User Load Research Articles

Flexible load optimal regulation strategy of commercial buildings under the environment of electricity market

Under the electricity market environment, the flexible load of power users can not only reduce the cost of power consumption by adjusting their own power consumption behaviors according to the real-time price (RTP), but also can obtain additional benefits by participating in auxiliary services. The load of commercial buildings (CBs) is concentrated and makes a greater contribution to the peak load of the power system. If the flexible load of CBs can be regulated, it has great benefits for shifting the peak load of the power system in addition to reducing the electricity cost. On the basis of analyzing the characteristics of flexible loads in CBs, a flexible load regulation model including short-time interruptible load (IL), reducible load (RL), translatable load (TL), air conditioning system (ACS) and energy storage system (ESS) is established. Considering the electrical comfort and temperature preference, the objective function is jointly constructed with the electrical cost, temperature discomfort and ancillary service income. An optimal regulation strategy for multiple types of flexible loads in CBs based on the electricity market environment to realize the economic power consumption of CBs is formulated. The simulation results show that the proposed strategy can reduce the power cost of CBs and lower the peak demand under the condition of satisfying the power consumption comfort.

Reputation-based electricity scheduling scheme for complex network of user electricity consumption

With the emergence of various high-powered electrical equipment, the demand for electric energy has increased rapidly. Subsequently, it has highlighted some issues of electricity consumption, such as the adjustment of electricity consumption peak. Although many electricity scheduling schemes have been proposed to adjust and control user load of electricity consumption, the current regulation of user load is not accurate and effective because the load regulation of different regional grid users is a complex network system. In this paper, we propose a reputation-based user electricity scheduling scheme for the complex network of user electricity consumption, whose purpose is to accurately adjust the electricity consumption of related users to further improve the adjustment of electricity consumption peak. In our scheme, we first model a complex network of user electricity consumption. Then we construct a reputation calculation method for electricity users, where the calculated reputation of users is one of the basis for assigning scheduling tasks to users and calculating the price subsidy received by users who complete the scheduling tasks. Further, we use the machine learning method to train a computation model to calculate the adjustment coefficients of electricity load, and then the electricity scheduling tasks are adjusted based on the calculated adjustment coefficients. Finally, the corresponding electricity scheduling tasks are assigned to the selected electricity users respectively for adjusting the electricity consumption of these users. Experiment results show the effectiveness of our proposed scheme. Our scheme can effectively calculate the reputation values of users based on their historical data, and the corresponding electricity scheduling tasks are effectively assigned to related users to accurately adjust the electricity consumption of these users according to their reputation values and the real-time adjustment coefficients, so as to efficiently improve the adjustment of electricity consumption peak.

Evolution of Hybrid LiFi-WiFi Networks: A Survey.

Given the growing number of devices and their need for internet access, researchers are focusing on integrating various network technologies. Concerning indoor wireless services, a promising approach in this regard is to combine light fidelity (LiFi) and wireless fidelity (WiFi) technologies into a hybrid LiFi and WiFi network (HLWNet). Such a network benefits from LiFi's distinct capability for high-speed data transmission and from the wide radio coverage offered by WiFi technologies. In this paper, we describe the framework for the HWLNet architecture, providing an overview of the handover methods used in HLWNets and presenting the basic architecture of hybrid LiFi/WiFi networks, optimization of cell deployment, relevant modulation schemes, illumination constraints, and backhaul device design. The survey also reviews the performance and recent achievements of HLWNets compared to legacy networks with an emphasis on signal to noise and interference ratio (SINR), spectral and power efficiency, and quality of service (QoS). In addition, user behaviour is discussed, considering interference in a LiFi channel is due to user movement, handover frequency, and load balancing. Furthermore, recent advances in indoor positioning and the security of hybrid networks are presented, and finally, directions of the hybrid network's evolution in the foreseeable future are discussed.

