Abstract

A novel time–current-rate-based inverse characteristic curve for relays in a DC microgrid is proposed in this paper. Line current rise rate is used as actuating quantity, ensuring quick line fault clearing (relay operating time is in order of a few . The advantage of using line current rise rate as actuating quantity is that for a line short-circuit fault, it does not vary significantly for grid-connected and islanded modes of operation and varying network topologies of DC microgrid. Consequently, using the proposed characteristic, a single set of optimal relay settings is obtained using an optimization solver in MATLAB. The obtained settings ensure reliable and selective coordination between primary and backup relays for various pole-to-ground and pole-to-pole line faults under different operating conditions with multiple sources in two different 4-bus 400V low voltage DC microgrids. The maximum relay operating time for a high resistance fault with the proposed curve is for the first considered low voltage DC microgrid. Simulations in the Real Time Digital Simulator and comparisons with previous schemes (one comparison on the second considered DC microgrid), conventional standard inverse, and extremely inverse curves for DC microgrid protection indicate the effectiveness of the proposed scheme.

Introduction

The ease of integration of solar Photo-Voltaic (PV) and Battery Energy Storage Systems (BESSs) based renewable energy resources in a DC microgrid, compared to an AC microgrid, has led to the increased expansion and research interest of DC microgrids over AC microgrids [1], [2]. Also, DC microgrid can be a great way to power remote towns, shipboards, spaceships, and grids with sensitive loads, where the quality of power is crucial [3], [4]. However, the rate of rise of current is very high in a DC microgrid during a line fault due to the rapid discharge of the DC link capacitor and the associated low line impedance [5]. Hence, in general, the DC microgrid protection schemes follow a three-time frame based protection methodology in which the protection of the line, feeder, and the source should be done within a few milliseconds, and seconds, respectively [6]. Several DC Circuit Breakers (CBs) have been developed to support the quick isolation of lines during a fault in a DC microgrid, among which the solid-state CBs have a response time in the range of a few

Conclusion

This paper proposes a novel inverse time relay characteristic for the protection of DC microgrids against line faults using line current rise rate as an actuating quantity. It can coordinate relays in a DC microgrid with non-unit fault detection schemes or act as a backup scheme for relays with communication-based primary method. The relay coordination using the proposed characteristic is tested on a V LVDC microgrid. A single set of optimal relay settings ( and ) and a common set of values of and in (8) are obtained, considering all possible modes of operation, network configurations, and worst-case PG, PP LRFs and PG HRFs. Consequently, the proposed approach does not require an online update of and with a change in operating condition of DC microgrid. The efficacy of the proposed curve is validated with the obtained optimal unique relay settings for various PG, PP LRFs, PG HRFs for GC, islanded modes of operation, ring, radial network configurations, and intermittency of generation from renewable energy sources. Zero relay miscoordinations with a maximum primary relay operating time with the proposed curve are and for LRF and HRF faults, respectively, proving the effectiveness of the proposed technique in LVDC microgrid.

Further, the issues of using SI and EI curves to coordinate relays in a LVDC microgrid using line current and line current rise rate as actuating quantity and issues with using line current as actuating quantity in proposed scheme are discussed. It is observed that the conventional EI curve with line current rise rate may be used for relay coordination in DC microgrid. However, the high relay operating time (maximum primary relay operating time being ) may lead to prolonged line fault isolation. Additionally, the proposed scheme is fast and reliable, compared to EI curves, since the sum of primary relay operating times, considering all fault scenarios, is with the proposed curve, compared to the EI curve, which gives . The comparison with [16], [17] reveals the efficacy of the proposed scheme with the relay operating times in the proposed scheme being in the range of a few for line faults in LVDC microgrid.

Abstract

Application of micro-grids (MGs) as a solution for future energy systems are significantly increasing in recent years. On the other hand, more stringent requirements are added to grid codes (GCs) of low-voltage distribution networks. Accordingly, this paper proposes a low-voltage ride-through (LVRT) control scheme for four-wire multi-source MGs. The novel strategy is composed of two layers for controlling each phase of the grid-connected sources independently. The primary layer contains a reverse-droop function for each phase and fractional-order sliding-mode-control (FOSMC). The secondary layer determines the total requested reactive power for each phase during symmetrical and asymmetrical voltage drops. Then, the mentioned power is shared among the phases of each inverter based on the reactive power-sharing strategy and GC requirements. Based on the proposed grid-following controller, the voltage of faulty phases is compensated by reactive power injection. In addition, power quality indexes are kept in acceptable ranges during abnormalities, and the FOSMC performs better than the conventional methods. The effectiveness of the proposed scheme is verified through offline simulations in MATLAB/Simulink as well as validation by real-time results.

