CN116565874A - Multi-time scale optimal scheduling method for power distribution network considering grid connection of optical storage users - Google Patents
Multi-time scale optimal scheduling method for power distribution network considering grid connection of optical storage users Download PDFInfo
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Abstract
The invention discloses a power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection, which comprises the following steps: acquiring branch impedance information of a power distribution network, the number of adjustable power supplies and position information according to the structure of the power distribution network; acquiring output prediction data of each distributed photovoltaic power supply of the power distribution network and load prediction data of each node of the power distribution network in the next day; dividing day-ahead and day-in scheduling periods, and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period; the method comprises the steps of (1) establishing a day-ahead scheduling optimization model considering operation economy by taking the lowest operation cost of a power distribution network as a target; establishing an intra-day scheduling optimization model considering the power quality by taking the lowest voltage offset rate of the power distribution network as a target; and solving the day-ahead scheduling optimization model and the day-in scheduling optimization model by adopting a particle swarm algorithm to obtain day-ahead and day-in scheduling plans.
Description
Technical Field
The invention relates to the technical field of power distribution network control, in particular to a power distribution network multi-time scale optimal scheduling method considering grid connection of optical storage users.
Background
With the large-scale access of the distributed new energy power supply to the power distribution network, the permeability of the distributed power supply of the low-voltage distribution network is gradually improved. The distributed renewable energy source has the characteristics of flexibility, reliability and the like, can be deployed near a load point, avoids electric energy loss caused by long-distance transmission, and reduces transmission pressure of a transmission line. The distributed photovoltaic power supply has the advantages of high energy utilization rate, simple installation mode, low deployment cost and the like, can be deployed on a large amount on a building roof, and is a main form of accessing the distributed power supply into a power distribution network. However, the large-scale access of the distributed photovoltaic power supply introduces more uncertain factors to the power distribution network, so that the problem of power distribution network scheduling is challenged, and the existing power distribution network scheduling method cannot meet the scheduling requirement of the large-scale distributed photovoltaic power supply. The distributed energy storage unit can store a small amount of electric energy, so that buffering is provided for the photovoltaic power supply, and the output characteristic of the distributed photovoltaic power supply is effectively improved. Therefore, a distributed energy storage device with a certain capacity proportion is configured for the distributed photovoltaic power supply, so that a light storage user is formed and the distributed energy storage device participates in power distribution network dispatching uniformly, and an effective mode for absorbing the distributed photovoltaic power supply is realized. The existing power distribution network dispatching method generally starts from overall power balance, and concerns on power quality problems such as voltage deviation and the like caused by local distributed power sources are less, and the problems that a model is not converged when the distributed power source output fluctuates exist.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention aims to provide a power distribution network multi-time scale optimal scheduling method considering grid connection of optical storage users, which solves the technical problems that the control of a distributed power supply in the power distribution network is difficult and the running state cannot be effectively perceived at present.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a power distribution network multi-time scale optimization scheduling method considering grid connection of optical storage users, including:
acquiring branch impedance information of a power distribution network, the number of adjustable power supplies and position information according to the structure of the power distribution network;
dividing day-ahead and day-in scheduling periods, and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period;
the method comprises the steps of (1) establishing a day-ahead scheduling optimization model considering operation economy by taking the lowest operation cost of a power distribution network as a target;
establishing an intra-day scheduling optimization model considering the power quality by taking the lowest voltage offset rate of the power distribution network as a target;
and solving the day-ahead scheduling optimization model and the day-in scheduling optimization model by adopting a particle swarm algorithm to obtain day-ahead and day-in scheduling plans.
The multi-time scale optimal scheduling method for the power distribution network, which is used for considering the grid connection of the optical storage users, comprises the following steps: the pre-day and intra-day scheduling periods include,
the minimum time units of the day-ahead scheduling and the day-in scheduling decisions are respectively;
the day-ahead schedule period is on the order of hours, and the day-in schedule period is on the order of minutes.
The multi-time scale optimal scheduling method for the power distribution network, which is used for considering the grid connection of the optical storage users, comprises the following steps: the day-ahead schedule optimization model includes,
the specific formula is as follows:
wherein C is the total running cost of the power distribution network, C G (t) generating cost for power supply nodes of power distribution network in each scheduling period, C loss (t) for each scheduleNetwork loss cost in time period distribution network C mar (t) is the margin cost in the distribution network per scheduling period, C PESS (t) the power cost of the optical storage users in the distribution network per scheduling period, N t Scheduling the number of time periods in a day;
all the set constraint conditions are met;
and acquiring accurate load of a scheduling period in a day and accurate output data of the distributed photovoltaic power supply.
