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A Parallel Solution to Stochastic Power System Operation with Renewable Energy 5 th Southeast Symposium on Contemporary Engineering Topics (SSCET), 2014.

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Presentation on theme: "A Parallel Solution to Stochastic Power System Operation with Renewable Energy 5 th Southeast Symposium on Contemporary Engineering Topics (SSCET), 2014."— Presentation transcript:

1 A Parallel Solution to Stochastic Power System Operation with Renewable Energy 5 th Southeast Symposium on Contemporary Engineering Topics (SSCET), 2014 Yong Fu, Ph.D. Associate Professor Electrical and Computer Engineering Mississippi State University New Orleans, LA September 19 th, 2014

2 Parallel Computing o With development of high performance computing technique, parallel computing technique can significantly improve computational efficiency of optimization problem with utilization of multi-processors and multi-threads. o These improvements cannot be achieved by the architectures of the machines alone, it is equally important to develop suitable mathematical algorithms and proper decomposition & coordination technique in order to effectively utilize parallel architectures

3 A Typical Power System Operation Problem – Security Constrained Unit Commitment  Objective Function – Minimize  Generating Unit Constraints  System Operation Constraints  Generation capacity  Minimum ON/OFF time limits  Ramping UP/DOWN limits  Must-on and area protection constraints  Forbidden operating region of generating units  Power balance  System reserve requirements  Power flow equations  Transmission flow and bus voltage limits  Limits on control variables  Limits on corrective controls for contingencies  Generation and startup/shutdown costs Unit 1 Unit 2 Unit 3 Large Scale, Non-Convex, Mixed Integer Nonlinear Problem

4 Who Use SCUC and How? GENCOs TRANSCOs ISO Security-Constrained Unit Commitment DISTCOs  ISOs: PJM, MISO, ISO New England, California ISO, New York ISO and ERCOT ISO (SCUC) and Market Participants  Day Ahead Market (DAM) determines the 24-hourly status of the generating units for the following day based on financial bidding information such as generation offers and demand bids.  Day Ahead UC for Reliability (RUC), which focuses on physical system security based on forecasted system load, is implemented daily to ensure sufficient hourly generation capacity at the proper locations.  Look-Ahead UC (LAUC), as a bridge between day- ahead and real-time scheduling, constantly adjusts the hourly status of fast start generating units to be ready to meet the system changes usually within the coming 3-6 hours.  Real-Time Market (RTM) further recommits the very fast start generating units based on actual system operating conditions usually within the coming two hours in 15-minute intervals.

5 Stochastic SCUC  In stochastic programming, the decision on certain variables has to be made before the stochastic solution is disclosed, whereas others could be made after.  The set of decisions is then divided into two groups:  A number of decisions are made before performing experiments. Such decisions are called first- stage decisions and the period when these decisions are made is called the first stage.  A number of second-stage decisions are made after the experiments in the second stage.  Stochastic models containing above two groups of variables, first-stage and second-stage decision variables, are called two-stage stochastic programming.

6 Stochastic SCUC --- Example G3 16 $/MWh 10MW~40MW Load G1 13 $/MWh 40MW~80MW G2 42 $/MWh 15MW~ 40MW System L1 75MW L2 75MW 1 2 20 MW 80 MW 50 MW 52.5 MW 75 MW 0 MW 100 MW 105 MW 95 MW Base Case Scenario 1 Scenario 2 W 0 MW 15 MW 75 MW 15 MW 23 MW ? MW G3 can adjust dispatches by 5 MW G2 is quick-start unit with 30 MW QSC 20 MW 65 MW 42.5 MW 52.5 MW 75 MW 15 MW 0 MW 20 MW 100 MW 105 MW 95 MW Base Case Scenario 1 Scenario 2 0 MW 15 MW 60 MW 30 MW 23 MW 52 MW 0 MW Solution 1 Solution 2 Cases Equipment Outage Wind (WM) Load (MW) Base case-20100 Scenario 1G315105 Scenario 2L22395

7 Current Work o Amdahl’s law: an upper bound on the relative speedup achieved on a system with multi-processors is decided by the execution time of the application operating sequentially.

8 Proposed Approach o Structure of Algorithm: Scenario-based stochastic model is adopted to analyze the uncertainties of load and wind energy in this paper. Instead of master-and-slave structure, UC and OPF subproblems are solved simultaneously in the proposed parallel calculation method. o Convergence performance: In an iterative solution process, the number of iterations affects the overall computational time. Several convergence acceleration options, including initialization and update of penalty multipliers, truncated auxiliary problem principle and trust region technique, are used to improve the convergence performance and efficiency in a scenario-based study.

9 Decomposition Strategy  Mathematically, the stochastic SCUC can be formulated as a mixed integer programming (MIP) problem as shown in  Variable Duplication Technique  Augmented Lagrangian Method

10 Algorithms for Parallel Solutions  Auxiliary Problem Principle (APP) Method  Diagonal Quadratic Approximation (DQA) Method  Alternating Direction Method of Multipliers (ADMM)  Analytical Target Cascading (ATC) Method

11 Iterative Solution Procedure Two separated auxiliary problem: Decomposition structure: Given values from the previous iteration Decision variables for the current iteration

12 Case Study – IEEE 118-bus Testing System o Case 1: Deterministic case o Case 2: Stochastic case with 3 scenarios 54 thermal units 3 wind farms 118 buses 186 branches

13 Deterministic Case Study The converged result is obtained after 39 iterations. Unit 45 at Hour 5 Unit 45 at Hour 21 Unit 36 at Hour 5 Unit 36 at Hour 21

14 Deterministic Case Study Items Centralized SCUC Parallel SCUC Changes Total Cost ($)1,583,7001,584,997 +0.08% Time (Seconds)198 -58%

15 Stochastic Case Study (3 scenarios) Items Centralized SCUC Parallel SCUC Changes Cost ($) 1,582,8401,583,565+0.046% Time (Seconds) 1,08320-96%

16 Case Study – A 1168-bus Power System o A practical 1168-bus power system with 169 thermal units, 10 wind farms, 1474 branches, and 568 demand sides. o It could be nearly impossible to get a near-optimal stochastic SCUC solution for this system by applying a traditional centralized SCUC algorithm. o However, the proposed parallel stochastic SCUC algorithm provides solutions. Unit 8 at Hour 1

17 Case Study – A 1168-bus Power System # of Scenarios# of IterationTotal Time (sec.) 0315109.55 1330146.06 2299139.31 3327163.47 4277142.28 5278140.28 6248139.52 7243130.94 8242133.25 9237148.33 10231131.95

18 Conclusions o The proposed stochastic SCUC approach minimizes the operation cost of system by possibility expectation of each scenarios, which can adaptively and robustly adjust generation dispatch in response to constraints in different scenarios. o In comparison with traditional stochastic SCUC, optimal power flow problem does not have to wait for unit commitment decision, both problems can be solved simultaneously, which is more computational efficient in both day-head and real-time power markets. o The ideas can be applied to various power system applications: state estimation, economic dispatch, and planning.

19 Thanks !

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