2816 IEEE TRANSACTIONS ON SMART GRID, …morris/papers/15/tsg_yuwen.pdf2816 IEEE TRANSACTIONS ON SMART GRID, VOL. 6, NO. 6, NOVEMBER 2015 EMaaS: Cloud-Based Energy Management Service

2816 IEEE TRANSACTIONS ON SMART GRID, VOL. 6, NO. 6, NOVEMBER 2015

EMaaS: Cloud-Based Energy Management Servicefor Distributed Renewable Energy Integration

Yu-Wen Chen, Student Member, IEEE, and J. Morris Chang, Senior Member, IEEE

Abstract—The increasing penetration of renewable energy hasbecome a critical issue in recent years. The future power sys-tem is foreseen to depend on distributed energy resource (DER)excessively for continuous load support. Yet, DER providers arealso facing limited choices in their produced renewable energy.Massive information and complicated cooperation emerging frominvolvers intensify issues in terms of efficiency, reliability, andscalability. In this paper, a cloud-based framework is proposedto provide a customer-oriented energy management as a ser-vice (EMaaS) for green communities, which are formed as virtualretail electricity providers (REPs) by involved DERs providers.It can be adopted by existing REPs or utilities. For each greencommunity, the multiperiod global cost is minimized to promoterenewable energy, and renewable energy consumption is sta-bilized to enhance integration. A solvable linear programmingmodel is formulated for EMaaS. The case studies results revealthe proposed EMaaS retains satisfactory performances.

Index Terms—Cloud computing, distributed renewableresource, energy management as a service (EMaaS), green com-munity, linear programming (LP), renewable energy integration,virtual retail electricity provider.

NOMENCLATURE

Parameters

Gb Production capacity of large-scale renewablegenerators.

Gs Production capacity of small-scale renewablegenerators.

D Electricity demand.Tc Assigned capacity for power distribution line.Pm Price for importing conventional energy.Ps Price for exporting renewable energy to power grid.Pr Price for trading renewable energy in green

community.Tu Upper bound for renewable energy integration.Tl Lower bound for renewable energy integration.Cs Corresponding cost for prosumers.Cb Corresponding cost for large-scale renewable gener-

ators providers.

Manuscript received May 29, 2014; revised October 27, 2014,January 19, 2015, and April 24, 2015; accepted June 15, 2015. Dateof publication July 17, 2015; date of current version October 17, 2015.Paper no. TSG-00520-2014.

The authors are with the Department of Electrical and ComputerEngineering, Iowa State University, Ames, IA 50011 USA (e-mail:[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSG.2015.2446980

Variables

Decision Variables for Small-Scale Renewable Generators:Id Import renewable energy for demand.Is Import renewable energy to storage.Im Import conventional energy for demand.Err Export renewable energy to green community.Erm Export renewable energy to power grid.Esr Export stored renewable energy to green community.Esm Export stored renewable energy to power grid.

Decision Variables for Large-Scale Renewable Generators:Ebm Export renewable energy to power grid.Ebr Export renewable energy to green community.

State Variables for Storage Systems:S State-of-charge for storage.

Subscripts

i ith small-scale renewable generator.j jth large-scale renewable generator.t tth time step.z zth power distribution line.

Sets

N For small-scale renewable generators.B For large-scale renewable generators.T For time step from 1 to K.Z For power distribution lines.L For customers connected to the same power distribu-

tion line.

I. INTRODUCTION

GREENHOUSE gases are believed to be a major causefor climate changes in temperature, storm severity, and

sea level. Increasing the energy efficiency to reduce thegreenhouse emissions from the combustion of fossil fuelsbecomes a critical goal for many countries, for example,the European Union has agreed to reduce greenhouse gasesby 30% by 2020 [1]. Among different approaches, increasingthe penetration of renewable energy in the country’s electricityhas been an important target for many states and coun-tries. According to Renewable Portfolio Standard Program inCalifornia, the 2014 tracking progress report states the genera-tion target as serving 33% of retail electricity from renewableresources by the end of 2020 [2]. Large-scale renewable gener-ators (such as solar parks, wind farms) and distributed rooftopphotovoltaic (PV) power can be adopted by companies and res-idents in order to achieve that goal. Proper management and

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CHEN AND CHANG: EMaaS: CLOUD-BASED ENERGY MANAGEMENT SERVICE FOR DISTRIBUTED RENEWABLE ENERGY INTEGRATION 2817

trading strategies could maximize the incentives for renewableresource development and usage, and optimize operation costsand reduce carbon footprint for renewable generators.