Introduction

1.1. Background
The utilization of small-scale distributed energy resources (DERs) in low and medium-voltage networks is increasing, and as a result, their sudden and collective disconnection from the power system might cause voltage collapse on the grid during severe voltage perturbations. Therefore, the severe impact of the DERs cannot be ignored since their contribution to the power supply is considerable. Thus, the incremental usage of DER systems has become leverage for setting new requirements for power systems. These regulations are called grid codes (GCs), which vary from country to country and cover some grid-connected power sources such as wind turbines and photovoltaic (PV) systems [1]. The mentioned regulations force the sources to stay connected to the main grid during temporary voltage sags and support it by injecting reactive current. An essential requirement of the GC, which has gained much attraction in recent studies, is the low-voltage ride-through (LVRT) capability of the distributed generators (DGs). LVRT curves of the German and Danish GCs are highlighted in Fig. 1a [2]. Besides, in Fig. 1b amount of the reactive power injection per voltage drop is depicted for different scenarios [3]. Most studies cover symmetrical voltage disturbances that only include a minor percentage of the faults such as [4,5], etc. However, asymmetrical faults such as single-phase short-circuits are more probable with an 85 % occurrence rate [6]. In most cases, the conventional three-phase control methods for compensating voltage drops are not applicable for asymmetrical faults since they might affect the operation of safe phases by deteriorating voltage imbalance. Also, reactive power-sharing and reference power tracking in multi-source MGs are important operation factors, which has not been addressed sufficiently in previous studies.

Conclusion

MGs integration into power systems has boosted the system’s reliability and caused operational challenges for the network operators. Hence, some countries have considered a set of requirements to improve the functionality of their power systems. For this purpose, in this paper, an LVRT scheme is proposed for four-wire MGs, which can control each phase of the DGs independently through a grid-following method. The local controller of the DGs, known as the primary layer, utilizes reverse-droop control for voltage and frequency regulation and a nonlinear FOSMC. On the other hand, the secondary layer fulfills the modern GC requirements by calculating the required reactive power for the faulty phases. Next, this amount is shared between the corresponding phases of the DGs based on their free capacity. The effectiveness of the presented controller is proved by analyzing the results obtained from implementing 30 % single-phase, two-phase, and three-phase voltage sags. Accordingly, each DG phase injects reactive power during voltage sags according to the determined amount. Also, comparing the injected reactive power with the amounts of German GC demonstrates that the reactive power injection is done with acceptable accuracy. The results point out that during the external sags, the voltage of PCC, inverter terminals, and load terminals are restored to their nominal values, 30 % compensation, as if there is no perturbation. Consequently, the presented controller can realize the GC requirements and ride through during severe voltage conditions. Moreover, this control method keeps the power quality indices like THD and voltage imbalance factors in acceptable ranges during the sags. Also, the superiority of designed FOSMC over conventional current controllers such as PI and SMC are demonstrated. This outperformance is highlighted with several aspects such as MPAE index, THD values, voltage compensation, and reactive power tracking during the imposed voltage sags. It is shown that the best value for the alpha is 0.76, by which the response of the controller is fast and accurate. Finally, the simulations are validated through real-time results. Our future work is focused on the LVRT capability of the MGs including both grid-forming and grid-following inverters. In addition, the effect of grid strength and R/X ratio of the power lines can be discussed in the future investigations, which has not been addressed sufficiently in this literature.

Abstract

1. Introduction

2. PUC inverters

3. Microgrid

4. proportional–integral (PI) control of PUC inverters

5. MPC of PUC inverters

6. Simulation results

7. Hardware-in-loop (HIL) validations

8. Conclusions

Declaration of competing interest

References

Abstract

     In this paper, an outline of microgrid arrangements and control methods at various hierarchical levels of Packed U-Cell (PUC) converters are provided. The paper discusses the control of these topologies using various techniques. The goal of these control strategies is to maintain a small THD, superior steady-state, fast-dynamic response, and high-power factor while balancing capacitor voltages under different operating conditions. The PI current controller is modelled on five and seven-level PUC inverters. The PI voltage and current controllers have also been modelled on a seven-level PUC inverter. Thereafter, model predictive control of five, seven and fifteen level inverters are also formulated and then simulated using MATLAB/Simulink. Finally, HIL validation of different PUC inverters is performed.

Introduction

     The electrical power generated from renewable energy resources (solar, wind and hydro, etc.) are either used for domestic purposes directly or they are supplied to the grid [1]. For example, the power generated by a solar plant is of dc nature which is first conditioned and then inverted to get an alternating output. This ac power is then synchronized with the grid according to the grid’s characteristics (frequency and phase angles) [2]. In each of the steps, there are many parameters to be controlled in an accurate manner so that power quality is maintained. For this purpose, the most suitable consideration is the multi-level family converters.

