Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Julieta Gir á ldez Graduate Student Division of Engineering CSM March 2011 PowerPoint Presentation
Download Presentation
Julieta Gir á ldez Graduate Student Division of Engineering CSM March 2011

Julieta Gir á ldez Graduate Student Division of Engineering CSM March 2011

76 Vues Download Presentation
Télécharger la présentation

Julieta Gir á ldez Graduate Student Division of Engineering CSM March 2011

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. JulietaGiráldez GraduateStudent Division of Engineering CSM March 2011 PLANNING DISTRIBUTION SYSTEM RESOURCE ISLANDS CONSIDERING RELIABILITY, COST AND THE IMPACT OF PENETRATION OF PLUG-IN HYBRID ELECTRIC VEHICLES

  2. Outline Introduction Design of distributed resource islands Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on distributed resource islands Conclusions and future work

  3. Outline Introduction Optimization of islanded distribution systems from a design perspective Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

  4. Introduction Smart Grid Initiative [1]: what is the evolution of electric power distribution systems? Distributed Energy Resources (DER) or Distributed Generation(DG) Incorporate ways of physical and virtual storage to balance consumption and production including PHEVs Increased used of technologies: advanced meters, advanced inverters, distribution automation, communication systems, etc. [1] 110thCongress of the United States, "Title XIII (Smart Grid)," in Energy Independence and Security Act of 2007. Washington, DC: Dec. 2007, pp. 292 –303.

  5. Introduction Why? Contribute to the load relief of the transmission system by increasing the generation in the distribution system and new ways of energy management Higher reliability and power quality Integration of green technologies into the grid

  6. Introduction How to implement the smart grid? Microgrid concept: a distributed resource island Self-contained autonomous subset of the area electric power system Has local Distributed Energy Resources (DER) Operates semi-autonomouslyof the grid, being able to island and reconnect as circumstances dictate Able to provide power quality and reliability different from general macro-grid standards [2] N.Hatziargyriou, H.Asano, R.Iravani, and C.Marnay , “Microgrids”, IEEE Power & Energy Magazine, pp.78-94, July/Aug. 2007.

  7. [3] “Distributed Energy Resources Integration”, Consortium for Electric Reliability Technology Solutions (CERTS), [Online]. Available:http://certs.lbl.gov/certs-der.html

  8. Outline Introduction Design of distributed resource islands Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

  9. Design of distributed resource islands Distribution systems Traditional electric distribution systems: Grid Grid Infinite bus Transformer Line Load

  10. Design of distributed resource islands Distribution systems Evolving distribution systems: Grid DG Grid Increase annual RELIABILITY at a feasible COST

  11. Design of distributed resource islands Modeling of annual load Annual demand: two ways of modeling annual load annual average demand at every load: i.e. 1 load level representative of the annual demand 6 step-load duration curve representation (hourly demand reordered in increasing demand): i.e. 6 load levels representative of the annual demand ∆T2=1900h ∆T1 =100 h [4] R. Billinton, S. Kumar, et al., "A Reliability Test System for Educational Purposes - Basic Data," IEEE Transactions on Power Systems, vol. 4, pp. 1238-1244, August 1989.

  12. Design of distributed resource islands Modeling of DG DG: aggregate power output of Renewable Energy (RE) and Conventional Distributed Generation (CDG) [5] Pout = CDG + RE + DS Capacity Factor: ratio of the actual output of a power source and its output if it had operated at full capacity Total DG rating R=RRE +RCDG [5] H. Brown, “Implications on the Smart Grid Initiative on Distribution System Engineering: Improving Reliability on Islanded Distribution Systems with Distributed Generation Sources, M.S thesis, Dept. Elec. Eng., Colorado School of Mines, 2010.

  13. Design of distributed resource islands Basic Reliability Concepts: ASAI: The time as a fraction of a year for which the system is available Annual Outage Time, U : Time as a fraction of a year for which the system is NOT available ~ Power Not Supplied (PNS) [MW]: Unserved load or demand that the system cannot attend Reliability metric: Energy Not Supplied [MWh]

  14. Design of distributed resource islands I. Enter the power system component data Slack bus: slack bus is modeled as a generator that absorbs or supplies generation in order to balance the load and generation ~Power Not Supplied~ Power systems simulation tool: Computer program to solve a power flow: Generation supplies the demand, to control the frequency of the system Bus voltage magnitudes remain close to the rated values Lines and transformers are not overloaded PowerWorldSimulatorTM is used II. Solve the Power Flow under balanced three phase conditions 2. 1. 3.

