CN117767259A - Power grid new energy consumption deviation tracing method and device considering multiple uncertainties - Google Patents
Power grid new energy consumption deviation tracing method and device considering multiple uncertainties Download PDFInfo
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Abstract
The invention discloses a power grid new energy consumption deviation tracing method and device considering multiple uncertainties, comprising the following steps: (1) Establishing a system optimization scheduling model aiming at maximizing new energy consumption; (2) According to the basic data of the power grid, solving the system optimization scheduling model to obtain new energy theory power discarding and actual power discarding after various uncertainty factors are changed; (3) And comparing the theoretical electric power abandoned by the new energy source with the actual electric power abandoned by the new energy source after various uncertainty factors are changed to obtain the influence of the uncertainty factors on the new energy source consumption of the power grid. The invention can trace multiple uncertainty factors of new energy consumption deviation of the power grid.
Description
Technical Field
The invention relates to the electric power information technology, in particular to a power grid new energy consumption deviation tracing method and device considering multiple uncertainties.
Background
The new energy represented by wind energy and solar energy has certain intermittence, volatility and randomness in the generation mechanism, and the new energy absorbing capacity of the power grid system is further limited. The fluctuation of the new energy source is large, the installed capacity is adversely affected, and after the new energy source enters the power system, the total power generation amount of the traditional power source is continuously changed, so that the adjustment burden of the power system is further increased. In order to ensure the dynamic balance of the power system, a large amount of wind and light discarding phenomena can occur in a new energy large-emission period.
The electricity generation amount of new energy in China is rapidly increased, the installed capacity of wind power and photovoltaic power generation in western provinces is continuously increased, and meanwhile, the phenomena of wind and light abandoning are frequently generated. In recent years, the wind and light discarding phenomenon is relieved to a certain extent, but a large new energy discarding amount still exists. Therefore, the new energy waste cause needs to be analyzed, and a new energy consumption deviation tracing technology considering multiple uncertainties is provided.
Disclosure of Invention
The invention aims to: aiming at the problems existing in the prior art, the invention provides a power grid new energy consumption deviation tracing method and device considering multiple uncertainties.
The technical scheme is as follows: the invention relates to a power grid new energy consumption deviation tracing method considering multiple uncertainties, which comprises the following steps:
(1) Establishing a system optimization scheduling model aiming at maximizing new energy consumption;
(2) According to the basic data of the power grid, solving the system optimization scheduling model to obtain new energy theory power discarding and actual power discarding after various uncertainty factors are changed;
(3) And comparing the theoretical electric power abandoned by the new energy source with the actual electric power abandoned by the new energy source after various uncertainty factors are changed to obtain the influence of the uncertainty factors on the new energy source consumption of the power grid.
Further, the system optimization scheduling model specifically includes:
wherein: f is an objective function, and N is a simulation period number; p (P) wind (t) is the wind power quantity which can be consumed by the system at the moment t; p (P) pv (t) is the photovoltaic electric quantity which can be consumed by the system at the moment t; Δt is the simulation period length;
the constraint conditions include:
a. power balance constraint
Wherein: n is n gen The number of the conventional units in the system; p (P) g (i, t) is the power of the conventional unit i at the time t; p (P) load (t) is the total load of the system at time t; p (P) link (t) is the link outgoing power at time t;
b. rotation reserve constraint
Wherein: p (P) g max (i) The maximum technical output of the conventional unit i; p (P) re The system is used for positive rotation; x (i, t) is a binary variable of the running state of the conventional unit i at the moment t, when X (i, t) is equal to 0, the conventional unit i at the moment t is in a stop state, and when X (i, t) is equal to 1, the conventional unit i at the moment t is in a running state;
c. output constraint of conventional unit
P g min (i)X(i,t)≤P g (i,t)≤P g max (i)X(i,t)
Wherein: p (P) g min (i) The minimum technical output of the conventional unit i;
d. climbing constraint of conventional unit
Wherein: p (P) up (i) And P down (i) Maximum upward and downward climbing rates allowed by the conventional unit i respectively;
e. minimum start-stop time constraint of conventional unit
Wherein: y (i, t) and Z (i, t) are binary variables of the starting and stopping states of the conventional unit i at the moment t respectively, when Y (i, t) is equal to 0, the conventional unit at the moment t is not in the starting state, and when Y (i, t) is equal to 1, the conventional unit at the moment t is in the starting state; z (i, t) is equal to 0, and indicates that the conventional unit is not in shutdown operation at the moment t, and Z (i, t) is equal to 1, and indicates that the conventional unit is in shutdown operation at the moment t; t (T) on And T off Minimum continuous running time and minimum continuous downtime of the conventional unit respectively;
f. logical constraint of normal unit operation, start-up and stop states
g. New energy output constraint
Wherein:and->The maximum theoretical output of wind power and photovoltaic at the time t is respectively.
