Demand Response Load Selection Using Electrical Distance Metrics
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Solution Overview
Problem
Current demand response selection methods in power distribution networks are inefficient due to their random nature and inability to optimize load reduction in complex topologies, leading to suboptimal selection of demand responsive loads and increased power loss.
Innovation Solution
A mathematical network model is used to evaluate power loss in power distribution networks, allowing for the selection of demand responsive loads that maximize reduction in power loss while meeting load reduction targets and network constraints, accommodating mesh networks and distributed generation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random selection method is used for demand responsive loads, then selection process is simple, but power loss reduction is suboptimal
Solution Approach 1:
The invention changes the selection criterion from random selection to selection based on electrical distance metric. The electrical distance is calculated using power flow equations and impedance values, transforming the selection parameter from arbitrary to physically meaningful, thereby optimizing power loss reduction while maintaining computational feasibility.
Solution Approach 2:
The invention replaces the random mechanical selection process with a mathematical optimization approach using power flow equations and electrical distance calculations. This substitution enables systematic optimization of load selection to minimize power loss while satisfying demand response targets.
2Loss of energy
If electrical distance model is used for load selection, then power loss evaluation is improved, but application to mesh networks and complex topologies is complicated
Solution Approach 1:
The invention creates a universal load selection methodology based on electrical distance that works across different network topologies including radial and mesh networks. The power flow equations and electrical distance calculations are formulated to be topology-agnostic, enabling the same approach to be applied universally without requiring topology-specific modifications.
Solution Approach 2:
The invention segments the complex network analysis into manageable components: (1) power flow equation formulation for each branch, (2) electrical distance calculation for each load, (3) sorting and selection based on electrical distance metrics. This segmentation makes the approach computationally tractable for complex mesh networks while maintaining accuracy.
3Loss of energy
If model-based load selection is used, then power loss reduction is optimized, but computational complexity increases
Solution Approach 1:
The invention applies partial action by calculating electrical distance metrics only for candidate loads that meet basic demand response criteria, rather than performing full power flow analysis for all loads in the network. This selective approach optimizes power loss reduction while limiting computational complexity to only the necessary subset of loads.
Solution Approach 2:
The invention performs preliminary filtering of candidate loads based on demand response eligibility criteria before applying the computationally intensive electrical distance calculations. This preliminary action reduces the number of loads requiring detailed analysis, thereby optimizing power loss reduction while managing computational resources efficiently.
Data Source
AI summary
In one aspect of the teachings herein, demand responsive loads are selected for involvement in a given DR event using an advantageous approach to selection that is based on using a mathematical network model to evaluate power loss in a power distribution network as a function of different combinations of demand responsive load selections and corresponding load reduction values. The mathematical network model comprises a mathematical representation of the power distribution network as a multi-phase unbalanced distribution network, including mathematical representations of the physical components in the power distribution network and the connecting relationships of those components. As overall power loss in the system is a function of different combinations of demand responsive load selections, the mathematical network model is used to evaluate system power loss under different demand response load selections, in a manner that automatically accommodates mesh networks and other complex network topologies, distributed generation sources, etc.