Resources In Microgrid Research Articles

Frequency regulation of hybrid shipboard microgrid system using butterfly optimization algorithm synthesis fractional‐order controller

AbstractInclusion of intermittent natured renewable energy resources in microgrid to reduce global warming, especially in shipboard power system and due to highly fluctuating propulsion load, frequency control strategy of isolated shipboard hybrid microgrid (ISHMG) is major point of attraction. Hence, a power system of marine vessel with dish‐stirling solar thermal system (DSTS), wind‐driven generation (WDG), solid oxide fuel cell (SOFC), super‐conducting magnetic energy storage (SMES), two different AC‐loads, and propulsion loads are considered as an ISHMG. The main objective of this paper is reducing the mismatch between generation and demand with the help of fractional‐order proportional integral‐derivative (FOPID) controller. To improve the frequency control, the tuning of the controller coefficients plays a major role as the controller's performance fully dependent on the parameters. Accordingly, the recently developed butterfly optimization algorithm (BOA) has been used to optimize the FOPID controller's parameters. Comparative analysis of several techniques like FA, PSO, GOA, SSA, and BOA used for PID, PI, FOPID controllers' optimization, and sensitivity analysis under different parametric variation has been presented to prove the stability of the ISHMG model. Analyzing all the results, it is found that BOA‐based FOPID controller is performing the frequency control far better than other techniques.

Review of Model Predictive Control of Distributed Energy Resources in Microgrids

In recent years, in response to increasing environmental concerns, advances in renewable energy technology and reduced costs have caused a significant increase in the penetration of distributed generation resources in distribution networks. Nonetheless, the connection of distributed generation resources to distribution networks has created new challenges in the control, operation, and management of network reliability. This article is a review on the model predictive control (MPC) for distributed energy resources (DER) in microgrids. The solutions of MPC for energy conversion of solar photovoltaic, wind, and energy storage systems are covered in detail. MPC’s applications for increasing reliability of grid-connected converters under (a)symmetrical grid faults are also discussed. The promising potentials of the applications of MPC to the stable multi-variable control performance of DERs are highlighted. This work reflects strong symmetry on MPC control strategies and provides guidance map for readers to facilitate future research works in these exciting fields.

Correction: Optimal Sizing and Allocation of Distributed Energy Resources in Microgrids Considering Internal Network Reinforcements

Correction: Optimal Sizing and Allocation of Distributed Energy Resources in Microgrids Considering Internal Network Reinforcements

Sizing of centralized shared energy storage for resilience microgrids with controllable load: A bi-level optimization approach

To improve the utilization of flexible resources in microgrids and meet the energy storage requirements of the microgrids in different scenarios, a centralized shared energy storage capacity optimization configuration model for microgrids based on bi-level optimization is proposed. First, the response characteristics of the shared energy storage and controllable load in the resilience microgrid are analyzed, and the centralized shared energy storage operation mode meeting the regulatory demand of multi-scenarios is designed. Then, a bi-level optimal allocation model is constructed, which takes the maximum net income of centralized shared energy storage as the upper layer and the minimum payment cost of load in the microgrid as the lower layer. Furthermore, the multi-objective whale optimization algorithm is used to solve the bi-level optimization model. The results show that the shared energy storage can jointly meet the regulation demand of multi-scenarios by coordinating the transferable load and cuttable load in the microgrid and improving the utilization rate of shared energy storage.

A Comparative Study of the Influence of BESS and STATCOM on Post-Fault Voltage Recovery in Microgrid

Reliable and efficient power flow in the electrical grid is crucial given network expansion and the incorporation of numerous renewable sources. Microgrids are becoming more and more important in the global energy sector due to its numerous benefits. The variable nature of distributed energy resources (DER) in microgrid makes large-scale DER integration potentially unstable for system voltage. Grid regulations place restrictions on the voltage control of DER unit, which raises the need for reactive power support. Proper placement of flexible AC transmission system (FACTS) under post-fault conditions may enable faster voltage recovery. In this work, battery energy storage system (BESS) and static synchronous compensator (STATCOM) have been considered for improving the post-fault voltage in DER integrated weakly grid-connected microgrid system. Two operation scenarios have been considered for analyzing the behavior of BESS and STATCOM individually at post-fault situation. According to the results of the simulation, BESS requires lesser rating than STATCOM for faster recovery of post-fault voltage of the system.

