Resources In Microgrid Research Articles

Investigation on Tuning Power–Frequency Droop for Improved Grid–Forming Inverter and Synchronous Generator Transient Load Sharing

Poor transient load sharing has been observed during the parallel operation of synchronous generators (SGs) and droop-controlled grid-forming (GFM) inverter-based resources (IBRs) in islanded microgrids. This can result in overcurrent protections, causing the inverters to trip, which leads to the collapse of voltage and frequency. In this article, small-signal analysis supported by electromagnetic transients (EMTs) simulation is performed on a detailed model of a microgrid containing IBRs and an SG to identify underdamped modes and their sensitivities to the power–frequency (P-F) droop parameter. Further EMT simulation of a more complex microgrid model with multiple GFM IBRs and SGs is performed to determine whether trends identified from the modal analysis extend to cases where the number of SGs in operation may vary. The objective is to investigate the effectiveness of tuning this parameter to reduce the likelihood of overcurrents in response to load and generation disturbances. The work uncovers both fast and slow modes of concern. Primarily, the findings show that increasing the P-F droop value improves the damping of the slower mode while degrading the damping of the faster mode, leading to an upper limit on the damping improvement of the GFM IBR output current. In EMT simulations of the more complex microgrid, the greatest damping improvement still exhibits significant maximum overshoot. Furthermore, the optimal value of the P-F droop parameter is sensitive to the number of SGs in operation. The conclusion is that tuning the P-F droop parameter is not an effective means to avoid the overcurrent tripping of GFM IBRs during response to large load or generation disturbances when operating in parallel to SGs in islanded microgrids.

Distributed Event-Triggered Secondary Control of Microgrids With Quantization Communication

Taken communication networks with limited resources in practical microgrids into consideration, a communication scheme, combined with an event-triggered communication scheme and quantization technology, is designed in the distributed optimal secondary control for islanded AC microgrids to reduce communication loads. In addition, switching communication networks are considered in microgrids to enhance the flexibility and the extendibility. In comparison with the existing distributed secondary control methods, the proposed distributed control strategy based on the designed communication scheme not only achieves the frequency and voltage restoration and the optimal active power sharing, but also reduces both the communication frequency and the amount of transmitted data in networks. Moreover, the presented communication scheme can exclude the Zeno behavior. Simulation results are given to evaluate the effectiveness of the proposed method.

Risk-based preventive energy management for resilient microgrids

A microgrid's energy management system (EMS) is typically formulated as a deterministic optimization problem. However, more risks and uncertainties are emerging due to the increasing complexity of the power system structure with intelligent distributed energy integration. As a result, different types of risks must be considered while implementing the EMS to improve system resiliency. In this paper, a risk-based EMS is introduced for islanded microgrids. A scenario-based optimization method is formulated for the EMS, where each system component's probability of failure (PoF) is considered in each scenario. In addition, the loads are classified into critical, semi-critical, and non-critical loads to prioritize serving the essential loads during a shortage of generation resources in microgrids. The nonlinear power flow equation of the new problem formulation is relaxed into a convex form to reduce the computational burden. In detail, the system's resiliency is improved by employing three functionalities in the EMS as a preventive measure: a) prioritizing to serve the critical loads, b) lowering the total amount of load served to mitigate the impact of the failure of any system component, and c) reducing the dependency on the generation resources with high PoF. The proposed preventive and risk-based EMS is validated against IEEE-14, 30, and 118 bus systems. The results demonstrate that the proposed EMS sheds more loads than the base EMS, based on the PoF of each system component, due to the knowledge that risks of failure of any component may induce a cascading failure and even blackout if only base EMS is provided. Moreover, the proposed EMS precisely considers the criticality of loads for curtailment to match the available system resources. The curtailment indirectly improves the system's resiliency by increasing the chance of survivability in the islanded mode with the probability of future component failure.

