A Wireless Mesh Architecture for the Advanced Metering Infrastructure ...

2013 IEEE Green Technologies Conference

A Wireless Mesh Architecture for the Advanced Metering Infrastructure in Residential Smart Grids Mohamed Riduan Abid, Ahmed Khallaayoun, Hamid Harroud, Rachid Lghoul

Mohammed Boulmalf ELIT School International University of Rabat Rabat, Morocco [email protected]

School of Science and Engineering Alakhawayn University in Ifrane Ifrane, Morocco [email protected], [email protected], [email protected]. [email protected]

Driss Benhaddou Engineering Technology Department University of Houston Houston, TX, USA [email protected] Abstract—Future Smart Grids will consist of distributed Micro-Grids where the Advanced Metering Infrastructure (AMI) forms a central component. This consists basically of meters/sensors that are regularly communicating data towards/from a central Control Plane. Due to the ad-hoc topological nature of the meters/sensors, particularly in residential areas, Wireless Mesh Networks (WMNs) prove to be the ideal technology for AMI deployment. In this paper, we propose a wireless mesh network based architecture for AMI deployment that uses both Zigbee and IEEE 802.11. The paper presents the challenges and opportunity related to implementing such architecture in Moroccan market. This paper proposes a framework for renewable energy integration, and an appropriate Middleware design. The paper presents preliminary simulations on the wireless coexistence between the two technologies and draw conclusions on the channel to be used and node placement problems.

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panel for instance, and become producers. This excess of electricity can be fed back to the grid, creating a two way flow of electricity. With the feed in tariff law adopted by many countries, the consumer can sell electricity to operators and make profit (which will motivate consumers to adopt renewable energy and bring the operating cost down). However, injecting electricity into the grid is not a straightforward task as this might cause grid instability and induce blackouts in serious cases. Information Technology will play a key role in making sure control information is collected in real-time to enable electric flow into the grid [3, 4]. Another major novel aspect in SGs is the advent of microgrids. Typical scenarios of micro-grids deployments are university campuses, cities, and factories, where there is enough renewable and cogeneration of energy if it is cut from the grid, it can still function under certain circumstances and conditions. Thus, SG will be composed of a complex distributed system of generators that accommodate renewable power spread in diverse locations, including customers, some of which are micro-grids. To integrate renewables energy in homes and commercial buildings and to connect these buildings to smart grid, there must be a number of sensors and information sharing among consumers and service providers as dictated by SG standards such as demand response protocol through advanced metering infrastructure (AMI). Given the advantage of wireless technologies, the standard has adopted Zigbee (IEEE 802.15.4) as the protocol for home area network. In addition, the paper extends the network to a neighbourhood (or campus) level and proposes a wireless architecture for local AMI using wireless mesh networks (WMNs). WMNs Enable the communication between the sensors/meters and the control plane constitutes the role of the Advanced Metering Infrastructure (AMI) are widely recognized for their ease-ofdeployment, self-healing, and ad-hoc nature; characteristics

INTRODUCTION

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mart Grids (SGs) are emerging as a very promising technology being developed to cope with the increasing stringent worldwide demand on energy [1, 2]. They are expected to replace ordinary electrical Grid (OGs) to enable the implementation of distributed renewable energy, and thus allow for optimal use of electric energy. With the integration of Information Technology (IT) into OGs, different components that are currently automated or manually operated (e.g., meters) will be able to process and exchange data in order to make intelligent autonomous decision, thus exhibiting a smart behaviour. A central novel aspect in SGs, compared to OGs, is the two-way electricity flow in the grid towards and from the consumers. With the advances in renewable energy technologies and with the consistent decline in their cost, consumers will be able to generate electricity, through solar 978-0-7695-4966-8/13 $26.00 © 2013 IEEE DOI 10.1109/GreenTech.2013.58