Optimal Sizing and Allocation of Distributed Energy Resources in Microgrids Considering Internal Network Reinforcements

Microgrids have become valuable assets because they improve the reliability of consumers while integrating renewables via distributed energy resources (DERs). Thus, making them cost-efficient is essential to secure their proliferation. This paper proposes a new method for the optimal design of microgrids. The proposed two-stage method optimizes the size and the location of the DERs, i.e., the renewable energy sources (RESs), distributed generation (DG) units, and battery energy storage systems (BESSs). Furthermore, the overall operation of the microgrid is optimized using a stochastic scenario-based approach, considering grid-connected and unintentional islanded modes. The proposed method also considers internal network reinforcements. Thus, the first stage is an energy-based approach, formulated as a mixed-integer linear programming (MILP) problem, and it is used to size the DERs, whereas the second stage uses an optimal AC power flow (AC-OPF) to formulate a mixed-integer nonlinear programming (MINLP) model that allocates the DERs and selects the best conductor for each circuit. The multi-objective nature of the problem is addressed via Pareto optimization to analyze the trade-off between operational and capital costs. The MINLP model is linearized through piece-wise approximations and solved using commercial solvers. Furthermore, the impact of battery degradation is analyzed through a simple adaptation of the Stage 1 model. Results were obtained with data from the real university campus microgrid CampusGrid, located at the State University of Campinas (UNICAMP), in São Paulo, Brazil.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Investigating and Optimizing the Operation of Microgrids with Intelligent Algorithms

Microgrids need optimization to reduce economic problems and human losses. Scattered resources in power systems and microgrids have led to many environmental, economic and human, and animal losses. The most important part of these problems is related to voltage and frequency fluctuations when possible occurrences such as extreme load changes or errors in microgrids. These problems lead to microgrid collapse. Therefore, providing optimal solutions that can solve these challenges is essential. For this purpose, the present study has tried to provide a high-performance control structure in the time of internal and external disturbances based on short-term planning. The proposed approach is the use of an evolutionary neuro-fuzzy network. Perhaps the main reason for using this approach can be due to uncertainty in the distribution and distribution of loads in microgrids and power systems. Simulation has been performed in MATLAB and Simulink environments, and the results show that the optimal load distribution has been done evolution in microgrids.

Correction to: A novel approach for optimal power scheduling of distributed energy resources in microgrids

Correction to: A novel approach for optimal power scheduling of distributed energy resources in microgrids

Resilient Networked Control of Inverter-Based Microgrids against False Data Injections

Inverter-based energy resource is a fast emerging technology for microgrids. Operation of micorgrids with integration of these resources, especially in an islanded operation mode, is challenging. To effectively capture microgrid dynamics and also control these resources in islanded microgrids, a heavy cyber and communication infrastructure is required. This high reliance of microgrids on cyber interfaces makes these systems prone to cyber-disruptions. Hence, the hierarchical control of microgrids, including primary, secondary, and tertiary control, needs to be developed to operate resiliently. This paper shows the vulnerability of microgrid control in the presence of False Data Injection (FDI) attack, which is one type of cyber-disruption. Then, this paper focuses on designing a resilient secondary control based on Unknown Input Observer (UIO) against FDI. The simulation results show the superior performance of the proposed controller over other standard controllers.

