CN114723115A - Optimization method and device for power distribution system including wind power plant based on demand response coordination - Google Patents

Optimization method and device for power distribution system including wind power plant based on demand response coordination Download PDF

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CN114723115A
CN114723115A CN202210319969.XA CN202210319969A CN114723115A CN 114723115 A CN114723115 A CN 114723115A CN 202210319969 A CN202210319969 A CN 202210319969A CN 114723115 A CN114723115 A CN 114723115A
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李忠政
廖孟柯
董昱廷
付林
范建兵
田淼
王燕
李清
郑伟东
赵昂
黄亮
周红莲
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Economic and Technological Research Institute of State Grid Xinjiang Electric Power Co Ltd
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Abstract

The invention relates to the technical field of power distribution network optimization, in particular to a method and a device for optimizing a power distribution system including a wind power plant based on demand response coordination, wherein the method comprises the steps of constructing an energy storage system model; constructing a risk index model; respectively converting the energy storage system model and the risk index model into Markov decision process models; and solving the Markov decision process model by utilizing a particle swarm algorithm to obtain an optimal solution. The method is characterized in that an energy storage system model and a risk index model are established by combining the output uncertainty of the wind power plant, the demand response coordination is carried out, the energy storage system model and the risk index model are respectively converted into Markov decision process models based on the time correlation of the wind power plant, namely, each scene is independent in time sequence rather than parallel, then the optimal solution is obtained by utilizing a particle swarm algorithm, the goal of optimizing the operation of a power distribution network is achieved, the operation cost of the system can be reduced, the wind curtailment is greatly reduced, and the wind power utilization rate is improved.

