CN112713618B - 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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CN112713618B
CN112713618B CN202011597575.8A CN202011597575A CN112713618B CN 112713618 B CN112713618 B CN 112713618B CN 202011597575 A CN202011597575 A CN 202011597575A CN 112713618 B CN112713618 B CN 112713618B
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active power
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distribution network
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CN112713618A (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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    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • G—PHYSICS
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
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    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
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Abstract

The invention discloses a multi-scene technology-based active power distribution network load-storage cooperative optimization operation method, which comprises the steps of firstly carrying out scene division on active power output of distributed renewable energy power generation based on the multi-scene technology, thereby considering uncertainty of the active power output of the distributed renewable energy power generation; 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, aiming at the source network and storage load cooperative optimization model of the active power distribution network, combining with a particle swarm optimization algorithm, and solving the problem of mixed integer programming.

Description

Active power distribution network source-storage cooperative optimization operation method based on multi-scenario 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 so as to enable the schedulable resources to cooperatively and optimally operate and realize the full consumption of the distributed renewable energy power generation.
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-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 power output of the distributed renewable energy power generation by taking 15min as a basic step length DG (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 source power generation active power output by using a multi-scene technology to obtain S typical days P of the distributed renewable energy source power generation active power output DG,s (t) probability of occurrence per typical day p s Wherein S =1,2, \8230;, 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 a target function, carrying out collaborative optimization on the source network charge storage, wherein decision variables comprise the reactive power output of a distributed renewable energy power generation inverter, a dynamic reconstruction interconnection switch action scheme, a load interruptible scheduling strategy, an energy storage charge-discharge strategy, the reactive power output of an energy storage system inverter and the reactive power output of a reactive power compensation device on a network, and the target function is as follows:
min C＝C up +C loss +C DG +C grid +C DSM +C ess (1)
in the formula: c is the daily comprehensive operation cost of the active power distribution network; c up The cost for purchasing electricity to the upper level; c loss The cost of network loss; c DG Purchasing electricity costs for a DG investment operator; c grid Dynamically reconstructing tie switch action costs; c DSM Demand response cost for interruptible loads; c ess Operating maintenance costs for energy storage;
the 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 of formula s The probability of occurrence of the s-th operation scene; t is a 15min time interval; t is a calculation time period; c. C up The purchase price of the upper grade electricity is; p up,t The 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; p loss,t The network power loss 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. C DG The electricity purchase price for the operator is invested in the DG; p is DG,t The electric quantity of electricity purchased by the operator is invested in the DG in the t time period;
4) Dynamically reconstructing contact switch action cost of the active power distribution network:
Figure BDA0002867008920000034
in the formula: c. C grid Cost 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
F t ＝R-F (7)
Figure BDA0002867008920000036
Figure BDA0002867008920000037
in the formula: f t Demand 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. C R Compensating the price for the outage; delta P n Load reduction specified for the grid company; delta P a Representing the actual load reduction of the user; c. C F Penalty price;
6) Energy storage operation maintenance cost:
Figure BDA0002867008920000041
in the formula: c. C up The operation and maintenance cost of the energy storage unit electric quantity; p ess,t Storing the charged and discharged electric quantity for the t time period;
the constraints are as follows:
(1) Tidal current balance constraint
Figure BDA0002867008920000042
In the formula: p is DGi,s,t The 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; p essi,t 、P DSMi,t 、P Li,t Respectively 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; q DGi,t 、Q essi,t 、Q Li,t And Q Ci,t Respectively 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
U min ≤U i ≤U max (12)
In the formula: u shape min And U max Respectively are 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: p is DGi,min And P DGi,max Respectively is the upper limit and the lower limit of active power output by the node i distributed renewable energy source power generation; q DGi,min And Q DGi,max The 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 constraints
N total ≤N total,max (14)
N n ≤N n,max (15)
In the formula: n is a radical of total For the total number of switching operations, N total,max Is the upper limit of the total number of switching operations; n is a radical of n Number of times of operation of nth switch, N n,max An upper limit of the number of times of operation of the nth switch;
(5) Load shedding factor constraint
