Uncertain Behavior Research Articles

The role of smart communities integrated with renewable energy resources, smart homes and electric vehicles in providing ancillary services: A tri-stage optimization mechanism

Increasing the connection of renewable energy sources and electric vehicles (EVs) to modern cities with smart homes (SHs) makes the need for new mechanisms to coordinate them with the operation of transmission (TN) and distribution networks (DNs) inevitable since the uncertain behavior of the above-mentioned resources can create various technical and economic challenges for the operators. Hence, this article introduces a tri-stage mechanism for the joint management of the energy and ancillary services markets in coordinated TN and DN, taking into account wind and solar renewable resources, SHs and EVs. In the first stage of this mechanism, SHs do their daily planning with the possibility of participating in the regulation market and send it to the DN operator. In the second stage, DN operator determines its strategy for participation in the energy and ancillary services markets according to the programs received from SHs and sends it to the TN operator. Finally, in the third stage, the TN operator settles the markets according to the plans received from the DN operators. This model is designed in the mixed-integer linear programming (MILP) format and simulated using CPLEX solver in GAMS. The simulation outputs illustrate that SHs by controlling their internet-of-things (IoT)-based appliances and EVs have provided cheap services in the regulation market and subsequently reduced the costs of this market by 10.4%. Also, the results reveal that the participation of large-scale industrial units in the demand response program (DRP) not only reduces the costs of the energy and ancillary services markets but also improves the voltage profile during the peak period.

Smoothness of time series: a new approach to estimation

The assessment of the risk of occurrence of extreme phenomena is inherently linked to the theory of extreme values. In the context of a time series, the analysis of its trajectory toward a greater or lesser smoothness, i.e. presenting a lesser or greater propensity for oscillations, respectively, constitutes another contribution in the assessment of the risk associated with extreme observations. For example, a financial market index with successive oscillations between high and low values shows investors a more unstable and uncertain behavior. In stationary time series, the upper tail smoothness coefficient is described by the tail dependence coefficient, a well-known concept first introduced by Sibuya. This work focuses on an inferential analysis of the upper tail smoothness coefficient, based on subsampling techniques for time series. In particular, we propose an estimator with reduced bias. We also analyze the estimation of confidence intervals through a block bootstrap methodology and a test procedure to prior detect the presence or absence of smoothness. An application to real data is also presented.

Incorporating uncertainty in stress‐strain data acquisition: extended model‐free data‐driven identification

AbstractBased on the increasing ability to collect and store large amounts of data and information, data‐driven methods have recently gained importance in the context of computational mechanics. Compared to the traditional approach of defining a suitable constitutive model and fit the parameters to experimental data, these methods aim to capture complex material behavior without the assumption of a certain material formulation. Distinguished are model‐based data‐driven methods, leading to an approximation of the constitutive material description for example, by neural networks, and model‐free data‐driven methods. The approach of data‐driven computational mechanics (DDCM), introduced by Kirchdoerfer and Ortiz (2016), enables to circumvent any material modeling step by directly incorporating material data into the structural analysis. A basic prerequisite for both types of data‐driven methods is a large amount of data representing the material behavior, in solid mechanics consisting of stresses and strains. Obtaining these databases numerically by multiscale approaches is computationally expensive and requires the definition of lower scale models. In case of an experimental characterization, constitutive descriptions are generally required to compute the stress states corresponding to displacement fields, for example, identified by full‐field measurement techniques, such as digital image correlation (DIC). The method of data‐driven identification (DDI), introduced in Leygue et al. (2018) based on the principles of DDCM, enables the determination of detailed information about the constitutive behavior based on displacement fields and applied boundary conditions without a specific material model. Stresses corresponding to given strains are identified by iteratively clustering and enforcing equilibrium. The algorithm has shown to be applicable to synthetic as well as real data taking linear and non‐linear material behavior into account. Generalized polymorphic uncertainty models, resulting as a combination of aleatoric and epistemic uncertainty models, are utilized to take variability, imprecision, inaccuracy and incompleteness of data into account. The consideration of uncertain material properties by data‐driven simulation approaches leads to the requirement of data sets representing uncertain material behavior. In this contribution, different sources of uncertainty occurring within DDI of stress‐strain relations are addressed and an efficient method for the identification of data sets representing uncertain material behavior based on the concept of DDI is proposed. In order to demonstrate the developed methods, numerical examples are carried out.

