CN109271678B - Storage battery charge-discharge scheduling optimization method based on photovoltaic micro-grid operation cost - Google Patents
Storage battery charge-discharge scheduling optimization method based on photovoltaic micro-grid operation cost Download PDFInfo
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Abstract
The invention relates to a storage battery charge-discharge scheduling optimization method based on photovoltaic microgrid operation cost. The invention provides an optimal operation scheduling strategy of a photovoltaic micro-grid storage battery in the future 24 hours based on 24-hour prediction data of photovoltaic micro-grid power load and photovoltaic power generation. The patent gives a photovoltaic microgrid operating cost model consisting of four sub-terms: (1) battery aging costs characterized based on depth of discharge; (2) a power demand charge based on peak electricity prices; (2) energy costs based on time-of-use electricity prices; and (4) generating benefits based on the photovoltaic internet electricity price. The constraint conditions of charging and discharging of the storage battery in the model are combined, so that the optimization of the operation cost model is realized, the operation cost of the photovoltaic micro-grid is obviously reduced, and the popularization and application of the photovoltaic micro-grid system are facilitated.
Description
Technical Field
The invention belongs to the field of new photovoltaic energy, and relates to a storage battery charge-discharge scheduling optimization method based on the operation cost of a photovoltaic micro-grid.
Background
By using the dispatching optimization method for charging and discharging the storage battery, the operation cost of the photovoltaic micro-grid can be obviously reduced. The scheduling optimization process of the charging and discharging of the storage battery needs to consider: (1) The dynamic influence of the depth of each charge and discharge of the storage battery on the service life; (2) impact of peak electricity prices on electricity demand costs; (3) influence of time-of-use electricity price on energy cost; (4) Influence of photovoltaic internet electricity price on photovoltaic microgrid income. However, in practical applications, people focus on the configuration of the capacity and investment cost of the storage battery in the construction period of the photovoltaic micro-grid on one hand, and on the other hand, how to utilize the charge and discharge of the storage battery to improve the photovoltaic power supply quality. The current storage battery charge-discharge scheduling optimization model rarely considers the four factors at the same time, generally avoids or simplifies the running cost problem of the photovoltaic micro-grid, and is unfavorable for improving the running income of the photovoltaic micro-grid to the greatest extent under the condition of fixed hardware configuration.
Disclosure of Invention
The invention considers that: (1) Based on the photovoltaic micro-grid electricity load and the electricity generation history data under the condition of similar weather, the integral fluctuation of the electricity load in seasons and the local fluctuation characteristic in a week are considered, and meanwhile, the fluctuation of the photovoltaic electricity generation along with the seasons and weather changes is considered, so that 24-hour prediction of the photovoltaic micro-grid electricity load and the photovoltaic electricity generation can be effectively realized; (2) At present, the charging and discharging of the storage battery in the photovoltaic micro-grid is usually only focused on improving the photovoltaic power supply quality or only focused on the electricity demand cost and the energy cost, so that the change of the service life of the storage battery caused by an unreasonable storage battery charging and discharging scheduling strategy in the operation of the photovoltaic micro-grid is avoided, and the change becomes a non-negligible part in the operation cost of the photovoltaic micro-grid. Therefore, the invention provides a storage battery charge-discharge scheduling optimization method based on the photovoltaic micro-grid operation cost based on 24-hour prediction of the photovoltaic micro-grid power load and photovoltaic power generation by comprehensively considering the storage battery charge-discharge dynamic cost and the composite electricity price cost.
The invention provides a storage battery charge-discharge scheduling optimization method based on photovoltaic microgrid operation cost, which comprises the following steps:
and (2) based on the photovoltaic micro-grid electricity load and photovoltaic power generation historical data under similar type daily conditions, the future 24-hour prediction of the photovoltaic micro-grid electricity load and photovoltaic power generation is realized.
And (2) calculating the charge state of the storage battery in each charge and discharge period.
