Networked Microgrids | Encyclopedia MDPI

Networked Microgrids: History

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The increasing impact of climate change and rising occurrences of natural disasters pose substantial threats to power systems. Strengthening resilience against these low-probability, high-impact events is crucial. The proposition of reconfiguring traditional power systems into advanced networked microgrids (NMGs) emerges as a promising solution.

networked microgrids
configuration
operation
power flow
communication
control

1. Introduction

The intensification of climate change poses a threat to power systems, leading to potential challenges like increased electricity demand and adverse impacts on power equipment. This convergence may result in critical issues, such as overloading and overheating, reminiscent of the 2003 blackout incident in the United States. The increasing annual intensity of climate change elevates the likelihood of severe weather events, contributing to a notable uptick in major power outages, as depicted in Figure 1 [1]. Major power outages, exemplified by events in Texas and Quebec, can inflict substantial economic losses, as seen with the USD 130 billion impact in Texas [2], and USD 50 million impact in Quebec [3]. Additionally, such outages pose significant challenges for affected households, enduring prolonged periods without electricity.

Figure 1. Major power outages in the U.S.

The extensive scholarly literature has been dedicated to enhancement of power networks’ capability to withstand adverse weather conditions—a practice commonly referred to as enhancement of power system resilience. Researchers suggested diverse strategies for resilience enhancement, such as strategic planning techniques and system hardening methods [4]. A notably promising solution among the various proposed methods involves integrating controllable and smart technologies into the power system and strategically establishing networked microgrids (NMGs). NMGs encompass interconnected microgrids (MGs) capable of exchanging both power and information. This configuration is formed by partitioning distribution systems, linking multiple MGs to create a larger and more resilient power system, as defined in IEEE standard 1547.4 [5]. This interconnected structure enhances resilience in managing energy resources and meeting electricity demand. Findings from [6] underscore the benefits of NMGs in reducing operating costs and improving power supply resilience compared to independent MGs. An illustrative example of the practical significance of this interconnected setup is observed in Adjuntas, Puerto Rico, where the resilience of two microgrids is notably elevated when integrated into a networked microgrid, as detailed in [7]. The versatility of NMGs positions them as a promising means to enhance overall system resilience.

2. Networked Microgrids’ Configuration

The emerging field of networked microgrids holds the potential to revolutionize traditional power grids, offering increased flexibility, sustainability, and resilience. Utilizing advanced configuration techniques, these networked microgrids can transform the way electricity is generated, distributed, and consumed in the future.

The configuration of networked microgrids encompasses three key aspects: formation, power distribution, and operation. Formation involves allocating distributed energy resources (DERs) in each microgrid, establishing boundaries, and determining the physical and operational connections between microgrids to shape the overall structure of the networked microgrids. Power distribution involves conducting power flow analysis, calculating voltage magnitudes, phase angles, and power flows at different points in the system. The integration of power flow analysis, also known as load flow analysis, is crucial for understanding and managing the distribution of electrical power within microgrids, incorporating various elements such as distributed energy resources, energy storage, and loads. Operation defines the behavior of networked microgrids over time under different conditions.

2.1. Formation

The establishment of NMGs involves restructuring distribution systems into interconnected or independent MGs. NMGs’ formation is crucial for ensuring coordinated functionality, control, and resource sharing among microgrids. This adaptation allows them to respond effectively to dynamic conditions, accommodating changes in load demand, generation capacity, and overall system conditions. Several proposed methodologies focus on organizing networked microgrids by determining optimal structures, boundaries, and partitions. The objective is to efficiently allocate resources, ensuring a continuous power supply, even in the face of unexpected disruptions. This section categorizes and examines a range of techniques developed by researchers and practitioners, each offering distinct advantages and considerations.

In the following subsections, a comprehensive review of each of these approaches is conducted to identify their characteristics, and the findings, including both features and limitations, are succinctly summarized in Table 1.

Table 1. Categorization of approaches for forming NMGs.

