CN106228462B - Multi-energy-storage-system optimal scheduling method based on genetic algorithm - Google Patents
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Abstract
The invention discloses a multi-energy-storage-system optimal scheduling method based on a genetic algorithm, which is based on historical data of power loads, uses a linear regression prediction method to predict the power loads on the same day, carries out modeling respectively based on working parameters and characteristics of a plurality of battery energy storage systems on the basis of load prediction, combines the characteristics of cooperative work of the plurality of energy storage systems, takes power change conditions of different batteries at different moments as decision variables, takes the sum of power utilization peak values of each user as an optimization target, adopts a heuristic genetic algorithm to solve a problem model and carries out optimal scheduling, and achieves the aim of more remarkable reduction of the peak value of the whole user on the premise of not changing the power utilization behavior of the user. The method improves the utilization rate of a single energy storage system, and can realize the optimization result of comprehensive benefit maximization and integral optimization for the user group as a whole. The method has important scientific significance and application value for research and popularization of the energy storage system.
Description
Technical Field
The invention relates to an optimization problem for urban power grid user sides, belongs to the field of power systems, and particularly relates to a genetic algorithm-based multi-energy-storage-system optimization scheduling method.
Background
With global warming, the rapid development of clean energy has reached a consensus worldwide, renewable energy represented by wind power generation and photovoltaic power generation is rapidly developed, the existing power system industry is challenged unprecedentedly, and the traditional power supply mode of a power grid has defects. The load in the power system has the characteristics that the peak-valley load difference increases year by year and the maximum load utilization hours decrease year by year, so that the scale of the power equipment increases along with the increase of the annual maximum load, but the annual maximum load utilization hours of the equipment gradually decrease, the economical efficiency of investment of the power equipment is reduced, and the utilization of power resources is low.
To solve the above problems to some extent, energy storage technology is introduced into the power system. The user side energy storage is an important energy storage technology, and is different from the power generation side energy storage and the power transmission and distribution level energy storage, and the single project is much smaller and is closer to the common power users. The energy storage mode can effectively realize demand side management, and has the functions of eliminating peak-valley difference, smoothing load, promoting the utilization of new energy, reducing power supply cost and the like.
In practical application, the existing single energy storage system and the existing single energy storage technology can better realize functions of reducing peak-valley difference, smoothing load and the like, but compare with the electricity charge saved in the day, the cost of the lithium battery and the battery management module of the energy storage system is higher, in practical use, the energy storage system works in only 1-2 load peak sections in one day, the utilization rate of the energy storage system is not high, and the actual internal yield IRR is lower. A new turn of domestic power system reform has determined that a power grid only charges the power grid and can gradually release a demand side, and with the development of modern power grid technology, in a certain range, the multi-user energy storage system can realize collaborative optimization through a new power grid structure, namely, a plurality of energy storage systems collaboratively optimize power load curves of a plurality of users to obtain an overall optimal result. For common power users, the method can improve the utilization rate of a single energy storage system and improve daily electricity charge saving; for a power grid company, the method can reduce peak-valley difference and smooth function more obviously.
The genetic algorithm is an intelligent optimization method with wide application, is a new algorithm developed by the theory of computer simulated evolution, is an important branch in the field of artificial intelligence, has few limiting conditions for optimization problems, has strong universality and is easy to realize by programming.
Disclosure of Invention
The invention aims to provide an improved method aiming at the problems that the utilization rate of an energy storage system is not high and the actual internal profitability is low when a single energy storage system is used for user load optimization. In a certain area range, a plurality of energy storage systems of multiple users cooperate to perform collaborative optimization aiming at a plurality of load curves of a user group, so that the result with the best overall economic benefit is obtained.
