Microgrid Power Research Articles

Impedance Modeling and Stability Analysis of Three-Phase Four-Wire Inverter with Grid-Connected Operation

With the continuous penetration and development of renewable energy power generation, distributed grids and microgrids are becoming increasingly important in power systems. In the distribution networks and microgrids, the grid impedance is comparatively large and cannot be ignored. Usually, the parallel compensation is used to improve the grid quality. In these three-phase four-wire power systems, analyzing the impedance characteristics of the grid-connected inverter is vital to carry out the small-signal stability analysis. Thus, it is vital to consider the influence of the zero-sequence component in addition to the positive-sequence component and the negative-sequence component when it comes to analyzing system stability. In this paper, the impedances of three-phase four-wire split capacitor inverter and three-phase four-leg inverter are established. Based on the achieved impedance, the similarities and differences between the impedances of three-phase four-wire split capacitor inverter and impedance of three-phase four-leg inverter are studied. The main difference is reflected in zero-sequence impedance. Additionally, the zero-sequence impedance characteristics and the dominating factors deciding the zero-sequence impedance are analyzed. Then, the stability of the system considering the grid impedance and impedance of three-phase four-wire inverter is investigated by separately considering the stability of the positive–negative-sequence component and the stability of the zero-sequence component. Several cases of small-signal instability caused by the positive–negative-sequence component or zero-sequence component are revealed. The experimental results validate the theoretical analysis.

Energy management optimization of integrated energy system with electric hydrogen hybrid energy storage

Abstract Integrated energy system (IES) has attracted wide attention as an efficient solution to a comprehensive utilization of hybrid energy system including electricity, heat, and hydrogen in recent years. This paper investigates the stochastic optimal operation for the micro integrated electric power, heat, and hydrogen delivery system. Firstly, the mathematical model of the micro integrated energy system for residential communities and other similar scenarios is discussed, including the energy interface, power microgrid, micro-heat system and micro-hydrogen system with considerations of distributed energy supply, energy storage, load, upstream energy network interface and their mathematical models. Secondly, the correlated uncertainty, such as wind power, solar power, and loads, are considered and handled in micro integrated energy system. Finally, a scenario-based approach is proposed for the stochastic operation of micro integrated energy system. The objective function is formulated to minimize the expected total operation and environmental costs, the hourly schedule of controllable components in micro integrated energy system. Numerical results demonstrated that the effectiveness of the proposed method. The correlated uncertainty and the corresponding impacts are accurately presented in the optimization model.

Agent-Based System for Microgrid Power Real-Time Pricing with Client Preferences

Microgrid technology is actually the most practical way for Distributed Energy Resources (DERs) deployment in urban and rural communities. The factors that motivate reliance to the microgrid technology include energy security, economy and environment. The economical sustainability of microgrids depends on the energy market within a community. This can be challenged by issues related to the scalability of the microgrid and the setting up of a pricing system. Various energy management systems and energy pricing methods developed so far offer little or no option to the end user/client to accept or reject the proposed price. Thus, this paper presents a client focused agent that automatically negotiates various available economical options in real-time pricing context within a microgrid. The proposed agent collects and analyses the prices as communicated by the microgrid network operator. Moreover, based on client predefined preferences, it regulates the client load demand in a predefined sequence with the aim of minimizing the power consumption during high price period. Therefore, the proposed system will reduce the client overall energy cost and contributes towards demand side management from the network operator perspective. The agent mathematical model and algorithm are simulated in MATLAB environment. The findings of the study demonstrate the effectiveness of the proposed agent approach in offering the client a real-time control over the energy cost control in a microgrid context.

