CN114626639A - Multi-microgrid collaborative optimization economic dispatching method considering wind and light uncertainty - Google Patents

Multi-microgrid collaborative optimization economic dispatching method considering wind and light uncertainty Download PDF

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CN114626639A
CN114626639A CN202210410500.7A CN202210410500A CN114626639A CN 114626639 A CN114626639 A CN 114626639A CN 202210410500 A CN202210410500 A CN 202210410500A CN 114626639 A CN114626639 A CN 114626639A
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朱西平
李姿霖
罗惠文
江强
刘明航
黄磊
龙文涛
张燕
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Southwest Petroleum University
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Abstract

The invention provides a multi-microgrid collaborative optimization economic dispatching method considering wind and light uncertainty, and belongs to the field of multi-microgrid dispatching. Firstly, generating a typical scene through Latin hypercube sampling and a K-Means algorithm; secondly, risk cost for quantifying wind and light uncertainty by using a condition risk value theory is added into a day-ahead scheduling model of the multi-microgrid system; then, mixed demand response based on electricity price and excitation is considered to reduce peak load and ensure the reliability of the micro-grid; and finally, importing the day-ahead prediction data of the electric load, the wind power and the photovoltaic into a day-ahead scheduling model, and solving the model by using an improved particle swarm algorithm to obtain an optimized scheduling result of the multi-microgrid system. Compared with the prior art, the method and the system consider the risk cost brought by the uncertainty of the wind and light energy, encourage the user side to add demand response to reduce the power consumption cost, and provide a flexible scheduling optimization scheme for multiple micro-grids.

Description

Multi-microgrid collaborative optimization economic dispatching method considering wind and light uncertainty
Technical Field
The invention belongs to the technical field of optimized dispatching of multiple micro-grids, and particularly relates to a multi-micro-grid collaborative optimization economic dispatching method considering wind and light uncertainty.
Background
The single micro-grid has limited absorption capacity and weak anti-interference capacity, and the influence of uncertainty of renewable energy sources on the grid is difficult to balance. The multiple micro-grids which are adjacent geographically are interconnected to form the multiple micro-grids, the power grid technology is a new power grid technology for dealing with renewable energy consumption, environmental pollution and power grid pressure, and the reliability, flexibility and sustainability of a power system can be improved. With the continuous increase of load demand, the peak-valley difference is increased, a large amount of renewable energy enters the multi-microgrid, and the peak regulation and voltage regulation difficulty of the system is increased.
With the opening of the power market at the power distribution network side, a plurality of geographically adjacent micro-grids are interconnected to form a multi-micro-grid system, so that the consumption rate of renewable energy sources can be improved, the anti-interference capability of the system is improved, and good economic benefits are achieved. Although wind and photovoltaic energy sources have the advantage of being pollution free and inexhaustible, they are susceptible to uncertainty due to output power fluctuations caused by variable wind speed and solar irradiance. In recent years, the power generation capacity of renewable energy sources is continuously expanded, and the energy supply pressure and the environmental crisis are effectively relieved; but due to the randomness and the fluctuation of the power generation of the renewable energy sources, the operation of the power system has risks; in addition, the reform of the electric power market in China is continuously promoted, and the demand for renewable energy sources is increasingly increased, so that the output deviation of the renewable energy sources is required to be reduced, and the stability of the market is improved; the demand response is an important measure for realizing the economic and efficient operation of the micro-grid system, and has important significance for promoting the sustainable development of the micro-grid system; on the other hand, as the demand response of the power system matures, the introduction of the demand response may reduce the negative impact of the uncertainty on the power system.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing microgrid optimization method has the technical problem of weak anti-interference capability, and provides a multi-microgrid collaborative optimization economic dispatching method considering wind and light uncertainty; the method can enhance the anti-interference capability of the micro-grid, improve the solving precision and reduce the comprehensive operation cost based on the condition risk value and the demand response.
