DP_input_data.rar_DP_DP_input_data_input_data_unit commitment_un

% Input Data for Dynamic Programming based Unit Commitment %----------------------------------------------------------------------- % Data from: % A. J. Wood and B. F. Wollenberg: "Power Generation Operation and Control", 1984, John Wiley, New York % obtained identical results to the reported ones. %----------------------------------------------------------------------- % cost coeff: a + b*PG + c*PG^2 % PARAMETERS setting MIN_UP_DOWN_TIME_FLAG = 1; % take minimum up and down times into account (1) or not (0) RAMP_UP_DOWN_FLAG = 0; % take ramp up and down rates into account (1) or not (0) N_PRED = 1; % number of predecesors to be searched (N_PRED >= 1) COMPLETE_ENUMERATION_FLAG = 1; % 1 - complete enumeration, 0 - priority list DETAIL_PRINT_FLAG = 0; % detailed results printing: 0 - no, 1 - yes DISPATCH_METHOD = 3; % 1 - quadprog, 2 - linprog, 3 - quick dispatch RESERVE_FLAG = 0; % take spinning reserve in calculation (1) or not (0) START_UP_COST_METHOD = 1; % 1-cold start-up (const), 2-cold/hot start-up, 3-exponential start-up ----------------------------------- % Unit_no. Pmin Pmax Inc.heat_rate No_load_cost Start_cost_cold Fuel_cost Min_up_time Min_down_time In.status Start_cost_hot Cold_start_[h] Ramp-up Ramp-down coef_a coef_b coef_c shut_down_cost TAU % [MW] [MW] [BTU/kWh] [£/h] [£] [£/MBTU] [h] [h] [h] [£] [h] [MW/h] [MW/h] [£] [£/MWh] [£/MW^2h] [£] [h] gen_data = [... 1 25 80 10440 213.00 350 2.00 4 2 -5 150 4 50 75 NaN NaN NaN 0 NaN 2 60 250 9000 585.62 400 2.00 5 3 +8 170 5 80 120 NaN NaN NaN 0 NaN 3 75 300 8730 684.74 1100 2.00 5 4 +8 500 5 100 150 NaN NaN NaN 0 NaN 4 20 60 11900 252.00 0.02 2.00 1 1 -6 0 0 80 120 NaN NaN NaN 0 NaN ]; DEMAND = [450;530;600;540;400;280;290;500]; K_RES_UP = 0.00; % if not specified, reserve up is calculated as RES_UP(HOUR) = K_RES_UP*DEMAND(HOUR) K_RES_DN = 0.00; % if not specified, reserve down is calculated as RES_DN(HOUR) = K_RES_DN*DEMAND(HOUR) % %----------------------------------------------------------------------- % % Data from: % % A.Y.Abdelaziz, M.Z.Kamh, S.F.Mekhamer, M.A.L.Badr: "An Augmented Hopfield Neural Network for % % Optimal Thermal Unit Commitment", International Journal of Power System Optimization, % % January-June 2010, Volume 2, No. 1, pp. 37-49 % % % % PARAMETERS setting: % MIN_UP_DOWN_TIME_FLAG = 1; % take minimum up and down times into account (1) or not (0) % RAMP_UP_DOWN_FLAG = 0; % take ramp up and down rates into account (1) or not (0) % N_PRED = 1; % number of predecesors to be searched (N_PRED >= 1) % COMPLETE_ENUMERATION_FLAG = 1; % 1 - complete enumeration, 0 - priority list % DETAIL_PRINT_FLAG = 0; % detailed results printing: 0 - no, 1 - yes % DISPATCH_METHOD = 1; % 1 - quadprog, 2 - linprog, 3 - quick dispatch % RESERVE_FLAG = 1; % take spinning reserve in calculation (1) or not (0) % START_UP_COST_METHOD = 1; % 1-cold start-up (const), 2-cold/hot start-up, 3-exponential start-up % % reported solution: £539353, my solution £535273; there is a slight difference between reported commited units and my solution % % % % Unit_no. Pmin Pmax Inc.heat_rate No_load_cost Start_cost_cold Fuel_cost Min_up_time Min_down_time In.status Start_cost_hot Cold_start_[h] Ramp-up Ramp-down coef_a coef_b coef_c shut_down_cost TAU % % [MW] [MW] [BTU/kWh] [£/h] [£] [£/MBTU] [h] [h] [h] [£] [h] [MW/h] [MW/h] [£] [£/MWh] [£/MWh^2] [£] [h] % %----------------------------------------------------------------------- % % Unit_no. Pmin Pmax Inc.heat_rate No_load_cost Start_cost_cold Fuel_cost Min_up_time Min_down_time In.status Start_cost_hot Cold_start_[h] Ramp-up Ramp-down coef_a coef_b coef_c shut_down_cost TAU % % [MW] [MW] [BTU/kWh]* [£/h]* [£] [£/MBTU]* [h] [h] [h] [£] [h] [MW/h] [MW/h] [£] [£/MWh] [£/MWh^2] [£] [h] % gen_data = [... % 1 30.0 100.0 NaN NaN 2050 NaN 5 4 -10 NaN NaN NaN NaN 820 9.023 0.00113 0 NaN % 2 130.0 400.0 NaN NaN 1460 NaN 3 2 10 NaN NaN NaN NaN 400 7.654 0.00160 0 NaN % 3 165.0 600.0 NaN NaN 2100 NaN 2 4 -10 NaN NaN NaN NaN 600 8.752 0.00147 0 NaN % 4 130.0 420.0 NaN NaN 1480 NaN 1 3 -10 NaN NaN NaN NaN 420 8.431 0.00150 0 NaN % 5 225.0 700.0 NaN NaN 2100 NaN 4 5 -10 NaN NaN NaN NaN