Smart grid concepts at TSO level

Transcription

1 Smart grid concepts at TSO level Dr. István Vokony, Dr. Bálint Hartmann, Dániel Divényi Autumn Academy nd November 2013, Budapest Hungary Budapest University of Technology and Economics Department Of Electric Power Engineering Power Systems & Environment Group

2 Contents P-f regulation problems in Hungary Multi agent solutions Wind power integration problems New regulation proposals for the decision makers U-Q regulation possibilities Decreasing losses 11/25/2013 2

3 Problem description Power-frequency control of Hungarian System Few controllable power plants Potential solution: integrate distributed energy resources Study: analyse the control potential of DERs Diverse technologies cogeneration units, renewables Several specifications gas-engines, gas-turbines, combined-cycle gas-turbine (CCGT) Further influences District heating service, wind-speed forecast, laws Economic questions: offical prices, open markets, costs Implement multiagent model 11/25/2013 3

4 Introduction of agents Agents are individual, intelligent units. They live in the environment, perceive its changes and make actions to increase their performance. Each DERs are represented by an Agent. Environment Time, weather, market prices Sensors Environment, inner parameters, status of generation units Actors Choose the ideal operating state Performance Profit 11/25/2013 4

5 Agent-program Input: percepts, estimations Output: action to do Object function: profit maximalisation Considers Diverse technologies cogeneration units, renewables Several specifications gas-engines, gas-turbines, combined-cycle gas-turbine (CCGT) Further influences District heating service, wind-speed forecast, laws Economic questions: offical prices, open markets, costs How can we do that? 11/25/2013 5

6 Different algorithm for each unit? Gas-engines: Heat: from exhausted gas 40% electric, 45% heat efficiency Power range 1-6MW Operation constraints 60% minimum load Can produce electric power without heat (bypass) Gas-turbine: Heat: from exhausted gas 35% electric, 50% heat efficiency Power range 5-50MW Operation constraints Can produce electric power without heat (bypass) 11/25/2013 CCGT Heat: from steam of steam-turbine 45% electric, ~50% heat efficiency Power range 20-50MW Operation constraints: Very complex system (e.g auxiliary boiler) Steam-turbine Heat: from steam of steam-turbine 30% electric, 60% heat efficiency Power range 20-50MW Operation constraints: Very complex system (e.g auxiliary boiler) Usually biomass fueled VERY COMPLEX!! 6

7 Generic state-based approach Actors = Operating states Performance measure = profit Goal: implement a model where the performance measure of each actors can be calculated independently on the applied technology Generic state-based approach developed Unified agent-program was implemented Parameters of generic actors Independent of the technology, generation units Enough to estimate the expected profit 11/25/2013 7

8 Electric power [MW] Generic state-based approach Parameters for generic states Powers Electric Heat (produced, consumed, wasted) Consumed gas Efficiency can be calculated Types and numbers of each operating units Conditions of application Control time for rampings 3,6 2 engines 3,3 2,9 2,5 2,2 waste heat 1,8 1 engine 1,5 1,1 0,7 0,4 0 engine 0,0 0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 Thermal power [MW] 11/25/2013 8

9 Electric power [MW] Electric power [MW] Different state-spaces 6,4 5,7 5,1 4,5 3,8 3,2 2,6 1,9 1,3 0,6 0,0 0,0 1,3 2,6 3,9 5,2 6,5 7,8 9,1 10,4 11,7 13,0 Thermal power [MW] a) 6 gas-engines, 2 secondary boiler 44,8 40,3 35,9 31,4 26,9 22,4 17,9 13,4 9,0 4,5 0,0 0,0 4,7 9,4 14,1 18,8 23,5 28,2 32,9 37,6 42,3 46,9 Thermal power [MW] b) combined cycled gas turbine 11/25/2013 9