And (3) calculating the depth of discharge of the storage battery in each charge-discharge period. The depth of discharge of the battery is defined as the average of the decreasing and increasing magnitudes of the state of charge of the battery during each charge-discharge cycle.
And (4) calculating the ageing cost of the storage battery in each charge-discharge period according to the discharge depth of the storage battery.
And (5) setting a photovoltaic micro-grid operation cost objective function. For a micro-grid system consisting of photovoltaic cells and storage cells, an objective function J consisting of electricity demand cost, energy cost, photovoltaic power generation internet income and storage cell aging cost is set.
Step (6) setting constraint conditions of a photovoltaic micro-grid operation cost objective function: the energy supply and demand balance condition of the storage battery in the discharging and charging mode, the electric power purchased from the external power grid and the storage battery discharging and charging requirements meet the stability condition, and the maximum allowable power limiting condition.
And (7) solving the charge and discharge power of the storage battery in each optimization period by using a particle swarm algorithm according to the photovoltaic micro-grid operation cost objective function set in the step (5) under the constraint condition of the step (6).
The invention has the beneficial effects that:
1. according to the integral fluctuation of the power load in seasons and the local fluctuation characteristic in the week, the fluctuation of the photovoltaic power generation along with the change of seasons and weather is considered, and 24-hour prediction of the power load and the photovoltaic power generation of the photovoltaic micro-grid based on similar types of days is realized;
2. the dynamic cost and the composite electricity price cost caused by the storage battery charge and discharge depth are comprehensively considered, and a photovoltaic micro-grid operation cost model formed by four sub-items is provided: (1) battery aging costs characterized based on depth of discharge; (2) a power demand charge based on peak electricity prices; (2) energy costs based on time-of-use electricity prices; and (4) generating benefits based on the photovoltaic internet electricity price.
3. Based on 24-hour prediction data of photovoltaic micro-grid power load and power generation, an optimal operation scheduling strategy of a photovoltaic micro-grid storage battery in the future 24 hours is provided. And the constraint conditions of charging and discharging of the storage battery in the model are combined, so that the optimization of the operation cost model is realized, and the operation cost of the photovoltaic micro-grid is obviously reduced.
Drawings
FIG. 1 is a graph of load versus photovoltaic output prediction.
Fig. 2 is a schematic diagram of a battery charging and discharging process.
FIG. 3 is a flow chart of a particle swarm optimization algorithm.
Detailed Description
The invention sets the optimization unit time deltat to 15 minutes, so the optimization cycle number T in 24 hours is 96. With reference to fig. 1,2 and 3, the specific implementation steps of the invention are as follows:
and (2) based on the photovoltaic micro-grid electricity load and the power generation history data under the similar type daily condition, the future 24-hour prediction of the photovoltaic micro-grid electricity load and the photovoltaic power generation is realized. The seasons are divided into 4 season types of spring, summer, autumn and winter, the weather is divided into 3 weather types of sunny, cloudy and snowy, and the week is divided into 6 date types of Monday, tuesday, wednesday, thursday, friday and holidays. Firstly, determining a prediction day corresponding to 24 hours of scheduling optimization, and searching candidate similar type days from photovoltaic micro-grid electricity load and photovoltaic power generation historical data according to the date type of the prediction day; then further screening candidate similar types according to seasons and weather types respectively; finally, according to the minimum difference value between the similar type day and the highest air temperature of the predicted day, determining the optimal similar type day, taking the corresponding photovoltaic micro-grid power load and photovoltaic power generation historical data as the predicted result of every 15 minutes within 24 hours in the future, and respectively marking as P d (i) And P RE (i) I=1, 2,..t. As shown in fig. 1.
And (2) calculating the charge state of the storage battery in each charge and discharge period. Assuming the state of charge SOC (i) of the battery at the i-th optimization cycle, the calculation process is shown in equation (1).