h. Line tide constraint
P min (m,n)≤P(m,n,t)≤P max (m,n)
Wherein: p (m, n, t) is the active power flowing from node m to node n at time t; p (P) min (m, n) and P max (m, n) are the minimum and maximum active powers allowed to flow on branch mn, respectively;
i. section constraint
Wherein: p (P) m,l The transmission power of the line l in the section of the node m; p (P) m,section,max Maximum transmission power for node m section; k is the total number of lines of the section.
Further, the grid basic data specifically comprises grid frame data, unit data, load data, new energy data and tie line data.
Further, the step (2) specifically includes:
(2-1) solving a system optimization scheduling model in the future according to the basic data of the power grid to obtain the maximum output of new energy before the future and the theoretical power discarding power of the new energy, and simultaneously optimizing the running state of a conventional unit in the future to be used as a unit combination in the future;
and (2-2) acquiring daily actual new energy output and daily actual system load level based on daily unit combination arrangement, changing various uncertainty factors one by one, solving a system optimization scheduling model in the daily to obtain the maximum output of the daily new energy and the actual power discarding power of the new energy after changing various uncertainty factors.
Further, the uncertainty factors include new energy output, load, section constraints, and tie-line plans.
The invention relates to a power grid new energy consumption deviation tracing device considering multiple uncertainties, which comprises:
the model building module is used for building a system optimization scheduling model aiming at maximizing new energy consumption;
the power discarding power calculation module is used for solving the system optimization scheduling model according to the basic data of the power grid to obtain new energy theory power discarding power and actual power discarding power after various uncertainty factors are changed;
the consumption deviation tracing module is used for obtaining the influence of each uncertainty factor on the consumption of new energy of the power grid by comparing the theoretical electric power of the new energy and the actual electric power of the new energy after changing various uncertainty factors.
Further, the system optimization scheduling model specifically includes:
wherein: f is an objective function, and N is a simulation period number; p (P) wind (t) is the wind power quantity which can be consumed by the system at the moment t; p (P) pv (t) is the photovoltaic electric quantity which can be consumed by the system at the moment t; Δt is the simulation period length;
the constraint conditions include:
a. power balance constraint
Wherein: n is n gen The number of the conventional units in the system; p (P) g (i, t) is the time t of the conventional unit iA power; p (P) load (t) is the total load of the system at time t; p (P) link (t) is the link outgoing power at time t;
b. rotation reserve constraint
Wherein: p (P) g max (i) The maximum technical output of the conventional unit i; p (P) re The system is used for positive rotation; x (i, t) is a binary variable of the running state of the conventional unit i at the moment t, when X (i, t) is equal to 0, the conventional unit i at the moment t is in a stop state, and when X (i, t) is equal to 1, the conventional unit i at the moment t is in a running state;
c. output constraint of conventional unit
P g min (i)X(i,t)≤P g (i,t)≤P g max (i)X(i,t)
Wherein: p (P) g min (i) The minimum technical output of the conventional unit i;
d. climbing constraint of conventional unit
Wherein: p (P) up (i) And P down (i) Maximum upward and downward climbing rates allowed by the conventional unit i respectively;
e. minimum start-stop time constraint of conventional unit
Wherein: y (i, t) and Z (i, t) are binary variables of the starting and stopping states of the conventional unit i at the moment t respectively, when Y (i, t) is equal to 0, the conventional unit at the moment t is not in the starting state, and when Y (i, t) is equal to 1, the conventional unit at the moment t is in the starting state; z (i, t) is equal to 0, and indicates that the conventional unit is not in shutdown operation at the moment t, and Z (i, t) is equal to 1, and indicates that the conventional unit is in shutdown operation at the moment t; t (T) on And T off Minimum continuous running time and minimum continuous downtime of the conventional unit respectively;
f. logical constraint of normal unit operation, start-up and stop states
g. New energy output constraint
Wherein:and->The maximum theoretical output of wind power and photovoltaic at the time t is respectively.
