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468 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 2, NO. 4, OCTOBER 2011

Combined Operations of Renewable Energy Systemsand Responsive Demand in a Smart GridCarlo Cecati, Fellow, IEEE, Costantino Citro, and Pierluigi Siano, Member, IEEE

AbstractThe integration of renewable energy systems (RESs)in smart grids (SGs) is a challenging task, mainly due to the in-termittent and unpredictable nature of the sources, typically windor sun. Another issue concerns the way to support the consumersparticipation in the electricity market aiming at minimizing thecosts of the global energy consumption. This paper proposes anenergy management system (EMS) aiming at optimizing the SGsoperation. The EMS behaves as a sort of aggregator of distributedenergy resources allowing the SG to participate in the openmarket.By integrating demand side management (DSM) and active man-agement schemes (AMS), it allows a better exploitation of renew-able energy sources and a reduction of the customers energy con-sumption costs with both economic and environmental benefits.It can also improve the grid resilience and flexibility through theactive participation of distribution system operators (DSOs) andelectricity supply/demand that, according to their preferences andcosts, respond to real-time price signals using market processes.The efficiency of the proposed EMS is verified on a 23-bus 11-kVdistribution network.

Index TermsActive management, demand side management(DSM), energy management systems (EMSs), smart grid (SG),wind turbines.

I. INTRODUCTION

T ODAY, the integration of large amounts of renewableenergy systems (RESs) with the grid [1][6] is widelystudied by many researchers, but only few of them addressthese problems in connection with a consumers potentialparticipation to the electricity market [7][9], or analyze theadditional balancing costs due to intermittent and partiallypredictable availability of RESs [10][12]. On the other hand,continuous changes of power system generation capacityimpose significant energy reserves, imported energy, and theuse of efficient storage systems [13][15], thus higher costs.Usually, stabilization of the available power is based on au-tomatic resources such as primary and secondary frequency

Manuscript received November 19, 2010; revised May 24, 2011; acceptedJune 26, 2011. Date of publication July 14, 2011; date of current versionSeptember 21, 2011.C. Cecati is with the Department of Industrial and Information Engineering,

and Economics, University of LAquila, and DigiPower Ltd., LAquila 67100,Italy (e-mail: [email protected]).C. Citro is with the Department of Electrical Engineering, Poly-

technic University of Catalunya (UPC), Barcelona, 08222, Spain (e-mail:[email protected]).P. Siano is with the Department of Industrial Engineering, University of

Salerno, Fisciano (SA), 84084, Italy (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSTE.2011.2161624

control devices, reacting within seconds up to minutes, and fastmanual resources (spinning and nonspinning reserves), usuallyprovided by diesel generators, responding within 1015 min.Generation and load forecast systems can provide adequate

solutions to face these problems even if they usually are affectedby errors requiring suitable regulation capabilities. Predictionerrors can be strongly reduced if wind-forecasting errors are in-dependent of those on the demand forecasting [16], and shortforecast lead time can generally ease the need for standby bal-ancing resources [17].One further element that could reduce balancing require-

ments is the flexibility of load demand which can be obtained byissuing price-based signals, and allowing customers to decreasethe energy demand according to their real-time availability[17], [18].Demand side management (DSM) includes mechanisms of

both price responsive demand and demand response programs[19]. The first one refers to those changes applied by consumersto their electric load profile in response to energy market pricesignals for improving the economic efficiency of their energyconsumption. This mechanism increases the economic effec-tiveness of electricity markets by encouraging the energy loaddemand when the real-time price is low and discouraging itwhen the price is high. As a consequence, the peak demandcan be decreased and the additional generation and transmis-sion infrastructures may be avoided or reduced [20] and neweco-friendly standard of living encouraged [21].Demand response, instead, is defined as the customers ability

to alter their own electricity demand in response to signals fore-casted by the system when reliability is put at risk. Essentially,it refers to curtailment service programs actualized by payingend-users to take their electrical load off the grid when it is defi-cient in capacity or operating reserves. There are many differentpotential balancing resources, for instance the management ofspace heatings, air-conditioners, refrigerators, washing/dryingmachines, electric vehicles, etc. [17]. Thousands of such poten-tial balancing loads can quickly provide (within seconds up toone minute) stable and predictable response without any earlywarning of curtailment. However, a common characteristic ofsuch a kind of load storage is that it is limited in duration as cus-tomers may not accept a sustained outage period of discomfort,considering that the value of lost load is always a very impor-tant issue [22]. Whereas real-time pricing options are alreadyavailable for large industrial and commercial consumers [23],such schemes have limited implementations for domestic cus-tomers [24][26], where not all the types of loads are able toparticipate in responsive demand programs. Some researchespointed out that active control of consumer loads could enable