In order to achieve the purpose, the provided technical scheme is a multi-microgrid collaborative optimization economic dispatching method considering wind and light uncertainty, and the method is characterized by comprising the following steps of:
1) establishing and obtaining typical power rules and error ranges of wind power and photovoltaic energy output in a multi-microgrid, and obtaining a probability density distribution function in a mathematical modeling mode;
2) a Latin hypercube sampling method is adopted to construct a probabilistic scene set of wind power and photovoltaic, and scene reduction is completed through a K-Means clustering algorithm;
3) processing risks brought by wind and light uncertainty by adopting a condition risk value theory CVaR, and establishing a multi-microgrid risk evaluation model;
4) taking the sum of the quantified condition risk value cost and the system operation cost as a target function 1 and the environmental governance cost as a target function 2, establishing a multi-microgrid system day-ahead optimization scheduling model based on the condition risk value, and determining the state constraint and the power constraint of the multi-microgrid during power interaction;
5) adding a demand response behavior of users in the microgrid on a multi-microgrid system model considering power interaction, and considering the influence of demand response based on time-of-use electricity price and excitation on a multi-microgrid day-ahead scheduling model;
6) and calculating and solving a day-ahead optimized scheduling model of the multi-microgrid system according to the confidence level and the risk evasion parameters, and solving the model by using an improved particle swarm algorithm to obtain an optimized scheduling result of the multi-microgrid system.
According to the above, the recommendation method is used in the field of multiple micro-grids, and the beneficial effects of the invention are as follows:
aiming at the uncertainty of wind and light energy, the optimal scheduling planning of multiple micro-grids is realized, and the main research idea can be divided into the targets of taking economy and environmental protection as priority; aiming at an economic target, the minimum value of system scheduling cost is taken into consideration as a target function, and the random output characteristic of the multi-micro-grid is taken into consideration by adopting a condition risk value theory in a multi-micro-grid with large renewable energy grid-connected proportion represented by wind power and photovoltaic; aiming at the environmental protection target, mainly considering that the pollutant emission treatment cost is lowest, constructing a day-ahead scheduling model and solving an optimal scheduling result; the scheduling method considers the wind and light uncertainty in the real power transaction, can reflect the power transaction process more truly, and is beneficial to the realization of economic scheduling.
Drawings
FIG. 1 is an overall flow chart of the steps performed by the present invention;
FIG. 2 is the scheduling model optimization result of the present invention.
Detailed Description
The invention relates to a multi-micro-grid collaborative optimization economic dispatching method considering wind and light uncertainty, and a specific flow thereof is shown in figure 1, and is characterized by comprising the following steps:
1) establishing and obtaining a typical power rule and an error range of the wind power and photovoltaic energy output in the multi-microgrid, and obtaining a probability density distribution function in a mathematical modeling mode;
2) a Latin hypercube sampling method is adopted to construct a probabilistic scene set of wind power and photovoltaic, and scene reduction is completed through a K-Means clustering algorithm;
3) processing risks brought by wind and light uncertainty by adopting a condition risk value theory CVaR, and establishing a multi-microgrid risk evaluation model;
4) taking the sum of the quantified condition risk value cost and the system operation cost as a target function 1 and the environmental governance cost as a target function 2, establishing a multi-microgrid system day-ahead optimization scheduling model based on the condition risk value, and determining the state constraint and the power constraint of the multi-microgrid during power interaction;
5) adding a demand response behavior of users in the microgrid system on a multi-microgrid system model considering power interaction, and considering the influence of demand response based on time-of-use electricity price and excitation on a multi-microgrid day-ahead scheduling model;
6) and calculating and solving a day-ahead optimized scheduling model of the multi-microgrid system according to the confidence level and the risk evasion parameters, and solving the model by using an improved particle swarm algorithm to obtain an optimized scheduling result of the multi-microgrid system.
More specifically, as shown in fig. 2, the specific implementation process of the system scheduling model optimization result of the multi-microgrid collaborative optimization economic scheduling method considering wind and photovoltaic uncertainty is as follows:
firstly, the uncertainty of wind power is generally expressed by adopting a probability distribution function, and the Weir distribution probability function is suitable for simulating the randomness of wind speed under various conditions:
Figure BDA0003603476750000041
where v is the wind speed, KsIs the shape factor, λ is the scale factor;
photovoltaic uncertainty typically employs a beta distribution function to simulate the randomness of the photovoltaic under various conditions:
Figure BDA0003603476750000042
where s is the solar irradiance and α and β are parameters of the beta probability density function.
