Power system regulation potential measuring and calculating model based on time sequence production analog simulation

Abstract

The invention belongs to the technical field of power system regulation potential calculation, and particularly relates to a power system regulation potential calculation model based on time sequence production analog simulation. Comprising the following steps: S1, preparing input data required for completing time sequence operation simulation of the power system; s2, establishing a power system time sequence operation simulation model based on unit combination; s3, determining a solution algorithm of the power system time sequence operation simulation model; according to the power system regulation potential measurement and calculation model based on time sequence production simulation, the problem that states at the end of the last month and at the beginning of the next month are not connected is solved by setting an overlapped calculation period, meanwhile, in order to ensure that a reliable result can be obtained through operation simulation of each month, a solution-free automatic rollback mechanism is further introduced, and the calculation efficiency is improved. According to the method disclosed by the invention, the dependence of the time sequence operation simulation of the power system on the actual measurement data of the output of the renewable energy source is solved, the simulation calculation speed is accelerated, and a feasible solution is ensured to be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power system regulation potential calculation, and in particular relates to an electric power system regulation potential calculation model based on time-series production simulation. Background Art

[0002] The power system needs to maintain balance and stability at all times. A large number of new energy sources are connected to the grid for power generation, resulting in an increasing proportion of new energy installed capacity in the power grid. However, new energy sources such as photovoltaics and wind power have characteristics such as volatility, intermittency and randomness, and cannot output power stably, which can easily lead to short-term power imbalance. In addition, the volatility of new energy power generation causes voltage fluctuations or volatility after large-scale access to the power grid, which will also affect the power flow distribution of the system. The measurement of the power system regulation potential refers to the evaluation and prediction of the power system's ability to meet load changes by adjusting various resources in the future. It is mainly to establish a quantitative relationship model between related variables. It is necessary to collect the operating data of various resources in the power system, such as generator output, energy storage equipment status, load changes, etc. According to the collected data, a prediction model for the power system regulation potential is established. The established model is used to predict and analyze the future power system regulation potential. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a power system regulation potential calculation model based on time-series production simulation, so as to solve the dependence of power system time-series operation simulation on actual measured data of renewable energy output, speed up the simulation calculation speed and ensure a feasible solution.

[0004] The object of the present invention is achieved in this way: a power system regulation potential calculation model based on time-series production simulation includes the following steps:

[0005] S1. Prepare the input data required for completing the power system timing operation simulation;

[0006] S2. Establish a power system sequential operation simulation model based on unit combination;

[0007] S3. Determine the solution algorithm of the power system timing operation simulation model.

[0008] The input data required to complete the power system timing operation simulation in step S1 include annual wind power and photovoltaic output data, generator unit operating characteristics, annual load forecast and interconnection line transmission plan data; wherein the annual hourly output curves of wind power and photovoltaic power stations are obtained by converting the wind speed, surface light and surface temperature data in the hourly meteorological data, and the thermal load curve of the cogeneration unit is estimated by combining the surface temperature data with the administrative division data and the population distribution data. Finally, the regional power system is divided into several subsystems with the restricted interconnection line as the dividing point.

[0009] The step S2 of establishing a power system sequential operation simulation model based on unit combination includes:

[0010] (1) Objective function

[0011] For a regional power grid consisting of k subsystems, its sequential operation simulation model is a mixed-integer linear programming model (MILP), the objective function of which is the power generation cost of the generator. Start-up cost Downtime costs and the power curtailment penalty for renewable energy, and its form is shown in formula (1).

[0012]

[0013] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalty for abandoned photovoltaic power and abandoned wind power for each megawatt (in yuan); p S，k (t) and p W，k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t obtained from meteorological data.