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that will benefit AMI, and especially in residential areas where the home electrical supplies are numerous and can be located anywhere in the house (redundant). In addition, the residences/homes exhibit an ad-hoc topology as the network is not under centralized control and topology may change depending on signal strength and notes going up and down. In general, two WMNs are used: 1. a Zigbee WMN to interconnect the sensors to the meter gateway, and 2. an IEEE 802.11 based WMN to interconnect the gateways/meters to the control plane. This paper proposes an open architecture that will enable reliable communication of information among participants emphasizing the scenario in Morocco Market. Given the heterogeneous nature of the network, the paper presents preliminary results on the wireless coexistence of Zigbee and IEEE 802.11s using OPNET simulations. These results are used to determine the optimum channel distribution and node placement problem in homes and campus environment. The reset of the paper is organized as follows: Section 2 highlights the review of smart grid architectures and AMI and current state of the art, in Section 3, we describe the proposed architecture. In Section 4, we highlight the middleware design. Section 5 describes the renewable energy integration. Wireless Mesh Networks are highlighted in Section 6. Section 7 assesses the Wireless Coexistence between the Zigbee and IEEE 802.11, and we finally conclude in Section 8.

The SmartGrids ETP, European Technology Platform for Electricity Networks of the Future, is a key European forum to develop technologies for the smart grids sector, as well as link among EU-level related initiatives [12]. Networking technologies that can be used by advanced meter infrastructure (AMI) have been surveyed by Bennett and Highfill [13]. The survey covers the requirement identified by the Utility AMI standard [14] and enhancement needed by the wireless technologies to meet those requirements. The survey indicates that a more in depth study is needed in order to determine the applicability of Zigbee. This paper proposes an open architecture to address this research gap and presents a preliminary study of the impact of WiFi on Zigbee in residential area. The papers in [15, 16] stress the importance of computational intelligence and ubiquitous computing to implement algorithms that are fast, scalable and dynamic to implement advanced monitoring, control and optimization algorithms that are required to carry out functions necessary by smart grid. The proposed architecture take in consideration issues related to implementing solar-based renewable energy in smart grid. Ultimately, integration of smart grid technologies with the highest efficiency solar panels would provide for the maximum benefit to customers, utilities, and the nation at large. III.

WIRELESS MESH ARCHITECTURE FOR AMI DEPLOYMENT

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REVIEW OF SMART GRIDS TECHNOLOGIES AND RESEARCH CHALLENGES

To optimally manage the electric power resource, power consumers use a 2-way communication channel with the SG control system, e.g., consumers reporting electricity meter levels and receiving electricity prices at any time in order to manage power consumption following a demand response protocol. In certain scenarios where a group of buildings (campus or a neighborhood) can communicate to optimize energy consumption, WMNs emerge as an easy-to-deploy technology that requires no wiring and uses data forwarding where intermediate nodes relay data on behalf of other nodes. At the heart of SG lies the Advanced Metering Infrastructure (AMI). AMI consists of a set of smart meters that communicate with a central system. The communication is a two-way one: 1. Smart meters periodically (e.g., 10 min) report power consumption levels. 2. Central system issues monitoring requests to Smart meters (e.g., switching On/Off). By collecting reports from different meters, the AMI can mine the data and analyze it in order to decide on the optimal usage of the electricity and respond to critical problems in real-time. On the other hand, local AMI infrastructure can be developed to enable a group of building to develop a microgrid. To address this scenario, the proposed architecture for AMI deployment connects four main Smart Grid Components, see Fig. 1: 1. Residential Smart Micro-Grid: This consists of residences, along with the corresponding electrical appliances. The appliances are equipped with Zigbee sensors and form a wireless mesh network. In fact, the Zigbee sensors can be configured to operate either in the mesh or tree mode. We opt in this architecture for

Smart gird related research and development is being investigated by utility companies, standards bodies, and university research groups, and is currently one of the top technologies that will give the economy a competitive advantage. Several consortiums have been established to develop technologies that will enable the migration of the current electric power grid toward a reliable and efficient smart grid [3-8]. The US Department of Energy has initiated a Modern Grid Initiative (MGI) to investigate the key technologies that are needed to enable smart grid [9]. The report identifies five key technology areas that can be grouped in integrated communications, sensing and measurement, advanced components, advanced control methods, and improved interfaces and decision support. National Institute of Standards and Technology (NIST) is undertaking the responsibility of developing standards that will enable interoperability of technologies developed to enable the smart grid, as chartered by the Energy Independent and Security Act of 2007 [10, 11]. Other standards bodies, such as IEEE (Institute of Electronics and Electrical Engineers), IEC (International Electrotechnical Commission) and IETF (Internet Engineering Task Force) are being involved to help in this huge process [11]. The standardization priorities define several areas: Demand Response and Consumer Efficiency; Wide Area Situational Awareness; Electric Storage; Electric Transportation; Advanced Metering Infrastructure; Distribution Grid Management; Cyber Security; and Network Communications.

