Hybrid energy storage optimization method and system considering electric vehicle and photovoltaic

[0054] Next, the technical solutions in the embodiments of the present invention will be described in connection with the drawings of the embodiments of the present invention, and it is understood that the described embodiments are merely the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art are in the range of the present invention without making creative labor premise.

[0055] The "Embodiment" mentioned herein means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of the present application. This phrase is not necessarily a separate or alternative embodiment of the same embodiment in each position in the specification. Those skilled in the art is, and the embodiments described herein may be combined with other embodiments.

[0056] The specification of the present application and the claims, "first", "second", "third", and "fourth", and the fourth ", and the fourth, and the fourth, and the fourth", and the fourth ", and the fourth", not to describe a particular order. . Moreover, the terms "including" and "have" and any variations, intended to cover the inclusion of his inclusion. For example, comprising a series of steps, processes, methods, etc. is not limited to the listed steps, but optionally further comprises the step of not listed, or alternatively further comprising for such process, method, article, or device-specific element other steps.

[0057] Object of the present invention is to provide an electric vehicle based on the battery charge amount of recession control method and system, while ensuring the meet user needs, by controlling the state of charge of the electric vehicle battery section can be reduced with lithium batteries of electric vehicles battery recession, with economic value.

[0058] In order to make the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] The present embodiment, if the user can estimate the required before the next charging point furthest with mileage, can be guided to the positive electric vehicle charging user behavior, to guide the user to an electric vehicle charging amount control in an appropriate range. So without compromising the car user experience, reduce battery consumption costs, promote the popularity of electric vehicles.

[0060] figure 1 Optimization method of hybrid energy storage embodiment of the method of the present invention provides a flow chart, such as figure 1 , The present invention provides a method for a hybrid electric vehicle to optimize the photovoltaic energy storage considerations, comprising:

[0061] Step 100: evaluate different types of hybrid energy storage, in order to determine the appropriate type of energy storage configuration;

[0062] Step 200: impact load on the grid decomposition, decomposition results obtained; the decomposition of the charge-discharge energy storage type configuration instruction;

[0063] Step 300: based on the charge-discharge instruction, the storage cost integrated day as the objective function to hybrid energy storage capacity remaining charge and discharge power of the energy storage capacity constraint hybrid establish optimal allocation model, and the hybrid energy storage optimization capacity model is solved to obtain the optimal configuration of the storage capacity type of configuration;

[0064] Step 400: the electric vehicle based on the load of the photovoltaic output data, a two-stage optimization model in accordance with the capacity of the configuration; the two-stage optimization model comprises a model before optimization and optimization model days;

[0065] Step 500: a load fluctuation of the objective function to optimize the two stage model optimization, to obtain optimal results hybrid energy storage.

[0066] figure 2 Hybrid energy storage schematic overall optimization method in the embodiment of the present invention provides, as figure 2 As shown in this embodiment, the overall process is based on the capacity of the energy storage type of hybrid energy storage system is arranged to select studies, there is a large number of grid impact load, such as rolling mills, and the like railway traction. Such equipment generates electric load is large sudden changes periodically or aperiodically, and the variation of the load time is very short, belonging to impact load. These loads impact on the power grid, grid stability destruction. Therefore it is necessary to access the storage system to reduce its mixed effects on the power system. Hybrid energy storage comprising energy storage and energy type power storage. Typical power energy storage includes: a super capacitor energy storage, and flywheel energy storage SMES. A typical type of energy storage include: lead-acid batteries, lithium batteries. Hybrid energy storage capacity before executing the command, first hybrid energy storage selection. Selected energy and power density, lifetime, sustainable time, charge and discharge efficiency, the cost as an evaluation index, the storage system evaluation index characteristics to reflect. Integrated AHP and data envelopment analysis for different types of storage were evaluated.

[0067] Preferably, the assessment of different types of hybrid energy storage, in order to determine the appropriate type of energy storage configuration, comprising:

[0068] Select energy, power density, lifetime, sustainable time, charge and discharge efficiency and cost as an evaluation index, the storage system evaluation index reflecting characteristics;

[0069] AHP and comprehensive data envelopment analysis method, the evaluation system of different types of hybrid energy storage was evaluated to obtain the energy storage type configuration; the configuration storage and a super capacitor type comprises lithium.