  15. Outline Introduction Optimization of islanded distribution systems from a design perspective Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

  16. MOGA Multi-objective redesign problem Investment cost versus reliability: Pareto-optimality ~ no single optimal solutions but a set of alternative solutions ~ Non-linear problem, discrete and non-convex feasible region Intractability of the problem as the size of the system grows [5] Evolutionary methods [5] H. Brown, “Implications on the Smart Grid Initiative on Distribution System Engineering: Improving Reliability on Islanded Distribution Systems with Distributed Generation Sources, M.S thesis, Dept. Elec. Eng., Colorado School of Mines, 2010.

  17. MOGA Mathematical formulation Variables Objective function 1: COST CC :Cost of conductor [$/km] li: Length of connection i[km] CDG: Cost of DG [$/MW] PDGj: Power output of DGlocated at bus j [MW]

  18. MOGA Mathematical formulation Objective function 2: RELIABILITY~ Energy Not Supplied Annual average loads: Six step load duration curve:

  19. MOGA Mathematical formulation Constraints Voltage within 5% of the nominal value at every bus j: Loading of the Line from bus j to k:

  20. MOGA * * GAs and the fitness function A population is comprised of individuals or chromosomes ~ a potential solutionto the optimization problem Evolutionary operators are used to create randomly individuals which may move to a higher level of fitness such as mutation, recombination, and crossover. MatlabTM Genetic Algorithm Optimization Toolbox (GAOT) inbuilt functions The fitness function determines how likely an individual is to survive to the next generation ~ output of fitness function ~ * *

  21. MOGA Importance of the initial population for convergence Explore 3 ways of selecting the initial population

  22. MOGA: RBTS test system Application to a test system RBTS System [6]: Possible Connections 302. We input only the 164 connections which length is less than 3km. Possible DG Location 27 buses DG: [6] R. Billinton and S. Jonnavithula, "A Test System for Teaching Overall Power System Reliability assessment," IEEE Transactions on Power Systems, vol. 11, pp. 1670-1676, November 1996.

  23. MOGA: RBTS Test system Application to a test system Results: “look-up table” for the decision maker A more expensive solution may be chosen if the Value of Lost Load (VOLL) [$] of the system is greater than the investment cost

  24. MOGA: RBTS Test system If VOLL ≤ Cost Solution 6 might be chosen

  25. MOGA: RBTS Test system Application to a test system Very similar redesign solutions for the RBTS with annual average loads and with step-load duration curve ENS overestimated with annual average demand Computational time : modeling of the annual load connection from Matlab to PowerWorld Simulator initial population

  26. Outline Introduction Optimization of islanded distribution systems from a design perspective Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

  27. Impact of PHEVs in distributed resource islands Introduction to PHEVs IEEE definition: “vehicles that have a battery storage system rating of 4 kWh or more, a means of recharging the battery form an external source, and the ability to drive at least 10 miles in all electric mode” Vehicle-to-grid (V2G): using the battery of a vehicle as a Distributed Energy Resource (DER) New way of electric energy management Existing power system infrastructure may not be adequate to deal with the increased demand and new patterns of consumption and power flows in the grid [7] “Vehicle to Grid (V2G) Electricity” , Global Greenhouse Warming, [Online]. Available: http://www.global-greenhouse-warming.com/vehicle-to-grid.html

  28. Impact of PHEVs in distributed resource islands Modeling PHEVs in distribution systems How many PHEVs? What is the behavior of the driver? For how long does a PHEV behave as a load? For how long does a PHEV behave as DG? ~ KEY ASSUMPTIONS TO STUDY THE IMPACT ~

  29. Impact of PHEVs in distributed resource islands Modeling PHEVs in a distribution system • How many vehicles? • How many PHEVs in the system? • What kind of PHEVs? • What design and operational characteristics? • What is the behavior of the driver? • For how long does the PHEV behaves as a load? • … and as a generator? • Electric customer consumes 2 kW and has 1.5 vehicles for residential; 38 workers per office building and 17 workers per commercial and 1.5 vehicles per worker • 30% penetration of the total transportation fleet • Probabilistic simulation methodology • Driving factors • Peak-shaving • Owner’s benefit • Linear Programming (LP) algorithms to optimize charging patterns

