EV Charging Control for Energy Cost. Optimization Using Model Predictive Control

Transcription

1 Contemporary Engneerng Scences, Vol. 7, 214, no. 22, HIKARI Ltd, EV Chargng Control for Energy Cost Optmzaton Usng Model Predctve Control Chang-Jn Boo Department of Electrcal Energy Engneerng Jeju Internatonal Unversty Jeju-s , Korea Kwang Y. Lee Department of Electrcal and Computer Engneerng Baylor Unversty, One Bear Place Waco, TX , USA Ho-Chan Km* Department of Electrcal Engneerng, Jeju Natonal Unversty 12 Jejudaehakno, Jeju-s , Korea *Correspondng Author Copyrght 214 Chang-Jn Boo et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Abstract The electrc vehcle (EV) chargng control algorthm for energy cost optmzaton s proposed n ths paper. A model predctve control (MPC) wth lnear programmng (LP) s used for optmal control, and the tme-of-use (TOU) prce s ncluded to calculate the energy costs. Smulaton results show that the reductons of energy cost and peak power can be acheved usng the proposed algorthms. Keywords: EV chargng control, Model predctve control, Lnear programmng, Optmzaton

2 1132 Chang-Jn Boo et al. 1. Introducton The development of EV s an mportant drecton of modern automoble vehcles. However, the restrcton of the EV s rapd growth s manly due to the lack of the EV chargng facltes [1]. Wth contnued development n EV chargng facltes, EV loads are expected to ncrease dramatcally n the near future and ths wll brng negatve mpacts on the stablty of power grds. EV loads are seldom controlled n the current practce of power system plannng, whch results n rsks n power system operaton and management. The electrcty prcng polcy provdes gudes for power demand and consumpton mode of customers. Customers wll respond to varable electrcty prces, decdng whether they prefer chargng or dschargng and actvely adjustng chargng rate and tme. In countres wth a mature electrcty market envronment, research has been focused n ths area. For nstances, Cao et al. used the tme-of-use (TOU) prce to fnd optmal chargng loads, whch mnmzes the chargng cost n a regulated market [1]. Delam et al. proposed a two-stage control strategy to maxmze the proft of the chargng staton and mnmze the peak load of the dstrbuton transformer [2]. Electrcty market n Korea may be a typcal example of the regulated market, where electrcty prces are fxed by the government and reman unchanged for a relatvely long perod of tme. At present, the electrcty prcng mechansm n Korea manly ncludes the stepwse power ff and the TOU prce. Wth such a transparency, the EVs n Korea wll play a sgnfcant role n balancng power supply and demand. Based on the state-of-charge (SOC), ths paper focuses on the applcaton of the model predctve control (MPC) to electrc vehcle that s charged on the TOU rates. An MPC approach wth the lnear programmng (LP) s selected to model and smulate the EV systems. An MPC strategy s selected, because ts perodc re-optmzaton characterstc provdes stablty durng external dsturbances [3]. By usng the proposed method, EVs are able to adjust chargng power and reduce the cost of costumers n load demand. 2. Effcent EV Chargng Control An EV s an automoble whch s drven by an electrc motor that uses electrcal energy stored n a battery pack. The battery pack of an EV s the major component that determnes the range and rechargng tmes, and t tends to be heavy and expensve. The capacty of the battery pack vares dependng on the type and sze of the vehcle. There s a 16.4 kwh capacty battery for KIA Ray, but only 1.7 kwh of the full capacty (65%) s avalable for consumpton. The Nssan Leaf, KIA Soul and Tesla Roadster have 24 kwh, 27kWh and 53 kwh capacty battery packs, respectvely.

