Power Flow Management Research Articles

Optimal Power Flow Management in Microgrids using Distributed Energy Resources

The effective and stable functioning of microgrids using distributed energy resources (DERs) is greatly dependent on ideal power stream management. To get the optimal power flow in microgrids, this study provides an inquiry into the use of cutting-edge optimization techniques. To fulfil load demand and consider DER requirements, system running costs must be kept to a minimum. The microgrid integrates a variety of distributed energy resources, including solar photovoltaic systems, wind turbines, and energy storage systems. The research examines several optimization strategies, such as mixed-integer programming, evolutionary algorithms, and linear programming, to decide on the best DER dispatch and scheduling. The findings give important information on how to efficiently use and manage distributed energy resources to raise the performance and resilience of microgrids.

An improved control for a stand-alone WEC system involving a Vienna rectifier with battery energy storage management

This study focused on efficiency improving, the power flow management and control problem of the standalone wind energy conversion system. Specifically, the system under study consists of a Permanent Magnet Synchronous Generator (PMSM) driving by wind turbine, Vienna rectifier, a Li-ion battery and a DC load. Battery life is vulnerable to fluctuations due to the intermittent nature of the wind, as well as to the demand for energy charging, which reduce considerably battery state of health (SOH) and consequently charging capacity. To overcome these shortcomings, a nonlinear battery charge controller is used to regulate the current supplied to the battery and to the load, and thus guarantee best charging performances. Based on the wind power, the load demand and the battery state of charge (SOC), three operating modes are considered. Specifically, MPPT mode, Constant Current (CC) charging mode and Constant Voltage (CV) charging mode. We also design a new energy management method to protect the energy storage system and increase its lifetime. This algorithm ensure a smooth switching between three controllers designed to provide the three operating modes. The high performances and the efficiency of the proposed controller and management algorithm are highlighted using several MATLAB/SIMULINK simulations.

Sizing Batteries for Power Flow Management in Distribution Grids: A Method to Compare Battery Capacities for Different Siting Configurations and Variable Power Flow Simultaneity

Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.

Design and experimental validation of an artificial neural network-SVPWM controller for a novel micro grid-tied fuel cell-based 3-phase boost inverter

A grid-tied fuel cell (FC) system demands efficient power conversion, power quality preservation, grid stability, power flow management, renewable energy source (RES) integration, and enhanced grid resilience. Achieving these goals requires a precise inverter circuit switch approach. A boost power converter connected to the FC stack ensures voltage regulation, power conditioning, efficient power transfer, system integration, control, and protection. This enhances FC system adaptability and compatibility across various applications, minimizing input current ripples for prolonged FC lifespan.This study introduces a novel DC-DC boost converter with an artificial neural network (ANN) controller to reduce FC input current ripples and enhance FC stack-generated voltage for grid applications. It also presents a space vector sinusoidal pulse width modulation (SVPWM) technique for FC-based three-phase grid-tied inverters. This offers improved voltage utilization, precise voltage and current control, reduced harmonic distortion, rapid response, flexibility, scalability, reduced total harmonic distortion (THD), and over-modulation capability. The proposed SVPWM technique utilizes a digital signal processing (DSP)-based controller, combining high-speed processing, precision, and real-time capabilities to enhance system performance and efficiency.

Hybrid optimized-ANFIS based MPPT for hybrid microgrid using zebra optimization algorithm and artificial gorilla troops optimizer

This paper presents an effective hybrid renewable energy system. This system utilizes primary energy sources represented by both photovoltaic (PV) and wind energy conversion system (WECS). In addition, integrating the battery energy storage system (BESS), the hydrogen energy storage system (HESS), and the supercapacitor energy storage system (SESS), as backup sources which providing reliable simultaneous power supply and loading. A new design for a wind energy conversion system based on a hybrid excitation permanent magnet synchronous generator (HEPMSG) is proposed, and then artificial intelligence strategies are utilized to determine the optimal flow of energy when the system is operating immediately. Moreover, new hybrid maximum power point tracking (MPPT) techniques have been proposed including zebra optimization algorithm (ZOA)-ANFIS-based MPPT and gorilla troops optimizer (GTO)-ANFIS-based MPPT to obtain the maximum power of solar panels and wind turbines, which leads to enhancing the performance of these energy sources. The implemented power flow management (PFM) model is designed by MATLAB/Simulink, considering three various operating cases to evaluate the performance and effectiveness of various backup system configurations under different operating scenarios. According to the results, power can be generated, and load requirements can be efficiently met by applying the proposed PFM strategy, and this leads to the system operating at optimal performance. When comparing two optimization techniques, it will become clear that both give robust performance, but ZOA technique significantly outperforms the GTO technique in the computation time (26.17 % reduction) for the photovoltaic system. On the other hand, in the case of the wind energy conversion system, the ZOA technique achieves a significant reduction in calculation time by 35.5 %. The ZOA technique achieves computation times of 1616.80 s and 672.43 s for the photovoltaic and wind energy conversion system, respectively.

