System Load Level Research Articles

ТЕНДЕНЦІЇ ЕЛЕКТРИФІКАЦІЇ ТРАНСПОРТНИХ ЗАСОБІВ

The market of electric vehicles in Ukraine and the world has been studied. Global electric vehicle sales are found to reach a record 3 million in 2022 and continue to increase. The main sales leaders are automobile companies: Nissan, Tesla, Toyota, Volkswagen Group, BYD. It is a joint effort between several industries: automobiles, utilities, gov-ernment and private property owners such as shopping centers and apartment complexes. When these paths align, vehi-cle electrification trends will grow exponentially. About 40 thousand electric vehicles are registered in Ukraine. The mar-ket is growing every year, reflecting the changing infrastructure for electric vehicles. This indicates significant changes in green transport and is expected to have far-reaching implications for transport and environmental policy in Ukraine. The popularity of used electric vehicles also suggests that cost may be a significant factor in driver choice, which could influ-ence future market trends and government incentives for electric vehicle adoption. The main companies providing ser-vices in the charging network for electric vehicles are Auto Enterprise, Toka, DTEK. The main factors influencing the ef-fective operation of electric vehicles, namely the development of infrastructure, are analyzed: a) modern equipment for charging stations corresponding to the appropriate level; b) provision of additional charging station services for electric vehicles (for example, individual lanes for parking and refueling and c) software for drivers, cost discounts for the use of electricity in accordance with system load levels, etc. Leading car factories are expected to account for more than 70% of global electric vehicle production by 2030, meaning one in four new passenger cars sold will be an electric vehicle. This is the beginning of the era of zero-emission alternative fuel vehicles. Keywords: electric vehicle, infrastructure, charging network for electric vehicles, operation, factor.

Intelligent Power Control of Inverter Air Conditioners in Power Systems: A Brain Emotional Learning-Based Approach

Inverter air-conditioning (IAC) units have been proved to be effective in frequency regulation by providing flexible capacities. This paper proposes a brain emotional learning (BEL)-based controller to provide the IACs with control signals to be efficiently involved in the frequency regulation in power systems. The BEL-based controller can learn quick-auto, making it appropriate in systems facing uncertainty. To assess the BEL controller performance in realistic conditions, the uncertainties as a consequence of variations in system parameters and load level are considered. The goal is to use the BEL controller to increase the IAC units' ability to track regulation signals accurately in uncertain circumstances. The controller is compared to a fuzzy-PI control, a proportional control scheme, a model predictive control and a linear quadratic regulator control. A delay-dependent stability criterion is used to calculate the highest time delay in the IACs response under which the system maintains stability. In addition, this paper presents an BEL-based coordinator to coordinate the IACs and traditional generation units for compensating considerable frequency variations caused by the time delays. Case studies are accomplished on a multi-area power system in MATLAB/Simulink environment. Eventually, real-time verifications by OPAL-RT real-time digital simulator on the simulated power system are executed to assess the control method.

Non-Iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems

Quasi-dynamic energy flow calculation is an indispensable tool for the heat and electricity integrated energy system (HE-IES) analysis. One solves the nonlinear partial differential algebraic equations to obtain thermal, hydraulic and electric variations. However, mainstream iteration solvers face the challenges of inefficiency and bad robustness. For one thing, the frequent update and factorization of Jacobian matrices utilize high CPU time. For another, the per-step iteration numbers grow exponentially as the system loading level creeps up. This paper presents a novel non-iterative algorithm for the quasi-dynamic energy flow calculation. The kernel of the proposed algorithm is to transform these nonlinear equations into linear recursive ones, by solving which, we obtain explicit closed-form solutions of unknown variables. In each step, the proposed algorithm requires only one matrix factorization and fixed times of arithmetic operations regardless of the loading levels, so that it achieves small and consistent per-step time costs. A semi-discrete scheme is used in PDE solution to avoid dissipative and dispersive errors that are often overlooked in previous literature. To ensure convergence, we also propose to control the temporal step sizes adaptively by estimating the simulation errors. Case studies showed that the proposed method manifested efficient and robust time performance compared with the iterative algorithms, and meanwhile preserved high accuracy.

