Power Flow Management Research Articles

RETRACTED: An efficient control strategy for optimal power flow management from a renewable energy source to a generalized three-phase microgrid system: A hybrid squirrel search algorithm with whale optimization algorithm approach

This paper introduces a hybrid approach for power flow management (PFM) of the hybrid renewable energy source (HRES) connected microgrid (MG) system. Here, the proposed approach is the combination of both the squirrel search algorithm (SSA) with whale optimization algorithm (WOA) named as SSAWO. Here, the SSA is developing the control signals of the voltage source inverter subject to the difference of power exchange between the source side and load side. The multi-objective function is shaped by the grid required active and reactive power varieties generated based on the accessible source power. The WOA procedures guarantee to locate the online control signals by utilizing the parallel execution against the active and reactive power varieties. The proposed technique-based control model enhances the control parameters of the power controller in light of the power flow varieties. By using the proposed methodology, the PFM of the MG system is controlled based on the source side and load side parameters variations. In order to render the power demand by the grid, the present system is accountable for dominating the energy sources using, optimally, both renewable energy sources and energy storage devices. Finally, the proposed model is implemented in MATLAB/Simulink working platform and the execution equates with the existing techniques.

ANFIS-Based Control of Multi-objective Grid Connected Inverter and Energy Management

As a consequence of fast socio-economic progress, the requirement of auxiliary energy sources is creating new opportunities in the field of renewable energy sources (RES). This paper presents a hybrid photovoltaic (PV)–fuel cell (FC) energy structure for grid-connected applications. The key challenges associated with the integration of RES are managing harmonic distortions, voltage fluctuations and power sharing. To mitigate these problems and to enhance the overall performance of the system, multi-objective inverter based on hybrid intelligent controller, adaptive neuro-fuzzy inference system has been designed. In the proposed system, PV acts as a primary energy source, while FC serves as a secondary energy source. An overall power management strategy has been designed to manage power flows among the RES, load and grid. The effectiveness of the proposed multi-objective inverter control and energy management of the system is validated under MATLAB/Simulink platform.

A novel predictive power flow control strategy for hydrogen city rail train

Hydrogen-based vehicular traction has already reached a mature technological level and can replace the more polluting diesel engines. The adoption of this technology can also alleviate the carbon footprint issue of the rail trains running on non-electrified lines.This study presents a model and a numerical performance analysis of an electric hybrid train in an urban context. The train uses hydrogen as fuel and operates over non-electrified lines with zero local emission.The electric traction motors of the train are fed by a hybrid power unit consisting of several hydrogen fuel cell stacks operating independently in on/off mode and a set of flywheel energy storage devices.Each component of the power train is modeled separately and its operating limits are chosen on the base of technical literature.An innovative predictive logic to manage power flows is defined and proposed with the aim to minimize the fuel consumption. Furthermore, this approach uses a regenerative electrical braking and eliminates dissipative devices, like rheostats, which are commonly utilized onboard electric trains.This predictive approach is based on the optimal management of the power unit components according to the advanced knowledge of the data of the rail vehicle, the characteristics of path, drive cycle and payload for an established route.The fuel cell stacks operate accordingly to the average traction power requirement in each railway line section, whereas the flywheel energy storage system manages the dynamic power.A parametric model of the system and a respective software tool have been developed; this implementation, that incorporates many tunable parameters of the train and rail path, is able to simulate the rail train operating on a specific railway path by implementing the novel control strategy.An existing single track non-electrified line, designed again for urban service, has been selected as a case study to evaluate the performance of the proposed system.The specific fuel consumptions obtained with the novel control strategy and with a single fuel cell system operating at constant power are compared under the same operating conditions.The results highlight that significant fuel savings can be achieved.

A Real-Time Energy Management and Speed Controller for an Electric Vehicle Powered by a Hybrid Energy Storage System

A real-time unified speed control and power flow management system for an electric vehicle (EV) powered by a battery-supercapacitor hybrid energy storage system (HESS) is developed following a nonlinear control system technique. In view of the coupling between energy management and HESS sizing, a HESS sizing model is developed in this article to optimally determine the size of HESS to serve an EV using the controller designed. The objectives of the controller are to track the set speed of the vehicle with globally exponential stability and to make use of the HESS wisely to reduce battery stress. The design provides a compound controller by exploiting the physical origins of the vehicles’ power demand. The controller and HESS sizing system designed are simulated on a standard urban dynamometer driving schedule and a recorded actual city driving cycle for a full-size EV to demonstrate their effectiveness.

