Networked Microgrids Research Articles

Two-Stage Optimization of Mobile Energy Storage Sizing, Pre-Positioning, and Re-Allocation for Resilient Networked Microgrids with Dynamic Boundaries

Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model’s effectiveness and applicability in improving system resilience.

Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids

Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids

Application of Dual-Tree Complex Wavelet Transform in Islanding Detection for a Hybrid AC/DC Microgrid with Multiple Distributed Generators

This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is aimed to detect and discriminate between the different fault/disturbance conditions that result in islanding and/or non-islanding conditions in a hybrid microgrid. For the islanding and non-islanding conditions detection by the AIDM, firstly, fault/disturbance signals are obtained from a test microgrid. Secondly, these signals are decomposed using Dual-Tree Complex Wavelet Transform. Thirdly, a Synthetic Minority Oversampling Technique (SMOTE) is applied for data preprocessing to increase the accuracy of the classifier. Finally, an artificial neural network (ANN) is used as the classifier for training and testing the proposed AIDM for different microgrid configurations and event scenarios. The proposed method is tested with different data categories from three different microgrid test systems with different scenarios. All modeling and simulations are executed in MATLAB Simulink Version 2023a. Results indicate that the proposed scheme could detect and discriminate between islanding and non-islanding conditions accurately in terms of dependability, precision, and accuracy. An average accuracy of 99–100% could be achieved when tested and validated with microgrid networks adapted from IEEE 13-bus systems.

Engineering Microgrids Amid the Evolving Electrical Distribution System

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system’s resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries.

Stability enhancement for seamless control in networked microgrids with energy storage: Nonlinear spatio-temporal control perspective

The integration of distributed energy resources into modernized networked microgrids, combined with the increasing variability in load dynamics, presents significant stability challenges. This research offers a comprehensive analysis of the stability of cascaded interactions in nonlinear multi-timescale systems, including grid-following and grid-forming inverters. The proposed grid-forming controller, integrated with energy storage systems and a nonlinear Lyapunov function, facilitates seamless control and stabilization of these inverters. This approach addresses challenges related to nonlinear plant and network dynamics, uncertainties in state variables, and unmodeled system dynamics. The closed-loop control system ensures asymptotic spatio-temporal dynamical stability through constrained time state convergence to stable equilibrium points while minimizing control oscillations. Comparative analysis and high-fidelity hardware-in-loop emulations demonstrate the superior performance of the proposed controller over traditional ones in diverse dynamic scenarios. Its ability to rapidly reject disturbances, minimize overshoot, converge efficiently, and enhance power quality confirms its effectiveness and highlights its potential for real-world applications.

Distributed Data-Driven Frequency Control in Networked Microgrids via Voltage Regulation

Distributed Data-Driven Frequency Control in Networked Microgrids via Voltage Regulation

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.

Optimized energy management for interconnected networked microgrids: A hybrid NEGCN-PFOA approach with demand response and marginal pricing

This manuscript proposes a hybrid approach for operating cost reduction in networked microgrids. The proposed hybrid system is the combined performance of both the Neighbour Enhanced Graph Convolutional Networks (NEGCN) and the Piranha Foraging Optimization Algorithm (PFOA). Commonly it is named as NEGCN-PFOA technique. The primary goal of the proposed strategy is operating cost minimization in networked micro grids. The NEGCN is used for the demand prediction in networked microgrids and the PFOA is used for the operating cost optimization of networked microgrids. Moreover, the result of the system critic behaviour is researched, where the critic is a function of the control range. By then, the proposed method has been included in the working MATLAB platform, and its execution is calculated using the existing procedures. Compared to all current approaches, the proposed technique yields better outcomes like Deep Reinforcement Learning, Long Short-Term Memory and Deep Learning Artificial Neural Network. The proposed method is useful in reducing the operating cost in the networked micro grids. From the outcome, it is concluded that the proposed technique based costs is less contrasted with existing methods.

