CN115619441B - Declaration method, medium and equipment for energy storage power station to participate in day-ahead power transaction - Google Patents
Declaration method, medium and equipment for energy storage power station to participate in day-ahead power transaction Download PDFInfo
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Abstract
The invention discloses a declaration method, medium and equipment for an energy storage power station to participate in day-ahead power transaction, which comprises the following steps: acquiring full life cycle limiting parameters participating in an electric power trading market; establishing an optimization income model participating in the day-ahead power trading market according to the acquired full life cycle limiting parameters; carrying out optimization solving on the optimization gain model by adopting an intelligent optimization algorithm by taking the maximum gain as an objective function to obtain a charge-discharge state and a charge-discharge amount corresponding to each moment in the day; and determining a reporting strategy of the charge and discharge power corresponding to each moment in the day according to the obtained charge and discharge state and charge and discharge amount corresponding to each moment in the day. The invention can improve the economic benefit of the energy storage power station participating in the transaction, simultaneously can realize the optimal configuration of the charge and discharge power, more effectively assist the frequency modulation and peak shaving of the power grid and relieve the fluctuation of the power grid; and an intelligent optimization algorithm is adopted for solving, so that the reporting strategy of the charging and discharging power can be quickly determined.
Description
Technical Field
The invention relates to the technical field of energy storage power, in particular to a declaration method, medium and equipment for an energy storage power station to participate in day-ahead power transaction.
Background
At present, domestic energy storage power stations participate in spot-shipment transactions before the day, for example, a certain province needs to declare 96 points of planned power (one point every 15 minutes) of one whole day in tomorrow before 12 points every day, but the industry also adopts a relatively original mode of staring at one team, so that the influence of artificial factors in the process of determining the 96 points participating in the spot-shipment market before the day in the declaration plan is large, meanwhile, the cost is high, and the income type cannot be guaranteed.
The Chinese patent with the publication number of CN106773715A provides an energy storage operation control method and system based on power spot price prediction and tracking, which are mainly used for guiding an energy storage unit to participate in electric energy trading of a daily market and power auxiliary service of a real-time market, guiding energy storage equipment to select a proper time node for charging and discharging operation according to a power price prediction result, judging the current time all the time, and outputting the action of the energy storage equipment after 1 hour and adjusting a power trading plan matched with the energy storage equipment. However, the patent participates in the daily market, only outputs a curve of 1 hour in the future, and cannot solve the declaration problem of participating in the day-ahead power trading market.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a declaration method, medium and equipment for an energy storage power station to participate in day-ahead power transaction.
The invention provides a declaration method for an energy storage power station to participate in day-ahead power transaction, which comprises the following steps:
s1, acquiring a full life cycle limiting parameter participating in an electric power trading market;
s2, establishing an optimal earning model participating in the electric power trading market in the day ahead according to the acquired full life cycle limiting parameters;
s3, carrying out optimization solving on the optimization gain model by using the maximum gain as an objective function and adopting an intelligent optimization algorithm to obtain a charge-discharge state and a charge-discharge amount corresponding to each moment in the day-ahead;
and S4, determining a reporting strategy of the charge and discharge power corresponding to each moment in the day according to the obtained charge and discharge state and charge and discharge amount corresponding to each moment in the day.
Further, in S1, the full lifecycle limit parameters include: the energy storage system comprises energy storage capacity, energy storage rated power, an energy storage soc upper limit value, an energy storage soc lower limit value, a current energy storage soc value, a day-ahead predicted electricity price, a daily charge and discharge frequency limit and a lowest charge and discharge price difference of electricity per degree.
Further, the functional expression of the charge-discharge state at each time of day obtained in S3 is represented by the following formula:
X i =f(min_cost, countLimit, soc,soc_up, soc_down, capacity, price i , RP)
wherein i represents at the i-th time, X i Indicating the charge-discharge state at the ith time; x i =1 indicates in a discharge state; x i =1 represents being in a charging state; x i =0 means in an unfilled state; countLimit tableShowing the limitation of the number of charging and discharging times per day, min _ cost showing the lowest charging and discharging price difference of each kilowatt-hour, capacity showing the energy storage capacity, RP showing the rated power of the energy storage, soc _ down showing the lower limit value of the energy storage soc, soc _ up showing the upper limit value of the energy storage soc, soc showing the state of charge and price i Indicating the predicted day-ahead electricity rate at the ith time.
Further, the method for establishing the optimal revenue model participating in the day-ahead power trading market in S2 specifically includes:
s21, judging the charge-discharge state at the current moment, and calculating the benefit at the current moment and the charge-discharge amount at the current moment according to the charge-discharge state at the current moment and the full life cycle limiting parameter;
s22, determining the benefit at the current moment according to the energy storage capacity, the energy storage rated power and the charging and discharging frequency limit;
s23, performing iteration of S21-S22 for N times to obtain the charge and discharge state, the charge and discharge amount and the benefit at each moment in the day; wherein N =24/T, T is the unit time for calculating the price of the discharged electricity;
s24, updating the charge-discharge state and the charge-discharge amount corresponding to each moment according to the obtained charge-discharge state, charge-discharge amount and predicted electricity price at each moment in the day;
and S25, calculating the total income according to the updated charge-discharge state and charge-discharge amount corresponding to each moment and the predicted electricity price in the day ahead.
Further, in S21, determining the charge-discharge state at the current time, and calculating the benefit at the current time and the charge-discharge amount at the current time according to the charge-discharge state at the current time and the full-life-cycle limiting parameter, specifically including:
assuming that the current time is in a charging state
If soc [ i ] is not less than soc _ up, then profit [ i ] = profit [ i-1] -Penalty Profit;
in the formula, soc [ i ] represents soc at the ith moment, soc _ up represents an energy storage soc upper limit value, config [ i ] represents total income from the ith moment, config [ i-1] represents total income from the ith moment, and PenaltyProfit represents punitive income;
if soc [ i ] < soc _ up, then charge [ i ] = charge [ i-1] +1,
soc[i]’=soc[i]-X i ×RP/capcity；
in the formula, charge [ i]Indicates the total number of charges to the ith time, charge [ i-1]Indicates the total number of charges to the i-1 st time, soc [ i ]]' prediction soc, X representing the i-th time i The charging and discharging state at the ith moment is represented, capacity represents the energy storage capacity, and RP represents the energy storage rated power;
when soc [ i]' soc _ up is less than or equal to, then fits [ i]=profit[i-1]+X i ×RP×T×price i ；
Let soc [ i ] = soc [ i ]', calculate chareacity [ i ] = RP × T;
in the formula, chareacity [ i ]]Represents the charging capacity at the ith moment, and T represents the unit time of clear price calculation, price i Indicating the predicted day-ahead electricity price at the ith time;
when soc [ i ]' > soc _ up,
then profit [ i]=profit[i-1]+X i ×（soc_up-soc[i]）×capacity×price i ，soc[i]=soc_up；
Assuming that the current time is in a discharge state
If soc [ i ] is less than or equal to soc _ down, then profit [ i ] = profit [ i-1] -PenaltyProfit;
if soc [ i ] > soc _ down, discharge [ i ] = discharge [ i-1] +1,
and calculates soc [ i ]]’=soc[i]-X i ×RP/capacity；
In the formula, discharge [ i ] represents the total discharge frequency to the ith time, discharge [ i-1] represents the total discharge frequency to the ith-1 time, and soc _ down represents the lower limit value of the stored energy soc;
when soc [ i ]' > soc _ down,
then profit [ i]=profit[i-1]+X i ×RP×T×（price i ABH), let soc [ i]=soc[i]', calculate DischareCapacity [ i]=RP×T；
In the formula, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment;
when soc [ i ]' < soc _ down,
then profit [ i]=profit[i-1]+X i ×soc_down×capacity×（price i -ABH）；
Let soc [ i ] = soc _ down, then discarecapacity [ i ] = soc _ down × capacity;
wherein ABH is an anti-optimizing abnormal value;
assuming that the current time is in a non-discharge and non-charge state, then profit [ i ] = profit [ i-1], discharge [ i ] = discharge [ i-1], charge [ i ] = charge [ i-1], and charcapicity [ i ] =0; discarecapcity [ i ] =0.
