In this paper, a planning-oriented resilience assessment framework is proposed that can help to design a more resilient power transmission system against extreme weather-induced events, such as typhoon disasters. It consists of three major models, i) a wind field-based probabilistic typhoon model to determine the level of uncertainty in the intensity and path of the typhoon; ii) a spatiotemporal fragility model of the power transmission corridor to quantify the spatiotemporal impacts of extreme wind speed on the failure probability of transmission corridor, and iii) a resilience assessment model of the planning system that allows us to develop quantitative resilience indices from both the system and component perspectives. The combinatorial enumeration method is introduced into the proposed framework to generate various potential typhoon disasters. Meanwhile, the impact-increment-based state enumeration (IISE) method is embedded within the proposed framework to improve its computational efficiency. The proposed method can provide the key information to identify the weak points of the system as well as inform the design of enhanced resilient planning schemes of transmission system against typhoon disasters. Extensive simulations carried out on the IEEE RTS-79 system considering realistic typhoon scenarios demonstrate the effectiveness of the proposed method.

With the increasing utilization of electric vehicles (EVs), transportation systems and electrical power systems are becoming increasingly coupled. However, the interaction between these two kinds of systems are not well captured, especially from the perspective of transportation systems. This paper studies the reliability of integrated transportation and electrical power system (ITES). A bidirectional EV charging control strategy is first demonstrated to model the interaction between the two systems. Thereafter, a simplified transportation system model is developed, whose high efficiency makes the reliability assessment of the ITES realizable with an acceptable accuracy. Novel transportation system reliability indices are then defined from the view point of EV's driver. Based on the charging control model and the transportation simulation method, a daily periodic quasi sequential reliability assessment method is proposed for the ITES system. Case studies based on RBTS system demonstrate that bidirectional charging controls of EVs will benefit the reliability of power systems, while decreasing the reliability of EVs travelling. Also, the optimal control strategy can be obtained based on the proposed method. Finally, case studies are performed based on a large scale test system to verify the practicability of the proposed method.

With the expansion of power grids and the growth of renewable energy generation, more uncertainties of topology (e.g. components outages) and injection (e.g. renewable energy outputs variations) need to be analyzed. As a result, large numbers of system states have to be evaluated by optimal power flow (OPF) and this brings a significant challenge to the efficiency of reliability assessment. To address this, a Lagrange-multiplier-based reliability assessment method (LM-T&I) is proposed to accelerate the evaluation of system states. The Lagrange-multiplier-based function is constructed to obtain the optimal load shedding of topology changes and injection variations, avoiding time-consuming OPF computations. Moreover, combined with the impact-increment-based state enumeration method (IISE), the computational efficiency can be further improved. Case studies are conducted on the RTS-79 and IEEE 118-bus systems. The scalability of the LM-T&I method is verified on the practical Brazilian system. The results demonstrate that the LM-T&I method has a superior performance on the reliability assessment compared with traditional methods.

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

The multi-energy microgrid (MEMG) improves the energy supply economy through a multi-energy coupling operation. However, due to faults or maintenance, outages may occur in the main grid, forcing the MEMG switch to the islanded mode, which heavily impacts the energy supply reliability. To solve this problem, a multi-objective stochastic optimization approach for planning the MEMG considering unscheduled islanded operation is proposed in this paper. This method aims to optimize the planning decision, improve the operation economy in the grid-connected mode and enhance the energy supply reliability in the islanded mode. A planning-operation co-optimization model is established with two objective functions: the minimum total planning cost and the minimum expected economic loss caused by islanded operation. The model incorporates equipment capacity allocation constraints, operation constraints in two operation modes and load curtailment constraints in the islanded mode. In addition to considering the uncertainties of load and renewable energy, the model of the uncertain islanded mode is established using four correlated random variables, and the scenario tree is used for islanded scenario generation. The adaptive weighted sum (AWS) method is used to solve the multi-objective model and obtain an optimal and evenly distributed Pareto frontier. On this basis, the technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine the final planning scheme. Numerical results illustrate the effectiveness of the proposed method, which makes a trade-off between planning economy and energy supply reliability.

Renewable energy based distributed generation (DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tie-line in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources (DSRs) including DGs, electric vehicles and thermostatically-controlled loads at the demand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs, considering the response order and the comfort levels. Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased. Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.