CN115146870A - Method, system, equipment and medium for generating day-to-day power supply plan of isolated island - Google Patents
Method, system, equipment and medium for generating day-to-day power supply plan of isolated island Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a medium for generating an isolated island in-day power supply plan, wherein the method comprises the steps of solving a day-ahead target power supply model to obtain a day-ahead target strategy; performing rolling optimization on the initial power supply model in each day in a preset day power supply scene according to a day-ahead target strategy and a multivariable control strategy to obtain a plurality of day target power supply models; and solving the target power supply model in each day, and determining a power supply plan in each day according to the solving result. The technical problems that in the prior art, research is not carried out on an isolated island power generation system, and the island power generation system is greatly influenced by uncertainty of new energy output and fluctuation of load demand, so that the island clean energy consumption capability is poor are solved. The invention fills the gap that the isolated island power generation system participates in the optimization operation of the power supply plan in the day, and the uncertainty of the output of new energy and the fluctuation of load demand are considered, so that the clean energy consumption capability of the island is further improved.
Description
Technical Field
The invention relates to the technical field of power supply, in particular to a method, a system, equipment and a medium for generating an isolated island power supply plan in a day.
Background
With the commercial development and utilization of offshore islands, more and more passenger ships, container ships and ocean engineering ships participate in the business related to ocean development, and the economic development and the inter-island trade of ocean islands are promoted while the daily life of residents in the islands is guaranteed. However, relevant researches show that in the annual operation scheduling period, the ships are in a neutral idle state in a part of time periods, such as fishing ships in a fishing off period, passenger ships in a slack season, and the like, so that the utilization rate of the ships is not high, and the capital recovery period is long. If clean energy such as wave energy, wind energy, solar energy and the like enriched in the island power generation system can be transported back to the continent in a mode of carrying a storage battery by the ship and participate in a continent power supply plan by means of the transport capacity of the idle ships, the island clean energy absorption capacity can be further improved while extra economic benefits are brought to the island power generation system.
The prior art focuses on an electric power supply model under a large power grid or an active power distribution network, and no research is carried out on an isolated island power generation system which is not connected with the power grid or is in weak connection. And the island clean energy consumption capability is poor due to the fact that the isolated island power generation system is greatly influenced by uncertainty of new energy output and fluctuation of load demand.
Disclosure of Invention
The invention provides a method, a system, equipment and a medium for generating an isolated island power supply plan in the day, which solve the problem that the prior art focuses on a power supply model under a large power grid or an active power distribution network, and no research is carried out on an isolated island power generation system which is not connected with a grid or is in weak connection. And the isolated island power generation system is greatly influenced by the uncertainty of new energy output and the fluctuation of load demand, so that the technical problem of poor island clean energy consumption capability is caused.
The invention provides a method for generating an island power supply day-to-day plan, which comprises the following steps:
responding to a received power supply request, and acquiring historical power supply data corresponding to the power supply request;
constructing a day-ahead target power supply model according to the historical power supply data, and performing scene simulation to obtain a plurality of in-day power supply scenes;
solving the day-ahead target power supply model to obtain a day-ahead target strategy;
performing rolling optimization on the initial power supply model in the preset day in each day power supply scene according to the day-ahead target strategy and the multivariable control strategy to obtain a plurality of day target power supply models;
and solving the power supply model of each day target, and determining a day power supply plan according to a solving result.
Optionally, the historical data includes photovoltaic power supply data, fan power supply data, and wave energy power supply data, and before the step of constructing a day-ahead target power supply model according to the historical power supply data, and performing scene simulation to obtain a plurality of day-ahead power supply scenes, the method further includes:
acquiring a distributed photovoltaic output characteristic model, a small fan output characteristic model and a wave energy power generation device output model;
inputting the photovoltaic power supply data into the distributed photovoltaic output characteristic model to obtain photovoltaic output power;
inputting the fan power supply data into the small fan output characteristic model to obtain fan output power;
and inputting the wave energy power supply data into the wave energy power generation device output model to obtain wave energy output power.
Optionally, the historical power supply data further includes load power and power storage vessel operation data, the step of constructing a day-ahead target power supply model according to the historical power supply data, and performing scene simulation to obtain a plurality of day-inside power supply scenes includes:
constructing an upper layer day-ahead power supply model and a lower layer day-ahead power supply model according to the photovoltaic power supply data, the fan power supply data, the wave energy power supply data, the load power and the electricity storage ship operation data;
performing single-layer model conversion on the upper layer day-ahead power supply model and the lower layer day-ahead power supply model to obtain a day-ahead target power supply model;
and (3) carrying out scene simulation by adopting a Monte Carlo simulation method and combining a plurality of preset random variables to obtain a plurality of in-day power supply scenes.
Optionally, the step of constructing an upper layer day-ahead power supply model and a lower layer day-ahead power supply model according to the photovoltaic power supply data, the fan power supply data, the wave energy power supply data, the load power and the power storage ship operation data includes:
respectively constructing a space-time transfer constraint and an energy conservation constraint according to the photovoltaic power supply data, the fan power supply data, the wave energy power supply data, the load power and the electricity storage ship operation data, and combining to obtain a first constraint condition;
constructing an upper layer day-ahead power supply model by adopting the first constraint condition and a preset first objective function;
constructing a plurality of updating constraint conditions, and combining to obtain a second constraint condition;
and constructing a lower layer day-ahead power supply model by adopting the second constraint condition and a preset second objective function.
Optionally, the step of performing single-layer model conversion on the upper layer day-ahead power supply model and the lower layer day-ahead power supply model to obtain a day-ahead target power supply model includes:
performing partial derivation on variables corresponding to the lower layer day-ahead power supply model to obtain a Couen-Tack balance constraint condition;
inputting Boolean variables into the Couen-Tack direction and the complementary constraint condition corresponding to the lower layer day-ahead power supply model, and converting the complementary constraint condition by applying a penalty factor method to obtain a cut plane complementary constraint condition;
adding the Couen-Tak balance constraint condition and the secant plane complementary constraint condition to the second constraint condition to obtain a third constraint condition;
transforming the first objective function according to the necessary condition of a preset optimal solution and the Couen-Tack balance constraint condition to obtain a third objective function;
and constructing a day-ahead target power supply model according to the third objective function and the third constraint condition.
