CN114372608A - Park energy storage and electricity price coordination optimization method for new energy consumption on site - Google Patents
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Abstract
The invention relates to a park energy storage and electricity price coordination optimization method facing new energy local consumption, which considers the space-time matching degree relation between new energy power and load power predicted by a park at the upper layer, and establishes a park day-ahead dynamic time-sharing electricity price optimization model according to the new energy and load power data predicted by the day-ahead; on the basis, a park energy storage optimization model is established on the lower layer by taking the maximum local consumption rate of new energy as a target, considering energy storage configuration and operation cost. Compared with the prior art, the method has the advantages of improving the consumption capacity of new energy, improving the utilization efficiency of the energy, coordinating the optimization of energy storage and electricity price, reducing the consumption cost of the new energy and the like.
Description
Technical Field
The invention relates to the technical field of planning of high-proportion renewable energy power systems, in particular to a park energy storage and electricity price coordination optimization method for new energy consumption on site.
Background
At present, new energy power generation becomes an important development direction of power production. However, due to the fact that the self-regulation capacity of a power grid is limited, the market mechanism is not complete enough, and the problems that new energy power generation is difficult to grid and insufficient in consumption are increasingly prominent at present. Aiming at the problem of difficulty in consumption of new energy of a park caused by poor space-time matching of new energy output and park load, the park usually adopts methods such as electricity price optimization adjustment, energy storage optimization configuration and the like to promote the local consumption of the new energy, so that the electric energy utilization efficiency of the park is improved, and the strategic goals of energy conservation and emission reduction are realized.
The new energy consumption in place is promoted in the park through optimization and adjustment of the electricity price, but the traditional time-of-use electricity price optimization method only takes load power data as a basis, does not take source storage factors into consideration, generally has the problem of single influence factor into consideration, does not perform coordinated optimization from the source charge storage whole body to improve the new energy consumption in place in the park, has limited degree of new energy consumption promotion, cannot further improve the new energy consumption in place, and has high energy consumption cost; the park configuration energy storage promotes the on-site consumption of new energy, but the problems of poor economy of energy storage configuration and the like generally exist.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a park energy storage and electricity price coordination optimization method for new energy consumption on site.
The purpose of the invention can be realized by the following technical scheme:
a park energy storage and electricity price coordination optimization method for new energy consumption on site comprises the following steps:
s1: and in the upper-layer electricity price optimization model, according to the new energy and load power data predicted by the park in the day ahead, dynamically dividing the time-sharing time period predicted by the park in the day ahead by adopting a fuzzy membership function.
The specific steps of dynamically dividing the time-sharing time interval predicted by the campus day ahead by adopting the fuzzy membership function comprise:
s101: calculating the day-ahead net load power data of the park according to the new energy and load power data predicted by the park day-ahead:
Pnet(t)＝P0(t)-PPV(t)-PWT(t)
in the formula, Pnet(t) the net load power data at time t predicted by the park day ahead; p0(t) load power data at time t predicted by the park day ahead; pPV(t)、PWT(t) new energy photovoltaic and fan power data at t moment predicted by the park day ahead are respectively obtained; taking t as 1, 2, … and 24;
s102: payload power data P predicted from the day ahead of the parknet(t) determining the maximum and minimum membership mu of each load point by a fuzzy membership functionx1And mux2The expressions of the two are:
in the formula, PxFor load x period campus net load power, x is 1, 2, …, 24; pmin、PmaxRespectively the minimum value and the maximum value of the net load power in all periods of the load of the park;
s103: using translational range transform to pair maximum and minimum membership mux1And mux2Carrying out standardization processing to obtain a membership degree matrix mu'xk：
Wherein x is 1, 2, …, 24, k is 1, 2; mean value of
Standard deviation of
S104: calibrating the matrix after the translational polar difference transformation by adopting an Euclidean distance method to obtain a fuzzy similar matrix R; the calculation formula of the x-th row and y-th column elements in R is as follows:
wherein x is 1, 2, …, 24, y is 1, 2, …, 24; s ∈ [0,1 ];
s105: processing the fuzzy similar matrix R by adopting a transitive closure method to obtain a transitive fuzzy similar equivalent matrix RkNamely:
R→R2→R4→…→R2k
up to Rk×Rk＝RkAt this time, RkNamely a fuzzy equivalent matrix;
s106: selecting a constant lambda to blur the similarity matrix RkThe element with the length being larger than lambda is 1, the element with the length being smaller than lambda is 0, and a lambda intercept matrix R is obtainedλ；
Rλ＝(tij(λ))24×24
Wherein i and j are 1, 2, 3, … and 24; λ ∈ [0,1 ];
s107: dividing the time of day into peak periods omega according to the similarity of row elements or column elements according to the off-diagonal elements in the lambda intercept matrixpAnd the normal period omegafValley period omegagThree periods of time.
