CN115882523A - Optimal operation method, system and equipment for power system with distributed energy storage - Google Patents
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Abstract
The invention relates to the technical field of distributed energy storage, in particular to an optimized operation method, system and equipment of a power system containing distributed energy storage, which comprises the following steps: step 1: acquiring initial parameters of a system; step 2: establishing a power distribution network optimization operation model containing distributed energy storage; determining constraint conditions of each part of system operation by taking the lowest one-day operation cost of the power distribution network as an optimization target, and solving the established day-ahead economic dispatching model of the power distribution network by using a mixed integer programming method and a second-order conic linear programming method; and 3, step 3: calling a Gurobi solver to carry out optimization solution on the model; and 4, step 4: the influence of the position of the energy storage device accessed to the power distribution network system or the charge and discharge power parameters on the wind power consumption and the system operation economic condition is researched by changing the position of the energy storage device accessed to the power distribution network system or the charge and discharge power parameters of the energy storage device. The invention can optimize the power system better.
Description
Technical Field
The invention relates to the technical field of distributed energy storage, in particular to an optimized operation method, system and equipment of a power system containing distributed energy storage.
Background
Currently, research on an optimized operation strategy after new energy is connected to a power distribution network becomes one of hot subjects in the field of power systems. Compared with the energy output of the traditional power system, the new energy power generation has the advantages of environmental protection, high energy utilization rate, high economic benefit and the like, and meanwhile, the uncertainty of the new energy output also brings challenges to the stable operation of a power grid. The problems of power distribution network safety and reliability reduction caused by uncertainty of new energy output can be solved by accessing the energy storage device, and meanwhile, the access of the distributed energy storage device has a positive effect on reducing the economic cost of the power distribution network.
The wind power generation technology is to convert wind energy into electric energy, and is one of new energy technologies which have more perfect technical theory development and most development potential in the current new energy power generation. The large-scale access of new energy such as wind power and the like to a power grid enriches energy structures, but the power distribution network structure can be deeply transformed, and as the randomness of a power supply side and the randomness of a power utilization side of a system are too strong and the independence is high, the power grid output and the load are possibly unbalanced, and the safety and the economical efficiency of the power grid operation are reduced. A large number of researches prove that for the bottleneck encountered by new energy power generation, a method of accessing a distributed energy storage device can be adopted, and the damage to a power grid caused by the uncertainty and intermittence of the output of new energy power generation modes such as wind power and the like is reduced.
The distributed energy storage technology is characterized in that an energy storage device is connected to a specific position in a power distribution network according to the load and the power position, and the distributed energy storage method is mostly applied to the embodiment and provides a peak-load-shifting operation optimization strategy method for the power distribution network with wind power integration. The method comprises the steps of establishing an objective function which takes the highest economic benefit of optimizing the whole-grid one-day operation as an optimization target function, considering node power balance constraint, voltage drop and branch current constraint, thermal power unit output constraint, thermal power unit climbing rate constraint, thermal power unit start-stop constraint, wind power output constraint, distributed energy storage device capacity constraint and distributed energy storage device charge-discharge operation constraint, loosening the node balance nonlinear constraint to a certain extent, and converting the node balance nonlinear constraint into a mixed integer second-order cone optimization model for solving. The power transmission and distribution side, the micro-grid and the user side participate in various aspects such as peak load regulation, valley filling, grid blockage relieving, power supply reliability improving and the like, the access mode is more flexible, and the scale of the power and the capacity of distributed energy storage is relatively small.
The distributed energy storage technology has the most projects and the most obvious potential increases in the field of renewable energy sources, and the projects generally refer to the application of the distributed energy storage technology to a power grid accessed by new energy sources such as wind power and the like, and the energy storage and technology are adopted to carry out peak load regulation and energy coordinated dispatching of a power system.
The energy structure is mainly transferred from coal to the leap-over type conversion mainly based on new energy. The grid connection of the distributed power supply mainly based on new energy greatly influences the safe and economic operation of the power distribution network, so that the quality problems of the electric energy caused by the grid connection of the distributed power supply arouse high attention at home and abroad.
New energy penetration rates are increasing, which also presents a significant challenge to power distribution networks. A distributed energy storage device and a distributed power supply are introduced to form a multi-energy complementary comprehensive energy power system, coordinated scheduling and peak and valley load regulation are carried out, and the safety and the economy of the system are maintained. The purpose of the distributed energy storage device access system is to participate in optimized scheduling of the power distribution network, such as peak clipping and valley filling, so that the operation cost of the power distribution network is reduced. The fundamental purpose of investment optimization of the distributed energy storage devices in the prior art is to "improve asset economy of the power distribution network", only the problem of investment optimization of the distributed energy storage devices is discussed in an optimization strategy of the distributed energy storage devices singly, if corresponding optimization is carried out, output changes of other units of a system can be caused, and the running cost of the whole system is not reduced or increased, so that the situation is obviously not favorable for long-term running and healthy development of the power distribution network. Therefore, when configuration and operation optimization of the distributed energy storage device is considered, operation cost optimization of the whole system is made by combining other units and power loads in the system into consideration.
Disclosure of Invention
The invention provides an optimized operation method, system and equipment for a power system with distributed energy storage, which can solve the problem that a distributed energy storage device participates in optimized scheduling of an energy system with distributed power supplies.
The invention relates to an optimized operation method of a power system with distributed energy storage, which comprises the following steps:
step 1: acquiring initial parameters of a system, including power distribution network structure parameters, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost for an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
step 2: establishing an optimized operation model of a power distribution network containing distributed energy storage, wherein the model comprises the following steps: the system comprises a thermal power generating unit, a wind generating set, an energy storage device and a power load; determining constraint conditions of each part of system operation by taking the lowest one-day operation cost of the power distribution network as an optimization target, and solving the established day-ahead economic dispatching model of the power distribution network by using a mixed integer programming method and a second-order conic linear programming method;
and 3, step 3: calling a Gurobi solver in Matlab software to carry out optimization solution on the model, and forming day-ahead electricity purchasing, wind power generation unit and thermal power generation unit output, distributed energy storage device charging and discharging plans, the function of the distributed energy storage devices in participating in system peak load regulation and the improvement condition of the distributed energy storage devices on the economic benefit of the system;
and 4, step 4: the influence of the position of the energy storage device accessed to the power distribution network system or the charge and discharge power parameters on the wind power consumption and the system operation economic condition is researched by changing the position of the energy storage device accessed to the power distribution network system or the charge and discharge power parameters of the energy storage device.