1949-3029/$26.00 2011 IEEE

CECATI et al.: COMBINED OPERATIONS OF RESs AND RESPONSIVE DEMAND IN AN SG 469

additional on-shore wind farms [27]. In [28], it has been demon-strated that fast/emergency reserve can be provided by respon-sive loads such as residential and small commercial air-condi-tioners; in [29] the control of residential heaters and pumps havebeen applied for managing daily peak demands. In [17], it hasbeen reported that the value of the implementation of real-timepricing in the U.K. would be at least 2.6 to 3.6 billion, dueto peak loads reductions during low wind speed, thus justifyingthe expense of installing and operating smart meters.These new mechanisms require active management schemes

(AMS) as well as end-user-level complex communicationsystems, necessary for making available information onreal-time-pricing and availability of the electrical energy. Dueto the previous considerations, this paper proposes an energymanagement system (EMS) for smart grid (SG) managementthrough DSM and AMS [30]. In the following, Section IIdescribes the EMS and a scheme for the active control of anSG, and Sections III and IV present and analyze the proposedmethod and different case studies, respectively. Conclusionsare drawn in Section V.

II. EMS FOR SGsAs known, the term SG refers to a fully automated electric

power system controlling and optimizing the operation of allits interconnected elements, in order to guarantee safe and effi-cient operations of energy generation, transmission, and distri-bution [31], [32]. Today, many interesting examples of SGs areavailable in many countries, including, for instance, the U.S.,Canada, Germany, Japan, India, and Australia [33], [34]. Micro-grids (MGs) are small-scenario versions of the centralized elec-tricity systems that locally generate, distribute, and regulate theflow of electric energy to consumers. They are connected to thebulk power grid and allow higher reliability and energy cost re-ductions by encouraging the end consumers to locally purchasegenerated electric power with privileged tariffs [32].Further initiatives towards the future SGs are concerned with

the so-called virtual power plants (VPPs), i.e., aggregationsof interconnected distributed generations (DGs) located indifferent places but managed in order to work as an uniquevirtual power plant managing a well defined amount of energy.This solution allows even the smallest DGs (aggregated inthe VPP) to access the electricity market and contribute to theenergy cost reduction process [35]. Examples of VPPs can befound in Germany, Australia, and the U.S. [36], [37].Regardless of the possible different implementations, inno-

vative EMSs are required to achieve a dynamic control of thedifferent interconnected elements. A possible scenario for theimplementation of this infrastructure is shown in Fig. 1. Themain elements of this system are: Energy management system (EMS); Supervisory control and data acquisition (SCADA); Remote terminal units (RTUs); Advanced metering infrastructure (AMI); State estimation algorithms (SEAs); Generation and load forecast system (GLFS).Optimization, monitoring, and control of the SG per-

formances are entrusted to a suite of hardware/softwareapplications constituting the EMS [38]. The SCADA system

Fig. 1. EMS in the SG infrastructure.

transmits the measurement data, provided by an AMI and by aset of remote collecting data devices (RTUs) placed in strategicpositions along the SG, to the EMS. The latter determines theactions required for the optimum state of the SG by using SEAsand a GLFS.