10 Electric power [MW] Electric power [MW] Different state-spaces 8,0 7,2 6,4 5,6 4,8 4,0 3,2 2,4 1,6 0,8 0,0 0,0 2,3 4,5 6,8 9,1 11,4 13,6 15,9 18,2 20,5 22,7 Thermal power [MW] c) biomass plant with extraction-type turbine 15,0 13,5 12,0 10,5 9,0 7,5 6,0 4,5 3,0 1,5 0,0 0,0 3,0 6,0 9,0 12,0 15,0 18,0 21,0 24,0 27,0 30,0 Wind speed [m/sec] d) wind-farm (6 towers) 11/25/

11 Unified agent-program in nutshell Strategies evaluate each actors according different aspects: Power service Heat service Fuel service Generation unit management Storage heater Further aspect can be implemented by adding new strategies modularized program The problem is too complex to give a priori knowledge Artificial intelligence was applied Reinforcement learning methods was implemented Agent learns the optimality of its actions in different circumstances based on later rewards. 11/25/

12 Power (MW) Verification: DG with gas-engines Registered Modeled /25/

13 Power (MW) Verification: DG with CCGT unit Registered Modeled /25/

14 Distribution [%] Output power [kw] Examination of the ramping events of wind power plants Wind power is a near stochastic producer Power is the function of the cube (third power) of the wind speed 2000 Rapid change of wind speed results 1500 in rapid change of 1250 output power 70 Ramping capabilities of the power system are sometimes 50 0 insufficient 1750 Downward Upward Downward 1000 MW Upward 1000 MW Wind speed [m/s] Wind power gradient [MW/min] 11/25/

15 Distribution [%] Examination of the scheduling errors of wind power plants Scheduling of wind power production is difficult due to the stochastic nature of wind speed Prediction of wind speed has Gaussian error, but the prediction of the production Inaccurate prediction results scheduling errors Actual capacity Capacity rescaled to 330 MW Capabilities of the 8 power system are sometimes 6 insufficient Error of the schedule [MW] 11/25/

16 Reduction of the amount of control reserves required by wind power plants Due to the scheduling error of wind power plants, big reserve requirements are created Scheduling error of Hungarian wind power plants is significantly higher than international examples Introduction of the penalty tariff did not prove to be efficient to change the situation 11/25/

17 Decreasing balancing reserves induced by wind power recommendation New scheduling and obligatory electricity purchase system Deterministic scheduling supplemented with level of uncertainty Purely rewarding tariff system instead of present rewardingpenailizing tariff system (Base and Bonus) Formulation of the Aimed revenue function, necessary to determine the Bonus price Balancing reserves kept only to regulate the scheduling errors inside the range committed by the wind power plants 11/25/

18 [MW] Decreasing balancing ±99% reserves ±95% ±90% induced by wind power results Production and schedule data of a Hungarian wind power plant ±22.56 MW, ±18.18 MW, ±14.63 MW 8-month recording divided to 2-month long subparts RMSE: 22, 18, 16 and 17% :00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 Time of the simulation 11/25/

19 Decreasing balancing reserves induced by wind power results Determination of the Aimed revenue function Every 1% difference of RMSE committed compared to RMSE actual is penalized by 2% decrease in Aimed revenue Current income of the wind power plants calculated, using the Hungarian tariffs Peak: 114 EUR/MWh Off-peak: EUR/MWh Deep-valley: EUR/MWh Penalty: EUR/MWh Income between 603,333 and 876,666 EUR 11/25/

20 Decreasing balancing reserves induced by wind power results Determination of Base and Bonus tariffs Difference between current and proposed income below 10% 70 combinations of Base and Bonus Possible boundary conditions Base should be equal to the amount of money that is to be paid for a unit with unpredictable scheduling error. Sum of Base and Bonus should be equal to the amount of money that is to be paid for a unit with a predictable scheduling error. Bonus should be greater than Base, thereby indicating the rewarding nature of the tariff system. Bonus should be equal to the cost of balancing energy that is needed as the result of an inaccurate schedule. Base: EUR/MWh Bonus: 100 EUR/MWh 11/25/

