Enhancing Power Grid Sustainability with Environment-Friendly and Resilient Operation Strategies LEI Shunbo Dept. of Electrical & Electronic Eng. The University of Hong Kong April 25, 2018
Outline Background and motivation Environment-friendly operation strategies Robust unit commitment considering air pollutant dispersion Distribution system dynamic reconfiguration Resilient operation strategies Remote-controlled switch allocation enabling prompt restoration Mobile emergency generator dispatch Co-optimize service restoration with dispatch of repair crews and mobile power sources
Outline Background and motivation Environment-friendly operation strategies Robust unit commitment considering air pollutant dispersion Distribution system dynamic reconfiguration Resilient operation strategies Remote-controlled switch allocation enabling prompt restoration Mobile emergency generator dispatch Co-optimize service restoration with dispatch of repair crews and mobile power sources
Background and motivation Power grid sustainability Sustainable power grids Environment-friendly RES integration Environmental sustainability operation Emission issue Economic sustainability Resilient operation …… …… Social sustainability Challenges Effective integration of renewable energy sources (RESs) (transmission-level & distribution-level) Emission control (air pollutants, etc.) Resilience against natural disasters
Outline Background and motivation Environment-friendly operation strategies Robust unit commitment considering air pollutant dispersion Distribution system dynamic reconfiguration Resilient operation strategies Remote-controlled switch allocation enabling prompt restoration Mobile emergency generator dispatch Co-optimize service restoration with dispatch of repair crews and mobile power sources
Robust UC considering air pollutant dispersion Air pollution problem One of the worst environmental health risks PM2.5: deeply penetrate into human lungs and blood stream Millions premature deaths, trillions economic cost (UNEP, WHO) Responsibility of the power system Major consumer of fossil fuels A high portion of air pollutants emissions (coal-fired plants, etc.)
Robust UC considering air pollutant dispersion Under-utilized environmental benefits of wind powers Uncertainty and variability (spinning reserve requirement, etc.) Only limiting total emissions (spatial distribution of air pollutants should be accounted for) Research gap C. Wang, Z. Lu, and Y. Qiao , “A Consideration of the Wind Power Benefits in Day -Ahead Scheduling of Wind- Coal Intensive Power Systems,” IEEE Trans. Power Syst. , vol. 28, no. 1, pp. 236-245, Feb. 2013. E. Denny and M. O'Malley, “Wind generation, power system operation, and emissions reduction,” IEEE Trans. Power Syst. , vol. 21, no. 1, pp. 341-347, Feb. 2006. … Generation scheduling that explicitly considers wind powers’ Gap: environmental benefits to reduce ground-level air pollutant concentration (GLAPC) at load centers.
Robust UC considering air pollutant dispersion Air pollutant dispersion models Emission rate function 2 b b ( ) E c P b P a it i it i it i Plume and puff models z z x x Δ h n n o o i i t t c c e e r r i i d d d d H n n i i y W y W (a) (b) 2 d 2 1 E H y it exp C ijt 2 2 2 2 2 2 2 r w I I d I d I d y z x y x z x Considers meteorological conditions and the system’s geographical distribution , to estimate GLAPC due to emissions of generations.
Robust UC considering air pollutant dispersion Problem formulation Objective function min( ) CC BDC APDC N T G Commitment cost: start-up/shut-down of generators CC ( su SU sd SD ) i it i it 1 1 t i N T G b 2 b BDC [ ( P ) P O ] Base-case dispatch cost: real power outputs of generators i it i it i it 1 1 t i Cost due to air pollutant dispersion (APDC) N N T D G 2 b 2 b [ ( ) ] APDC K c P b P a O s NP BP j ij i it i it i it j j j t 1 j 1 i 1 Consider population density & background pollution Minimize people’s exposure to air pollutants
Robust UC considering air pollutant dispersion Problem formulation Constraints Uncertainty set of wind power outputs. Robust feasibility check: ensure adequate spinning reserve, to avoid environmentally unfriendly operations of more polluting coal-fired units in real time.
Robust UC considering air pollutant dispersion Solution algorithm Compact matrix form A mixed-integer quadratic programing problem with a robust feasibility check Robust feasibility check Iterative algorithm 1) Set the number of iterations K =0. Choose a tolerance δ(> 0) for the robust feasibility check. * , … , y 2 , K 2) Solve the MP to update the current optimal solution ( x * , y 1 * , y 2 , 1 * ). 3) K = K +1. Solve the SP with x K = x * , to obtain u K . 4) If objective_of_SP ≤ δ, return ( x * , y 1 * ) and terminate. Otherwise, go to step 2).
