Increasing Renewable Generation Feed-InCapacity Leveraging Smart Meters

Maurantonio Caprolu∗, Javier Hernandez Fernandez∗†, Abdulrahman Alassi†‡, Roberto Di Pietro∗

∗Hamad Bin Khalifa University (HBKU) - College of Science and Engineering (CSE)Division of Information and Computing Technology (ICT) — Doha, Qatar

†Iberdrola Innovation Middle East, Doha, Qatar‡Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UK

Abstract—The amount of energy that can be fed intoa network is limited by the physical capacity of thecomponents that form the grid and the load it serves. Elec-trical utilities use Hosting Capacity (HC) methodologies toanalyze and calculate the level of generation that a networkcan accommodate. Currently, HC analyses are based onconservative static models with the objective of ensuringthat technical limitations are not exceeded, hence curbingthe optimal network capacity. Demand Response (DR)programs seek to optimize energy resources by loweringor deferring consumption. As a result, technical solutionshave focused on reducing demand. These solutions areefficient when there is no local generation of energy, or toreduce individual consumption, but they hamper with feed-in programs. To solve the above introduced issues, in thispaper we propose a system with the objective to maximizethe limit of the HC of each customer while ensuringstability of the grid. Each customer is assigned a nominalHC value, calculated using the existing methodologies,but has the option of requesting a temporary increasein the feed-in limit. The utility provider will grant orreject the request based on the current conditions of thegrid by utilizing the existing smart meter infrastructureand the energy-balancing metering at the transformersubstation. The architecture supporting the cited objectivesis detailed, a PoC rooted on real experiments is showed, andfuture directions are also highlighted. Finally, the achievedexperimental results show the viability of our proposal.

Index Terms—Hosting Capacity, Smart Grids, ActiveDistribution Networks, Blockchain, AMI, Smart Meters,Energy Trading.

I. INTRODUCTION

In recent years, problems such as climate change andthe depletion of natural resources have raised grow-ing concerns about fossil-based energy production tech-niques. As a result, more and more entities, from compa-nies to private citizens, have begun to produce electricitylocally. Consequently, power grids are evolving intosmart grids, adapting to the new market, and leverag-ing new technologies to provide increasingly advanced

This is a personal copy of the authors. Not for redistribution. Thefinal version of the paper will be soon available through the IEEExploreDigital Library, in the proceedings of the IEEE Green Energy andSmart Systems Conference.

services to both producers and consumers. The field ofActive Distributions Networks (ADN) deals with thepenetration of intermittent Renewable Energy Sources(RES) into the electrical grid. Proper identification of thePhotovoltaic (PV) penetration limit in Low Voltage (LV)distribution networks is a vital parameter for reliableplanning and operation by the Distribution System Op-erator (DSO). This limit may be identified using severaldifferent reference parameters, including the maximumpermissible percentage of the distribution network’s load,the substation transformer capacity, and the availablerooftop space. Moreover, the limiting factors for defin-ing the maximum tolerable penetration limits may alsovary. Namely, a penetration limit can be defined to beachieved when voltage regulations exceeds the adopteddistribution codes (i.e. when a voltage rise conditionoccurs). Similarly, Total Harmonic Distortion (THD) andtransformer saturation may be also be used as penetrationlimit indicators, as well as the network and feederthermal and electrical limits [1]. Authors of [2] summa-rized the preliminary Hosting Capacity (HC) estimationswhich are followed by some international DSOs forthe connection of distributed generation. RES systemsinstalled on LV, such as residential buildings, typicallyinclude a second bidirectional smart meter (in addition tothe smart meters owned by the utility provider), whichis connected to a controller that balances the invertersto ensure that the output power entering the DSO gridnever exceeds a certain limit. Despite achieving itspurpose, such an arrangement is far from optimal, asit requires the duplication of the metering assets. Thisinefficiency is usually a product of either regulation,technical limitations in the hardware, or the lack of anappropriate interface to be used by the household [3].

