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BackgroundRefractory cardiogenic shock (CS) patients receiving venous-arterial extracorporeal membrane oxygenation (VA-ECMO) have a wide range of mortality, machine algorithm methods may explain the potential heterogeneity of these patients.MethodsBetween January 2018 and May 2021, 210 patients with CS who were receiving VA-ECMO support were enrolled and analyzed retrospectively. The k-means consensus agnostic algorithm was used. Patients were divided into three clusters based on covariates, such as platelet count (PLT), aspartic acid transaminase (AST), Interleukin-6 (IL-6), prothrombin time (PT), and serum lactate level 24 hours after ECMO initiation. The clinical and laboratory profiles were analyzed.ResultsAmong 210 CS with CS receiving ECMO, 148 (70.5%) were men, with a median age of 62 years (interquartile range (IQR): 53-67). Overall, 104 (49.5%) patients survived to discharge with 142 (67.6%) survived on ECMO. The patients were phenotyped into three clusters: (1) “platelet preserved (I)” Phenotype (36 [17.1%] patients), characterized by a preserved platelet count; (2) “hyperinflammatory (II)” phenotype (72 [34.3%] patients), characterized by a significant inflammatory response with higher Interleukin-6 (IL-6), and Interleukin-10 (IL-10) levels; and (3) “hepatic-renal (III)” phenotype (102 [48.6%] patients), characterized by unfavorable conditions in creatinine, aspartic acid transaminase, alanine aminotransferase, direct bilirubin, and prothrombin time. The in-hospital mortality rates were 25.0%, 52.8%, and 55.9% for phenotypes I, II, and III, respectively (P = 0.005).ConclusionThe consensus k-means algorithm analysis identified three phenotypes in refractory patients with CS receiving VA-ECMO: “platelet preserved,” “hyperinflammatory,” and “hepatic-renal.” The phenotypes are associated with the clinical profile and mortality, allowing treatment strategies for subsets of patients with CS receiving ECMO to be developed.

The ongoing quest for transportation electrification with the massive proliferation of EV charging stations (EVCSs) will deepen the interaction and require the further coordination of coupled power and transportation networks (PTN). The individually-owned EVCSs located in an urban transportation network (UTN) will compete using price signals to maximize their respective payoffs. In this paper, a multi-agent deep reinforcement learning (MA-DRL) method is proposed to model the pricing game in UTN and determine the optimal charging prices for a single EVCS. The EVCS charging demand is first analyzed using a modified user equilibrium traffic assignment problem (UE-TAP) with elastic traveling demands and different charging prices. The price competition problem is then formulated as a game with incomplete information in which the market environment is very complex due to nonlinear traffic assignments. Thus, the MA-DRL approach is proposed to learn the charging pricing strategies of multiple EVCSs and approximate the Nash Equilibrium (NE) of the pricing game using the incomplete information. The proposed solution will determine the optimal pricing strategies for an EVCS in UTN. The case studies on a 24-node Sioux-Falls network and real-world Xian city are conducted to verify the effectiveness of the proposed approach.

The proliferation of distributed renewable energy resources and plug-in electric vehicles (EVs) have helped residential electricity consumers evolve into prosumers as they participate in the local energy market (LEM) by engaging in transactions of surplus electricity. In this system, the budgetbalance problem is a frequent issue, particularly when Vickrey-Clarke-Groves (VCG)-based mechanisms are applied to managing the two-sided nature of LEM. Although this issue could be partially addressed by manually modifying the LEM, the variance in the LEM environment needs to be better understood. This paper proposes a deep learning-based automatic mechanism design (AMD) method to improve VCG for tackling the budget-balanced two-sided LEM, as a way to avoid tedious manual adjustments. A convolutional neural network (CNN) with self-attention mechanism is constructed to extract features from biddings and to provide robust generalization capabilities for participating prosumers. The gated recurrent units (GRUs) are utilized to extend the proposed approach to the non-stationary bidding environment. This improved mechanism is targeted as efficient and incentive compatible, with the ability to keep the balance between the budget-balance and individual rationality. Case studies are conducted to demonstrate effectiveness of the proposed automatically improved mechanism and adaptive ability to various bidding environments.

