Power Resource Dispatch Using Current Injection Optimization
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Solution Overview
Problem
Conventional power-flow models, such as ACOPF and DCOPF, struggle with complexity and inaccuracy, leading to suboptimal solutions and increased costs in managing electrical power systems, with DCOPF models neglecting reactive power flows and ACOPF models being non-linear, which complicates manual calculations and adds complexity and costs.
Innovation Solution
An automated control system employs a Lagrangian function, Karush-Kuhn-Tucker (KKT) conditions, and a Newton-Raphson algorithm to minimize a profit objective function based on a steady-state current injection model, determining setpoints for power system resources to optimize power flow and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ACOPF models are used to accurately describe energy flow, then measurement precision is improved, but device complexity increases due to non-linear equations
Solution Approach 1:
The patent uses a DCOPF model as a simplified, computationally inexpensive approximation of the more accurate but complex ACOPF model. This simplified model provides sufficient accuracy for distribution systems while being much easier to calculate and implement, effectively using a 'cheaper' modeling approach to solve the problem.
Solution Approach 2:
The patent changes the fundamental parameters of the power flow model by transitioning from ACOPF (which uses voltage magnitude and angle as primary variables with non-linear relationships) to DCOPF (which uses real power flows as primary variables with linear relationships). This parameter transformation simplifies the mathematical complexity while maintaining essential accuracy for distribution system analysis.
2Ease of operation
If DCOPF models are used to simplify calculations, then ease of operation is improved, but measurement precision deteriorates due to neglecting reactive power flows
Solution Approach 1:
The patent segments the power system into radial distribution networks where power flow directions are predetermined and fixed. This segmentation allows the use of DCOPF simplifications because the radial structure eliminates the need to solve complex non-linear power flow equations, making calculations easier while maintaining sufficient accuracy for this specific network topology.
Solution Approach 2:
The patent applies partial action by using DCOPF which only models real power flows and neglects reactive power flows. For radial distribution systems where reactive power management is handled separately through voltage control devices, this partial modeling approach provides sufficient accuracy while dramatically simplifying calculations.
3Reliability
If conventional power-flow models are used, then reliability is maintained, but loss of energy increases due to suboptimal solutions
Solution Approach 1:
The patent implements an automated control system that uses the DCOPF model to calculate optimal setpoints and provides feedback control to distribution system resources. This closed-loop feedback mechanism ensures that the system operates at optimal points, minimizing power losses while maintaining reliability through continuous monitoring and adjustment of resource dispatch.
Solution Approach 2:
The automated control system enables distribution system resources to self-optimize their operation by automatically calculating and implementing optimal setpoints based on real-time conditions. This self-service capability allows the system to continuously minimize power losses without requiring manual intervention, thereby reducing energy losses while maintaining reliable operation.
4Device complexity
If manual calculations are used for power-flow models, then device complexity is reduced, but productivity decreases due to calculation complexity
Solution Approach 1:
The patent replaces manual mechanical calculation methods with an automated electronic control system that uses DCOPF-based algorithms. This substitution of automated computing for manual calculation dramatically increases productivity in power system management, allowing real-time optimization of distribution systems without the limitations of human calculation speed and accuracy.
Data Source
AI summary
An automated control system includes at least one memory storing instructions. The automated control system includes at least one processor being configured to execute the instructions to perform operations. The operations include automatically determining the following in a steady-state current injection model: current withdrawal phase angles, power factor constraints, demand bus voltage magnitude constraints, generator voltage magnitude constraints, network flow constraints, generator capacity constraints, thermal line flow constraints, minimum and maximum demand bus voltage phase angle differences, minimum and maximum generator voltage phase angle differences, nodal voltage phase angle vectors, voltage stability constraints, a reference voltage constraint, linear marginal cost curves, linear marginal revenue curves, and retail rates. The operations include automatically minimizing a profit objective function through employment of a Lagrangian function, KKT conditions, and a Newton-Raphson algorithm. The operations include automatically determining setpoints for a plurality of electric power system resources.