A. Motivations

The existing HC mechanisms, due to their staticnature, are not optimizing the feed-in capacity of LVRES. The feed-in limits set by regulation and utilities [1],[2] are predetermined based on calculations that take

into consideration the existing grid infrastructure andadopt a conservative approach that guarantees the safetyof the networks. Accordingly, the set limit is genericand does not take into account the constant variationsin the network. Therefore, it offers only a baseline levelof power to be exported to each customer, which cannotbe considered an optimal solution. A relevant exampleis when the local installed PV generation capacity forsome clients exceeds their designated export limit bythe utility (e.g., installing a 10 kW PV system whenthe utility imposed HC export limit is 5 kW). Suchlocal overcapacity can be justified to increase the userself-consumption against typical high loads. Though,under occasional light loading conditions, the availableexport PV capacity might exceed the allowed one bythe DSO, forcing the inverter to operate the PV—in thiscase, below its maximum power point, and leading topartial, undesired, energy curtailment. Extending the useof distributed generation requires a dynamic approachthat considers and adapts to the current conditions ofthe grid to optimize generation resources. Moreover,maximizing the feed-in capacity of local resources viaa flexible HC, also provides a new controllable inputparameter for Demand Response (DR) programs to moreefficiently distribute energy consumption. Such an ap-proach is a logical step towards more sustainable andself-sufficient energy communities while preserving anenvironment of trust where transactions are transparentto all stakeholders.

B. Contributions

This paper introduces a generation response systemthat can help to maximize the output of distributed re-newable sources in the LV by increasing, for certain pe-riods, the assigned feed-in capacity of customers wheretechnical conditions allow it. Our system is similar inconcept to traditional DR programs but has the oppositeemphasis, as the baseline feed-in is fixed and the benefitis granted by request and based on the current statusof the grid. The system design is also enriched witha blockchain-based application in order to provide atrustworthy and transparent mechanism that records thereasons behind the approval and rejection of requests.The contributions of this work are manifold, and can besummarized as follows:

• Proposing a novel system capable of dynamicallymanaging the HC in LV networks, by increasingcustomers’ assigned feed-in capacity for certainperiods.

• Designing and implementing a protocol that incor-porates a dynamic HC system using smart meters.

• Developing an experimental testbed to validate thedesigned system operation in an emulated environ-ment with three practical scenarios.

II. BACKGROUND

A. Hosting Capacity

The concept of HC was introduced by Andre Even in2004 [4] and later refined by Math Bollen [5]. HC definesthe amount of Distributed Generation (DER) that can beintegrated into a power system without adversely affect-ing the quality of power and without requiring to upgradeinfrastructure [6]. The HC of a network is affected byvarious factors. Some of these factors are related tothe network characteristics, such as the configuration,load profiles, protection devices, etc.. Other factors arerelated to the DER, such as the employed technologyor the location. According to [6], the accuracy of HCis influenced by multiple factors. However, taking allof them into account is computationally unfeasible. Inthis regard, studies such as [6] have provided PrincipalComponent Analysis of the influencing factors, and havediscussed the implications of using each factor to aid inthe selection process.

B. Smart Metering Architecture

As depicted in Figure 1, customers’ homes are con-nected to the utility power grid and equipped witha smart meter that provides computational and stor-age capability to each end-point in the network. Com-munication between smart meters and utility meteringsystems can be implemented using various technolo-gies commonly used in power grid architecture, suchas Power Line Communication (PLC), DSL, WirelessMesh, Cellular (GSM / GPRS, 3G, LTE), or LPWAN.Given the wide choice of technologies available, thetelecommunication architecture varies considerable fromprovider to provider. This paper uses PLC as the basetelecommunication technology, as it is currently the mostused in smart metering deployments [7], [8]. The mainadvantage of PLC over other technologies is that it doesnot require the deployment of any new infrastructure, asexisting power lines are used for signal transmission.Besides these infrastructural and operational benefits,security is also strengthened by using PLC, as sniffingcommunications requires physical coupling to the LV ordirect access to the equipment. In the standard archi-tecture for PLC deployments, each home is connectedvia PLC to the Data Concentrator Unit (DCU), whichis usually located in the local transformer substationthat supplies the household with electricity. The DCU isconnected to the IT infrastructure of the utility providervia standard TCP/IP protocols. Systems such as theMeter Data Management System (MDMS) manage thecontrol and operation of the smart meters [9].

