CN112713618A - Active power distribution network source network load storage cooperative optimization operation method based on multi-scene technology - Google Patents

Active power distribution network source network load storage cooperative optimization operation method based on multi-scene technology Download PDF

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CN112713618A
CN112713618A CN202011597575.8A CN202011597575A CN112713618A CN 112713618 A CN112713618 A CN 112713618A CN 202011597575 A CN202011597575 A CN 202011597575A CN 112713618 A CN112713618 A CN 112713618A
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active power
distribution network
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power distribution
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CN112713618B (en
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刘洪�
程雪颖
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Hefei Institute Of Innovation And Development Tianjin University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/46—Controlling the sharing of generated power between the generators, sources or networks
    • H02J3/48—Controlling the sharing of active power
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/12—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load
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    • H—ELECTRICITY
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    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
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    • H—ELECTRICITY
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    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
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Abstract

本发明公开了一种基于多场景技术的主动配电网源网荷储协同优化运行方法，首先基于多场景技术将分布式可再生能源发电的有功出力进行了场景划分，从而考虑了分布式可再生能源发电有功出力的不确定性；其次，以主动配电网日内综合运行成本最低为目标建立了主动配电网源网荷储协同优化模型，在上述只考虑了源荷储协同优化运行模型的基础上，还考虑了网络重构、分布式可再生能源发电逆变器和储能逆变器的无功出力与分布式可再生能源发电有功出力的协同优化运行，实现了主动配电网源网荷储协同优化运行；最后针对主动配电网源网荷储协同优化模型，与粒子群优化算法相结合，求解了混合整数规划问题。

Figure 202011597575

The invention discloses an active distribution network source-network-load-storage collaborative optimization operation method based on multi-scenario technology. First, the active power output of distributed renewable energy power generation is divided into scenarios based on the multi-scenario technology, so as to consider distributed availability. The uncertainty of the active power output of renewable energy power generation; secondly, the active distribution network source-grid-load-storage collaborative optimization model is established with the goal of the lowest daily comprehensive operation cost of the active distribution network. In the above, only the source-load-storage collaborative optimization operation model is considered. On the basis of the network reconfiguration, the coordinated optimal operation of the reactive power output of distributed renewable energy generation inverters and energy storage inverters and the active power output of distributed renewable energy power generation are also considered to realize the active distribution network. Source-grid-load-storage collaborative optimization operation; finally, for the active distribution network source-grid-load-storage collaborative optimization model, combined with the particle swarm optimization algorithm, the mixed integer programming problem is solved.

Figure 202011597575

Description

Active power distribution network source network load storage cooperative optimization operation method based on multi-scene technology
Technical Field
The invention relates to the technical field of power distribution network operation, in particular to an active power distribution network source network load storage cooperative optimization method based on a multi-scene technology, which is suitable for formulating an operation strategy of an active power distribution network and realizing economic dispatching of the active power distribution network.
Background
Active output of distributed renewable energy power generation has uncertainty, and compared with traditional economic dispatching, the economic dispatching of the active power distribution network should scientifically and reasonably make a source network load storage cooperative optimization operation strategy, so that full consumption of distributed renewable energy power generation is realized, and the economy of the active power distribution network is improved. Therefore, source network load storage collaborative optimization operation of the active power distribution network becomes a hot spot concerned in the current field. In order to fully absorb the renewable energy power generation, the uncertainty of the active output of the distributed renewable energy power generation needs to be considered firstly when the source network load storage cooperative optimization operation of the active power distribution network is carried out. Secondly, how to utilize various schedulable resources in the operation link, including source network load storage and the like, needs to be considered to enable the schedulable resources to cooperatively and optimally operate, and the full consumption of the distributed renewable energy power generation is realized.
In the existing research, on the basis of considering the uncertainty of the active power output of the distributed renewable energy power generation, operation strategies of schedulable resources such as peak-valley electricity price, flexible load scheduling strategies, energy storage charging and discharging strategies and the like of demand side time are formulated, and the cooperative optimization operation of source charge storage is realized, but the cooperative optimization operation of network reconstruction, the schedulable resources such as the reactive power output of a distributed renewable energy power generation inverter and an energy storage inverter and the active power output of the distributed renewable energy power generation is not considered. On the other hand, the research considering the source network load storage cooperative optimization operation considers the active output of the distributed renewable energy power generation as constant, and although various schedulable resources are considered, the active output of the distributed renewable energy power generation is limited, the uncertainty of the distributed renewable energy power generation is not considered, and the distributed renewable energy power generation cannot be fully absorbed.
