Microturbine Research Articles

Real-time monitoring and operation of microgrid using distributed cloud–fog architecture

In this paper, a new distributed multi-agent framework based on the three layers’ fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.

Sizing of Energy Storage with Network Constraints Economic Dispatch of Fuel Cell Micro Turbine and Renewable Energy Sources

Micro-Grid is the appropriate solution to various problems in the power system. Different types of energy sources, likewise Fuel cell (FC), Micro-Turbine (MT) and renewable energy sources can be integrated with micro-grids (MG). The battery energy storage has played a crucial role to support the power mismatch of on-grid or off-grid MG. Therefore the optimal size of battery energy storage along with the optimal cost-based calculation has become an essential part for the micro-grid operator. The piecewise linear cost method is used for the cost based analysis. The main contribution of this paper is: (i) the optimal size of battery energy storage has been determined with a Fuel cell (FC) and Micro-Turbine (MT) based distribution generation (DG). (ii) The impact of battery storage with DG and renewable energy sources (RES) has been considered. (iii) The total benefit and market benefit has been maximised. (iv) The unit-commitment cost of FC and MT with spinning reserve, piecewise linear cost function, ramp rate, minimum up and downtime constraints has been considered for the sizing of battery storage. (v) The network constrained has been found to obtain minimum daily energy loss for the optimal size of battery storage. (vi) The state of charge (SOC) of battery, the power output of DG’s and RES, power loss, battery cost per day, operating cost of generation, etc. have been determined. The optimal sizing of battery energy storage determination is helpful for the both Microgrid operators as well as designers. The IEEE-33 bus test system with ZIP load has been carried out for analysis and result validation. The general algebraic modeling system (GAMS) is used to solve the deterministic optimisation problem.

Optimal energy management of microgrids under environmental constraints using chaos enhanced differential evolution

Optimal energy management of microgrids is one of the challenging tasks in modern power systems. It helps to achieve maximum societal benefits in terms of economy and reduced environmental effects. Microgrid can be operated in grid connected as well as isolated mode. A novel hybrid optimization technique using differential evolution (DE) and chaos theory is presented in this paper for optimal operation of a microgrid comprising of both renewable and non-renewable energy sources. The reduction of pollutants is also considered because of integration of non-renewable energy sources with the microgrid. The motivation for the proposed hybrid algorithm is to avoid premature convergence and stagnation. The optimal operation is formulated as a bi-objective optimization problem considering minimization of operating cost and reduction of pollutants simultaneously over a 24-h scheduling horizon. It is a complex non-linear optimization problem under a set of equality as well as inequality constraints. A microgrid consisting of wind turbine (WT), photovoltaic (PV), micro turbine (MT) fuel cell (FC) and battery units as storage device is considered for the present study. The microgrid is assumed to be grid connected. The proposed algorithm is tested on two systems in order to verify its effectiveness and efficiency. Further, the results obtained by the proposed technique are validated by comparing the same obtained by other recent methods. It observed that the proposed technique is capable of producing superior results in terms of cost and pollution.

Improved-GWO designed FO based type-II fuzzy controller for frequency awareness of an AC microgrid under plug in electric vehicle

The paper proposes to use an improved Grey Wolf Optimization (GWO) optimized Fractional Order (FO) based type-II fuzzy controller for frequency regulation of an AC microgrid in presence of plug-in electric vehicles. The AC microgrid comprises various renewable-based energy sources such as wind turbine generator (WTG), photovoltaic cell (PV), microturbine (MT), aqua electrolyzer based fuel cell (FC) and diesel engine generator (DEG) along with various storage devices (ESD) like battery energy storage (BES), flywheel energy storage (FES) and electric vehicles (EV). The uncertain nature of solar, wind technologies and load demand makes the system more complicated and introduces the frequency oscillations in the system. It is highly needed to establish power balance among generation by designing an appropriate controller for frequency regulation. This paper proposes a robust fractional order type-II fuzzy PID (FO-T2-FPID) where the parameters of the FO-T2-FPID controllers are optimized by suggesting an improved GWO (I-GWO) algorithm. To demonstrate the capability of proposed I-GWO algorithm few comparative analyses have been performed over particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithm. It is also observed that for the same FO-T2-FPID controller structure, the percentage improvement in the objective function (ITAE) under load uncertainty by I-GWO compared to GWO and PSO are 29.74% and 65.94% respectively. Finally, it is conferred that proposed I-GWO optimized FO-T2-FPID controller significantly improves microgrid dynamic performance under various operational conditions.

