Demand Response Forecasting for Peak Load Reduction

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Solution Overview

Problem

Utility companies face challenges in managing peak electrical demand due to the difficulty in storing energy and fluctuating power demand, which strains power systems, and existing demand response methods are either costly or require significant public education and participation.

Innovation Solution

A method and system for forecasting load and managing control plans for households with controlled appliances, using processing devices to preprocess historical consumption data, create forecast models, and send control instructions based on environmental and user behavior parameters to optimize appliance activation during peak periods.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If generators are taken on and offline to control power production, then power supply can be adjusted to match demand, but operating costs increase and response time is delayed

Engineering Contradiction:

Improvepower production adjustmentVSAvoidgenerator response time

Core Design Contradiction:

PowerVSLoss of time

Solution Approach 1:

The system pre-cools or pre-heats thermal mass (water, concrete, masonry) before peak demand periods occurs. This preliminary action stores cooling or heating capacity in advance, allowing the HVAC system to maintain comfort during peak periods without running expensive generators or consuming peak power.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the operational parameters of HVAC equipment by adjusting setpoints and运行 schedules. During off-peak periods, the system operates at different parameters (pre-cooling/heating) compared to peak periods (maintaining temperature with reduced or no HVAC operation), thereby avoiding peak demand while maintaining comfort.

Inventive Principle:

Principle #35Parameter changes

2Power

If local power shut-downs are initiated to reduce power consumption, then peak demand is reduced, but service reliability deteriorates and customer satisfaction decreases

Engineering Contradiction:

Improvepeak demand reductionVSAvoidpower service reliability

Core Design Contradiction:

PowerVSReliability

Solution Approach 1:

The system uses automated control algorithms and building automation systems to manage demand response without manual intervention or customer action. The HVAC system automatically adjusts operation based on pre-cooled thermal mass, eliminating the need for customer education programs or manual enrollment processes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-cools or pre-heats thermal mass before peak demand periods, storing cooling or heating capacity in advance. This allows the building to maintain comfort during peak periods without requiring power shut-downs or customer intervention, thereby maintaining service reliability while reducing peak demand.

Inventive Principle:

Principle #10Preliminary action

3Power

If financial incentives are provided to consumers to postpone appliance operation, then peak demand is reduced, but program complexity increases and requires significant public education

Engineering Contradiction:

Improvepeak demand reductionVSAvoiddemand response program complexity

Core Design Contradiction:

PowerVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

4Loss of energy

If demand response control is applied to reduce peak consumption, then operational costs are reduced, but prediction accuracy of consumption patterns becomes more difficult

Engineering Contradiction:

Improvepeak consumption reductionVSAvoidconsumption prediction accuracy

Core Design Contradiction:

Loss of energyVSMeasurement precision

Solution Approach 1:

The system incorporates feedback loops that continuously monitor actual consumption patterns, thermal mass temperature, and environmental conditions. This feedback is used to adjust control algorithms and improve prediction accuracy over time, allowing the system to adapt to changing building characteristics and external conditions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10804700B2Method and system for optimizing and predicting demand response

Publication Date: 2020.10.13 GRID4C

AI summary

The present invention provides a method for forecasting load and managing a control plan for households having electric appliances wherein the control plan determines the activation of the electric appliances at pre-defined control periods. The method comprises the steps of: pre-processing per meter of households historical consumption of electric appliances at control period in relation to time dependent environmental parameters and household profiles and control program parameters, creating forecast model of consumption of each controlled appliance during next control plan period based on said pre-processing enabling to simulate control program parameters according to predefined goals parameter including at least target cost or consumption, determining control plan parameters for incoming control period, based on forecast models using defined goal parameters and sending control instructions to each group member control module based on determined control plan parameters, time dependent parameters and measured environmental parameters within the household.