III. RELATED WORK

The determination of the maximum HC is a well-studied topic, with research focusing on the most ap-

Fig. 1: Utility’s infrastructure.

propriate methodologies and models to determine theoptimal HC of a system [10], [11], factors affecting sucha capacity [12], [13], and techniques to mitigate theireffect [14], [15], [16]. Authors of [17] showed that realtime access to network parameters can have a significantimpact on the increase of HC thresholds. Efforts toextend the current use of smart meters to applicationssuch as planning, DR programs, and load control arealso becoming more frequent now since an increasedpenetration rate of installed smart meters has becomethe norm. The European Commission reported that by2020 over 200 million meters will be smart, representingover 70 percent of European households.r [18]. In [19],the authors presented a microgrid composed of seventransformer substations connected to loads, distributedgeneration resources including wind turbines, PV andmini-hydraulic and storage capacity in the form offlywheels and electrochemical lead-acid and lithium-ionbatteries. Each element of the microgrid was equippedwith PLC based smart meter using the PRIME protocol,an open international PLC standard for AMI and SmartGrid applications [20], [21], to provide remote control,monitoring, and load planing and improve the qualityof service of the network. Data provided by smartmeters was used in [22] to increase the HC by applyingan optimization method for balancing the impact ofasymmetrical power in-feed of LV networks with highPV penetration . The Authors of [23] simulated thearchitecture of an AC/DC microgrid for a residentialbuilding with smart meters using radio mesh with stan-dard EN 13757-5 to prove its applicability for EnergyManagement Systems (EMS) and Power Quality (PQ).Smart meters can also be used in street lighting control[24], [25] or fault location in distribution systems [26].

Fig. 2: Flowchart of the proposed protocol. The dottedlines represent PLC messages, while dashed blocks areevents that may or may not occur.

IV. DESIGN AND ARCHITECTURE

The main goal of the proposed system is to cre-ate a dynamic HC management technique to optimizethe feed-in capacity of LV customers. The architectureshould also be able to take advantage of the existingequipment and infrastructure to facilitate seamless in-tegration into the DSO network. To achieve the abovegoals, we designed a request-based protocol for thedynamic management of HC, depicted in Figure 2. Inour system, even if the baseline feed-in is fixed, eachcustomer can request a temporary increase in their exportPV energy capacity when necessary. This request willbe evaluated by the DSO, and granted or denied basedon the current conditions of the grid. When a requestis granted, the conditions of the grid are monitored forthe entire duration of the request. If the state of thenetwork no longer supports this concession, the extraexport capacity is immediately revoked, reverting to theinitial HC limit assigned by the DSO. Any request made,whether granted, refused, or revoked, is registered on ablockchain-based application. This ensures transparency,accountability, and auditability, which are not guaranteedby current systems.

A. Architecture

The architecture of the proposed system is depicted inFigure 3. The system can be gracefully integrated intothe standard architecture of any power grid implementingsmart meters and DCUs or any kind of LV supervisionin the transformer substation, and is complemented by ablockchain-based application that runs on cloud systemscontrolled by the DSO. The request to increase theenergy export limit is triggered on the smart meter byopening a request to the DCU. The DCU handles the

Fig. 3: High level architecture of the proposed system.

request by checking locally whether it is technicallyfeasible to temporarily increase the feed-in limit. If suc-cessful, the DCU calculates the new capacity thresholdand the period for which this increase in capacity isgranted, and communicates the decision, along with itsparameters, to the smart meter. The decision is alsoforwarded to the blockchain-based application, to recordthe status of the network at the time of the request andthe response to the request.

The proposed solution relies on existing smart me-ter infrastructure, including the balancing meter at thetransformer substation, and requires no additional equip-ment by the DSOs. The system will rely on a privateblockchain implementation where customers and retail-ers can access the technical reasons supporting eitherthe approval or the rejection of their requests for extrafeed-in capacity. The system is designed to operate ina real-world power grid environment, leveraging thehardware deployed in the existing utility infrastructure,as described in Section II-B. For those cases where asecond meter is used along with the RES controller, thenany of the interfaces cited in [3] could be used.

V. EXPERIMENTAL VALIDATION

The experimental tests described here are speciallydesigned to investigate the feasibility of our architec-tural proposal. In a first approximation, for the sake ofsimplicity and experimental control, The system param-eters are voltages from the substation and connectedcustomers. In a real full-system implementation, moreparameters should be considered, such as the feederscurrent capacity. Here, the proposed dynamic HC algo-rithm was experimentally validated by testing differentscenarios using a scaled lab setup. Through the con-ducted experiments, we also tested the possibility of

varying the HC based on real-time data measurements.Our experiments involve three different scenarios, whereeach one included two customers (represented by theirsmart meters, with PLC support), and a decision makingDCU, that can output the following commands:

i Request Granted: Client 1 sends a request toincrease their export capacity from 5A to 10A tothe DCU at the transformer substation. This latterone checks the local voltage at the transformersubstation and both clients’ connection points,then grants the request if both voltages are withinpermissible limits.