III. METHOD DESCRIPTION

A. EMS PolicyAccording to the EUDirective EC 2006/32 on energy end-use

efficiency and energy services [39], a mechanism of real-timepricing (RTP) tariff should be offered to the market. In thisstudy, the hourly spot market price is assumed as the real-timeelectricity price for consumers available one day in advance,as adopted by Denmark and Ireland in Europe [26], [40]. Evenif fluctuations between predicted and actual prices occur, thiserror usually goes to zero [41]. In order to reduce the elec-tricity costs, those consumers with demand regulation capabilitycan reschedule their bids according to the real-time electricityprice. The scenario of Ireland Single Electricity Market (SEM)[41] demonstrated that DSM, optimized on one-day-ahead pre-dicted electricity prices may promote the use of wind generatedelectricity. Moreover, variable service subscription (VSS)-typeprograms are assumed for customers that, under demand lim-iting and demand subscription service, subscribe to a demandthreshold. The solution is a centrally controlled limiting loaddevice: when the generation capacity is insufficient or due toreliability requirements, the EMS can limit the demand to thetotal subscribed capacity and responsive loads are paid by thedistribution system operator (DSO) according to the VSS [42].The EMS behaves as a sort of aggregator of distributed energy

470 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 2, NO. 4, OCTOBER 2011

resources [43], that allows the SG (or MG) participating in theopen market, buying and selling active and reactive power tothe bulk grid and optimizing the local (renewable) productioncapabilities. It takes into account the bids received by energyproducers and consumers.When buying active and reactive power from the grid, the

EMS tries to maximize the benefit function of demand whileminimizing the costs of energy and the costs paid to consumersfor demand limiting. When selling active and reactive powerto the bulk power grid, due to an excess of low price renew-able generation, the EMS also tries to maximize revenues byexchanging power with the grid. The SG (or MG) can also re-lieve possible network congestions by transferring energy to thenearby feeders of the distribution network [44].In other words, the complementary operations executed by

the EMS are:1) A one day-ahead schedule of distributed generators andresponsive loads according to the market prices, with eachtrade day comprising 48 half hourly trading periods. Alldispatchable generators and responsive loads bid the oneday-ahead active and reactive power generation or loaddemand by providing price and quantity information foreach trading period one day ahead. For each trading periodthe dispatch schedules are determined [45].

2) A real-time intraday optimization operation that everyminutes, e.g., 5 min, modifies the scheduling in order toconsider the operation and economic requirements.

As both price and reliability demand response (e.g., ancillaryservice) are considered, the scheduling is modified according toboth the real-time electricity price and the support offered bydistributed generators and responsive loads to the active net-work operation.

B. Mathematical Problem Formulation

During each time interval, the objective function to be maxi-mized is the sum of the total demand benefits, minus the sum ofthe total generation costs and the costs paid for load curtailingunder VSS [46]

(1)

where is the vector of dependent variables, containing the am-plitudes and angles of the buses voltages; is the vector of con-trol variables, including the secondary voltage of the on-loadtap-changers (OLTCs) and the active and reactive power in-jected or absorbed by generators and loads; is the set of poolload buses; is the set of pool generator buses; is theset of responsive loads; is the cost for curtailing 1 MWh ofthe th responsive load under VSS; is the curtailed en-ergy for the th responsive load; is the demandvector; is thebenefit of consumer ; is the supply vector; and

is the cost of supplier. Subscript and subscript specify a relationship with activeor reactive power, respectively. In the pool model, productioncosts and benefit functions are quadratic functions of active andreactive power of pool loads and generators, as follows:

(2)(3)(4)(5)

The price-dependent load is modeled with a consumer benefitfunction , concave and increasing, with including boththe real and reactive power demand [46].In order to integrate the simulation of reactive power ex-

change, price-dependent reactive loads are considered. Since re-active power acts more as a service enabling the consumption ofreal power, a benefit function different from the real power ben-efit equation is determined. Accordingly, the benefit of the re-active power is considered as the avoidance of its shifting froma given desired level for a specified active power consumption.Desired reactive power demand is that required by the load atthe given load level and can be defined as a function of the realpower demand [46] . Assuming themagnitudeof the function increasing with as and con-sidering a concave function for as(5) is obtained.In order to maximize the objective function, the nonlinear

programming formulation of the OPF, described in [47][51],is modified including the AMS and DSM.

C. Discrete Variables Handling

The OPF can be approached as a mixed discrete-continuousoptimization nonlinear problem with a single integer variable:the OLTC transformer tap. The solution of this problem is im-plemented by a two-stage approach [52], [53]. First, a solutionover the full range of variables is generated while assuming thatall variables are continuous; then, the discrete variable is movedto the nearest discrete setting, and treated as constant during asecond-stage solution.