Prediction method of primary frequency modulation capability of power system based on MEA-BP algorithm

In order to accurately predict the primary frequency modulation capability of power system when frequency deviation occurs, a method for predicting primary frequency modulation performance of power system based on improved neural network with thought evolution algorithm (MEA) is presented. Nine-dimensional parameters, such as frequency deviation before and after disturbance, system backup capacity, load level, mechanical power and electrical power, are selected as input eigenvalues, and the output is the system power change curve. This paper takes CEPRI36v8 system as an example of simulation, obtains the primary frequency modulation curve of the system by setting the shear load disturbance with PSASP, uses the disturbance data and the primary frequency modulation response data as training set, test set and validation set data, trains the prediction model of primary frequency modulation capability based on MEA-BP, and finds the prediction error is about 0.5% compared with the actual data, which proves that the proposed method can accurately and quickly predict the primary frequency modulation capability after the frequency deviation of the power network. This method can assist power dispatcher to analyze the primary frequency modulation response of power grid after power disturbance.

Optimal Capacitor Placement in Power Distribution System

Optimal Capacitor Placement (OCP) determines the locations, the size and the control settings of these capacitors during each load level of a three-phase distribution system. That will reduce peak power and energy losses and minimise the investment costs and installation of the capacitor banks. The optimal solution is obtained by testing various combinations of capacitor banks and candidate nodes and the distribution system and calculating the resultant savings. The capacitor placement problem is a combinational optimisation problem having an objective function composed of energy losses and capacitor bank installation costs subject to the load constraints, network constraints and operational constraints at different load levels. OCP seeks to maximise the net savings resulting from the reduction in power and energy losses less the total costs of capacitors. An Artificial Intelligence (A.I.) based Genetic Algorithm (G.A.) has been used to solve the OCP problem. The proposed method allows the modelling of loads of different power factors for other portions of the power distribution system. The solution algorithm has been implemented and tested on a ten bus power distribution system, and the simulation results of the proposed algorithm are well executed.

Evaluation of Adaptive Partitioning and Real-Time Capability for Virtualization With Xen Hypervisor

In this article, the fundamental suitability of the open-source hypervisor Xen for safety-critical real-time application is analyzed. In the field of avionics, satisfying criteria such as safety criticality and real-time capability are mandatory and pose one of the greatest challenges when developing systems and functions that are to be qualified for the installation in passenger aircraft. System architectures that additionally aspire adaptive behavior in terms of self-configuration and reconfiguration complicate these challenges even more. Spatial and temporal partitioning is a fundamental requirement for the implementation of safety-critical systems but is of static behavior in current avionic standards such as A653. Implementing a fully functional partitioning that is able to reconfigure during runtime would be a great leap forward in the field of adaptive avionics. The open-source hypervisor Xen offers features to dynamically create and manage virtual machines that natively come with architecture-related segregation. In order to determine the feasibility of Xen in the field of safety-critical real-time application, its spatial and temporal partitioning capabilities and occurring latencies are analyzed in this article. The analysis is based on specifically designed benchmarks that measure the accuracy of periodic task execution with different system load levels featuring various Xen schedulers and guest operating system kernels. The overall results for the spatial partitioning and real-time capabilities turn out to be promising but remain with minor interferences, varying based on the setup benchmarking configuration. The temporal partitioning enforced by Xen remains with clearly identified and fixable issues before a deterministic function execution in hard real time is reached.

Chance-Constrained OPF: A Distributed Method With Confidentiality Preservation

This paper focuses on the global chance-constrained optimal power flow problem of a multi-regional interconnected grid. In this global problem, however, multiple regional independent system operators (ISOs) participate in the decision-making process, raising the need for distributed but coordinated approaches. Most notably, due to the regulation and security concerns, regional ISOs may refuse to share confidential information with others, including generation cost, load data, system topologies, and line parameters. Accordingly, this paper proposes a distributed CC-OPF method with strict confidentiality preservation, which theoretically enables regional ISOs to determine the exact global optimal dispatchable generations within their regions without disclosing confidential data. The proposed method neither requires parameter tunings nor has convergence issues. Results from a number of test cases show that the proposed method has considerably high accuracy, regardless of the system size, load level, and amount of regions, thereby outperforming the state-of-the-art obfuscation approaches.

