Aalborg Universitet. Published in: PowerTech, 2015 IEEE Eindhoven. DOI (link to publication from Publisher): /PTC.2015.

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1 Aalborg Universitet Optimized Energy Management o a Single-House Residential Micro-Grid Wit Automated Demand Response Anvari-Mogaddam, Amjad; Monse, Hassan; Raimi-Kian, Askan; Guerrero, Josep M.; Quintero, Juan Carlos Vasquez Publised in: PowerTec, 25 IEEE Eindoven DOI (link to publication rom Publiser):.9/PTC Publication date: 25 Document Version Early version, also known as pre-print Link to publication rom Aalborg University Citation or publised version (APA): Anvari-Mogaddam, A., Monse, H., Raimi-Kian, A., Guerrero, J. M., & Vasquez, J. C. (25). Optimized Energy Management o a Single-House Residential Micro-Grid Wit Automated Demand Response. In PowerTec, 25 IEEE Eindoven IEEE Press. DOI:.9/PTC General rigts Copyrigt and moral rigts or te publications made accessible in te public portal are retained by te autors and/or oter copyrigt owners and it is a condition o accessing publications tat users recognise and abide by te legal requirements associated wit tese rigts.? Users may download and print one copy o any publication rom te public portal or te purpose o private study or researc.? You may not urter distribute te material or use it or any proit-making activity or commercial gain? You may reely distribute te URL identiying te publication in te public portal? Take down policy I you believe tat tis document breaces copyrigt please contact us at vbn@aub.aau.dk providing details, and we will remove access to te work immediately and investigate your claim. Downloaded rom vbn.aau.dk on: august 9, 28

2 Tis document downloaded rom is te preprint version o te paper: A. Anvari-Mogaddam, H. Monse, A. Raimi-Kian, J. M. Guerrero, J. C. Vasquez, "Optimized energy management o a single-ouse residential micro-grid wit automated demand response," in IEEE PowerTec'5, 25. Optimized Energy Management o a Single-House Residential Micro-Grid Wit Automated Demand Response Amjad Anvari-Mogaddam Hassan Monse Askan Raimi-Kian Scool o ECE, College o Engineering University o Teran Teran, Iran a.anvari, monse,arkian@ut.ac.ir Abstract In tis paper, an intelligent multi-objective energy management system (MOEMS) is proposed or applications in residential LVAC micro-grids were ouseolds are equipped wit smart appliances, suc as wasing macine, diswaser, tumble dryer and electric eating and tey ave te capability to take part in demand response (DR) programs. Te superior perormance and eiciency o te proposed system is studied troug several scenarios and case studies and validated in comparison wit te conventional models. Te simulation results demonstrate tat te proposed MOEMS as te capability to reduce residential energy use and improve te user s satisaction degree by optimal management o demand/generation sides. Index Terms-- Dispersed generation, residential AC microgrid, energy management, master-slave control. I. INTRODUCTION Te current electric grids were conceived many years ago wen te needs o electricity were simple and small and te power generations were localized and built around communities. Suc grids were designed or te utilities to provide electricity or te end users and ten bill tem once a mont. Tis limited unidirectional interaction creates a ard situation or te grid to meet to te evercanging and rising energy demands o te 2 st century. On te oter and, by te emergence o te smart grids, two-way communications can be introduced between te utility and customers wile electricity and inormation can be excanged easily. In tis environment, not only te grid can be operated more eicient, reliable, secure and greener, but also newer tecnologies suc as wind and solar energy production can be enabled to be integrated []. Wit active participations o end users as inormed consumers and better communications between tem and te utilities, te smart grid will also replace te aging inrastructure o today s grid and manage te user s electricity need. More importantly, troug utilization o a Josep M. Guerrero Juan C. Vasquez Department o Energy Tecnology Aalborg University Aalborg, Denmark joz,juq@et.aau.dk ttp://microgrids.et.aau.dk communications means suc as a ome area network (HAN) it is possible to connect dierent smart devices and measurement units to an energy management system (EMS) and manage te operation o domestic devices in a cost eective way [2]. Wit a ocus on residential EMSs, a large number o researc and demonstration projects ave been done recently and related indings ave been publised in dierent scientiic papers [3]-[8]. As an example, autors o [3] ave proposed a residential energy system or grid support applications to manage dierent distributed energy resources (DERs) considering minimum operation cost. Likewise, a single-objective energy management algoritm or domestic load sceduling as been outlined in [4] wit regard to minimum electricity consumption cost. Autors o [5] ave developed