Further, in S22, updating the profit at the current time according to the energy storage capacity, the energy storage rated power, and the limit of the number of charging and discharging times, specifically including:
when MIN (charge [ i ], discharge [ i ]) > countLimit x (capacity/RP), then profit [ i ]' = profit [ i ] -PenaltyProfit; in the formula, charge [ i ] represents the total number of charging times to the ith time, discharge [ i ] represents the total number of discharging times to the ith time, profit [ i ]' represents the total income from the ith time after updating, profit [ i ] represents the total income to the ith time, countLimit represents the charging and discharging times limit of each day, penaltyProfit represents the penalty income, capacity represents the energy storage capacity, and RP represents the energy storage rated power.
Further, in S24, updating the charge/discharge state and the charge/discharge amount corresponding to each time according to the obtained charge/discharge state, charge/discharge amount, and predicted power rate at each time in the day, specifically includes:
s241, performing segmentation processing by taking the charging time in each time as a time segmentation point, and searching whether m discharging times exist between every two adjacent charging times; wherein m is an integer greater than or equal to 2, the charging time represents the time in a charging state, and the discharging time represents the time in a discharging state;
when m discharging moments exist, the m discharging moments are recorded as Tm, and m discharging quantities corresponding to Tm are sequenced from large to small to obtain a discharging quantity sequence;
acquiring the day-ahead predicted electricity price of each discharging time between two adjacent charging times, predicting the electricity price according to the acquired day-ahead predicted electricity price of each discharging time between two adjacent charging times, and recording m discharging times corresponding to the largest m day-ahead predicted electricity prices as Tm';
updating the charge-discharge state corresponding to Tm' into a discharge state, and updating the charge-discharge state corresponding to the other discharge time between the two adjacent charge times into a non-charge and non-discharge state; sequencing the Tm ' according to the order of the predicted electricity prices from big to small in the day before the Tm ' corresponds to the Tm ' to obtain a first time sequence;
establishing a mapping relation between a discharge capacity sequence and a first time sequence;
updating the charge-discharge state and the discharge amount corresponding to each discharge moment in the first time sequence according to the mapping relation between the discharge amount sequence and the first time sequence;
when m discharging moments do not exist, the charging and discharging state and the charging and discharging amount corresponding to the discharging moment between the two adjacent charging moments are not updated;
s242, carrying out segmentation processing by taking the discharging time in each time as a segmentation point, and searching whether n charging times exist between every two adjacent discharging times, wherein n is an integer greater than or equal to 2;
when n charging moments exist, the n charging moments are recorded as Tn, and n charging quantities corresponding to the Tn are sequenced from large to small to obtain a charging quantity sequence;
acquiring the day-ahead predicted electricity price of each charging time between two adjacent discharging times; predicting the electricity price according to the acquired day ahead of each charging time between two adjacent discharging times, and marking n times corresponding to the minimum n day ahead predicted electricity prices as Tn';
updating the charge-discharge state corresponding to Tn' to a charge state, and updating the charge-discharge state of other charge moments between the two adjacent discharge moments to a non-charge and non-discharge state; sequencing Tn' according to the order of the predicted electricity prices from small to large in the day ahead to obtain a second time sequence;
establishing a mapping relation between the charging quantity sequence and the second time sequence;
updating the charging amount corresponding to each charging time in the second time sequence according to the mapping relation between the charging amount sequence and the second time sequence;
and when the n charging moments do not exist, neither the charging and discharging state nor the charging and discharging amount corresponding to the charging moment between the two adjacent discharging moments is updated.
Further, the establishing of the mapping relationship between the discharge amount sequence and the first time sequence in S241 specifically includes:
and mapping the discharge quantity in the discharge quantity sequence to the discharge time with the same sequencing position in the first time sequence.
Further, the establishing of the mapping relationship between the charging quantity sequence and the second time sequence in S242 specifically includes:
and mapping the charging quantity in the charging quantity sequence to the charging time with the same sequencing position in the second time sequence.
Further, in S25, the total profit is calculated according to the updated charge and discharge state and charge and discharge amount corresponding to each time and the predicted electricity price in the day ahead, and specifically includes:
s251, calculating the sum of optimization gains according to the corresponding charge-discharge state, charge-discharge amount and electricity price at each updated moment;
wherein, the calculation formula of the optimal profit sum is as follows
Benifit=BenifitC[i max ]+BenifitDC[i max ]；
Wherein Benifit is the sum of the optimization gains, i max Denotes the maximum value of i, benifitc [ i max ]Is shown to the i-th max Total charge gain at time, benifitDC [ i ] max ]Is expressed to the i-th max The total yield of discharge at a moment;
wherein Benifitc [ i ]]=BenifitC[i-1]-ChareCapacity[i]×price i ；
In the formula, benifitc [ i ]]Indicates the total charge gain to time i, benifitc [ i-1]Indicates the total charge gain to the i-1 th time, chareCapacity i]Indicating the amount of charge at time i, price i Indicating the predicted day-ahead electricity price at the ith time;
wherein BenifitDC [ i ]]=BenifitDC[i-1]+DischareCapacity[i]×（price i -ABH）；
In the formula, benifitDC [ i ] represents the total discharge yield to the ith moment, benifitC [ i-1] represents the total discharge yield to the ith moment, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment, and ABH is an abnormal value for preventing optimization;
s252, calculating a real theoretical benefit and an actual lowest charging and discharging price difference of each degree of electricity according to the updated charging and discharging state and the updated charging and discharging amount corresponding to each moment, and updating an optimization benefit sum according to the lowest charging and discharging price difference of each degree of electricity and the actual lowest charging and discharging price difference;
wherein the calculation formula of the real theoretical gain is as follows
RCBenifit=BenifitC[i max ]+Benifit2[i max ]；
In the formula, RCBenifit represents the true theoretical yield, benifitc [ i [ ] max ]Is expressed to the i-th max Total charge gain at time, benifit2[ i max ]Is expressed to the i-th max The theoretical discharge total yield at the moment;
Benifit2[i]=Benifit2[i-1]+DischareCapacity[i]×price i ；
in the formula, benifit2[ i]Representing the total theoretical discharge yield to the ith moment; benifit2[ i ]]Shows the theoretical discharge Total yield to time i-1, dischareCapacity [ i]Indicating the discharge capacity at time i, price i Indicating the predicted day-ahead electricity price at the ith time;
the calculation formula of the actual lowest charge-discharge cost difference per watt hour is as follows
real_min_cost=RCBenifit/(DischareCapacity)；
In the formula, real _ min _ cost represents the actual lowest charge-discharge cost difference of each watt of electricity, RCBenifit represents the real theoretical benefit, and DischareCapacity represents the total discharge capacity;
wherein, dischareCapacity = ∑ DischareCapacity [ i ]; in the formula, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment;
wherein when real _ min _ cost is less than or equal to min _ cost, benifit' = Benifit-PenaltyProfit; in the formula, min _ cost represents the lowest charge-discharge price difference per watt hour, benifit represents the sum of the optimization gains, and Benifit' represents the sum of the updated optimization gains.
