Real Voltage Research Articles

Adaptive constraint differential evolution for optimal power flow

The optimal power flow (OPF) problem featured as a non-linear, non-convex, large-scale and constrained, still remains a popular and challenging work in power systems optimization. Although various optimization algorithms have been devoted to solving this problem, they suffer from some weak points such as insufficient accuracy as well as most of them are unconstrained optimization algorithms that result in optimal solutions that violate certain security operational constraints. To this end, this paper presents an adaptive constraint differential evolution (ACDE) algorithm, in which the novelty lies primarily in these three points: i) the crossover rate (CR) sorting mechanism is employed to build the relationship of CR and individual fitness values; ii) reusing successful evolution direction is proposed to guide the individual evolution towards promising regions; iii) an advanced constraint handling technique named superiority of feasible solutions (SF) is introduced to effectively deal with constraints in power systems. In order to verify the performance of the presented approach to the OPF problem, the standard IEEE-30 bus system is selected as the test case, in which six optimization objectives including total fuel cost, total fuel cost considering the valve-point effect, real active power losses, voltage deviation, voltage stability and emission are studied. The experimental results demonstrate that the presented approach can provide the smaller cost (800.41132$/h), reducing by up to 3.76% compared to the MPIO-COSR. In terms of the emission, ACDE emits the least emissions (0.204817ton/h). In addition, the proposed method also obtains the best results on the real active power losses (3.084041 MW) and voltage deviation (0.085636p.u.) when compared with other state-of-the-art methods.

Development of Intelligent Smart Metering System through Remote Monitoring and Control under Robust Conditions

Abstract This paper discusses the development of intelligent smart metering infrastructure for smart buildings based on information and communication technologies. The system collects electrical parameters, like real power, reactive power, voltage, current, power factor, and energy consumption, and physical parameters, such as temperature, humidity, and occupancy of the building. The electrical data are collected using Raspberry Pi 3B along with Smart Pi module. The physical parameters are collected by using a wide range of sensors communicating with the data acquisition module wirelessly by Wi-Fi technology. The monitoring platform is developed both in the customer end for the energy user and remote end for monitoring and control by the system operator. The developed system also has a parallel mode of communication based on LoRa technology, which will come into the picture if the primary mode of communication fails, ensuring there is always a continuous transfer of data under natural disaster situations. In addition, the system has an occupancy detection–based Internet of Things control module through which the system operator can remotely control selected appliances in the customer side based on the occupancy of the building as well as during disaster situations. The developed system also paves the way for secured smart buildings by continuous tracking of the building physical parameters, like temperature, humidity, and occupancy of the building during emergency situations like floods, earthquakes, and terrorist attacks, by which relief operations can be provided.

Synthetic Domestic Electricity Demand in Thailand using A Modified High Resolution Modelling Tool by CREST

A residential electricity demand profile is one of the key roles for investigating the impacts of high penetration of low carbon technologies, such as photovoltaic systems and electric vehicles, on distribution networks. However, it is difficult to identify the true daily electricity consumption of Thailand household, caused by the lack of routine real time demand monitoring and residential electricity meter is normally on monthly which is a low time resolution. In this paper, the CREST Demand Model is employed to simulate a high resolution domestic electricity demand in Thailand, without installing new monitoring devices and customer interruption, through a stochastic process which is a combination of patterns of active occupancy, the outdoor ambient light characteristic and daily activity profiles. Due to the model is based on time use survey data in UK, the outdoor irradiance and appliance configuration are adapted to fit for the Thailand case study. In order to verify the model, the synthetic load profiles by CREST Demand Model is compared against measured data from the actual monitoring in a real low voltage network in Thailand. The results show that it is promising to apply the high resolution demand model by CREST to simulate the domestic electricity demand profiles in Thailand.

