A Named Data Networking

16th Annual Mediterranean Ad Hoc Networking Workshop

Review on Microgrid Communications Solutions: A Named Data Networking – Fog Approach Kate Monteiro, Michel Marot, Hatem Ibn-khedher Département Réseaux et Services de Télécommunications CNRS SAMOVAR, Institut Mines-Telecom, TSP Palaiseau, France [email protected], [email protected], [email protected]

Abstract—In the Internet of Things (IoT) scenario, smart grids and more particularly microgrids, bring more intelligence and innovation to the historic and stablished power grid. However, the communication demands are elevated. To address microgrid requirements, an efficient and highly available communication network is required. This paper focuses on communication solutions for the microgrid considering the amount of data generated, the latency requirements and the security of the communication infrastructure. Named Data Networking (NDN) is a new Internet paradigm that is contentcentric and it can expedite the data dissemination within the microgrid communication network. Fog computing pushes the cloud to the edge, promising low latency which is in accordance with microgrid communications needs. This paper analyses the microgrid communications and control and it proposes a NDNFog approach for microgrid communications. Internet of Things; Microgrids; Named Data Networking; Fog.(Keywords)

I. INTRODUCTION Microgrids are small scale smart grids that can be incorporated in small communities, hospitals, schools, industrials and commercials areas, reducing transmissions and distributions losses [1]. To accomplish microgrid proficiency, parameters such as current, voltage and frequency measures, and equipment status reports have to be gathered from multiple sensors and Intelligent Electronic Devices (IEDs) across the entire power system. The collected data must be processed, transmitted and stored securely to reliably control and monitor the whole microgrid. The microgrid controllers must properly receive that data to timely instruct actuators. To this end, the communication infrastructure must provide highly available and secure twoway communications among several microgrid elements as well as fulfill time sensitive communication demands and data priority. The bi-directional communication between the microgrid and the customers introduces more interactivity. Hence, it is expected a large amount of information generated between customer applications, smart appliances, smart homes, smart building and microgrids. The microgrid communication network has to efficiently deliver data but the classical TCP/IP architecture and protocols are not suited for that. Indeed, in the past the Internet was essentially built to interconnect remote computers networks and to share resources, for example computer mainframes. The main principle behind the classic

Internet design, based on the TCP/IP protocol suite is a hostcentric model where firstly, two hosts establish a connection between them and subsequently, they exchange information. The host-centric communication implies that the hosts must be location aware. However, this paradigm is not suited for microgrid where data may be accessed by several components/actors of the microgrid. Also, more generally speaking, the nature of the communications has changed. Nowadays, the Internet is mostly used to disseminate audio, video and data contents. What content is delivered has become more noteworthy than where it is delivered. In this way, an efficient content delivery has been required. New exigencies that were not envisioned in the original Internet design, such as mobility and security have surfaced. Also, the Internet of Things (IoT) has brought new challenges to TCP/IP networks, as detailed in [2]: i) Most things operate in restricted conditions, for instance short battery lifetime, low processing capabilities and few memory capacities. Because of those limitations, IoT layer 2 networks often present small MTU, for example 127 bytes considering IEEE 802.15.4. Since the number of things is significant, a large address space is needed, thus IPv6 is seemly rather than IPv4 whose address space is diminishing. Yet, for processing optimization, IPv6 has a fixed header of 40 bytes plus optional headers which is a considerable header size for small packets. Also, IPv6 requires a minimum MTU of 1280 bytes to prevent segmentation but this is unsuitable for constrained IoT networks. For those reasons, a mechanism to adapt IPv6 to the packet size of constrained layer 2 protocols is required. ii) Most things are connected through mesh networks. IoT mesh networks present multiple layer 2 protocols joined together without any layer 3 devices, namely routers between them. They embrace multi-link subnets models that are not supported by the original IP addressing scheme. iii) Multicast and broadcast consume a substantial amount of energy. Many things enter to sleeping mode to save power. However, a single multicast operation may require a series of multi-hop forwarding and potentially wake-up constrained things. iv) TCP reliable in-order delivery and congestion control are often inappropriate for IoT networks. The TCP three-way handshaking introduces latency which most delay-sensitive things are not able to tolerate. Still, most IoT communications require the exchange of small amount of