Preferably, in the step 2, a power distribution network optimization operation model containing distributed energy storage is established, and the total operation cost of the power distribution network is minimized as an optimization target; the operating cost of the power distribution network comprises: the power distribution network obtains the objective function of the power distribution network optimization operation model from the electricity purchase cost of a superior power grid, the power generation cost of a thermal power generating unit, the wind abandoning penalty cost of a wind power generating unit, the operation cost of an energy storage device and the loss load cost as follows:
wherein:
charging fee for purchasing electricity from higher-level power grid for power distribution network>
For the operating cost of the thermal power unit>
Penalty cost for wind curtailment>
For the operating cost of the energy storage device>
The cost for losing load; />
Is composed oftBased on the electricity purchase cost of the superior power grid, the time-interval distribution network>
Is composed oftThe active power of electricity purchase of a time-interval power distribution network from a superior power grid; A. b and C are power generation cost coefficients of the thermal power generating unit; />
Indicating thermal power generating unitgIn thattThe output of the time period; />
Wind turbine generator system->
The wind abandon penalty cost; />
Representing wind farmswIn thattPredicted outcomes for a time period; />
Representing wind farmswIn thattThe output of the time period; />
For the cost of energy storage, the device is used>
Indicating stored energydIn thattThe amount of discharge of the time period; />
For the load-shedding cost of the distribution network, the system>
Is shown inshedIs attLoad shedding capacity over a period of time.
Preferably, in step 2, a DistFlow model is used to describe power balance of the power distribution network node, and system operation constraints are expressed as:
node power balance constraint:
wherein, aggregate
For connection to nodes of the distribution networkjA set of devices of; />
In a power distribution networkjA set of branch end nodes that are head-end nodes; />
For the distribution lineijIn combination with a resistor>
For the distribution lineijA reactance of (d); />
Is composed oftTime interval distribution lineijThe current of (a); />
Is composed oftTime interval nodeiThe voltage magnitude of (2); />
Is composed oftPeriod of time IgThe active power generation power of the thermal power generating unit; />
Is a firstwThe typhoon generator is arranged ontActive generated power of a time period; />
Representing distributed energy storage devicesdIn thattActive discharge amount of a time period>
Indicating stored energydIn thattAn active charge amount for the time period; />
Is composed oftActive power transmitted to a power distribution network by a superior power grid in a time period; />
Is composed oftReactive power transmitted to the power distribution network by the superior power grid in the time period; />
Is composed oftPower of load lost at any moment，/>
Is composed oftTime interval loaddThe power factor of (c); />
Finger-shapedtTime interval distribution lineijActive power of the segment; />
Finger-shapedtTime interval distribution lineijReactive power of the segment; />
Finger-shapedtTime interval distribution lineijActive power of the segment; />
FingertTime interval distribution lineijReactive power of the segment; />
Are respectively astTime interval loaddThe active load value and the reactive load value of (2); />
And &>
For transmission linesijCurrent magnitude limits of the segments; />
And &>
Is a nodeiVoltage magnitude limitation of (2).
Preferably, in step 2, the specific operation constraint conditions of the thermal power generating unit are represented as follows:
thermal power unit output constraint:
；/>
thermal power generating unit climbing restraint:
；
and (3) limiting the start and the stop of the thermal power generating unit:
；
wherein:
the state variable represents the working state of the thermal power generating unit; />
Minimum limit of thermal power unit output;
the thermal power generating unit provides the maximum limit of output; />
And &>
Respectively being thermal power generating unitsgThe up-and-down climbing rate of the unit>
And
respectively indicating a starting time counter and a stopping time counter of the thermal power generating unit g; />
And &>
Respectively refer to gas unitsgMinimum boot and downtime.
Preferably, in step 2, the specific operation constraint conditions of the wind turbine generator are expressed as follows:
output constraint of the wind turbine generator:
；
wind turbine generator set actual output and large wind abandonThe relationship between the predicted output of the small wind turbine generator and the predicted output of the wind turbine generator is as follows:
；
wherein:
is as followswThe typhoon generator is arranged ontPredicted generation power of a time period->
Is as followswThe typhoon generator is arranged attThe air abandon quantity of the time interval.
Preferably, in step 2, the specific constraint conditions of the energy storage device are expressed as follows:
and (3) charging and discharging power constraint of the energy storage device:
；
electric quantity constraint before and after each charge and discharge:
；
and (3) limiting the power of charge and discharge at each time:
；
and after 24 hours of charge and discharge, the electric quantity is equal to the electric quantity at the initial moment of the day, and the restraint is as follows:
wherein:
、/>
limiting the minimum value and the maximum value of the charging and discharging power of the energy storage device; />
Is an energy storage devicetThe amount of electricity over a period of time; />
Is the electric quantity at the initial moment of the energy storage device>
Is the electric quantity of the energy storage device at the end of a day; />
Is an energy storage devicetCharging and discharging power of a time period; />
Is the charge-discharge efficiency of the energy storage device;
is an energy storage devicetThe value of the state variable of time interval charge and discharge is 0 or 1; />
And limiting the maximum value of the charging and discharging power of the energy storage device.