Dynamic Output-Feedback Decentralized Control Synthesis for Integration of Distributed Energy Resources in AC Microgrids

This work proposes a control design procedure based on Linear Matrix Inequalities (LMI) for integrating Distributed Energy Resources (DERs) in microgrid systems. Each DER uses local measurements as feedback, so this proposal results in a method to design a decentralized primary control. The dynamic model employed to synthesize such a controller presents an unmeasured state vector, model uncertainties, and random disturbances. To simultaneously deal with all these difficulties while ensuring the closed-loop stability and multiple performance specifications, this work is formulated in terms of a single Lyapunov function. To find this Lyapunov function and synthesize the controller gains, a convex optimization problem that involves LMIs is required to be solved. The controller results in a linear discrete-time output-feedback form, facilitating its implementation. The time-domain simulation of a microgrid system is performed in the software PSCAD/EMTDC with four DERs to validate the control design procedure’s effectiveness.

Scheduling of multi-fuel energy resources in microgrid using grey wolf optimisation

Scheduling of multi-fuel energy resources in microgrid using grey wolf optimisation

Virtual Conductance Based Cascade Voltage Controller for VSCs in Islanded Operation Mode

Voltage source converters have become the main enabler for the integration of distributed energy resources in microgrids. In the case of islanded operation, these devices normally set the amplitude and frequency of the network voltage by means of a cascade controller composed of an outer voltage control loop and an inner current control loop. Several strategies to compute the gains of both control loops have been proposed in the literature in order to obtain a fast and decoupled response of the voltages at the point of common coupling. This paper proposes an alternative and simple methodology based on the introduction of a virtual conductance in the classic cascade control. This strategy allows to design each control loop independently, obtaining a closed-loop response of a first-order system. In this way, the gains of each control loop are easily derived from the parameters of the LC coupling filter and the desired closed-loop time constants. Furthermore, a state observer is included in the controller to estimate the inductor current of the LC filter in order to reduce the number of required measurements. A laboratory testbed is used to validate and compare the proposed controller. The experimental results demonstrate the effectiveness of the proposal both in steady-state and transient regimes.

Scheduling of multi-fuel energy resources in microgrid using grey wolf optimisation

Scheduling of multi-fuel energy resources in microgrid using grey wolf optimisation

Fast Online Parameter Identification for Current Source Operated PV Modules in DC Microgrids

This paper presents a non-iterative online approach to identify the modeling parameters of a photovoltaic (PV) module. It is motivated by the fact that accurate and reliable modeling of distributed energy resources (DERs) in DC Microgrids improves their stability and efficiency under a wide range of operational conditions. In particular, the case where PV modules are used as DERs in their current-source region is considered. The proposed method addresses the limitations associated with parameter identification in these settings. Specifically, the method works under varying temperature and insolation conditions relying only on current and voltage sensors that already exist in the power electronic converters tying the PV DER to the Microgrid. Also, its algorithm is practical and reliable as it does not rely on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge or an initial guess, and it is non-iterative, so it does not risk divergence (or require proof of convergence) as other iterative algorithms. Moreover, it is both fast and of a low computational complexity, which enables its implementation on microcontrollers within PV DER systems. The development of this method is detailed in the paper along with its application steps to facilitate its adoption. Furthermore, an experimental setup was used to test the proposed method under different ambient conditions and demonstrated its efficacy with algorithm execution times of under 1 second and high modeling accuracy on a microcontroller.

Adaptive Synthetic Inertia Control Framework for Distributed Energy Resources in Low-Inertia Microgrid

Bulk integration of Distributed Energy Resources (DERs) into the power grids reduces the system&#x2019;s inherent inertial response.The reduced inertial response causes a high Rate-of-Change-of-Frequency (RoCoF) and poses formidable operational challenges for the grid frequency stability. Interconnections around the world comprehend the role and value of the Synthetic Inertia Control (SIC), which is considered as a subset of the Fast Frequency Response (FFR) and as one of the potential solutions to arrest high RoCoF in low-inertia power systems. This paper proposes an intelligent SIC model with an adaptive Fuzzy Logic Controller for a low-inertia microgrid. The proposed Fuzzy-SIC (FSIC) design optimizes the DER output to fulfill the FFR requirements of the system for various operating conditions. The particle swarm optimization algorithm is applied to tune the SIC unit parameters along with the secondary Proportional&#x2013;Integral&#x2013;Derivative control. The proposed approach is examined in a control area with distinct degrees of DERs and load. Case studies and numerical results demonstrate about 87% improvement in RoCoF and frequency nadir in comparison to the system without a synthetic inertia emulation. Furthermore, the robustness of the proposed approach is evaluated using various case studies and a time-domain analysis is conducted to demonstrate the impact of incremental SIC parameters on the system parameters.