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data, making the connection setup to absorb a significant part of the session time. Also, TCP requires the buffering of data both at the source and at the destination for retransmission and in-order delivery, consuming constrained thing resources [3]. v) Naming configuration and service discovery through traditional Domain Name Servers (DNS) are inadequate for most things, particularly for sensors and actuators. To face these new issues, Information Centric Networking (ICN) has been proposed. Several solutions appeared like Named Data Networking (NDN) which is a new concept for the future Internet architecture based on a content-centric and location unaware model that can help solving IoT communication demands [4]. NDN has been applied to IoT domain, for example smart building, smart homes and smart grids [5], [6], [7], [8] and [9], more specifically to sensor and actuator things. In this way, it is arguable that NDN can improve data dissemination in the microgrid communication context. The goal of this position paper is to analyze in which extent microgrid communication solutions can take profit of these recent advances in networking. We analyze the requirements of microgrid in terms of communication needs and the features of NDN and then we propose an architecture based on an NDN approach for microgrid, in combination with fog. Actually, how the collected data is processed and stored is an important mater to microgrid communications. Fog computing pushes cloud computing to the edge, improving communication speed, flexibility and security. It is well suited for industrial sectors like smart grid where commands have strong delay constraints. The rest of this paper is organized as follows. Section II reviews the microgrid and its control methods and communication solutions. Section III overviews the Named Data Networking Architecture. Section IV addresses the NDN-Fog approach. The conclusion closes the paper. II.

MICROGRID CONTROL AND COMMUNICATIONS BACKGROUD

Several research efforts have been done to define the concept, control methods, and technological aspects of microgrid. The Conseil International des Grands Réseaux Électriques (CIGRÉ) Working Group C6.22 Microgrid Evolution Roadmap has been established since 2010, focused on outlining the microgrid architecture as well as its performing features. In [10] and [11] are presented the results of such studies. According to WG C6.22, Microgrid can be defined as a controllable, coordinated electricity distribution system of small size. Its components are loads, Distributed Energy Resources (DER), Energy Storage (ES) and control devices. Microgrid can operate either connected to the main grid, through a Point of Common Coupling (PCC) or islanded. While in islanded mode, the microgrid is disconnected from the main grid due to economic reasons,

maintenance or failure. Loads are consumers of power, e.g. plants, households. DER includes different power sources such as microturbines, fuel cells, photovoltaic generators, wind generators, Combined Heat and Power (CHP) and small hydropower units. ES are storage devices, e.g. batteries, that save the excess of the energy produced. The Energy Management System (EMS) performs control and management functions within the microgrid. It is viewed as a set of control strategies and operational practices, implemented both in software and hardware, to optimize the energy management of the microgrid [12]. The main goals of the EMS are guaranteeing the efficient control of the different DER power output, the load consumption, the ES storage levels, and assuring the resynchronization after transitions between grid-connected and island mode [13]. Although microgrids have no specific size, they can be classified by capacity. In [14] microgrids are listed as simple, corporate, feeder area, substation area and independent. Simple microgrids have a capacity inferior to 2 MW and they serve small independent institutes such as schools and hospital with multiple loads. Corporate microgrids have a capacity between 2 and 5 MW, and they include a small number of household loads, but not commercial or industrial loads. Feeder area microgrids have a capacity between 5 and 20 MW and they cover some large commercial and industrial loads. Substation area microgrids have a capacity superior to 20 MW and embrace household, commercial and industrial loads. Independent microgrids are meant to remote off-grid areas, for instance islands, rural zones, mountains, and villages. Their capacity depends on the loads attached to them. In [15] are given some examples of microgrids in Europe as well as their respective control methods and communications technologies. The Gridnice [16] project installed in Carros, in Nice city nearby, France, is the first commercial microgrid pilot in Europe. The project is coordinated by Electricité Réseau Distribution France (ERDF), and it was launched in 2012 under the European program GRID4EU. The microgrid has a capacity of 3.5 MW, and it serves approximately 2500 voluntaries clients. The main DER components are photovoltaic generators and the ES are batteries which have an autonomy of 5 hours when operating in island mode. A. Control Methods Concerning the EMS control methods, the centralized and the decentralized approaches are considered, [17], [18]. Regarding to centralized control, the coordination functions are attributed to a sole central controller. The centralized solution is habitually based on Supervisory Control and Data Acquisition (SCADA) systems. The standard IEC 61850 [19] is typically used by SCADA based electric power grids for communication among the diverse devices, enabling manufacturers interoperability. Central control is easy to implement and to operate, and it is appropriate for small-scale microgrids. However, there are constrains such as strong computational efforts, e.g. memory

