CN115173453A - Energy storage auxiliary power grid peak regulation optimal configuration method - Google Patents
Energy storage auxiliary power grid peak regulation optimal configuration method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
Abstract
The application is suitable for the technical field of peak regulation control of a power system, and provides an optimal configuration method for energy storage auxiliary power grid peak regulation, which comprises the following steps: acquiring a daily load curve, a new energy power generation curve and the peak-load-regulation output maximum value of a traditional fire-water power generation unit, and determining a daily net load curve according to the daily load curve and the new energy power generation curve; constructing a multi-objective optimization model with an economic target of energy storage auxiliary power grid peak shaving as a first objective function and a peak clipping and valley filling effect target as a second objective function based on a daily net load curve; based on the multi-objective optimization model, converting the first objective function and the second objective function into a single-objective optimization model only containing a third objective function by using a linear weighted sum method; and (3) performing optimization solution by adopting an immune particle swarm optimization algorithm based on the constructed single-target optimization model of the third objective function to obtain the optimal configuration power and the optimal configuration capacity of the energy storage auxiliary power grid peak shaving. The method and the device can improve the economic benefit and the auxiliary peak shaving effect of the energy storage auxiliary power grid peak shaving.
Description
Technical Field
The application belongs to the technical field of power system peak shaving control, and particularly relates to an optimal configuration method for energy storage auxiliary power grid peak shaving.
Background
With the continuous improvement of the permeability of new energy, the output of the traditional generator set is gradually reduced, and the problem of imbalance of the flexibility supply and demand of the power system is increasingly highlighted. The energy storage technology can improve the controllability and stability of a new energy power grid with high permeability, but large-scale application of energy storage is limited due to high investment cost. In the aspect of economy of energy storage configuration, new energy configuration and energy storage schemes are issued by multiple provinces such as Ningxia, qinghai, inner Mongolia and Guizhou, and photovoltaic and energy storage will become a mainstream mode of future photovoltaic power station development. However, it is necessary to note that the economy of energy storage is still not perfect, and the rigid requirement of photovoltaic power plant configuration energy storage inevitably brings additional cost increase to the development owner. Therefore, in order to fully utilize the capacity of the energy storage battery, an efficient energy storage optimal configuration method needs to be fully considered so as to realize the economic operation of the new energy power grid with high permeability.
In addition, the reduction in traditional generator set resources makes it difficult for the system to cope with the large changes in the new energy daytime output. At the moment that the permeability of new energy is high, the output plan of the synchronous machine is low, and the utilization rate is low; and at the moment of exiting the new energy, the synchronous machine needs to quickly make up for the shortage of power generation. Because the wind power output and the load curve trend in the day are opposite, the photovoltaic output quickly rises in the morning and quickly exits in the evening, and the net power generation load of the synchronous machine greatly changes in the day. Meanwhile, the output of the new energy power supply is influenced by continuously changing meteorological factors, the output characteristic of the new energy power supply has randomness and volatility, and new uncertainty is brought to the source end of the power system. Uncertainty of new energy output is superposed with uncertainty of existing loads of a system and forced outage of a generator, and the degree of active imbalance in the worst scene is aggravated. Taking a photovoltaic system as an example, the net power load within a synchronous machine day is represented by a typical "duck curve" characteristic. Meanwhile, the synchronous machine needs to provide a standby for the uncertainty (such as N-1) of the system, the operation range of the synchronous machine set is limited, and the difficulty of power generation-standby combined scheduling is further increased. Therefore, in order to cope with the output and load change with larger degree and time scale, a capacity optimization configuration method for stabilizing the peak-to-valley value of the load by storing energy needs to be researched.
However, most of the existing optimal configuration methods for peak shaving of the energy storage auxiliary power grid carry out the assessment and calculation of the gains from the economic perspective, and the peak shaving economic gains and the peak shaving effects cannot be comprehensively considered; on the other hand, the existing optimal configuration method for peak shaving of the energy storage auxiliary power grid only utilizes traditional optimization algorithms such as a particle swarm algorithm and a genetic algorithm, and rarely utilizes an efficient intelligent optimization algorithm to perform optimal configuration on energy storage.
Disclosure of Invention
In order to solve the problems in the related art, the embodiment of the application provides an energy storage auxiliary power grid peak shaving method, which is used for improving the economic benefit and the auxiliary peak shaving effect of energy storage auxiliary power grid peak shaving.