Preferably, in step 2, the equation of the node current of the power distribution network represented by the voltage drop and branch current constraints is linear and non-convex, so that the node balance non-linear constraints are loosened to a certain degree and converted into a second-order cone optimization model to be solved:
first, define
And &>
These two intermediate variables, in combination with the branch current versus power relationship: />
And (3) carrying out relaxation treatment on the joint voltage drop equation to obtain:
further conversion yields a second order cone equation:
the current and voltage constraints after the second order cone relaxation are:
。
preferably, in step 3, system parameters, target functions and constraint conditions are converted into statements in Matlab software for compiling, and a Gurobi commercial solver is called in the Matlab software to optimize and solve the model, so that output plans of the wind power generating unit and the thermal power generating unit are obtained, and the conditions of the distributed energy storage device in participating in peak load shifting and valley filling of the system and improving the economic effect of the system are obtained.
The invention also provides an optimized operation system of the power system containing the distributed energy storage, which adopts the optimized operation method of the power system containing the distributed energy storage and comprises the following steps:
the data acquisition module is used for acquiring structural parameters of a distribution network in one day, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost for an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
the modeling module is used for constructing a wind power grid-connected distributed energy storage device participation power distribution network optimized scheduling model based on the output and running state of each unit of the power distribution network and the running state data of the energy storage device in the day ahead;
the optimization calculation module is used for constructing the scheduling model into a mixed integer programming model easy to solve, introducing 0-1 binary state variables to constrain partial variables, adopting a mixed integer second-order cone programming method to rewrite nonlinear power flow constraints of the power distribution network nodes, and analyzing and solving results by setting examples;
and the output plan module is used for outputting the optimization calculation result of the optimization calculation module and obtaining the day-ahead output plan of each unit of the system.
The invention also provides an optimized operation device of the power system with the distributed energy storage, which comprises a processor, wherein the processor executes a computer program to realize the optimized operation method of the power system with the distributed energy storage.
The method simulates a power distribution network system containing distributed energy storage, establishes a day-ahead optimized dispatching model of the power distribution network accessed by the wind power and distributed energy storage devices, and simulates the running condition of the actual system. The technical effects that the invention can achieve are as follows:
(1) And determining a power distribution network system framework, and establishing output mathematical models for wind power generation, thermal power generating unit power generation and distributed energy storage devices in the power distribution network. On the basis of the mathematical model, a power distribution network day-ahead economic dispatching model in a grid-connected mode is established; according to the output characteristics of each distributed power supply, an objective function aiming at the minimum one-day operation cost is provided, the electricity purchasing cost, the power generation cost of a thermal power generating unit, the wind abandoning punishment cost of a wind power generating unit, the energy storage one-day operation cost and the load loss cost of a power distribution network from a higher-level power grid are considered in the objective function, and the model is closer to an actual power distribution network system through power balance constraint, power grid voltage and current safety constraint, wind power generator output constraint, power grid safety constraint, thermal power generating unit output ramp rate constraint, energy storage device charge and discharge power constraint and the like.
(2) The scheduling model is constructed into a mixed integer programming model easy to solve, 0-1 binary state variables are introduced to constrain partial variables, the mixed integer second-order cone programming method is adopted to rewrite nonlinear power flow constraints of the nodes of the power distribution network, the technical effect of the method is embodied by setting example analysis solving results, and a commercial solver Gurobi is adopted to solve after programming in Matlab. In different examples, influences of the accessed wind turbine generator and the distributed energy storage device, wind permeability, the position of the energy storage device and power parameters on system operation and system economic benefits are respectively considered, and a day-ahead plan of the output of each unit of the system is obtained. By analyzing the data of each example, the technology of the invention can draw conclusions that: the introduction of the distributed energy storage device can ensure that the residual electric quantity of the power distribution network is stored in the energy storage device in the load valley period, and the consumption of the system to wind power is improved; the distributed energy storage device discharges to compensate when the power is in shortage, a large amount of electricity is prevented from being purchased when the electricity price is high, the effect of peak load regulation is achieved, and the economic benefit of system operation is improved. The location of the distributed energy storage devices, and the parameters of the devices also affect the participation in system energy scheduling.
Drawings
Fig. 1 is a flowchart of an optimized operation method of an electric power system with distributed energy storage in embodiment 1;
fig. 2 is an application environment diagram of the dynamic economic dispatch optimization method for the power system in embodiment 1;
FIG. 3 is a schematic view of a computer apparatus according to embodiment 1;
FIG. 4 is a node structure diagram of a power distribution network system in embodiment 2;
FIG. 5 (a) is a schematic diagram of the day-ahead plan of the output of each unit obtained by optimizing example 1 in embodiment 2;
FIG. 5 (b) is a schematic diagram of the day-ahead plan of the output of each unit obtained after the optimization of example 2 in embodiment 2;
FIG. 6 is a schematic system configuration diagram of three operation modes in example 3 in embodiment 2;
FIG. 7 shows the internal charge of the energy storage device in example 3 of embodiment 2
Schematic diagram of the variation of (1);
FIG. 8 shows the internal electric quantity of the energy storage device in example 4 of embodiment 2
Schematic diagram of the variation of (1).
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Example 1:
as shown in fig. 1, the present embodiment provides an optimized operation method for a power system with distributed energy storage, which includes the following steps:
step 1: acquiring initial parameters of the system, including power distribution network structure parameters, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost to an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
step 2: establishing an optimized operation model of a power distribution network containing distributed energy storage, wherein the model comprises the following steps: the system comprises a thermal power generating unit, a wind generating set, an energy storage device and a power load; determining constraint conditions of each part of system operation by taking the lowest one-day operation cost of the power distribution network as an optimization target, and solving the established day-ahead economic dispatching model of the power distribution network by using a mixed integer programming method and a second-order conic linear programming method;
and step 3: calling a Gurobi solver in Matlab software to carry out optimization solution on the model, and forming day-ahead electricity purchasing, wind power generation unit and thermal power generation unit output, distributed energy storage device charging and discharging plans, the function of the distributed energy storage devices in participating in system peak load regulation and the improvement condition of the distributed energy storage devices on the economic benefit of the system;
and 4, step 4: the influence of the position of the energy storage device connected to the power distribution network or the charge and discharge power parameters on the wind power consumption and the system operation economic condition is researched by changing the position of the energy storage device connected to the power distribution network or the charge and discharge power parameters of the energy storage device.
