CN117767445A - Active power coordination control method and system with participation of offshore wind power and energy storage - Google Patents
Active power coordination control method and system with participation of offshore wind power and energy storage Download PDFInfo
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Abstract
The invention provides an active power coordination control method and system for participation of offshore wind power and energy storage, wherein the method comprises the following steps: constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after active power of the offshore wind farm is absorbed as targets; establishing an optimized scheduling model aiming at the minimum deviation of the active power; acquiring offshore wind power output data, energy storage data and load data, performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and acquiring an upper-layer control optimized offshore wind power plant scheduling plan; judging whether the dispatching plan of the offshore wind farm after the upper-layer control optimization is smaller than the actual output value of the offshore wind farm at the current moment; and if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized. The method provided by the invention can maintain active power balance of the offshore wind farm and ensure safe and stable operation of the power distribution network.
Description
Technical Field
The invention relates to the technical field of coordination control, in particular to an active power coordination control method and system for participation of offshore wind power and energy storage.
Background
In order to realize the distant targets of carbon peak and carbon neutralization, the continuous development and utilization of new energy are important. At present, the new energy power generation mainly comprises power generation modes such as solar power generation, wind power generation, biomass energy power generation and the like, and in wind power generation, the offshore wind power resources are abundant due to wide coastal lines and sea level, a large amount of land resources are not required to be occupied, and the power generation quality is better than that of onshore wind power generation, so that the offshore wind power generation is rapid in development. However, due to the intermittence and uncertainty of the offshore wind power generation, the power fluctuation is large in the offshore wind power generation grid-connected process, so that the offshore wind power generation has a certain limitation.
Disclosure of Invention
The invention aims to provide an active power coordination control method and system for participation of offshore wind power and energy storage, so as to solve the technical problems, an energy storage multi-objective function optimization scheduling model is built to realize upper control of an offshore wind power plant, and an optimization scheduling model is built for minimum deviation of the active power to realize lower control, so that active power balance of the offshore wind power plant is maintained, and safe and stable operation of a power distribution network is ensured.
In order to solve the technical problems, the invention provides an active power coordination control method for participation of offshore wind power and energy storage, which comprises the following steps:
constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after active power of the offshore wind farm is absorbed as targets;
establishing an optimized scheduling model aiming at the minimum deviation of the active power;
acquiring offshore wind power output data, energy storage data and load data, performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and acquiring an upper-layer control optimized offshore wind power plant scheduling plan;
acquiring actual output values of the offshore wind farm at the current moment based on the offshore wind power output data, the energy storage data and the load data, and judging whether an upper-layer control optimized offshore wind farm scheduling plan is smaller than the actual output values of the offshore wind farm at the current moment; if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized; otherwise, returning to perform upper control.
According to the scheme, the upper control of the offshore wind farm is realized by constructing the energy storage multi-objective function optimal scheduling model, and meanwhile, the lower control is realized by constructing the optimal scheduling model aiming at the minimum deviation of the active power, so that the active power balance of the offshore wind farm is maintained, and the safe and stable operation of the power distribution network is ensured.
The invention provides an energy storage multi-objective function optimal scheduling model which is constructed, so that the system running cost is minimum under the condition of meeting the energy storage scheduling. The output prediction error of the offshore wind farm can have a certain influence on the balance of the active power of the system, so that an optimal scheduling model is established for the minimum deviation of the active power on the basis of upper control so as to minimize the output prediction error of the offshore wind farm.
Further, the energy storage multi-objective function optimization scheduling model is constructed by taking the maximum capacity after the active power of the offshore wind farm is consumed and the minimum system maintenance cost as targets, and specifically comprises the following steps:
the energy storage multi-objective function optimization scheduling model comprises a maximum capacity objective function after active power of the offshore wind farm is absorbed and a minimum maintenance cost objective function of the offshore wind farm; wherein:
the maximum capacity objective function after the active power of the offshore wind farm is absorbed is specifically expressed as follows:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th time period;
the minimum maintenance cost objective function of the offshore wind farm is specifically expressed as:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;is the j thThe working state of the energy storage device in the t time period is that the energy storage device discharges when the value of the working state is 1, and the energy storage device charges when the value of the working state is 0; />Representing the charge and discharge power of the jth energy storage device in the t time period; />Representing the maintenance cost of the jth energy storage device during the t-th time period.