     Due to improved power quality compared to their two-level counterparts in recent times, multilevel inverter (MLI) topology has become increasingly accepted in commercial applications. The reduced harmonic deformation, improved waveform like a sinusoidal waveform and enhanced use of switches due to diminished voltage stress. MLI have their uses in various fields of renewable energy sources including, HVDC, distributed generation (DG) system, application of industrial drive systems, uninterruptible power supplies, etc. [3], [4], [5], [6]. Based on a few semiconductor devices coupled with different dc links, MLIs generate sinusoidal waveforms at their outputs through staircase waveforms. MLI has traditionally been used in three major topologies in commercial applications within the last few decades: Cascade H-Bridge (CHB) inverters, Flying Capacitor (FC) inverters and Neutral Point Clamped (NPC) inverters. Each MLI topology, however, have some drawbacks, such as the increasing count of source necessities, the balancing of capacitor voltage, and the high necessity for switches counting in the CHB, FC, and NPC network topologies, respectively [3].

Conclusions

     The main purpose of this work is to present that MPC can be a better alternative to the classical PI control when applying such control techniques on different PUC topologies. Although MPC is known for its limitations in terms of the steady-state error, MPC can be one of the greatest alternatives to the classical PI due to the simplicity of designing this control, the ability to achieve multiple objectives, and the high accuracy in transient conditions.

     In this paper, MPC was successfully applied on five, seven and fifteen level PUC inverters and compared with PI control when applied on the same topologies. Although the converters’ voltages usually have higher THD when MPC is applied, the easy implementation of MPC and the capability to control more than one control variables are the some of the advantages which increase the chances of using MPC in such applications.

     In this work, using MPC, the capacitor voltages of the different PUC inverters were well regulated to follow their reference values using a single controller. However, in order to achieve similar performance using the classical PI control, two control loops, voltage and current control loops, were required to be designed which significantly increase the complexity of this control.

Highlights

Abstract

Keywords

1. Introduction

2. Cyber–physical DC microgrids

3. Attack detection design

4. Performance validation

5. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Appendix.

References

Abstract

DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.

1. Introduction

DC microgrids (MGs), known as next-generation power systems, have received significant attention in recent years because of their ability to transmit power from renewable energy sources and energy storage devices to various loads with greater efficiency and reliability than the AC grid. As a distributed power supply, the DC MG can be operated independently or connected to the utility grid. Such applications can be found in power generations [1], smart houses [2], transportation systems [3], etc.

Due to the rapid developments of the Industry 4.0 paradigm, information technology-based solutions have been widely used in industrial processes. The revolutionary changes have seen the emergence of cyber–physical systems where large amounts of data are exchanged between multiple devices in real time [4]. Accordingly, the framework of DC MG tends to be more distributed, intelligent and tightly integrated with the network. However, due to the strong reliance on the communication technologies, DC MGs are more vulnerable to security threats [5] and have a higher risk of being compromised by malicious attackers.

In general, the functionality of a potential microgrid controller relies heavily on the reliability of the data received by the measurement devices or sensors. For example, if the sensors or communication links are compromised by an attacker, the controllers may receive faulty data and therefore make inappropriate control decisions [6], leading to the undesirable power-sharing [7], frequency oscillating [8] and stability issues [9]. As a result, renewable energy generating units may not be able to produce the maximum amount of available power from nature or meet the appropriate power sharing between microgrids, and energy storage devices may not be able to provide the required amount of power or operate with optimal economic dispatch [10]. More seriously, attackers may be able to disrupt the system without adequate security protection in terms of hardware or software policies, leading to significant social losses. Examples include the nuclear facility struck by Stuxnet malware [11], power outage event [12], [13] and nuclear plant blackout accident [14]. Considering the huge impact of attacks on microgrid systems, it is vital to provide an effective detection scheme to counter cyber attacks.

Highlights

Abstract

Keywords

Abbreviations

1. Introduction

2. Microgrid model

3. Description of the proposed multi-functional algorithm

4. Simulation results

5. Comparison with other existing techniques

6. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

References

Abstract

A large part of the current microgrids (MGs) operate in grid-connected mode and act as slaves following the voltage and frequency dictated by the main grid. Therefore, these MGs are not expected to be operated in parallel as islands. Hence, according to the IEEE 1457-2018 standard, the power generation units in these grid-connected MGs (GCMGs) have to be disconnected in less than 2 s if islanding occurs. On the contrary, if the islanding operation (IO) does not occur, these MGs exchange power with the electrical grid. This paper explores the feasibility of a multi-functional algorithm (MA) for these GCMGs in an attempt to address three tasks simultaneously; (i) power sharing, (ii) voltage support and, (iii) islanding detection (ID). The MG object of study comprises an electronically interfaced photovoltaic (PV) unit supplemented by a battery energy storage system (BESS) equipped in combination with a bidirectional charger. The model of the GCMG has been implemented in MATLAB, where an extensive set of simulations has been performed. The results demonstrate the effectiveness and benefits of the proposed MA, which fulfils several functions all at once.

