CN115759375B - Modularized mobile battery energy storage configuration method applied to fault distribution network - Google Patents
Modularized mobile battery energy storage configuration method applied to fault distribution network Download PDFInfo
- Publication number
- CN115759375B CN115759375B CN202211398876.7A CN202211398876A CN115759375B CN 115759375 B CN115759375 B CN 115759375B CN 202211398876 A CN202211398876 A CN 202211398876A CN 115759375 B CN115759375 B CN 115759375B
- Authority
- CN
- China
- Prior art keywords
- value
- power
- mobile battery
- distribution network
- cost
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to a modularized mobile battery energy storage configuration method applied to a fault power distribution network, which comprises the following steps: designing a modularized mobile battery optimal configuration based on perception; establishing an expected model of a loss value of the power distribution network; establishing an expected model of the reliability of the power distribution network; establishing a desired model of delay network expansion; a desired model of the cycle life cost is built. The beneficial effects of the invention are as follows: according to the invention, an optimal configuration model based on a prediction theory is established, and the prediction value of the comprehensive energy utilization rate is maximized under the condition of considering uncertainty faults.
Description
Technical Field
The invention relates to the field of energy storage configuration, in particular to a modularized mobile battery energy storage configuration method applied to a fault power distribution network.
Background
Modern energy utilization requires flexible resources, and a mobile energy storage system (mobile energy storage system, MESS) can move between different load units to provide various resource allocation services such as load transfer, voltage regulation and the like. Because of the great potential of MESS in various applications, the application range of mobile energy storage in the future is continuously expanded, and the optimal configuration and operation of the mobile energy storage are researched, so that the elasticity of a power distribution network is improved, and the method has great significance for safe operation of the power grid. With the rapid development of battery technology, the preparation cost of the energy storage battery is continuously reduced, and the performances such as energy and power density are obviously improved. The modularized mobile battery gradually becomes an important component of the power distribution network energy storage system, has excellent characteristic of common battery energy storage, and expands space for flexible configuration and multipurpose application in extreme scenes. At present, energy allocation of a power system mainly starts from three aspects of a power source side, a user side and a power grid side, and the energy storage allocation method of the power grid side can obtain various benefits according to different operation strategies. Technicians and related scholars have proposed various application and distribution network operating strategies in which mobile battery based energy storage configuration methods are optimized. However, most configuration methods and configuration models do not consider the mobility of the stored energy nor the randomness and uncertainty of the power distribution network faults.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a modularized mobile battery energy storage configuration method applied to a fault power distribution network.
In a first aspect, a method for configuring energy storage of a modularized mobile battery applied to a fault power distribution network is provided, including:
s1, designing a modularized mobile battery optimal configuration based on perception;
s2, building an expected model of a loss value of the power distribution network;
s3, building an expected model of the reliability of the power distribution network;
s4, building a delay network expansion expected model;
s5, building an expected model of the cycle life cost.
Preferably, in S1, the integrated desired cost function F is determined by a numerical function a (x) and a weight function w (p), and expressed as:
F＝w + (p)a + (x)+w - (p)a - (x) (1)
wherein w is + (p) and w - (p) is a desired weight function of gain and loss, respectively; a, a + (x) And a - (x) The decision maker's expected numerical functions for gain and loss, respectively; p is the probability that the control object represents gain or loss in the uncertain scene; x is the value of the control object.