S2: on the basis of park time-sharing time-interval division, a park operator time-sharing electricity price optimization model is constructed according to each operation cost of an upper park operator and the power, capacity configuration and operation total cost of lower energy storage, and the park time-sharing electricity price is obtained. The specific steps of constructing the park operator time-of-use electricity price optimization model comprise:
s201: constructing a park time-sharing electricity price optimization model with the maximum profit of a park operator as a target:
maxRf＝I-C＝(Isell-CNE-Cgrid-Cope-Cline)
in the formula, RfEarnings for the operator of the park; i, the income of electricity sale of operators in the park; c is the operation cost of the operator in the park; i issellRevenue for electricity sold to the user by the campus operator; cNEThe electricity purchasing cost to the new energy photovoltaic and the fan is saved for the park operators; cgridThe unbalanced power exchange cost of the lower park and the distribution network; copeThe power and capacity configuration and the running total cost of the lower-layer energy storage are achieved; clinePenalizing cost for power fluctuation of the park tie line;
s202: the operation cost and the income of the park operator are as follows:
in the formula, p (t) is the optimized time-of-use electricity price of the park operator at the time t; pout(t) real-time power at time t after the park load demand response after electricity price optimization; the time interval delta t is 1 h; pPV(t)、PWT(t) power data of the new energy photovoltaic and the fan at the t moment are respectively predicted at the t moment before the park day; p is a radical ofPV(t)、pWT(t) the grid-surfing electricity prices of the new energy photovoltaic and the fan in the park at the time t are respectively, namely the electricity purchasing prices of the new energy fan and the photovoltaic from the park operator; p is a radical of0(t) optimizing the time-of-use electricity price at the previous t moment for the park operator; pgrid(t) is unbalanced power exchanged between the park and the power distribution network at the moment t; klinePunishing a cost coefficient for power fluctuation of a park and a power distribution network tie line; p is a radical ofp、pf、pgRespectively the peak, flat and valley electricity prices; omegap、Ωf、ΩgRespectively peak, plateau and valley periods.
S3: and setting constraint conditions of the park time-of-use electricity price optimization model.
The constraint conditions of the upper park time-of-use electricity price optimization model comprise electricity consumption constraint, time-of-use electricity price constraint and unit electricity cost constraint.
The expression of the power consumption constraint is as follows:
in the formula, P0(t) load power data at time t predicted by the park day ahead;
the change rate of the daily electric quantity of the load in the park is determined;
the expression of the time-of-use electricity price constraint is as follows:
0≤pg＜pf＜pp≤pmax
wherein γ is a constant limiting the peak-to-valley electrovalence ratio; p is a radical ofmaxThe upper limit of the peak time electricity rate; p is a radical ofp、pf、pgRespectively the peak, flat and valley electricity prices;
the expression of the unit electricity cost constraint is as follows:
in the formula, the unit electricity utilization cost of the user after the time-of-use electricity price optimization of the park operator is shown on the left side; the unit electricity cost of the user before time-of-use electricity price optimization is on the right side.
S4: and calculating and determining real-time power after park load response according to the time-of-use electricity price information generated in the S2, and providing data support for power and capacity configuration of lower-layer energy storage.
Taking 24h a day as a period of pricing and demand response, the real-time power P after the park load response in the t-th periodoutThe formula for (t) is:
in the formula, Pout(t) real-time power at time t after the park load demand responds; p0(t) load power data at time t predicted by the park day ahead; e (t, t) is a self-elastic matrix; e (t, j) is a cross elastic matrix; p is a radical of0(t) the initial time-of-use electricity price of the campus at the tth time period; p (t) is the time-of-use electricity price after the campus is optimized in the t time period; t is 1, 2, …, 24, j is 1, 2, …, 24.
S5: and fitting a relation curve of the discharge depth and the cycle life of the energy storage battery, establishing a capacity loss index of the energy storage battery in single discharge, and obtaining the capacity loss of the battery in single discharge by combining the discharge capacity of the energy storage battery.
The specific steps for acquiring the capacity loss of the battery in single discharge comprise:
s501: and (3) calling an MATLAB tool box to perform relational curve fitting, and performing relational curve fitting by adopting a power function method:
in the formula: n is a radical ofDFor the cycle life of the energy storage cell, DbatThe charge and discharge depth of the energy storage battery;
s502: establishing a loss index Z of single discharge capacity of the energy storage battery:
s503: according to the discharge capacity loss index Z of the energy storage battery at the time t, combining the corresponding discharge capacity to obtain the discharge capacity loss E at the time tloss(t)：
Eloss(t)＝-ZPbat(t)ΔtPbat(t)＜0
In the formula, Pbat(t) is the charging and discharging power of stored energy at time t, P during discharging of stored energybat(t) < 0; the time interval Δ t is taken to be 1 h.
S6: in the energy storage optimization model of the lower park, according to real-time power data after park load response, the power and capacity of park energy storage are used as decision variables, a park energy storage optimization configuration model with the largest local consumption rate of park new energy is constructed, and meanwhile, the power, capacity configuration and running total cost of energy storage are calculated and returned to the upper park to optimize time-sharing electricity price.
S7: and setting constraint conditions of energy storage charging and discharging and safe system operation of the lower park.
The constraint conditions for configuring energy storage in the lower park comprise electric power balance constraint, tie line interaction power constraint, energy storage system operation constraint, energy storage system charge state constraint and energy storage power and capacity constraint.