Hybrid pulse-width-modulation-based simultaneous wireless laser information and power transfer system using PWM sampling

Simultaneous wireless laser information and power transfer can be widely used in the Internet-of-Things and energy Internet power supply systems to improve system performance. Herein, a hybrid pulse width modulation (PWM)-based simultaneous wireless laser information and power transfer system using PWM sampling is proposed and is investigated theoretically and experimentally. The output voltage of the receiver is adjusted based on the duty cycle of the PWM loaded on the laser diodes at the transmitter. Communication is achieved by leveraging sampling of the PWM by loading a modulation disturbance on the modulation wave of the PWM generator. An output voltage of 1.5 V, output power of up to 0.75 W, and communication rate of up to 10 kbps are achieved in the proposed system. The system can be applied in one-to-many transmissions of power and signals in microgrid power supply systems, in which signals and power can be loaded on the direct current bus through the proposed system node, such that the signals and power can be transmitted to each powered node without additional signal conditioning, modulation, or coupling. The proposed system can thus enhance the interference immunity of communication subsystems, as well as the simplicity, and safety of power transfer subsystems.

Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS)

The aim of this study is to develop very short-term and accurate energy consumption forecasts for educational building. The purpose is to develop a predictive model for building energy planning to balance the supply from renewable power systems and the building electrical load demand). For the methodology, an adaptive neuro-fuzzy inference system (ANFIS) was used as a machine learning approach for building energy forecasting. The data for training (80%), testing (10%), and validation (10%) was used from smart energy meters installed in the building and weather data. A total of 20520 dataset instances was used for developing the forecasting model. The originality of this study is the ANFIS model was validated for the training, testing, and validation data, and the accuracy and reliability of the forecasting model was assessed over various very short time horizon of past data (0.5–4 h ahead). The main results reveal that the predictive model is extremely accurate in predicting the energy consumption of a building. The values of coefficient of correlation R for training, testing, and validation are 0.98017, 0.9778, and 0.97593, respectively, for the 30 min ahead energy forecasting. The R values for all the data are respectively 0.97951, 09854, and 0,96778 for the 0.5, 1, 4 h ahead energy forecasting. The low nMSE and nMAE errors for the ANFIS model demonstrate how well the model replicates the reported results from smart energy meters. The research findings are very important for the energy planning of microgrid power systems (energy purchase, building operations and maintenance, and demand side management).

An optimal dispatch model of adiabatic compressed air energy storage system considering its temperature dynamic behavior for combined cooling, heating and power microgrid dispatch

Adiabatic compressed air energy storage (A-CAES) technology naturally has the ability of cogenerating cooling heating and electric power. It is a promising energy storage technology in the application of combined cooling, heating and power (CCHP) dispatch. This paper explores a new modelling method of the A-CAES for CCHP dispatch by taking the temperature dynamic behavior of the A-CAES into detailed consideration. With the help of the binary technique and piecewise approximation method, this A-CAES model is formulated based on mixed-integer linear programming (MILP), which can be easily solved. On the basis of this, an optimal day-ahead dispatch model for a CCHP microgrid containing an A-CAES is proposed. The empirical study based on a pilot plant and system data in China is carried out. The simulation results verify the effectiveness of the proposed A-CAES model. With the comparison of the results using the proposed A-CAES model and the conventional A-CAES model without considering temperature dynamic behavior, the results show that the system operation cost obtained by the proposed model can be 13.7% lower than the results obtained by the conventional A-CAES model.

Real-time wavelet-based adaptive algorithm for low inertia AC microgrids power measurements

This paper proposes a real-time wavelet-based power estimator using a real-time modified stationary discrete wavelet packet transform (RT-MSDWPT). The proposed method overcomes the conventional SDWPT main constraints of power estimations related to the lack of reactive power flow direction and susceptibility to off-nominal frequency operation. The introduction of a digital sampling-shifting of a quarter of the fundamental, applied to the filtered voltages, assures the reactive power flow direction. Additionally, the insertion of an adaptive sampling time procedure based on the power grid frequency estimation mitigates the errors introduced by the network frequency deviations. These modifications make the RT-MSDWPT suitable for being embedded into the standard hierarchical power flow control strategies of ac microgrids. Experimental results assessed using an ac microgrid laboratory setup, operating under different operational scenarios, demonstrate the feasibility of the proposed solution.