However, integrating distributed energy resources (DERs)can be challenging due to the fluctuations and uncertain-ties of uncontrollable and intermittent renewable energyresources [3], [4]. Several potential challenges exist for renew-able energy integration, including reserve capacity [5], [6],scheduling, load management, and forecasting [7].

In the literature, several approaches have been attemptedto enhance renewable energy integration. Distributed genera-tions (DG) and their integration attempts [8], [9] have beeninvestigated to dispatch bidirectional flows on aged distribu-tion network designed for unidirectional flows. Due to the factthat conventional distribution management systems infrastruc-ture commonly lack the capability of DG dispatching, powergrids and local loads have to passively accept unmanaged DGoutputs. In order to provide a managed renewable integration,a cluster of DG installations can be collectively bundled as avirtual power plant (VPP) [10] and operated by a centralizedcontrol entity. As the core of VPP, the energy managementsystems of a scheduling coordinator can maximize the clusterprofit by managing its DERs while considering their physicalpositions [11]. Demand response (DR) is also available as anenergy management technique from demand-side perspective.As an important ingredient of smart grid, DR promotes bothmarket efficiency and operational reliability, and is currentlyevolved in existing programs at different independent systemoperators (ISOs)/Regional Transmission Organizations, suchas California ISO (CAISO) and New York ISO [12]. Microgridfunctions autonomously by incorporating localized DGs, stor-age systems, and controllable load. Microgrid control centeroptimizes operating states and coordinates components to pur-sue economic benefits in either grid-connected or standalonemode [13]. Multiple microgrids can participate in the marketand effectively utilize the DERs through a two level multiagentsystems [14]. According to the above four renewable integra-tion schemes (unmanaged DG, VPP, DR, and microgrid), adedicated, centralized control entity must be set up to imple-ment corresponding control mechanisms, which is costly tooperate in practice. The complexity of utilities’ business andoperation models will significantly increase as more DERs aredeveloped.

Net metering [15] is an existing mechanism to max-imize the incentive for individual renewable generationproviders, and is supported national wide in 39 states accord-ing to the Database of State Incentives for Renewablesand Efficiency [16]. Supported programs allow customers toreceive payment for excess produced renewable energy in thecompetitive market, where customers are free to choose aretail provider that provides the excess generation buy-backprogram that they prefer. However, only few retail electricityproviders (REPs) [17] offer buy-back programs, like GreenMountain Energy in Texas. Individual renewable generationproviders, especially homeowners with small-scale renewablegenerators, will face difficulty in having sufficient choices,such as not only selling their surplus renewable generation tofew REPs.

To tackle the aforementioned technical difficulties, thispaper proposed a cloud-based framework to provide thecustomer-oriented energy management as a service (EMaaS)for green communities, which are formed as virtual REPsby involved DERs providers. The proposed EMaaS couldbe adopted and operated by existing REPs or utilities. Anintermediate service between utilities and customers is pro-vided to promote the generation, trading, and consumptionof renewable energy. EMaaS actively facilitates among differ-ent components through a linear programming (LP) model toachieve two purposes for each green community: 1) maximiz-ing the incentive (minimizing the global cost) to increase thewillingness of both companies and residents to equip renew-able energy generators and use the service, and 2) enhancingrenewable energy integration by determining and honoringcommitments, which is similar to the unit commitment [18]but for DERs in distributed networks. Commitments would bedesigned and agreed between the EMaaS provider and localutilities as contracts, which already exist between REPs andutilities [17].

EMaaS is essentially a service provided on an extensiveframework and also a business model for distributed renew-able energy integration, that creates attempt to futuristic energyInternet [19]. When EMaaS helps the green communities toform as virtual REPs, a new price appears for customerstrading produced renewable energy within the community.By trading with other customers within the same commu-nity following this price, customers can reduce their overallcost and benefit mutually while no dedicated physical con-troller or operating entity is required. With the option oftrading within the green community and various components,customers have more options for their produced renewableenergy. Finding the optimal global cost for each communitybecomes nontrival due to the potential number of variouscombinations of options, especially when multiperiod fluctu-ated prices, renewable generation forecasting, storage systems,and electricity demand are considered together. To solve theproblem efficiently, an LP model is formulated for EMaaShelping each community to achieve the optimal global costby suggesting the ideal options for each customer’s renewablegeneration.