Hydrogen has shown great potential in the renewable power integration and urban mobility decarbonization like the hydrogen fuel cell vehicles (HFCVs). The HFCV refueling as an essential hydrogen load is of great flexibility. Considering the HFCV demand response, this paper studies the integrated electric power and hydrogen system (IPHS) operation. First, the HFCV refueling load model is formulated with its routing on the transportation network considered. Second, the optimal IPHS operation model is developed in which the electric power operation, tube-trailer based hydrogen delivery and HFCV refueling are coordinated. Third, a Lagrangian Relaxation (LR) based method is developed to solve the proposed model efficiently, which corresponds to a price-based demand response mechanism for HFCVs. Compared with existing works on IPHS, the influence of transportation networks is delicately analyzed on both HFCV refueling and hydrogen delivery. The case studies have proven the effectiveness of the proposed method and demonstrated that the overall operation cost is decreased via the proper guidance of HFCV refueling. The HFCV demand response shows great potential in exploring the synergy of energy and transportation systems.

The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted.

The production cost modeling (PCM) plays an important role in the mid/long-term power system planning. With the increasing penetration of renewable power, it becomes solving a series of unit commitment (UC) problems for time periods up to one or multiple years. This process is greatly challenged by the UC solution efficiency which hinders its practical use. In this paper, a fast UC method is proposed based on the load state transition curve (LSTC). Firstly, LSTC is proposed and obtained via load clustering. It simplifies the load model as well as maintains the necessary temporal characteristics. Secondly, the LSTC based UC model is developed by accommodating the constraint formulation to LSTC. Compared with the conventional ones, the scale of LSTC-based UC is dramatically reduced. The stochastic and volatile wind power is also taken into consideration. The case studies on the modified IEEE-RTS 1979 have verified the validity and feasibility of the proposed method in which the solution time is reduced from ∼300s to ∼12s with the cost error lower than 0.2%. It provides an efficient tool for the optimal planning and operation of large-scale power systems.

There is a lack of data regarding mid-term outcomes of extracorporeal membrane oxygenation (ECMO) for refractory postcardiotomy cardiogenic shock (PCS). In this context, this research aimed to assess the mid-term survival and quality of life of PCS patients who receive ECMO by comparing them with cardiac surgery patients who do not receive ECMO. A retrospective analysis was performed on the clinical data of patients who had undergone ECMO treatment after cardiac surgery from January 2013 to June 2017 in a tertiary hospital (n = 102); non-ECMO patients who had undergone cardiac surgery and were discharged successfully were selected as the control group (n = 102). Survival and mid-term quality of life were assessed and compared through the Short-Form 36 (SF-36). Both groups were followed up by telephone, and SF-36 scores were obtained from the surviving patients. The data were available for 89 patients (87.3%) and 88 patients (86.3%) in the ECMO group and the control group, respectively. After discharge, the control group outperformed the ECMO group in survival (93.1% vs. 82.4%; p = 0.013). No significant differences in complications, all-cause mortality, first readmission for any cause, or work condition between the ECMO group and the control group were observed. The SF-36 scores in general health (GH) and vitality (VT) were significantly lower among the ECMO survivors (p0.05). The results of this study indicate that ECMO can provide acceptable mid-term survival with good quality of life for patients with refractory cardiogenic shock.

Objective This study aimed to evaluate the relationship between plasma soluble ST2 (sST2) levels 24 h after extracorporeal membrane oxygenation (ECMO) initiation and continuous renal replacement therapy (CRRT) in patients receiving venoarterial ECMO (V-A ECMO) support. Methods and Results Data of patients who received ECMO support for postcardiotomy cardiogenic shock between January 2017 and July 2019 were retrospectively collected from Beijing Anzhen Hospital, Capital Medical University. Ultimately, 116 patients were included in the present study for analysis. The concentration of sST2 was determined by enzyme-linked immunosorbent assay (ELISA). The log10 sST2 levels were higher in patients undergoing CRRT than those who did not (6.06 vs. 6.22, p = 0.019). Patients undergoing CRRT had a lower survival rate than those who did not (32.8% vs. 67.3%, p < 0.001). In the univariate logistic regression analysis, sST2, HCO3-, lactate, and creatinine levels 24 h after ECMO initiation were related to CRRT ( p < 0.05). In the multivariate logistic regression analysis, HCO3- and sST2 were identified as independent risk factors for CRRT use in patients undergoing ECMO ( p < 0.05). The area under receiver operator characteristic curve (AUC) for sST2 and HCO3- together was 0.72 (95% confidence interval (CI), 0.79–0.91), which was better than those of sST2 or HCO3- alone (0.63 vs. 0.67). Conclusions sST2 and HCO3−levels at 24 h after ECMO initiation were associated with CRRT and could predict CRRT use in postcardiotomy cardiogenic shock patients undergoing ECMO.