D. Constraints

The equality constraints represent the static loadflow equations such as Kirchhoff current law , whereis the set of busses (indexed by ) and Kirchhoff voltage law

, where is the set of lines (indexed by ) [51], [52]. Theinequality constraints are listed in the following:1) Active and reactive power constraints for the interconnec-tion to external network (slack bus)

(6)

where is the set of external sources (indexed by ),and are the active and reactive power outputs of ,respectively, and and are the min/maxvalues they can assume.

CECATI et al.: COMBINED OPERATIONS OF RESs AND RESPONSIVE DEMAND IN AN SG 471

2) Active and reactive power constraints for generators:

(7)

where and are the active and reactive power out-puts of , respectively, and and are themin/max values they can assume.

3) Active and reactive power constraints for consumers load,

(8)

where and are the active and reactive powerabsorbed by consumer , respectively, and and

are the min/max values it can assume.4) Voltage level constraints

(9)

where is the voltage at , and are the max/minvalues it can assume.

5) Flow constraints for lines and transformers

(10)

where and represent the active and reactive powerinjections onto , respectively, and the maximum powerflow on .

The additional constraints derived from the AMS are relatedto the coordinated OLTC voltage, theWTs and diesel generators(DGens) power factor angles.1) Coordinated OLTC voltage constraint

(11)

where is the secondary voltage of the OLTC,are the max/min values it can assume.

2) Coordinated generator reactive power constraints,

(12)

where is the power factor angle of , are themax/min values it can assume.

IV. CASE STUDIES

The proposed technique is applied to a 23-bus 11-kV radialdistribution system, shown in Fig. 2. The three feeders are sup-plied by a 6-MVA 33/11-kV transformer; the tap position allowsnine different voltages with a step p.u. Voltagelimits are taken to be 10% of the nominal value and feederthermal limits are 1.5 MVA (81 A/phase). The phasor dynamic

Fig. 2. Test network.

TABLE INETWORK LOADING

TABLE IIWTS GENERATED ACTIVE AND REACTIVE POWER

models for the WTs, the DGens, the OLTC and the other dis-tribution system elements are implemented using Matlab Sim-PowerSystems.The load at each bus is assumed to track a load curve [30]:

discrete load bands across one year are considered: maximum,normal, medium, and minimum load. The load levels for eachband are summarized in Table I.In the test network, two wind turbines (WT1 and WT2) are

connected at nodes 7 and 16, respectively. Each WT generatesabout 1.05 MW at a wind speed of 12 m/s, operating within apower factor varying between 0.85 leading and lagging [54].The power extracted from a WT is a function of the availablewind power, the power curve of the machine and the ability ofthe machine to react to wind variations. The WTs generated ac-tive and reactive power dependence on the wind speed is givenin Table II.A high cost DGen generating a maximum active power of

600 kW is connected at bus 9. The cost curve used for theDGen is approximated by a second-order polynomial function[55], considering the diesel generator starting cost assessed at15 euros [56]. The values of the cost coefficients are calculatedconsidering the fuel consumption curve of a real diesel gener-ator obtained from the data provided in [57], setting a fuel priceof 1 euro per liter [58]. The diesel unit can operate between 25%and 100% of its rate capacity. As regards with the consumers, it

472 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 2, NO. 4, OCTOBER 2011

TABLE IIISUPPLIERS CHARACTERISTICS

TABLE IVCUSTOMER CHARACTERISTICS SCENARIO 1

TABLE VCUSTOMER CHARACTERISTICS SCENARIO 2

has been assumed that each consumer has both fixed and priceresponsive load.Operation of the considered EMS endowed with AMS is first

evaluated considering discrete load and wind speed states [30],varying in the range between minimum and maximum load andbetween 0 and 12 m/s, respectively.The following analysis considers different DSMmechanisms

such as Price Responsive Demand and Demand Response Pro-grams [19] and aims at evaluating the benefits of real-time elec-tricity price. Two different scenarios are analyzed as follows:1) consumers are involved in a demand response program;2) consumers are involved in a demand response program andalso participate as price responsive demand.

Suppliers and customers coefficients are given in Tables III,IV, and V, respectively [59], [60].In both minimum and maximum load scenarios, the suppliers

and customer characteristics are equal, thus the supplied loadand the total cost paid for energy delivering change only fornormal and maximum loads.

