Resilient Microgrids Research Articles

Market‐clearing framework of a resilient microgrid with renewable energy considering emission reduction targets

AbstractWith the gradual attention of global climate change, a series of carbon emission plans have been formally implemented in the field of energy. There is a consistent relationship between the carbon market and the electricity market in promoting clean energy development and improving carbon dioxide emission reduction targets. The market design of a low‐carbon power system should be considered as a whole to promote the coordinated development of carbon and power markets. Taking an industrial park in China as the research object, an internal market trading model of an industrial park considering carbon dioxide emission reduction targets is proposed. The proposed trading mechanism considers both carbon dioxide emission reduction targets and social welfare maximization. The proposed market trading model connects day‐ahead and intraday electricity markets, which deal with uncertainty about renewable energy sources.

Design, control, reliability, economic and energy management of microgrid: A review

Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids.

Distributed secondary optimal control for self‐maintaining microgrids on pelagic islands

AbstractConsidering the geographic distance, self‐maintaining microgrids are widely built to supply power on pelagic islands, where developing distributed autonomous control strategies while guaranteeing microgrids’ resilience and energy security have gained increasing attention. However, most existing distributed control strategies restore frequencies in an asymptotic manner, which may not meet the requirement for safe and rapid recovery after disasters. Meanwhile, self‐maintaining microgrids have relatively low percentages of fossil fuels. Power sharing among distributed energy sources (DERs) under most existing control relies on the droop coefficients, which may lead to inefficient operations. To overcome these challenges, this paper proposed a distributed control strategy for self‐maintaining microgrids. The frequency restoration time can be explicitly optimized and is robust to changes of system states, which provides faster recovery performance under disasters. The steady‐state powers of different DERs are regulated for different kinds of DERs separately and are independent of the droop coefficients, where the proper use of renewable energy under energy security can be achieved. The proposed control enhances the resilience of microgrids against disasters and meets the requirement of energy security on islands. The algorithms in the paper are verified by both simulation and experiment studies under various conditions.

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.

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

Distributed observer-based resilient control of islanded DC microgrids under actuator faults

In this paper, a distributed observer-based resilient control scheme is proposed to solve the voltage regulation and current sharing problems of islanded direct current (DC) microgrids under actuator faults. First, an observer-based distributed fault estimation design is presented to estimate fault signals with high accuracy. Then, based on the estimated information, a primary decentralized fault-tolerant control layer is designed to ensure accurate voltage reference tracking. A secondary cooperative control layer is employed to achieve the goal of current sharing. Besides, both the fault estimation observers and the fault-tolerant controllers are designed in a plug-and-play fashion, which guarantees the scalability of the DC microgrid. The simulation and experimental results show that the proposed control strategy can efficiently improve the reliability and resilience of the DC microgrid.

Enhancing the economic performance and resilience in a multi-area multi-microgrid system by a decentralized operation model

This paper proposes a multi-area multi-microgrid system (MAMMS) and uses a decentralized operation model to enhance the economic performance of microgrids (MGs) in normal operation mode and their resilience against extreme weather events. The proposed MAMMS includes distantly spaced multi-microgrid systems (MMSs), each has its own local network (LN) and several MGs with independent owners. In this system, MGs participate in the day-ahead intra-regional market to exchange power with neighboring local MGs (NLMGs) and LN. Also, to reduce the prediction error of loads, RESs' outputs, and events' impacts, MGs use a model predictive control-based approach and exchange power with NLMGs for each next hour by participating in the real-time intra-regional market (RTIRM). In the event of an incident, the distribution network operator interconnects the islanded MMSs together and grid-connected MMSs via available tie lines. So, if MGs of MMSs affected by the incident cannot supply their total required power by RTIRM, they can participate in the inter-regional emergency market and buy power from other MMSs' MGs. This market is formed for each next hour and continues until the end of the incident. Numerical results show the effectiveness of this model for enhancing the economic performance and resilience of MGs.

Improve multi-energy supply microgrid resilience using mobile hydrogen trucks based on transportation network

Nowadays, multi-energy supply utility grid system has witnessed the destruction of increasing natural disasters. Under the disasters, the energy supply capability from the utility grid system to the end-user microgrids is decreased, which is due to the destruction of the system infrastructure. Thus, how to improve the resilience of the microgrids under disasters is an essential problem. In this paper, a mobile hydrogen truck-assisted methodology is proposed to deliver hydrogen tanks to end-user microgrids via transportation network to resist to the natural disasters. First, a temporal–spatial destructive model of the natural disasters based on the grid division is presented, and the dynamical energy supply ability of an IEEE30+gas20+heat14 utility grid system is derived. Second, a hydrogen tank delivering model from hydrogen company to microgrids based on transportation network is presented. Third, a real-world transportation network based on SUMO simulator is linked with Matlab to simulate the real-time coupling between transportation network and power network. Last, microgrids optimal operation based on the temporal–spatial destructive model and hydrogen tank delivering model is presented. The simulation results show that with the assistance of the arrived hydrogen tanks through real-world transportation network in microgrid, one can indeed reduce load shedding. However, when considering the damaged transportation network, the saving loads are reduced due to the increase of the mobile hydrogen storage delivery time. It reveals that delivering mobile hydrogen tanks to end-user microgrids can effectively improve the system resilience.