te same task sceduling and domestic energy management or a residential building taking into account dierent tecnical and operational issues. Oter autors ave also investigated suc problem in multiple ways considering a time-domain simulation wit dynamic termo-electrical constraints [6], price-elastic load siting [7] and incentive-based demand response (DR) actions [8]. As can be observed rom te related literature, tere is a large and growing body o researc addressing te energy management problem witin smart residential micro-grids considering dierent objectives and related constraints. Altoug tis literature covers te extent o te problem o energy sceduling in uture smart grids, tere exist several callenges wit smart management o energy-related production and consumption units. Tus, in tis paper we propose a multi-objective dispatcing model o a residential smart EMS (MOEMS) taking into account dierent DERs and smart ouseold devices. To tis end, irst matematical models o te mentioned system components togeter wit te master-slave control o distributed generation units (DGs) are presented. Ten te optimization model is introduced wit regard to a

3 meaningul balance between saving energy and living comortably. Finally, te control strategies wit demand side participation under dierent operating conditions will be set up and tested. II. SYSTEM CONFIGURATION AND MATHEMATICAL MODELLING A block diagram o te proposed LVAC gridconnected residential micro-gird is sown in Fig.. Fig. 2. Master-slave control o a grid-connected micro-grid Fig.. Block diagram o a typical residential smart micro-grid Witin te mentioned system, bot DG units and DR actions are operated in a way to enance te system s eiciency and meet te user s requirements. In te mentioned structure, MOEMS plays te major role and manages te operation o domestic energyrelated production and consumption units optimally wit regard to dierent objectives and related constraints. As can be seen in te same igure, renewable energy sources suc as potovoltaic (PV) and wind turbine (WT) are utilized as uncontrollable primary power sources o te system wile te macro-grid (or utility) is used as an uninterruptible power supply tat guarantees te system stability. In oter word, since te micro-gird operates in a grid-connected mode, te utility as te capability to maintain AC bus voltage and requency at teir nominal values and oter controllable DG units regulate teir output powers according to te load level and power reerences coming rom te MOEMS. Suc controlling sceme known as master-slave is depicted in Fig. 2 and te related algoritm is sown in Fig. 3. Since optimal energy management o te aorementioned micro-grid can be regarded as a decision-making problem wit multiple objectives and system constraints, te matematical modeling o suc problem can be presented as ollows: Fig. 3. Control algoritm o DG units were ρ is te air density, C p is te eiciency coeicient o te wind turbine, A is te rotor swept area (as sown in Fig. 4), V is te wind velocity and inally λ and β denote te tip speed ratio and pitc angle, respectively. Similarly, in a PV system, te ollowing equation could be used to calculate te output power: ( / ), α (, ) PPV = YPV PV GT GT STC p Tc T + c STC (2) were, Y PV is te rated capacity o te PV array, PV is te PV derating actor, G T is te solar radiation incident on te PV array at any moment, G T,STC is te incident radiation at standard test conditions, α p is te temperature coeicient o power, T c is te PV cell temperature at any moment, and T c,stc is te PV cell temperature under standard test conditions. A. Distributed Energy Sources ) Renewable Energy Sources (RESs) RESs suc as solar energy and wind can be used extensively in micro-girds as a clean and economic way o energy generation. Te generated power o a WT can be expressed as: ρ A 3 PWT = Cp ( λ, β ) V () 2 Fig. 4. Wind turbine swept area

4 2) Micro-Combined Heat and Power System As sown in Fig. 5, a co-generation system generally consists o tree units including a prime mover, an auxiliary boiler and a termal storage tank and generates eat and electricity according to te ollowing equations: e t P = g η = P η / η (3) ( ) CHP CHP e CHP e t e e e t t t PCHP [ P, P ] ; PCHP [ P, P ] (4) were, g CHP represents te total uel low into te prime e e t t mover and P ( P ) and P ( P ) are te lower and upper limits on te CHP electrical (termal) output, respectively. It sould be noted tat te same termal equation and constraints must be met or an auxiliary boiler. Fig. 5. Combined eat and power generation system Considering te energy equivalent o te ot water inside te tank, te water storage temperature at eac time can be also stated as ollow: HWD ( Tcw Tst ) + Vtot Tst Tst ( + ) = + Vtot (5) t t PCHP + Paux V C tot w Tst [T st,t st ] (6) 3) Battery Energy Storage System (BESS) Basically, te beavior o a BESS can be described matematically troug te ollowing energy update unction: c dc ( ( ) ( )) PBESS PBESS setp SOC( + ) = SOC + (7) E BESS SOC [ SOC, SOC] (8) were, SOC() is