The contributions of this paper are summarized as follows.1) To the best of our knowledge, this is the first work pro-

viding the customer-oriented EMaaS through a cloud-based framework to achieve the multiperiod globaloptimal costs for each green community, and promoterenewable energy integration by determining and honor-ing commitments. The concept of forming green com-munities as virtual REPs by involved DERs providersallows customers to trade their produced renewableenergy to each other, and be able to benefit mutu-ally. Customers can have more trading options whilethe multiperiod fluctuated prices, renewable generationforecasting, storage systems, and electricity demand areconsidered altogether.

2) EMaaS is a beneficial attempt for cloud applicationin power system community. It significantly reducesinfrastructure costs of decision making and increases


efficiency, reliability and scalability, based on the pro-posed virtual renewable energy trading system onthe cloud.

3) An LP model is formulated for EMaaS, and case studiesare conducted to estimate and evaluate the performancesof the proposed EMaaS.

The remainder of this paper is organized as follows.Section II presents the system model, and Section III givesthe formulation model. Section IV discusses the case studies,and the conclusion is summarized in Section V.

II. SYSTEM MODEL

In this paper, the four essential factors are the multiperiodfluctuated prices, renewable generation forecasting, states-of-storage systems, and electricity demand. They are consideredtogether by EMaaS so that the optimal global cost for eachgreen community could be achieved. For example, if the fluc-tuated prices and the storage systems are the only consideredfactors, customers would store more renewable energy forfuture usage when the price is higher. However, they couldstore more renewable energy than their future demand, anddegrade the service of others. Therefore, the electricity demandand the forecasted renewable generation must be consideredtogether.

In the following sections, first, a cloud computing-basedarchitecture is introduced, which represents the key enabler ofEMaaS. Then, the framework with various components is sec-ond presented, and several utilized data are summarized. Sincethe physical power distribution lines may not exist betweencustomers, the renewable energy trading scheme is performedvirtually through a mapping in a cooperative procedure, whichis addressed with an adjusting process in the last section.

A. Cloud Computing-Based Architecture

Cloud computing brings enormous advantages includ-ing avoiding capital investments, reducing maintenanceexpenses, providing secure managements, and simplifyingimplementations [20], [21]. It is defined in National Instituteof Standards and Technology [22] as the model for enablingubiquitous, convenient, on-demand network access to a sharedpool of configurable computing resources, which can berapidly provisioned and released with minimal managementeffort or service provider interaction.

Providing the energy management service on the cloudinfrastructure allows various components to access it easilythrough public or private cloud with thin client interfaces, suchas Web browser or application programming interface (API).Multiple green communities could be easily formed withoutany limitation, and the energy management is able to copewith the issues of efficiency, reliability and scalability eventhe complexity of coordinating the massive data is increased.Comparing to providing the service without the cloud infras-tructure for serving M green communities, the cloud-basedframework could reduce the cost M − 1 times, which includesthe cost for establishing the local computational machine toexecute the service, the duplicated implementation for theservice and the maintenance. Existing major service models

Fig. 1. Framework.

of cloud computing are infrastructure as a service, platformas a service (PaaS) and software as a service [22]. Theproposed EMaaS is an extension of PaaS model and specif-ically designed for DERs providers forming virtual REPs toachieve optimal global costs and improve the renewable energyintegration. It is applicable to large-scale power system byincluding more involvers as customers.

B. Framework

The framework is presented in Fig. 1. An information pooland a service manager run on the cloud infrastructure. They areaccessible to other components through the thin client interfacevia the information exchanging lines. Sequential time-seriesdata depending on various geographic locations are stored inthe information pool, and utilized by the service manager. Theservice manager provides EMaaS for multiple green commu-nities and suggests ideal choices to every customer within thegreen community.