The Effect of Cross-Coupling on the Dynamic Performance of a Dual Phase Shift Controlled BiWPT System

In a bidirectional wireless power transfer (BiWPT) system, dual phase-shift (DPS) control is used to maintain the optimal efficiency at constant output power over varying misalignment. The presence of multiple control and output variables in DPS control creates cross-coupling between transmitter and receiver side, making BiWPT a multiple-input multiple-output (MIMO) system. Due to cross coupling, any change in control variable in one side of the BiWPT system will affect the output of the same side as well as output and control input in the other side. This results in an uncertain dynamic behaviour of the output response. The cross-coupling effect has not been adequately researched in the literature. Hence, this paper presents an accurate dynamic model using a generalized state-space averaging approach (GSSA) and an extended describing function (EDF) method for a multivariable control of the S-S compensated BiWPT system. This model is used to derive the system’s relative gain array (RGA), which is used to study the effect of cross-coupling terms on the output variables. A 1 kW S-S compensated BiWPT experimental set-up is used to validate the effect of cross-coupling on the dynamic behavior of the system by comparing DPS and single phase-shift (SPS) controlled experimental waveforms. The steady-state and transient behavior of the BiWPT system has also been investigated with simulation and experimental results.

Quadtree SBFEM and optimization based forward and inverse interval analysis for PCM-integrated walls

Interval model is employed to describe the uncertain behavior of phase change materials (PCM) integrated walls. A numerical model is developed for the forward interval analysis, by which the interval bounds of thermal response, such as the temperature on the inner wall and the degree of phase change of PCM, can be estimated when materials and environmental parameters are interval variables. This model integrates the advantages of the quadtree-scaled boundary finite element method (quadtree SBFEM), the Legendre polynomial surrogate (LPS), interval model, and optimization method, to gain a comprehensive benefit of convenient mesh regeneration, reliable bounds estimation, and the alleviation of heavy computational burden in the optimization process. On the other side, a numerical model is presented for the inverse interval analysis, by which the interval bounds of material parameters can be identified when the intervals of measurement of temperature and the degree of phase change of PCM are provided, and the impact of nosy data is taken into account. This model may be enlightening for a deeper investigation of inverse interval analysis of PCM-integrated walls since no attention seems to have been paid to this issue. In terms of computing accuracy, computational cost, and numerical examples are provided to elucidate the effectiveness of proposed approaches, and satisfactory results are achieved in both forward and inverse interval analysis.

Decentralized Control Strategy for Participation of Electric Vehicles in Distribution Voltage Control

With the accelerating transportation electrification, increasing numbers of electric vehicles (EVs) are connected to distribution networks via electric vehicle charging stations (EVCSs). Due to the uncertain charging behaviors of EV users, concentrated high-power EV charging during peak hours can result in significant undervoltage issues, affecting the regular operation of distribution networks and having detrimental effects on regular EV charging. Conventional EV charging scheduling methods are conducted in a centralized way, requiring extensive investment in communication infrastructure and intensive computing power. Accordingly, this paper proposes a novel decentralized voltage control scheme with the participation of EVs and EVCSs. The proposed control scheme first divides the distribution network into clusters based on a modified modularity index. Then, it applies the rolling optimization-based control of the EVs and EVCSs within each cluster to solve the regional voltage problems. The optimization window length is self-adapted based on the monitored states of the grid-connected EVs and EVCSs. Case studies show that the proposed decentralized voltage control strategy could effectively address the undervoltage violation issues and alleviate the pressure on peak shaving during peak hours, while satisfying the charging requirements of EV users.

