Optimal operation of reservoir based on dynamic programming and particle swarm optimization

Li, Shun-xin; Du, Hui Bin

doi:10.3724/sp.j.1087.2010.01550

Cited by 1 publication

(1 citation statement)

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“…The global optimal solution can be found quickly through inter-particle cooperation and information interaction, and the global search capability is strong, which is suitable for optimizing continuous variables. Li et al (2022) introduced inertia weights of the adaptive mechanism to dynamically adjust its convergence speed to avoid PSO getting into a local optimal solution, which can lead to premature convergence. Song (2020) and Ning et al (2022) considered the complexity of a nonconvex planning reactive-voltage optimization model solution, using a highly robust PSO algorithm to solve the model without analyzing the nature of the model itself.…”

Section: Solving Methodology For the Stackelberg Gamementioning

confidence: 99%

Overvoltage risk regulation strategy with distributed energy application in a distribution network based on the Stackelberg game

Qiu,

Wang,

Zhang

et al. 2024

Front. Energy Res.

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Along with the increasing low-carbon demand of the power system, the access of a high percentage of renewable energy resources to the distribution network has a large impact on the voltage fluctuation of the system and reduces the operational reliability. In this paper, we consider utilizing the reactive capacity of distributed resources to participate in system voltage regulation to reduce node loss of load probability (LOLP) caused by node overvoltage faults and propose an overvoltage risk regulation strategy for the interaction between distribution network operators (DSOs) and distributed users in the framework of the Stackelberg game. First, the nodes are clustered and analyzed based on the two-dimensional indexes of node voltage regulation ability, and different voltage regulation compensation tariffs are assigned. Second, the cost-benefit model of voltage regulation for the leader and follower sides and the node LOLP model are constructed to measure the reliability of the system. The Stackelberg game is used to co-optimize the two parties’ compensation tariffs and voltage regulation strategies. The optimal solution of voltage regulation under the equilibrium of the game is obtained by solving using the particle swarm optimization (PSO) algorithm. Based on the IEEE-33 node system, a case study is carried out to verify that the proposed overvoltage risk regulation strategy can maximize the benefits of the regulator participants while enhancing the operational reliability of the system.

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Section: Solving Methodology For the Stackelberg Gamementioning

confidence: 99%