Microgrids Resiliency Enhancement against Natural Catastrophes Based Multiple Cooperation of Water and Energy Hubs

With the ever-growing frequency of natural catastrophe occurrences such as hurricanes, floods, earthquakes, etc., the idea of resilient microgrids (MGs) has attracted more attention than before. Providing the opportunity for a multi-carrier energy supply after a natural catastrophe can lessen power losses and improve power resiliency and reliability. Critical loads within the MG can be prioritized and restored in the shortest possible time based on the condition of the network after the damaging occurrence by considering the energy hub (EH) systems and the optimum design and allocation of these multi-carrier systems. To this end, this paper aims to address the resilience framework in MGs considering sets of water and EHs (WEHs) consisting of CHP (combined heat and power), a boiler, energy storage, and a desalination unit. This study focused on considering an effective resilient scheme to restore critical loads in a short period after a natural catastrophe when the MG experiences an unpredictable event. By applying the idea of WEHs, there would be a chance of restoring the system by using two sets of WEH systems in the appropriate islanded points to restore the system and critical loads of electricity, heat, and water. For this purpose, different scenarios were considered for assessing the resiliency of the system against a natural catastrophic event that causes serious damage to the network by analyzing the energy-not-supplied (ENS) factor. Moreover, the allocated WEHs can adequately supply the electrical, water, and thermal demand loads throughout the day after the natural catastrophe. To mitigate the unforeseen variations in the renewable sources, a battery is located in the WEH, which can attend to the optimal scheduling effectively. A scenario-based method is also introduced to improve the resiliency of MGs in an uncertain environment such as electrical, heat, and water stochastic demands. The appropriate efficiency of the offered model was considered on a modified IEEE test system.

A control with differential power-flow compensating of microgrid under symmetrical fault conditions

The load side failure of an independent microgrid will lead to an imbalance in power supply and demand and voltage drop in the fault area. The use of traditional reactive power compensation to achieve fault traversal in microgrids cannot effectively solve. This article proposes a differential power flow compensation control strategy based on micro source feeder impedance. Utilize micro source redundant power to increase active power output and compensate for the required power flow in the fault area. Calculate the power flow compensation coefficient based on the difference in feeder impedance, and dynamically adjust the reference value of power flow compensation according to the required power in the fault area. By rationalizing the distribution of fault flow, the transient power balance and voltage support in the fault area can be improved. To a certain extent, it enhances the fault resilience of the independent microgrid, and improves the working reliability during fault traversal. At the same time, it reduces the sharp increase in line losses caused by fault currents, and even endangers the security of network structures. The effectiveness of the strategy was verified through simulation and experiments.

Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems

This study presents a novel framework for improving the resilience of microgrids based on the power-to-hydrogen concept and the ability of microgrids to operate independently (i.e., islanded mode). For this purpose, a model is being developed for the resilient operation of microgrids in which the compressed hydrogen produced by power-to-hydrogen systems can either be used to generate electricity through fuel cells or sold to other industries. The model is a bi-objective optimization problem, which minimizes the cost of operation and resilience by (i) reducing the active power exchange with the main grid, (ii) reducing the ohmic power losses, and (iii) increasing the amount of hydrogen stored in the tanks. A solution approach is also developed to deal with the complexity of the bi-objective model, combining a goal programming approach and Generalized Benders Decomposition, due to the mixed-integer nonlinear nature of the optimization problem. The results indicate that the resilience approach, although increasing the operation cost, does not lead to load shedding in the event of main grid failures. The study concludes that integrating distributed power-to-hydrogen systems results in significant benefits, including emission reductions of up to 20 % and cost savings of up to 30 %. Additionally, the integration of the decomposition method improves computational performance by 54 % compared to using commercial solvers within the GAMS software.