Preferably, S1 includes:
s101, defining optimal configuration parameters, and extracting 4 parameter characteristics related to an energy storage system, wherein the parameter characteristics comprise a power distribution network loss value, power distribution network reliability, delay network expansion and modularized mobile battery cycle life cost;
s102, establishing a modularized mobile battery optimal configuration model; an optimal configuration model is established based on expected theory, such as a formula (2) -a formula (7), wherein an optimal target is to maximize comprehensive prediction value in an MMB operation period, and the expression is as follows:
max F＝w los F los +w pro F pro +w exp F exp +w cyc F cyc (2)
0≤w los ,w pro ,w exp ,w cyc ≤1 (5)
w los +w pro +w exp +w cyc ＝1 (6)
in the formula (2), F los Representing the loss value of the distribution network, w los Weight value representing loss value of distribution network, F pro Representing the reliability of the distribution network, w pro Weight value representing reliability of distribution network, F exp Representing delay network spread, w exp Weight value representing delay network spread, F cyc Representing modular mobile battery cycle life cost, w cyc A weight value representing a modular mobile battery cycle life cost; in formula (3), m represents a distributed power plant index, N PS Representing the number of distributed power stations, p MMB,t Representing the output power of the MMB; p is p input,t Represents input power, N represents load index, N load Representing the number of loads, p n,t Representing the power demand of the nth load, p los,t Representing the power loss of the distribution network at a sampling time point t; in the formula (4), P a-s Representing the effective power of the transmission lines a to b; equation (3) -equation (6) are constraint conditions, equation (3) is an active power balance constraint condition, equation (4) is an effective power value range, and equation (5) and equation (6) are constraint conditions of 4 parameter feature weight values.
Preferably, in S2, the loss efficiency a (B los ) And expected loss value F los Has a corresponding relationship, and the formula is shown as (7):
F los ＝w + (p los )a + (B los )+w - (p los )a - (B los )
wherein k is the year of operation and the value range is 1-N y ，p los For power distribution network loss, B los To reduce the gain value of the loss value, B los.0 Is the expected value; Δp los,tj Configuring power distribution network loss change values before and after the modularized mobile battery; n (N) y The service life of the modularized mobile battery is prolonged; e, e 0 For the reference yield, n j Days for the j-th load to meet the load power value; l (L) cost Is the unit electricity price; the decision variable being the capacity value C n Rated power P n And state of charge lower limit value SOC min The 3 decision variables are used for determining configuration parameters and an optimal control strategy of the modularized mobile battery;
the complete cycle formula of the optimal configuration of the modularized mobile battery is shown as (9) and (10):
SOC t ∈(SOC min ,SOC max ) (10)
in SOC t For the SOC value at sampling time t, SOC min And SOC (System on chip) max The minimum SOC value and the maximum SOC value of the modularized mobile battery are respectively, eta is the charge and discharge efficiency of MMB configuration, and delta T is the unit time interval.
Preferably, in S3, the reliability parameter a (B pro ) Is expressed as F pro ：
F pro ＝w + (p pro )a + (B pro )+w - (p pro )a - (B pro )
Wherein, kappa is a loss reduction coefficient, alpha is a risk coefficient, and beta is a risk avoidance coefficient; p is p pro For the reliability power value, B pro To improve the reliability benefit value, B pro,0 Is a parameter characteristic reference value;
the unpowered electric quantity is expected as an index parameter for reliability evaluation, as shown in formula (12):
wherein B is pro To increase the reliability of the distribution network, Δeens is the difference in expected unpowered power EENS after the modular mobile battery is configured, r IEA The coefficients are rated for the consumer. l (L) sell To average price of electricity, l comp Is the power generation efficiency.
Preferably, in S3, the calculation formula of the expected unpowered electric quantity EENS is as follows:
in the method, in the process of the invention,h is the number of application scene categories under different SOC values, X is the total number of samples of the SOC values, a h EENS for the sample size of the h SOC application scenario n For the expected unpowered charge of the nth load, SOC h For the expected value of the modularized mobile battery state of charge in the h SOC application scene, E rec,n Probability value, N, that a modular mobile battery can supply power to an nth load during a fault n The number of components to cause the nth load failure; lambda (lambda) q Sum mu q The failure probability and the repair probability of the component q are respectively; p is p sup,n B, power required for supplying power to nth island load hi For the value of sample I in the h SOC application scene, I q The module is in the same island operation condition as the island load after the fault of the modularized mobile battery.
Preferably, in S4, the desired function of the delay network spread is F exp The calculation formula is as follows:
F exp ＝w + (p exp )a + (B exp )+w - (p exp )a - (B exp )
wherein p is exp To delay network power value, B exp A benefit value for the delay network; v inv For cost value of network extension, Δt y For the extended years of a modular mobile battery delay network, τ is the annual load growth rate,
to configure forMaximum load of the power system before the mobile battery is modularized.