The multi-time scale optimal scheduling method for the power distribution network, which is used for considering the grid connection of the optical storage users, comprises the following steps: the operating costs of the distribution network include,
the power supply node power generation cost, the network loss cost, the optical storage user power use cost and the capacity margin cost;
the power generation cost of the power supply node is determined by the power output of the power supply node in the scheduling period;
the network loss cost is used for calculating network loss of the power distribution network and is determined by the loss power of each branch in the current scheduling period;
the capacity margin cost is used for representing the cost required by reserving the scheduling margin in the day-ahead scheduling stage and is determined by the standby power of the adjustable equipment;
the light storage user power use cost is used for representing cost required by dispatching the light storage user in the future, and consists of light storage user power cost and peak regulation compensation cost.
The multi-time scale optimal scheduling method for the power distribution network, which is used for considering the grid connection of the optical storage users, comprises the following steps: constraints of the day-ahead schedule optimization model include,
electric power balance constraint, tide constraint, node voltage and current range constraint, power supply climbing constraint, renewable energy consumption constraint and optical storage user charge state constraint;
the electric power balance constraint is used for ensuring that all power sources in the distribution network generate enough electric power to support all loads and power loss in the distribution network every scheduling period in the distribution network;
the tide constraint is used for representing the transmission relation of electric power in the power distribution network, so that the running state of the power distribution network can be solved;
the node voltage and current range constraint is used as a load flow operation boundary condition, so that a calculation result is ensured to be established, and the solved voltage is ensured to accord with the actual operation condition;
the power supply climbing constraint is used for limiting the power range of the adjustable equipment which is allowed to be adjusted in unit scheduling time;
the renewable energy consumption constraint is used for limiting the typical daily consumption rate of the distributed photovoltaic power supply;
and the charge state constraint of the optical storage user is used for representing the relationship between the charge and discharge conditions of the optical storage user and the charge state.
The multi-time scale optimal scheduling method for the power distribution network, which is used for considering the grid connection of the optical storage users, comprises the following steps: the intra-day scheduling optimization model includes,
the specific formula is as follows:
wherein U is N (t) is the rated voltage per unit value of the node, U i (t) is the node i voltage at time t, N n The number of nodes in the distribution network;
detecting the deviation condition of an operation plan, judging the deviation reason of the plan, and updating the constraint condition of the intra-day scheduling optimization model according to the deviation reason of the plan;
gradually rolling the scheduling time period, repeatedly updating constraint conditions of the intra-day scheduling optimization model until all the scheduling time periods are traversed to correct the day-ahead scheduling plan in real time;
the constraint condition of the intra-day scheduling optimization model is the same as the constraint condition of the pre-day scheduling optimization model, the range of the intra-day scheduling optimization model is correspondingly reduced along with the time scale reduction, and the intra-day scheduling optimization model is further adjusted according to the planned deviation reason so as to ensure the pertinence of the adjustment process;
judging a planned deviation reason, and if the power generation planned deviation exceeds a threshold value, operating an intra-day scheduling optimization model;
if the power generation plan deviation does not exceed the threshold, continuing to operate according to the day-ahead dispatch plan.
The multi-time scale optimal scheduling method for the power distribution network, which is used for considering the grid connection of the optical storage users, comprises the following steps: the adjustment process of the constraint conditions of the intra-day scheduling optimization model comprises the following steps:
for the offset event caused by the load offset day-ahead scheduling plan, the allowable adjustment range of the optical storage user is strictly constrained, and the allowable adjustment output range of the power supply node is loosely constrained;
and for the power generation plan deviation caused by the photovoltaic power supply output deviation, the output range of the optical storage user is loosely constrained, and the output of the power supply node is strictly constrained. Thereby ensuring the pertinence of the intra-day scheduling;
wherein the strict constraint indicates that the adjustable device allows the adjustment range to be further reduced, and the loose constraint indicates that the adjustable device allows the adjustment range to be slightly enlarged.
In a second aspect, an embodiment of the present invention provides a power distribution network multi-time scale optimized scheduling system that considers grid connection of optical storage users, including,
the acquisition module is used for acquiring branch impedance information of the power distribution network, the quantity and position information of the adjustable power supplies, and output prediction data of each distributed photovoltaic power supply of the power distribution network and load prediction data of each node of the power distribution network in the next day according to the structure of the power distribution network;
the dividing module is used for dividing day-ahead and day-in scheduling periods and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period;
the method comprises the steps of (1) a model building module, wherein the model building module is used for building a day-ahead scheduling optimization model considering running economy and building an intra-day scheduling optimization model considering electric energy quality by taking the lowest running cost of a power distribution network as a target and taking the lowest voltage deviation rate of the power distribution network as a target;
and the algorithm module is used for solving the day-ahead scheduling optimization model and the day-in scheduling optimization model by adopting a particle swarm algorithm to obtain day-ahead and day-in scheduling plans.