[0070] Specifically, the embodiment and the integrated use of AHP data envelopment analysis method of the present embodiment, different types of storage evaluated to select the appropriate type storage configuration, the energy storage type evaluation results are shown in Table 1 and Table 2. Table 1 Evaluation sort of energy storage type, Table 2 Evaluation of power energy storage sorting.

[0071] Table 1

[0072]

[0073] Preferably, the impact load on the grid for decomposition, decomposition results obtained; the decomposition of the charge-discharge energy storage configuration type instruction, comprising:

[0074] Using empirical mode decomposition algorithm to improve the impact load decomposition, decomposition results obtained; decomposition comprises the intrinsic mode functions and components aftermath.

[0075] Specifically, the present embodiment employs an improved empirical mode decomposition (Improved ensemble empirical modedecomposition, MEEMD) algorithm exploded impact load, automatically generate several intrinsic mode function (IntrinsicMode Functions, IMFs) with a aftermath component according decomposition hybrid energy storage discharge power allocation, a lithium battery and supercapacitor charging and discharging of the reference instruction.

[0076] Further, a system for impact load MEEMD decomposition of the time domain plot of the frequency domain decomposed in the space, resulting IMFs characterize different frequencies, such as image 3 , As a super capacitor and Lithium batteries instructions.

[0077] Preferably, the overall cost of the formula storage date:

[0078] Z = (A su + A ba ) / 365 + B y E B + C y E C;

[0079] Among them, B y Maintenance cost factor is the operation of the super capacitor; C y Maintenance cost factor is of the lithium battery operation; A su Value of the investment cost of the super capacitor; A ba Value of the investment cost of the lithium battery;

[0080] Wherein, in the formula for the value of the investment cost r, N and APR respectively cycle length; C su And C ba The investment costs are ultracapacitors and investment cost of the lithium battery;

[0081] Wherein, the investment cost of the formula B k And C k Are the coefficients and power cost of the lithium battery power cost factor supercapacitor; B e And C e Respectively, the cost factor and the capacity of the lithium battery capacity cost factor super capacitor; P B And P C Ultracapacitors are rated power and rated power of the lithium battery; E B E C Respectively, the super capacitor and the rated capacity of the lithium battery rated capacity.

[0082]Preferably, the constraint condition specifically includes a residual capacity constraint of the supercapacitance, a charge and discharge capacity constraint, a power constraint of the supercapacitor, a power constraint and a voltage constraint of the lithium battery.

[0083] Further, the mixed energy storage capacity optimization configuration model is established in this embodiment, and the multivariate planning problem of the nonlinear constraint is solved by the internal point method to obtain a capacity configuration. Capacity optimization configuration model is minimized for mixed storage energy daily, with mixed storage capacity SOC and charge and discharge power.

[0084] Further, the target function is a comprehensive cost of mixing reservoirs, including daily operating costs and daily investment costs. Investment cost refers to the funds of investment in investors, with time. Under different development phases and influencing factors, the same amount of funds in different years is different. The hybrid energy storage system of supercapacitance and lithium battery is usually more than ten years, so the time value of funds will be introduced first before the configuration is optimized.

[0085] The value of funds refers to the value-added value of the currency over time, which is the value added after the capital turnover. That is, the currently held value is greater than the value of the same number of funds in the future. The current amount of funds held is the present value. The amount of funds held in the future is the final value. The value of the equal funds in the mixed energy storage life cycle is the annual value. The three are proportional to the relationship, and the calculation formula is as follows. .

[0086] F = (1 + r) N × P (1)

[0087]

[0088] In the formula F, P, A - is the final value, present value, annual value; R, N - is annual interest rate, period length (year).

[0089] The total investment cost of mixed storage capacity is the following formula for one-time investment cost.

[0090]

[0091] Central C su , C ba - The cost of supercapacitor investment, lithium battery investment, respectively; B k , C k - The cost coefficient of supercapacitor power is respectively, and the power cost coefficient of lithium battery; B e , C e - The cost coefficient of supercapsant capacitance, and lithium battery capacity; B , P C - The rated power, lithium battery rated power, respectively; E B E C - rating the rated capacity of supercapacitor, rated capacity of lithium battery;

[0092] One-time investment cost is the present value, according to the annual value and present value conversion formula (2) can be obtained by mixing energy storage annual equal investment costs:

[0093]

[0094] Among A su - Super capacitor investment cost annual value; A ba - Lithium battery investment cost annual value.

[0095] Capacity Configuration Target Functions consist of a comprehensive cost of investment costs with lithium batteries with a lithium battery, which is described below.