and processing, because of the huge amount of data collected from several microgrids components, low flexibility and scalability since it is difficult to add new elements, high bandwidth, single point of failure which may compromise the complete system, and Denial of Service (DoS). Instead, decentralized control allows each element to perform their self-directed monitoring. The decisions are taken by local controllers (LC) that command their corresponding microgrid component. The advantages are the improvement of communication speed, lower bandwidth requirements, high modularity, plug and play capabilities which augments the system expandability, and better fault tolerance. Nevertheless, the control system should be wellplanned to avoid disorder, leading to microgrid malfunction. Thus, the implementation of decentralized control is a complex task. MAS (Multi Agent-based control System) is a possibility for decentralized control. The Working Group IEEE Power Engineering MAS [20] has been studying different technologies, concepts, standards, and challenges so as to apply MAS on electric power fields [21], [22]. Also in [23] is examined the application of MAS in microgrid control. MAS can be considered as a set of two or more intelligent agents, which are computing entities inserted in some environment. Intelligent agents react to changes in their environments, they are autonomous, they are goaloriented and they cooperate with each other [21], [22], [23]. They perform their own task independently and asynchronously, in order to reach predefined objectives [21], [22], [23]. For instance, in the microgrid context each DER local controller can be regarded as a single intelligent agent.

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scalability. The second level englobes a central controller designated as Microgrid Central Controller (MGCC). The MGCC monitors the entire microgrid system, and it controls the frequency, voltage and power deviations of the local controllers, assuring power quality control. It interacts with the main grid, enabling smooth transition between islanded and grid connected mode. The third level is the distribution dispatch center. The distribution networks dispatch the microgrid economically and efficiently. Critical and important decisions are made by the third level which controls the microgrid. Figure 1 shows the microgrid hierarchical control model. B. Communications Technologies The general smart grid communications network is segregated in many extents namely Wide Area Network (WAN), Field Area Network (FAN), Neighboring Area Network (NAN), Home Area Network (HAN), Business/Building Area Network (BAN) and Industrial Area Networks (IAN) [27]. Figure 2 shows the smart grid end-toend communication architecture where the microgrid communication network is included [24].

Figure 2. Smart Grid communications architecture [24]

LC

Figure 1. Microgrid hierarchical control model. Adapted from [14] Presently, the common microgrid is composed of a threelevel centralized hierarchical control [14], [17]. The fist level consists of local controllers that interact with sensors connected to DER, ES and controllable loads, and it regulates the frequency, voltage and power, enabling

1) HAN/BAN/IAN comunication technologies The BAN and the IAN are at industrial and commercial customer premises. Building automation, heating, ventilation and air conditioner (HVAC) and industrial energy management are provided to commercial and industrial clients. The HAN connects smart appliances, electrical devices and smart meters at residential customer premises. Also, HAN assures communications to Home Energy Management System (HEMS). The main communication technologies for HAN/BAN/IAN are short range wireless technologies such as ZigBee, Lo6PWAN and Bluetooth, Wi-Fi and wired technologies namely Ethernet and PLC [25], [26], [27]. 2) NAN/FAN Advanced Metering Infrastructure (AMI) is composed of smart meters, computer hardware and software and

