Fine time sequence operation simulation method based on novel rural power system

Abstract

The invention belongs to the technical field of power system control, and particularly relates to a refined time sequence operation simulation method based on a novel rural power system. Comprising the following steps: S1, collecting economic and technical parameters of each element in a power system; s2, constructing a target function of a time sequence operation simulation model based on an alternating current power flow model; s3, establishing a time sequence operation simulation model based on the refined power flow; s4, adding a second-order cone relaxation constraint and a phase angle recovery condition into the model; s5, solving the time sequence operation simulation model in different time periods through commercial software; through the above steps, the fine time sequence operation simulation method based on the rural novel electric power system can set a long time sequence decomposition time period to realize fine analysis of the annual operation condition of the rural electric power system while ensuring the solving efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of power system control, and in particular relates to a refined sequential operation simulation method based on a new rural power system. Background Art

[0002] At present, the timing operation simulation technology is mainly used to analyze the operation and planning of large-scale power systems with high voltage levels, such as the entire provincial power grid or regional power grid. Due to the complex node lines of this type of power system, the large number of units, and the long time period of timing operation simulation, it is limited by the current large-scale optimization problem solving technology and capabilities, and considering the characteristics of large-scale power systems, the timing operation simulation model is usually established based on the DC power flow model. The DC power flow model ignores the distribution of reactive power in the power system and assumes that the voltage of each node is not much different from the rated voltage. In the high-voltage transmission system, the resistance is much smaller than the reactance and the voltage value is constant near the per-unit value. The error of this approximate model is acceptable. However, in rural power systems, the voltage level is lower and the distribution line distance is longer than that of urban distribution networks. The voltage problem is more serious, and there are problems such as insufficient power supply capacity and low voltage quality. In order to consider more reliable safe operation constraints at the simulation operation level and improve the accuracy of operation simulation, it is of great significance to establish a timing operation simulation model based on AC power flow for rural power systems. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a refined time-series operation simulation method based on a new rural power system to finely analyze the annual operation of the rural power system.

[0004] The object of the present invention is achieved as follows: A refined sequential operation simulation method based on a new rural power system comprises the following steps:

[0005] S1. Collect economic and technical parameters of each component in the power system;

[0006] S2, constructing the objective function of the sequential operation simulation model based on the AC power flow model;

[0007] S3. Establish a time-series operation simulation model based on refined power flow;

[0008] S4, adding second-order cone relaxation constraints and phase angle recovery conditions to the model;

[0009] S5. Solve the time series operation simulation model in different time periods using commercial software.

[0010] Furthermore, the various components inside the power system in step S1 include an AC power grid, a tie line, a biomass unit, a wind power and photovoltaic unit, and an energy storage element.

[0011] Furthermore, the objective function constructed in step S2 is to minimize the economic operation cost of the rural power system, and the objective function is as follows:

[0012]

[0013] Among them, T N is the total number of simulation time periods, t is the time period index number, F pv (t) is the light abandonment penalty in time period t, F wp (t) is the wind abandonment penalty in period t, F mgp (t) is the active power generation cost of the biomass generator set at time t, F loss (t) is the line loss penalty term at time t, F ep (t) is the electricity purchase cost in period t; their calculation methods are shown in formula (2), formula (3), formula (4), formula (5) and formula (6), respectively.

[0014] F pv (t) = P cpv (t)×f pv (2)

[0015] F wp (t) = P cwp (t)×f wp (3)

[0016] F mgp (t) = P mg (t)×f mgp (4)

[0017] F loss (t) = P loss (t)×f loss (5)

[0018] F ep (t) = P ep (t)×f ep (6)

[0019] Where P cpv (t) is the abandoned optical power at time t, P cwp (t) is the wind power abandoned at time t, P mg (t) is the active power output of the biomass unit at time t, P loss (t) is the system network loss power at time t, P ep (t) is the external power transmission power at time t; f pv is the light abandonment penalty coefficient, f wp is the wind abandonment penalty coefficient, f mgp is the active output cost coefficient of biomass power generation, floss is the network loss penalty coefficient, f ep is the unit electricity purchase cost;

[0020] Among them, P loss The calculation formula of (t) is as follows:

[0021]

[0022] Where n is the line index number, Brn is the number of transmission lines in the system, is the square of the current flowing through the nth transmission line at time t, r n is the resistance of the nth line.