In a third aspect, embodiments of the present invention provide a computing device comprising:
a memory and a processor;
the memory is configured to store computer executable instructions that, when executed by the one or more processors, cause the one or more processors to implement a power distribution network multi-time scale optimized scheduling method that accounts for optical storage user grid-tie according to any of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium storing computer executable instructions that when executed by a processor implement the method for optimizing and scheduling multiple time scales of a power distribution network in consideration of grid connection of optical storage users.
The invention has the beneficial effects that: compared with the prior art, the distributed energy storage unit and the distributed photovoltaic equipment are combined to perform integrated scheduling, and the problems of voltage fluctuation caused by the fact that a distributed power supply is connected into a power distribution network and power discarding of the distributed power supply are solved. By taking the operation economy of the distribution network and the stability of the electric energy quality as optimization targets respectively in different time scales, the voltage stability is ensured while the operation economy of the system is ensured, and meanwhile, the problem that the model is not converged when the output of the distributed power supply fluctuates is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flow chart of a method for optimizing and dispatching a power distribution network in multiple time scales by considering grid connection of optical storage users.
Fig. 2 is a schematic diagram of a power distribution network structure of a power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection.
Fig. 3 is a schematic diagram of a day-ahead scheduling and day-ahead scheduling strategy of a power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection.
Fig. 4 is a flowchart of an intra-day scheduling constraint condition updating mode of a power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection.
Fig. 5 is a solution flow chart of different time scale optimization scheduling models of the power distribution network multi-time scale optimization scheduling method considering the grid connection of the optical storage users.
Fig. 6 is a comparison chart of output conditions of a day-ahead dispatching power supply of the power distribution network multi-time scale optimization dispatching method considering grid connection of optical storage users.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Further, in describing the embodiments of the present invention in detail, the cross-sectional view of the device structure is not partially enlarged to a general scale for convenience of description, and the schematic is only an example, which should not limit the scope of protection of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Example 1
Referring to fig. 1, for one embodiment of the present invention, a power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection is provided, including:
as shown in fig. 1, the specific steps of the present invention are as follows:
s1: and acquiring branch impedance information of the power distribution network, the quantity and position information of the adjustable power sources according to the structure of the power distribution network.
S2: and dividing day-ahead and day-in scheduling periods, and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period. It should be noted that:
the day-ahead scheduling time period and the day-ahead scheduling time period are the minimum time units for day-ahead scheduling and day-ahead scheduling decisions respectively;
the day-ahead schedule period is on the order of hours, and the day-in schedule period is on the order of minutes.
S3: and (3) establishing a day-ahead scheduling optimization model considering the running economy by taking the lowest running cost of the power distribution network as a target. It should be noted that:
the specific formula of the day-ahead scheduling optimization model is expressed as follows:
wherein C is the total running cost of the power distribution network, C G (t) generating cost for power supply nodes of power distribution network in each scheduling period, C loss (t) is the network loss cost in the distribution network per scheduling period, C mar (t) is the margin cost in the distribution network per scheduling period, C PESS (t) the power cost of the optical storage users in the distribution network per scheduling period, N t Scheduling the number of time periods in a day;
and acquiring accurate load of a scheduling period in a day and accurate output data of the distributed photovoltaic power supply.
The operating costs of the distribution network include,
the power supply node power generation cost, the network loss cost, the optical storage user power use cost and the capacity margin cost;
the power cost is determined by the power node output of the scheduling period, and can be expressed as:
wherein P is G,i (t) active output power for power source i per scheduling period, c PG1 、c PG2 And c PG3 As a power cost factor, N G The number of power supply nodes in the distribution network is the number.
The network loss is used for calculating the network loss of the power distribution network, and is determined by the loss power of each branch in the current scheduling period, and the network loss is specifically as follows:
wherein P is loss,i (t) Power is lost for tributary i per scheduling period, C loss (t) penalty cost per unit power loss per scheduling period for tributaries, N b G is the number of branches in the distribution network jk For conductance between j and k nodes, U j For the node j voltage at time t, U k θ is a point in the network different from point j jk And the power angle difference between the j and k nodes.
The margin cost is used for characterizing the cost required by reserving the scheduling margin in the day-ahead scheduling stage, is determined by the standby power of the adjustable equipment and can be expressed as follows:
wherein E is mar,i (t) optically storing the user i reserve energy storage capacity, c, for each scheduling period mar Spare cost for optical storage user unit power, N PESS The number of users is stored for the light in the distribution network.