Demand side management in a microgrid to reduce the peak consumption cost

Microgrid stands for the participation of renewable sources such as solar and wind in the existing energy system in order to increase the availability and reliability of energy for consumers and to make the atmosphere carbon-free by reducing dependence on the main grid. However, the non-linearities inherent in the nature of the microgrid and network components make the programming process complex. Therefore, the efficient but linear model for microgrid resource planning algorithms is gaining interest today due to its simplicity and computational speed. On the contrary, for demand-side management, reducing the peak demand price by using different methods such as load trimming and valley filling by varying the use of flexible loads on the consumer side, which is an easier option for microgrid operators, rush hour price for consumer satisfaction. This paper gives an idea of the above purpose by designing an average model of a solar microgrid with the implementation of the maximum power point tracking (MPPT) algorithm method for stability of the system frequency and a battery storage system with a controller. You then plan the microgrid using the demand side management (DSM) method in order to reduce the cause of the price spikes.

Fuzzy Q-learning-based approach for real-time energy management of home microgrids using cooperative multi-agent system

The development of plug-in electrical vehicles (PEVs) and distributed energy resources in home microgrids (H-MGs) is gaining attention in recent years. Accordingly, it is vital to apply a suitable energy management system to provide the required energy of the PEV and ensure the health of the energy storage systems in the H-MG. This paper proposes a continuous real-time cooperative multi-agent system (MAS) for H-MG. Real-time MAS interacts with H-MG agents to perform as independent learners applying a distributed and cooperative reinforcement learning method, while state variables are shared to coordinate their real-time behavior. Therefore, the fuzzy Q-learning (FQL) method is investigated to control the agents in the continuous state space and achieve an efficient solution. The experimental results highlight the effectiveness of the suggested control strategy to guarantee the energy supply and reduce the electricity market price. The results of the simulation demonstrate that using the proposed cooperative MAS with the real-time FQL method in the energy management of the H-MG will reduce the electricity market price by 10%, increase the health of the storage system by 35.67%, and enhance the utilization of the PV system to charge PEV for 8% at the peak load demand.

Modified virtual frequency‐voltage frame control scheme with zero sharing error for islanded AC microgrids

AbstractThe active power‐virtual frequency (P‐ω′) and reactive power‐virtual voltage (Q‐V′) droop control scheme has been suggested for coordinated control of distributed energy resources (DERs) in islanded microgrids. While this method mitigates the issue of active and reactive power coupling in the droop controller, its main limitation is power‐sharing error. Due to the local property of the virtual frequency and voltage, significant steady‐state errors occur for both active and reactive power sharing. In this paper, a decentralized method is proposed to eliminate the mentioned errors without utilization of communication links. In the proposed scheme, P‐ω′ and Q‐V′ droops are realized at the microgrid point of common coupling (PCC). By employing the PCC virtual frequency/voltage for the active/reactive power sharing of all DERs, the sharing errors caused by the local property are eliminated. Small signal stability of the proposed scheme is studied, and Hardware‐in‐the‐Loop (HIL) experimental results are presented to validate the proposed method and highlight the improvements compared with the conventional P‐ω′/Q‐V′ scheme. The results verify the efficacy of the proposed method for providing accurate active and reactive power sharing while regulating the frequency and voltage close to the rated values.

Cyber-Resilient Control of an Islanded Microgrid Under Latency Attacks and Random DoS Attacks

The information exchange among distributed energy resources (DERs) in microgrids (MGs) is through sensing and communication systems, which are prone to expose cyber-attack threats. This article investigates the stability issue of MG systems with distributed secondary control under latency attacks and random denial-of-service (DoS) attacks. Considering these two kinds of attack modes, the corresponding attack consequences including network jamming and time-varying latency in the communication network are simultaneously studied. First, a new metric is defined to quantify the DoS attacks by considering different network jamming choices. Then, the time-domain stability study is conducted considering both attack consequences. Next, a cyber-resilient control strategy is proposed with two control modes: 1) An adaptive-gain resilient controller to sustain the fast stabilization of MG systems under nonuniform time-varying latency attacks, which is proved by the stochastic stability analysis using Lyapunov–Krasovskii functional method. 2) An event-trigger topology reconfiguration controller against excessive latency and damaged cyber connectivity caused by DoS attacks. A switching mechanism for coordinating the above control modes is also designed to guarantee the secondary control functions of MG systems. A modified IEEE 13-bus MG system with five DERs is tested and the effectiveness of the proposed controller under different attack scenarios is verified by OPAL-RT real-time tests.

