Virtual Power Plant Dispatch for Renewable Energy Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Electric utilities face challenges in accurately predicting and utilizing demand response (DR) and distributed energy resource (DER) assets to mitigate the variability and unpredictability of renewable energy resources, leading to costly adjustments in traditional generation resources and inadequate flexible reserve management.
Innovation Solution
A system and method for accurately modeling the capacity, ramping, and effective durational use of DR and DER resources, aggregating them into Virtual Power Plants (VPPs) for real-time balancing and economic optimization, utilizing advanced communication technologies and data modeling to predict and dispatch these resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional generation resources are adjusted to compensate for renewable energy variability, then supply and demand balance is maintained, but operational costs increase significantly
Solution Approach 1:
The system performs preliminary actions by forecasting renewable energy generation and demand in advance, creating flexible reserve capacity beforehand through demand response and distributed energy resources. This allows the system to pre-position resources to mitigate variability, avoiding costly real-time adjustments of traditional generation facilities.
Solution Approach 2:
The patent introduces flexible reserve capacity from demand response and distributed energy resources as an intermediary between variable renewable generation and traditional baseload plants. This intermediary layer absorbs the variability and uncertainty, shielding traditional generation resources from frequent ramping adjustments and reducing their operational costs.
2Reliability
If demand response and distributed energy resources are utilized to mitigate renewable variability, then flexible reserve capacity is improved, but accurate prediction and modeling becomes more difficult
Solution Approach 1:
The system changes parameters by developing specialized forecasting models that account for the specific characteristics of demand response and distributed energy resources. These models incorporate weather conditions, consumer behavior patterns, and resource availability to predict the flexible reserve capacity that can be provided, transforming uncertain variables into quantifiable predictions.
Solution Approach 2:
The patent implements feedback mechanisms where actual performance data from demand response and distributed energy resources is collected and used to refine forecasting models. This continuous feedback loop improves prediction accuracy over time by learning from historical performance and adjusting models to better capture the behavior of these resources.
3Loss of energy
If renewable energy resources are expanded to meet demand, then environmental benefits and cost reduction are achieved, but variability and unpredictability increase
Solution Approach 1:
The system performs preliminary forecasting of renewable energy generation based on weather predictions and historical data. By predicting renewable output in advance, the system can proactively schedule demand response and distributed energy resource activation to compensate for expected variability, maintaining reliability while preserving the environmental and economic benefits of renewable expansion.
Solution Approach 2:
The patent introduces flexible reserve capacity from demand response and distributed energy resources as an intermediary layer between variable renewable generation and the grid. This intermediary absorbs the variability and uncertainty of expanded renewable resources, allowing them to be integrated at higher levels without compromising grid reliability or requiring expensive traditional generation adjustments.
Data Source
AI summary
A system and process/method of modeling demand-response (DR) and distributed-energy resource (DER) assets is provided, facilitating the aggregation of said assets in virtual power plants (VPPs), and using VPPs to provide energy balancing services needed to minimize the impact of Variable Energy Resources (VER) on power system. The method accurately accounts for assets by qualifying assets based on various parameters and by forecasting the capabilities of the resulting VPPs for provision of balancing services. Asset performance factors that may affect the aggregated VPP's capabilities are monitored and recalculated when necessary. Near-term forecasted VPP capability is compared to near-term forecasted imbalances in the electric-power-supply system, and the VPP is dispatched to minimize the system imbalances. The dispatch signal is disaggregated to control commands to individual assets. This process provides a reliable and cost effective approach to support higher penetrations of renewable generation in the electric power system.