CN111009896A - Day-ahead optimization scheduling method and system for power distribution network - Google Patents
Day-ahead optimization scheduling method and system for power distribution network Download PDFInfo
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention relates to a day-ahead optimal scheduling method and system for a power distribution network. The method comprises the following steps: s1: and establishing a day-ahead optimization scheduling model of the power distribution network. S2: and solving the day-ahead optimized scheduling model of the power distribution network by using a particle swarm algorithm to obtain the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network. S3: and determining the day-ahead scheduling capacity of each schedulable device in the same type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network. The types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit. Based on the technical scheme provided by the application, the distributed power generation unit is taken as one of elements scheduled in the day-ahead and is comprehensively considered with other elements, so that the efficient operation of the power distribution network can be realized.
Description
Technical Field
The invention relates to full resource coordination optimization of a power distribution network, in particular to a day-ahead optimization scheduling method and system of the power distribution network.
Background
The large-scale access of the distributed renewable energy sources has obvious influence on the original electrical characteristics such as the system tide distribution, the voltage level, the short-circuit capacity and the like, so that the traditional power distribution network cannot meet the access operation and high-efficiency utilization requirements of high-permeability renewable energy source power generation under the low-carbon economic background. Coordinated optimization scheduling is a core technology and an important means for the power distribution network to actively manage controllable resources such as distributed power sources and controllable loads and realize efficient operation.
The traditional day-ahead scheduling compiles a power generation plan of the next day according to predicted load, a unit power generation and maintenance plan, a tie line exchange power plan, unit consumption characteristics and the like, and is one of the core contents of the economic scheduling of the power system. After the distributed power generation units (solar photovoltaic power generation units, wind power generation units, micro gas turbine power generation units and the like) are considered to be connected to the grid in a large scale, the traditional day-ahead scheduling method ignores the influence of uncertainty of output of the renewable energy sources, so the traditional day-ahead scheduling method is not suitable any more, and the search for a new day-ahead scheduling method is very important.
Disclosure of Invention
Therefore, it is necessary to provide a day-ahead optimized scheduling method and system for a power distribution network system after large-scale grid connection of distributed power generation units.
A day-ahead optimal scheduling method for a power distribution network, the method comprising:
s1: establishing a day-ahead optimized scheduling model of the power distribution network;
s2: solving the day-ahead optimized scheduling model of the power distribution network by using a particle swarm algorithm to obtain the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
s3: determining the day-ahead scheduling capacity of each schedulable device in each type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
the types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit.
In one embodiment, the S1 includes: establishing a day-ahead optimized scheduling objective function of the power distribution network;
the method for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: the method comprises the steps of establishing a network loss minimum objective function of the power distribution network, establishing a peak clipping and valley filling objective function and establishing a maximum absorption objective function of the distributed energy.
In one embodiment, the S1 further includes: the constraint condition of the day-ahead optimized scheduling objective function of the power distribution network;
the constraint condition for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: establishing a power flow constraint, establishing an output constraint of the distributed power generation unit, establishing an energy constraint of the energy storage unit, establishing a charge and discharge power constraint of the energy storage unit, establishing a controllable load power constraint and establishing a position constraint of the interconnection switch.
In one embodiment, the establishing a network loss minimum objective function of the power distribution network includes:
establishing a network loss minimum objective function minf of the power distribution network according to the following formula1：
Wherein T is the T-th time interval in the period, T is the time interval number in the period, k is the node number in the power distribution network system,
the active power is injected for the ith node during the time period t,
for the injection of active power of the jth node during the period t, BijIs a quadratic term of the coefficient B, Bi,0Is a primary term of the coefficient B, B0,0Is a constant term for the B coefficient.
In one embodiment, the establishing a peak clipping and valley filling objective function includes:
establishing the peak clipping and valley filling objective function minf according to the following formula2：
Wherein T is the T-th time interval in the cycle, T is the number of time intervals in the cycle, Pd,tRemoving loads of distributed power generation units and energy storage units in power distribution network systemThe rest load is the first distributed power generation unit in the power distribution network system, NGFor the number of distributed power generating units, P, in a power distribution network systemmax,tThe maximum value of the system load of the power distribution network in the period t,
for average load of the distribution network system within a cycle, N0α is penalty factor, x is installed capacity of distributed generation unit in power distribution network systeml,tAnd generating power for the ith distributed generation unit in the power distribution network system in the tth period.
