Search

An accurate estimation of the remaining useful life (RUL) and capacity of lithium-ion batteries (LIBs) can guarantee safe and reliable operation and help to make wise replacement decisions. This paper presents an improved approach for predicting the RUL and capacity of LIB using a long short-term memory (LSTM) deep neural network-integrated with a gravitational search algorithm (GSA) to address the challenges associated with predicting battery life. Initially, data cleaning is carried out to minimize any negative impacts that can reduce the convergence rate. Abnormal data are replaced with highly correlated data, and the data is standardized. Moreover, the LSTM model hyperparameters are optimized using the GSA optimization technique. To evaluate the robustness of the proposed method, 15 prediction samples are generated to calculate the uncertainty levels (95% CI) of the predicted RUL. The proposed method is assessed using aging data from the NASA battery dataset. Its performance is compared with baseline LSTM, baseline GRU, BiLSTM, and LSTM-based particle swarm optimization (PSO) models across various error metrics. The robustness of the proposed method is verified by benchmarking it against other existing approaches for predicting RUL and capacity. The results indicate that the LSTM-GSA model outperforms in prediction accuracy, achieving RMSE values of 1.04%, 1.15%, 1.26%, and 0.92% across different battery cases at both early and later cycle stages. Overall, this research provides a promising solution for predicting RUL and the future capacity of LIBs with uncertainty, which is essential for ensuring the safe and efficient operation of energy storage systems.

In concern of energy trilemma such as energy security, energy equity, and environmental sustainability, the electric infrastructures are significantly developing the manifold avenues for decentralization, decarbonization, and digitalization. Over the years, the growth of decentralized power systems using distributed energy resources is increasing rapidly, bringing new prospects for local energy trading concerning economic and control operations in a single framework called transactive energy (TE). However, the continuity of power supply after high-disruptive events has remained a potential challenge for the energy market operator. Thus, to triumph over the challenges, this article proposes a novel TE framework with resiliency consideration. On the other hand, cyber-attacks are an increasing concern, which impacts the digital platform, such as energy digitization. Considering this, homomorphic Blockchain technology is applied to secure the TE platform against cyber-attacks. To show the leveraging capabilities, this article considers various case studies in the wake of high-disruptive events such as microgrid outage conditions and cyber-attack. The result shows that the proposed TE framework enhances the microgrid benefit and guarantees optimal trading performance. In addition, empowering Blockchain realizes that the security of the system is vital for the TE system when it faces data tampering or any external attacks. The effectiveness and feasibility of the proposed system are evaluated by cost, benefit, and probability of success.

The increasing penetration of photovoltaic (PV) systems in low-voltage residential feeders has elevated the need for grid support at the distribution level to prevent violations of local voltage constraints. In this article, a coordinated reactive power support (RPS) methodology is presented that utilizes the demand-side flexibilities of the end user to keep local voltage levels within allowed levels. A cloud-based architecture is implemented to optimally coordinate consumers’ reactive power capable demand-side resources such as electric vehicles, solar PV systems, flexible home appliances, etc., considering their varying characteristics, ratings, and purposes. An optimization-based two-stage device scheduling and management model is presented for the cloud server that schedules consumers’ devices in day ahead for cost minimization, and optimally allocates the required RPS in real time among the candidate devices based on priority. Two device prioritization strategies are proposed that consider the reliability of reactive power capable consumer devices and management complexity, thereby, allowing consumers to either enhance the candidate devices’ lifetime or reduce the management complexity while participating in grid support. The proposed RPS methodology is validated using simulation studies, and an experimental setup is established to verify the viability of the proposed cloud-based coordination system for RPS. Case studies indicate that the proposed method can effectively prevent overvoltage situations by using coordinated RPS from consumers’ devices while maximizing their reliability. Results also indicate that the proposed methodology is economically more viable than state-of-the-art voltage control strategies.

The increased integration of light-duty electric vehicles (EVs) into low-voltage (LV) residential networks imposes capacity issues for the grid operators. For example, the uncoordinated and clustered charging of residential EVs can often overload grid assets, jeopardize network reliability, and violate local voltage constraints. This article proposes a coordinated management system for EVs in LV residential networks with power grid support functionalities to address grid overloading and local voltage constraints violation. The charging and discharging of EV batteries in the network are coordinated via a local EV aggregator. The coordination is realized using multiagent system architecture that provides the EV owners with full decision-making authority and preserves their privacy. The EV coordination and vehicle-to-grid (V2G) resource optimization of the EV aggregator is formulated as a mixed-integer programming-based optimization model to minimize the electricity cost for the EV owners based on a real-time tariff while complying with local grid constraints. The proposed methodology is evaluated via simulation of an LV residential network in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed strategy for the electricity cost reduction of the EV owners while mitigating grid overloading and maintaining desired bus voltages.

False data injection attack (FDIA) is a critical cyber-attack that can cause disrupt operations and subsequently blackouts in smart grid networks. Cleverly constructed stealthy false measurement vectors can circumvent the bad data detector unit and mislead the state estimation process. This article proposes a novel belief propagation (BP)-based algorithm to detect both random and stealthy-type FDIAs in smart grids with higher detection rate than the state-of-the-art machine learning classifiers such as Naive Bayes, support vector machines, Random Forest, OneR, and AdaBoost. Another novel feature of the proposed algorithm is to detect FDIAs without using any historical cyber-attack data, which are sketchy due to security constraints and infinitesimal in occurrence numbers. The proposed BP method utilizes local sensor measurement data to calculate local belief and send it as a message signal to the control center. Then, the control center determines final/global belief and compares the result with a predefined threshold value derived from the uncompromised measurement database. The real-time steady-state load data are utilized for dc state estimation. From the obtained results, performance parameters such as detection rate, receiver operating characteristic curve, precision, recall, and F-measure of the proposed BP algorithm are found superior to the aforementioned state-of-the-art machine learning algorithms.