Further, in S4, determining a reporting policy of the charge and discharge power corresponding to each time in the day according to the charge and discharge state and the charge and discharge amount corresponding to each time, specifically includes:
calculating the charge and discharge power corresponding to each moment in the day;
if the current time is in a charging state, power [ i ] = charapacity [ i ]/charge;
the Power [ i ] represents the declared Power at the ith moment, the charcapcity [ i ] represents the charging capacity at the ith moment, and the charge E represents the charging efficiency of the energy storage Power station;
if the current time is in a discharging state, power [ i ] = DischarCapacity [ i ]/Discharge;
wherein Power [ i ] represents declared Power at the ith moment, discharCapacity [ i ] represents discharge electric quantity at the ith moment, and DischargeE represents discharge efficiency of the energy storage Power station.
Further, in S4, the method of calculating the charge/discharge power corresponding to each time before the day further includes:
and carrying out time interpolation on the charge and discharge power corresponding to each moment in the day ahead according to a time interpolation method to obtain a time interpolation estimated value.
Further, in the S3, in the optimizing solution of the optimizing profit model by using the intelligent optimization algorithm, the particle swarm optimization algorithm is introduced to optimize the optimizing profit model, and the particle swarm optimization algorithm specifically includes:
s31, initializing and setting particle swarm optimization algorithm parameters, comprising the following steps: population size, particle dimension, acceleration factor, maximum and minimum flight speeds of particles, and maximum iteration number;
s32, randomly initializing the speed and the position of the particles in a specified search range;
s33, calculating the inertia weight and the acceleration factor of the particles before next iteration according to a calculation formula improved by the inertia weight and the acceleration factor;
the inertia weight adopts a segmentation strategy, and an improved formula of the inertia weight is shown as the following formula:
in the formula, ω d i Representing the inertial weight, ω, at the ith time instant, at the d-th iteration min Representing the minimum inertial weight, ω max Representing the maximum inertia weight, and d representing the current iteration number; f. of d average Representing the average fitness of all particles at the d-th iteration; f. of d max =max{f(x 1 d )，f(x 2 d )，…，f(x n d ) I.e. represents the maximum fitness of all particles at the d-th iteration.
Wherein, the acceleration factor adopts a dynamic learning factor, and an improved formula of the acceleration factor is shown as the following formula:
c 1 =c 1 start +(c 1 end -c 1 start ) ×(d/d max )；
in the formula, c 1 Denotes a self-learning factor, c 1 start Represents the minimum value taken by the self-learning factor, c 1 end Represents the maximum value taken by the self-learning factor, d max Representing the maximum number of iterations;
c 2 =c 2 start +(c 2 end -c 2 start ) ×(d/d max )；
in the formula, c 2 Represents a social cognitive learning factor, c 2 start Represents the minimum value taken by the social cognitive learning factor, c 2 end Represents the maximum value taken by the social cognitive learning factor;
s34, calculating the speed and the position of the particles;
s35, solving the fitness value fi of each particle according to the set fitness function, and solving the individual extreme value P according to fi best And global extreme G best And then compares it with the individual extremum P best Comparing, if the result is better than the individual extreme value, P best ＝fi；
S36, determining individual extreme value P of each particle best And global extreme G in the population best Comparing, if the result is better than the global extreme value, G best ＝P best ；
S37, judging whether a termination condition is met or a preset maximum iteration number is reached, if the termination condition is met, stopping the operation of the algorithm, entering Step38, and if the termination condition is not met, returning to Step33;
and S38, outputting the global optimal value and the charge-discharge state and the charge-discharge amount corresponding to each moment.
The invention also provides a declaration system of the energy storage power station participating in day-ahead power transaction, which comprises the following steps:
the acquisition module is used for acquiring the full life cycle limiting parameters participating in the electric power trading market;
the optimizing model modeling module is used for establishing an optimizing income model participating in the electric power trading market in the day-ahead;
the optimization module is used for carrying out optimization solving on the optimization gain model by taking the maximum gain as an objective function and adopting an intelligent optimization algorithm to obtain the charge-discharge state and the charge-discharge amount corresponding to each moment in the day-ahead;
and the analysis module is used for determining a reporting strategy of the charging and discharging power corresponding to each moment in the day according to the obtained charging and discharging state and the charging and discharging amount corresponding to each moment.
The invention also proposes a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, implements a method of declaring participation of an energy storage plant in a day-ahead power transaction as defined in any one of the above.
The invention also proposes an electronic device comprising: a processor and memory for storing one or more programs; when executed by the processor, the one or more programs implement a method of reporting on participation of an energy storage plant in a day-ahead power transaction as described in any one of the above.
According to the method, the medium and the equipment for reporting the energy storage power station participating in the day-ahead power transaction, an optimal gain model which is constructed based on the full life cycle limiting parameters participating in the power transaction market and by taking the maximum gain as a target function is constructed, the economic gain of the energy storage power station participating in the transaction is improved, the optimal configuration of charging and discharging power can be realized, the frequency modulation and peak shaving of a power grid are effectively assisted, and the fluctuation of the power grid is relieved; and the intelligent optimization algorithm is adopted for solving, so that the reporting strategy of the charge and discharge power can be quickly determined, and the method can be applied to the reporting strategy of predicting the charge and discharge power for multiple days in the future.
Drawings
Fig. 1 is a flowchart of a reporting method for an energy storage power station to participate in a day-ahead power transaction according to an embodiment of the present invention.
Fig. 2 is a flow diagram of S2 in an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1 and fig. 2, the method for reporting that an energy storage power station participates in a day-ahead power transaction provided by the invention comprises the following steps:
s1, acquiring a full life cycle limiting parameter participating in an electric power trading market;
s2, establishing an optimal earning model participating in the electric power trading market in the day ahead according to the acquired full life cycle limiting parameters;
s3, carrying out optimization solving on the optimization gain model by using the maximum gain as an objective function and adopting an intelligent optimization algorithm to obtain a charge-discharge state and a charge-discharge amount corresponding to each moment in the day-ahead;
and S4, determining a reporting strategy of the charge and discharge power corresponding to each moment in the day according to the obtained charge and discharge state and charge and discharge amount corresponding to each moment in the day.
The optimization gain model is constructed based on the full life cycle limiting parameters participating in the electric power trading market and taking the maximum gain as an objective function, so that the economic gain of the energy storage power station participating in the trading is improved, the optimal configuration of charging and discharging power can be realized, the frequency modulation and peak shaving of the power grid are effectively assisted, and the fluctuation of the power grid is relieved; and an intelligent optimization algorithm is adopted for solving, so that the reporting strategy of the charging and discharging power can be quickly determined.
In the present embodiment, the full lifecycle constraint parameters in S1 include: the system comprises an energy storage capacity, an energy storage rated power, an energy storage soc upper limit value, an energy storage soc lower limit value, a current energy storage soc value, a day-ahead predicted electricity price, a daily charge and discharge frequency limit and a lowest charge and discharge price difference of electricity per degree.