The electric power balance constraint is expressed as:
PPV(t)+PWT(t)+Pgrid(t)＝Pout(t)+Pbat(t)
in the formula (I), the compound is shown in the specification,Pout(t) real-time power at time t after park load demand response after optimization of electricity price, PPV(t)、PWT(t) power data P of new energy photovoltaic and wind turbine t moment predicted respectively for t moment before the park daygrid(t) is the unbalanced power exchanged between the park and the distribution network at time t, Pbat(t) is the charge and discharge power stored at time t;
the expression of the tie line interaction power limit constraint is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively, cross-line power Pline(t) minimum and maximum values;
the expression of the energy storage system operation constraint is as follows:
-Pbess≤Pbat(t)≤Pbess
wherein S (t) is the state of charge at the moment of energy storage t, ηch、ηdisRespectively representing the charging efficiency and the discharging efficiency of the stored energy, wherein sigma is the self-discharging rate of the stored energy; pbessPower allocated for park energy storage, EbatCharging and discharging capacity for storing energy at the time t;
the expression of the energy storage system state of charge constraint is as follows:
Smin≤S(t)≤Smax
S(0)＝S(24)
in the formula, Smin、SmaxMinimum and maximum states of charge of the stored energy, respectively; s (0) and S (24) are respectively that the energy storage system is in a state of 00: time 00And 24: state of charge at 00;
the expression of the energy storage power and capacity constraint is as follows:
in the formula (I), the compound is shown in the specification,
respectively configuring the minimum and maximum capacity of the energy storage battery;
and respectively configuring the minimum power and the maximum power for the energy storage battery.
S8: and calculating the scale of power and capacity of the park time-sharing electricity price and energy storage configuration, and completing the coordination optimization of the park energy storage and the electricity price of the new energy consumption on the spot.
Further, the power and capacity scales of the park time-of-use electricity price and energy storage configuration are calculated by adopting a moth flame optimization-particle swarm optimization intelligent optimization algorithm, and the specific steps comprise:
s801: the upper-layer electricity price optimization model adopts a moth flame optimization algorithm to obtain park time-of-use electricity prices, the lower-layer energy storage optimization model adopts a particle swarm algorithm to obtain the power and capacity of energy storage configuration, the optimal power and capacity configuration of energy storage corresponding to the upper-layer time-of-use electricity prices is calculated, and the power and capacity configuration and the total running cost of the energy storage are calculated and returned to the upper-layer electricity price optimization model;
s802: when the difference value of the results of the two operations is smaller than a preset value, stopping iteration and outputting the result; and if the difference is not smaller than the preset value, continuing iteration.
Compared with the prior art, the park energy storage and electricity price coordination optimization method for new energy local consumption at least has the following beneficial effects:
the method provided by the invention fully utilizes the optimization and adjustment capability of the electricity price and the stored energy as the adjustment means of flexible resources, carries out coordination and optimization on the whole source charge storage of the garden, considers the space-time matching degree relation between the new energy power and the load power predicted by the garden day ahead, establishes a double-layer optimization model of the energy storage and the electricity price of the garden, can effectively promote the on-site consumption of new energy of the garden, and has important reference significance for improving the consumption of the new energy, the electric power operation efficiency and the energy conservation and emission reduction under the strategic background of double carbon.
Secondly, building a park energy storage and electricity price double-layer optimization model, and dynamically dividing the time-sharing time period predicted by the park in the day by adopting a fuzzy membership function to build a park time-sharing electricity price optimization model constraint condition; the extracted model is solved, relevant result configuration analysis is carried out, the space-time matching degree relation between the new energy power and the load power predicted by the park in the day is considered, the park new energy consumption on the spot can be effectively promoted, the energy utilization efficiency can be improved, the energy storage and electricity price optimization can be coordinated, and the new energy consumption cost is reduced.
The upper-layer park electricity price optimization method provided by the invention integrates new energy and load power data to perform dynamic time-sharing time-interval division, so that the peak-to-valley time interval division is more reasonable, the park electricity price is optimized and adjusted on the basis, the flexible adjustment capability of the load on the charge side is fully exerted, and the local consumption capability of the new energy in the park is further improved to a certain extent.
And fourthly, solving the park energy storage and electricity price double-layer optimization model by adopting a moth flame optimization algorithm, and improving the optimization precision and the convergence speed.
And fifthly, the method improves the economy of energy storage configuration of the lower park under the condition of not damaging the power utilization benefit of a user through time-period division of time-of-use electricity price and optimization adjustment of the time-of-use electricity price of the upper park, and provides a scientific and practical method for energy storage configuration of the park oriented to on-site consumption of new energy.
Drawings
FIG. 1 is a schematic flow chart of a park energy storage and electricity price coordination optimization method for new energy consumption on site in the embodiment;
FIG. 2 is a solving flow chart of the park energy storage and electricity price coordination optimization method for new energy consumption on site in the embodiment;
fig. 3 is a schematic diagram of time-sharing electricity at each time before optimization by a campus operator in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The invention relates to a park energy storage and electricity price coordination optimization method for new energy local consumption.
The invention relates to a park energy storage and electricity price coordination optimization method for new energy local consumption, which has the main principles that: one, using maximum and minimum membership functions mux1And mux2New energy power P predicted by park day-aheadPV(t)、PWT(t) and load power P0(t) carrying out membership degree division on the basis of data, adopting a fuzzy clustering method, and taking maximum and minimum membership degree values mu of each moment in the day beforex1And mux2The peak-valley time-sharing time interval is obtained according to the division, so that the time-sharing time interval of the park time-sharing electricity price has dynamic characteristics. Secondly, on the basis of time-sharing time interval division, a park operator electricity price optimization model is constructed, and the electricity price p of each time-sharing time interval of the park peak, the park peak and the park valley is obtained in an optimized modep、pfAnd pg. Thirdly, on the basis of time-of-use electricity price determination, determining the load power P after electricity price optimization by adopting electricity price type demand responseout(t) calculating the local consumption rate R of the new energy of the park under the optimization of the electricity pricesel. Fourthly, load power data P with the gardenout(t) according to the local new energy consumption rate R of the parkselAnd constructing a park energy storage optimization configuration model at the maximum target. And calculating the configuration and operation cost C of energy storageopeReturning to the upper-layer electricity price optimization model to obtain the time-of-use electricity price P (t) and the energy storage power P predicted by the park day aheadbessAnd capacity EbessAnd (5) configuring the scale.
