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									Simulation of Power Systems
   with integrated time-
   dependent resources

    Yannick Degeilh and Pr. George Gross
            Friday, May 20th 2011
            Power Affiliate Program
                  Outline
• Context and Motivation
• The challenges of modeling time dependent
  resources
  – Wind
  – Demand Response Resources (DRRs)
  – Utility-Scale Storage Device
• Overview of the proposed simulation
  approach
• Application study results
                       Context
• There is a growing interest in integrating wind
  resources into the smart grid due to its benefits:
   –   Renewable
   –   emission free
   –   0 fuel cost
   –   Meets energy independence goals
• New policies currently being implemented to
  foster wind power development include:
   – tax credits and incentives
   – Federal stimulus package
   – Renewable Portfolio Standards (RPS)
                Renewable Portfolio Standards
                                                           www.dsireusa.org / April 2011
                                                                                                         VT: (1) RE meets any increase       ME: 30% x 2000
 WA: 15% x 2020*                                                                                                                             New RE: 10% x 2017
                                                                     MN: 25% x 2025                          in retail sales x 2012;
                               MT: 15% x 2015                           (Xcel: 30% x 2020)                 (2) 20% RE & CHP x 2017           NH: 23.8% x 2025
OR: 25% x 2025 (large utilities)*                     ND: 10% x 2015                         MI: 10% & 1,100 MW                               MA: 22.1% x 2020
                                                                                                  x 2015*                                      New RE: 15% x 2020
 5% - 10% x 2025 (smaller utilities)
                                                                                                                                              (+1% annually thereafter)
                                                       SD: 10% x 2015 WI: Varies by utility;
                                                                                                         NY: 29% x 2015                    RI: 16% x 2020
                                                                      10% x 2015 statewide
         NV: 25% x 2025*                                                                                                                   CT: 23% x 2020
                                                                           IA: 105 MW           OH: 25% x 2025†
                                       CO: 30% by 2020      (IOUs)                                                                         PA: ~18% x 2021†
                                  10% by 2020 (co-ops & large munis)*
                                                                                     IL: 25% x 2025         WV: 25% x 2025*†               NJ: 22.5% x 2021
CA: 33% x 2020          UT: 20% by 2025*                 KS: 20% x 2020                                      VA: 15% x 2025*               MD: 20% x 2022
                                                                           MO: 15% x 2021                                                  DE: 25% x 2026*
                 AZ: 15% x 2025
                                                               OK: 15% x 2015                    NC: 12.5% x 2021     (IOUs)      DC
                                                                                                                                           DC: 20% x 2020
                                                                                                 10% x 2018 (co-ops & munis)
                                  NM: 20% x 2020 (IOUs)
                                         10% x 2020 (co-ops)

                                                                                                                                             PR: 20% x 2035
                                                   TX: 5,880 MW x 2015

                     HI: 40% x 2030

                                                                                                                                    29 states +
     Renewable portfolio standard                                Minimum solar or customer-sited requirement                        DC and PR have
     Renewable portfolio goal                                                                                                           an RPS
     Solar water heating eligible                         *
                                                          †
                                                                 Extra credit for solar or customer-sited renewables
                                                                 Includes non-renewable alternative resources
                                                                                                                                         (7 states have goals)
Complicating factors in the integration
               of wind
Wind power salient features:

  – High variability/intermittency

  Wind has a highly time-dependent nature

  – Uncertainty

  – Lack of predictability

  – Limited dispatchability
                         Wind time-varying nature
                                        7 consecutive days of wind power output and load
                         250                                                                            8000
                                                                                                  wind power
                                                                                                  load


                         200                                                                            7000
wind power output (MW)




                         150                                                                            6000




                                                                                                               load (MW)
                         100                                                                            5000




                         50                                                                             4000




                          0                                                                             3000
                               0   24    48          72          96          120           144   168
                                                             hour
Wind power outputs are uncertain
                                 7 consecutive days of wind power output (wind data from Adair, IA)
                   250
                                                               day 4
                                                                                         day 2
                                                                                                              day 6


                   200                                             day 5
                                                                                                                   day 1
                                                                                                           day 3


                   150
 wind power (MW)




                                                                                            average
                   100




                   50                                                                                         day 7




                    0
                         0   4                8                 12                 16                 20                   24
                                                             day time
                                             DRRs
                   Demand response resources (DRRs) = Market demand-side
                   players who:
                   • offer load curtailment services that compete with generators
                      offers during peak-hours.
Time - Dependent