In the present embodiment, the functional expression of the charge/discharge state at each time before the day obtained in S3 is represented by the following formula:
X i =f(min_cost, countLimit, soc,soc_up, soc_down, capacity, price i , RP)
wherein i represents the time at i, X i Indicating the charge-discharge state at the ith time; x i =1 represents being in a discharge state; x i =1 represents being in a charging state; x i =0 indicates an unfilled state; the countLimit represents the daily charge-discharge frequency limit, the min _ cost represents the lowest charge-discharge price difference of each electricity degree, the capacity represents the energy storage capacity, the RP represents the energy storage rated power, the soc _ down represents the lower limit value of the energy storage soc, the soc _ up represents the upper limit value of the energy storage soc, the soc represents the state of charge, and the price represents the state of charge i Indicating the predicted day-ahead electricity rate at the ith time.
In this embodiment, the method for establishing the optimal profit model participating in the power trading market in S2 specifically includes:
s21, judging the charge-discharge state at the current moment, and calculating the benefit at the current moment and the charge-discharge amount at the current moment according to the charge-discharge state at the current moment and the full life cycle limiting parameter;
s22, determining the benefit at the current moment according to the energy storage capacity, the energy storage rated power and the charging and discharging frequency limit;
s23, performing iteration of S21-S22 for N times to obtain the charge and discharge state, the charge and discharge amount and the benefit at each moment in the day; wherein N =24/T, T is the unit time for calculating the price of the discharged electricity;
s24, updating the charge-discharge state and the charge-discharge amount corresponding to each moment according to the obtained charge-discharge state, charge-discharge amount and predicted electricity price at each moment in the day;
and S25, calculating the total income according to the updated charge-discharge state and charge-discharge amount corresponding to each moment and the predicted electricity price in the day ahead.
By the arrangement, the optimization gain model can be combined with the fluctuation of the day-ahead predicted electricity price, and the follow-up solution is facilitated, so that the optimal charge and discharge power declaration strategy is obtained while the obtained gain is maximum.
It should be understood that when the unit time T of calculating the price of electricity is 1 hour, and N is equal to 24, then i is the maximum value, i.e. i max =24; when the unit time of the calculation of the price of the clear electricity is 0.5h, N is equal to 48, i max =48；
When the unit time of the calculation of the price of the discharged clear electricity is 15min, N is equal to 96, i max =96。
In this embodiment, in S21, determining the charge/discharge state at the current time, and calculating the benefit at the current time and the charge/discharge amount at the current time according to the charge/discharge state at the current time and the full-life-cycle limiting parameter specifically include:
assuming that the current time is in a charging state
If soc [ i ] is not less than soc _ up, then profit [ i ] = profit [ i-1] -Penalty Profit;
in the formula, soc [ i ] represents soc at the ith moment, soc _ up represents an energy storage soc upper limit value, config [ i ] represents total income from the ith moment, config [ i-1] represents total income from the ith moment, and PenaltyProfit represents punitive income;
if soc [ i ] < soc _ up, then charge [ i ] = charge [ i-1] +1,
soc[i]’=soc[i]-X i ×RP/capcity；
in the formula, charge [ i]Indicates the total number of charges to the ith time, charge [ i-1]Indicates the total number of charges to the i-1 th time, soc [ i]' denotes the prediction soc, X at the i-th time i The charging and discharging state at the ith moment is represented, capacity represents the energy storage capacity, and RP represents the energy storage rated power;
when s isoc[i]' soc _ up is less than or equal to, then fits [ i]=profit[i-1]+X i ×RP×T×price i (ii) a Update soc [ i ]]=soc[i]', calculate ChareCapacity [ i]=RP×T；
In the formula, chareacity [ i ] represents the charging capacity at the ith moment, and T represents the unit time for calculating the price of electricity;
when soc [ i ]' > soc _ up,
then profit [ i]=profit[i-1]+X i ×（soc_up-soc[i]）×capacity×price i ，soc[i]=soc_up；
Assuming that the current time is in a discharge state
If soc [ i ] is less than or equal to soc _ down, then profit [ i ] = profit [ i-1] -PenaltyProfit;
if soc [ i ] > soc _ down, then discharge [ i ] = discharge [ i-1] +1;
and calculates soc [ i ]]’=soc[i]-X i ×RP/capacity；
In the formula, discharge [ i ] represents the total discharge frequency to the ith time, discharge [ i-1] represents the total discharge frequency to the ith-1 time, and soc _ down represents the lower limit value of the stored energy soc;
when soc [ i ]' > is equal to soc _ down,
then profit [ i]=profit[i-1]+X i ×RP×T×（price i ABH), let soc [ i]=soc[i]', calculate DischareCapacity [ i]=RP×T；
In the formula, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment, and ABH is an abnormal value for preventing optimization;
when soc [ i ]' < soc _ down,
then profit [ i]=profit[i-1]+X i ×soc_down×capacity×（price i -ABH）；
Let soc [ i ] = soc _ down, then discarecapacity [ i ] = soc _ down × capacity;
assuming that the current time is in a state of not discharging and not charging, then profit [ i ] = profit [ i-1]; charge [ i ] = charge [ i-1]; discharge [ i ] = discharge [ i-1]; charecacity [ i ] =0; discarecapcity [ i ] =0.
It should be understood that the setting of the ABH is to optimize the scene of charging and discharging when the price of electricity is the same.
In order to increase the optimizing speed, in this embodiment, in S22, the updating the profit at the current time according to the energy storage capacity, the energy storage rated power, and the limit of the number of charging and discharging times specifically includes:
when MIN (charge [ i ], discharge [ i ]) > countLimit x (capacity/RP), then profit [ i ]' = profit [ i ] -PenaltyProfit;
in the formula, charge [ i ] represents the total charging times to the ith time, discharge [ i ] represents the total discharging times to the ith time, profit [ i ]' represents the total income to the ith time after updating, profit [ i ] represents the total income to the ith time, countLimit represents the charging and discharging times limit of each day, penaltyProfit represents the punishment income, capacity represents the energy storage capacity, and RP represents the energy storage rated power.