A Hybrid Controller With Hierarchical Architecture for Microgrid to Share Power in an Islanded Mode

One challenge of utilizing inverter-based distributed energy resources in islanded microgrids is the proper power distribution between them. Improper power allocation causes resource overload and disturbs the balance of supply and demand, which can be eliminated using a droop controller. However, different operating points and loading conditions decrease the accuracy of this method. This paper aims to present a plan to improve the power distribution in islanded microgrids. To enhance the performance of droop control, the virtual impedance is exploited, giving the operator the ability to control the impedance between the inverter and the load effectively. The proposed method is evaluated and validated in the case of linear, nonlinear, and unbalanced loads.

A Novel Reconfigurable Coordinated Control for Autonomous Low Voltage DC Microgrid

DC microgrids with a small electrical network of locally available energy resources and distributed loads are becoming popular in real-life applications, especially in remote areas and in some specific urban applications. This, in turn demands a serious insight into the control and reliability enhancement of such systems so that the availability of secure and safe energy can be assured to the users. But the integration of intermittent renewable energy resources in dc microgrids lead to a serious concern regarding the stability of the microgrid bus voltage under continuous change in available source power and the loading conditions. While the microgrid management is still a challenge under steady state operating conditions, it becomes much complex under some emergency events like faults, failure of controllers/sources and overloads etc. This paper proposes a novel reconfigurable coordinated control architecture for a Low Voltage DC (LVDC) microgrid comprises of solar photovoltaic, fuel cell based renewable energy sources supported Energy Storage System (ESS) formed with battery and ultra-capacitor banks. The reliability of the system is ensured in the proposed reconfigurable coordinated control with three layered hierarchy viz. Emergency control layer sandwiched between the supervisory and local control layers. The proposed control strategy is implemented through a new Microgrid Bus Controller (MGBC) strengthened by an “emergency” control layer which handles the responsibility in emergency conditions to coordinate the energy sources, loads and controllers. The MGBC facilitates the fault identification and isolation to ensure the reliable operation of the healthy microgrid sections and speedy fault recovery. The MGBC is dedicated for observing the bus parameters and identify the fault location by monitoring the bus branching currents. Some of the key hardware and simulations results validating the proposed architecture along with the details of the control implementation are presented in this paper.

Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems

Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid’s energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers’ discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%,23.94%, 25.49%, and 31.65%) and (10.69%,3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system’s reliability with considering the weather uncertainty.

Reserve Evaluation and Energy Management of Micro-grids in Joint Electricity Markets Based on Non-Intrusive Load Monitoring

The heating, ventilation, and air-conditioning (HVAC) units are regarded as major demand response (DR) resources in micro-grids. However, due to privacy concerns and technical constraints, it is difficult for the system operator to obtain the complete information on each individual appliance. In this paper, we present a non-intrusive load monitoring (NILM) based framework for the operation strategy of the micro-grid in the joint energy, reserve, and regulation markets. The NILM technologies enable the operator to disaggregate the power of the HVAC units from the reading of the smart meters. Hence, the operation state of the appliances and the behavior of consumers can be studied without obtaining detailed data of each individual appliance. An advanced NILM algorithm is proposed, and the Coupled Generative Adversarial Networks (C-GAN) are utilized to enhance the generalizability of the trained model. Based on the NILM result, a novel method based on Hidden Markov Model (HMM) is proposed to evaluate the upward and downward reserve capacity of the HVAC units. The evaluated reserve capacity can help the operator better bid in the joint market based on the proposed optimization model. The proposed framework and methodology are verified through case studies. The simulation result reveals that with NILM, the market operator can save more energy consumption costs and load curtailment costs and earn more revenues in the joint market by selling excessive energy and providing ancillary services.