te battery state o carge at our bounded by certain limits, and E BESS is te battery capacity in kw. Likewise, P c BESS and P dc BESS are te carging and discarging power o te battery. B. Termal Model o a Residential House For eicient perormance o a MOEMS, it is necessary to understand te eat dynamics o a residential building wit regard to dierent internal/external eat sources and sinks. Generally, te internal eat gain o a building is estimated according to te eat generated by te occupants, ligts and appliances suc as stove, television, radio, etc. Heat could also be added to te space mainly due to te presence o eating system and te eect o solar radiation. Regarding a radiant loor eating/cooling system (RFH/CS), te amount o eat tat is supplied to te loor at eac time step is calculated as ollows: Q = u η u η P (9) ( ( ) ) RH RH H RH C RH PRH [, PRH ] ; ηh [ ηh, ηh ] ; ηc [ ηc, ηc ] () were, u RH is a binary variable sowing te operating mode o te RFH/CS ("": eating, "": cooling), and P RH () is te power consumption o te eat pump at our ; η Η (η C ) is te eating (cooling) perormance coeicient or te above mentioned system. Likewise, te Sun plays a great role on te actual eating/cooling load o a building by its direct and diuse radiation. Te amount o eat obtained directly rom solar radiation (I) wen it enters troug te glazing and is absorbed by te loor area (A ) wit solar absorptivity o α could be computed as ollowing: Qs = α A I () Similarly, at eac time step, te eat lows into an exterior surace o a building subjected to solar radiation, and contributes to te eat gain o te building as ollows: αsi Tout + o Q = U A ( ) si s T ( 4 ( ) 4 in εσ Tout Tsurr ) (2) o were, σ is Stean-Boltzmann constant, o is te combined convection and radiation eat transer coeicient and α s, ε, U are te solar absorptivity, emissivity, and te overall eat transer coeicient o te exposed surace wit te area o A s, respectively. In a similar manner, T in, T out, and T surr are te indoor, outdoor and te average surrounding surace temperatures. Considering te mentioned eat lows, te termal beavior o a ouse in terms o temperature update unctions could be determined as ollows: Q i + Qsi Qio T in = T in ( ) + step mi c (3) p, i QRH Q i T = T ( ) + step m c (4) p, were, Q io, is te eat low between te indoor air node and te outdoor environment, Q i is te eat low between te loor and te indoor, and m (m i ) and c p, (c p,i ) are te mass and speciic eat capacity coeicients o te loor (indoor air), respectively. C. Automated Demand Response and Load Sceduling Automated DR programs can be a more cost-eective alternative tan adding generation capabilities to meet te peak and or occasional demand spikes. Te objective o DR is to actively engage customers in modiying teir consumption in response to pricing signals. Tis objective

5 could be met troug wise management o controllable loads suc as wasing macine, tumble dryer, diswaser, etc. Eac scedulable load (ere named as task) as its own list o operating parameters (LOP) tat need to be set by residents or eicient sceduling as: Start Time, End Time, Runtime, Power RTi P s, i st, i et, i TLOPi = (5) Desired Operating Interval, Priority DOIi IDi Moreover, or suc tasks several constraints must be met correspondingly. First, eac task must be completed witin its valid start-end time period T v,i =[ st,i, et,i ]. Second, some tasks cannot be cancelled or set to standby once tey are started. Tird, operation o one task (e.g., task j) may depend on te completion o anoter task (e.g., task i). Finally, parallel operation o tasks at eac time step sould be limited to a certain level named as te upper power consumption o a ouse ( P D ). D. Objective Function In tis work, te ollowing mixed objective unction is considered as te model o optimization: ρ grid Pgrid + ρ gas ( uchp gchp Min : J = ζ T + uaux gaux ) + ζ ( IDi DSLi ) 2 T i N (6) were, P grid () is te amount o power excanged wit utility at our, ρ gas and ρ grid are te natural gas and realtime electricity prices, respectively. g aux () is te total amount o gas consumed by te auxiliary boiler at our and u CHP and u aux are te on/o states o te corresponding units. Likewise, DSL i () is te user s dissatisaction level wen task i is executed at our as sown in Fig. 6. speed, solar radiation and real time electricity prices were considered as te 24-our proiles depicted in Fig. 7. TABLE I OPERATING PARAMETERS FOR DG UNITS AND SCHEDULABLE TASKS Parameter Value Unit Parameter Value Unit Renewable Energy Sources ρ.8 Kg/m 3 A 2 m 2 D rotor 2.35 m α p -.44 %/ C Y PV.25 kw PV 77 % G T,STC kw/m 2 T c,stc 25 o C Micro-Combined Heat and Power System e e t t P, P.3,.5 kw P, P 4,9 kw g CHP m 3 / η e, η t, η aux 3,7, 86 % T,T st st 6, 8 aux aux o C T cw Battery Energy Storage System E BESS 24 kw SOC, SOC o C 2-8 % P c, P 3.3, 3.3 kw η dc c, η dc 87, 9 % Radiant Floor Heating/Cooling System P RH 2 kw T set 25 o C η, η, 4 % η, η, 3 % H H TABLE II SCHEDULABLE TASKS PARAMETERS Appliance T v DOI RT P s ID Wasing Macine