Fig. 2 is an application environment diagram of the dynamic economic dispatch optimization method for the power system provided in this embodiment. The dynamic economic dispatch optimization method for the power system provided by the embodiment can be applied to the application environment but is not limited to the application environment. As shown in fig. 2, the application environment includes: power data collection equipment, computer equipment 104 and dispatch center 105.
The power data acquisition devices 101 to 103 may be smart meters such as electromechanical integrated meters and all-electronic meters, and are not limited herein. The intelligent data transmission system has intelligent functions of user side control, bidirectional data communication in multiple data transmission modes and the like. The computer device 104 may be a Server (Server), a Terminal (Terminal), etc., and is not limited thereto. The Server is a special computer which can provide certain service for a Client (a Client generally refers to a computer which can send a scheduling demand instruction) in a network environment, and the Server is provided with a network operating system (such as Windows Server, linux, unix and the like) and various Server application system software (mainly refers to Matlab software which can complete an optimized operation strategy process of a power system with distributed energy storage). Terminals generally refer to those user devices that are connected to a centralized host system (e.g., IBM mainframe computer), receive keyboard-entered data acquisition instructions and optimization calculation instructions from a user, and send these inputs to the host system. The dispatching center 105 is configured with the online dispatching computer device 104, and completes the calculation of the optimized operation strategy of the power system with distributed energy storage through a man-machine conversation mode.
The dispatch center 105 sends optimization instructions to the computer device 104. The computer device 104 is used for sending acquisition instructions to the electric power data acquisition devices 101 to 103 in the target area after receiving the optimization instructions. The electric power data acquisition equipment 101 to 103 are used for acquiring the operation data of each unit and the operation data of the energy storage device of the electric power system in one day after receiving the acquisition instruction, and sending the acquired data to the computer equipment 104. After receiving the operating data of each unit and the operating data of the energy storage device in a day of the power system, the computer device 104 establishes a power distribution network day-ahead optimization scheduling model accessed by the wind power and the distributed energy storage devices, and sends the obtained daily operating plan output result of each unit to the scheduling center 105.
wherein:
charging fee for power distribution network from higher level power grid>
For the operating cost of the thermal power unit>
Penalty cost for wind curtailment>
For the operating cost of the energy storage device, the device is switched on>
For lost load costs; />
Is composed oftBased on the electricity purchase cost of the superior power grid in the time interval distribution network>
Is composed oftThe method comprises the steps that active power of electricity purchase of a superior power grid of a time-interval power distribution network is obtained; A. b and C are power generation cost coefficients of the thermal power generating unit; />
Indicating thermal power generating unitgIn thattForce output in time period; />
Wind turbine generator system->
The wind abandon penalty cost; />
Representing wind farmswIn thattPredicted outcomes for a time period; />
Representing wind farmswIn thattForce output in time period; />
For the cost of energy storage use>
Indicating stored energydIn thattThe amount of discharge over a period of time; />
For the load-shedding cost of the distribution network, the system>
Is shown inshedIs attLoad shedding power of a time period.
Constraint conditions
1) Describing power distribution network node power balance by using a DistFlow model, wherein system operation constraint conditions are expressed as follows:
node power balance constraint:
wherein, aggregate
For connection to nodes of the distribution networkjA set of devices of; />
In a power distribution networkjA set of branch end nodes that are head-end nodes; />
For the distribution lineijIs greater than or equal to>
For the distribution lineijA reactance of (d); />
Is composed oftTime interval distribution lineijThe current of (a); />
Is composed oftTime interval nodeiThe voltage magnitude of (2); />
Is composed oftPeriod of time IgThe active power generation power of the thermal power generating unit; />
Is a firstwThe typhoon generator is arranged ontActive generated power of a time period; />
Representing distributed energy storage devicesdIn thattActive discharge amount for a time period>
Indicating stored energydIn thattAn active charge amount for the time period; />
Is composed oftActive power transmitted to a power distribution network by a superior power grid in a time period; />
Is composed oftThe reactive power transmitted to the power distribution network by the superior power grid in the time period; />
Is composed oftThe power of the load is lost at any moment and is combined with the power of the load>
Is composed oftTime interval loaddThe power factor of (c); />
Finger-shapedtTime interval distribution lineijActive power of the segment; />
FingertTime interval distribution lineijReactive power of the segment; />
Finger-shapedtTime interval distribution lineijActive power of the segment; />
FingertTime interval distribution lineijReactive power of the segment; />
Are respectively astTime interval loaddActive load value and reactive load value of; />
And &>
For transmission linesijCurrent magnitude limits of the segments; />
And &>
Is a nodeiIs limited by the voltage level of (c).
2) Because thermal power generating unit performance and quantity are definite, its output has certain restriction, and in addition, thermal power generating unit still opens and stops restraint and climbing restraint, and the thermal power generating unit concrete operation constraint condition shows as:
output restraint of the thermal power generating unit:
；
and (3) climbing restraint of the thermal power generating unit:
；/>
thermal power unit opens and stops restraint:
；
wherein:
the state variables (0 and 1) represent the working state of the thermal power generating unit; />
Minimum limit of thermal power unit output; />
The maximum limit of the output provided by the thermal power generating unit; />
And &>
Are respectively thermal power generating unitsgThe up and down climbing rate of (c);
and &>
Respectively indicating a starting time counter and a stopping time counter of the thermal power generating unit g; />
And &>
Respectively refer to gas unitsgMinimum boot and downtime.
3) Because the performance and the number of the wind turbine generators are determined, the output of the wind turbine generators is limited to a certain extent, the output transmitted from the wind turbine generators to a power distribution network is limited by upper and lower limits, and the specific operation constraint conditions of the wind turbine generators are expressed as follows:
output constraint of the wind turbine generator:
；
the relationship between the actual output of the wind turbine generator, the size of the abandoned wind and the predicted output of the wind turbine generator is as follows:
；
wherein:
is a firstwThe typhoon generator is arranged ontPredicted generation power of a time period->
Is as followswThe typhoon generator is arranged attThe air abandon amount of time slot.