A. Scenario 1: Consumers Involved in a Demand ResponseProgramIn this scenario, consumers can be limited only for relia-

bility requirements (i.e., for avoiding constraints violations) by

TABLE VITOTAL ACTIVE POWER ABSORBED BY DEMAND AT BUSES 3, 12, AND 17 [kW]

TABLE VIISUM OF THE TOTAL NETWORK DEMAND [kW]

means of a centrally controlled limiting load device. When,during maximum and normal load, demand is limited to thetotal subscribed capacity, consumers are paid by the DSO at200 euros/MWh according to a VSS [43].During normal operation each load is supplied at its max-

imum value for a wind speed equal to 12 m/s or when the loadis minimum. When wind speed varies between 0 and 10 m/s,all consumers are supplied at their desired demand level, ex-cept those connected at buses 3, 12, and 17, which, are, instead,limited in order to satisfy reliability requirements, as shown inTable VI. Since variable loads operate at fixed power factor,the absorbed reactive power exhibits a similar trend. Due to theoverall load increase, the active power absorbed by consumersconnected at buses 3, 12, and 17 is limited by the thermal con-straints on the wires 01 and 012. The sum of total networkdemand is shown in Table VII.The percentage peak demand reduction is shown in Fig. 3 for

wind speeds below or equal to 6 m/s. During maximum load,the total demand at buses 3, 12, and 17 decreases from 1706 to1032 kW (or by 39%) and to 829 kW (or by 51%), when thewind speed is 6 and 0 m/s, respectively.For instance, in case of maximum load and wind speed

varying from 0 to 8 m/s, it is worth noting that: the total curtailed power decreases proportionally to thewind speed from 878 to 139 kW;

the DGen always generates its maximum active power of600 kW, except for a wind speed of 8 m/s, when it gener-ates 320 kW.

The DGen supplies active power only during maximum andnormal load states and wind speeds varying from 0 up to 8 m/s.

CECATI et al.: COMBINED OPERATIONS OF RESs AND RESPONSIVE DEMAND IN AN SG 473

Fig. 3. Percentage peak demand reduction in Scenario 1.

Its output power varies from a minimum value around 293 kW,in case of normal load and wind speed of 6 m/s, to a maximumvalue of 600 kW, in coincidence with maximum load and windspeeds lower than 8 m/s.The active and reactive power imported from the grid tend to

increase while decreasing the wind speed and with an increasingload value. While this trend is always verified in case of reactivepower, the relationship between the tendencies of the imported/exported active power in relationshipwith the load value is morecomplex as it depends on the active power generated by theWTsand the DGen and on the active power absorbed by variableloads. When the wind speed is equal to 12 m/s and the load isminimum, about 285 kW of active power are exported to thebulk power grid. Due to the implemented AMS and, in orderto relieve the voltage constraints, the WTs always supply lead(capacitive) power.

B. Scenario 2: Consumers Involved in a Demand ResponseProgram also Participating as Price Responsive DemandAs in the previous scenario, consumers can be limited due

to reliability constraints, moreover price responsive consumersunder RTP tariff (at buses 3, 12, and 17) can modify their de-mand in response to high real-time electricity prices occurringduring normal and maximum load.Real-time electricity price signals, available to both con-

sumers and producers, represent an effective coordinationmechanisms suitable to drive both to change their bids/offersin both constrained and unconstrained feeder conditions.While for wind speeds higher or equal to 8 m/s, the impact

of price responsive demand and RTP tariff is negligible dueto the low-price wind energy, consumers under RTP tariff ad-just their demand bids in response to high real-time electricityprice for wind speeds below 8 m/s. In the case of maximumload, RTP tariff induces consumers to move consumption awayfrom costly peak hours and the total demand decreases fromabout 1706 kW to about 678 kW ( 60%) and to about 341 kW( 80%) in correspondence of a wind speed of 6 and 0 m/s, re-spectively, as shown in Fig. 4.RTP also allows reducing the power generated by the DGen,

that is equal to about 240 kW during maximum load.

Fig. 4. Percentage peak demand reduction in Scenario 2.