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functionalities that allow providing homes with collected

the mesh mode as this is the optimal mode for ad-hoc topologies. Furthermore, residences can be of a large space, and thus we might have appliances located in dark spots (i.e., spots that are isolated or located in a farther location from the sink gateway). With a mesh topology, sensors can operate as routers on behalf of each other, thus augmenting the sink visibility to dark spots. Every single residence is equipped with a Zigbee-IEEE 802.11 gateway (e.g., meter) which plays both the roles of a Sink and an IEEE 802.11 node which will connect the residence, and thus appliances, to the IEEE 802.11s Mesh Network. This latter will be interfaced to the backbone network (e.g., Internet, Operator network) via a Mesh gateway (i.e., a Mesh Portal Point in IEEE 802.11s terminology) 2. Control Plane: This is the back office where all computation will be done. This will consist mainly of a Database Server that will store all data communicated from the sensors, and an Application Server that will provide an interface for accessing the data and issuing appropriate operations (e.g., switching On/Off appliances). 3. End user (Home Owner): He will be capable of tracking the energy consumption at his residence, and interacting with the Application Server at the Control Plane. Specific end-user applications should be developed for Mobile devices. 4. Electricity Provider: It should have access to the realtime data collected at the Control Plane. Typical applications can be shaped and deployed at the Application Server based on the Operator need, e.g., Applications tracking the electricity consumption behaviour in a whole Residential Smart Micro-Grid. An important point which is very worth mentioning, at this stage, is the nature of the backbone network. In fact, there can be two independent networks one managed by the Electricity Operator, should be a Private Network in order to assure a high level of security, and the other under the control of the user through an Internet access point. However, this variability/imprecision in the nature of the backbone network does not impact the proposed architecture since most private networks are connected to Internet where special leased lines are purchased. In this work, we are planning to deploy this architecture using Internet as a backbone. Thus, security does is not concern in this case since this is meant to be an open testbed for Smart Grid. IV.

ResidentialSmartMicroGrid Home Owner

ControlPlane BackOffice

Solar Panel

Backbone Network

IEEE802.11s MeshNetwork Mesh Gateway

ZigbeeIEEE802.11 Gateway

Zigbee IEEE802.11

Database Application Server Server

Electricity Provider

Figure1:SmartGridAdvancedMeteringInfrastructureArchitecture

energy, and WMN infrastructure layer consisting of diverse types of sensors and meters. The use of a separate WMN infrastructure layer allows higher flexibility in adding various energy sensors and devices without handling low-level programming at the level of home services/functions. The middleware layer gathers data from different devices in the WMN, filters, transforms and aggregates collected data before storing it into a database. The volumes of data generated by the different WMN sensors require significant data filtering to extract the most relevant information. Various filtering policies are monitored by the middleware to filter data depending on the characteristics of the energy consumption and/or provisioning. The data filtering includes also the removal of duplicate data that maybe generated by multiple sensors. The middleware allows transforming raw data into a form which is suitable for homes smart grid level interactions. So, from an end-user service perspective, it is desirable to provide a mechanism that turns the low-level captured data into the corresponding business event.

MIDDLEWARE DESIGN

Our approach consists of using a middleware management that helps managing the configuration of the different AMI components, and provides the main following capabilities: x Add, configure, and modify connected WMN elements, x Process collected data, and store it in a database, x Manage end-user services and disseminate appropriate information and services based on subscribe/notify protocol. As shown in the Fig. 2, AMI is organized as a three-tier architecture consisting of clients’ service layer, AMI middleware layer applying a set of business logic to trigger

Figure 2: Smart Grid AMI Middleware Architecture

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The fine-grained data has implicit meanings and associated relationships with other data, and need to be aggregated into summaries and/or proper inferences. V.

