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The implementation of high-reliability microgrids requires proper protection schemes that effectively function in both grid-connected and island modes. This article presents a communication-assisted, four-level hierarchical protection strategy for a loop-structured microgrid, with reference to an existing functional microgrid at the Illinois Institute of Technology. [ABSTRACT FROM AUTHOR]

In this article, a privacy-preserving distributed control strategy is proposed for realizing the optimal economic operation of islanded reconfigurable microgrids (MGs). Using the proposed distributed control strategy, participating DERs would only exchange the frequency data with their neighbors while the generation data are held privately by each participant. The proposed distributed control strategy reduces the communication burdens among DERs and exploits the operational flexibility of reconfigurable MGs. It also demonstrates the versatility for considering a variety of operational objectives without requiring any additional communication and control infrastructures. Using the proposed control strategy, the optimality and the stability of an MG system's equilibrium point are demonstrated by the Lyapunov theory. The effectiveness of the proposed control strategy is validated in a 12-bus MG system for various operating conditions, including load variations, DER disconnection and reconnection operations, and MG reconfiguration operations. [ABSTRACT FROM AUTHOR]

The networking of geographically-close microgrids (MGs) offers additional operational flexibility for realizing the benefits of various DERs. The failures of CMC (central master controller) or master controllers (MCs) in certain MGs due to natural or human-made disturbances might impede the performance of networked MGs and even compromise the system security. This paper proposes a unification scheme to restore the MG function in a networked MG system with failed CMC and certain MCs. In the unification scheme, failed CMC delegates its control authority to certain MCs and failed MCs relinquish their control responsibilities to designated MCs in adjacent MGs. The neighboring functional MCs are accordingly allowed to take the lead for managing the operations in malfunctioning MGs. The unification control signal provided by the designated MCs is imposed on pinned DERs and propagated among remaining DERs in the MGs with failed MCs, which reduces the communication complexity among MGs significantly. The proposed unification scheme is expected to achieve desired control performances even when the available communication resources are not sufficient to maintain continuous unification control signals in networked MG system. The effectiveness of the proposed unification scheme is validated by the time-domain PSCAD/EMTDC simulations of two networked MG systems. [ABSTRACT FROM AUTHOR]

Networking a series of autonomous microgrids (MGs) is a strategic effort toward the resilience enhancement in extreme conditions. In this paper, flexible division and unification control strategies are proposed to help networked MGs prepare adequately for extreme events and adapt comprehensively to subsequent changing conditions, which enhance the system resilience. According to the proposed strategies, networked MGs can switch between two distinct modes of division and unification by utilizing a sparse communication network without requiring any additional communication infrastructures or controllers. In division mode, each MG is regulated by its local master controllers (MCs) for active power sharing, which ensures that disruptions are handled effectively by local energy resources without utilizing those in adjacent MGs. Thus, any islanding or resynchronization of individual MGs would not introduce further disruptions to the remaining networked system. The proposed control strategies imply that the networked MGs system in division mode is managed in a proactive way to adequately prepare the networked system for extreme events. In the unification mode, the remaining networked MGs, which are still functional, use the proposed algorithm to share all available energy resources and adapt to continuously changing operating conditions in order to respond to extreme events. The proposed control algorithm for devising a flexible networked MGs system is a cost-effective scheme that can fully exploit the system operation flexibility corresponding to different operation stages for enhancing resilience. The proposed control strategies are applied to a networked AC MGs system and the performance is tested using time-domain PSCAD/EMTDC simulations. [ABSTRACT FROM AUTHOR]

In concert with the transformation of conventional passive power distribution system, distributed energy resources (DERs) have progressively become participants in the provision of electricity services in active distribution networks (ADNs). In this paper, we propose a systematic valuation process to quantify the value of DERs in the ADN context. The paper first provides comprehensive insights into the impacts of DERs on ADN and the society as a whole. Given the technological, locational, and temporal diversity of DERs, a two-part scheme is developed to value and compensate DER portfolios proposed by customers and independent third parties. In particular, DERs are valued for their benefits and costs in both short and long terms. An integrated resource planning model is formulated to quantify the value of a given DER portfolio to be installed, where bi-level optimization techniques are applied to coordinate decisions on ADN planning and operations. In order to determine the short-term operation benefits of the DER portfolio on a continuous basis, a retail market operation model is developed based on peer-to-peer energy transactions among prosumers, when the impacts of DERs on ADN operations are monetized by distribution locational marginal prices. It is finally concluded in the paper that the proposed valuation scheme will not only contribute to the proactive investment of DERs in ADN but also help enhance the role of DERs in offering affordable, reliable, resilient and sustainable electricity services to customers. [ABSTRACT FROM AUTHOR]