Sensor Fault-Resilient Control of Electronically Coupled Distributed Energy Resources in Islanded Microgrids

In this paper a sensor fault resilient control approach for electronically interfaced distributed energy resources (DERs) in islanded microgrids is proposed. The proposed control strategy consists of a sliding mode observer (SMO) and a H infinity output feedback controller that ensures resiliency of the DER units in an islanded microgrid against erroneous measurements due to sensor malfunctioning. Any error in the measurements due to malfunctioning of the associated sensors are modelled as sensor faults in this paper. The SMO in the proposed approach estimates the sensor faults, and the estimated faults are used to devise fault tolerant control strategy so that the H infinity output feedback control remains unperturbed by the erroneous measurements. In order to demonstrate the efficacy of the proposed control strategy, rigorous simulation studies are carried out on a test islanded microgrid system with single DER and multiple DERs. The efficacy of the proposed approach has been further validated through an experimental study carried out on a prototype islanded microgrid system

Joint energy and flexiramp market equilibrium model for regional‐integrated electricity‐heat systems considering flexibility exploitation of district heating networks

AbstractThe increasing penetration of renewable energy resources into regional‐integrated energy systems (RIES) holds higher standards on operational flexibility. To alleviate the negative effects of insufficient operational flexibility, this paper proposes a joint market equilibrium model to obtain flexible resources in microgrids (MGs) and district heating networks (DHNs) through economic incentives. Based on the incorporation of the flexiramp (FRP) into the energy markets, a joint market mechanism is first established. Through multi‐energy synergy, the FRP provisions on the supply and demand sides in MGs are then introduced. Considering the flexibility exploitation of DHNs, a collaborative FRP supply method is developed to formulate the optimal bidding strategy for MGs. Moreover, a hierarchical model is designed to achieve joint market equilibrium, where MGs strategically bid in the external layer and the RIES operator optimally clears the market in the internal layer. The combined utilization of the Q‐learning algorithm and path‐tracking interior point method is applied to solve the hierarchical model efficiently. The performance of the proposed approach is verified by case studies. The results demonstrate the efficacy of the proposed approach for operational flexibility improvement through minimizing renewable energy curtailment, the overall cost, and the average energy prices.

A novel approach for optimal power scheduling of distributed energy resources in microgrids

A novel approach for optimal power scheduling of distributed energy resources in microgrids

Microgrid Protection through Adaptive Overcurrent Relay Coordination

The inclusion of distributed energy resources (DER) in Microgrids (MGs) comes at the expense of increased changes in current direction and magnitude. In the autonomous mode of MG operation, the penetration of synchronous distributed generators (DGs) induces lower short circuit current than when the MG operates in the grid-connected mode. Such behavior impacts the overcurrent relays and makes the protection coordination difficult. This paper introduces a novel adaptive protection system that includes two phases to handle the influence of fault current variations and enable the MG to sustain its operation. The first phase optimizes the power flow by minimizing the generators’ active power loss while considering tolerable disturbances. For intolerable cases, the second phase opts to contain the effect of disturbance within a specific area, whose boundary is determined through correlation between primary/backup relay pairs. A directional overcurrent relay (DOCR) coordination optimization is formulated as a nonlinear program for minimizing the operating time of the relays within the contained area. Validation is carried out through the simulation of the IEEE 9, IEEE 14, and IEEE 15 bus systems as an autonomous MG. The simulation results demonstrate the effectiveness of our proposed protection system and its superiority to a competing approach in the literature.