bidirectional communications [25]. The NAN provides bidirectional connection between the smart meters and the utility grid. The smart meters periodically measure energy consumption of the customers. Data Concentrators (DCs) at the vicinity aggregate the measured data from several smart meters and send those values to the utility grid. The measurements are sent to Meter Data Management System (MDMS) in the utility grid for billing and evaluation. The measured values can be reported in real time back to the customers that hence manage their energy consumptions. Also, the utility can analyze the electricity demand throughout the time, optimizing the energy production and distribution accordingly. Real time pricing implicates to broadcast the electricity price to smart meters and smart appliances so that the power usage can be planned taking into consideration the best hour price. Communication technologies that can be applied to NAN are for example, Low Power Area Network (LPWAN), 5G and PLC. LPWAN [28], [29] enables low power consumption and long range in contrast to cellular mobile networks that have long range but consume significant amount of power, and shortrange wireless networks which are low power networks but are short ranged. LPWAN technologies work in licensed and unlicensed spectrum. Proprietary technologies namely SigFox [30] and LoRa [31] operate in the sub 1GHz unlicensed spectrum, more precisely in the Industrial, Scientific and Medical (ISM) 868/915 MHz band. Cellular mobile IoT optimized technologies such as LTE-M, NB-IoT and EC-GSM-IoT [32] function in LTE and GSM licensed spectrum. However, SigFox and LoRa may present interference issues since they operate in the free-unlicensed spectrum. Moreover, the AMI frequently requires the exchange of data, which can be a limitation for SigFox, whose number of uplink messages per day is 140 messages of 12 bytes. Also, SigFox presents downlink and uplink asymmetry. 5G [33] is considered the future mobile network. It is under specification and is predictable to reach the market in 2020. 5G will be optimized for IoT and it will enable various connected devices, low latency, peak rate of 1 Gbps, energy efficiency and scalability. In order to provide high capacity and many connected devices, 5G is exploring the millimetric waves (mmWaves) band from 30 to 300 GHz. Nevertheless, mmWaves show considerable signal attenuation, absorption due to the atmosphere and rain, propagation and penetration issues into buildings. Studies have been conducting in the 28, 38 and 73 GHz bands as alternatives to the congested 700 MHz to 2,6 GHz bands used by contemporary mobile technologies. Many research groups are developing the 5G technology. 5GPPP has presented in [34] the view for 5G in the context of smart grids. Accordingly, 5G can play an important role within smart grids assuring low latency, reliability and availability. The FAN connects distribution substations, Distributed Generation (DG) and microgrids to the utility grid. The

technologies suitable to FAN are for example 3G, 4G, 5G, and PLC. 3) WAN comunication technologies The WAN connects bulk generation, substations, NAN and FAN to the utility grid. It covers a large geographic area. The Phasor Measurement Unit (PMU) is a device that measures the phase and the magnitude of the electromagnetic wave. It is introduced in strategic points of the distribution and transmission lines in order to monitor the power quality and to detect faults in the entire power grid, including the microgrid. The data collected from several PMUs are sent to Phasor Data Concentrator (PDC). The reporting rate of the measurements are 10-30 samples per second considering the norm IEEE C37-118 and 80-256 samples per second for IEC 61850-90-5 [35]. A Global Position System (GPS) clock synchronizes the PMU so that the data is precisely sampled. The WAN connects the PMUs to the utility grid. Communications technologies such as 4G, 5G, WiMax and Optical Fiber [25], [26], [27] can be applied, taking into account the distance, frequency of collected data, and the bandwidth required. 4) EMS Concerning processing, computing and storage of data collected for monitoring and controlling activities, cloud computing and fog computing can be considered as options. Cloud computing [36], [37] enables to share resources namely processing, memory, storage, and applications. It employs the Internet to connect to data centers which provide services to multiple clients. The cloud computing offers flexible services to clients since it allows to increase or to decrease the resources in accordance to the client needs. The clients have the alternative of not managing equipment nor dedicated specialized workforce. On the order hand, the cloud providers can virtualize servers, operating systems, software etc. The cloud computing business model furnishes services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). IaaS delivers to clients computing infrastructure such as virtual machines, processing, storage and networking. The clients manage their own operations systems, middleware, development and execution platforms, databases and applications. PaaS offers the computing infrastructure, the operation systems, the middleware and a cloud platform in which programmers can develop new applications. This platform includes an application development and execution environment. In this case, the clients only manage their database and applications. SaaS furnishes to client all the resources including applications and databases. In the client premise, there is no need to manage or install software and equipment since the client connects directly to the cloud to access to services and applications. The EMS can be hosted in the cloud since cloud computing offers flexibility, huge storage capacity, processing and virtualization. Fog computing [38], [39], [40], [41] is the extension of cloud computing to the network edge. It is a new paradigm