Two-stage robust distribution system operation by coordinating electric vehicle aggregator charging and load curtailments

In this paper, a comprehensive two-stage robust distribution system operation model is proposed by adjusting the charging of electric vehicle aggregators (EVAs) and curtailing loads. Because uncertainties in EVA charging demands are involved in the second stage of the adopted two-stage framework, distributionally robust optimization is used to improve the average economic performance of the proposed model, and security of distribution system operation is guaranteed by applying the Farkas lemma and robust optimization. The proposed model is solved by iteratively adding optimality cuts and feasibility cuts through a novel constraint generation algorithm, whose mathematical Proof is provided. The case studies show that the proposed model is capable of properly handling EVA uncertainties and coordinating EVA charging and load curtailments. The optimal coordination depends on several key parameters including the cost coefficients of delaying EVA charging and curtailing loads, the limits on delaying EVA charging, the system load level, and the EVA uncertainty level.

Resilient Identification of Distribution Network Topology

Network topology identification (TI) is an essential function for distributed energy resources management systems (DERMS) to organize and operate widespread distributed energy resources (DERs). In this paper, discriminant analysis (DA) is deployed to develop a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Following, to improve the TI resiliency against the interruption of communication channels, a quadratic programming optimization approach is proposed to recover the missing signals. By deploying the propounded data recovery approach and Bayes' theorem together, a benchmark is developed afterward to identify anomalous measurements. This benchmark can make the TI function resilient against cyber-attacks. Having a low computational burden, this approach is fast-track and can be applied in real-time applications. Sensitivity analysis is performed to assess the contribution of different measurements and the impact of the system load type and loading level on the performance of the proposed approach.

Identification of marginal generation units based on publicly available information

Identification of marginal generation units is essential to the development of effective demand response (DR) programs and quantification of locational marginal emissions (LMEs). The real-time marginal units, however, are not revealed by the ISOs/RTOs. This paper develops a framework to identify marginal units from the perspective of a market participant. Through the analysis of the relationship between marginal units and locational marginal prices (LMPs), it is impossible to determine the marginal generators based solely on knowledge of the LMPs. In the proposed framework, a simple data driven approach of exclusion is developed using LMPs, masked bid prices with a 4-month delay, and annual data on power generation and fuel consumption; all are publicly available. Using the link between load profiles and marginal units, the identification results based on historical data can be used for marginal unit predictions assuming the dispatch merit order in the prediction case is the same as in the historical case. A simulation study shows that the proposed framework is effective in detecting marginal units when system load levels are relatively high. Two applications, one to trace load changes back to marginal units and the other to calculate locational marginal emissions, are provided to show the practical value of marginal unit identification in power systems.

A Distributed EV Navigation Strategy Considering the Interaction Between Power System and Traffic Network

Increasing number of drivers are switching to electric vehicles (EV) for higher efficiency, lower environmental impact and less maintenance cost. To promote the EV revolution, charging issues of EVs needs to be well addressed. Since the traffic flow of the transportation network and the operating conditions of the power systems are time-varying, it is important to implement real-time charging navigation for EV drivers. In this paper, a novel navigation approach is proposed to search the fast charging station with the lowest overall objective, which consists both of the time consumption and the financial cost. The traffic condition and distribution system loading level are reflected by the time consumption on each road section and locational marginal price (LMP) at each fast charging station, respectively. The LMP can act as a signal to divert the EV load and thus relieve the traffic and power line congestions. The proposed navigation approach is based on the multi-agent system framework utilizing the distributed min/max- consensus and the biased min consensus algorithms. Simulations demonstrate the effectiveness of the proposed navigation approach in different scales of systems.

An Enhanced Random Access With Inter-Frame Successive Interference Cancellation for Stationary Cellular IoT Networks

In 5G cellular networks, it is required to accommodate a massive number of Internet-of-Things (IoT) devices in a resource-efficient way. In this letter, we propose a random access with an inter-frame successive interference cancellation (RA-ISIC) for stationary IoT networks. In our proposed scheme, the BS does not discard the collided packets and attempts to recover them through inter-frame SIC operations. We evaluate the performance of our proposed scheme in terms of resource efficiency. Results show that a certain level of system load is required to maximize the efficiency of our proposed scheme, which can be achieved through a probabilistic retransmission (PR) policy. Consequently, our proposed scheme can effectively improve the resource efficiency and can be suitable to accommodate more IoT devices with a smaller amount of resources, compared to the conventional one.