h. Line tide constraint
P min (m,n)≤P(m,n,t)≤P max (m,n)
Wherein: p (m, n, t) is the active power flowing from node m to node n at time t; p (P) min (m, n) and P max (m, n) are the minimum and maximum active powers allowed to flow on branch mn, respectively;
i. section constraint
Wherein: p (P) m,l The transmission power of the line l in the section of the node m; p (P) m,section,max Maximum transmission power for node m section; k is the total number of lines of the section.
Further, the grid basic data specifically comprises grid frame data, unit data, load data, new energy data and tie line data.
Further, the electric power discarding calculation module specifically includes:
the first calculation unit is used for solving the system optimization scheduling model in the future according to the power grid basic data to obtain the maximum output of the new energy before the future and the theoretical power discarding power of the new energy, and optimizing the running state of the conventional unit in the future and combining the conventional unit in the future as the unit in the future;
the second calculation unit is used for acquiring the daily actual new energy output and the daily actual system load level based on the daily unit combination arrangement, changing various uncertainty factors one by one, solving the system optimization scheduling model in the daily, and obtaining the maximum output of the daily new energy and the actual power of the new energy after changing various uncertainty factors.
Further, the uncertainty factors include new energy output, load, section constraints, and tie-line plans.
Compared with the prior art, the invention has the beneficial effects that: various uncertainty factors in the day are fully considered, targeted research is carried out on the influence of the uncertainty factors in the day, and a new energy consumption deviation tracing technology considering multiple uncertainties is provided; the feasibility and the effectiveness of the method are verified by adopting actual data, and the method has practical significance and application value.
Drawings
FIG. 1 is a general flow chart of a power grid new energy consumption deviation tracing method considering multiple uncertainties;
FIG. 2 is a network topology employed by the method of the present invention;
FIG. 3 is a graph of the day-ahead forecast and the day-ahead actual for the total load of the system electricity;
FIG. 4 is a graph of the day-ahead predictions and the day-ahead actual of wind and photovoltaic outputs;
FIG. 5 is a pre-day plan and intra-day actual curve of link outgoing power;
FIG. 6 is a graph of new energy source and various conventional unit outputs;
FIG. 7 shows the daily new energy and the output of each conventional unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The embodiment provides a power grid new energy consumption deviation tracing method considering multiple uncertainties, which is shown in fig. 1 and comprises the following steps:
(1) And establishing a system optimization scheduling model aiming at maximizing new energy consumption.