In one embodiment, the establishing the maximum absorption objective function of the distributed energy resource includes:
establishing a maximum absorption objective function minf of the distributed energy source according to the following formula:
wherein N is the nth photovoltaic power station in the power distribution network system, and NfNumber of photovoltaic power stations, P, in a distribution network systemgivePlanning the power generation for photovoltaic plant guidance, PplanAnd reporting the planned generating power for the photovoltaic power station.
In one embodiment, the output constraints of the distributed power generation unit include:
the output of the distributed power generation units in the power distribution network system is less than or equal to the maximum value of the output of the distributed power generation units and is greater than or equal to the minimum value of the output of the distributed power generation units.
In one embodiment, the energy storage unit energy constraint includes:
the energy of the energy storage units in the power distribution network system is smaller than or equal to a first preset threshold value and larger than or equal to a second preset threshold value.
In one embodiment, the first preset threshold is 70% -100% of the rated capacity of the energy storage unit.
In one embodiment, the second preset threshold is 10% -30% of the rated capacity of the energy storage unit.
In one embodiment, the energy storage unit charging and discharging power constraint includes:
the charging power of the energy storage unit in the power distribution network system is less than or equal to the maximum value of the self charging power and is greater than or equal to the minimum value of the self charging power;
the discharging power of the energy storage units in the power distribution network system is less than or equal to the maximum value of the self discharging power and more than or equal to the minimum value of the self discharging power.
In one embodiment, the controllable-load power constraint includes:
the power of the controllable load in the power distribution network system is less than or equal to the maximum value of the controllable power of the controllable load and greater than or equal to the minimum value of the controllable power of the controllable load.
In one embodiment, the tie switch position constraint comprises:
the interconnection switch is in a radial structure in the power distribution network system.
In one embodiment, the S3 includes:
determining a day-ahead scheduling capacity Q of the mth schedulable device in the distributed generation unit according tom:
Determining the day-ahead scheduling capacity Q of the ith schedulable device in the energy storage unit according to the following formulay：
Determining the day-ahead scheduling capacity Q of the s-th schedulable device in the controllable load unit according tos：
Q' is the total day-ahead schedulable capacity of the distributed power generation units, M is the number of schedulable devices in the distributed power generation units, and M belongs to [1, M ]; q' is the total schedulable capacity of the energy storage unit in the day ahead, Y is the number of schedulable devices in the energy storage unit, and Y belongs to [1, Y ]; q' is the total day-ahead schedulable capacity of the controllable load unit, S is the number of schedulable devices in the controllable load unit, S is [1, S ].
A day-ahead optimized dispatch system for a power distribution network, the system comprising:
the establishing module is used for establishing a day-ahead optimized scheduling model of the power distribution network;
the acquisition module is used for solving the day-ahead optimized scheduling model of the power distribution network by utilizing a particle swarm algorithm to acquire the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
the determining module is used for determining the day-ahead scheduling capacity of each schedulable device in each type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
the types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit.
The embodiment of the application provides a day-ahead optimization scheduling method for a power distribution network, which comprises the following steps: and establishing a day-ahead optimization scheduling model of the power distribution network. And solving the day-ahead optimized scheduling model of the power distribution network by using a particle swarm algorithm to obtain the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network. And determining the day-ahead scheduling capacity of each schedulable device in the same type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network. The types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit. According to the technical scheme provided by the embodiment of the application, the distributed power generation unit is taken as one of elements scheduled in the day-ahead and is comprehensively considered with other elements, so that the efficient operation of the power distribution network can be realized.
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Fig. 1 is a flowchart of a method for day-ahead optimal scheduling of a power distribution network according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a system for optimizing and scheduling a power distribution network in the future according to an embodiment of the present application.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
As shown in fig. 1, in an embodiment of the present application, a method for day-ahead optimal scheduling of a power distribution network is provided, where the method includes:
s1: and establishing a day-ahead optimization scheduling model of the power distribution network.
S2: and solving the day-ahead optimized scheduling model of the power distribution network by using a particle swarm algorithm to obtain the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network.
S3: and determining the day-ahead scheduling capacity of each schedulable device in the same type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network.
Specifically, the day before is the day or days before the actual scheduling day. The types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit. The distributed power generation unit generally includes a solar photovoltaic power generation unit, a wind power generation unit, a micro gas turbine power generation unit, and the like. The solar photovoltaic power generation unit and the wind power generation unit are used as clean renewable energy sources, are generally utilized to the maximum extent during normal operation, and generally adopt a Maximum Power Point Tracking (MPPT) mode. The micro gas turbine power generation unit is generally operated at a higher load rate as a schedulable power generation unit. The energy storage unit can rapidly adjust the output or storage power of the energy storage unit according to the scheduling requirement of the system, and can provide the functions of peak clipping and valley filling, power fluctuation stabilization, electric energy quality improvement and the like. When the controllable load unit serving as a schedulable power utilization unit receives a scheduling request issued by scheduling, the response can be fast, and the functions of peak clipping and valley filling are achieved.
In one embodiment, the S1 includes: and establishing a day-ahead optimized dispatching objective function of the power distribution network and establishing a constraint condition of the day-ahead optimized dispatching objective function of the power distribution network. The method for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: the method comprises the steps of establishing a network loss minimum objective function of the power distribution network, establishing a peak clipping and valley filling objective function and establishing a maximum absorption objective function of the distributed energy. The constraint condition for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: establishing a power flow constraint, establishing an output constraint of the distributed power generation unit, establishing an energy constraint of the energy storage unit, establishing a charge and discharge power constraint of the energy storage unit, establishing a controllable load power constraint and establishing a position constraint of the interconnection switch.
Specifically, under the joint optimization of the multiple objective functions and the multiple constraint conditions, the high-efficiency, energy-saving and effective operation of the power distribution network system can be realized under the condition that the safety of the power distribution network system is met.
In one embodiment, the establishing a network loss minimum objective function of the power distribution network includes:
establishing a network loss minimum objective function minf of the power distribution network according to the following formula1：
Wherein T is the T-th time interval in the period, T is the time interval number in the period, k is the node number in the power distribution network system,
the active power is injected for the ith node during the time period t,
for the injection of active power of the jth node during the period t, BijIs a quadratic term of the coefficient B, Bi,0Is a primary term of the coefficient B, B0,0Is a constant term for the B coefficient.
The establishing of the peak clipping and valley filling target function comprises the following steps:
establishing the peak clipping and valley filling objective function minf according to the following formula2：
Wherein T is the T-th time interval in the cycle, T is the number of time intervals in the cycle, Pd,tThe residual load after the load of the distributed power generation unit and the load of the energy storage unit are removed in the power distribution network system, wherein l is the first distributed power generation unit in the power distribution network system, NGFor the number of distributed power generating units, P, in a power distribution network systemmax,tThe maximum value of the load of the power distribution network system in the period t, P is the average load of the power distribution network system in the period, N0α is penalty factor, x is installed capacity of distributed generation unit in power distribution network systeml,tAnd generating power for the ith distributed generation unit in the power distribution network system in the tth period.
The establishing of the maximum consumption objective function of the distributed energy comprises the following steps:
establishing a maximum absorption objective function minf of the distributed energy source according to the following formula:
wherein N is the nth photovoltaic power station in the power distribution network system, and NfNumber of photovoltaic power stations, P, in a distribution network systemgivePlanning the power generation for photovoltaic plant guidance, PplanAnd reporting the planned generating power for the photovoltaic power station.
In one embodiment, the output constraint of the distributed power generation unit is established as follows: the output of the distributed power generation units in the power distribution network system is less than or equal to the maximum value of the output of the distributed power generation units and is greater than or equal to the minimum value of the output of the distributed power generation units. The maximum value and the minimum value of the self-output are determined by the equipment parameters of the distributed power generation unit and the capacity of the inverter.
In one embodiment, the establishing the energy constraint of the energy storage unit includes: the energy of the energy storage units in the power distribution network system is smaller than or equal to a first preset threshold value and larger than or equal to a second preset threshold value.
Specifically, the energy of the energy storage unit can maintain high efficiency within a certain range, so that the energy of the whole energy storage system must be ensured to be within a reasonable range. The first preset threshold value can be 70% -100% of the rated capacity of the energy storage unit. The second preset threshold may be 10% -30% of the rated capacity of the energy storage unit. As a preferred embodiment, the first preset threshold may be 90% of the rated capacity of the energy storage unit, and the second preset threshold may be 20% of the rated capacity of the energy storage unit. In addition, the energy constraint of the energy storage unit can reasonably consider the energy storage reserve capacity required by the smooth distributed power generation fluctuation.
In one embodiment, the establishing the charge and discharge power constraint of the energy storage unit includes: the charging power of the energy storage unit in the power distribution network system is less than or equal to the maximum value of the self charging power and is greater than or equal to the minimum value of the self charging power. The discharging power of the energy storage units in the power distribution network system is less than or equal to the maximum value of the self discharging power and more than or equal to the minimum value of the self discharging power. The maximum value of the self-charging power, the minimum value of the self-charging power, the maximum value of the self-discharging power and the minimum value of the self-discharging power are determined by equipment parameters of an energy storage unit and the capacity of an inverter. The device parameters are determined by the device manufacturing process.
In one embodiment, the establishing the controllable-load power constraint includes: the power of the controllable load in the power distribution network system is less than or equal to the maximum value of the controllable power of the controllable load and greater than or equal to the minimum value of the controllable power of the controllable load.
Specifically, the maximum value of the self-controllable power and the minimum value of the self-controllable power may be obtained by the AMI system.
In one embodiment, the establishing tie switch position constraints comprises: the interconnection switch is in a radial structure in the power distribution network system.
In one embodiment, in S2, the solving the day-ahead optimization scheduling model of the power distribution network by using a particle swarm optimization comprises: s21: initializing the particle swarm. S22: the fitness value for each particle is calculated. S23: and comparing the adaptive value of each particle with the adaptive value of the best position experienced, and if the adaptive value of the particle is better than the adaptive value of the best position experienced, updating the current best position by using the adaptive value of the particle. S24: and comparing the adaptive value of each particle with the adaptive value of the best global experienced position, and updating the global best position by using the adaptive value of the particle if the adaptive value of the particle is better than the adaptive value of the best global experienced position. S25: the velocity and position of the particles are updated. S26: and judging whether a termination condition is reached. If the end condition is reached, the solution process is ended and the result is output, and if the end condition is not reached, the flow goes to S22. The termination condition may be a preset maximum number of iterations or other suitable conditions.
In one embodiment, the S3 includes: determining a day-ahead scheduling capacity Q of the mth schedulable device in the distributed generation unit according tom:
Determining the day-ahead scheduling capacity Q of the ith schedulable device in the energy storage unit according to the following formulay：
Determining the day-ahead scheduling capacity Q of the s-th schedulable device in the controllable load unit according tos：
Q' is the total day-ahead schedulable capacity of the distributed power generation units, M is the number of schedulable devices in the distributed power generation units, and M belongs to [1, M ]; q' is the total schedulable capacity of the energy storage unit in the day ahead, Y is the number of schedulable devices in the energy storage unit, and Y belongs to [1, Y ]; q' is the total day-ahead schedulable capacity of the controllable load unit, S is the number of schedulable devices in the controllable load unit, S is [1, S ].
As shown in fig. 2, an embodiment of the present application provides a system for optimizing scheduling of a distribution network in the future, including: the device comprises an establishing module, an obtaining module and a determining module.
Specifically, the establishing module is used for establishing a day-ahead optimized dispatching model of the power distribution network. And the obtaining module is used for solving the day-ahead optimized scheduling model of the power distribution network by utilizing a particle swarm algorithm to obtain the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network. And the determining module is used for determining the day-ahead scheduling capacity of each schedulable device in the same type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network. The types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit.
In one embodiment, the establishing module comprises: a first establishing unit and a second establishing unit. The first establishing unit is used for establishing a day-ahead optimized dispatching objective function of the power distribution network. The second establishing unit is used for establishing a constraint condition of a day-ahead optimization scheduling objective function of the power distribution network. The method for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: establishing a network loss minimum objective function of the power distribution network, establishing a peak clipping and valley filling objective function and establishing a maximum absorption objective function of the distributed energy; the constraint condition for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: establishing a power flow constraint, establishing an output constraint of the distributed power generation unit, establishing an energy constraint of the energy storage unit, establishing a charge and discharge power constraint of the energy storage unit, establishing a controllable load power constraint and establishing a position constraint of the interconnection switch.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (15)
1. A day-ahead optimal scheduling method for a power distribution network is characterized by comprising the following steps:
s1: establishing a day-ahead optimized scheduling model of the power distribution network;
s2: solving the day-ahead optimized scheduling model of the power distribution network by using a particle swarm algorithm to obtain the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
s3: determining the day-ahead scheduling capacity of each schedulable device in each type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
the types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit.
2. The method according to claim 1, wherein the S1 includes: establishing a day-ahead optimized scheduling objective function of the power distribution network;
the method for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: the method comprises the steps of establishing a network loss minimum objective function of the power distribution network, establishing a peak clipping and valley filling objective function and establishing a maximum absorption objective function of the distributed energy.
3. The method according to claim 1, wherein the S1 further comprises: the constraint condition of the day-ahead optimized scheduling objective function of the power distribution network;
the constraint condition for establishing the day-ahead optimized scheduling objective function of the power distribution network comprises the following steps: establishing a power flow constraint, establishing an output constraint of the distributed power generation unit, establishing an energy constraint of the energy storage unit, establishing a charge and discharge power constraint of the energy storage unit, establishing a controllable load power constraint and establishing a position constraint of the interconnection switch.
4. The method of claim 2, wherein establishing the network loss minimization objective function for the power distribution network comprises:
establishing a network loss minimum objective function minf of the power distribution network according to the following formula1：
Wherein T is the T-th time interval in the period, T is the time interval number in the period, k is the node number in the power distribution network system, Pi tThe active power is injected for the ith node during the time period t,
for the injection of active power of the jth node during the period t, BijIs a quadratic term of the coefficient B, Bi,0Is a primary term of the coefficient B, B0,0Is a constant term for the B coefficient.
5. The method of claim 2, wherein the establishing a peak clipping and valley filling objective function comprises:
establishing the peak clipping and valley filling objective function minf according to the following formula2：
Wherein T is the T-th time interval in the cycle, T is the number of time intervals in the cycle, Pd,tThe residual load after the load of the distributed power generation unit and the load of the energy storage unit are removed in the power distribution network system, wherein l is the first distributed power generation unit in the power distribution network system, NGFor the number of distributed power generating units, P, in a power distribution network systemmax,tThe maximum value of the system load of the power distribution network in the period t,
for average load of the distribution network system within a cycle, N0α is penalty factor, x is installed capacity of distributed generation unit in power distribution network systeml,tAnd generating power for the ith distributed generation unit in the power distribution network system in the tth period.
6. The method of claim 2, wherein establishing a maximum absorption objective function for the distributed energy resource comprises:
establishing a maximum absorption objective function minf of the distributed energy source according to the following formula:
wherein N is the nth photovoltaic power station in the power distribution network system, and NfNumber of photovoltaic power stations, P, in a distribution network systemgivePlanning the power generation for photovoltaic plant guidance, PplanAnd reporting the planned generating power for the photovoltaic power station.
7. The method of claim 3, wherein the output constraints of the distributed generation unit comprise:
the output of the distributed power generation units in the power distribution network system is less than or equal to the maximum value of the output of the distributed power generation units and is greater than or equal to the minimum value of the output of the distributed power generation units.
8. The method of claim 3, wherein the energy storage unit energy constraint comprises:
the energy of the energy storage units in the power distribution network system is smaller than or equal to a first preset threshold value and larger than or equal to a second preset threshold value.
9. The method according to claim 8, wherein the first preset threshold is 70% -100% of the rated capacity of the energy storage unit.
10. The method according to claim 8, wherein the second preset threshold is 10% -30% of the rated capacity of the energy storage unit.
11. The method of claim 3, wherein the energy storage unit charging and discharging power constraints comprise:
the charging power of the energy storage unit in the power distribution network system is less than or equal to the maximum value of the self charging power and is greater than or equal to the minimum value of the self charging power;
the discharging power of the energy storage units in the power distribution network system is less than or equal to the maximum value of the self discharging power and more than or equal to the minimum value of the self discharging power.
12. The method of claim 3, wherein the controllable-load power constraint comprises:
the power of the controllable load in the power distribution network system is less than or equal to the maximum value of the controllable power of the controllable load and greater than or equal to the minimum value of the controllable power of the controllable load.
13. The method of claim 3, wherein the tie switch position constraint comprises:
the interconnection switch is in a radial structure in the power distribution network system.
14. The method according to claim 1, wherein the S3 includes:
determining a day-ahead scheduling capacity Q of the mth schedulable device in the distributed generation unit according tom:
Determining the day-ahead scheduling capacity Q of the ith schedulable device in the energy storage unit according to the following formulay：
Determining the day-ahead scheduling capacity Q of the s-th schedulable device in the controllable load unit according tos：
Q' is the total day-ahead schedulable capacity of the distributed power generation units, M is the number of schedulable devices in the distributed power generation units, and M belongs to [1, M ]; q' is the total schedulable capacity of the energy storage unit in the day ahead, Y is the number of schedulable devices in the energy storage unit, and Y belongs to [1, Y ]; q' is the total day-ahead schedulable capacity of the controllable load unit, S is the number of schedulable devices in the controllable load unit, S is [1, S ].
15. A system for day-ahead optimal scheduling of a power distribution network, the system comprising:
the establishing module is used for establishing a day-ahead optimized scheduling model of the power distribution network;
the acquisition module is used for solving the day-ahead optimized scheduling model of the power distribution network by utilizing a particle swarm algorithm to acquire the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
the determining module is used for determining the day-ahead scheduling capacity of each schedulable device in each type of schedulable resource according to the day-ahead schedulable capacity of each type of schedulable resource in the power distribution network;
the types of the schedulable resources comprise a distributed power generation unit, an energy storage unit and a controllable load unit.
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