Monte Carlo Simulation for Intermittent Generator Risk Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The unpredictability of intermittent energy generators, such as solar and wind turbines, makes it challenging for energy consumption management industries to accurately predict peak demand reduction, leading to uncertainties in utility savings and increased peak demand charges.
Innovation Solution
A method and system that generate a probability assessment for peak demand reduction by using customer data, historical generator production data, and Monte Carlo simulations to create demand reduction probability distribution curves, allowing for the assignment of probability-weighted economic values and the implementation of peak demand reduction systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If intermittent generators (solar panels, wind turbines) are used to reduce reliance on grid power, then utility savings and reduced dependence on the grid are achieved, but the unpredictability of energy production due to weather conditions creates uncertainty in determining actual savings
Solution Approach 1:
The system performs preliminary actions by collecting historical weather data and generator production data before the actual energy production occurs. Monte Carlo simulations are run in advance to predict a range of possible production outcomes and their probabilities, allowing stakeholders to make informed decisions about energy procurement and risk management before the intermittent generator actually produces energy.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual generator production against predicted production ranges. This feedback loop allows the system to refine its probability assessments and improve the accuracy of utility savings calculations over time, reducing the uncertainty mentioned in the contradiction.
2Measurement precision
If detailed predictions of intermittent generator performance are attempted, then precision in predicting peak demand reduction is improved, but the high degree of uncertainty in weather conditions far in advance makes precise predictions difficult
Solution Approach 1:
The system changes the parameter of prediction from deterministic single-value forecasts to probabilistic distribution curves. Instead of attempting to predict exactly how much energy a generator will produce, the system predicts the probability distribution of possible production levels, transforming the problem from seeking precise point predictions to characterizing ranges of likely outcomes with associated probabilities.
Solution Approach 2:
The system performs partial action by focusing predictions on the most relevant time intervals for peak demand charge calculation rather than attempting to predict all possible outcomes with equal precision. The Monte Carlo simulations generate comprehensive distribution curves, but the system selectively applies these predictions to the specific time intervals that matter most for utility billing and peak demand management.
3Reliability
If Monte Carlo simulations are used to generate probability distribution curves for generator production, then a guaranteed probability of peak demand reduction is provided, but the complexity of the simulation process and data requirements increases
Solution Approach 1:
The system uses copying by creating virtual replicas of the intermittent generator's performance characteristics through probability distribution curves. Instead of physically testing or monitoring every possible weather scenario, the system creates computational copies of historical production data matched with historical weather data, allowing Monte Carlo simulations to efficiently explore thousands of possible future scenarios without requiring complex physical experimentation.
Data Source
AI summary
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.