Economic dispatch (ED) aims to minimize the generation cost subject to power balance constraints. It is extensively used in power system operation and planning. ED problem as well as other problems with the same formulation are named as ED-type problems in this letter and a fast solution method is provided. The proposed method is achieved by solving a series of relaxed problems. With a closed-form solution for the relaxed ED-type problems, it is demonstrated that the proposed method consumes far less computing time and memory space than the off-the-shelf solvers and other quadratic programming (QP) methods. Finally, the effectiveness and computational efficiency of the proposed method are verified by the case studies, which shows the great potential in power system planning and operation.

The extreme fast charging (XFC) technology helps to reduce refueling time, alleviate mile anxiety, extend driving range and finally promote the popularity of electric vehicles (EVs). However, it would also pose great challenges on the power grid infrastructure especially distribution networks, due to the large-scale and intermittent power demand. This paper proposes a coordinated planning method for power distribution networks and XFC EV charging stations, with the on-site batteries considered. Firstly, considering the traffic flow pattern, the operation of XFC stations is analyzed on both energy and power demand. Secondly, the coordinated planning model is developed to satisfy the time-varying XFC load, with both transportation and electricity constraints considered. In addition, the on-site batteries are introduced to flatten the XFC energy used and supplement its power supply. The case studies have verified the effectiveness of the proposed method. The influence of XFC on the distribution networks and the effects of the on-site storage are also studied.

The increasing concerns of energy utilization and climate change have promoted the permeation of various smart energy subsystems on the distribution level, such as integrated energy systems (IESs) and electric vehicle charging stations (EVCSs). These subsystems typically act separately during operation and their transaction values have not yet been well investigated. In this paper, we propose an energy trading model based on the Nash bargaining game to study cooperative benefits between an IES and several EVCSs. The proposed model not only considers individual interests, but also enables the players to fairly benefit from cooperation. In particular, the uncertainties of the market prices, renewable energies and integrated demand response are considered. To ensure that the entire game is computationally tractable, the original problem is decomposed into a major energy trading problem and an additional payment bargaining problem. Furthermore, a distributed algorithm based on modified Benders decomposition is used to overcoming the players’ privacies. The results show the considerable benefits where the costs of the IES may be reduced by 3.89% and the profits associated with the EVCSs may be increased by at least 7.8%. The proposed algorithm is proven to be able to find the optimal global solutions efficiently and accurately.

Electric power and transportation networks become increasingly coupled through electric vehicles (EV) charging station (EVCS) as the penetration of EVs continues to grow. In this paper, we propose a holistic framework to enhance the operation of coordinated electric power distribution network (PDN) and urban transportation network (UTN) via EV charging services. Under this framework, a bi-level model is formulated to optimally determine EVCS charging service fees (CSF) for guiding EV charging behaviors and minimizing the total social cost. At the upper level, PDN with wind power generation is formulated as a second-order cone problem (SOCP) where CSF is determined. Given the settings calculated at the upper level, the lower level problem is described as a traffic assignment problem (TAP) which is subject to the user equilibrium (UE) principle and captures the individual rationality of single EV owners in UTN. The uncertainties in wind power output and origin-destination (O-D) traffic demands are considered in the proposed model and a deep reinforcement learning (DRL)-based solution framework is developed to decouple and approximately solve the stochastic bi-level problem. Both gradient-based and gradient-free training algorithms are implemented in this paper and the respective results are compared. The case studies on a 5-node system, 24-node Sioux-Falls system and real-world Xi’an city in China are conducted to verify the effectiveness of the proposed model, which demonstrates the enhanced operation of coordinated PDN and UTN networks by reducing the traffic congestion and improving the integration of renewable energy.