[0014] (2) Power balance constraints

[0015] For any subsystem k, there is a power balance constraint:

[0016]

[0017] where p G，i (t) is the power output of generator i at time t; p W，k (t) is the wind power output at time t in system k; p S，k (t) is the photovoltaic output of system k at time t; T I，k (t) and T O，k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L，k (t) is the load power at time t in system k

[0018] (3) Backup constraints

[0019] For any subsystem k, the safe spare capacity of this subsystem must be ensured, so the spare constraint is:

[0020]

[0021] where u i (t) is the start / stop state of generator i, which is 1 when the generator is running and 0 when it is stopped; is the rated capacity of generator i; W，k and ε S，k They are the maximum prediction error of wind power output and photovoltaic output of subsystem k, respectively. The prediction error here is defined as the maximum error between the predicted renewable energy output and the actual renewable energy output. Since the accuracy of wind power hourly output prediction is still poor, the renewable energy output is often not considered when determining the minimum startup mode of the power system, that is, the prediction error is considered to be 100%; η L，k is the maximum reserve demand factor of subsystem k, which is generally 5%;

[0022] (4) Grid constraints

[0023] The meaning of grid constraint is that at any time, the power transmitted on any line cannot exceed the maximum allowable transmission power of the line. The mathematical expression is:

[0024]

[0025] in represents the power transmission distribution coefficient of the i-th thermal power unit to the l-th line; N represents the power transfer distribution coefficient of load m to line l; G Represents the set of thermal power units; M k represents a set of loads; represents the line transmission capacity; L is the number of lines in the system;

[0026] Considering the case of wind power and photovoltaic grid connection (wind power represents renewable energy), formula (4) is expanded to:

[0027]

[0028] in, N represents the power transmission distribution coefficient of the j-th wind turbine to the l-th line; W Represents the set of wind turbines in the system;

[0029] (5) Power output range constraints

[0030] Due to the need for stable combustion of thermal power units, the output of thermal power units in the startup state can often only vary within the minimum technical output and installed capacity range. Hydropower units also have minimum output restrictions. At the same time, the output of renewable energy sources such as wind power and photovoltaic power cannot be higher than the maximum output determined by wind speed and light conditions, so there are power output range constraints:

[0031]

[0032] in p G，i is the minimum technical output of generator i when it is running;

[0033] (6) Generator climbing constraints

[0034] The ramp capability of a generator set limits the maximum value of the output that the generator set can increase or decrease in a unit time. In addition to the operating range constraints, the output power of unit i at time t also needs to meet the ramp constraint of the unit:

[0035]

[0036] where r U，i and r D，i are the maximum ramp-up speed rate and the maximum ramp-down speed rate of generator i, respectively. N is a large constant used to ensure u i (t) or u i When (t-1) is not all 1, formula (9) and formula (10) are always valid; when the generator is just started or about to be shut down, the output of the generator must also meet the unit climbing speed constraint. The output constraints when the generator is started and stopped are shown in formula (11) and formula (12) respectively:

[0037]

[0038] Among them, s U，i Indicates the starting state of generator i; s D，i The shutdown state of generator i; N is a large constant;

[0039] (7) Minimum start and stop time constraints

[0040] The minimum start and stop time constraint of the generator set is:

[0041]

[0042] where u i (t) is the working state of generator i at time t; T U，i and T D，i They represent the time that generator i needs to continue to run and remain in shutdown state at time t, respectively. The time can be calculated by the following formula:

[0043] T U，i (t) = min{M U，i ,T-t+1}(15)

[0044] T D，i (t) = min{M D，i ,T-t+1}(16)

[0045] Where M U，i and M D，i are the minimum start-up time and minimum downtime of generator i, that is, the generator must keep running for M after each start-up. U，i After each shutdown of the generator, it needs at least M D，i In the process of rolling solution, the final state of the previous rolling period will be used as the initial state input of the current rolling period. Therefore, it is assumed that the U has been started for the final state of the previous rolling period. i0 Hours and Closed D i0 The initial start and stop constraints of the unit in the current rolling period are:

[0046]

[0047] (8) Energy storage constraints

[0048] E ch，t =αP ch，t dt,E ds，t =βP ds，t dt (19)

[0049] Where: E ch，t 、E ds，t are the energy storage charging and discharging energies respectively; α and β are the energy storage charging and discharging efficiency coefficients respectively; P ch，t , P ds，t are the charge and discharge powers at time t respectively;