Electric vehicles (EVs) are getting popular as one of the effective solutions for increased energy efficiency in commercial systems. This paper proposes an improved algorithm for commercial peak-load management using EVs, battery-energy-storage systems, and photovoltaic units. It uses the bidirectional vehicle-to-grid technique to utilize the energy from EVs in a parking lot. The proposed system has been tested in a real power distribution network in realistic load and weather conditions. The financial benefit of the system is also investigated, and it is found that the industrial peak load can be reduced by 50%, and the energy cost can be reduced by up to 27.3%. It also enhances the load factor by 9%. The performance of the proposed control algorithm is compared with that of an artificial-neural-network-based technique and tested in a laboratory prototype. From simulated and experimental results, it is found that the proposed approach provides substantial savings, while reducing the peak demand of the existing grids.

Objective:: There is limited data characterizing the risk and impact of surgical site infection (SSI) in neonates; this makes it difficult to identify factors that increase neonatal SSI risk and to determine how SSI affects outcomes in this special population. Study Design:: The American College of Surgeons National Surgical Improvement Program Pediatric (NSQIP-P) collected data on children undergoing surgery at children’s hospitals from 2012 to 2014. Neonates undergoing general surgical procedures were characterized with regard to demographic characteristics and comorbidities. Perioperative variables such as wound class, type of surgery and length of operation were also evaluated. Results:: Seven thousand three hundred and seventy-nine neonates were identified in the NSQIP-P participant user file. The overall SSI rate was 2.6%. Only wound class and length of surgery were significantly associated with SSI. Furthermore, SSI was independently associated with longer length of stay, even after adjusting for covariates. Conclusions:: This is the largest study to date analyzing SSI in neonates. We found that perioperative variables have a more significant impact on SSI than patient factors, suggesting that operation-related characteristics are influencing SSI. Furthermore, neonates with SSI are more likely to have prolonged hospitalizations even after adjusting for patient comorbidities.

This paper presents a new distributed agent-based scheme to enhance the transient stability of power systems by maintaining phase angle cohesiveness of interconnected generators through proper relay coordination with critical clearing time (CCT) information. In this distributed multi-agent infrastructure, intelligent agents represent various physical device models to provide dynamic information and energy flow among different physical processes of power systems. The agents can communicate with each other in a distributed manner with a final aim to control circuit breakers (CBs) with CCT information as this is the key issue for maintaining and enhancing the transient stability of power systems. The performance of the proposed scheme is evaluated on a standard IEEE 39-bus New England benchmark system under different large disturbances such as three-phase short-circuit faults and changes in loads within the systems. From the simulation results, it is found that the proposed scheme significantly enhances the transient stability of power systems as compared to a conventional scheme of static CB operation.

This paper presents an analytical approach to establish a relationship between the voltage variation and distributed generation (DG) integration for the planning and operation of distribution networks with DG. The proposed approach is mainly based on the derivation of a voltage variation formula for distribution networks with DG and the consideration of the worst case scenario, which establishes a relationship between the amount of voltage variation and maximum permissible DG. Some recommendations are presented based on the worst case voltage variation formula and DG integration to counteract the voltage variation effect. The relationship between the connection cost and voltage level is also presented in this paper. The feasibility of the proposed approach is validated by comparing the voltage profile obtained from the derived formula to that with the existing power system simulation software.

This paper presents a robust stabilization scheme for a three-phase grid-connected photovoltaic system to control the current injected into the grid and dc-link voltage to extract maximum power from photovoltaic (PV) units. The scheme is mainly based on the design of a robust controller using a partial feedback linearizing approach of feedback linearization, where the robustness of the proposed scheme is ensured by considering uncertainties within the PV system model. In this paper, the uncertainties are modeled as structured uncertainties based on the satisfaction of matching conditions. The performance of the proposed stabilization scheme is evaluated on a three-phase grid-connected PV system in terms of delivering maximum power under changes in atmospheric conditions.

This paper presents a new approach to control the grid current and dc-link voltage for maximum power point tracking and improvement of the dynamic response of a three-phase grid-connected photovoltaic (PV) system. To control the grid current and dc-link voltage, the zero dynamic design approach of feedback linearization is used, which linearizes the system partially and enables controller design for reduced-order PV system. This paper also describes the zero dynamic stability of the three-phase grid-connected PV system, which is a key requirement for the implementation of such controllers. Simulation results on a large-scale grid-connected PV system show the effectiveness of the proposed control scheme in terms of delivering maximum power into the grid.

To enhance the Chromobacterium viscosum lipase (glycerol-ester hydrolase; EC 3.1.1.3) activity for the reaction of water-insoluble substrates, the AOT/isooctane reverse micellar interface was modified by co-adsorption of a non-ionic surfactant. Polyoxyethylene sorbitan trioleate (Tween 85) was used as the non-ionic surfactant and olive oil as a water-insoluble substrate. An appreciable increase of lipase activity was observed and at higher Wo values (where Wo = molar ratio of water to total surfactants of the micellar system) there was no sharp fall of the enzyme activity such as a typical bell-shaped profile. The kinetic model for the lipase-catalysed hydrolysis of olive oil in AOT/isooctane reverse micellar system was applied to the enzymatic reaction in this mixed reverse micellar system. It was found that the predictions of the model agree well with the experimental kinetic results and that the adsorption equilibrium constant of olive oil molecules between the micellar phase and the bulk phase of the organic solvent is smaller in AOT/Tween 85 mixed reverse micellar systems than in simple AOT reverse micellar systems. © 1999 Society of Chemical Industry