The increasing growth in the installation of natural-gas fired units has raised necessitates on synergy between the electricity and natural gas networks. This paper discusses how synergistic operation of electricity and natural gas networks can be achieved in a distributed fashion using alternating direction method of multipliers (ADMM). A standard ADMM approach and a consensus-based ADMM approach are developed, respectively, to solve the gas-electric integrated optimal power flow problem with and without a coordination operator. In both cases, the optimization formulation for each operator is modeled, and the solution procedure as well as data exchange among multiple decision-makers are explained. Case studies on both small and large gas-electric integrated systems show that the two ADMM-based distributed approaches have significant efficiencies over the traditional Lagrange relaxation (LR) and augmented LR-based methods.

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.

Due to essential differences in modeling and computation, the traditional sequential cross-entropy method that is greatly effective for generation systems may lose its effectiveness for composite systems. An enhanced sequential cross-entropy method for composite systems is presented to achieve the effectiveness. In the pre-sample stage, a sequential cross-entropy optimization algorithm rather than using a traditional non-sequential optimization algorithm is proposed so that the samples in this stage could also contribute to calculations of reliability indices. In the second stage, an accelerated contingency analysis is presented for further reducing computational burdens, in which a bisection optimization algorithm is used to rapidly identify failure system states. The new likelihood ratios, i.e., frequency likelihood ratio and duration likelihood ratio are developed, resulting in accurate corrections of loss of load frequency (LOLF), loss of load probability (LOLP), and expected energy not supplied (EENS) indices. The performance of the proposed method is demonstrated by comparisons between the proposed and traditional methods using several composite power systems, including the IEEE RTS-79, a modified IEEE RTS-79, and the IEEE RTS-96.

This paper proposes a noncooperative game-based planning method for microgrid (MG) considering the interconnection between developing and developed privately-owned clustered MGs on an island. A noncooperative game with imperfect information is designed to regulate the decision-making in planning and operating strategies for rational MG owners. The optimization models for both developing and developed MGs are presented. Different levels of maturity of MG development are modeled by diverse objective functions and constraints, while the data privacy of privately-owned MGs is preserved through imperfect information and prior beliefs. To tackle the seasonality of renewable energy and load on an island, hydrogen storage is considered in the developing MG on the island. Diagonalization and realization are incorporated to develop an effective solving methodology in searching the mixed strategy equilibrium for each MG. The effectiveness of the proposed method is verified using comprehensive case studies.

A novel concept called the committed carbon emission operation region (CCEOR) of integrated energy systems (IESs) is proposed in this paper, which provides a powerful tool for low-carbon operation analyses (LCOA) of IESs. The CCEOR of IESs is first defined, and a CCEOR model is then proposed by considering the uncertainty of renewable power outputs and the characteristics of low-carbon sequential operation. Based on the nonlinear constraint transformation method and distributionally robust chance-constrained (DRCC) approach, a reformulation method is developed to convert the proposed uncertain nonlinear CCEOR model into a deterministic mixed-integer convex CCEOR model. The characteristics of the CCEOR are investigated. Based on the transformed CCEOR model, the CCEOR is a high-dimensional and bounded region composed of finite convex sub-regions. A region approximation method is then presented to construct the CCEOR. The effectiveness and applicability of the proposed CCEOR model and method are validated by numerical simulation on three test systems. Simulation results indicate that the proposed CCEOR can characterize the low-carbon feasible space (LCFS) visually, the visual coupling relationships between observation variables and LCFS, and the comprehensive low-carbon operation information of operation points.