As shown in fig. 1, the park energy storage and electricity price coordination optimization method for new energy consumption on the spot specifically includes the following steps:
step one, in an upper-layer electricity price optimization model, according to new energy and load power data predicted in the park in the day ahead, dynamic division is carried out on time-sharing time periods predicted in the park in the day ahead by adopting a fuzzy membership function; specifically, the method comprises the following steps:
11) calculating the day-ahead net load power data of the park according to the new energy and load power data predicted by the park day-ahead:
Pnet(t)＝P0(t)-PPV(t)-PWT(t)
in the formula, Pnet(t) the net load power data at time t predicted by the park day ahead; p0(t) load power data at time t predicted by the park day ahead; pPV(t)、PWT(t) new energy photovoltaic and fan power data at t moment predicted by the park day ahead are respectively obtained; take t as 1, 2, …, 24.
12) Payload power data P predicted from the day ahead of the parknet(t) determining the maximum and minimum membership mu of each load point by means of a fuzzy membership functionx1And mux2；
In the formula, PxFor net load of x time period parkPower-on, x ═ 1, 2, …, 24; pmin、PmaxRespectively the minimum value and the maximum value of the net load power in all periods of the load of the park;
13) using existing translation range transformation to pair maximum and minimum membership degree mux1And mux2Carrying out standardization processing to obtain a membership matrix;
wherein x is 1, 2, …, 24, k is 1, 2; mean value of
Standard deviation of
14) Calibrating the matrix after the translational polar difference transformation by using an Euclidean distance method to obtain a fuzzy similar matrix R; the calculation formula of the x-th row and y-th column elements in R is as follows:
wherein x is 1, 2, …, 24, y is 1, 2, …, 24; s ∈ [0,1], selecting a suitable parameter s, in this embodiment, preferably, s ═ 0.9751, ensuring that R (x, y) ∈ [0,1 ];
15) processing the fuzzy similar matrix R by using a transitive closure method to obtain a fuzzy similar equivalent matrix R with transitivitykNamely:
R→R2→R4→…→R2k
up to Rk×Rk＝RkAt this time, RkNamely a fuzzy equivalent matrix;
16) selecting a constant lambda to blur the similarity matrix RkThe element with the length being larger than lambda is 1, the element with the length being smaller than lambda is 0, and a lambda intercept matrix R is obtainedλ；
Rλ＝(tij(λ))24×24
Wherein i and j are 1, 2, 3, … and 24; lambda belongs to [0,1]]In the present embodiment, λ is preferably 0.8972, resulting in λ intercept matrix Rλ；
17) At this time, λ intercept matrix RλA 24-order square matrix composed of elements 0 and 1, wherein the rows and the columns respectively represent 24 hours, and the time of day can be divided into peak periods omega according to the similarity of the row elements or the column elements according to the non-diagonal elements in the lambda intercept matrixpAnd the normal period omegafValley period omegag3 periods of time.
Step two, on the basis of time-sharing and time-sharing partition of the park, according to each operation cost C of the upper-layer park operator and the power, capacity configuration and operation total cost C of the lower-layer stored energyopeBuilding a park operator time-of-use electricity price optimization model, and solving a park time-of-use electricity price p (t); specifically, the method comprises the following steps:
21) constructing a park time-sharing electricity price optimization model with the maximum profit of a park operator as a target:
maxRf＝I-C＝(Isell-CNE-Cgrid-Cope-Cline)
in the formula, RfEarnings for the operator of the park; i, the income of electricity sale of operators in the park; c is the operation cost of the operator in the park; i issellRevenue for electricity sold to the user by the campus operator; cNEThe electricity purchasing cost to the new energy photovoltaic and the fan is saved for the park operators; cgridThe unbalanced power exchange cost of the lower park and the distribution network; copeThe power and capacity configuration and the running total cost of the lower-layer energy storage are achieved; clinePenalizing cost for power fluctuation of the park tie line;
22) the operation cost and the income of the park operator are as follows:
in the formula, p (t) is the optimized time-of-use electricity price of the park operator at the time t; pout(t) real-time power at time t after the park load demand response after electricity price optimization; the time interval delta t is 1 h; pPV(t)、PWT(t) power data of the new energy photovoltaic and the fan at the t moment are respectively predicted at the t moment before the park day; p is a radical ofPV(t)、pWT(t) are respectively the grid-surfing electricity price of the park new energy photovoltaic and the wind turbine at the time t, namely the electricity price purchased by the park operator to the new energy wind turbine and the wind turbine, and the embodiment pPV(t) is 0.38 yuan/(kW. h), pWT(t) is set to 0.42 yuan/(kW. h); p is a radical of0(t) the time-of-use electricity price at the previous t moment is optimized for the park operator, and the trade electricity price between the park operator and the power distribution network also adopts p0(t), i.e. the time of use electricity price before optimization, invention p0(t) see FIG. 3 in detail; pgrid(t) is unbalanced power exchanged between the park and the power distribution network at the moment t; klinePunishment cost coefficient for power fluctuation of junctor of park and distribution network, K is set for this embodimentlineIs 0.30 yuan/(kWh & h); p is a radical ofp、pf、pgRespectively the peak, flat and valley electricity prices; omegap、Ωf、ΩgRespectively peak, plateau and valley periods.