RENEWABLE ENERGY INTEGRATION

A significant contribution of Smart Grid is the integration of renewable energy [17, 18], thus promoting green and efficient energy. Currently, various countries around the globe are integrating more renewables through subsidies (i.e., feed in tariff) and promoting saving through efficiency regulations. Morocco, like other countries, has put the legal aspects pertaining to the integration of renewable energies (Loi13-09) [19], institutional and technical instruments (e..g, MASEN – The largest Solar Energy Plant in the world) [20], and capacities (20GW of renewables) at its disposal to enable greater renewable energy capacity [21], and improve on energy efficiency. The Moroccan law enables industrials to produce their own energy but can only feed back to the grid at the medium, high or very high voltage. A law enabling the end user to sell back electricity to the electric company is being drafted and should take effect in the coming year. With the significant decrease in the cost of KWh produced by solar or wind, Morocco and other developing countries will see a significant increase in their DG (Distributed Generation) capacity. The latter will cause an issue for the dispatcher, who will need to have a good estimate on the load profile of the country, a challenge when the DG capacity of the country is increased significantly. In this context, an AMI infrastructure becomes imperative for proper management of the grid. A current project undertaken by the authors involves equipping a residential household with renewables (solar/wind) and a metering infrastructure, and this to deduce a load profile for a typical Moroccan household, and optimize the renewable energy size for the household of interest. In this paper, we propose a general architecture for renewable energy integration that can be easily shaped to fit any Residential area. Fig. 3 depicts the relevant main components. The customer (home owner) is powered via the electric grid, and a solar panel is rendering the customer an electricity producer as well. The power generated by the panel (DC form) is converted to an AC form to enable low voltage grid injection of excess energy. A power switch box will be used to couple the solar power to the residence and to the electricity provider at low voltage. The two-way meter will record the energy consumption at both sides, i.e., the power generated locally and the power provided by the grid. Using the AMI infrastructure, the customer will have access to real-data about consumption levels and tariffs. This will enable him/her to manage the house loads in order to efficiently use the locally produced energy. The adopted architecture will make the loads adjustable and shiftable (the house load profile will be rearranged based on the local production curve). It is worth noting that some loads are classified as sensitive and they might not tolerate being disconnected from the grid. Finally, the metering infrastructure will enable cost optimization in case where

 Figure 3: Renewable Energy Integration

 variable-tariff contract and Demand Response are adopted by the electricity provider. VI.

WIRELESS MESH NETWORKS

Due to their easy-to-deploy, self-healing, and reduced-cost features, WMNs [22] are emerging as a promising technology, and are attracting considerable attention in academia and industry as well. In this context, IETF (Internet Engineering Task Force) is actively working to finalize the IEEE 802.11s standard which will pave the way towards a successful worldwide industrialization of this promising technology. Even though the IEEE 802.11s standard is not final yet (Last meeting was held in July, 2011) [23], its main traits are already set, e.g., architecture and MAC routing. The IEEE 802.11s TG (Task Group) set HWMP (Hybrid Wireless Mesh Protocol) [24] as a default routing protocol along with Airtime [25] as the default routing metric. WMNs are easy-to-deploy as they involve minimal wiring and configuration overhead: Placing the WMN nodes and powering them On is all that is required to operate a WMN. On the other hand, WMNs are self-healing thanks to the redundancy of wireless links: A failure in a wireless link incites the network to seek an alternative operational link, thus maintaining the network operational. Indeed, in WMNs, no wiring is required except for the AP (Access Point) which will serve as a gateway towards the backbone network (e.g., AMI, Internet), even though the gateway AP can be wirelessly connected as well. All other APs serve as routers and route data on behalf of each other towards/from the gateway AP. Therefore, covering a cell (e.g., a residence) reduces to adding an AP without wiring it