The green community is formed by two types of nondis-patchable DERs providers. The first type DER is large-scalerenewable generators, which are built by individual companieswith the purpose of raising revenue, such as wind farms andsolar parks. The second type DER is small-scale renewablegenerators, e.g., PV panels equipped on the rooftop of vari-ous types of buildings. These small-scale renewable generatorsproviders are called prosumers, since they are both the energyproducer and the energy consumer. Reducing the electricitycost for daily demands is their priority. Both large- and small-scale renewable generators connect to a power gird followingthe signed interconnection agreements with their local electrictransmission and distribution utility, such as the electric sub-stantive rule 25.211 addressed in public utility commission ofTexas [23]. The power grid is also supported by the conven-tional power companies with various conventional generators,like fossil fueled generators. They are capable of provid-ing sufficient energy to the loads in power grid but are notenvironment-friendly.

Storage system is another essential component for the pro-posed framework structure. It is assumed to be efficient and


able to store and release energy quickly, such as super-capacitors. The storage system is maintained by the servicemanager, and cooperates with renewable energy generators.Individual storage and distributed renewable resource are con-nected in pairs at same location to a set of DERs, and areonly able to charge from the renewable energy produced byrenewable generators while operating in the proposed EMaaS.The maximum storage capacity is assigned as Smax for eachindividual storage.

C. Utilized Data

EMaaS utilizes the sequential time-series data gatheredfrom different components, which includes the parametersof {Gb, Gs, D, Tc, Pm, Pr, and Ps} for K time steps ahead.Gb and Gs are the production capacities of renewable gener-ators. They can be forecasted according to the local weatherreport with other environment conditions, such as the differ-ent angles, available spaces or positions for each renewablegenerator [24], [25]. D is the electricity demand that could bepredicted by historical data, or entered directly from customersin advance. Tc is the assigned capacity for each power distri-bution line. It is decided by local power utilities and dependson the physical distribution network, including the supportedloads from both non-EMaaS and EMaaS customers.

{Pm, Pr, Ps} are three price indicators used by EMaaSto calculate the corresponding costs for satisfying customers’electricity demands. Pm indicates the price of importing powerfrom conventional power grid. It is time-variant and is pro-vided to EMaaS as a fixed known input value for each timestep by conventional power companies or local utilities basedon their predictions. Environment concern is included in theprice indicators. Ps is viewed as a lower price than Pm since itexcludes environment costs, such as CO2 emission. The rela-tionship between Ps and Pm is showed in (1), where α dependson various environment penalties in each region. Pr appearswhen the green community is formed as a virtual REP. It ispresented in (2) as a value between Pm and Ps, where β isassigned by the contract according to the agreement betweencustomers and EMaaS provider. It is similar to the electricityprice sold to customer by existing REPs

Ps = αPm, 0 < α < 1 (1)

Pr = β(1 − α)Pm + Ps, 0 < β < 1. (2)

D. Cooperative Procedure

A practical cooperative procedure shown in Fig. 2 is pro-posed for the service manager interacting with other compo-nents. In the beginning of the procedure, the service managergathers K time steps ahead utilized data from information pooland other components. Then the energy management is run foreach green community to achieve the optimal global benefitsand determine each decision variable. Corresponding cost foreach customer is calculated based on various combinationsof decision variables and is recorded as the billing reference.Suggested amount of imported/exported power for each timestep in K are provided by the service manager to renewableresource providers as their ideal demands. Suggestions are sent

Fig. 2. Cooperative procedure.

through APIs, which are presented as the dashed informationexchanging lines in Fig. 1. Large-scale renewable generatorswill export the total produced power, and Ad

i,t in (3) is mappedas the amount for each prosumer to import/export while it ispositive/negative. As

i,t in (4) is indicated to prosumers as theamount of charging or discharging their storage system

Adi,t = Id

i,t + Isi,t + Im

i,t − Erri,t − Esr

i,t − Esmi,t − Erm

i,t (3)

Asi,t = Is

i,t − Esri,t − Esm

i,t . (4)

The service is not practical if the suggested ideal demandscannot be satisfied due to other physical power distributionnetwork constraints or the inaccurate gathered data caused byforecast error, such as unpredicted sudden changes in elec-tricity demands. Therefore, an adjusting process is required tocomplete the cooperative procedure. When unexpected situa-tions or forecast errors are observed, the insufficient or surplusamount (εa) of energy will import from the external power gridwith the current price P∗ or export to the external power gridwith the price αP∗, and cause a differential cost (εc). The dif-ference between P∗ and the utilized Pm would also be treatedas forecast error and affects εc.