A Markov Chain Monte Carlo approach for the estimation of photovoltaic system parameters

Knowledge of the installation parameters of photovoltaic systems is essential in the context of grid management: by relating these parameters to performance data, forecasting models may be optimised to improve the management of power flow into the grid. In the case of small residential systems, these parameters are often not available. We present a novel method for determining the azimuth (ϕ), tilt (θ) and rated power (P) of photovoltaic systems, using openly available data over the course of 2016–2018 of 12 photovoltaic systems in PVOutput. This method consists of two steps: firstly we identify a candidate list of clear days by computing descriptive statistics of a larger set of 80 PVOutput system profiles. In the second step we compare the observed clear-day profiles, of the aforementioned 12 systems, with modelled clear-sky profiles from the PVLib library. The fits are performed employing a Markov Chain Monte Carlo (MCMC) approach, implemented with the Emcee package: the most favoured parameters and their associated uncertainties, for any given day, are obtained by sampling from the posterior assuming a Gaussian sampling distribution. The results for our 12 systems are in good agreement with the PVOutput metadata.

A hybrid model based on discrete wavelet transform (DWT) and bidirectional recurrent neural networks for wind speed prediction

Wind speed is the main driver of wind power output, but its inherent fluctuations and deviations present significant challenges for power system security and power quality. Accurate short-term wind power forecasting is necessary to ensure the stability and seamless integration of wind energy into the grid, thereby bolstering grid reliability, optimizing power flow management, and minimizing disruptions, ultimately contributing to a more sustainable and efficient energy ecosystem. Non-stationarity is a major challenge in analyzing wind speed data, and change-point detection is essential for optimal resource allocation. This paper addresses the issue of short-term wind power forecasting for stable and effective wind energy system operation. To predict non-stationary data and detect change points, non-stationary data must first be transformed into stationary data. Discrete wavelet transform (DWT) is used to decompose wind speed traces into low- and high-frequency components for more accurate predictions using deep learning algorithms. The proposed approach utilizes a combination of DWT and recurrent neural network (RNN) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), complementing each other to efficiently predict short-term and long-term dependencies in wind speed data and achieve the most accurate prediction results. Experimental results substantiate the consistent superiority of the proposed approach over the baseline method, showcasing a significant enhancement in prediction accuracy, with improvements spanning from 11.36% to 16.63% for MAPE, 16.67%–26.47% for RMSE, and 21.74%–30.77% for MAE.

Real-time power flow analysis and management for a long-endurance solar UAV during continuous flight

Real-time power flow analysis and management for a long-endurance solar UAV during continuous flight

A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids

Incorporating renewable Distributed Energy Resources (DER) into the main grid is crucial for achieving a sustainable transition from fossil fuels. However, this generation system is complicated by the fluctuating behavior of renewable resources and the variable load demand, making it less reliable without a suitable energy storage system (ESS). This study proposes an Optimal Power Flow Management (OPFM) strategy for a grid-connected hybrid Micro Grid (MG) comprising a wind turbine (WT), a photovoltaic (PV) field, a storage battery, and a Micro Gas turbine (MGT). This proposed strategy includes (i) minimizing the MG’s daily energy cost, (ii) decreasing CO2 emissions by considering the variable load, weather forecast, and main grid fees to optimize the battery charging/discharging strategy, and (iii) optimizing the decision-making process for power purchase/sell from/to the main grid. The suggested OPFM approach is implemented using a Genetic Algorithm and compared with the Bellman Algorithm and a restricted management system via several simulations under the Matlab environment. Furthermore, the hybridization of the Bellman Algorithm and the Genetic Algorithm is proposed to enhance the OPFMC strategy’s efficiency by leveraging both algorithms’ strengths. The simulation results demonstrate the effectiveness of the proposed strategy in lowering energy costs and CO2 emissions and enhancing reliability. Additionally, the comparison of the hybridized GA algorithm reveals a cost 16% higher than the Bellman Algorithm; however, the use of the hybridized GA algorithm leads to a reduction in GHG emissions by 31.4%. These findings underscore the trade-off between cost and environmental impact in the context of algorithmic optimization for microgrid energy management.