Diswaser Tumble Dryer Microwave Electric Kettle TABLE III POWER STAGE AND CONTROL PARAMETERS Parameters Symbol Value Unit Power Stage Nominal bus voltage V * 23 V Nominal bus requency * 5 Hz Filter capacitance C 22 µf Filter inductance o master unit L in.8 mh Filter inductance o slave units L 3.6 mh Output inductance L o. mh Control System Parameters Voltage Loop PI k pv, k iv., 2 -, s - Current Loop PI k pi, k ii 5,5 -, s - Frequency Droop m d.3 rad s - /W C C DOI i 4 2 Fig. 6. Deinition o user s dissatisaction degree III. SIMULATION STUDY In tis section, perormance analysis o our proposed model or optimal energy management in a residential smart micro-grid is presented troug dierent computer simulations. Te case study is one o te variations o a real single-zone, low-energy ouse in Sydney, Australia, wose constructional elements are adopted rom [9]. For tis case study, dierent types o DG units as well as scedulable tasks are introduced using te parameters sown in Tables I-II. Te power stage and control system parameters are also tabulated in Table III. In te perormed simulation studies, te ourly outdoor temperatures in ot and cold weater conditions, wind RTP ( /kw) ; V wind (m/s) ; T outdoor (oc) 2 Temp cold RTP Temp ot Irradiation cold Irradiation ot Wind Speed Fig. 7. Environmental inormation proiles or te proposed problem Fig. 8 sows te perormance o te proposed MOEMS compared to a conventional one troug Irradiation (W/m2)

6 dierent operating conditions. It sould be mentioned tat te conventional EMS (CEMS) gets real-time price signal and determines te tasks sceduling in a cost eective way under RTP canges; owever user s preerence is not considered as an objective. It also tries to maintain te ouse witin te comortable temperature ranges. sceduling o te ouseold appliances is done eectively taking into account te operational constraints and users preerences Controllable Loads Fixed Loads Demand Proile Load (kw) Fig. 8. Perormance comparison between MOEMS and CEMS As can be observed rom te simulation results, altoug te CEMS gets te RTP signal and determines te tasks sceduling in a cost eective way, te mixed objective unction tat includes te user s preerences and satisaction level gets a worse value wit te proposed MOEMS. On te oter and, te proposed structure beneits rom an advanced EMS and could manage bot demand and supply sides in a way to reduce domestic energy usage and ensure optimal task sceduling or te inabitants. Based on te simulations results, te MOEMS as improved te mixed objective unction value up to 33% and 4% wit respect to te CEMS in ot and cold weater conditions, respectively. Regarding a cold weater condition, te optimal management o te controllable appliances and DG units togeter wit te amount o power excange between te ouse and te utility or a given demand proile as been also demonstrated in Fig. 9. As it can be observed rom Fig. 9, MOEMS tracks te electricity usage by te our and inds te most eicient solution to supply te demand and/or sit (load) activities into time-rames wen te electricity price is less expensive wile considering user s comort level. In a similar manner, most o te residential demand is supplied by te utility during some periods o time wen te RTP is relatively low and te carging process o te BESS is carried out wit less cost. On te oter and, wit te rise o RTP and te need or more electricity during te oter ours o te day, MOEMS dispatces controllable DG units in a way not only to meet te load, but also to sell te surplus o energy to te utility and make proits. It is also worty to note tat in te mentioned residential grid-connected micro-grid, te utility plays te role o master unit and tries to maintain te requency constant and regulate te voltage at PCC using current and voltage controllers. Likewise, oter controllable DGs beave as slave units and regulate teir output powers by te use o an inner-loop PI current controller according to te reerences signals coming rom te MOEMS. At te same time, te optimal BESS (kw) CHP (kw) Utility (kw) Reerence Signal Voltage at PCC (V) a a b Unit Response Reerence Signal Frequency (Hz) Unit Response b c Reerence Signal c Unit Response Fig. 9. Optimal operation o controllable ouseold appliances and DG units using MOEMS Fig. illustrates te termal beavior o te building in dierent weater conditions. As it can be seen, eat can be transerred between te indoor air node and outdoor environment based on te temperature dierences at te mentioned nodes, wic in turn aects te eating/cooling load o te building. In a given ot summer day, te ouse not only captures te eat rom te Sun directly, but also absorbs te solar radiation in te walls and te roo and emits tat eat later in te day. Regarding tis point, te RFH/CS must work more to remove extra eat rom te indoor environment. On te contrary, te RFH/CS must be operated in te eating mode in a cold winter day to keep te indoor temperature witin te comort range, altoug te internal and external eat gains o te building assist te eating process.