The system optimization scheduling model specifically comprises the following steps:
wherein: f is an objective function, and N is a simulation period number; p (P) wind (t) is the wind power quantity which can be consumed by the system at the moment t; p (P) pv (t) is the photovoltaic electric quantity which can be consumed by the system at the moment t; Δt is the simulation period length;
the constraint conditions include:
a. power balance constraint
Wherein: n is n gen The number of the conventional units in the system; p (P) g (i, t) is the power of the conventional unit i at the time t; p (P) load (t) is the total load of the system at time t; p (P) link (t) is the link outgoing power at time t;
b. rotation reserve constraint
Wherein: p (P) g max (i) Is normalMaximum technical output of the gauge set i; p (P) re The system is used for positive rotation; x (i, t) is a binary variable of the running state of the conventional unit i at the moment t, when X (i, t) is equal to 0, the conventional unit i at the moment t is in a stop state, and when X (i, t) is equal to 1, the conventional unit i at the moment t is in a running state;
c. output constraint of conventional unit
P g min (i)X(i,t)≤P g (i,t)≤P g max (i)X(i,t)
Wherein: p (P) g min (i) The minimum technical output of the conventional unit i;
d. climbing constraint of conventional unit
Wherein: p (P) up (i) And P down (i) Maximum upward and downward climbing rates allowed by the conventional unit i respectively;
e. minimum start-stop time constraint of conventional unit
Wherein: y (i, t) and Z (i, t) are binary variables of the starting and stopping states of the conventional unit i at the moment t respectively, when Y (i, t) is equal to 0, the conventional unit at the moment t is not in the starting state, and when Y (i, t) is equal to 1, the conventional unit at the moment t is in the starting state; z (i, t) is equal to 0, and indicates that the conventional unit is not in shutdown operation at the moment t, and Z (i, t) is equal to 1, and indicates that the conventional unit is in shutdown operation at the moment t; t (T) on And T off Minimum continuous running time and minimum continuous downtime of the conventional unit respectively;
f. logical constraint of normal unit operation, start-up and stop states
g. New energy output constraint
Wherein:and->The maximum theoretical output of wind power and photovoltaic at the time t is respectively.
h. Line tide constraint
P min (m,n)≤P(m,n,t)≤P max (m,n)
Wherein: p (m, n, t) is the active power flowing from node m to node n at time t; p (P) min (m, n) and P max (m, n) are the minimum and maximum active powers allowed to flow on branch mn, respectively;
i. section constraint
Wherein: p (P) m,l The transmission power of the line l in the section of the node m; p (P) m,section,max Maximum transmission power for node m section; k is the total number of lines of the section.
(2) And according to the basic data of the power grid, solving the system optimization scheduling model to obtain the new energy theory power discarding power and the actual power discarding power after various uncertainty factors are changed.
The power grid basic data specifically comprise grid frame data, unit data, load data, new energy data and tie line data.
In specific implementation, the method specifically comprises the following steps: (2-1) solving a system optimization scheduling model in the future according to the basic data of the power grid to obtain the maximum output of new energy before the future and the theoretical power discarding power of the new energy, and simultaneously optimizing the running state of a conventional unit in the future to be used as a unit combination in the future; and (2-2) acquiring daily actual new energy output and daily actual system load level based on daily unit combination arrangement, changing various uncertainty factors one by one, solving a system optimization scheduling model in the daily to obtain the maximum output of the daily new energy and the actual power discarding power of the new energy after changing various uncertainty factors.
Wherein the uncertainty factors include new energy output, load, section constraint and tie line planning.
(3) And comparing the theoretical electric power abandoned by the new energy source with the actual electric power abandoned by the new energy source after various uncertainty factors are changed to obtain the influence of the uncertainty factors on the new energy source consumption of the power grid.
Example two
The embodiment provides a power grid new energy consumption deviation tracing device considering multiple uncertainties, which comprises:
the model building module is used for building a system optimization scheduling model aiming at maximizing new energy consumption;
the power discarding power calculation module is used for solving the system optimization scheduling model according to the basic data of the power grid to obtain new energy theory power discarding power and actual power discarding power after various uncertainty factors are changed;
the consumption deviation tracing module is used for obtaining the influence of each uncertainty factor on the consumption of new energy of the power grid by comparing the theoretical electric power of the new energy and the actual electric power of the new energy after changing various uncertainty factors.