1. Introduction

Conceptually, MGs are self-sufficient entities formed by an aggregation of generation units, energy storage systems and a set of controllable loads [1]. These MGs can be either connected to the grid (i.e., the abovementioned GCMGs) or operating in stand-alone mode [2]. Even though these GCMGs can be powered by different technologies (Wind, PV, mini-hydro, etc.), rooftop PV units are becoming very popular in residential and industrial applications due to their multiple benefits. Even though these GCMGs require the main grid, if both generation and storage devices are properly coordinated and sized, the amount of energy exchanged with the grid can be significantly reduced. Therefore, these GCMGs are becoming less grid-dependents in terms of exchanged energy.

In this regard, multi-functional algorithms are gaining momentum in MGs. For example, a fully distributed MA control has been proposed in [3], while references [4], [5], [6] used MAs for optimal MG management. If the peak power is shaved and the energy supplied by the grid is curtailed, this will imply a reduction in the electricity cost [7], [8]. Undoubtedly, this aspect is crucial for these MGs; for instance, see the techno-economic analysis carried out in [9].

Albeit these MGs offer several advantages for the users and the DNs (e.g., losses reduction and voltage support) [10], [11], some issues may occur due to their inherent intermittency. In particular, voltage regulation is among the most concerning issues for the distributor operators (DSOs). Although these PV-based distributed generations (DGs) are commonly operated at unity power factor, some grid codes require reactive power support by the voltage source converters (VSCs). Therefore, optimal Volt/VAR strategies have been investigated for such purposes, see [12]. Presently, the electronically-interfaced grid-connected generation units which provide grid support are also known as grid-following VSCs, see [13].

Abstract

1. Introduction

2. Microgrid Control

3. Overview of AI framework for microgrid control

4. AI in Hierarchical control – review and possible improvement

5. Networked microgrid

6. Discussion and future scopes

7. Conclusion

Acknowledgement

References

Abstract

     Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and forming essential consumer/prosumer centric integrated energy systems. Integration, coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task. Classical control techniques are not enough to support dynamic microgrid environments. Implementation of Artificial Intelligence (AI) techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks. Therefore, this paper briefly reviews the control architectures, existing conventional controlling techniques, their drawbacks, the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers. This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments. It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.

Introduction

     Microgrids can be distinguished from any distribution network containing DERs by two distinct features. First, their capabilities to operate in an islanded mode confirms the resiliency and reliability of the network. Second, to appear as controlled and coordinated units viewing from the upstream network [1]. Microgrids provide noteworthy benefits to consumers as well as utilities, the majority of which include; higher reliability by incorporating flexibility at the community layer distribution network, improved power quality by managing flexible loads, reduced carbon emission by the diverse DERs, lowering transmission & distribution losses, cheap energy supply utilising more renewable resources, and the possibility of active participation in the energy markets [2], and must fully satisfy the network and load requirements at islanded mode [3].

     Taking into consideration the large share of stochastic natured DERs comes with lots of uncertainty and a range of instances where effective controlling of the microgrid network becomes very critical. Interconnecting multiple microgrids as a network of microgrids can be an effective solution to accommodate and improve the operation quality of the large number of DERs. It has also been recognised that when multiple microgrids, geographically close to each other, are tied together through the distribution line to form a networked microgrid, the reliability and resilience of the interconnected microgrid can be significantly increased [4, 5].

Conclusion

     This paper provides an overall review of AI-based control in microgrid environments. An overview of existing traditional control methods, their drawbacks, the need for AI techniques and their implementation at the different levels have been reviewed and future scopes have been presented. Despite system model complexities and challenges, it is found that AI can certainly be an important tool to enable the seamless integration and control of DERs at the local and networked levels. NN-based models are getting more focus on all levels. Most of the implemented AI techniques are physical model-based, whereas data-driven techniques should also gain more interest. Since a data-driven model doesn’t require extensive physical system information, the design becomes less complex. However, in some specific applications like inertia estimation in the primary control, there is a lack of data available at the low voltage distribution network level. As ESS is becoming a core part of decarbonising the smart and microgrid networks, the datasets for ESS SoC levels which are accessible and open to the public require extensive preprocessing to improve the data quality and better predictability.

Abstract

     The main approach used to model uncertainties in microgrid planning is the Probabilistic Power Flow (PPF). However, this technique has a high computational cost due to the need to solve a system of nonlinear equations for different scenarios of microgrid operation. This paper aims to propose low-cost computational power flow algorithms to evaluate nodal voltages in islanded Direct Current (DC) microgrids under uncertainty. An approximated power flow is proposed based on the Admittance Summation Method for radial microgrids. In addition, iterative power flow algorithms, previously developed by the authors for Alternating Current (AC) microgrids, have been adapted for DC microgrids. The proposed iterative and approximated algorithms were combined with Monte Carlo Simulation to obtain a PPF method. The proposed methods were tested and validated in relation to the Newton-Raphson Method in DC radial microgrids with 33 and 906 nodes and in DC meshed microgrids with 33 e 144 nodes. The results showed that the developed methods have good accuracy and obtain considerable saving in the computational cost of the PPF.