Wherein, SOC (0) represents the initial charge state of the storage battery; sigma represents the self-discharge rate of the battery; c (C) BAT Representing the capacity of the battery; η (eta) CB And eta DB Respectively representing the charge and discharge efficiency of the storage battery; PB (PB) C (i) And PB D (i) Respectively representing the charging and the charging of the storage battery in the ith optimization periodDischarge power.
Taking the charge and discharge process of the battery shown in fig. 2 as an example, each charge and discharge cycle is composed of a local maximum value and a local minimum value of a state of charge (SOC) curve of the battery in the figure. For example, a-B in the figure corresponds to a half cycle, indicating a discharge process; and B-C represents a charging process, so A-B-C forms a complete charge-discharge cycle.
It is assumed that the number of battery charge/discharge cycles is M and the corresponding half cycle number is 2M in the 24-hour schedule planning process, and thus the sum of the numbers of the local maxima and local minima of the SOC curve is 2m+1. The occurrence time of 2M+1 local extrema is denoted as t i I=1, 2, … 2m+1, which correspond to the ct < th > k And (3) optimizing the period as shown in the formula (2).
In the formula, floor represents a downward rounding function. The states of charge of the 2M+1 local extrema can be recorded as SOCs (ct i ),i＝1,2,…,2M+1。
And (3) calculating the depth of discharge of the storage battery in each charge-discharge period. The depth of discharge (DOD) of the battery is defined as the average of the decreasing and increasing values of the state of charge of the battery at each charge-discharge cycle, as shown in formula (3).
In the formula, DOD (k) represents the depth of discharge corresponding to the kth charge-discharge cycle of the storage battery in the 24-hour scheduling process.
And (4) calculating the ageing cost of the storage battery in each charge-discharge period. Determining the aging cost C of the kth charge-discharge period of the storage battery according to the depth of discharge DOD (k) of the storage battery D (k) As shown in formula (4).
Wherein a, b, alpha, beta correspond to manufacturing parameters of the battery, C I Representing the investment cost per unit capacity of the battery.
And (5) setting a photovoltaic micro-grid operation cost objective function. For a micro-grid system consisting of photovoltaic cells and storage cells, a charge C for power demand is set Demand Cost of energy C Engergy Internet surfing benefits E of photovoltaic power generation ET Battery aging cost C D The objective function J is constructed as shown in equation (5).
J＝min(C Demand +C Energy -E ET +C D ) (5)
Wherein min represents a minimum function; electric power demand charge C Demand Cost of energy C Engergy Internet surfing benefits E of photovoltaic power generation ET Battery aging cost C D The calculations are shown in the formula (6), the formula (7), the formula (8) and the formula (9), respectively.
C Demand ＝P max ×PR DC (6)
Wherein P is max Representing peaks in power load that occur during a 24 hour dispatch plan; PR (PR) DC The peak load unit electricity price is represented; p (P) EF (i) Representing the power purchased from the external grid during the ith optimization period; PR (PR) EC (i) Representing the time-of-use electricity price in the ith optimization period; p (P) ET (i) Representing the internet power of photovoltaic power generation in the ith optimization period; PR (PR) ET (i)And (5) representing the photovoltaic internet electricity price in the ith optimization period.
And (6) setting constraint conditions of the photovoltaic micro-grid operation cost objective function.
Constraint (1): the energy supply and demand balance conditions in the discharging and charging modes of the storage battery are respectively shown in the formulas (10) and (11).
P d (i)＝P EF (i)+P RE (i)-P ET (i)+PB D (i) (10)
P d (i)＝P EF (i)+P RE (i)-P ET (i)-PB C (i) (11)
Wherein P is d (i)，P RE (i)，P EF (i)，P ET (i)，PB D (i) And PB C (i) Is as defined above.
Constraint (2): electric power P purchased from an external grid EF (i) The stability condition is required to be satisfied as shown in the formula (12).