In the above scheme, the number of the collection time periods can be divided into a plurality of time periods, and the energy storage equipment is timely mobilized to charge and discharge when required by observing the running state of the system in each time period, so that the peak clipping and valley filling capacity of the offshore wind power generation system is regulated, and the active power balance is maintained. If a time period is taken to be 20min, a day 24h can be divided into 72 time periods, and then t=72.
The energy storage device can be quickly adjusted in response to energy change, can absorb the surplus energy generated by the system, and can release the surplus energy when the power generation is insufficient so as to maintain the power balance of the offshore wind power generation system.
Further, the method establishes an optimized scheduling model aiming at the minimum deviation of the active power, which is specifically expressed as follows:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
Further, the method comprises the steps of obtaining the output data, the energy storage data and the load data of the offshore wind power plant, performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and obtaining an upper-layer control optimized offshore wind power plant scheduling plan, wherein the method comprises the following specific steps:
acquiring offshore wind power output data, energy storage data and load data under a plurality of multi-time scales;
calculating a maximum capacity objective function after active power of the offshore wind farm is absorbed based on the offshore wind power output data, the energy storage data and the load data so as to obtain maximum capacity;
judging whether the maximum capacity is smaller than 0, if so, discharging the energy storage device; otherwise, further judging whether the maximum capacity is smaller than the grid-connected maximum allowable transmission power, if not, solving a minimum maintenance cost objective function of the offshore wind farm so as to enable the maintenance cost of the offshore wind farm to be the lowest, and acquiring an upper-layer control optimized offshore wind farm scheduling plan.
In the scheme, the minimum maintenance cost objective function of the offshore wind farm can be solved by utilizing a multi-objective differential algorithm, and the obtained upper-layer control optimized offshore wind farm scheduling plan comprises an upper-layer control optimized offshore wind power output plan and an energy storage charging and discharging plan. The particle swarm optimization can be adopted to solve the optimized dispatching model, and the obtained lower-layer control optimized offshore wind farm dispatching plan comprises a lower-layer control optimized offshore wind power output plan and an energy storage charging and discharging plan, namely the next-time offshore wind power output plan and the next-time energy storage charging and discharging plan.
Further, the energy storage multi-objective function optimization scheduling model and the optimization scheduling model are required to meet power balance constraint, active power balance constraint of the offshore wind farm and energy storage charging and discharging constraint so as to ensure that each device works normally.
In particular, the power balance constraint may be expressed as:
in the method, in the process of the invention,indicating the active power sent out in the t time period; this moment is:
in the method, in the process of the invention,for t time periods load initial active power consumption, < >>Active power consumed by the jth energy storage device for the t time period.
The active power balance constraint of an offshore wind farm can be expressed as:
wherein P is WTmax And allowing the wind power single machine to output the maximum active power.
The energy storage charge-discharge constraint can be expressed as:
wherein P is ESmin And P ESmax Maximum and minimum charge and discharge power and SOC respectively allowed by energy storage min And SOC (System on chip) max The stored energy allows maximum and minimum states of charge, respectively.
The scheme not only provides a control flow of gradual progressive upper control and lower control, but also divides a plurality of time nodes for sampling, namely, collects corresponding state data of a plurality of time periods, and ensures the control accuracy and instantaneity.