The light storage user cost is used for representing the cost required by the day-ahead dispatching light storage user, and consists of the light storage user power cost and the peak shaving compensation cost, and can be expressed as:
wherein P is PESS,i (t) optically storing user i output power, c, for each scheduling period PESS (t) the unit power use cost of the optical storage user, P PV,i (t) storing user i distributed photovoltaic maximum output power for each scheduling period, c comp And (t) peak shaving compensation unit power cost for the optical storage user.
All the set constraint conditions are met;
and acquiring accurate load of a scheduling period in a day and accurate output data of the distributed photovoltaic power supply.
Constraint conditions of the day-ahead dispatching optimization model comprise electric power balance constraint, tide constraint, node voltage and current range constraint, power supply climbing constraint, renewable energy source absorption constraint and light storage user charge state constraint;
the electric power balance constraint is used for ensuring that all power sources in the distribution network generate enough electric power to support all loads and power loss in the distribution network every scheduling period, and the electric power balance constraint is specifically as follows:
wherein P is L,i (t) load point i load value, N for each scheduling period b 、N L And N GT The number of the distribution network branches, the number of the load points and the number of the optical storage users are respectively.
The flow constraint is expressed as:
wherein P is in,i (t) injecting power for node I at time t, I in,i (t) node i injection for time tCurrent per unit value, Y i,j N is admittance between node i and node j n Is the number of nodes in the distribution network.
The node voltage current range constraint can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->The upper and lower limits of the voltage of the ith node of the power distribution network are respectively +.>And->The upper limit and the lower limit of the current of the ith branch of the power distribution network are respectively set;
the power ramp constraint is used to limit the power range that the tunable device is allowed to tune in a unit of scheduling time, and can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,the climbing rate is the power supply i of the power distribution network;
renewable energy consumption constraints are used to limit typical intra-day consumption rates of distributed photovoltaic power sources, which can be expressed as:
wherein P is DG,i (t) i output power of distributed power supply for each scheduling period, N t For the number of distributed photovoltaic nodes,predicting maximum power per schedule period for distributed power source i, c d The lowest daily renewable energy consumption rate is the single distributed power supply of the power distribution network.
The state of charge constraint of the optical storage user is used for representing the relationship between the charge and discharge conditions of the optical storage user and the state of charge, and can be expressed as follows:
E ess,i (t)＝E ess,i (t-1)-P ess,i (t)+P sto,i (t)
wherein E is ess,i (t) is the state of charge of the energy storage device i in the t scheduling period, P ess,i (t) and P sto,i And (t) respectively releasing electric energy power and absorbing electric energy power for the optical storage user i at the time t.
S4: and (3) establishing an intra-day scheduling optimization model considering the power quality by taking the lowest voltage offset rate of the power distribution network as a target. It should be noted that:
the specific formula of the intra-day scheduling optimization model is expressed as follows:
wherein U is N (t) is the rated voltage per unit value of the node, U i (t) is the node i voltage at time t, N n The number of nodes in the distribution network;
detecting the deviation condition of an operation plan, judging the deviation reason of the plan, and updating the constraint condition of the intra-day scheduling optimization model according to the deviation reason of the plan;
gradually rolling the scheduling time period, repeatedly updating constraint conditions of the intra-day scheduling optimization model until all the scheduling time periods are traversed to correct the day-ahead scheduling plan in real time;
the constraint condition of the daily scheduling optimization model is the same as that of the daily scheduling optimization model, the range of the daily scheduling optimization model is correspondingly reduced along with the time scale reduction, and the daily scheduling optimization model is further adjusted according to the planned deviation reason so as to ensure the pertinence of the adjustment process;
and judging the reason of the plan deviation, and if the power generation plan deviation exceeds a threshold value, operating a day-ahead dispatching optimization model.
The constraint condition adjustment process of the intra-day scheduling optimization model comprises the following steps:
for the offset event caused by the load offset day-ahead scheduling plan, the allowable adjustment range of the optical storage user is strictly constrained, and the allowable adjustment output range of the power supply node is loosely constrained;
and for the power generation plan deviation caused by the photovoltaic power supply output deviation, the output range of the optical storage user is loosely constrained, and the output of the power supply node is strictly constrained. Thereby ensuring the pertinence of the intra-day scheduling;
wherein, the strict constraint indicates that the adjustable device allows the adjustment range to be further reduced, and the loose constraint indicates that the adjustable device allows the adjustment range to be slightly enlarged.
