Hybrid Distributed Generation Research Articles

A communication-less islanding detection scheme for hybrid distributed generation systems using recurrent neural network

The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems that include both synchronous and inverter-based DG devices. The scheme consists of three stages: time-domain feature extraction (FE) from the three-phase voltage signal at the point of common coupling (PCC), feature selection using the wrapper method, and detection using a long short-term memory (LSTM) RNN algorithm. To assess the performance of the proposed scheme, a comprehensive set of islanding and grid-connected scenarios was created using the IEC 61850-7-420 test system in MATLAB/Simulink. After feature extraction in MATLAB, the feature selection and LSTM model training/testing steps were conducted in Python. Thirteen features were extracted from the voltage signal, and the most significant features were selected for detection using the wrapper method. The detection process encompassed various events, such as line faults, load switching, and capacitor switching, occurring in both islanded and grid-connected modes. The proposed scheme was compared against existing techniques, artificial neural networks, and support vector machines under the same conditions. The proposed scheme demonstrated excellent accuracy (99.68 %), selectivity (99.44 %), and sensitivity (100 %) under normal conditions with all features. It also performed well in noisy environments, with accuracies of 98.94 % and 97.04 % with 40 dB and 30 dB noise, respectively. This research highlights the importance of quality and quantity of data for training intelligent RNN-based IDSs and demonstrates the effectiveness of the proposed scheme in detecting islanding in hybrid DG systems.

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

<span lang="EN-US">Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.</span>

Energy management and harmonic compensation of micro‐grids via multi‐agent systems based on decentralized control architecture

AbstractHere, a new energy management strategy based on a multi‐agent structure is presented by introducing hybrid control of AC micro‐grid (MG), where current harmonics compensation is also considered. This energy management strategy is based on Mealy machine in a multi‐agent system (MAS), which is implemented by generating coordination signals and sending them to the renewable energy sources (RES). In RES, maximum power point tracking (MPPT) or reverse of Hill‐climbing (RHC) is used based on these received coordination signals to limit the exchange power between the MG and the grid. Also, the multi‐agent structure of shunt active power filters (SAPFs) and passive filters have been studied in power MGs. The comparison of MG current harmonic compensation by these filters by fast Fourier transform (FFT) algorithm has been investigated in the proposed network. The simulation results show that the proposed energy management system (EMS) with proper management of MG different agents has been able to have a suitable performance for hybrid distributed generation (DG) systems. Also, the SAPF with better and more appropriate harmonic compensation than the SC‐RC‐LCL passive filter can eliminate the harmonic pollution of MG and be used with high performance in hybrid DG systems.

A hybrid IMFO-fuzzy algorithm for performance enhancement of microgrid under uncertainty via DGs integration and reconfiguration

Microgrids (MGs) are distributed generation and distribution systems that include distributed generation (DG) units, energy storage systems (ESSs), distributed reactive sources (DRSs), and resilient loads that can operate in either connected or isolated modes. When dealing with uncontrolled DGs such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES), MGs planners have a difficult time making decisions. The work proposed in this paper addresses three interconnected works: (i) the implementation of a rigorous hybrid optimization approach for reconfiguration and DGs placement; (ii) the performance investigation under uncertain behavior of RES-based DGs and demand; and (iii) performance enhancement realization through the replacement of hybrid DGs for RES-based DGs. An Improved Moth Flame Optimization (IMFO), which is a multi-objective optimization method, has been linked with fuzzy logic in order to handle multiple objectives in an efficient manner. These objectives include the minimization of voltage deviation, the reduction of generation cost, and the reduction of loss. The quality of the power, the amount of money saved by consumers, and the benefits to the Distribution System Operator (DSO) might all be improved with the help of a hybrid algorithm. This research is also extended to address the uncertainties of RES-based DGs by replacing hybrid DGs in the most optimal locations. IEEE 33 bus RDS is used to test radial distribution system (RDS) microgrids. For validation purposes, uses 24-hour load patterns to mimic WES and PVES’ 24-hour load dispatching behavior. The research findings clearly demonstrate the advantages of microgrids over traditional architectures. The hybrid DG requires an average generating cost of 185.33 $/kW in order to produce 100 kW of power throughout the day with significantly reduced emissions.