Robust structured controller synthesis for interleaved boost converters using an H∞ control method

The regulation of output voltage and equivalent distribution of phase currents of multi-phase converters which have non-minimum phase characteristic are still challenges, especially in the presence of uncertainties in real parameters, duty cycle, input voltage, and load disturbances. However, in classical third-order integral-lead (Type-III) controller design methodologies, the controller is synthesized considering only the nominal performance conditions. This paper proposes a structured [Formula: see text] synthesis framework based on an optimization methodology to the design of a robust Type-III controller for interleaved boost converters. The structured [Formula: see text] control approach is adapted for optimization of Type-III feedback and feedforward controllers in two-degree-of-freedom (2-DOF) control system configuration. The robust stability of the closed-loop interleaved boost converter system against model uncertainties is ensured via the classical [Formula: see text]-analysis technique. Numerical comparisons are made among the classical, i.e. unstructured or full order, [Formula: see text]-based controller design method, a dual-loop PI controller, and proposed 1-DOF and 2-DOF structured controller synthesis approaches on an interleaved boost converter model. Simulation results verify the effectiveness and advantages of the proposed approach from the viewpoint of the output voltage regulation under different disturbance points.

A local flexibility market mechanism with capacity limitation services

Local flexibility markets have a substantial potential to unlock the flexibility of distributed energy resources in the distribution level. Capacity limitation services have been perceived as one of the most appealing products to be traded in these markets. This work argues why classical market-clearing and pricing mechanisms such as pay-as-bid, uniform pricing and Vickrey-Clarke-Groves (VCG) are not compatible with a market that trades capacity limitations. As a solution, we propose a local flexibility market mechanism which is built upon an adapted VCG-based auction. The mechanism achieves a trade-off among various desirable economic properties, including budget-balancedness, incentive-compatibility and stability. The suitability of the proposed mechanism is illustrated using a case study which is based on a real medium voltage feeder, located on the Danish island of Bornholm. Results show that aggregators and the distribution system operator benefit from the trade of capacity limitation services. We eventually conclude by providing a set of policy recommendations for the real-life operation of such a market.

Optimization of IEDs Position in MV Smart Grids through Integer Linear Programming

In the paper, an analytical method for determining the optimal positioning of intelligent electronic devices in medium voltage grids is proposed. Intelligent electronic devices are automated devices able to communicate one with each other and command the circuit breaker in order to localize and isolate a line fault as fast as possible. However, the number of intelligent electronic devices to install has to be limited, due to the relevant installation costs and the reduction in the transmission bandwidth caused by the increased number of exchanged messages. So, the electrical distributor has to carefully detect the nodes of the grid where the intelligent electronic devices have to be installed. The authors propose a method based on integer linear programming, which, given the number of intelligent electronic devices to install, finds their optimal position, i.e., the one that minimizes the penalties associated with the power down experienced by customers. In order to highlight the offered advantages in terms of computational effort, the proposed approach has been assessed with a real medium voltage grid.

Stratified Control Applied to a Three-Phase Unbalanced Low Voltage Distribution Grid in a Local Peer-to-Peer Energy Community

This paper presents control relationships between the low voltage distribution grid and flexibilities in a peer-to-peer local energy community using a stratified control strategy. With the increase in a diverse set of distributed energy resources and the next generation of loads such as electric storage, vehicles and heat pumps, it is paramount to maintain them optimally to guarantee grid security and supply continuity. Local energy communities are being introduced and gaining traction in recent years to drive the local production, distribution, consumption and trading of energy. The control scheme presented in this paper involves a stratified controller with grid and flexibility layers. The grid controller consists of a three-phase unbalanced optimal power flow using the holomorphic embedding load flow method wrapped around a genetic algorithm and various flexibility controllers, using three-phase unbalanced model predictive control. The control scheme generates active and reactive power set-points at points of common couplings where flexibilities are connected. The grid controller’s optimal power flow can introduce additional grid support functionalities to further increase grid stability. Flexibility controllers are recommended to actively track the obtained set-points from the grid controller, to ensure system-level optimization. Blockchain enables this control scheme by providing appropriate data exchange between the layers. This scheme is applied to a real low voltage rural grid in Austria, and the result analysis is presented.