Since the predicted power rates in the day ahead are fluctuating, in order to effectively alleviate the assisting of the grid frequency modulation peak shaving, in this embodiment, in S24, the charging and discharging state and the charging and discharging amount corresponding to each time are updated according to the obtained charging and discharging state, the charging and discharging amount and the predicted power rates in the day ahead at each time, which specifically includes:
s241, performing segmentation processing by taking the charging time in each time as a time segmentation point, and searching whether m discharging times exist between every two adjacent charging times; wherein m is an integer greater than or equal to 2, the charging time represents the time in a charging state, and the discharging time represents the time in a discharging state;
when m discharging moments exist, the m discharging moments are recorded as Tm, and m discharging quantities corresponding to Tm are sequenced from large to small to obtain a discharging quantity sequence;
acquiring the day-ahead predicted electricity price of each discharging time between two adjacent charging times, predicting the electricity price according to the acquired day-ahead predicted electricity price of each discharging time between two adjacent charging times, and recording m discharging times corresponding to the largest m day-ahead predicted electricity prices as Tm';
updating the charge-discharge state corresponding to Tm' into a discharge state, and updating the charge-discharge state corresponding to the other discharge time between the two adjacent charge times into a non-charge and non-discharge state;
sequencing the Tm ' according to the order of the predicted electricity prices from big to small in the day before the Tm ' corresponds to the Tm ' to obtain a first time sequence;
establishing a mapping relation between a discharge capacity sequence and a first time sequence;
updating the charge-discharge state and the discharge amount corresponding to each discharge moment in the first time sequence according to the mapping relation between the discharge amount sequence and the first time sequence;
when m discharging moments do not exist, the charging and discharging state and the charging and discharging amount corresponding to the discharging moment between the two adjacent charging moments are not updated;
s242, carrying out segmentation processing by taking the discharging time in each time as a segmentation point, and searching whether n charging times exist between every two adjacent discharging times, wherein n is an integer greater than or equal to 2;
when n charging moments exist, the n charging moments are recorded as Tn, and n charging quantities corresponding to the Tn are sequenced from large to small to obtain a charging quantity sequence;
acquiring the day-ahead predicted electricity price of each charging time between two adjacent discharging times; predicting the electricity price according to the acquired day ahead of each charging time between two adjacent discharging times, and marking n times corresponding to the minimum n day ahead predicted electricity prices as Tn';
updating the charge-discharge state corresponding to Tn' to a charge state, and updating the charge-discharge state of other charge moments between the two adjacent discharge moments to a non-charge and non-discharge state;
sequencing Tn' according to the order of the predicted electricity prices from small to large in the day ahead to obtain a second time sequence;
establishing a mapping relation between the charging quantity sequence and the second time sequence;
updating the charging amount corresponding to each charging time in the second time sequence according to the mapping relation between the charging amount sequence and the second time sequence;
when there are no n charging times, the charge/discharge state and the charge/discharge amount corresponding to the charging time between the two adjacent discharging times are not updated.
It should be noted that when N =24, when the 1 st time point, that is, the state of the charge and discharge point corresponding to the zero point, is the non-charge and non-discharge state, and when the time point of the first non-charge and non-discharge state after the zero point is the discharge time point, the zero point performs the slicing processing according to S241; if the time of the first non-charging and non-discharging state after the zero point is the charging time, performing segmentation processing on the zero point according to the S242; if the last time, that is, if the charge/discharge state at 23 points is the non-charge/discharge state, the slicing process is performed at that time in S241.
Since the optimization process is performed according to time, the SOC is strongly correlated with time, and the benefit is correlated with the SOC, therefore, assuming that the time of the highest power price during discharging is time T, the optimization in time sequence will have the condition that time T discharges at the maximum power and time T +1 discharges at a low power, and the benefit in this case is greater than the benefit when time T-1 discharges at the maximum power and time T discharges at a low power, because the SOC searches for the point T, T +1, which should be time T-1,T (the power price at time T-1 is greater than the time T + 1), therefore, in this embodiment, to solve this problem, the mapping relationship between the discharging amount sequence and the first time sequence is established in S241, which specifically includes:
and mapping the discharge quantities in the discharge quantity sequence to the discharge moments with the same sequencing position in the first moment sequence.
Therefore, the discharge amount corresponding to each time in the first time sequence is updated to the discharge amount with the same sequencing position in the discharge amount sequence.
Similarly, since the optimization process is performed according to time, the SOC is strongly correlated with the time, and the benefit is correlated with the SOC, therefore, if the time corresponding to the lowest electricity price during charging is T, the optimization in time sequence may have a case where the T-1 time is charged with the maximum power and the T time is charged with the low power, although the charging time is normally determined, the charging power corresponding to each charging time is not correct, and the selected point is locally optimal due to the SOC limitation. In order to solve this problem, in the present embodiment, the step S242 of establishing a mapping relationship between the charge amount sequence and the second time sequence specifically includes:
and mapping the charging quantity in the charging quantity sequence to the charging time with the same sequencing position in the second time sequence.
In this way, the charge amount corresponding to each time in the second time series is updated to the charge amount at the same rank position in the charge amount series.
In the present embodiment, in S25, the total profit is calculated based on the charge/discharge state and the charge/discharge amount corresponding to each time after the update and the predicted power rate in the day ahead, and specifically includes:
s251, calculating the sum of optimization gains according to the corresponding charge-discharge state, charge-discharge amount and electricity price at each updated moment;
wherein, the calculation formula of the optimal profit sum is as follows
Benifit=BenifitC[i max ]+BenifitDC[i max ]；
Wherein Benifit represents the sum of the search yields, i max Denotes the maximum value of i, benifitc [ i max ]Is shown to the i-th max Total charge gain at time, benifitDC [ i ] max ]Is expressed to the i-th max The total yield of discharge at that moment;
wherein Benifitc [ i ]]=BenifitC[i-1]-ChareCapacity[i]×price i ；
In the formula, benifitc [ i ]]Indicates the total charge gain to time i, benifitc [ i-1]Indicates the total charge gain to the i-1 th time, chareCapacity i]Indicating the amount of charge at time i, price i Indicating the predicted day-ahead electricity price at the ith time;
wherein, benifitDC [ i ]]=BenifitDC[i-1]+DischareCapacity[i]×（price i -ABH）；
In the formula, benifitDC [ i ] represents the total discharge yield to the ith moment, benifitC [ i-1] represents the total discharge yield to the ith moment, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment, and ABH is an abnormal value for preventing optimization;
s252, calculating a real theoretical benefit and an actual lowest charging and discharging price difference of each degree of electricity according to the updated charging and discharging state and the updated charging and discharging amount corresponding to each moment, and updating an optimization benefit sum according to the lowest charging and discharging price difference of each degree of electricity and the actual lowest charging and discharging price difference;
wherein the calculation formula of the real theoretical gain is as follows
RCBenifit=BenifitC[i max ]+Benifit2[i max ]；
Where RCBenifit represents the true theoretical yield, benifitc [ i [ ] max ]Is expressed to the i-th max Total charge gain at time, benifit2[ i max ]Is shown to the i-th max The theoretical discharge total yield at the moment;
wherein, benifit2[ i]=Benifit2[i-1]+DischareCapacity[i]×price i ；
In the formula, benifit2[ i]Representing the total theoretical discharge yield to the ith moment; benifit2[ i ]]Shows the theoretical discharge Total yield to time i-1, dischareCapacity [ i]Indicating the discharge capacity at time i, price i Indicating the predicted day-ahead electricity price at the ith time;
the calculation formula of the actual lowest charge-discharge cost difference per watt hour is as follows
real_min_cost=RCBenifit/(DischareCapacity)；
In the formula, real _ min _ cost represents the actual lowest charge-discharge cost difference of each kilowatt-hour, and DischareCapacity represents the total discharge capacity;
wherein, dischareCapacity = ∑ DischareCapacity [ i ]; in the formula, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment;
wherein when real _ min _ cost is less than or equal to min _ cost, benifit' = Benifit-PenaltyProfit;
in the formula, min _ cost represents the lowest charge-discharge price difference of each degree of electricity, benifit represents the sum of the optimization gains, benifit' represents the updated sum of the optimization gains, and PenaltyProfit represents the penalty gains.
Of course, it will be understood by those skilled in the art that when real _ min _ cost>min _ cost, benifit' = Benifit. It should be understood that i max =N=24/T。
In the present embodiment, in S4, the determining a reporting policy of the charge/discharge power corresponding to each time of day based on the charge/discharge state and the charge/discharge amount corresponding to each time of day specifically includes:
calculating corresponding charge and discharge power at each moment in the day ahead;
if the current time is in a charging state, power [ i ] = chargeability [ i ]/charge;
the Power [ i ] represents the declared Power at the ith moment, the charcapcity [ i ] represents the charging capacity at the ith moment, and the charge E represents the charging efficiency of the energy storage Power station;
if the current time is in a discharging state, power [ i ] = DischarCapacity [ i ]/Discharge;
wherein Power [ i ] represents declared Power at the ith moment, discharCapacity [ i ] represents discharge electric quantity at the ith moment, and DischargeE represents discharge efficiency of the energy storage Power station.