[0050] The stored energy E at the end of time t t It can be expressed as:

[0051]

[0052] Where: E ini is the energy stored in the initial stage of energy storage; dt is the scheduling period;

[0053] The energy storage charging and discharging power limits are:

[0054] -CH t P ch max，t ≤P ch，t ≤0,0≤P ds，t ≤DS t Pds max，t (twenty one)

[0055] Where: CH t DS is the charging state of the energy storage at time t. A value of 1 indicates charging, and a value of 0 indicates no charging. t is the discharge state of the energy storage at time t, a value of 1 indicates discharge, and 0 indicates no discharge; P chmax，t , P dsmax，t are the maximum charging and discharging power of energy storage at time t respectively;

[0056] Energy storage limitations:

[0057] E min，t ≤E t ≤E max，t , E ini =E T (twenty two)

[0058] Where: E min，t 、E max，t are the minimum and maximum values ​​of energy stored at time t; E ini Indicates the initial energy storage state; E T Indicates the energy storage state at time T;

[0059] To ensure that the charging and discharging of energy storage are not performed at the same time, the energy storage charging and discharging state constraints are:

[0060] CH t +DS t ≤1(23).

[0062] The step S3 determines the solution algorithm of the power system sequential operation simulation model, including:

[0063] The time domain decomposition technology and the no-solution automatic rollback technology are used to solve the power system timing operation model; the time domain decomposition technology is: on the basis of decomposing the annual operation simulation into 12 periods in units of months, a 5-day overlap period is set between adjacent periods, such as Figure 3 As shown in the figure; by introducing the overlapping period, the influence of the initial conditions used in each month of simulation on the simulation results of that month is reduced, thereby reducing the inconsistency of the results between months;

[0064] The unsolvable automatic rollback technology includes:

[0065] When the rolling solution encounters no solution, it does not exit the solution process directly, but includes the previous simulation period in the simulation. If there is still no solution, it continues to roll back. When rolling back the simulation results is feasible, the simulation results obtained by rolling back are used to overwrite the original simulation results of the previous period. The process of the automatic rollback mechanism is as follows: Figure 4For a rolling simulation with n days as the unit, the automatic rollback mechanism mainly includes the following three steps (in the initial state, m is 0 and k is 1):

[0066] Step 1: Use the simulation result at the last moment of day km-1 as the initial condition and simulate the operation from day km to day k+n;

[0067] Step 2: If the simulation from km to k+n days has a solution, save the simulation result; if the simulation has no solution, roll back one day, that is, set m=m+1 and jump to step 1;

[0068] Step 3: If the k+nth day is the last day of this month, end the rolling simulation of this month and output all simulation results of this month; if the k+nth day is not the last day of this month, set k=k+n, m=0 and jump to the first step.

[0069] Beneficial effects of the present invention: the power system regulation potential calculation model based on time-series production simulation of the present invention includes steps S1, preparing input data required for completing the power system time-series operation simulation; S2, establishing a power system time-series operation simulation model based on unit combination; S3, determining the solution algorithm of the power system time-series operation simulation model; the power system regulation potential calculation model based on time-series production simulation of the present invention solves the problem of the disconnection between the end of the previous month and the beginning of the next month by setting overlapping calculation periods. At the same time, in order to ensure that reliable results can be obtained in the operation simulation of each month, an automatic rollback mechanism for no solution is also introduced to ensure that the operation simulation of a long time scale will not be unsolvable due to the rolling interval problem. The present invention solves the dependence of the power system time-series operation simulation on the actual measured data of renewable energy output, speeds up the simulation calculation speed, and ensures that a feasible solution is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] 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.

[0071] Figure 1 Schematic diagram of the three-stage simulation framework for simulating the timing operation of the power system;

[0072] Figure 2 It is the piecewise linearization diagram of coal consumption curve;

[0073] Figure 3 It is a schematic diagram of the timing decomposition of the simulation for the full-year timing operation;

[0074] Figure 4This is the flow chart of the automatic rollback algorithm. DETAILED DESCRIPTION

[0075] The present invention will be further described below in conjunction with the accompanying drawings.