Description

Optimization method and device for power distribution system including wind power plant based on demand response coordination
Technical Field
The invention relates to the technical field of power distribution network optimization, in particular to a method and a device for optimizing a power distribution system including a wind power plant based on demand response coordination.
Background
With the increase of the permeability of the renewable power, the influence of the permeability of the renewable power on the operation of a power distribution network is larger and larger, and a wind power plant has strong randomness, so that a certain error always exists in a prediction result of wind power output, and further, the phenomenon of unbalanced supply and demand of the wind power output and load demand is caused. How to balance load under the condition that a system is stable and users are satisfied and the cost is the lowest is the main task of optimizing operation of a modern power distribution system, and the consumption capacity of power generation of a wind power plant is improved. The demand response has good load balancing capacity and is widely applied to the field. In the previous research of demand response, uncertainty of distribution network type parameters is not considered, uncertainty and time correlation of the output of a wind power plant under the action of an energy storage system and a distribution network system on a balanced random wind power plant are not considered, the previous research adopts a multi-stage stochastic programming Method (MSP) to solve, and random variables with time dependency are not considered.
Disclosure of Invention
The invention provides a method and a device for optimizing a power distribution system including a wind power plant based on demand response coordination, overcomes the defects of the prior art, and can effectively solve the problems that the output uncertainty and the time correlation of the wind power plant are not considered in the existing method for optimizing the power distribution system including the wind power plant by adopting a multi-stage random planning method.
One of the technical schemes of the invention is realized by the following measures: a method for optimizing a power distribution system including a wind power plant based on demand response coordination comprises the following steps:
constructing an energy storage system model;
constructing a risk index model;
respectively converting the energy storage system model and the risk index model into Markov decision process models;
and solving the Markov decision process model by using a particle swarm algorithm to obtain an optimal solution.
The following is further optimization or/and improvement of the technical scheme of the invention:
the energy storage system model is shown as follows:
HaSoC(k+1)＝HaSoC(k)+ηchPch(k)Δt-Pdis(k)Δt/ηdis
Pch_min≤Pch(k)≤Pch_max
Pdis_min≤Pdis(k)≤Pdis_max
wherein SoC (k), Pch(k)，Pdis(k) The charging state and the charging active power of the battery at the moment k are respectivelyPower, discharge active power; etach，ηdisPercent efficiency of charge and discharge, respectively; ha，Pch_max，Pch_minThe maximum value and the minimum value of the capacity and the charging active power of the energy storage system are respectively; p isdis_max，Pdis_minRespectively the maximum value and the minimum value of the discharge active power.
The risk indicator model is shown as follows:
Figure BDA0003571251310000021
VaR＝inf(γ|Prob(Z≤γ)≥η)
wherein η is the confidence; etasIs a secondary non-negative variable whose value is equal to the distance between the random variable Z and VaR; rhosAnd NS is the probability of the scene s and the total number of scenes, respectively.
The Markov decision process model comprises:
Figure BDA0003571251310000022
Sk+1＝T·(Sk，yk)
wherein S iskIs the current state of the system { Pw(k)，Pw(k-1)，Pw(k-2)，..，Pw(k-n +1) }; t is the system transition probability given by the current state and the current operation; y iskFor the current action, from { PTCL(k)，Pch(k)，Pds(k)，Qrec(k) Is formed by the following steps; f. ofkIs a cost function; qrec(k) Is a reconstruction scheme.
The second technical scheme of the invention is realized by the following measures: a wind farm-containing power distribution system optimization device based on demand response coordination comprises:
the first model unit is used for constructing an energy storage system model;
the second model unit is used for constructing a risk index model;
the conversion unit is used for respectively converting the energy storage system model and the risk index model into Markov decision process models;
and the solving unit is used for solving the Markov decision process model by utilizing a particle swarm algorithm to obtain an optimal solution.
The invention aims at the problem that the supply and demand relationship is unbalanced due to the uncertainty of the output of the wind power plant after the wind power plant is accessed into a distribution network. A wind power plant-containing power distribution system optimization method based on demand response coordination is provided. The method comprises the steps of establishing an energy storage system model and a risk index model by combining the output uncertainty of a wind power plant, carrying out demand response coordination, converting the energy storage system model and the risk index model into Markov decision process models respectively based on the time correlation of the wind power plant, namely, each scene is independent in time sequence rather than parallel, and then obtaining an optimal solution by utilizing a particle swarm algorithm, so that the aim of optimizing the operation of a power distribution network is achieved, meanwhile, the operation cost of the system can be reduced, the wind curtailment is greatly reduced, and the wind power utilization rate is improved.
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FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the structure of the device of the present invention.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described with reference to the following examples and figures:
example 1: as shown in the attached figure 1, the embodiment of the invention discloses a method for optimizing a power distribution system including a wind power plant based on demand response coordination, which comprises the following steps:
s101, constructing an energy storage system model;
step S102, constructing a risk index model;
step S103, respectively converting the energy storage system model and the risk index model into Markov decision process models;
and step S104, solving the Markov decision process model by using a particle swarm algorithm to obtain an optimal solution.
The invention aims at the problem that the supply and demand relationship is unbalanced due to the uncertainty of the output of the wind power plant after the wind power plant is accessed into a distribution network. A wind power plant-containing power distribution system optimization method based on demand response coordination is provided. The method comprises the steps of establishing an energy storage system model and a risk index model by combining the output uncertainty of a wind power plant, carrying out demand response coordination, converting the energy storage system model and the risk index model into Markov decision process models respectively based on the time correlation of the wind power plant, namely, each scene is independent in time sequence rather than parallel, and then obtaining an optimal solution by utilizing a particle swarm algorithm, so that the aim of optimizing the operation of a power distribution network is achieved, meanwhile, the operation cost of the system can be reduced, the wind curtailment is greatly reduced, and the wind power utilization rate is improved.