λ imin ＜λ i ＜λ imax (16)
In the formula: lambda i Load reduction factor for node i; lambda [ alpha ] i,max 、λ i,min Respectively the upper limit and the 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 of c 、p d Actual charging and discharging power for energy storage; p is a radical of formula c,max 、p d,max Respectively the upper limit of the charge and discharge power; u. of c 、u d A 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
S min E S ≤E SOC ≤S max E S (19)
In the formula: e SOC Residual capacity for stored energy; e S Rated installation capacity for energy storage; s min And S max Respectively a minimum state of charge and a maximum state of charge of the stored energy;
(8) Energy storage inverter reactive power output constraint
Q essi,min ≤Q essi,t ≤Q essi,max (20)
In the formula: q essi,min ，Q essi,max Storing the upper limit and the lower limit of the reactive power output by the inverter for the node i;
(9) Reactive power output constraint of reactive power compensation device on network
Q Ci,min ≤Q Ci,t ≤Q Ci,max (21)
In the formula: q Ci,min ，Q Ci,max The 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: c i (t)，C i (t-1) is the access capacity of the reactive power compensation device on the node i network at the t moment and the t-1 moment; n is cmax And the maximum switching times of the reactive 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: v i Denotes the particle flight velocity, X i Representing the position of the particle, k being the number of iterations, P i 、P g Representing the current individual extremum and the global extremum; individual learning factor c 1 And social learning factor c 2 The value is generally 2; r is a radical of hydrogen 1 And r 2 Is 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 within the range of 0.9-0.4, and the calculation formula of the inertia weight factor is as follows:
Figure BDA0002867008920000063
wherein maxiter 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 load 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 The optimal result is decoded to obtain the optimal operation strategy of the source network load storage.
The invention has the advantages that: on one hand, the influence of uncertainty of the active power output of the distributed renewable energy power generation is considered, and compared with the traditional scheduling method for limiting the active power output of the distributed renewable energy power generation, the distributed renewable energy power generation can be fully consumed, and the economy of the system 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.
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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 length DG (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 source power generation active power output by using a multi-scene technology to obtain S typical days P of the distributed renewable energy source power generation active power output DG,s (t) andprobability of occurrence per typical day p s Wherein S =1,2, \8230;, 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 storage collaborative optimization model takes the lowest comprehensive operation cost of the active power distribution network in a day as an objective function, source network 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 charging and discharging strategy, reactive power output of an energy storage system inverter and reactive power output of reactive power compensation devices on a network, and the objective function is as follows:
min C＝C up +C loss +C DG +C grid +C DSM +C ess (1)
in the formula: c is the daily comprehensive operation cost of the active power distribution network; c up The cost for purchasing electricity to the upper level; c loss The cost of network loss; c DG The electricity purchase cost for the operator is invested in the DG; c grid Dynamically reconstructing tie switch action costs; c DSM Demand response cost for interruptible load; c ess And 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 of s The probability of occurrence of the s-th operation scene; t is a 15min time interval; t is a calculation time period (24 h); c. C up Is directed upwardsThe price of grade electricity purchase; p is up,t The 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; p is loss,t The 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. C DG Purchase electricity prices for the operator investing in DG; p DG,t And (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. C grid Cost of switching actions for tie switches; d is the switching times of the communication switch in the day.
5) Demand response cost of interruptible load:
Figure BDA0002867008920000094
F t ＝R-F(7)
Figure BDA0002867008920000095
Figure BDA0002867008920000096
in the formula: f t The demand response cost of the interruptible load in the t 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. C R Compensating the price for the outage; delta P n Load reduction specified for the grid company; delta P a Representing the actual load reduction of the user; c. C F Is a penalty price.
6) Energy storage operation maintenance cost:
Figure BDA0002867008920000097
in the formula: c. C up The operation and maintenance cost of the energy storage unit electric quantity; p ess,t The 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: p DGi,s,t The 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; p essi,t ，P DSMi,t ，P Li,t Respectively, 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; q DGi,t ，Q essi,t ，Q Li,t And Q Ci,t The 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
U min ≤U i ≤U max (12)
In the formula: u shape min ，U max The 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: p is DGi,min ，P DGi,max The upper limit and the lower limit of active power output by the node i distributed renewable energy source power generation; q DGi,min ，Q DGi,max For the upper and lower limits of the reactive power output by the node i distributed renewable energy power generation inverter, the distributed renewable energy power generation inverter is considered to be capable of generating two kinds of capacitive or inductive reactive power.
