To enhance the resilience of power grids against typhoon disasters, this study proposes a novel preventive scheduling method by coordinating source, network, and load resources. An evaluation index for unexpected load shedding that considers the importance of load is provided on the basis of the utility function and employed as the objective function. Moreover, the piecewise function based objective function is transformed into a mixed-integer linear formulation by introducing auxiliary variables with clear physical meanings. The startup and shutdown arrangements of units and the optimal scheduling of their outputs, as well as the opening and closing of line switches, for protecting the power supply of important loads and various demand-side management measures are comprehensively modeled. In response to the difficulty in solving the problem caused by numerous factors considered in the model, an identification method for variable and constraint reduction by narrowing the range of switchable lines is proposed to verify the effectiveness of the switchable lines in optimal transmission switching (OTS) in reducing the congestion caused by typhoon disasters. The proposed method is applied to the modified IEEE two-area system and the IEEE-118 system. Results indicate that the utility function based collaborative preventive scheduling method can effectively reduce the loss of important loads during disasters, without excessively cutting off other loads, compared with the widely used load shedding based method. Furthermore, the model dimensionality reduction method based on the effectiveness identification of switchable lines can greatly improve the solving efficiency with on decrease in the accuracy.

This paper investigates the stochastic optimal operation of microgrid considering the influence of energy storage system (ESS). The uncertain factors related to renewable energies are also fully considered. Monte Carlo simulation (MCS) and scenario reduction technique are used to capture the randomness of renewable energy output. The objective is to minimize the operation cost and pollutant treatment cost of the microgrid system. Then, the multi-objective optimization problem is transformed to a single-objective problem by a line weighted sum method. A nonlinear programming tool—General Algebraic Modeling System (GAMS)—is adopted to solve the single-objective problem. Simulation results indicate that the total cost of the microgrid system can be reduced effectively when considering the effect of ESS and verify the correctness of the proposed strategy.

In this paper, a novel contingency-aware method for N-2 security-constrained transmission expansion planning is proposed. To ensure that the transmission construction plan satisfies the N-2 security criterion, the proposed method takes advantage of the adjustable robust optimization (ARO) framework and upgrades it. We construct a discrete uncertainty set in which component failures are treated as uncertain events that are handled as binary variables. In addition to the failure of existing lines and generators, we explicitly model the failure of candidate lines. The proposed model comprises a master problem that makes the transmission construction decision, and a series of subproblems that can detect not only the worst contingency, but also the potential contingencies. Computational studies on the IEEE RTS 24-bus and IEEE 118-bus test systems are carried out to validate the effectiveness of the proposed method. Compared with the deterministic method and ARO method in the literature, the proposed method has higher computational efficiency.

In recent years, failure recovery after faults is concerned and discussed worldwide as an important and hot topic. Facing the challenge of heavy loads and ultra-high voltage transmission, it’s urgent to propose some solutions to enhance transient voltage stability for failure recovery. Therefore, a novel dynamic volt ampere reactive (VAR) planning methodology is proposed in this paper to help failure recovery and improve transient voltage stability after contingencies. First, a transient voltage fluctuation (TVF) index is proposed to evaluate transient voltage condition after faults. Then dynamic compensation sensitivity is presented for searching the best candidate locations. Following that, the dynamic VAR planning methodology based on an improved Tent chaos multi-objective algorithm is discussed in detail. There are two optimization objects in the optimization. One is to minimize TVF to enhance transient voltage stability for failure recovery. And the other optimization is to minimize dynamic VAR investment cost and operation cost. Finally, IEEE 39 power system and a practical power system are analyzed and discussed. The proposed dynamic VAR planning methodology can support enough reactive power for failure recovery. With the least SVC planning amount and the power loss cost, it can greatly decrease the system TVF index and enhance the transient voltage stability. It’s proved that the proposed dynamic VAR planning optimization is effective and helpful for safety operation of power system.

N-k contingency estimation plays a very important role in the operation and expansion planning of power systems, the method of which is traditionally based on heuristic screening. This paper stringently analyzes the best and worst states of power systems given the uncertainties of N-k contingency and interval load. For the sake of simplification and tractable computation, an approximate direct current (DC) power flow model was used. Rigorous optimization models were established for identifying the worst and best scenarios considering the contingencies of generators and transmission lines together with their uncertain loads. It is very useful to identify the worst N-k contingencies with interval loads. If the worst existing scenario meets security standards, all scenarios must satisfy it. The mathematical model established for finding the worst N-k contingency with interval load is a bi-level optimization model. In this paper, strong duality theory and mathematical linearization were applied to the solution of bi-level optimization. The computational results of standard cases validate the effectiveness of the proposed method and illustrate that generator contingency has more impact on minimum load shedding than transmission line contingency.