Voltage regulation-based sensorless position observer with high-frequency signal injection topology for BDFIGs in ship power microgrid systems

The aim of this paper is to propose a new rotor position observer based on the approach of high-frequency signal (HFS) injection for sensorless direct voltage control (DVC) strategy of the stand-alone brushless doubly-fed induction generators (BDFIGs). The proposed new observer is relied on the injection of HFS into the control winding (CW) side, (CW-HFS), while the resulted induced signal detected from the power winding (PW) side will observe the information of rotor position. The suggested new sensorless control method neither need any saliencies in the rotor side nor require any flux computation process, which can ensure the simplicity and efficacy of the proposed method. Comprehensive experimental results are obtained with a prototype wound-rotor BDFIG. The corresponding results ensure the capability and efficacy of the suggested sensorless DVC strategy based on the proposed rotor position observer using the CW-HFS injection approach with a good transient behavior under different operating conditions for the stand-alone BDFIG systems.

Optimal operation of CCHP microgrids with multiple shiftable loads in different auxiliary heating source systems

This paper studies the optimal operation of integrated system from the perspective of the demand side load shifting, aims at adapting to the unbalance between energy demand and supply in the combined cooling, heating and power (CCHP) microgrid. A shiftable electrical load model is proposed, which combined with the time-of-use (TOU) price strategy and environmental benefits of solar and wind power. Based on the TOU price strategy, the shiftable load models are established according to the user’s room temperature comfort demand and the allowable fluctuation range of the domestic hot water’s temperature, respectively. Then the optimization model is constructed, which takes into account factors such as economy and environment, and solved by a CPLEX solver. Finally, in order to deeply study the influence of TOU price on the system, the influences of heating/cooling and electrical load shift on the CCHP microgrids with different auxiliary heating sources are studied. The results show that the influence of Shiftable loads in the system using an electric boiler as an auxiliary heating source is significantly better than using a gas boiler; the proposed shiftable load models can reduce the operational cost by up to 17.02%; and have better effect than the existing one that only considers TOU.

A new type of microgrid power quality improvement strategy based on harmonic suppression

Current harmonics and distorted voltage are two important factors that affect the power quality of microgrids. Combining with the operating characteristics of microgrids, this article uses a new strategy to control grid-connected inverters and UPQC to improve grid quality. Pointed out its advantages relative to traditional active filters, and in view of the current harmonic detection problems, In this paper, the inverter adopts an improved ip-iq harmonic detection method to improve the accuracy of harmonic current detection and has strong interference. The UPQC structure uses SVPWM and PWM control methods as its new control strategies on the series side and parallel side to compensate for distortion voltage and harmonic current. A detailed model is built in MATLAB/Simulink to verify the feasibility of this method.

Optimizing demand response of a modular water reuse system in a remote Arctic microgrid

Reliance on imported diesel fuel, with high transportation costs, has made power and water treatment expensive in remote diesel microgrids in the Arctic. Past attempts at implementing piped water in these areas have proven difficult due to the high cost of energy to pump, transport, and heat water with imported diesel fuel. A modular Water Reuse (WR) system has been developed to provide more affordable, distributed water service for an individual home lacking running water. However, these WR systems still consume substantial electricity and can burden a household with high energy costs, if powered by the community diesel microgrid. Here we expand a mixed-integer linear optimization model — Food-Energy-Water Microgrid Optimization with Renewable Energy (FEWMORE) — to treat the effects of operating WR systems as dispatchable loads connected to a microgrid. We apply the model to a western Alaska community without piped water to analyze demand response (DR) of WR systems with solar and wind energy. Such an analysis has not yet been articulated by current energy optimization, water treatment, and demand response models for modular water service in microgrids. Integrating a solar photovoltaics (PV) array to power a WR system, as opposed to operating solely off of diesel generation, results in a 3% decrease in total project costs (installing and maintaining solar PV, and electricity purchases from the diesel microgrid) over a 20-year lifetime. Optimally dispatching the water treatment processes results in more savings: a 13% decrease in total project costs and a 37% reduction in diesel use.

Microgrid Power Quality Improvement Using Active Power Conditioner

This paper discusses power system power quality issues and possible remedies using power electronics. Several worldwide standards This research shows how a Shunt active power filter can improve power quality in a microgrid system at the distribution level. The major goal of this study is to find an appropriate pulse generating strategy for improving the shunt active power filter's compensation capabilities. The device's compensating capability is mostly determined by the DC link capacitor voltage regulation. A fixed hysteresis current control approach has been employed in the past. An adaptive hysteresis current control technique has been developed here to improve the performance of a shunt active power filter. The suggested technique's performance has been verified in the MA TLAB/SIMULINK model platform under various operating conditions.