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to the backbone network. This way, the WMN technology is considerably easing wireless coverage, especially when compared to the actual Wi-Fi WLANs solution. A fact which is of tremendous importance to industry since easing wireless coverage equates affording more coverage; and thus supporting more user connections. To illustrate the functionality of the IEEE 802.11s mesh networks, we depict in Fig. 4 a scenario where an end-user, with a Wi-Fi enabled station, is accessing an Application Server located in the Internet: The end-user, at station Sta1, is connected to a legacy IEEE 802.11 network through a mesh access point (MAP1) which serves as an interface between the Wi-Fi network and the WMN. MAP1 is responsible for routing frames towards the destination, using an appropriate routing protocol (e.g., HWMP). MAP1selects a mesh point (MP1) as a next-hop. This latter performs only routing and forwards the data towards a mesh portal point (MPP1) who serves as a gateway between the WMN and the Internet where the server resides. Ideally, MPPs should interface WMNs with whatever type of non-mesh network, e.g., Smart Grid AMI. In Smart Grids, residential electric appliances form an “adhoc” topology since their locations cannot be determined in advance in any way. This fact applies to residences’ locations as well. Thus, WMNs prove to be the perfect Wireless technology to deploy Smart Grid AMI, where appliances and residences need to be connected to a backbone network. This latter embeds the computing power, e.g., servers and databases. In this context, we propose a wireless mesh architecture for AMI deployment, where IEEE 802.11s WMNs are used to connect residences, and ZigBee WMNs [26] to connect the appliances. Next Section highlights the architecture.

Figure 5: Zigbee and IEEE 802.11 Coexistence Scenario

mobile IEEE 802.11 nodes (Mobile_1 and Mobile_2) are constantly moving towards the vicinity where two Zigbee nodes (a sensor and a gateway) are communicating, see Fig. 5. The Zigbee Sensor is sending data towards the Zigbee Gateway with a rate of 100 kbps. Ihe IEEE 802.11 stations (i.e., Mobile_1 and Mobile_2) are uniformly moving towards the Zigbee vicinity with a speed of 10 miles per hour, and are transmitting at a data rate of 1 Mbps. Using different configurations for the IEEE 802.11 bands, we verified the impact of interferences between the two wireless technologies by tracking the Zigbee throughput variance depending on proximity of IEEE 802.11 mobile nodes to the Zigbee vicinity. Three different experiments sets were run, each pertaining to one of the three IEEE 802.11 standards, i.e., IEEE 802.11 a/b/g. Next section illustrates the results. A. Zigbee 2.4 GHz vs. IEEE 802.11a IEEE 802.11a was designed to operate in the 5-GHz Unlicensed National Information Infrastructure (U-NII) bands in the United States. Thus, theoretically, IEEE 802.11a should not interfere with Zigbee 2.4 GHs as they are operating in different bands. To assess the fact, we configured the IEEE 802.11 mobile nodes to transmit using IEEE 802.11a. Results showed that indeed there are no interferences, See Fig. 6, as the throughput (100 packets/sec) remained unchanged (In all experiments, we set a packet to be 1Kb).

ZIGBEE AND IEEE 802.11 COEXISTENCE

VII.

To assess the wireless coexistence between Zigbee and IEEE 802.11, we run OPNET [27] simulations where two



Other non-mesh network, e.g., Electricity Operator Network

Internet Application Server

MPP1Gateway

MPP2

MP3 WirelessMesh Network

MP1

MP2 MAP1

WiFiNetwork Sta1

Figure 6: Zigbee Throughput (Packets per Sec) with IEEE 802.11a

Sta2

 Figure 4: Wireless Mesh Network for Last-mile Internet Access

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Figure 8: Zigbee Throughput (Packets per Sec) with IEEE 802.11g

Figure 7: Zigbee Throughput (Packets per Sec) with IEEE 802.11b

B. Zigbee 2.4 GHz vs. IEEE 802.11b IEEE 802.11b operates in the 2.4 GHz band. This latter is divided into 13 channels with an inter-space of 5 MHz (Channels 12 and 13 are not allowed in USA, thus resulting in 11 channels). Theoretically IEEE 802.11b and Zigbee should interfere as they are operating in the same band. To assess the fact, we configured the mobile stations to operate in IEEE 802.11b, and run experiments for every single IEEE 802.11b channels, resulting in 13 experiments sets in total, i.e., one for each of the IEEE 802.11b 13 channels. We found that Zigbee throughput is affected only when IEEE 802.11b is transmitting in Channel 13, see Fig. 7.

2min16. This corresponds to a throughput of 14.5% which is excessively unacceptable, when compared to the throughput of 26.6% for IEEE 802.11b. From the three experiments sets above, we evaluated the impact of IEEE 802.11 on Zigbee 2.4 GHz and specially the degradation of Zigbee in the presence of channel 13 in IEEE 802.11b. Even though there is no apparent interfering, and thus they can both coexist (exception for channel 13 in IEEE 802.11b and IEEE 802.11g), the results show that we should take in consideration interference and pay attention to note placement in the network. Previous experimental results [28] showed that wireless nodes are susceptible to other interference and topology design should be taken consideration.

