DER Aggregation Engine With Site-Level Feedback for Peak Power Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to effectively aggregate distributed energy resources (DERs) to provide coordinated responses to electricity grid demands, as they do not consider site-specific activities, DER sizes, and operating costs, leading to inefficient utilization and high costs in peak demand management.
Innovation Solution
A centralized aggregation engine coordinates with site controllers to optimize the aggregation of DERs, adjusting benefits and apportionment to ensure efficient participation from various sites, balancing costs and benefits to achieve a target power change while minimizing overall electricity-related costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional centralized VPP systems aggregate DERs without considering site-specific factors, then coordination capability is improved, but cost efficiency and reliability deteriorate
Solution Approach 1:
The system segments the VPP aggregation process into two levels: centralized coordination for overall target setting and distributed optimization for site-specific execution. Each DER site maintains local autonomy to optimize based on its own constraints (battery state, load profiles, generation capacity) while contributing to the aggregate target, resolving the contradiction between centralized coordination and site-specific efficiency.
Solution Approach 2:
The system implements local quality by allowing each DER site to apply its own optimization criteria and constraints locally. Sites with different characteristics (storage-capable vs. generation-only, commercial vs. residential) can optimize their participation strategies locally, ensuring cost efficiency and reliability are maintained at each site while achieving overall coordination.
2Reliability
If DERs operate independently to optimize local costs, then site-level cost efficiency is improved, but aggregate coordination and grid response capability deteriorate
Solution Approach 1:
The system implements feedback mechanisms where DER sites report their local optimization results, availability, and constraints back to the centralized coordinator. The coordinator adjusts aggregate targets and incentives based on this feedback, creating a closed-loop system that maintains both local cost efficiency and aggregate coordination. The feedback loop enables dynamic adjustment of aggregation targets based on real-time site conditions.
3Power
If additional T&D infrastructure is constructed to meet peak demand, then power delivery capability is improved, but construction costs and resource utilization efficiency deteriorate
Solution Approach 1:
The system merges multiple distributed DERs across different locations into a coordinated virtual power plant. By combining the capabilities of numerous small-scale generation and storage resources, the VPP achieves power delivery capability comparable to traditional centralized infrastructure without requiring new T&D assets. This merging approach eliminates the need for expensive duplicate infrastructure while maintaining peak demand response capability.
Solution Approach 2:
The VPP system enables existing DER infrastructure to serve multiple functions: local cost optimization, aggregate peak demand response, and grid stability support. This multi-functionality allows the system to replace dedicated peak-demand infrastructure with versatile distributed resources that provide multiple benefits throughout the year, improving resource utilization efficiency.
4Reliability
If DER aggregation considers detailed site-specific factors, then cost efficiency and reliability are improved, but system complexity and computational requirements deteriorate
Solution Approach 1:
The system performs preliminary actions by having DER sites pre-report their capabilities, constraints, and optimization parameters before aggregation optimization begins. Sites provide advance information about battery state of charge, generation capacity, load profiles, and operational constraints. This preliminary data collection simplifies the subsequent optimization process by reducing the computational complexity of real-time decision-making while still incorporating all site-specific factors.
Data Source
AI summary
Apparatuses, systems, and methods of apportioning an aggregate maneuver is disclosed. An aggregation engine receives aggregation opportunity information requesting an aggregate change in power of one or more sites. The aggregation engine transmits participation opportunity information to a plurality of site controllers. The participation opportunity information indicates a site benefit for providing a site change in power. The aggregation engine also receives commitment information from the plurality of site controllers, the commitment information indicating levels of commitment of the plurality of site controllers to contribute site change in power to a target change in power of the aggregation opportunity. The aggregation engine, as appropriate, further transmits updated participation opportunity information including an adjusted proposed site benefit, an adjusted proposed site change in power, or both to the plurality of site controllers, until an aggregate change in power of the one or more sites is within a threshold level of the target change in power.