The purpose of the invention is realized by the following technical scheme: a multi-energy-storage-system optimal scheduling method based on a genetic algorithm comprises the following steps:
(1) acquiring historical power load data of a user, dividing a prediction day into N time stages, wherein the time interval of each stage is delta t, and performing short-term power load prediction by adopting a multiple linear regression prediction method to obtain predicted load data; (2) according to the physical characteristics, the working characteristics and the relevant parameters of the energy storage systems, K energy storage systems are respectively modeled, and the energy storage system model of the kth user at the moment i is as follows:
SoCk_min≤SoCk(i)≤SoCk_max
SoCk(i)＝SoCk(i-1)+bk(i)·Δt
bk_min≤bk(i)≤bk_max
wherein, SoCk(i) For the battery residual capacity of the energy storage system i moment of the kth user, SoCk_max、SoCk_minRespectively the upper and lower limits of the energy storage system SoC when overcharge and overdischarge protection are considered, bkIs the average power of the energy storage system per time interval, bk_max、bk_minIs b iskUpper and lower limits of (b), wherein bk_minIs negative and represents the maximum discharge power, bk_maxIs a positive number, representing the maximum charging power;
(3) given an objective function to be optimized, the objective function is defined as follows:
wherein, c1Is a charge charged according to the maximum value of the power consumption of the user over a period of time, c2Is the cost of depletion of the battery, Pk_net(i) The load condition of the power grid end at the moment of the kth user i is obtained;
(4) considering the collaborative optimization of the multiple energy storage systems, providing constraint conditions:
bk(i)＝bk_itself(i)+bk_others(i)
αk·bk_min≤bk_others(i)≤αk·bk_max
bkj(i)·bjk(i)≥0j≠k
bk_itself(i)·bkj(i)≥0j≠k
Pk_net(k)＝Pk_load(k)+bk_itself(i)+b'k_others(i)
wherein b isk_itself(i) The charging and discharging condition of the energy storage system i of the kth user to the self load at the moment, bk_others(i) For charging and discharging the energy storage system to other user loads, bk_others(i) By charging or discharging variables b to or from different userskj(i) Constitution, αkDefining coefficients for the users, wherein the coefficients indicate indexes, b ', of the users participating in collaborative optimization of the energy storage system at unit time'k_others(i) For the load charging and discharging conditions of other energy storage equipment to the kth user, the charging and discharging variables b of the energy storage systems of different users to the kth userjk(i) Constitution Pk_load(i) The actual load condition of the kth user;
(5) and optimizing according to the constructed optimization problem, solving by adopting a heuristic genetic algorithm to obtain a charge-discharge power sequence of the multiple energy storage systems, and finishing the optimized scheduling of the battery energy storage system by using the charge-discharge power sequence as an execution standard for predicting the charge-discharge behavior of the daily energy storage system.
Further, the expression of the multiple linear regression prediction value at the prediction day i in step 1 is as follows:
wherein
To predict the result of the prediction at time i, QmHistorical data required for multiple linear regression prediction methodsPoint, amIs partial regression coefficient, where M is 0, 1, 2, … …, M; m is the number of selected historical data points.
The method comprises the following specific steps:
d. aiming at the predicted value of the moment i, selecting historical data of M points before the moment i and power load data of the moment i in the previous week, wherein the abscissa is time, and the ordinate is a power load value, and drawing a scatter diagram;
e. from multiple linear regression models
According to the least square principle, solving a partial regression coefficient amWherein M is 0, 1, 2, … …, M;
f. the regression equation and the action magnitude of each variable are checked and evaluated, and the power load at the moment i is predicted according to the equation to be solved, so that the power load at the moment i is solved
g.
Further, the step 5 specifically includes the following steps:
a. taking the charging and discharging behaviors of K user energy storage systems at N moments as decision variables, randomly generating G individuals meeting the value range of the decision variables, wherein each individual contains K x N parameters, namely the sum of the charging and discharging behaviors of the K user energy storage systems at the N moments to form a population;
b. determining iteration times and setting coding and decoding modes and genetic parameters according to the general principle of a genetic algorithm; designing a fitness function according to the constraint condition of the optimization problem;
c. performing variation and selection according to the solving principle of the genetic algorithm to obtain the optimal decision variable meeting the iteration condition and the fitness function, namely the optimal charging and discharging behaviors of the K user energy storage systems with the optimal target function at N moments, and calculating to obtain Jmin。
Compared with the prior art, the invention has the advantages that: the method is based on historical data of the power load, uses a linear regression prediction method to predict the power load of the day, carries out modeling respectively based on working parameters and characteristics of a plurality of battery energy storage systems on the basis of load prediction, combines the characteristics of cooperative work of the plurality of energy storage systems, takes power change conditions of different batteries at different moments as decision variables, takes the sum of power consumption peak values of each user as an optimization target, adopts a heuristic genetic algorithm to solve a problem model and carries out optimization scheduling, and achieves the aim of more remarkable reduction of the integral user peak value on the premise of not changing the power consumption behavior of the user. Particularly, the method is characterized in that the utilization rate of the energy storage system is not high by optimizing the single user load by aiming at the existing single energy storage system, a collaborative optimization method of a plurality of energy storage systems aiming at a plurality of user load curves is innovatively provided, and compared with the optimization method of the single energy storage system aiming at the single load curve, the method improves the utilization rate of energy storage equipment while ensuring the peak clipping and valley filling effects, thereby improving the overall profit of the system. The method has important scientific significance and application value for research and popularization of the energy storage system.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the load change of a user I after two energy storage systems are adopted to optimize two load curves;
fig. 3 is a schematic diagram of the load change of the second user after two energy storage systems are adopted to optimize two load curves.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
As shown in fig. 1, the optimal scheduling method for multiple energy storage systems based on genetic algorithm provided by the invention comprises the following steps:
(1) acquiring historical power load data of a user, dividing a prediction day into N time stages, wherein the time interval of each stage is delta t, and performing short-term power load prediction by adopting a multiple linear regression prediction method to obtain predicted load data;
the expression of the multiple linear regression prediction value at the prediction day i is as follows:
wherein
To predict the result of the prediction at time i, QmHistorical data points required for the multiple linear regression prediction method, amIs partial regression coefficient, where M is 0, 1, 2, … …, M; m is the number of selected historical data points.