[0023] Furthermore, the step S3 of establishing a time series operation simulation model based on the refined power flow includes:

[0024] After determining the objective function, add the following constraints to the system;

[0025] (1) Power balance constraints

[0026]

[0027] In the formula, i, j, k are node index numbers, set B represents the set of all nodes in the network, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, and p j is the active injection power of node j, P jk is the active power flowing from node j to node k, P ij is the active power flowing from node i to node j, is the square of the current flowing from node j to node j, r ij is the resistance from node i to node j, g j is the conductance of node j to ground, V j 2 is the square of the voltage value at node j; q j is the reactive injection power of node j, Q jk is the reactive power flowing from node j to node k, Q ij is the active power flowing from node i to node j, x ij is the reactance value from node i to node j, b j is the earth susceptance of node j;

[0028] (2) Ohm’s law constraints

[0029]

[0030] In the formula, E represents the set of all branches in the system;

[0031] (3) Second-order cone constraint

[0032] The apparent power expression of the first section of the line is shown in formula (10):

[0033]

[0034] Multiplying both ends of equation (10) by the corresponding conjugate quantity yields

[0035]

[0036] Equation (11) describes the relationship between node voltage, branch current and branch power;

[0037] (4) Voltage over-limit constraint

[0038]

[0039] In the formula, represent Set the variable to It can reduce the dimension of variables and simplify the calculation of the model; is the minimum value of the square of the voltage per unit value at node j, is the maximum value of the square of the voltage per unit value at node j;

[0040] (5) Current limit constraint

[0041]

[0042] In the formula, represent Similarly, set the variable to It can reduce the dimension of variables and simplify the calculation of the model; is the minimum value of the square of the per unit current of line ij, is the maximum value of the square of the per-unit value of the current in line ij;

[0043] (6) Line active power flow constraints

[0044] -P ijmax ≤P ij ≤P ijmax (14)

[0045] Where P ijmax is the maximum active power transmission value of line ij;

[0046] (7) Biomass unit operation constraints

[0047] The operation constraints of the biomass unit include: upper and lower limits of active power output of the biomass unit (15), upper and lower limits of reactive power output of the biomass unit (16), reactive climbing power constraint of the biomass unit (17), active climbing power constraint of the biomass unit (18), and start-stop time constraint (19);

[0048] P mgmin (j)U(j,t)≤P mg (j,t)≤P mgmax (j)U(j,t) (15)

[0049] Q mgmin (j)U(j,t)≤Q mg (j,t)≤Q mgmax (j)U(j,t) (16)

[0050]

[0051] Where P mgmin (j) is the lower limit of the active output of the j-th biomass generator; U(j,t) is the state of the j-th biomass generator at time t, "0" represents shutdown and "1" represents startup; P mgmax (j) is the upper limit of the active output of the jth biomass generator; Q mgmin (j) is the lower limit of reactive power output of the jth biomass generator; Q mgmax (j) is the upper limit of reactive power output of the jth biomass generator; Q up (j) is the active power ramp limit of the jth biomass generator, Q down (j) is the upper limit of reactive power ramp of the jth biomass generator; P up (j) is the active power ramp limit of the jth biomass generator, P down (j) is the upper limit of the active power ramp of the j-th biomass generator; S(j,t) is the shutdown status of the j-th biomass generator at time t, "0" represents that it is not shut down at time t, and "1" represents that it is shut down at time t; O(j,t) is the startup status of the j-th biomass generator at time t, "0" represents that it is not started at time t, and "1" represents that it is started at time t;

[0052] (8) Wind power and photovoltaic output constraints

[0053]

[0054] Where P wp (j,t) is the actual output of the j-th wind farm at time t, is the maximum output of the jth wind farm at time t, P pv(j,t) is the actual output of the j-th PV power station at time t, is the predicted output of the j-th PV power station at time t;

[0055] (9) Energy storage operation constraints

[0056] The energy storage operation constraints include charging power constraint (21), discharging power constraint (22), energy storage charging and discharging state constraint (23), energy storage charge state constraint (24), and energy storage capacity constraint (25);

[0057] u c (j,t)P cmin (j)≤P c (j,t)≤u c (j,t)P cmax (j) (21)

[0058] u d (j,t)P dmin (j)≤P d (j,t)≤u d (j,t)P dmax (j) (22)

[0059] u c (j,t)+u d (j,t)≤1 (23)

[0060] E(j,t)=E(j,t-1)+η c P c (j,t-1)-P d (j,t-1) / η d (twenty four)

[0061] E min (j)≤E(j,t)≤E max (j) (25)

[0062] In the formula, u c (j, t) is the charging status of the jth energy storage station at time t, "1" represents the charging status at this time, P cmin (j) is the minimum charging power of the jth energy storage station, P cmax (j) is the maximum charging power of the j-th energy storage station; u d (t, j) is the energy storage discharge state, "1" represents the discharge state at this moment, P dmin (j) is the minimum discharge power of the jth energy storage power station, P dmax (j) is the maximum discharge power of the j-th energy storage station; E(j,t) represents the storage capacity of the j-th energy storage station at time t, η cis the charging efficiency of the energy storage station, η d is the discharge efficiency of the energy storage power station; E min (j) is the minimum storage capacity of the jth energy storage station, E max (j) is the maximum storage capacity of the jth energy storage power station;

[0063] (10) Tie-line power constraints

[0064] The tie line power constraints are as follows.

[0065]

[0066] Where P out (t) is the power transmitted outward by the system at time t, P CLmax The upper limit of the transmission power of the interconnection line.