Secondly, uncertainty factors of micro-grid operation mainly comprise the volatility of wind power and photovoltaic output power; according to the random characteristics of wind power and photovoltaic, Latin hypercube sampling is carried out to obtain various operation scenes representing the multi-microgrid, uncertainty is converted into a deterministic model, and then the scenes are reduced by utilizing a K-Means clustering algorithm, so that multi-microgrid wind-light scene modeling considering uncertainty factors is completed.
Third, when [0,1 ] is given]Using a special convex function F at the confidence of the range betaβ(x, α) to characterize the conditional risk value CVaR and the risk value VaR. Thus, CVaR minimization is defined as follows:
Figure BDA0003603476750000043
wherein f (x, gamma) is a loss function of the model, x is a decision variable, gamma is a random variable, and when [0,1 ] is given]When the confidence coefficient beta of the range is beta, ξ (gamma) is a probability density function of the wind and light; p is a radical ofmThe probability of the mth scene generated by wind and light is M, and M is the total number of generated wind and light scenes;
and (3) modeling the wind abandoning and light abandoning risk cost by using the CVaR with the confidence coefficient of beta, wherein the conditional risk cost is composed of the wind abandoning risk cost and the light abandoning risk cost, and the CVaR values are respectively as follows:
Figure BDA0003603476750000044
Figure BDA0003603476750000045
Figure BDA0003603476750000051
Figure BDA0003603476750000052
in the formula, alphaWTA risk loss threshold value for wind abandon; alpha is alphaPVA light abandoning risk loss threshold value;
Figure BDA0003603476750000053
is the wind abandon penalty coefficient of the wind power,
Figure BDA0003603476750000054
representing the air abandon amount at the t moment in the scene m;
Figure BDA0003603476750000055
is the light abandonment penalty coefficient of the light,
Figure BDA0003603476750000056
representing the amount of light rejected at time t in scene m;
the conditional risk cost of considering the wind-solar uncertainty is thus:
CVaR＝CVaRWT+CVaRPV
fourthly, introducing a risk avoidance parameter lambda in the range of [0-2.5] to reflect the balance of the multiple micro-grids on the expected cost and the cost variability risk; the objective function 1 is that the economic cost of the multiple micro-grids in one operation period is minimum:
Figure BDA0003603476750000057
in the formula (I), the compound is shown in the specification,
Figure BDA0003603476750000058
the cost for purchasing electricity;
Figure BDA0003603476750000059
for operating costs;
Figure BDA00036034767500000510
for maintenance costs;
Figure BDA00036034767500000511
earning for selling electricity;
Figure BDA00036034767500000512
after participation in DRIncentive costs, CVaR is conditional risk cost;
the objective function 2 is that the pollutant discharge treatment cost is lowest:
Figure BDA00036034767500000513
wherein k is a contaminant (containing CO)2，SO2Etc.) a type number; beta is akThe cost for treating the k-th pollutants; alpha is alphankIs the emission coefficient of the pollutant k of the nth diesel generator set;
Figure BDA00036034767500000514
is the emission coefficient of the distribution network pollutant k;
the method comprises the following steps of power interaction constraint of the microgrid and a power distribution network and power constraint between the microgrid:
Figure BDA00036034767500000515
Figure BDA00036034767500000516
in the formula, sigma represents a binary variable of a power interaction state; sigma1+σ2≤1，σ1/σ2Taking 0 or 1; sigma3+σ4≤1，σ3/σ4Take 0 or 1.
Fifthly, reducing the load quantity depending on the demand elasticity and the electricity price of the user, and optimizing the time-sharing electricity price by adopting a load price elasticity linear function; price elasticity of a load is defined as the sensitive response of the load to electricity prices, and the demand price elasticity is calculated as follows:
Figure BDA0003603476750000061
in the formula (I), the compound is shown in the specification,
Figure BDA0003603476750000062
is the initial load demand at time t;
Figure BDA0003603476750000063
is the initial electricity price at time t; Δ etIs the electricity price change after participating in DR;
Figure BDA0003603476750000064
in the formula, DRtIs the load after participation in DR at time t, ηDRIs the percentage of load involved in DR, the greater the value, the greater the incentive compensation value given to the user.
