System Congestion Research Articles

Femoral Vein Pulsatility: What Does It Mean?

In this report, the clinical evolution of a 72-year-old patient transferred to the surgical intensive care unit after cardiac surgery is described. The presence of a pulsatile Doppler signal of the common femoral vein was noted after surgery. On postoperative day 5, diuretics in addition to a combination of inhaled epoprostenol and milrinone were associated with normalization of femoral vein pulsatility. The observations seen in peripheral venous flow reinforce the hypothesis that pulsatility of the common femoral vein represents an associated echocardiographic sign of right ventricular dysfunction and may be used to monitor systemic venous congestion. Pulsatility in the venous system may be improved by reducing volume overload and improving right ventricular function.

Traffic load access barring scheme for random‐access channel in massive machine‐to‐machine and human‐to‐human devices coexistence in LTE‐A

SummaryA major challenge for the development of the upcoming generations of wireless systems is to match the massive expansion of the Internet of things (IoT) applications along with the traditional human communication. Recently, 3rd Generation Partnership Project (3GPP) has suggested the use of LTE‐A to support machine‐to‐machine (M2M) communications to realize the IoT applications. One of the key concerns is the random‐access procedure in LTE‐A, the existence of a massive number of M2M devices results in system overload and congestion in the random‐access channel (RACH). In this paper, we present a new extension to the active barring‐based random‐access scheme. The proposed traffic load access barring (TLAB) RACH access scheme considers the dynamic changes in system traffic when setting the barring information. At the same time, the proposed scheme avoids unnecessary barring of some M2M devices. Our scheme improves the access delay that results from the barring of M2M devices by applying a new rule in the selection of classes to bar and classes to unbar. The simulation results show the superiority of our scheme in terms of success probability, average access delay, and average power consumption.

Storage-Transmission Joint Planning Method to Deal with Insufficient Flexibility and Transmission Congestion

The insufficient power system flexibility and transmission congestion are two fundamental reasons for wind power curtailment. As the scale of the wind power in the power system is growing rapidly, the two factors of wind power curtailment events coexist and have a certain coupling relationship. The configuration of the energy storage system can not only increase the flexibility of the system but also alleviate transmission congestion. Therefore, the joint planning of energy storage and transmission grid that takes into account the flexibility of the system and the transmission congestion is of great significance to solve the wind curtailment. Hence, this paper first decouples the insufficient flexibility and transmission congestion wind power curtailment, and quantitatively analyzes the impact of transmission capacity on the coupling relationship between the two; second, reveals the principle of joint planning of energy storage system and transmission congestion, and constructs an optimization model, and proposes to set up the capacity of the wind power which connects the power network. The solution procedure, which deals with grid planning and the energy storage system optimization in turn, not only ensures the accuracy of the model, but also significantly reduces the calculation cost. Finally, a case study of a wind power base in Northeast China and an improved Garver-6 system are carried out to verify the effectiveness of the proposed method.

Global asymptotic consensus of multi-agent internet congestion control system.

This paper considers the Internet congestion control and consensus problems of a multi-agent system composed of a congested node, some aggregation nodes and their following nodes. First, a novel multi-agent Internet congestion control system with both discrete and distributed delays is proposed. Second, the stability and Hopf bifurcation of the congested node are discussed. Subsequently, a simple error controller designed for aggregation nodes, some necessary and sufficient conditions have been established to solve the bifurcation consensus problem between aggregating nodes and the congested node. Bifurcation consensus of aggregation nodes and their following nodes are also obtained through consensus protocols which based on a novel predictive controller. So far, the bifurcation asymptotically consensus of multi-agent Internet congestion control system is achieved. It is worth noting that conditions for global stability of system can be obtained by selecting appropriate control parameters, and conclusions of global asymptotic stability consensus is given to draw conditions for avoiding network congestion in the whole system. Finally, numerical examples illustrated the correctness and validity of the main results.