Application of QOCGWO-RFA for maximum power point tracking (MPPT) and power flow management of solar PV generation system

A hybrid technique for solar PV array (SPV) generating system for maximizing the power to load is proposed in this dissertation. The proposed hybrid technique is the joint execution of both the Quasi Oppositional Chaotic Grey Wolf Optimizer (QOCGWO) with Random Forest Algorithm (RFA) and hence it is named as QOCGWO-RFA technique. Here, QOCGWO optimizes the exact duty cycles required for the DC-DC converter of SPV based on the voltage and current parameters. RFA predicts the control signals of the voltage source inverter (VSI) based on the active and reactive power variations in the load side. With this control technique, the system parameter variations and external disturbances are reduced and the load demands are satisfied optimally. The proposed strategy is implemented in MATLAB/Simulink working platform with three different case studies and compared with existing techniques. With these case studies, the proposed technique generates the optimal PV power of 2.1 kW.

Power flow management scheme of hybrid renewable energy source to maximize power transfer capability using I2HOSOA approach

Due to the intermittent nature of renewable sources, the generation of power is varied which is the main problem in renewable energy system. Miss-matching between the power generation and load power causes a deviation from the desired voltage and frequency in power supply. Therefore, a new efficient smart grid system is required for an optimal power flow management. In this paper, a hybrid approach is presented for power flow management of HRES connected smart grid system. The novelty of the proposed approach is the combined execution of IHHO with SOA named as I2HOSOA technique. In the established work, the HHO is integrated by crossover and mutation function, it is known as IHHO. The main contribution of the proposed strategy is to control the power flow based on source and load side parameters variations. In the proposed approach, the control signals of the voltage source are developed by the IHHO based on the variety of power exchange between the source and load side. Similarly, the online control signals are located by the SOA procedure by utilizing the parallel execution against the active and reactive power varieties. The multi-objective function is shaped by the grid required active and reactive power varieties created based on accessible source power. Here, the control parameters of the power controller are enhanced by the proposed technique based control models in light of the power flow varieties. The comparison between established and existing methods is analyzed in terms of reactive current injection, grid code, current amplitude limitation control, active power control, zero active power oscillations, and injection of active and reactive power. Furthermore, the statistical evaluation of established, and existing methods of mean, median, and standard deviation, is evaluated. Finally, the proposed model is executed in MATLAB/Simulink working platform and the execution is compared with the existing techniques.

Analysis, Modeling and Control of a Hybrid Drive Wind Turbine With Hydrogen Energy Storage System

Hybrid drive wind power generation system, equipped with the speed regulating differential mechanism (SRDM), is able to be friendly connected to power grid without the need of partly- or fully-rated converters. The novel transmission schemes can promote not only the output power quality but also the low voltage ride through (LVRT) capability of the existing wind turbines (WTs). For the purpose of further improving the grid-connected operating performances of SRDM-based WT, this paper aims to develop a hybrid power production unit, in which the hydrogen storage system (HSS), comprising an electrolyzer, a hydrogen fuel cell and a supercapacitor, is integrated into SRDM-based WT. The basic architecture and numerical modelling methods of key subsystems in SRDM-based WT as well as in HSS are analyzed. After determining the eight different operating modes, a power supervision approach is synthesized for the proposed SRDM-based WT with HSS, by which the power flow management between energy sources and storage elements can be realized. Case studies are carried out in the presence of different randomly varying wind speeds and grid voltage faults. The satisfactory operating performances of the proposed wind-hydrogen hybrid system in terms of maximizing wind energy utilization, suppressing output power fluctuation and improving system continuous operating stability are verified.

Power Flow Management in Multi-Source Electric Vehicle Charging Station

Grid-tied renewable energy sources (RES) with battery-behind-meter (BBM) architectures have successfully been used to ensure effective energy cooperation between the grid and RES-based microgrids. Such environments are quite stochastic, thus making power management very challenging. This paper presents the use of an asynchronous Q-learning in performing a power flow management task in a multi-source electric vehicle charging station with the integration of vehicle-to-microgrid technology. The power scheduling problem is first formulated as a Markov decision process. Asynchronous Q-learning is then used to solve it. The algorithm is tested with a typical charging station load profile over a 24-hour period and compared with a simple rule-based algorithm. Simulation results show that the proposed method is able to select a power schedule that reduces the energy cost with a better utilization of both the battery storage system and the vehicle to microgrid energy compared to the rule-based method.