Optionally, the step of performing scene simulation by using a monte carlo simulation method in combination with a plurality of preset random variables to obtain a plurality of power supply scenes in a day includes:
classifying random numbers corresponding to a plurality of preset random variables into each normal distribution interval according to a preset prediction deviation; the random variables comprise predicted new energy output power, predicted load demand and predicted output demand;
generating corresponding initial scenes by adopting the predicted new energy output power, the predicted load demand and the predicted output demand corresponding to each normal distribution interval;
extracting a target threshold value intermediate scene from the initial scene where the predicted new energy output power, the predicted load demand and the predicted output demand are located by adopting Monte Carlo sampling;
normalizing the distribution probability corresponding to each normal distribution interval to obtain the cumulative normalized probability corresponding to each interval;
judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene;
if so, setting the cumulative normalized probability corresponding to the initial scene to be less than or equal to the distribution probability corresponding to the interval where the random variable is located, and setting the binary parameter vector corresponding to the initial scene as a first threshold;
if not, the binary parameter vector corresponding to the initial scene corresponding to the unselected interval is a second threshold;
and skipping to execute the step of judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene, and generating a plurality of day power supply scenes.
Optionally, the multivariable control strategy comprises optimizing a time window; the step of performing rolling optimization on the initial power supply model in the preset day in each day power supply scene according to the day-ahead target strategy and the multivariable control strategy to obtain a plurality of day target power supply models comprises the following steps:
constructing a fourth objective function according to a day-ahead scheduling plan and day-internal operation data corresponding to the day-ahead objective strategy, and making a scheduling plan in an optimized time scale period corresponding to the optimized time window;
superposing a day-ahead scheduling plan constraint condition corresponding to the day-ahead target strategy on the first constraint condition and the third constraint condition to obtain a fourth constraint condition;
constructing an intra-day initial power supply model according to the fourth objective function and the fourth constraint condition;
and executing a rolling optimization operation instruction on the in-day initial power supply model in the in-day power supply scene at an average time interval according to the scheduling plan to obtain a plurality of in-day target power supply models.
A second aspect of the present invention provides a system for generating an intra-day power supply plan of an island, the system comprising:
the historical power supply data module is used for responding to the received power supply request and acquiring historical power supply data corresponding to the power supply request;
the in-day power supply scene module is used for constructing a day-ahead target power supply model according to the historical power supply data and carrying out scene simulation to obtain a plurality of in-day power supply scenes;
the day-ahead target strategy module is used for solving the day-ahead target power supply model to obtain a day-ahead target strategy;
the intra-day target power supply module is used for performing rolling optimization on a preset intra-day initial power supply model in each intra-day power supply scene according to the day-ahead target strategy and the multivariable control strategy to obtain a plurality of intra-day target power supply models;
and the in-day power supply plan module is used for solving the in-day target power supply models and determining an in-day power supply plan according to the solution result.
A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the island-in-sea-island power supply plan generating method according to any one of the above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements the island-in-the-sea daily power supply plan generating method according to any one of the above.
According to the technical scheme, the invention has the following advantages:
the method comprises the steps of responding to a received power supply request, analyzing the power supply request to obtain historical power supply data carried by the power supply request, constructing a day-ahead target power supply model according to the historical power supply data, carrying out scene simulation to obtain a plurality of day-ahead power supply scenes, solving the day-ahead target power supply model by using a solver, obtaining a day-ahead target strategy according to a solving result, carrying out rolling optimization on a preset day-ahead initial power supply model in each day-ahead power supply scene according to the day-ahead target strategy and a multivariable control strategy to obtain a plurality of day-ahead target models, solving each day-ahead target power supply model, and determining a day-ahead power supply plan according to a solving result. The problem that the existing technology focuses on an electric power supply model under a large power grid or an active power distribution network is solved, and no research is carried out on an isolated island power generation system which is not connected with the power grid or is in weak connection. And the isolated island power generation system is greatly influenced by the uncertainty of new energy output and the fluctuation of load demand, so that the technical problem of poor absorption capability of island clean energy is caused. The invention fills the gap that the isolated island power generation system participates in the optimization operation of the power supply plan in the day, further provides a multi-time scale coordination optimization strategy based on model prediction control in consideration of the uncertainty of new energy output and the fluctuation of load demand, corrects the optimization strategy formulated at the previous stage, realizes the maximization of the power supply of the isolated island power generation system, and further improves the clean energy consumption capability of the island.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a flowchart illustrating steps of a method for generating a day-to-day power supply plan of an island according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a method for generating an intra-day power supply plan of an isolated island according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of basic operating parameters of an electric storage vessel according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a scheduling plan for optimizing a time window at an intra-day phase according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a typical island isolated power generation system provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of node marginal electricity prices of island and continental load areas according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an intra-day optimized operation strategy of the power storage ship according to the embodiment of the invention;
fig. 8 is a block diagram of a system for generating an isolated island power supply plan in the day according to a third embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a system, equipment and a medium for generating an isolated island power supply plan in the day, which are used for solving the problem that the existing technology focuses on a power supply model under a large power grid or an active power distribution network, and no research is carried out on an isolated island power generation system which is not connected with a grid or in a weak connection mode. And the isolated island power generation system is greatly influenced by the uncertainty of new energy output and the fluctuation of load demand, so that the technical problem of poor absorption capability of island clean energy is caused.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for generating an intra-day power supply plan of an island according to an embodiment of the present invention.
The invention provides a method for generating a day-to-day power supply plan of an isolated island, which comprises the following steps of:
The power supply request refers to a request for sending out the power resources of the island power generation system to the load center area via the idle capacity resources existing in the isolated island;
the historical power supply data refers to power resource related data of the conventional island power generation system, and comprises photovoltaic power supply data, fan power supply data, wave energy power supply data, load power, power storage ship operation data and the like.
In a specific embodiment, the received power supply request is responded, and the power supply request is analyzed to obtain data carried by the power supply request, wherein the data comprises photovoltaic power supply data, fan power supply data, wave energy power supply data, load power, power storage ship operation data and the like.
And 102, constructing a day-ahead target power supply model according to historical power supply data, and performing scene simulation to obtain a plurality of in-day power supply scenes.
The day-ahead power supply model is a single-layer mathematical model which is converted from an upper layer day-ahead power supply model and a lower layer day-ahead power supply model and contains KKT (optimality condition), and the day-ahead power supply model comprises a third objective function and a third constraint condition;
the scene simulation refers to the simulation of the predicted new energy output, the predicted load demand and the predicted output power based on a multi-scene optimization method;
the in-day power supply scenario refers to a scenario of predicting a power supply plan in a recent period of time.