Analysis of performance and suitable users of CCHP systems with active thermal energy storage

Introducing a thermal energy storage (TES) unit in the combined cooling, heating, and power (CCHP) system can help alleviate the supply–demand energy mismatch and improve the system performance. However, the active regulation function of TES has not been fully exerted and the applicable user scenarios of the CCHP-TES system are not clear due to the limitations of case studies. In this work, based on the supply–demand energy matching principle, an improved design of active thermal energy storage is proposed to further develop the role of TES units. Meanwhile, to overcome the limitations of case studies and obtain the universal applicable user scenarios, dimensionless user load characteristic parameters are used to analyse the correlation between the user characteristics and the system performances. The system thermodynamic model and integrated design methodology are developed. The results indicate that the average energy-saving rates of the proposed active CCHP-TES system are higher than 14.63%, and the maximum differences compared with those of the conventional CCHP system and passive CCHP-TES system are 22.27 and 29.20 percentage points, respectively. Moreover, among the user load characteristic parameters, the cooling/heating-to-electricity ratio has the greatest impact on the performances of the proposed system, the duration time of the peak electricity load the next, and the peak-to-valley ratio of electricity load the least. In most cases, the larger the cooling/heating-to-electricity ratio and the duration time of the peak electricity load are and the smaller the peak-to-valley ratio of electricity load is, the better the system performance will be. Finally, the applicable user scenarios of the proposed system are given quantitatively and the system is applicable once the peak-to-valley ratio of electricity load is less than 2 and the cooling/heating-to-electricity ratio is greater than 0.5.

Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

AbstractLocal integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.

Industrial users load pattern extraction method based on multidimensional electrical consumption feature construction

The rapid development of renewable energy generation aggravates the imbalance between supply and demand in power grid, and exploring the potential of demand side resource can effectively improve such problems. Industrial users (IU) is an important demand response resource of power grid, and mining the load patterns of IU is the basis of studying the demand response ability of IU, which plays an important role in the safe operation and lean management of power grid. Lately, the popularity of advanced metering infrastructures provides data support for studying the load patterns of IU. However, the high dimensionality and the complex non-linear relationship of IU’s load data bring difficulties to the task of clustering. To solve the above problems, this paper proposes a load pattern extraction method based on multidimensional electrical consumption feature construction. Firstly, industrial load characteristic set of IU is created with five load characteristic indexes weighted by improved entropy weight method. In addition, convolutional autoencoder is established to extract the temporal feature of industrial load data which is combined with industrial load characteristic set to build a multidimensional feature set (MFS) for IU and finish multidimensional electrical consumption feature construction (MECFC). Then, MFS is used as the input of Self-Organization Map network to select the initial clustering centers of K-means algorithm, overcoming the problem of local optimal solution, and complete the IU daily load clustering. The experiment shows that the algorithm based on MECFC solves the local optimal problem and have better performance in stability and clustering effect than traditional methods.

The path enabling storage of renewable energy toward carbon neutralization in China

In the coming years, renewable energy generation and new power systems will become the dominant trends toward alleviating extreme climate change and realizing carbon neutrality. In attempt to absorb significant amount of renewable energy, the new power systems are confronted with rising electricity prices and declining grid stability. To solve this problem, energy storage has emerged as a core component of the power systems in addition to the traditional source-grid-load structure; thus, various energy-storage techniques are being studied. Through comprehensive examination on the cost and industrial foundation of various energy storage methods in China, this paper clarified the advantages of lithium-ion batteries and hydrogen at duration less than 10h and higher than 48h respectively, especially after 2035. After combining with scenario demand in China, three promising energy storage application to support the clean energy revolution are proposed, including large-scale hydrogen energy storage for renewable energy base at Northeastern China, the centralized lithium-ion battery stations for the regulation of power grid, and distributed electric vehicles for user load terminal at developed coastal region. Furthermore, the development status, technical bottlenecks and solutions of these energy storage paths are discussed in detail to indicating the technical paths. Finally, the establishment of an everyone-involved energy storage market is proposed in future scenarios to promote the widespread popularization of energy storage technology and the achievement of carbon neutrality.