An Incentive-Compatible Frequency Regulation Market for Flexible Resources in Microgrid

Frequency regulation services can be provided by emerging flexible resources such as electric vehicles and energy storage which can rapidly adjust the output in response to regulation signals, as long as there is an effective market to provide proper incentives. However, current regulation markets were designed for conventional thermal generating units, and it is difficult for the mileage-based bidding structure to express the cost characteristics of flexible resources. Therefore, the current mechanisms are not conducive to encouraging the participation of flexible resources in providing frequency regulation services. This paper proposes an incentive-compatible frequency regulation market for flexible resources, whose revisions to the existing mechanism include the bidding structure, clearing rules, and settlement rules that adapt to the characteristics of new resources. Compared with the current mechanism, the proposed mechanism can reduce the costs of deploying regulation and more reasonably allocate the revenues for regulation services. The case studies verify the effectiveness of the proposed mechanism.

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

HIGHLIGHTS Novel data-driven approach to uncertainties in microgrid resources Faster convergence that stochastic optimization Reduced conservativeness compared to robust optimization Microgrid system with comprehensive selection of distributed energy resources

Machine Learning Based Efficient Protection Scheme for AC Microgrid

Micro grids have become popular as a way to reduce carbon emissions and use nonrenewable energy sources to produce power. Microgrids allow users to generate and regulate energy as needed, reducing their reliance on the utility grid. They may also sell excess electricity to the grid and make money. Due to its simple design, fast installation, and easy maintenance, photovoltaic systems are a vital microgrid resource. Microgrids threaten the reliability and optimum functioning of major power grids. It's crucial to discover defects early and fix them before catastrophic system breakdown. This research proposes a unique method based on Discrete wavelet transform and ensemble of Decision tree classifier for detecting and classifying microgrid faults. Once the particular fault type is recognised and categorised, a suitable protective strategy may be used to address it early, enhancing the system's overall safety.

分散型蓄電システム管理のためのマイクログリッド周波数制御

This paper proposes a novel frequency control method for the management of distributed energy storage resources in AC microgrid. Distributed autonomous controls by the individual prosumers perform an efficient coordination of SOCs which are automatically controlled to a same value that indicates the total SOC in the microgrid. The coordination is realized by the proposed control law that allows frequency deviation in a specified bandwidth. The proposed method has been developed in order for the microgrid operation by SSI, the single-phase synchronous inverters developed by the authors, while useful for general AC microgrid to increase the resilience of power supply.

Frequency and Voltage Control Techniques through Inverter-Interfaced Distributed Energy Resources in Microgrids: A Review

Microgrids (MG) are small-scale electric grids with local voltage control and power management systems to facilitate the high penetration and grid integration of renewable energy resources (RES). The distributed generation units (DGs), including RESs, are connected to (micro) grids through power electronics-based inverters. Therefore, new paradigms are required for voltage and frequency regulation by inverter-interfaced DGs (IIDGs). Notably, employing effective voltage and frequency regulation methods for establishing power-sharing among parallel inverters in MGs is the most critical issue. This paper provides a comprehensive study, comparison, and classification of control methods including communication-based, decentralized, and construction and compensation control techniques. The development of inverter-dominated MGs has caused limitations in employing classical control techniques due to their defective performance in handling non-linear models of IIDGs. To this end, this article reviews and illustrates advanced controllers that can deal with the challenges that are created due to the uncertain and arbitrary impedance characteristics of IIDGs in dynamics/transients.

Power Loss Minimization of Parallel-Connected Distributed Energy Resources in DC Microgrids Using a Distributed Gradient Algorithm-Based Hierarchical Control

This paper presents a distributed hierarchical control to minimize the distribution power loss, including both line loss and converter loss, of distributed energy resources (DERs) in DC microgrids. The proposed control comprises a distributed gradient algorithm (DGA) at the top layer, a consensus control at the secondary layer, and a local droop and dual-loop control at the bottom layer. Since the distribution power loss of DERs is modelled a concave function of the output currents, the DGA with addition and subtraction operators can be directly used to derive the optimal current allocation coefficients based on peer-to-peer data exchange between the neighboring DERs. The derived optimal current allocation coefficients are adopted by the distributed secondary control to generate adaptive voltage terms for the local control. The local control consists of a droop control and a classic dual-loop control for grid-connected converters. Both simulation and experimental results have validated that the proposed control can reduce more distribution power loss of DERs than the conventional control by only considering line loss by various cases, including normal operation, power clip, plug-in-and-out of DERs, and communication failure. Experimental results also verify that the execution speed of the proposed control is faster than that of the conventional Lagrange multiplier-based centralized control.