[0076] 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.

[0077] 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.

[0078] like Figure 1 As shown, the power system regulation potential calculation model based on time-series production simulation of the present invention includes the following steps:

[0079] S1. Prepare the input data required for completing the power system timing operation simulation;

[0080] S2. Establish a power system sequential operation simulation model based on unit combination;

[0081] S3. Determine the solution algorithm of the power system timing operation simulation model.

[0082] The input data required to complete the power system timing operation simulation in step S1 include annual wind power and photovoltaic output data, generator unit operating characteristics, annual load forecast and interconnection line transmission plan data; wherein the annual hourly output curves of wind power and photovoltaic power stations are obtained by converting the wind speed, surface light and surface temperature data in the hourly meteorological data, and the thermal load curve of the cogeneration unit is estimated by combining the surface temperature data with the administrative division data and the population distribution data. Finally, the regional power system is divided into several subsystems with the restricted interconnection line as the dividing point.

[0083] The step S2 of establishing a power system sequential operation simulation model based on unit combination includes:

[0084] (1) Objective function

[0085] The operation simulation model used arranges the start and stop and output of all units in the system to balance the electrical load and thermal load with the goal of minimizing the total operating cost. At the same time, in order to promote the consumption of renewable energy, the objective function also includes penalties for wind and solar abandonment.

[0086] For a regional power grid consisting of k subsystems, its sequential operation simulation model is a mixed-integer linear programming model (MILP), the objective function of which is the power generation cost of the generator. Start-up cost Downtime costs and the power curtailment penalty for renewable energy, and its form is shown in formula (1).

[0087]

[0088] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalty for abandoned photovoltaic power and abandoned wind power for each megawatt (in yuan); p S，k (t) and p W，k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t obtained from meteorological data.

[0089] Power generation costs The relationship between the unit output (cost curve) is as follows Figure 2 As shown; although the coal consumption curve of the thermal power unit is nonlinear (dashed line in the figure), the nonlinear coal consumption curve can be processed piecewise linearly. By selecting appropriate power segmentation points, the interval is approximated as several continuous straight lines (solid straight lines in the figure), where p G，i , Respectively represent the minimum and maximum technical output of the i-th thermal power unit; p G，i，1 、p G，i，2 are the starting powers of the second and third segments of the coal consumption curve (here the coal consumption curve is divided into three segments only); f1, f2, f3 are the slopes of the first, second, and third segments of the coal consumption curve after segmentation; is the minimum operating cost of the i-th thermal power unit.

[0090] (2) Power balance constraints

[0091] For any subsystem k, there is a power balance constraint:

[0092]

[0093] where p G，i (t) is the power output of generator i at time t; p W，k(t) is the wind power output at time t in system k; p S，k (t) is the photovoltaic output at time t in system k; T I，k (t) and T O，k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L，k (t) is the load power at time t in system k

[0094] (3) Backup constraints

[0095] For any subsystem k, the safe spare capacity of this subsystem must be ensured, so the spare constraint is:

[0096]

[0097] where u i (t) is the start / stop state of generator i, which is 1 when the generator is running and 0 when it is stopped; is the rated capacity of generator i; W，k and ε S，k They are the maximum prediction error of wind power output and photovoltaic output of subsystem k, respectively. The prediction error here is defined as the maximum error between the predicted renewable energy output and the actual renewable energy output. Since the accuracy of wind power hourly output prediction is still poor, the renewable energy output is often not considered when determining the minimum startup mode of the power system, that is, the prediction error is considered to be 100%; η L，k is the maximum reserve demand factor of subsystem k, which is generally 5%;

[0098] (4) Grid constraints

[0099] The meaning of grid constraint is that at any time, the power transmitted on any line cannot exceed the maximum allowable transmission power of the line. The mathematical expression is:

[0100]

[0101] in represents the power transmission distribution coefficient of the i-th thermal power unit to the l-th line; N represents the power transfer distribution coefficient of load m to line l; G Represents the set of thermal power units; M k represents a set of loads; represents the line transmission capacity; L is the number of lines in the system;