Background: The relationship between the magnitude of platelet count decrease and mortality in post-cardiotomy cardiogenic shock (PCS) patients undergoing venoarterial extracorporeal membrane oxygenation (VA-ECMO) has not been well-reported. This study was designed to evaluate the association between the relative decrease in platelet count (RelΔplatelet) at day 1 from VA-ECMO initiation and in-hospital mortality in PCS patients.Methods: Patients (n = 178) who received VA-ECMO for refractory PCS between January 2016 and December 2018 at the Beijing Anzhen Hospital were reviewed retrospectively. Multivariable logistic regression analyses were performed to assess the association between RelΔplatelet and in-hospital mortality.Results: One hundred and sixteen patients (65%) were weaned from VA-ECMO, and 84 patients (47%) survived to hospital discharge. The median [interquartile range (IQR)] time on VA-ECMO support was 5 (3–6) days. The median (IQR) RelΔ platelet was 41% (26–59%). Patients with a RelΔ platelet ≥ 50% had an increased mortality compared to those with a RelΔ platelet < 50% (57 vs. 37%; p < 0.001). A large RelΔplatelet (≥50%) was independently associated with in-hospital mortality after controlling for potential confounders (OR 8.93; 95% CI 4.22–18.89; p < 0.001). The area under the receiver operating characteristic curve for RelΔ platelet was 0.78 (95% CI, 0.71–0.85), which was better than that of platelet count at day 1 (0.69; 95% CI, 0.61–0.77).Conclusions: In patients receiving VA-ECMO for post-cardiotomy cardiogenic shock, a large relative decrease in platelet count in the first day after ECMO initiation is independently associated with an increased in-hospital mortality.

Different from conventional low-rise buildings, in high-rise buildings, the gas pressure can easily exceed the safe limit, and the hot water cannot be easily transported to the higher floors owing to the effect of gravity. Therefore, overcoming these problems to supply heating for high-rise buildings remains a great challenge. Accordingly, gas-hanging stoves are introduced in this study as a provision for decentralised heating supply. To analyse the influence of gas hanging stoves on high-rise buildings and on the entire urban micro integrated energy system (UMIES), two models were developed: a scenario-based two-stage stochastic model for optimal scheduling of the UMIES and a gas overpressure model taking both additional overpressures caused by the frictional resistance and gravity into account. Numerical experiments based on two microgrid test systems show the efficiency of gas hanging stoves with regard to reducing operation costs of the UMIES; moreover, the gas pressure in high-rise buildings becomes more uniform and concentrated. Thus, the supply of gas and heat to high-rise buildings can be significantly improved.

The smart grid with flexible topologies receives intensive attention recently. Transmission switching (TS) alters the power system topology during operation, and has been demonstrated for the advantage of economic and secure operation of power systems. TS includes a chain of sequential switching actions which bring disturbances to the system if the switching actions are not properly designed. Unfortunately, it is not considered or well-studied in existing works. In this paper, a new multi-period TS model that considers the transition security and a two-stage iterative method are proposed. In the TS model, we take into account the fact that only one line is permitted to switch up or down at a time and the security of each switching action is considered. The proposed iterative solution makes the TS model more tractable under AC framework. Case studies on a 6-bus system and the IEEE 57-bus test system have varified the effectiveness of the proposed model. Numerical results show that: ➀ the consideration of transition security of TS is essential; ➁ the transition path is directly related to secure and fast the transmission switching; ➂ the proposed model and solution method give an effective way to determine the switching sequence and switching timing under transition security criteria.

Long-term electricity consumption (EC) forecasting is a very important part for the expansion planning of power system. Instead of point forecasting, based on fuzzy Bayesian theory and expert prediction, a novel long-term probability forecasting model is proposed to predict the Chinese per-capita electricity consumption (PEC) and its variation interval over the period 2010–2030. The special model structure can improve the reliability and accuracy of expert prediction through econometric methodology. It contains three components: fuzzy relation matrix, prior prediction, and fuzzy Bayesian formula. To contend with the long-term uncertainty, the prior prediction is implemented to combine the advantages of expert's experience with other time-based methods from the perspective of probability. With the utilization of fuzzy technique, the multiple effects of influencing factors (IFs) on PEC can be expressed as a fuzzy relation matrix. It can rule the results of prior prediction to obey the long-run equilibrium relationship of natural evolution thorough probability calibration. To demonstrate its efficiency and applicability, the result of this method is compared with that of other 6 approaches and 4 agencies. The case study shows that the proposed methodology has higher accuracy and adaptability.