Review on Microgrid Power Quality Improvement Using Active Power Conditioner

This paper discusses power system power quality issues and possible remedies using power electronics. Several worldwide standards for power quality control are briefly discussed, as well as some significant methods for analysis. Non-sinusoidal waveforms are introduced and assessed in an electrical circuit. The compensatory features and principles of operation of passive filters, shunt, hybrid, and series active power filters are presented. For each type of power filter, the topologies and control schemes of various power circuits are examined. Simulation and experiment are used to prove the compensating features of each topology with its corresponding control scheme. Harmonic current, power factor, and load unbalance can all be compensated for by the filter. Harmonic current, power factor, and load unbalance can all be compensated for by the filter. A power filter that has been employed there continuously monitors the load current and adapts to changes in load harmonics.

A Review on Renewable Energy Sources in Microgrid

The utilization of RES to address electrical energy requirements is gaining traction as a potential solution to this issue of electrical energy shortages. The use of renewable energy sources in electrical generation systems is done in a variety of ways, including in microgrid systems. Microgrid technologies have massive benefits for both the user as well as the electric utility provider. There has been a lot of microgrid development in numerous nations since micro grids have a lot of benefits, such as higher power quality and being more ecologically friendly. Power generation, microgrid architecture, power electronics, control systems, and security devices are all areas of microgrid innovation. In this paper, it is demonstrated that persistence as well as artificial neural networks have certain qualities that make them useful to investigators of controllers at various levels of control that micro grids must have in order to be cost-effective, productive, as well as capable of meeting energy power quality and quantity requirements. Keywords: Renewable energy sources, Microgrid, persistence, Artificial Neural Network

Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization

Combined cooling, heating and power (CCHP) microgrid can effectively improve the efficiency of renewable energy and reduce the operating cost of microgrids. However, the uncertainties of renewable energy sources and variety load demands make the optimal scheduling of CCHP microgrid complex and difficult to achieve. In this paper, a novel multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale. In the day-ahead scheduling scale, the uncertainties of CCHP microgrid are introduced by describing the day-ahead forecast data as interval numbers with upper and lower bounds. The scheduling decision-making variables and the interaction power with power grid are obtained by considering the regulation cost in the worst-case scenario. In the intraday scheduling operation scale, the rolling optimization strategy is applied to obtain intraday optimal power allocation by minimizing the scheduling cost at each prediction horizon based on the intraday ultra-short-term forecast data, which preserves the day-ahead scheduling decision. Finally, the power deviation of the intraday ultra-short-term optimal power allocation is smoothed based on real-time operating data. The simulation results demonstrate that the proposed strategy can effectively reduce the operating cost and improve the robustness for the coordinated operation of CCHP microgrid.

Online Controller Tuning for DC Microgrid Power Converters With the Ability to Track Maximum Allowable Bandwidth

Possible dynamic interactions among multiple dc microgrid power converters that are connected to a common dc bus can lead to performance degradation or even stability issues in some control loops. Thus, in order to maintain a desired dynamic behavior when operating conditions or system parameters are changing, an interesting approach is to adopt autotuning for the controller parameters. In general, existing autotuning methods are based on the evaluation of the stability specifications (e.g., the crossover frequency and the phase margin) and the adaptation of the controller to ensure the specified dynamic properties. However, the desired bandwidth and phase margin may not be achievable due to parameter changes, unexpected additional delays, or unmodeled dynamics in the feedback loop. This article addresses the infeasibility issue by adding an external control loop on top of the autotuning unit. This external loop adjusts the reference crossover frequency until the phase margin specification is fulfilled. As a result, the proposed scheme is capable of tracking the highest bandwidth at which the reference phase margin is achievable, i.e., the maximum allowable bandwidth. The proposed method is applied to a dc microgrid prototype, and the simulation and experimental results are discussed.