A Convex Micro-Grid-Based Optimization Model for Planning of Resilient and Sustainable Distribution Systems Considering Feeders Routing and Siting/Sizing of Substations and DG Units

Integration of distributed generation (DG) and renewable energy resources (DERs) has affected planning and operation of recent distribution networks (DNs). On the other side, resiliency and sustainability of DNs is the other concern of distribution network operators. In this paper, a new formulation is developed for optimal planning of resilient DNs based on optimal formation of resilient micro-grids (MGs). The formulation aims to implement optimal siting and sizing of conventional and renewable-based DGs, optimal routing and type of feeders, as well as optimal sizing and placement of HV/MV substations to configure adequate and resilient MGs against extreme events. The investment, operational, emissions, and resiliency costs have been included in the objective function, and all the problem constraints as well as uncertainty of renewable DGs have been taken into account. The new formulation uses line-flow-based (LFB) model of AC power flow equations, and reserve feeders (tie-lines) are employed to enhance resiliency after severe outages via reconfiguration concept. All the relations have been convexfied in order to compose a mixed-integer quadratically-constrained (MIQCP) model to be solved with global optimum solvers in GAMS. Efficiency of the conducted methodology has been evaluated by different experiments on the 24-node system, and the results are investigated.

Utilization of Battery Electric Buses for the Resiliency of Islanded Microgrids

During a grid outage, microgrids can benefit from energy resources that enhance the reliability and resiliency of serving critical loads and overall protection. The present study develops a methodology to assess the efficacy of battery electric buses (BEBs) as a microgrid resiliency attribute during islanded operations, and applies the methodology to an existing 20 MW-class microgrid equipped with a fleet of 20 BEBs. Each BEB comprises 324 kWh of stored energy and can charge/discharge at 80 kW which translates to 1.6 MW and the potential of 6.48 MWh of stored power and energy respectively depending on the state-of-charge of each bus. For the application evaluated, the methodology reveals that a BEB fleet can serve as a viable resiliency and flexible energy resource for an islanded microgrid by enabling Mobility Services+, examples of which include (1) deployment to serve critical loads within the microgrid (e.g., an emergency control center, shelter, clinic), (2) deployment to serve critical loads outside the microgrid (e.g., a hospital, fire station, grocery store), and (3) service as a blackstart resource should the microgrid prime power generator trip. In the present case, the BEB fleet has the ability to support a microgrid emergency center for at least 16 h, and serve as a blackstart resource with a probability of 100% by a combination of parked BEBs and BEBs recalled from on route transit service.

Microgrids and distribution system resilience assessment: A multi-objective robust-stochastic optimization approach considering social behavior using a deep learning modeling

In this paper, a new multi-objective two-stage robust-stochastic (MOTSRS) optimization approach for assessing microgrids and distribution system resilience is proposed. A novel approach based on deep learning is presented to model social behavior, specifically Social Reactions to Disasters (SRDs), in assessing microgrid and distribution system resilience. It is initially demonstrated that the proposed robust-stochastic approach is efficient for assessing the resilience of microgrids and distribution systems. A new resilience criterion is also proposed to be used for sensitivity analyses at the conservative level of the model. Subsequently, the effect of SRDs on system resilience is investigated through a supervised deep regression method. First, a large dataset is generated through a proposed nested Monte Carlo simulation. A numerical feature and five binary social behavior features are included in the dataset. Next, a feedforward neural network is utilized to predict the load during both normal and disaster circumstances based on the six features. The proposed supervised deep regression model with mean squared errors between 2e-5 and 2e-6 is capable of predicting electrical load during normal and disaster events. It is concluded that the resilience metrics of MGs, as well as expected operation costs and regret values, are influenced by the SRD effect.

An auto-tuned droop coefficient-based controller for microgrid system to enhance grid resilience during blackouts

This paper presents a reliable auto-tuned droop coefficient-based controller (ATDC) for a microgrid (MG) system for enhancing grid resilience during blackouts. Existing power system networks experience blackouts due to inevitable failures such as overloading on lines, failure of a protection system, and imbalance in the power lines. The consequences of blackouts can be resolved by adapting to the concept of resilient MGs with high dynamic stability for power restoration. However, impedance parameters of power lines will change during blackouts, this eventually affects system's performance. Therefore, synchronization of MG sources to the utility grid and providing power supply to the critical loads during power system blackouts are the key factors to be resolved by maintaining system within permissible limits. The proposed ATDC-based controller adapts to changes in system parameters and auto-tunes the droop coefficients to provide proper control coordination between the each of distributed generator (DG) in MG. The proposed ATDC-based controller is designed in silico, and its effectiveness is tested under various conditions of MG operations. Results are compared with conventional and improved droop controller strategies. The test results demonstrate that the proposed controller maintains grid supply frequency within IEEE-1547 standards while ensuring appropriate power-sharing between load and MG sources.

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles).