[0067] Furthermore, the step S4 of adding a second-order cone relaxation constraint and a phase angle recovery condition to the model includes:

[0068] According to the second-order cone relaxation theory, equation (10) is relaxed to the inequality constraint (27),

[0069]

[0070] Formula (27) can be rewritten into the form of Second Order Cone Programming (SOCP) as follows:

[0071]

[0072] In the formula, ||·|| represents the second norm; the relaxed model is a SOCP problem, which can be solved by commercial solvers (Cplex, Gurobi, etc.).

[0073] Since there is no voltage phase angle parameter in the timing operation simulation model constructed in step 3, in order to ensure that the operation results have practical and feasible physical meanings, that is, the voltage phase angle parameters can be restored according to the operation results, another condition to ensure the accuracy of the above relaxation is the loop phase angle constraint, as shown in equations (29) and (30);

[0074]

[0075] In the formula, β ij represents the voltage phase angle difference between node i and node j; c is the minimum loop set in the system;

[0076] The intuitive meaning of the loop phase angle constraint is that for each loop in the system topology, the cumulative voltage phase difference in a certain direction is equal to 0 after taking the modulus of 2π.

[0077] The loop phase angle constraint described by equation (30) introduces new variables and cannot be directly added to the timing operation simulation model established in step 3. ij Written in the form shown in formula (31),

[0078]

[0079] From formula (31), we can separate The real and imaginary parts of , first the real part, in per unit value, The value of varies around 1, while P ij r ij , Q ij x ij , P ij x ij and Q ij r ij is usually much smaller than 1, so β ​​can be used ij The approximate expression method of β ij The approximate expression of is as follows:

[0080]

[0081] Formula (32) gives β ij Approximate expression formula, thus the loop phase angle constraint formula (33) is converted into formula (34),

[0082]

[0083] Equation (34) is a linear equality constraint. After adding it to the sequential operation simulation model established in step 3, the loop phase angle constraint can be considered without affecting the model solution efficiency.

[0084] Furthermore, the step S5 of solving the time series operation simulation model by time periods using commercial software includes:

[0085] The segmented solution method is adopted to decompose the whole year into several short time segments, and the solutions are solved sequentially. The operation results of the last few segments of the previous sub-period are used as the starting operation state of the next sub-period to meet the segment coupling constraints and ensure the complete feasibility of the solution results.

[0086] After decomposing the annual time series operation simulation model into several sub-period problems, solve each sub-period in turn, and finally add up the solution results of each sub-period to obtain the complete annual time series operation simulation results. For large power systems, under acceptable computational complexity, the length of the sub-period is generally set to one day or one week, but too many sub-periods will affect the accuracy of the operation simulation. For rural power systems, the number of lines and units is small. Under the premise of computing power permitting, the sub-period can be set to a longer length, reducing the number of period decompositions to obtain more accurate solution results.

[0087] Beneficial effects of the present invention: A refined sequential operation simulation method based on a new rural power system of the present invention comprises the following steps: S1, collecting economic and technical parameters of various components inside the power system; S2, constructing an objective function of a sequential operation simulation model based on an AC power flow model; S3, establishing a sequential operation simulation model based on a refined power flow; S4, adding a second-order cone relaxation constraint and a phase angle recovery condition to the model;

[0088] S5. Solve the time series operation simulation model by time period through commercial software; through the above steps, a refined time series operation simulation method based on a new rural power system of the present invention can set a longer time series decomposition period to achieve refined analysis of the annual operation of the rural power system while ensuring the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0090] Figure 1 It is a flow chart of the method for simulation of refined sequential operation based on new rural power system;

[0091] Figure 2 β ij Schematic diagram of the graphic representation of

[0092] Figure 3 A schematic diagram of the decomposition of the simulation period for the time series operation;

[0093] Figure 4 Schematic diagram of the piecewise solution method for sequential operation simulation;

[0094] Figure 5 This is a wiring diagram of a rural county power system;

[0095] Figure 6It is the load time series curve throughout the year;

[0096] Figure 7 This is a schematic diagram of the annual sequential output curve of the photovoltaic power station;

[0097] Figure 8 This is a schematic diagram of the annual output curve of each wind farm;

[0098] Fig. 9 This is a schematic diagram for verifying the relaxation of the second-order cone constraint;

[0099] Fig.10 This is a schematic diagram of the maximum and minimum square values ​​of the voltage per unit value at each node throughout the year. DETAILED DESCRIPTION

[0100] The present invention will be further described below in conjunction with the accompanying drawings.

[0101] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0102] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0103] like Figure 1-10 As shown, a refined sequential operation simulation method based on a new rural power system of the present invention comprises the following steps:

[0104] S1. Collect economic and technical parameters of each component in the power system;

[0105] S2, constructing the objective function of the sequential operation simulation model based on the AC power flow model;

[0106] S3. Establish a time-series operation simulation model based on refined power flow;

[0107] S4, adding second-order cone relaxation constraints and phase angle recovery conditions to the model;

[0108] S5. Solve the time series operation simulation model in different time periods using commercial software.

[0109] Furthermore, the various components inside the power system in step S1 include an AC power grid, a tie line, a biomass unit, a wind power and photovoltaic unit, and an energy storage element.