                   • Potentially recover a fraction of the load that has not been
                      consumed during peak hours in the low load hours: “Payback
                      effect”
                   DRR players effectively shift the demand from peak hours to
                   low load hours => can reduce overall consumer payments,
                   improve system reliability. Impact on emissions?
     Utility-Scale Storage



MW
                   Storage discharge during
 p
                         peak hours


                    Modified load to be met by
                       non-storage units


                    Storage charge during low
 0                         load hours
     day hours
                                                                    V
       Wind/Storage Interactions
     energy discharged during
            peak hours
MW
                                                 unit i+3    higher cost
     daily load shape                            unit i+2
                                                 unit i+1
                                                 unit i

              daily load                         unit i-1
              minus wind
                                                 unit i-2




                                                  …
                                               Base-loaded
                                                   units     lower cost
 0            12              24             hours

                 energy charged during low
                         load hours
             Operational Planning
                     conventional
   load                 units
 forecast

wind power                           Commitment of the
 forecast                             conventional units
                   unit commitment
                          (UC)       for every subperiod
                                         h of period T
  DRRs



 storage
                     transmission
                       resources
             Hourly DAM realization

   load              conventional
                        units      unit
                                commitment
wind power
                     transmission-                 resource
                      constrained                  dispatch
  DRRs              DAM for hour h



 storage
                     transmission       evaluation of :
                       resources        • economic indices
                                        • Reliability metrics
                                        • Environmental impacts
       Nature and Scope of the study
• The proposed research focuses on the assessment over longer
  term periods of wind, storage and demand response
  resources (DRRs) impacts on:
   – power system economics (LMPs, congestion rents…)
   – pollutant emissions (including greenhouse gas)
   – power system reliability (LOLP, EUE…)

• Direct applications of the approach include:
  – resource planning
  – production costing
  – reliability analysis
  – investment risk analysis
  – policy analysis and study of their potential impacts
                   Main Contribution
• A simulation approach that emulates the hourly transmission-
  constrained day-ahead markets (DAMs) for power systems
  integrating wind, demand response and storage resources
   – modeling of wind into daily wind patterns
   – modeling of DRR offers in the market and recovery effect
   – modeling of storage arbitrage via economic and cycle-efficiency
     considerations
• The simulation approach is based upon the principles of a
  Monte Carlo simulation so that it can accommodate:
   – resource variability/intermittency   used to approximate the market outcome
   – resource uncertainty.                probabilistic distribution for each snapshot

• Definition of representative simulation periods and
  implementation of Latin hypercube sampling (LCS) to ensure
  computational tractability
Capture of both time-dependency and
             uncertainty
     • Capture of time-dependency…
   snapshot 1        …       snapshot h          …         snapshot H

   subperiod 1     ...       subperiod h     ...       subperiod H



  period 1       . .. ..       period t          ...           period T
                                                                          time

                            study period
                     …        …              …

                 Monte Carlo Simulation of each period t

       … and uncertainty.
Conceptual Approach
     Insights in storage operation
• In such conditions, how to emulate storage
  operations?
• Premises:
  – Storage unit operation decided by the ISO.
  – Storage exploited in view of maximizing social welfare
  – Economic criterion: stored energy must be sold at
    higher price than that at which it has been bought.
  – Storage cycle efficiency consideration. Over a period
    of one week, what is charged must be discharged
  Insights in storage operation
                       The load is greater than
                       8000 MW 40% of the time
                           Pr  L  8000   0.4




Time domain           “Time abstracted” domain
          Insights in storage operation

“Time Abstracted” domain                                               Time domain
MW




                                                       MW
                 Discharged Energy

     p                                                  p




                                     charging energy

         unit i+1                                               unit i+1
          unit i                                                unit i
             …




                                                                   …
         base-loaded units                     p.u.         base-loaded units
                                                                                     hour
     0                                1
      Insights in storage operation