Background: Sepsis a complex clinical syndrome represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses; an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models; notably, within the first 12 hours of patient admission their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects-the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI. [ABSTRACT FROM AUTHOR]

This paper studies the role of hourly economic demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly economic demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units. [ABSTRACT FROM AUTHOR]

This paper presents a co-optimization planning model that considers the long-term interdependency of natural gas and electricity infrastructures. The model incorporates the natural gas transportation planning objective in the co-optimization planning of power generation and transmission systems. The co-optimization planning model is decomposed into a least-cost master investment problem for natural gas and electricity systems which interacts with two operation subproblems representing the feasibility (security) and the optimality (economic) of the proposed co-optimization. In addition, the natural gas subproblem would check the feasibility of fuel supply transportation system as part of the proposed co-optimization planning. The co-optimization planning of electricity and natural gas infrastructures would satisfy the desired power system reliability criterion. The iterative process will continue between the co-optimization investment and the operation subproblems until an economic, secure, reliable, and fuel-supply feasible planning for the two interdependent infrastructures is obtained. Numerical simulations demonstrate the effectiveness of the proposed co-optimization planning approach. [ABSTRACT FROM PUBLISHER]

This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations. [ABSTRACT FROM PUBLISHER]

• This paper proposes a dc grid controller for multiterminal high-voltage direct current (MT-HVDC) networks or grids. Its distinct features are. • It offers the possibility to optimize the performance of MT-HVDC networks for any meaningful operational objectives and within the physical constraints of the system components. • Easily scalable to any MT-HVDC grids, independent of number and topology of converters employed, and grid topology (meshed or radial). • Suitable for online calculations during real-time or co-simulation in electromagnetic transient and phasor environments. • Permits incorporation of all practical equality and inequality constraints such as thermal limits of the dc cables and converters. Generally, multi-terminal dc grids provide full control over the dc powers that converter terminals exchange with their respective host ac grids. Nevertheless, the same level of controllability does not exist over the power flow in the individual dc lines of a highly meshed HVDC network, and this increases the risk of overloading the dc cables that present lower resistances. To address the highlighted shortcoming, this paper presents a generic dc grid controller that uses nonlinear constrained optimization to optimize the performance of multi-terminal HVDC networks for any desirable operational objective, within system physical constraints such as dc cable and converter thermal limits, and minimum and maximum dc voltage limits. The presented dc grid controller performs online optimization at regular intervals to dynamically estimate and update the set-points of the converter terminals as system operating conditions vary. The technical viability of the proposed controller is assessed using a generic seven-terminal HVDC network that uses voltage sourced modular multilevel converters. Simulations from the scenarios that prioritize dc grid power loss or operational cost minimization show that the presented dc grid controller exhibits good performance during normal operation as converter terminals set-points vary, and during dc grid reconfiguration following simulated successive outages of the dc cables. It has been shown that the performance of the proposed DC grid control is not affected by the parameter variation such as changes of resistance with temperature. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for enhancing the power system flexibility, which can respond properly to contingencies in real-time operations. The proposed approach introduces a unified power flow controller (UPFC) in a three-stage security-constrained optimal power flow (SCOPF). The pre- and post-contingency system operation states are divided into three stages including the base case, post-contingency short-term, and post-contingency long-term periods. The UPFC applications re-route active power flow and provide reactive power to mitigate overloads and voltage violations when line outages occur in power systems. UPFC is adopted as a fast-response corrective control device during the post-contingency short-term period, which is coordinated with the conventional slow-response corrective control system during the post-contingency long-term period. A convex approach is applied to reformulate the original nonlinear nonconvex SCOPF problem into a second-order cone programming (SOCP) problem. A two-level algorithm using Benders decomposition and sequential cone programming (SCP) is applied to solve the large-scale SOCP problem. An improved covering cut bundle (CCB) strategy is proposed to accelerate the convergence of the Benders decomposition algorithm. Numerical results show the effectiveness of the proposed model and its solution technique for enhancing the power system flexibility. [ABSTRACT FROM AUTHOR]

