MULTIPERIOD/MULTISCALE MILP MODEL FOR OPTIMAL PLANNING OF ELECTRIC POWER INFRASTRUCTURES

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1 MULTIPERIOD/MULTISCALE MILP MODEL FOR OPTIMAL PLANNING OF ELECTRIC POWER INFRASTRUCTURES Cristiana L. Lara* and Ignacio E. Grossmann* *Department of Chemical Engineering, Carnegie Mellon University Center for Advanced Process Decision-making CAPD Meeting, Mar. 6 Mar 9,

Renewable electricity generation by fuel type (trillion

2 Electricity mix gradually shifts to lower-carbon options Electricity generation by fuel type (trillion kwh) Renewable electricity generation by fuel type (trillion kwh) Source: EIA, Annual Energy Outlook 2015 Reference case 2

Potential accelerated retirements Coal-fired Cumulative

Accelerated Coal Retirements 90 60 Accelerated Coal and

0 2010 2020 2030 2040 Nuclear Cumulative retirement of

Retirements/ Accelerated Coal and Nuclear Retirements 30

3 Potential accelerated retirements Coal-fired Cumulative retirement of coal-fired generating capacity 120 Accelerated Coal Retirements Accelerated Coal and Nuclear Retirements High Oil and Gas Resource Reference Nuclear Cumulative retirement of nuclear generating capacity Accelerated Nuclear Retirements/ Accelerated Coal and Nuclear Retirements Reference Source: EIA, Annual Energy Outlook

High variability on the renewables capacity factor Increasing contribution of renewable power generation in the grid make it crucial to include operation details in the

4 High variability on the renewables capacity factor Increasing contribution of renewable power generation in the grid make it crucial to include operation details in the hourly level in long term planning models to capture their variability Source: U.S. Energy Information Administration, based on the Electric Reliability Council of Texas (ERCOT) 4

5 Problem statement By taking the viewpoint of a central planning, identify type source capacity of future power generation infrastructure that can meet the projected electricity demand while minimizing: capital investment of all new generating units the operating and maintenance costs of both new and existing units environmental costs 5

6 Problem statement Hourly time resolution In order to be able to capture the variability of generation by renewable source units, and assure that the load demand is met at anytime, operational decisions are also taken ramping limits unit commitment status Long term investment plans 6

MILP Model Objective function: Minimization of the discounted total cost over

operating cost Cost of investments in new capacities Penalty for not meeting the

balance: ensures that the sum of instantaneous power equal load at all times

7 MILP Model Objective function: Minimization of the discounted total cost over the planning horizon comprising Variable operating cost Startup cost Fixed operating cost Cost of investments in new capacities Penalty for not meeting the minimum renewable annual energy production requirement subject to Energy balance: ensures that the sum of instantaneous power equal load at all times plus a slack for potential excess generation by the renewable source generators (wind and solar) 7

8 MILP Model Objective function: Minimization of the discounted total cost over the planning horizon comprising Variable operating cost Startup cost Fixed operating cost Cost of investments in new capacities Penalty for not meeting the minimum renewable annual energy production requirement subject to Unit minimum and maximum power output for thermal generators Minimum % of the capacity nameplate Capacity nameplate 8

Variable operating cost Startup cost Fixed operating cost

meeting the minimum renewable annual energy production

9 MILP Model Objective function: Minimization of the discounted total cost over the planning horizon comprising Variable operating cost Startup cost Fixed operating cost Cost of investments in new capacities Penalty for not meeting the minimum renewable annual energy production requirement subject to Capacity factor for renewable source generators 9

10 MILP Model Objective function: Minimization of the discounted total cost over the planning horizon comprising Variable operating cost Startup cost Fixed operating cost Cost of investments in new capacities Penalty for not meeting the minimum renewable annual energy production requirement subject to Minimum reserve margin requirement: ensures that the generation capacity is greater than the peak load by a predefined margin Minimum annual Renewable Energy Source (RES) contribution requirement: establish that if the RES quota target (imposed by environmental treaties) is not satisfied, there will be a penalty applied to the deficit in RES production 10

capacities Penalty for not meeting the minimum renewable annual energy production requirement subject