Preferably, the expected function of cycle life cost is expressed as F cyc The calculation formula is as follows:
F cyc ＝w + (p cyc )a + (D cyc )+w - (p cyc )a - (D cyc )
wherein p is cyc For cycle life power, D cyc Life costs configured for modular mobile batteries;
D cyc (C n ,P n ) For cycle life cost, it is composed of three parts: initial investment cost and construction cost D con Cost of maintenance D ope And recovery cost D rec The method comprises the steps of carrying out a first treatment on the surface of the Construction cost D con Including the energy cost required by the battery and the power cost for energy conversion, the calculation formula is:
D con ＝d s C n +d p P n (21)
wherein d s Investment cost per unit capacity of modular mobile battery, d p Investment cost per unit power; d (D) ope The method comprises the steps of determining fixed cost by rated power and power loss cost by charge and discharge electric quantity, wherein a calculation formula is as follows:
wherein D is ope Annual operating and maintenance costs per unit power, Q ch And Q dch Annual charge and discharge electric quantity of the modularized mobile battery respectively; d (D) rec For the cost difference between the battery production expenditure and the recovery income, the calculation formula is:
wherein d r Battery recovery value per unit weight ρ e For specific energy of battery, d h The treatment cost of the waste battery in unit mass is i is the battery number and d uv For the recovery price of metal u ρ uv To configure the metal u content per unit weight in a modular mobile battery.
In a second aspect, a modular mobile battery energy storage configuration device applied to a fault distribution network is provided, and the modular mobile battery energy storage configuration method applied to the fault distribution network in any one of the first aspects is performed, including:
the design module is used for designing the modularized mobile battery optimal configuration based on perception;
the first building module is used for building an expected model of the loss value of the power distribution network;
the second building module is used for building an expected model of the reliability of the power distribution network;
a third building module for building a desired model of the delay network expansion;
and a fourth building module for building a desired model of the cycle life cost.
The beneficial effects of the invention are as follows:
(1) The invention verifies the operation capability and coordination capability of the Modularized Mobile Battery (MMB) in the normal working state and the fault state of the power distribution network, and screens out 4 parameter attributes configured by the energy storage system, namely the power distribution network loss, the power distribution network reliability, the delay network expansion and the MMB cycle life cost.
(2) According to the invention, an optimal configuration model based on a prediction theory is established, and the prediction value of the comprehensive energy utilization rate is maximized under the condition of considering uncertainty faults.
Drawings
Fig. 1 is a flowchart of a modular mobile battery energy storage configuration method applied to a faulty distribution network.
Detailed Description
The invention is further described below with reference to examples. The following examples are presented only to aid in the understanding of the invention. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present invention without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Example 1:
the invention provides a modularized mobile battery energy storage configuration method applied to a fault distribution network, which maximizes the expected value of the comprehensive energy utilization rate under the condition of considering uncertainty failure, and comprises the following steps:
s1, designing a modularized mobile battery optimal configuration based on perception;
s2, building an expected model of a loss value of the power distribution network;
s3, building an expected model of the reliability of the power distribution network;
s4, building a delay network expansion expected model;
s5, building an expected model of the cycle life cost.
In S1, the comprehensive expected cost function F is determined by a numerical function a (x) and a weight function w (p), and the expression is:
F＝w + (p)a + (x)+w - (p)a - (x) (1)
wherein w is + (p) and w - (p) is a desired weight function of gain and loss, respectively; a, a + (x) And a - (x) The decision maker's expected numerical functions for gain and loss, respectively; p is the probability that the control object represents gain or loss in the uncertain scene; x is the value of the control object.