(4) Tie switch action times constraint
N total ≤N total,max (14)
N n ≤N n,max (15)
In the formula: n is a radical of total For the total number of switching operations, N total,max Is the upper limit of the total number of switching operations; n is a radical of n Number of times of operation of nth switch, N n,max The upper limit of the number of times of operation of the nth switch.
(5) Load shedding factor constraint
λ imin ＜λ i ＜λ imax (16)
In the formula: lambda i Load reduction factor for node i; lambda i,max 、λ i,min Upper 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 of c 、p d Actual charging and discharging power for energy storage; p is a radical of formula c,max 、p d,max Respectively, the upper limit of the charge and discharge power; u. of c 、u d For charging or discharging of stored energyAnd (4) an electric flag bit. 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
S min E S ≤E SOC ≤S max E S (19)
In the formula: e SOC Residual capacity for energy storage; e S Rated installation capacity for energy storage; s. the min And S max Respectively a minimum state of charge and a maximum state of charge of the stored energy.
(8) Energy storage inverter reactive power output constraint
Q essi,min ≤Q essi,t ≤Q essi,max (20)
In the formula: q essi,min ，Q essi,max The 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
Q Ci,min ≤Q Ci,t ≤Q Ci,max (21)
In the formula: q Ci,min ，Q Ci,max The 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: c i (t)，C i (t-1) is the access capacity of the reactive power compensation device on the node i network at the t moment and the t-1 moment; n is cmax And the maximum switching times of the reactive 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: v i Denotes the particle flight velocity, X i Denotes the particle position, k is the number of iterations, P i 、P g Representing the current individual extremum and the global extremum; individual learning factor c 1 And social learning factor c 2 The value is generally 2; r is 1 And r 2 Is 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 adopted to be linearly decreased from 0.9 to 0.4, and accordingly, the following inertia weight factors can be calculated according to the following formula:
Figure BDA0002867008920000122
wherein maximer is an ideal iteration number, and iter is a current iteration number. At the beginning of searching, the inertia weight factor is the largest, and the method has the strongest global searching capability and is beneficial to directly locking the position of the optimal solution; 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 distribution network source network load storage collaborative optimization model, namely the 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 is required, 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 optimum adaptation value is the optimum 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 Decoding the optimal result to obtain the optimal operation strategy of the source network load storage.

Claims (1)

1. An active power distribution network source-network load-storage cooperative optimization operation method based on a multi-scenario technology 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) Based on the active power distribution network source network load storage cooperative optimization model, discrete variables are continuously subjected to iteration by adopting a particle swarm optimization algorithm, and after an optimal solution is solved, the integration is carried out;
the specific process of the step (1) is as follows:
firstly, predicting time sequence values of illumination intensity and wind speed in a long time according to a prediction model; secondly, establishing a time sequence output model P of the active power output of the distributed renewable energy power generation by taking 15min as a basic step length DG (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 output DG,s (t) probability of occurrence per typical day p s Wherein S =1,2, \ 8230;, 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 a target function, carrying out collaborative optimization on the source network charge storage, wherein decision variables comprise the reactive power output of a distributed renewable energy power generation inverter, a dynamic reconstruction interconnection switch action scheme, a load interruptible scheduling strategy, an energy storage charge-discharge strategy, the reactive power output of an energy storage system inverter and the reactive power output of a reactive power compensation device on a network, and the target function is as follows:
min C＝C up +C loss +C DG +C grid +C DSM +C ess (1)
in the formula: c is the daily comprehensive operation cost of the active power distribution network; c up The cost for purchasing electricity to the upper level; c loss The cost of network loss; c DG The electricity purchase cost for the operator is invested in the DG; c grid Dynamically reconstructing contact switch action cost; c DSM Demand response for interruptible loadsCost; c ess Operating and maintaining costs for energy storage;
the respective part cost calculation is as follows:
1) The cost of purchasing electricity from the active power distribution network to the superior power grid is as follows:
Figure FDA0004048075320000011