The system optimization scheduling model specifically comprises the following steps:
wherein: f is an objective function, and N is a simulation period number; p (P) wind (t) is the wind power quantity which can be consumed by the system at the moment t; p (P) pv (t) is the photovoltaic electric quantity which can be consumed by the system at the moment t; Δt is the simulation period length;
the constraint conditions include:
a. power balance constraint
Wherein: n is n gen The number of the conventional units in the system; p (P) g (i, t) is the power of the conventional unit i at the time t; p (P) load (t) is the total load of the system at time t; p (P) link (t) is the link outgoing power at time t;
b. rotation reserve constraint
Wherein: p (P) g max (i) The maximum technical output of the conventional unit i; p (P) re The system is used for positive rotation; x (i, t) is a binary variable of the running state of the conventional unit i at the moment t, when X (i, t) is equal to 0, the conventional unit i at the moment t is in a stop state, and when X (i, t) is equal to 1, the conventional unit i at the moment t is in a running state;
c. output constraint of conventional unit
P g min (i)X(i,t)≤P g (i,t)≤P g max (i)X(i,t)
Wherein: p (P) g min (i) The minimum technical output of the conventional unit i;
d. climbing constraint of conventional unit
Wherein: p (P) up (i) And P down (i) Maximum upward and downward climbing rates allowed by the conventional unit i respectively;
e. minimum start-stop time constraint of conventional unit
Wherein: y (i, t) and Z (i, t) are respectively the time t of the conventional unit iThe binary variables of the starting and stopping states are that the conventional unit is not in the starting state at the moment t when Y (i, t) is equal to 0, and the conventional unit is in the starting state at the moment t when Y (i, t) is equal to 1; z (i, t) is equal to 0, and indicates that the conventional unit is not in shutdown operation at the moment t, and Z (i, t) is equal to 1, and indicates that the conventional unit is in shutdown operation at the moment t; t (T) on And T off Minimum continuous running time and minimum continuous downtime of the conventional unit respectively;
f. logical constraint of normal unit operation, start-up and stop states
g. New energy output constraint
Wherein:and->The maximum theoretical output of wind power and photovoltaic at the time t is respectively.
h. Line tide constraint
P min (m,n)≤P(m,n,t)≤P max (m,n)
Wherein: p (m, n, t) is the active power flowing from node m to node n at time t; p (P) min (m, n) and P max (m, n) are the minimum and maximum active powers allowed to flow on branch mn, respectively;
i. section constraint
Wherein: p (P) m,l The transmission power of the line l in the section of the node m; p (P) m,section,max Broken for node mMaximum transmission power of the face; k is the total number of lines of the section.
The power-saving power calculation module specifically comprises:
the first calculation unit is used for solving the system optimization scheduling model in the future according to the power grid basic data to obtain the maximum output of the new energy before the future and the theoretical power discarding power of the new energy, and optimizing the running state of the conventional unit in the future and combining the conventional unit in the future as the unit in the future; the power grid basic data specifically comprise grid frame data, unit data, load data, new energy data and tie line data.
The second calculation unit is used for acquiring the daily actual new energy output and the daily actual system load level based on the daily unit combination arrangement, changing various uncertainty factors one by one, solving the system optimization scheduling model in the daily, and obtaining the maximum output of the daily new energy and the actual power of the new energy after changing various uncertainty factors. The uncertainty factors include new energy output, load, section constraints and tie line plans.
The device provided by the embodiment of the invention can be used for executing the method provided by the first embodiment of the invention, and has the corresponding functions and beneficial effects of executing the method.
It should be noted that, in the embodiment of the determining apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
The embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. It will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course, may be implemented solely by hardware, as long as the function or function is achieved.
In the basic data of the power grid, the network frame data mainly comprises network topology, line capacity and reactance among nodes, wherein the network topology is shown in figure 2, and the line capacity and reactance among nodes are shown in table 1; the unit data mainly comprise minimum starting and stopping time, climbing speed and maximum and minimum technical output of the unit, the operation parameters of the conventional units are shown in table 2, and each conventional unit is respectively positioned at nodes 1, 2 and 6 in fig. 2; the load data mainly comprises a day-ahead prediction and an actual day-in curve of the total load of the system electricity consumption, which are shown in fig. 3, wherein the electricity consumption loads are respectively positioned at nodes 3, 4 and 5 in fig. 2 according to the following steps of 3:4.5:2.5 ratio distribution; the new energy data mainly comprise the future forecast and the intra-day actual curves of wind power and photovoltaic output, as shown in fig. 4, and the wind power station and the photovoltaic power station are respectively positioned at a node 4 and a node 3 in fig. 2; the link data is mainly provided with a daily schedule of link outgoing power and a daily actual curve as shown in fig. 5.