In the embodiment of the invention, historical power supply data is analyzed to obtain past power supply data and predicted power supply data, a day-ahead power supply simulation is constructed, and scene simulation is carried out on the day-ahead predicted power supply data by the past power supply data and the predicted power supply data, so that a plurality of day-ahead power supply scenes can be obtained.
And 103, solving the day-ahead target power supply model to obtain a day-ahead target strategy.
It should be noted that the day-ahead target strategy refers to a day-ahead optimal power supply scheme or plan.
In the embodiment of the invention, a solution method is used for solving the day-ahead target model so as to obtain the day-ahead target strategy.
And step 104, performing rolling optimization on the initial power supply model in the preset day in each day power supply scene according to a day-ahead target strategy and a multivariable control strategy to obtain a plurality of day target power supply models.
It should be noted that the multivariable control strategy refers to a control method based on prediction of a controlled object;
the intra-day initial power supply model refers to a power supply model for predicting new energy power, load demand and output power in a certain period of time in the near future;
the in-day target power supply model refers to a power supply model of new energy power, load demand and output power which are optimal in a recent period of time.
In the embodiment of the invention, according to the optimal power supply plan in the day ahead and the control method for predicting the prediction object, the rolling optimization is respectively carried out on the initial power supply model in the preset day in each day power supply scene, and the rolling optimization is continuously carried out until a plurality of day target power supply models are obtained.
And 105, solving the target power supply model in each day, and determining a power supply plan in each day according to the solving result.
It should be noted that the in-day power supply plan refers to a power supply plan in a certain period of time in the near future.
In the embodiment of the invention, the solution method is used for solving the power supply model of the target in each day, so that a power supply plan in each day is obtained through the solution result, and people can conveniently supply power by means of the power supply plan in each day.
The method comprises the steps of responding to a received power supply request, analyzing the power supply request to obtain historical power supply data carried by the power supply request, constructing a day-ahead target power supply model according to the historical power supply data, carrying out scene simulation to obtain a plurality of day-ahead power supply scenes, solving the day-ahead target power supply model by using a solver, obtaining a day-ahead target strategy according to a solving result, carrying out rolling optimization on a preset day-ahead initial power supply model in each day-ahead power supply scene according to the day-ahead target strategy and a multivariable control strategy to obtain a plurality of day-ahead target models, solving each day-ahead target power supply model, and determining a day-ahead power supply plan according to a solving result. The problem that the existing technology focuses on an electric power supply model under a large power grid or an active power distribution network is solved, and no research is carried out on an isolated island power generation system which is not connected with a grid or in a weak connection mode. And the isolated island power generation system is greatly influenced by the uncertainty of new energy output and the fluctuation of load demand, so that the technical problem of poor absorption capability of island clean energy is caused. The invention fills the gap that the isolated island power generation system participates in the optimization operation of the power supply plan in the day, further provides a multi-time scale coordination optimization strategy based on model prediction control in consideration of the uncertainty of new energy output and the fluctuation of load demand, corrects the optimization strategy formulated at the previous stage, realizes the maximization of the power supply of the isolated island power generation system, and further improves the clean energy consumption capability of the island.
Referring to fig. 2-7, fig. 2 is a flowchart illustrating a method for generating an intra-day power supply plan of an island according to an embodiment of the present invention.
The invention provides a method for generating an isolated island power supply plan in the day, which comprises the following steps:
before executing step 201, the following steps S11-S14 are also included:
s11, acquiring a distributed photovoltaic output characteristic model, a small fan output characteristic model and a wave energy power generation device output model;
s12, inputting photovoltaic power supply data into a distributed photovoltaic output characteristic model to obtain photovoltaic output power;
s13, inputting power supply data of the fan into a small fan output characteristic model to obtain fan output power;
and S14, inputting wave energy power supply data into a wave energy power generation device output model to obtain wave energy output power.
It should be noted that the photovoltaic power supply data refers to relevant data generated in the photovoltaic power supply process, and includes data such as solar energy to dc electric energy conversion efficiency, inverter ac/dc conversion efficiency, total photovoltaic module area, and total solar radiance. The fan power supply data refers to related data generated in the fan power supply process, and comprises data such as the density of air flow around the rotor, the swept area of the rotor, the local wind speed and the like. The wave energy power supply data refers to relevant data generated in the wave energy power supply process and comprises data such as seawater density, swept area of a rotor, local wind speed and the like.
The distributed photovoltaic output characteristic model is as follows:
in the formula (I), the compound is shown in the specification,
for distributed photovoltaic real-time electric power, η 4 For the conversion efficiency of solar energy into DC electric energy, eta inv For the AC-DC conversion efficiency of the inverter, A pv The total area of the photovoltaic module is the total area,
total solar Irradiance (GHI),
the PV plant is given off the optical power at time t,
is the rated electric power of the PV power station. T is a unit of ref For reference temperature, T t cell Is the actual temperature, gamma, of the photovoltaic array t Is the temperature coefficient at time t.
The small fan output characteristic model is as follows:
in the formula, P t WT Is the electrical energy (kW) produced by a horizontal axis wind turbine; ρ is a unit of a gradient local Is the density of the air flow around the rotor (kg/m 3), A R Is the swept area of the rotor (m 2); v local (t) is the local wind speed (m/s). λ (t) is the tip ratio (dimensionless), θ is the pitch angle (deg), C p (λ (t), θ) are power coefficients, which are regression functions that approximate (dimensionless) sums.
The output model of the wave energy power generation device is as follows:
in the formula, P wave,mcl (t) is time tThe equivalent power density of the wavefront width of the wave energy power generation device is shown as P, rho is the density of seawater (kg/m 3), g is the acceleration of gravity (m/s), h (T) is the effective wave height, T is the wave period, and when the equivalent width of the wave energy power generation device is L, the wave power captured by the wave energy power generation device can be represented as P wv (t)：
P wv (t)＝η wv P wave，mel (t)L/1000
In the formula eta wv The value is 0.1-0.15 for the wave energy absorption efficiency.