Machine learning based classifiers for dynamic and transient disturbance classification in smart microgrid system

The smart microgrid system should have the ability to rapidly detect and classify every type of disturbance that happens in the network to operate the protection scheme and maintain the power quality. Both dynamic and transient types of disturbances are considered in this study, and the classification of each type of disturbance has been done using different machine learning-based techniques. A novel ensemble classifier has been proposed after comparing the performance of the decision tree (DT), k nearest neighbor (KNN), support vector machine (SVM), and ensemble classifier. The performances of all the classifiers have been evaluated in terms of accuracy, sensitivity, confusion matrices, and receiver operating characteristics (ROC). A very satisfactory result has been obtained in the ensemble tree classification technique with 99.3% accuracy in 2.1 s of training time. Therefore, it can be suggested to identify and classify every transient as well as steady-state events of the microgrid network.

Developing a cooperative approach under normal and contingency conditions for generation expansion planning of microgrids

AbstractThe issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario.

A new fault detection and relay coordination technique for low voltage DC microgrid

Integration of various distributed generation units in the DC distribution network creates an outsized impact on magnitude and direction of fault current. The specific behaviour of fault current during a short circuit fault demands quick and reliable detection and isolation of fault, which poses a significant challenge in developing an effective protection and coordination scheme for DC microgrids. In this regard, a new fault detection and relay coordination algorithm is proposed for a Low voltage DC (LVDC) microgrid, which utilizes inverse time relay characteristics based on variance of fault current. While the relay responds and picks up during both internal and external faults, its operation is automatically limited upon operation of the downstream circuit breaker without the need for any communication channel. The efficacy of the proposed strategy is evaluated by modelling a mesh-connected DC microgrid network using PSCAD/EMTDC software. The protection coordination problem is developed as a constraint non-linear optimization problem to determine optimal relay settings. The proposed approach has been tested by simulating various types of faults at different fault locations and fault resistances along with various microgrid operating modes. The results suggest that the proposed strategy stands out by optimizing relay operating time and providing backup protection while upholding relay coordination. A comparative assessment of the proposed technique with existing ones proves its effectiveness in terms of swift relay tripping time, better immunity against noise, and independency against microgrid operating mode and topology.

Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.

Energy management system in networked microgrids: an overview

Energy management system in networked microgrids: an overview

Features of the Method for Synchronizing Active Electrical Networks with Microgrid

Currently, with the increasing degree of decentralization of power system management, small distributed generation is becoming the main source of power for consumers in local power connections. Such networks acquire Microgrid properties and adapt to work both independently and in parallel with an external network. Operating independently, Microgrid networks are able to transfer excess electricity to an external network under certain conditions. Due to the low inertia of power units and the stochasticity of their own loads, connecting Microgrid to an external network can lead to sharp power surges and drops, causing a shock moment on the shaft of generating equipment. The purpose of this paper is to develop and study an algorithm for managing Microgrid synchronization with an external network. A synchronization control algorithm is investigated and proposed, which makes it possible to connect generating units for parallel operation with an external network with minimal impact moment on the shaft. A comparison of the simulation results of the proposed method with the standard one is given. Based on the research data, statistical data were collected and analyzed.

Leveraging media for demand control in an optimal network of renewable microgrids with hydrogen facilities in South Korea