  30. Impact of PHEVs in distributed resource islands Probabilistic simulation of PHEV fleet for 8760 hours [8] PHEV Class 4 PHEVClass 1 PHEV Class 2 PHEV Class 3 Methodology Probabilistic simulation methodology [8] Contributions made by this thesis: LP algorithm ~ determine the loading Impact on design and reliability of distributed resource islands Tools & methods Daily vehicle data for optimization Departure time Miles driven LP Optimization of daily charging pattern of PHEVs for 1 year Energy required Arrival time Objective/s: maximize owners profit and/or utility peak shaving (Demand response) Incorporate optimized PHEV load (hourly) to load duration curve of distribution system Impact of PHEV fleet on annual reliability of islanded legacy radial distribution systems Impact of PHEV fleet on annual reliability of islanded networked distribution systems [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009. Results

  31. Impact of PHEVs in distributed resource islands Parameters for the Prob. Sim. Methodology [8] Four vehicle classes (types) Design characteristics (SOC): Battery size [8] S. Meliopoulos, J. Meisel and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  32. Impact of PHEVs in distributed resource islands Parameters for the Prob. Sim. Methodology [8] Amount of driving supplied from electric battery? From fuel? kphev=0 represents a charge sustaining (CS) mode in which on average all the drive energy comes from gasoline kphev=1 represents a charge depleting (CD) mode, all of the drive energy comes from electricity Simulations run in Powerdrive Simulation Analysis Tool (PSAT) Performance parameter Ec: required energy per mile [kWh/mi.] [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  33. Impact of PHEVs in distributed resource islands Parameters for the Prob. Sim. Methodology [8] Vehicle control strategy: drive in CD from battery while in SOC ranges and switch to CS to maintain SOC relying on gas Charge depleting distance MD [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  34. Impact of PHEVs in distributed resource islands Parameters for the Prob. Sim. Methodology [8] Four random paramaters kphevcandBc # Vehicles per class Daily Miles driven per vehicle Driver’s behavior ~ Time parameters [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  35. Impact of PHEVs in distributed resource islands Probabilistic simulation methodology [8] Vehicle design characteristics kphevc and usable battery capacity Bc are distributed according to a bivariate normal distribution with mean vector μ and covariance matrix ∑ with 0.8 parameter correlation Performance parameter Ec is determined knowing kphevc [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  36. Impact of PHEVs in distributed resource islands Probabilistic Sim. Meth. applied to the RBTS test system[8] Vehicles per class: normal distribution with mean #PHEVs*Probability vehicle class and 1% standard deviation Total # vehicles (light transportation fleet): 15, 269 = 14,925 res + 230 com+ 114 off Uniform distribution of the #PHEVs throughout the load points of the RBTS per demand type~ daily parameters generated only for the #PHEVs in one load type~ Vehicle population size per class: Approximate to the average #PHEV per class per load type [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  37. Impact of PHEVs in distributed resource islands Probabilistic simulation methodology [8] Miles driven per vehicle per day Md,c,v : log normal distribution with mean 3.37 and standard deviation of 0.5 Daily energy required per vehicle from the grid [kWh]: , if MD ≤ Md,c,v , if Md,c,v≤ MD [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  38. Impact of PHEVs in distributed resource islands Probabilistic simulation methodology [8] Driver’s behavior ~ time parameters: Gaussian distribution Only residential charging in [8], what about office and commercial loads? average urban driving speed 25 [mi./h] [8] S. Meliopoulos, J. Meiselrge and T. Overbye, ―Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report),‖ PSERC Document 09-12, Oct. 2009.