Example 2: as shown in the attached figure 1, the embodiment of the invention discloses a method for optimizing a power distribution system including a wind power plant based on demand response coordination, which comprises the following steps:
step S201, constructing an energy storage system model;
because the energy storage system is an important method for peak clipping, valley filling and wind curtailment reduction, it is very important to construct an energy storage system model, and the energy storage system model in this embodiment is as follows:
HaSoC(k+1)＝HaSoC(k)+ηchPch(k)Δt-Pdis(k)Δt/ηdis
Pch_min≤Pch(k)≤Pch_max
Pdis_min≤Pdis(k)≤Pdis_max
wherein, SoC (k), Pch(k)，Pdis(k) The state of charge (expressed in percentage), the charging active power (W), and the discharging active power (W) of the battery at time k, respectively; etach，ηdisPercent efficiency of charge and discharge, respectively; ha，Pch_max，Pch_minRespectively, the capacity of the energy storage system, the maximum value and the minimum value of the active power of charging, where at any moment P, since the energy storage system cannot be charged and discharged simultaneouslych(k)，Pdis(k) At least one value is 0；Pdis_max，Pdis_minRespectively the maximum value and the minimum value (unit: W) of the discharge active power.
Step S202, constructing a risk index model;
the CVaR is used as a common risk index in the field of financial risk measurement and can be used for measuring the risk of the random variable Z exceeding the limit. Here, the present embodiment is used to measure the risk of the user voltage crossing and the energy storage system discharge state crossing, so the risk index model is as follows:
Figure BDA0003571251310000041
VaR＝inf(γ|Prob(Z≤γ)≥η)
wherein η is the confidence; etasIs a secondary non-negative variable whose value is equal to the distance between the random variable Z and VaR; rhosAnd NS is the probability of the scene s and the total number of scenes, respectively.
Step S203, respectively converting the energy storage system model and the risk index model into Markov decision process models;
the markov decision process models a stochastic dynamic problem into a system with states and state transition probabilities. At each time stage, the decision maker selects the best action for each state that minimizes the cost function. The transition probability to reach the future state depends only on the current behavior and the current state of the system. The Markov decision process model includes:
Figure BDA0003571251310000042
Sk+1＝T·(Sk，yk)
wherein S iskIs the current state of the system { Pw(k)，Pw(k-1)，Pw(k-2)，..，Pw(k-n +1) }; t is the system transition probability given by the current state and the current operation; y iskFor the current action, from { PTCL(k)，Pch(k)，Pds(k)，Qrec(k) Is formed by the following steps; f. ofkIs a cost function; qrec(k) To a reconstruction scheme.
The markov decision process problem can be solved by stochastic dynamic programming. With dynamic programming, each state is assigned a value function V. Dynamic programming optimizes its value function for the current state and expects other value functions to be derived from the current state. It decomposes the dynamic optimization problem into simpler sub-problems. The above equation can be rewritten as a recursive form of a number of values:
Vk(Sk)＝min{fk(Sk，yk)+E[Vk+1(Sk+1)|Sk，yk|}
and step S204, solving the Markov decision process model by using a particle swarm algorithm to obtain an optimal solution.
The particle swarm algorithm simulates birds in a bird swarm by designing a particle without mass, which has only two attributes: speed, which represents how fast the movement is, and position, which represents the direction of the movement. And each particle independently searches an optimal solution in a search space, records the optimal solution as a current individual extremum, shares the individual extremum with other particles in the whole particle swarm, finds the optimal individual extremum as a current global optimal solution of the whole particle swarm, and adjusts the speed and the position of each particle in the particle swarm according to the found current individual extremum and the current global optimal solution shared by the whole particle swarm.
The particle swarm algorithm framework is as follows:
step1 population initialization, which can be random initialization or design specific initialization method according to the optimized problem, then calculate the individual adaptive value, thus selecting the individual local optimal position vector and the population global optimal position vector;
step2 iterates to set: setting iteration times, and setting the current iteration times to be 1;
step3 speed update: updating the velocity vector of each individual;
step4 location update: updating the position vector of each individual;
step5 local position and global position vector update: updating the local optimal solution of each individual and the global optimal solution of the population;
step6 termination condition judgment: and when the iteration times are judged, the maximum iteration times are reached, if the iteration times are met, a global optimal solution is output, otherwise, the iteration is continued, and the step3 is skipped.
Example 3: as shown in fig. 1, an embodiment of the present invention discloses a demand response coordination-based optimization device for a power distribution system including a wind farm, including:
the first model unit is used for constructing an energy storage system model;
the second model unit is used for constructing a risk index model;
the conversion unit is used for respectively converting the energy storage system model and the risk index model into Markov decision process models;
and the solving unit is used for solving the Markov decision process model by utilizing a particle swarm algorithm to obtain an optimal solution.
Example 4: the embodiment of the invention discloses a storage medium, wherein a computer program capable of being read by a computer is stored on the storage medium, and the computer program is set to execute a wind power plant-containing power distribution system optimization method based on demand response coordination during operation.
The storage medium may include, but is not limited to: u disk, read-only memory, removable hard disk, magnetic or optical disk, etc. various media capable of storing computer programs.
Example 5: the embodiment of the invention discloses electronic equipment which comprises a processor and a memory, wherein a computer program is stored in the memory and loaded and executed by the processor to realize a wind power plant-containing power distribution system optimization method based on demand response coordination.
The electronic equipment further comprises transmission equipment and input and output equipment, wherein the transmission equipment and the input and output equipment are both connected with the processor.
The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect, and unnecessary technical features can be increased or decreased according to actual needs to meet the requirements of different situations.

