With the development of energy intelligent technology, integrated energy system (IES) has been widely used in the field of energy supply. While the IES significantly improves energy efficiency, the interaction between different energy systems may also bring multiple operational risks to the reliability of the energy supply, which necessitates an effective IES reliability assessment technique. Considering the dynamic characteristics of gas and heat systems can reduce energy losses during contingencies, which provides an instrument to improve the IES reliability. This paper for the first time contributes to a novel emergency dispatch formulation based on the IES dynamic optimal energy flow (OEF). A reliability assessment framework for IES is proposed by combining the IES dynamic OEF and Monte Carlo simulation (MCS). By taking into account the emergency dispatch formulation, the unreliability degree of IES can be mitigated. The IES dynamic OEF model is transformed into a linear programming problem under reasonable assumptions and simplifications due to its high degree of nonlinearity. Several novel reliability indices for heat system are proposed by incorporating the impacts of the IES dynamic characteristics on the reliability. The proposed model and method are validated by using an integrated gas, power and heat testing system.

This paper proposes a multi-data driven hybrid learning method for weekly photovoltaic (PV) power scenario forecast that is coordinately driven by weather forecasts and historical PV power output data. Patterns of historical data and weather forecast information are simultaneously captured to ensure the quality of the generated scenarios. By combining bicubic interpolation and bidirectional long-short term memory (BiLSTM), a super resolution algorithm is first presented to enhance the time resolution of weather forecast data from three hours to one hour and increase the precision of weather forecasting. A weather process-based weekly PV power classification strategy is proposed to capture the coupling relationships between meteorological elements, continuous weather changes and weekly PV power. A gated recurrent unit (GRU)-convolutional neural network (CNN)-based scenario forecast method is developed to generate weekly PV power scenarios. Evaluation indices are presented to comprehensively assess the quality of the generated weekly scenarios of PV power. Finally, the PV power, weather observation and weather forecast data collected from five PV plants located in Northeast Asia are used to verify the effectiveness and correctness of the proposed method.

The integration of the variable renewable energies makes the operation conditions of the power system ever-changeable. Consequently, the power system operational reliability evaluation is increasingly important. This paper introduces the concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation. Unlike the exogenous uncertainties, DDU reveals that the decisions of the system operation could significantly affect the resolution of the uncertainties which influence the reliability metrics. In this paper, the proposed DDU modeling method links the device reliability indices, i.e., the forced outage rate, and the operational-decision variables. The impacts of DDU on operational reliability are analyzed based on a reliability-constrained stochastic unit commitment (UC) model. An adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem. Case studies underline the necessity of considering DDU in power system operational reliability evaluations.

Transmission defense hardening (TDH) is an effective practice to ensure the safe operation of the power system during and after natural disasters, such as typhoons. However, the coupling among the typhoon disaster, hardening decision, and transmission line status is not addressed in the previous research works. Besides, the enhancement measures for different types of transmission line failures have not yet been assessed. This paper proposes a comprehensive framework to improve power transmission system resilience against typhoon disasters. Firstly, the motion path and the wind field of typhoons are simulated using Monte Carlo sampling to quantify the spatiotemporal impacts of wind speed on the transmission line status. The decision-dependent uncertainty (DDU), which reflects the effects of hardening decisions on the transmission lines, is addressed by sampling according to the corresponding failure probabilities. Then, the scenario simulation is integrated into the proposed two-stage stochastic mixed-integer programming (SMIP) model considering various enhancement measures for different failures. Since the way to handle the DDU needs a large number of scenarios, it leads to high computation complexity. Hence, the sample average approximation (SAA) algorithm is introduced to cope with it. Finally, the proposed framework and its solution algorithm are carried out on the modified IEEE RTS-79 system and the modified IEEE 118-Bus system. The significant cost saving and resilience improvement demonstrate the effectiveness of the proposed framework.

The ever-increasing penetration of renewable generation is demanding additional flexibility for the operation of power systems. In this sense, it has been proved that wind power generators (WPGs) can provide part of the required operating reserve through pitch angle control (PAC) under a deloaded strategy. Unlike conventional operating reserve sources, however, the operating reserve provided by WPGs can't always be considered reliable, which renders the power system dispatch more challenging. In this research, the reserve capacity of the WPGs is modeled considering the decision-dependent uncertainty (DDU). The random wind speed introduces uncertainty to the available reserve capacity of the WPGs while the decisions on the deloading degrees of the WPGs directly affect their coefficients between wind speed and usable wind power, making the uncertainty decision-dependent. Moreover, the operational technical limits of the WPGs and the multiple possible wind speed scenarios are also considered in the wind power reserve capacity modeling. Then, a two-stage reliability constrained stochastic economic dispatch model with WPGs’ reserve provision (SED-WRP) is developed to jointly dispatch the generation and reserve. A group-based multi-cut Benders decomposition method is applied to improve the convergence performance of the solution algorithm. Case studies are solved to quantify the impact of considering the reserve provision of WPGs and the inherent DDU in power system dispatch decisions.

Ensuring the resilience of power system is the prerequisite for maintaining its normal operation after the occurrence of natural disasters. This paper proposes a data-driven transmission defense planning (DTDP) model to address the power system resilience issue against extreme weather events. DTDP enables the determination of the optimal resilience enhancement portfolio, including line hardening and construction. The target events of DTDP are selected according to survivability based severity indices. The component survivability and post-disaster system failure state are treated as random variables in two separate levels, and the selected historical data are used to construct the confidence sets of their ambiguous probability distribution. Thus, the man-made uncertainty budget can be avoided and the expected economic loss under the worst-case distribution is minimized. Moreover, a sparsification method of system failure states is designed to boost computational efficiency. The effectiveness of DTDP and its solution method are verified on a six-bus test system and RTS-79 system. The results demonstrate that the DTDP can obtain a better defense plan compared to other optimization techniques.