Disclosure of Invention
The invention aims to make up for the defects of the prior art, provides a source network load storage collaborative optimization operation method of an active power distribution network based on a multi-scene technology, takes uncertainty of active output of distributed renewable energy power generation into consideration to carry out collaborative optimization on source network load storage of the active power distribution network, and provides an optimized operation strategy of the source network load storage.
Aiming at the defects of the existing research, the problem of uncertainty of the active power output of the distributed renewable energy power generation is solved based on the multi-scene technology, the uncertainty of the active power output of the distributed renewable energy power generation is considered, the lowest comprehensive operation cost in the day of the active power distribution network is taken as a target function, a source network and storage collaborative optimization model of the active power distribution network is established, the source network and storage are collaboratively optimized, and the operation safety and economy of the active power distribution network are guaranteed.
The invention is realized by the following technical scheme:
a multi-scenario technology-based active power distribution network source network load storage cooperative optimization operation method specifically comprises the following steps:
(1) dividing the active power output of the distributed renewable energy source power generation into a plurality of scenes by using a multi-scene technology and giving the occurrence probability of each scene;
(2) establishing an active power distribution network source network storage collaborative optimization model by taking the lowest comprehensive operation cost of the active power distribution network in a day as a target;
(3) and continuously performing iteration on discrete variables by adopting a particle swarm optimization algorithm based on the active power distribution network source-network load-storage cooperative optimization model, solving an optimal solution, and then performing normalization.
The specific process of the step (1) is as follows:
firstly, predicting time sequence values of illumination intensity and wind speed in a longer time according to a prediction model; secondly, establishing a time sequence output model P of the active output of the distributed renewable energy power generation by taking 15min as a basic step lengthDG(t), the active output of the distributed renewable energy power generation is considered to be unchanged within 15 min; finally, clustering the active power output of distributed renewable energy power generation by using a multi-scene technology to obtain S sub-componentsTypical day P of active output of distributed renewable energy power generationDG,s(t) probability of occurrence per typical day psWhere S is 1,2, …, S.
The specific process of the step (2) is as follows:
the method comprises the following steps of taking the lowest comprehensive operation cost of an active power distribution network in a day as an objective function, carrying out collaborative optimization on source network charge storage, wherein decision variables comprise reactive power output of a distributed renewable energy power generation inverter, a dynamic reconstruction interconnection switch action scheme, a scheduling strategy of interruptable load, a charge-discharge strategy of stored energy, reactive power output of an energy storage system inverter and reactive power output of a reactive power compensation device on a network, and the objective function is as follows:
min C＝Cup+Closs+CDG+Cgrid+CDSM+Cess (1)
in the formula: c is the daily comprehensive operation cost of the active power distribution network; cupThe cost for purchasing electricity to the upper level; clossThe cost of network loss; cDGThe electricity purchase cost for the operator is invested in the DG; cgridDynamically reconstructing tie switch action costs; cDSMDemand response cost for interruptible loads; cessOperating and maintaining costs for energy storage;
the respective part cost calculation is as follows:
1) the electricity purchasing cost of the active power distribution network to a superior power grid is as follows:
Figure BDA0002867008920000031
in the formula: s is the total number of the operation scenes of the active power distribution network; p is a radical ofsThe probability of occurrence of the s-th operation scene; t is a 15min time interval; t is a calculation time period; c. CupThe price is the upper electricity purchase price; pup,tThe electric quantity purchased to the upper level in the t time period;
2) the network loss cost of the active power distribution network is as follows:
Figure BDA0002867008920000032
in the formula: c is the price of selling electricity to the active power distribution network; ploss,tThe network loss capacity in the t time period is;
3) the electricity purchasing cost of the active power distribution network to DG investment operators is as follows:
Figure BDA0002867008920000033
in the formula: c. CDGThe electricity purchase price for the operator is invested in the DG; pDG,tThe electric quantity purchased by the operator is invested to the DG in the t time period;
4) the active power distribution network dynamically reconstructs the contact switch action cost:
Figure BDA0002867008920000034
in the formula: c. CgridCost of switching actions for tie switches; d is the number of times of switching the contact switch action in the day;
5) demand response cost of interruptible load:
Figure BDA0002867008920000035
Ft＝R-F (7)