The application is realized by the following technical scheme, comprising the following steps:
acquiring a daily load curve, a new energy power generation curve and the peak-load-regulation output maximum value of a traditional fire-water power generation unit, and determining a daily net load curve according to the daily load curve and the new energy power generation curve; constructing a multi-objective optimization model with an economic target of energy storage auxiliary power grid peak shaving as a first objective function and a peak clipping and valley filling effect target as a second objective function based on the daily net load curve data; based on the constructed multi-objective optimization model, converting the first objective function and the second objective function into a single-objective optimization model only containing a third objective function by utilizing a linear weighted sum method; and performing optimization solution by adopting an immune particle swarm algorithm based on the constructed single-target optimization model of the third objective function to obtain the optimal configuration power and configuration capacity of the energy storage auxiliary power grid peak regulation.
In a possible implementation manner of the energy storage auxiliary power grid peak shaving method, acquiring a new energy power generation curve includes:
the output power of the wind power frequency modulation unit is obtained, the output randomness of the fan is caused by the random distribution of wind energy, and a large amount of statistical data analysis shows that the probability density of average wind speed in most areas follows Weibull distribution, and the expression is as follows:
in the formula, v is the actual wind speed, c is a scale coefficient, representing the wind speed distribution discrete degree, and k is a shape coefficient. The models of the wind turbines in the power grid are assumed to be the same, and the wind speed correlation among the turbines is ignored.
The expression of the output power of the wind power frequency modulation unit is as follows:
in the formula, P WT For fan output power, P r To a rated maximum power, v r Rated wind speed of the fan, v actual wind speed, v ci For the fan to cut into the wind speed, v co And cutting the wind speed for the fan.
And obtaining the power of the photovoltaic generator set, wherein the solar illumination intensity generally obeys Beta distribution.
The probability density function of the illumination intensity is:
wherein Γ (·) is a Gamma function, r and r max The actual illumination intensity and the maximum illumination intensity within the time are respectively delta,
Are the shape parameters of the Beta distribution,
u is the average value of the illumination intensity.
The photovoltaic output power can be expressed as:
P PV ＝P ST K AC [1+k W (T c -T ST )]/K ST
in the formula, P PV For photovoltaic output power, P ST Is under standard test conditions (the light incidence intensity is 1 kW/m) 2 And the ambient temperature is 298.15K) under the condition of photovoltaic output powerRate, K AC As the actual light intensity, k W Is the power temperature coefficient, T c To the photovoltaic operating temperature, T ST For standard test of ambient temperature, K ST The light intensity under standard test conditions.
In a possible implementation manner of the energy storage auxiliary grid peak shaving method, constructing an economic target of energy storage auxiliary grid peak shaving as a first target function includes:
and constructing an economic objective function of the energy storage auxiliary power grid peak shaving based on the daily net load curve data.
Constructing a first objective function with the maximum net profit of the energy storage auxiliary power grid peak shaving, wherein the expression of the first objective function is as follows:
C 1 ＝C in -C cost
in the formula, C 1 Is a first objective function, C in For assisting the peak shaving of the grid for energy storage, i.e. the return of the grid from the energy storage station, C cost The peak shaving total cost; the peak shaving total cost is determined based on the peak shaving mileage compensation gain and the peak shaving capacity compensation gain, and the peak shaving total cost is determined based on the auxiliary peak shaving cost of the traditional water-gas power generating unit, the auxiliary peak shaving cost of the new energy generating unit and the auxiliary peak shaving cost of the energy storage system.
Constructing a second objective function of the peak clipping and valley filling effect target of the energy storage auxiliary power grid peak shaving, wherein the expression of the second objective function is as follows:
in the formula (I), the compound is shown in the specification,
for a net load value at time t, P grid.av Is the mean of the net load.
In one possible implementation manner of the energy storage auxiliary power grid peak shaving method, the power grid power balance constraint is determined based on the peak shaving value of the traditional hydroelectric generating set, the peak shaving value of the new energy source set, the energy storage system power value and the daily load power value; and the peak-load-regulating output value of the traditional fire-water electric generating set, the peak-load-regulating output value of the new energy generating set, the output value of the energy storage system and the daily load power value are respectively determined by the constraint of the physical properties of the traditional fire-water electric generating set.