[0102] Considering the case of wind power and photovoltaic grid connection (wind power represents renewable energy), formula (4) is expanded to:

[0103]

[0104] in, N represents the power transmission distribution coefficient of the j-th wind turbine to the l-th line; W Represents the set of wind turbines in the system;

[0105] (5) Power output range constraints

[0106] Due to the need for stable combustion of thermal power units, the output of thermal power units in the startup state can often only vary within the minimum technical output and installed capacity range. Hydropower units also have minimum output restrictions. At the same time, the output of renewable energy sources such as wind power and photovoltaic power cannot be higher than the maximum output determined by wind speed and light conditions, so there are power output range constraints:

[0107]

[0108]

[0109] in p G，i is the minimum technical output of generator i when it is running;

[0110] (6) Generator climbing constraints

[0111] The ramp capability of a generator set limits the maximum value of the output that the generator set can increase or decrease in a unit time. In addition to the operating range constraints, the output power of unit i at time t also needs to meet the ramp constraint of the unit:

[0112]

[0113] where r U，i and r D，i are the maximum ramp-up speed rate and the maximum ramp-down speed rate of generator i, respectively. N is a large constant used to ensure u i (t) or u i When (t-1) is not all 1, formula (9) and formula (10) are always valid; when the generator is just started or about to be shut down, the output of the generator must also meet the unit climbing speed constraint. The output constraints when the generator is started and stopped are shown in formula (11) and formula (12) respectively:

[0114]

[0115] Among them, s U，i Indicates the starting state of generator i; s D，i The shutdown state of generator i; N is a large constant;

[0116] (7) Minimum start and stop time constraints

[0117] For a generator set, once it is connected to the grid, it must be able to shut down after a sufficient amount of time. Similarly, once the generator set is shut down, it must also be able to shut down after a certain amount of time before it can be connected to the grid again. Therefore, the minimum start-stop time constraint for the generator set is:

[0118]

[0119]

[0120] where u i (t) is the working state of generator i at time t; T U，i and T D，i They represent the time that generator i needs to continue to run and remain in shutdown state at time t, respectively. The time can be calculated by the following formula:

[0121] T U，i (t) = min{M U，i ,T-t+1} (15)

[0122] T D，i (t) = min{M D，i ,T-t+1} (16)

[0123] Where M U，i and M D，i are the minimum start-up time and minimum downtime of generator i, that is, the generator must keep running for M after each start-up. U，i After each shutdown of the generator, it needs at least M D，i In the process of rolling solution, the final state of the previous rolling period will be used as the initial state input of the current rolling period. Therefore, it is assumed that the U has been started for the final state of the previous rolling period. i0 Hours and Closed D i0 The initial start and stop constraints of the unit in the current rolling period are:

[0124]

[0125] (8) Energy storage constraints

[0126] E ch，t =αP ch，t dt,E ds，t =βP ds，t dt (19)

[0127] Where: E ch，t 、E ds，t are the energy storage charging and discharging energies respectively; α and β are the energy storage charging and discharging efficiency coefficients respectively; P ch，t , P ds，tare the charge and discharge powers at time t respectively;

[0128] The stored energy E at the end of time t t It can be expressed as:

[0129]

[0130] Where: E ini is the energy stored in the initial stage of energy storage; dt is the scheduling period;

[0131] The energy storage charging and discharging power limits are:

[0132] -CH t P ch max，t ≤P ch，t ≤0,0≤P ds，t ≤DS t P ds max，t (twenty one)

[0133] Where: CH t DS is the charging state of the energy storage at time t. A value of 1 indicates charging, and a value of 0 indicates no charging. t is the discharge state of the energy storage at time t, a value of 1 indicates discharge, and 0 indicates no discharge; P chmax，t , P dsmax，t are the maximum charging and discharging power of energy storage at time t respectively;

[0134] Energy storage limitations:

[0135] E min，t ≤E t ≤E max，t , E ini =E T (twenty two)

[0136] Where: E min，t 、E max，t are the minimum and maximum values ​​of energy stored at time t; E ini Indicates the initial energy storage state; E T Indicates the energy storage state at time T;

[0137] To ensure that the charging and discharging of energy storage are not performed at the same time, the energy storage charging and discharging state constraints are:

[0138] CH t +DS t ≤1 (23).