[0110] Furthermore, the objective function constructed in step S2 is to minimize the economic operation cost of the rural power system, and the objective function is as follows:

[0111]

[0112] Among them, T N is the total number of simulation time periods, t is the time period index number, F pv (t) is the light abandonment penalty in time period t, F wp (t) is the wind abandonment penalty in period t, F mgp (t) is the active power generation cost of the biomass generator set at time t, F loss (t) is the line loss penalty term at time t, F ep (t) is the electricity purchase cost in period t; their calculation methods are shown in formula (2), formula (3), formula (4), formula (5) and formula (6), respectively.

[0113] F pv (t) = P cpv (t)×f pv (2)

[0114] F wp (t) = P cwp (t)×f wp (3)

[0115] F mgp (t) = P mg (t)×f mgp (4)

[0116] F loss (t) = P loss (t)×f loss (5)

[0117] F ep (t) = P ep (t)×f ep (6)

[0118] Where P cpv (t) is the abandoned optical power at time t, P cwp (t) is the wind power abandoned at time t, P mg (t) is the active power output of the biomass unit at time t, P loss (t) is the system network loss power at time t, P ep (t) is the external power transmission power at time t; f pv is the light abandonment penalty coefficient, f wp is the wind abandonment penalty coefficient, f mgp is the active output cost coefficient of biomass power generation, f loss is the network loss penalty coefficient, f ep is the unit electricity purchase cost;

[0119] Among them, Ploss The calculation formula of (t) is as follows:

[0120]

[0121] Where n is the line index number, Brn is the number of transmission lines in the system, is the square of the current flowing through the nth transmission line at time t, r n is the resistance of the nth line.

[0122] Furthermore, the step S3 of establishing a time series operation simulation model based on the refined power flow includes:

[0123] After determining the objective function, in order to build a time series operation simulation model based on refined power flow, the following constraints need to be added to the system;

[0124] (1) Power balance constraints

[0125]

[0126] In the formula, i, j, k are node index numbers, set B represents the set of all nodes in the network, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, and p j is the active injection power of node j, P jk is the active power flowing from node j to node k, P ij is the active power flowing from node i to node j, is the square of the current flowing from node j to node j, r ij is the resistance from node i to node j, g j is the conductance of node j to ground, V j 2 is the square of the voltage value at node j; q j is the reactive injection power of node j, Q jk is the reactive power flowing from node j to node k, Q ij is the active power flowing from node i to node j, x ij is the reactance value from node i to node j, b j is the earth susceptance of node j;

[0127] (2) Ohm’s law constraints

[0128]

[0129] In the formula, E represents the set of all branches in the system;

[0130] (3) Second-order cone constraint

[0131] The apparent power expression of the first section of the line is shown in formula (10):

[0132]

[0133] Multiplying both ends of equation (10) by the corresponding conjugate quantity yields

[0134]

[0135] Equation (11) describes the relationship between node voltage, branch current and branch power;

[0136] (4) Voltage over-limit constraint

[0137]

[0138] In the formula, represent Set the variable to It can reduce the dimension of variables and simplify the calculation of the model; is the minimum value of the square of the voltage per unit value at node j, is the maximum value of the square of the voltage per unit value at node j;

[0139] (5) Current limit constraint

[0140]

[0141] In the formula, represent Similarly, set the variable to It can reduce the dimension of variables and simplify the calculation of the model; is the minimum value of the square of the per unit current of line ij, is the maximum value of the square of the per-unit value of the current in line ij;

[0142] (6) Line active power flow constraints

[0143] -P ijmax ≤P ij ≤P ijmax (14)

[0144] Where P ijmax is the maximum active power transmission value of line ij;

[0145] (7) Biomass unit operation constraints

[0146] The operation constraints of the biomass unit include: upper and lower limits of active power output of the biomass unit (15), upper and lower limits of reactive power output of the biomass unit (16), reactive climbing power constraint of the biomass unit (17), active climbing power constraint of the biomass unit (18), and start-stop time constraint (19);

[0147] P mgmin (j)U(j,t)≤P mg (j,t)≤P mgmax (j)U(j,t) (15)

[0148] Q mgmin (j)U(j,t)≤Q mg (j,t)≤Q mgmax (j)U(j,t) (16)

[0149]

[0150]

[0151] Where P mgmin (j) is the lower limit of the active output of the j-th biomass generator; U(j,t) is the state of the j-th biomass generator at time t, "0" represents shutdown and "1" represents startup; P mgmax (j) is the upper limit of the active output of the jth biomass generator; Q mgmin (j) is the lower limit of reactive power output of the jth biomass generator; Q mgmax (j) is the upper limit of reactive power output of the jth biomass generator; Q up (j) is the active power ramp limit of the jth biomass generator, Q down (j) is the upper limit of reactive power ramp of the jth biomass generator; P up (j) is the active power ramp limit of the jth biomass generator, P down (j) is the upper limit of the active power ramp of the j-th biomass generator; S(j,t) is the shutdown status of the j-th biomass generator at time t, "0" represents that it is not shut down at time t, and "1" represents that it is shut down at time t; O(j,t) is the startup status of the j-th biomass generator at time t, "0" represents that it is not started at time t, and "1" represents that it is started at time t;

