Evaluation of centralized and distributed microgridtopologies and comparison to Open Energy Systems

(OES)Annette Werth

The University of TokyoSony Computer Science Laboratories Inc.

Tokyo, Japanannette@csl.sony.co.jp

Nobuyuki KitamuraThe University of Tokyo

Tokyo, Japannobuyuki.kitamura@gmail.com

Ippei Matsumotoand Kenji Tanaka

The Univeristy of TokyoTokyo, Japan

i_ilsea3918syou@yahoo.co.jp,tanaka@triton.naoe.t.u-tokyo.ac.jp

Abstract—In this study we examine microgrid topologies thatcombine solar panels and batteries for a community of 20residential houses: In the first case we consider a systemwith centralized PV panels and batteries that distributes theenergy to the 20 homes. In the second case we consider 20standalone home systems with roof-top PV panels and batteries.Using real electricity consumption and solar irradiation data wesimulated the overall demand energy that could replaced by solarenergy for both topologies. The centralized-resources approachachieves better performance but it requires extended planningand high initial investments, while the distributed approach canbe gradually built bottom-up. We analyze the additional resourceinvestment needed to reach the same electricity savings as for thecentralized topology. Finally, we compare it to a hybrid approachnamed Open Energy Systems (OES), a 2-layered microgridmade of interconnected nanogrids and show that it improvesthe solar replacement ratio by autonomously exchanging energywith neighbors.

Keywords—DC power distribution, Distributed power system,DC Power transmission, microgrid, smart grid

I. INTRODUCTION

Increasingly high penetration of renewables and DistributedGeneration (DG) makes the grid infrastructure fragile andmore prone to cascading blackouts. As the electricity gener-ation is shifting to renewable sources, the grid infrastructurefaces multiple challenges: intermittency and variability of awide range of renewable sources, geographical distribution(Distributed Generation - DG), bi-directional power flow and aneed for Energy Storage Systems (ESS) for meeting demand-response requirements[1][2]. Microgrids and nanogrids, espe-cially when combined with storage, are promising solutionsbecause they can manage demand-response fluctuations locallyand hence actively reduce the pressure on the utility grid[3].A wide range of new energy grid systems have been proposedwith different levels of distribution both in terms of hardwareresources as well as energy management. However, currentlythere is no widely accepted consensus on how to categorizethese systems [4]. Therefore, we first propose a rough clas-sification of the terms nanogrid, microgrid and Virtual PowerPlants (VPP). A qualitative comparison is given in table I.

In general terms, microgrids are defined as systems thatcombine a set of DGs, Loads and optional storage modules.The number and arrangement of these building blocks can varygreatly and so do the control schemes used but the main aimis to shift peak demand and flatten consumption patterns. Asof today, the connection of building blocks is predominantlydone using alternative current (AC) but direct current (DC)microgrids are expected to increase in the coming years [5].

Although there is no clear distinction, we define nanogridsas technologically simpler microgrids, often serving a singlebuilding or load [6]. Thanks to the smaller complexity andreduced regulatory pressure, they can be developed bottom-upas for instance, Home Energy Management Systems (HEMS).

On the other hand, VPPs are considered a top-down ap-proach tackling the grid challenge. VPP are clusters of DGs,ESS, loads and even entire microgrids that are connected andmanaged at a higher level so that they can be seen as a singleentity [7]. They usually serve to upgrade existing infrastructurewith smart meters and controllers and use intelligent softwareto improve demand-response (DR) and optimize renewableenergy generation[5]. A hybrid approach can be described asOpen Energy Systems which indeed combines characteristicsof all 3 previously described grid structures (see table I forcomparison): building blocks are a flexible number of DCnanogrids, interconnected via a local DC power grid andcontrolled in a distributed way. The general concept may beseen as a 2-level DC grid system where each house is equippedwith one subsystem, a DC nanogrid including batteries, thatis connected to a dedicated, shared DC power bus as wellas a communication line allowing power exchanges within acommunity. This kind of interconnected grid could be seen asan a type of Multi-Microgrids as they are described in [8].

