Power Distribution Automation Upgrade Under Reliability Constraints
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Solution Overview
Problem
Current methods for transforming circuit breakers and switchgears in power distribution networks are inefficient, taking long times, requiring large memory, generating unstable solutions, and failing to guarantee optimal search results, thus hindering ideal automation equipment transformation.
Innovation Solution
A method is developed to optimize the transformation of automation equipment in power distribution networks by determining installation states and operation criteria for fault isolation, load transfer, and fault recovery, while minimizing total transformation cost through a target function and reliability constraints, using a mixed integer linear programming model to solve for optimal equipment status and reliability indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heuristic optimization algorithms (ant colony algorithm, genetic algorithm, simulated annealing algorithm) are used for transforming circuit breakers and switchgears, then the transformation can be performed, but the calculation time is long, memory space requirement is large, solutions are unstable, and optimal search results cannot be guaranteed
Solution Approach 1:
The patent replaces heuristic optimization algorithms (which are iterative and probabilistic) with a mixed-integer linear programming model combined with branch-and-bound algorithm. This substitution transforms the optimization problem from a heuristic search process into a deterministic mathematical programming approach, eliminating the instability and long calculation time associated with heuristic methods while guaranteeing optimal solutions through systematic exploration of the solution space.
2Reliability
If the entire power distribution network is transformed to automation equipment, then the reliability and flexibility of the power distribution network is improved, but the investment cost becomes huge
Solution Approach 1:
The patent applies local quality by determining optimal automation transformation configurations for different feeder lines and equipment based on their specific characteristics, failure rates, and reliability requirements. Rather than uniformly transforming all equipment, the model identifies critical components that need automation while allowing non-critical components to remain manual, thereby achieving reliability improvements with minimized investment.
Solution Approach 2:
The patent implements partial action by transforming only the necessary portion of the power distribution network to automation equipment. The mixed-integer linear programming model calculates the optimal set of equipment to upgrade, transforming just enough components to meet reliability constraints without the excessive cost of transforming the entire network, thus achieving cost-effective reliability improvement.
3Reliability
If more automation equipment is installed to improve reliability indices (CIF, CID, SAIFI, SAIDI, EENS), then the power supply reliability is improved, but the transformation cost increases
Solution Approach 1:
The patent uses parameter changes by formulating reliability constraints (CIF, CID, SAIFI, SAIDI, EENS) as mathematical inequalities in the mixed-integer linear programming model. The optimization process dynamically adjusts equipment transformation decisions based on these reliability parameters, automatically determining the minimum transformation cost configuration that satisfies all reliability requirements without manual trial-and-error analysis.
Data Source
AI summary
The present disclosure provides a method for optimizing transformation of automation equipment in a power distribution network based on reliability, including determining installation states of respective components in the power distribution network and operation criterions for fault isolation, load transfer and fault recovery after a fault occurred in a feeder segment; determining a target function which is a target function for minimizing a total transformation cost of the power distribution network; determining constraint conditions including reliability constraints; establishing an optimization model for evaluating the reliability of the power distribution network based on the reliability constraints in accordance with the target function and the constraints; and solving the established optimization model for evaluating the reliability of the power distribution network based on the reliability constraints to obtain optimal solutions as optimization results of the automation transformation state of the circuit breaker and the switch and the reliability index.