The invention provides an active power coordination control system with the participation of offshore wind power and energy storage, which comprises the following components: the upper control model construction module is used for constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after the active power of the offshore wind farm is absorbed as targets; the lower control model building module is used for building an optimized scheduling model aiming at the minimum deviation of the active power; the data acquisition module is used for acquiring the offshore wind power output data, the energy storage data and the load data and acquiring the actual output value of the offshore wind farm at the current moment; the upper control module is used for performing upper control on the offshore wind farm based on the offshore wind power output data, the energy storage data, the load data and the energy storage multi-objective function optimization scheduling model to obtain an upper control optimized offshore wind farm scheduling plan; the lower control module is used for judging whether the dispatching plan of the offshore wind farm after the upper control optimization is smaller than the actual output value of the offshore wind farm at the current moment; if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized; otherwise, returning to perform upper control.
The system provided by the scheme is simple to construct and easy to realize, and is used for realizing an active power coordination control method for the participation of the offshore wind power and energy storage.
Further, in the upper control model building module, the energy storage multi-objective function optimization scheduling model is built by taking the maximum capacity after active power of the offshore wind farm is absorbed and the minimum system maintenance cost as targets, specifically: the energy storage multi-objective function optimization scheduling model comprises a maximum capacity objective function after active power of the offshore wind farm is absorbed and a minimum maintenance cost objective function of the offshore wind farm; wherein:
the maximum capacity objective function after the active power of the offshore wind farm is absorbed is specifically expressed as follows:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th time period;
the minimum maintenance cost objective function of the offshore wind farm is specifically expressed as:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;the working state of the jth energy storage device in the t time period is that the energy storage device is discharged when the value of the j energy storage device is 1, and the charging of the energy storage device is indicated when the value of the j energy storage device is 0; />Representing the charge and discharge power of the jth energy storage device in the t time period; />Representing the maintenance cost of the jth energy storage device during the t-th time period.
In the above scheme, the number of the collection time periods can be divided into a plurality of time periods, and the energy storage equipment is timely mobilized to charge and discharge when required by observing the running state of the system in each time period, so that the peak clipping and valley filling capacity of the offshore wind power generation system is regulated, and the active power balance is maintained.
Further, in the lower control model building module, the minimum deviation for the active power builds an optimized scheduling model, which is specifically expressed as:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
Further, in the upper control module, the upper control is performed on the offshore wind farm based on the offshore wind power output data, the energy storage data, the load data and the energy storage multi-objective function optimization scheduling model, and an upper control optimized offshore wind farm scheduling plan is obtained, which specifically comprises the following steps:
calculating a maximum capacity objective function after active power of the offshore wind farm is absorbed based on the offshore wind power output data, the energy storage data and the load data so as to obtain maximum capacity;
judging whether the maximum capacity is smaller than 0, if so, discharging the energy storage device; otherwise, further judging whether the maximum capacity is smaller than the grid-connected maximum allowable transmission power, if not, solving a minimum maintenance cost objective function of the offshore wind farm so as to enable the maintenance cost of the offshore wind farm to be the lowest, and acquiring an upper-layer control optimized offshore wind farm scheduling plan.
Further, the active power coordination control system with the participation of the offshore wind power and the energy storage further comprises a constraint module, wherein the constraint module is used for constructing power balance constraint, active power balance constraint of the offshore wind power plant and energy storage charge-discharge constraint for the energy storage multi-objective function optimization scheduling model and the optimization scheduling model.
Drawings
FIG. 1 is a schematic flow chart of an active power coordination control method for offshore wind power and energy storage participation according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an active power coordination control system for offshore wind power and energy storage participation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a main circuit of an active power coordination control system for offshore wind power and energy storage participation according to an embodiment of the present invention;
fig. 4 is a flowchart of a specific application of an active power coordination control method involving offshore wind power and energy storage according to an embodiment of the present invention.
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.
Referring to fig. 1, the embodiment provides an active power coordination control method for participation of offshore wind power and energy storage, which includes the following steps:
s1: constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after active power of the offshore wind farm is absorbed as targets;
s2: establishing an optimized scheduling model aiming at the minimum deviation of the active power;
s3: acquiring offshore wind power output data, energy storage data and load data, performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and acquiring an upper-layer control optimized offshore wind power plant scheduling plan;
s4: acquiring actual output values of the offshore wind farm at the current moment based on the offshore wind power output data, the energy storage data and the load data, and judging whether an upper-layer control optimized offshore wind farm scheduling plan is smaller than the actual output values of the offshore wind farm at the current moment; if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized; otherwise, returning to perform upper control.