Setting park time-of-use electricity price optimization model constraint conditions, wherein the constraint conditions of the upper park time-of-use electricity price optimization model comprise power consumption constraint, time-of-use electricity price constraint and unit power consumption cost constraint; specifically, the method comprises the following steps:
31) and (3) restricting the electricity consumption:
in the formula, P0(t) load power data at time t predicted by the park day ahead;
in the present embodiment, the rate of change of the daily power consumption of the load in the campus is preferably set
5.0 percent;
32) and (3) time-of-use electricity price constraint:
0≤pg＜pf＜pp≤pmax
where γ is a constant that limits the peak-to-valley valence ratio, in the present embodiment, it is preferable to set the peak-to-valley valence ratio γ to 5; p is a radical ofmaxIn the present embodiment, it is preferable to set p as the upper limit of the peak time electricity ratemax1.2 yuan/(kWh & h);
33) unit electricity cost constraint:
in the formula, the unit electricity utilization cost of the user after the time-of-use electricity price optimization of the park operator is shown on the left side; the unit electricity cost of the user before time-of-use electricity price optimization is on the right side.
Step four, rootCalculating and determining real-time power P after park load response according to the generated time-of-use electricity price informationout(t) providing data support for power and capacity configuration of lower layer stored energy; specifically, the method comprises the following steps:
with 24h a day as a period for pricing and demand response, the campus load demand expression for the t-th period is:
in the formula, Pout(t) real-time power at time t after the park load demand responds; p0(t) load power data at time t predicted by the park day ahead; e (t, t) is a self-elastic matrix; e (t, j) is a cross elastic matrix; p is a radical of0(t) the initial time-of-use electricity price of the campus at the tth time period; p (t) is the time-of-use electricity price after the campus is optimized in the t time period; t is 1, 2, …, 24, j is 1, 2, …, 24.
Step five, providing a calculation method of the running capacity loss of the energy storage battery, which comprises the following steps: fitting a relation curve of the discharge depth and the cycle life of the energy storage battery, establishing a capacity loss index Z of the energy storage battery in single discharge, and combining the discharge capacity P of the energy storage batterybatΔ t, obtaining the capacity loss E of a single discharge of the batteryloss(t); specifically, the method comprises the following steps:
51) and (3) calling an MATLAB tool box to perform relational curve fitting, and fitting by adopting a power function method:
in the formula: n is a radical ofDFor the cycle life of the energy storage cell, DbatThe charge and discharge depth of the energy storage battery;
52) establishing a loss index Z of single discharge capacity of the energy storage battery:
53) according to discharge of energy storage battery at t momentThe capacity loss index Z is combined with the corresponding discharge capacity, and the discharge capacity loss E at the time t is obtainedloss(t) is:
Eloss(t)＝-ZPbat(t)Δt Pbat(t)＜0
in the formula, Pbat(t) is the charging and discharging power of stored energy at time t, P during discharging of stored energybat(t) < 0; the time interval Δ t is taken to be 1 h.
Step six, in the energy storage optimization model of the lower-layer park, building the on-site consumption rate R of the new energy of the parkselOptimizing and configuring the model for the largest energy storage of the park, and calculating the power and capacity configuration of the energy storage and the operation cost CopeReturning to the upper layer to optimize the electricity price; specifically, the method comprises the following steps:
61) optimally configuring the park energy storage with the maximum local consumption rate of the new energy as a target;
PSC(t)＝min{PPV(t)+PWT(t),Pout(t)}
in the formula, RselThe consumption rate of new energy in situ is increased; pSC(t) the new energy power consumed by the load in real time at time t of the park; pbat(t) is the charging and discharging power at the moment of energy storage t, P during energy storage chargingbat(t)＞0；
62) Calculating the configuration and operation total cost C of energy storage of a park operatoropeThe method comprises the steps of calculating the daily replacement cost corresponding to the capacity loss of the energy storage battery, including the energy storage capacity, the power configuration cost and the maintenance cost; the method comprises the following specific steps:
62a) configuration and running total cost C of energy storage of park operatoropeThe expression is as follows:
Cope＝Cin+Crep+Com
in the formula, CinDaily investment cost for energy storage power and capacity configuration; crepDaily replacement cost corresponding to energy storage operation capacity loss; comDaily maintenance costs for energy storage;
62b) the operating costs of energy storage in the garden are as follows:
Cin＝KD(Cbess+Ccon)
Com＝KDKomPbess
Cbess＝KEEbess+KPPbess
in the formula, CbessThe total investment cost for energy storage; cconThe initial construction cost of the energy storage system; pbessAnd EbessPower and capacity configured for park energy storage respectively; kDIs an equal-daily coefficient; kP、KEFor the unit power and unit capacity cost of stored energy, respectively, K is set in this embodimentP、KE1085 yuan/kW and 3224 yuan/kW.h respectively; komFor the annual maintenance cost coefficient of energy storage, K is set in this embodimentomIs 155 yuan/kW; r is the discount rate, and in this example, r is set to be 4%; y isbessFor the investment age of the energy storage system, y is set in this embodimentbessFor 15 years.