S1 comprises the following steps:
s101, defining optimal configuration parameters, and extracting 4 parameter characteristics related to an energy storage system, wherein the parameter characteristics comprise a power distribution network loss value, power distribution network reliability, delay network expansion and modularized mobile battery cycle life cost; the definition of the optimized configuration parameters is shown in table 1;
TABLE 1 optimization model parameter definition
S102, establishing a modularized mobile battery optimal configuration model; an optimal configuration model is established based on expected theory, such as a formula (2) -a formula (7), wherein an optimal target is to maximize comprehensive prediction value in an MMB operation period, and the expression is as follows:
max F＝w los F los +w pro F pro +w exp F exp +w cyc F cyc (2)
0≤w los ,w pro ,w exp ,w cyc ≤1 (5)
w los +w pro +w exp +w cyc ＝1 (6)
in the formula (2), F los Representing the loss value of the distribution network, w los Weight value representing loss value of distribution network, F pro Representing the reliability of the distribution network, w pro Weight value representing reliability of distribution network, F exp Representing delay network spread, w exp Weight value representing delay network spread, F cyc Representing modular mobile battery cycle life cost, w cyc A weight value representing a modular mobile battery cycle life cost; in formula (3), m represents a distributed power plant index, N PS Representing the number of distributed power stations, p MMB,t Representing the output power of the MMB; p is p input,t Represents input power, N represents load index, N load Representing the number of loads, p n,t Representing the power demand of the nth load, p los,t Representing the power loss of the distribution network at a sampling time point t; in the formula (4), P a-b Representing the effective power of the transmission lines a to b; equation (3) -equation (6) is a constraintThe formula (3) is an active power balance constraint condition, the formula (4) is an effective power value range, and the formula (5) and the formula (6) are constraint conditions of 4 parameter characteristic weight values.
In S2, the configuration parameters of the MMB affect the loss values of the upper-layer power grid and the transformers in the station. When the operation strategy is determined and errors are not considered, the loss value of the power distribution network has obvious corresponding relation with the decision variable. In particular, loss efficiency a (B loc ) And expected loss value F los Has a corresponding relationship, and the formula is shown as (7):
F los ＝w + (p los )a + (B los )+w - (p los )a - (B los )
wherein k is the year of operation and the value range is 1-N y ，p los For power distribution network loss, B los To reduce the gain value of the loss value, B los.0 Is the expected value; Δp los,tj Configuring power distribution network loss change values before and after the modularized mobile battery; n (N) y The service life of the modularized mobile battery is prolonged; e, e 0 For the reference yield, n j Days for the j-th load to meet the load power value; l (L) cost Is the unit electricity price; the decision variable being the capacity value C n Rated power P n And state of charge lower limit value SOC min The 3 decision variables are used for determining configuration parameters and an optimal control strategy of the modularized mobile battery;
the complete cycle formula of the optimal configuration of the modularized mobile battery is shown as (9) and (10):
SOC t ∈(SOC min ,SOC max ) (10)
in SOC t For the SOC value at sampling time t, SOC min And SOC (System on chip) max The minimum SOC value and the maximum SOC value of the modularized mobile battery are respectively, eta is the charge and discharge efficiency of MMB configuration, and delta T is the unit time interval.
In S3, when the primary port of the distribution network transformer fails, the MMB may supply power to the plurality of loads during the fault recovery period to improve the reliability of the distribution network. Improving reliability parameter a (B) pro ) Is expressed as F pro ：
F pro ＝w + (p pro )a + (B pro )+w - (p pro )a - (B pro )
Wherein, kappa is a loss reduction coefficient, alpha is a risk coefficient, and beta is a risk avoidance coefficient; p is p pro For the reliability power value, B pro To improve the reliability benefit value, B pro,0 Is a parameter characteristic reference value;
the unpowered electric quantity is expected as an index parameter for reliability evaluation, as shown in formula (12):
wherein B is pro To increase the reliability of the distribution network, Δeens is the difference in expected unpowered power EENS after the modular mobile battery is configured, r IEA The coefficients are rated for the consumer. l (L) sell To average price of electricity, l comp Is the power generation efficiency.
The invention introduces a fault priority analysis method to calculate MMB residual power. And (3) completing SOC statistical value calculation according to the operation strategy of the step (2), and obtaining the reliability of each SOC value. Finally, the comprehensive expected value of the reliability is weighted and calculated, and the expected unpowered electric quantity EENS has the following calculation formula:
wherein H is the number of application scene categories under different SOC values, X is the total number of samples of the SOC values, a h EENS for the sample size of the h SOC application scenario n For the expected unpowered charge of the nth load, SOC h For the expected value of the modularized mobile battery state of charge in the h SOC application scene, E rec,n Probability value, N, that a modular mobile battery can supply power to an nth load during a fault n The number of components to cause the nth load failure; lambda (lambda) q Sum mu q The failure probability and the repair probability of the component q are respectively; p is p sup,n B, power required for supplying power to nth island load hi For the value of sample I in the h SOC application scene, I q The module is in the same island operation condition as the island load after the fault of the modularized mobile battery.
