Dynamic Hosting Capacity for DER Integration on Distribution Grids
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for calculating hosting capacity for distributed energy resources (DERs) in distribution grids are static and worst-case based, failing to account for locational differences, flexible assets, and time-dependent variations, leading to inefficient interconnection processes and costly upgrades.
Innovation Solution
A dynamic hosting capacity methodology and system that uses a high-power grid analytics engine with real-time state estimation, scenario analysis, and probabilistic methods to determine hosting capacity at each location and time interval, enabling active management of DERs through real-time or planning-based commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static worst-case approach is used to calculate hosting capacity, then utility system reliability is maintained, but available hosting capacity is severely limited and interconnection costs increase
Solution Approach 1:
The patent transforms the static hosting capacity calculation into a dynamic one by incorporating real-time or near-real-time measurements of actual grid conditions (voltage, power flow, loading). The system continuously updates hosting capacity values based on actual operating conditions rather than relying on predetermined worst-case scenarios, allowing the hosting capacity to dynamically adapt to changing grid states while maintaining reliability constraints.
Solution Approach 2:
The invention changes the parameters used in hosting capacity calculation from fixed worst-case assumptions to variable parameters based on actual measured grid conditions. By using real-time measurements of voltage magnitudes, power flows, and loading conditions, the system adjusts the hosting capacity parameters to reflect actual operational states, thereby increasing available capacity without compromising system reliability.
2Device complexity
If a static worst-case approach is used, then planning simplicity is maintained, but locational differences and time-dependent variations in hosting capacity are not captured
Solution Approach 1:
The patent segments the distribution grid into multiple zones or feeders and calculates hosting capacity separately for each segment. This allows the system to capture locational differences in hosting capacity by analyzing each segment's specific characteristics, constraints, and operating conditions independently, thereby providing precise location-specific hosting capacity values rather than a single grid-wide estimate.
Solution Approach 2:
The system implements dynamic hosting capacity calculation that updates values based on time-dependent grid conditions. By continuously monitoring and recalculating hosting capacity for each segment based on actual operating conditions, the system captures temporal variations and provides time-specific hosting capacity information, enhancing measurement precision without requiring overly complex manual planning processes.
3Device complexity
If flexible assets are not considered, then calculation simplicity is maintained, but the ability to mitigate impact on hosting capacity is lost
Solution Approach 1:
The patent implements a feedback mechanism where real-time measurements of grid conditions and flexible asset performance are continuously monitored and fed back into the hosting capacity calculation system. This feedback loop allows the system to adjust hosting capacity values based on actual asset performance and grid responses, enabling the incorporation of flexible assets' mitigating effects while maintaining manageable calculation complexity through automated iterative processes.
Solution Approach 2:
The system dynamically adjusts hosting capacity parameters based on the presence and performance of flexible assets. By incorporating measurements of flexible asset operations (such as demand response, energy storage, or distributed generation) into the calculation, the system modifies hosting capacity values to reflect the mitigating effects these assets provide, thereby increasing hosting capacity flexibility without requiring excessively complex manual assessments.
4Productivity
If dynamic hosting capacity calculation is implemented, then available hosting capacity and interconnection efficiency increase, but system complexity and data requirements increase
Solution Approach 1:
The patent implements a self-service approach where the system automatically collects, processes, and analyzes grid data using existing utility measurement infrastructure and control systems. The hosting capacity calculation system serves itself by utilizing already-available telemetry data, sensor readings, and operational parameters from the grid, thereby reducing the need for additional complex measurement devices or manual data collection processes while still achieving dynamic calculation capabilities.
Solution Approach 2:
The invention leverages existing multi-functional utility systems and infrastructure to support dynamic hosting capacity calculation. By utilizing existing SCADA systems, energy management platforms, and measurement devices that already perform multiple functions (monitoring, control, analysis), the system avoids the need for dedicated specialized equipment, thereby reducing overall system complexity while enabling dynamic hosting capacity assessment across multiple grid segments and time periods.
Data Source
AI summary
A distributed energy resource management system utilizes capacity on a distribution grid to host further distributed energy resource interconnections. In an embodiment, the system utilizes a data store including operational values associated with distributed electric resources on an electric power distribution network and a processor or processors coupled to the data store and in communication with the distributed electric resources. The computer processors are programmed, upon receiving one or more requests, to create 3-phase AC power flows for each location across the electric power distribution network. Optimizations of the 3-phase AC power flows to are used to calculate hosting capacity for each of the locations, for each of a plurality of time intervals, and each of a plurality of types of distributed energy resource, as a dynamic quantity that can change depending on time, day, season, and location. The hosting capacity is translated into calculated operational values which are used to determine and send a direction to actively manage distributed energy resources on the electric power distribution network.