Energy Systems Operational Optimisation Emmanouil (Manolis) Loukarakis Pierluigi Mancarella Workshop on Mathematics of Energy Management University of Leeds, 14 June 2016
Overview What s this presentation about? 1: Perspective 2: Electrical Distribution Networks Management 3: Other Problems 2/18 (Overview)
What s the Problem? Towards increased energy efficiency & reduced emissions Large-Scale Storage Distribution medium/low voltage radial networks Inflexible Demand Distributed Generation Large-Scale Renewables Conventional Generation CHP high voltage meshed networks Heating Flexible Demand Distributed Storage Resources Coordination Over Time Gas Network Heat Network Electrical Distribution Networks Management Optimising Gas (or other fuel) Usage Optimising Heat Networks Operation Hot Water / Other Processes 3/18 (Perspective)
Current State of Play Unit Commitment (every 24h to <1h) Network Simplified models Operating status Large Generators Detailed model / bids Optimise! Forecasts Distribution System Aggregate model/bids significant uncertainty integer variables typically coupled with reliability requirements Is this the right time to optimise devices at the end-user level? other energy vectors? Not really! Probably not in detail! Economic Dispatch (every 15min) Network Large Generators Distribution System limited number of discrete controls contingency considerations limited look-ahead Detailed models Detailed models Optimise! Local Device Controls (instant) Aggregate measurements Operating state / Control mode / Power set-points Is this the right time to optimise devices at the end-user level? other energy vectors? If not now when?! Network Large Generators real-time 4/18 (Perspective)
Distribution Extending Dispatch energy power Distribution Microgrid Large-scale generation (conventional & renewable) system (multiple areas) Bus Aggregate Demand IGs TSOs Users Area 1 Area 2 1 3 4 5 6 Area 3 2 4 401 413 419 431 444 450 456 402 403 414 415 420 421 432 433 445 446 451 452 457 458 404 405 416 417 422 423 434 435 436 447 448 453 454 459 460 406 407 418 424 425 426 437 438 439 449 455 461 462 463 408 409 410 427 428 440 441 464 465 411 412 429 430 442 443 466 467 Very large scale! Uncertainty! Peculiarities of individual devices. Need for one more optimisation step! Large-scale generation (conventional & renewable) system (multiple areas) infeasible infeasible Distribution system (high/medium voltage feeders) curtailment 2 4 6 8 10 12 time-step curtailment 2 4 6 8 10 12 time-step Distribution system (medium/low voltage feeders) Individual Users (inflexible & flexible demand / small scale renewables) 5/18 (Perspective)
A Step Further Unit Commitment (every 24h to <1h) Network Large Generators Forecasts Distribution System Simplified models Operating status Detailed model / bids Optimise! Aggregate model/bids Disaggregating the network operators schedule Economic Dispatch (every 15min) Large Generators Network Forecasts Distribution System Microgrids Microgrid (Local) Dispatch (every 1min) Microgrids Users Detailed model / bids OPF Aggregating function / OPF Aggregate models Network constraints Flexible & inflexible energy offers / requests Optimise! Operating state / Control mode / Power set-points Operating state / Control mode / Power set-points Optimise! Local Device Controls (instant) Network Large Generators Users 6/18 (Perspective)
Microgrid Dispatch or in other words: close-to-real-time distribution network management IEEE-123 the good old days IEEE-123 in a test case with lots of EVs if left uncontrolled Objectives follow a given power output (market signal) serve customers! alleviate constraints violations Requirements solution time up to a few minutes Controls many discrete: tap changers, capacitor banks, loads some continuous: smallscale generation, storage, some EVs 7/18 (Distribution Networks Management)
Modelling Considerations (part 1) Point 1 Return currents not of interest Kron s reduction! Point 2 Symmetrical components no advantage in 1p/2p loads Point 3 Constant power models not good enough go ZIP + VI formulation Non-linear! Non-convex! 8/18 (Distribution Networks Management)
Modelling Considerations (part 2) Point 4 If V in polar coordinates the energy balance (right part) is non-linear use rectangular coordinates! Point 5 Voltage constraints non-convex Still non-linear! 9/18 (Distribution Networks Management)
Modelling Considerations (part 3) imag{i} (p.u. I max ) real{i} (p.u. c P ) 1 0.8 linear approximation feasibility region non-linear exact curve 1 0.5 0-0.5 outer approximation Approximation 2 Approximate P-part, as a ZI-part 0.6 0.8 0.9 1 1.1 1.2 voltage (p.u.) -1-2 -1 0 1 2 real{i} (p.u. I max ) Approximation 3 Imbalance / capacity bounds linearize! Linear (assuming Z part is fixed)! 10/18 (Distribution Networks Management)