[0096] Z = (a su + A ba ) / 365 + b y E B + C y E C (5)

[0097] Central B y - Super capacitor operation maintenance cost coefficient; C y - Lithium battery operation maintenance cost coefficient.

[0098] Further, the capacity configuration optimization model constraint condition is a mixed energy storage residual capacity constraint and charge and discharge power constraint, wherein the remaining capacity constraint of the supercapacitor is shown in:

[0099] 0.1e B ≤ S su ≤0.95E B (6)

[0100] Somex su - The remaining capacity of the super capacitor; the remaining capacity calculation formula of the super capacitor is shown in the following formula:

[0101]

[0102] Somex su (t + 1) - The remaining capacity of the T + 1 super capacitor; su (t) - The remaining capacity of the time T super capacitor; h (t) - Time t High frequency load power; η sdis , Η scha - The supercapacitor discharge efficiency and charging efficiency, respectively. When the high frequency load power is positive, the load is absorbed from the grid, and the supercapacitor is discharged; when the high frequency load power is negative, the load feeds feedback power, the supercapacitor is charged.

[0103] The charge and discharge capacity constraint of the lithium battery is shown in:

[0104] 0.3e C ≤ S ba ≤E C (8)

[0105] Somex ba - The remaining capacity of the lithium battery. The residual capacity calculation formula of the lithium battery is shown in the following formula:

[0106]

[0107] Somex ba (T + 1) - Remaining capacity of T + 1 lithium battery; s ba (T) - Remaining capacity of T lithium battery; p l (T) - Time t low frequency load power; η bdis , Η bcha - A lithium battery discharge efficiency and charging efficiency, respectively.

[0108] When the low frequency load power is positive, the load is absorbed from the grid, and the lithium battery is discharged; when the low frequency load power is negative, the load feeds feedback power, the lithium battery is charged.

[0109] The power constraint of the super capacitor is shown in the following formula:

[0110] -P B ≤ P h (t) ≤p B (10)

[0111] The power constraint of the lithium battery is shown in:

[0112] -P C ≤ P l (t) ≤p C (11)

[0113] The specified value of the voltage allowed deviation according to the voltage max With u min Perform voltage constraints, see the following formula:

[0114] U min ≤ u (t) ≤ u max (12)

[0115] Based on the YalMip + CPLEX platform, the above model is solved.

[0116] Alternatively, in the present embodiment, the supercapacitance and discharge command is based on the charge and discharge command of the lithium battery, and the overall cost of the energy storage date is the target to mix the storage capacity and charge and discharge power. The decision variable is a linear function of the supercapacitor and the rating of the lithium battery and the rated capacity, the target function is the linear function of the decision variable, the constraint condition is a nonlinear function of the decision variable, using YalMip + CPLEX to solve the optimal supercapacitor and lithium battery capacity configuration As a result, as shown in Table 3. Table 3 is a result of mixing energy storage capacity configurations.

[0117] table 3

[0118]

[0119] Preferably, the target function of the reception optimization model is:

[0120]

[0121]

[0122] Where Z OF And Z OF1 The total operating cost of the system and the operation and maintenance of energy storage equipment; Z OF2 And Z OF3 Electrop costs and electric vehicle service costs, respectively; 1 t Output power for Time distribution network; and Discharge power and battery charging power of Time energy storage, respectively; λ t And N P The length of the adjustment coefficient and the control domain of the electricity price, respectively; α EN Run maintenance costs for energy storage equipment;

[0123] The constraint conditions of the recever optimization model include first equation constraints and first non-equation; the first non-equally constrained includes a first energy storage capacity constraint and power maximum and minimum constraints; wherein the first equity The formula of the constraint is The load power of the T hour system; the formula of the first energy storage capacity constraint is s n and It is a state of storage of rated capacity and energy storage, respectively; the power maximum value and the minimum constraint formula is P cmax For energy storage rated power.

[0124] Preferably, the target function of the intraday optimization model is:

[0125]

[0126] Where P b (i) and P s (i) the charge and discharge power of the lithium battery and the supercapacitance charge and discharge power; P, respectively; P f (i) and p (i) are I time the original load power and the alarm load power; P mean In order to be relay, load power mean; LF is a volatility parameter of the secondary load data P;

[0127] The constraints of the intraday optimized model include second equation constraints and second inequality; the second inequality constraint includes a second energy storage capacity constraint and a voltage constraint; the formula of the second equation constraint is: For the execution time domain N c = 1H lithium battery charge and discharge power optimization results calculated the resulting lithium battery charge state; s (T 0 + ΔT) and S (T 0 ), Respectively, the end-load state and the head end charge state, respectively; the second energy storage capacity constraint is 0.3 ≤ S (T) ≤ 1 0... T 0 + ΔT>; where S (t) is a charge state of the T time; the formula of the voltage constraint is U min ≤u ≤u max; maxThe maximum value of the voltage allows the deviation specified value; the U min The maximum value of the voltage allows the deviation specified value; U is the current voltage value.