In a possible implementation manner of the energy storage auxiliary grid peak shaving method, the first objective function and the second objective function are converted into a single-objective optimization model only containing a third objective function by using a linear weighted sum method, which includes:
because the dimensions of the first objective function and the second objective function are different, the min-max standardization method is adopted to eliminate the influence of different orders and dimensions between the objective functions and convert the first objective function and the second objective function, and the expression of the min-max standardization method is as follows:
wherein f and f' are respectively the true value and the normalized value of the objective function, f max 、f min The maximum value and the minimum value of the objective function are respectively.
The third objective function expression obtained by the linear weighted sum method is:
in the formula: c 3 Is a third objective function, a 1 Is the weight occupied by the first objective function, C 1max 、C 1min Respectively the maximum and minimum values, C, of the first objective function 2max 、C 2min The maximum value and the minimum value of the second objective function are respectively.
In a possible implementation manner of the energy storage auxiliary power grid peak shaving method, an algorithm for solving the optimal configuration power and configuration capacity of the energy storage auxiliary power grid peak shaving is an immune particle swarm algorithm, and the method comprises the following steps:
step 1: setting parameters, setting the maximum iteration times k of the algorithm max Size N of particle population, dimension nv of particle populationar, immune memory library volume K, learning factor c 1 And c 2 Inertia weight ω;
step 2: particle initialization, generating an initial particle population A with the population number N through a random function 0 According to the initial moving speed of the particles
And position
Calculating population A 0 All particle fitness values f (x) i ) (i =1,2,3, …, N), and is set as the particle individual extremum P i k (i =1,2,3, …, N) location and fitness value;
and step 3: updating the global extreme value to obtain the current population A k All the individual extreme values and the global extreme value of the medium-sized particles
Comparing and updating the global extreme value;
and 4, step 4: whether to terminate the iteration, if the current iteration number k is more than k max If so, terminating the iteration, and taking the fitness function value corresponding to the global extreme value of the particle population obtained by the current iteration as a global optimal solution obtained by the algorithm; otherwise, executing step 5;
and 5: generating new particles, randomly generating M new particles through a vaccination mechanism of immune particle swarm optimization, and updating individual extreme value P of each new particle i k (i = N +1, N +2, …, N + M) and global extremum for particle population
Step 6: calculating particle selection probability, and calculating selection probability P (x) of N + M particles in the population based on particle concentration according to formula (4-2) and formula (4-3) i )(i＝1,2,3,…,N+M)；
And 7: creating and updating a memory library, preferentially selecting S particles with the largest fitness value in the current particle population by adopting an elite retention strategy, and preferentially storing the S particles into an immune memory library; then selecting K-S high-quality particles from the rest N + M-S particle populations according to the population particle selection probability and storing the K-S high-quality particles into an immune memory library;
and 8: forming a parent group, and selecting N high-quality particles from the N + M particle groups according to the particle selection probability of the particle groups to form a new particle group B k ；
And step 9: particle renewal for a new particle population B k The speed, the position, the fitness value and the individual extreme value of the particle of all the particles are updated;
step 10: merging the particle groups, and combining K excellent particles in the current immune memory bank with the current particle group B k Merging, eliminating K inferior particles with relatively poor adaptability value in the current merged particle swarm to form a new particle swarm A with the capacity of N k And 3, jumping to the step 3, and continuing the algorithm iteration.
Drawings
Fig. 1 is a framework diagram of a design solving process according to the peak shaving optimization configuration method for the energy storage auxiliary power grid of the present application.
Fig. 2 is a photovoltaic unit output prediction curve graph obtained by the energy storage auxiliary power grid peak shaving optimal configuration method.
Fig. 3 is a wind turbine output prediction curve graph obtained by the optimal configuration method for peak shaving of the energy storage auxiliary power grid.
Fig. 4 is a typical daily load curve diagram of the regional power grid obtained by the optimal configuration method for peak shaving of the energy storage auxiliary power grid.