And step seven, setting constraint conditions of energy storage charging and discharging and system safe operation. The constraint conditions for configuring energy storage in the lower park comprise electric power balance constraint, tie line interaction power constraint, energy storage system operation constraint, energy storage system charge state constraint and energy storage power and capacity constraint; specifically, the method comprises the following steps:
71) electric power balance constraint:
PPV(t)+PWT(t)+Pgrid(t)＝Pout(t)+Pbat(t)
72) tie line interaction power limit constraints:
in the formula (I), the compound is shown in the specification,
and
respectively, cross-line power PlineMinimum and maximum values of (t), the present embodiment being set
And
500kW and 500kW respectively;
73) and (4) operation restraint of the energy storage system:
-Pbess≤Pbat(t)≤Pbess
wherein S (t) is the state of charge at the moment of energy storage t; etach、ηdisEta is set for the charging and discharging efficiencies of the stored energy, respectively, in this embodimentch、ηdis0.95 and 0.96 respectively; σ is the self-discharge rate of stored energy, and σ is set to 0.02 in this embodiment; ebatCharging and discharging capacity for storing energy at the time t;
74) and (3) energy storage system charge state constraint:
Smin≤S(t)≤Smax
S(0)＝S(24)
in the formula, Smin、SmaxS is set for the minimum and maximum states of charge of the stored energy, respectively, in this embodimentmin、Smax0.20 and 0.90, respectively; in this embodiment, S (0) and S (24) are set as follows: 00 hours and 24: state of charge at 00;
75) energy storage power and capacity constraint:
in the formula (I), the compound is shown in the specification,
the minimum and maximum configuration capacities of the energy storage battery are set by the embodiment
Respectively 1000 kW.h and 2000 kW.h;
the minimum and maximum configuration power of the energy storage battery are set by the embodiment
100kW and 500kW respectively.
Step eight, calculating the power P (t) of the park time-of-use electricity price P (t) and the energy storage configuration by using a moth flame optimization-particle swarm optimization (MFO-PSO) intelligent optimization algorithmbessAnd capacity EbessScale; further, a process of solving the garden energy storage and electricity price double-layer optimization model by using a moth flame optimization-particle swarm optimization (MFO-PSO) intelligent optimization algorithm is shown in fig. 2, and includes:
81) the upper-layer electricity price optimization model uses a moth flame optimization algorithm to obtain the electricity price P (t) of the park, and the lower-layer energy storage optimization model uses a particle swarm algorithm to obtain the power P of the energy storage configurationbessAnd capacity EbessCalculating the optimal power and capacity configuration of stored energy corresponding to the upper time-of-use electricity price, and calculating the stored energyReturning the power and capacity configuration and the running total cost to the upper electricity price optimization model;
82) when the difference of the two operation results
When the current value is less than the preset value, stopping iteration and outputting a result; if the difference is not the same
When the current value is not less than the preset value, continuing iteration; the preset value is set according to the actual situation, and the preset value set by the method is 0.01; n is the iteration number of the moth flame optimization algorithm, and n is 1, 2, 3 and ….
The method of the invention considers the space-time matching degree relation between the new energy power and the load power predicted by the park in the day ahead at the upper layer, and establishes a dynamic time-of-day electricity price optimization model of the park in the day ahead according to the new energy and load power data predicted in the day ahead; on the basis, a park energy storage optimization model is established on the lower layer by taking the maximum local consumption rate of new energy as a target, considering energy storage configuration and operation cost. The method carries out coordinated optimization on the source charge storage through the optimization adjustment of the upper-layer electricity price and the electric energy transfer of the lower-layer stored energy, can obviously improve the local consumption capability of new energy in the park and improve the economy of stored energy configuration.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A park energy storage and electricity price coordination optimization method for new energy consumption on site is characterized by comprising the following steps:
s1: in an upper-layer electricity price optimization model, according to new energy and load power data predicted by a park in the day ahead, dynamic division is carried out on time-sharing time periods predicted by the park in the day ahead by adopting a fuzzy membership function;
s2: on the basis of park time-sharing time-interval division, a park operator time-sharing electricity price optimization model is constructed according to each operation cost of an upper park operator and the power, capacity configuration and operation total cost of lower stored energy, and the park time-sharing electricity price is obtained;
s3: setting a time-of-use electricity price optimization model constraint condition of an upper park;
s4: calculating and determining real-time power after park load response according to the time-of-use electricity price information generated by the S2, and providing data support for power and capacity configuration of lower-layer energy storage;
s5: fitting a relation curve of the discharge depth and the cycle life of the energy storage battery, establishing a capacity loss index of the energy storage battery in single discharge, and obtaining the capacity loss of the battery in single discharge by combining the discharge capacity of the energy storage battery;
s6: in the energy storage optimization model of the lower park, according to real-time power data after park load response, the power and capacity of park energy storage are used as decision variables, a park energy storage optimization configuration model with the largest local consumption rate of park new energy is constructed, and meanwhile, the power, capacity configuration and running total cost of energy storage are calculated and returned to the upper layer to optimize time-sharing electricity price;
s7: setting constraint conditions of energy storage charging and discharging and system safe operation of a lower-layer park;
s8: and calculating the scale of power and capacity of the park time-sharing electricity price and energy storage configuration, and completing the coordination optimization of the park energy storage and the electricity price of the new energy consumption on the spot.