4) Because the energy storage device is limited by the current transformer in the manufacturing process and the system, the charging and discharging power of the battery existsMaximum and minimum values, so that the charging and discharging power of the energy storage device is restricted; the constraint that the electric quantity is satisfied before and after each charge and discharge is as follows; taking into account the upper limit of charge-discharge power
The distributed energy storage device can only be in one of 3 states of charging, discharging and non-charging and non-discharging at any moment, and the physical infeasible phenomenon of charging and discharging does not exist, so that the power limit of each charging and discharging is obtained; and the electric quantity of the energy storage device after 24 hours of charging and discharging is equal to the electric quantity at the initial moment of the day. The specific constraints of the energy storage device are expressed as:
and (3) charge and discharge power constraint of the energy storage device:
electric quantity constraint before and after each charge and discharge:
；
and (3) limiting the power of charge and discharge at each time:
；
and after 24 hours of charge and discharge, the electric quantity is equal to the electric quantity at the initial moment of the day:
；
wherein:
、/>
the minimum value and the maximum value of the charge and discharge power limit of the energy storage device are obtained; />
Is an energy storage devicetThe amount of electricity over a period of time; />
Is the electric quantity at the initial moment of the energy storage device>
Is the electrical quantity of the energy storage device at the end of a day;
、/>
is an energy storage devicet(ii) charge-discharge power over a period of time; />
Is the charge-discharge efficiency of the energy storage device;
is an energy storage devicetThe value of the state variable of time interval charge and discharge is 0 or 1; />
And limiting the maximum value of the charging and discharging power of the energy storage device.
The node current equation of the power distribution network represented by voltage drop and branch current constraint is linear and non-convex, so that the node balance non-linear constraint is loosened to a certain degree and converted into a second-order cone optimization model for solving:
first, define
And &>
These two intermediate variables, in combination with the branch current versus power relationship:
and (3) carrying out relaxation treatment on the voltage drop equation of the contact point to obtain:
further conversion yields a second order cone equation:
the current and voltage constraints after the second order cone relaxation are:
；
and step 3, converting system parameters, target functions and constraint conditions into statements in Matlab software for compiling, calling a Gurobi commercial solver in the Matlab software to carry out optimization solution on the model, and obtaining output plans of the wind power generation unit and the thermal power generation unit and improvement conditions of the distributed energy storage device in participating in system peak load regulation and valley filling and on the system economic effect.
The embodiment provides an optimized operation system of an electric power system with distributed energy storage, which adopts the above optimized operation method of an electric power system with distributed energy storage, and includes:
the data acquisition module is used for acquiring structural parameters of a distribution network in one day, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost for an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
the modeling module is used for constructing a wind power grid-connected distributed energy storage device participation power distribution network optimized scheduling model based on the output and running state of each unit of the power distribution network and the running state data of the energy storage device at the day before;
the optimization calculation module is used for constructing the scheduling model into a mixed integer programming model easy to solve, introducing 0-1 binary state variables to constrain partial variables, adopting a mixed integer second-order cone programming method to rewrite nonlinear power flow constraints of the power distribution network nodes, and analyzing and solving results by setting examples;
and the output plan module is used for outputting the optimization calculation result of the optimization calculation module and obtaining the day-ahead output plan of each unit of the system.
The embodiment provides an optimized operating device of an electric power system with distributed energy storage, that is, a computer device 300 (also, the computer device 104 in fig. 2), where the computer device 300 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server, and a schematic block diagram of the computer device refers to fig. 3, but fig. 3 is merely an example of the computer device 300, and does not limit the computer device 300, and may include more or less components than those shown in the drawings, or combine some components, or different components, for example, a terminal may further include an input/output device, a network access device, a bus, and the like. The computer device 300 comprises a processor 301, a power supply 302, a wired or wireless interface 308, an input output interface 309, a memory 307, and an operating system 303, a computer program 304, a database 305, a non-volatile storage medium 306 in the memory 307 and on the processor 301. The processor 301, when executing the computer program 304, implements a method for optimized operation of a power system with distributed energy storage as described above.
1) The processor 301 is a very large scale integrated circuit built into the computer apparatus 300 and includes an arithmetic logic unit, a register unit, a control unit, and the like. It can fetch the instruction from memory, place it in instruction register, decode the instruction, decompose the instruction into a series of micro-operations, then send out various control commands, execute the micro-operation series so as to implement the execution of an instruction. The Processor 301 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc., and is not limited herein.
2) The power supply 302 is a means for providing power for the proper functioning of all the units or modules in the computer device 300.
3) The memory 307, as well as the operating system 303, computer programs 304, database 305 in the memory and on the processor, may be stored as an integrated module in the non-volatile storage medium 306 of the computer.
The operating system 303 is the system software (or set of programs) that facilitate the user's management and control of the computer's hardware and software resources, and is the core and foundation of the computer system, which acts as an intermediary between hardware and software. It may provide an interactive command interface, batch command interface, program interface, etc. for the user. The operating system 303 may be Windows, maxos x, linux, etc., and is not limited thereto.
The main function of the memory 307 is to store programs and data. Programs are the basis for computer operations, and data are the objects of computer operations. Whether program or data, are represented in binary form, and are collectively referred to as information, in memory. The memory is mainly divided into a main memory (internal memory) and a secondary memory (external memory). The storage 307 may be a Random Access Memory (RAM), a main memory (internal memory), a Read Only Memory (ROM), a memory, a hard disk, a secondary storage (external memory), a floppy disk, an optical disk, etc., and is not limited thereto.