3 EV chargng control for energy cost optmzaton Energy cost functon for EV chargng Snce the purpose s to ensure EV chargng wth mnmal energy consumpton, the cost functon must reflect ts performance n a mathematcal formulaton. Thus, the proposed cost functon mnmzes energy consumpton, subject to constrants on the SOC, whch should be hgher or equal to the lower lmt of the comfort range. Ths formulaton beng lnear, allows the use of the LP method for solvng the optmzaton problem. In ths paper, we used the followng energy cost functon to represent the daytme electrcty expense. N M mn J u( k) p( k) c( k) k 1 1 (1) where the varable u ( k ) s the chargng functon whch needs to be solved by the optmzaton algorthm over a control horzon (H), p ( k ) s the power consumpton at the tme k, M s the number of chargng statons, and ck ( ) accounts for the TOU electrcty rates n the k-th swtchng nterval. If a control horzon (H) of a 9-hour daytme s dvded nto 15 mn swtchng ntervals, then, N = 36 s the total number of tme steps per daytme. In ths paper, a practcal rate plan n Table 1 s appled, where the tme perod s dvded nto on-peak, md-peak and off-peak. Table 1. Summary of TOU electrcty rates n electrc vehcle Energy charge (KRW/kWh) Tme perod Summer Sprng/fall Wnter Off-peak Md-peak On-peak The SOC s defned as the remanng capacty of a battery and t s affected by ts operatng condtons such as load current and temperature. If the rated capacty s used, the SOC at tme t can be expressed as 1 t SOC ( t) SOC P( t) dt (2) nt Q t r where SOC nt represents the ntal SOC of an EV battery at tme t and Q r s the rated capacty of the EV battery. Therefore, n ths paper, the tme duraton T n mnutes of the chargng process can be derves as [4]

4 1134 Chang-Jn Boo et al. T ( SOC SOC ) E nt 6 (3) Pc, where SOC represents the get SOC of the EV battery, E s the battery capacty n kwh, P s the chargng staton level n kw, and c, s the chargng effcency. Eq. 1 s an optmzaton problem. Addtonal nequalty constrants can also be drectly mposed on the chargng to regulate the chargng tme wthn a range. Ths mples that constrants need to be added to the cost functon: SOC ( k) SOC There stll exsts potental to reduce the peak load, and then save money on a TOU prcng, by wsely pre-desgnng the chargng set-pont schedules. However, performng an effcent strategy requres sgnfcant amount of knowledge and efforts from chargng staton operators. The MPC algorthm wll be compared wth the conventonal on/off chargng algorthm of the EV system. The on/off control algorthm s based on the revsed swtchng levels, and t s defned as (4) 1 when SOC ( k) SOC u ( k) when SOC ( k) SOC 2.2. MPC Control Algorthm usng Lnear Programmng (5) The MPC control strategy can be explaned further wth Fg. 1, whch shows the result of a hypothetcal controller that controls the SOC levels of EV. The control model n Fg. 1 uses 15 mn swtchng ntervals, and a control horzon (H) of 9 h. The process of the MPC controller n Fg. 1 can be descrbed as follows [5]: At the current tme (11 h) the controller samples the SOC levels of EV, apples all the constrants, and predcts the future statuses of the EV system that wll optmze cost over the next 7 h. It shows that the current tme s 11 h whch means that the nputs and output pror to 11 h are hstorcal and the nputs and output after 11 h are the future predcted values. Note that the MPC samplng ntervals are chosen to concde wth the swtchng ntervals of the EV systems. However, once the predcted nputs are calculated only the frst predcted nput s mplemented and the rest of the predcted nputs are dscarded. After the frst predcted nput s mplemented the entre optmzaton process s repeated. Ths means that the EV system s swtched on for 15 mn, and when the 15 mn nterval lapses the level of the reservor s sampled agan, the constrants are re-appled and the future statuses of the SOC are computed over the next 7 h.