ii Request Denied: Client 1 sends a similar requestto increase their export capacity, but the request isdenied by the DCU even though the local voltageat Client 1 distribution board is approximately240V (the nominal value). This is because thelocal voltage at the neighboring connection point(Client 2) is 258V (beyond the permissible band),and thus increasing the export capacity for Client1 could result in a violation of the voltage limitfor Client 2.

iii Request Revoked after being Granted: Similarlyto scenario (i), Client 1 sends a request that isgranted. Subsequently, the voltage of Client 2increases beyond the permissible 5% limit. Asthe violation persists for some time, the grantedpermission for Client 1 is revoked, returning theirexport PV capacity energy to the normal HC limitdefined by the utility, in order to alleviate thevoltage violation by Client 2.

A. Proof-of-concept

To demonstrate the feasibility of the system, we fo-cused on the implementation of the hardware and soft-ware components necessary for the execution of the threescenarios described above. The resulting system includedsmart meters and a DCU, able to communicate via

Fig. 4: Testbed used for the experimental evaluation.

(a) (b)

(c) (d)

Fig. 5: Experimental Test Results: (a): Steady-state oscilloscope measurements, (b): Results for scenario I: Permission grantedto increase HC, (c): Results for scenario II: Denied permission to increase HC (Overvoltage), (d): Results for scenario III:Permission granted to increase HC, followed by a revoke of such a grant due to detected capacity violation.

PLC and sense the network parameters, such as voltage,necessary to make a decision. Other system componentsnot required for the feasibility assessment, such as theblockchain-based applications, were considered out ofscope and will be implemented in our future research.The architecture shown in Figure 4 consisted of thefollowing elements:

1) Smart Meter: Designed to emulate the behaviourof a standard smart meter, it is obtained by com-bining 4 devices, as shown in Figure 4.

• Raspberry Pi 4 Model B, which was pro-grammed to run the logic of bith the smartmeter and the controller managing the renew-able generation source (relay and output feed-in levels).

• Metrology Meter connected via Ethernet to theRaspberry Pi, which provided the electrical

values. For this experiment, we used an en-ergy analyzer for indirect connections, EM330from Carlo Gavazzi, coupled with its gatewaycontroller.

• Communication Module, which managed thePLC communications between the differentdevices in the network and abstracted the com-munication layer from the operational logic.We used an ATMEL/Microchip ATPL230APLC base band modem loaded with the PHYlayer of the PRIME protocol specification,which was configured to work as a servicenode in the network and mimic the behaviourof a PLC meter.

• Relay Module, a standard JBtek 4 Channel DC5V Relay, connected to the Raspberry Pi viathe GPIO interface.

2) DCU: Replicated the same hardware setup asthe smart meters but without the relay and withthe ATPL230A configured to act as a basenode/master. The logic of the DCU simulated fourdistinct functional areas: (i) Meter data aggrega-tion; (ii) Balancing Meter; (iii) Real-time local HCcalculations, to automatically verify the stabilityof the grid, pausing and resuming the operationsaccordingly; and, (iv) Communication between themeters and the private blockchain-based applica-tion.

3) Power Components: The experimental setup con-sisted of two independently controlled variable ACvoltage sources (mimicking the grid-connectionpoint for two neighboring clients within the net-work), in addition to two independently controlledvariable AC loads that represented the real-timeenergy exports of each customer. Adjustments tothe export capacity in this experimental representa-tion is emulated by adjusting the AC load demandfor the corresponding client.

Within the experimental testbed, the smart meters withPLC protocol were connected at each end to communi-cate the parameters for decision making to the DCU.Requests and decisions were represented by logic sig-nals that would, in reality, determine the energy-exportcontrol logic for the PV inverters to adjust the operatingpoint between Maximum Power Point Tracking (MPPT)and voltage regulation modes [27].

B. Discussion and Results

The experimental results for the tests described earlierwere collected using an oscilloscope sensing voltagesand currents from the power and logic componentsfor each scenario. Figure 5a illustrates the steady-statevoltage wave-forms at client 1 and 2 connection points,identified by the light blue and purple signals, respec-tively. The export current from Client 1 to the networkis depicted in red, while the client request and DCUdecision logic signals are represented in yellow andgreen, respectively.

Figure 5b shows the results for scenario (i), whereClient 1 sent a request to the DCU (yellow signalincreased from zero to 1). After checking the localvoltage at the transformer substation and at both clients’connection points, the DCU sent the approval signal toClient 1 (green signal increased from zero to one). Afterreceiving the confirmation, Client 1 increased its exportcurrent (red signal).