According to the embodiment, the upper control of the offshore wind farm is realized by constructing the energy storage multi-objective function optimal scheduling model, and meanwhile, the lower control is realized by constructing the optimal scheduling model aiming at the minimum deviation of the active power, so that the active power balance of the offshore wind farm is maintained, and the safe and stable operation of the power distribution network is ensured.
The invention provides an energy storage multi-objective function optimal scheduling model which is constructed, so that the system running cost is minimum under the condition of meeting the energy storage scheduling. The output prediction error of the offshore wind farm can have a certain influence on the balance of the active power of the system, so that an optimal scheduling model is established for the minimum deviation of the active power on the basis of upper control so as to minimize the output prediction error of the offshore wind farm.
Further, the energy storage multi-objective function optimization scheduling model is constructed by taking the maximum capacity after the active power of the offshore wind farm is consumed and the minimum system maintenance cost as targets, and specifically comprises the following steps:
the energy storage multi-objective function optimization scheduling model comprises a maximum capacity objective function after active power of the offshore wind farm is absorbed and a minimum maintenance cost objective function of the offshore wind farm; wherein:
the maximum capacity objective function after the active power of the offshore wind farm is absorbed is specifically expressed as follows:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th time period;
the minimum maintenance cost objective function of the offshore wind farm is specifically expressed as:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;the working state of the jth energy storage device in the t time period is that the energy storage device is discharged when the value of the j energy storage device is 1, and the charging of the energy storage device is indicated when the value of the j energy storage device is 0; />Representing the charge and discharge power of the jth energy storage device in the t time period; />Representing the maintenance cost of the jth energy storage device during the t-th time period.
In this embodiment, the number of the collection time periods may be a number of time periods obtained by dividing one day into a plurality of time periods, and by observing the running state of the system in each time period, the energy storage device is timely mobilized to perform charging and discharging when needed, so as to adjust the peak clipping and valley filling capacity of the offshore wind power generation system and maintain active power balance. If a time period is taken to be 20min, a day 24h can be divided into 72 time periods, and then t=72.
The energy storage device can be quickly adjusted in response to energy change, can absorb the surplus energy generated by the system, and can release the surplus energy when the power generation is insufficient so as to maintain the power balance of the offshore wind power generation system.
Further, the method establishes an optimized scheduling model aiming at the minimum deviation of the active power, which is specifically expressed as follows:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
Further, the method comprises the steps of obtaining the output data, the energy storage data and the load data of the offshore wind power plant, performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and obtaining an upper-layer control optimized offshore wind power plant scheduling plan, wherein the method comprises the following specific steps:
acquiring offshore wind power output data, energy storage data and load data under a plurality of multi-time scales;
calculating a maximum capacity objective function after active power of the offshore wind farm is absorbed based on the offshore wind power output data, the energy storage data and the load data so as to obtain maximum capacity;
judging whether the maximum capacity is smaller than 0, if so, discharging the energy storage device; otherwise, further judging whether the maximum capacity is smaller than the grid-connected maximum allowable transmission power, if not, solving a minimum maintenance cost objective function of the offshore wind farm so as to enable the maintenance cost of the offshore wind farm to be the lowest, and acquiring an upper-layer control optimized offshore wind farm scheduling plan.
In this embodiment, the minimum maintenance cost objective function of the offshore wind farm may be solved by using a multi-objective differential algorithm, and the obtained upper-layer control optimized offshore wind farm scheduling plan includes an upper-layer control optimized offshore wind power output plan and an energy storage charging and discharging plan. The particle swarm optimization can be adopted to solve the optimized dispatching model, and the obtained lower-layer control optimized offshore wind farm dispatching plan comprises a lower-layer control optimized offshore wind power output plan and an energy storage charging and discharging plan, namely the next-time offshore wind power output plan and the next-time energy storage charging and discharging plan.
