Distributed Generator Energy Storage Control for Grid Resilience
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Solution Overview
Problem
Traditional power grid resilience measures focus on predictable failures, but extreme events often cause widespread damage and large-scale power outages, necessitating enhanced energy storage control methods for distributed generators (DGs) to improve network resilience and post-disaster recovery.
Innovation Solution
A resilience enhancement-oriented energy storage control method and system for DGs in distribution networks, which involves determining objective functions and constraints to optimize investment costs, load shedding, and post-disaster operations by configuring energy storage systems and hardened lines, while considering line failures and node power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy storage systems and hardened lines are configured to enhance resilience against extreme events, then the ability to resist failures and ensure uninterrupted power supply is improved, but the investment cost increases
Solution Approach 1:
The patent applies preliminary action by configuring energy storage systems and hardened lines before extreme events occur. The optimization model determines the optimal placement and capacity of energy storage systems in advance, and identifies which lines should be hardened beforehand. This allows the distribution network to be pre-prepared for potential disasters, ensuring resilience when needed while controlling investment costs through optimized planning rather than universal hardening.
2Reliability
If the distribution network is hardened and energy storage is increased to prevent outages, then power supply reliability is improved, but the complexity of system configuration and control increases
Solution Approach 1:
The patent applies feedback by implementing a tri-level optimization model that continuously adjusts energy storage control strategies based on system state. The model considers line failure states, load conditions, and energy storage status to dynamically determine charging/discharging decisions. This feedback mechanism simplifies control by providing clear decision rules based on system conditions, rather than requiring complex manual coordination of multiple hardening measures and energy storage units.
3Reliability
If comprehensive resilience measures are implemented across the entire distribution network, then post-disaster recovery capability is improved, but the cost and complexity of implementation increases
Solution Approach 1:
The patent applies local quality by optimizing energy storage placement and line hardening at specific locations rather than uniformly across the entire network. The tri-level model identifies critical nodes and lines that benefit most from resilience enhancement, concentrating resources where they provide maximum post-disaster recovery capability. This localized approach reduces overall implementation complexity and cost while maintaining effective recovery capability where it matters most.
Data Source
AI summary
A resilience enhancement-oriented energy storage control method and system for a distributed generator (DG) in a distribution network. The method includes: determining an objective function including a first sub-objective function and a second sub-objective function; determining an outer-level constraint based on a quantity of configured energy storage systems (ESSs), a quantity of hardened lines, and a rated power and capacity configuration of an ESS; determining a middle-level constraint based on a line failure; determining an inner-level constraint based on node power of a distribution network, a line load capacity, a line power flow, a power output of a unit, a node voltage, climbing of the unit, operation of the ESS, and LS; determining parameters based on the outer-level constraint, the first sub-objective function, the middle-level constraint, the inner-level constraint, and the second sub-objective function; and controlling energy storage of a DG in the distribution network.