Adaptive Threshold for Fingerprint Recognition System Based on Threat Level and System Load

Abstract A fingerprint is a widely used trait for person recognition in civilian applications. User authentication takes place when the matching score is higher than the acceptance threshold. The performance of the fingerprint system (FS) is evaluated based on a false acceptance rate (FAR) and false rejection rate (FRR). Usually, the FS is set to work at a rate where FAR and FRR are equal (EER). However, operating at EER allows finite FAR, which is risky during the critical threat. In response, the acceptance threshold must shifts towards zero FAR to mitigate the threat. It increases FRR, system load, and user inconvenience. In the civilian application acceptance threshold is set by the vendor, and currently, there is no research attempt to change it dynamically. It is necessary as; 1) system must respond to external parameters like load and threat level; 2) system must balance security and user convenience due to high traffic. This paper describes a method to change the acceptance threshold over the interval EER to zero FAR based on system load and threat level. The proposed method uses a fuzzy inference system (FIS) and an artificial neural network (ANN).

On the Impact of the Dead-Band of Power System Stabilizers and Frequency Regulation on Power System Stability

This letter studies the impact of the dead-band in the input signal of power system stabilizers (PSSs) on the transient response of power systems. The letter provides a taxonomy of the dynamic behavior of the system in various scenarios: no PSS, PSS without dead-band, and PSS with dead-band. For the latter, the dead-band stability margin and the interaction of the PSS with the system loading level as well as primary and secondary frequency regulators are discussed. The oscillatory behavior of the system is rigorously studied through the monodromy matrix and limit cycles are characterized by means of Floquet multipliers. The IEEE 14-bus system serves to illustrate the aforementioned scenarios.

Sliding Mode Control for We-energy Based on Markovian Jumping Systems

Sliding mode control (SMC) is a promising robust control approach for the abrupt variations in complex System. Energy internet (El), as a dynamic nonlinear strong coupling System, has different operating states under different operating conditions (system connection, load levels and faults, etc.). We-Energy (WE), as a basic energy unit in El, is proposed for describing the essential characteristics and operating state of EL According the operating states of El, four operating modes of WE presented in this paper can be randomly converted, which correspond the stochastic markov process. Then, based on the coupling characteristics of El, a set of state equations is established to embody the markovian jumping System (MJS) of WE. Moreover, sliding surface and contorl scheme for WE based on MJS is designed. Finally, a numerical simulation is shown to illustrate the proposed method.

Optimal power consumption analysis of a load-dependent server activation policy for a data service center

This paper analyzes a data service center with optimal power consumption policy by using an M/M/s/∞ queueing system with queue-dependent heterogeneous and unreliable servers. This load-dependent server activation policy is that the servers deploying in a data service center were divided into fundamental and reserved servers. The fundamental servers are always available and reserved servers are switched off initially. The decision of switching the reserved server on or off is depending on the load level of system. Using the matrix-analytical approach, we derive the formulae for computing the rate matrix and obtain the stationary probabilities. Except for investigating the effects of system parameters on the various system characteristics, we also propose a mathematical model to determine the optimal threshold values of switching on/off reserved servers so as minimize the power consumption on a given expected waiting time.

Optimization of steel frame building systems in terms of parameters and reliability requirements

