paper. Scenarios are generated using Seasonal Autoregressive Integrated Moving Average (SARIMA) model and joint probability distribution function of the forecast errors to model the uncertainties of the real-time prices, PV generations and demands. The proposed scenario generation model of this paper treats forecast errors as random variable, which allows to reflect new information observed in the real-time market into scenario generation process without retraining SARIMA or re-fitting probability distribution functions over the forecast errors. This approach significantly improves the computational time of the proposed model. A simulation study is conducted for an aggregation of 6 residential units, and the results highlights the benefits of aggregation as well as the proposed stochastic decision-making model.