[0140] Due to the limitation of the solution scale of mixed linear programming problems, for a regional power system with hundreds of generators, the annual operation simulation cannot be solved by a single calculation. Therefore, the rolling simulation method is currently widely used for the time series operation simulation of the power system. However, since the rolling operation simulation must be carried out in rolling time periods, the annual operation simulation of the regional power system with hundreds of units often takes hours or even days. In order to speed up the solution speed of the time series operation simulation, the method of time series decomposition can be used to decompose the annual operation simulation into multiple time periods for parallel calculation. Although time series decomposition and parallel calculation can effectively speed up the calculation speed of the annual operation simulation, after decomposing the whole year into multiple time periods, it will not be possible to ensure that all operation constraints are established between time periods. That is, time series decomposition will cause the calculation results to be unable to be combined to obtain feasible annual operation simulation results. Barro C and Hummon M et al. found that the influence of various constraints in the operation simulation on the simulation results will gradually weaken as the operation time is pushed back. For example, the operation constraints on January 1 have almost no effect on the operation results on January 10. Based on this, the annual operation simulation can be decomposed into 12 periods in monthly units, and a 5-day overlap period can be set between adjacent periods, such as Figure 3 By introducing the overlapping period, the impact of the initial conditions used in each month of the simulation on the simulation results of that month is reduced, thereby reducing the inconsistency of the results between months.

[0141] The time series decomposition method can effectively speed up the calculation speed of the annual time series operation simulation at the expense of a small amount of result consistency, but it still cannot solve the problem of rolling simulation being prone to no solution due to tight flexibility in regional power systems with a high proportion of renewable energy. The reason why rolling simulation has no solution is often because the results of the previous one or several simulation periods make the system flexibility unable to meet the system's operation needs in the current solution period. Based on this, a no-solution automatic rollback mechanism can be developed. When the rolling solution encounters no solution, it does not directly exit the solution process, but includes the previous simulation period in the simulation. If there is still no solution, continue to roll back; when the rollback simulation result is feasible, the simulation result obtained by rolling back is used to overwrite the original simulation result of the previous period.

[0142] The step S3 determines the solution algorithm of the power system sequential operation simulation model, including:

[0143] The time domain decomposition technology and the no-solution automatic rollback technology are used to solve the power system timing operation model; the time domain decomposition technology is: on the basis of decomposing the annual operation simulation into 12 periods in units of months, a 5-day overlap period is set between adjacent periods, such as Figure 3 As shown in the figure; by introducing the overlapping period, the influence of the initial conditions used in each month of simulation on the simulation results of that month is reduced, thereby reducing the inconsistency of the results between months;

[0144] The unsolvable automatic rollback technology includes:

[0145] When the rolling solution encounters no solution, it does not exit the solution process directly, but includes the previous simulation period in the simulation. If there is still no solution, it continues to roll back. When rolling back the simulation results is feasible, the simulation results obtained by rolling back are used to overwrite the original simulation results of the previous period. The process of the automatic rollback mechanism is as follows: Figure 4 For a rolling simulation with n days as the unit, the automatic rollback mechanism mainly includes the following three steps (in the initial state, m is 0 and k is 1):

[0146] Step 1: Use the simulation result at the last moment of day km-1 as the initial condition and simulate the operation from day km to day k+n;

[0147] Step 2: If the simulation from km to k+n days has a solution, save the simulation result; if the simulation has no solution, roll back one day, that is, set m=m+1 and jump to step 1;

[0148] Step 3: If the k+nth day is the last day of this month, end the rolling simulation of this month and output all simulation results of this month; if the k+nth day is not the last day of this month, set k=k+n, m=0 and jump to the first step.