21 [MW] Decreasing balancing ±99% reserves ±95% ±90% induced by wind power results ±10.07 MW, ±7.9 MW, ±6.73 MW :00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 Time of the simulation 25 November, 2013 IYCE 2013, Siófok, Hungary 21

22 Former assessments, results Developing, operating, integrating smart networks, grids the stability assessments are essential The appropriateness for island operation of these grids is assumed To determine conditions and criterias for stable operation P-f regulation examination were simulated 11/25/

23 System integration criteria It is known that the correlation between these two subsystems is not too strong: the P-f and U-Q regulation can be separated, possible to examine separately. Analysing U-Q regulation seemed to be necessary at any case. As long as the smart grid Has no negative affect to the power system, Appropriate for island operation. Analysing power quality parameters in case of island operation is unavoidable. (e.g.: nominal voltage level (U n ), power loss (P loss )) 11/25/

24 U-Q regulation: model network 11/25/

25 Q [%] U [kv] U [kv] Problems: local voltage peak MO1 bus s voltage conditions, the SVS is out of operation % 100% 80% Voltage- and reactive power conditions in Mosonszolnok branch 125, ,5 124 Q-Slacktrafo 60% 40% 20% 123, ,5 122 Q-MO1S ΔQ U-MO1S 0% 121, windspeed [m/s] 11/25/

26 U-Q regulation possibilities Static Var System Generating reactive power from synchronous machines (gas turbines at MO1G bus) Changing power factor with power electronics (at wind turbines) 11/25/

27 Reactive power generation [MVAr] reactive power generation [MVAr] U-Q regulation: affect of SVS System slack s reactive power generation, the SVS is in operation System slack s reactive power generation, the SVS is out of operation /25/

28 U [%] U [%] U-Q regulation: generating reactive power MO1 bus s voltage conditions without generating Q MO1 bus s voltage conditions with generating Q 100% 100% 99% 99% 98% 98% 97% 97% 96% 96% 95% % 100% 99% 99% 98% 98% 97% 97% 96% 96% 95% /25/

29 Q [MVAr] U [kv] U-Q regulation: changing wind turbines power factor, cos(φ) 0.95 System slack s reactive power generation (wind turbine cos(φ)=0.95) MO1 bus s voltage conditions (wind turbine cos(φ)=0.95) /25/

30 Summarizing regulation methods The voltage- reactive power regulation in micro grids should be realized in several steps in order to reduce network losses caused by the unnecessary reactive power flows; the first step is local reactive power regulation: reactive power needs could be served by the inverters of wind power plants at 0.95 power factor, as well as on high and medium voltage levels The second step is to involve synchronous machines of the system into the reactive power regulation, if the need is close enough in electric aspect The third and a really effective solution is to install an SVS (Static VAr System), in case if application of the first two steps are not satisfactory 11/25/

31 Regulation strategy 11/25/

32 Results Settings range to minimize the loss windspeed [m/s] /4 load 0,99 0,95 0,95 0,95 0,95 0,95 0,95 0,95 cosϕ 1/2 load n.a. n.a. 0,98 0,95 0,95 0,95 0,95 0,95 3/4 load n.a. n.a. 0,95 0,95 0,95 0,95 0,95 0,95 1/1 load n.a. n.a. n.a. 0,99 0,95 0,95 0,95 0,95 1/4 load 0,93 0,95 0,95 0,95 0,95 0,95 0,95 0,95 U n 1/2 load n.a. n.a. 0, /4 load n.a. n.a. 1,05 1,05 1,05 1,05 1,05 1,05 1/1 load n.a. n.a. n.a. 1,1 1,1 1,1 1,1 1,1 11/25/

33 Results 18% 232 KW 14,5% 135 KW 11/25/

34 Summary Here a load dependent mixed-multi-stage control strategy is introduced. The controllable equipment are dispersed in space and time, and the control is made stepwise minimising the network losses and keeping the node voltages between the set ranges. 11/25/

35 Thank you for your attention! 11/25/