Traditional reactive power optimization aims to minimize the total transmission losses by control reactive power compensators and transformer tap ratios, while guaranteeing the physical and operating constraints, such as voltage magnitudes and branch currents to be within their reasonable range. However, large amounts of renewable resources coming into power systems bring about great challenges to traditional planning and operation due to the stochastic nature. In most of the practical cases from China, the wind farms are centrally integrated into active distribution networks. By the use of conic relaxation based branch flow formulation, the reactive optimization problem in active distribution networks can be formulated as a mixed integer convex programming model that can be tractably dealt with. Furthermore, to address the uncertainties of wind power output, a two-stage robust optimization model is proposed to coordinate the discrete and continuous reactive power compensators and find a robust optimal solution that can hedge against any possible realization within the uncertain wind power output. Moreover, the second order cone programming-based column-and-constraint generation algorithm is employed to solve the proposed two-stage robust reactive power optimization model. Numerical results on 33-, 69- and 123-bus systems and comparison with the deterministic approach demonstrate the effectiveness of the proposed method.

This paper proposes a new method for reliability evaluation of active distribution systems with multiple microgrids based on a Monte Carlo simulation. Multi-state models are developed on the basis of generalized capacity outage tables (GCOTs) to better represent various types of distributed generators in reliability evaluation. Then, the virtual power plant (VPP) is introduced to model microgrids with intermittent sources. Furthermore, the reliability behavior of VPP is efficiently characterized by an equivalent GCOT. The nonsequential Monte Carlo method is then adopted to evaluate the reliability of active distribution systems considering different operation modes under single or multiple contingencies. Some techniques-such as two-step state sampling, zone partitioning and minimal path search-are proposed to facilitate the state evaluation process and improve the Monte Carlo simulation speed. The effectiveness and efficiency of the proposed method are validated through extensive numerical tests on an IEEE test system and a real-life active distribution network.

A novel multi-stage stochastic programming model is proposed for the expansion co-planning of gas and power net-works considering the uncertainties in net load demand. Mean-while, the nonanticipativity constraints are taken into account to guarantee the decisions should only depend on the information of realized uncertainties up to the present stage. Compared with the traditional two-stage stochastic programming model, the proposed multi-stage stochastic programming model yields se-quential investment decisions with the uncertainties revealed gradually over time, such that the investment decisions are ca-pable of keeping future options open and can shift from “never be changed” decisions to a flexible “wait and see” decisions. The test on three systems shows the effectiveness of the proposed multi-stage stochastic programming model.

With the growing trend of extreme weather events in the Northeast U.S., a region of dense vegetation, evaluating hazard effects of wind storms on power distribution systems becomes increasingly important for disaster preparedness and fast responses in utilities. In this paper, probabilistic wind storm models for the study region have been built by mining 160-year storm events recorded in the National Oceanic and Atmospheric Administration's Atlantic basin hurricane database (HURDAT). Further, wind storms are classified into six categories according to NOAA criteria and IEEE standard to facilitate the evaluation of distribution system responses under different levels of hazards. The impacts of wind storms in all categories are accurately evaluated through a Sequential Monte Carlo method enhanced by a temporal wind storm sampling strategy. Extensive studies for the selected typical distribution system indicate that our models and methods effectively reveal the hazardous effects of wind storms in the study region, leading to useful insights towards building better system hardening schemes.

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.