  39. Impact of PHEVs in distributed resource islands LP algorithms By now we know: Size and design characteristics of the PHEV fleet Daily energy required per vehicle from the grid Daily available time for charging per vehicle DETERMINE DAILY CHARGING PATTERNS: Utility peak shaving or benefit of the owner

  40. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Sets: • I = set of load types , from 1 … NI • C= set of PHEV classes, from 1…NC • V= set of PHEVs per class, from 1 … NV • D=set of days in a year, from 1…ND • T= set of hours in a day, from 1 … NT

  41. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Parameters: • Bc = Battery size per vehicle class c [kWh] • DEd,c,v= Daily energy required per day d, vehicle class c and vehicle v [kWh] • Ad,c,v= Daily arrival time per day d, vehicle class c and vehicle v [h] • Dd,c,v= Daily departure time per day d, vehicle class c and vehicle v [h] • Cmaxc= Maximum hourly charge rate per vehicle class c [kW] • Lbased,i,t= Base load (without PHEVs) on day d, load type iand hour t [kW] • Lavd,i= Average base load (without PHEVs) on day d and load type i [kW] • Pd,t = Price of energy on day d and hour t [$/kWh] From the Probabilistic Simulation Methodology

  42. Impact of PHEVs in distributed resource islands Application to the RBTS test system Base load of the system: [9] Reliability Test System Task Force of the Application of Probability Methods Subcommittee, “IEEE reliability test system,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-98, no. 6, pp. 2047-54, November 1979.

  43. 0.052 0.052 0.19 0.21 0.19 0.95 0.85 *the numbers inside the pie charts express the energy rate in $/kWh Impact of PHEVs in distributed resource islands Midnight Midnight Application to the RBTS test system Charge rates assumptions: Residential: Classes 1&2 ~ Level 1 (120V;15A) Classes 3&4 ~ Level 2 (240V;30A) Non-residential: all classes at Level 2 Price of energy: Time Of Use (TOU) pricing 2 seasons 3 price levels: on-peak, medium peak and off-peak Noon Noon

  44. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Variables: • C+d,c,v,t= Amount charged on day d, vehicle class c, vehicle v and time t [kW] • C-d,c,v,t= Amount discharged on day d, vehicle class c, vehicle v and time t [kW] • Cd,c,v,t= Energy stored on day d, vehicle class c, vehicle v and time t [kWh] Hourly charge (+) or discharge (-) If positive, a change in the direction of power in the battery • W+d,c,v,t= Absolute value of the difference between C+d,c,v,tand C+d,c,v,t+1[kW] • Ld,i,t= New load on day d, load type iand hour t [kW] • Zd,i,t= Absolute value of the difference between Ld,i,tand Lavd,i [kW] Energy inventory

  45. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Battery constraints: C+d,c,v, t ≤ Cmaxc for every d, c, v, t C-d,c,v, t ≤ Cmaxc for every d, c, v, t Limit the charge/discharge to the available connection Energy in the battery when the PHEV arrives home Cd,c,v, t = Bc - DEd,c,v for t=Ad,c,v – 1 and every d,c,v Inventory balance Cd,c,v, t = Cd,c,v, t-1 + C+d,c,v, t - C-d,c,v, t for Ad,c,v≤ t ≤ Dd,c,vand every d,c,v Battery fully charged by dep. time Cd,c,v, t = Bc for t=Dd,c,vand every d,c,v

  46. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Battery constraints: -W+d,c,v,t≤ C+d,c,v, t - C+d,c,v, t+1 ≤ W+d,c,v,t for Ad,c,v≤ t ≤ Dd,c,v -1 and every d, c, v ∑W+d,c,v,t≤ 3*Cmaxcfor Ad,c,v≤ t ≤ Dd,c,v -1 and every c, v W+d,c,v,t?

  47. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Load constraints: New load with PHEVs for every d, i, t Peak-shaving measure for every d, i, t

  48. Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Objective function: Utility peak-shaving Customer profit SOLVE ONE OBJECTIVE AT A TIME AND COMPARE IMPACT IN RELIABILITY

  49. (1) RBTS Base Load (2) RBTS Base load + PHEV for peak shaving (3) RBTS Base load + PHEV for customer benefit • (4) RBTS Base load + PHEV uncontrolled charging & no V2G RBTS Power demand [kW] Time [h] Impact of PHEVs in distributed resource islands Results Loading of the RBTS system with PHEVs Peak demand [kW] Base Load[kW]

  50. (1) RBTS Base Load (2) RBTS Base load + PHEV uncontrolled charging & no V2G (3) RBTS Base load + PHEV delayed charging & no V2G RBTS Power demand [kW] Time [h] Impact of PHEVs in distributed resource islands Results Loading of the RBTS system with PHEVs Peak demand [kW] Base Load[kW]