Feasibility of hybrid wind and photovoltaic distributed generation and battery energy storage systems under techno-economic regulation

The Brazilian electric energy compensation system (EECS) states that 100% of the energy generated and inserted into the grid should be returned to prosumers as credits to their energy bill. Ongoing regulatory changes propose that compensation should only be provided for the energy cost (43%). From this perspective, with awareness of the complementarity between wind and solar sources, energy storage systems (ESS) applied to hybrid distributed generation (DG) can become attractive. This study aimed to assess the economic feasibility of hybrid DG power plants with battery banks. Stochastic analyses were carried out by varying nine of the variables (nominal power, solar radiation, wind speed, electricity demand, energy tariffs, discount rate, battery bank investment, and solar and wind installation costs) in three types of hybrid power plants (micro, mini, and small). All scenarios presented a high probability of viability. The main conclusions are as follows. The complementarity of the sources yields benefits to the system: batteries have a high investment cost, which require incentives for their insertion in the DG as a decrease in battery cost or a specific subsidy. The regulatory framework should consider the possibility of payout for the benefits provided by the use of an ESS for DG, i.e., energy storage is directly related to the smart grid concept.

Novel Control Approach with FRT capability for Grid connected HYBRID distributed generation system using ANN controller based DVR

Abstract: For ever increasing power demand and depletion of conventional energy resources, Renewable Energy Systems (RES) became an alternative source of electricity to reduce the load stress on the Power Grid. Although several control & design modifications are presented in past literature to improve reliability & performance of through Distribution Generation (DG) technologies, they always fall short in some aspects of voltage stability and Fault Ride Through (FRT) capabilities. The main aim of the project is Protecting Critical load from Grid side altercations which occur due to harmonics generated by DG’s and Short circuit faults near to load center. This project proposes the application of a Dynamic Voltage Restorer (DVR) to enhance the power quality and improve the Fault Ride Through (FRT) capability of a three-phase medium-voltage network connected to a hybrid distribution generation (DG) system. In this hybrid farm, the Photo Voltaic (PV) plant via single-stage energy conversion (DC-AC inverter) & DFIG (Doubly-Fed Induction Generator) based Wind power plant are connected to the same Point of Common Coupling (PCC). For MPPT of wind power plant, we use Pitch Angle Control (PAC) technique. This topology allows Perturb and observe (P&O) based MPPT algorithm for PV plant through connection of the DG (Distribution generation) system to the public grid through a step-up transformer. In addition, the DVR based on Artificial Neural Network (ANN) controller is connected to the same PCC. Different fault condition scenarios are tested for improving the efficiency and the quality of the power supply and compliance with the requirements of the sensitive Load. The efficiency of this control technique is that it enhances restoration and harmonics suppression capabilities of DVR which are far superior than that of PI controller used in existing model. Keywords: RES, DG, LVRT, FRT, PV, DFIG, PCC, MPPT, P&O, DVR, PI, ANN, THD, Voltage stability.

Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed Generation System

In this manuscript, a genetic algorithm (GA) is proposed for the power management (PM) of a grid integrated hybrid distributed generation (DG) system. The hybrid distribution generation (DG) system incorporates photovoltaic (PV), wind turbine (WT), fuel cell (FC) and battery. The power fluctuations are produced in the distributed generation system, because the hybrid resource utilization and the generation of power is changeable. The major purpose of the proposed control method is “to control the power flow (PF) of active with reactive energy amid the source and grid side. The proposed GA-based power control system is mainly utilized for optimizing active with reactive power flow controllers. By controlling charge with discharge of battery, the proposed system met the energy requirement of the charge and managed the sensitivity of the charge. The proposed method provides an optimal power flow in DG systems. To evaluate the management of PF, the equality with inequality constraints is determined that is used to specify the accessibility of renewable energy sources (RES), the demand for electricity and storage components load level. The security of power system is improved with the help of proposed control system. Moreover, the battery is used to allow the renewable energy system and maintain a stable power output. The proposed method is activated in MATLAB / Simulink work site and the performance is compared with existing methods. The statistical analysis of mean, median and standard deviation (SD) are also analyzed for proposed with existing methods. The proposed technique mean value is 1.5784, median is 1.4892, SD is 0.5883.

Grid – connected operation and performance of hybrid DG having PV and PEMFC

Purpose“the purpose of this study/paper” or “this study/paper aims to” in the Purpose section of the . The integration of distributed generation (DG) to the utility grid is yet another approach to provide reliable and secured power.Design/methodology/approachThe significant concern in this contemporary world are the day-to-day increasing power demand, lack of energy and increasing environmental pollution, which are threatening the existence of living things.FindingsThe research focus here is to adequacy and security in the grid-integrated hybrid distributed generation (DG) having photovoltaic (PV) and proton exchange membrane fuel cell.Originality/valuePV system is a clean source of generation and suitable for many applications. Photovoltaic cell captures the energy from solar irradiation. To track the maximum power from PV, perturb and observe method is used. As it is intermittent in nature, integrating PV with fuel cell makes the hybrid source more reliable. Power electronic interfacing devices are used to integrate this hybrid DG source to microgrid. The simulation of this grid-connected hybrid DG is performed using Matlab/Simulink environment.

Power Management System of a Particle Swarm Optimization Controlled Grid Integrated Hybrid PV/WIND/FC/Battery Distributed Generation System

In this manuscript, the Power management of grid integrated hybrid dis-tributed generation (DG) system with Particle swarm optimization (PSO)algorithm is proposed. The hybrid DG system combines with photovoltaic,wind turbine, fuel cell, battery. Depending on the use of hybrid sources andthe changes of power production the variation of power can occurs in the DGsystem. The major purpose of the proposed method restrains the power flow(PF) on active with reactive power between the source and grid side. In thepower system control the proposed PSO method is utilized to maximize theactive with reactive PF and the controllers. The proposed method interact theload requirement energy and maintain the load sensitivity due to chargingand discharging battery control. In the DG system, the proposed PSO methodallows maximum power flow. To assess the PF, the constraints of equalityand inequality have been evaluated and they are utilized to determine theaccessibility of renewable energy source (RES), electricity demand, and thestorage elements of charge level. The protection of the power system is enhanced based on the proposed PSO method. Additionally, for retaininga stable output the renewable power system and battery is used. The pro-posed method is activated in MATLAB/Simulink working platform and theefficiency is likened with other existing methods.

A new passive islanding detection method for hybrid distributed generation under zero power imbalance

To solve the energy consumption demand of the world and environmental problems in the future, the distributed generation is considered an alternative approach. In this paper, a new passive islanding detection technique is proposed for the hybrid distributed generation (HDG) system based on changes in negative sequence voltage (NSV) and currents during an unintentional islanding. Islanding is caused in the distributed generation system due to failures in the power grid. As per distribution energy resources interconnection standards, it should be detected within 2 sec with the equipments connected to it. It is difficult to detect islanding during zero power imbalance condition. Sequence analyser will separate the positive, negative and zero sequence components of voltages and currents from the voltages and currents obtained at the point of common coupling (PCC). During a wide range of power imbalance conditions, the change in negative sequence voltage and currents are examined for islanding detection. The simulation shows that this method is free from non-detection zone, even at zero power imbalances between load and distribution generation. The computer simulations made in MATLAB/Simulink laboratory show the effectiveness of this method.

A new passive islanding detection method for hybrid distributed generation under zero power imbalance

To solve the energy consumption demand of the world and environmental problems in the future, the distributed generation is considered an alternative approach. In this paper, a new passive islanding detection technique is proposed for the hybrid distributed generation (HDG) system based on changes in negative sequence voltage (NSV) and currents during an unintentional islanding. Islanding is caused in the distributed generation system due to failures in the power grid. As per distribution energy resources interconnection standards, it should be detected within 2 sec with the equipments connected to it. It is difficult to detect islanding during zero power imbalance condition. Sequence analyser will separate the positive, negative and zero sequence components of voltages and currents from the voltages and currents obtained at the point of common coupling (PCC). During a wide range of power imbalance conditions, the change in negative sequence voltage and currents are examined for islanding detection. The simulation shows that this method is free from non-detection zone, even at zero power imbalances between load and distribution generation. The computer simulations made in MATLAB/Simulink laboratory show the effectiveness of this method.

Benefit Cost Analysis of Electrification of Urban Districts: Case Study of Philadelphia, Pennsylvania

AbstractIn this paper, the results of a feasibility analysis are summarized to assess the energy, economic, and environmental benefits of electrification and renewable energy integration for an urban district of Old City, Philadelphia, Pennsylvania. First, the energy demand for the district is reduced through the implementation of cost-effective energy efficiency measures (EEMs) appropriate for Philadelphia’s climate. Then, a combination of distributed generation (DG) systems including wind, photovoltaics, and hydropower is evaluated to determine optimized hybrid systems that meet the energy demand of non-electrified and electrified districts. The analysis indicates that the implementation of common and proven EEMs to all the district buildings can reduce its annual energy consumption and CO2 emissions by 13% and 13.8%, respectively. These EEMs are estimated to be cost-effective based on the current electricity price of $0.082/kWh offered by Philadelphia’s utility, Philadelphia Electric Company (PECO). Through cost optimization analysis, a hybrid DG system combining wind and hydropower connected to the grid is found to lower the cost of energy for the non-electrified district to $0.007/kWh, lower than the current PECO rate, with the added benefit to lower carbon emissions by 10%. Moreover, the analysis considered the case of an electrified district which reduces life cycle costs by 3.5%. By implementing electrification and EEMs, the electricity usage decreases by 14% and peak demand by 19.5% as well as CO2 emissions by 18%. Through cost optimization to design a hybrid DG system that can meet energy demands of the electrified district, wind and hydropower connected to the grid is found to achieve a cost of energy of $0.008/kWh and carbon emissions reduction of 34.9%.

Optimal design of hybrid DG systems for microgrid reliability enhancement

While the concept of microgrids and renewable energy systems is not entirely new, these integrated technologies have become a special topic of interest for researchers, utility providers and governments. Many challenges must be overcome to achieve better integration of renewable sources into the energy framework. This study presents some viable possibilities for the utilisation of a hybridised microgrid system. The hybridisation is achieved by an efficient design approach for the enhancement of both load and system reliability indices through the intelligent placement and sizing of hybrid distributed generation (DG) systems. Real-time models of solar photovoltaics, wind turbines, batteries and thermal DGs are presented and implemented. Also, network component failures are stochastically modelled via Monte Carlo simulations, and a general tie-set algorithm using an adapted breadth-first search is proposed. Moreover, mixed-integer multi-objective particle swarm optimisation is employed, giving a four-dimensional Pareto solution that is attained by optimising four reliability-related objectives, namely system average interruption frequency index, System Average Interruption Duration Index (SAIDI), Energy Not Supplied (ENS) and total cost.

A passive islanding detection method for hybrid distributed generation system under balanced islanding

<p>To solve the energy consumption demand of the world and environmental problems in the future the distributed generation is considered an alternative approach. In this paper a new passive islanding detection technique was proposed for the Hybrid distributed generation (HDG) system based on changes in negative sequence voltage (NSV) and currents during an unintentional islanding. Islanding is caused in the distributed generation system due to failures in the power grid. As per distribution energy resources interconnection standards, it should be detected within 2 sec with the equipments connected to it. It is difficult to detect islanding during zero power imbalance condition. Sequence analyzer will separate the positive, negative and zero sequence components of voltages and currents from the voltages and currents obtained at the point of common coupling (PCC). During a wide range of power imbalance conditions the change in negative sequence voltage and currents are examined for islanding detection. The simulation shows that this method is free from Non detection zone, even at zero power imbalances between load and distribution generation. The computer simulations made in Matlab/ Simulink laboratory show the effectiveness of this method</p>

Distributed generation with energy storage systems: A case study

Due to its relatively high efficiency, Distributed Generation (DG) is widely used to supply energy sources (generally power, heating and cooling) for on-site needs. This, however, presents a challenge to deal with an abrupt increase of electricity demand. To satisfy 100% of electricity demand with a high level dynamic performance energy storage is one of the most promising options for the DG system. In this study a hybrid DG system integrated with Compressed Air Energy Storage (CAES) and Thermal Energy Storage (TES) is proposed. Coupled with energy storage the DG system can perform a ‘peak shaving’ function and maintain the power output requirement properly, resulting in a lower core engine power rating and better process efficiency. To carry out technical evaluation the process flow chart is created and process models are developed. The results of simulation are also validated by IET’s CAES experimental data. The results reveal that the hybrid system’s exergy efficiency is 41.5%, and the primary fuel saving ratio is 23.13%. The CAES expander system is operated in a sliding pressure mode, satisfying various load profiles while its exergy efficiency for one day cycle is 64.7%. Compared with conventional DG system, within the hybrid system the core engine size can be downgraded by 35.3%, the fuel saving ratio is 11.06%.

A meta-heuristic firefly algorithm based smart control strategy and analysis of a grid connected hybrid photovoltaic/wind distributed generation system

An integrating distributed generation to power systems causes several technical issues, especially system stability like voltage at point of common coupling (PCC) and system. Therefore, to fully address the issue, current exiting power systems should be upgraded to the advanced power grid. To make the power grid become ‘advanced’, particularly in terms of stability and flexibility, one must need to make a controller which capable to control the above issue after integration of hybrid distribution generation system into the utility grid. All the issues have been attempted to address in this paper.Dynamic model for the main system components, namely, wind energy conversion system (WECS), PV energy conversion system (PVECS) and control for PVECS and the power electronics devices are addressed in this paper. The overall control strategy for grid connected hybrid wind/PV distributed generation system has also been presented. Different energy sources in the system are integrated through a DC bus into the utility grid. Based on the dynamic component models, a simulation model for the hybrid distributed generation system connected with utility grid has been developed using MATLAB/Simulink. Hybrid system comprises of Wind Turbine (WT) and solar Photovoltaic (SPV). For control the voltage and frequency at PCC Firefly based controller is used. Performance of several controllers such as Proportional Integral (PI), and Proportional Integral Derivatives (PID) are evaluated to control the frequency of the system. The controller gains are simultaneously optimized by powerful meta-heuristic firefly algorithm. Comparison of the dynamic responses reveals better performance of the PID controller. Here, it has been observed that the values of gain designed by firefly algorithm are robust which is verified and validated by case studies. Investigations reveal that the FA is successfully applied for simulation studies and it has been carried out to verify the controller and system performance under different scenarios which reveals that overall control strategy are robust and perform well.

Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling

As the development of China's economy, environmental problems in China become more and more serious. Solar energy and wind energy are considered as ones of the best choices to solve the environmental problems in China and the hybrid wind/solar distributed generation (DG) system has received increasing attention recently. However, the instability and intermittency of the wind and solar energy throw a huge challenge on designing of the hybrid system. In order to ensure the continuous and stable power supply, optimal unit sizing of the hybrid wind/solar DG system should be taken into consideration in the design of the hybrid system. This paper establishes a multi-objective optimization framework based on cost, electricity efficiency and energy supply reliability models of the hybrid DG system, which is composed of wind, solar and fuel cell generation systems. Detailed models of each unit for the hybrid wind/solar/fuel cell system were established. Advanced ε-constraints method based on Hammersley Sequence Sampling was employed in the multi-objective optimization of the hybrid DG system. The approximate Pareto surface of the multi-objective optimization problems with a range of possible design solutions and a logical procedure for searching the global optimum solution for decision makers were presented. In this way, this work provided an efficient method for decision makers in the design of the hybrid wind/solar/fuel cell system.