Bi-objective robust optimization for reliability-oriented power network planning by considering distributed generation effects: A case study in Iran

In the present study, a new mixed-integer linear programming is presented for a reliability-oriented power network. The reliability approach is conducted using the Average Energy Not Supplied (AENS) index and System Average Interruption Duration Index (SAIDI) based on the feeder’s failure rate. In this formulation, the constraints include standard ranges for electric power and current magnitude, acceptable voltage drop, power law, and power balance considering loss power. Moreover, the relation between feeder failure rate and electric power level and subsequently SAIDI for outage duration control is added in constraints to improve network reliability. The model is studied on a real case study in Iran in two scenarios with or without considering DG. The obtained results indicate that optimization for DG location and its capacity in the power system decrease AENS cost. Furthermore, DG incorporation in the grid reduces load congestion in substations and system’ total cost. Augmented Epsilon Constraint (AEC) method is utilized to solve two-objective mixed-integer linear programming. To overcome the uncertain environment, the robust optimization is applied, and the effect of Γ parameters is investigated on Pareto sets and system’s total costs. As confirmed by numerical results, by increasing demand constraint conservative level, the objective function gets away from the nominal value, requiring that Decision-Makers create an appropriate balance between conservative levels and system costs. The obtained results reveal the efficiency and effectiveness of the proposed mathematical model in the real world. • New reliability-oriented multi stage power network planning. • Determination the feeders failure rates via power storage and AENS cost. • Considering uncertainty in demand forecasting and facility installation costs. • DG incorporation as a facility to costs reduction and reliability reinforcement.

Effects of imperfect IoT-enabled diagnostics on maintenance services: A system design perspective

Although many firms have deployed Internet of Things (IoT)-based diagnostics to predict equipment failures and maintenance requirements, they receive imperfect demand signals due to inadequate data quality and the use of ineffective predictive tools. A strategic queuing model was developed in this study to investigate a maintenance service provider’s optimal capacity allocation and pricing decisions in the presence of imperfect IoT-based diagnostics. In addition, we considered the heterogeneous service rates resulting from the accelerating effect of IoT-based diagnostics. The results of the study revealed that, on the one hand, the optimal service rate always increases with the diagnostic quality; on the other hand, if customer error cost is higher than that of the service provider, the optimal price increases with the diagnostic quality; otherwise, it decreases with the diagnostic quality. Furthermore, we found that the error costs for the two major stakeholders—the service provider and the customer—may affect the equilibrium in different ways. Finally, in the presence of the accelerating effect, we found that although the accelerating effect can improve the average service rate, system congestion actually increases if the effect becomes increasingly obvious; however, the service provider’s profit improves in this case.

COVID-19 and chronic fatigue syndrome: Is the worst yet to come?

There has been concern about possible long-term sequelae resembling myalgic encephalomyelitis/chronic fatigue syndrome in COVID-19 patients. Clarifying the mechanisms underlying such a “post-COVID-19 fatigue syndrome” is essential for the development of preventive and early treatment methods for this syndrome. In the present paper, by integrating insights pertaining to the glymphatic system and the nasal cerebrospinal fluid outflow pathway with findings in patients with chronic fatigue syndrome, idiopathic intracranial hypertension, and COVID-19, I provide a coherent conceptual framework for understanding the pathophysiology of post-COVID-19 fatigue syndrome. According to this hypothesis, this syndrome may result from damage to olfactory sensory neurons, causing reduced outflow of cerebrospinal fluid through the cribriform plate, and further leading to congestion of the glymphatic system with subsequent toxic build-up within the central nervous system. I further postulate that patients with post-COVID-19 fatigue syndrome may benefit from cerebrospinal fluid drainage by restoring glymphatic transport and waste removal from the brain. Obviously, further research is required to provide further evidence for the presence of this post-viral syndrome, and to provide additional insight regarding the relative contribution of the glymphatic-lymphatic system to it. Other mechanisms may also be involved. If confirmed, the glymphatic-lymphatic system could represent a target in combating post-COVID-19 fatigue syndrome. Moreover, further research in this area could also provide new insights into the understanding of chronic fatigue syndrome.

Mitigating congestion by optimal rescheduling of generators applying hybrid PSO\u2013GWO in deregulated environment

Deregulation in power system and increased power demand has introduced congestion in power system. With the depletion of fossil fuels, power sector has turned towards incorporation of the Renewable Energy Systems through private generators. This paper focuses on the non-cost-free method to mitigate congestion by rescheduling the generators for their active power output. The rescheduling is done in such a way that the cost of rescheduling is minimum. This paper presents a new method to merge two tested algorithms Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to generate a new hybrid algorithm, HPSOGWO. The active power output is rescheduled to mitigate congestion with minimum cost of rescheduling. The priority of participating generators in rescheduling is set by generator sensitivity factor and its output is optimized by proposed HPSOGWO. The applied algorithm has reduced the rescheduled power to 16% less as done by GWO. HPSOGWO has moderated the congestion cost to 25% less as compared to GWO. The effectiveness of proposed HPSOGWO algorithm is validated on standard IEEE 30 bus system and results confirms the outperformance of proposed method over GWO and PSO in reducing congestion cost with reduction in power losses to mitigate congestion.

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.

An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling

In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

Storage Assignment Optimization in Robotic Mobile Fulfillment Systems

Robotic mobile fulfillment system (RMFS) is a new type of parts‐to‐picker order picking system, where robots carry inventory pods to stationary pickers. Because of the difference in working mode, traditional storage assignment methods are not suitable for this new kind of picking system. This paper studies the storage assignment optimization of RMFS, which is divided into products assignment stage and pods assignment stage. In the products assignment stage, a mathematical model maximizing the total correlation of products in the same pods is established to reduce the times of pod visits, and a scattered storage policy is adopted to reduce system congestion. A heuristic algorithm is designed to solve the model. In the pods assignment stage, a model is established minimizing the total picking distance of the mobile robots considering the turnover rate and the correlation of pods as well as the workload balance among picking corridors. A two‐stage hybrid algorithm combining greedy algorithm and improved simulated annealing is designed to solve the model. Finally, a simulation experiment is carried out based on the historical order data of an e‐commerce company. Results show that the storage assignment method proposed in the paper significantly improves the efficiency of order picking.

Methodology for Security Analysis of Grid- Connected Electric Vehicle Charging Station With Wind Generating Resources

The project Carbon-Free Island Jeju by 2030 promoted by the Republic of Korea aims to expand the renewable energy sources centered on wind power in Jeju Island and supply electric vehicles for eco-friendly mobility. However, the increased penetration rate of electric vehicles and expansion of variable renewable energy sources can accelerate the power demand and uncertainty in the power generation output. In this paper, power system analysis is performed through electric vehicle charging demand and wind power outputs prediction, and an electric vehicle charging decentralization algorithm is proposed to mitigate system congestion. In order to predict electric vehicle charging demand, the measurement data were analyzed, and random sampling was performed by applying the weight of charging frequency for each season and time. In addition, wind power outputs prediction was performed using the ARIMAX model. Input variables are wind power measurement data and additional explanatory variables (wind speed). Wind power outputs prediction error (absolute average error) is about 9.6%, which means that the prediction accuracy of the proposed algorithm is high. A practical power system analysis was performed for the scenario in which electric vehicle charging is expected to be higher than the wind power generation due to the concentration of electric vehicle charging. The proposed algorithm can be used to analyze power system problems that may occur due to the concentration of electric vehicle charging demand in the future, and to prepare a method for decentralizing electric vehicle charging demand to establish a stable power system operation plan.

Abstract 14595: The Clinical Impact of Portal Vein Pulsatility on the Prognosis of Hospitalized Acute Heart Failure Patients

Introduction: Heart failure (HF) especially right-heart failure causes hepatic portal system congestion. The Portal vein (PV) pulsatility can be influenced by right atrial pressure (RAP). However, the association between PV pulsatility and the condition of HF remains unclear. Hypothesis: In this study, we aim to evaluate usefulness of PV pulsatility as a prognostic marker as well as a therapeutic indicator for hospitalized acute HF patients. Methods: We enrolled 54 patients with acute HF and 17 patients without HF served as controls. PV flow velocity was measured by ultrasonography at admission and discharge phase. PV pulsatility ratio (PVPR) was calculated by dividing minimal velocity by peak velocity. The primary endpoint for prognostic analysis was cardiac death and unexpected re-hospitalization for recurrent HF. The observation period was one year from first hospitalization for HF. Results: On admission, PVPR was significantly higher in controls compared to acute HF patients (0.91±0.08 vs. 0.71±0.04, p&lt0.01).PVPR did not change during the hospitalization in controls (admission 0.91±0.08 vs discharge 0.93±0.06, p=0.31). However, in acute HF patients, PVPR was significantly elevated after the improvement of HF (admission 0.71±0.04 vs discharge 0.82±0.02, p&lt0.05) due to the increase in minimal velocity (admission 12.6±4.5 cm/s vs. discharge 14.6±4.6 cm/sec, p&lt0.05), indicating the decrease in RAP. To elucidate the association between PVPR and primary endpoint, the patients were divided into three groups according to the tertile of PVPR at discharge (PVPR-Q1:0.92&ltPVPR&lt1, PVPR-Q2: 0.73&ltPVPR&lt0.92, PVPR-Q3:PVPR&lt0.73). Kaplan-Meier analysis found that the patients with higher PV pulsatility at discharge had significantly higher event rate among the groups (Figure). Conclusions: PVPR at discharge would reflect the condition of HF. It also can be a novel prognostic marker for hospitalized acute HF patients.

An Incentive-Based Mechanism to Alleviate Active Power Congestion in a Multi-Agent Distribution System

Increasing the integration of distributed energy resources (DERs) inspired by environmental and governmental incentives, beside the introduction of multi-agent structure perspective have led to a paradigm shift of operational conditions in distribution systems. In this context, the traditional concept of fit and forget in the distribution grid management would not be efficient in the modern distribution systems and consequently new mechanisms should be developed in order to enable the distribution system operator (DSO) to efficiently manage the grid congestion caused by peak power output of DERs or load demands requests. In this article, the Stackelberg game concept is employed to develop an incentive-based mechanism that facilitates the contribution of local flexible resources operated by independent agents in the congestion management of distribution grids. Therefore, the proposed bi-level formulation would result in congestion alleviation in the multi-agent system; while the objectives associated with DSO and independent agents are fulfilled. Additionally, the developed framework enables DSO to reconfigure the network to decrease the operational costs associated with the congestion alleviation procedure. Finally, the strong-duality concept is utilized in order to combine the two-level problem into a one-level problem and the obtained model is implemented on IEEE-37 bus test system to investigate its effectiveness in congestion management in the distribution system.

Bid responsive generation rescheduling for congestion management in electricity market

AbstractElectricity market economics is one of the vital factors which play a key role in achieving the efficient operation of the power system. Economic operation along with secure & reliable operation is must for ensuring optimum utilization of the resources in order to maximize social welfare. This article presents a method for achieving economic operation of electricity market in case of congestion occurring in the system. The generator sensitivity (GS) method alone can manage the system congestion, however it does not consider the economic benefits which can be achieved by considering the bids submitted by the generators and as a result it leads to situation of market power. To avoid such scenario, bid sensitivity factor (BSF) is proposed to select the generators to participate and regulate their real power generation for alleviating congestion from the system optimally. The BSF takes into account the GS as well as bids of the generators. In this paper, the effectiveness of the proposed method in eliminating market power is realized by changing the bids of the generators. The proposed method is tested on IEEE 30‐bus system and IEEE 118‐bus system. The obtained results show an encouraging pattern for managing the congestion more optimally and economically.

How Emergency Care Congestion Increases COVID-19 Mortality: Evidence from Lombardy, Italy

Background: The COVID-19 pandemic has caused generous and well-developed healthcare systems to collapse. This paper quantifies how much system congestion may have increased mortality rates, using distance to the ICU as a proxy for access to emergency care.Methods: We match daily death registry data for almost 1,500 municipalities in Lombardy, Italy, to data on geographical location of all ICU beds in the region. We then analyze how system congestion increases mortality in municipalities that are far from the ICU through a differences-in-differences regression model.Findings: We find that COVID-19 mortality is up to 60% higher in the average municipality - which is 15 minutes driving away from the closest ICU - than in a municipality with an ICU in town. This difference is larger in areas and in days characterized by an abnormal number of calls to the emergency line.Interpretation: We interpret these results as suggesting that a sudden surge of critical patients may have congested the healthcare system, forcing emergency medical services to prioritize patients in the most proximate communities in order to maximize the number of lives saved. Through some back-of-the-envelope calculations, we estimate that Lombardy's death toll from the first COVID-19 outbreak could have been 25% lower had all municipalities had ready access to the ICU. Drawing a lesson from Lombardy's tale, governments should strengthen the emergency care response and palliate geographical inequalities to ensure that everyone in need can receive critical care on time during new outbreaks.