Power Flow Management With Demand Response Profiles Based on User-Defined Area, Load, and Phase Classification

In recent times, the electric power management based on customers’ demand has drawn significant attention of smart-grid (SG) governors. The SG requires real-time management of dynamic load to maintain the quality of service (i.e., balance between supply and demand) by interfacing with users. In this paper, we propose an approach to respond to the active demand (AD) based on user-defined energy-management policy. An algorithm is also proposed for the smart-controller device (SCD) modeled with the load aggregator and connected to the customers. The total contracted load and the user ADs are determined based on area, load, and phase classification, which specify the individual energy consumption. The proposed scheme is implemented in MATLAB/Simulink using the load-information of the IEEE 30-bus system, and the feasibility is assessed in IEEE 13 and IEEE 34 node test feeder systems. By applying the customer’s controlled SCD device both the deficiency and redundancy of generation in terms of grid controllable load have been improved that lead the maximization of generation and distribution services. The voltage regulation and power factor of the particular area have been enhanced by integrating appropriate distributed generation and power factor improvement devices. The results garnered from the performance analysis show that the proposed scheme can optimize power generation based on the user-defined demand.

Managing Power Flow and Support Provided by SVCS/STATCOMS in Maintaining Grid Stability: A Case Study and Learnings there off

Call requested by Prime Minister of India to switch of domestic lights from 9pm to 9.09 pm on 05/04/2020.The call was an expression of Solidarity, Integrity cum Discipline towards Nation to fight against the COVID-19 epidemic. The time chosen was so crucial when each household, public places power requirement is almost at full. It was a heroic task and a challenge for electric power grid operators to maintain grid stability during the period i.e such a small spell of time. Failing which if collapsed would be difficult to restore and normalize. It was a great challenge for the National power load control center to maintain stability and continuity with quality of power electricity parameters like V, F. In this paper, efforts had been made to understand how Indian power could remain stable during the period without fear and behavior of regional load patterns during the period.

Electric Power Systems in More and All Electric Aircraft: A Review

Narrow body and wide body aircraft are responsible for more than 75% of aviation greenhouse gas (GHG) emission and aviation, itself, was responsible for about 2.5% of all GHG emissions in the United States in 2018. This situation becomes worse when considering a 4-5% annual growth in air travel. Electrified aircraft is clearly a promising solution to combat the GHG challenge; thus, the trend is to eliminate all but electrical forms of energy in aircraft power distribution systems. However, electrification adds tremendously to the complexity of aircraft electric power systems (EPS), which is dramatically changing in our journey from conventional aircraft to more electric aircraft (MEA) and all electric aircraft (AEA). In this article, we provide an in-depth discussion on MEA/AEA EPS: electric propulsion, distributed propulsion systems (DPS), EPS voltage levels, power supplies, and EPS architectures are discussed. Publications on power flow (PF) analysis and management of EPS are reviewed, and an initial schematic of a potential aircraft EPS with electric propulsion is proposed. In this regard, we also briefly review the components required for MEA/AEA EPS, including power electronics (PE) converters, electric machines, electrochemical energy units, circuit breakers (CBs), and wiring harness. A comprehensive review of each of the components mentioned above or other topics except for those related to steady state power flow in MEA/AEA EPS is out of this article's scope and should be found somewhere else. At the close of the paper, some challenges in the path towards AEA are presented. Unless the discussed challenges are satisfactorily addressed and solved, arriving at an AEA that can properly operate over commercial missions will not be possible.

A Brief Review on Partially Isolated Bidirectional Multiport Converters For Renewable Energy Sourced DC Microgrids

The increasing energy demand and the growing concern over the environmental effects of conventional sources augmented the integration of renewable energy sources as distributed generation units to the power grid. But integrating the renewable energy sources into the existing aged utility AC grids can introduce new difficulties such as an increase in voltage, synchronization, and protection issues. DC microgrids are becoming a solution to these problems by providing a reliable interface for connecting the renewable energy sources, storage, and the grid. Static power converters are required for matching the voltage levels of renewable energy sources and storages with that of the grid. Recently, multiport power electronic converters are gaining importance because of their compact size, centralized control and simple power flow management techniques. In renewable energy sourced micro-grid, it is required to have a battery back-up to overcome the on-off nature of renewable energy sources. Also to meet the safety requirements, it is required to have an isolated bidirectional power transfer between the energy sources, battery and load. In such a case, a multiport converter with bi-directional power flow capability with necessary isolation is required to connect various renewable energy sources with load and battery facilities. Among the different categories of multiport converters, partially isolated multiport converters combine the advantages of isolated and non-isolated circuits by providing required isolation while improving the power density by maintaining compact circuit size. In this paper, a brief overview of partially isolated multiport dc-dc converter topologies for harvesting renewable energy in DC microgrids is presented.