In the embodiment of the invention, data such as solar energy-to-direct current electric energy conversion efficiency, inverter alternating current-to-direct current conversion efficiency, total photovoltaic module area, total solar radiance and the like are input into a distributed photovoltaic output characteristic model, and photovoltaic output power in a specific certain time period can be calculated; inputting data such as the density of airflow around the rotor, the swept area of the rotor, the local wind speed and the like into a small fan output characteristic model, and calculating the fan output power in a certain time period; and inputting data such as seawater density, swept area of a rotor, local wind speed and the like into a wave energy power generation device output model so as to obtain wave energy output power in a certain time period.
In the embodiment of the present invention, the specific implementation process of step 201 is similar to step 101, and is not described herein again.
Optionally, step 202 further comprises the following steps S21-S24:
s21, respectively constructing a space-time transfer constraint and an energy conservation constraint according to photovoltaic power supply data, fan power supply data, wave energy power supply data, load power and electricity storage ship operation data, and combining to obtain a first constraint condition;
s22, constructing an upper layer day-ahead power supply model by adopting a first constraint condition and a preset first objective function;
s23, constructing a plurality of updating constraint conditions, and combining to obtain a second constraint condition;
and S24, constructing a lower-layer day-ahead power supply model by adopting a second constraint condition and a preset second objective function.
As shown in fig. 3, the power storage vessel operation data refers to power storage data of the power storage vessel in a period of time ahead of the day or data generated in the driving process, and includes data such as a position state and a position change situation of the power storage vessel at a certain moment in the day-ahead stage, a position node of the power storage vessel at a certain moment, and a departure node of the power storage vessel at a certain moment; the upper layer day-ahead power supply model refers to a mathematical model of the power storage ship participating in day-ahead power market bidding, and the lower layer day-ahead power supply model refers to a mathematical model of the power storage ship participating in day-ahead power market clearing; spatiotemporal transition constraints include:
and space-time continuity constraint:
spatial state uniqueness constraint:
scheduling periodicity constraints:
and logically constraining the starting state:
reach the state logic constraint:
and (3) restricting the running time:
wherein, the first and the second end of the pipe are connected with each other,
all the variables are Bohr variables and are used for describing the position state and position change conditions (departure and arrival) of the power storage ship at the moment t in the day-ahead.
A value of 1 represents the location node i of the power storage vessel at time t,
a value of 1 represents the departure of the power storage vessel from node i at time t,
a value of 1 indicates that the electric storage vessel arrives at node i at time t,
is the time of flight from node i to node j.
The energy conservation constraints include:
and (3) limiting the upper and lower limits of charge and discharge power:
and (3) constraint of upper and lower limits of charge level:
charge level periodicity constraint:
charge level time continuity constraint:
competitive bidding power constraint:
wherein the content of the first and second substances,
the charging power and the discharging power of the electric storage ship at the node i at the time t are respectively, pmax is the maximum charging and discharging power of the storage battery,
the charge level, SOC, of the electric storage ship at the node i at the time t min 、SOC max The charge levels are respectively the minimum charge level and the maximum charge level of the storage battery, eb is the rated capacity of the storage battery, eta is the charge-discharge efficiency, and gamma is the self-loss coefficient of the storage battery.
The first objective function is:
wherein R is DA And I is a node set accessed by the power storage ship in the power network, and T is an optimization time window.
For the clearing price of node i at time t,
the bidding powers of the electric storage ship at the node i at the time t are taken as the power supply and the load respectively.
The plurality of updating constraint conditions are respectively node power balance constraint, line capacity constraint, generator output upper and lower limit constraint, load upper and lower limit constraint and phase angle constraint, and the second constraint condition comprises:
node power balance constraint:
and (3) line capacity constraint:
upper and lower limit of generator outputBundling:
and (4) load upper and lower limit constraint:
phase angle constraint:
wherein, b ij For the branch ij susceptance,
for the power angle of node i at time t,
for the maximum transmission capacity of the line ij,
respectively the upper limit and the lower limit of the output of the generator m,
respectively an upper limit and a lower limit of the competitive bidding capacity of the load n.
And the dual variables respectively corresponding to the inequality constraints are obtained.
The second objective function is market utility maximization, expressed as:
wherein phi is M Representative of a set of generator units participating in the day-ahead market, phi N Represents a set of users, phi I Representing a collection of nodes.
Are respectively provided withFor the quotation of the generator set m and the load n at the moment t in the market in the day ahead,
respectively the winning power of the generator set m and the load n.
Respectively the charging and discharging quotations of the power storage ship at the node i at the moment t,
the power is the medium power.
In a specific embodiment, the first objective function is to aim at the maximum market income, the second objective function is to aim at the maximum market balance, and constraints such as space-time continuity constraint, space state uniqueness constraint and the like are constructed according to data such as photovoltaic power supply data, fan power supply data, wave energy power supply data, load power, and power storage ship operation data; therefore, a first constraint condition, a first objective function, a second constraint condition and a second objective function are constructed, an upper layer day-ahead power supply model is constructed through the first constraint condition and the first objective function, and a lower layer day-ahead power supply model is constructed through the second constraint condition and the second objective function.
And 203, performing single-layer model conversion on the upper layer day-ahead power supply model and the lower layer day-ahead power supply model to obtain a day-ahead target power supply model.
Optionally, step 203 may include the following steps S31-S35:
s31, performing partial derivation on variables corresponding to the lower layer day-ahead power supply model to obtain a Cohen-Tack balance constraint condition;
s32, inputting Boolean variables into the Couen-Take direction and the complementary constraint conditions corresponding to the lower-layer day-ahead power supply model, and converting the complementary constraint conditions by applying a penalty factor method to obtain cut plane complementary constraint conditions;
s33, adding a Cohen-Tack balance constraint condition and a secant plane complementary constraint condition on the second constraint condition to obtain a third constraint condition;
s34, transforming the first objective function according to the necessary condition of the preset optimal solution and the Couen-Tack balance constraint condition to obtain a third objective function;
and S35, constructing a day-ahead target power supply model according to the third objective function and the third constraint condition.
In a specific embodiment, according to the strong dual theory, first, the variables are paired
And (3) performing partial derivation to obtain a Cohen-Tack balance constraint condition:
secondly, writing out the Couen-tach direction and the complementary constraint condition corresponding to the inequality constraint in the lower layer day-ahead power supply model:
further, introducing a boolean variable and applying a penalty factor method (large M method) to convert the complementary constraint into a cut-plane constraint, as shown in the following formula:
where M is a very large integer, capable of covering the range of the scaled variable,
are all boolean variables.