[0149] After calculating the model, the results of the parallel calculations of each month can be combined to obtain the annual operation simulation results, and the annual operation statistical information such as the wind abandonment rate, solar abandonment rate, annual utilization hours of each power generation equipment, total coal consumption, etc. can be obtained, and a report output can be generated, such as Figure 1 as shown in .

[0150] In summary, the electric power system regulation potential calculation model based on time-series production simulation of the present invention includes steps S1, preparing input data required for completing the electric power system time-series operation simulation; S2, establishing the electric power system time-series operation simulation model based on unit combination; S3, determining the solution algorithm of the electric power system time-series operation simulation model; the electric power system regulation potential calculation model based on time-series production simulation of the present invention solves the problem of the state disconnection between the end of the previous month and the beginning of the next month by setting overlapping calculation periods. At the same time, in order to ensure that reliable results can be obtained in the operation simulation of each month, an automatic rollback mechanism for no solution is also introduced to ensure that the operation simulation of a long time scale will not be unsolvable due to the rolling interval problem. The present invention solves the dependence of the electric power system time-series operation simulation on the actual measured data of renewable energy output, speeds up the simulation calculation speed, and ensures that a feasible solution is obtained.

[0151] 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.

[0152] 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 power system regulation potential calculation model based on time-series production simulation, characterized in that: The following steps are involved: S1. Prepare the input data required for completing the power system timing operation simulation; S2. Establish a power system sequential operation simulation model based on unit combination; S3. Determine the solution algorithm of the power system timing operation simulation model.

2. The power system regulation potential calculation model based on time-series production simulation as claimed in claim 1 is characterized in that: The input data required to complete the power system timing operation simulation in step S1 include annual wind power and photovoltaic output data, generator unit operating characteristics, annual load forecast and interconnection line transmission plan data; wherein the annual hourly output curves of wind power and photovoltaic power stations are obtained by converting the wind speed, surface light and surface temperature data in the hourly meteorological data, and the thermal load curve of the cogeneration unit is estimated by combining the surface temperature data with the administrative division data and the population distribution data. Finally, the regional power system is divided into several subsystems with the restricted interconnection line as the dividing point.