Static and dynamic evaluation of different architectures for an actual HVDC link project

The employment of High-Voltage Direct Current (HVDC) transmission in modern power systems is mainly driven by requirements of increasing power transfer over high distances in a cost-efficient and controllable way. HVDC control can ensure fast and accurate power flow control to improve the whole system performance as well as increased flexibility to manage power flows from remote renewable generation to load centers, especially in a zonal market configuration. However, in the network planning phase the implications of a new HVDC connection, both on the steady-state and on the dynamic operation of a power system, must be carefully evaluated with particular reference to possible outage events, depending on the operating conditions and on the exploited control schemes. In this paper, a real HVDC project within the Italian transmission network is considered in both the Voltage Source Converter (VSC) and Line Commutated Converter (LCC) technologies. The study implies a methodology to analyze the impact on steady-state and dynamic performances in the presence of faults, with a focus on grid security and market efficiency aspects. The behavior of the interconnected Italian power system is simulated in a development planning viewpoint by means of software tools exploited by the System Operator.

Power Flow Management of Grid Connected Micro-grid

In this paper, a novel control method for the power flow management of grid connected with transformer-battery based system including bidirectional (BL) DC-DC converter is presented. The objective of this proposed system is to satisfy the load demand and control the power flow management from the different sources. A BL boost converter is used to boost the power from the wind and connected with battery charging or discharging. Rectifier is used here to convert AC to DC and that the received DC supply gets boost up by DC-DC boost converter. A BL converter is used for supplying loads. The advantage of the proposed work is simple in operation, minimizes the losses and feeds the extra amount of power into the grid. The battery can be charged from the grid also whenever it is required.

Active and reactive power flow management in parallel transmission lines using static series compensation (SSC) with energy storage

<p>The power flow controlled in the electric power network is one of the main factors that affected the modern power systems development. The Static Series Compensatior with storage energy, is a FACTS powerful device that can control the active power flow control of multiple transmission lines branches. In this paper, a simulation model of power control using static series compensator with parallel transmission lines is presented. The control system using adaptive neuro-fuzzy logic is proposed. The results show the ability of static series compensator with storage energy to control the flow of powers components "active and reactive power" in the controlled line and thus the overall power regulated between lines. </p>

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

AbstractThere are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles’ hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle’s running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV’s hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

Power System Security with Contingency Technique by using SSSC & UPFC

Now days’ electrical power requirement has enlarged expanding as expansion & restructuring of electrical power system (PS) for generation & transmission in power sector is critically limited due to current resources & environmental circumstances. As outcome, approximately of corridors of power transmission overhead lines are greatly loaded & congested. Also major issue of power system voltage stability becomes power transfer restricted and capability issue. A Modern power electronics technology FATCS considered device Static Synchronous Series Compensator (SSSC) is VSC demanded series FACTS equipment. Unified power flow controller (UPFC) is to manage power flow (PF), voltage magnitude & phase angle. In this research paper suggested to maintain voltage magnitude as well as PF of faulty lines. The consequence of mutation of PS parameters like voltage, phase angle, active power, reactive power, & overall power factor with & without SSSC & UPFC have also incorporated. Assessment of PS safety is essential in society to expand customs to sustain system functions when one or more components fail. A PS is "secure" when it can defy loss of one or more ingredients & still go on working without major problems. The Contingency event investigation technique is taken to identify electrical node PF in faulty transmission lines (TL). The Performance of PS has been tested on IEEE 14-Bus System.

Power Flow Management in MTdc Grids Using Series Current Flow Controllers

Power flow control in multiterminal HVdc (MTdc) grids is essential to restrict operation within permissible limits. A current flow controller (CFC) can achieve this. In this paper, the modeling, control, and simulation of resistive, RC -circuit-based, and capacitive CFCs is carried out. CFC prototypes have been developed and an MTdc grid test-rig has been employed for experimental validation. Results show that all devices achieve an effective power flow management. The impact of CFC deployment in future MTdc grids is assessed on droop-controlled converters. A voltage compensation-based method is proposed to minimize power deviations during unscheduled line current control. A protection scheme has been assessed under faults.

Losses Minimization Control for an Integrated Multidrive Topology Devoted to Hybrid Electric Vehicles

This paper deals with the study and implementation of a losses minimization control strategy for an integrated multidrive (IMD) topology used in the drivetrain of parallel hybrid electric vehicles. In this IMD, the stator sections of a multiwinding induction machine are interfaced to the storage units through standard three-phase inverters. The goal of this study is to optimize the management of multidirectional power flows in an IMD configuration, reducing both the power losses of the induction machine and the energy storage units while keeping the computational complexity suitably low. To this aim, the proposed approach consists in a control strategy that continuously searches for the best compromise between the torque response and the power capability of each storage unit while minimizing the overall power losses. Using such an approach, a significant improvement in the overall efficiency is obtained. Simulations and experimental tests confirm the effectiveness of the proposed method.