4) The non-volatile storage medium 306 is a storage medium of the computer program 304, and is characterized in that data is not lost when the computer is shut down or suddenly and unexpectedly shut down. In many write operations of the storage system, the memory serves as an important bridge between the controller and the hard disk, providing faster performance, but the non-volatile storage medium can effectively protect the data in the memory from being lost if a sudden power failure occurs. The non-volatile storage medium 306 may be any tangible medium that can contain or store a program and may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, more specific examples including but not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, an optical fiber, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
5) The wired or wireless interface 308 is responsible for processing digital communication from the end user device to the wireless medium, and ensures that data interaction is performed among the power data acquisition devices 101 to 103, the computer device, and the dispatch center through a line, and data interaction may also be performed through a network or bluetooth, which is not limited herein. The input/output interface 309 is a connection circuit for exchanging information between the processor 301 and an external device in the computer device 300, and is connected to the processor 301 through a bus. The input/output interface is divided into a bus interface and a communication interface, and is generally made into a circuit card, and the input/output interface 309 may be in the form of a floppy disk drive adapter card, a hard disk drive adapter card, a parallel printer adapter card, and the like, which is not limited herein.
6) The design and division of the modules of the computer device 300 are determined according to the system optimization scheduling requirement, and then the computer device system needs to be analyzed and designed integrally. In practical engineering application, different functional units and modules in computer equipment need to be called according to actual requirements to complete the target. The existing form of the above units or modules can be changed flexibly, all the units or modules can be integrated in the same processing unit, and any units can be integrated in one unit according to the design of computer equipment. The functional implementation form of the above units or modules may be a software form or a hardware form, and is specifically selected according to actual engineering requirements, and is not limited herein.
Example 2:
the embodiment provides a distributed energy storage participating peak load regulation optimizing operation strategy method for a power distribution network with wind power grid connection. The method comprises the steps of establishing a target function which takes the highest economic benefit of optimizing the whole-network one-day operation as a target function, considering node power balance constraint, voltage drop and branch current constraint, thermal power unit output constraint, thermal power unit climbing rate constraint, thermal power unit start-stop constraint, wind power output constraint, distributed energy storage device capacity constraint and distributed energy storage device charge-discharge operation constraint, loosening the node balance nonlinear constraint to a certain degree, and converting the node balance nonlinear constraint into a mixed integer second-order cone optimization model for solving.
In the aspect of wind power output modeling, wind power output is considered as a deterministic output mode, and specific output conditions refer to actual output conditions of a certain wind power plant at a certain day. In order to promote the distributed energy storage device to consume the wind power, the wind abandoning punishment cost is increased, and the wind abandoning is reduced by consuming the wind power as much as possible in the optimization process of the system. In the aspect of modeling of the distributed energy storage device, the capacity, the charge-discharge state and the charge-discharge power of the energy storage device are reasonably limited.
The technical idea of the present invention is explained by the technical effects of the following examples, in which data are expressed in per unit value form.
In the example, a power distribution network system containing distributed energy storage is based on a standard IEEE33 node power distribution network system, a node 1 is a substation node, namely a power distribution network purchases an electricity node from a superior power grid, the node has no power load, and other nodes all have power loads, in addition, a thermal power unit, three wind power units and four distributed energy storage devices are connected into the system, the positions of wind power plants are arranged at nodes 12, 19 and 27, the nodes of the thermal power unit are arranged at node 2, and the positions of the distributed energy storage devices are arranged at nodes 13, 14, 20 and 29, and the structure diagram of the power distribution network system is shown in figure 4.
The peak load regulation and valley filling of the energy storage device in the system and the digestion effect of the enhancement system on wind power are analyzed by setting the following examples, example 1: the system is connected to a wind turbine generator but has no energy storage device; example 2: the system is connected with a wind turbine generator and an energy storage device; example 3: the influence of the change of the access point of the energy storage device on the operation of the system; example 4: the charging and discharging power change of the energy storage device has influence on the system operation.
The one-day operating economics of examples 1 and 2 are given, as in table 1.
TABLE 1 one-day running economics scenarios for examples 1 and 2
As can be seen from the output conditions of the units in the systems in fig. 5 (a) and 5 (b), in the low valley period of the power load, such as the time period from 0 point to 8 points, since most residents have a rest, few living activities and low power load, the electric quantity generated by the wind turbine generator is insufficient, and at this time, the energy storage device can store part of the surplus wind power; when the electricity consumption peak period is reached, such as the time period from 11 to 14 points and the time period from 17 to 20 points, the living activities of residents are more, the electricity consumption load is increased, and the energy storage device releases the stored electricity to supply power to the system. The measures reduce the electricity purchasing cost of the system to a certain extent, reduce the power supply pressure of the system and improve the operation economy.
As can be seen from the economic benefit condition of the operation of two examples of the table 1, because the energy storage device is connected into the system, the consumption of the system to wind power is promoted, the output of the power purchasing unit and the output of the thermal power generating unit are both reduced, and the economic benefit of the system is improved.
Compared with the prior art, the embodiment has the following advantages:
example 3 Power Generation with respect to wind by changing four energy storage devicesThree different system operation modes (mode 3.1, mode 3.2 and mode 3.3) are provided for the distance of the unit, the system structures of the three operation modes are shown in fig. 6, and the principle that the access point of the energy storage device is changed is that the access point is farther away from the wind generating set on the line where the access point is located. The system arranged in the three modes obtains the internal electric quantity of the energy storage device after the optimization technology of the invention
The change of (a) is shown in fig. 4, and the economic efficiency of the operation of the mode 3.1, the mode 3.2, and the mode 3.3 for one day is shown in table 2.
TABLE 2 economic benefits of one day operation of mode 3.1, mode 3.2, mode 3.3
It can be seen from table 2 that along with the increase of energy memory access point distance wind generating set position, the system cost of purchasing electricity constantly risees, and energy memory use cost constantly reduces, and the system moves the total cost constantly and risees a day, and this shows that, when energy memory was more close to wind generating set's position, energy memory was better to the condition of consuming of wind-powered electricity generation, and energy memory's use is more sufficient, and is more obvious to the most usefulness of system's peak regulation valley filling, can improve the economic nature of system operation to a certain extent.