Introduction

     A microgrid can be described as a cluster of loads and generators that can operate in an interconnected or islanded way from the electrical distribution network [1, 2]. The islanded operation occurs when there is a disturbance in the utility’s system and the microgrid is automatically disconnected from the Common Coupling Point (CCP). During the islanded operation, the microgrid loads are supplied by their own native generation. Therefore, it is expected that the reliability of the microgrid will be improved.

     An important aspect that has gained importance in the design of microgrids is the application of Direct Current (DC) microgrids and hybrid microgrids (DC and Alternating Current (AC)) [3, 4]. The use of DC voltage is motivated by the following facts: (i) increased DC loads (Light-Emitting Diode (LED) lamps, computers, printers, etc.); (ii) DC renewable Distributed Generation (DG) (solar photovoltaic and fuel cells); (iii) insertion of battery energy storage systems to increase the use of renewable DG. The DC microgrids can offer the following advantages when compared with AC microgrids [5],[6]: (i) minimization of conversion losses; (ii) greater power transfer capacity; (iii) elimination of the need for frequency synchronism. At this point, it is important to mention that the algorithms proposed in this paper are oriented towards DC microgrids, that is, hybrid AC-DC microgrids are not considered.

Conclusions

     This paper presented approximate and iterative power flow algorithms for islanded Direct Current microgrids with radial and meshed topologies. These algorithms were developed based on the combination of the following techniques: Admittance Summation Method, Current Summation Method, Gauss-Zbus Method, Modified Augmented Nodal Analysis and Superposition Principle. The proposed algorithms were embedded in a Probabilistic Power Flow, based on Monte Carlo Simulation, in order to demonstrate that these algorithms are suitable for planning studies under uncertainties. The tests results with large scale microgrids demonstrate that the proposed algorithms have good accuracy in relation to the Newton-Raphson Method in the evaluation of the voltage profile and losses. In addition, the proposed methods achieve significant reductions in the computational cost associated with the Probabilistic Power Flow. Future work associated with the proposed power flow algorithms is oriented to towards expanding these algorithms to model the following aspects related to DC microgrids: secondary control, voltage unbalance in bipolar configuration and networked (interconnected) microgrids.

1- Introduction

2- Preliminaries

3- DC microgrid model

4- Battery scheduling by optimization-based control

5- Simulation results

6- Comparison with other models and control strategies

7- Conclusion

References

Abstract

This paper considers a discrete-time scheduling method for the power balancing of a continuous-time DC microgrid system. A high-order dynamics and a resistor network are used for modelling the electrical storage unit and the DC bus of the centralized microgrid system, respectively. A PH (Port-Hamiltonian) formulation on graphs is employed to explicitly describe the microgrid topology. This modelling approach allows us to derive a discrete-time model which preserves the power and energy balance of the physical system. Next, a constrained economic MPC (Model Predictive Control) using the proposed control model is formulated for efficiently managing the microgrid operation. The systematic combination of the network modelling method and optimization-based control allows us to generate the appropriate power profiles. Finally, the benefits of the proposed approach are validated through simulation and comparison results over a particular DC microgrid elevator system under different scenarios and using real numerical data.

Introduction

Nowadays, industrial and research communities are concentrating their attention to microgrid systems and their development for improving the energy reliability of the classical power grid. A microgrid is represented by a group of interconnected customer loads and Distributed Energy Resources (DER) within clearly defined electrical boundaries which acts as a single controllable entity that can connect and disconnect from the grid (known as “islanding”) [1]. DERs are small power sources that can be aggregated to provide the power necessary to meet (part of) a regular demand. It includes the distributed energy storage and generation systems. The distributed energy generation systems are integrated to the local system to reduce the impact on the environment of the fossil fuel resources. However, the electricity price of the external grid varies during a day. It may be expensive when the energy demand is high. Moreover, the power supplied by the distributed energy generation system is unstable [2,3]. Consequently, the distributed energy storage system is used to store energy when it is available and cheap. Then, it is reused in the contrary case. In microgrids, DERs are connected to the load systems through converters to satisfy the energy demand [4,5].