In this paper we investigate how the grid-topology impactsthe efficiency in solar usage depending on the size of PVpanels and ESS. For this, we examine microgrid topologiesthat combine solar panels and batteries to provide energyfor a community of 20 residential houses. In the first case,

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Table ICHARACTERISTICS OF ENERGY DISTRIBUTION NETWORKS (PARTIALLY BASED ON [9], [6], [10])

Level Nanogrid Microgrid Virtual Power Plant Open Energy Systems(multi-level microgrid)

Building blocks DG, Loads, (ESS) DG, Loads, (ESS) DG, Loads, (ESS),nanogrid, microgrid

DC nanogrids withbatteries

Storage usually often sometimes yes (inside nanogrids)Resource mix static static mix & match mix & match

Geographic mix confined to load confined to network wide & variable wide & variablePhysical connection often DC AC or DC uses existing power lines

-> ACDC

Grid-tied sometimes sometimes yes not directly (nanogrids maybe grid connected)

Islanding usually yes no yesUtility owned no sometimes yes no

Regulative pressure no some strong no

each house is equipped with a standalone nanogrid with PVand ESS. In the second case, all houses are connected to amicrogrid with central PV and ESS. The aim is to numericallycompare the difference and thus obtain a theoretical gainthat could be achieved by interconnecting the standalonenanogrids. Note that since the aim is to manage demandfluctuations within the community, feeding-in electricity to theutility grid is not considered in this paper.

II. COMPARISON OF STANDALONE NANOGRIDS ANDSINGLE MICROGRID

Two grid topologies of equal overall capacities are analyzed.In the first case we consider a system with centralized PVpanels and batteries that distributes the energy to the 20homes (Fig. 1 - centralized microgrid). This approach could becomparable to a megasolar-type microgrid with central energystorage system (ESS) and distributed loads. In the second casewe consider 20 standalone home systems with roof-top PVpanels and batteries (Fig. 1 - distributed standalone nanogrids).This kind of home energy system is now being commercializedin many countries and can be bought by individuals as aninvestment towards reducing their own electricity bill.

The centralized-resource approach usually requires up-frontplanning and management as well as high initial investments,but it can be very well optimized for the customers’ needs.When resources (PV and batteries) are managed centrally,the local demand fluctuations can be spread over 20 housesand significantly more of the overall demand energy can bereplaced by solar generated energy. On the other hand, thedistributed approach has as structural advantages as it can bebuilt bottom up by individuals who decide to invest in a homesystem (does not require any central coordination). It is alsoinherently robust against grid related failures.

A. Simulation data and set-up

We compare both topologies by considering the community-wide generation and storage capacity as variables. We ex-tracted at random 20 households’ annual electricity de-mand data from a demand database of 100 houses inKyushu, Japan, all with a standard electricity subscrip-tion, that is, a 6kV A pay-as-you-go deal (Fig. 2). For

Fig. 1. Comparison of microgrid topologies

solar irradiation we used NEDO’s irradiation database(http://app7.infoc.nedo.go.jp/index.html) for Naha, Okinawa,Japan. We simulated the overall demand energy that could bereplaced by solar energy depending on a given battery capacityand PV size.

The Solar Replacement Ratio (SRR) is an indicator thatexpresses the percentage of electricity demand that could bereplaced by solar energy (see equation 1). This indicator canalso be interpreted as the energy self-sufficiency of the system.

SRR =EDemand − EAC − ESOC

EDemand(1)

For this analysis we assumed a usable battery charge cyclefrom 30% to 100% of the capacity. Whenever the battery’sSOC is below 30%, AC power from the utility power is usedto cover the demand and avoid discharging the battery further.The difference between PV generated energy and consumptionis used to charge or discharge the battery. We do not considerconversion, transmission or stand-by losses.

In the first case, the electricity demand of 20 houses issummed to form the community’ electricity demand. We ranthe algorithm for a total PV size from 40 to 320KWpeak anda total battery size from 60KWh to 580KWh.

In the second case, the algorithm is applied for each house’sdemand data separately using 1/20th of the capacities for eachhouse, that is, a PV size from 2 to 16KWpeak and batteriesfrom 3KWh to 29KWh.

Fig. 2. Annual Electricity demand and solar irradiation for a solar panel of3KWpeak

B. Self-sufficiency per topology depending on PV and batterycapacity

We computed the amount of electricity demand that couldbe replaced by solar energy using both topologies in functionof various system configurations, that is, combinations ofPV and battery capacities (see SRR in Fig. 3 a and b).As expected, the single microgrid with shared solar panelsand batteries achieves a higher SRR than the standalonenanogrids because local electricity demand fluctuations arebetter absorbed. Indeed, standalone nanogrids are stronglyexposed to both weather fluctuations and electricity demandfluctuations so that even with very high capacity batteries andPV panels it is difficult to reach a SRR higher than 90%.