3. The power system regulation potential calculation model based on time-series production simulation according to claim 1 is characterized in that: The step S2 of establishing a power system sequential operation simulation model based on unit combination includes: (1) Objective function For a regional power grid consisting of k subsystems, its sequential operation simulation model is a mixed integer linear programming model, the objective function of which is the power generation cost of the generator. Start-up cost Downtime costs and the power curtailment penalty for renewable energy, and its form is shown in formula (1). Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalty for abandoned photovoltaic power and abandoned wind power for each megawatt (in yuan); p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t obtained from meteorological data. (2) Power balance constraints For any subsystem k, there is a power balance constraint: where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k (3) Backup constraints For any subsystem k, the safe spare capacity of this subsystem must be ensured, so the spare constraint is: where u i (t) is the start / stop state of generator i, which is 1 when the generator is running and 0 when it is stopped; is the rated capacity of generator i; W,k and ε S,k are the maximum prediction errors of wind power output and photovoltaic output of subsystem k, respectively, and η L,k is the maximum reserve demand factor of subsystem k; (4) Grid constraints The meaning of grid constraint is that at any time, the power transmitted on any line cannot exceed the maximum allowable transmission power of the line. The mathematical expression is: in represents the power transmission distribution coefficient of the i-th thermal power unit to the l-th line; N represents the power transfer distribution coefficient of load m to line l; G Represents the set of thermal power units; M k represents a set of loads; represents the line transmission capacity; L is the number of lines in the system; Considering the connection of wind power and photovoltaic power to the grid, equation (4) is expanded to: in, N represents the power transmission distribution coefficient of the j-th wind turbine to the l-th line; W Represents the set of wind turbines in the system; (5) Power output range constraints in p G,i is the minimum technical output of generator i when it is running; (6) Generator climbing constraints In addition to the operating range constraints, the output power of unit i at time t also needs to meet the unit's ramp constraints: where r U,i and r D,i are the maximum ramp-up speed rate and the maximum ramp-down speed rate of generator i, respectively. N is a large constant used to ensure u i (t) or u i When (t-1) is not all 1, formula (9) and formula (10) are always valid; when the generator is just started or about to be shut down, the output of the generator must also meet the unit climbing speed constraint. The output constraints when the generator is started and stopped are shown in formula (11) and formula (12) respectively: Among them, s U,i Indicates the starting state of generator i; s D,i The shutdown state of generator i; N is a large constant; (7) Minimum start and stop time constraints The minimum start and stop time constraint of the generator set is: where u i (t) is the working state of generator i at time t; T U,i and T D,i They represent the time that generator i needs to continue to run and remain in shutdown state at time t, respectively. The time can be calculated by the following formula: T U,i (t)=min{M U,i ,T-t+1} (15) T D,i (t)=min{M D,i ,T-t+1} (16) Where M U,i and M D,i are the minimum start-up time and minimum downtime of generator i, that is, the generator must keep running for M after each start-up. U,i After each shutdown of the generator, it needs at least M D,i In the process of rolling solution, the final state of the previous rolling period will be used as the initial state input of the current rolling period. Therefore, it is assumed that the U has been started for the final state of the previous rolling period. i0 Hours and Closed D i0 The initial start and stop constraints of the unit in the current rolling period are: (8) Energy storage constraints E ch,t =αP ch,t dt,E ds,t =βP ds,t dt (19) Where: E ch,t 、E ds,t are the energy storage charging and discharging energies respectively; α and β are the energy storage charging and discharging efficiency coefficients respectively; P ch,t , P ds,t are the charging and discharging powers at time t respectively; The stored energy E at the end of time t t It can be expressed as: Where: E ini is the energy stored in the initial stage of energy storage; dt is the scheduling period; The energy storage charging and discharging power limits are: -CH t P chmax,t ≤P ch,t ≤0,0≤P ds,t ≤DS t P dsmax,t (21) Where: CH t DS is the charging state of the energy storage at time t. A value of 1 indicates charging, and a value of 0 indicates no charging. t is the discharge state of the energy storage at time t, a value of 1 indicates discharge, and 0 indicates no discharge; P chmax,t , P dsmax,t are the maximum charging and discharging power of energy storage at time t respectively; Energy storage limitations: AND min,t ≤E t ≤E max,t ,AND ini =And T (22) Where: E min,t 、E max,t are the minimum and maximum values ​​of energy stored at time t; E ini Indicates the initial energy storage state; E T Indicates the energy storage state at time T; To ensure that the charging and discharging of energy storage are not performed at the same time, the energy storage charging and discharging state constraints are: CH t +DS t ≤1 (23)。 4. The power system regulation potential calculation model based on time-series production simulation according to claim 1 is characterized in that: The step S3 determines the solution algorithm of the power system sequential operation simulation model, including: The time domain decomposition technology and the no-solution automatic rollback technology are used to solve the power system time sequence operation model; the time domain decomposition technology is: on the basis of decomposing the annual operation simulation into 12 periods in units of months, a 5-day overlap period is set between adjacent periods, as shown in Figure 3; The unsolvable automatic rollback technology includes: When the rolling solution encounters no solution, it does not exit the solution process directly, but includes the previous simulation period in the simulation. If there is still no solution, it continues to roll back. When rolling back the simulation results is feasible, the simulation results obtained by rolling back are used to overwrite the simulation results of the previous period. For a rolling simulation with n days as the unit, the automatic rollback mechanism mainly includes the following three steps: Step 1: Use the simulation result at the last moment of day km-1 as the initial condition and simulate the operation from day km to day k+n; Step 2: If the simulation from km to k+n days has a solution, save the simulation result; if the simulation has no solution, roll back one day, that is, set m=m+1 and jump to step 1; Step 3: If the k+nth day is the last day of this month, end the rolling simulation of this month and output all simulation results of this month; if the k+nth day is not the last day of this month, set k=k+n, m=0 and jump to the first step.

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