TABLE VIIITOTAL COSTS [Euro/h]

A reduction of the peak demand can be evidenced in Scenario2 when compared to Scenario 1: for wind speeds below 8 m/s,the percentage decrease of the peak demand is within 21%37%and 34%60%, during normal and maximum load, respectively.This reduction leads to significant economic benefits for bothconsumers and DSO, that avoids paying consumers according toa VSS at 200 euros/MWh for demand limiting, as in Scenario 1.A maximum cost reduction, varying from 301 to 28 euros/h,

can be achieved during maximum load in Scenario 2 when com-pared with Scenario 1. The total cost reductions, if compared tothe previous Scenario, are shown in Table VIII.Moreover, RTP encourage consumers shifting consumptions

during periods of high wind energy production and supportingthe use of renewable energy resources.

C. Base-Year AnalysisThe benefits of the AMS and price responsive consumers

under RTP tariff during one year are assessed following theapproach used in [61]. Based on their joint probability ofoccurrence, defining the number of coincident hours over theyear, wind availability and demand have been aggregated intoa number of wind/demand scenarios. Actual data for bothdemand and wind production have been taken from [61].The set of scenarios obtained by combining wind availability

and load demand real data for one year are shown in Fig. 5.

474 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 2, NO. 4, OCTOBER 2011

Fig. 5. Coincident hours for demand/generation scenarios.

Fig. 6. Total costs without AMS.

Each scenario represents the combination between wind speedand load demand values, indicated in percentage terms (x-axis),and is characterized by a defined number of hours over theyear (y-axis). Such a number represents the time (number ofhours) during which each combination wind/demand occurs inthe course of the year.As shown in Figs. 6 and 7, by summing the costs of each

scenario, the proposed EMS endowed with AMS allows a totalannual cost reduction around 383 keuros, if compared to thescenario without AMS. The annual curtailed energy is equalto about 634 MWh/year without considering the AMS, while itdecreases down to about 573 MWh/year when using the AMS,with a reduction of about 9% of the curtailed energy.AMS, such as the coordinated voltage regulation of OLTC

and the power factor control ofWTs, are able, in fact, to increasethe total energy absorbed by loads. For instance, when the loaddemand is within 70% and 100%, the power factor control ofWTs can increase the energy absorbed by loads up to 10% ifcompared with the scenario in which only a regulation of the

Fig. 7. Total costs with AMS.

OLTC is applied, and up to 20% if compared with the scenariowithout AMS.Combining AMS with price responsive consumers under

RTP tariff (at buses 3, 12, and 17) an additional 93 keurosannual cost reduction over the scenario with the sole AMS isachieved.It is worth noting that, by mitigating network constraints, the

sole use of AMS may increase the energy absorbed by loads upto 20%.Moreover, real-time electricity price signals drive consumers

to find a different time schedule of their consumptions, thus re-ducing expensive peak power demands further contributing tonetwork constraints reduction.Hence, a combination of both mechanisms through the active

participation of producers and consumers, represents a good op-tion for improving both resilience and flexibility of SGs and forsupporting the use of renewable energy resources.

D. Computational Performances EvaluationSimulation results demonstrate that the proposed method is

fast enough to be executed in real-time: for the considered net-work, a personal computer with an Intel CoreTM i7 processorrunning at 2.67 GHz and with 8-GByte RAM requires less than3 min for the solution of a single OPF.The proposed optimization approach (i.e., Sequential

Quadratic Programming), requires low computational re-sources while providing very good results, comparable withperformances obtained using interior point method solution ofOPF relaxation [53]. It is worth noting that this method, alsocoded in the AIMMS optimization modeling environment [30],is scalable, i.e., it can be used with a larger number of controlvariables.

V. DISCUSSION AND CONCLUSIONIn this paper, an EMS for the optimization of SGs has been

proposed.The EMS behaves as a sort of aggregator of distributed energy

resources allowing the SG participating in the open market in

CECATI et al.: COMBINED OPERATIONS OF RESs AND RESPONSIVE DEMAND IN AN SG 475

order to optimize the local production capabilities as well as tominimize the cost of bought energy.The proposed system integrates AMS with DSM without re-

quiring significant additional hardware.Simulation results evidenced that the combined operations

of RES and Price Responsive Demand mitigate network con-straints while satisfying higher demand levels and reducing theenergy costs.AMS offer technical benefits: they allow a better coordina-

tion between DSOs and electricity supply and demand that, sat-isfying their preferences at minimum costs, can respond to real-time price signals using market processes.It is worth pointing out that each active or DSM solution, or

the combination of them, should be evaluated on a case-by-casebasis as the implementation and cost-effectiveness of each solu-tion depends on network characteristics. A combination of bothmechanisms will, however, represent in most cases, the best op-tion to improve the SGs resilience and flexibility through re-source use optimization and peak loads reduction.The implementation of AMS and DSM requires both a hard-

ware as well as a software infrastructure that are expected to be-come standard in SGs. Conversely, the actual implementationof AMS and DSM also requires a new regulatory frameworkbased on economic signals and providing incentives for bothconsumers and generator owners and special bilateral contractsbetween them and the DNOs.

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Carlo Cecati (M90SM03F06) received the Dr. Ing. degree in elec-trotechnic engineering from the University of LAquila, LAquila, Italy, in1983.He is currently with the Department of Industrial and Information Engi-

neering, and Economics, where he is a Professor of Industrial Electronics andDrives and he is a Rectors Delegate. He is the Founder and Coordinator of thePh.D. courses on management of renewable energies and sustainable buildingsat the University of LAquila. In 2007, he was the Founder of DigiPowerLtd., a spin-off company dealing with industrial electronics and renewableenergies. His research and technical interests cover several aspects of powerelectronics and electrical drives. In those fields he authored more than 100papers published in international journals and on conference proceedings.Dr. Cecati is a Coeditor-in-Chief of the IEEE TRANSACTIONS ON

INDUSTRIAL ELECTRONICS; he has been a Technical Editor of the IEEE/ASMETRANSACTIONS ON MECHATRONICS. He has been a General Cochair ofthe IEEE International Symposium on Industrial Electronics (ISIE) 2002,IEEE-ISIE2004, IEEE-ISIE2008, a Honorary Cochair of the IEEE-ISIE2010,a Technical Program Cochair of the IEEE Industrial Electronics Conference(IECON) 2007, and a Track Cochair or Special Session Chair of IEEE-ISIEand IEEE-IECON conferences. From 2000 to 2004, he was an AdCom memberof the IEEE Industrial Electronics Society (IES), and from 2005 to 2006, anIES Vice President. Since 2007, he has been an IES Senior AdCom memberand, until 2008 IEEE, IES Region 8 Coordinator. He is a member of IEEE IESCommittees on Power Electronics and on Renewable Energy Systems and aCochair of IEEE IES Committee on Smart Grids.

Costantino Citro received the M.Sc. degree with honors in electronic engi-neering from the University of Salerno in 2010. In the same year, he joined theDepartment of Information and Electrical Engineering, University of Salerno,with a research fellowship in the fields of power electronics and electrical powersystems. Since March 2011, he has been working toward the Ph.D. degree inelectrical engineering at Polytechnic University of Catalunya (UPC), Spain.His research interests include power electronic converters and integration of

renewable energy systems in smart grids.

Pierluigi Siano (M09) received the M.Sc. degree in electronic engineering andthe Ph.D. degree in information and electrical engineering from the Universityof Salerno, Fisciano, Italy, in 2001 and 2006, respectively.Currently, he is Assistant Professor with the Department of Industrial Engi-

neering, University of Salerno, Fisciano, Italy. His research activities are cen-tered on the integration of renewable distributed generation into electricity net-works and Smart Grids and on the application of soft computing methodologiesto analysis and planning of power systems. In these fields, he has publishedmore than 70 technical papers including 30 international journal papers and 40international conference papers.Dr. Siano is Associate Editor of IEEE TRANSACTIONS ON INDUSTRIAL

INFORMATICS, member of the editorial board of the International Journal onPower System Optimization, Energy and Power Engineering, Smart Grid andRenewable Energy. He served as reviewer and session chairman for manyinternational conferences. He has been Special Sessions Cochair of IEEE-ISIE2010 and Guest Editor of the Special Sections of the IEEE TRANSACTIONSON INDUSTRIAL ELECTRONICS on Methods and Systems for Smart GridsOptimization and on Smart Devices for Renewable Energy Systems. He issecretary of the Technical Committee on Smart Grids and member of theTechnical Committee on Renewable Energy Systems of the IEEE IES.