Due to the randomness of the faults of the energy storage system, the traditional delay network expansion evaluation method has higher calculation redundancy. In S4, the desired function of the delay network expansion is E exp The calculation formula is as follows:
F exp ＝w + (p exp )a + (B exp )+w - (p exp )a - (B exp )
wherein p is exp To delay network power value, B exp A benefit value for the delay network; v inv For cost value of network extension, Δt y For the extended years of a modular mobile battery delay network, τ is the annual load growth rate,
to configure the maximum load of the power system before the modular mobile battery.
The expected function of cycle life cost is expressed as F cyc The calculation formula is as follows:
F cyc ＝w + (p cyc )a + (D cyc )+w - (p cyc )a - (D cyc )
wherein p is cyc For cycle life power, D cyc Life costs configured for modular mobile batteries;
D cyc (C n ,P n ) For cycle life cost, it is composed of three parts: initial investment cost and construction cost D con Cost of maintenance D ope And recovery cost D rec The method comprises the steps of carrying out a first treatment on the surface of the Construction cost D con Including the energy cost required by the battery and the power cost for energy conversion, the calculation formula is:
D con ＝d s C n +d p P n (21)
wherein d s Investment cost per unit capacity of modular mobile battery, d p Investment cost per unit power; d (D) ope The method comprises the steps of determining fixed cost by rated power and power loss cost by charge and discharge electric quantity, wherein a calculation formula is as follows:
wherein D is ope Annual operating and maintenance costs per unit power, Q ch And Q dch Annual charge and discharge electric quantity of the modularized mobile battery respectively; d (D) rec For the cost difference between the battery production expenditure and the recovery income, the calculation formula is:
wherein d r Battery recovery value per unit weight ρ e For specific energy of battery, d h The treatment cost of the waste battery in unit mass is i is the battery number and d uv For the recovery price of metal u ρ uv To configure the metal u content per unit weight in a modular mobile battery.
Example 2:
the model coefficients of the invention are set as follows: α=0.88, β=0.88, and κ=2.25. The reference value unit of the profit/cost parameter is millions of yuan, and the reference value is set as follows: b (B) los,0 ＝0.5，B pro,0 ＝0.9，B exp,0 ＝1，D cyc,0 ＝1.45。B total And synthesizing a predicted value for the parameter characteristic value. And (3) carrying out simulation according to the parameters, wherein convergence can be achieved after 200 iterations, and the maximum iteration number is 300. The MMB optimal configuration results are shown in table 2, and the benefit/cost of the parameter feature and the expected value of the parameter feature are shown in table 3. The data according to tables 2 and 3 show that: (1) Optimization capacity of MMB configurationThe amount is 4988.73kWh, the rated power is 1575.5kW, the minimum value of SOC during operation is 0.8, and the net benefit is 2793.85 ten thousand yuan. (2) Expected value a of delay network expansion del The maximum credit capacity of MMB is 3330kW. (3) The load fluctuation of the selected area is small, the lower limit of the SOC obtained by optimization is high, and the network loss is not reduced. (4) To maximize net revenue, the overall result of the MMB configuration is C n ＝9043.75kWh，P n ＝1712.1kW，B total ＝27.5，F＝0.286。
TABLE 2 MMB optimal configuration results
TABLE 3 benefit/cost of parameter characterization and expected value of parameter characterization
Claims (3)
1. The modularized mobile battery energy storage configuration method applied to the fault power distribution network is characterized by comprising the following steps of:
s1, designing a modularized mobile battery optimal configuration based on perception;
in S1, the comprehensive expected cost function F is determined by a numerical function a (x) and a weight function w (p), and the expression is:
F＝w + (p)a + (x)+w - (p)a - (x) (1)
wherein w is + (p) and w - (p) is a desired weight function of gain and loss, respectively; a, a + (x) And a - (x) The decision maker's expected numerical functions for gain and loss, respectively; p is the probability that the control object represents gain or loss in the uncertain scene; x is the value of the control object;
s1 comprises the following steps:
s101, defining optimal configuration parameters, and extracting 4 parameter characteristics related to an energy storage system, wherein the parameter characteristics comprise a power distribution network loss value, power distribution network reliability, delay network expansion and modularized mobile battery cycle life cost;