In a further embodiment, after the charge and discharge power corresponding to each time point before the day is calculated in S4, the method further includes: and carrying out time interpolation on the charge and discharge power corresponding to each moment in the day according to a time interpolation method to obtain a time interpolation estimated value.
In the embodiment, the charging and discharging power is corrected by adopting an interpolation method, so that the accuracy of the specific execution of the energy storage after reporting can be improved, and the benefit is indirectly improved.
Specifically, if the unit time is calculated by taking 1 hour as the price of the clear electricity in the current method, 24 Power [ i ] are generated, but when the Power grid requirement is declared according to 15min, the Power grid requirement is split according to a time interpolation method for 1 hour, the original Power [ i ] is expanded into 4 points for declaration, and each Power value is Power [ i ] as well.
In the embodiment, in S3, an intelligent optimization algorithm is adopted to perform optimization solution on the optimization gain model, and a particle swarm optimization algorithm is introduced to perform optimization solution on the optimization gain model, where the particle swarm optimization algorithm specifically includes:
s31, initializing and setting parameters of the particle swarm optimization algorithm, wherein the parameters comprise: population scale, particle dimension acceleration factors, maximum and minimum flight speeds of particles and maximum iteration times;
s32, randomly initializing the speed and the position of the particles in a specified search range;
s33, calculating the inertia weight and the acceleration factor of the particles before next iteration according to a calculation formula improved by the inertia weight and the acceleration factor;
the inertia weight adopts a segmentation strategy, and an improved formula of the inertia weight is shown as the following formula:
in the formula, ω d i Representing the inertial weight, ω, at the ith time instant, at the d-th iteration min Representing the minimum inertial weight, ω max Representing the maximum inertia weight, and d representing the current iteration number; f. of d average Representing the average fitness of all particles at the d-th iteration; f. of d max =max{f(x 1 d )，f(x 2 d )，…，f(x n d ) Represents the maximum fitness of all particles at the d-th iteration;
wherein, the acceleration factor adopts a dynamic learning factor, and an improved formula of the acceleration factor is shown as the following formula:
c 1 =c 1 start +(c 1 end -c 1 start ) ×(d/d max )；
in the formula, c 1 Denotes a self-learning factor, c 1 start Represents the minimum self-learning factor, c 1 end Represents the maximum self-learning factor, d max Representing the maximum number of iterations;
c 2 =c 2 start +(c 2 end -c 2 start ) ×(d/d max )；
in the formula, c 2 Represents a social cognitive learning factor, c 2 start Represents the minimum social cognitive learning factor, c 2 end Represents the maximum social cognitive learning factor;
s34, calculating the speed and the position of the particles;
s35, according to the set fitness functionCounting, calculating the fitness value fi of each particle, and calculating the individual extreme value P according to fi best And global extreme G best And compares it with an individual extremum P best Comparing, if the result is better than the individual extreme value, P best ＝fi；
S36, setting individual extreme value P of each particle best And global extreme G in the population best Comparing, if the result is better than the global extreme value, G best ＝P best ；
S37, judging whether a termination condition is met or a preset maximum iteration number is reached, if the termination condition is met, stopping the operation of the algorithm, entering Step38, and if the termination condition is not met, returning to Step33;
and S38, outputting the global optimal value and the charge-discharge state and the charge-discharge amount corresponding to each moment.
The embodiment adopts a sectional random inertia, cognitive factors and a dynamic adjustment method, solves the problem that the particle swarm is too early or the accuracy is poor, and can quickly find the optimal solution of the model.
In a further embodiment, c 1 start =1.2，c 1 end =0.4，c 2 start =0.4，c 2 end =1.2. In a further embodiment, ω min =0.4，ω max =0.9。
Of course, in other embodiments, in S3, the optimization solution is performed on the optimization gain model by using an intelligent optimization algorithm, and the optimization solution is performed on the optimization gain model by introducing simulated annealing or a genetic algorithm.
The invention also provides a system for reporting that the energy storage power station participates in the day-ahead power transaction, which comprises the following steps:
the acquisition module is used for acquiring the full life cycle limiting parameters participating in the electric power trading market;
the optimizing model modeling module is used for establishing an optimizing profit model participating in the day-ahead power trading market;
the optimization module is used for carrying out optimization solving on the optimization gain model by taking the maximum gain as an objective function and adopting an intelligent optimization algorithm to obtain the charge-discharge state and the charge-discharge amount corresponding to each moment in the day-ahead;
and the analysis module is used for determining a reporting strategy of the charging and discharging power corresponding to each moment in the day according to the obtained charging and discharging state and the charging and discharging amount corresponding to each moment.
The invention also proposes a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, implements a method of declaring participation of an energy storage plant in a day-ahead power transaction as defined in any one of the above.
The invention also proposes an electronic device comprising: a processor and memory for storing one or more programs; when executed by the processor, the one or more programs implement a method of reporting on participation of an energy storage plant in a day-ahead power transaction as described in any one of the above.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (13)
1. A declaration method for an energy storage power station to participate in day-ahead power transaction is characterized by comprising the following steps:
s1, acquiring a full life cycle limiting parameter participating in an electric power trading market;
s2, establishing an optimal earning model participating in the electric power trading market in the day ahead according to the acquired full life cycle limiting parameters;
the method for establishing the optimal revenue model participating in the day-ahead power trading market in the S2 specifically comprises the following steps:
s21, judging the charge-discharge state at the current moment, and calculating the benefit at the current moment and the charge-discharge amount at the current moment according to the charge-discharge state at the current moment and the full life cycle limiting parameter;
s22, determining the benefit at the current moment according to the energy storage capacity, the energy storage rated power and the charging and discharging times limit;
s23, performing iteration of S21-S22 for N times to obtain the charge and discharge state, the charge and discharge amount and the benefit at each moment in the day; wherein N =24/T, T is the unit time for calculating the price of the discharged electricity;
s24, updating the charge-discharge state and the charge-discharge amount corresponding to each moment according to the obtained charge-discharge state, the charge-discharge amount and the predicted electricity price in each moment in the day;
s25, calculating total income according to the corresponding charge-discharge state and charge-discharge amount at each moment after updating and the predicted electricity price in the day ahead;
s3, carrying out optimization solving on the optimization gain model by using the maximum gain as an objective function and adopting an intelligent optimization algorithm to obtain a charge-discharge state and a charge-discharge amount corresponding to each moment in the day-ahead; the method comprises the following steps that in the optimization solution of an optimization gain model by adopting an intelligent optimization algorithm, a particle swarm optimization algorithm is introduced to perform optimization solution on the optimization gain model;
s4, determining a reporting strategy of the charge and discharge power corresponding to each moment in the day according to the obtained charge and discharge state and charge and discharge amount corresponding to each moment in the day;
in S4, determining a reporting policy of the charge and discharge power corresponding to each moment in the day according to the charge and discharge state and the charge and discharge amount corresponding to each moment, specifically includes:
calculating the charge and discharge power corresponding to each moment in the day;
if the current time is in a charging state, power [ i ] = chargeability [ i ]/charge;
the Power [ i ] represents the declared Power at the ith moment, the charcapcity [ i ] represents the charging capacity at the ith moment, and the charge E represents the charging efficiency of the energy storage Power station;
if the current time is in a discharging state, power [ i ] = DischarCapacity [ i ]/Discharge;
wherein Power [ i ] represents declared Power at the ith moment, discharCapacity [ i ] represents discharge electric quantity at the ith moment, and DischargeE represents discharge efficiency of the energy storage Power station.