Maximum price at
 which energy is                              Minimum price of
bought ($/MWh)     Overall cycle efficiency   displaced energy
                                                  ($/MWh)        20
    Insights in storage operation
• Translating insights from the “time-
  abstracted” domain back into the
  chronological time domain
  – We have obtained the sets of units that can
    participate in charging the storage. Let m be the
    marginal cost of the most expensive unit of such
    set.
  – We have obtained the sets of units that can be
    displaced by the storage. Let u be the marginal
    cost of the cheapest unit of such set.
      Insights in storage operation
• In the hourly DAM:
   – if there is no congestion, scrupulously follow the
     nominal schedule yielded by the analysis in the “time-
     abstracted” domain.
   – if there is congestion, maximize the social welfare by
     both bidding and offering the storage unit:
       o Bid the storage as a load with willingness to pay m
       o Offer the storage as a generator with willingness to sell u
• It can be shown that the optimal solution only
  allows the storage to be used either as a load or a
  generator
           Application study results
• IEEE 118-bus test system
• Annual peak load of 8090.3 MW.
• Reserve margin: 20%
• Conventional generation: 9714 MW of installed capacity
• 4 Midwestern wind farms for a total nameplate capacity of
  2040 MW (25% of the peak load)
• 300 MW utility-scale storage unit with a 10,000 MWh
  reservoir.

We illustrate the capabilities of the approach by examining
the impacts of storage on power system economics in the
following cases:               Wind     Storage
                 Base case
                 Case 1                X
                 Case 2       X
                 Case 3       X        X
      Hourly average storage utilization
                                                                      Hourly average storage utilization
                                       200                                                                                               7000

                                                                                                                  case 3: both wind and storage
                                                                                                                  case 1: storage only
                                                                                                                  Load


                                       100                                                                                               6000
Storage charge and discharge (MWh/h)




                                                                                                                                                Load (MW)
                                         0                                                                                               5000




                                       -100                                                                                              4000




                                       -200                                                                                              3000
                                              0   12   24   36   48   60   72     84     96    108    120   132     144   156    168
                                                                                   hours
                                         Total Consumption Payments
                                             6
                                         x 10                    Hourly average total consumption payment
                                   4.5

                                                                                                              base case: no storage no wind
                                    4
                                                                                                              case 1: storage only
                                                                                                              case 2: wind only
                                   3.5                                                                        case 3: both wind and storage
total consumption payment (in $)




                                    3



                                   2.5


                                    2



                                   1.5



                                    1



                                   0.5



                                    0
                                         0       12   24   36   48   60    72    84   96    108   120   132   144   156   168
                                                                                  hours
                                                            6
                                                        x 10                   Hourly average total emission of carbon dioxyde
                                                  1.5

                                                                                                                                       base case: no storage no wind
                                                  1.4                                                                                  case 1: storage only
                                                                                                                                       case 2: wind only
                                                                                                                                       case 3: both wind and storage
total total emission of carbon dioxyde (in kgC)




                                                  1.3


                                                  1.2


                                                  1.1


                                                   1


                                                  0.9


                                                  0.8


                                                  0.7


                                                  0.6


                                                  0.5
                                                        0       12   24   36    48    60    72    84    96    108   120   132    144    156   168
                                                                                                   hours
                            Outcomes average values
              average consumption                                    average congestion                              average carbon
  case                                                                                                                                                                                      LOLP                                                                    EUE (MWh)
                  payments ($)                                            rents ($)                               dioxyde emissions (kg)



base case                   1,206,700                                             62,717                                          1,127,300                                              0.0093                                                                         11.0681



 case 1                     1,100,700                                             68,618                                          1,133,100                                              0.0066                                                                         4.7062



 case 2                     691,930                                               58,399                                          972,690                                                0.003                                                                          5.1626



 case 3                     579,870                                               61,409                                          976,380                                                0.0026                                                                         2.7877




            base case                                         base case                                           base case                                  base case                                                                                  base case




               case 1                                            case 1                                              case 1                                     case 1                                                                                     case 1




               case 2                                            case 2                                              case 2                                     case 2                                                                                     case 2




               case 3                                            case 3                                              case 3                                     case 3                                                                                     case 3




                    0   2   4   6       8   10   12      14           0   1   2    3       4   5   6          7           0   2    4    6   8   10      12           0   0.001   0.002   0.003   0.004   0.005   0.006   0.007   0.008   0.009   0.01           0   2    4    6    8   10   12
                                    $                    5
                                                      x 10                             $                  4
                                                                                                       x 10                            kg               5
                                                                                                                                                     x 10                                                                                                                    MWh
                 Concluding remarks
Findings
• Wind resources may reduce electricity prices
• Storage drives overall consumption payments down and improves
   system reliability
• The impact of storage increases with deepening wind penetrations


Future work
• Improve the emulation of storage operation
• Extend the simulation capabilities so as to take into account
   multiple storage units
• Integrate a unit commitment to the simulation framework
• Account for the intra-hourly variability of wind and its impacts on
   power system economics and reliability
Thank you!




 Any question?

								
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