In modern distribution system, the distribution system operator (DSO) acts as a market facilitator and data manager as well as an energy supplier and operation controller. In this circumstance, the DSO should comprehensively consider the diversified participants of the modern distribution system when making investment decisions of distributed generation (DG). This paper proposes a DG planning model considering the behavior of the diversified participants, which are motivated to cooperate with distributed renewable energy resources to promote their integration, and to achieve the optimal DG investment plan. The optimization model takes a centralized structure but fully considers the preferences, profits and comfort levels of the aggregators and consumers. The model is linearized into a mixed-integer linear programming (MILP) problem and is solved by CPLEX. Results of the case study show that when the DSO spares subsidies to the aggregators and consumers to encourage their participation in demand response (DR) programs, it earns more compared with providing no subsidies for DR participation. It is also demonstrated that the overall profit increases as the subsidies increase within a certain range, but decreases when the subsidies exceed this range. Therefore, the DSO needs to carefully choose the subsidization level to achieve the optimal utilization of renewable energy and demand flexibility. The optimal subsidization level is derived from the model proposed in this paper. Therefore, this paper puts forward a new pattern to utilize the distributed renewable energy sources, and provides guidance in policy making and DR program implementation.

The integrated energy system (IES) is an important development direction of future energy. Realizing the optimal dispatch of the integrated energy system is beneficial to improve its economic and environmental benefits. Aiming at the problem of insufficient consumption of renewable energy such as wind power, this paper proposes an optimization dispatch model of combining CHP unit and wind power generation. Coordinated constraints such as the uncertainty modeling of wind power in source side, the modeling of multi-energy flow of regional electricity in grid side, the comprehensive demand response on the load side, and the electrical heating of the storage side are included. Therefore, the optimization dispatch model proposed will optimize the output and load curve of each unit to increase the grid-connected wind power consumption and reduce the system load shedding level. In addition, this model aims to minimize the operating cost of the system, integrating “source-network-load-storage’ constraints, and calling Gurobi to solve it. Finally, the analysis results of specific calculation examples prove that this model can effectively promote wind power consumption and reduce system operation costs.

In this paper, we developed an optimal planning model for an integrated urban energy system in order to explore the potential for integrated operation between different energy forms. Both the respective modes of operation of the grid and the natural gas network are considered, as well as the application of coupling units and demand response. Nonlinearities in the operation of the energy network are handled by the piecewise linearization methods. Therefore, a mixed-integer linear planning (MILP) model is developed that can be efficiently solved by existing solvers. A case study of 26 electric nodes and 20 gas node systems is conducted to validate the effectiveness of the method. The case results show that the integrated demand response can reduce system cost and improve the load curve.

Demand response enables numerous small-scale consumers to minimize their costs as independent decision-makers. Consequently, the power system operation is dominated by the equilibrium of multiple end-users. This paper proposes the concept of user equilibrium of DR in the smart grid representing a state that no end-users can profit more by adjusting their own consumption. Then the user equilibrium is reformulated as the solution of a convex programming problem, named as the user equilibrium problem. The corresponding proof is also provided. An efficient solution algorithm is proposed to obtain the equilibrium among numerous end-users based on the branch-and-price method. Compared with the existing works, the operating constraints of flexible loads are modelled in details and can be considered. Numerical examples are presented to validate the proposed concept and method.

Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter-network applications, e.g., cross-network recommendation, cross-network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users' accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this paper, we firstly discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Secondly, we propose the framework of Multi-View Cross-Network User Alignment (MCUA) method, which uses a multi-view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top-k valuable features of different types of name matchings for MCUA over our experimental data sets.

The Benders Decomposition method is widely used to manage large-scale problems in the power system optimization. In this letter, a simple but effective method is proposed to improve the Benders Decomposition efficiency with the security constrained unit commitment (SCUC) problem taken as an example. The heuristic weights are introduced for constraint violations to accelerate the elimination of them. Thus the Benders Decomposition efficiency can be improved. The validity of the proposed method has been verified by case studies on multiple IEEE test systems.

The accurate quantitative reliability evaluation of off-shore wind power plants (OWPPs) is an important part in planning and helps to obtain economic optimization. However, loop structures in collector systems and large quantities of components with correlated failures caused by shared ambient influences are significant challenges in the reliability evaluation. This paper proposes an ana-lytical approach to evaluate the reliability of OWPPs considering environmental impact on failures and solve the challenges by protection zone models, equivalent power unit models and common cause failure (CCF) analysis. Based on investigation of the characteristics of OWPP and related failures mechanisms, the components are divided into three CCF subsets. With the aid of the protection zone model and equivalent power unit model merged with CCF, the faulty collector system state eval-uation is applied to reduce the computational burden. The case studies present the necessity and improved per-formance of merging CCF analysis into modeling via the comparison with other two simplified methods. A sensi-tivity analysis is also carried out to account for inaccu-racy of failure data. The results show that the assump-tion of independent failures in the conventional method might lead to over-optimistic or over-pessimistic evalua-tion depending on the CCF style.