A fully robust home energy management model considering real time price and on-board vehicle batteries

On-board batteries from electric vehicles can be used in domestic installations as an auxiliary source of energy through their vehicle-to-home features. To properly manage this kind of assets, Home Energy Management systems are expected to be essential tools in future smart grids, enabling a more efficient use of energy in residential applications. However, these tools normally perform under severe uncertain environments caused by intermittent renewable generation, uncertain prices, uncertain demand as well as uncertain behaviour of electric vehicles. In this regard, further research should be conducted in order to develop advanced robust energy management tools that account for the effect of uncertainties. So far, most of the existing literature focused on simple stochastic approaches, that may pose various issues, while other more sophisticated uncertainty models have not been sufficiently studied. In this regard, this paper proposes a fully robust Home Energy Management model, which accounts for all the inherent uncertainties that may arise in domestic installations. In contrast to previous researches, robust optimization is applied to all the uncertainties, resulting in a tractable, but robust yet paradigm model which allows to adapt the degree of robustness. A case study is conducted on a benchmark prosumer paradigm to validate the new proposal as well as highlight the benefits of flexible appliances. Results reveal the usefulness of the new tool, which allows to handle very unfavourable uncertain profiles, especially in case of the electric vehicles, for which a very low initial state-of-charge and early departure time can be assumed, leading to more conservative results that suppose incrementing the electricity bill by 38 %.

Modelling and Simulation of Thermostatically Controlled Loads for Power System Regulation in Building-to-Grid Integration Framework

Since last few decades the inclusion of renewable energies in the grid power system is rapidly increasing with the increase in technologies to minimize the use of conventional fossil fuels for minimizing the emission of greenhouse gases. But the uncertain behaviour of the renewable energy sources lead to a challenge of maintaining the stability and reliability of the grid system and to optimize the supply and demand balance. It is identified that more than 40% energy consumption is made by the consumers of buildings. To minimize the power fluctuation, caused by inclusion of renewable systems, the thermostatically controlled loads (TCLs) like ACs, refrigerators, heaters of buildings can be considered as flexible loads for regulation purpose as TCLs share the major part of the total power consumption and also TCLs can be adjusted without compromising the comfort level. In this paper, a practical linear network model of the multi layered construction materials of building is developed with 3R2C electrical circuit model and corresponding state-space equations are formulated, and also a 2ndorder TCL model is proposed as an RC network equivalent where main focus is on the works, which develop specific control strategies by modelling the building energy systems.

Quality indicators: completeness, validity and timeliness of cancer registry data contributing to the European Cancer Information System.

All malignant tumours, except skin non-melanoma, and in situ and uncertain behaviour of bladder were obtained from 130 European general PBCRs for patients older than 19 years. Proportion of cases with death certificate only (DCO%), proportion of cases with unknown primary site (PSU%), proportion of microscopically verified cases (MV%), mortality to incidence (M:I) ratio, proportion of cases with unspecified morphology (UM%) and the median of the difference between the registration date and the incidence date were computed by sex, age group, cancer site, period and PBCR. A total of 28,776,562 cases from 130 PBCRs, operating in 30 European countries were included in the analysis. The quality of incidence data reported by PBCRs has been improving across the study period. Data quality is worse for the oldest age groups and for cancer sites with poor survival. No differences were found between males and females. High variability in data quality was detected across European PBCRs. the results reported in this paper are to be interpreted as the baseline for monitoring PBCRs data quality indicators in Europe along time.

