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.

Posts

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

/in /by

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.

Read more

Aggregation of BTM Battery Storages to Provide Ancillary Services in Wholesale Electricity Markets

/in /by

The behind the meter battery energy storage systems (BTM-BESSs) have been deployed widely by indus-trial/commercial buildings to manage electricity transaction with utilities in order to reduce customers’ electricity bills. Commercial BTM battery storages are mainly employed to cut the customers’ monthly demand peaks, which is rewarded by significant decrease in the monthly demand charge. However, given complexity of demand charge management problems, the rates of return on investments for installation of BTM-BESSs are not appealing enough. In this paper, an aggregation model for BTM-BESSs is proposed in order to provide the opportunity for the BTM-EMS units to participate in the multiple wholesale markets to provide ancillary services, in addition to the demand charge management, to maximize owners’ payoff from installation of BTM-BESSs. Finally, the efficiency of the proposed aggregation model is validated through the simulation studies on the real value data.

Read more