An adjustment interval is used as q minutes in the adjustingprocess. It can be assigned by following the current operationin real-time market, where CAISO uses 5 min in real-timeeconomic dispatch process [26]. During each adjustment inter-val, customers check their actual amount of produced andacquired power, which is operated and distributed in paral-lel by local electric distribution utilities in real time throughthe solid physical distribution lines in Fig. 1. Service managerobtains these real-time data from customers through APIs, andrecords a factor (δ) as the ratio of the differential cost (εc)to the total corresponding costs (Copt) for the entire greencommunity. If δ is larger than a threshold (ρ), the servicemanager will retrigger the energy management for the nextK time steps period. Otherwise, the determined decision vari-ables will be followed by each customer continuously in thenext adjustment interval. When the time (K − q) is reached,the energy management will be retriggered for the succeedingK time steps.


III. FORMULATION FOR EMAAS

The proposed EMaaS cooperates with various componentsto achieve two purposes for each green community: max-imizing the incentive as minimizing the global cost andenhancing renewable energy integration. An LP model isformulated for EMaaS and limited to individual green com-munity with massive information and involvers. Operationcosts for storage systems and renewable generations areassumed to be negligible. The model is formed by decisionvariables {Id, Is, Im, Err, Erm, Esr, Esm, Ebm, Ebr} and a statevariable {S}.

The objective function for the model is to maximize thebenefits as minimizing the global corresponding cost for everyprosumer (Cs) and large-scale renewable generator provider(Cb) in the entire green community during K time steps period.It is shown in (5), where prosumers tend to minimize theirelectricity cost in (6), and large-scale renewable generatorsproviders are trying to maximize their revenues in (7)

min∑

t∈T

Coptt =

∑

t∈T

⎛

⎝∑

i∈N

Csi,t +

∑

j∈B

Cbj,t

⎞

⎠ (5)

Csi,t = Im

i,t × Pmt − (

Esmi,t + Erm

i,t

) × Pst

+(

Idi,t + Is

i,t − Esri,t − Err

i,t

)× Pr

t (6)

Cbj,t = Ebm

j,t × (−Pst

) + Ebrj,t × (−Pr

t

). (7)

The following constraints subject to the objective function inthe formulated model to ensure the limitation of power systemor physical equipments will not be conflicted, and the ability ofrenewable energy integration could be achieved. Constraint (8)shows the basic criterion to attract customers to the energymanagement, which is providing the guaranty to satisfy therequested electricity demand for prosumers regardless of otherload management algorithms

Di,t = Idi,t + Im

i,t. (8)

For each time step, the summation of the suggested demandsfor customers connected to the same power distribution lineneeds to satisfy the assigned capacity for corresponding powerdistribution line. It is shown in constraint (9), where Ad

i,t isaddressed in (3)

∑

i∈Lz

Adi,t +

∑

j∈Lz

(Ebm

j,t + Ebrj,t

)≤ Tc

z,t ∀z ∈ Z. (9)

For the renewable generation, constraints (10) and (11)assure that the exported energy is equal to the producedamount. Since the total available renewable energy in thegreen community at each time step depends on differentchoices of other prosumers, it also needs to be tracedwith (12) to avoid the imported amount exceeding the availableamount

Ebrj,t + Ebm

j,t = Gbj,t (10)

Erri,t + Erm

i,t = Gsi,t (11)

∑

j∈B

Ebrj,t +

∑

i∈N

(Esr

i,t + Erri,t

) =∑

i∈N

(Idi,t + Is

i,t

). (12)

The storage is assumed to be efficient so the energy conver-sion loss can be ignored, and the operational range for stateof charge is set from 0 to Smax. The state variable, S, dependson the state variable in the previous time step and the charg-ing or discharging decision in current stage, and their relationis indicated in (13). Constraint (14) guarantees the exportedenergy from the storage will not exceed the current existingamount in storage, and constraint (15) promises that storagewill not be saturated after importing energy from renewablegenerators

Si,t+1 = Si,t − (Esm

i,t + Esri,t

) + Isi,t (13)

Si,t − (Esm

i,t + Esri,t

) ≥ 0 (14)

Si,t + Isi,t ≤ Smax

i . (15)