Scenario (ii) is depicted in Figure 5c. The request forClient 1 was rejected by the DCU because the localvoltage at the neighbouring client’s connection pointwas 258V , which could lead to voltage violation alsoat Client 2 as well as impacting the exporting client.

Finally, Figure 5d shows scenario (iii), where therequest was first approved, and later on revoked. Theresults were similar to those of scenario (i), until thevoltage of Client 2 increased, as shown by the increasein the purple signal. As the violation persisted, theDCU revoked the temporary permission to supply extracurrent, previously granted to Client 1 (the green signalfell from 1 to 0). As soon as Client 1’s smart meterreceived the revoke, the request logic (yellow signal) alsofell from 1 to 0, bringing the export PV energy withinthe normal HC limit (red signal).

The successful execution of the three scenariosdemonstrates the ability of the proposed system to dy-namically manage HC using real-time measurements andour HC algorithm, in addition to helping maximize theoutput of RES in LV networks.

VI. CONCLUSION

The visibility of network parameters provided by res-idential smart meters, combined with an unprecedentedpossibility of reach, is enabling the deployment of abrand new set of applications to further control andmanage LV networks. In particular, DSOs and users canleverage the metering data to increase their baseline feed-in capacity in certain situations. In this paper, we pro-posed a generation response system aimed at maximizingthe output of distributed renewable sources in the LV net-work. This objective is achieved by temporarily increas-ing the assigned feed-in capacity when technical condi-tions allow. The design of the system is enriched with alightweight blockchain application to record the decisiontaken in order to grant or reject each request, making thesystem fully transparent and auditable by stakeholders,including customers and retailers. Our proof-of-conceptexperiments illustrated the viability of the solution bysimulating three typical scenarios and recording thesystem’s response. The achieved results show that ourproposal fully attains the design goals. Future researchaims at accommodating different parameters, such asvoltage and feeders capacity, as well as to implementa proof of concept of the blockchain presented in ourarchitectural design. Other extensions could provide animproved understanding of how dynamic HC systemscan complement DR programs and increase the use andadoption of distributed RES.

ACKNOWLEDGEMENT

This publication is supported by Iberdrola S.A. aspart of its innovation department research studies. Itscontents are solely the responsibility of the authorsand do not necessarily represent the official views ofIberdrola Group.

REFERENCES

[1] T. Aziz and N. Ketjoy, “Pv penetration limits in low voltagenetworks and voltage variations,” IEEE Access, vol. 5, pp.16 784–16 792, 2017.

[2] S. M. Ismael, S. H. A. Aleem, A. Y. Abdelaziz, and A. F. Zobaa,“State-of-the-art of hosting capacity in modern power systemswith distributed generation,” Renewable energy, vol. 130, pp.1002–1020, 2019.

[3] F. Benzi, N. Anglani, E. Bassi, and L. Frosini, “Electricitysmart meters interfacing the households,” IEEE Transactions onIndustrial Electronics, vol. 58, no. 10, pp. 4487–4494, 2011.

[4] M. H. Bollen and F. Hassan, Integration of distributed generationin the power system. John wiley & sons, 2011, vol. 80.

[5] J. Deuse, D. Benintendi, P. Agrell, and P. Bogetoft, “Powersystem and market integration of der, the eu-deep approach,” inCIRED 2005-18th International Conference and Exhibition onElectricity Distribution. IET, 2005, pp. 1–4.

[6] EPRI. (2018) Impact factors and recommendations on how toincorporate them when calculating hosting capacity control ofresources.

[7] N. Andreadou, M. Guardiola, and G. Fulli, “Telecommunicationtechnologies for smart grid projects with focus on smart meteringapplications,” Energies, vol. 9, no. 5, p. 375, 2016.

[8] G. Van de Kaa, T. Fens, J. Rezaei, D. Kaynak, Z. Hatun, andA. Tsilimeni-Archangelidi, “Realizing smart meter connectivity:Analyzing the competing technologies power line communica-tion, mobile telephony, and radio frequency using the best worstmethod,” Renewable and Sustainable Energy Reviews, vol. 103,pp. 320–327, 2019.

[9] K. Sharma and L. M. Saini, “Power-line communications forsmart grid: Progress, challenges, opportunities and status,” Re-newable and Sustainable Energy Reviews, vol. 67, pp. 704–751,2017.

