Abstract: Methods and systems for generating a probability assessment for peak demand reduction for utility customers using a conditional-output energy generator are described. One method includes providing a customer data set and one or more historical generator production data sets for one or more intermittent generators that meteorologically correspond with the customer data set. Time intervals are defined in the data sets and a production distribution curve is generated for each time interval. A simulation is performed using the historical customer consumption data and the production distribution curves to obtain a net demand distribution curve for each time interval. These methods and systems may provide probability-based economic evaluation of consumption management systems.

Abstract: Methods and systems for generating a probability assessment for peak demand reduction for utility customers using a conditional-output energy generator are described. One method includes providing a customer data set and one or more historical generator production data sets for one or more intermittent generators that meteorologically correspond with the customer data set. Time intervals are defined in the data sets and a production distribution curve is generated for each time interval. A simulation is performed using the historical customer consumption data and the production distribution curves to obtain a net demand distribution curve for each time interval. These methods and systems may provide probability-based economic evaluation of consumption management systems.

Abstract: Methods and systems for generating a probability assessment for peak demand reduction for utility customers using a conditional-output energy generator are described. One method includes providing a customer data set and one or more historical generator production data sets for one or more intermittent generators that meteorologically correspond with the customer data set. Time intervals are defined in the data sets and a production distribution curve is generated for each time interval. A simulation is performed using the historical customer consumption data and the production distribution curves to obtain a net demand distribution curve for each time interval. These methods and systems may provide probability-based economic evaluation of consumption management systems.

Abstract: Methods and systems for generating a probability assessment for peak demand reduction for utility customers using a conditional-output energy generator are described. One method includes providing a customer data set and one or more historical generator production data sets for one or more intermittent generators that meteorologically correspond with the customer data set. Time intervals are defined in the data sets and a production distribution curve is generated for each time interval. A simulation is performed using the historical customer consumption data and the production distribution curves to obtain a net demand distribution curve for each time interval. These methods and systems may provide probability-based economic evaluation of consumption management systems.