Figure BDA0002867008920000036
Figure BDA0002867008920000037
in the formula: ftDemand response cost for interruptible load in the t-th time period; r is the profit when the load response can be interrupted; f is punishment when the interruptible load does not reach the specified response; c. CRCompensating the price for the outage; delta PnLoad reduction specified for the grid company; delta PaRepresenting the actual load reduction of the user; c. CFPenalty price;
6) energy storage operation maintenance cost:
Figure BDA0002867008920000041
in the formula: c. CupThe operation and maintenance cost of the energy storage unit electric quantity; pess,tThe electric quantity for charging and discharging the stored energy in the t time period;
the constraints are as follows:
(1) tidal current balance constraint
Figure BDA0002867008920000042
In the formula: pDGi,s,tThe active power output by the distributed renewable energy source power generation under the s-th operation scene in the t-th time period of the node i is obtained; pessi,t、PDSMi,t、PLi,tRespectively storing the active power stored in the tth time period of the node i, the active power consumed by interruptible loads and the active power consumed by other loads; qDGi,t、Qessi,t、QLi,tAnd QCi,tRespectively obtaining reactive power output by the distributed renewable energy power generation inverter, reactive power output by the energy storage inverter, reactive power consumed by a load and reactive power output by a reactive power compensation device on a network in the tth time period of the node i;
(2) node voltage constraint
Umin≤Ui≤Umax (12)
In the formula: u shapeminAnd UmaxRespectively representing the upper limit and the lower limit of the node voltage amplitude of the active power distribution network;
(3) distributed renewable energy power generation output constraint
Figure BDA0002867008920000043
In the formula: pDGi,minAnd PDGi,maxRespectively is the upper limit and the lower limit of active power output by the node i distributed renewable energy source power generation; qDGi,minAnd QDGi,maxThe upper limit and the lower limit of the reactive power output by the node i distributed renewable energy power generation inverter are respectively set;
(4) tie switch action times constraint
Ntotal≤Ntotal,max (14)
Nn≤Nn,max (15)
In the formula: n is a radical oftotalFor the total number of switching operations, Ntotal,maxAn upper limit of the total number of switching operations; n is a radical ofnNumber of times of operation of nth switch, Nn,maxAn upper limit of the number of times of operation of the nth switch;
(5) load shedding factor constraint
λimin＜λi＜λimax (16)
In the formula: lambda [ alpha ]iLoad reduction factor for node i; lambda [ alpha ]i,max、λi,minRespectively an upper limit and a lower limit of the load reduction coefficient of the node i;
(6) energy storage charge and discharge power constraint
Figure BDA0002867008920000051
In the formula: p is a radical ofc、pdActual charging and discharging power for energy storage; p is a radical ofc,max、pd,maxRespectively, the upper limit of the charge and discharge power; u. ofc、udA charge-discharge flag bit for energy storage; because the energy storage device can not be charged and discharged simultaneously, the charging and discharging flag bit of the energy storage also meets the following requirements:
Figure BDA0002867008920000052
(7) remaining capacity constraint of energy storage system
SminES≤ESOC≤SmaxES (19)
In the formula: eSOCResidual capacity for stored energy; eSRated installation capacity for energy storage; sminAnd SmaxRespectively a minimum state of charge and a maximum state of charge of the stored energy;
(8) energy storage inverter reactive power output constraint
Qessi,min≤Qessi,t≤Qessi,max (20)
In the formula: qessi,min，Qessi,maxUpper and lower limits of reactive power output by the energy storage inverter for the node i;
(9) reactive power output constraint of reactive power compensation device on network
QCi,min≤QCi,t≤QCi,max (21)
In the formula: qCi,min，QCi,maxThe upper limit and the lower limit of the reactive power output by the reactive power compensation device on the node i network are set;
(10) switching times constraint of reactive power compensation device on network
Figure BDA0002867008920000061
In the formula: ci(t)，Ci(t-1) the access capacity of the reactive power compensation device on the node i network at the time t and the time t-1; n iscmaxAnd the maximum switching times of the reactive power compensation device on the network in one day are represented.
The particle swarm optimization algorithm in the step (3) comprises the following specific processes:
in order to solve the balance problem of the local searching capability and the global searching capability of the particle swarm algorithm, an inertia weight factor omega is introduced, and accordingly, the speed updating formula of the particle swarm algorithm is obtained as follows:
Figure BDA0002867008920000062
in the formula: viDenotes the particle flight velocity, XiTo representPosition of the particle, k number of iterations, Pi、PgRepresenting the current individual extremum and the global extremum; individual learning factor c1And social learning factor c2The value is generally 2; r is1And r2Is located at [0,1 ]]Random numbers within the interval;
in the optimization process, a large inertia weight factor has strong global searching capability, and a small inertia weight factor has strong local searching capability; the inertia weight factor is linearly decreased by 0.9-0.4, and the calculation formula of the inertia weight factor is as follows:
Figure BDA0002867008920000063
wherein maximer is an ideal iteration number, and iter is a current iteration number.
