Networked Microgrids (NMG)

The traditional electrical utility faces great challenges in terms of the coordination processes of the different actors that participate in its operation. Electrical distribution networks have maintained a centralized scheme, in which the control and coordination of the operating parameters are maintained to ensure the correct interconnection of all the generators. This results in an increased vulnerability under failure occurrence and a slower rate of restoration after fault conditions. This reality is exacerbated when considering the growing inclusion of renewable energy resources, with their fast dynamic response characteristic, which makes it difficult to maintain a centralized control scheme.

Another important concept that challenges the centralized control paradigm are microgrids (MG). They are defined as electrical distribution systems that contain distributed generation loads and sources (DER) that can act as a single controllable unit (Feng et al., 2018) . Microgrids are a fundamental part of the transition to the concept SmartGrid, where a participatory and bidirectional scheme between generators and consumers is proposed (Sigrist et al., 2016).

The formation and dynamic coordination of MG becomes an important tool to increase the levels of resilience of the electrical infrastructure of a country. The capability of detecting, isolating, and implementing self-healing actions should be a key goal in the transition to the SmartGrid. Nevertheless, this required the development of new control concepts focused on multi-agent systems (MAS) and consensus techniques between the agents (Shobole & Wadi, 2021).

For this reason, a new building block for the electrical utility of the future is needed. Networked Microgrids (NMG) are defined as the clustering of adjacent MG with the capacity of interconnection, interaction, and power sharing (Liu et al., 2018). This creates a boundaryless operation which can be dynamically reconfigure to enhance the resiliency, improve the economic dispatch of electricity, and promote the integration of renewable energy resources (RES). These technical characteristics make the case for the consideration of NMG as the central building block for the transition to the concept of SmartGrid.

Important technical challenges, regarding the coordination and the required changes in the electrical infrastructure, need to be faced to deploy NMG systems and begin the decentralization process of the electrical utility. Realities such as climate change, dependence on nonrenewable energy resources, and the distributed nature of renewable energy are a call to attention on the necessity of addressing the challenges related to the deployment of NMG.

Technical realization of Networked Microgrids

Although there is a wide literature that refers to the use of distributed control in microgrid systems, this is still an open and active field of research. The authors (Espina et al., 2020) present a review of works that have focused on this topic, referring to three major trends: multi-agent control implementations, implementations based on machine learning systems, and traditional schemes based on strategies such as drop control.

A key point in understanding the implementation of the coordination strategy for NMG is the definition of the control levels that are present in a NMG system. As can be seen on Fig. 1, there are three levels of control that can be found in the interconnected operation of MG. Primary control focuses on the internal functioning of the MG members of the NMG, techniques such as droop control or P-f control are used to ensure the frequency stability inside the MG, islanding detection is also used to establish the frequency regulation reference. In the secondary control level, the objective is the interconnected stability of MG, techniques for safe synchronization and disturbance handling must be implemented at this level, small scale fault handling and distributed DER operation can be implemented. Finally, the tertiary control level regulates large NMG interconnection, in which power flow considerations and optimization of the electrical market are the primary objectives, large scale faults can be accommodated by the reconfiguration of the NMG and the isolation of the faulted agents.

Figure 1. Control levels present in networked Microgrids.

It is clear that the secondary and tertiary control level requires the coordination of distributed agents. An example of a multi-agent control system is the one presented by (Eskandari et al., 2020). The authors propose the study of consensus-based multi-agent control techniques to regulate the droop control interaction of each individual agent. The authors model the interaction within the NMG, adding states related to the consensus rules for each instance of DG. A FUZZY controller is used for the consensus rules dedicated to power flow optimization and a feedback control scheme with fixed gains for voltage consensus.

Optimization is a key point for the evaluation of the consensus strategy. In (Wang et al., 2020) the use of a peer-to-peer strategy to perform multi-agent control and develop it in a dedicated system, based on shared consensus signals between each agent. His proposal is tested in a HIL simulation system. After performing the relevant modeling, optimization models based on the consensus rules that have been defined are proposed. This is extended to a secondary and tertiary level of control using the methodology of multipliers in alternating directions to solve the problem of consensus raised. The authors comment on the added robustness when using the optimization methodology with the method of multipliers in alternate directions when compared to other types of implementations, improving stability compared to controllers without this optimization loop. Additionally, they comment on how the simulation in the physical system was satisfactory, validating the proposed approach.