Further, according to the strong dual theory, the necessary conditions for obtaining the optimal solution by the market in the future are as follows:
according to the above conditions and the Cohen-Tak balance condition, the first objective function is transformed to obtain a third objective function:
and adding the Couen-Tack balance constraint condition and the secant plane complementation constraint condition on the second constraint condition to obtain a third constraint condition, wherein the third constraint condition comprises the second constraint condition, the Couen-Tack balance constraint condition and the secant plane complementation constraint condition. The first objective function does not contain bilinear terms any more after conversion, the double-layer multi-objective programming problem is converted into a mixed integer linear programming problem with balance constraint conditions and secant plane constraint, and then the mixed integer linear programming problem can be solved through a commercial solver gurobi and the like.
And 204, carrying out scene simulation by combining a Monte Carlo simulation method with a plurality of preset random variables to obtain a plurality of in-day power supply scenes.
Optionally, step 204 may include the following steps S41-S48:
s41, classifying random numbers corresponding to a plurality of preset random variables into normal distribution intervals according to a preset prediction deviation; the random variables comprise predicted new energy output power, predicted load demand and predicted output demand;
s42, generating corresponding initial scenes by adopting the predicted new energy output power, the predicted load demand and the predicted output demand corresponding to each normal distribution interval;
s43, extracting a target threshold value intermediate scene from initial scenes where the predicted new energy output power, the predicted load demand and the predicted output demand are located by adopting Monte Carlo sampling;
s44, carrying out normalization processing on the distribution probability corresponding to each normal distribution interval to obtain the cumulative normalization probability corresponding to each interval;
s45, judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene;
s46, if yes, the cumulative normalization probability corresponding to the initial scene is smaller than or equal to the distribution probability corresponding to the interval where the random variable is located, and the binary parameter vector corresponding to the initial scene is set as a first threshold;
s47, if not, the binary parameter vector corresponding to the initial scene corresponding to the unselected interval is a second threshold value;
and S48, skipping to execute the step of judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene, and generating a plurality of in-day power supply scenes.
It should be noted that the preset prediction deviation is 5%, and the normal distribution interval is seven; the random variables include predicted new energy output power, predicted load demand, and predicted output demand. The first threshold is 1, the second threshold is 0, and the target threshold is 53.
In a specific embodiment, first, random numbers between [0,1] are generated for each input random variable, and the random numbers are uniformly distributed in a normal distribution interval; secondly, all prediction uncertainties are simulated according to errors determined by the probability distribution function.
The probability distribution of the random variables is represented by a group of limited initial scenes, each initial scene is realized corresponding to the random variable singly, and the load demand and the new energy output level under each initial scene can be represented as follows:
in the formula (I), the compound is shown in the specification,
for the actual requirements of photovoltaic, wind power, wave energy and load,
to predict forces and demands
To predict the deviation.
Specifically, the detailed process of the monte carlo scene generation method is as follows:
firstly, dividing a distribution function with a zero mean value as a center into seven normal distribution intervals according to the required 5% precision, determining a standard deviation width for each normal distribution interval, and associating each normal distribution interval with a distribution probability.
Secondly, according to different normal distribution intervals and the obtained distribution probability, a roulette algorithm is applied to generate an initial scene of each hour, the distribution probabilities of the different normal distribution intervals are normalized, the sum is equal to 1, and each normal distribution interval is associated with the accumulated normalized probability.
Optionally, each initial scene includes a binary parameter vector for identifying photovoltaic power, wind power, wave energy, and load power at each time interval: wind power, photovoltaic power, wave power and load power are generated along with the initial scenes, each initial scene has a group of data, and each group of data can be used as a binary parameter vector.
In the formula (I), the compound is shown in the specification,
representing the corresponding segment for a Bohr variableAnd selecting whether the photovoltaic power, the wind power, the wave power and the load power are selected in the initial scene s, selecting a first interval of which the cumulative normalized probability is less than or equal to the random number, so that the associated binary parameter is equal to 1, and the parameter of the non-selection interval is equal to 0.
Finally, the normalized probability for each initial scene is calculated according to the following equation:
in the formula, beta l,t 、β pv,t 、β wt,t 、β wv,t The probability that each normal distribution interval of the load, the photovoltaic power, the wind power and the wave energy output is selected is respectively corresponded.
In further detail, on the basis of the power prediction before the day and the travel demand prediction, the prediction deviation of the new energy output, the load power and the output demand in the day stage is considered to be 5% (the prediction deviation follows normal distribution, and is expected to be 0, and the standard deviation σ = 5%/3). The Monte Carlo sampling is utilized to respectively extract 5 initial scenes from three types of random variables of new energy output, load demand and output demand, 53 intermediate scenes are generated in total, and the backward subtraction method is adopted to reduce the power supply scenes to 6 intraday power supply scenes.
And 205, solving the day-ahead target power supply model to obtain a day-ahead target strategy.
In the embodiment of the present invention, the specific implementation process of step 205 is similar to that of step 103, and is not described herein again.
And step 206, performing rolling optimization on the initial power supply model in each day power supply scene according to a day-ahead target strategy and a multivariable control strategy to obtain a plurality of day-ahead target power supply models.
Optionally, step 206 further comprises the following steps S51-S54:
s51, constructing a fourth objective function according to a day-ahead scheduling plan and day-internal operation data corresponding to the day-ahead objective strategy, and making a scheduling plan in an optimized time scale period corresponding to an optimized time window;
s52, superposing a day-ahead scheduling plan constraint condition corresponding to a day-ahead target strategy on the first constraint condition and the third constraint condition to obtain a fourth constraint condition;
s53, constructing an intra-day initial power supply model according to a fourth objective function and a fourth constraint condition;
and S54, executing a rolling optimization operation instruction on the day initial power supply model in a day power supply scene at average time intervals according to the scheduling plan to obtain a plurality of day target power supply models.
In a particular embodiment, in the intraday market, the objective function of the storage vessel under the time window/is the yield
Max, the fourth objective function is:
in the formula (I), the compound is shown in the specification,
for the discharge price of the node i at the time t under the power supply scene omega in the day,
respectively charging and discharging winning power s of the electricity storage ship at the t moment node i under the power supply scene omega in the day ω Is the probability of the occurrence of scene omega.