2. The coordination optimization method for energy storage and electricity prices of the campus oriented to new energy local absorption according to claim 1, wherein in S1, the specific step of dynamically dividing the time-sharing period predicted by the campus day ahead by using the fuzzy membership function comprises:
s101: calculating the day-ahead net load power data of the park according to the new energy and load power data predicted by the park day-ahead:
Pnet(t)＝P0(t)-PPV(t)-PWT(t)
in the formula, Pnet(t) the net load power data at time t predicted by the park day ahead; p0(t) load power data at time t predicted by the park day ahead; pPV(t)、PWT(t) new energy photovoltaic and fan power data at t moment predicted by the park day ahead are respectively obtained; taking t as 1, 2, … and 24;
s102: payload power data P predicted from the day ahead of the parknet(t) determining the maximum and minimum membership mu of each load point by a fuzzy membership functionx1And mux2The expressions of the two are:
in the formula, PxFor load x period campus net load power, x is 1, 2, …, 24; pmin、PmaxRespectively the minimum value and the maximum value of the net load power in all periods of the load of the park;
s103: using translational range transform to pair maximum and minimum membership mux1And mux2Carrying out standardization processing to obtain a membership degree matrix mu'xk：
Wherein x is 1, 2, …, 24, k is 1, 2; mean value of
Standard deviation of
S104: calibrating the matrix after the translational polar difference transformation by adopting an Euclidean distance method to obtain a fuzzy similar matrix R; the calculation formula of the x-th row and y-th column elements in R is as follows:
wherein x is 1, 2, …, 24, y is 1, 2, …, 24; s ∈ [0,1 ];
s105: processing the fuzzy similar matrix R by adopting a transitive closure method to obtain a transitive fuzzy similar equivalent matrix RkNamely:
R→R2→R4→…→R2k
up to Rk×Rk＝RkAt this time, RkNamely a fuzzy equivalent matrix;
s106: selecting a constant lambda to blur the similarity matrix RkThe element with the length being larger than lambda is 1, the element with the length being smaller than lambda is 0, and a lambda intercept matrix R is obtainedλ；
Wherein i and j are 1, 2, 3, … and 24; λ ∈ [0,1 ];
s107: dividing the time of day into peak periods omega according to the similarity of row elements or column elements according to the off-diagonal elements in the lambda intercept matrixpAnd the normal period omegafValley period omegagThree periods of time.
3. The coordination optimization method for energy storage and electricity prices of the park oriented to new energy local consumption according to claim 2, wherein in the step S2, the concrete steps of constructing the park operator time-of-use electricity price optimization model comprise:
s201: constructing a park time-sharing electricity price optimization model with the maximum profit of a park operator as a target:
max Rf＝I-C＝(Isell-CNE-Cgrid-Cope-Cline)
in the formula, RfEarnings for the operator of the park; i, the income of electricity sale of operators in the park; c is the operation cost of the operator in the park; i issellRevenue for electricity sold to the user by the campus operator; cNEThe electricity purchasing cost to the new energy photovoltaic and the fan is saved for the park operators; cgridThe unbalanced power exchange cost of the lower park and the distribution network; copeThe power and capacity configuration and the running total cost of the lower-layer energy storage are achieved; clinePenalizing cost for power fluctuation of the park tie line;
s202: the operation cost and the income of the park operator are as follows:
wherein p (t) is preferablyTime-of-use electricity price at time t of the operator of the chemical park; pout(t) real-time power at time t after the park load demand response after electricity price optimization; the time interval delta t is 1 h; pPV(t)、PWT(t) power data of the new energy photovoltaic and the fan at the t moment are respectively predicted at the t moment before the park day; p is a radical ofPV(t)、pWT(t) the grid-surfing electricity prices of the new energy photovoltaic and the fan in the park at the time t are respectively, namely the electricity purchasing prices of the new energy fan and the photovoltaic from the park operator; p is a radical of0(t) optimizing the time-of-use electricity price at the previous t moment for the park operator; pgrid(t) is unbalanced power exchanged between the park and the power distribution network at the moment t; klinePunishing a cost coefficient for power fluctuation of a park and a power distribution network tie line; p is a radical ofp、pf、pgRespectively the peak, flat and valley electricity prices; omegap、Ωf、ΩgRespectively peak, plateau and valley periods.
4. The campus energy storage and electricity price coordination optimization method for new energy local consumption according to claim 2, wherein in S3, the constraint conditions of the upper campus time-of-use electricity price optimization model include power consumption constraint, time-of-use electricity price constraint and unit power consumption cost constraint.
5. The park energy storage and electricity price coordination optimization method oriented to new energy local consumption according to claim 4, characterized in that the expression of the electricity consumption constraint is as follows:
in the formula, P0(t) load power data at time t predicted by the park day ahead;
the change rate of the daily electric quantity of the load in the park is determined;
the expression of the time-of-use electricity price constraint is as follows:
0≤pg＜pf＜pp≤pmax
wherein γ is a constant limiting the peak-to-valley electrovalence ratio; p is a radical ofmaxThe upper limit of the peak time electricity rate; p is a radical ofp、pf、pgRespectively the peak, flat and valley electricity prices;
the expression of the unit electricity cost constraint is as follows:
in the formula, the unit electricity utilization cost of the user after the time-of-use electricity price optimization of the park operator is shown on the left side; the unit electricity cost of the user before time-of-use electricity price optimization is on the right side.