And sixthly, solving the optimized scheduling model by using an improved particle swarm optimization after determining the objective function and the constraint condition, wherein the method comprises the following specific steps of:
(1) inputting predicted values of wind power, photovoltaic and load demands of different micro-grids, time-of-use electricity price values and inputting equipment parameters of the wind power, the photovoltaic, the fuel cell and the diesel generator;
(2) algorithm initialization: setting improved particle swarm algorithm parameters including population number, optimization times, inertia weight, learning factors and the like;
(3) calculating an objective function value of each particle;
(4) calculating pbest of the particles, updating gbest of the particles, and updating the position of each particle;
(5) and if the stopping condition is met, outputting an optimization result. Otherwise, returning to the step 3 for continuing.
The above description is only for the purpose of describing the embodiments of the present invention with reference to the accompanying drawings, and the embodiments are not intended to limit the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (7)

1. A multi-micro-grid collaborative optimization economic dispatching method considering wind and light uncertainty is characterized by comprising the following steps:
1) establishing and obtaining a typical power rule and an error range of the wind power and photovoltaic energy output in the multi-microgrid, and obtaining a probability density distribution function in a mathematical modeling mode;
2) a Latin hypercube sampling method is adopted to construct a probabilistic scene set of wind power and photovoltaic, and scene reduction is completed through a K-Means clustering algorithm;
3) processing risks brought by wind and light uncertainty by adopting a condition risk value theory CVaR, and establishing a multi-microgrid risk evaluation model;
4) taking the sum of the quantified condition risk value cost and the system operation cost as a target function 1 and the environmental governance cost as a target function 2, establishing a multi-microgrid system day-ahead optimization scheduling model based on the condition risk value, and determining the state constraint and the power constraint of the multi-microgrid during power interaction;
5) adding a demand response behavior of users in the microgrid on a multi-microgrid system model considering power interaction, and considering the influence of demand response based on time-of-use electricity price and excitation on a multi-microgrid day-ahead scheduling model;
6) and calculating and solving a day-ahead optimized scheduling model of the multi-microgrid system according to the confidence level and the risk evasion parameters, and solving the model by using an improved particle swarm algorithm to obtain an optimized scheduling result of the multi-microgrid system.
2. The method for the multi-microgrid collaborative optimization economic scheduling considering wind and light uncertainty as claimed in claim 1, wherein the detailed steps of the step 1) are as follows:
in step 1), the uncertainty of wind power is usually represented by a probability distribution function, and the weiler distribution probability function is suitable for simulating the randomness of wind speed under various conditions:
Figure FDA0003603476740000011
where v is the wind speed, KsIs the shape factor, λ is the scale factor;
photovoltaic uncertainty typically employs a beta distribution function to simulate the randomness of the photovoltaic under various conditions:
Figure FDA0003603476740000021
where s is the solar irradiance and α and β are parameters of the beta probability density function.
3. The method for multi-microgrid collaborative optimization economic dispatching considering wind and light uncertainty as claimed in claim 1, wherein the detailed steps of step 2) are as follows:
uncertainty factors of the operation of the microgrid mainly comprise the fluctuation of wind power and photovoltaic output power; according to the random characteristics of wind power and photovoltaic, Latin hypercube sampling is carried out to obtain various operation scenes representing the multi-microgrid, uncertainty is converted into a deterministic model, and then the scenes are reduced by utilizing a K-Means clustering algorithm, so that multi-microgrid wind-light scene modeling considering uncertainty factors is completed.