7 Heat Fow (kw) Heat Flow (kw) (a) Q i Q si Q o Q io Q i Q si Q io Q o REFERENCES [] A.Anvari Mogaddam, and A.R.Seii, A Compreensive Study on Future Smart Grids: Deinitions, Strategies and Recommendations, Journal o te Nort Carolina Academy o Science, Vol.27, no., pp , Spring 2. [2] F. Benzi, N. Anglani, E. Bassi, and L. Frosini, Electricity Smart Meters Interacing te Houseolds," IEEE Trans. Industrial Electronics, vol.58, no., pp , Oct. 2. [3] H. Karami, M.J. Sanjari, S.H. Hosseinian, and G.B. Garepetian, An Optimal Dispatc Algoritm or Managing Residential Distributed Energy Resources, IEEE Trans. Smart Grid, vol.5, no.5, pp , Sept. 24. [4] A. Barbato, A. Capone, G. Carello, M. Delanti, M. Merlo, and A. Zaminga, House energy demand optimization in single and multiuser scenarios, IEEE International Conerence on Smart Grid Communications, pp , Oct. 2. [5] M. Tasdigi, H. Gasemi, and A. Raimi-Kian, "Residential Microgrid Sceduling Based on Smart Meters Data and Temperature Dependent Termal Load Modeling," IEEE Trans. Smart Grid, vol.5, no., pp , Jan. 24. [6] F. De Angelis, M. Boaro, D. Fuselli, S. Squartini, F. Piazza, and W. Qinglai, Optimal Home Energy Management under Dynamic Electrical and Termal Constraints, IEEE Trans. Industrial Inormatics, vol.9, no.3, pp , Aug. 23. [7] A. J. Roscoe and G. Ault, Supporting ig penetrations o renewable generation via implementation o real-time electricity pricing and demand response, IET Renewable Power Generation, vol. 4, pp , Apr. 2. [8] M.Parvizimosaed, F.Farmani, and A.Anvari-Mogaddam, Optimal Energy Management o a Micro-Grid wit Renewable Energy Resources and Demand Response, Journal o Renewable and Sustainable Energy, vol.5, no.5, pp. 3-48, 23. [9] S.M. Bambrook, A.B. Sproul, and D. Jacob, Design optimization or a low energy ome in Sydney, Energy and Buildings, vol. 43, no.7,pp. 72-7, (b) Fig.. Dierent eat lows based on te termal model o te residential building: a) summer, b) winter IV. CONCLUSION AND FUTURE WORK In tis paper, an intelligent multi-objective energy management system or applications in LVAC micro-grid was described and validated via dierent operating conditions. Te proposed ramework covered dierent key modeling aspects suc as master-slave control o DG units and termal dynamics o a residential building were ouseolds were supposed to be equipped wit smart appliances and ad te capability to take part in demand response programs. Moreover, dierent distributed eat and electricity generators suc as radiant-loor eating/cooling system, renewable energy sources, battery and micro-chp unit were integrated to provide energy saving and a comortable liestyle or residents. It was demonstrated tat te proposed energy management system as te capability to reduce domestic energy usage and improve te user s satisaction degree troug management o loads and generations witin te smart micro-gird.