allowing connection, virtualization, processing, computing, and storage functionalities near to the users where the data is generated and consumed. To guarantee the standardization of the fog architecture, the OpenFog Consortium was formed in 2015. The fog computing has a hierarchical architecture [42]. The layers are composed of devices, gateways, fog nodes, and the last layer is the cloud which is regarded as a counterpart of the fog. The fog nodes are geographically distributed and at client neighboring. They can be elements placed anywhere, for example routers, switchers, servers, access points, base stations and surveillance cameras with virtualization, storage, and processing capabilities. The processing and memory capacities of those equipments are reinforced. The fog nodes and the cloud complement mutually with benefits, assuring a continuous service between the cloud and the end users. Although cloud computing is widely used by services and applications, it shows constrains namely delay, since the servers are located far from the clients, and the bandwidth is not preserved considering the large amount of traffic in the core network. Real time applications which are time sensitive pose delay requirements that fog computer can better achieve. Comparatively to cloud computing which is a centralized solution, fog computing presents a distributed approach, with many fog nodes spread over the network edge. The advantages of fog computing over cloud computing are lower delay, lower jitter, better security since the data is less exposed and fewer hops between the fog nodes and the client, typically one hop reachable in contrast with many hops for cloud computing, considering IP networks. Moreover, fog reduces the traffic in the core network decreasing congestion and latency, and it eliminates the single point of failure. Taking into account the advantages, fog has been proposed to the smart grid domain, particularly to the decentralized microgrid control approach [43]. III.

its location, location is unaware. The publisher must cryptographically sign the content with his public key, assuring content security. A. NDN Architecture In NDN communications there are two types of packets, INTEREST and DATA, illustrated in Figure 4. Both INTEREST and DATA packets carry a name that identifies a specific content. The DATA packet also holds the publisher cryptographic signature. The subscriber sends an INTEREST packet to request a given content. When the content is found, a DATA packet comprising the desired piece of data is sent towards the subscriber. This DATA packet tracks the INTEREST packet reverse path. Packets are forwarded hop-by-hop by routers through a name-based routing scheme. Each router maintains three data structures which are the Forwarding Information Base (FIB), the Pending Interest Table (PIT) and the Content Store (CS). The FIB consists of a routing table and it is built employing name-based routing protocols. It maps outcoming interfaces to each name-prefix. Routers forward INTEREST packets towards the publisher considering the information in the FIB. The PIT saves all the unsatisfied requests whose INTEREST packets have already been forwarded by the router. Each PIT entry records the content name along with the respective incoming interfaces. This allows the aggregation of various requests for the same content. Furthermore, the routers only forward the first INTEREST packet. Others INTEREST packets for the same content are discarded. The routers forward DATA packets towards the subscriber taking in to account the information in the PIT. The CS temporally caches the DATA packets in order to satisfy future requests for the same content.

NAMED DATA NETWORKING OVERVIEW

The necessity to reconsider the classic Internet architecture, motivated by the need to move the Internet from the user centric paradigm to the information centric paradigm, made the researcher community to quest for updated solutions. The Information-Centric Networking (ICN) is a novel proposal for the next generation Internet architecture. Among various ICN research projects, summarized in [44], [45], [46] is Name-Data Networking (NDN) [47], [48], [49], a further advance of the Content Centric Networking (CCN) project [50], [51]. The NDN future Internet architecture proposes a switch from the host-centric perspective to the content-centric viewpoint. The content is named and its delivery is ruled by the name rather than the IP address. The content to be named can be anything, for instance a data chunk in a movie or a book, a command to turn on some lights, etc. [49]. Besides the name based paradigm, the mobility and the security are native to the NDN architecture. While the content is delivered based on its name instead of

