DER Aggregation Control for Utility Demand and Site Cost Constraints
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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 activities, DER size, and operating costs.
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 that maximizes benefits and minimizes penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If DERs are aggregated in a centralized manner to provide utility-requested power levels, then the ability to meet utility demand is improved, but the complexity of coordination and control increases
Solution Approach 1:
The system segments the aggregation problem into two independent layers: a centralized aggregation engine that handles high-level coordination and commitment decisions, and local site controllers that handle real-time DER control. This segmentation reduces overall system complexity by distributing computational tasks and avoiding the need for a single complex centralized controller to manage all real-time decisions.
Solution Approach 2:
The aggregation engine acts as an intermediary between the utility and local site controllers. It receives requests from the utility, formulates commitment decisions, and transmits them to site controllers, which then execute the actual DER control. This intermediary layer simplifies the coordination complexity by providing a structured communication interface and decision-making framework.
2Productivity
If centralized control is used to coordinate DERs, then the ability to meet utility demand is improved, but the impact on local consumer service quality may worsen
Solution Approach 1:
The system implements local quality by enabling each site controller to make decisions optimized for its local conditions and consumer needs. The site controller receives commitment decisions from the aggregation engine but has the autonomy to determine the specific DER control actions that best serve local consumers, ensuring that utility demand response does not adversely affect local service quality.
Solution Approach 2:
By segmenting control authority between the centralized aggregation engine and local site controllers, the system allows utility-level objectives to be met while preserving local flexibility. The site controller can adjust DER operations to maintain consumer service quality, preventing the centralized control from having negative local impacts.
3Ease of manufacture
If DER aggregation considers site-specific activities and operating costs, then the economic efficiency is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The site controller performs self-service by automatically collecting and processing its own site-specific data (activities, DER characteristics, operating costs) and using this information to determine optimal control actions. This eliminates the need for a complex centralized data collection system, as each site independently prepares and communicates only the necessary commitment decisions to the aggregation engine.
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.