Methods	Categorizes	Features	Limits	Ref.
Clustering	Partitional, Hierarchical, and Density-Based	Create a straightforward approach with minimal mathematical complexity to support large-scale NMG by focusing on specific MGs.	Designed for the formation of uncoupled multi-microgrids. No assurance of finding optimal solutions.	[8,9,10,11,12,13,14,15,16,17]
Graph theory	MST, and BFS	Facilitate visualization of distributed-grid problems to find optimal solution rapidly.	Designed for the formation of uncoupled multi-microgrids. Efficiency degrades for medium to large systems. Lacks consideration for transient response. Fails to address protection concerns.	[18,19,20,21,22,23,24,25,26]
MIP	MINLP, MILP, and MISOCP	Capable of finding the optimal solution for problems in which decision variables can take on both continuous and discrete values.	Computationally expensive. Practically infeasible when the size of the system is large or for real-time decision making. Less consideration for transient response. Need for a thorough and accurate mathematical model of the environment Non-convex characteristics of power flow constraints.	[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]
Heuristic	BFS, BSO, Tabu, ABS, and PSO	Discover close-to-optimal solutions within a reasonable timeframe.	Less consideration for transient response of NMGs No assurance of an optimal NMGs’ formation. Lacks consideration for transient protection and frequency deviation.	[42,43,44,45,46]
Game theory	Cooperative, and Dart Game	Modeling interactions and strategic interdependence among microgrids.	Computationally expensive. Lacks consideration for transient protection and frequency deviation. Practically infeasible when the size of the NMG is large or for real-time decision making.	[47,48,49,50,51,52]
DRL	DQN and multi agent DQN	Advanced machine learning techniques with a model-free nature enable dynamic configuration, allowing for their application in an online mode.	Lack of maturity and reliability in power system applications. Lacks consideration for transient protection and frequency deviation. Complex implementation poses challenges in deployment. Dependence on online and historical data of the network for effective functioning.	[53,54,55,56,57,58,59,60,61]

2.2. Power Distribution

The configuration of NMGs is significantly reliant on power flow (PF) calculations. Analyzing the power flow or voltage profile is crucial for understanding the distribution of power within the network. This information plays a key role in dispatching microgrids optimally, ensuring their stable and reliable operation. Additionally, it aids in identifying areas with high load concentration and interconnected DERs, which are deemed as promising candidates for microgrid formation. According to Table 2, researchers used different PF techniques in configuring NMGs.

PF calculations frequently employ various numerical techniques to linearize nonlinear equations and solve them within electrical power systems. The PF calculation typically consumes a significant amount of execution time and involves complexities, mainly because it necessitates updating the voltage magnitude and angle in each iterative process [65]. These challenges become particularly pronounced in NMGs due to their dependency on various factors, including the operational mode, types of microgrids, and network topologies. The detailed discussions on these factors will be presented in the subsequent subsections.

Table 2. PF techniques employed for configuration of NMGs.

PF Techniques	Ref.
AC PF	[33,34,51,53,55,58,59,60,64]
Linear DistFlow	[31,39,40,41,44,61,63,66]
NR	[8,9,10,16,20,57]
BFS	[27,32,42,43]
Kirchhoff’s law	[19,25,30,56]
Gauss-Seidel	[26]

2.3. Operation

There are two primary types of networked microgrids based on their operational characteristics: predetermined networked microgrids (PNMGs) and dynamic networked microgrids (DNMGs). A predefined networked microgrid maintains a consistent switching status and network configuration regardless of the system’s operating conditions and customer priorities. The boundaries of the microgrid are determined based on factors such as supply adequacy, reliability indices, and maximum coverage. These predetermined networked microgrids operate according to established rules and agreements. For example, grid-tied microgrids are connected to the main grid and coordinate their operation with the utility grid, following predetermined agreements and regulations for power sharing and exchange. Virtual power plants integrate various distributed energy resources and function as a single controllable entity, with power generation and sharing predetermined based on the capabilities and capacities of the distributed energy resources [88]. Community microgrids, designed to serve specific communities or areas, also fall into the category of predetermined networked microgrids [89]. They have predefined connections, power sharing arrangements, and operational strategies tailored to meet the specific needs of the community.

On the other hand, DNMGs, an evolved form of networked microgrids, have gained popularity due to their advanced structure. As per [90,91,92], dynamic microgrids can be described as microgrids with adaptable boundaries that dynamically adjust to maintain a balance between generation and load. This flexibility enables dynamic microgrids to optimize their operations in real time, ensuring efficient utilization of resources and meeting the evolving demands of the system. DNMGs exhibit real-time adaptability and flexibility, utilizing advanced control algorithms, communication technologies, and intelligent decision-making capabilities to optimize resource utilization and ensure reliable operation. DNMGs are capable of self-healing, automatically detecting and responding to faults or disruptions, and reconfiguring their operations to restore power supply [91,93,94]. Additionally, demand-responsive microgrids dynamically adjust power consumption and load profiles based on grid conditions and user preferences, enabling efficient utilization of energy resources. Multi-agent systems are also a type of dynamic networked microgrid that facilitate real-time coordination and cooperation among interconnected components, optimizing power sharing and load balancing [95,96].