The problem of ensuring safety and reliability of building structures in modern socio-economic conditions is especially urgent. An important aspect of this issue is to determine the rational costs of manufacturing the load-bearing systems. This should take into account the possible emergency situations and their consequences. The purpose of the present studies is to create a methodology for the optimal design of normally operated steel frames considering material and social losses from the possible failure of structural elements. A method for parametric optimization of steel frame systems during selection of rod cross-sections and structure reliability levels is developed. The problem is to minimize structure manufacturing costs and recover damage caused by any material and social losses in the event of possible malfunctions and damage. Constraints on the strength, stiffness, and stability of the frame have all been taken into consideration. A two-stage optimization scheme is proposed. The first stage includes several parametric optimization processes using a genetic algorithm with minimization of construction costs for various system load levels. Then the structure's reliability is assessed within a probabilistic formulation for each of the obtained parameter combinations. Eventually variant of the frame is selected based on the minimum sum of the manufacturing cost and the expected value of material and social losses possible during operation period of the structure, expressed in monetary terms. An example of optimum design of a spatial frame with personnel present in the areas of possible damage is considered. The presented method allows implementing a comprehensive approach to optimum designing, while taking into account both the manufacturing cost and the possible failures of the constructive system.

Wavelet-fuzzy based protection scheme of EHV-AC transmission system and efficacy of discrete Fourier transform

Extra high voltage (EHV) transmission system is the vital links between the generating units and the transmission substations. Transmission of power to the utilities located over long distances through extra high voltage (EHV) transmission is efficient and cost effective. It has the advantage of allowing the bulk amount of energy transmission from generating units to the widely distributed load centers economically. However, detection of faults on EHV transmission system is critical and a challenging task for a large variation in fault operating condition such as high fault resistance involved, inception of fault at the voltage zero crossing situation and the variation in load angles and the system loading levels. Moreover, fault characteristics on EHV transmission lines differ to those on medium and high voltage (MV/HV) due to the presence of larger shunt capacitance, higher voltage level, longer transmission radius and weakly damped decaying dc current caused by the high system reactance to resistance ratio. A number of deterministic and intelligent models based protection schemes of transmission systems are endorsed by several authors where attempts have been made to detect, classify and to locate the fault and the faulted section of the line but they have their own limitations and complexities and the results are not satisfactory. Keeping in view of the aforesaid difficulties, this paper proposes a new approach for the protection of EHV-AC transmission system based on novel application of discrete wavelet transform (DWT) in combination with fuzzy logic using discrete Fourier transform (DFT) at the fundamental frequency phasor. The proposed method retains both fundamental and the high frequency components of the fault current signals to achieve high reliability and selectivity of the protection. The obtained results have been validated with the well established mathematical model of the given EHV-AC transmission system. It is found that the proposed method is accurate, computationally simpler, faster and valid for a wide range of fault operating conditions.

Bulk system reliability impacts of forced wind energy curtailment

With rapid growth of wind power in power systems, it becomes important to accurately model the behavior of wind, its interaction with conventional sources and also with other wind resources in order to conduct a realistic assessment of system reliability and benefits from wind energy utilization. At low wind penetration levels, all the wind energy generated is utilized to serve the load. However, at higher penetration levels, wind energy is spilled due to limitations in the ramping capability of the scheduled generating units and transfer capability of transmission lines. The benefits from wind energy are reduced as its spillage increases. Hence, accurate wind models should be developed to include forced wind energy curtailment in the reliability modelling, considering factors such as the system load level, unit dispatch order, ramp rates of the generating units and wind profile diversity between multiple wind farms. A new technique is proposed in this paper to create a comprehensive wind absorption capability model, and embed it in the composite generation and transmission system reliability model. The presented methodology to evaluate bulk system adequacy and wind energy benefits considering wind curtailment due to both the generation and transmission constraints is illustrated on an example system.

Non-linear high impedance fault distance estimation in power distribution systems: A continually online-trained neural network approach

This paper presents a new methodology for high impedance fault (HIF) location in overhead power distribution systems. A polynomial function was used to model the voltage at fault point as function of the fault current. Additionally, a continuously trained neural network after the occurrence of HIF was used. This neural network is used to estimate unknown parameters present in the equations that model the feeder during an HIF. The proposed algorithm uses measurements of voltage and current taken only at the substation together with the feeder parameters. A typical 13.8kV distribution system is used to test and validate the proposed scheme. The performance of the method was evaluated according to the fault current amplitude and the system load level. In addition, a comparative analysis with a state-of-the-art method was also performed. The HIF distance estimation errors remained below 1% in 86% of the tested cases. The maximum error obtained was 2.3%. Hence, such good performance along with the simplicity of the method and low cost of implementation, make this methodology suitable for real distribution feeder.