[10] S. F. Santos, D. Z. Fitiwi, M. Shafie-khah, A. W. Bizuayehu,C. M. Cabrita, and J. P. Catalao, “New multi-stage and stochasticmathematical model for maximizing res hosting capacity—partii: Numerical results,” IEEE Transactions on Sustainable Energy,vol. 8, no. 1, pp. 320–330, 2016.

[11] H. Al-Saadi, R. Zivanovic, and S. F. Al-Sarawi, “Probabilistichosting capacity for active distribution networks,” IEEE Trans-actions on Industrial Informatics, vol. 13, no. 5, pp. 2519–2532,2017.

[12] K. Coogan, M. J. Reno, S. Grijalva, and R. J. Broderick,“Locational dependence of pv hosting capacity correlated withfeeder load,” in 2014 IEEE PES T&D Conference and Exposition.IEEE, 2014, pp. 1–5.

[13] J. Seuss, M. J. Reno, R. J. Broderick, and S. Grijalva, “Improv-ing distribution network pv hosting capacity via smart inverterreactive power support,” in 2015 IEEE Power & Energy SocietyGeneral Meeting. IEEE, 2015, pp. 1–5.

[14] S. Wang, S. Chen, L. Ge, and L. Wu, “Distributed generationhosting capacity evaluation for distribution systems consideringthe robust optimal operation of oltc and svc,” IEEE Transactionson Sustainable Energy, vol. 7, no. 3, pp. 1111–1123, 2016.

[15] A. Navarro-Espinosa and L. F. Ochoa, “Increasing the pv hostingcapacity of lv networks: Oltc-fitted transformers vs. reinforce-ments,” in 2015 IEEE Power & Energy Society Innovative SmartGrid Technologies Conference (ISGT). IEEE, 2015, pp. 1–5.

[16] C. Bucher, G. Andersson, and L. Kung, “Increasing the pvhosting capacity of distribution power grids–a comparison ofseven methods,” in 28th European Photovoltaic Solar EnergyConference and Exhibition (PVSEC), Paris, 2013, pp. 4231–4235.

[17] N. Etherden and M. H. Bollen, “Increasing the hosting capacityof distribution networks by curtailment of renewable energyresources,” in 2011 IEEE Trondheim PowerTech. IEEE, 2011,pp. 1–7.

[18] E. Commission. Smart grids and meters. [On-line]. Available: https://ec.europa.eu/energy/en/topics/markets-and-consumers/smart-grids-and-meters/overview

[19] N. Uribe-Perez, L. Hernandez, R. Gomez, S. Soria, D. de la Vega,I. Angulo, T. Arzuaga, and L. Gutierrez, “Smart management ofa distributed generation microgrid through plc prime technology,”in 2015 International Symposium on Smart Electric DistributionSystems and Technologies (EDST). IEEE, 2015, pp. 374–379.

[20] P. A. TWG, “Specification for powerline intelligent meteringevolution,” R1.3.6, April 2013.

[21] PRIME Alliance TWG, “Specification for powerline intelligentmetering evolution,” R1.4, October 2014.

[22] B. Bletterie, S. Kadam, R. Pitz, and A. Abart, “Optimisation of lvnetworks with high photovoltaic penetration—balancing the gridwith smart meters,” in 2013 IEEE Grenoble Conference. IEEE,2013, pp. 1–6.

[23] E. J. Palacios-Garcia, E. Rodriguez-Diaz, A. Anvari-Moghaddam,M. Savaghebi, J. C. Vasquez, J. M. Guerrero, and A. Moreno-Munoz, “Using smart meters data for energy management opera-tions and power quality monitoring in a microgrid,” in 2017 IEEE26th International Symposium on Industrial Electronics (ISIE).IEEE, 2017, pp. 1725–1731.

[24] A. Ozadowicz and J. Grela, “The street lighting control systemapplication and case study,” in 2015 International Conferenceon Event-based Control, Communication, and Signal Processing(EBCCSP). IEEE, 2015, pp. 1–8.

[25] S. Cho and V. Dhingra, “Street lighting control based on lon-works power line communication,” in 2008 IEEE InternationalSymposium on Power Line Communications and Its Applications.IEEE, 2008, pp. 396–398.

[26] F. C. Trindade, W. Freitas, and J. C. Vieira, “Fault locationin distribution systems based on smart feeder meters,” IEEEtransactions on Power Delivery, vol. 29, no. 1, pp. 251–260,2013.

[27] A. Alassi and O. Ellabban, “Design of an intelligent energymanagement system for standalone pv/battery dc microgrids,” in2nd IEEE SGRE Conference, November 2019.