This paper proposes a novel energy management system (EMS) for an isolated structure of networked microgrids (NMGs). The interconnected microgrids consist of the cyber-physical connections for information and power exchanges. A bi-level EMS is presented in which the outer-level EMS is aimed to exchange the required information and power between the interconnected microgrids, and the inner-level EMS is intended for energy scheduling of each on-fault microgrid in case of separation from other microgrids. This paper focuses on the operation of interconnected microgrids. A step-wise demand response program (DRP) is also considered in the energy management to attain the cost-effective operation. Furthermore, a new pricing model based on microgrid marginal pricing (MGMP) is introduced for the power exchanges between the interconnected microgrids. To cope with the uncertainties of the renewable energy sources and loads, some scenarios are generated using the scenario-based analysis. Also, a backward scenario reduction method is used to reduce the number of the scenarios. Besides, a mixed integer linear programming (MILP) is applied to the stochastic optimization problem of the NMGs. The proposed model is implemented on a test system with five NMGs. The simulation is run over a 24- hour scheduling time horizon. Both cases without and with demand response program (DRP) are compared in the numerical results. The results of the simulation demonstrate that using the proposed DRP in the energy management increases the performance of the generation units and decreases the total operational cost of the proposed NMGs. Also, the voltages of the buses converge to their rated values.

Introduction

Environmental concerns and rising energy consumption today increase the usage of distributed energy resources, energy storages (ESs) and demand response programs (DRP). Besides, renewable energy sources (RES) play an important role in clean energy generation [1]. Although controlling many these resources presents difficult challenges in the safe and efficient operation of the network, these challenges are managed by the concept of microgrid [2]. Microgrid is a distribution system consisting of distributed generations (DGs), ESs and responsive loads. Indeed, microgrids are operated as an interconnected or isolated network. From the upstream grid perspective, the microgrid is a controllable system that is operated as a controllable load or a power source. In grid-connected mode, the microgrids send or receive power from the main grid and other microgrids in the system. However, factors such as major disruption to the main grid, decreasing the power quality of the main grid according to the certain standards, or maintenance programs cause to isolate the microgrid from the main grid [3–5]. Given the uncertainties in DER, the availability of ESs and responsive loads along with RES-based DGs is a proper solution for achieving higher reliability and ensuring balance between generation and load demand in the microgrids. This issue not only delays the increase of the generation capacity, but also reduces system operating costs and greenhouse gas emissions. Furthermore, deploying a microgrid can facilitate the implementation of the efficient DRP [6,7].

1- Introduction

2- Modeling of a radial MMG system oriented to performance sharing

3- A BN-based reliability evaluation method for the radial MMG systems

4- The system modeling and BN parameter modeling

5- Case study

6- Conclusion

References

Abstract

The multi-microgrid (MMG) system is studied as a rudiment of the smart grid, and the radial MMG system is regarded as the comprehensive and typical one. The reliability evaluation of MMG systems are widely discussed to ensure its reliable and steady operation. The power balance between supply and demand is the core criterion for the reliability evaluation of MMG systems, which will be broken by unexpected events or inherent limits, such as the failure of equipment and the insufficient transmission capacity of distribution network (DN). In existing research, few have established a reliability model considering partial failure and imperfect repair of the equipment, and the DN transmission capacity usually is ignored. In this study, we abstract the radial MMG system as a performance-sharing system and present a Bayesian-network-based (BN-based) unified modeling method for performance and reliability to solve the deficiencies in existing research. First, the operation of radial MMG systems is analyzed to establish an abstract model to simplify the problem. Afterwards, the executive program of the BN-based reliability evaluation method is given. Further, the system modeling and the BN parameter modeling are introduced. Finally, a radial MMG system including 9 microgrids (MGs) is studied as a case, the variation of the system reliability index in grid-connected and island modes is analyzed. The results show that the method supports the reliability evaluation and analysis of radial MMG systems considering unexpected events and inherent limits.

Introduction

The concept of an MMG is developed on the EU’s “More Microgrid” program proposed in 2006 (Xu et al., 2018), which forms at the medium-voltage (MV) level, and consists of several low-voltage (LV) MGs and distributed generation (DG) units connected to adjacent MV feeders (Gil & Lopes, 2007). With the popularization of renewable energy power generation, an increasing number of neighboring MGs interconnected to the DN will form a multilevel (and high-voltage) radial MMG system in a wide area (Madureira et al., 2011), which is more typical representative of the future grid. The radial MMG systems are regarded as the rudiment of the smart grid, which have the complex architecture, energy dispatch and failure modes. Compared to traditional power systems, the reliability evaluation of MMG systems is more difficult due to its more complex structure and operating modes. MG is an independently operating unit in an MMG system, and there are many reliability evaluation methods of MG. The typical characteristics of the MG, such as uncertainties, energy storage, and multiple operating modes, are taken into account in existing methods (Bae & Kim, 2008; Bai, Miao, Zhang, & Bai, 2015; Conti, Nicolosi, & Rizzo, 2012; Conti, Rizzo, El-Saadany, Essam, & Atwa, 2014; Costa & Matos, 2009; Moslehi & Kumar, 2010); which are the foundation of MMG reliability evaluation. The MMG reliability evaluation method has the following development trend: first, it only focused on the power balance between supply and demand, for example, Nikmehr and Najafi-Ravadanegh (2015, 2016) optimized power dispatch of an MMG system constructed by three interconnected MGs and evaluated the satisfaction of loads demands. However, equipment failure and maintenance will affect system reliability seriously, which is gradually taken into account in the latter methods. In general, the equipment has some intermediate states in addition to the states of “normal” and “failure”. For example, in a DG consisting of multiple diesel units, if one unit fails while the others generate power properly, the system is in the state of derating operation, which is an intermediate state. This state, which was ignored completely in the existing reliability model of the MMG, is called partial failure.

