Zhaohong Bie

Robust Distribution Network Reconfiguration

We propose a two-stage robust optimization model for the distribution network reconfiguration problem with load uncertainty. The first-stage decision is to configure the radial distribution network and the second-stage decision is to find the optimal a/c power flow of the reconfigured network for given demand realization. We solve the two-stage robust model by using a column-and-constraint generation algorithm, where the master problem and subproblem are formulated as mixed-integer second-order cone programs. Computational results for 16, 33, 70, and 94-bus test cases are reported. We find that the configuration from the robust model does not compromise much the power loss under the nominal load scenario compared to the configuration from the deterministic model, yet it provides the reliability of the distribution system for all scenarios in the uncertainty set.

Microgrids for Enhancing the Power Grid Resilience in Extreme Conditions

This paper presents a framework for analyzing the resilience of an electric power grid with integrated microgrids in extreme conditions. The objective of this paper is to demonstrate that controllable and islandable microgrids can help improve the resiliency of power grids in extreme conditions. Four resilience indices are introduced to measure the impact of extreme events. Index

Battling the Extreme: A Study on the Power System Resilience

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A New Model for Resilient Distribution Systems by Microgrids Formation

measures the expected number of lines on outage due to extreme events. Index loss of load probability measures the probability of load not being fully supplied. Index expected demand not supplied measures the expected demand that cannot be supplied. Index

A Geometric Programming to Importance Sampling for Power System Reliability Evaluation

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Adequacy evaluation of generating system recognizing random fuzzy wind speed

measures the difficulty level of grid recovery. The mechanism of extreme events affecting power grid operation is analyzed based on the proposed mesh grid approach. The relationship among transmission grid, distribution grid, and microgrid in extreme conditions is discussed. The Markov chain is utilized to represent the state transition of a power grid with integrated microgrids in extreme conditions. The Monte Carlo method is employed to calculate the resilience indices. The proposed power grid resilience analysis framework is demonstrated using the IEEE 30-bus and 118-bus systems assuming all loads are within microgrids.

A Data-Driven Stochastic Reactive Power Optimization Considering Uncertainties in Active Distribution Networks and Decomposition Method

The electricity infrastructure is a critical lifeline system and of utmost importance to our daily lives. Power system resilience characterizes the ability to resist, adapt to, and timely recover from disruptions. The resilient power system is intended to cope with low probability, high risk extreme events including extreme natural disasters and man-made attacks. With an increasing awareness of such threats, the resilience of power systems has become a top priority for many countries. Facing the pressing urgency for resilience studies, the objective of this paper is to investigate the resilience of power systems. It summarizes practices taken by governments, utilities, and researchers to increase power system resilience. Based on a thorough review on the existing metrics system and evaluation methodologies, we present the concept, metrics, and a quantitative framework for power system resilience evaluation. Then, system hardening strategies and smart grid technologies as means to increase system resilience are discussed, with an emphasis on the new technologies such as topology reconfiguration, microgrids, and distribution automation; to illustrate how to increase system resilience against extreme events, we propose a load restoration framework based on smart distribution technology. The proposed method is applied on two test systems to validify its effectiveness. In the end, challenges to the power system resilience are discussed, including extreme event modeling, practical barriers, interdependence with other critical infrastructures, etc.

Multiobjective Transmission Network Planning considering the Uncertainty and Correlation of Wind Power

Forming multiple micorgrids with distributed generators offers a resilient solution to restore critical loads from natural disasters in distribution systems. However, more dummy binary and continuous variables are needed with the increase of the number of microgrids, which will therefore increase the complexity of this model. To address this issue, this letter presents a new model to reformulate the micorgrid formulation problem in resilient distribution networks. Compared with the traditional model, the number of both binary and continuous variables is greatly reduced, such that the computational performance is significantly improved. Numerical results on IEEE test systems verify the effectiveness of the proposed model.

A second order cone based relaxation and decomposition algorithm for multi-period reactive power optimization considering uncertain PV integration in active distribution networks

A novel Geometric Programming (GP) is presented in the first time by the optimization model of importance sampling parameters (ISP) in Variance Minimization (VM) for importance sampling (IS) of power system reliability evaluation. The key point of the proposed method is that the equality constraints of VM optimization model can be relaxed into inequalities because of its special structure, thus a new GP-VM convex optimization model can be built exactly to solve the difficulty of obtaining the optimal ISP. Numerical results of two test systems verify the effectiveness of the proposed method.

Energy-Reserve Co-Optimization for Energy Internet Considering Reserve Control Zone Determination

A new method based on fuzzy extension principle which can represent and propagate the possibilistic uncertainties of wind power in power system adequacy evaluation is proposed. In this method, wind speed is modeled as random fuzzy variable, and the corresponding system reliability indices are represented by fuzzy numbers instead of crisp values. Moreover, the relationship of different variables is recognized in this method. The paper illustrates the effectiveness of the approach by its application to IEEE Modified Reliability Test System.