Loose Cocrystal Strategy for High‐Efficiency Near‐Infrared Phosphorescence

AbstractHarvesting triplet excitons of large π‐conjugate aromatic molecules via cocrystal engineering for attaining near‐infrared (NIR) phosphorescence has aroused wide attention due to its fundamental significance and practical application. Herein, the pyrene (Py)‐N, N’‐bis(1‐ethylpropyl)‐2,5,8,11‐tetrakis(p‐methyl‐phenyl)‐perylenediimide (MP) organic cocrystal is designed and fabricated via the solution self‐assembly method, which demonstrates the NIR phosphorescence with a maximum peak at 730 nm and a photoluminescence quantum yield of 34.2%. The singlet states with an intermediate energy level from MP molecules bridges singlet and triplet states of Py molecules, resulting in efficient NIR phosphorescence. Importantly, the loose packing pattern makes MP an excellent elementary molecule that can be assembled with a variety of aromatic molecules, from ternary to six‐membered benzene rings and even larger benzene skeletons. In contrast to the limitations of traditional cocrystal systems, cocrystal engineering based on MP molecules with weak π–π interactions is no longer limited to the careful selection and collocation of molecular structures, uncertain assembly behavior, and unpredictable properties. This work holds considerable insights for the exploration of novel NIR phosphorescent materials via a universal strategy.

Generalized Stratified Sampling for Efficient Reliability Assessment of Structures against Natural Hazards

Performance-based engineering for natural hazards facilitates the design and appraisal of structures with rigorous evaluation of their uncertain structural behavior under potentially extreme stochastic loads expressed in terms of failure probabilities against stated criteria. As a result, efficient stochastic simulation schemes are central to computational frameworks that aim to estimate failure probabilities associated with multiple limit states using limited sample sets. In this work, a generalized stratified sampling scheme is proposed in which two phases of sampling are involved: the first is devoted to the generation of strata-wise samples and the estimation of strata probabilities, whereas the second phase aims at the estimation of strata-wise failure probabilities. Phase-I sampling enables the selection of a generalized stratification variable (i.e., not necessarily belonging to the input set of random variables) for which the probability distribution is not known a priori. To improve the efficiency, Markov Chain Monte Carlo Phase-I sampling is proposed when Monte Carlo simulation is deemed infeasible, and optimal Phase-II sampling is implemented based on user-specified target coefficients of variation for the limit states of interest. The expressions for these coefficients are derived with due regard to the sample correlations induced by the Markov chains and the uncertainty in the estimated strata probabilities. The proposed stochastic simulation scheme reaps the benefits of near-optimal stratified sampling for a broader choice of stratification variables in high-dimensional reliability problems with a mechanism to approximately control the accuracy of the estimators of multiple failure probabilities. The practicality and efficiency of the scheme are demonstrated using two examples involving the estimation of failure probabilities associated with highly nonlinear responses induced by wind and seismic excitations.

Central giant cell granuloma in a 10-year-old girl: microsurgical reconstruction with a microvascularized fibula flap

Central giant cell granuloma (CGCG) are rare, proliferative, benign bone tumors characterized by an abundance of multinucleated giant cells. It affects children and young adults, predominantly women, in the second and third decade of life, it’s commonly found in the mandible. The main treatment for giant cell tumors is surgical excision with free histologic margins; in case of mandibular involvement, the use of a vascularized composite bone flap represents the standard for a functional reconstruction. We present the case of a female patient in the first decade of life with a CGCG of aggressive type, located in the ascending branch and right mandibular angle. En bloc resection of the lesion was performed, with a 2-cm margin and mandibular reconstruction at the same surgical time with a free microvascularized flap from the right fibula. CGCG are rare and infrequent tumors during childhood. Due to its uncertain behavior, it is important to rule out other differential diagnoses prior to definitive treatment.

English

Renewable sources are essential in fulfilling affordable, clean, and sustainable energy requirements. Consistent wind speed forecasting and accurate solar radiation prediction are required to minimize economic losses and enhance the security of power usage. Predicting solar radiation and wind speed is difficult due to their uncertain behaviour. Motivated by these aspects, this study implemented an Internet of Things (IoT) and artificial neural network (ANN) based system for predicting solar radiation and wind speed. Initially, IoT and long-range (LoRa) enabled hardware was developed for obtaining real-time data. In this study, customized hardware was developed with the amalgamation of LoRa, WI-Fi and many sensors. Solar grid and windmill sensor nodes are customized hardware deployed to collect real-time data to create datasets that predict solar radiation and wind speed using wind speed, solar radiation, humidity, temperature, voltage, and current parameters. For the prediction, in this study, we have employed the ANN techniques, i.e., Levenberg Marquardt (LM) and Bayesian regularization (BR) methods. We have found that the proposed ANN framework using the LM algorithm provides accuracy in the prediction compared to the BR algorithm with less root mean square error (RMSE), mean square error (MSE), fast convergence speed, and more significant Correlation coefficient (R).

