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Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Chaoxian Lv, Rui Liang, Yuanyuan Chai

2023Journal of Modern Power Systems and Clean Energy16 citationsDOIOpen Access PDF

Abstract

Owing to potential regulation capacities from flexible resources in energy coupling, storage, and consumption links, central energy stations (CESs) can provide additional support to power distribution network (PDN) in case of power disruption. However, existing research has not explicitly revealed the emergency response of PDN with leveraging multiple CESs. This paper proposes a decentralized self-healing strategy of PDN to minimize the entire load loss, in which multi-area CESs' potentials including thermal storage and building thermal inertia, as well as the flexible topology of PDN, are reasonably exploited for service recovery. For sake of privacy preservation, the co-optimization of PDN and CESs is realized in a decentralized manner using adaptive alternating direction method of multipliers (ADMM). Furtherly, bilateral risk management with conditional value-at-risk (CVaR) for PDN and risk constraints for CESs is integrated to deal with uncertainties from outage duration. Case studies are conducted on a modified IEEE 33-bus PDN with multiple CESs. Numerical results illustrate that the proposed strategy can fully utilize the potentials of multi-area CESs for coordinated load restoration. The effectiveness of the performance and behaviors' adaptation against random risks is also validated.

Topics & Concepts

CVARComputer scienceDemand responseAdaptation (eye)Power (physics)Coupling (piping)Network topologyReliability engineeringDistributed computingMathematical optimizationTopology (electrical circuits)Risk managementEngineeringExpected shortfallComputer networkElectrical engineeringElectricityBusinessMechanical engineeringMathematicsFinanceOpticsPhysicsQuantum mechanicsOptimal Power Flow DistributionMicrogrid Control and OptimizationPower System Optimization and Stability
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