Introduction

System-wide benefits

The benefits and costs of microgrids

Israel electricity sector

Methodology and data sources

Summary of findings and their relation to prior studies

Recommendations for microgrid policymaking

Conclusion and directions for further research

References

Abstract

This paper addresses the costs and benefits associated with microgrid development relative to the costs and benefits of conventional generation interconnected to a bulk transmission and distribution grid. The costs and benefits are classified as: environmental (avoided environmental damage costs); economic (mainly employment multiplier effects); deferral or avoidance of transmission and distribution investment costs; and greater access to electricity supply that is highly reliable and resilient. Deficiencies due to the lack of relevant available data and of research on economic modeling of microgrids at the societal level are discussed. The context in which these costs and benefits are measured is the Israeli electricity market, which features a highly centralized, vertically integrated electricity company (Israel Electric Corporation, IEC) with some legacy distribution companies. Moreover, because Israel’s transmission and distribution infrastructure investment have declined significantly over the past several years, the Israeli market provides a useful basis for analyses of microgrids as an alternative to such large-scale investments.
The analysis reveals that under reasonable assumptions reflecting the current state of microgrid technologies, microgrids may constitute a viable, cost-effective alternative to additional central- station generation requiring new investments in transmission and distribution infrastructure. Specifically, using reasonable assumptions regarding 10-MW incremental investments in a microgrid and in central-station generation with necessary transmission and distribution investments, the analysis indicates that, when considering the reliability, T&D investment deferral, local economic, environmental, and social costs and benefits of each alternative, the net benefits to the Israeli economy from selecting the incremental 10-MW investment in a representative Israeli microgrid may exceed $13,000,000 per year. However, when local economic benefits are not considered, the net benefits decline to approximately $260,000 per year. For perspective, generation capacity additions by the Israel Electric Corporation have averaged 166 MW from 2008 through 2018, reaching 13,775 MW of installed capacity by the end of 2018. Total annual capital investment has averaged approximately $1 billion since 2015, about $400 million of which has been in the generation sector. The paper concludes with future research directions, with an emphasis on integrating engineering analysis, scenario simulation, flexibility, and quantifying social/equity (“fairness”) effects of microgrids.

Introduction

Over the past several years, microgrid development has been a significant topic for energy policy development (Hirsch, Parag, & Guerrero, 2018). While a large share of this development has taken place in developing countries with limited access to reliable energy supply, there is some progress being made in microgrid development in the OECD countries, particularly in North America (Sioshansi, 2018). This development depends in large part on a combination of technical, economic, and regulatory factors. In these OECD countries, regulators have attempted to prioritize microgrid development by using their existing sets of cost-benefit analytical tools. Such tools have been used to determine the prudency of utility investments (e.g., California Public Utilities Commission, 2018), but may be inadequate for evaluating microgrid projects at specific locations throughout such a utility’s transmission and distribution systems. Consequently, in many OECD countries, policy tools have reinforced an existing bias toward larger centralized infrastructure rather than distributed systems, including microgrids (Levin & Thomas, 2016; Sioshansi, 2018). This paper attempts to provide a framework for assessing benefits and costs of microgrid integration, based on the current state of microgrid development. Notably, some of the benefits, such as improved resiliency, have not been defined well, making quantification difficult.

1- Introduction

2- Resilience of power systems

3- Microgrids as a resilience resource: microgrid formation

4- Strategies used by microgrids for enhancing resilience

5- Communication, event-specific, and component resilience

6- Research gaps and future directions

7- Conclusion

References

Abstract

Microgrids are considered as a potential solution to deal with major power disruption events due to their ability of islanding and potential to sustain the penetration of renewables. In order to elaborate the role of microgrids in enhancing the resilience of power systems, a three-step analysis is carried out in this paper. In the first step, the general backgrounds of resilience in power systems are presented, which comprise of disaster modeling, resilience analysis methods, and resilience enhancement efforts by different regions. In the second step, the use of microgrids as a resilience resource is analyzed, where formation of microgrids, networked microgrids, and dynamic microgrids along with resiliency of multi-energy networks are explored. In the third step, the strategies utilized by microgrids for enhancing their resilience during major outage events are analyzed. These strategies include proactive scheduling, outage management, feasible islanding, and advanced operation strategies for reducing the impact of major disruptions. The classification of these operation strategies is based on the event occurrence and clearance times. In addition, the resilience strategies used by different types of microgrids, types of energy management systems, communication resilience, and resilience of individual components in microgrids are also analyzed. Finally, research gaps in the existing literature and future directions for improving the available resilience-oriented operation methods for enhancing the resilience of microgrids are presented.