Further, the energy storage multi-objective function optimization scheduling model and the optimization scheduling model are required to meet power balance constraint, active power balance constraint of the offshore wind farm and energy storage charging and discharging constraint so as to ensure that each device works normally.
In particular, the power balance constraint may be expressed as:
in the method, in the process of the invention,indicating the active power sent out in the t time period; this moment is:
in the method, in the process of the invention,for t time periods load initial active power consumption, < >>Active power consumed by the jth energy storage device for the t time period.
The active power balance constraint of an offshore wind farm can be expressed as:
wherein P is WTmax And allowing the wind power single machine to output the maximum active power.
The energy storage charge-discharge constraint can be expressed as:
wherein P is ESmin And P ESmax Maximum and minimum charge and discharge power and SOC respectively allowed by energy storage min And SOC (System on chip) max The stored energy allows maximum and minimum states of charge, respectively.
The embodiment not only provides a control flow of gradually progressive upper control and lower control, but also divides a plurality of time nodes for sampling, namely, collects corresponding state data of a plurality of time periods, and ensures the accuracy and instantaneity of control.
Referring to fig. 2, the present embodiment provides an active power coordination control system for offshore wind power and energy storage participation, including: the upper control model construction module is used for constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after the active power of the offshore wind farm is absorbed as targets; the lower control model building module is used for building an optimized scheduling model aiming at the minimum deviation of the active power; the data acquisition module is used for acquiring the offshore wind power output data, the energy storage data and the load data and acquiring the actual output value of the offshore wind farm at the current moment; the upper control module is used for performing upper control on the offshore wind farm based on the offshore wind power output data, the energy storage data, the load data and the energy storage multi-objective function optimization scheduling model to obtain an upper control optimized offshore wind farm scheduling plan; the lower control module is used for judging whether the dispatching plan of the offshore wind farm after the upper control optimization is smaller than the actual output value of the offshore wind farm at the current moment; if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized; otherwise, returning to perform upper control.
The system provided by the embodiment is simple to construct and easy to realize, and is used for realizing an active power coordination control method for the participation of the offshore wind power and energy storage.
Further, in the upper control model building module, the energy storage multi-objective function optimization scheduling model is built by taking the maximum capacity after active power of the offshore wind farm is absorbed and the minimum system maintenance cost as targets, specifically: the energy storage multi-objective function optimization scheduling model comprises a maximum capacity objective function after active power of the offshore wind farm is absorbed and a minimum maintenance cost objective function of the offshore wind farm; wherein:
the maximum capacity objective function after the active power of the offshore wind farm is absorbed is specifically expressed as follows:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th time period;
the minimum maintenance cost objective function of the offshore wind farm is specifically expressed as:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;the working state of the jth energy storage device in the t time period is that the energy storage device is discharged when the value of the j energy storage device is 1, and the charging of the energy storage device is indicated when the value of the j energy storage device is 0; />Representing the charge and discharge power of the jth energy storage device in the t time period; />Representing the maintenance cost of the jth energy storage device during the t-th time period.
In this embodiment, the number of the collection time periods may be a number of time periods obtained by dividing one day into a plurality of time periods, and by observing the running state of the system in each time period, the energy storage device is timely mobilized to perform charging and discharging when needed, so as to adjust the peak clipping and valley filling capacity of the offshore wind power generation system and maintain active power balance.
