A large number of renewable energy resources are integrated into the integrated energy system (IES), which complicates the IES dispatching, especially for accommodating anti-peak-regulation of wind power. To cope with that, a day-ahead IES optimal dispatching method considering power to gas (P2G) units and dynamic pipeline networks is proposed in this article. First, by introducing P2G, an IES structure based on energy hub is established to implement bidirectional flow between power and natural gas systems. Second, the dynamic characteristic of gas pipelines is modeled with energy storage capability, which can improve the flexibility of the natural gas system by regulating the pressure level of pipeline networks. Furthermore, a cooperative dispatching strategy for P2G and pipeline storage capability is presented to catch the flexibility of IES, in which the unbalanced wind power is converted into natural gas and stored in pipeline networks. Finally, case study is verified on the modified IEEE39-NGS20-HS20 and IEEE118-NGS40-HS20 IES systems with different typical wind power scenarios. The proposed cooperative dispatching strategy can effectively increase wind power consumption and reduce operating cost of the whole system without high computation burden.

A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users’ benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method.

Owing to a higher energy supply efficiency and operational flexibility, integrated energy system (IES), including power, heating and gas systems, will be the primary form of energy supply in the future. However, with the increase of large-scale stochastic wind power integrated, the IES planning will face a significant challenge as traditional power system. Therefore, a probability-interval based IES planning considering wind power integrated is proposed in this paper. Firstly, a conditional value-at-risk (CVaR) based probability-interval method is developed to describe the uncertain wind power. Secondly, besides traditional facilities, electricity storage system (ESS) is introduced to improve the flexibility of IES. Then, an expansion planning model for IES is established to minimize the total cost including investment, operation, CVaR cost and unserved energy cost. Moreover, the piecewise linearization method is used to deal with the nonlinear integral terms of the proposed model to improve the solution efficiency. Finally, IEEE14-NGS14 and IEEE118-NGS40 systems are constructed and the planning model is solved by GAMS/CPLEX. The numerical results illustrate the correctness and effectiveness of the proposed method.

This paper proposes a non-parametric probabilistic wind turbine power forecast (WTPF) approach based on the empirical dynamic modeling. According to Takens' theorem, the approach applies historical wind turbine power (WTP) time series to form the reconstructed state space. The moving trajectories of state vectors in the space reflect the evolution laws of WTP, and the divergence of adjacent trajectories indicates the uncertainty existing in the forecast. An enhanced simplex projection algorithm is developed to forecast the probability distribution as well as the prediction interval of WTP with respect to the trajectories. Without any assumptions on the distribution types or physical equations, the proposed approach can overcome the drawbacks of distribution type misspecification or physical model incorrectness in the conventional forecast approaches. The real-time updated state vector set in the reconstructed space further merits the reliability and flexibility of the proposed approach. Case studies employ the proposed approach as well as several benchmarks to achieve 5-min, 15-min and 30-min ahead probabilistic WTPFs of Penglai wind farm located in Eastern China, and the superior performance demonstrates the effectiveness of the proposed approach.

To improve the adaptability for renewable energy sources (RES), a multi-time scale optimal dispatch method, which considers the multiple correlation of RES, is proposed in the active distribution network (ADN). The proposed method includes a multiple correlation model of RES and a scheduling framework. Multiple correlation model of RES is formulated by t Copula function, Sklar's theorem and conditional distribution of multivariate t distribution. The multiple correlation model can estimate the forecast error of the future period according to the historical data and the latest prediction. The scheduling framework further employs the revised forecast to coordinate the fast/slow-response regulation resources in the ADN under three timescales. In the day-ahead stage, the operation state of slow-response resources is optimized and determined. In the intraday stage, the output of the fast-response resource, which is transferred to the real-time stage as reference trajectory, is optimized based on the short-term prediction. In the real-time stage, the stochastic model predictive control is adopted to adjust the output of fast-response resources based on ultra-short-term prediction and reference trajectory. Case analyses indicate that the proposed method can effectively reduce the voltage fluctuation, promote the utilization of demand-side resources, and improve the system economic performance by introducing the multiple correlation model.

This article presents a model predictive control (MPC) based multitime scale co-optimized dispatch for integrated electricity and natural gas system (IEGS) considering the bidirectional interactions and renewable uncertainties. In the proposed model, optimal dispatch is extended into three substages optimization problems of day-ahead, intraday, and real-time to coordinate the economy and accuracy of operations. The different optimization strategy is designed in each dispatching stage according to the operating characteristics of multienergy coupled units and high-penetration intermittent wind power. In the day-ahead stage, the unit commitment is tackled to arrange the start-stop plan of slow-response units, maintained in intraday and real-time stages to ensure full-term optimal economic operation. In the intraday stage, MPC-based rolling optimization is implemented to obtain the unit output scheme with the lowest operating cost target. Furthermore, the output scheme is rolling adjusted in the real-time stage based on the ultrashort-term forecast value, and a closed-loop feedback mechanism is introduced to achieve accurate power balance. In each stage, stochastic decision-making is executed based on the stochastic scenario method, efficiently capturing the uncertainty in IEGS. Besides, the proposed nonlinear model is transformed into a mixed-integer second-order cone programming problem using McCormick relaxation, which can be efficiently solved by commercial solvers. Simulation results on IEEE39-Gas20 and IEEE118-GAS40 test systems demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to accurately track random fluctuations without obvious computational burden.