[0128] Specifically, the present embodiment is based on the arrangement of the lithium battery and the supercapacitor, and considers the electric vehicle and photovoltaic prediction data, such as Figure 4 Indicated. Further establish two phased optimization models, including recent optimization models and intraday optimization models, such as Figure 5 Indicated.

[0129] Further, the present embodiment first constructs a recent optimization model, based on the aforementioned constraint conditions, solving the target function, the recent optimization model belongs to the problem of mixed integer planning, using YalMip + CPLEX to calculate, Image 6 Indicated.

[0130] Alternatively, the electric vehicle and photovoltaic prediction data are considered in this embodiment, establish an energy storage system model, and the energy storage power is p 3 , Energy storage discharge power is p 2 , The SOC of the energy storage is dynamically changed with the charging power of charge, see the following formula:

[0131]

[0132] Somex SOC (t + 1), s SOC (t) - T + 1 time, Time energy storage state; P 3 (t + 1), P 2 (t + 1) - T + 1 time storage energy, discharge power; c , Η d - The energy storage power and discharge power are accumulated.

[0133] In this embodiment, the lithium battery and supercapacitance are determined, two stage optimization model is established. Recent optimization models are targeted at the smallest system operating cost, and the intraday optimization model is targeted with minimal load fluctuations. Among them, the prior to optimization model is optimized to optimize the operating cost of the distribution network system with a mixed energy storage system, and the specific formula is as follows:

[0134]

[0135] Z OF ,Z OF1 - The total operating cost of the system, the energy storage equipment operation and maintenance, respectively; Z OF2 ,Z OF3 - Take electricity cost, electric vehicle service cost; P 1 t --T time distribution network output power; - Discharge power, energy storage power of energy, energy storage; λ t , N P - The length of time of the electricity price is changed with the load changes, respectively; α EN - The energy maintenance cost of the energy storage device.

[0136] Optimized decision variable is p 1 t , P 2 t , P 3 t λ t , N P , The cost of operation and maintenance cost in the energy storage device is 0.0827 yuan / kWh, ρ p The price of the peak is 1.3500 yuan / kWh, ρ v The electricity price of the valley is in this article is 0.4700 yuan / kWh.

[0137] The equation of the above-mentioned optimization model is as follows:

[0138]

[0139] In - The system load power is at T moment.

[0140] Reprofitting conditions for the recent optimization model include storage capacity constraints, power maximum and minimum constraints. Among them, the energy storage capacity constraint is preferably in the condition of 30% to 100%, so the capacity constraints are shown in the following formula.

[0141]

[0142] Somex n , - The storage of rated capacity and energy storage is used.

[0143] The energy maximum value of the power maximum value and the minimum value constraint in this embodiment is P cmax Therefore, the maximum power is the largest, the minimum is constrained.

[0144]

[0145] This embodiment takes into account the smaller scale optimization operation of the mixed reservoir to perform the small time scale optimization operation of the hybrid reservoir, which is the optimization target. Target function calculation formula is shown in the following formula.

[0146]

[0147] Pieces in P b (i), p s (i) - Time lithium battery charge and discharge power, super capacitor charge and discharge power; f (i), p (i) - is the original load power, secondary load power; P mean - is a parental load power mean; LF - characterizes the volatility of the secondary load data P. The indicator parameters of the volatility of the logging data are typically the difference or variance of load standards, and their calculation formula is shown in the following formula.

[0148]

[0149] The constraints of this embodiment also include equation constraints and inequality constraints in the date of operation, wherein the equation constraints are limited by the phase 1 day-pre-optimization results, and the inequality constraint is a lithium battery capacity constraint.

[0150] Performed time domain N according to stage 1 c The previously optimized scheduling results, constraints on the lithium battery charge and discharge SOC in the day optimization phase, as follows:

[0151]

[0152] In - by execution time domain N c = 1H lithium battery charge and discharge power optimization results calculate the lithium battery charge state;

[0153] S 0 + ΔT), S (T 0 ) - The endonal state of the end of the day, the head end is electrically conductive.