in the formula: s is the total number of the operation scenes of the active power distribution network; p is a radical of formula s The probability of occurrence of the s-th operation scene; t is a 15min time interval; t is a calculation time period; c. C up The price is the upper electricity purchase price; p up,t The 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 FDA0004048075320000021
in the formula: c is the price of selling electricity to the active power distribution network; p loss,t The network power loss 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 FDA0004048075320000022
in the formula: c. C DG Purchase electricity prices for the operator investing in DG; p DG,t The electric quantity of electricity purchased by the operator is invested in the DG in the t time period;
4) The active power distribution network dynamically reconstructs the contact switch action cost:
Figure FDA0004048075320000023
in the formula: c. C grid Cost of switching actions for tie switches; d is the switching times of the communication switch action in the day;
5) Demand response cost of interruptible load:
Figure FDA0004048075320000024
F t ＝R-F (7)
Figure FDA0004048075320000025
Figure FDA0004048075320000026
in the formula: f t Demand 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. C R Compensating the price for the outage; delta P n Load reduction specified for the grid company; delta P a Representing the actual load reduction of the user; c. C F Punishment of price;
6) Energy storage operation maintenance cost:
Figure FDA0004048075320000027
in the formula: c. C up The operation and maintenance cost of the energy storage unit electric quantity; p ess,t Storing the charged and discharged electric quantity for the t time period;
the constraints are as follows:
(1) Flow balance constraints
Figure FDA0004048075320000031
In the formula: p is DGi,s,t The active power output by the distributed renewable energy source in the tth operation scene in the node i is obtained; p is essi,t 、P DSMi,t 、P Li,t Respectively 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; q DGi,t 、Q essi,t 、Q Li,t And Q Ci,t Respectively 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
U min ≤U i ≤U max (12)
In the formula: u shape min And U max Respectively are 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 FDA0004048075320000032
In the formula: p is DGi,min And P DGi,max Respectively is the upper limit and the lower limit of active power output by the node i distributed renewable energy source power generation; q DGi,min And Q DGi,max The 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
N total ≤N total,max (14)
N n ≤N n,max (15)
In the formula: n is a radical of total For the total number of switching operations, N total,max An upper limit of the total number of switching operations; n is a radical of n Number of times of operation of nth switch, N n,max An upper limit of the number of times of operation of the nth switch;
(5) Load shedding factor constraint
λ imin ＜λ i ＜λ imax (16)
In the formula: lambda [ alpha ] i Is a node iLoad reduction coefficient of (2); lambda i,max 、λ i,min Respectively 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 FDA0004048075320000041
In the formula: p is a radical of c 、p d Actual charging and discharging power for energy storage; p is a radical of formula c,max 、p d,max Respectively, the upper limit of the charge and discharge power; u. of c 、u d A 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 FDA0004048075320000042
(7) Remaining capacity constraint of energy storage system
S min E S ≤E SOC ≤S max E S (19)
In the formula: e SOC Residual capacity for energy storage; e S Rated installation capacity for energy storage; s min And S max Respectively a minimum state of charge and a maximum state of charge of the stored energy;
(8) Energy storage inverter reactive power output constraint
Figure FDA0004048075320000043
In the formula: q essi,min ，Q essi,max Upper 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
Q Ci,min ≤Q Ci,t ≤Q Ci,max (21)
In the formula: q Ci,min ，Q Ci,max For nodes i on the networkThe upper and lower limits of the reactive power output by the reactive power compensation device;
(10) Switching times constraint of reactive power compensation device on network
Figure FDA0004048075320000044
In the formula: c i (t)，C i (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 is a radical of an alkyl radical cmax The maximum switching times of the reactive 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 optimization, an inertia weight factor omega is introduced, and accordingly, a speed updating formula of the particle swarm optimization is obtained as follows:
Figure FDA0004048075320000051
in the formula: v i Denotes the particle flight velocity, X i Representing the position of the particle, k being the number of iterations, P i 、P g Representing the current individual extremum and the global extremum; individual learning factor c 1 And social learning factor c 2 Typically 2; r is a radical of hydrogen 1 And r 2 Is 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 FDA0004048075320000052
wherein, maxiter is an ideal iteration number, 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;
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 The optimal result is decoded to obtain the optimal operation strategy of the source network load storage.
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