A Novel Mathematical Approach to Model Multi-Agent-Based Main Grid and Microgrid Networks for Complete System Analysis

Penetration of renewable energy resources in modern power systems has increased rapidly. The integration of different renewable and nonrenewable resources for the purpose of electricity generation is referred to as Distributed Generation (DG) units. The penetration of DG units gave birth to the concept of power microgrid. Power inverters play a major role in the integration of DGs in the power system. Control and stability analysis of microgrids in power systems is a challenging task for the control community. Dynamic microgrid modeling demands knowledge of fundamental engineering laws to detailed theoretical analysis. To model the dynamic behavior of the power microgrid, a basic understanding of the power converter operation modes and their control schemes is necessary. The main microgrid modeling components are power converters, power lines, transformers, protection systems, load, and faults. In this paper, preliminary concepts of power systems along with graph theoretic approach are used to develop the model of the microgrid and main grid networks. A mathematical model of a power microgrid in islanded mode, as well as the grid-connected mode, is developed and comprises of generation sources, power inverter interface, protection mechanism, load, faults, and transmission lines. The developed mathematical model can be used to address the stability issues as well as resilience in the power networks for complete system analysis. To validate the mathematical model, a renewable energy-based main grid and microgrid model is simulated. The graphical result of simulated model presents the generation and load curves.

Research on Microgrid Scheduling Based on Improved Crow Search Algorithm

Considering the characteristics of distributed power in microgrid, in order to maximize the advantages of distributed power generation technology in economy, environment, and energy, a multiobjective dispatching model of microgrid is proposed under the condition of satisfying system constraints and considering the operating costs and environmental costs of microgrid. The crow search algorithm (CSA) has the advantages of less parameter setting, simple implementation, and strong optimization ability and is often used in theoretical analysis and practical engineering applications. However, its disadvantage is that crows only search for candidate solutions according to their own experience, and their development ability is poor, especially for solving high-dimensional functions. In order to overcome these shortcomings, an improved crow search algorithm (CSA-PSO) is proposed based on the particle swarm optimization (PSO) algorithm. The two main improvements are as follows: (1) in order to avoid the blind selection of crow in search, the global optimal solution is adopted to modify the solution search equation to guide the search of new candidate solutions, so as to improve the development ability; (2) introducing a levy flight strategy to improve the single search mechanism of the CSA. In order to verify the performance of the CSA-PSO algorithm, 17 benchmark functions are simulated with other intelligent algorithms. The results show that the CSA-PSO algorithm has a good optimization effect in search accuracy, convergence speed, and robustness. Finally, this algorithm and other five algorithms are applied to the optimal scheduling problem of microgrid. By solving the objective function, the optimal scheduling output scheme of each distributed power supply is obtained. By using the CSA-PSO algorithm, the total operating cost of microgrid is reduced by at least 21.5%, which further verifies the effectiveness of the CSA-PSO algorithm.

Design and Implementation of Electric Vehicle Technology by Using ANN Controller

Abstract: It is possible to utilise EVs as both a load and provider of energy using the Vehicle-to-Grid (V2G) approach (or Gridto-Vehicle technique if EVs are used as a load). With this technology, industrial microgrids may have voltage and power flow regulation and congestion management. An no of electric vehicles with a variety of charging profiles, battery states of charge and electric vehicle counts may benefit from two separate controllers (grid regulation and charger controller), according to the controllers, It is possible to regulate the main power flow and voltage drop in an industrial microgrid by allowing bidirectional power flow. Simulations indicate that the suggested controllers can regulate an industrial microgrid's voltage levels and power flow. According to industrial microgrids include solar, wind farms, electric car fleets, industrial loads, commercial loads, and a diesel generator. MATLAB/SIMULINK is used to simulate and analyze the results. Keywords: Electric vehicles (EV), State of Charge (SOC), Grid Regulation Power Genereation Controller(GRPGC), Charge Controller(CC), Grid to Vehicle(G2V), Vehicle to Grid(V2G), Industrial Microgrid(IMG), Grid Regulation Controller(GRC), Destributed Energy Resources(DER), Diesel Generators(DGs).

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network.

This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.