FIG. 7 shows the internal electric quantity of the energy storage device
The change condition of (2) illustrates the use condition of the energy storage device when the energy storage device changes relative to the position of the wind turbine generator, and from the mode 3.1 and the mode 3.3, the total charge quantity of the energy storage device continuously decreases and the total discharge quantity also continuously decreases in the operation of the energy storage device per day, so that the use of the energy storage device is reduced on the whole, and the effect on the peak regulation and valley filling of the system is also reduced.
Example 4 by varying the upper limit of the charging and discharging power of the energy storage device
Three different system operation modes (mode 4.1, mode 4.2, mode 4.3, mode 4.4 and mode 4.5) are proposed in the change, and the change rule of the five operation modes is to use
The value of (c) is increasing. The output results of the units obtained by the system arranged in the five modes through the optimization technology of the invention are shown in fig. 8, and in addition, the economic benefit conditions of the operation in one day of the mode 4.1, the mode 4.2, the mode 4.3, the mode 4.4 and the mode 4.5 are obtained, as shown in table 3.
TABLE 3 economic benefits of one day operation of mode 4.1, mode 4.2, mode 4.3, mode 4.4, mode 4.5
FIG. 8 depicts energy storage device electrical quantities under five operating modes
In the manner 4.1 to the manner 4.5, it can be seen from the figure that the upper limit of the charging and discharging power is greater or less than the maximum value of the charging and discharging power of the energy storage device>
Continuously rising, the electric quantity of the energy storage device is->
The variation range is larger, that is, the usage amount of the energy storage device is continuously increased, and the electric quantity of the energy storage device is greater or less in the modes 4.4 and 4.5>
The change curves are completely overlapped with each other, this is indicated in->
After, is combined with>
Is no longer a factor limiting the use of the energy storage device. />
Table 3 compares the cost of each output with the total cost for the five operating modes of example 4. It can be seen from the table that the upper limits of the charging and discharging power of the energy storage device are from mode 4.1 to mode 4.5
When the temperature is continuously increased, the system cost is obviously reduced, the cost of the thermal power generating unit is slightly reduced, the use cost of the energy storage device continuously rises, the total operating cost of the system continuously rises every day, and the system explains that the system can be used for changing the condition of the energy storage device>
Has an influence on the use of the energy storage device when->
The value of (2) is improved, and the energy storage device can be used for scheduling the system energy to a greater extent. It is to be noted that mode 4.4 and mode 4.5 have the same part cost, which indicates the case in which a +>
Already without a parameter limiting the use of the energy storage device, in this system, if not paired->
And limiting, wherein the maximum charging and discharging power of the energy storage device is between 0.5 and 0.6.
In addition, analogy to example 4, it can be proved by a similar control variable method that the capacity of the energy storage device is also an important parameter for limiting the participation of the energy storage device in system peak load shifting, and the upper limit of the capacity of the energy storage device
When the temperature is continuously increased, the system cost is obviously reduced, the cost of the thermal power generating unit is slightly reduced, the use cost of the energy storage device is continuously increased, and the total cost of the system operating every day is continuously increased, which indicates that the system is more or less well>
Has an influence on the use of the energy storage device when->
The value of (2) is improved, and the energy storage device can be used for scheduling the system energy to a greater extent.
The present invention and its embodiments have been described above schematically, and the description is not intended to be limiting, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, without departing from the spirit of the present invention, a person of ordinary skill in the art should understand that the present invention shall not be limited to the embodiments and the similar structural modes without creative design.
Claims (10)
1. The optimal operation method of the power system containing distributed energy storage is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring initial parameters of the system, including power distribution network structure parameters, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost to an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
step 2: establishing an optimized operation model of a power distribution network containing distributed energy storage, wherein the model comprises the following steps: the system comprises a thermal power generating unit, a wind generating set, an energy storage device and a power load; determining constraint conditions of each part of system operation by taking the lowest one-day operation cost of the power distribution network as an optimization target, and solving the established day-ahead economic dispatching model of the power distribution network by using a mixed integer programming method and a second-order conic linear programming method;
and step 3: calling a Gurobi solver in Matlab software to carry out optimization solution on the model, and forming a day-ahead electricity purchasing plan, a wind power generation unit and a thermal power generation unit output plan, a distributed energy storage device charging and discharging plan, a distributed energy storage device participating in system peak regulation and valley filling and a system economic benefit improvement condition;
and 4, step 4: the influence of the position of the energy storage device connected to the power distribution network or the charge and discharge power parameters on the wind power consumption and the system operation economic condition is researched by changing the position of the energy storage device connected to the power distribution network or the charge and discharge power parameters of the energy storage device.
2. The method for optimizing the operation of a power system including distributed energy storage according to claim 1, wherein: step 2, establishing a power distribution network optimization operation model containing distributed energy storage, and taking the total operation cost of the minimized power distribution network as an optimization target; the operating cost of the power distribution network comprises: the power distribution network obtains the objective function of the power distribution network optimization operation model from the electricity purchasing cost of a superior power grid, the power generation cost of a thermal power generating unit, the wind abandoning penalty cost of a wind power generating unit, the operation cost of an energy storage device and the load loss cost as follows:
wherein:
charging fee for purchasing electricity from higher-level power grid for power distribution network>
For the operating costs of a thermal power plant>
Penalty cost for wind curtailment>
For the operating cost of the energy storage device, the device is switched on>
The cost for losing load; />
Is composed oftBased on the electricity purchase cost of the superior power grid, the time-interval distribution network>
Is composed oftThe active power of electricity purchase of a time-interval power distribution network from a superior power grid; A. b and C are power generation cost coefficients of the thermal power generating unit;
indicating thermal power generating unitgIn thattThe output of the time period; />
Wind turbine generator system->
The wind abandon penalty cost; />
Representing wind farmswIn thattPredicted outcomes for a time period; />
Representing wind farmswIn thattThe output of the time period; />
For the cost of energy storage, the device is used>
Indicating stored energydIn thattThe amount of discharge over a period of time; />
For the load-shedding cost of the distribution network, the system>
Is shown inshedIs located intLoad shedding power of a time period. />
3. The method for optimizing the operation of a power system comprising distributed energy storage according to claim 2, wherein: in step 2, a DistFlow model is used for describing power balance of the power distribution network nodes, and system operation constraint conditions are expressed as follows:
node power balance constraint:
wherein, set
For connection to nodes of the distribution networkjA set of devices of; />
In a power distribution networkjA set of branch end nodes that are head-end nodes; />
Is a distribution lineijIn combination with a resistor>
Is a distribution lineijA reactance of (d); />
Is composed oftTime interval distribution lineijThe current of (a); />
Is composed oftTime interval nodeiVoltage magnitude of (d); />
Is composed oftIn the first periodgThe active power generation power of the thermal power generating unit; />
Is as followswThe typhoon generator is arranged attActive generated power of a time period; />
Representing distributed energy storage devicesdIn thattActive discharge amount of a time period>
Indicating stored energydIn thattAn active charge amount for the time period; />
Is composed oftActive power transmitted to a power distribution network by a superior power grid in a time period; />
Is composed oftThe reactive power transmitted to the power distribution network by the superior power grid in the time period; />
Is composed oftPower of load on power-off at any moment>
Is composed oftTime interval loaddThe power factor of (c); />
FingertTime interval distribution lineijActive power of the segment; />
Finger-shapedtTime interval distribution lineijReactive power of the segment; />
Finger-shapedtTime interval distribution lineijActive power of the segment; />
FingertTime interval distribution lineijReactive power of the segment; />
Are respectively astTime interval loaddThe active load value and the reactive load value of (2); />
And &>
For transmission linesijCurrent magnitude limits of the segments; />
And &>
Is a nodeiIs limited by the voltage level of (c).
4. The method for optimizing operation of a power system comprising distributed energy storage according to claim 3, wherein: in step 2, the specific operation constraint conditions of the thermal power generating unit are represented as follows:
output restraint of the thermal power generating unit:
；
output restraint of the thermal power generating unit:
；
thermal power unit opens and stops restraint:
；
wherein:
the state variable represents the working state of the thermal power generating unit; />
Minimum limit of thermal power unit output; />
The maximum limit of the output provided by the thermal power generating unit; />
And &>
Are respectively thermal power generating unitsgUp and down hill climbing rate of->
And
respectively indicating a starting time counter and a stopping time counter of the thermal power generating unit g; />
And &>
Respectively refer to gas unitsgMinimum boot and downtime.
5. The method for optimizing operation of a power system comprising distributed energy storage according to claim 4, wherein: in step 2, the specific operation constraint conditions of the wind turbine generator are expressed as follows:
output restraint of the wind turbine generator:
;
the relation between the actual output of the wind turbine generator, the size of the abandoned wind and the predicted output of the wind turbine generator is as follows:
;
wherein:
is as followswThe typhoon generator is arranged attPredicted generation power of a time period->
Is as followswThe typhoon generator is arranged attThe air abandon amount of time slot.
6. The method for optimizing the operation of a power system comprising distributed energy storage according to claim 5, wherein: in step 2, the specific constraint conditions of the energy storage device are expressed as follows:
energy storage device chargerAnd (3) discharge power constraint:
;
electric quantity constraint before and after each charge and discharge:
;
power limitation of each charge and discharge:
;
and after 24 hours of charge and discharge, the electric quantity is equal to the electric quantity at the initial moment of the day, and the restraint is as follows:
;
wherein:
、/>
limiting the minimum value and the maximum value of the charging and discharging power of the energy storage device; />
Is an energy storage devicetThe amount of electricity over a period of time; />
Is the electric quantity at the initial moment of the energy storage device>
Is the electrical quantity of the energy storage device at the end of a day;
is an energy storage devicetCharging and discharging power of a time period; />
Is the charge-discharge efficiency of the energy storage device；
Is an energy storage devicetThe value of the state variable of time interval charge and discharge is 0 or 1; />
And limiting the maximum value of the charging and discharging power of the energy storage device.
7. The method for optimizing operation of a power system comprising distributed energy storage according to claim 6, wherein: in the step 2, a power distribution network node current equation represented by voltage drop and branch current constraint is linear and non-convex, so that node balance non-linear constraint is loosened to a certain degree, and the node balance non-linear constraint is converted into a second-order cone optimization model to be solved:
first, define
And &>
These two intermediate variables, in combination with the branch current versus power relationship:
and (3) carrying out relaxation treatment on the voltage drop equation of the contact point to obtain:
further conversion yields a second order cone equation:
the current and voltage constraints after the second order cone relaxation are:
。
8. the method for optimizing operation of a power system comprising distributed energy storage according to claim 7, wherein: and step 3, converting system parameters, target functions and constraint conditions into statements in Matlab software for compiling, calling a Gurobi commercial solver in the Matlab software to carry out optimization solution on the model, and obtaining output plans of the wind power generation unit and the thermal power generation unit and improvement conditions of the distributed energy storage device in participating in system peak load regulation and valley filling and on the system economic effect.
9. The power system that contains distributed energy storage optimizes the operation system, its characterized in that: which adopts a method for optimizing the operation of a power system comprising distributed energy storage according to any of claims 1-8 and comprises:
the data acquisition module is used for acquiring structural parameters of a distribution network in one day, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost for an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
the modeling module is used for constructing a wind power grid-connected distributed energy storage device participation power distribution network optimized scheduling model based on the output and running state of each unit of the power distribution network and the running state data of the energy storage device at the day before;
the optimization calculation module is used for constructing the scheduling model into a mixed integer programming model easy to solve, introducing 0-1 binary state variables to constrain partial variables, adopting a mixed integer second-order cone programming method to rewrite nonlinear power flow constraints of the power distribution network nodes, and analyzing and solving results by setting examples;
and the output plan module is used for outputting the optimized calculation result of the optimized calculation module and obtaining the day-ahead output plan of each unit of the system.
10. Contain distributed energy storage's electric power system optimization operation equipment, its characterized in that: comprising a processor which, when executing a computer program, carries out a method for optimized operation of a power system comprising distributed energy storage according to any of claims 1-8.
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