Prediction of Potential Acid Mine Drainage Formation in High Sulfidation Epithermal Deposit using Geochemical and Mineralogy Approaches

Identification of acid mine drainage (AMD) can be predicted using a geochemical and mineralogy characterization approach to identify rocks forming and neutralizing acids. Geochemical characterization using static and kinetic tests. The static test is the first step to determining the potential for acid formation in rocks by comparing the potential for forming and neutralizing acids, which are characterized by uncertain behavior that causes predictions to be inaccurate. Kinetic tests were carried out to predict the long-term acid-producing potential better. Furthermore, mineralogical characterization is needed to assess the importance of sample mineral content in predicting AMD. Tests were conducted on rock samples from high sulfidation epithermal (HSE) deposits in North Sulawesi, Indonesia. The samples represent the different geological domain conditions found in HSE deposits. The main acid-producing components are pyrite and secondary minerals (alunite) capable of buffering acidity. The results showed a lower change in leachate pH (from day 30 to 180) and were accompanied by the release of species (high dissolved metals).

Experiential learning, institutional quality and Chinese state-owned firms’ cross-border acquisitions

PurposeState-owned firms play important roles in Chinese cross-border acquisition (CBA) activities. However, compared with private firms, state-owned firms have a lower likelihood of acquisition completion and take longer to complete a deal. This paper aims to determine why this phenomenon exists and how state-owned firms can overcome the constraints of their identity.Design/methodology/approachBy integrating organizational learning theory with institutional theory, this paper attempts to answer the research questions from a legitimacy perspective. Employing Chinese CBA data from 1982 to 2014, the authors use a logit model and a random effects model to test the hypothesis.FindingsThe results show that a state-owned identity easily causes legitimacy concerns among host country regulatory agencies; thus, it may result in longer and more uncertain evaluation behaviors, which lead to a lower likelihood of CBA completion and a longer deal duration. Only experience with failed acquisitions can increase CBA completion probability. Furthermore, in very complex decision-making environments, such as that surrounding deal duration, only specific types of experience (i.e. experience of failed international acquisitions) can trigger learning behavior, whereas general experience (i.e. failed acquisition experience) has little influence. Favorable bilateral relationships may not improve the completion rate and efficiency of state-owned firms, but high-quality host country institutions lead to a higher likelihood of CBA completion among state-owned firms; however, this may be not conducive to decreasing the time needed to complete an acquisition deal.Originality/valueFirst, by discussing the completion rate and duration of CBAs conducted by state-owned firms and analyzing the factors that influence them, this paper enriches and develops the theory of organizational overseas mergers and acquisitions (M&As). Second, by adopting a legitimacy perspective and integrating institutional theory, the authors theorize how state-owned status influences firms’ M&A completion rate and time and test the hypotheses empirically; thus, this paper improves and deepens institutional theory. Third, by discussing how different types of experience (i.e. successful experience vs failed acquisition experience) influence the acquisition completion rate and duration and how general experience and specific types of experience affect these two dependent variables differently, this paper explains how state-owned firms can learn effectively from experience, contributing to organizational learning theory.