S5: and solving a day-ahead scheduling optimization model and a day-in scheduling optimization model by adopting a particle swarm algorithm to obtain day-ahead and day-in scheduling plans. It should be noted that:
the particle swarm optimization is utilized to solve a day-ahead scheduling optimization model and a day-in scheduling optimization model, and the method comprises the following steps:
initializing the running condition of each particle unit device by taking a certain running state of the power distribution network in the scheduling period as a particle unit;
solving the corresponding power flow of each particle unit according to the scheduling constraint conditions in the day-ahead scheduling day;
calculating the optimization target value of each particle unit;
updating the optimal solution of the particle population and the optimal solution of the individual;
updating the inertia weight and the learning factor of the particle unit according to the optimal solution;
updating the running condition of the particle unit equipment;
repeating the steps from calculating the optimization target value of each particle unit to updating the running condition of the particle unit equipment until the maximum iteration times are reached, and outputting the running condition of the equipment with the optimal optimization target value in the example group;
the embodiment also provides a power distribution network multi-time scale optimization scheduling system considering grid connection of optical storage users, which comprises the following steps:
the acquisition module is used for acquiring branch impedance information of the power distribution network, the quantity and position information of the adjustable power supplies, and output prediction data of each distributed photovoltaic power supply of the power distribution network and load prediction data of each node of the power distribution network in the next day according to the structure of the power distribution network;
the dividing module is used for dividing day-ahead and day-in scheduling periods and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period;
the method comprises the steps of (1) a model building module, wherein the model building module is used for building a day-ahead scheduling optimization model considering running economy and building an intra-day scheduling optimization model considering electric energy quality by taking the lowest running cost of a power distribution network as a target and taking the lowest voltage deviation rate of the power distribution network as a target;
and the algorithm module is used for solving a day-ahead scheduling optimization model and a day-in scheduling optimization model by adopting a particle swarm algorithm to acquire day-ahead and day-in scheduling plans.
The embodiment also provides a computing device, which is suitable for a situation of a power distribution network multi-time scale optimization scheduling method considering grid connection of optical storage users, and comprises the following steps:
a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the power distribution network multi-time scale optimization scheduling method considering the grid connection of the optical storage users.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor implements a multi-time scale optimized scheduling method for a power distribution network, which is proposed in the above embodiment and considers grid connection of optical storage users.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
Example 2
Referring to fig. 2 to fig. 6, for another embodiment of the present invention, a verification test of a power distribution network multi-time scale optimization scheduling method considering grid connection of optical storage users is provided, and technical effects adopted in the method are verified and described.
The above method is used for practical implementation, the embodiment adopts an improved IEEE33 node power distribution network system as shown in fig. 2, and adds optical storage users on the basis of the improved IEEE33 node power distribution network system to match the embodiment, wherein node 1 is a power source node, and optical storage users are added at nodes 2, 3, 7, 13 and 30, when day-ahead scheduling is performed, a scheduling period is one day and divided into 24 scheduling periods, a scheduling period is one hour and 3 minutes are scheduling periods and divided into 20 scheduling periods.
As shown in fig. 3, in the day-ahead scheduling process, not only the power balance and the electric energy quality constraint need to be met, but also for the node with higher distributed photovoltaic energy permeability, considering that the output fluctuation risk is higher, in unit time, the output power change amplitude of the power supply node is limited by the thermal stress of the unit, and the output power change value of the unit power in unit time is the climbing rate of the unit, so that the distributed energy storage device of the node should maintain a certain state of charge level to ensure a certain adjustment margin in the actual running process, and the lowest running cost of the distribution network is taken as a main optimization target; in addition, in order to ensure that renewable energy sources can be fully consumed, the ratio of the output power of each distributed photovoltaic power source to the maximum possible output power of each distributed photovoltaic power source is ensured to be higher than a certain ratio in the day-ahead scheduling link, and the ratio is defined as the consumption rate.
The daily scheduling link adopts a daily rolling method to correct the daily scheduling plan of the power distribution network on the same day in real time, and when in actual operation, the load and the distributed photovoltaic output have a certain difference compared with the predicted value, the daily scheduling plan is followed by the distributed energy storage device, so that the output of the optical storage user is matched with the daily scheduling plan as much as possible, and the scheduling uncertainty of the power distribution network is reduced; in addition, real-time scheduling should also be protected against voltage fluctuations caused by changes in photovoltaic output on a short time scale.
In order to prove that the scheduling method provided by the invention is superior to the traditional method, the embodiment sets the following three scenes including the scene 1 as the traditional method for calculation and analysis.
Scenario 1: the distributed photovoltaic is not provided with energy storage, and residual electricity is discarded.
Scenario 2: the distributed optical storage user is formed, and the distributed energy storage with the photovoltaic capacity of 10% is configured.
Scenario 3: the distributed light storage user is formed, and the distributed energy storage with the photovoltaic capacity of 30% is configured.
In three situations, the electricity price of a power supply node is 0.4 yuan/kW h, a photovoltaic and energy storage device is a power distribution network asset, wherein the distributed energy storage configuration cost is 1800 yuan/kW h, and the discharge rate of an energy storage battery is 3h; in order to only show the effect of the light storage users on the scheduling, the load is the same as the distributed photovoltaic scale in three situations.