SOLID STATE TRANSFORMER (SST) AND THE CHALLENGES OF THE FUTURE GRID

Power flow management in both transmission and distribution grid networks is continuously being challenged by increased integration of distributed generations that are stochastic and intermittent in nature. This paper investigates the anticipated roles or functionalities required from solid state transformer (SST) to enable it address the challenges faced by the grid network. The SST is a power electronics based transformer build with flexibilities and intelligence required for performing ancillary services to the grid. The paper shows that, with the aid of SST, smooth integration of renewable energy sources and other functionalities such as power factor correction, reactive power compensation, harmonics mitigation, fault ride through and or fault isolation can be achieved in the future grid.

Implementation and Control of Multiport Grid Integrated Hybrid Energy Interface for EV Charging Application

EV charging combined with distributed energy storage (ES) devices could be utilized to send power to the grid during peak hours, mitigating the effects of load shedding. In order to achieve these goals, a hybrid multi-port charging system is developed in this study to properly manage power flows and balance energy. It is made up of a PFC bridge rectifier that is linked to an isolated three-port dc-dc converter that includes a series resonant (SRC) and a DAB converter for fast and slow charging applications, respectively. To maximize efficiency and power density, the ports share a 400 V common DC link. Grid-to-vehicle (G2V) mode is used to charge EV and ES vehicles from the utility grid; PV-to-grid (PV2G) mode is used to deliver PV energy to the grid; renewable-to-vehicle (R2V) mode is used to charge EV and ES batteries; and vehicle-to-grid (V2G) mode is used by EV and ES to deliver stored energy to the grid. The charging system’s performance and operation are validated in real time using MATLAB/Simulink under various operating situations. The system’s total harmonic distortion (THD) for the supply current is 2.07%. The suggested system is also compared against a state-of-the-art multiport charging system to assess its performance.

A Review of Advanced Control Strategies of Microgrids with Charging Stations

In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, operating independently or alongside the main grid to facilitate power flow management among interconnected sources and different loads locally. This review paper aims to offer a comprehensive overview of the different control strategies proposed in the literature to control microgrids with electric vehicle charging stations. The surveyed research is primarily categorized according to the employed control algorithms, although distinctions are also made based on defined microgrid architecture, utilization of specific power sources, and charging stations configurations. Additionally, this paper identifies research gaps in the current research. These gaps encompass the use of oversimplified models for charging stations and/or renewable sources operation, limited simulation time periods, or lack of experimental testing of proposed approaches. In the light of these identified shortcomings, this manuscript presents recommendations for guiding future research.

Improvement of the Power System's Transient Stability Using the Unified Power Flow Controller with Fuzzy Logic Technique

By combining the functions of many FACTS (flexible alternative current transmission systems) devices into a single unit, the UPFC (unified power flow controller) is the most efficient FACTS device available. Two voltage converters work together to form the Unified Power Flow Controller, a flexible tool for enhancing voltage regulation, power flow management, and power system stability. Both converters rely heavily on their control methods. This article discusses the power flow mechanism in a transmission line, which is necessary for understanding the functioning of UPFC in a transmission line and using a standard PI controller with voltage references in the control circuit of both converters. This study aims to enhance the power system's dependability and stability by developing a smart support-based UPFC device. Fuzzy logic is a useful tool for controlling UPFC equipment in a variety of failure scenarios. The fuzzy logic controller contrasts power grid characteristics such as a rotor speed deviation to a reference value to create operational pulses for the voltage source converter in the UPFC system. The suggested model will be tested using MATLAB simulation. The success of the proposed model is shown by thoroughly comparing simulation results to those of previously established models. This research paper's original contribution is the creation of a smart support-based UPFC device with fuzzy logic control. By comparing power system parameters such as rotor speed deviation to a reference value and generating operational pulses for the voltage source converter in the UPFC system, this technique increases the electricity grid's dependability and stability. The effectiveness of the suggested model is proved by a comparison of simulation results obtained in a MATLAB environment with those obtained from previously developed models.