Modelling Considerations (part 4) Formulation Multi-time-step? stochastic? 650 646 645 632 633 634 632A 632B 632C 632D Formulation Single-time-step? deterministic? Approximation 4 Modified utility function to prioritise demand Follow the market power reference 632E 611 684 671 675 652 680 Point 6 Do we really need tight voltage bounds? 11/18 (Distribution Networks Management)
Does It Work? Algorithm 1 650 646 645 632 633 634 611 684 632A 632B 632C 632D 632E 671 675 Collect info from smart meters Approximate problem at a given voltage reference frame 848 652 680 822 846 820 844 818 864 842 800 802 806 808 812 814 850 816 824 826 858 834 860 836 840 832 888 890 862 810 838 YES Needs adjustment? NO 852 828 830 854 856 Send energy schedules to devices 33 27 32 31 24 22 26 20 11 14 10 2 149 1 29 30 3 4 28 25 19 23 48 21 9 7 8 12 18 5 6 250 47 44 42 13 40 35 34 49 37 45 17 15 16 50 43 41 59 46 36 51 52 58 96 95 38 39 57 65 66 64 63 62 60 53 54 55 56 94 93 92 91 300 111 110 112 113 114 61 90 89 109 107 108 104 106 103 105 450 102 100 101 99 71 98 97 70 69 68 75 67 74 73 610 72 85 79 78 77 76 80 84 88 81 87 86 82 83 IEEE-13 0.0176 0.0031 IEEE-34 0.0056 0.0003 IEEE-37 0.0002 0.0001 IEEE-123 0.0079 0.0012 0.0161 0.0024 0.0168 0.0006 0.0005 0.0001 0.0099 0.0013 0.9945 0.9999 0.9929 1.0000 0.9998 1.0000 0.9964 1.0000 Time (sec) 0.17 (0.25) 0.24 (0.50) 0.23 (0.43) 0.33 (1.80) 12/18 (Distribution Networks Management)
Tap-Changers Algorithm 2 822 848 846 Approximation 5 Taps are continuous 820 844 818 864 842 800 802 806 808 812 814 850 816 824 826 858 834 860 836 840 832 888 890 862 810 852 838 Collect info from smart meters Approximate demand & taps at a given voltage reference frame Solution time (sec) Iterations 828 830 854 856 Max. tap rounding errors Power change IEEE-13 0.32 5 0.0029-3.78% IEEE-34 0.89 4 0.0038-5.16% IEEE-37 0.49 4 0.0030-8.03% IEEE-123 2.62 16 0.0031-4.42% Solve for state and taps (approximate) YES Needs adjustment? NO Send energy schedules to devices Update trust-region 13/18 (Distribution Networks Management)
Discrete Controls Algorithm 3 Mixed integer programming Approximation 5 Solve continuous relaxation restricting deviations from nearest integral solution Tap controls number Added EVs number Solution time (sec) IEEE-13 3 592 7.7 IEEE-34 9 316 4.3 IEEE-37 3 409 4.9 IEEE-123 9 623 14.9 An feasible integral solution was recovered Due to high number of small controls no significant difference between the continuous relaxation objective value Collect info from smart meters Approximate demand & taps at a given voltage reference frame Solve for state and taps (approx. continuous relaxation) YES Needs adjustment? NO YES Is integral? Send energy schedules to devices NO Update trust-region Adjust penalty 14/18 (Distribution Networks Management)
Summing Up months/ years ahead minutes / hours ahead min. ahead sec. ahead real-time Model detail Model detail Model detail Model detail Uncertainty Uncertainty Uncertainty Not now! There are more problems out there! How should we solve them? Important! Problem characteristics! Solver characteristics! 15/18 (Distribution Networks Management)
Another Problem : energy district management (1) The problem optimising over time subject to network constraints and detailed device and building models boiler CHP other gas demand HEAT / POWER GENERATION INSTALLATION heat exchanger pump BUILDING from gas supply network gas network heat network from / to electricity supply network electrical network OTHER BUILDINGS / INSTALLATIONS Computational difficulties thermal network storage capacity thermal network dynamics building heating hot water renewables storage other electrical demand 16/18 (Other Challenges)
Another Problem : energy district management (2) The Manchester University test case electricity gas Solution Fast enough? Reliable enough? heat 17/18 (Other Challenges)
Distribution marginal price (m.u./mwh) Another Problem : distributed optimisation applications Solving very large scale problems Getting closer to control 60 40 20 <10-1 <10-2 <10-3 <10-4 0 0 200 400 600 800 iteration Optimization Problem Structure Power System Decomposition Large-scale generation (conventional & renewable) system (multiple areas) Bus Aggregate Demand IGs TSOs Users Area 1 Area 2 1 3 4 5 6 Area 3 2 TSO 1 TSO 2 TSO 3 IG IG 1 Users 2 Users 4 Users 1 Users 3 IG IG 1 Users 5 Users 6 Agent/subproblem representing users Large Generator subproblem Network Operator subproblem 18/18 (Other Challenges)
Thank you for your attention Questions?