An Online Deep Reinforcement Learning-Based Order Recommendation Framework for Rider-Centered Food Delivery System

As an important part of intelligent transportation systems, On-demand Food Delivery (OFD) becomes a prevalent logistics service in modern society. With the continuously increasing scale of transactions, rider-centered assignment manner is gaining more attraction than traditional platform-centered assignment among food delivery companies. However, problems such as dynamic arrivals of orders, uncertain rider behaviors and various false-negative feedbacks inhibit the platform to make a proper decision in the interaction process with riders. To address such issues, we propose an online Deep Reinforcement Learning-based Order Recommendation (DRLOR) framework to solve the decision-making problem in the scenario of OFD. The problem is modeled as a Markov Decision Process (MDP). The DRLOR framework mainly consists of three networks, i.e., the actor-critic network that learns an optimal order ranking policy at each interaction step, the rider behavior prediction network that predicts the grabbing behavior of riders and the feedback correlation network based on attention mechanism that identifies valid feedback information from false feedbacks and learns a high-dimensional state embedding to represent the states of riders. Extensive offline and online experiments are conducted on Meituan delivery platform and the results demonstrate that the proposed DRLOR framework can significantly shorten the length of interactions between riders and the platform, leading to a better experience of both riders and customers.

Safety and effectiveness of continuous denosumab for unresectable or advanced giant cell tumor

Introduction. Giant cell tumor of bone is a relatively rare, locally aggressive osteolytic skeletal neoplasm with uncertain behavior: recurrence rates up to 70 % and distant metastases occur 2–6 % of cases. Nowadays denosumab is the choice of therapy for patients with unresectable or advanced disease. However, the efficiency, duration or administration and most of all safety of continuous denosumab are not established.Materials and methods. Fourty advanced or unresectable giant cell tumor cases were observed from 2005 till 2020 in N.N. Blokhin National Medical Research Center of Oncology. The average age of pts was 33,6 ± 13,1 years (18–64), and the women and men ratio was about 2,1 : 1. The most commonly affected sites were long bones of the lower extremities (22,5 %), sacrum (22,5 %), long bones of the upper extremities (17,5 %), spine (17,5 %), pelvis (10 %) and others. 70 % of cases were anatomically compounded due to tumor localization and 27,5 % of cases were primary disease. 37,5 % of cases were with pulmonary metastases. Patients underwent computed tomography / magnetic resonance imaging every 3 months during the first three years and then once every six months. Patient received subcutaneous denosumab 120 mg every 4 weeks with a loading dose of 120 mg subcutaneous on study days 8 and 15. After 2 years monthly therapy and confirmed stabilization effect patient then received maintenance therapy: once in three months injection. All patients received daily calcium and vitamin D supplement.Results. Median follow-up was 52,8 ± 41,3 months (3–219 months). The average denosumab injections were 25 ± 16 (4–85). Clinical and radiographically stabilization of the effect occurred on average at 12 ± 8 (4–32) injections. Hypocalcemia was registered in one case (2.5 %). There was significant improvement of Karnofsky scale, Visual analogue scale (VAS) and Watkins scale (p <0.001). 5-year progression-free survival for was 70.1 % (95 % confidence interval 55.7–88.0), the median was not reached. Progression of disease was observed only in subgroup with violations in denosumab administration or its cancellation (32,5 %). 3-year progression-free survival in subgroup with violations in denosumab administration or its cancellation was 10 % (95 % confidence interval 15.5–64.1). In subgroup with continuous denosumab and once in three months injection after 2 years monthly therapy there was no signs of progression.Conclusions. In this study we showed evidence of safety and effectiveness of continuous denosumab for unresectable or advanced giant cell tumor even with once in three months injection therapy. Denosumab for advanced giant cell tumor of bone became a choice of treatment, but we need further investigation for observation long term denosumab effectiveness and complications.