Tu and Tl are two values used for enhancing renewableenergy integration. They are assigned as the upper and lowerbound according to various commitments. Since the formedgreen community allows EMaaS to coordinate several DERsand demands as a single one, it is able to restrict and sta-bilize the amount of produced renewable energy in betweenthese two values. The smaller the absolute value of Tu minusTl is the less capacity that conventional generators need toreserve. Therefore, the renewable generators within the greencommunity would be able to work in a grid-friendly manner,with low spinning reserve requirement for conventional gener-ations. Constraint (16) presents the amount of total requestedelectricity demand minus the total imported energy from theconventional generators for each time step. This indicatesthe amount of renewable energy production for the greencommunity at each time step

Tl ≤ Dtotal −⎡

⎣∑

i∈N

(Imi,t − Esm

i,t

) −∑

j∈B

Ebmj,t

⎤

⎦ ≤ Tu. (16)

The formulated optimization problem is solvable by state-of-the-art commercial or open-source LP solvers, with theobjective function in (5). It is subjected to various constraintsfrom (8) to (16), and all variables are greater or equal to 0.

IV. CASE STUDIES

This section presents case studies with various numbersof EMaaS customers, two scenarios with different ratios ofrenewable energy production to the electricity demand, andthree commitment cases. Cost savings performance, renewableenergy integration performance, effects of storage systems, andcomputational performance are discussed in detail.

A. Test Cases

The time horizon K for EMaaS is set to 24 by followingthe current existing day-ahead operation interval provided byCAISO, and each time step represents an hour. Fig. 3 illus-trates the utilized data in our case studies. Only one large-scalerenewable resource is assumed to exist in the green commu-nity, and its productivity is generated in a uniform distributionwith the range from 30 to 170 kW. Several DERs exist inthe community as well, and their productions follow the basis


Fig. 3. Utilized information for case studies. (a) Basis for the productionof Gs. (b) Electricity demands basis. (c) Price Indicators.

in Fig. 3(a). This basis is designed according to the databasefrom CAISO, where PV-based renewable generations are ableto work from clock of 6 to 20 a day during the summer time.Each PV generator has various coefficients, like different sizesand positions. Thus, Gs is calculated as the basis multipliedby a uniform distribution in the case studies.

The electricity demand basis is based on the typical house-hold load profile showed in Fig. 3(b). With different pref-erences that depend on the environment or habits of eachhousehold, the requested electricity demands for each pro-sumer are generated by multiplying the basis to a uniformdistribution with the range from 0.5 to 1.5. Customers arerandomly connect to different distribution lines, and the avail-able capacities (Tc) for each line are assigned as the numberof connected customers times 115 kWh, which is smaller thanthe maximum in electricity demand basis.

To bring the reality into the case studies, the price indi-cator Pm is treated as a quadratic fuel cost function in (17),which is widely used by thermal power plants [27]. We assumethere is a conventional generator supporting 3600 residents,including both non-EMaaS and EMaaS customers. It is ableto produce enough power (Pout) to support the total electric-ity demands under its output capacity constraints, which is500 MW for the maximum and 100 MW for the minimum asthe data provided in [27]. With the cost function coefficients(a, b, c) = (240, 7, 0.007), Pm is set between 1 to 6 cents perkWh. The α and β used for Ps and Pr are set as 0.4 and 0.5.The values of the price indicators are shown in Fig. 3(c). Themaximal storage capacities are all set as equals to 30 kWh

Cost(Pout) = a + b(Pout) + c(Pout)2. (17)

The ratio of renewable energy production to the electricitydemand is the critical factor for virtually trading renewableenergy within the green community, and can be expressedin terms of (

∑Gs + ∑

Gb)/D. With a higher ratio, moreavailable renewable energy could be traded to achieve a lower

TABLE ISCENARIO SETTINGS FOR CASE STUDIES

global cost for each green community. To present the effectsfrom different ratios of produced renewable energy to theelectricity demand, two scenarios in Table I are used in thecase studies. The ratio is calculated in percentage as 35.8%to present the low-ratio case in scenario 1, and as 66%to indicate the high-ratio case in scenario 2. For both sce-narios, the renewable energy production capacities complyinterconnection agreements.

B. Energy Management Schemes

Two management schemes with different strategies are usedto compare with the proposed EMaaS. The first scheme iswithout green community and is multitime (MT) steps-based.It is widely used in unmanaged DGs [8]. Customers make theirdecisions based on the fluctuated price indicators individually.They tend to store the produced renewable energy when Ps

is low, and use it when Pm is high. Thus, the cost under MTsteps could be reduced.