ζ 2 P 0 ≤P EF (i)≤ζ 1 P 0 (12)
Wherein P is 0 Representing a reference power; zeta type 1 And zeta 2 Representing P EF (i) The upper and lower bound fluctuation coefficients of (2) may be set to 0.8 and 0.2, respectively.
Constraint (3): the battery is discharged and charged to meet the maximum allowable power limit conditions as shown in the formulas (13) and (14).
0≤PB D (i)≤P MDB (13)
0≤PB C (i)≤P MCB (14)
Wherein P is MDB And P MCB The upper limits of the discharge power and the charge power of the battery are indicated, respectively.
Step (7) solving the discharge power PB of the storage battery in each optimization period by using a particle swarm algorithm according to the photovoltaic micro-grid operation cost objective function set in the step (5) under the constraint condition of the step (6) D (i) And charging power PB C (i) The implementation flow is shown in figure 3.
Claims (2)
1. The storage battery charge-discharge scheduling optimization method based on the photovoltaic micro-grid operation cost is characterized by comprising the following steps of:
the method comprises the following steps that (1) based on the photovoltaic micro-grid electricity load and photovoltaic power generation historical data under similar type daily conditions, the future 24-hour prediction of the photovoltaic micro-grid electricity load and photovoltaic power generation is realized; setting the optimization unit time delta T as 15 minutes and the optimization cycle number T within 24 hours as 96; determining the optimal similar type day, taking the corresponding photovoltaic micro-grid electricity load and photovoltaic power generation historical data as the predicted result of every 15 minutes within 24 hours in the future, and respectively marking as P d (i) And P RE (i)，i＝1,2,...,T；
Step (2) calculating the charge state of the storage battery in each charge-discharge period; assuming that the state of charge SOC (i) of the storage battery in the ith optimization period, the calculation process is shown as a formula (1);
wherein, SOC (0) represents the initial charge state of the storage battery; sigma represents the self-discharge rate of the battery; c (C) BAT Representing the capacity of the battery; η (eta) CB And eta DB Respectively representing the charge and discharge efficiency of the storage battery; PB (PB) C (i) And PB D (i) Respectively representing the charge and discharge power of the storage battery in the ith optimization period;
assuming that the charge and discharge cycle number of the storage battery is M and the corresponding half cycle number is 2M in the scheduling process of 24 hours, so that the sum of the numbers of the local maximum value and the local minimum value of the SOC curve is 2M+1; the occurrence time of 2M+1 local extrema is denoted as t i I=1, 2, … 2m+1, which correspond to the ct < th > k The optimization period is as shown in the formula (2):
in the formula, floor represents a downward rounding function, and thereforeThe states of charge of 2M+1 local extrema can be respectively noted as SOC (ct i ),i＝1,2,…,2M+1；
Step (3) calculating the depth of discharge of the storage battery in each charge-discharge period; in each charge-discharge period, the discharge depth of the storage battery is defined as the average value of the decrease and increase amplitude values of the charge state of the storage battery; as shown in formula (3);
wherein DOD (k) represents the depth of discharge corresponding to the kth charge-discharge cycle of the storage battery in the 24-hour scheduling process; m is the charge and discharge cycle number of the storage battery in the 24-hour scheduling process;
step (4) according to the discharging depth of the storage battery, calculating the aging cost C of the storage battery in each charging and discharging period D (k) As shown in formula (4);
wherein a, b, alpha, beta correspond to manufacturing parameters of the battery, C I Representing the investment cost of the unit capacity of the storage battery;
setting a photovoltaic micro-grid operation cost objective function; setting an objective function J consisting of electricity demand cost, energy cost, photovoltaic power generation online income and storage battery aging cost for a micro-grid system consisting of photovoltaic and storage batteries, wherein the objective function J is shown in a formula (5);
