This article proposes a risk-based contingency-constrained optimal power flow model by leveraging the methods of both adjustable uncertainty set and distributionally robust optimization. In the proposed model, an adjustable uncertainty set of wind power is developed with network contingencies explicitly incorporated. Based on this uncertainty set, the proposed model is capable of securing the network against both wind power fluctuations and contingencies in a probabilistic manner with the optimal balance between operation cost and risk. Meanwhile, a data-driven L 1-norm-based ambiguity set is employed so that the proposed model is distributionally robust to the ambiguous probability distribution of wind power and the size of the model remains unchanged as the available wind power data increases. A decomposition-based algorithm is also derived so that the proposed model is solvable by off-the-shelf solvers. Numerical studies on IEEE 14- and 118-bus systems are conducted to verify the effectiveness of the proposed model.

This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.

In northern China, the combined heat and power (CHP) units account for a large proportion of the local generation capacity. However, the inherent heat-power coupling of CHP units poses a large barrier for wind power accommodation. To facilitate the local wind integration, the key is to improve the flexibility of the CHP system, and there are two types of feasible methods. One is the addition of heat storage tanks and electric boilers into the CHP system, and this method has been considered in the scheduling of the CHP system, such as the unit commitment (UC) optimization. The other type of method is the flexibility retrofit of CHP units, including the two-stage bypass modification and low-pressure cylinder removal (LPCR). However, prior research has not attempted to integrate the flexibility retrofit into the UC scheduling. To fill this gap, this paper proposes a novel UC model incorporating the bypass and LPCR retrofit of CHP units. In this model, the constraints on the safe operation region, fuel cost, reserve provision, ramping and mode switching of different types of CHP units are thoroughly described. Cases studies are conducted on a test system. Simulation results show that the proposed UC model is capable of utilizing the bypass and LPCR retrofit to facilitate wind accommodation, with the wind curtailment reduced by 81.94% and 20.45% under the bypass and LPCR retrofit, respectively.