The second scheme is a single time step version ofEMaaS (ST-C), i.e., customers in the green community areorganized in a cooperative pattern. This scheme is followingthe idea from Bazan and German [28]. In the ST-C approach,at each time step, customers tend to satisfy their electricitydemands first by using available generations and local storagesystems. The unsatisfied demands will be supplied by purchas-ing the available renewable energy from the community withprice Pr, or from the conventional power grid with price Pm.After the demands are satisfied, customer will store the surplusproduced renewable energy to his local storage system for hisfuture demands with zero cost. When the storage system issaturated, the surplus renewable energy will be traded to othercustomers within the green community with price Pr, and tothe main power grid with the price Ps.

Three energy management schemes, including the approachof MT, ST-C, and the proposed EMaaS, are based on LP prob-lems. CPLEX 12.5 [29] was employed as the LP solver in thecase studies. In the following sections, different performanceindices are investigated for the proposed EMaaS. In the per-formance of cost savings and storage system, Tu and Tl are setto be the values (extremely large Tu and extremely small Tl)that did not provide the actual bounding in the commitments.

C. Cost Savings Performance

With different numbers of customers under two scenarios,Table II lists the absolute cost values for three energy man-agement schemes and the cost ratios to EMaaS. In scenario 1,the cost for MT scheme requires 3.86% more than EMaaS,and ST-C scheme requires 3.84% more. When the ratioof produced renewable energy to the electricity demand isincreased in scenario 2, more renewable energy is able tobe traded within the green community so the cost can be


TABLE IICOST SAVINGS PERFORMANCE

TABLE IIICOMMITMENT CASES UNDER SCENARIO 1

TABLE IVRENEWABLE ENERGY INTEGRATION PERFORMANCE

significantly reduced by the proposed EMaaS. The cost forMT scheme would be 31.7% more than EMaaS, and ST-Cscheme requires 30.9% more. The achievable profits remainstable while the number of customers increases, and are sig-nificant when the ratio of produced renewable energy to theelectricity demand is higher. It is promising since increasingrenewable energy production capacity is expectable in manystates, like California [2]. The proposed EMaaS is able tomaximize the incentive for individual companies or residentsequipping renewable energy generators and using the service.

D. Renewable Energy Integration Performance

To demonstrate the capability of the bounds used in thecommitment for renewable energy integration, three cases arepresented in Table III with the customer size of 500 underscenario 1. Table IV presents the renewable energy production

Fig. 4. Costs with various commitment cases.

Fig. 5. Cost ratio to EMaaS with storage system.

from the green community with different energy managementschemes. As expected, when Tu is set to be a lower value,EMaaS is able to reduce the difference between the highest andthe lowest production from the green community. Therefore,conventional generators could decrease the reserve capacitymore, and enhance the ability of integrating renewable genera-tors with conventional generators. However, as the value of Tu

decreases, the cost will increase as shown in Fig. 4. A tradeoffexists between the cost and the integration ability. Such ref-erence provides valuable information for EMaaS provider andlocal utilities to determine suitable bounds for commitmentsin the contract.

E. Effects of Storage Systems

Fig. 5 shows the comparison of cost ratio to the case ofEMaaS with storage system under scenario 2 (in Table I) to


Fig. 6. Computation time.

discuss the impact of the storage systems. While the storagesystems are not involved in the energy management, the costfor EMaaS is 7.65% more, and yet 20.3% less than the schemeof ST-C. This illustrates that the proposed EMaaS is able toreduce the cost for green communities regardless of the stor-age systems, and the storage systems can further enhance thecapability of EMaaS. The comparison between the schemeof ST-C regardless of storage systems also indicates that theadvantages of storage systems can only be guaranteed whenthe multiperiod information is considered altogether. Withoutconsidering the information in future time step, customers willmiss the opportunity to lower their costs by selling the sur-plus renewable energy with higher Pr or Ps at the current timestep and purchasing with lower price Pm or Pr in the futuretime step.