Privacy-Preserving Hybrid Cloud Framework for Real-Time TCL-Based Demand Response

Widespread advanced metering infrastructure and wide-area monitoring systems generate a significant amount of electricity load consumption data, which can facilitate eliciting end users temperature flexibility for demand response programs. However, the direct delivery of users load profiles is a threat to users privacy. So this paper proposes a privacy-preserving hybrid cloud framework for TCL-based demand response programs, composed of user private clouds and aggregation cloud. User clouds store users load profiles and elicit temperature flexibility by the proposed stable temperature-related regression model. In the aggregation cloud, this paper proposes the slope-priority flexibility aggregation method for the mean-variance analysis of aggregate flexibility and the XGBoost-accelerated disaggregation model for real-time selecting users based on users fitting coefficients. Hybrid cloud achieves privacy-preserving by separating flexibility eliciting models and aggregation/disaggregation methods into user private clouds and aggregation cloud. Numerical experiments verify that: 1) in user clouds, the stable regression model achieves less predict errors; 2) in aggregation cloud, the slope-priority method can achieve higher aggregate flexibility, and XGBoost-accelerated disaggregating reduces the solving time by nearly three orders of magnitude.

A Novel DC Transmission and Distribution System with 100% New Energy Consumption

With the continuous development of new energy technology and the continuous improvement of people’s green and low-carbon awareness, distributed photovoltaic, wind power, and other new energy are more and more widely used. On the other hand, the development of new industries, such as electric vehicle charging stations and data centers, has increased the DC load significantly, showing the characteristics of the coexistence of AC and DC user loads. Therefore, it is of great practical significance to study how to effectively utilize new energy and realize the direct power supply of AC and DC loads to achieve the dual carbon goal. Combined with the development trend of new energy and DC transmission, this paper proposes a ±10 kV DC transmission and distribution architecture with 100% new energy consumption, which can realize the complete local consumption of new energy and reliable power supply for diversified AC and DC loads, maximizing the reduction of power loss caused by AC-DC conversion. This paper discusses the key devices and key technologies in the above architecture and analyzes the advantages and disadvantages and application scope of various methods in the current research results.

A Novel Day Optimal Scheduling Strategy for Integrated Energy System including Electric Vehicle and Multisource Energy Storage

Aiming at problems such as high operating costs, the low utilization efficiency of renewable energy, and the increase in the peak-valley difference in user load caused by disordered charging of electric vehicles (EVs), the operation mode of “setting electricity by heat” or “setting heat by electricity” is adopted in the integrated energy system of intelligent residential areas on the distribution side. In this paper, a method is proposed to use multiple energy storage, demand response technology, and make EV orderly charge and discharge plan to consume renewable resources and realize optimal economic scheduling in the integrated energy system of combined heat and power (CHP). Firstly, the demand response model of electricity load adjusting according to renewable energy output and time-of-use electricity prices has been established. Secondly, considering the comfort of the space thermal load, the orderly charge and discharge plan of electric energy storage (ESS), thermal energy storage (HSS), and EV and aiming at the minimum operating cost of the integrated energy system, the optimal output operation of each equipment was scheduled. Finally, through simulation analysis and comparative experiments, it is verified that the proposed method can promote the local consumption of renewable energy, realize the decoupling of thermoelectric constraints, and reduce the operating cost of the integrated energy system.

Hybrid distributed finite-time neurodynamic optimization of electric vehicle charging schemes management in microgrid considering time-varying factors

In this paper, two distributed finite-time neurodynamic algorithms are proposed to collaboratively manage the charging scheme of electric vehicles (EVs) in the microgrid scenario. First, the upper level model is constructed to optimize the disorderly charging problem of EV users under private charging posts , and explore the optimal charging scheme under charging constraints and time-varying conditions to ensure the benefits of users. The lower layer model explores the optimal public charging scheme under the system operation constraint and the supply–demand balance constraint with the objective of minimizing the overall microgrid operation cost. The optimal solution of the upper model, i.e., the load of EV users under the private charging post, is considered as a parameter of the lower model. In this context, two finite-time neurodynamics with fast convergence rate, executed in a distributed manner, are proposed to track the optimal solution of the problem in real time. Furthermore, the stability and convergence in finite time of the two proposed algorithms are proved using Lyapunov theorem and finite time theorem. Numerical case studies of small-scale and large-scale power systems demonstrate the effectiveness, robustness, and real-time performance of the two proposed algorithms.