TABLE 1 line capacity and reactance between nodes
TABLE 2 conventional unit operating parameters
Taking t=96, i.e. dividing the whole day into 96 time periods of 1 time period every 15 min. Solving the theoretical output of each conventional unit before the day is shown in fig. 6, and determining the unit combination of the operation of the conventional units 1 and 2 in all days and the shutdown of the conventional unit 3 in all days. The dissipatable amount of the new energy theory is basically consistent with the predicted power, and the system only generates waste electricity at the moment of 6:15, and the waste electricity power is 82.046MW. Based on the unit combination of the normal units 1 and 2 running all the day and the normal unit 3 stopping all the day, the actual dissipatable amount of the new energy is solved in the day through a system optimization scheduling model for maximizing the new energy dissipation, the electric power is abandoned at each moment, the total electric power is 1037.92MW, and the energy consumption is improved.
Table 3 Power distribution at various times of day
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Under the condition of the day ahead, various uncertainty factors (new energy output, load, section constraint and tie line plan) are changed one by one, and the new energy consumption deviation is traced.
Firstly, only the output of new energy is changed, the grid data, the unit data, the load data and the connecting line data are unchanged, and the electric power is abandoned at each moment as shown in table 4.
Table 4 power distribution at various moments
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Various data of the system in the generation and power rejection time period are shown in table 5, and the new energy output is predicted to be smaller in the time period, so that the system generates power rejection 814.937MW.
Table 5, 11:30 to 12:15 system data
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And secondly, only changing load data, net rack data, unit data, new energy output and tie line data, and discarding electric power at each moment as shown in table 6.
TABLE 6 Power distribution at various moments
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The data of the system in the generated power-off time period is shown in table 7, and the predicted load is larger in the time period, so that the system generates power-off 24.494MW.
Table 7, 22:15 to 23:00 system data items
Then only the grid data, the load data, the unit data, the new energy output and the tie line data are changed, and the electric power is discarded at each moment as shown in table 8.
Table 8 power distribution at various moments
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The data of the system in the generated power-discarding time period are shown in table 9, and the section power flow is reduced in the time period, so that the system generates 198.166MW power-discarding.
Table 9, data of each item of the systems from 45:45 to 4:30
And finally, only the data of the connecting lines, the data of the net rack, the data of the load, the data of the unit and the output of new energy are changed, and the system does not generate waste electricity. Since the system generates 82.046MW power-off at the moment of 6:15, the transmission power of the connecting line is increased in the time period of 6:15-7:00, and various data of the system are shown in table 10.
Table 10, 6:15 to 7:00 system data items
In summary, the smaller new energy output prediction results in more 814.937MW power rejection of the system, the larger load prediction results in more 24.494MW power rejection of the system, the larger section constraint results in more 198.166MW power rejection of the system, and the increased power sent by the connecting line results in less 82.046MW power rejection of the system. Thereby knowing the influence of uncertainty factors on new energy consumption.
Claims (10)
1. A power grid new energy consumption deviation tracing method considering multiple uncertainties is characterized by comprising the following steps:
(1) Establishing a system optimization scheduling model aiming at maximizing new energy consumption;
(2) According to the basic data of the power grid, solving the system optimization scheduling model to obtain new energy theory power discarding and actual power discarding after various uncertainty factors are changed;
(3) And comparing the theoretical electric power abandoned by the new energy source with the actual electric power abandoned by the new energy source after various uncertainty factors are changed to obtain the influence of the uncertainty factors on the new energy source consumption of the power grid.