2. A method of claim 1 wherein in S1 the full life cycle limiting parameters include: the system comprises an energy storage capacity, an energy storage rated power, an energy storage soc upper limit value, an energy storage soc lower limit value, a current energy storage soc value, a day-ahead predicted electricity price, a daily charge and discharge frequency limit and a lowest charge and discharge price difference of electricity per degree.
3. A method for declaring participation of an energy storage plant in a day-ahead power transaction according to claim 1, wherein the functional expression of the charge-discharge state at each moment in the day-ahead obtained in S3 is as follows:
X i =f(min_cost, countLimit, soc,soc_up, soc_down, capacity, price i , RP)
wherein i represents the time at i, X i Indicating the charge-discharge state at the ith time; x i =1 indicates in a discharge state; x i =1 represents being in a charging state; x i =0 indicates an unfilled state; the countLimit represents the daily charge-discharge frequency limit, the min _ cost represents the lowest charge-discharge price difference of each kilowatt-hour, the capacity represents the energy storage capacity, the RP represents the energy storage rated power, the soc represents the state of charge, the soc _ down represents the lower limit value of the energy storage soc, the soc _ up represents the upper limit value of the energy storage soc, and the price i Indicating the predicted day-ahead electricity rate at the ith time.
4. A method according to claim 1, wherein in S21, the charging and discharging state at the current time is determined, and the income at the current time and the charging and discharging amount at the current time are calculated according to the charging and discharging state at the current time and the full life cycle limiting parameter, and specifically comprises:
assuming that the current time is in a charging state
If soc [ i ] is more than or equal to soc _ up, then profit [ i ] = profit [ i-1] -Penalty Profit;
in the formula, soc [ i ] represents soc at the ith moment, soc _ up represents the upper limit value of the energy storage soc, config [ i ] represents the total income from the ith moment, config [ i-1] represents the total income from the ith moment to the ith-1 moment, and Penalty Profit represents punished income;
if soc [ i ] < soc _ up, then charge [ i ] = charge [ i-1] +1,
soc[i]’=soc[i]-X i ×RP/capcity；
in the formula, charge [ i]Indicates the total number of charges to the ith time, charge [ i-1]Indicates the total number of charges to the i-1 th time, soc [ i]' represents the predicted soc at time i, capacity represents the storage capacity, RP represents the storage rated power, X i Indicating the charge-discharge state at the ith time;
when soc [ i ]]' soc _ up is less than or equal to, then fits [ i]=profit[i-1]+ X i ×RP×T×price i ；
Updating soc [ i ] = soc [ i ]', calculating chareacity [ i ] = RP × T;
in the formula, chareacity [ i ]]Indicating the amount of charge at time i, price i The predicted electricity price in the day before the ith moment is shown, and T shows the unit time for calculating the clear electricity price;
when soc [ i ]' > soc _ up,
then profit [ i]=profit[i-1]+ X i ×（soc_up-soc[i]）×capacity×price i ，soc[i]=soc_up；
Assuming that the current time is in a discharge state
If soc [ i ] is less than or equal to soc _ down, then profit [ i ] = profit [ i-1] -PenaltyProfit;
if soc [ i ]]>soc _ down, then discharge [ i]=discharge[i-1]+1, and calculate soc [ i ]]’=soc[i]- X i ×RP/capacity；
In the formula, discharge [ i ] represents the total discharge frequency to the ith time, discharge [ i-1] represents the total discharge frequency to the ith-1 time, and soc _ down represents the lower limit value of the stored energy soc;
when soc [ i ]' > soc _ down,
then profit [ i]=profit[i-1]+ X i ×RP×T×（price i -ABH），soc[i]=soc[i]’，DischareCapacity[i]=RP×T；
In the formula, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment;
when soc [ i ]' < soc _ down,
then p isrofit[i]=profit[i-1]+ X i ×soc_down×capacity×（price i -ABH）；
Let soc [ i ] = soc _ down, then discarecapacity [ i ] = soc _ down × capacity;
wherein ABH is an anti-optimizing abnormal value;
assuming that the current time is in a state of not discharging and not charging, then profit [ i ] = profit [ i-1];
discharge[i]=discharge[i-1]；charge[i]=charge[i-1]；
ChareCapacity[i]=0；DischareCapacity[i]=0。
5. a method for reporting participation of energy storage power stations in day-ahead power transactions according to claim 1, wherein in S22, the gains at the current time are updated according to the energy storage capacity, the energy storage rated power and the limitation of the number of charging and discharging times, and specifically comprises:
when MIN (charge [ i ], discharge [ i ]) > countLimit x (capacity/RP),
then profit [ i ]' = profit [ i ] -PenaltyProfit;
in the formula, charge [ i ] represents the total number of charging times to the ith time, discharge [ i ] represents the total number of discharging times to the ith time, profit [ i ]' represents the total income from the ith time after updating, profit [ i ] represents the total income to the ith time, countLimit represents the charging and discharging times limit of each day, penaltyProfit represents the penalty income, capacity represents the energy storage capacity, and RP represents the energy storage rated power.
6. A declaration method of the energy storage power station participating in the day-ahead power transaction as claimed in claim 1, wherein in S24, the charging and discharging state and the charging and discharging amount corresponding to each time are updated according to the obtained charging and discharging state, the charging and discharging amount and the day-ahead predicted power price at each time, specifically comprising:
s241, performing segmentation processing by taking the charging time in each time as a time segmentation point, and searching whether m discharging times exist between every two adjacent charging times; wherein m is an integer greater than or equal to 2, the charging time represents the time in a charging state, and the discharging time represents the time in a discharging state;
when m discharging moments exist, the m discharging moments are recorded as Tm, and m discharging quantities corresponding to Tm are sequenced from large to small to obtain a discharging quantity sequence;
acquiring the day-ahead predicted electricity price of each discharging time between two adjacent charging times, predicting the electricity price according to the acquired day-ahead predicted electricity price of each discharging time between two adjacent charging times, and recording m discharging times corresponding to the largest m day-ahead predicted electricity prices as Tm'; updating the charge-discharge state corresponding to Tm' into a discharge state, and updating the charge-discharge state corresponding to the other discharge time between the two adjacent charge times into a non-charge and non-discharge state; sequencing the Tm ' according to the order of the predicted electricity prices from big to small in the day before the Tm ' corresponds to the Tm ' to obtain a first time sequence;
establishing a mapping relation between a discharge capacity sequence and a first time sequence; updating the charge-discharge state and the discharge amount corresponding to each discharge moment in the first time sequence according to the mapping relation between the discharge amount sequence and the first time sequence;
when m discharging moments do not exist, the charging and discharging state and the charging and discharging amount corresponding to the discharging moment between the two adjacent charging moments are not updated;
s242, carrying out segmentation processing by taking the discharging time in each time as a segmentation point, and searching whether n charging times exist between every two adjacent discharging times, wherein n is an integer greater than or equal to 2;
when n charging moments exist, the n charging moments are recorded as Tn, and n charging quantities corresponding to the Tn are sequenced from large to small to obtain a charging quantity sequence;
acquiring the day-ahead predicted electricity price of each charging time between two adjacent discharging times; predicting the electricity price according to the acquired day ahead of each charging time between two adjacent discharging times, and marking n times corresponding to the minimum n day ahead predicted electricity prices as Tn'; updating the charge-discharge state corresponding to Tn' to a charge state, and updating the charge-discharge state of other charge moments between the two adjacent discharge moments to a non-charge and non-discharge state; sequencing Tn' according to the order of the predicted electricity prices from small to large in the day ahead to obtain a second time sequence;
establishing a mapping relation between the charging quantity sequence and the second time sequence; updating the charging amount corresponding to each charging time in the second time sequence according to the mapping relation between the charging amount sequence and the second time sequence;
and when the n charging moments do not exist, neither the charging and discharging state nor the charging and discharging amount corresponding to the charging moment between the two adjacent discharging moments is updated.