Introduction

Resilience enhancement of power grids against natural disasters has become a major consideration for power and energy sector researchers and engineers in recent years. Due to lesser incidences of natural disasters, these events were initially known as low-probability high-impact events. However, the frequency of these events has increased in the last few decades due to climate change [1]. Among various other major events, the intensity and severity of weather-related events have significantly increased in the last decades [2]. Seven of the ten major storms that occurred during the last four decades have occurred in the last 10 years and each event caused damages of over 1-billion dollars [3]. In 2017 only, eight major weather-related events have struck the world, especially the US [4–11]. Five of these eight events have disrupted power to over a million consumers and the least number of consumers affected during any single event were around 0.3million. The existing electrical power systems can assure service reliability during normal conditions and abnormal but foreseeable and low impact contingencies. However, the continuity of service during unexpected and high impact events is still a challenge [12]. Therefore, existing power systems are known to be reliable but not resilient. The resiliency of a system is defined as its ability to return to the equilibrium (stable operation point) after a major disruption event [13]. Due to the absence of a universally accepted definition of resilience in power systems, the authors in [14] have analyzed various aspects of system resilience and defined the power system resilience.

I. INTRODUCTION

II. PRIMARY CONTROL STRATEGY OF DC MICROGRIDS

III. UPPER-LEVEL CONTROL STRATEGY FOR DC MICROGRIDS

IV. LOAD STABILITY ANALYSIS IN THE DC MICROGRIDS

V. CONCLUSION AND PROSPECTS

REFERENCES

In order to overcome the problem of power generation in distributed energy, microgrid(MG) emerges as an alternative scheme. Compared with the ac microgrids, the dc microgrids have the advantages of high system efficiency, good power quality, low cost, and simple control. However, due to the complexity of the distributed generation system, the conventional droop control shows the drawbacks of low current sharing accuracy. Therefore, the improved primary control methods to enhance current sharing accuracy are systematically reviewed, such as particle swarm optimization programming, probabilistic algorithm and voltage correction factor scheme. However, it is difficult to achieve stable and coordinated operation of the dc microgrids by relying on the primary control. Hence, the various secondary control approaches, such as dynamic current sharing scheme, muti-agent system (MAS) control and virtual voltage control methods have been summarized for voltage regulation. Furthermore, the energy management system (EMS), modular-based energy router (MBER) and other coordinated control methods are reviewed to achieve power management. Besides, various control methods to compensate the effect of communication delay are summarized. Moreover, linear matrix inequality (LMI), Lyapunov-Krasovskii functional stability and Takagi–Sugeno model prediction scheme can be adopted to eliminate the influence of communication delay. In addition, due to the constant power loads (CPL) exhibit negative impedance characteristics, which may result in the output oscillation of filter. Thus, various control approaches have been reviewed to match the impedance, such as the nonlinear disturbance observer (NDO) feedforward compensation method, linear programming algorithm, hybrid potential theory and linear system analysis of polyhedral uncertainty. The merits and drawbacks of those control strategies are compared in this paper. Finally, the future research trends of hierarchical control and stability in dc microgrids and dc microgrid clusters are also presented.

INTRODUCTION

In recent years, in order to solve the problem of environmental pollution and reduce the demand of fossil fuels for conventional power generation, distributed generation (DGs) including renewable energy (RES) and energy storage systems (ESS) have been widely developed [1], [2]. Moreover, to coordinate the contradiction between the conventional grid and the DG units, while exploiting the advantages of the distributed power source, the concept of the microgrid (MG) have emerged at the beginning of this century [3]. MG can be divided into dc MG and ac MG, compared to ac MG, dc MG possesses the advantages of high efficiency, more natural interface with various RES and ESS, and in compatible with the requirements of consumer electronic products, thus dc MG has been widely applied [4]. Furthermore, dc MG can be adopted to renewable energy (photovoltaic arrays, wind turbines, etc.), aerospace equipment, ship electrical systems, energy storage equipment, electric vehicles, data centers, telecommunication systems [5]–[9]. In addition, when components are coupled around a dc bus, there are no issues with reactive power flow and frequency adjustment [10]–[12]. Therefore, dc MGs are increasingly attracting considerable attention.