Indeed, we clearly notice that IEEE 802.11b (channel 13) is noticeably interfering with Zigbee 2.4GHz: At time 2m19sec, the received packets is dropping to a minimum value of 26.6 packets per second which corresponds to a 26.6 %, and this is largely an unacceptable throughput degradation.

VIII.

CONCLUSION AND FUTURE WORK

In this paper, we presented a highly flexible Wireless Mesh Network architecture for deploying Smart Grid Advanced Metering Infrastructure for a cluster of buildings (e.g. neighborhood, campus). The proposed architecture uses both Zigbee and IEEE 802.11, and we demonstrated through simulation that Zigbee and IEEE 802.11 can largely coexist if we exclude using Channel 13 in IEEE 802.11b/g. However simulations don’t take real world environment in consideration and provide simplified view of the network. It is our expectation to use the test bed to collect real world measurements and assess the performance of the network. We also delineated a highly flexible architecture for energy integration and proposed an appropriate middleware plane that facilitates the dissemination of information among different components of the architecture. The WMN enable the network to adapt to dynamic change in the network and support the open environment to research different implementation strategies of the smart grid technologies. One of the research problems that will be addressed in the future is the development of middleware framework that will facilitate the integration of heterogeneous networks and sensors. The platform will be used to implement algorithms and protocols that will enable us to demonstrate and test

C. Zigbee 2.4 GHz vs. IEEE 802.11g IEEE 802.11g is transmitting in the same band as IEEE 802.11b and has the same channels; the two standards differ only in the modulation techniques, which result in different transmission rates and ranges. Theoretically, since IEEE 802.11g is operating in the same band as IEEE 802.11b, its interference with Zigbee should be similar to the one observed in previous experiments. To assess the fact, we configured the mobile nodes to operate in IEEE 802.11g, and checked the interference of Zigbee with all 13 IEEE 802.11g channels. It turned out, that indeed, IEEE 802.11g is exhibiting a similar behavior to the one of IEEE 802.11b. However, IEEE 802.11g showed severe interference levels when compared to IEEE 802.11b. For instance, using IEEE 802.11g Channel 13, see Fig. 8, the number of received packets at the gateway is dropping to a minimum of 14.5 (packets per second) at time

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[22] F. Akyildiz and X. Wang, “Wireless Mesh Networks: A Survey,” Computer Networks and ISDN Systems, Vol. 47, No. 4, pp. 445-487, 2005 [23] IEEE TGs, Status of Project IEEE 802.11s: http:// www.ieee802.org/11/Reports/tgs_update.htm, 2011 [24] M. Bahr, “Update on the Hybrid Wireless Mesh Protocol of IEEE 802.11s,” IEEE Conference on Mobile Adhoc and Sensor Systems, Pisa, 2007, pp. 1-6. [25] M. Bahr, “Proposed Routing for IEEE 802.11s WLAN Mesh Networks,” The 2nd Annual International Wireless Internet Conference, Boston, 2006, pp. 6-13. [26] V. Gungor, D. Sahin, T. Kocak, and S. Ergüt, “Smart grid communications and networking,” Türk Telekom, Tech. Rep. 11316-01, Apr. 2011. [27] OPENT Network Simulator, http://www.opnet.com [28] D. Benhaddou, M. Balakrishnan, X. Yuan, J. Chen, M. Rungta, R. Barton, H. Yang, “Wireless Sensor Networks for Space Applications: Network Architecture and Protocol Enhancements”, Sensors & Transducers Journal. MEMS: From Micro Devices to Wireless Systems, Vol. 7, Special Issue, Oct. 2009. 

smart grid technologies such as demand response, efficient energy utilization and renewable energy integration.. As a future work, we are planning to implement this architecture in a real-world Residential Smart Micro-Grid at Alakhawayn Univeristy in Ifrane.

ACKNOWLEDGMENT We acknowledge the Seed Money Research Fund, at Alakhawayn University in Ifrane, for funding this Research.

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