Centralised coordination of EVs charging and PV active power curtailment over multiple aggregators in low voltage networks

Widespread application of residential photovoltaic (PV) systems and electric vehicles (EVs) has led the distribution system operators (DSOs) to face new technical challenges such as overloading and significant voltage variations, especially on low voltage (LV) grids. This adverse effect may be mitigated by employing the aggregators as intermediary actors to coordinate the operation at the distribution level. Therefore, this paper proposes a centralised coordination strategy to mitigate both PV and EV impacts, such as voltage rising/dropping at noon or evening, respectively, by defining the optimal export limit of PV power and EVs charging among multiple aggregators at the DSO level. The latter is in charge to dictate the optimal aggregated signals to every aggregator by employing a mixed-integer quadratic programming (MIQP) approach. The aggregated PV power is evenly managed for each aggregator by weighting. Two convex optimisation models are defined to satisfy both power and voltage constraints of the LV network. Each proposed optimisation approach can be utilised when there is or no detailed information about the LV network topology. The concepts discussed in this paper are tested on a real low voltage network considering a critical penetration level of EVs and PVs.

A Support Vector machine-Based method for parameter estimation of an electric arc furnace model

In the iron and steel industry, electric arc furnaces (EAFs) are used in the melting and refining process of metals. They are known to demand large amounts of reactive power and cause significant power quality (PQ) problems due to their highly non-linear time varying voltage-current characteristic. Several EAF models have been proposed with the purpose to predict the voltage and current waveforms, to assess the performance of different compensating devices such as static var compensator, synchronous static compensator, active power filters, and –still under study– energy storage systems, and also for planning the installation of iron and steel facilities considering existing real data from similar facilities. An important aspect of these models is related to the estimation of their parameters. This paper presents a new method to estimate the parameters of an EAF model. The method utilizes a multiple-input multiple-output regressor based on support vector machine, that maps from voltage characteristics of the electric arc to the values of the model parameters. The multidimensional support vector regressor (M-SVR) is designed in the training phase, using data from several simulations of the EAF model. These simulations are carried out adjusting the parameters of the model within the search space, and considering the real arc current as input to the model. Then, in the validation phase, for the real voltage waveform, the estimated parameters are obtained using each regressor of the M-SVR. The proposed method is validated by the comparison between the waveforms obtained using the EAF model with actual data from a steel plant. Results show that the relative error between the fundamental component of the current and voltage, for real and simulated waveforms, are 2.1% and 6.3% respectively.

Modeling of Gapless MOSA at Low Current Region Considering Harmonic Content of Operating Voltage

A surge arrester is modeled as a simplified electrical circuit in power frequency applied voltage that consists of a capacitance branch in parallel with a nonlinear resistor. Under applied harmonic voltage, the obtained resistive current of the simplified model exhibits some deficiency, especially in its harmonic contents. This paper presents a harmonic model for metal-oxide surge arresters that can be utilized to simulate their performances under applied harmonic voltage. It has three parallel RC branches to simulate fundamental, third and fifth harmonics contents of total leakage current. Proposed and conventional models have been simulated under different applied harmonic voltages to show the capability of the proposed model. Also, real voltage and leakage current signals, which have been measured experimentally from a 20 kV metal-oxide surge arrester, have been compared with simulation results. Results show that the proposed model can simulate the performance of metal-oxide surge arresters more accurately than the conventional ones under applied harmonic voltages.

Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems

This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

An operator-independent artificial finger can differentiate anterior vaginal wall indentation parameters between control and prolapse patients

In this study, the reproducibility and validity of an automated artificial finger for evaluating properties of vaginal wall tissue was assessed. The effect of angle and rate of indentation on displacing the anterior vaginal wall (AVW) was studied in control and prolapse patients.Following IRB approval, an automated artificial finger equipped with a calibrated piezoresistive sensor at its tip was used to induce 3-second AVW deformation sequences (10°, 15°, and 20° indentation). Measurements were taken in patients in supine position, either awake in clinic or under anesthesia in the operating room (OR). The real time voltage output of a sensor (linearly proportional to the reaction force) was recorded for each motion profile to calculate key parameters: baseline voltages, amplitude changes over indentation intervals, and slopes of indentation curves. 23 women (9 controls and 14 prolapse) were studied, 6 in clinic and 17 in OR. No differences in mean reproducibility was noted across groups. There was a significant difference in sensor output based on selected motion profile parameters between different degrees of indentation for all women (p < 0.001) and in baseline voltage between age-matched and non-age-matched controls (p < 0.02). From these findings, we can conclude that indentation reaction properties of prolapsed and non-prolapsed AVW can be objectively measured using an operator-independent artificial finger with significant differences between patient groups.

New Method of Detection, Identification, and Elimination of Photovoltaic System Faults in Real Time Based on the Adaptive Neuro-Fuzzy System

This article proposes an adaptive neuro-fuzzy system (ANFIS)-based method to detect, identify, and eliminate defects/faults in a photovoltaic (PV) system. The control is conducted using two tests. The first test measures the difference between the power estimated by the ANFIS and the real power of the generator. This test detects defects and isolates the affected branch using controlled switching devices to prevent shutting down the entire power supply by disconnecting the inverter. The second test measures the consistency between the real current and voltage of the generator and the short-circuit current and open-circuit voltage estimated by the ANFIS, to identify open-circuit and short-circuit conditions. Each system has its own dedicated irradiation and ambient temperature sensors whose positions differ to avoid the influence of faults overlap. The method was studied through simulation using MATLAB Simulink and experimentally validated via panels of different power ratings and technological makeup that are more than five years old, utilizing a DS1104 dSPACE controller board. The technique overcomes the limitations of existing methods that are based on comparisons between the estimated and measured variables as the discrepancy between real and simulated behavior; system change because of environment, topology, or age; and the need for frequent intelligent system updates as it does not rely on real data for learning. Additionally, this method includes an aging analysis, and it can be used to control most typical PV installations via a gain factor—the ratio of the new value to the learn value.

Analysis of the Influence of the Insulation Parameters of Medium Voltage Electrical Networks and of the Petersen Coil on the Single-Phase-to-Ground Fault Current

Settings of protection are essential to ensure the sensitivity and selectivity needed to detect defects. Making the correct settings requires the calculation of the fault currents with as little error as possible. Fault currents are influenced by the parameters of the electrical networks, including the state of the insulation and the Petersen coil, which changes during their operation electrical networks. This paper analyzes how the insulation parameters of medium voltage power lines, the parameters of the Petersen coil used to treat the neutral of the medium voltage electrical network and the value of the resistance at the fault location influence the fault current in the case of a single-phase fault. The large number of single-phase faults that occur in medium voltage electrical networks justifies this analysis. The symmetrical components method was used to calculate the fault current. The results obtained by calculation were verified experimentally by causing a single-phase-to-ground fault in a real medium voltage network. The paper presents the situations in which the analytical calculation of the single-phase-to-ground fault current can lead to inadmissibly large errors, even over 50%, but also the situations in which the errors fall below 3%.

Modeling of German Low Voltage Cables with Ground Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

This paper aims to propose a new multiobjective algorithm for multiobjective distributed network reconfiguration (DNR) with the placements of distributed generation (DG) in radial distribution networks (RDNs). The new proposed algorithm, called the nondominated sorting stochastic fractal search (NSSFS), is a new multiobjective version of the original SFS algorithm. NSSFS incorporated fast nondominated sorting strategies, crowding distance computation, and selection mechanism into SFS to find and maintain the best nondominated solutions. The proposed NSSFS algorithm was tested with eight multiobjective benchmark test functions to validate its performance. The NSSFS was then implemented to define the optimal network configuration, positions, and sizes of DG units in the RDNs, where real power loss, voltage profile, and voltage stability index were optimized simultaneously. The implementation of multiobjective DNR-DG (MODNR-DG) significantly enhanced the performance of the system. Based on the comparison outcomes, the NSSFS algorithm obtained better solution quality than other multiobjective techniques, proving the effectiveness of NSSFS in dealing with the MODNR-DG problem.