Figure 3 – Interest and Data Packets [48] B. Naming NDN names are hierarchically structured. For example, a command to adjust a DER voltage deviation may have the name /DER/Solar Panel/ADJUSTVOLTAGE/Value. The symbol ‘/’ delimits name components in text representations like URLs and it is not part of the name. Another example illustrates segmentation property, that is the segment 1 and version 1 of the video class.mpg is named /TSP/videos/courses/microgrids/class.mpg/1/1. In longest name-prefix matching a request for a content whose name is /TSP/videos/courses/microgrids/class.mpg matches the name /TSP/videos/courses/microgrids/class.mpg/1/1 since the last one has the first one as prefix. The NDN

hierarchical namespace allows not only to emphasize the relationship and the context of data elements but also to aggregate names increasing routing scalability. C. Packet Routing and Fowarding The packet routing and forwarding scheme in NDN is namebased. Publishers announce the name prefix of new content via the NDN routing system. Once an INTEREST packet is received, the router examines the content name and it checks in the CS for name-prefix matching. If the content name already exists in the CS, the router sends a DATA packet to the incoming interface. Otherwise, the router searches for the content name in its PIT. If there is an entry in the PIT with the content name, the router attaches the incoming interface to that entry and the INTEREST packet is discarded. If the content name is not encountered neither in the CS nor in the PIT, the CR looks up in its FIB. If a longest name-prefix match occurs, a new entry is added to the PIT with the content name and its incoming interface. Then, the INTEREST packet is forwarded towards the publisher considering the next hop outcoming interface in the FIB. In case of mismatch, the router broadcasts the INTEREST packet to all outcoming interfaces or it discards the packet, depending on the forwarding policies. When a DATA packet arrives, the router stores its content in the CS. Then, the router looks up for the name content in its PIT. If an entry is found in the PIT, the router sends the DATA packet to all the incoming interfaces recorded in that PIT entry. This enables multicasting. Next, that PIT entry is deleted. If a longest name-prefix matching not occurs, the DATA packet is dropped. IV.

MICROGRID NDN-FOG COMMUNICATION APPROACH

The operations of the microgrid involve the collection of data from multiple sensors, equipment and intelligent devices throughout the microgrid system. This data must be transmitted to controllers for storage, processing and decisions to be sent to actuators. Furthermore, a certain level of interaction between the customer and the microgrid is expected. For instance, the customer is informed about the energy consumed, and based on that information the energy usage can be adapted to reduce the electricity bill. Also, in customer smart homes, intelligent appliances generate information which is sent to smart meters and it is used for the microgrid for balance supply. Those operations require a significant amount of exchanged data that must be disseminated to various microgrid components. The NDN, as viewed, presents a content-centric approach that can help the microgrid communications to achieve an efficient and secure data delivery. Firstly, NDN name-based paradigm releases the need to configure network-dependent addresses for each microgrid component interface [4]. Names are discovered and advertised directly at network layer, becoming unnecessary additional association between names and interface identifiers [4]. Secondly, NDN cashing allows storing widespread

information such as real-time electricity price information and weather forecasts across the network. This information is widely available for customer and controllers, thus reducing network load. Data such as notifications and alerts for some or all the microgrid components can be multicast or broadcast respectively. Lastly, NDN assures security of the data by demanding publisher to sign DATA packets with a cryptographic key. The data is secure at network layer which means that it is needless to secure a channel or a session in microgrid communications. Also, with the advent of smart homes and smart building based on NDN [5], [6], [8] it is expected a profitable synergy and integration between smart homes, smart buildings and microgrid, all sustained on the NDN architecture, namely in energy saving and management. On the other hand, fog computing extends cloud computing to the edge of the network. Microgrid time sensitive data for example, control signals and equipment alarms and faults can be processed and actuated near to the controlled devices reducing the transmission delay and improving communication speeds and microgrid operations. Remote microgrids can benefit from the advantages of fog computing such as virtualization, processor capacity and flexibility, since fog nodes can be placed anywhere namely in base stations, railway stations etc. Data collected from several microgrid sensors can be processed, filtered or aggregated and temporally stored in fog nodes and then transferred to the cloud for permanent storage. Combining the advantages of NDN and fog computing leads to a more flexible, efficient and secure microgrid communications. A. NDN-Fog High Level Architecture The proposed microgrid NDN-Fog architecture is illustrated in Figure 4. The microgrid local controllers are in the fog nodes since the data must be processed closed to the controlled microgrid components namely DERs and ESs. Sensors at DERs and ESs send measured data to LCs for temporally storage and processing. The MGCC is in the cloud since it processes a huge amount of data from several local controllers. The PMUs carefully located in the microgrid power generation and distribution line sends those data to PDC via a NDN WAN. The DC close to the customers, aggregates the data from multiple smart meters (SM) through the NDN NAN and it sends the measurements to the MDMS. NDN gateways (GWs) forward the data to the fog nodes that collect and temporarily store the data from sensors. The fog nodes and the cloud are interconnected through the NDN WAN whose routers forward and route data using NDN interest-data mechanism. The NDN-Fog architecture is flexible and scalable, enabling the addition of more microgrid components for example DERs or loads and the increasing of the MGCC capacities for instance, memory, processing, storage. Also, it suits any type of microgrid, simple, independent or proprietary.

