Robust Optimal Operation of Active Distribution Network Based on Minimum Confidence Interval of Distributed Energy Beta Distribution

Abstract

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

Improved Symbiotic Organisms Search Algorithm for Optimal Operation of Active Distribution Systems Incorporating Renewables and Emerging Data‐Center Resources

“…The classical cost function of THG units is in the form such as (9), where x i ,y i , and z i are parameters related to generation cost of the ith THG.…”

“…To solve the intermittency and uncertainty of RESs in the distribution network, a robust optimization strategy on the basis of minimum confidence interval is conducted to solve the problem of optimal operation of active distribution network. 9 Zeng et al 10 established a dynamic twostage optimization model for active distribution network with the consideration of economy and environment, which was solved by discrete genetic algorithm. Reference 11 established a two-stage robust optimization model considering economy and robustness, which was solved by benders' dual decomposition algorithm.…”

Multi-time scale trading profit model of pumped storage power plant for electricity market

“…Li et al (2021) proposed a p-robust algorithm to calculate the risk caused by the uncertainty of electricity price on the revenue of pumped storage power plants, but it needs to sacrifice part of the profit while reducing the risk of pumped storage revenue. Luo et al (2021) considered the uncertainty of distributed energy sources and the role of energy storage devices on multiple time scales to optimize the operation of the electric grid. Yang et al (2020) proposed a demand response model of energy storage operators to take part in the MLTM to reduce the uncertainty risk while lowering the power purchase cost for operators through flexible energy storage systems.…”

A Modified Long Short-Term Memory-Deep Deterministic Policy Gradient-Based Scheduling Method for Active Distribution Networks

“…However, it suffers a heavy computational burden due to the need to consider many scenarios. Furthermore, studies (Li et al, 2020a;Luo et al, 2021) establish robust optimal operation strategies to deal with the randomness of DERs. In (Luo et al, 2021), the uncertainty of DERs is described based on beta distribution, and a robust optimization model is established to optimize the network loss, power purchase cost, and voltage distribution.…”

“…Furthermore, studies (Li et al, 2020a;Luo et al, 2021) establish robust optimal operation strategies to deal with the randomness of DERs. In (Luo et al, 2021), the uncertainty of DERs is described based on beta distribution, and a robust optimization model is established to optimize the network loss, power purchase cost, and voltage distribution. Li et al propose a distributed adaptive robust VVC method (Li et al, 2020a).…”