Model-based state of health estimation of a lead-acid battery using step-response and emulated in-situ vehicle data

Lead-acid (PbA) batteries are one the most prevalent battery chemistries in low voltage automotive applications. In this work, we have developed an equivalent circuit model (ECM) of a 12V PbA battery while preserving the major dynamics of a semi-empirical model we have developed previously. Thereafter, two batteries are aged according to a modified IEC 60095-1 standard and the evolution of ECM parameters investigated. The trend derived from these aging campaigns are used to determine the state of health (SOH) of three either new or aged on the vehicle batteries. The contribution from this approach stems from the fact that the ECM model while being simple is capable of approximating the sum of charge transfer resistances of both electrodes from a short step response. In addition, another SOH estimation technique is developed based on frequency response analysis of a metric dubbed instantaneous dynamic resistance derived from voltage response of the battery during engine cranking. Real world voltage response of the battery during cranking is imitated in the lab and superposed on IEC 60095-1 standard profile, according to which another battery is aged. This data is used to assess the performance of SOH estimation technique.

Comparative Analysis of a Performance of Metaheuristic Algorithms in Solving Optimal Power Flow Problems with UPFC Device in the Transmission System

Incremental industrialization and urbanization is the cause of enhanced energy use as it increases the building of new lines and more inductive loads. As a result, the transmission system losses increased, and the magnitudes of voltage profile values deviated from the stated value, resulting in increased cost of active power generation. To mitigate these issues, adequate reactive power compensation in the transmission line and bus systems should be done. Reactive power is regulated by the proper position of the Flexible AC Transmission System (FACTS). Unified Power Flow Controller (UPFC) is a voltage converter system that increases the voltage profile and reduces loss. In this paper, the optimal power flow solution is considered using a FACTS device based on Multi Population Modified Jaya (MPMJ) optimization algorithm. Using the Analytical Hierarchy Process (AHP) system, the optimal position of the UPFC device is determined by considering the most useful objective function provided by priorities and weighting factors. Therefore, on the standard IEEE-57 bus test system, the proposed MPMJ optimization algorithm is implemented with UPFC for optimal fuel cost values of generation, real power loss, voltage deviation and sum of squared voltage stability index. The result obtained by the proposed algorithm is contrasted with the recent literature algorithm.

Relieving Tensions on Battery Energy Sources Utilization among TSO, DSO, and Service Providers with Multi-Objective Optimization

The European strategic long-term vision underlined the importance of a smarter and flexible system for achieving net-zero greenhouse gas emissions by 2050. Distributed energy resources (DERs) could provide the required flexibility products. Distribution system operators (DSOs) cooperating with TSO (transmission system operators) are committed to procuring these flexibility products through market-based procedures. Among all DERs, battery energy storage systems (BESS) are a promising technology since they can be potentially exploited for a broad range of purposes. However, since their cost is still high, their size and location should be optimized with a view of maximizing the revenues for their owners. Intending to provide an instrument for the assessment of flexibility products to be shared between DSO and TSO to ensure a safe and secure operation of the system, the paper proposes a planning methodology based on the non-dominated sorting genetic algorithm-II (NSGA-II). Contrasting objectives, as the maximization of the BESS owners’ revenue and the minimization of the DSO risk inherent in the use of the innovative solutions, can be considered by identifying trade-off solutions. The proposed model is validated by applying the methodology to a real Italian medium voltage (MV) distribution network.

Real time voltage security assessment using adaptive fuzzified decision tree algorithm

Real time voltage security assessment using adaptive fuzzified decision tree algorithm