s102, establishing a modularized mobile battery optimal configuration model; an optimal configuration model is established based on expected theory, such as a formula (2) -a formula (7), wherein an optimal target is to maximize comprehensive prediction value in an MMB operation period, and the expression is as follows:
max F＝w los F los +w pro F pro +w exp F exp +w cyc F cyc (2)
0≤w los ，w pro ，w exp ，w cyc ≤1 (5)
w los +w pro +w exp +w cyc ＝1 (6)
in the formula (2), F los Representing the loss value of the distribution network, w los Weight value representing loss value of distribution network, F pro Representing the reliability of the distribution network, w pro Weight value representing reliability of distribution network, F exp Representing delay network spread, w exp Weight value representing delay network spread, F cyc Representing modular mobile battery cycle life cost, w cyc A weight value representing a modular mobile battery cycle life cost; in formula (3), m represents a distributed power plant index, N PS Representing the number of distributed power stations, p MMB，t Representing the output power of the MMB; p is p input，t Represents input power, N represents load index, N load Representing the number of loads, p n，t Representing the power demand of the nth load, p los，t Representing the power loss of the distribution network at a sampling time point t; in the formula (4), P a-b Representing the effective power of the transmission lines a to b; formula (3) -formula(6) As constraint conditions, the formula (3) is an active power balance constraint condition, the formula (4) is an effective power value range, and the formula (5) and the formula (6) are constraint conditions of 4 parameter characteristic weight values;
s2, building an expected model of a loss value of the power distribution network;
in S2, loss benefit a (B) los ) And expected loss value F los Has a corresponding relationship, and the formula is shown as (7):
F los ＝w + (p los )a + (B los )+w - (p los )a - (B los )
wherein k is the year of operation and the value range is 1-N y ，p los For power distribution network loss, B los To reduce the gain value of the loss value, B los.0 Is the expected value; Δp los，tj Configuring power distribution network loss change values before and after the modularized mobile battery; n (N) y The service life of the modularized mobile battery is prolonged; e, e 0 For the reference yield, n j Days for the j-th load to meet the load power value; l (L) cost Is the unit electricity price; the decision variable being the capacity value C n Rated power P n And state of charge lower limit value SOC min The 3 decision variables are used for determining configuration parameters and an optimal control strategy of the modularized mobile battery;
the complete cycle formula of the optimal configuration of the modularized mobile battery is shown as (9) and (10):
SOC t ∈(SOC min ，SOC max ) (10)
in SOC t For the SOC value at sampling time t, SOC min And SOC (System on chip) max The minimum SOC value and the maximum SOC value of the modularized mobile battery are respectively, eta is the charge and discharge efficiency of MMB configuration, and delta T is the unit time interval;
s3, building an expected model of the reliability of the power distribution network;
in S3, the reliability parameter a (B pro ) Is expressed as F pro ：
F pro ＝w + (p pro )a + (B pro )+w - (p pro )a - (B pro )
Wherein, kappa is a loss reduction coefficient, alpha is a risk coefficient, and beta is a risk avoidance coefficient; p is p pro For the reliability power value, B pro To improve the reliability benefit value, B pro，0 Is a parameter characteristic reference value;
the unpowered electric quantity is expected as an index parameter for reliability evaluation, as shown in formula (12):
wherein B is pro To increase the reliability of the distribution network, Δeens is the difference in expected unpowered power EENS after the modular mobile battery is configured, r IEA Evaluating the coefficients for the consumer; l (L) sel l is the average price of electricity selling, l comp The power generation efficiency is achieved;
s4, building a delay network expansion expected model;
in S4, the desired function of the delay network spread is F exp The calculation formula is as follows:
F exp ＝w + (p exp )a + (B exp )+w - (p exp )a - (B exp )
wherein p is exp To delay network power value, B exp A benefit value for the delay network; v inv For cost value of network extension, Δt y For the extended years of a modular mobile battery delay network, τ is the annual load growth rate,
maximum load of the power system before configuring the modular mobile battery;