[0152] (8) Wind power and photovoltaic output constraints

[0153] The output of wind power and photovoltaic power generation is affected by climate conditions, and the actual output in each period is constrained by the maximum predicted output;

[0154]

[0155] Where P wp (j,t) is the actual output of the j-th wind farm at time t, is the maximum output of the jth wind farm at time t, P pv(j,t) is the actual output of the j-th PV power station at time t, is the predicted output of the j-th PV power station at time t;

[0156] (9) Energy storage operation constraints

[0157] The energy storage operation constraints include charging power constraint (21), discharging power constraint (22), energy storage charging and discharging state constraint (23), energy storage charge state constraint (24), and energy storage capacity constraint (25);

[0158] u c (j,t)P cmin (j)≤P c (j,t)≤u c (j,t)P cmax (j) (21)

[0159] u d (j,t)P dmin (j)≤P d (j,t)≤u d (j,t)P dmax (j) (22)

[0160] u c (j,t)+u d (j,t)≤1 (23)

[0161] E(j,t)=E(j,t-1)+η c P c (j,t-1)-P d (j,t-1) / η d (twenty four)

[0162] E min (j)≤E(j,t)≤E max (j) (25)

[0163] In the formula, u c (j, t) is the charging status of the jth energy storage station at time t, "1" represents the charging status at this time, P cmin (j) is the minimum charging power of the jth energy storage station, P cmax (j) is the maximum charging power of the j-th energy storage station; u d (t, j) is the energy storage discharge state, "1" represents the discharge state at this moment, P dmin (j) is the minimum discharge power of the jth energy storage power station, P dmax (j) is the maximum discharge power of the j-th energy storage station; E(j,t) represents the storage capacity of the j-th energy storage station at time t, η cis the charging efficiency of the energy storage station, η d is the discharge efficiency of the energy storage power station; E min (j) is the minimum storage capacity of the jth energy storage station, E max (j) is the maximum storage capacity of the jth energy storage power station;

[0164] (10) Tie-line power constraints

[0165] Since the rural power system is connected to the large power grid, the exchange power between the rural power system and the large power grid is determined by the transmission power upper limit of the interconnection line. The interconnection line power constraint is as follows.

[0166]

[0167] Where P out (t) is the power transmitted outward by the system at time t, P CLmax The upper limit of the transmission power of the interconnection line.

[0168] Furthermore, the step S4 of adding a second-order cone relaxation constraint and a phase angle recovery condition to the model includes:

[0169] In the above-mentioned sequential operation simulation model based on AC power flow, equation (10) is a non-convex constraint, which makes the entire model non-convex and requires relaxation to ensure the convexity of the model. According to the second-order cone relaxation theory, equation (10) is relaxed to the inequality constraint (27),

[0170]

[0171] Formula (27) can be rewritten into the form of Second Order Cone Programming (SOCP) as follows:

[0172]

[0173] In the formula, ||·|| represents the second norm; the relaxed model is a SOCP problem, which can be solved by commercial solvers (Cplex, Gurobi, etc.).

[0174] Since there is no voltage phase angle parameter in the timing operation simulation model constructed in step 3, in order to ensure that the operation results have practical and feasible physical meanings, that is, the voltage phase angle parameters can be restored according to the operation results, another condition to ensure the accuracy of the above relaxation is the loop phase angle constraint, as shown in equations (29) and (30);

[0175]

[0176] In the formula, β ij represents the voltage phase angle difference between node i and node j; c is the minimum loop set in the system;

[0177] The intuitive meaning of the loop phase angle constraint is that for each loop in the system topology, the cumulative voltage phase difference in a certain direction is equal to 0 after taking the modulus of 2π.

[0178] The above-established sequential operation simulation model is a mixed integer SOC planning model that does not consider the loop phase angle constraint, which can be effectively solved by some commercial solvers. However, if the phase angle cannot be restored, the solution of the above model may not be feasible in the ring network. How to ensure that the loop phase angle constraint is added while maintaining the excellent characteristics of the model being easy to solve is crucial. The loop phase angle constraint described by equation (30) will introduce new variables and cannot be directly added to the sequential operation simulation model established in step 3. Replace β ij Written in the form shown in formula (31),

[0179]

[0180] From formula (31), we can separate The real and imaginary parts of , first the real part, in per unit value, The value of varies around 1, while P ij r ij , Q ij x ij , P ij x ij and Q ij r ij is usually much smaller than 1, so β ​​can be used ij An approximate expression of Figure 2 As shown, it is deduced that β ij The approximate expression of is as follows:

[0181]

[0182] Formula (32) gives β ij Approximate expression formula, thus the loop phase angle constraint formula (33) is converted into formula (34),

[0183]

[0184] Equation (34) is a linear equality constraint. After adding it to the sequential operation simulation model established in step 3, the loop phase angle constraint can be considered without affecting the model solution efficiency.

