This paper develops an optimal electricity-gas flow (OEGF) framework considering power line and pipeline congestions to coordinate the energy supplies, flexible electricity and gas loads, electricity and gas storages within an integrated electricity and gas system. The objective is to maximize the social welfare, while balancing the supply and demand, and relieving the congestion. The formulated OEGF with nonlinear power/gas flow models is a nonlinear programming (NLP) problem. In order to improve the computational efficiency of OEGF and guarantee the accuracy of solutions, a modified DC power flow model embedded power losses is adopted to model the power system for simplification first; two different two-stage convexification methods are then designed to address the nonlinear OEGF model. The obtained two types of OEGF models are convex in both stages and can be easily solved. Numerical results validate the effectiveness of the proposed two OEGF methods compared to the nonlinear OEGF method. In addition, effects of congestion management are also investigated.

Background: Recent studies have indicated a potential correlation between intestinal bacteria and primary ovarian insufficiency (POI). However, the causal relationship between the gut microbiota (GM) and POI remains unclear.Methods: A bidirectional two-sample Mendelian randomization (MR) study was conducted to investigate the relationship between the GM and POI. Data on the GM were based on the MiBioGen consortium's summary statistics from the most comprehensive genome-wide association study meta-analysis to date (n=13,266), and POI data were obtained from the R8 release of the FinnGen consortium, containing a total of 424 cases and 181,796 controls. A variety of analytical methods, including inverse variance weighting, maximum likelihood, MR-Egger, weighted median, and constrained maximum likelihood and model averaging and Bayesian information criterion, were utilized to explore the connection between the GM and POI. The Cochran's Q statistics were used to evaluate the heterogeneity of instrumental variables. The MR-Egger and MR-pleiotropy residual sum and outlier (PRESSO) methods were used to identify the horizontal pleiotropy of instrumental variables. The MR Steiger test was used to evaluate the strength of causal relationships. A reverse MR study was performed to investigate the causal relationship between POI and the targeted GMs which were indicated to have a causal relationship with POI in the forward MR evaluation.Results: The inverse variance weighted analysis indicated that Eubacterium (hallii group) (odds ratio [OR]=0.49, 95% confidence interval [CI]: 0.26–0.9, P=0.022) and Eubacterium (ventriosum group) (OR=0.51, 95% CI: 0.27–0.97, P=0.04) had protective effects on POI, and Intestinibacter (OR=1.82, 95% CI: 1.04–3.2, P=0.037) and Terrisporobacter (OR=2.47, 95% CI: 1.14–5.36, P=0.022) had detrimental effects on POI. Results of the reverse MR analysis indicated that POI had no significant influence on the four GMs. No significant heterogeneity or horizontal pleiotropy was observed in the performance of the instrumental variables.Conclusion: This bidirectional two-sample MR study revealed a causal link between Eubacterium (hallii group), Eubacterium (ventriosum group), Intestinibacter, and Terrisporobacter and POI. Additional clinical trials are needed to gain a clearer understanding of the beneficial or detrimental effects of the GMs on POI and their mechanisms of action.

The existing studies on probabilistic steady-state analysis of integrated energy systems (IES) are limited to integrated electricity and gas networks or integrated electricity and heating networks. This paper proposes a probabilistic steady-state analysis of integrated electricity, gas and heating networks (EGH-IES). Four typical operation modes of an EGH-IES are presented at first. The probabilistic energy flow problem of the EGS-IES considering its operation modes and correlated uncertainties in wind/solar power and electricity/gas/heat loads is then formulated and solved by the Monte Carlo method based on Latin hypercube sampling and Nataf transformation. Numerical simulations are conducted on a sample EGH-IES working in the “electricity/gas following heat” mode to verify the probabilistic analysis proposed in this paper and to study the effects of uncertainties and correlations on the operation of the EGH-IES, especially uncertainty transmissions among the subnetworks.

This paper studies the uncertainty analysis of the regional integrated electricity and gas system (IEGS) composed of a three-phase unbalanced power distribution system and a gas distribution network. The well-known probabilistic energy flow (PEF) analysis of IEGS is extended to a comprehensive probabilistic-interval energy flow (PIEF) problem by modeling uncertainties in pipeline parameters as interval variables and spatial dependency between them as correlation angles. The polynomial chaos expansion (PCE) method is introduced to IEGS analysis and an improved PCE-based method combining the technique of dimension-reduction and the sparse-PCE is proposed to address the numerous uncertainties involved in a regional IEGS. A methodological framework considering probabilistic/interval uncertainties, linear/rank correlations among probabilistic variables, and dependency between interval variables is then developed for both PEF and PIEF analysis. Numerical simulations of a sample regional IEGS are used to verify the accuracy and efficiency of the proposed method, as well as the superiority of the proposed PIEF analysis over the existing PEF analysis.

Owing to spatiotemporal flexibility of electrical vehicles (EVs), EVs will introduce new synergies between the urban traffic system (UTS) and power distribution system (PDS). As a mobile charging load, arranged EV charging can not only provide more flexibility for power system optimization, but also influence the traveling time and other mobility-related characteristics of EVs. However, there are few studies that consider the impact of the coupling facility (such as parking lot with charging facilities) on UTS reliability, as well as the reliability of UTS and PDS (referred to as PTS). Therefore, it is necessary to extend the original research scope to incorporate the traffic system simulation into the collaborative analysis of the coordinated PTS. This paper first develops a new simulation framework for the coordinated UTS and PDS coupled with commercial charging lots (CLs). Then, a new method is illustrated for measuring the reliability of a coordinated UTS and PDS. At last, a novel unified PTS reliability index considering both UTS and PDS reliability indices are presented.

The continuing growth of an offshore wind farm integrated into a power grid via high‐voltage DC has posed great challenges for automatic generation control (AGC) of the power system. To address these challenges, a new concept of ‘dynamic dispatch of AGC units (DDA)’ under control performance standards from the view of economic dispatch (ED) has been proposed in the previous work, and proved to be an effective technique to co‐operate the AGC units with different ramping rates and to fill the gap between ED and AGC. However, the existing DDA model is deterministic in nature, which can hardly deal with the uncertain forecasting error of the offshore wind power output. A novel stochastic DDA model based on chance‐constrained programming is proposed considering a random offshore wind power forecasting error, and a hybrid algorithm combining the evolutionary programming algorithm and point estimate method is then developed to solve the stochastic model. Numerical results from a two‐area test system with additional offshore wind power generation demonstrate the accuracy and computation efficiency of the hybrid algorithm and the benefits offered by the stochastic AGC dispatch method.