Further, in the lower control model building module, the minimum deviation for the active power builds an optimized scheduling model, which is specifically expressed as:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
Further, in the upper control module, the upper control is performed on the offshore wind farm based on the offshore wind power output data, the energy storage data, the load data and the energy storage multi-objective function optimization scheduling model, and an upper control optimized offshore wind farm scheduling plan is obtained, which specifically comprises the following steps:
calculating a maximum capacity objective function after active power of the offshore wind farm is absorbed based on the offshore wind power output data, the energy storage data and the load data so as to obtain maximum capacity;
judging whether the maximum capacity is smaller than 0, if so, discharging the energy storage device; otherwise, further judging whether the maximum capacity is smaller than the grid-connected maximum allowable transmission power, if not, solving a minimum maintenance cost objective function of the offshore wind farm so as to enable the maintenance cost of the offshore wind farm to be the lowest, and acquiring an upper-layer control optimized offshore wind farm scheduling plan.
Further, the active power coordination control system with the participation of the offshore wind power and the energy storage further comprises a constraint module, wherein the constraint module is used for constructing power balance constraint, active power balance constraint of the offshore wind power plant and energy storage charge-discharge constraint for the energy storage multi-objective function optimization scheduling model and the optimization scheduling model.
The system provided by the embodiment not only provides a control flow of progressive upper control and progressive lower control, but also divides a plurality of time nodes for sampling, namely, collects corresponding state data of a plurality of time periods, and ensures the accuracy and instantaneity of control.
In order to further explain the technical characteristics of the invention and highlight the technical advantages, the embodiment provides a specific application of the active power coordination control method involving the offshore wind power and the energy storage, and a main circuit schematic diagram of the system can be shown in fig. 3. The single fan is connected to a transmitting-end converter station through a PSMG (permanent magnet synchronous motor) to rectify electric energy, then is connected through a direct-current capacitor, and inverts the electric energy through a receiving-end converter station and transmits the electric energy to a power grid; the storage battery is connected to the power grid through a bidirectional DC/DC converter and an energy storage inverter.
Specifically, constructing an energy storage multi-objective function optimization scheduling model, which comprises the following steps: maximum capacity objective function after active power consumption of offshore wind farm:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th period.
Further comprises: minimum maintenance cost objective function for offshore wind farm:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;the working state of the jth energy storage device in the t time period is that the energy storage device is discharged when the value of the j energy storage device is 1, and the charging of the energy storage device is indicated when the value of the j energy storage device is 0; />Representing the charge and discharge power of the jth energy storage device in the t time period; />Representing the maintenance cost of the jth energy storage device during the t-th time period.
An optimized scheduling model is established for the minimum deviation of the active power, and the method is specifically expressed as follows:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
Further, in order to ensure the normal operation of the system, the energy storage multi-objective function optimization scheduling model and the optimization scheduling model are required to meet the power balance constraint, the active power balance constraint of the offshore wind farm and the energy storage charging and discharging constraint.
Based on the above model, see the control flow of fig. 4, first, upper layer control is performed: collecting offshore wind power output data, energy storage data and load data, collecting once every 20min, collecting 72 times in a day, conveying the collected data to formula (1) for calculation, and judging Y 1 If the wind power output is less than 0, the wind power output is insufficient to maintain the load consumption, the energy storage is discharged, and the energy storage charge and discharge protection is restrained; if not, then next judging Y 1 If the power is smaller than the maximum allowable transmission power, if so, the excess energy can be connected through a line, if not, the method (2) is solved, so that the maintenance cost of the offshore wind power is lowest, and meanwhile, the power balance constraint, the active power output balance constraint of the offshore wind power and the energy storage charge-discharge balance constraint are carried out, so that the upper control is finished, an upper control optimized offshore wind power output plan and an energy storage charge-discharge plan are obtained, and the lower control is carried out.
Judging whether the dispatching plan of the offshore wind farm after the upper control optimization is smaller than the actual output value of the offshore wind farm at the current moment, if not, indicating that the actual output does not reach the minimum maintenance cost standard requirement, and increasing the actual output, thereby returning to the upper control; if so, the actual output reaches the minimum maintenance cost standard requirement, but the power deviation is required to be reduced, the solution (3) is solved, so that the deviation is minimized, and meanwhile, the active power balance constraint, the active power output balance constraint of the offshore wind farm and the energy storage charging and discharging constraint are carried out, so that the lower control is finished, the offshore wind power output plan after the lower control optimization is obtained, the energy storage charging and discharging plan is finished, and the control is finished.