The fourth constraint condition comprises a first constraint condition, a third constraint condition and a day-ahead scheduling plan constraint condition, wherein the day-ahead scheduling plan constraint condition mainly transmits position information u and SOC level in a day-ahead optimization result for the kth time (k =1,2, …, 24-T) l + 1) the location information and SOC guidelines delivered by the rolling optimization may be expressed as follows:
in the formula, κ (0 < κ < 1) is a relaxation coefficient, and a smaller κ represents a stronger day-ahead SOC optimization result for an in-day SOC guiding factor.
As shown in fig. 4, the optimization time scale of the multivariable Control (MPC) is t during the in-day phase l Optimizing the time window by taking T l Every 1 h. Specifically, the power storage ship takes a day-ahead scheduling plan constraint condition corresponding to a day-ahead target strategy as guidance (full-time SOC, position information and the like), combines the current charge level according to the new energy output and load power prediction level and takes a time window T l Maximum internal yield is the target, and the future T to T + T is formulated l Scheduling in time interval, and executing t time interval to t-1+1/t 1 And executing the instructions in the time period to obtain a plurality of in-day target power supply models.
And step 207, solving the target power supply model in each day, and determining a power supply plan in each day according to the solving result.
It should be noted that the in-day power supply plan refers to a power supply plan for delivering new energy on an island to a continental load center area by means of an electricity storage vessel in a recent period of time. The simulation platform is set as follows: intel (R) Core (TM) i5-10400 CPU@2.90GHz desktop computer. The yalmould toolkit and gurobi optimization software are called based on a Matlab R2021a platform. As shown in fig. 5, a typical island isolated power supply situation.
In the specific embodiment, as shown in fig. 6-7, it can be seen that the marginal electricity price of the continental load area is higher than that of the island 1 and the island 2 in one day, and is highest in 18-19 th evening and 400 yuan/MWh, lowest in 4-5 th morning and 360 yuan/MWh, so that the optimal time for supplying power to the power storage ship is 18-19 th evening, and the worst time is 4-5 th morning. Under the influence of wind power output, the electricity price of the island 1 is at a lower level of 100 yuan/MWh in most time intervals in one day, and at the moment, the wind turbine generator is a marginal unit; under the influence of photovoltaic output, the marginal electricity price of the island 2 is at a lower level of 80 yuan/MWh within 9-18 time period, and at the moment, the photovoltaic unit quotes the node marginal price. In a dispatching cycle, the power storage ship has 15 path transfer behaviors in total and has 7 complete charging and discharging behaviors. As charging loads, the electric storage vessel charges at the 8 node of the island 2 at time 7, 30 minutes, 15 minutes, 11 hours, and 15 minutes, and charges at the 12 node of the island 1 at time 2, 4 hours, 45 minutes, 15 hours, and 22 hours, respectively, at which the marginal electricity price of the node is the lowest. As a power source, the electric storage ship discharges at the node of the continental load center region 11 at 0 hour 30, 3 hours 15, 8 hours 45, 12 hours 30, 16, 18 and 23 hours, and discharges at the node 8 at 6 and 23 hours 45, respectively.
The method comprises the steps of responding to a received power supply request, analyzing the power supply request to obtain historical power supply data carried by the power supply request, constructing a day-ahead target power supply model according to the historical power supply data, carrying out scene simulation to obtain a plurality of day-ahead power supply scenes, solving the day-ahead target power supply model by using a solver, obtaining a day-ahead target strategy according to a solving result, carrying out rolling optimization on a preset day-ahead initial power supply model in each day-ahead power supply scene according to the day-ahead target strategy and a multivariable control strategy to obtain a plurality of day-ahead target models, solving each day-ahead target power supply model, and determining a day-ahead power supply plan according to a solving result. The problem that the existing technology focuses on an electric power supply model under a large power grid or an active power distribution network is solved, and no research is carried out on an isolated island power generation system which is not connected with the power grid or is in weak connection. And the isolated island power generation system is greatly influenced by the uncertainty of new energy output and the fluctuation of load demand, so that the technical problem of poor absorption capability of island clean energy is caused. The invention fills the gap that the isolated island power generation system participates in the optimization operation of the power supply plan in the day, further provides a multi-time scale coordination optimization strategy based on model prediction control in consideration of the uncertainty of new energy output and the fluctuation of load demand, corrects the optimization strategy formulated at the previous stage, realizes the maximization of the power supply of the isolated island power generation system, and further improves the clean energy consumption capability of the island.
Referring to fig. 8, fig. 8 is a block diagram illustrating a system for generating an intra-day power supply plan of an isolated island according to an embodiment of the present invention.
The invention provides a system for generating an isolated island power supply plan in the day, which comprises:
a historical power supply data module 801, configured to respond to the received power supply request and obtain historical power supply data corresponding to the power supply request;
the in-day power supply scene module 802 is configured to construct a day-ahead target power supply model according to historical power supply data, and perform scene simulation to obtain a plurality of in-day power supply scenes;
a day-ahead target strategy module 803, configured to solve the day-ahead target power supply model to obtain a day-ahead target strategy;
the intra-day target power supply module 804 is used for performing rolling optimization on the preset intra-day initial power supply model in each intra-day power supply scene according to a day-ahead target strategy and a multivariable control strategy to obtain a plurality of intra-day target power supply models;
and a power supply plan for day module 805, configured to solve the power supply target model for each day, and determine a power supply plan for day according to a solution result.
Optionally, before the historical power supply data module 801, the following is further included:
the new energy submodule is used for acquiring a distributed photovoltaic output characteristic model, a small fan output characteristic model and a wave energy power generation device output model;
the photovoltaic output power sub-module is used for inputting photovoltaic power supply data into the distributed photovoltaic output characteristic model to obtain photovoltaic output power;
the fan output power submodule is used for inputting fan power supply data into the small fan output characteristic model to obtain fan output power;
and the wave energy output power submodule is used for inputting wave energy power supply data into the wave energy power generation device output model to obtain wave energy output power.
Optionally, the power-on-day scenario module 802 further includes:
constructing an upper layer day-ahead power supply model and a lower layer day-ahead power supply model submodule, which are used for constructing an upper layer day-ahead power supply model and a lower layer day-ahead power supply model according to photovoltaic power supply data, fan power supply data, wave energy power supply data, load power and power storage ship operation data;
the day-ahead target power supply module is used for carrying out single-layer model conversion on the upper layer day-ahead power supply model and the lower layer day-ahead power supply model to obtain a day-ahead target power supply model;
and the in-day power supply scene submodule performs scene simulation by combining a Monte Carlo simulation method with a plurality of preset random variables to obtain a plurality of in-day power supply scenes.