Claims (8)

1. A method for optimizing a power distribution system including a wind power plant based on demand response coordination is characterized by comprising the following steps:
constructing an energy storage system model;
constructing a risk index model;
respectively converting the energy storage system model and the risk index model into Markov decision process models;
and solving the Markov decision process model by utilizing a particle swarm algorithm to obtain an optimal solution.
2. The method of optimizing a wind farm-based power distribution system based on demand response coordination according to claim 1, wherein the energy storage system model is represented by the following equation:
HaSoC(k+1)＝HaSoC(k)+ηchPch(k)Δt-Pdis(k)Δt/ηdis
Pch_min≤Pch(k)≤Pch_max
Pdis_min≤Pdis(k)≤Pdis_max
wherein, SoC (K), Pch(k)，Pdis(k) Respectively representing the charging state, the charging active power and the discharging active power of the battery at the moment k; etach，ηdisPercent efficiency of charge and discharge, respectively; ha，Pch_max，Pch_minThe maximum value and the minimum value of the capacity and the charging active power of the energy storage system are respectively; pdis_max，Pdis_minThe maximum value and the minimum value of the discharge active power are respectively.
3. A method of optimizing a wind farm-based power distribution system based on demand response coordination according to claim 1 or 2, characterized in that the risk indicator model is represented by the following equation:
Figure FDA0003571251300000011
VaR＝inf(γ|Prob(Z≤γ)≥η)
wherein η is the confidence; etasIs a secondary non-negative variable whose value is equal to the distance between the random variable Z and VaR; ρ is a unit of a gradientsAnd NS is the probability of the scene s and the total number of scenes, respectively.
4. A method of optimizing a wind farm-containing power distribution system based on demand response coordination according to claim 1 or 2, characterized in that said markov decision process model comprises:
Figure FDA0003571251300000012
Sk+1＝T·(Sk，yk)
wherein S iskIs the current state of the system { Pw(k)，Pw(k-1)，Pw(k-2)，..，Pw(k-n +1) }; t is the system transition probability given by the current state and the current operation; y iskFor the current action, from { PTCL(k)，Pch(k)，Pds(k)，Qrec(k) Is formed by the following steps; f. ofkIs a cost function; qrec(k) To a reconstruction scheme.
5. The method of optimizing a wind farm-containing power distribution system based on demand response coordination according to claim 3, wherein said Markov decision process model comprises:
Figure FDA0003571251300000021
Sk+1＝T·(Sk，yk)
wherein S iskIs the current state of the system { Pw(k)，Pw(k-1)，Pw(k-2)，..，Pw(k-n +1) }; t is the system transition probability given by the current state and the current operation;ykfor the current action, from { PTCL(k)，Pch(k)，Pds(k)，Qrec(k) Is formed by the following steps; f. ofkIs a cost function; qrec(k) Is a reconstruction scheme.
6. A wind power plant-containing power distribution system optimization device based on demand response coordination, wherein the wind power plant-containing power distribution system optimization method based on demand response coordination is used by the rule generalization and attack reconstruction network attack detection device, and the method is as defined in any one of claims 1 to 5, and comprises the following steps:
the first model unit is used for constructing an energy storage system model;
the second model unit is used for constructing a risk index model;
the conversion unit is used for respectively converting the energy storage system model and the risk index model into Markov decision process models;
and the solving unit is used for solving the Markov decision process model by utilizing a particle swarm algorithm to obtain an optimal solution.
7. A storage medium having stored thereon a computer program readable by a computer, the computer program being arranged to perform when executed the method of optimizing a wind farm power distribution system based on demand response coordination according to any of claims 1 to 5.
8. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program that is loaded into and executed by the processor to implement the method for demand response coordination based optimization of a wind farm-containing power distribution system according to any of claims 1 to 5.
CN202210319969.XA 2022-03-29 2022-03-29 Optimization method and device for power distribution system including wind power plant based on demand response coordination Pending CN114723115A (en)

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