In this embodiment, the optimization objective of the day-ahead scheduling optimization model is that the running cost of the power distribution network is the lowest, where the running cost of the power distribution network includes the power generation cost of the power source node, the network loss cost, the power usage cost of the optical storage user and the capacity margin cost, and may be expressed as:
wherein C is power distributionNet total running cost, C G (t) generating cost for power supply nodes of power distribution network in each scheduling period, C loss (t) is the network loss cost in the distribution network per scheduling period, C mar (t) is the margin cost in the distribution network per scheduling period, C PESS (t) the power cost of the optical storage users in the distribution network per scheduling period, N t The number of time slots is scheduled in one day.
In this embodiment, the power cost is determined by the power node output of the scheduling period, and the specific formula is as follows:
wherein P is G,i (t) active output power for power source i per scheduling period, c PG1 、c PG2 And c PG3 As a power cost factor, N G The number of power supply nodes in the distribution network is the number.
In this embodiment, the network loss is used to calculate the network loss of the power distribution network, and is determined by the loss power of each branch in the current scheduling period, and the specific formula is as follows:
wherein P is loss,i (t) Power is lost for tributary i per scheduling period, C loss (t) penalty cost per unit power loss per scheduling period for tributaries, N b G is the number of branches in the distribution network jk For conductance between j and k nodes, U j For the node j voltage at time t, U k θ is a point in the network different from point j jk And the power angle difference between the j and k nodes.
In this embodiment, the margin cost is used to characterize the cost required for reserving the scheduling margin in the day-ahead scheduling stage, and is determined by the standby power of the adjustable device, and the specific formula is as follows:
wherein E is mar,i (t) optically storing the user i reserve energy storage capacity, c, for each scheduling period mar Spare cost for optical storage user unit power, N PESS The number of users is stored for the light in the distribution network.
In this embodiment, the light storage user cost is used for representing the cost required by the day-ahead scheduling light storage user, and is composed of the light storage user power cost and the peak shaving compensation cost, and the specific formula is as follows:
wherein P is PESS,i (t) optically storing user i output power, c, for each scheduling period PESS (t) the unit power use cost of the optical storage user, P PV,i (t) storing user i distributed photovoltaic maximum output power for each scheduling period, c comp And (t) peak shaving compensation unit power cost for the optical storage user.
In the embodiment, constraint conditions of the day-ahead optimization scheduling model include electric power balance constraint, tide constraint, node voltage and current range constraint, power supply climbing constraint, renewable energy source absorption constraint and light storage user charge state constraint;
in this embodiment, the electric power balance constraint is used to ensure that all power sources in the distribution network generate enough electric power to support all loads and power losses in the distribution network per scheduling period, and the specific formula is as follows:
wherein P is L,i (t) load point i load value, N for each scheduling period b 、N L And N GT The number of the distribution network branches is respectively,The number of load points and the number of optical storage users.
In this embodiment, the specific formula of the power flow constraint is as follows:
wherein P is in,i (t) injecting power for node I at time t, I in,i (t) injecting current per unit value for node i at time t, Y i,j N is admittance between node i and node j n Is the number of nodes in the distribution network.
In this embodiment, the specific formula of the node voltage and current range constraint is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,the upper and lower limits of the voltage of the ith node of the power distribution network are respectively +.>And the upper and lower limits of the current of the ith branch of the power distribution network are respectively set.
In this embodiment, the power ramp constraint is used to limit the power range that the adjustable device is allowed to adjust in a unit scheduling time, and the specific formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,the climbing rate of the power supply i of the power distribution network is achieved.
In this embodiment, the renewable energy consumption constraint is used to limit the typical daily consumption ratio of the distributed photovoltaic power source, and the specific formula is as follows:
wherein P is DG,i (t) i output power of distributed power supply for each scheduling period, N t For the number of distributed photovoltaic nodes,predicting maximum power per schedule period for distributed power source i, c d The lowest daily renewable energy consumption rate is the single distributed power supply of the power distribution network.
In this embodiment, the state of charge constraint of the optical storage user is used to characterize the relationship between the charge and discharge conditions of the optical storage user and the state of charge, and the specific formula is as follows:
E ess,i (t)＝E ess,i (t-1)-P ess,i (t)+P sto,i (t)
wherein E is ess,i (t) is the state of charge of the energy storage device i in the t scheduling period, P ess,i (t) and P sto,i And (t) respectively releasing electric energy power and absorbing electric energy power for the optical storage user i at the time t.
The day-ahead scheduling time period and the day-ahead scheduling time period are respectively the minimum time units for day-ahead scheduling and day-ahead scheduling decisions; preferably, the day-ahead schedule period is on the order of hours and the day-in schedule period is on the order of minutes.