J＝min(C Demand +C Energy -E ET +C D ) (5)
wherein min represents a minimum function; electric power demand charge C Demand Cost of energy C Engergy Internet surfing benefits E of photovoltaic power generation ET Battery aging cost C D The calculation is shown as a formula (6), a formula (7), a formula (8) and a formula (9) respectively;
C Demand ＝P max ×PR DC (6)
wherein P is max Representing peaks in power load that occur during a 24 hour dispatch plan; PR (PR) DC The peak load unit electricity price is represented; p (P) EF (i) Representing the power purchased from the external grid during the ith optimization period; PR (PR) EC (i) Representing the time-of-use electricity price in the ith optimization period; p (P) ET (i) Representing the internet power of photovoltaic power generation in the ith optimization period; PR (PR) ET (i) Representing the photovoltaic internet electricity price in the ith optimization period;
step (6) setting constraint conditions of a photovoltaic micro-grid operation cost objective function: constraint (1): the energy supply and demand balance conditions of the storage battery in the discharging and charging modes are respectively shown in the formula (10) and the formula (11);
P d (i)＝P EF (i)+P RE (i)-P ET (i)+PB D (i) (10)
P d (i)＝P EF (i)+P RE (i)-P ET (i)-PB C (i) (11)
wherein P is d (i)，P RE (i)，P EF (i)，P ET (i)，PB D (i) And PB C (i) Is as defined above;
constraint (2): electric power P purchased from an external grid EF (i) The stability condition is required to be satisfied, as shown in a formula (12);
ζ 2 P 0 ≤P EF (i)≤ζ 1 P 0 (12)
wherein P is 0 Representing a reference power; zeta type 1 And zeta 2 Representing P EF (i) The upper and lower boundary fluctuation coefficients of (2) can be set to 0.8 and 0.2 respectively;
constraint (3): the discharging and charging of the storage battery are required to meet the maximum allowable power limiting conditions, as shown in the formulas (13) and (14);
0≤PB D (i)≤P MDB (13)
0≤PB C (i)≤P MCB (14)
wherein P is MDB And P MCB Respectively representing the upper limit of the discharging power and the charging power of the storage battery;
step (7) solving the discharge power PB of the storage battery in each optimization period by using a particle swarm algorithm according to the photovoltaic micro-grid operation cost objective function set in the step (5) under the constraint condition of the step (6) D (i) And charging power PB C (i)。
2. The storage battery charge-discharge scheduling optimization method based on the photovoltaic micro-grid operation cost is characterized in that the step (1) is based on photovoltaic micro-grid electricity load and photovoltaic power generation historical data under similar type daily conditions, and future 24-hour prediction of the photovoltaic micro-grid electricity load and photovoltaic power generation is achieved; the method comprises the following steps: the seasons are divided into four season types of spring, summer, autumn and winter, the weather is divided into 3 weather types of sunny, cloudy and snowy, and the week is divided into six date types of Monday, tuesday, friday and holiday; firstly, determining a prediction day corresponding to 24 hours of scheduling optimization, and searching candidate similar type days from photovoltaic micro-grid electricity load and photovoltaic power generation historical data according to the date type of the prediction day; then further screening candidate similar types according to seasons and weather types respectively; finally, according to the minimum difference value between the similar type day and the highest air temperature of the predicted day, the optimal similar type day is determined, and the corresponding photovoltaic micro-grid power load and photovoltaic power generation historical data are used as every 15 minutes in the future 24 hoursThe prediction results of (2) are respectively denoted as P d (i) And P RE (i) I=1, 2,. -%, T; where T corresponds to an optimized cycle number within 24 hours, equal to 96.
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| Economic Dispatch of Distribution Network with Multi-Microgrid;Zhao Qiongyao;《Proceedings of the 34th Chinese Control Conference》;20150730;全文 * |
| 含多微源的微网并网优化运行研究;燕颖等;《电子设计工程》;20160430;第24卷(第07期);全文 * |
| 基于相似日的光伏发电短期预报模型;罗小兰等;《杭州电子科技大学学报(自然科学版)》;20150915;第35卷(第05期);全文 * |
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