Fig. 5 is a comparison diagram of net load curves before and after optimization obtained by the optimal configuration method for peak shaving of the energy storage auxiliary power grid according to the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The present application will be described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a schematic flow chart of a method for peak shaving of an energy storage auxiliary power grid according to an embodiment of the present application, and referring to fig. 1, the method may not include steps 101 to 104, and the method is described in detail as follows:
in step 101, a daily load curve, a new energy power generation curve and the peak load regulation output maximum value of a traditional fire-water power generation unit are obtained, and a daily net load curve is determined according to the daily load curve and the new energy power generation curve.
In a possible implementation manner of the energy storage auxiliary power grid peak shaving method, acquiring a new energy power generation curve includes:
illustratively, the output power of the wind turbine generator is obtained, the output randomness of the wind turbine generator is caused by the random distribution of wind energy, and a large amount of statistical data analysis shows that the probability density of the average wind speed in most areas follows Weibull distribution, and the expression is as follows:
in the formula, v is the actual wind speed, c is a scale coefficient, representing the wind speed distribution discrete degree, and k is a shape coefficient. The models of the wind generation sets in the power grid are assumed to be the same, and the wind speed correlation among the sets is ignored.
The expression of the output power of the wind power frequency modulation unit is as follows:
in the formula, P WT For fan output power, P r At rated maximum power, v r Rated wind speed of the fan, v actual wind speed, v ci For the fan to cut into the wind speed, v co The wind speed is cut for the fan.
For example, the power of a photovoltaic generator set is obtained, the solar illumination intensity generally follows Beta distribution, and the probability density function is as follows:
wherein Γ (-) is a Gamma function, r and r max The actual illumination intensity and the maximum illumination intensity within the time are respectively delta,
Are the shape parameters of the Beta distribution,
u is the average value of the illumination intensity.
The photovoltaic output power can be expressed as:
P PV ＝P ST K AC [1+k W (T c -T ST )]/K ST
in the formula, P PV For photovoltaic output power, P ST Is under standard test conditions (the light incidence intensity is 1 kW/m) 2 Ambient temperature of 298.15K) photovoltaic output power, K AC As the actual light intensity, k W Is the power temperature coefficient, T c To the photovoltaic operating temperature, T ST For standard test of ambient temperature, K ST The light intensity under standard test conditions.
In step 102, based on the daily net load curve data, a multi-objective optimization model is constructed, wherein an economic objective of energy storage auxiliary power grid peak shaving is used as a first objective function, and a peak shaving and valley filling effect objective is used as a second objective function.
Illustratively, the expression of the first objective function is:
C 1 ＝C in -C cost
in the formula, C 1 Is a first objective function, C in For energy storage, the peak shaving total yield of the auxiliary grid, i.e. the yield of the grid from the energy storage station, C cost The peak shaving total cost.
Optionally, the peak shaving total income of the energy storage auxiliary power grid is determined based on the peak shaving mileage compensation income and the peak shaving capacity compensation income.
The peak regulation total cost is determined based on the auxiliary peak regulation cost of the traditional water-fire power unit, the auxiliary peak regulation cost of the new energy unit and the auxiliary peak regulation cost of the energy storage system.
For example, the expression of the peak shaving total yield of the energy storage auxiliary power grid is as follows:
in the formula, C inA Compensating for the benefits of peak-shaving mileage, C inB The gain is compensated for the peak shaver capacity at time t,
provides peak regulation mileage for the energy storage unit at the moment t,
the price is settled for the unit mileage at the time t, the settlement is generally carried out according to the time-of-use electricity price of the power grid,
for the auxiliary peak shaving capacity of the energy storage unit at the time t,
for the peak shaving service time of the energy storage unit at the time t,
and compensating the price per unit capacity of the energy storage auxiliary peak shaving service at the moment t.
Optionally, the peak shaving total cost is determined based on the auxiliary peak shaving cost of the traditional water-fire power generating unit, the auxiliary peak shaving cost of the new energy generating unit and the auxiliary peak shaving cost of the energy storage system.