The embodiment designs a multi-time scale coordination control method of upper control and lower control, which divides one day into a plurality of time nodes, observes the running state in each node, and timely transfers the energy storage equipment to charge and discharge as required, so as to adjust the peak clipping and valley filling capacity of the offshore wind power generation system and maintain active power balance. Meanwhile, constraint conditions are set up, and normal operation of each device is guaranteed.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (10)
1. The active power coordination control method for the participation of the offshore wind power and the energy storage is characterized by comprising the following steps of:
constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after active power of the offshore wind farm is absorbed as targets;
establishing an optimized scheduling model aiming at the minimum deviation of the active power;
acquiring offshore wind power output data, energy storage data and load data, performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and acquiring an upper-layer control optimized offshore wind power plant scheduling plan;
acquiring actual output values of the offshore wind farm at the current moment based on the offshore wind power output data, the energy storage data and the load data, and judging whether an upper-layer control optimized offshore wind farm scheduling plan is smaller than the actual output values of the offshore wind farm at the current moment; if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized; otherwise, returning to perform upper control.
2. The method for active power coordination control of offshore wind power and energy storage participation according to claim 1, wherein the method is characterized in that the method is used for constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after active power of an offshore wind power plant is absorbed as targets, and specifically comprises the following steps:
the energy storage multi-objective function optimization scheduling model comprises a maximum capacity objective function after active power of the offshore wind farm is absorbed and a minimum maintenance cost objective function of the offshore wind farm; wherein:
the maximum capacity objective function after the active power of the offshore wind farm is absorbed is specifically expressed as follows:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th time period;
the minimum maintenance cost objective function of the offshore wind farm is specifically expressed as:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;the working state of the jth energy storage device in the t time period is that the energy storage device is discharged when the value of the j energy storage device is 1, and the charging of the energy storage device is indicated when the value of the j energy storage device is 0; />Representing the charge and discharge power of the jth energy storage device in the t time period; />Indicating that the jth energy storage device is at the t-th timeMaintenance costs of the segments.
3. The method for active power coordination control of offshore wind power and energy storage participation according to claim 2, wherein the method is characterized in that an optimal scheduling model is established for the minimum deviation of active power, and specifically comprises the following steps:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
4. The method for active power coordination control of offshore wind power and energy storage participation according to claim 3, wherein the steps of obtaining the output data, the energy storage data and the load data of the offshore wind power, and performing upper-layer control on the offshore wind power plant based on an energy storage multi-objective function optimization scheduling model, and obtaining an upper-layer control optimized offshore wind power plant scheduling plan are as follows:
acquiring offshore wind power output data, energy storage data and load data under a plurality of multi-time scales;
calculating a maximum capacity objective function after active power of the offshore wind farm is absorbed based on the offshore wind power output data, the energy storage data and the load data so as to obtain maximum capacity;
judging whether the maximum capacity is smaller than 0, if so, discharging the energy storage device; otherwise, further judging whether the maximum capacity is smaller than the grid-connected maximum allowable transmission power, if not, solving a minimum maintenance cost objective function of the offshore wind farm so as to enable the maintenance cost of the offshore wind farm to be the lowest, and acquiring an upper-layer control optimized offshore wind farm scheduling plan.
5. The method for active power coordination control of offshore wind power and energy storage participation according to any one of claims 1-4, wherein the energy storage multi-objective function optimal scheduling model and the optimal scheduling model are required to meet power balance constraint, active power balance constraint of an offshore wind farm and energy storage charge-discharge constraint.