4. The method for multi-microgrid coordinated optimization economic dispatching considering wind-solar uncertainty according to claim 1, characterized in that the detailed steps of step 3) are as follows:
when given [0,1]Using a special convex function F at the confidence of the range betaβ(x, α) to characterize the conditional risk value CVaR and the risk value VaR. Thus, CVaR minimization is defined as follows:
Figure FDA0003603476740000022
wherein f (x, gamma) is a loss function of the model, x is a decision variable, gamma is a random variable, and when [0,1 ] is given]When the confidence coefficient beta of the range is beta, ξ (gamma) is a probability density function of the wind and light; p is a radical ofmThe probability of the mth scene generated by wind and light is M, and M is the total number of generated wind and light scenes;
and (3) modeling the wind abandoning and light abandoning risk cost by using the CVaR with the confidence coefficient of beta, wherein the conditional risk cost is composed of the wind abandoning risk cost and the light abandoning risk cost, and the CVaR values are respectively as follows:
Figure FDA0003603476740000023
Figure FDA0003603476740000024
Figure FDA0003603476740000031
Figure FDA0003603476740000032
in the formula, alphaWTA risk loss threshold value for wind abandon; alpha is alphaPVA risk loss threshold value for light abandonment;
Figure FDA0003603476740000033
is the wind abandon penalty coefficient of the wind power,
Figure FDA0003603476740000034
representing the air abandon amount at the t moment in the scene m;
Figure FDA0003603476740000035
is the light abandonment penalty coefficient of the light,
Figure FDA0003603476740000036
representing the amount of light rejected at time t in scene m;
the conditional risk cost of considering the wind-solar uncertainty is thus:
CVaR＝CVaRWT+CVaRPV。
5. the method for multi-microgrid collaborative optimization economic dispatching considering wind and light uncertainty as claimed in claim 1, wherein the detailed steps of step 4) are as follows:
introducing a risk avoidance parameter lambda in the range of [0-2.5] to reflect the balance between the expected cost and the cost variability risk of the multi-microgrid; the objective function 1 is that the economic cost of the multiple micro-grids in one operation period is minimum:
Figure FDA0003603476740000037
in the formula (I), the compound is shown in the specification,
Figure FDA0003603476740000038
for the cost of electricity purchase;
Figure FDA0003603476740000039
for operating costs;
Figure FDA00036034767400000310
for maintenance costs;
Figure FDA00036034767400000311
earning for selling electricity;
Figure FDA00036034767400000312
is the incentive cost after participation in DR, CVaR is the conditional risk cost;
the objective function 2 is that the pollutant discharge treatment cost is lowest:
Figure FDA00036034767400000313
wherein k is a contaminant (containing CO)2，SO2Etc.) a type number; beta is akThe cost for treating the k-th pollutants; alpha is alphankIs the nth firewoodThe emission coefficient of the oil power generation unit pollutant k;
Figure FDA00036034767400000314
is the emission coefficient of the distribution network pollutant k;
the method comprises the following steps of power interaction constraint of the microgrid and a power distribution network and power constraint between the microgrid:
Figure FDA00036034767400000315
Figure FDA00036034767400000316
in the formula, sigma represents a binary variable of the power interaction state; sigma1+σ2≤1，σ1/σ2Taking 0 or 1; sigma3+σ4≤1，σ3/σ4Take 0 or 1.
6. The method for the multi-microgrid collaborative optimization economic dispatching considering wind and light uncertainty is characterized in that the detailed steps of the step 5) are as follows:
the reduction of the load quantity depends on the demand elasticity and the electricity price of a user, and the time-sharing electricity price is optimized by adopting a load price elastic linear function; price elasticity of a load is defined as the sensitive response of the load to electricity prices, and the demand price elasticity is calculated as follows:
Figure FDA0003603476740000041
in the formula, Pt loadIs the initial load demand at time t;
Figure FDA0003603476740000042
is the initial electricity price at time t; Δ etIs the electricity price change after participating in DR;
the demand response model based on time-of-use electricity price optimization is as follows:
Figure FDA0003603476740000043
in the formula, DRtIs the load after participation in DR at time t, ηDRIs the percentage of load involved in DR, the greater the value, the greater the incentive compensation value given to the user.
7. The method as claimed in claim 1, wherein the detailed steps of step 6) are as follows:
after determining the objective function and the constraint condition, solving the optimized scheduling model by using an improved particle swarm algorithm, and specifically comprising the following steps:
(1) inputting predicted values of wind power, photovoltaic and load demands of different micro-grids, time-of-use electricity price values and inputting equipment parameters of the wind power, the photovoltaic, the fuel cell and the diesel generator;
(2) algorithm initialization: setting improved particle swarm algorithm parameters including population number, optimization times, inertia weight, learning factors and the like;
(3) calculating an objective function value of each particle;
(4) calculating pbest of the particles, updating gbest of the particles, and updating the position of each particle;
(5) and if the stopping condition is met, outputting an optimization result. Otherwise, returning to the step 3 for continuing.
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