5 EV chargng control for energy cost optmzaton 1135 Swtchng Md peak Peak Md peak Peak Md peak SoC[%] Off 1 8 or 1 On Tme(hour) Fg. 1. Control horzon swtchng strategy. 3. Smulaton Results In order to verfy the effectveness of optmzed chargng model presented n ths paper, we consder the mult-ev and mult-staton case for performance comparson. The number of EVs and the number of chargng statons are 4 and 2, respectvely n Fg. 2. It s assumed that the ntal SOCs of 4 EVs at 9 o clock are.2,.3,.31,.41 and the get SOC of all vehcles s set to.8 at 18 o clock. The total battery capacty of all vehcles and the power level of chargng statons have 24 kwh and 6.6 kw, respectvely. The chargng effcency s consdered as.9. EV 1 On Port 1 2 channel EVSE EV 2 On AC EV 3 Port 2 Standby Interface module (Avalable two port) Power supply module # 1 (6.6kW) Grd power Port 3 EV 4 Standby Power supply module # 2 ((6.6kW) Port 4 Fg. 2. Chargng staton and EV confguraton. Ths paper smulates and compares the followng two control algorthms. (1) On/off control algorthm ( u ( k) or 1, k 1,, N ). (2) MPC control algorthm wth LP ( u ( k) 1, k 1,, N ). Fg. 3 and Fg. 4 show the smulaton results of the two control algorthms. In Fg. 3, we see that the SOCs n four electrc vehcles are operated n the upper bound of the defned SOC regon ( SOC1,.8 and SOC2,.8). If the SOC of

6 1136 Chang-Jn Boo et al. an EV charged by the staton reach the get SOC, then the chargng staton connects wth another EV. Furthermore, the proposed MPC wth LP method s more effectve than on/off method, because the two electrc vehcles are charged n off-peak and md-peak perods. Fg. 4 shows that when MPC wth LP method s used, the loads are moved out of the on-peak perods and the energy level of power wth MPC control method at peak perod s lower than the On/off control method. Ths s caused by the movng control horzon (H) of the MPC control method, whch means that after each mplemented control step the MPC algorthm s optmzng more nto the next cycle. Fg. 4 shows that MPC control method result n a TOU savng of 4.7% for on/off per day. Ths shows that the MPC wth LP control method s better than the on/off control algorthm. Fg. 3. Comparsons of SOCs n two chargng statons: (a) On/off, (b) MPC wth LP Energy[kW] Energy[kW] Tme[hour] (a) Tme[hour] (b) Fg. 4. Energy costs for EV chargng: (a) On/off, (b) MPC wth LP.

7 EV chargng control for energy cost optmzaton Concluson Ths paper proposed the MPC control algorthm wth LP for savng energy cost and reducng peak demand. In the MPC algorthm, the optmzaton problem wth constrants s transformed nto an LP algorthm and solved n each tme step. Future works nclude the applcatons of the MPC controller for varous types of electrc vehcles. Acknowledgements. Ths work was supported n part by the Inter-ER Cooperaton Projects through Korea Insttute for Advancement of Technology (KIAT), and n part by the Power Generaton and Electrcty Delvery through the Korea Insttute of Energy Technology Evaluaton and Plannng (KETEP) grant funded by the Korea Government Mnstry of Knowledge and Economy. References [1] Y. Cao, S. Tang, C. L P. Zhang, Y. Tan, Z. Zhang and J. L An optmzed EV chargng model consderng TOU prce and SOC curve, IEEE Trans. on Smart Grd, 3 (212), [2] S. Delam A. S. Masoum, P. S. Moses and M. A. S. Masoum, Real-tme coordnaton of plug-n electrc vehcle chargng n smart grds to mnmze power losses and mprove voltage profle, IEEE Trans. on Smart Grd, 2 (211), [3] I. Hazyuk, C. Ghaus and D. Penhouet, Optmal temperature control of ntermttently heated buldngs usng model predctve control: Part I - Buldng modelng, Buldng and Envronment, 51 (212), [4] L. Chen, C. Y. Chung, Y. Ne and R. Yu, Modelng and optmzaton of electrc vehcle chargng load n a parkng lot. In: 5th IEEE PES Asa-Pacfc Power and Energy Engneerng Conference, 271. IEEE PES, Hong Kong (213). [5] C. J. Boo, J. H. Km, H. C. Km, M. J. Kang and K. Y. Lee, Energy effcent temperature control for peak power reducton n buldng coolng systems. Internatonal Journal of Control and Automaton. 6 (213) Receved: August 9, 214