Massive amounts of misinformation have been spreading over social media during the 2016 U.S. election season, causing wide public concern about our information ecosystem. How does fact checking compete with misinformation for user attentions? How do misinformation and fact checking differ in their spreading patterns? And what strategies are used by social bots to promote the spread of misinformation? In this paper, we address these research questions by analyzing datasets collected by Hoaxy. We answer the first question by conducting: 1) a survival analysis shows that about 70% of claims will be fact-checked in one week and about 800 tweets with claim links are posted during this period, on average and 2) a cross-correlation analysis shows that the sharing of fact-checking articles typically lags that of misinformation by about one day. Regarding the second question, we answer it from three points of view: 1) we find that false claims can become more popular than the corresponding debunking; 2) when looking at the distribution of types of tweets, we find that fact checking tends to spread in a conversational way; and 3) bot behavior analysis shows that the most active accounts sharing misinformation behave more like social bots. To better understand how social bots spread misinformation, we conduct case studies to answer the third question. By presenting strategies such as the production of a large number of original tweets, the alternating and hijacking of hashtags, and the injection of content into conversations, we demonstrate how social bots take advantages of the recommendation features of Twitter to amplify the spread of misinformation.

Power systems are exceedingly faced with extreme events such as natural disasters and deliberate attacks. In comparison, the underground natural gas system is considered less vulnerable to such extreme events. We consider that the overhead power grid can be hardened by replacing segments of electric power grid with underground natural gas pipelines as an energy transportation system to countereffect extreme events which can damage interdependent infrastructures severely. In this paper, an integrated electricity and natural gas transportation system planning algorithm is proposed for enhancing the power grid resilience in extreme conditions. A variable uncertainty set is developed to describe the interactions among power grid expansion states and extreme events. The proposed planning problem is formulated as a two-stage robust optimization problem. First, the influence of extreme events representing natural disasters is described by the proposed variable uncertainty set and the proposed robust model for the integrated planning is solved with the grid resilience represented by a set of constraints. Second, the investment decisions are evaluated iteratively using the conditional events. The integrated electricity and natural gas planning options are analyzed using the modified IEEE-RTS 1979 for enhancing the power grid resilience. The numerical results point out that the proposed integrated planning is an effective approach to improving the power grid resilience.

Power system operation has recently witnessed major challenges, which are often due to large-scale integrations of wind power generation. In this paper, a two-stage robust security-constrained unit commitment (SCUC) model is proposed for managing the wind power uncertainty in the hourly scheduling of power system generation. Different from previous studies on robust SCUC, which considered a predefined uncertainty set, the proposed method applies a flexible uncertainty set for managing the variable wind power generation. The proposed method seeks a feasible and economic dispatch in the flexible uncertainty set, takes into account wind spillage and load curtailment risks, and makes a tradeoff between the optimal wind power absorption and the economic grid operation. Several case studies are applied to the proposed method and the corresponding solutions are analyzed in the paper. The impacts of major factors, including flexible generation resources and power transmission capacity, on the proposed solution are also discussed. The numerical results demonstrate the merits of the proposed method for managing large variations in the hourly wind power generation and lowering the power system operation cost in uncertain conditions.

A novel solution to integrating offshore wind power via the fractional frequency transmission system (FFTS) is introduced in this paper. The basic idea of FFTS is to improve the technical and economic performance of ac transmission systems by lowering the grid frequency. FFTS is especially suitable for transmitting offshore wind power because the low frequency dramatically reduces the charging current in the cable. In addition, as a novel ac transmission system, FFTS performs much better than HVDC in constructing a multiterminal (MT) grid. The basic structures and characteristics of a typical PMSG-based FFTS offshore wind power system and a MT-FFTS offshore grid are described first. Then two offshore wind power cases are used to compare the technical and economical pros and cons of the FFTS solution with the conventional solutions: HVAC and HVDC. The feasibility studies show that the performance of FFTS offshore wind power system is superior to that of HVAC and HVDC in both point-to-point (PP) and multiterminal cases. The uniform annual value of PP-FFTS and MT-FFTS is 5.01% and 5.07% cheaper than PP-HVDC and MT-HVDC. Therefore, FFTS is a promising solution to offshore wind power integration and building offshore grids.

