Coverage for src/distopf/matrix_models/lindist.py: 50%

1from typing import Optional 

2import pandas as pd 

3from distopf.matrix_models.base import LinDistBase 

4import distopf as opf 

5 

6 

7class LinDistModel(LinDistBase): 

8 """ 

9 LinDistFlow Model class for linear power flow modeling. 

10 

11 This class represents a linearized distribution model used for calculating 

12 power flows, voltages, and other system properties in a distribution network 

13 using the linearized branch-flow formulation from [1]. The model is composed of several power system components 

14 such as buses, branches, generators, capacitors, and regulators. 

15 

16 Parameters 

17 ---------- 

18 branch_data : pd.DataFrame 

19 DataFrame containing branch data including resistance and reactance values and limits. 

20 bus_data : pd.DataFrame 

21 DataFrame containing bus data such as loads, voltages, and limits. 

22 gen_data : pd.DataFrame 

23 DataFrame containing generator data. 

24 cap_data : pd.DataFrame 

25 DataFrame containing capacitor data. 

26 reg_data : pd.DataFrame 

27 DataFrame containing regulator data. 

28 

29 References 

30 ---------- 

31 [1] R. R. Jha, A. Dubey, C.-C. Liu, and K. P. Schneider, 

32 “Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters 

33 With Voltage Control Devices,” 

34 IEEE Trans. Power Syst., vol. 34, no. 3, pp. 1801–1813, 

35 May 2019, doi: 10.1109/TPWRS.2018.2890613. 

36 

37 Examples 

38 -------- 

39 This example demonstrates how to set up and solve a linear distribution flow model 

40 using a provided case, and visualize the results. 

41 

42 >>> import distopf as opf 

43 >>> # Prepare the case data 

44 >>> case = opf.DistOPFCase(data_path="ieee123_30der") 

45 >>> # Initialize the LinDistModel 

46 >>> model = LinDistModel( 

47 ... branch_data=case.branch_data, 

48 ... bus_data=case.bus_data, 

49 ... gen_data=case.gen_data, 

50 ... cap_data=case.cap_data, 

51 ... reg_data=case.reg_data, 

52 ... ) 

53 >>> # Solve the model using the specified objective function 

54 >>> result = opf.lp_solve(model, opf.gradient_load_min(model)) 

55 >>> # Extract and plot results 

56 >>> v = model.get_voltages(result.x) 

57 >>> s = model.get_apparent_power_flows(result.x) 

58 >>> p_gens = model.get_p_gens(result.x) 

59 >>> q_gens = model.get_q_gens(result.x) 

60 >>> # Visualize network and power flows 

61 >>> opf.plot_network(model, v=v, s=s, p_gen=p_gens, q_gen=q_gens).show() 

62 >>> opf.plot_voltages(v).show() 

63 >>> opf.plot_power_flows(s).show() 

64 >>> opf.plot_gens(p_gens, q_gens).show() 

65 """ 

66 

67 def __init__( 

68 self, 

69 branch_data: Optional[pd.DataFrame] = None, 

70 bus_data: Optional[pd.DataFrame] = None, 

71 gen_data: Optional[pd.DataFrame] = None, 

72 cap_data: Optional[pd.DataFrame] = None, 

73 reg_data: Optional[pd.DataFrame] = None, 

74 ): 

75 super().__init__(branch_data, bus_data, gen_data, cap_data, reg_data) 

76 self.build() 

77 

78 

79if __name__ == "__main__": 

80 # Prepare the case data 

81 case = opf.DistOPFCase(data_path="ieee123_30der") 

82 # Initialize the LinDistModel 

83 model = LinDistModel( 

84 branch_data=case.branch_data, 

85 bus_data=case.bus_data, 

86 gen_data=case.gen_data, 

87 cap_data=case.cap_data, 

88 reg_data=case.reg_data, 

89 ) 

90 # Solve the model using the specified objective function 

91 result = opf.lp_solve(model, opf.gradient_load_min(model)) 

92 # Extract and plot results 

93 v = model.get_voltages(result.x) 

94 s = model.get_apparent_power_flows(result.x) 

95 # s = model.get_apparent_power_flows(result.x) 

96 # p_gens = model.get_p_gens(result.x) 

97 # q_gens = model.get_q_gens(result.x) 

98 # # Visualize network and power flows 

99 # opf.plot_network(model, v=v, s=s, p_gen=p_gens, q_gen=q_gens).show() 

100 # opf.plot_voltages(v).show() 

101 # opf.plot_power_flows(s).show() 

102 # opf.plot_gens(p_gens, q_gens).show()