2. The method for tracing new energy consumption deviation of a power grid taking multiple uncertainties into consideration according to claim 1, wherein the system optimization scheduling model is specifically:
wherein: f is an objective function, and N is a simulation period number; p (P) wind (t) is the wind power quantity which can be consumed by the system at the moment t; p (P) pv (t) is the photovoltaic electric quantity which can be consumed by the system at the moment t; Δt is the simulation period length;
the constraint conditions include:
a. power balance constraint
Wherein: n is n gen The number of the conventional units in the system; p (P) g (i, t) is the power of the conventional unit i at the time t; p (P) load (t) is the total load of the system at time t; p (P) link (t) is the link outgoing power at time t;
b. rotation reserve constraint
Wherein: p (P) gmax (i) The maximum technical output of the conventional unit i; p (P) re The system is used for positive rotation; x (i, t) is a binary variable of the running state of the conventional unit i at the moment t, when X (i, t) is equal to 0, the conventional unit i at the moment t is in a stop state, and when X (i, t) is equal to 1, the conventional unit i at the moment t is in a running state;
c. output constraint of conventional unit
P gmin (i)X(i,t)≤P g (i,t)≤P gmax (i)X(i,t)
Wherein: p (P) gmin (i) The minimum technical output of the conventional unit i;
d. climbing constraint of conventional unit
Wherein: p (P) up (i) And P down (i) Maximum upward and downward climbing rates allowed by the conventional unit i respectively;
e. minimum start-stop time constraint of conventional unit
Wherein: y (i, t) and Z (i, t) are binary variables of the starting and stopping states of the conventional unit i at the moment t respectively, when Y (i, t) is equal to 0, the conventional unit at the moment t is not in the starting state, and when Y (i, t) is equal to 1, the conventional unit at the moment t is in the starting state; z (i, t) is equal to 0, and indicates that the conventional unit is not in shutdown operation at the moment t, and Z (i, t) is equal to 1, and indicates that the conventional unit is in shutdown operation at the moment t; t (T) on And T off Minimum continuous running time and minimum continuous downtime of the conventional unit respectively;
f. logical constraint of normal unit operation, start-up and stop states
g. New energy output constraint
Wherein:and->The maximum theoretical output of wind power and photovoltaic at the time t is respectively.
h. Line tide constraint
P min (m,n)≤P(m,n,t)≤P max (m,n)
Wherein: p (m, n, t) is the active power flowing from node m to node n at time t; p (P) min (m, n) and P max (m, n) are the minimum and maximum active powers allowed to flow on branch mn, respectively;
i. section constraint
Wherein: p (P) m,l The transmission power of the line l in the section of the node m; p (P) m,section,max Maximum transmission power for node m section; k is the total number of lines of the section.
3. The power grid new energy consumption deviation tracing method considering multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps of: the power grid basic data specifically comprise grid frame data, unit data, load data, new energy data and tie line data.
4. The power grid new energy consumption deviation tracing method considering multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps of: the step (2) specifically comprises:
(2-1) solving a system optimization scheduling model in the future according to the basic data of the power grid to obtain the maximum output of new energy before the future and the theoretical power discarding power of the new energy, and simultaneously optimizing the running state of a conventional unit in the future to be used as a unit combination in the future;
and (2-2) acquiring daily actual new energy output and daily actual system load level based on daily unit combination arrangement, changing various uncertainty factors one by one, solving a system optimization scheduling model in the daily to obtain the maximum output of the daily new energy and the actual power discarding power of the new energy after changing various uncertainty factors.
5. The power grid new energy consumption deviation tracing method considering multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps of: the uncertainty factors include new energy output, load, section constraints and tie line plans.
6. The utility model provides a take into account power grid new energy consumption deviation traceability device of multiple uncertainty which characterized in that includes:
the model building module is used for building a system optimization scheduling model aiming at maximizing new energy consumption;
the power discarding power calculation module is used for solving the system optimization scheduling model according to the basic data of the power grid to obtain new energy theory power discarding power and actual power discarding power after various uncertainty factors are changed;
the consumption deviation tracing module is used for obtaining the influence of each uncertainty factor on the consumption of new energy of the power grid by comparing the theoretical electric power of the new energy and the actual electric power of the new energy after changing various uncertainty factors.