[0185] Furthermore, the step S5 of solving the time series operation simulation model by time periods using commercial software includes:

[0186] The time series operation simulation solution is relatively complex and difficult to solve directly. The model complexity is mainly reflected in three aspects: time dimension, unit dimension and model dimension. For rural power systems, the number of units and line nodes is small, and the unit dimension and model dimension are both low. The main factor causing the difficulty in solving the time series operation simulation is the large time dimension caused by the long time scale simulation operation of 8760h. Figure 3 As shown in the figure, a segmented solution method is used to decompose the whole year into several short time segments, which are solved sequentially. The operation results of the last few segments of the previous sub-period are used as the starting operation state of the next sub-period to meet the time period coupling constraints and ensure the complete feasibility of the solution results.

[0187] After decomposing the annual time series operation simulation model into several sub-period problems, solve each sub-period in turn, and finally add up the solution results of each sub-period to obtain the complete annual time series operation simulation result. The complete solution process is as follows: Figure 4 For large power systems, under acceptable computational complexity, the length of the sub-period is generally set to one day or one week, but too many sub-periods will affect the accuracy of the operation simulation. For rural power systems, the number of lines and units is small, and the sub-period can be set to a longer length if the computing power allows, reducing the number of period decompositions and obtaining more accurate solution results.

[0188] The present invention is further described below by taking a certain county power system as an example. The actual wiring diagram is as follows: Figure 5 As shown. In terms of power supply, there is a 24MW straw burning power plant and a 15MW waste incineration power plant. In addition, the county system has a total of 470MW of wind power generation and 200MW of photovoltaic power generation. In addition, it also has a 9.6MWh / 1.2MW energy storage power station. The system is connected to the main grid by interconnection lines at nodes 1, 2, 4, and 6 (the interconnection lines are not drawn in the figure). The power system is not an isolated system operating off the grid. In terms of substations, the rural power system has 2 220kV substations, 8 110kV substations, and 12 35kV substations.

[0189] After data sorting, the hourly load time series data of the county from August 1, 2021 to August 1, 2022 were obtained, of which the maximum load was 471MW. The normalized load data is as follows: Figure 6 shown.

[0190] Similarly, the output time series data of the photovoltaic power station in the county from August 1, 2021 to August 1, 2022 are obtained, such as Figure 7As shown in the figure, the photovoltaic output record value of the county is normal throughout the year, that is, the photovoltaic output is high at noon and zero at night. In addition, in summer, the sunshine time is long, and the photovoltaic output time is long, while in winter, the sunshine time is short, and the photovoltaic output time is reduced.

[0191] The annual output of several wind farms in the county is as follows: Figure 8 As shown in the figure, each wind farm has obtained its own 8760h output data, which can ensure the integrity and accuracy of the input data. Since the installed capacity of each wind farm is different, the output size varies greatly. Since the system is connected to the external power grid, it can be regarded as connected to the infinite power grid, which can ensure the full absorption of wind and solar power generation. Therefore, the recorded new energy output curves are all actual output curves without reduction.

[0192] The operating parameters of the biomass unit and energy storage power station and the related cost coefficients are shown in Table 1. The cost of external power purchase needs to take into account the environmental cost in order to promote the development of renewable energy power generation.

[0193] Table 1 Timing operation related parameters

[0194]

[0195] The actual data of the county is input into the refined power flow timing operation simulation model established above, the sub-period is set to 730h, the overlapping period is set to 24h, and the sequential segmentation solution is performed to obtain the annual operation results of the county power system as shown in Table 2. Since the county power system is connected to the large power grid, when the power generation of the power supply inside the county is less than the load, external power can be obtained from the large power grid. When the power generation of renewable energy inside the county is surplus, the excess power can be transmitted to the outside world through the interconnection line. There is no benefit item for external power transmission, but due to the setting of the power abandonment penalty item, the excess power will be sent out, which can ensure the consumption rate of renewable energy. In addition, the generators inside the county are all renewable energy power generation, so the proportion of the electricity load inside the county provided by the internal power supply is the proportion of renewable energy power supply.

[0196] Table 2 Simulation results of sequential operation based on refined power flow

[0197]

[0198]

[0199] According to the operation results, the average annual utilization hours of the county's biomass units are 5749 hours, which is higher than the current average annual utilization hours of 4414 hours of biomass units in my country. The annual utilization hours of photovoltaic power generation and wind power generation are just close to the national average. In addition, the proportion of renewable energy power supply in the county is 63.23%, and the remaining load power needs to rely on external power supply.

[0200] Verify the refined timing simulation results:

[0201] (1) Second-order cone relaxation verification

[0202] The following formula is The calculation formula is substituted into this formula by the data obtained after running the timing simulation model. The calculated value of is then compared with the value obtained by solving the model The error index is defined as follows:

[0203]

[0204] In the formula, Δ ij It is the second-order cone relaxation error index of line ij.

[0205] Because an error index can be calculated for each line in each period, the maximum absolute value of the error of each line throughout the year is taken, such as Fig. 9 As shown, it can be seen that the maximum error of each line is 10 -5 The magnitude of indicates that the deviation after relaxation meets the operation requirements. Here, the second-order cone relaxation is an accurate relaxation, which will not affect the optimal solution of the original problem. If the loop phase angle constraint can also be met, it can be shown that the solution obtained by the above timing generation model will have practical physical significance.