[0154] The relationship between the end-end charge state and the head-load state of the end of the day is as follows.

[0155]

[0156] Pieces in P f (t) - T Allowance to charge and discharge power.

[0157] In short-scale date optimization, lithium battery charge and discharge capacity still obeys the lithium battery capacity constraint as follows.

[0158] 0.3 ≤ S (T) ≤ 1 0... T 0 + ΔT> (22)

[0159] In short-term date, the scheduling of the energy storage must not only consider the capacity limit of the energy storage itself, but also guarantee the voltage quality of the distribution network. The specified value of the voltage allowed deviation according to the voltage max With u min The voltage constraint is performed as shown below.

[0160] U min ≤u ≤u max (twenty three)

[0161] Based on the YalMip + CPLEX platform, the above model is solved.

[0162] Further, in the day optimization, optimization of the super capacitor and the lithium battery is optimized, and the high-frequency signal in the low capacitance is used to secreate the low frequency signal in the net load power. With load fluctuations as target functions, supercapacitor charge and discharge capacity and supercapacitor charging power are equal to the discharge power in the day-optimized time period, and supercapacitance is optimized. On the other hand, according to the target function and the constraint condition, the lithium battery compensation charge and discharge power is obtained, and the load fluctuation is the target function, with lithium battery capacity and the charging power of the lithium battery in the day-optimized time period, the discharge power is constrained, for lithium Battery optimization. Get the final mixed storage capacity optimization, Figure 7 , 8 Shown

[0163] This embodiment also provides a hybrid storage energy optimization system considering electric vehicles and photovoltaic, including:

[0164] The evaluation module is used to evaluate different hybrid energy storage to determine the appropriate energy storage configuration type;

[0165] Decompose module, is used to decompose the impact load of the grid to obtain a decomposition result; the decomposition result is a charge and discharge command for the storage configuration type;

[0166] The energy storage model establishes the module for the minimum cost of the energy storage day, and the mixed energy storage capacity optimization configuration model is established by the mixing energy storage capacity and charge and discharge power of the energy storage and discharge instruction. The hybrid energy storage capacity optimized configuration model is solved, resulting in the capacity configuration of the optimal energy storage configuration type;

[0167] Two-stage optimization model establishment module for the establishment of two-stage optimization model based on the capacity configuration results based on the electric vehicle load and photovoltaic output; the two-stage optimization model includes a recent optimization model and the intraday optimization model;

[0168] Optimization module is used to optimize the two-stage optimization model to obtain a mixed storage energy optimization result with load fluctuation.

[0169] Preferably, the evaluation module comprises:

[0170] The system establishes units for selecting energy, power density, life, sustainable time, charge and discharge efficiency, and cost as an evaluation index, and establish an evaluation index system reflecting the energy storage characteristics;

[0171] The analysis unit is used to comprehensively use the hierarchical method and the data envelope analysis method, evaluate different mixing energy storage types in the evaluation index system to obtain the type of energy configuration; the energy storage configuration type includes a lithium battery And super capacitors.

[0172] Preferably, the decomposition module comprises:

[0173] The decomposing unit is used to use improved empirical modal decomposition algorithm to decompose the impact load to obtain an decomposition result; the decomposition result includes an intrinsic modal function and a remaining wave component.

[0174] The beneficial effects of the present invention are as follows:

[0175]The present invention establishes a mixed energy storage capacity optimized configuration model for a power system in which the impact-loaded load is established to obtain a capacity configuration. Based on this, it is further considered that the electric vehicle and photovoltaic prediction data and the energy storage system model are constructed, and two phased optimization models are constructed. Reduce system operating costs during the recent stage, reduce system load fluctuations during the intraday phase, and further implement the system's economic security operation.

[0176] In this specification, various embodiments are described in the manner, each of which is focused on the different embodiments, and the same similar part of each embodiment can be seen in each other. For the system disclosed in the examples, since it corresponds to the method disclosed in the examples, the description is relatively simple, and the relevant method is referred to as described in the method portion.

[0177] Specific examples are described herein to illustrate the principles and embodiments of the present invention, and the above embodiments are intended to help understand the method of the present invention and their core ideas; at the same time, for the general articles of the art Thoughts, there will be changes in the specific embodiments and applications. In summary, the contents of this specification should not be construed as limiting the invention.