The real-time dispatching optimization target is that the total voltage offset rate of the power distribution network is the lowest, and the specific formula is as follows:
wherein U is N (t) is the rated voltage per unit value of the node, U i (t) is the node i voltage at time t, N n Is the number of nodes in the distribution network.
The time scale of the daily scheduling is reduced to a minute level, and on the scheduling optimization problem of a short time scale, the adjustment range of a power supply node is relatively limited due to the limitation of the time scale, relatively accurate load and renewable energy output prediction data can be obtained at the stage, so that the updated data can be used for correcting a plan on the basis of daily scheduling, and node voltage fluctuation caused by output change of photovoltaic equipment is restrained; meanwhile, as shown in fig. 4, the constraint condition range is updated according to the planned deviation cause.
As shown in fig. 5, the particle swarm algorithm is used to solve the day-ahead and day-in optimal scheduling model, and the steps include:
initializing the running condition of each particle unit device by taking a certain running state of the power distribution network in the scheduling period as a particle unit;
according to the constraint condition model, solving the corresponding power flow of each particle unit;
calculating the optimization target value of each particle unit;
updating the optimal solution of the particle population and the optimal solution of the individual;
updating the inertia weight and the learning factor of the particle unit according to the optimal solution;
updating the running condition of the particle unit equipment;
repeating the steps from the calculation of the optimization target value of each particle unit to the updating of the running condition of the particle unit equipment until the maximum iteration times are reached, and outputting the running condition of the equipment with the optimal optimization target value in the example group.
As shown in fig. 6, after three situations are subjected to day-ahead scheduling configuration to form an optical storage user, the output characteristics of the power supply node are changed, compared with the case that the energy storage is not configured, the peak output power of the power supply node is reduced by 1.2MW after 10% of energy storage is configured for the distributed photovoltaic power supply, and the unit operation cost is reduced by a small margin; after 30% energy storage is configured, the output characteristic of the power supply node is greatly improved, the peak output power is reduced by 2.4MW, and meanwhile, 15:00-19: and the unit output power adjustment quantity during 00 hours is reduced by 38% compared with that of the scene 1, so that the output characteristic of the power supply node unit is greatly smoothed.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection is characterized in that: comprising the steps of (a) a step of,
acquiring branch impedance information of a power distribution network, the number of adjustable power supplies and position information according to the structure of the power distribution network;
dividing day-ahead and day-in scheduling periods, and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period;
the method comprises the steps of (1) establishing a day-ahead scheduling optimization model considering operation economy by taking the lowest operation cost of a power distribution network as a target;
establishing an intra-day scheduling optimization model considering the power quality by taking the lowest voltage offset rate of the power distribution network as a target;
and solving the day-ahead scheduling optimization model and the day-in scheduling optimization model by adopting a particle swarm algorithm to obtain day-ahead and day-in scheduling plans.
2. The power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection as claimed in claim 1, wherein the method comprises the following steps: the pre-day and intra-day scheduling periods include,
the minimum time units of the day-ahead scheduling and the day-in scheduling decisions are respectively;
the day-ahead schedule period is on the order of hours, and the day-in schedule period is on the order of minutes.
3. The power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection as claimed in claim 1, wherein the method comprises the following steps: the day-ahead schedule optimization model includes,
the specific formula is as follows:
wherein C is the total running cost of the power distribution network, C G (t) generating cost for power supply nodes of power distribution network in each scheduling period, C loss (t) is the network loss cost in the distribution network per scheduling period, C mar (t) is the margin cost in the distribution network per scheduling period, C PESS (t) the power cost of the optical storage users in the distribution network per scheduling period, N t Scheduling the number of time periods in a day;
all the set constraint conditions are met;
and acquiring accurate load of a scheduling period in a day and accurate output data of the distributed photovoltaic power supply.
4. The power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection as claimed in claim 1, wherein the method comprises the following steps: the operating costs of the distribution network include,
the power supply node power generation cost, the network loss cost, the optical storage user power use cost and the capacity margin cost;
the power generation cost of the power supply node is determined by the power output of the power supply node in the scheduling period;
the network loss cost is used for calculating network loss of the power distribution network and is determined by the loss power of each branch in the current scheduling period;
the capacity margin cost is used for representing the cost required by reserving the scheduling margin in the day-ahead scheduling stage and is determined by the standby power of the adjustable equipment;
the light storage user power use cost is used for representing cost required by dispatching the light storage user in the future, and consists of light storage user power cost and peak regulation compensation cost.
5. A power distribution network multi-time scale optimized scheduling method considering optical storage user grid connection as claimed in claim 3, wherein: constraints of the day-ahead schedule optimization model include,
electric power balance constraint, tide constraint, node voltage and current range constraint, power supply climbing constraint, renewable energy consumption constraint and optical storage user charge state constraint;
the electric power balance constraint is used for ensuring that all power sources in the distribution network generate enough electric power to support all loads and power loss in the distribution network every scheduling period in the distribution network;
the tide constraint is used for representing the transmission relation of electric power in the power distribution network, so that the running state of the power distribution network can be solved;
the node voltage and current range constraint is used as a load flow operation boundary condition to ensure that a calculation result is established;
the power supply climbing constraint is used for limiting the power range of the adjustable equipment which is allowed to be adjusted in unit scheduling time;
the renewable energy consumption constraint is used for limiting the typical daily consumption rate of the distributed photovoltaic power supply;
and the charge state constraint of the optical storage user is used for representing the relationship between the charge and discharge conditions of the optical storage user and the charge state.
6. The power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection as claimed in claim 1 or 5, wherein the method comprises the following steps of: the intra-day scheduling optimization model includes,
the specific formula is as follows:
wherein U is N (t) is the rated voltage per unit value of the node, U i (t) is the node i voltage at time t, N n The number of nodes in the distribution network;
detecting the deviation condition of an operation plan, judging the deviation reason of the plan, and updating the constraint condition of the intra-day scheduling optimization model according to the deviation reason of the plan;
gradually rolling the scheduling time period, repeatedly updating constraint conditions of the intra-day scheduling optimization model until all the scheduling time periods are traversed to correct the day-ahead scheduling plan in real time;
the constraint condition of the intra-day scheduling optimization model is the same as the constraint condition of the pre-day scheduling optimization model, the range of the intra-day scheduling optimization model is correspondingly reduced along with the time scale reduction, and the intra-day scheduling optimization model is further adjusted according to the planned deviation reason so as to ensure the pertinence of the adjustment process;
judging a planned deviation reason, and if the power generation planned deviation exceeds a threshold value, operating an intra-day scheduling optimization model;
if the power generation plan deviation does not exceed the threshold, continuing to operate according to the day-ahead dispatch plan.
7. The power distribution network multi-time scale optimization scheduling method considering optical storage user grid connection as claimed in claim 6, wherein the method comprises the following steps: the adjustment process of the constraint conditions of the intra-day scheduling optimization model comprises the following steps:
for the offset event caused by the load offset day-ahead scheduling plan, the allowable adjustment range of the optical storage user is strictly constrained, and the allowable adjustment output range of the power supply node is loosely constrained;
for the power generation plan deviation caused by the photovoltaic power supply output deviation, the output range of the optical storage user is loosely constrained, the power supply node output is strictly constrained, and the intra-day scheduling pertinence is ensured;
wherein the tight constraint indicates that the adjustable device allows the adjustment range to be further reduced, and the loose constraint indicates that the adjustable device allows the adjustment range to be slightly increased.
8. A power distribution network multi-time scale optimization scheduling system considering optical storage user grid connection is characterized by comprising,
the acquisition module is used for acquiring branch impedance information of the power distribution network, the quantity and position information of the adjustable power supplies, and output prediction data of each distributed photovoltaic power supply of the power distribution network and load prediction data of each node of the power distribution network in the next day according to the structure of the power distribution network;
the dividing module is used for dividing day-ahead and day-in scheduling periods and acquiring output data of each distributed photovoltaic power supply of the power distribution network and load data of each node of the power distribution network in each scheduling period;
the method comprises the steps of (1) a model building module, wherein the model building module is used for building a day-ahead scheduling optimization model considering running economy and building an intra-day scheduling optimization model considering electric energy quality by taking the lowest running cost of a power distribution network as a target and taking the lowest voltage deviation rate of the power distribution network as a target;
and the algorithm module is used for solving the day-ahead scheduling optimization model and the day-in scheduling optimization model by adopting a particle swarm algorithm to obtain day-ahead and day-in scheduling plans.
9. A computing device, comprising:
a memory and a processor;
the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, where the computer executable instructions when executed by the processor implement the steps of a power distribution network multi-time scale optimization scheduling method that considers optical storage user grid connection according to any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of a power distribution network multi-time scale optimized scheduling method for light storage user grid connection considered as claimed in any one of claims 1 to 7.
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| CN117996861A (en) * | 2024-04-02 | 2024-05-07 | 浙江大学 | Scheduling method for light-water coupling residual electricity hydrogen production of power distribution network and energy management device |
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| CN117996861A (en) * | 2024-04-02 | 2024-05-07 | 浙江大学 | Scheduling method for light-water coupling residual electricity hydrogen production of power distribution network and energy management device |
| CN117996861B (en) * | 2024-04-02 | 2024-06-11 | 浙江大学 | Scheduling method for light-water coupling residual electricity hydrogen production of power distribution network and energy management device |
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