The method comprises the following specific steps:
h. aiming at the predicted value of the moment i, selecting historical data of M points before the moment i and power load data of the moment i in the previous week, wherein the abscissa is time, and the ordinate is a power load value, and drawing a scatter diagram; the sampling interval may take 15 minutes and M may take 10.
i. From multiple linear regression models
According to the least square principle, solving a partial regression coefficient amWherein M is 0, 1, 2, … …, M;
j. the regression equation and the action magnitude of each variable are checked and evaluated, and the power load at the moment i is predicted according to the equation to be solved, so that the power load at the moment i is solved
(2) According to the physical characteristics, the working characteristics and the relevant parameters of the energy storage systems, K energy storage systems are respectively modeled, and the energy storage system model of the kth user at the moment i is as follows:
SoCk_min≤SoCk(i)≤SoCk_max
SoCk(i)＝SoCk(i-1)+bk(i)·Δt
bk_min≤bk(i)≤bk_max
wherein, SoCk(i) Storing i-th-time electricity of energy storage system for k-th userResidual battery capacity, SoCk_max、SoCk_minRespectively the upper and lower limits of the energy storage system SoC when overcharge and overdischarge protection are considered, bkIs the average power of the energy storage system per time interval, bk_max、bk_minIs b iskUpper and lower limits of (b), wherein bk_minIs negative and represents the maximum discharge power, bk_maxIs a positive number, representing the maximum charging power;
(3) given an objective function to be optimized, the objective function is defined as follows:
wherein, c1Is a charge charged according to the maximum value of the power consumption of the user over a period of time, c2Is the cost of depletion of the battery, Pk_net(i) The load condition of the power grid end at the moment of the kth user i is obtained;
(4) considering the collaborative optimization of the multiple energy storage systems, providing constraint conditions:
bk(i)＝bk_itself(i)+bk_others(i)
αk·bk_min≤bk_others(i)≤αk·bk_max
bkj(i)·bjk(i)≥0j≠k
bk_itself(i)·bkj(i)≥0j≠k
Pk_net(k)＝Pk_load(k)+bk_itself(i)+b'k_others(i)
wherein b isk_itself(i) The charging and discharging condition of the energy storage system i of the kth user to the self load at the moment, bk_others(i) For energy storage system to other usersCharge and discharge of the load, bk_others(i) By charging or discharging variables b to or from different userskj(i) Constitution, αkDefining coefficients for the users, wherein the coefficients indicate indexes, b ', of the users participating in collaborative optimization of the energy storage system at unit time'k_others(i) For the load charging and discharging conditions of other energy storage equipment to the kth user, the charging and discharging variables b of the energy storage systems of different users to the kth userjk(i) Constitution Pk_load(i) The actual load condition of the kth user;
(5) optimizing according to the constructed optimization problem, solving by adopting a heuristic genetic algorithm to obtain a charge-discharge power sequence of the multiple energy storage systems, and completing the optimized scheduling of the battery energy storage system by taking the charge-discharge power sequence as an execution standard for predicting the charge-discharge behavior of the daily energy storage system, wherein the method specifically comprises the following steps:
a. taking the charging and discharging behaviors of K user energy storage systems at N moments as decision variables, randomly generating G individuals meeting the value range of the decision variables, wherein each individual contains K x N parameters, namely the sum of the charging and discharging behaviors of the K user energy storage systems at the N moments to form a population;
b. determining iteration times and setting coding and decoding modes and genetic parameters according to the general principle of a genetic algorithm; designing a fitness function according to the constraint condition of the optimization problem;
c. performing variation and selection according to the solving principle of the genetic algorithm to obtain the optimal decision variable meeting the iteration condition and the fitness function, namely the optimal charging and discharging behaviors of the K user energy storage systems with the optimal target function at N moments, and calculating to obtain Jmin。
FIG. 2 is a schematic diagram of load change of a user I after two load curves are optimized by two energy storage systems, wherein the abscissa is time h, and the ordinate is power consumption kW; fig. 3 is a schematic diagram of load change of a second user after two load curves are optimized by two energy storage systems, wherein the abscissa is time h, and the ordinate is power consumption kW. As can be seen from the figure, the method of the invention achieves the effect of more remarkable reduction of the integral peak value of the user on the premise of not changing the power utilization behavior of the user.