Hybridization Chain Reaction Lateral Flow Assays for Amplified Instrument-Free At-Home SARS-CoV-2 Testing.

The lateral flow assay format enables rapid, instrument-free, at-home testing for SARS-CoV-2. Due to the absence of signal amplification, this simplicity comes at a cost in sensitivity. Here, we enhance sensitivity by developing an amplified lateral flow assay that incorporates isothermal, enzyme-free signal amplification based on the mechanism of hybridization chain reaction (HCR). The simplicity of the user experience is maintained using a disposable 3-channel lateral flow device to automatically deliver reagents to the test region in three successive stages without user interaction. To perform a test, the user loads the sample, closes the device, and reads the result by eye after 60 min. Detecting gamma-irradiated SARS-CoV-2 virions in a mixture of saliva and extraction buffer, the current amplified HCR lateral flow assay achieves a limit of detection of 200 copies/μL using available antibodies to target the SARS-CoV-2 nucleocapsid protein. By comparison, five commercial unamplified lateral flow assays that use proprietary antibodies exhibit limits of detection of 500 copies/μL, 1000 copies/μL, 2000 copies/μL, 2000 copies/μL, and 20,000 copies/μL. By swapping out antibody probes to target different pathogens, amplified HCR lateral flow assays offer a platform for simple, rapid, and sensitive at-home testing for infectious diseases. As an alternative to viral protein detection, we further introduce an HCR lateral flow assay for viral RNA detection.

Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants

There is a consensus regarding the need to realize the transformation of renewable energy by enhancing demand-side regulating ability. This paper proposes a peak shaving potential assessment model based on the price elasticity mechanism and consumer psychology, focusing on the adjustable user load in virtual power plants. The values of deterministic parameters and the distribution of the uncertain parameter of the model are obtained through the long short-term memory network (LSTM) and mixture density network (MDN). Then, the refined distribution of peak shaving potential considering external conditions, incentive inputs, and spatial and temporal scales is obtained. Based on the evaluation results, a peak shaving decision-making model for virtual power plants is constructed using a scenario scheme. Differentiated schemes for traditional, risk-averse, and risk-seeking virtual power plant decision-makers are considered. Case studies using the data of a virtual power plant pilot area show that the proposed model can better characterize the features of virtual power plant users, and a refined control strategy with better economic benefits can be obtained.

Review of Cloud Computing Framework for the Implementation of eLearning Systems

The rapid growth in Information and Communication Technology (ICT), combined with recent events such as the Covid-19 lockdown, has had a significant impact on traditional educational systems. Today, eLearning has become a widely accepted method for delivering educational services. As a result, the ICT infrastructure needed to be expanded to keep up with the increasing numbers of users, a wide range of learning services, and the growth of educational content. However, the volatile user load and the massive storage and transfer of rich data have led to a need for the effective utilisation of server-side system resources in providing eLearning services. Cloud computing offers a dynamic provision of virtualised resources, elasticity, scalability, pay-per-use and measured service with the ability to dynamically provision and de-provision computing resources as needed. In this paper, a review of cloud-based eLearning frameworks is conducted to identify the state of the art. Different frameworks for the implementation of cloud computing-based eLearning are identified and presented. Additionally, the review provides insights into the use of cloud computing in education, current challenges in eLearning implementation, deployment strategies, and the benefits of using cloud computing for eLearning implementation.

Charging Behavior Analysis Based on Operation Data of Private BEV Customers in Beijing