We propose a capability based dependency model of interdependent network that takes two node dependency properties into account. One is support capability and the other is required capability. The redundancy degree of an interdependent network is also defined, whose value is the ratio of its total support capability and total required capability. Through the numerical simulations, we found that: (1) Interdependent networks without redundant support-dependence links are extremely vulnerable, even the failure of one node could cause the collapse of whole network; (2) Increasing support-dependence links and redistributing the nodes’ dependency properties can enhance the robustness of network without changing its redundancy degree; (3) Improving the redundancy degree could enhance network robustness without adding support-dependence links. These conclusions enlighten the design of interdependent networks: when network’s redundancy degree is fixed, we can take strategy from results (2), and when network structure is settled, we can apply strategy from results (3).

In this paper, an mixed integer linear programming (MILP) method is proposed for calculating the optimal power flow in a multicarrier energy system. A state variable-based linear energy hub model is developed, which avoids the introduction of dispatch factor variables applied traditionally to the optimal power flow problem. The multidimensional piecewise linear approximation method is proposed for representing nonconvex natural gas transmission constraints in which the approximation error is further analyzed. Accordingly, the optimal power flow is reformulated as an MILP problem. Compared with the nonlinear models, the proposed model can be solved by the existing optimization techniques, which can be easily implemented in the optimal power system planning problem. The proposed method is verified by case studies applied to the modified six-bus and the IEEE-118 systems. The test results show that the proposed method can provide a fast solution for the optimal power flow which can be applied to large scale hub systems with sufficient accuracy. The results also demonstrate that the proposed method outperforms the existing MILP methods in calculation time especially in large scale hub applications.

In this paper, an economic dispatch model considering a flexible generation redispatch is proposed for managing the wind power variability in electric power systems. The model considers the base-case operation cost as well as the steady-state secure region for the variable wind energy. The generation schedule in the secure region provides grid operators with the boundary for absorbing wind power. The proposed model is formulated as a generalized semi-infinite programming problem and the corresponding solution is presented. The impact of flexible thermal resources and transmission capacity on the calculation of the wind power secure region is also discussed. The proposed solution method is analyzed using the modified IEEE-RTS 1979.

The electric vehicles (EVs) can introduce new operation strategies, such as charging management and vehicle-to-grid control, which could provide a considerable level of distributed storage to the power grid. In this paper, a partial decomposition method which is based on the Lagrangian relaxation framework is proposed for the EV charging control in transmission-constrained power systems. The partial decomposition method helps reduce the number of dual multipliers and stabilize the iterative process. The proposed partial decomposition framework is applied to a day-ahead SCUC algorithm which can be easily implemented in the existing hierarchical power system operations. The proposed EV charging control method can not only help reduce the total generation cost of power systems but also alleviate the transmission grid congestion. The feasibility of the proposed method is validated by the case studies applied to the modified IEEE-RTS1979.

Concentrating solar power (CSP) generation provides a new way to exploit solar energy. Its thermal energy storage (TES) can improve the output flexibility of CSP greatly and mitigate the peak load regulation problem brought by renewable energy. The proper configuration of TES capacity can promote the efficient utilization of CSP resource as well as lower the general cost. This paper proposes a TES capacity configuration model which can work out an optimal configuration scheme of TES capacity as well as generation schedule. It can reflect the actual role TES plays in electricity transfer and takes the influence of different load scales and illumination conditions into consideration which ensures its optimality and generality over a relatively long time scale. The case studies proved the effectiveness and correctness of the proposed method. It can provide an effective tool for TES capacity configuration of CSP units as well as generation expansion of systems containing CSP.

In this paper, an optimal planning model is developed for the integrated district energy system to explore the synergy between different energy forms. The individual operating characteristics of electric distribution networks and natural gas pipes are both taken into account, as well as the coupling components. The nonlinearity lying in the energy transportation and conversion is managed by the piecewise linearization method. Thus, a mixed integer second-order cone programming (MISOCP) model is constructed which can be solved by the off-the-shelf tool efficiently. The case studies have been carried out on the modified IEEE-33 system, which verifies the validility and effectiveness of the proposed method. The test results also show the investment and operation cost of district energy systems can be reduced significantly via the coordinated planning of electricity and gas distribution networks.