6. The method for the coordination and optimization of energy storage and electricity prices of the campus oriented to new energy local consumption of claim 1, wherein in S4, with 24h a day as a pricing and demand response period, the real-time power P after load response of the campus at the t-th time periodoutThe formula for (t) is:
in the formula, Pout(t) real-time power at time t after the park load demand responds; p0(t) load power data at time t predicted by the park day ahead; e (t, t) is a self-elastic matrix; e (t, j) is a cross elastic matrix; p is a radical of0(t) the initial time-of-use electricity price of the campus at the tth time period; p (t) is the time-of-use electricity price after the campus is optimized in the t time period; t is 1, 2, …, 24, j is 1, 2, …, 24.
7. The method for the coordination and optimization of energy storage and electricity price in the campus oriented to new energy local consumption of claim 1, wherein in S5, the specific step of obtaining the capacity loss of the battery in single discharge comprises:
s501: and (3) calling an MATLAB tool box to perform relational curve fitting, and performing relational curve fitting by adopting a power function method:
in the formula: n is a radical ofDFor the cycle life of the energy storage cell, DbatThe charge and discharge depth of the energy storage battery;
s502: establishing a loss index Z of single discharge capacity of the energy storage battery:
s503: according to the discharge capacity loss index Z of the energy storage battery at the time t, combining the corresponding discharge capacity to obtain the discharge capacity loss E at the time tloss(t)：
Eloss(t)＝-ZPbat(t)Δt Pbat(t)＜0
In the formula, Pbat(t) is the charging and discharging power of stored energy at time t, P during discharging of stored energybat(t) < 0; the time interval Δ t is taken to be 1 h.
8. The campus energy storage and electricity price coordination optimization method for new energy local consumption according to claim 1, wherein in S7, the constraint conditions of energy storage charging and discharging and system safe operation of the lower layer campus comprise electric power balance constraint, tie line interaction power constraint, energy storage system operation constraint, energy storage system state of charge constraint and energy storage power and capacity constraint.
9. The park energy storage and electricity price coordination optimization method oriented to new energy local consumption according to claim 8, characterized in that the expression of the electric power balance constraint is as follows:
PPV(t)+PWT(t)+Pgrid(t)＝Pout(t)+Pbat(t)
in the formula, Pout(t) real-time power at time t after park load demand response after optimization of electricity price, PPV(t)、PWT(t) power data P of new energy photovoltaic and wind turbine t moment predicted respectively for t moment before the park daygrid(t) is the unbalanced power exchanged between the park and the distribution network at time t, Pbat(t) is the charge and discharge power stored at time t;
the expression of the tie line interaction power limit constraint is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively, cross-line power Pline(t) minimum and maximum values;
the expression of the energy storage system operation constraint is as follows:
-Pbess≤Pbat(t)≤Pbess
wherein S (t) is the state of charge at the moment of energy storage t, ηch、ηdisRespectively representing the charging efficiency and the discharging efficiency of the stored energy, wherein sigma is the self-discharging rate of the stored energy; pbessPower allocated for park energy storage, EbatCharging and discharging capacity for storing energy at the time t;
the expression of the energy storage system state of charge constraint is as follows:
Smin≤S(t)≤Smax
S(0)＝S(24)
in the formula, Smin、SmaxMinimum and maximum states of charge of the stored energy, respectively; s (0) and S (24) are respectively that the energy storage system is in a state of 00: 00 hours and 24: state of charge at 00;
the expression of the energy storage power and capacity constraint is as follows:
in the formula (I), the compound is shown in the specification,
respectively configuring the minimum and maximum capacity of the energy storage battery;
and respectively configuring the minimum power and the maximum power for the energy storage battery.
10. The coordination optimization method for energy storage and electricity price in a park oriented to new energy local consumption of claim 1, wherein in S8, a moth flame optimization-particle swarm optimization intelligent optimization algorithm is adopted to calculate the power and capacity scales of the energy storage and electricity price in the park time-of-day configuration, and the specific steps include:
s801: the upper-layer electricity price optimization model adopts a moth flame optimization algorithm to obtain park time-of-use electricity prices, the lower-layer energy storage optimization model adopts a particle swarm algorithm to obtain the power and capacity of energy storage configuration, the optimal power and capacity configuration of energy storage corresponding to the upper-layer time-of-use electricity prices is calculated, and the power and capacity configuration and the total running cost of the energy storage are calculated and returned to the upper-layer electricity price optimization model;
s802: when the difference value of the results of the two operations is smaller than a preset value, stopping iteration and outputting the result; and if the difference is not smaller than the preset value, continuing iteration.
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Application publication date: 20220419 Assignee: SHANGHAI HEZE ELECTRIC POWER ENGINEERING DESIGN AND CONSULTING Co.,Ltd. Assignor: Shanghai University of Electric Power Contract record no.: X2023310000110 Denomination of invention: A coordinated optimization method for energy storage and electricity price in industrial parks for on-site consumption of new energy License type: Common License Record date: 20230625 Application publication date: 20220419 Assignee: SHANGHAI CHENSHI TECHNOLOGY DEVELOPMENT Co.,Ltd. Assignor: Shanghai University of Electric Power Contract record no.: X2023310000108 Denomination of invention: A coordinated optimization method for energy storage and electricity price in industrial parks for on-site consumption of new energy License type: Common License Record date: 20230625 |
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