Optionally, constructing the sub-modules of the upper layer day-ahead power supply model and the lower layer day-ahead power supply model further includes:
the first constraint condition submodule is used for respectively constructing a space-time transfer constraint and an energy conservation constraint according to photovoltaic power supply data, fan power supply data, wave energy power supply data, load power and electricity storage ship operation data, and combining the space-time transfer constraint and the energy conservation constraint to obtain a first constraint condition;
the upper-layer day-ahead power supply module is used for constructing an upper-layer day-ahead power supply model by adopting a first constraint condition and a preset first objective function;
the second constraint condition submodule is used for constructing a plurality of updating constraint conditions and combining the updating constraint conditions to obtain a second constraint condition;
and the lower-layer day-ahead power supply module is used for constructing a lower-layer day-ahead power supply model by adopting a second constraint condition and a preset second objective function.
Optionally, the day-ahead target power supply module further comprises:
the Cohen-Tack balance constraint condition submodule is used for performing partial derivation on variables corresponding to the lower layer day-ahead power supply model to obtain a Cohen-Tack balance constraint condition;
the cut plane complementary constraint condition submodule is used for inputting Boolean variables into the corresponding Kuen-Take direction and the complementary constraint condition of the lower layer day-ahead power supply model, and converting the complementary constraint condition by applying a penalty factor method to obtain a cut plane complementary constraint condition;
the third constraint condition submodule is used for adding a Cohen-Tack balance constraint condition and a secant plane complementary constraint condition on the second constraint condition to obtain a third constraint condition;
the third objective function submodule is used for transforming the first objective function according to the necessary condition of the preset optimal solution and the Couen-Take balance constraint condition to obtain a third objective function;
and constructing a day-ahead target power supply module for constructing a day-ahead target power supply model according to the third objective function and the third constraint condition.
Optionally, the intra-day power supply scenario sub-module further includes:
the normal distribution interval submodule is used for classifying random numbers corresponding to a plurality of preset random variables into each normal distribution interval according to the preset prediction deviation; the random variables comprise predicted new energy output power, predicted load demand and predicted output demand;
the initial scene submodule is used for generating corresponding initial scenes by adopting the predicted new energy output power, the predicted load demand and the predicted output demand corresponding to each normal distribution interval;
the target threshold value intermediate scene is used for extracting the target threshold value intermediate scene from initial scenes where the predicted new energy output power, the predicted load demand and the predicted output demand are located by adopting Monte Carlo sampling;
the normalization probability submodule is used for performing normalization processing on the distribution probability corresponding to each normal distribution interval to obtain the cumulative normalization probability corresponding to each interval;
the initial scene judging submodule is used for judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene;
the first threshold submodule is used for setting the binary parameter vector corresponding to the initial scene as a first threshold if the cumulative normalized probability corresponding to the initial scene is smaller than or equal to the distribution probability corresponding to the interval where the random variable is located;
the second threshold submodule is used for determining that the binary parameter vector corresponding to the initial scene corresponding to the unselected interval is a second threshold if the binary parameter vector corresponding to the initial scene corresponding to the unselected interval is not the second threshold;
and the skip execution submodule is used for skipping execution of the step of judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene, and generating a plurality of in-day power supply scenes.
Optionally, the intra-day target power supply module 804 further includes:
the scheduling plan submodule is used for constructing a fourth objective function according to a day-ahead scheduling plan corresponding to the day-ahead target strategy and day-internal operation data and formulating a scheduling plan in an optimized time scale period corresponding to an optimized time window;
the fourth constraint condition submodule is used for superposing a day-ahead scheduling plan constraint condition corresponding to the day-ahead target strategy on the first constraint condition and the third constraint condition to obtain a fourth constraint condition;
the intra-day initial power supply module is used for constructing an intra-day initial power supply model according to a fourth objective function and a fourth constraint condition;
and the in-day target power supply model submodule is used for executing a rolling optimization operation instruction on the in-day initial power supply model in the in-day power supply scene at average time intervals according to the scheduling plan to obtain a plurality of in-day target power supply models.
The method comprises the steps of responding to a received power supply request, analyzing the power supply request to obtain historical power supply data carried by the power supply request, constructing a day-ahead target power supply model according to the historical power supply data, carrying out scene simulation to obtain a plurality of day-ahead power supply scenes, solving the day-ahead target power supply model by using a solver, obtaining a day-ahead target strategy according to a solving result, carrying out rolling optimization on a preset day-ahead initial power supply model in each day-ahead power supply scene according to the day-ahead target strategy and a multivariable control strategy to obtain a plurality of day-ahead target models, solving each day-ahead target power supply model, and determining a day-ahead power supply plan according to a solving result. The problem that the existing technology focuses on an electric power supply model under a large power grid or an active power distribution network is solved, and no research is carried out on an isolated island power generation system which is not connected with a grid or in a weak connection mode. And the isolated island power generation system is greatly influenced by the uncertainty of new energy output and the fluctuation of load demand, so that the technical problem of poor absorption capability of island clean energy is caused. The invention fills the gap that the isolated island power generation system participates in the optimization operation of the power supply plan in the day, further provides a multi-time scale coordination optimization strategy based on model prediction control in consideration of the uncertainty of new energy output and the fluctuation of load demand, corrects the optimization strategy formulated at the previous stage, realizes the maximization of the power supply of the isolated island power generation system, and further improves the clean energy consumption capability of the island.
An embodiment of the present invention further provides an electronic device, where the electronic device includes: the computer system comprises a memory and a processor, wherein a computer program is stored in the memory; the computer program, when executed by the processor, causes the processor to execute the isolated island-in-the-sea power plan generating method according to any one of the embodiments described above.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the method for generating the power supply plan in an island day as in any of the above embodiments is implemented.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for generating a power supply plan in an island day is characterized by comprising the following steps:
responding to a received power supply request, and acquiring historical power supply data corresponding to the power supply request;
constructing a day-ahead target power supply model according to the historical power supply data, and performing scene simulation to obtain a plurality of in-day power supply scenes;
solving the day-ahead target power supply model to obtain a day-ahead target strategy;
performing rolling optimization on the initial power supply model in the preset day in each day power supply scene according to the day-ahead target strategy and the multivariable control strategy to obtain a plurality of day target power supply models;
and solving each day target power supply model, and determining a day power supply plan according to a solving result.