Transient stability enhancement in renewable energy integrated multi-microgrids: A comprehensive and critical analysis

Multi-microgrids offer various benefits including the decreased overloading of a single microgrid, more options during faulty conditions, and more utilization of renewable energy resources. However, the implementation of a multi-microgrid brings the challenges such as power system complexity, interconnection issues, bidirectional power flow management, and power flow balancing. In the presence of these challenges, the power flow stability of the multi-microgrids is a challenging problem. In this context, this study evaluates a transient stability analysis model in multi-microgrids using solar photovoltaics, wind power, and a unified power flow controller (UPFC). UPFC offers a more robust power flow control strategy compared with other flexible alternating current transmission systems (FACTS) devices. First, a multi-microgrid structure consisting of the two microgrids was designed in DIgSILENT PowerFactory software. Second, the load flow calculation was performed in the absence and presence of UPFC, short circuit fault, and grid connection. Third, the electromagnetic transients (EMT) simulation was performed for all these situations. The results exhibited that the UPFC would offer significant power flow stability in the multi-microgrids. It was observed that the UPFC resulted in more transient stability in the microgrid where it was located. However, it improved the power flow quality at all the locations in the multi-microgrids. In addition, UPFC offered significant transient stability during the fault occurrence. The results offer various insights into power flow management in multi-microgrids.

A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems

This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton’s Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model’s practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability.

Distributed predictive control design to achieve economically optimal power flow for DC microgrid clusters

AbstractDC microgrid clusters are a collection of interconnected microgrids that allow for flexible power flow, leading to economic benefits and improved resilience from distributed generation. However, managing power flow among interconnected microgrids with different components such as photovoltaic, wind turbine, and battery energy storage systems, as well as various dynamic operation scenarios, presents a significant challenge for proportional‐integral (PI)‐based controllers. To address this challenge, this paper proposes a distributed predictive control design in the hierarchical control paradigm that aims to achieve economically optimal power flow (EOPF) for DC microgrid clusters. The predictive controller considers multiple objectives optimisation, including generation cost models, converter losses, and transmission losses over both local lines and tie‐lines, and design and implementation of the two‐layer tertiary control for the EOPF of DCMGCs are presented. Hardware‐in‐the loop (HIL) experimental results demonstrate the effectiveness of the controller design.

THE PHOTOVOLTAIC SYSTEM CONNECTED WITH THE GRID FOR A RESIDENTIAL BUILDING IN BAGHDAD CITY

contribution of solar photovoltaic (PV) energy to the electrical power sector is rapidly rising. Photovoltaic generators (PVGs) account for 47% of small network renewable energy sources (RESs). This paper focuses on a strategy to control the photovoltaic power systems connected to the grid for a residential building in the city of Baghdad, based on the solar radiation which is taken from average NASA data to make a scientifically reliable and significant contribution to the sustainable energy supply and to further improve the performance and stability of the power system, a comprehensive technique is being carried out, This investigation includes an examination of various control techniques, such as maximum power point tracking (MPPT) for grid-connected single-phase photovoltaic inverters. This research aims to develop efficient and effective control strategies for power flow management in grid-connected photovoltaic systems, to reduce energy consumption. By advancing our understanding of power flow management techniques, we can help to accelerate the transition to a more sustainable and resilient energy future. Phase locked loop (PLL) circuit is implemented to synchronize with the domestic 11 kV/380 V, 250 kVA transformer. The model in this work includes the current and voltage regulator and smart meters. The simulation results demonstrate that the proposed control systems can regulate the energy flow and guarantee the efficient transmission of maximum power to the grid. The perturb and observe (P&O) and Incremental Conductance Algorithm (IC) are used to estimate the Maximum Power Point Tracking (MPPT) of the Solar Panel. The initial cost of the PV used, according to the prevailing prices in Baghdad at the present time, is about 3000 dollars, The results indicate that the PV system that is connected to the grid can substantially reduce the monthly energy consumption and the monthly costs to about 12% and 6% respectively.

Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS

Battery energy storage systems (BESSs) are a promising solution for increasing efficiency and flexibility of distribution networks (DNs) with a significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV systems and BESS that need to be addressed to maximize DN performance. This paper deals with day-ahead optimal active–reactive power dispatching in unbalanced DNs with integrated single-phase PV generation and BESS. The objectives are the minimization of cost for electricity, energy losses in the DN, and voltage unbalance at three-phase load buses by optimal management of active and reactive power flows. To solve this highly constrained non-linear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and a chaotic gravitational search algorithm (CGSA)called the PSOS-CGSA algorithm is proposed. A scenario-based approach encompassing the Monte Carlo simulation (MCS) method with a simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through aseries test cases in a modified IEEE 13-bus feeder. The simulation results show that the proposed approach enables a large reduction in daily costs for electricity, as well as a reduction in expected daily energy losses in the DN by 22% compared to the base case without BESS while ensuring that the phase voltage unbalance rate (PVUR) is below the maximum limit of 2% for all three-phase buses in the DN.

Adaptive Fuzzy Logic-Based Control and Management of Photovoltaic Systems with Battery Storage

Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT’s state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS.