Site Controller Coordination for Cost-Aware DER Aggregation
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Solution Overview
Problem
Existing systems struggle to effectively aggregate distributed energy resources (DERs) in a coordinated manner to provide utility-requested power levels without adversely affecting electricity consumers, failing to consider site-specific factors like planned activities and DER size.
Innovation Solution
A centralized aggregation engine optimizes the aggregation of DERs by determining economically efficient maneuvers for individual sites, considering their unique conditions and costs, to achieve a net power change while minimizing total electricity-related costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DERs are aggregated in a coordinated manner to provide utility-requested power levels, then grid stability and power delivery are improved, but site-specific factors like planned activities and DER size are not considered, leading to increased costs for individual sites
Solution Approach 1:
The system segments the aggregation problem into two levels: centralized aggregation engine that coordinates overall DER aggregation for grid stability, and site controllers that optimize individual site operations. This segmentation allows simultaneous achievement of grid-wide reliability and site-specific cost optimization by distributing decision-making authority appropriately.
Solution Approach 2:
The patent applies local quality by enabling each site controller to consider site-specific factors (planned activities, DER size, local costs) while contributing to the overall aggregation. Each site operates with customized parameters and constraints that reflect its unique characteristics, rather than applying uniform aggregation rules to all sites.
2Power
If additional T&D systems are constructed to satisfy peak demand, then power delivery capability is improved, but construction costs increase significantly with infrequent utilization
Solution Approach 1:
The system merges multiple distributed energy resources across different sites into a coordinated virtual power plant. By aggregating DERs from numerous sites, the system achieves peak demand capacity that would be prohibitively expensive to build through traditional centralized T&D infrastructure, while avoiding the high construction costs of duplicating utility-scale generation and transmission assets.
Solution Approach 2:
The aggregation engine provides multi-functionality by enabling DERs to serve multiple purposes: local site optimization, utility-requested aggregation for peak demand, and autonomous operation. This universal system replaces the need for dedicated infrastructure built solely for peak demand scenarios.
3Ease of operation
If independent DERs operate autonomously without coordination, then site autonomy and simplicity are maintained, but coordinated power delivery to maintain grid stability is compromised
Solution Approach 1:
The system implements dynamic operation modes that allow site controllers to switch between autonomous operation and coordinated aggregation based on utility requests and site conditions. This dynamic flexibility maintains site autonomy when aggregation is not needed while enabling coordinated power delivery when grid stability requires it, resolving the contradiction between independence and coordination.
Data Source
AI summary
The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.