Enhanced Power and Energy Coordination for Batteries Under the Real-Time Closed-Loop, Distributed Microgrid Control

Hierarchical controls, consisting of primary, secondary, and tertiary layers to realize different functionalities at different time scales, have been applied for microgrids to achieve the stable and economic operations. However, in islanded microgrids with pure renewables (i.e., wind, solar, and battery storage systems (BSS) only), the three layers shall interact rather tightly and frequently in order to effectively leverage limited capabilities of available resources against disturbances. To this end, this paper discusses a more responsive control framework, including the distributed schemes for individual primary, secondary, and tertiary control layers as well as the real-time closed-loop scheme for a prompt coordination of the three layers. Thus, the proposed control framework could promote real-time interaction of resources in islanded microgrids with limited communication and computation burdens. Based on this, an enhanced control module design for BSSs of different configurations is further discussed to optimally coordinate their power and energy states with improved operational stability and economics. The steady-state analysis confirms the compatibility of the designed power and energy goals in tracking optimal power and energy equilibria simultaneously, and a customized Lyapunov candidate function is used to prove the asymptotic stability of the overall control framework. The hardware-in-the-loop simulation validates efficacy of the designed control framework in promptly mitigating disturbances and effectively achieving convergence to the optimal economic equilibria.

Optimal energy management of a photovoltaic-batteries-grid system

Microgrid becomes an attractive concept to meet the rapidly increasing demands for energy in worldwide and deal with air pollutions. Distributed energy resources (DERs) in Microgrid are the vital role to meet the demand of the grid locally. The performance and the PV source grid connected system's response depend on vital parameters like transient response, voltage or current overshoot, steady state error, and harmonics distortions. This research aims to optimize the proportional-integral (PI) controller for improving power quality performance and energy management of the PV battery of three phase inverter of grid connected mode scheme. Besides to the PI conventional method, three different meta-heuristic optimization algorithms are proposed and implemented in order to enhance the PI and hence all system parameters. These three algorithms are (Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA)) that can tune the PI controller's parameters by minimizing voltage regulator error, hence approving the goal of optimization in finding the optimal local or global solutions. The experimental results have been demonstrating the variety and the effectiveness of these optimization methods in convergence generation, computation time, and improving all required system parameters.

Optimum operation management of microgrids with cost and environment pollution reduction approach considering uncertainty using multi‐objective NSGAII algorithm

AbstractGenerating electricity near the consumption can provide more flexibility to supply various services to consumers as well as reduce system losses. The limited fossil fuels and air pollution are among the main incentives for the expansion of this technology. One of the perspectives proposed for effective increase in these resources’ involvement is to combine these resources with the objectives of the visibility of the relationship between distributed generation resources and power network, as well as control of these resources more efficiently. One of the methods of combining distributed generation sources is a new concept called micro grid; a topic that is of great importance in this regard is the proper management of distributed generation resources in micro grid aimed to reduce the costs of generating electricity and polluting the environment. The resource management in micro grid is a completely non‐linear and top‐order problem. In this paper, micro grid optimum programming has been studied considering the capacities available in the electricity market. In order to realize this, as well as reduce pollution and cost simultaneously, NSGAII multi‐objective genetic algorithm is used. Studies have been conducted in the form of three scenarios on a sample micro grid consisting of solar, wind, micro turbine, fuel cell and battery resources, considering the uncertainty of load and solar and wind generations. The probabilistic distribution function (PDF) and Roulette Wheel were used to create variables with uncertainty. The first scenario objective was to reduce costs by PSO, GA, and ABC algorithms’ optimization. In the second scenario, pollution was reduced and the studies were repeated using these three algorithms. Finally, in the third scenario, there was a simultaneous reduction in pollution and cost by these three algorithms, taking into account weight coefficients as well as NSGAII algorithm.