Abstract In pursuit of a sustainable 2030 strategy in the Republic of Korea, this study addresses the oversight in recent optimal renewable energy microgrid designs, which, despite encompassing all feasible renewable sources, neglected the pivotal role of hydrogen as an energy carrier. This research explores the feasibility of reprogramming media platforms to dynamically shape energy consumption during peak intervals. It further proposes the retrofitting of microgrids with industrial hydrogen production and storage facilities, aligning with controlled electricity demand. A comprehensive social survey investigates the impact of media content on energy-conscious behaviour and cooperation, specifically targeting energy savings during peak hours. Utilizing a probabilistic model, the study quantifies responses from the surveyed sample and decomposes the energy demand time series to reveal three new consumption patterns: demand reduction by lowering residential electricity consumption at peak intervals without shifts, intense demand shifting by redistributing electricity consumption from peaks to valleys without human intervention, and moderate demand shifting achieved through cooperation with consumers. With these novel energy demand patterns in hand, the study optimally designs renewable microgrids in 17 sites in South Korea, comparing two strategies: Plan A, involving electrolysis-based hydrogen production and storage tanks, and Plan B, which excludes hydrogen facilities. Comparative results demonstrate that media content contributes to a 10.28% and 16.11% reduction in peak electricity consumption, with and without human intervention, respectively. In Plan B, a demand cut saves 937.3 MWh/yr, resulting in a 12.88% reduction in the levelized costs of electricity (LCOE) and a 4.67% reduction in net present costs (NPC) of optimal renewable microgrids in Korea. Conversely, in Plan A, intense demand reduction exhibits superior performance, leading to $981K less NPC, 1,046 MWh/yr less excess electricity, and a 3.76% smaller LCOE. The study recommends the implementation of smart gadgets to control residential electricity consumption, producing industrial hydrogen at Korean sites based on consumer attention and agreement with specific media content. However, it underscores the importance of studying the socio-psychological effects of this plan in future research.

A Bi-Level Approach for Networked Microgrid Planning Considering Multiple Contingencies and Resilience

A Bi-Level Approach for Networked Microgrid Planning Considering Multiple Contingencies and Resilience

Providing an Intelligent Frequency Control Method in a Microgrid Network in the Presence of Electric Vehicles

Due to the reduction in fossil fuel abundance and the harmful environmental effects of burning them, the renewable resource potentials of microgrid (MG) structures have become highly highly. However, the uncertainty and variability of MGs leads to system frequency deviations in islanded or stand-alone mode. Usually, battery energy storage systems (BESSs) reduce this frequency deviation, despite limitations such as reducing efficiency in the long term and increasing expenses. A suitable solution is to use electric vehicles (EVs) besides BESSs in systems with different energy sources in the microgrid structure. In this field, due to the fast charging and discharging of EVs and the fluctuating character of renewable energy sources, controllers based on the traditional model cannot ensure the stability of MGs. For this purpose, in this research, an ultra-local model (ULM) controller with an extended state observer (ESO) for load frequency control (LFC) of a multi-microgrid (MMG) has been systematically developed. Specifically, a compensating controller based on the single-input interval type fuzzy logic controller (FLC) was used to remove the ESO error and improve the LFC performance. Since the performance of the ULM controller based on SIT2-FLC depends on specific parameters, all of these coefficients were adjusted by an improved harmony search algorithm (IHSA). Simulation and statistical analysis results show that the proposed controller performs well in reducing the frequency fluctuations and power of the system load line and offers a higher level of resistance than conventional controllers in different MG scenarios.

Optimal operation of an electricity-hydrogen DC microgrid with integrated demand response

Uncertainties introduced by the high penetration of renewable sources, plug-in-hybrid-electric vehicle load demand, and limited capacity of firm generation available in a DC microgrid make the energy scheduling task rather challenging. This paper proposes a decentralized energy management scheme with a real-time pricing-based demand response implementation for a DC microgrid, considering the sectoral coupling between electricity and hydrogen energy. The objectives of the scheduling strategy are to maximize the profit of the DC microgrid operator and reduce the cost of energy use by the consumers, considering the interaction and interdependence of the electrical and hydrogen systems with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the upstream grid, microturbines, battery energy storage, hydrogen storage, electrolyzer and fuel cell) and sets real-time prices. The consumers set their consumption patterns according to the real-time price. The DC microgrid operator side flexibilities are coordinated with the consumer side flexibilities (thermostatically controlled load like air-conditioner and plug-in-hybrid electric vehicle) using the decentralized “Alternating Direction Method of Multipliers” approach. The probabilistic Copula theory models correlated input uncertainties. Simulation results on a six-bus DC microgrid test system reveal that the operating cost of the DC microgrid operator reduces by ∼11.06% while the energy use cost of consumers reduces by ∼4.80% using the proposed approach for the system under study.

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.