[0206] (2) Phase Angle Recovery Condition Verification

[0207] Since the loop phase angle constraint adopted by the present invention requires some approximate numerical conditions, such as the square of the voltage per unit value of the node needs to be maintained near "1", in the operation results of the timing operation simulation model based on the refined power flow, the annual maximum and minimum values ​​of the square of the voltage per unit value of each node are as follows: Fig.10 As shown in the figure, since the county power system belongs to a high-voltage distribution system with a maximum voltage level of 220kV, the simulation results show that the square of the per-unit voltage value of each node varies between 0.95 and 1.05, which meets the approximate requirements of the loop phase angle constraint.

[0208] In summary, a refined timing operation simulation method based on a new rural power system of the present invention comprises the following steps: S1, collecting economic and technical parameters of each component inside the power system; S2, constructing an objective function of a timing operation simulation model based on an AC power flow model; S3, establishing a timing operation simulation model based on a refined power flow; S4, adding second-order cone relaxation constraints and phase angle recovery conditions to the model; S5, solving the timing operation simulation model by time period through commercial software; through the above steps, a refined timing operation simulation method based on a new rural power system of the present invention can set a longer timing decomposition period to achieve a refined analysis of the annual operation of the rural power system while ensuring the solution efficiency.

[0209] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0210] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A refined sequential operation simulation method based on a new rural power system, characterized in that: The following steps are involved: S1. Collect economic and technical parameters of each component in the power system; S2, constructing the objective function of the sequential operation simulation model based on the AC power flow model; S3. Establish a time-series operation simulation model based on refined power flow; S4, adding second-order cone relaxation constraints and phase angle recovery conditions to the model; S5. Solve the time series operation simulation model in different time periods using commercial software.

2. A refined sequential operation simulation method based on a new rural power system as claimed in claim 1, characterized in that: The various components inside the power system in step S1 include an AC power grid, a tie line, a biomass unit, a wind power and photovoltaic unit, and an energy storage element.

3. A refined sequential operation simulation method based on a new rural power system as claimed in claim 2, characterized in that: The objective function constructed in step S2 is to minimize the economic operation cost of the rural power system. The objective function is as follows: Among them, T N is the total number of simulation time periods, t is the time period index number, F pv (t) is the light abandonment penalty in time period t, F wp (t) is the wind abandonment penalty in period t, F mgp (t) is the active power generation cost of the biomass generator set at time t, F loss (t) is the line loss penalty term at time t, F ep (t) is the electricity purchase cost in period t; their calculation methods are shown in formula (2), formula (3), formula (4), formula (5) and formula (6), respectively. F pv (t)=P cpv (t)×f pv (2) F wp (t)=P cwp (t)×f wp (3) F mgp (t)=P mg (t)×f mgp (4) F loss (t)=P loss (t)×f loss (5) F ep (t)=P ep (t)×f ep (6) Where P cpv (t) is the abandoned optical power at time t, P cwp (t) is the wind power abandoned at time t, P mg (t) is the active power output of the biomass unit at time t, P loss (t) is the system network loss power at time t, P ep (t) is the external power transmission power at time t; f pv is the light abandonment penalty coefficient, f wp is the wind abandonment penalty coefficient, f mgp is the active output cost coefficient of biomass power generation, f loss is the network loss penalty coefficient, f ep is the unit electricity purchase cost; Among them, P loss The calculation formula of (t) is as follows: Where n is the line index number, Brn is the number of transmission lines in the system, is the square of the current flowing through the nth transmission line at time t, r n is the resistance of the nth line.

