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 variation and uncertainty of renewable energy potentially impact power system stability. This article proposes an efficient uncertainty evaluation algorithm to evaluate the influence of renewable energy uncertainty on power system dynamic performance. This algorithm applies probabilistic collocation method (PCM), which greatly reduces the simulation burden without compromising the result accuracy, to approximate the dynamic simulation results. The proposed algorithm also considers the correlation among adjacent renewable generators by using the Copula function, which is suitable to model nonlinear correlations. Additionally, this article extends the utilization of PCM. The actual historical data of renewable generation productions can be utilized directly, and kernel density estimation (KDE) is used to capture the nonparametric distribution of renewable generation production. To improve the accuracy of the approximation results, the proposed method adopts K-means clustering technique to select the approximation samples of input variables. The proposed probabilistic dynamic simulation algorithm is compared with Monte Carlo simulations (MCS) with the probabilistic results on the IEEE 39-bus system with multiple renewable generators, and the accuracy and efficiency of the proposed algorithm are validated.

With the increase in wind generation installed capacity, the uncertainty of wind power has brought great challenges to wind power accommodation. The accurate assessment result of wind power accommodation capability (WPAC) has important guiding significance to the formulation of the day-ahead wind power trading plan for the multi-energy microgrid (MEMG). This paper proposes a WPAC assessment method for the MEMG. A liner energy flow model is first derived based on the equivalent infinitesimal replacement and the mild assumption that supply temperatures of different heat load nodes are nearly the same. Then, the operational risk model easily embedded into the assessment model is established based on the probability distributions of prediction error corresponding to multiple wind power output ranges. Finally, the WPAC assessment model is constructed as a two-stage robust optimization problem and solved by the column-and-constraint generation (C&CG) algorithm. Simulations on a MEMG composed of the IEEE 33-bus power system and the 23-node district heating network (DHN) verify the effectiveness of the proposed method.

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.

This paper proposes an optimal planning method for the dual-zero microgrid (DZMG) on an island. The DZMG is the off-grid microgrid that exchanges zero power with entity grids and operates in a net-zero carbon emission mode. A net-zero emission operating strategy is designed considering the positive interaction between $\rm CO_{2}$ flow and energy flow. The multi-scale circulation of $\rm CO_{2}$ flow is realized by coordinating the carbon capture system, solvent storage tank (SST), and direct air capture (DAC), while the seasonal shift of energy flow is completed by the hydrogen storage system (HSS) and DAC. An optimization planning model for DZMG is developed to size the SST, DAC, and HSS optimally. The net-zero emission target and the net-zero operating strategy are involved in the model to balance the environmental and economic concerns in the planning. Numerical experiments are carried out on two IEEE test systems and a real-world island microgrid to validate the effectiveness and adaptability of the proposed method. Simulation results reveal that the proposed method reduces the planning cost by over 25% compared with the extant zero-carbon-based method. Besides, the economy of the DZMG positively correlates to carbon prices and technological maturity, while inversely relates to fuel prices.

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.