2. The isolated island power supply day-to-day plan generation method according to claim 1, wherein the historical data includes photovoltaic power supply data, fan power supply data and wave power supply data, and before the step of constructing a day-ahead target power supply model according to the historical power supply data and performing scene simulation to obtain a plurality of day-to-day power supply scenes, the method further comprises:
acquiring a distributed photovoltaic output characteristic model, a small fan output characteristic model and a wave energy power generation device output model;
inputting the photovoltaic power supply data into the distributed photovoltaic output characteristic model to obtain photovoltaic output power;
inputting the fan power supply data into the small fan output characteristic model to obtain fan output power;
and inputting the wave energy power supply data into the wave energy power generation device output model to obtain wave energy output power.
3. The isolated island power supply day-to-day plan generating method of claim 2, wherein the historical power supply data further comprises load power and power storage ship operation data, and the step of constructing a day-ahead target power supply model according to the historical power supply data and performing scene simulation to obtain a plurality of day-to-day power supply scenes comprises:
constructing an upper layer day-ahead power supply model and a lower layer day-ahead power supply model according to the photovoltaic power supply data, the fan power supply data, the wave energy power supply data, the load power and the electricity storage ship operation data;
performing single-layer model conversion on the upper layer day-ahead power supply model and the lower layer day-ahead power supply model to obtain a day-ahead target power supply model;
and (3) carrying out scene simulation by adopting a Monte Carlo simulation method and combining a plurality of preset random variables to obtain a plurality of in-day power supply scenes.
4. The isolated island in-the-sea day power supply plan generating method according to claim 3, wherein the step of constructing an upper layer day-ahead power supply model and a lower layer day-ahead power supply model from the photovoltaic power supply data, the fan power supply data, the wave energy power supply data, the load power and the power storage vessel operation data comprises:
respectively constructing a space-time transfer constraint and an energy conservation constraint according to the photovoltaic power supply data, the fan power supply data, the wave energy power supply data, the load power and the electricity storage ship operation data, and combining to obtain a first constraint condition;
constructing an upper layer day-ahead power supply model by adopting the first constraint condition and a preset first objective function;
constructing a plurality of updating constraint conditions, and combining to obtain a second constraint condition;
and constructing a lower layer day-ahead power supply model by adopting the second constraint condition and a preset second objective function.
5. The island-in-sea day power supply plan generating method of claim 4, wherein said step of converting said upper layer day-ahead power supply model and said lower layer day-ahead power supply model into a single layer model to obtain a day-ahead target power supply model comprises:
performing partial derivation on variables corresponding to the lower layer day-ahead power supply model to obtain a Couen-Tack balance constraint condition;
inputting Boolean variables into the Couen-Take direction and complementary constraint conditions corresponding to the lower layer day-ahead power supply model, and converting the complementary constraint conditions by applying a penalty factor method to obtain cut plane complementary constraint conditions;
adding the Couen-Tak balance constraint condition and the secant plane complementary constraint condition to the second constraint condition to obtain a third constraint condition;
transforming the first objective function according to the necessary condition of a preset optimal solution and the Couen-Tack balance constraint condition to obtain a third objective function;
and constructing a day-ahead target power supply model according to the third objective function and the third constraint condition.
6. The isolated island power supply day-to-day plan generating method of claim 3, wherein the step of performing scene simulation by combining a plurality of preset random variables by using a Monte Carlo simulation method to obtain a plurality of day-to-day power supply scenes comprises:
classifying random numbers corresponding to a plurality of preset random variables into each normal distribution interval according to a preset prediction deviation; the random variables comprise predicted new energy output power, predicted load demand and predicted output demand;
generating corresponding initial scenes by adopting the predicted new energy output power, the predicted load demand and the predicted output demand corresponding to each normal distribution interval;
extracting a target threshold value intermediate scene from the initial scene where the predicted new energy output power, the predicted load demand and the predicted output demand are located by adopting Monte Carlo sampling;
normalizing the distribution probability corresponding to each normal distribution interval to obtain the cumulative normalized probability corresponding to each interval;
judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene;
if so, setting the cumulative normalized probability corresponding to the initial scene to be less than or equal to the distribution probability corresponding to the interval where the random variable is located, and setting the binary parameter vector corresponding to the initial scene as a first threshold;
if not, the binary parameter vector corresponding to the initial scene corresponding to the unselected interval is a second threshold;
and skipping to execute the step of judging whether the initial scene is selected or not according to the binary parameter vector corresponding to the initial scene, and generating a plurality of in-day power supply scenes.
7. The method of claim 5, wherein the multivariate control strategy comprises an optimization time window, and the step of performing rolling optimization on the initial power supply model in each day according to the day-ahead target strategy and the multivariate control strategy to obtain a plurality of day-ahead target power supply models comprises:
constructing a fourth objective function according to a day-ahead scheduling plan and day-internal operation data corresponding to the day-ahead objective strategy, and making a scheduling plan in an optimized time scale period corresponding to the optimized time window;
superposing a day-ahead scheduling plan constraint condition corresponding to the day-ahead target strategy on the first constraint condition and the third constraint condition to obtain a fourth constraint condition;
constructing an intra-day initial power supply model according to the fourth objective function and the fourth constraint condition;
and executing a rolling optimization operation instruction on the in-day initial power supply model in the in-day power supply scene at an average time interval according to the scheduling plan to obtain a plurality of in-day target power supply models.
8. An island-in-the-sea intra-day power plan generation system, the system comprising:
the historical power supply data module is used for responding to the received power supply request and acquiring historical power supply data corresponding to the power supply request;
the in-day power supply scene module is used for constructing a day-ahead target power supply model according to the historical power supply data and carrying out scene simulation to obtain a plurality of in-day power supply scenes;
the day-ahead target strategy module is used for solving the day-ahead target power supply model to obtain a day-ahead target strategy;
the in-day target power supply module is used for performing rolling optimization on a preset in-day initial power supply model in each in-day power supply scene according to the day-ahead target strategy and the multivariable control strategy to obtain a plurality of in-day target power supply models;
and the power supply plan in the day module is used for solving the target power supply model in each day and determining a power supply plan in the day according to the solution result.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to perform the steps of the island intra-day power supply plan generating method according to any one of claims 1 to 7.
10. A computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed, implements the island-in-sea-day power supply plan generating method according to any one of claims 1 to 7.
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