Stochastic scheduling of a renewable-based microgrid in the presence of electric vehicles using modified harmony search algorithm with control policies

Modern power systems are seeking for alternative energy sources will less emission to conventional fossil-fuel power plant to mitigate the global concerns on environmental issues. On one hand, renewable energies have turned to the first choice of system operators. On the other hand, the system should provide the required infrastructure to appropriately accommodate such energy sources. In this respect, microgrids (MG) would provide the needed conditions for integrating renewable energy sources (RESs). Thus, the optimal energy management of such systems in the presence of highly uncertain renewable power generation is of great importance. Accordingly, this paper provides a stochastic programming framework for the optimal scheduling of an MG equipped with RESs and plug-in electric vehicles (PEVs). The power sources considered include a wind energy system in the form of wind turbine (WT), a solar photovoltaic (PV) system, a fuel cell (FC), a microturbine (MT), besides a battery storage system (BSS). The mentioned problem is formulated as a single-objective optimization problem aimed at minimizing the total operating cost over the scheduling period. The MG is considered in the grid-connected mode where it can transact power with the upstream system. The uncertainty of the problem is due to the intermittent power output of the wind energy system and the PV unit, as well as uncertain behavior of the EV owners in charging/discharging their vehicles. The proposed stochastic optimization problem is the solved using an effective and efficient optimization algorithm named “modified harmony search (MHS) algorithm”. Finally, the simulation results are discussed and the superior performance of the suggested algorithm is verified through making a comprehensive comparison with some well-known methods.

Design, manufacture and test of a micro-turbine renewable energy combustor

The ever-increasing demand on highly efficient decentralized power generation with low CO2 emission has made microturbines for power generation in micro gas turbine (MGT) systems popular when running on biofuels as a renewable source of energy. This document presents a state-of-the-art design, and optimization (in terms of design, performance and emission control) of a micro-turbine renewable energy combustor that fits into the existing Bladon 12kWe recuperated microturbine plenum while running on a range of biofuels as it can successfully provide the required power of the MGT. Governing equations for in-depth analysis of the combustor consist of manufacturer empirical data to simulate system-level operation with respect to replacement of the fossil with biofuels. The Model developed and validated at the company’s ISO conditions confirms the output power of the new combustor fits the conventional system with slight eco-energy improvements. The modeling of the combustor in a complete microturbine assembly system is performed, then was utilized to further analysis of the microturbine with the designed combustor. The results gave on average 46.7% electrical efficiency, 83.2% system efficiency, 12 kWe electrical power, and 90% recuperator effectiveness at nominal operating conditions of microturbine (MT). Sensitivity analyses evaluate changes in performance with respect to fuel phase (e.g., liquid or gaseous) and design variables (e.g., orientation, shape, and dimensions of combustor), leading to energy optimization of the unit. Findings demonstrate that the combustor in microturbine can meet the target performance specifications of a company conventional diesel microturbine with significant savings. An objective function including both combustor and recuperator technical energy data is defined for finding the best ratio of fuel and air and their flow rates to find the most effective operating points for the operation of MT. Annual time series simulations completed for Coventry, West Midlands, United Kingdom indicate a new combustor can reduce operational costs of diesel fuel combustor by 8%, 2%, 36%, and 25% when supplying bioethanol, DME, biogas, and NG, respectively. Annual operating time of the renewable microturbine combustor at rated capacity included an 11% reduction in exergy loss with biogas fuel relative to diesel fuel.

Balancing management of strategic aggregators using non-cooperative game theory

The aggregators are intermediary players at the distribution system level. They manage the financial transactions of resources such as micro-turbines (MTs), photo-voltaic (PV) production systems, flexible demands (FDs), and must-run demands (MDs) in the energy market. Based on the regulation of many transmission system operators (TSOs) in European countries, an aggregator is obliged to be assigned to a balancing group (BG) represented by a balancing group manager (BGM). The TSO measures the energy imbalances at the BG level. Meanwhile, the BGM is responsible for the management of financial transactions between the aggregators and the TSO. The strategic aggregators can establish a common BG instead of individual BGs. Then, they gain more payoffs since their energy imbalances are compensated more efficiently. However, the BGM should have the complete knowledge of the aggregators’ confidential information, which includes the evaluation of aggregators concerning the cost/utility functions of associated resources. The aggregators have no incentive to disclose this valuable information. To solve this challenge, an iterative distributed algorithm based on non-cooperative game theory is proposed. In the numerical results, the performance of proposed common BG has been evaluated thanks to the comparison with a benchmark case where the aggregators establish their own individual BGs.

Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty

This paper suggests a new energy management system for a grid-connected microgrid with various renewable energy resources including a photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT) and battery energy storage system (BESS). For the PV system operating in the microgrid, an innovative mathematical modelling is presented. In this model, the effect of various irradiances in different days and seasons on day-ahead scheduling of the microgrid is evaluated. Moreover, the uncertainties in the output power of the PV system and WT, load demand forecasting error and grid bid changes for the optimal energy management of microgrid are modelled via a scenario-based technique. To cope with the optimal energy management of the grid-connected microgrid with a high degree of uncertainties, a modified bat algorithm (MBA) is employed. The proposed algorithm leads to a faster computation of the best location and more accurate result in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The simulation results demonstrate that the use of practical PV model in a real environment improve the accuracy of the energy management system and decreases the total operational cost of the grid-connected microgrid.

Day-ahead profit-based reconfigurable microgrid scheduling considering uncertain renewable generation and load demand in the presence of energy storage

Due to the penetration of renewable energy sources (RESs) with probabilistic natures into microgrids (MGs), optimal scheduling and reconfiguration (RG) processes are associated with uncertainty. This paper presents the stochastic profit-based optimal day-ahead scheduling of a reconfigurable microgrid (RMG) as a new generation of the conventional microgrid. The proposed algorithm finds the optimal RMG's topology from the profit maximization point of view, the optimal hourly MG's unit set-points like micro-turbines (MTs) and energy storage, and power exchange with the main grid, simultaneously. The generated power of wind turbine (WT) and PV panel, as well as load demand are considered as uncertain parameters. To solve the profit maximization problem of RMG, time-varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is employed. Also, to ensure simulation accuracy in the presence of high-level uncertainties, the autocorrelation model is used based on actual data for the uncertainty of renewable power output. The feasibility and applicability of the proposed framework are demonstrated on a 69-bus radial RMG with various distributed generators in different cases. The results show the effectiveness of the proposed model.

Self-healing model for gas-electricity distribution network with consideration of various types of generation units and demand response capability

In this study, a model has been proposed to carry out Self-Healing (SH) in distribution level with consideration of combined gas and electrical network and with consideration of various generation types include Micro-Turbines (MTs), Energy Storages (ESs) and Combined Heat and Power (CHP) units. The model also determines the optimum generation amounts of all generation units with target of minimizing of total cost. It is to be noted that the supplying source of gas demands through CHP or directly from natural gas network are important in defining of the optimum amount of the generation units. In case of fault occurrence in the grid and outage of line/lines or zone/zones as a result, the model determines the new grid topology so that it will not violate the constraints of the gas and electric networks and the minimum amount of loads will be isolated from the grid. Hence, the new generation amounts of generation units will be determined based on the new topology of the grid. If the generation amount or ramp rate limit of generation units are not enough or the transfer capacity of lines reach to their limitation the model will take action for Demand Response (DR) for controllable loads and in critical cases zone/zones-shedding to reduce total demand of the grid. The proposed model, after linearization, would be as Mixed Integer Linear Programming (MILP) problem that is solved by commercial solver. It is implemented on a grid with six Microgrids (MGs) and also 44-node system for testing and verifying.

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.

Dynamic modeling gas-electricity combined system with wind power and its stability control method

The micro turbine (MT) is scheduled to catch up wind power uncertainty and thus lead to great variations in the state of natural gas system (NGS). To meet this challenge, firstly, a dynamic model of gas-electricity combined system with wind power is established, the dynamic model of NGS is based on differential equations, so that it can connect with MT model easily, and a droop control module is added to the Rowen's MT model, so that the improved model can adjust the output power according to the scheduling instructions. Then, a stability control method based on the variable regulation ratio gas pressure regulator is proposed, the essence of this method is to reduce the variation of natural gas storage in the gas pipeline, so as to ensure the stability of pressure. The simulation results show that the dynamic model is correct and the stability control method is effective.