Charging behavior is essential to understanding the real performance and evaluating the sustainability of battery electric vehicle (BEV) development and providing the basis for optimal infrastructure deployment. However, it is very hard to obtain the rules, due to lack of the data support, etc. In this research, analyzing the charging behavior of users with private charging piles (PCPs) is carried out based on the real vehicle data of 168 BEV users in Beijing, covering 8825 charging events for a one-year duration. In this study, the charging behaviors are defined by five indexes: the starting state of charge (SOC) of batteries, charging location selection, charging start time, driving distance, and duration between two charging events. To further find the influencing rules of the PCPs owning state, we setup a method to divide the data into two categories to process further analysis and comparison. Meanwhile, in order to better observe the impact of electric vehicle charging on the power grid, we use a Monte Carlo (MC) simulation to predict the charging load of different users based on the analysis. In addition, an agent-based trip chain model (ABTCM), a multinomial logistic regression (MLR), and a machine learning algorithm (MLA) approach are proposed to analyze the charging behavior. The results show that with 40% or lower charging start SOC, the proportion of users without PCPs (weekday: 55.9%; weekend: 59.9%) is larger than users with PCPs (weekday: 45.5%; weekend: 42.6%). Meanwhile, users without PCPs have a certain decrease in the range of 60–80% charging start SOC. The median charging time duration is 51.44 h for users with PCPs and is 17.25 h for users without PCPs. The charging peak effect is evident, and the two types of users have different power consumption distributions. Due to the existence of PCPs, users have lower mileage anxiety and more diverse charging time choices. The analysis results and method can provide a basis for optimal deployment and allocation of charging infrastructure, and to make suitable incentive policies for changing the charging behavior, targeting the carbon neutral objectives.

Analysis and prediction of industrial energy consumption behavior based on big data and artificial intelligence

To promote the energy-saving transformation of industrial enterprises effectively, it is important to accurately grasp the energy consumption behavior of enterprises. In this study, the multi-type industrial enterprises in an industrial park located in Shanghai are selected for analysis. Based on the collection and processing of the measured heating (steam) data, by employing the big data analysis method, the cluster analysis is carried out from different dimensions including user difference, load fluctuation and typical daily characteristics. Following which, the multi-type load characteristic curve is obtained, and the energy consumption characteristics of the whole industrial park and different types of industrial enterprises are discussed. On this basis, the energy load forecasting model of industrial enterprises is developed based on the LSTM neural network, and the effect of introducing meteorological data on the accuracy of load forecasting is analyzed.

GlobeCorr: interactive globe-based visualization for correlation datasets.

Increasingly complex omics datasets are being generated, along with associated diverse categories of metadata (environmental, clinical, etc.). Looking at the correlation between these variables can be critical to identify potential confounding factors and novel relationships. To date, some correlation globe software has been developed to aid investigations; however, they lack secure, dynamic visualization capability. GlobeCorr.ca is a web-based application designed to provide user-friendly, interactive visualization and analysis of correlation datasets. Users load tabular data listing pairwise variables and their correlation values, and GlobeCorr creates a dynamic visualization using ribbons to represent positive and negative correlations, optionally grouped by domain/category (such as microbiome taxa against other metadata). GlobeCorr runs securely (locally on a user's computer) and provides a simple method for users to visualize and summarize complex datasets. This tool is applicable to a wide range of disciplines and domains of interest, including the bioinformatics/microbiome and metadata examples provided within. See https://GlobeCorr.ca; Code provided under an open source MIT license: https://github.com/brinkmanlab/globecorr.

A low-power ankle-foot prosthesis for push-off enhancement.

Passive ankle-foot prostheses are light-weighted and reliable, but they cannot generate net positive power, which is essential in restoring the natural gait pattern of amputees. Recent robotic prostheses addressed the problem by actively controlling the storage and release of energy generated during the stance phase through the mechanical deformation of elastic elements housed in the device. This study proposes an innovative low-power active prosthetic module that fits on off-the-shelf passive ankle-foot energy-storage-and-release (ESAR) prostheses. The module is placed parallel to the ESAR foot, actively augmenting the energy stored in the foot and controlling the energy return for an enhanced push-off. The parallel elastic actuation takes advantage of the amputee's natural loading action on the foot's elastic structure, retaining its deformation. The actuation unit is designed to additionally deform the foot and command the return of the total stored energy. The control strategy of the prosthesis adapts to changes in the user's cadence and loading conditions to return the energy at a desired stride phase. An early verification on two transtibial amputees during treadmill walking showed that the proposed mechanism could increase the subjects' dorsiflexion peak of 15.2% and 41.6% for subjects 1 and 2, respectively, and the cadence of about 2%. Moreover, an increase of 26% and 45% was observed in the energy return for subjects 1 and 2, respectively.

User Association and Load Balancing Based on Monte Carlo Tree Search

User Association and Load Balancing Based on Monte Carlo Tree Search

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.