Since NDN can run above any layer 2 protocol, and it does not require a minimum MTU, it enables heterogeneous multi-link networks, with a broad set of technologies, without any mechanism to adapt data packet size. This facilitates the integration with fog which is mainly supported on various wireless technologies. Also, in NDN the control functionalities of transport layer such as retransmissions are pushed to the application layer. Moreover, it is possible to promptly shift from the hierarchical control architecture to full decentralized control one. The decentralized control requires interaction and cooperation among the local controllers without the presence of the MGCC which disappears, constituting a local controller mesh network. NDN facilitates the communication in mesh networks because it does not require managing IP addresses. Although the MGCC does not exist in decentralized control, the cloud is still needed to permanently store measured data, and it could be a backup in case of local controller breakdown. B. NDN Naming and data handling The microgrid controllers collect data from sensors and equipment, and they send control signals to actuators to perform a specific task. Following the sensor-controlleractuator mechanism in NDN-based smart homes and buildings described in [5], [6] and [8], the microgrid components are attached to names that describe tasks, commands, measures, alarms, notifications and information. The names are related to the corresponding microgrid component and specify a task, time, an action, a measure as exemplifying: /Microgrid_ID/DER_ID/Sensor/Voltage/Value/Timestamp/ /Microgrid_ID/ES_ID/Sensor/Level/Value/Timestamp/ /Microgrid_ID/Load_ID/Smartmeter_ID/Measure/Timestamp /Microgrid_ID/PMU_ID/Phase/Measure/Timestamp /Microgrid_ID/DER_ID/LC/ADJUSTVOLTAGE/Value/

The data is collected from sensors, smart meters, PMUs and IEDs considering the publish-subscribe mechanism described in [4] and implemented in [52]. After being notified, the local controllers, PDC and DC issue INTEREST packets, next they receive DATA packets with the measures. The MGCC can also request to local controllers the data collected sending INTEREST packets. However, for alerts or fault information that require a promptly action, the push data collection detailed in [4] is considered where a microgrid component or equipment may directly send data without following the interest-data procedure. The data can be inserted in the INTEREST packed. This solution is only adopted for high priority situations since it can be not considered as a general solution [4]. Controllers may send commands to actuators by issuing INTERESTS packets with the task to be executed in the name. For instance, a DER local controller may send an

INTEREST packet to its controlled component representing a command to regulate the voltage: Microgrid_ID/DER_ID/LC/REGULATEVOLTAGE/Value/codeKey

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Figure 4. NDN-Fog architecture Nevertheless, to assure that the command is from the controller, it must contain a codekey in the name for authentication of INTEREST packets by the actuators which should have access to the signature key of the controllers. It is well the case because the DATA packet already contains the publisher signature, so the content is secured. All the microgrid components should be assigned with a key for security improvement. After validating the INTEREST message from the controller, the actuator executes the command and it sends a DATA packet confirming the execution of the task. The MGCC can broadcast or multicast general instructions to the local controllers. The PIT and CS functionalities assure an efficient delivery. V.

CONCLUSIONS

This paper explored the communication solutions for the microgrid. It was proposed a NDN-Fog approach which can solve the current TCP/IP issues for data dissemination in the microgrids and achieve microgrid communications requirements such as low latency, security, storage, processing capabilities and data priority. The proposed solution is well suited for microgrids and adapted to further development in control method models.

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