7. The method for declaring participation of an energy storage power station in a day-ahead power transaction according to claim 6, wherein a mapping relation between a discharge capacity sequence and a first time sequence is established in S241, and specifically comprises:
and mapping the discharge quantity in the discharge quantity sequence to the discharge time with the same sequencing position in the first time sequence.
8. The declaration method of the energy storage power station participating in the day-ahead power transaction of claim 6, wherein the establishing of the mapping relationship between the charging quantity sequence and the second time sequence in the S242 specifically includes:
and mapping the charging quantity in the charging quantity sequence to the charging time with the same sequencing position in the second time sequence.
9. A declaration method of the energy storage power station participating in the day-ahead power trading according to claim 1, wherein in S25, the total profit is calculated according to the updated charge-discharge state and charge-discharge amount corresponding to each time and the day-ahead predicted power rate, specifically comprising:
s251, calculating the sum of optimization gains according to the corresponding charge-discharge state, charge-discharge amount and electricity price at each updated moment;
wherein, the calculation formula of the optimal profit sum is as follows
Benifit=BenifitC[i max ]+BenifitDC[i max ]；
In the formula, benifit denotes the sum of the search gains, i max Denotes the maximum value of i, benifitc [ i max ]Is shown to the i-th max Total charge gain at time, benifitDC [ i ] max ]Is expressed to the i-th max The total yield of discharge at a moment;
wherein Benifitc [ i ]]=BenifitC[i-1]-ChareCapacity[i]×price i ；
In the formula, benifitc [ i ]]Indicates the total charge gain to the ith time, benifitc [ i-1]Indicates the total charge gain to the i-1 th time, chareCapacity i]Indicating the amount of charge at time i, price i Represents the predicted power rate of the day before the ith time;
wherein BenifitDC [ i ]]=BenifitDC[i-1]+DischareCapacity[i]×（price i -ABH）；
In the formula, benifitDC [ i ] represents the total discharge yield to the ith moment, benifitC [ i-1] represents the total discharge yield to the ith moment, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment, and ABH is an abnormal value for preventing optimization;
s252, calculating a real theoretical benefit and an actual lowest charge-discharge cost difference of each degree of electricity according to the updated charge-discharge state and charge-discharge amount corresponding to each moment, and updating an optimization benefit sum according to the lowest charge-discharge cost difference of each degree of electricity and the actual lowest charge-discharge cost difference;
wherein the calculation formula of the real theoretical gain is as follows
RCBenifit=BenifitC[i max ]+Benifit2[i max ]；
In the formula, RCBenifit represents the true theoretical yield, benifitc [ i [ ] max ]Is shown to the i-th max Total charge gain at time, benifit2[ i max ]Is expressed to the i-th max The theoretical discharge total yield at the moment;
wherein, benifit2[ i]=Benifit2[i-1]+DischareCapacity[i]×price i ；
In the formula, benifit2[ i]Representing the total theoretical discharge yield to the ith moment; benifit2[ i ]]Shows the theoretical discharge Total yield to time i-1, dischareCapacity [ i]Represents the discharge capacity at the i-th time, price i Indicating the predicted day-ahead electricity price at the ith time;
the calculation formula of the actual lowest charge/discharge cost difference per degree of electricity is as follows
real_min_cost=RCBenifit/(DischareCapacity)；
In the formula, real _ min _ cost represents the actual lowest charge-discharge cost difference of each watt of electricity, RCBenifit represents the real theoretical benefit, and DischareCapacity represents the total discharge capacity;
wherein, dischareCapacity = ∑ DischareCapacity [ i ]; in the formula, dischareCapacity [ i ] represents the discharge electric quantity at the ith moment;
wherein, when real _ min _ cost is less than or equal to min _ cost, benifit' = Benifit-PenaltyProfit; in the formula, min _ cost represents the lowest charge-discharge price difference per watt hour, benifit represents the sum of the optimization gains, and Benifit' represents the sum of the updated optimization gains.
10. A declaration method of energy storage power stations participating in day-ahead power transactions as claimed in claim 1, wherein the charge and discharge power corresponding to each moment in day-ahead is calculated in S4, further comprising:
and carrying out time interpolation on the charge and discharge power corresponding to each moment in the day according to a time interpolation method to obtain a time interpolation estimated value.
11. A declaration method of energy storage power station participation in day-ahead power transaction according to claim 1, wherein in S3, the particle swarm optimization algorithm is specifically as follows:
s31, initializing and setting particle swarm optimization algorithm parameters, comprising the following steps: population size, particle dimension, acceleration factor, maximum and minimum flight speeds of particles, and maximum iteration number;
s32, randomly initializing the speed and the position of the particles in a specified search range;
s33, calculating the inertia weight and the acceleration factor of the particles before next iteration according to a calculation formula improved by the inertia weight and the acceleration factor;
the inertia weight adopts a segmentation strategy, and an improved formula of the inertia weight is shown as the following formula:
wherein, the acceleration factor adopts a dynamic learning factor, and an improved formula of the acceleration factor is shown as the following formula:
c 1 =c 1 start +(c 1 end -c 1 start ) ×(d/d max )；
in the formula, c 1 Denotes a self-learning factor, c 1 start Represents the minimum value taken by the self-learning factor, c 1 end Represents the maximum value taken by the self-learning factor, d max Representing the maximum number of iterations;
c 2 =c 2 start +(c 2 end -c 2 start ) ×(d/d max )；
in the formula, c 2 Represents a social cognitive learning factor, c 2 start Represents the minimum value taken by the social cognitive learning factor, c 2 end Represents the maximum value taken by the social cognitive learning factor;
s34, calculating the speed and the position of the particles;
s35, solving the fitness value fi of each particle according to the set fitness function, and solving the individual extreme value P according to fi best And global extremum G best And compares it with an individual extremum P best Comparing, if the result is better than the individual extreme value, then P best ＝fi；
S36, setting individual extreme value P of each particle best And global extreme G in the population best To carry outComparing, if the result is better than the global extreme value, G best ＝P best ；
S37, judging whether a termination condition is met or a preset maximum iteration number is reached, if the termination condition is met, stopping the operation of the algorithm, entering Step38, and if the termination condition is not met, returning to Step33;
and S38, outputting the global optimal value and the charge-discharge state and charge-discharge amount corresponding to each moment.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of declaring participation of an energy storage plant in a day-ahead power transaction as claimed in any one of claims 1-11.
13. An electronic device, comprising: a processor and memory for storing one or more programs; the one or more programs, when executed by the processor, implement a method of claim 1-11 of claim for claiming participation of an energy storage power station in a day-ahead power transaction.
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