F. Computational Performance

The computation time for cloud-based service can bedivided into two parts. The first part is for executing the ser-vice, and the second part is for the cloud managing the data,which includes the connection time from each component.The second part is relatively small, and several works havebeen proposed to improve the communication delays to enablereal-time operation for the cloud application [20]. This paperproposes the EMaaS through the cloud-based framework. Thecomputation performance is focusing on the first part of thecomputation time for executing the service to find optimalsuggestions. The execution time of the service depends ondifferent instance types provided by the existing cloud com-puting platform. To estimate the computation time in moderncloud computing platform, a publicly accessible Linux serverequipped with 32 cores and 126 GB memory under mediumload is used in case studies. The scale of resources of thisserver is comparable to most provided instances from AmazonElastic Compute Cloud [30].

The proposed EMaaS is formulated as an LP problem,which is efficiently solvable in polynomial time [31]. Fig. 6shows the exact computation time for the number of customersfrom 1000 to 15 000, where 15 000 can be considered as alarge enough amount to present as a massive community. Thisfurther demonstrates the proposed EMaaS is realistic and han-dles the day ahead operation and the adjustment interval verywell. The computation time for the size of 1000 is only 7 s, and

3.2 min for the size of 15 000. It is significantly smallerthan the day-ahead operation interval (24 h), and is suf-ficient for the adjustment interval in the adjusting process(5 min from the CAISO real-time economic dispatch process).Therefore, the energy management is manageable and practicalto overcome unexpected situations.

V. CONCLUSION

In this paper, a cloud-based framework is proposed to pro-vide the customer-oriented EMaaS for green communities,which are formed as virtual REPs by involved DERs providers.EMaaS facilitates comprehensively among different types ofgenerators, storage systems, and utilizes sequential time seriesdata. Furthermore, EMaaS could be adopted and operatedeconomically by existing REPs or utilities, and is practicalwith the cooperative procedure. With the formulated linearoptimization model, EMaaS is shown to be practical and man-ageable from the estimated computational time on a high-endpublicly accessible server. Two advantages for EMaaS are asfollows.

1) Achieving the Multiperiod Global Optimal Cost:Electricity price and environment concern are consid-ered together in the cost, and calculated based on variouscombination of decision variables, which are suggestedfor individual customers through an LP model. Theglobal optimal benefit as minimizing corresponding costfor each green community during the K time steps canbe achieved. EMaaS is able to increase the willingness ofboth companies and residents to equip renewable energygenerators and use the service.

2) Enhancing Renewable Energy Integration: The strat-egy in EMaaS successfully promotes renewable energyintegration by reducing the reserve capacity for thedispatchable conventional generators and honoring com-mitments for the green community. The existing tradeoffbetween minimizing the corresponding cost and enhanc-ing the capability of renewable energy integration isshown clearly in the case studies. Such reference pro-vides valuable information for EMaaS provider and localutilities to determine suitable bounds for commitmentsin the contract.

ACKNOWLEDGMENT

The authors would like to thank Dr. G. Geng from ZhejiangUniversity for the fruitful discussion.

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Yu-Wen Chen (S’14) received the B.S. degree incommunication engineering from National CentralUniversity, Taoyuan, Taiwan, in 2010, and the M.S.degree in electrical engineering from ColumbiaUniversity, New York, NY, USA, in 2012. She is cur-rently pursuing the Ph.D. degree in computer engi-neering with Iowa State University, Ames, IA, USA.

Her current research interests include communi-cation network, smart grid, wireless network, cloudcomputing, and Internet technology.

Ms. Chen was a recipient of the Best StudentAward from National Central University, in 2009. She is a Member of theIEEE-Eta Kappa Nu, and the Tau Beta Pi-The Engineering Honor Society.

J. Morris Chang (SM’08) received the Ph.D. degreein computer engineering from North Carolina StateUniversity, Raleigh, NC, USA.

His industrial experience includes positions atTexas Instruments Inc., Dallas, TX, USA; theMicroelectronic Center of North Carolina, Durham,NC; and AT&T Bell Laboratories, Murray Hill,NJ, USA. He is an Associate Professor with theDepartment of Electrical and Computer Engineering,Iowa State University, Ames, IA, USA. His researchprojects have been supported by National Science

Foundation, Defense Advanced Research Projects Agency, and Altera. Hiscurrent research interests include wireless networks, Java virtual machines,and computer architecture.

Dr. Chang was a recipient the University Excellence in Teaching Awardfrom the Illinois Institute of Technology, Chicago, IL, USA, in 1999. Heis currently the Handling Editor of the Journal of Microprocessors andMicrosystems and an Associate Editor-in-Chief of IEEE IT PROFESSIONAL.