4. A refined sequential operation simulation method based on a new rural power system as claimed in claim 3, characterized in that: The step S3 of establishing a time sequence operation simulation model based on the refined power flow includes: After determining the objective function, add the following constraints to the system; (1) Power balance constraints In the formula, i, j, k are node index numbers, set B represents the set of all nodes in the network, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, and p j is the active injection power of node j, P jk is the active power flowing from node j to node k, P ij is the active power flowing from node i to node j, is the square of the current flowing from node j to node j, r ij is the resistance from node i to node j, g j is the conductance of node j to ground, V j 2 is the square of the voltage value at node j; q j is the reactive injection power of node j, Q jk is the reactive power flowing from node j to node k, Q ij is the active power flowing from node i to node j, x ij is the reactance value from node i to node j, b j is the earth susceptance of node j; (2) Ohm’s law constraints In the formula, E represents the set of all branches in the system; (3) Second-order cone constraint The apparent power expression of the first section of the line is shown in formula (10): Multiplying both ends of equation (10) by the corresponding conjugate quantity yields Equation (11) describes the relationship between node voltage, branch current and branch power; (4) Voltage over-limit constraint In the formula, represent Set the variable to It can reduce the dimension of variables and simplify the calculation of the model; is the minimum value of the square of the voltage per unit value at node j, is the maximum value of the square of the voltage per unit value at node j; (5) Current limit constraint In the formula, represent Similarly, set the variable to It can reduce the dimension of variables and simplify the calculation of the model; is the minimum value of the square of the per unit current of line ij, is the maximum value of the square of the per-unit value of the current in line ij; (6) Line active power flow constraints -P ijmax ≤P ij ≤P ijmax (14) Where P ijmax is the maximum active power transmission value of line ij; (7) Biomass unit operation constraints The operation constraints of the biomass unit include: upper and lower limits of active power output of the biomass unit (15), upper and lower limits of reactive power output of the biomass unit (16), reactive climbing power constraint of the biomass unit (17), active climbing power constraint of the biomass unit (18), and start-stop time constraint (19); P mgmin (j)U(j,t)≤P mg (j,t)≤P mgmax (j)U(j,t) (15) Q mgmin (j)U(j,t)≤Q mg (j,t)≤Q mgmax (j)U(j,t) (16) Where P mgmin (j) is the lower limit of the active output of the j-th biomass generator; U(j,t) is the state of the j-th biomass generator at time t, "0" represents shutdown, "1" represents startup; P mgmax (j) is the upper limit of the active output of the jth biomass generator; Q mgmin (j) is the lower limit of reactive power output of the jth biomass generator; Q mgmax (j) is the upper limit of reactive power output of the jth biomass generator; Q up (j) is the active power ramp limit of the jth biomass generator, Q down (j) is the upper limit of reactive power ramp of the jth biomass generator; P up (j) is the active power ramp limit of the jth biomass generator, P down (j) is the upper limit of the active power ramp of the j-th biomass generator; S(j,t) is the shutdown state of the j-th biomass generator at time t, "0" represents that it is not shut down at time t, and "1" represents that it is shut down at time t; O(j,t) is the startup state of the j-th biomass generator at time t, "0" represents that it is not started at time t, and "1" represents that it is started at time t; (8) Wind power and photovoltaic output constraints Where P wp (j,t) is the actual output of the j-th wind farm at time t, is the maximum output of the jth wind farm at time t, P pv (j,t) is the actual output of the j-th PV power station at time t, is the predicted output of the j-th PV power station at time t; (9) Energy storage operation constraints The energy storage operation constraints include charging power constraint (21), discharging power constraint (22), energy storage charging and discharging state constraint (23), energy storage charge state constraint (24), and energy storage capacity constraint (25); u c (j,t)P cmin (j)≤P c (j,t)≤u c (j,t)P cmax (j) (21) u d (j,t)P dmin (j)≤P d (j,t)≤u d (j,t)P dmax (j) (22) u c (j,t)+u d (j,t)≤1 (23) E(j,t)=E(j,t-1)+η c P c (j,t-1)-P d (j,t-1) / η d (24) E min (j)≤E(j,t)≤E max (j) (25) In the formula, u c (j, t) is the charging status of the jth energy storage station at time t, "1" represents the charging status at this time, P cmin (j) is the minimum charging power of the jth energy storage station, P cmax (j) is the maximum charging power of the j-th energy storage station; u d (t, j) is the energy storage discharge state, "1" represents the discharge state at this moment, P dmin (j) is the minimum discharge power of the jth energy storage power station, P dmax (j) is the maximum discharge power of the j-th energy storage station; E(j,t) represents the storage capacity of the j-th energy storage station at time t, η c is the charging efficiency of the energy storage station, η d is the discharge efficiency of the energy storage power station; E min (j) is the minimum storage capacity of the jth energy storage station, E max (j) is the maximum storage capacity of the jth energy storage power station; (10) Tie-line power constraints Where P out (t) is the power transmitted outward by the system at time t, P CLmax The upper limit of the transmission power of the interconnection line.

5. A refined sequential operation simulation method based on a new rural power system as claimed in claim 4, characterized in that: The step S4 adds a second-order cone relaxation constraint and a phase angle recovery condition to the model, including: According to the second-order cone relaxation theory, equation (10) is relaxed to the inequality constraint (27), Formula (27) can be rewritten into the form of second-order cone programming as follows: In the formula, ||·|| represents the second norm; another condition to ensure the accuracy of the above relaxation is the loop phase angle constraint, as shown in formula (29) and formula (30); In the formula, β ij represents the voltage phase angle difference between node i and node j; c is the minimum loop set in the system; β ij Written in the form shown in formula (31), It is derived that β ij The approximate expression of is as follows: The loop phase angle constraint formula (33) is transformed into formula (34), Equation (34) is a linear equality constraint.

6. A refined sequential operation simulation method based on a new rural power system as claimed in claim 5, characterized in that: The step S5 of solving the time series operation simulation model by time periods using commercial software includes: The segmented solution method is adopted to decompose the whole year into several short time segments, and the solutions are solved sequentially. The operation results of the last few segments of the previous sub-period are used as the starting operation status of the next sub-period. After decomposing the annual time series operation simulation model into several sub-period problems, each sub-period is solved in turn, and finally the solution results of each sub-period are added together to obtain the complete annual time series operation simulation results.

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