A two-layer optimal consensus-based distributed control strategy is proposed for the coordinated operation of networked microgrids (MGs). Several networked MG operation objectives are optimized using the proposed control strategy, including frequency/voltage regulation, proportional active power sharing, and smooth MG islanding/reconnection operation. The global objective function of networked MGs is decomposed into a series of local objectives assigned to participating distributed energy resources (DERs). In the decomposed optimization, each control layer is realized in a distributed manner in which DER state variables reach consensus along with optimizing their assigned local objective functions. Each control layer in the two-layer control strategy is formulated as an optimal consensus problem in which the optimality and asymptotical stability of the system equilibrium point for each control layer are demonstrated. The effectiveness of the proposed control strategy is validated in a modified IEEE 33-bus distribution system using the PSCAD/EMTDC platform. [ABSTRACT FROM AUTHOR]

Truck-mounted mobile emergency resources (MERs) play a significant role in microgrid formation and regulation for power system resilience enhancement. This paper proposes a distributed fixed-time secondary control (DFSC) scheme to regulate the frequency and active power sharing in islanded microgrids with MERs. The proposed DFSC scheme is designed based on a general directed communication graph which considers MER characteristics (e.g., mobility, flexibility, and potential cyber threats). Each DER receives data from its neighbors through directional communication links. The proposed pinning control strategy selects only a small number of DERs for pinning, which have access to the reference frequency information and would guide the operation of remaining local DERs and MERs. In addition, the guaranteed convergence time is determined by control parameters and communication graph without applying the initial operation states. The paper demonstrates that the desired control performances (i.e., frequency restoration and accurate active power sharing) are realized in a timely fashion considering frequent operations of MERs. Case studies are tested and discussed for load variations, different control parameters, communication link failures, and MER operations through time-domain simulations in PSCAD/EMTDC platform. [ABSTRACT FROM AUTHOR]

In this paper, a compartmentalization strategy is proposed for the economic operation of a hybrid AC/DC microgrid. The hybrid AC/DC microgrid will be compartmentalized into several independently controlled and coordinated AC and DC nanogrids. Accordingly, the optimal economic operation of the microgrid consists of two segments, including the optimal power sharing among local DERs in individual nanogrids and the optimal power sharing among interconnected nanogrids. For each AC/DC nanogrid, the optimal power sharing and frequency/voltage restoration are realized simultaneously without the need for any real-time communication. In the compartmentalized microgrid, each nanogrid will communicate with neighboring nanogrids through a sparse communication network to regulate power exchanges among nanogrids. In islanded mode, incremental costs of nanogrids are unified for realizing the optimal economic operation of the microgrid. In grid-connected mode, the external electricity price (i.e., purchase/sale electricity price set by the utility grid) is the desired incremental cost, which would be imposed on some pinned nanogrids and propagated among the remaining nanogrids. Then, the optimal power sharing of nanogrids within the grid-connected microgrid would be modeled as a pinning synchronization problem. The proposed compartmentalization strategy ensures that the optimal economic operation of the hybrid AC/DC microgrid is achieved under both island and grid-connected modes while the utilized communication structure is simplified and the dependency on communication networks is reduced. The performance of the proposed compartmentalization strategy is tested and verified through time-domain simulations in PSCAD/EMTDC for a hybrid AC/DC microgrid located at Illinois Institute of Technology (IIT). [ABSTRACT FROM AUTHOR]

Based on Petri net (PN) models of automated manufacturing systems, this paper proposes a deadlock prevention method to obtain a maximally permissive (optimal) supervisor while minimizing its structure. The optimal supervisor can be achieved by forbidding all first-met bad markings (FBMs) and permitting all legal markings in a PN model. An FBM obtained via a single transition’s firing at a legal marking is a deadlock or marking that inevitably evolves into a deadlock. A lexicographic multiobjective integer programming problem with multiple objectives to be achieved sequentially is formulated to design such an optimal and structurally minimal supervisor. As a nonlinear function, the quantity of its directed arcs is minimized. A conversion method is proposed to convert the nonlinear model into a linear one. With the premise that each place in the supervisor is associated with a nonnegative place invariant, the controlled net holds all legal markings of the net model, and the supervisor has the minimal structure. Finally, some examples are used to illustrate the application of the proposed approach. [ABSTRACT FROM PUBLISHER]

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers' needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers' preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms. [ABSTRACT FROM AUTHOR]