6. An active power coordination control system with the participation of offshore wind power and energy storage is characterized by comprising:
the upper control model construction module is used for constructing an energy storage multi-objective function optimization scheduling model by taking the maximum capacity and the minimum system maintenance cost after the active power of the offshore wind farm is absorbed as targets;
the lower control model building module is used for building an optimized scheduling model aiming at the minimum deviation of the active power;
the data acquisition module is used for acquiring the offshore wind power output data, the energy storage data and the load data and acquiring the actual output value of the offshore wind farm at the current moment;
the upper control module is used for performing upper control on the offshore wind farm based on the offshore wind power output data, the energy storage data, the load data and the energy storage multi-objective function optimization scheduling model to obtain an upper control optimized offshore wind farm scheduling plan;
the lower control module is used for judging whether the dispatching plan of the offshore wind farm after the upper control optimization is smaller than the actual output value of the offshore wind farm at the current moment; if so, performing lower-layer control on the offshore wind farm based on the optimized scheduling model, and acquiring a scheduling plan of the offshore wind farm after the lower-layer control is optimized; otherwise, returning to perform upper control.
7. The active power coordination control system with the participation of the offshore wind power and the energy storage according to claim 6, wherein in the upper control model construction module, the objective of maximum capacity and minimum system maintenance cost after the active power of the offshore wind farm is absorbed is to construct an energy storage multi-objective function optimization scheduling model, which specifically comprises:
the energy storage multi-objective function optimization scheduling model comprises a maximum capacity objective function after active power of the offshore wind farm is absorbed and a minimum maintenance cost objective function of the offshore wind farm; wherein:
the maximum capacity objective function after the active power of the offshore wind farm is absorbed is specifically expressed as follows:
wherein Y is 1 Representing maximum capacity; n (N) WT For the total number of fans, i represents the ith fan; w (W) WTi Representing the surplus power of the ith fan; t represents the number of acquisition time periods, T represents the T-th time period;active power generated by the ith fan in the t time period; />Active power consumed by the load during the t-th time period;
the minimum maintenance cost objective function of the offshore wind farm is specifically expressed as:
wherein Y is 2 Representing a minimum maintenance cost; s is S WT The maintenance cost for the wind power single machine; n (N) ES The number j of the energy storage devices is the j th energy storage device;the working state of the jth energy storage device in the t time period is that the energy storage device is discharged when the value of the j energy storage device is 1, and the charging of the energy storage device is indicated when the value of the j energy storage device is 0; />Indicating the charge and discharge of the jth energy storage device in the t time periodA power; />Representing the maintenance cost of the jth energy storage device during the t-th time period.
8. The active power coordination control system with the participation of offshore wind power and energy storage according to claim 7, wherein in the lower control model construction module, an optimal scheduling model is established for the minimum deviation of the active power, specifically expressed as:
in the method, in the process of the invention,and (5) scheduling the optimized ith fan for the upper layer control in the offshore wind farm in the t time period.
9. The active power coordination control system with participation of offshore wind power and energy storage according to claim 8, wherein in the upper control module, the upper control is performed on the offshore wind farm based on the offshore wind power output data, the energy storage data, the load data and the energy storage multi-objective function optimization scheduling model, so as to obtain an upper control optimized offshore wind farm scheduling plan, which specifically is:
calculating a maximum capacity objective function after active power of the offshore wind farm is absorbed based on the offshore wind power output data, the energy storage data and the load data so as to obtain maximum capacity;
judging whether the maximum capacity is smaller than 0, if so, discharging the energy storage device; otherwise, further judging whether the maximum capacity is smaller than the grid-connected maximum allowable transmission power, if not, solving a minimum maintenance cost objective function of the offshore wind farm so as to enable the maintenance cost of the offshore wind farm to be the lowest, and acquiring an upper-layer control optimized offshore wind farm scheduling plan.
10. The active power coordination control system for the participation of offshore wind power and energy storage according to any one of claims 6-9, further comprising a constraint module, wherein the constraint module is used for constructing a power balance constraint, an offshore wind farm active power balance constraint and an energy storage charging and discharging constraint for the energy storage multi-objective function optimal scheduling model and the optimal scheduling model.
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