s5, building an expected model of the cycle life cost;
the expected function of cycle life cost is expressed as F cyc The calculation formula is as follows:
F cyc ＝w + (p cyc )a + (D cyc )+w - (p cyc )a - (D cyc )
wherein p is cyc For cycle life power, D cyc Life costs configured for modular mobile batteries;
D cyc (C n ，P n ) For cycle life cost, it is composed of three parts: initial investment intoCost of construction and cost of construction D con Cost of maintenance D ope And recovery cost D rec The method comprises the steps of carrying out a first treatment on the surface of the Construction cost D con Including the energy cost required by the battery and the power cost for energy conversion, the calculation formula is:
D con ＝d s C n +d p P n (21)
wherein d s Investment cost per unit capacity of modular mobile battery, d p Investment cost per unit power; d (D) ope The method comprises the steps of determining fixed cost by rated power and power loss cost by charge and discharge electric quantity, wherein a calculation formula is as follows:
wherein D is ope Annual operating and maintenance costs per unit power, Q ch And Q dch Annual charge and discharge electric quantity of the modularized mobile battery respectively; d (D) rec For the cost difference between the battery production expenditure and the recovery income, the calculation formula is:
wherein d r Battery recovery value per unit weight ρ e For specific energy of battery, d h The treatment cost of the waste battery in unit mass is i is the battery number and d uv For the recovery price of metal u ρ uv To configure the metal u content per unit weight in a modular mobile battery.
2. The method for configuring energy storage of a modular mobile battery applied to a faulty power distribution network according to claim 1, wherein in S3, the calculation formula of the expected unpowered electric quantity EENS is as follows:
wherein H is the number of application scene categories under different SOC values, X is the total number of samples of the SOC values, a h EENS for the sample size of the h SOC application scenario n For the expected unpowered charge of the nth load, SOC h For the expected value of the modularized mobile battery state of charge in the h SOC application scene, E rec,n Probability value, N, that a modular mobile battery can supply power to an nth load during a fault n The number of components to cause the nth load failure; lambda (lambda) q Sum mu q The failure probability and the repair probability of the component q are respectively; p is p sup,n B, power required for supplying power to nth island load hi For the value of sample I in the h SOC application scene, I q The module is in the same island operation condition as the island load after the fault of the modularized mobile battery.
3. A modular mobile battery energy storage configuration device applied to a faulty power distribution network, for performing the modular mobile battery energy storage configuration method applied to a faulty power distribution network according to any one of claims 1 to 2, comprising:
the design module is used for designing the modularized mobile battery optimal configuration based on perception;
the first building module is used for building an expected model of the loss value of the power distribution network;
the second building module is used for building an expected model of the reliability of the power distribution network;
a third building module for building a desired model of the delay network expansion;
and a fourth building module for building a desired model of the cycle life cost.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211398876.7A CN115759375B (en) | 2022-11-09 | 2022-11-09 | Modularized mobile battery energy storage configuration method applied to fault distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211398876.7A CN115759375B (en) | 2022-11-09 | 2022-11-09 | Modularized mobile battery energy storage configuration method applied to fault distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115759375A CN115759375A (en) | 2023-03-07 |
| CN115759375B true CN115759375B (en) | 2023-06-02 |
Family
ID=85369877
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211398876.7A Active CN115759375B (en) | 2022-11-09 | 2022-11-09 | Modularized mobile battery energy storage configuration method applied to fault distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115759375B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101764407A (en) * | 2010-01-08 | 2010-06-30 | 杨毅 | Planning method of distribution network based on fuzzy expected value model |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105305424B (en) * | 2015-10-19 | 2017-11-17 | 重庆大学 | A kind of distribution network reliability evaluation method of meter and electric automobile access |