.

C. Comparison of topologies using of solar replacement en-ergy

Finally, we computed the relative difference between thetopologies 4: The difference of the annual solar replacementenergy in kWh of both topologies divided by the total an-nual demand in kWh. This gives an indicator similar to theSolar Replacement Ratio corresponding to the percentage ofincreased self-sufficiency between the topologies.

When considering a PV size smaller than 4kWpeak, thedemand greatly exceeds generation and thus all generatedenergy is consumed locally. Generally this can be observed forsmaller systems aiming at a self-sufficiency of less than 50%where the topological difference remains rather small (<5%).However, for system that aim at covering more than 70% oftheir energy needs by solar (SRR>70%), the grid topologycan impact the solar replacement to over 10%. For systemswith a SRR of above 85% the topological difference decreasesagain because even standalone systems are big enough to covermost of their own demand electricity. However, in this casethe additional investment in batteries or PV needed to increasethe SOR further is hard to justify the small increase of SRR.

Fig. 3. Solar Replacement Ratio for a community of 20 houses throughoutone year. a) All batteries and solar panels are shared. b) All batteries andsolar panels are distributed in the houses without interconnections

Also, it can be noted that standalone nanogrids start stagnatingat around 85% of SRR, while centralized microgrids can reachup to 95%.

Fig. 4. Relative difference of electricity demand covered by solar for bothcases, standalone nanogrids and single microgrid

D. Discussion

In practice, however, SRR is only one of many factorsthat should be taken into account. Standalone systems aretechnologically simpler, significantly easier to install and oftenthe only way to set up a energy system. The inherent robust-ness, local confinement and minimal regulatory barriers pushesthese kind of bottom-up home energy systems (or nanogrids)into the market and recently they are starting to experiencesubstantial deployment [6]. They are considered especially

well suited for off-grid areas and with a clear preference forDC solutions [6].

However, currently most microgrids/nanogrids include autility grid connection in order to prevent outages and avoidwasting generated electricity. On the other hand, feed-in-tariffs(FIT) continue to drop due to the intermittency of powersources and the sequential higher is the costs for upgradingthe conventional grid and including energy storage [11]. Eventhough investment in storage has not yet reach grid parity,energy storage prices are decreasing yearly [5] making it anattractive way of improving the SRR as seen in the Fig. 3.

For instance, when the battery size per house is fixed to5kWh, we can see that an additional 3kWpeak of solar panelson each house would be required to reach the same solarreplacement as for the single microgrid topology (see Fig.5). In other words, this corresponds to about 7000kWh peryear of electricity savings. At the current electricity price ofabout $0.26/kWh in Japan (average price for 2011, Source:IEA, EIA, national electricity boards OANDA), this couldtheoretically amount to around $1800 of savings per year forthe community, which does not quite cover the investment costof the additional solar panes required. Note that in practice,distribution losses and conversion losses must also be takeninto account.

Fig. 5. Comparison for a fixed battery size of 5kWh per house

Since this difference in the topologies is due entirely tothe absorption of different consumption patterns of the 20households, it is strongly affected by outliers: vacant houses,untypical consumption pattern (daily or annual) strongly in-fluence comparison. The more heterogeneous the demandpatterns, the bigger the topological difference.

Also, in this study we only use solar energy as a source,meaning that generation is exactly the same for all households.However, it could also be possible to have some of thehouseholds use a standalone microgrid including a on windturbine or a bio-fuel generator. Considering all householdhave access to all resources would then make an even biggerdifference.

III. OPEN ENERGY SYSTEMS APPROACH

The discussion in the previous paragraph led us to developa third hybrid topology that consists in a 2-layered microgridthat consists in interconnecting the standalone nanogrids thatcan exchange energy with each other to spread local fluctu-ations. The idea is to preserve the structural advantages andbottom up approach that standalone nanogrids show, but to beable to exchange with neighbors using dedicated power linesand thus to achieve an SRR closer to the single microgridapproach.