Robust UC considering air pollutant dispersion Case studies IEEE 14-bus system Peak GLAPC µg/m 3 Average GLAPC µg/m 3 Bus Back-ground PGLAPC without wind PM2.5 µg/m 3 BP j NP j Case No. Case No. No. AGLAPC without wind 1 2 3 4 1 2 3 4 PGLAPC with wind 4 22 3 9203 22.00 22.00 22.00 22.00 20.00 22.00 22.00 22.00 70 29 AGLAPC (µg/m 3 ) AGLAPC with wind PGLAPC (µg/m 3 ) 5 50 5 1463 70.32 70.32 64.79 56.07 59.97 59.15 56.04 52.53 7 22 3 320 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 9 22 3 5680 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 10 30 4 1733 30.02 30.02 30.02 30.01 30.01 30.01 30.00 30.00 60 28.5 11 36 5 674 53.28 53.28 53.28 53.28 46.14 46.01 46.13 46.75 12 23 3 1174 32.36 32.36 31.99 31.46 28.60 28.34 27.71 27.61 13 18 3 2600 21.02 21.02 20.97 20.90 20.57 20.37 19.73 19.97 50 28 14 13 2 2869 13.13 13.13 13.13 13.13 13.06 13.06 13.02 13.06 0 2 4 6 8 10 12 AUCC (k$) UC cost Total emission Peak GLAPC Average GLAPC Total exposure Case 1: w/o wind power, w/o APDC µg/m 3 µg/m 3 ·10 5 µg/m 3 k$ Ton Case 2: w/ wind power, w/o APDC Case 1 224.58 100.25 70.32 29.37 150.92 Case 3: w/ wind power, w/o APDC, Case 2 200.19 93.43 70.32 29.22 150.42 w/ total emissions limit Case 3 213.38 65.00 64.79 28.29 148.09 Case 4: w/ wind power, w/ APDC Case 4 209.79 67.90 56.07 28.10 145.81 Emission issues need to be explicitly considered, so as to utilize wind power’s benefits in air pollution control. Only limiting total emissions is less cost effective.
Robust UC considering air pollutant dispersion Case studies Guangdong Grid system UC cost Total emission Peak GLAPC Average GLAPC Total exposure ·10 7 $ µg/m 3 µg/m 3 ·10 8 µg/m 3 Ton Case 1 1.6254 26640.70 74.36 58.88 133.59 Case 2 1.4596 24183.33 74.36 55.43 124.21 Case 3 1.5290 11500.00 74.36 33.60 66.54 Case 4 1.5158 19228.48 24.36 14.52 16.93 74.0 58.5 PGLAPC without wind Again : 1) need to explicitly 68.5 AGLAPC without wind 52.0 PGLAPC with wind consider emission issues; and 2) 62.0 AGLAPC with wind 45.5 only limiting total emission is not PGLAPC (µg/m 3 ) AGLAPC (µg/m 3 ) 55.5 effective. 39.0 49.0 Observation : wind power makes 32.5 42.5 a system more cost effective in and 26.0 36.0 more capable of air pollution 19.5 control. 29.5 23.0 1013.0 0 1 2 3 4 5 6 7 8 9 AUCC (10 5 $)
Outline Background and motivation Environment-friendly operation strategies Robust unit commitment considering air pollutant dispersion Distribution system dynamic reconfiguration Resilient operation strategies Remote-controlled switch allocation enabling prompt restoration Mobile emergency generator dispatch Co-optimize service restoration with dispatch of repair crews and mobile power sources
Distribution system dynamic reconfiguration What is DSDR? Reconfiguration 23 24 25 29 30 31 32 33 23 24 25 29 30 31 32 33 26 27 26 27 5 28 5 28 6 6 1 2 3 4 7 8 9 10 11 14 15 18 1 2 3 4 7 8 9 10 11 14 15 18 19 20 21 22 12 13 16 17 19 20 21 22 12 13 16 17 From a radial topology to another radial topology, by opening/closing switches. DSDR: dynamically reconfigure the network over time Applications Power loss reduction, operation cost minimization Electric service restoration, reliability enhancement Supply capacity improvement Renewable distributed generation (DG) integration
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