11 MILP Model Objective function: Minimization of the discounted total cost over the planning horizon comprising Variable operating cost Startup cost Fixed operating cost Cost of investments in new capacities Penalty for not meeting the minimum renewable annual energy production requirement subject to Unit commitment status and ramping limits for the thermal generators OFF ON OFF t - 1 Startup t Shutdown t

12 Modeling strategies for MULTISCALE Time scale approach Year 1, spring: Investment decisions x 93 x 94 x 89 x 89 Day Day Day Day Year 2, spring: Investment decisions Spring Summer Fall Winter Horizon: 30 years, each year has 4 periods (spring, summer, fall, winter) Each period is represented by one representative day on an hourly basis Varying inputs: load demand data, capacity factor of renewable source generators Each representative week is repeated in a cyclic manner (~3 months reduced to 1 day) Connection between periods: only through investment decisions 12

and starting up/shutting down a generator switched from binary to integer variables *Palmintier, B.

13 Modeling strategies for MULTISCALE Clustering representation* cluster Instead of representing each generator separately, aggregate same type of generators in clusters Decision of building/retiring and starting up/shutting down a generator switched from binary to integer variables *Palmintier, B., & Webster, M. (2014). Heterogeneous unit clustering for efficient operational flexibility modeling 13

Case study: ERCOT region 30 year time horizon Data from ERCOT database All costs in 2012 U$ Clusters considered: coal-st-old1 coal-igcc-new Discrete variables: 103,050 Continuous variables: 101,071

14 Case study: ERCOT region 30 year time horizon Data from ERCOT database All costs in 2012 U$ Clusters considered: coal-st-old1 coal-igcc-new Discrete variables: 103,050 Continuous variables: 101,071 Equations: 278,183 CPLEX optcr = 0.05% coal-st-old2 ng-ct-old ng-cc-old ng-st-old nuc-st-old pv-old wind-old coal-igcc-ccs-new ng-cc-new ng-cc-ccs-new ng-ct-new nuc-st-new pv-new wind-new csp-new Considers reference* case scenario *Based on EIA Annual Energy Outlook 2015 fuel price data 14

Case study: ERCOT region Power generation (TW) 600 500 400 300 200 100 Power

04% CPU time: 637 s Breakdown of the Total Cost 34% 44% 0 1 2 3 4 5 6 7 8 9 10 11

natural gas nuclear PV wind Natural gas generation will grow from 21% to 42% of

15 Case study: ERCOT region Power generation (TW) Power generation by source Minimum cost: $ billions Optimality gap: 0.04% CPU time: 637 s Breakdown of the Total Cost 34% 44% years 0% 18% 4% coal natural gas nuclear PV wind Natural gas generation will grow from 21% to 42% of the total generation Fuel Cost* Variable Operating Cost Fixed Operating Cost Startup Cost Investment Cost 15

16 Conclusions and Future Work Time scale and clustering approaches reduce considerably the size of the MILP, making it possible to solve large instances For ERCOT region, future investments will be focused on natural gas and wind generation Natural gas will be the major contributor for the overall generation by the end of the time horizon Future work: Include transmission in the model (multiple generation nodes) Apply decomposition techniques to speed up the solution Address the uncertainty by extending MILP model to multi-stage MILP stochastic programming model 16

17 Acknowledgments Funding sources: Thank you! 17

MILP FORMULATION AND NESTED DECOMPOSITION FOR PLANNING OF ELECTRIC POWER INFRASTRUCTURES

MILP FORMULATION AND NESTED DECOMPOSITION FOR PLANNING OF ELECTRIC POWER INFRASTRUCTURES Cristiana L. Lara 1, and Ignacio E. Grossmann 1 1 Department of Chemical Engineering, Carnegie Mellon University