| KR101862029B1 (en) * | 2016-09-19 | 2018-05-29 | 연암공과대학교산학협력단 | Adaptive Reclosing Method using Second-Order Difference of THD in Distribution System with BESS used as Uninterruptible Power Supply |
| WO2020060943A1 (en) * | 2018-09-17 | 2020-03-26 | Caban Systems, Inc. | Systems and methods for energy storage and power distribution |
| CN110119886B (en) * | 2019-04-18 | 2022-11-25 | 深圳供电局有限公司 | Active distribution network dynamic planning method |
| CN110890766B (en) * | 2019-11-28 | 2020-12-22 | 山东理工大学 | Power distribution network mobile energy storage configuration method based on Fourier-Legendre series |
| CN113313613B (en) * | 2021-07-06 | 2024-02-20 | 华北电力大学 | Power distribution network modularized movable battery energy storage MMBES optimal configuration method |
| CN115313374A (en) * | 2022-08-25 | 2022-11-08 | 国网上海市电力公司 | Active power distribution network fault recovery method fusing multi-agent architecture |
-
2022
- 2022-11-09 CN CN202211398876.7A patent/CN115759375B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101764407A (en) * | 2010-01-08 | 2010-06-30 | 杨毅 | Planning method of distribution network based on fuzzy expected value model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115759375A (en) | 2023-03-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tu et al. | Optimization of a stand-alone photovoltaic–wind–diesel–battery system with multi-layered demand scheduling | |
| CN102097808B (en) | Method for estimating reliability of electric distribution system containing distributive wind power, photovoltaic and energy storage devices | |
| CN107634518B (en) | Source-network-load coordinated active power distribution network economic dispatching method | |
| CN109948849B (en) | Power distribution network frame planning method considering energy storage access | |
| CN109149571A (en) | A kind of energy storage Optimal Configuration Method of the combustion gas of consideration system and fired power generating unit characteristic | |
| CN102915472A (en) | Comprehensive power distribution network optimization planning method based on gene modified chaos genetic algorithm | |
| CN111861029A (en) | Power supply reliability assessment method considering island division and network reconstruction | |
| CN115688970B (en) | Micro-grid two-stage self-adaptive robust optimal scheduling method based on interval probability uncertainty set | |
| CN114595868A (en) | Source network and storage collaborative planning method and system for comprehensive energy system | |
| CN116911076B (en) | Toughness support simulation method and device for power distribution network by multiple micro-grids and electronic equipment | |
| CN111009914A (en) | Active power distribution network-oriented energy storage device location and volume determination method | |
| CN115293457A (en) | Seasonal hydrogen storage optimization configuration method of comprehensive energy system based on distributed collaborative optimization strategy | |
| CN114626613A (en) | Wind-solar complementary considered energy-storage combined planning method | |
| CN115759375B (en) | Modularized mobile battery energy storage configuration method applied to fault distribution network | |
| CN110739719A (en) | Two-step decision method for optimized access of flexible multi-state switch | |
| Pruckner et al. | A study on the impact of packet loss and latency on real-time demand response in smart grid | |
| CN111525556B (en) | Multi-target optimal power flow calculation method considering wind power confidence risk | |
| CN107622331A (en) | A kind of generating set and the optimization method and device of power consumer direct dealing mode | |
| Martinez-Rico et al. | Energy storage sizing based on automatic frequency restoration reserve market participation of hybrid renewable power plants | |
| CN116993205A (en) | Urban comprehensive energy system day-to-day operation adjustment capability assessment method | |
| CN116436048A (en) | Multi-target-driven micro-grid group cloud energy storage optimal configuration method and device | |
| Xunjie et al. | Optimal Planning Method of IES with Multi-energy Storage and Wind Power | |
| CN115473284A (en) | Robust optimization method and system for operation of power distribution system under regional power exchange constraint and computer equipment | |
| Du et al. | Integrating a multi-microgrid system into real-time balancing market: Problem formulation and solution technique | |
| CN114358430A (en) | Novel rural power distribution network planning strategy based on robust optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |