CN116384559A - Modeling method of hybrid energy storage capacity configuration strategy based on integer linear programming solution - Google Patents
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Abstract
The invention discloses a modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution. The energy storage technology is combined with the distributed energy source to provide rapid power buffering and a transition power supply when the system is connected with the power grid and the independent operation mode is switched, so that the continuity and stability of the system connection are ensured. Then, aiming at the defect of damage to the storage battery under the condition of high-power fluctuation, the super capacitor is introduced to effectively process peak current by setting the climbing constraint of the storage battery, so that the response speed of the system to power change is greatly improved. Finally, a mathematical model of the hybrid energy storage system is established, an objective function in the aspects of micro-grid construction, installation and operation cost is established, a quadratic hybrid integer linearization method is adopted to linearize the multivariable objective function and constraint conditions, and a CPLEX solver is utilized to calculate a final configuration result.
Description
Technical Field
The invention belongs to research and application of energy storage technology in micro-grids, and particularly relates to a modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution.
Background
The energy storage system is an important component of the multi-source complementary power grid, and particularly when the system operates as an independent power system, the energy storage link can smooth the fluctuation of the output power of the power grid. In addition, the energy storage technology has the advantages of accurate adjustment, high response speed, high power throughput capacity and the like. The energy storage technology is combined with the distributed power generation system, so that the power quality of the system can be improved, rapid power buffering is provided, and transitional power is provided when the system is connected with a grid and is switched to an independent operation mode. The energy storage system can effectively improve the reliability of distributed power generation, ensure the continuity and stability of the grid-connected system, and the technology of the energy storage system is widely focused.
The current mainstream energy storage system is still a single energy storage mode mainly comprising batteries, the batteries are required to respectively provide and receive peak current in the power consumption peak period, and when the batteries are in a low SOC state, the internal resistance of the batteries is large, and the peak current cannot be processed due to easy overheating. After the super capacitor device is added, high-efficiency electric energy release and storage can be provided and realized under peak current, so that the performance of the energy storage system is improved, the efficiency and the running time are increased, and the service life of a battery is prolonged. The energy storage of the battery which is mainstream at present is economical and has high energy density, but the power density is low and the charge and discharge speed is low; the super capacitor has high energy storage power density and high charging and discharging speed, but has low energy density and high single machine cost. The hybrid energy storage system combining the battery and the super capacitor is constructed, so that the advantages of the two energy storage devices can be fully exerted, and the hybrid energy storage system has better economy, higher compensation coverage rate and higher stability.
Disclosure of Invention
The invention aims to provide a modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution, which solves the problems of the background technology. In order to achieve the above purpose, the present invention provides the following technical solutions:
step one: establishing an aggregation model of the storage battery and the super capacitor, establishing an energy and power calculation mode and constraint conditions of the energy and power calculation mode in a micro-grid system, performing climbing constraint on the storage battery, and setting rising and falling limits of charging and discharging power of the storage battery and current constraint of the super capacitor; the initial capacity requirement in the case of self-discharge of the battery is considered.
Step two: and establishing an objective function based on the storage battery acquisition cost, the super capacitor acquisition cost and the storage battery charge-discharge cycle life damage cost, and obtaining a mathematical expression of the objective function.
Step three: according to the linearization calculation concept, an improved secondary mixed integer variable linearization method is established based on CPLEX, a new method for accurately expressing a multi-variable multi-dimensional complex objective function is obtained, and a reliable algorithm foundation is provided for mixed energy storage capacity configuration and scheduling scheme research.
Step four: linearizing the established constraint conditions and the objective function by using a linearization method based on a CPLEX solver, and selecting proper parameters to obtain an optimal capacity configuration scheme of the hybrid energy storage system. The parameters are determined by reference model parameters and actual requirements.
The research strategy comprises the steps of extracting typical scene load data, utilizing a mode that a load curve is equal to a photovoltaic output curve integral, introducing a k value (the k value is determined by the maximum initial capacity calculated by initial capacity) under the premise of considering redundancy required by self-discharge of a storage battery, deducing a photovoltaic output curve, and solving a linearized objective function by utilizing a CPLEX solver under the premise of meeting load requirements to the maximum extent to obtain an optimal mixed energy storage capacity configuration scheme.
Further, the specific content of the first step includes:
1) Firstly, constraint conditions of a micro-grid system, namely system capacity constraint, are obtained:
wherein mu i The ratio of the capacity of each small system in the micro-grid system to the capacity of the micro-grid system is N 1 The number of small systems in the micro-grid system;
the output capacity constraint of photovoltaic power generation is expressed as:
wherein,,
capacity actually output for photovoltaic power generation, +.>
The capacity of the maximum output of the photovoltaic power generation;
the energy balance constraint is expressed as:
wherein P is L Power required by load, P PV For the output power of the photovoltaic,
and->
The charging power and the discharging power of the storage battery respectively.
2) The mathematical model of the storage battery and the climbing constraint can be expressed as follows:
wherein E is b (t) is the energy state of the storage battery at the time t,
the storage loss rate of the storage battery; η (eta) c And eta d The charge efficiency and the discharge efficiency of the battery, respectively.
The battery charge-discharge rate and charge-discharge current constraint equation expression can be expressed as follows:
wherein v is c And v d For the charge and discharge rate of the storage battery, v c_R And v d_R Giving it a charge-discharge rate; i c And I d For the charge and discharge current of the storage battery, I c_max And I d_max Is the maximum value of the charge and discharge current.
Battery power constraint:
wherein the method comprises the steps of
Maximum charge/discharge power of single accumulator, n b For the number of storage batteries lambda c (t) and lambda d (t) is a pair of binary quantities representing the switch states.
Battery climbing constraint:
wherein Q is c 、Q d 、W c 、W d Respectively represent the rise of the charge and discharge power of the storage battery in a unit time periodAnd a drop limit.
3) For the super capacitor, the reference model satisfies the following state equation expression on the premise of not considering the charge and discharge loss and the self-discharge rate of the super capacitor:
wherein E is sc (t) is the energy state of the super capacitor at the time t,
and->
The charging power and the discharging power of the super capacitor are respectively.
In the charging and discharging processes, the super capacitor must meet the voltage and current constraints of charging and discharging:
wherein I is s The working current of the super capacitor; u (U) 1 The charging working voltage of the super capacitor is obtained; u (U) 2 The discharge working voltage of the super capacitor; i S max The working current of the super capacitor is the maximum value; u (U) min And U max The minimum value and the maximum value of the working voltage of the super capacitor are respectively.
Supercapacitor power constraints can be expressed as:
wherein,,
maximum charge and discharge power of a single super capacitor, n sc Is the number of super capacitors.
The supercapacitor energy constraint can be expressed as:
wherein the method comprises the steps of
For the capacity of a single super capacitor, the influence of each part is comprehensively considered, so that the comprehensive design is performed.
Further, the second specific content includes:
the parameters are reasonably designed, and based on the parameters, an objective function of the hybrid energy storage system is established, wherein the objective function is obtained from the aspect of cost, and the objective function consists of three parts and can be expressed as follows:
the first part is the acquisition cost of the storage battery,
the costs required to meet the scheduled energy demand, power demand, climbing demand for the battery, respectively, are expressed as follows
Wherein,,
for the unit price of the storage battery->
For single battery capacity, P b And (t) is the power value of the storage battery at the time t, and the power value is positive during charging and negative during discharging.
The second part is the purchase cost of the super capacitor,
the energy demand cost and the power demand cost when the super capacitor meets the scheduling requirement are respectively expressed as follows:
wherein,,
is the unit price of the super capacitor, < >>
For single super capacitor capacity, P sc And (t) is the power value of the super capacitor at the time t, and the power value is positive during charging and negative during discharging.
It will be seen that in practice the number of energy storage units is varied to meet the constraints, so that all constraints are met, the maximum value is chosen, i.e
And->
The third part is to consider life-span damage cost of the storage battery charge-discharge cycle, firstly, the charge-discharge cycles with different depths are converted into complete cycle times to calculate life-span loss, and the calculation formula is shown as the following formula:
d is the number of cycles after conversion. The cost of the break can be expressed as:
D c for the maximum cycle number of the storage battery under the set charge-discharge depth, Y p Is the life cycle (day) of the battery.
Further, the specific content of the third step includes:
further studies were made on algorithms that linearize the above multivariate nonlinear objective functions and constraints.
1) For minmax objective function min (maxx i ) Linearization method: using the intermediate variable X instead of maxx i And adding constraints, wherein the specific process is as follows:
minX (18)
X≥x i (19)
2) For cases involving absolute values in an objective function, e.g.
(f represents unconstrained), which can be regarded as a special case of max for this problem, i.e., |x|=max (-x, x); linearization method using intermediate variable y i Substitution |x i The concrete process is as follows:
minc i y i (20)
ax＝b (21)
y i ≥x i ,y i ≥-x i (22)
x i f,c i ≥0 (23)
therefore, the absolute value may be linearized and then the maximum value may be linearized for the expression (14) including both the absolute value and the maximum value.
3) Secondary mixed integer variable linearization: for the objective function
And->
Wherein lambda is c (t),λ d (t)∈{0,1}，/>
Using two intermediate variables X c (t) and X d (t) are respectively replaced by X c (t) substitution of
For example, the following constraints are added:
0≤X c (t)≤Lλ c (t) (26)
wherein L is a relatively large constant, X d And (t) the same.
The parameters are further adjusted, and the initial values of the self-discharge phenomenon of the storage battery are summarized as follows.
The method for calculating the initial capacity of the storage battery comprises the following steps: if the self-discharge phenomenon of the storage battery is not considered, whether the initial capacity is considered or not does not influence the calculation result; when considering the self-discharge phenomenon of the battery, the calculation result of equation (4) is greatly deviated, so that it is necessary to correct the result by selecting an initial value. To achieve this, formula (4) is first converted into a representation comprising an initial amount:
E b (1) I.e. initial quantity, let E b (1) Battery energy state vector at=0 is E b ' then for each E b ' t < 0, the initial amount needed is easily obtained as follows:
accordingly, a correction amount is added at each other moment, and the corrected energy state value formula (29) shows:
so long as for each E is selected b ' maximum initial amount required for (t) < 0
Will->
Is substituted into E in formula (29) b (1) E which is a positive value after correction can be obtained b (t)。
The beneficial effects of the invention are as follows:
the invention utilizes the advantages of accurate regulation and large power throughput capacity of the energy storage technology, combines the energy storage technology with the distributed energy source to provide rapid power buffering and provides a transition power supply when the system is connected with the power grid and the independent operation mode is switched, thereby effectively improving the reliability of the distributed power generation and ensuring the continuity and the stability of the system connection with the power grid. Then, aiming at the defect of damage to the storage battery under the condition of high-power fluctuation, the super capacitor is introduced to effectively process peak current by setting the climbing constraint of the storage battery, so that the response speed of the system to power change is greatly improved. Finally, a mathematical model of the hybrid energy storage system is established, an objective function in the aspects of micro-grid construction, installation and operation cost is established, a quadratic hybrid integer linearization method is adopted to linearize the multivariable objective function and constraint conditions, and a CPLEX solver is utilized to calculate a final configuration result.
Drawings
FIG. 1 is a schematic diagram of a linearization process;
FIG. 2 is a schematic diagram of the initial capacity calculation principle;
FIG. 3 is a graph of a typical solar load curve, a typical solar photovoltaic output curve, and a net load curve;
FIG. 4 is a graph of battery and supercapacitor SOC;
FIG. 5 is a battery power profile;
fig. 6 is a graph of supercapacitor power.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The following examples are only for more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
A modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution comprises the following steps:
step one: establishing an aggregation model of the storage battery and the super capacitor, establishing an energy and power calculation mode and constraint conditions of the energy and power calculation mode in a micro-grid system, performing climbing constraint on the storage battery, and setting rising and falling limits of charging and discharging power of the storage battery and current constraint of the super capacitor; the initial capacity requirement in the case of self-discharge of the battery is considered.
Step two: and establishing an objective function based on the storage battery acquisition cost, the super capacitor acquisition cost and the storage battery charge-discharge cycle life damage cost, and obtaining a mathematical expression of the objective function.
Step three: according to the linearization calculation concept, an improved secondary mixed integer variable linearization method is established based on CPLEX, a new method for accurately expressing a multi-variable multi-dimensional complex objective function (shown in figure 1) is obtained, and a reliable algorithm basis is provided for mixed energy storage capacity configuration and scheduling scheme research.
Step four: linearizing the established constraint conditions and the objective function by using a linearization method based on a CPLEX solver, and selecting proper parameters to obtain an optimal capacity configuration scheme of the hybrid energy storage system. The parameters are determined by reference model parameters and actual requirements.
The invention relates to an off-grid micro-grid case, comprising a photovoltaic power station, a hybrid energy storage system consisting of a storage battery and a supercapacitor, and a consumer load. In this simulation, the load is a typical daily load value among annual load values of an industrial park. The photovoltaic output curve and the net load curve are deduced by the principle that the load curve is equal to the integral of the photovoltaic output curve and introducing a k value (the k value is determined by the maximum initial capacity calculated by the initial capacity) under the premise of considering the redundancy required by the self-discharge of the storage battery (as shown in figure 3).
Based on the cost-wise objective function of the hybrid energy storage system, the three-part composition can be expressed as follows:
the first part is the acquisition cost of the storage battery,
the required costs for the batteries to meet the scheduled energy demand, power demand, climbing demand, respectively, and have their expressions as follows:
wherein,,
for the unit price of the storage battery->
For single battery capacity, P b And (t) is the power value of the storage battery at the time t, and the power value is positive during charging and negative during discharging.
The second part is the purchase cost of the super capacitor,
the energy demand cost and the power demand cost when the super capacitor meets the scheduling requirement are respectively expressed as follows:
wherein,,
is the unit price of the super capacitor, < >>
For single super capacitor capacity, P sc And (t) is the power value of the super capacitor at the time t, and the power value is positive during charging and negative during discharging.
It will be seen that in practice the number of energy storage units is varied to meet the constraints, so that all constraints are met, the maximum value is chosen, i.e
And->
The third part is to calculate the life loss by considering the life loss cost of the charge-discharge cycle of the storage battery and converting the charge-discharge cycles with different depths into complete cycle times, wherein the calculation formula is as follows:
d is the number of cycles after conversion. The cost of the break can be expressed as:
D c for the maximum cycle number of the storage battery under the set charge-discharge depth, Y p Is the life cycle (day) of the battery.
Further, the aggregation model of the source load storage is analyzed, and the system constraint conditions are synthesized as follows:
1) Firstly, constraint conditions of a micro-grid system, namely system capacity constraint, are obtained:
wherein mu i The ratio of the capacity of each small system in the micro-grid system to the capacity of the micro-grid system is N 1 Is a micro-grid systemThe number of small systems in the system;
the output capacity constraint of photovoltaic power generation is expressed as:
wherein,,
capacity actually output for photovoltaic power generation, +.>
The capacity of the maximum output of the photovoltaic power generation;
the energy balance constraint is expressed as:
wherein P is L Power required by load, P PV For the output power of the photovoltaic,
and->
The charging power and the discharging power of the storage battery respectively.
The battery charge-discharge rate and charge-discharge current constraint equation expression can be expressed as follows:
wherein v is c And v d For the charge and discharge rate of the storage battery, v c_R And v d_R Giving it a charge-discharge rate; i c And I d For the charge and discharge current of the storage battery, I c_max And I d_max Charge and discharge current is the mostLarge value.
Battery power constraint:
wherein the method comprises the steps of
Maximum charge/discharge power of single accumulator, n b For the number of storage batteries lambda c (t) and lambda d (t) is a pair of binary quantities representing the switch states.
Battery climbing constraint:
wherein Q is c 、Q d 、W c 、W d The rising and falling limits of the charge and discharge power of the battery in a unit time period are respectively shown.
In the charging and discharging processes, the super capacitor must meet the voltage and current constraints of charging and discharging:
wherein I is s The working current of the super capacitor; u (U) 1 The charging working voltage of the super capacitor is obtained; u (U) 2 The discharge working voltage of the super capacitor; i S max The working current of the super capacitor is the maximum value; u (U) min And U max The minimum value and the maximum value of the working voltage of the super capacitor are respectively.
Supercapacitor power constraints can be expressed as:
wherein,,
maximum charge and discharge power of a single super capacitor, n sc Is the number of super capacitors.
The supercapacitor energy constraint can be expressed as:
wherein the method comprises the steps of
For the capacity of a single super capacitor, the influence of each part is comprehensively considered, so that the comprehensive design is performed.
The initial capacity of the storage battery is further set, and can be summarized as follows:
the method for calculating the initial capacity of the storage battery comprises the following steps: if the self-discharge phenomenon of the storage battery is not considered, whether the initial capacity is considered or not does not influence the calculation result; when considering the self-discharge phenomenon of the battery, a large deviation occurs in the calculation result, so that it is necessary to correct the result by selecting an initial value. To achieve this, the mathematical model of the battery can be converted into a representation containing the initial quantities:
E b (1) I.e. initial quantity, let E b (1) Battery energy state vector at=0 is E b ' then for each E b ' t < 0, the initial amount needed is easily obtained as follows:
accordingly, a correction amount is added at each other moment, and the corrected energy state value formula (18) shows:
so long as for each E is selected b ' maximum initial amount required for (t) < 0
Will->
Is substituted into E in formula (18) b (1) E which is a positive value after correction can be obtained b (t) (shown in fig. 2).
The following is a further setting of parameters required by the system, including:
In order to verify the effectiveness and feasibility of the energy storage capacity optimization configuration strategy introduced into the super capacitor, a related algorithm is established by utilizing MATLAB based on a CPLEX solver to obtain a corresponding capacity configuration scheme by combining the established source charge storage mathematical model.
FIG. 3 is a representative load curve selected to reflect load power usage in a microgrid system and to add to the calculated photovoltaic output curve to obtain a net load output curve, providing a data base for energy storage capacity configuration and scheduling;
according to the set parameters and in combination with a linearization algorithm, obtaining the SOC variation conditions of the storage battery and the super capacitor in the day, which are shown in the system in FIG. 4, and providing the SOC variation conditions at each moment in the day; FIG. 5 is a graph of power during a battery day; fig. 6 is a graph of power profile of supercapacitor during a day. The final configuration results obtained in the case are as follows:
the power of a storage battery in the hybrid energy storage system is 54.00kW, and the stored energy is 173.49kWh; the super capacitor is 19.94kW, and the stored energy is 10.83kWh; the final total cost was 71.96 ten thousand yuan.
The energy storage system taking the storage battery as a single energy storage mode is configured with the storage battery power of 58.07kW and the storage capacity of 203.67kWh; the power of the super capacitor is 0, and the stored energy is 0; the total cost is 206.34 ten thousand yuan, and the service life of the storage battery is greatly influenced by a pure storage battery energy storage system under the condition of power fluctuation.
The foregoing description of the preferred embodiments of the present invention has been presented only in terms of those specific and detailed descriptions, and is not, therefore, to be construed as limiting the scope of the invention. It should be noted that modifications, improvements and substitutions can be made by those skilled in the art without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (4)
1. The modeling method of the hybrid energy storage capacity configuration strategy based on integer linear programming solution is characterized by comprising the following steps of:
step one: establishing an aggregation model of the storage battery and the super capacitor, establishing an energy and power calculation mode and constraint conditions of the energy and power calculation mode in a micro-grid system, performing climbing constraint on the storage battery, and setting rising and falling limits of charging and discharging power of the storage battery and current constraint of the super capacitor; the requirement on initial capacity under the self-discharge condition of the storage battery is considered;
step two: establishing objective functions of storage battery acquisition cost, super capacitor acquisition cost and storage battery charge-discharge cycle life damage cost, and obtaining a mathematical expression of the objective functions;
step three: according to the linearization calculation concept, an improved secondary mixed integer variable linearization method is established based on CPLEX, and a method for linearizing and expressing a multivariable objective function is obtained;
step four: linearizing the established constraint conditions and the objective function by using a linearization method based on a CPLEX solver, and selecting proper parameters to obtain an optimal capacity configuration scheme of the hybrid energy storage system; the parameters are determined by reference model parameters and actual requirements.
2. The modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution as claimed in claim 1, wherein the first step specifically comprises:
1) Firstly, constraint conditions of a micro-grid system, namely system capacity constraint, are obtained:
wherein mu i The ratio of the capacity of each small system in the micro-grid system to the capacity of the micro-grid system is N 1 The number of small systems in the micro-grid system;
the output capacity constraint of photovoltaic power generation is expressed as:
wherein,,
capacity actually output for photovoltaic power generation, +.>
The capacity of the maximum output of the photovoltaic power generation;
power balance constraint:
wherein P is L Power required by load, P PV For the output power of the photovoltaic,
and->
The charging power and the discharging power of the storage battery are respectively;
2) The mathematical model of the storage battery and the climbing constraint are expressed as follows:
wherein E is b (t) is the energy state of the storage battery at the time t,
the storage loss rate of the storage battery;η c and eta d The charging efficiency and the discharging efficiency of the storage battery are respectively;
the battery charge-discharge rate and the charge-discharge current constraint equation expression are expressed as follows:
wherein v is c And v d For the charge and discharge rate of the storage battery, v c_R And v d_R Giving it a charge-discharge rate; i c And I d For the charge and discharge current of the storage battery, I c_max And I d_max The maximum value of the charge and discharge current of the storage battery;
battery power constraint:
wherein the method comprises the steps of
Maximum charge/discharge power of single accumulator, n b For the number of storage batteries lambda c (t) and lambda d (t) is a pair of binary quantities representing the state of the switch;
battery climbing constraint:
wherein Q is c 、Q d 、W c 、W d Respectively representing the rising and falling limits of the charge and discharge power of the storage battery in a unit time period;
3) For the super capacitor, the reference model satisfies the following state equation expression on the premise of not considering the charge and discharge loss and the self-discharge rate of the super capacitor:
wherein E is sc (t) is the energy state of the super capacitor at the time t,
and->
The charging power and the discharging power of the super capacitor are respectively;
in the charging and discharging processes, the super capacitor must meet the voltage and current constraints of charging and discharging:
wherein I is s The working current of the super capacitor; u (U) 1 The charging working voltage of the super capacitor is obtained; u (U) 2 The discharge working voltage of the super capacitor; i Smax The working current of the super capacitor is the maximum value; u (U) min And U max The minimum value and the maximum value of the working voltage of the super capacitor are respectively;
the supercapacitor power constraint is expressed as:
wherein,,
maximum charge and discharge power of a single super capacitor, n sc The number of the super capacitors;
the supercapacitor energy constraint is expressed as:
wherein the method comprises the steps of
The capacity of a single super capacitor;
4) The method for calculating the initial capacity of the storage battery comprises the following steps: if the self-discharge phenomenon of the storage battery is not considered, whether the initial capacity is considered or not does not influence the calculation result; when considering the self-discharge phenomenon of the battery, the calculation result of the formula (4) has a large deviation, so that it is necessary to correct the result by selecting an initial value; to achieve this, formula (4) is first converted into a representation comprising an initial amount:
E b (1) I.e. initial quantity, let E b (1) Battery energy state vector at=0 is E b ' (t), then for each E b ' t < 0, giving the initial quantities required for it are:
correspondingly, a correction amount needs to be added at each other moment, and the corrected energy state value is shown in the formula (15):
select for each E b ' t < 0 holds, the maximum initial amount required
Will->
Is substituted into E in formula (15) b (1) E is obtained which is all positive after correction b (t)。
3. The modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution as claimed in claim 2, wherein the specific content of the second step comprises:
the objective function of the hybrid energy storage system is established from the aspect of cost, and the objective function consists of three parts as follows:
the first part is the acquisition cost of the storage battery,
the cost required by the storage battery to meet the scheduled energy demand, power demand and climbing demand is expressed as follows:
wherein,,
for the unit price of the storage battery->
For single battery capacity, P b (t) is the power value of the storage battery at the time t, and the value is positive during charging and negative during discharging;
the second part is the purchase cost of the super capacitor,
the energy and power costs when the super capacitor meets the scheduling requirement are respectively expressed as follows:
wherein,,
is the unit price of the super capacitor, < >>
For single super capacitor capacity, P sc (t) is the power value of the super capacitor at the time t, and the power value is positive during charging and negative during discharging;
in summary, the number of energy storage units is actually changed to satisfy the constraint conditions, so that all the constraint conditions are satisfied, the maximum value of the cost function should be selected, namely
And->
The third part is to calculate the life loss by considering the life loss cost of the charge-discharge cycle of the storage battery and converting the charge-discharge cycles with different depths into complete cycle times, wherein the calculation formula is shown in the formula (19):
d is the circulation times after conversion; the cost of the break is expressed as:
D c for the maximum cycle number of the storage battery under the set charge-discharge depth, Y p Is the life cycle of the storage battery.
4. The modeling method of a hybrid energy storage capacity configuration strategy based on integer linear programming solution as claimed in claim 3, wherein the specific contents of the third step include:
1) For the objective function min (max x i ) Is a linearization method of (1): using intermediate variable X instead of max i And adding constraints, wherein the specific process is as follows:
min X (21)
X≥x i (22)
2) For cases involving absolute values in an objective function, e.g.
(f represents unconstrained), as is the special case of max, i.e., |x|=max (-x, x); the linearization method comprises the following steps: using the intermediate variable y i Substitution |x i The concrete process is as follows:
min c i y i (23)
ax＝b (24)
y i ≥x i ,y i ≥-x i (25)
x i f,c i ≥0 (26)
therefore, for the case that the expression (17) contains both the absolute value and the maximum value, the absolute value is linearized and then the maximum value is linearized;
3) Secondary mixed integer variable linearization: for the objective function
And->
Wherein lambda is c (t),λ d (t)∈{0,1}，/>
Using two intermediate variables X c (t) and X d (t) are respectively replaced by X c (t) substitution of
For example, the following constraints are added:
0≤X c (t)≤Lλ c (t) (29)
wherein L is a relatively large constant, X d And (t) the same.
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| CN117559495A (en) * | 2023-11-13 | 2024-02-13 | 南方电网能源发展研究院有限责任公司 | Power distribution network energy storage planning method and device based on service condition of storage battery |
| CN117791662A (en) * | 2024-02-27 | 2024-03-29 | 华北电力大学 | Hybrid energy storage capacity distribution method, system, electronic equipment and medium |
| CN117996802A (en) * | 2024-03-15 | 2024-05-07 | 广州智光储能科技有限公司 | Energy storage configuration method, device and system of power system and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117559495A (en) * | 2023-11-13 | 2024-02-13 | 南方电网能源发展研究院有限责任公司 | Power distribution network energy storage planning method and device based on service condition of storage battery |
| CN117791662A (en) * | 2024-02-27 | 2024-03-29 | 华北电力大学 | Hybrid energy storage capacity distribution method, system, electronic equipment and medium |
| CN117791662B (en) * | 2024-02-27 | 2024-05-17 | 华北电力大学 | Hybrid energy storage capacity distribution method, system, electronic equipment and medium |
| CN117996802A (en) * | 2024-03-15 | 2024-05-07 | 广州智光储能科技有限公司 | Energy storage configuration method, device and system of power system and storage medium |
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