A High-Speed Microturbine PMa-SynRG Emulation Using Power Hardware-in-the-Loop for Wind Energy Conversion Systems

In this paper, a high-speed microturbine (MT) permanent magnet assisted synchronous reluctance generator (PMa-SynRG) real-time emulation based on linear impedance regulator (LIR) using power hardware-in-the-loop (PHIL) for wind energy generation tests is presented. The LIR is designed without any feedback control loop for reshaping the $s$ -domain performances of the current filter along with the converter inside the PMa-SynRG emulated system. The PHIL platform not only provides a method for eliminating the high cost of using real renewable energy hardware but also it enables the developers to create new, rapid, and reliable controllers for renewable energy testing. This platform can be used in investigating the performance of energy system under various conditions even if the generator prototype is not yet developed or unavailable. PMa-SynRG mathematical model is emulated in the real-time using PHIL platform while the output voltage of the proposed emulator imitates the generated voltage through the simulated model. In addition, a voltage source converter is employed as a voltage amplifier for imitating the PMa-SynRG performance when supplying nonlinear/linear loads. In this paper, the proportional-integral resonant (PIR) controller is utilized at the voltage control loop for tracking the distorted output reference signal voltage. In order to investigate the performance of the proposed PMa-SynRG emulator, it has been simulated and compared with MATLAB/SIMULINK environment.

A Novel Sequential Sampling Algorithm for Reliability Assessment of Microgrids

Due to the polymorphic uncertainties in microgrids (MGs), prohibitive computational burden is produced in reliability assessment. In this work, a novel sequential sampling algorithm (NSSA) compatible with sequential Monte Carlo (SMC) simulation is developed to overcome the computational burden. First, optimal probability density functions (PDFs) of random variables are worked out based on variation method. Then, optimal PDFs are employed to chronologically simulate the random states of microturbine (MT), photovoltaics (PV) and time varying load with improved computational efficiency. Therefore, the convergence of reliability assessment is accelerated accordingly. A series of case studies have been conducted, and the computational results show that NSSA provides a favorable sampling efficiency and adaptability to system conditions in reliability assessment of MGs. At last, based on optimal PDFs produced by NSSA, dominant joint PDF (DJ-PDF) is defined and employed to quantify the contributions of different scenarios to the reliability indices. Case studies have confirmed that DJ-PDF can provide detailed information for scenario-based reliability analysis.

Robust Frequency Control of an Islanded AC Micro Grid using BDA Optimized 3DOF Controller under Plug in Electric Vehicle

The article presents the effectiveness of a Binary Dragonfly Algorithm (BDA) based 3-DOF controller for robust frequency control in an islanded AC micro-grid system under different uncertainties. A micro-grid is incorporated with the integration of various renewable energy based distributed generations (DG). The proposed micro-grid system is structured with wind turbine generator (WTG), Photo voltaic (PV) system, Diesel engine generator (DEG), Micro-turbines (MT), Aqua electrolyzer based Fuel Cells(FC) and with few energy storage devices i.e Battery energy storage (BES) and Flywheel energy storage (FES). Moreover a chargeable plug in electric vehicle is effected as load side demand while obtaining frequency control mechanism in micro-grid system. However large dynamics, low inertia and incurred uncertainties of most DG system affects system performance especially on system frequency seriously. In view of this to obtain robust control mechanism in islanded micro-grid system the article proposes a novel Binary Dragonfly Algorithm based 3-DOF controller to ensure servicing of good quality power to remote consumers. The performances of proposed BDA optimized 3-DOF controller is compared with conventional PSO, GA technique based PID and PI controller in order to justify supremacy of proposed approaches. Finally it has been suggested that the proposed BDA optimized 3-DOF controller is more effectiveness over other optimized controllers.

Singular Perturbation for the Dynamic Modeling of Integrated Energy Systems

An integrated energy system (IES) represents coordinated operations of constrained natural gas (NG), electric power, and thermal systems in three distinctly different time scales. Microturbines (MTs) are increasingly utilized in IES as suppliers of combined cooling, heating and power (CCHP) units, which optimize the delivery and the usage of interdependent NG, electric power, and thermal energy sectors in a distributed IES. In this paper, a dynamic IES model is proposed based on the singular perturbation theory (SPT), which denotes the dynamic interactions of the three energy systems. The dynamic IES model representing a boundary layer system (BLS) is solved and analyzed in three time-scales. The simulation results demonstrate the validity of the proposed dynamic IES model in three time-scales, highlighting the necessity of incorporating the dynamic characteristics of NG, electric power, and thermal energy in dynamic analyses of distributed IES.

Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm

This article offers a multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models. Many renewable and non-renewable energy resources like photovoltaic system (PV), micro-turbine (MT), fuel cell (FC), and wind turbine system (WT) are incorporated in a grid-connected hybrid power system to supply energy demand. The main aim of this article is to maximize environmental, technical, and economic benefits by minimizing various objective functions such as the annual cost, power loss and greenhouse gas emission subject to different power system constraints and uncertainty of renewable energy sources. For each load model, optimum DER size and its corresponding location are calculated. To test the feasibility and validation of the multi-objective water cycle algorithm (MOWCA) is conducted on the IEEE-33 bus and IEEE-69 bus network. The concept of Pareto-optimality is applied to generate trilateral surface of non-dominant Pareto-optimal set followed by a fuzzy decision-making mechanism to obtain the final compromise solution. Multi-objective non-dominated sorting genetic (NSGA-III) algorithm is also implemented and the simulation results between two algorithms are compared with each other. The achieved simulation results evidence the better performance of MOWCA comparing with the NSGA-III algorithm and at different load models, the determined DER locations and size are always righteous for enhancement of the distribution power system performance parameters.

Analyzing Customer Outage Cost in a Microgrid

In today’s era of socioeconomic and environmental concerns, conventional energy sources are incapable of meeting increasing energy demands which leads to the significance of Microgrid (MG) systems incorporating renewable energy sources. An ideally designed microgrid capable of operating in standalone and grid connected mode is considered with photovoltaic panels, wind turbines, fuel cell, battery and Microturbines (MT) as sources. The two noteworthy disadvantages of microgrid are very expensive power supply and intermittency of power generation. Besides ensuring a steady supply, the power delivered should be of moderate cost. So, this work focuses on reducing the cost associated with its operation and emission by simultaneously analyzing the impact of uncertainty. The uncertainties can be caused due to different DGs present in MG and the load varations. The variations of uncertainty are included as outage cost. The MG operating under islanded and grid connected modes are analyzed for different scenarios to obtain optimal value of customer outage cost and operating cost. So a heuristic optimization technique which can handle the multi-objective problem is considered. The optimal solution for different operational scenarios of grid connected and islanded mode are obtained using Multi-Objective Particle Swarm Optimization (MOPSO) algorithm.

Optimized FOPID controller for improving steady state and transient response of Microturbine Generation system

The power system structure is complicated and the demand is increasing rapidly. In order to minimize the power losses and to reduce the power shortage, Distribution Generation (DG) is added in the power system. Microturbine Generation (MTG) is a new type of DG which has become popular source of electric power industries due to their fuel flexibility, reliability, power quality and its size. In this paper, the modelling and simulation analysis of the MTG system are carried out with Fractional Order Proportional Integral Derivative (FOPID) controller to improve the overall performance of MTG system. The performance of the MTG system is studied under various linear and non-linear load conditions. The Microturbine model and converter controller models are simulated using MATLAB Software. FOPID controller is used to maintain constant voltage for various load conditions by maintaining the torque of the Microturbine (MT) system. The power quality of MTG system is analyzed using Fast Fourier Transform (FFT) Analysis. To improve the power quality, LCL filter is added in this paper. The overall performance of MTG system is analyzed with and without FOPID controller. When the results are compared with conventional PI controller, the MTG system shows better performance in FOPID controller.

Decentralised self‐healing model for gas and electricity distribution network

With regard to the global environmental challenges, low cost of the natural gas and fast developing of new gas-fired power generation technologies, conduce to unwanted integration of gas and electricity network. Moreover, one of the benefits of emerging distribution network as microgrids (MGs) would be self-healing capability. So, in this research a model has been formulated as Mixed Integer Linear Programming (MILP) problem which determines optimally the power generation of micro turbines (MTs), Energy Storages (ESs) and Combined Heat and Power (CHP) units during the normal operation of MGs with consideration of combined gas and electricity network. In case of fault occurrence in MG/MGs and need to compensate the power generation shortage the model determines the power transferring rate of all MGs with normal operation mode, adjacent to the faulty MG/MGs, according to the cost-based optimization. In critical situations load shedding procedure will be applied to the faulty MG/MGs. It would be considered that CHPs supply both heat and electricity loads and the remaining heat demands will be supplied by natural gas network. The proposed model has been applied to the test system with six MGs as three scenarios and also verified by applying to the network with 30 MGs.