This third topology can be seen as an Open Energy System(OES). We have proposed one possible implementation OESas follows (main structure is shown in Fig. 6): Every houseis equipped with DC nanogrids including batteries, energysource (i.e. PV panel) and loads constitute the basic standalonesubsystems. Subsystems are then interconnected over a DCpower bus with neighbors to form a community-wide DC mi-crogrid. Bidirectional DCDC converters that can be used eitherin current regulated or voltage regulated mode serve as theinterface between subsystems: they allow actively controllingpower flow and making abstraction of the internal subsystemdesign as for instance internal bus voltage. In this way, DCpower can be exchanged within a community, therefore helpbalancing demand-response requirements and thus increaseself-sufficiency

Fig. 6. Main architecture of Open Energy Systems (OES)

A. Simulation results and evaluation

For managing the demand-response, we implemented anautonomous energy exchange algorithm based on the State ofCharge (SOC) of each battery: depending on predefined triggerlevels, energy will be automatically sent to neighbors and thusbattery are leveled similarly to the centralized approach.

Using the same input data as in the previous data,we first simulated the different types of systems, thistime using real-time, multi-domain simulations in MAT-LAB/Simulink/SimScape (more details and results can befound in a related paper). We compare this time SRR foreach month of the year. Both previously described systems(standalone nanogrids and one, centralized microgrid) weresimulated, as well as the OES system with the autonomousexchange algorithm. For reference, we also show the directPV system, that is, a system without storage (solar only). Weused battery capacities of 6kWh and PV panel with 3kWp.This system takes into account DCDC conversion losses and

line transmission losses. It assumes that the batteries capacitiescan be used fully.

Fig. 7 shows that the OES approach can outperform thestandalone nanogrid topology without requiring any additionalPV or battery capacity. Of course, this system requires addi-tional investment for the interconnections (DCDC converter,bus lines and controller).

Fig. 7. Grid system comparison throughout the year - using 3kWp PV panelsand 6kWh batteries in each household

B. Feasibility and implementation

The feasibility of this concept has been tested on at ourfull-scale platform at the Okinawa Institute of Science andTechnology where 19 inhabited houses are equipped withnanogrids that are interconnected with a DC power bus line.A distributed and layered software architecture allows to haveplug-and-play like flexibility in terms of system size. Theenergy exchange deals are negotiated between the housesbased on battery SOC levels: Depending on the battery SOClevel each house can request or respond to energy deals. Forinstance, if one nanogrid’s SOC level exceeds 90%, it willask neighboring units if their batteries can store the energygenerated by its solar panels. Similarly, if the SOC leveldrops below a certain predefined level, the unit will requestneighbors whose battery level is still sufficiently high. Thecontrol logic is freely based on a multi-agent approach asshown in the flowchart shown in 8. For more details on theconfiguration/setup, the actual execution or regulation of thepower please refer to [12].

The first implementation of this autonomous algorithm onthe real system have been demonstrated successfully at thesecond International Symposium of Open Energy Systems in2-3 February 2015 [13]. Multiple parallel exchange deals wereautomatically agreed on subsequently executed. Numericalevaluation of the efficiency is not been made available yet.

IV. CONCLUSION

In this paper we provide a theoretical categorization andqualitative comparison of grid topologies. Using real elec-tricity demand and solar irradiation data we compared twotopologies in function of both PV size and battery capacitiesusing the Solar Replacement Ratio. We obtained a quantitative

Fig. 8. Control flow chart of Open Energy Systems

Fig. 9. Ongoing feasibility study of Open Energy Systems in Okinawa

measure of the amount of electricity demand covered by solarthat then can be translated in energy savings. Even thoughconversion and distribution losses are not taken into account,it allows to compare the impact on solar energy usage forcentralized and distributed microgrids in function of PV andbattery capacity. A brief qualitative discussion on the gridsystem features is provided.

Further, we present a hybrid grid system that interconnectsstandalone nanogrids to one microgrid in which energy isexchanged via an automatic exchange algorithm. The previ-ously obtained results can be seen as theoretical upper limitof efficiency of the exchange algorithm for given differenthardware (PV and batteries) configurations. We show that self-sufficiency can be improved by interconnecting standalonemicrogrids and thus conserving the advantages of the bottom-up approach.

This study considers homogenous home systems with samePV and battery capacities in all homes and electricity demandtaken from consumers with same utility subscription. However,

the topological differences will increase with the heterogeneityof the systems, which is expected to further drive incentivesfor interconnecting and sharing electricity bottom-up, even ina local community.

ACKNOWLEDGMENT

This research is partially supported by the “Subtropicaland Island Energy Infrastructure Technology Research SubsidyProgram” of the Okinawa Prefectural Government and carriedout by the research consortium of Sony Computer ScienceLaboratories, Inc., Okisoukou Co. Ltd., Sony Business Oper-ations Inc. and OIST.

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