The specific process of the step (3) is as follows:
1) taking a solution of an active power distribution network source network load storage collaborative optimization model, namely a source network load storage operation strategy, as a sequence, and expressing the sequence as a particle;
2) initializing ideal iteration times, population numbers, positions and speeds;
3) calculating the adaptive value of the particle according to a formula (23), and initializing an individual extreme value and a global extreme value;
4) updating the speed and position of the particles;
5) if the particles fly out of the learning space in the iterative process, namely the operation strategy of the source network charge storage does not meet the constraint condition, the positions of the particles need to be reset so that the particles are positioned at the boundary;
6) calculating an adaptive value corresponding to each particle in the population according to a formula (23);
7) judging whether each particle in the population is an active particle, if not, resetting and recalculating;
8) selecting the optimal particles according to the adaptive value; (each particle corresponds to an adaptation value, and the particle corresponding to the optimal adaptation value is the optimal particle)
9) Judging whether the algorithm is finished or not, if so, finishing the calculation and outputting the current optimal result; if the algorithm is not finished, proceeding to the step 4) to carry out iterative optimization again;
10) and decoding the optimal result to obtain an optimal source network load storage operation strategy.
The invention has the advantages that: on one hand, the influence of uncertainty of the distributed renewable energy power generation active power output is considered, and compared with the traditional scheduling method for limiting the distributed renewable energy power generation active power output, the distributed renewable energy power generation can be fully consumed, and the system economy is improved; on the other hand, the method considers more schedulable resources such as source network load storage and the like, carries out cooperative optimization on various controllable resources in the active power distribution network operation link, can obtain better effect compared with the independent scheduling control of the controllable resources, and further improves the economical efficiency of the active power distribution network operation.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, firstly, in the invention, for the problem that uncertainty of active output of distributed renewable energy power generation is not considered in the optimized operation of the active power distribution network, the active output of distributed renewable energy power generation is subjected to scene division based on a multi-scene technology, so that the uncertainty of the active output of distributed renewable energy power generation is considered; secondly, an active power distribution network source network charge-storage cooperative optimization model is established by taking the lowest daily comprehensive operation cost of the active power distribution network as a target, and network reconstruction, and cooperative optimization operation of reactive power output of a distributed renewable energy power generation inverter and an energy storage inverter and active power output of distributed renewable energy power generation are considered on the basis of only considering the source charge-storage cooperative optimization operation model, so that the source network charge-storage cooperative optimization operation of the active power distribution network is realized; and finally, solving the problem of mixed integer programming by combining a source network and storage collaborative optimization model of the active power distribution network with a particle swarm optimization algorithm.
1 Multi-scene partitioning
In order to fully absorb the renewable energy power generation, the uncertainty of the active output of the distributed renewable energy power generation needs to be considered in the source network load storage cooperative optimization operation of the active power distribution network. The method solves the problem of uncertainty of the distributed renewable energy power generation active power output by using a multi-scene technology, divides the distributed renewable energy power generation active power output into a plurality of scenes and gives the probability of each scene.
First, the time-series values of the light intensity and the wind speed over a long period of time are predicted from the prediction model. Secondly, establishing a time sequence output model P of the active output of the distributed renewable energy power generation by taking 15min as a basic step lengthDG(t), the active power output of the distributed renewable energy power generation is considered to be unchanged within 15 min. Finally, clustering the distributed renewable energy power generation active power output by using a multi-scene technology to obtain S typical days P of the distributed renewable energy power generation active power outputDG,s(t) probability of occurrence per typical day psWhere S is 1,2, …, S.
After typical scenes and probabilities of the active power output of the distributed renewable energy sources are obtained, the operation cost of the active power distribution network in each scene can be calculated, probability weighting is carried out on the operation cost in each scene, and therefore the influence of uncertainty of the active power output of the distributed renewable energy sources on the source network and the storage network of the active power distribution network on collaborative optimization is considered.
2 active power distribution network source-network load-storage cooperative optimization model
The active power distribution network source and grid charge storage collaborative optimization model takes the lowest comprehensive operation cost of the active power distribution network in a day as an objective function, source and grid charge storage is collaboratively optimized, decision variables comprise reactive power output of a distributed renewable energy power generation inverter, a dynamic reconstruction contact switch action scheme, a load interruptible scheduling strategy, an energy storage charge-discharge strategy, reactive power output of an energy storage system inverter and reactive power output of a reactive power compensation device on a network, and the objective function is as follows:
min C＝Cup+Closs+CDG+Cgrid+CDSM+Cess (1)
in the formula: c is the daily comprehensive operation cost of the active power distribution network; cupThe cost for purchasing electricity to the upper level; clossThe cost of network loss; cDGThe electricity purchase cost for the operator is invested in the DG; cgridDynamically reconstructing tie switch action costs; cDSMDemand response cost for interruptible loads; cessAnd the cost of energy storage operation and maintenance is saved.
The respective part cost calculation is as follows:
1) the electricity purchasing cost of the active power distribution network to a superior power grid is as follows:
Figure BDA0002867008920000081
in the formula: s is the total number of the operation scenes of the active power distribution network; p is a radical ofsThe probability of occurrence of the s-th operation scene; t is a 15min time interval; t is a calculation time period (24 h); c. CupThe price is the upper electricity purchase price; pup,tThe electric quantity purchased to the upper level in the t time period.
2) The network loss cost of the active power distribution network is as follows:
Figure BDA0002867008920000091
in the formula: c is the price of selling electricity to the active power distribution network; ploss,tThe network loss amount in the t-th time period is shown.
3) The electricity purchasing cost of the active power distribution network to DG investment operators is as follows:
Figure BDA0002867008920000092
in the formula: c. CDGThe electricity purchase price for the operator is invested in the DG; pDG,tAnd (5) investing the electric quantity purchased by the operator to the DG in the t-th time period.
4) The active power distribution network dynamically reconstructs the contact switch action cost:
Figure BDA0002867008920000093
in the formula: c. CgridCost of switching actions for tie switches; d is the number of times of switching the communication switch in the day.
5) Demand response cost of interruptible load:
Figure BDA0002867008920000094
Ft＝R-F(7)
Figure BDA0002867008920000095
Figure BDA0002867008920000096
in the formula: ftDemand response cost for interruptible load in the t-th time period; r is the profit when the load response can be interrupted; f is punishment when the interruptible load does not reach the specified response; c. CRCompensating the price for the outage; delta PnLoad reduction specified for the grid company; delta PaRepresenting the actual load reduction of the user; c. CFIs a penalty price.
6) Energy storage operation maintenance cost:
Figure BDA0002867008920000097
in the formula: c. CupThe operation and maintenance cost of the energy storage unit electric quantity; pess,tThe amount of electric energy charged and discharged for the t-th time period.
The main constraints are as follows:
(1) tidal current balance constraint
Figure BDA0002867008920000101
In the formula: pDGi,s,tDistributed regeneration is carried out under the s operation scene in the t time period of the node iThe active power output by the energy source power generation; pessi,t，PDSMi,t，PLi,tRespectively, the active power stored in the t-th time slot of the node i (positive during discharging and negative during charging), the active power consumed by the interruptible load and the active power consumed by other loads; qDGi,t，Qessi,t，QLi,tAnd QCi,tThe reactive power output by the distributed renewable energy power generation inverter, the reactive power output by the energy storage inverter, the reactive power consumed by the load and the reactive power output by the reactive power compensation device on the network in the tth time period of the node i are respectively.
(2) Node voltage constraint
Umin≤Ui≤Umax (12)
In the formula: u shapemin，UmaxThe voltage amplitude of the active power distribution network node is the upper limit and the lower limit of the voltage amplitude of the active power distribution network node.
(3) Distributed renewable energy power generation output constraint
Figure BDA0002867008920000102
In the formula: pDGi,min，PDGi,maxThe upper limit and the lower limit of active power output by the node i distributed renewable energy source power generation; qDGi,min，QDGi,maxThe upper limit and the lower limit of the reactive power output by the node i distributed renewable energy power generation inverter are considered in the invention, and the distributed renewable energy power generation inverter can send out capacitive or inductive reactive power.
(4) Tie switch action times constraint
Ntotal≤Ntotal,max (14)
Nn≤Nn,max (15)
In the formula: n is a radical oftotalFor the total number of switching operations, Ntotal,maxAn upper limit of the total number of switching operations; n is a radical ofnNumber of times of operation of nth switch, Nn,maxThe upper limit of the number of times of operation of the nth switch.
(5) Load shedding factor constraint
λimin＜λi＜λimax (16)
In the formula: lambda [ alpha ]iLoad reduction factor for node i; lambda [ alpha ]i,max、λi,minUpper and lower limits of the load reduction coefficient for the node i;
(6) energy storage charge and discharge power constraint
Figure BDA0002867008920000111
In the formula: p is a radical ofc、pdActual charging and discharging power for energy storage; p is a radical ofc,max、pd,maxRespectively, the upper limit of the charge and discharge power; u. ofc、udIs a charging and discharging zone bit of energy storage. Because the energy storage device can not be charged and discharged simultaneously, the charging and discharging flag bit of the energy storage can meet the following conditions:
Figure BDA0002867008920000112
(7) remaining capacity constraint of energy storage system
SminES≤ESOC≤SmaxES (19)
In the formula: eSOCResidual capacity for stored energy; eSRated installation capacity for energy storage; sminAnd SmaxRespectively a minimum state of charge and a maximum state of charge of the stored energy.
(8) Energy storage inverter reactive power output constraint
Qessi,min≤Qessi,t≤Qessi,max (20)
In the formula: qessi,min，Qessi,maxThe upper limit and the lower limit of the reactive power output by the node i energy storage inverter are considered in the invention, and the energy storage inverter can send out capacitive or inductive reactive power.
(9) Reactive power output constraint of reactive power compensation device on network
QCi,min≤QCi,t≤QCi,max (21)
In the formula: qCi,min，QCi,maxThe upper limit and the lower limit of the reactive power output by the reactive power compensation device on the node i network.
(10) Switching times constraint of reactive power compensation device on network
Figure BDA0002867008920000113
In the formula: ci(t)，Ci(t-1) the access capacity of the reactive power compensation device on the node i network at the time t and the time t-1; n iscmaxAnd the maximum switching times of the reactive power compensation device on the network in one day are represented.
3 solving method
In the active power distribution network source network charge storage cooperative optimization model, a dynamic reconstruction interconnection switch action scheme and reactive power output of a reactive power compensation device on a switchable network are discrete, and reactive power output of a distributed renewable energy power generation inverter, a scheduling strategy of interruptible load, a charging and discharging strategy of energy storage and reactive power output of an energy storage system inverter are continuous. Therefore, a mixed integer programming problem needs to be solved for the active power distribution network source-storage cooperative optimization. The solution is that based on the proposed active power distribution network source network load storage cooperative optimization model, discrete variables are required to be continuously subjected to iteration, and after an optimal solution is solved, the integral is carried out.
The invention adopts a particle swarm algorithm to solve. In order to solve the balance problem of the local search capability and the global search capability of the particle swarm algorithm, an inertia weight factor omega is introduced, and accordingly, a speed updating formula of the particle swarm algorithm is obtained as follows:
Figure BDA0002867008920000121
in the formula: viDenotes the particle flight velocity, XiRepresenting the position of the particle, k being the number of iterations, Pi、PgRepresenting current individual extrema and global polesA value; individual learning factor c1And social learning factor c2The value is generally 2; r is1And r2Is located at [0,1 ]]Random numbers within the interval.
In the optimization process, a large inertia weight factor has strong global searching capability, and a small inertia weight factor has strong local searching capability. If the inertial weight factor is kept unchanged in the whole searching process, the global contradiction and the local contradiction are easily caused. Therefore, the inertia weight factor is linearly decreased from 0.9 to 0.4, and the following calculation formula of the inertia weight factor is given as follows:
Figure BDA0002867008920000122
wherein maximer is an ideal iteration number, and iter is a current iteration number. At the beginning of searching, the inertial weight factor is the largest, the searching global capability is the strongest, and the position of the optimal solution is directly locked; in the later iteration stage, the inertia weight factor is gradually reduced, the local searching capability of the algorithm is enhanced, and the optimal solution position can be determined quite accurately.
The PSO algorithm is realized by the following steps:
1) the solution of the active power distribution network source network load storage collaborative optimization model, namely a source network load storage operation strategy, is used as a sequence and can be expressed as a particle;
2) initializing ideal iteration times, population numbers, positions and speeds;
3) calculating the adaptive value of the particle according to a formula (23), and initializing an individual extreme value and a global extreme value;
4) updating the speed and position of the particles;
5) if the particles fly out of the learning space in the iterative process, namely the operation strategy of the source network charge storage does not meet the constraint condition, the positions of the particles need to be reset so that the particles are positioned at the boundary;
6) calculating an adaptive value corresponding to each particle in the population according to a formula (23);
7) judging whether each particle in the population is an active particle, if not, resetting and recalculating;
8) selecting the optimal particles according to the adaptive value; (each particle corresponds to an adaptation value, and the particle corresponding to the optimal adaptation value is the optimal particle)
9) Judging whether the algorithm is finished or not, if so, finishing the calculation and outputting the current optimal result; if the algorithm is not finished, proceeding to the step 4) to carry out iterative optimization again;
10) and decoding the optimal result to obtain an optimal source network load storage operation strategy.

Claims (5)

1. A multi-scenario technology-based active power distribution network source network load storage cooperative optimization operation method is characterized by comprising the following steps: the method specifically comprises the following steps:
(1) dividing the active power output of the distributed renewable energy source power generation into a plurality of scenes by using a multi-scene technology and giving the occurrence probability of each scene;
(2) establishing an active power distribution network source network storage collaborative optimization model by taking the lowest comprehensive operation cost of the active power distribution network in a day as a target;
(3) and continuously performing iteration on discrete variables by adopting a particle swarm optimization algorithm based on the active power distribution network source-network load-storage cooperative optimization model, solving an optimal solution, and then performing normalization.
2. The active power distribution network source network storage cooperative optimization operation method based on the multi-scenario technology as claimed in claim 1, wherein: the specific process of the step (1) is as follows:
firstly, predicting time sequence values of illumination intensity and wind speed in a longer time according to a prediction model; secondly, establishing a time sequence output model P of the active output of the distributed renewable energy power generation by taking 15min as a basic step lengthDG(t), the active output of the distributed renewable energy power generation is considered to be unchanged within 15 min; finally, clustering the distributed renewable energy power generation active power output by using a multi-scene technology to obtain S typical days P of the distributed renewable energy power generation active power outputDG,s(t) probability of occurrence per typical day psWhere S is 1,2, …, S.
3. The active power distribution network source network storage cooperative optimization operation method based on the multi-scenario technology as claimed in claim 2, wherein: the specific process of the step (2) is as follows:
the method comprises the following steps of taking the lowest comprehensive operation cost of an active power distribution network in a day as an objective function, carrying out collaborative optimization on source network charge storage, wherein decision variables comprise reactive power output of a distributed renewable energy power generation inverter, a dynamic reconstruction interconnection switch action scheme, a scheduling strategy of interruptable load, a charge-discharge strategy of stored energy, reactive power output of an energy storage system inverter and reactive power output of a reactive power compensation device on a network, and the objective function is as follows:
min C＝Cup+Closs+CDG+Cgrid+CDSM+Cess (1)
in the formula: c is the daily comprehensive operation cost of the active power distribution network; cupThe cost for purchasing electricity to the upper level; clossThe cost of network loss; cDGThe electricity purchase cost for the operator is invested in the DG; cgridDynamically reconstructing tie switch action costs; cDSMDemand response cost for interruptible loads; cessOperating and maintaining costs for energy storage;
the respective part cost calculation is as follows:
1) the electricity purchasing cost of the active power distribution network to a superior power grid is as follows:
Figure FDA0002867008910000021
in the formula: s is the total number of the operation scenes of the active power distribution network; p is a radical ofsThe probability of occurrence of the s-th operation scene; t is a 15min time interval; t is a calculation time period; c. CupThe price is the upper electricity purchase price; pup,tThe electric quantity purchased to the upper level in the t time period;
2) the network loss cost of the active power distribution network is as follows:
Figure FDA0002867008910000022
in the formula: c is the price of selling electricity to the active power distribution network; ploss,tThe network loss capacity in the t time period is;
3) the electricity purchasing cost of the active power distribution network to DG investment operators is as follows:
Figure FDA0002867008910000023
in the formula: c. CDGThe electricity purchase price for the operator is invested in the DG; pDG,tThe electric quantity purchased by the operator is invested to the DG in the t time period;
4) the active power distribution network dynamically reconstructs the contact switch action cost:
Figure FDA0002867008910000024
in the formula: c. CgridCost of switching actions for tie switches; d is the number of times of switching the contact switch action in the day;
5) demand response cost of interruptible load:
Figure FDA0002867008910000025
Ft＝R-F (7)
Figure FDA0002867008910000026
Figure FDA0002867008910000027
in the formula: ftDemand response cost for interruptible load in the t-th time period; r is the profit when the load response can be interrupted; f is an interruptible loadPenalty when no specified response is reached; c. CRCompensating the price for the outage; delta PnLoad reduction specified for the grid company; delta PaRepresenting the actual load reduction of the user; c. CFPenalty price;
6) energy storage operation maintenance cost:
Figure FDA0002867008910000031
in the formula: c. CupThe operation and maintenance cost of the energy storage unit electric quantity; pess,tThe electric quantity for charging and discharging the stored energy in the t time period;
the constraints are as follows:
(1) tidal current balance constraint
Figure FDA0002867008910000032
In the formula: pDGi,s,tThe active power output by the distributed renewable energy source power generation under the s-th operation scene in the t-th time period of the node i is obtained; pessi,t、PDSMi,t、PLi,tRespectively storing the active power stored in the tth time period of the node i, the active power consumed by interruptible loads and the active power consumed by other loads; qDGi,t、Qessi,t、QLi,tAnd QCi,tRespectively obtaining reactive power output by the distributed renewable energy power generation inverter, reactive power output by the energy storage inverter, reactive power consumed by a load and reactive power output by a reactive power compensation device on a network in the tth time period of the node i;
(2) node voltage constraint
Umin≤Ui≤Umax (12)
In the formula: u shapeminAnd UmaxRespectively representing the upper limit and the lower limit of the node voltage amplitude of the active power distribution network;
(3) distributed renewable energy power generation output constraint
Figure FDA0002867008910000033
In the formula: pDGi,minAnd PDGi,maxRespectively is the upper limit and the lower limit of active power output by the node i distributed renewable energy source power generation; qDGi,minAnd QDGi,maxThe upper limit and the lower limit of the reactive power output by the node i distributed renewable energy power generation inverter are respectively set;
(4) tie switch action times constraint
Ntotal≤Ntotal,max (14)
Nn≤Nn,max (15)
In the formula: n is a radical oftotalFor the total number of switching operations, Ntotal,maxAn upper limit of the total number of switching operations; n is a radical ofnNumber of times of operation of nth switch, Nn,maxAn upper limit of the number of times of operation of the nth switch;
(5) load shedding factor constraint
λimin＜λi＜λimax (16)
In the formula: lambda [ alpha ]iLoad reduction factor for node i; lambda [ alpha ]i,max、λi,minRespectively an upper limit and a lower limit of the load reduction coefficient of the node i;
(6) energy storage charge and discharge power constraint
Figure FDA0002867008910000041
In the formula: p is a radical ofc、pdActual charging and discharging power for energy storage; p is a radical ofc,max、pd,maxRespectively, the upper limit of the charge and discharge power; u. ofc、udA charge-discharge flag bit for energy storage; because the energy storage device can not be charged and discharged simultaneously, the charging and discharging flag bit of the energy storage also meets the following requirements:
Figure FDA0002867008910000042
(7) remaining capacity constraint of energy storage system
SminES≤ESOC≤SmaxES (19)
In the formula: eSOCResidual capacity for stored energy; eSRated installation capacity for energy storage; sminAnd SmaxRespectively a minimum state of charge and a maximum state of charge of the stored energy;
(8) energy storage inverter reactive power output constraint
Qessi,min≤Qessi,t≤Qessi,max (20)
In the formula: qessi,min，Qessi,maxUpper and lower limits of reactive power output by the energy storage inverter for the node i;
(9) reactive power output constraint of reactive power compensation device on network
QCi,min≤QCi,t≤QCi,max (21)
In the formula: qCi,min，QCi,maxThe upper limit and the lower limit of the reactive power output by the reactive power compensation device on the node i network are set;
(10) switching times constraint of reactive power compensation device on network
Figure FDA0002867008910000051
In the formula: ci(t)，Ci(t-1) the access capacity of the reactive power compensation device on the node i network at the time t and the time t-1; n iscmaxAnd the maximum switching times of the reactive power compensation device on the network in one day are represented.
4. The active power distribution network source network storage cooperative optimization operation method based on the multi-scenario technology as claimed in claim 3, wherein: the particle swarm optimization algorithm in the step (3) comprises the following specific processes:
in order to solve the balance problem of the local searching capability and the global searching capability of the particle swarm algorithm, an inertia weight factor omega is introduced, and accordingly, the speed updating formula of the particle swarm algorithm is obtained as follows:
Figure FDA0002867008910000052
in the formula: viDenotes the particle flight velocity, XiRepresenting the position of the particle, k being the number of iterations, Pi、PgRepresenting the current individual extremum and the global extremum; individual learning factor c1And social learning factor c2The value is generally 2; r is1And r2Is located at [0,1 ]]Random numbers within the interval;
in the optimization process, a large inertia weight factor has strong global searching capability, and a small inertia weight factor has strong local searching capability; the inertia weight factor is linearly decreased by 0.9-0.4, and the calculation formula of the inertia weight factor is as follows:
Figure FDA0002867008910000053
wherein maximer is an ideal iteration number, and iter is a current iteration number.
5. The active power distribution network source network storage cooperative optimization operation method based on the multi-scenario technology as claimed in claim 4, wherein: the specific process of the step (3) is as follows:
1) taking a solution of an active power distribution network source network load storage collaborative optimization model, namely a source network load storage operation strategy, as a sequence, and expressing the sequence as a particle;
2) initializing ideal iteration times, population numbers, positions and speeds;
3) calculating the adaptive value of the particle according to a formula (23), and initializing an individual extreme value and a global extreme value;
4) updating the speed and position of the particles;
5) if the particles fly out of the learning space in the iterative process, namely the operation strategy of the source network charge storage does not meet the constraint condition, the positions of the particles need to be reset so that the particles are positioned at the boundary;
6) calculating an adaptive value corresponding to each particle in the population according to a formula (23);
7) judging whether each particle in the population is an active particle, if not, resetting and recalculating;
8) selecting the optimal particles according to the adaptive value;
9) judging whether the algorithm is finished or not, if so, finishing the calculation and outputting the current optimal result; if the algorithm is not finished, proceeding to the step 4) to carry out iterative optimization again;
10) and decoding the optimal result to obtain an optimal source network load storage operation strategy.
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