7. The power grid new energy consumption deviation tracing device considering multiple uncertainties according to claim 6, wherein the system optimization scheduling model is specifically:
wherein: f is an objective function, and N is a simulation period number; p (P) wind (t) is the wind power quantity which can be consumed by the system at the moment t; p (P) pv (t) is the photovoltaic electric quantity which can be consumed by the system at the moment t; Δt is the simulation period length;
the constraint conditions include:
a. power balance constraint
Wherein: n is n gen The number of the conventional units in the system; p (P) g (i, t) is the power of the conventional unit i at the time t; p (P) load (t) is the total negative of the system at time tA lotus; p (P) link (t) is the link outgoing power at time t;
b. rotation reserve constraint
Wherein: p (P) gmax (i) The maximum technical output of the conventional unit i; p (P) re The system is used for positive rotation; x (i, t) is a binary variable of the running state of the conventional unit i at the moment t, when X (i, t) is equal to 0, the conventional unit i at the moment t is in a stop state, and when X (i, t) is equal to 1, the conventional unit i at the moment t is in a running state;
c. output constraint of conventional unit
P gmin (i)X(i,t)≤P g (i,t)≤P gmax (i)X(i,t)
Wherein: p (P) gmin (i) The minimum technical output of the conventional unit i;
d. climbing constraint of conventional unit
Wherein: p (P) up (i) And P down (i) Maximum upward and downward climbing rates allowed by the conventional unit i respectively;
e. minimum start-stop time constraint of conventional unit
Wherein: y (i, t) and Z (i, t) are binary variables of the starting and stopping states of the conventional unit i at the moment t respectively, when Y (i, t) is equal to 0, the conventional unit at the moment t is not in the starting state, and when Y (i, t) is equal to 1, the conventional unit at the moment t is in the starting state; z (i, t) is equal to 0, and indicates that the conventional unit is not in shutdown operation at the moment t, and Z (i, t) is equal to 1, and indicates that the conventional unit is in shutdown operation at the moment t; t (T) on And T off Minimum continuous running time and minimum continuous downtime of the conventional unit respectively;
f. logical constraint of normal unit operation, start-up and stop states
g. New energy output constraint
Wherein:and->The maximum theoretical output of wind power and photovoltaic at the time t is respectively.
h. Line tide constraint
P min (m,n)≤P(m,n,t)≤P max (m,n)
Wherein: p (m, n, t) is the active power flowing from node m to node n at time t; p (P) min (m, n) and P max (m, n) are the minimum and maximum active powers allowed to flow on branch mn, respectively;
i. section constraint
Wherein: p (P) m,l The transmission power of the line l in the section of the node m; p (P) m,section,max Maximum transmission power for node m section; k is the total number of lines of the section.
8. The power grid new energy consumption deviation tracing device considering multiple uncertainties according to claim 6, wherein: the power grid basic data specifically comprise grid frame data, unit data, load data, new energy data and tie line data.
9. The power grid new energy consumption deviation tracing device considering multiple uncertainties according to claim 6, wherein: the power-saving power calculation module specifically comprises:
the first calculation unit is used for solving the system optimization scheduling model in the future according to the power grid basic data to obtain the maximum output of the new energy before the future and the theoretical power discarding power of the new energy, and optimizing the running state of the conventional unit in the future and combining the conventional unit in the future as the unit in the future;
the second calculation unit is used for acquiring the daily actual new energy output and the daily actual system load level based on the daily unit combination arrangement, changing various uncertainty factors one by one, solving the system optimization scheduling model in the daily, and obtaining the maximum output of the daily new energy and the actual power of the new energy after changing various uncertainty factors.
10. The power grid new energy consumption deviation tracing device considering multiple uncertainties according to claim 1, wherein: the uncertainty factors include new energy output, load, section constraints and tie line plans.
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