Dynamic networked microgrids offer distinct advantages when compared to predetermined networked microgrids. Their flexible boundaries, which expand or shrink based on the real-time generation and load conditions, enable superior adaptability to changing energy demands and resource availability. This flexibility enhances the overall resilience of the system, as dynamic networked microgrids can reconfigure themselves in response to disruptions or faults, isolating affected sections and ensuring uninterrupted operation. Moreover, dynamic networked microgrids optimize the utilization of distributed energy resources by dynamically adjusting connections and allocations, leading to improved energy efficiency and cost-effectiveness [97]. The scalability of dynamic networked microgrids allows for seamless integration of new microgrids and DERs, accommodating the growing demand for renewable energy sources. Additionally, their ability to balance loads and manage voltage and frequency fluctuations enhances grid stability. Overall, dynamic networked microgrids offer increased flexibility, resilience, optimal resource utilization, scalability, and grid stability, making them a promising solution for efficient and sustainable power distribution in the evolving energy landscape. While the benefits of DNMGs are evidently greater than those of PDNMGs, Table 3 indicates that over 40 percent of studies focus on configuring PNMGs.

Table 3. List of key studies in DNMGs and PNMGs.

Operation	Ref.
DNMGs	[9,11,24,26,27,28,31,33,46,53,55,56,57,58,59,60]
PNMGs	[8,10,13,16,19,20,25,30,32,34,42,43,44,51,63,64]

3. Networked Microgrids’ Control

Effective monitoring and control techniques play a crucial role in optimizing performance and bolstering the overall resilience of networked microgrids. These techniques aid in the efficient distribution of energy, reducing power losses, and enabling adaptive operation. They ensure that networked microgrids can swiftly adjust to changing conditions and optimize their functioning in response to disruptions. To implement advanced and real-time control techniques, a robust and reliable communication structure is necessary.

3.1. Communication

In the context of networked microgrids, effective communication infrastructure plays a crucial role in ensuring the smooth management of energy and coordination among various components. These communication tools facilitate the exchange of information not only between microgrids but also with the central energy management system and end users [98]. They enable the implementation of advanced functionalities, including load balancing, demand response, and fault detection, which rely on continuous and reliable communication [99].

3.2. Control

The control of NMGs involves overseeing and managing network functions to achieve goals such as energy trading, optimizing operational costs, maximizing power stability, ensuring reliability, enhancing user comfort, and achieving a resilience index. The control capabilities of networked microgrids are analyzed and evaluated through various perspectives, including the control architecture, control modes, and control schemes. The control architecture and control modes illustrate the framework for NMGs’ control, while the control scheme delineates the approach to managing interconnection and interchange among MGs. All these capabilities, along with their features and limitations, are succinctly presented in Table 4 and systematically and thoroughly examined, considering their formulation models, objectives, and features, in the subsequent subsections.

Table 4. Categorization of control techniques for NMGs.

Control Features	Categories	Features	Limits	Ref.
Architecture	Centralized	Effective in situations requiring precise coordination and centralized controller.	Single-point communication. Reliability issues. Struggle with a large number of agents.	[109,110,111,112,113,114,115,116,117,118,119]
	Decentralized	Enhance privacy protection of MGs, facilitates communication among MGs in different points.	Difficulty in achieving system-wide objectives Increased vulnerability to communication failures. Limited scalability with a growing number of agents.	[120,121,122,123,124,125,126]
	Distributed	Ensure regular operation of NMGs by adjusting voltage and frequency, even without communication with master controllers.	Privacy concern. Increased vulnerability to communication failures.	[127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148]
Modes	Master–Slave	Enable centralized coordination among MGs and DS.	Single-point communication. Reliability issues.	[109,110,111,112,113,149]
Modes	P2P	Allow decentralized decision making and mutual collaboration among MGs and DS.	Increased communication complexity in large-scale systems. Limited scalability with a growing number of peers.	[138,140,143,148,150,151,152,153,154,155,156,157,158,159,160]
Scheme	Hierarchical	Provide a structured approach with levels of decision making, facilitating coordination between MGs and DS.	Potential delays in decision making due to multi-level hierarchy. Increased vulnerability to failures in higher-level controllers. Complexity in ensuring alignment between local and global objectives.	[114,161,162,163,164,165,166,167,168,169,170]
	Droop-Based	Aid in load sharing and maintain voltage and frequency stability amidst variations with less reliance on communication systems.	Less able to manage all dynamic behaviors of NMGs. Less applicable in large-scale networks.	[171,172,173,174,175,176,177,178,179,180,181,182,183,184]
	Optimization	Assist in determining optimal setpoints for various operational parameters of NMGs.	Less applicable in large-scale networks. Model-based and centralized structure.	[109,110,112,113,137,138,139,140,141,142,185,186,187,188,189,190,191,192]
	AI	Allow NMGs to dynamically adapt and respond to changing conditions in real time.	Complexity in implementation. Less maturity in power systems. Dependent on historical and real-time data.	[124,131,193,194,195,196]

This entry is adapted from the peer-reviewed paper 10.3390/en17030715

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