Optimal stochastic self-scheduling of a water-energy virtual power plant considering data clustering and multiple storage systems

As the world moves towards the integration of different water and energy resources, as well as storage systems, it is necessary to develop the conventional structure of virtual power plants (VPPs). In this work, a mixed-integer nonlinear programming (MINLP) model is established for stochastic self-scheduling problem of a new VPP structure, namely water-energy VPP. The proposed VPP, which participates in the electricity market to maximize its daily profit, aggregates different energy and water resources including wind turbines, compressed air energy storage (CAES), gas boiler, electrical boiler, absorption chiller, ice storage, water storage, and water well to supply water demand, as well as different types of energy demands such as cooling, power, and heat demands. To cope with the uncertain behavior of wind power, a scenario-based approach is employed. First, with the use of Monte Carlo simulation in MATLAB, a number of scenarios are obtained, and afterwards, K-means technique, which is a fast and efficient data clustering method, is implemented for the scenario reduction. Additionally, the impact of the consideration of CAES system on the performance of the proposed water-energy is discussed. The numerical results indicate that with the deployment of CAES system into the structure of the water-energy VPP, the expected daily profit is increased up to 4.26 %.

Design and implementation of a data-driven intelligent water heating system for an island community: A case study

Water heating accounts for approximately 25% of household energy use in developed countries. Therefore, the optimal control of water heating through the deployment of intelligent residential Electric Water Heaters (EWH) brings significant benefits. This paper presents an innovative design and implementation of an easy-to-use device for intelligent residential water heating. The device relied upon machine learning techniques to forecast a consumer’s hot water demand and optimize the operation of an EWH using a novel data collection process that relied on non-intrusive vibration data alone. The device was deployed in a six-month pilot project on the island of São Miguel, Portugal. The major difficulties were the novel use of vibration data to forecast the volume of hot water used and the uncertain behavior of the consumers. The challenges of only using vibration data were solved by careful data collection and artificial intelligence methods. To tackle the issue of uncertain consumer behavior, an innovative ‘heat now’ function was incorporated into the device to override the novel control framework. Results show that the device could accurately forecast hot water demand and optimally operate the EWH to meet this demand. The results showed an average reduction of 1.33 kWh/day per consumer, which equates to an average decrease of 35.5% in water heating costs. Calculations show that these devices can reduce the total energy used by 2832 kWh daily or 0.21% of total electricity generated. Furthermore, a fleet of these devices could reduce thermal generation by 0.37%, reducing emissions by 693.31 tons of CO2 per year. The results from the consumer survey show that the device did not affect the consumer’s comfort, validating the benefits and efficacy of the proposed device. Hence, the paper shows that a simple-to-use, novel device using an innovative forecasting algorithm based on non-intrusive vibration data brings numerous quantifiable benefits to actual consumers and electrical utilities.

Voltage constraint-oriented management of low carbon technologies in a large-scale distribution network

With the rising concerns about climate change and technological developments, the deployment of low carbon technologies (LCT) is gaining importance for reliable and sustainable power system operation. To decarbonize the heat and transportation sectors, LCTs such as heat pumps (HP) and electric vehicles (EV) are integrated into the power system from the low voltage (LV) distribution network, where predominantly end-users are connected. However, the increasing penetration of EVs and HPs, together with distributed photovoltaic (PV) systems on the demand side, can cause technical challenges in LV distribution networks, such as under/over voltages at the far end of the feeders. Therefore, the hosting capacity of a grid for LCTs is limited due to network constraints such as line ampacity, bus voltage, and frequency. In this paper, voltage-constrained management approaches for LCTs are proposed to improve the grid hosting capacity in a real large-scale distribution network. Moreover, HPs, PVs, battery energy storage systems (BESS), and EVs as well as their vehicle-to-grid (V2G) feature are evaluated as LCTs. The uncertain behavior of these technologies is taken into consideration for more realistic analyses. In addition, the charging and discharging interactions of BESSs are controlled by following the power consumption profiles from the economic point of view. Furthermore, the hourly carbon data is used to control BESSs from an emission-sensitive aspect. To demonstrate the effectiveness of the proposed algorithms, a series of tests is conducted on a real distribution network model. The results show that the hosting capacity of the considered LV network can be increased from 35% to 50% using the proposed algorithms. The promising results obtained in this study pave the way for future active LCT management studies to improve hosting capacity in urban networks, and pilot demonstrations in the field.