A Comparative Study of Series Compensation, TCPAR and TCSR FACTS Controllers for Mitigation of Congestion

Power engineers face many challenges while designing and operating a power system. While operating a power grid, mitigation of congestion is one of the major issues. Congestion can take place in one or more transmission lines of a grid when outage of one or more lines takes place due to the fault. Current of a faulty line is transferred to other lines of the grid, and it may result in congestion in some of the healthy lines of a power system. Congestion may cause overloading and overheating of such lines. In this paper, authors have applied different techniques/FACTS Controllers to mitigate congestion in IEEE 5-bus transmission system and presented a comparative study. These are series compensation technique, Thyristor Controlled Phase Angle Regulator (TCPAR) FACTS Controller and Thyristor Controlled Series Reactor (TCSR) FACTS Controller. Analytical study was performed to find the efficacy of each controller, and it has been found that TCSR FACTS Controller provides the desired result.

On Queue-Length Information when Customers Travel to a Queue

Problem definition: We consider a service system in which customers must travel to the queue to be served. In our base model, customers observe the queue length and then decide whether to travel. We also consider alternative information models and investigate how the availability of queue-length information affects customer-equilibrium strategies, throughput, and social welfare. Academic/practical relevance: A common assumption in queueing models is that once a customer decides to join the queue, joining is instantaneous. This assumption does not fit real-life settings, where customers possess online information about the current wait time at the service, but while traveling to the service, its queue length may change. Motivated by this realistic setting, we study how queue-length information prior to traveling affects customers’ decision to travel. Methodology: We prove that a symmetric equilibrium exists in our base model. We perform the calculation numerically as a result of the model complexity, which is due to the fact that the arrival rate to the traveling queue depends on the current state of the service queue, and vice versa. The alternative models are tractable, and we present their analytical solution. Results: When customers can observe the service-queue length prior to traveling, their probability of traveling is monotonically nonincreasing with the observed queue length. We find that customers may adopt a generalized mixed-threshold equilibrium strategy: Travel when observing short queue lengths, avoid traveling when observing long queue lengths, and mix between traveling and not traveling when observing intermediate queue lengths, with a decreasing probability of traveling. Managerial implications: Our results imply that when system congestion is high, the provider can increase throughput by disclosing the queue-length information, whereas at low congestion, the provider benefits from concealing the information. With respect to social welfare, queue-length information prior to departure is beneficial when congestion is at intermediate to high levels and yields the same social welfare otherwise.

Islanded microgrid congestion control by load prioritization and shedding using ABC algorithm

The continued growth in load demand and the gradual change of generation sources to smaller distributed plants utilizing renewable energy sources (RESs), which supply power intermittently, is likely to strain existing power systems and cause congestion. Congestion management still remains a challenging issue in open access transmission and distribution systems. Conventionally, this is achieved by load shedding and generator rescheduling. In this study, the control of the system congestion on an islanded micro grid (MG) supplied by RESs is analyzed using artificial bee colony (ABC) algorithm. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time during islanding mode operation. This is to ensure as minimal load as possible is shed during a contingency that leads to loss of mains and ensure a congestion free microgrid operation. This is tested and verified on a modified IEEE 30-bus distribution systems on MATLAB platform. The results are compared with other algorithms to prove the applicability of this approach.

Coordinated operation of reconfigurable networks with dynamic line rating for optimal utilization of renewable generation

The large-scale penetration of renewable generation, the ever-increasing load, and aging of the transmission infrastructure all pose excessive stress on secure power system operation due to increasing congestion levels of the network. This arises the need for system operators to exploit the inherent flexibility in the system by using new cost-effective transmission topologies to better utilize the existing transmission assets. Dynamic Line Rating (DLR) and Optimal Transmission Switching (OTS) technologies both provide flexible ways to boost power system performance and maximize the use of existing infrastructure. In this paper, OTS and DLR are co-optimized in a stochastic unit commitment (SUC) formulation to characterize its influence on the scheduling of power system operation in the presence of renewable energy resources. Based on the predicted meteorological parameters and loading, the optimization problem i.e. Flexible Transmission Dynamic Line Rating (FTDLR) determines when and which line should be upgraded to adopt DLR and which line should be removed from the network. The inherent flexibility in transmission system is utilized by anticipating the capacity of transmission lines using DLR in co-ordination with reconfiguring network topology to relieve system congestions and reduce system operating cost. The flexibility obtained from the current transmission system can help in postponing the need for building new transmission corridors and deepen the integration of renewable generation. Numerical results establish that the enforcement of DLR and the practice of OTS are complementary, which can help minimize the system operating cost, wind curtailment, and network congestion.