Robustness plays a key point in this type of strategy; therefore, it is necessary to study the effect of adverse conditions on the proposed schemes. (Lai et al., 2021) implements a multi-agent control system at the primary and secondary level of a set of microgrids. Focusing on proposing test scenarios with extreme noise conditions and delays in communication channels. Additionally, certain tertiary control functions are implemented at the secondary level, simplifying the control scheme. All this by formulating a problem of consensus among the agents. The authors carry out a three-level consensus, where there is a leader-following consensus for the regulation of operating variables and a leaderless consensus for the optimal distribution of power. These consensus considerations implement different gains depending on the disturbances that are identified in the communication channel. These strategies are tested in a simulation environment. In their conclusion, the authors comment on how including the considerations of disturbances in the communication lines allows to improve the robustness of the system against noise conditions or delays in the communication channels. Additionally, they state how their results demonstrate that it is possible, by including power considerations in the consensus, to have a tertiary level of control implemented at a secondary level.

Even for determining consensus rules, machine learning techniques can play an important role. In the work presented by (Zhang et al., 2021) this is put to the test, it studies the use of machine learning methodologies of consensus policies that include restrictions to guarantee safe operations. In this way, it seeks to take advantage of approaches based on models and approaches based on data. The authors determine the different considerations that will be applied to the constrained learning problem, creating what they call a safe supervised multi-agent problem for policy learning.

As can be seen from the review of these works, the NMG control problem is not a trivial one, and there are limited commercial solutions available at this moment to address this challenging problem. Even so, current technical capabilities allow the deployment of this kind of solutions, it requires more research dedicated to promoting a safe transition to a decentralized electrical grid and to prepare a robust integration framework for the NMG.

SmartGrid and Networked Microgrids

The coordination of the dynamic formations of NMG is a key point in achieving the functional objectives of the SmartGrid. As can be seen on Fig. 2, there are categories assigned to a MG system, which depends on its placement with reference on the main substation. Coordination capabilities allows the integration of the diverse MG up to the substation level, forming and NMG. In a next step, it is possible to coordinate the integration of the NMG and form a meshed configuration integrated by several NMG.

Figure 2. Categorization of MG systems, taken from (Chen et al., 2021).

There are several possibilities for the interconnection of the MG forming the NMG structure. Typical configurations for NMG are shown on Fig. 3. These configurations (start, ring, and meshed) offer some redundancy level and the ability of interconnection. In the case of start configuration, there is a single point of failure (main distribution bus) but a failure inside a MG can be accommodated. In a ring configuration, up to two points of failure can be handled; when a failure occurs, the bidirectional ring allows the interconnection to the utility. For the meshed configuration, the quantity of failures depends on the interconnections available to the utility, in a tradeoff between economic cost and enhanced availability (Espina et al., 2020).

Figure 3. Configurations for NMG: a) Star Configuration. b) Ring Configuration. c) Meshed Configuration.

The adaptability capacity of NMG makes the case for considering this technology as the ideal building block for the SmartGrid. There are important additional functionalities in the SmartGrid model that can be also addressed using the NMG infrastructure, in Fig. 4 the technical evolution required for implementing the SmartGrid model are shown. The electrical generators, market, transmission, distributors, and consumers are all impacted by this transition. Inclusion of distributed generation, the decentralization and the elimination of boundaries, the downscaling of the electrical transmission network, and finally the bidirectional nature of the distribution and consumers, are all characteristics of the SmartGrid that can be addressed using NMG.

Figure 4. Comparison between traditional electrical utility and smart electrical utility. (Bartz/Stockmar,2018). CC-BY-SA-4.0.

NMG based electrical utilities will need to address the control and coordination challenges based on the technical functionalities required for the SmartGrid.  The correct modeling and the determination of the consensus policies are key point in a successful deployment of a NMG infrastructure.

PUCMM Microgrid Laboratory and NMG

PUCMM’s Microgrid research team is currently studying the control and optimization techniques of NMG for the enhanced resiliency of Dominican Republic electrical Utility. A star configuration test lab is under construction to test the implementation of consensus policies and the dynamical integration of MG under a NMG scheme. As show on Fig. 5, a total of six MG system can be simulated and interconnected using a hardware in the loop simulation setting. Furthermore, simulation scenarios are being constructed using as a reference information from Dominican Republic’s interconnected national electrical system.

This project sponsored by the USAID and NASEM aims to develop a reference framework for the integration of NMG in Dominican Republic electrical utility. Acting as a validation tool for optimization techniques, multi agent control implementations, and becoming a reference for the formulation of policies for Dominican Republic’s electric sector in order to promote the evolution to the concept of SmartGrid, harnessing its extended resiliency and improved operation capability.


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Figure 5. PUCMM’s PHIL laboratory proposed configuration.


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CI – Rafael O. Batista Jorge

This article is derived from the Subject Data funded in whole or part by NAS and USAID under the USAID Prime Award Number AID-OAA-A-11-00012. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors alone and do not necessarily reflect the views of USAID or NAS.

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