Blockchain Protocol-Based Secondary Predictive Secure Control for Voltage Restoration and Current Sharing of DC Microgrids

DC microgrids with distributed architectures inevitably encounter the threats of communication constraints and cyber attacks, and there is little research to address these two communication irregularities simultaneously. To provide DC microgrids with maximum resistance to communication constraints and cyber attacks, a distributed predictive secure control method based on blockchain protocol is proposed in this paper. The proposed control approach is the first attempt to address cyber attacks with a hybrid of data and models, improving the resilience and robustness of DC microgrids. With the help of practical Byzantine ideas, the proposed strategy preserves the resilience strengths of the blockchain against corruption and discards its shortcomings of low real-time performance, significantly enhancing the security without compromising the dynamic performance of the system by active intervention. Subsequently, a bound on the communication delay induced by the blockchain network under the expected security index is given based on the theoretical analysis. Unlike the existing practice of passively tolerating communication delays and packet dropouts, the predictive control method proposed in this paper actively compensates for them experienced by DC microgrids. Then, an analytical model of the closed-loop DC microgrid system is developed, and stability analysis is presented accordingly. Finally, several experimental tests performed on a PV-based DC microgrid hardware system are presented to demonstrate the effectiveness of the proposed control strategy.

Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: An efficiency comparison of state-of-the-art metaheuristics

The resilient microgrid (MG) capacity planning and optimisation problem is widely recognised as a non-deterministic polynomial time-hard (NP-hard) problem. Accordingly, metaheuristics – top-level algorithms inspired by various natural and physical processes – can be utilised to determine the near optimality in designing MGs. However, a comprehensive review of the mainstream literature has shown that the performance of several metaheuristics has not yet been evaluated. In response, this paper first systematically benchmarks the efficiencies of previously unexplored metaheuristics in MG sizing applications against the well-established metaheuristic in the literature, namely the particle swarm optimisation (PSO) algorithm. To this end, the metaheuristics are separately integrated into a novel MG sizing method, which is aware of the optimal demand response capacity procured from electric vehicle (EV)-charging loads. Two grid-independent, 100%-renewable MGs are modelled, which enable the reliable and robust supply of electrical loads in areas far removed from the grid. Furthermore, an advanced EV-charging demand response program is integrated into the overall method, whilst quantifying various sources of time-series data uncertainty and considering specific resilience constraints. The simulation results yielded from three real-world isolated community case studies in Aotearoa-New Zealand confirm the effectiveness of the proposed stochastic, resilience-oriented, EV-charging demand response-addressable MG sizing method. Importantly, the comprehensive statistics-based performance evaluations indicate that new metaheuristics have the potential to outperform the PSO by up to ∼6% in MG sizing applications. This indicates the potentially significant implications of using advanced metaheuristics for improving the economics – and, therefore, rolling out – capital-intensive grid-isolated 100%-renewable MGs.

Multi-Segment Decentralized Control Strategies for Renewables-Rich Microgrids in Extreme Conditions

Microgrids provide a promising approach to accommodating various distributed energy resources (DERs), while requiring significant communication infrastructures that may be affected by extreme conditions such as natural disasters and cyber-attacks. In this paper, a fully decentralized control strategy without the need for communication is proposed for islanded microgrids with high renewables penetration. First, special multi-segment power/frequency characteristic curves are designed, so that different DERs can be automatically coordinated in a prioritized manner such as renewables first to maintain power balance while DER frequencies are regulated at their reference values. Second, piecewise linear served load versus frequency models are designed to prioritize loads according to their significance, so that only noncritical loads will be curtailed as needed while critical loads are supplied without any interruptions during power deficiency. The proposed strategy can effectively deal with various normal and extreme system conditions including 100% renewables penetration, loads and renewables variations, power deficiencies requiring load curtailments, disconnection of existing DERs, connection of new DERs as well as network sectionalization and reconfiguration. The proposed control strategy is validated in the real-time digital simulator (RTDS) model of the IIT Campus Microgrid to demonstrate its effectiveness in enhancing the resiliency of renewables-rich microgrids in extreme conditions.

Autonomous Restoration of Networked Microgrids Using Communication-Free Smart Sensing and Protection Units

This paper presents autonomous restoration of networked microgrids using distributed energy resources (DERs), inverter-based resources (IBRs) in general, and controllable assets of intelligence without communication to achieve a extremely resilient power system restoration. The main innovation of this work lies in use of real-time smart sensing and decision making that enable autonomous recombination of islanded microgrids, which are individually black-started by local grid-forming (GFM) inverter units. The smart circuit breakers (SCBs) designed and demonstrated here play critical roles in the restoration process to achieve autonomy; the autonomous restoration scheme is not pre-engineered and may be subject to encounter overloading, live or downed segments as the boundary of the microgrids dynamically expands without prior knowledge and only with local measurement. We demonstrate autonomous microgrid restoration both in simulation of a modified IEEE 13-bus system, and in a hardware testbed comprising 24 GFM-IBRs and 6-SCBs, depicting several scenarios to evaluate their functionalities and interoperability. This work validates high potential of extremely resilient microgrid using novel approaches for inverter-based bottom-up system restoration in a communication-free paradigm.