Synergy of smart grids and hybrid distributed generation on the value of energy storage

In smart grids, demand response and distributed energy systems aim to provide a higher degree of flexibility for load-shifting operations and the leverage to control intermittent wind supply. In this more dynamic energy system, deployment of energy storage at the site of consumption is envisioned to create synergies with the local distributed generation (DG) system. From a large end-user perspective, this paper contributes to the practical understanding of smart grids by modelling the impact of real-time pricing schemes (smart grids) on a hybrid DG system (mixed generation for heating and electricity loads) coupled with storage units. Specifically, we address: How does the portfolio of DG units affect the value of energy storage? and, what is the value of energy storage when assessing different designs of demand response for the end-user? To this end, we formulate a dynamic optimization model to represent a real-life urban community’s energy system composed of a co-generation unit, gas boilers, electrical heaters and a wind turbine. We discuss the techno-economic benefits of complementing this end-user’s energy system with storage units (thermal storage and battery devices). The paper analyses the storages policy strategies to simultaneously satisfy heat and electricity demand through the efficient use of DG units under demand response mechanisms. Results indicate that the storage units reduce energy costs by 7–10% in electricity and 3% in gas charges. In cases with a large DG capacity, the supply–demand mismatch increases, making storage more valuable.

Impact of Communication Delay on Frequency Regulation in Hybrid Power System Using Optimized H-Infinity Controller

ABSTRACTA communication infrastructure is essential for safe monitoring and control of isolated hybrid power system. For the hybrid system, integration of different renewable resources is required, such as wind turbine generator, diesel engine generator, aqua-electrolyser, and fuel cell along with storage unit. However, impacts of communication delay on dynamic performance on the hybrid power system, mainly load frequency control have not been reported by researchers. This paper presents the impact of communication delay profile in isolated hybrid distributed generation (DG) system for minimization of frequency deviation profile. The communication delays are one of the major problems and always present in a system during transmission of signal from remote terminal unit to control centre to individual unit. The frequency control problem is addressed for the hybrid DG system with communication delay. The delay is modelled by the second-order Padé approximation method. Particle swarm optimization (PSO)-tuned H-infinity loop shaping controller is used in this study for minimization of frequency deviation profile and compared with genetic algorithm (GA)-tuned H-infinity loop shaping controller and proportional integral-tuned H-infinity controller for different load conditions and change in system parameters. Performance of controllers is also validated with introducing nonlinearities in the system, such as generation rate constraints, time varying delay, and uncertainties. The results show that frequency deviation in presence of delay is found to be minimum for controller design by PSO-tuned H-infinity loop shaping of controller.

Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System

In this paper, islanding detection in a hybrid distributed generation (DG) system is analyzed by the use of hyperbolic S-transform (HST), time-time transform, and mathematical morphology methods. The merits of these methods are thoroughly compared against commonly adopted wavelet transform (WT) and S-transform (ST) techniques, as a new contribution to earlier studies. The hybrid DG system consists of photovoltaic and wind energy systems connected to the grid within the IEEE 30-bus system. Negative sequence component of the voltage signal is extracted at the point of common coupling and passed through the above-mentioned techniques. The efficacy of the proposed methods is also compared by an energy-based technique with proper threshold selection to accurately detect the islanding phenomena. Further, to augment the accuracy of the result, the classification is done using support vector machine (SVM) to distinguish islanding from other power quality (PQ) disturbances. The results demonstrate effective performance and feasibility of the proposed techniques for islanding detection under both noise-free and noisy environments, and also in the presence of harmonics.