and Battery Condition

Transcription

1 1/8 Autonomous Distributed (Vehicle to Grid) considering Charging Request and Battery Condition Yutaka Ota, Haruhito Taniguchi, Tatsuhito Nakajima Jumpei Baba, and Akihiko Yokoyama (The University of Tokyo, Japan) K. M. Liyanage (University of Peradeniya, Sri Lanka) IEEE PES Conference on Innovative Smart Grid Technologies Europe , 12:3 15:, PP14: Grid Solutions for Plug in Vehicles October 12, 21, Gothenburg, Sweden

2 Integration of RESs toward future low carbon power grid p, gy, q y g New electricity demand with energy storage Heat Pump Water Heater, Plug in Electric Vehicle Issues : reverse power flow, excess energy, frequency regulation 2/8 Pump Storage Hydro Thermal WASA based on PMU/WAMS Ubiquitous Power Grid Nuclear Interconnected System Tie-line Distribution System MicroGrid Smart Charging and (Vehicle-to-Grid) Considering Charging Request Bulk Power System Load Dispatching Center Regional Energy Management System Coordinated dcontrol lstrategyt to be Virtual Power Storage Wind Park Battery Plug-in Hybrid Vehicle e Electric Vehicle ECU / BMU Battery SCADA Battery Energy Storage System DC-DC Charger Distribution System MicroGrid Converter Motor Inverter Photovoltaic Generation Heat Storage Heat Pump Water Heater Load Distributed Generator Mega Solar

3 Integration of RESs toward future low carbon power grid p, gy, q y g New electricity demand with energy storage Heat Pump Water Heater, Plug in Electric Vehicle Issues : reverse power flow, excess energy, frequency regulation 3/8 Pump Storage Autonomous Distributed WASA based on PMU/WAMS Hydro (Vehicle to Grid) Thermal Control Nuclear based on self terminal frequency and SOC (State of Charge) Ubiquitous Power Grid Interconnected System 1. Replying vehicle use s charging Tie-line request 2. Managing battery condition through utility grid Distribution System Load Dispatching 3. Contributing MicroGrid the power grid as a spinning reserve Center Smart Charging and (Vehicle-to-Grid) Considering Charging Request Bulk Power System Regional Energy Management System Coordinated dcontrol lstrategyt to be Virtual Power Storage Wind Park Battery Plug-in Hybrid Vehicle e Electric Vehicle ECU / BMU Battery SCADA Battery Energy Storage System DC-DC Charger Distribution System MicroGrid Converter Motor Inverter Photovoltaic Generation Heat Storage Heat Pump Water Heater Load Distributed Generator Mega Solar

4 Time control scheme 4/8 (a) gain according to Δf (b) SOC balance control P max K max V1G Charge wer [kw] Char ge po Discharge Δf sp V1G K max Δf max Droop K max Limiter ΔP max + SOC bl balance control Charge restraint in high SOC Discharge restraint in low SOC gain [kw W/.1Hz] Discharge Charge Spinning Reserve Mode Frequency deviation [Hz] r [kw] powe e Charg Diusc charge V1G (one way charge control) (c) power output under sinusoidal frequency input (SOC:3%) (SOC:5%) (SOC:7%) V1G (SOC:7%) V1G (SOC:3%) (SOC:5%) Battery SOC [%]

5 model and parameters 5/8 Battery pack (cell) pool (one vehicle) in Grid A Batte ery voltage [V V] Nominal Voltage V nom Nominal Capacity C nom Energy Capacity (3.7) [V] 5 (5) [Ah] (.185) [kwh] Number of vehicles 4, Maximum power P max gain K max 2[MW] (5 [kw]) 2[MW /.25Hz] Internal Resistance R int.352 (.4)[Ohm] Δf sp.1[hz] dsoc dt OCV CCV = = V I nom = OCV + αrt F + CC(5A)-Charge R int CC(5A)-Discharge ln C I OCV nom SOC SOC CV(4.1V)-Charge Battery SOC [%] 91.6% 2V, 25A Plug in, Plug out 3[%] > 9[%] Grid A (339[MW]) Tie-line pool (one vehicle) in Grid B Number of vehicles 1, Maximum power P max gain K max Δf sp 5[MW] (5 [kw]) 5[MW /.25Hz] Grid B (79[MW]) 1[Hz].1[Hz] Plug in, Plug out 5[%] > 5[%] 2V, 25A

6 Power grid model with RESs and LFC by thermals 6/8 2[MW/.25Hz] (5[kW]*4,) Governor free 1213[MW] ](5[%]) 2[puMW/puHz] Flat Frequency Control 496[MW] (1.5[%] of load) PI control : 1,.1 Random Noise 1213 [MW] with LPF Δfa ARa FFC Δfa Δfa ΔPa _a Renewable_a ARa Thermal_a ΔPtha Load_a Grid A 339[MW] 2[puMW/puHz] ΔP a [s] Ma.s Inertia_a System constant : 5 Dispatching Tie line ΔP 14 t Kab Center s Tie line Bias Control 16[MW] (1.5[%] of load) PI control : 1,.1 System constant : 5 Governor free 278[MW] (5[%]) 2[puMW/puHz] 5[MW/.25Hz] (5[kW]*1,) Random Noise 278 [MW] with LPF ΔPt ARb ARb Δfb ΔPthb Δfb ΔP TBC b 1 Thermal_b Mb.s Renewable_b Δfb ΔPb _b Load_b 9.2[s] Inertia_b 79[MW] 2[puMW/puHz] Grid B Δf a Δf b

7 Δfa[H Hz] ] Δfb[Hz ΔPt[MW] ΔPa[MW] ΔP Pb[MW] No control control / V1G V1G (Smart Charging) 7/ Grid-A:Freq Grid-B:Freq Tie-line Power b[kw W] a[ [kw] Discharge Ch harge Discharge Charge Grid-A:Outputs Renewable Thermal Load Grid-B:Outputs -4 Renewable Thermal Load Grid-A:One Vehicle Plugged in with 3% Grid-B:One Vehicle Plugged in with 5% [ hour] SOCa a[%] SOCb[% %]

8 8/8 Conclusions Autonomous Distributed ted (Vehicle to Grid) 1. Replying charging request by grid friendly smart charging 2. Balancing battery SOC by trickle charge/discharge 3. Supplying distributed spinning reserve for the grid Communication is not used for control. Future works Utility want to know the amount of available pool > > Identification of system constant Implementation into Plug in Electric Vehicles > > Experiment of few types of Li ION batteries > Development of available charger/discharger