Claims (2)
1. A multi-energy-storage-system optimal scheduling method based on a genetic algorithm is characterized by comprising the following steps:
(1) acquiring historical power load data of a user, dividing a prediction day into N time stages, wherein the time interval of each stage is delta t, and performing short-term power load prediction by adopting a multiple linear regression prediction method to obtain predicted load data;
(2) according to the physical characteristics, the working characteristics and the relevant parameters of the energy storage systems, K energy storage systems are respectively modeled, and the energy storage system model of the kth user in the i period is as follows:
SoCk_min≤SoCk(i)≤SoCk_max
SoCk(i)＝SoCk(i-1)+bk(i)·Δt
bk_min≤bk(i)≤bk_max
wherein, SoCk(i) For the battery residual capacity of the k user energy storage system in the period of i, SoCk_max、SoCk_minRespectively the upper and lower limits of the energy storage system SoC when overcharge and overdischarge protection are considered, bk(i) Average power of the energy storage system for period i, bk_max、bk_minAre respectively bk(i) Upper and lower limits of (b), wherein bk_minIs negative and represents the maximum discharge power, bk_maxIs a positive number, representing the maximum charging power;
(3) given an objective function to be optimized, the objective function is defined as follows:
wherein, c1Is a charge charged according to the maximum value of the power consumption of the user over a period of time, c2Is the cost of depletion of the battery, Pk_net(i) The load condition of the power grid end in the i-th period of the kth user is obtained; pk_netThe load condition of the grid end of the kth user in unit time is shown;
(4) considering the collaborative optimization of the multiple energy storage systems, providing constraint conditions:
bk(i)＝bk_itself(i)+bk_others(i)
αk·bk_min≤bk_others(i)≤αk·bk_max
bkj(i)·bjk(i)≥0j≠k
bk_itself(i)·bkj(i)≥0j≠k
Pk_net(i)＝Pk_load(i)+bk_itself(i)+b'k_others(i)
wherein b isk_itself(i) For the charging and discharging condition of the energy storage system of the kth user to the self load at the i time period, bk_others(i) For charging and discharging the energy storage system to other user loads, bk_others(i) By charging or discharging variables b to or from different userskj(i) Constitution, αkDefining coefficients for the users, wherein the coefficients indicate indexes, b ', of the users participating in collaborative optimization of the energy storage system at unit time'k_others(i) For the load charging and discharging conditions of other energy storage equipment to the kth user, the charging and discharging variables b of the energy storage systems of different users to the kth userjk(i) Constitution Pk_load(i) The actual load condition of the kth user;
(5) optimizing according to the constructed optimization problem, solving by adopting a heuristic genetic algorithm to obtain a charge-discharge power sequence of the multiple energy storage systems, and finishing the optimized scheduling of the battery energy storage system by using the charge-discharge power sequence as an execution standard for predicting the charge-discharge behavior of the daily energy storage system; the method specifically comprises the following steps:
a. taking the charging and discharging behaviors of K user energy storage systems in N time periods as decision variables, randomly generating G individuals meeting the value range of the decision variables, wherein each individual contains K x N parameters, namely the sum of the charging and discharging behaviors of the K user energy storage systems in the N time periods to form a population;
b. determining iteration times and setting coding and decoding modes and genetic parameters according to the general principle of a genetic algorithm; designing a fitness function according to the constraint condition of the optimization problem;
c. performing variation and selection according to the solving principle of the genetic algorithm to obtain the optimal decision variable meeting the iteration condition and the fitness function, namely the optimal charging and discharging behaviors of the K user energy storage systems with the optimal target function in N time periods, and calculating to obtain Jmin。
2. The method according to claim 1, wherein the multiple linear regression prediction value expression for the prediction day i period in step 1 is as follows:
wherein
To predict the result of the prediction of the i-th day period, QmHistorical data points required for the multiple linear regression prediction method, a0Is a regression constant, amIs partial regression coefficient, where M is 1, 2, … …, M; m is the number of the selected historical data points; the method comprises the following specific steps:
a. aiming at the predicted value of the i time period, selecting historical data of M points before the i time period and power load data of the i time period of the last week, and drawing a scatter diagram with the abscissa as time and the ordinate as a power load value;
b. from multiple linear regression models
According to the least square principle, solving a partial regression coefficient amWherein M is 0, 1, 2, … …, M;
checking and evaluating regression equation and action magnitude of each variable, and calculating according to the obtained equationPredicting the power load in the i-period to obtain
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