For example, the expression of the peak shaving total cost of the energy storage auxiliary power grid is as follows:
in the formula, C TU The peak shaving cost of the traditional water-fire machine set, C NEU Peak shaving cost for new energy unit, C ESS In order to assist the peak shaving cost of the energy storage system,
for the peak regulation output value of the traditional water-fire unit at the moment t, comprising the regulation of a thermal power unitPeak output value and peak regulation output value of hydroelectric generating set, a i 、b i 、c i Respectively is each secondary coefficient in the quadratic function of the economic dispatching power generation cost of the traditional water-fire unit,
the actual peak regulation output value of the new energy unit in the period of t comprises two parts of a photovoltaic part and a fan, c op For the operation and maintenance cost coefficient, C, of the new energy unit cost For assisting peak shaving costs of the energy storage unit, c om In order to store energy, operate and maintain the price,
for storing the force output value for t period, C ESS Allocating a price for unit energy storage, N life The energy storage life is.
In some embodiments, an economic objective function for energy storage auxiliary grid peak shaving is determined based on a grid power balance constraint, an energy storage power constraint, and an energy storage capacity constraint.
Optionally, the power grid power balance constraint is determined based on the peak regulation output value of the traditional water-fire unit, the peak regulation output value of the new energy unit, the output value of the energy storage system and the load power value.
Illustratively, the expression of the grid power balance constraint is:
in the formula (I), the compound is shown in the specification,
for the time period t the payload power value,
for the time period t the load power value,
is positive, representing energy storage charging,
negative, representing an energy storage discharge.
Furthermore, the peak-shaving output value of the traditional water-fire unit, the peak-shaving output value of the new energy unit and the output value of the energy storage system are respectively constrained by the physical properties of the traditional water-fire unit.
Illustratively, the expression that the peak-shaving output value of the traditional water-fire machine set, the peak-shaving output value of the new energy machine set and the output value of the energy storage system are respectively constrained by the physical attributes of the expressions is as follows:
in the formula (I), the compound is shown in the specification,
the peak load regulation output upper limit value of the thermal power generating unit,
is the lower limit value of the peak-load regulating output of the thermal power generating unit,
is the peak-load-adjusting output upper limit value of the hydroelectric generating set,
is the lower limit value of the peak-load-adjusting output of the hydroelectric generating set,
is the peak load regulation output upper limit value of the wind turbine generator,
is the lower limit value of the peak load regulation output of the wind turbine generator,
is the peak load regulation output upper limit value of the photovoltaic unit,
peak shaving output for photovoltaic unitThe lower limit value is set as the lower limit value,
the peak load regulation output upper limit value of the energy storage system,
and the peak load regulation output lower limit value is the energy storage system peak load regulation output lower limit value.
Optionally, the energy storage power constraint is determined based on the state of charge of the energy storage system, the properties of the energy storage system and the charging and discharging conditions of the energy storage system.
Illustratively, the expression of the energy storage power constraint is:
wherein the content of the first and second substances,
in the formula (I), the compound is shown in the specification,
is the charge state of the energy storage unit at the moment t, epsilon is the self-discharge rate of the energy storage unit, epsilon belongs to [0,1 ∈ ]]And E is the capacity of the energy storage unit,
for the charging power of the energy storage unit at time t,
is the discharge power of the energy storage unit at the time t, eta is the charge-discharge efficiency of the energy storage unit,
for charging energy-storage unitsIn the electrical state, the voltage of the power supply is set,
take a value of 0 or 1, wherein
Indicating that the energy storage unit is in a charging state,
in order to be in the discharge state of the energy storage unit,
taking a value of 0 or 1, wherein
Indicating that the energy storage unit is in a discharged state, SOC min Is the minimum value of the state of charge of the energy storage unit, SOC max Is the maximum value of the charge state of the energy storage unit.
It should be noted that the expression of the energy storage power constraint ensures that the energy storage unit cannot perform both the charging operation and the discharging operation at the same time, which accords with the actual working condition, and at the same time indicates that the SOC of the energy storage unit at the starting time and the ending time of the scheduling period are equal, so that the energy storage unit can continue to perform the cyclic scheduling in the next period.
Optionally, the energy storage capacity constraint is determined based on an expected configuration of the energy storage system.
Illustratively, the expression of the energy storage capacity constraint is:
E min ≤E≤E max
in the formula, E min For minimum energy storage capacity, E max The maximum value of the energy storage capacity.
Illustratively, the expression of the second objective function is:
in the formula, C 2 In order to be the second objective function,
is a net load value of t period, P grid.av Is the mean payload value.
In step 103, based on the constructed multi-objective optimization model, the first objective function and the second objective function are converted into a single-objective optimization model only containing the third objective function by using a linear weighted summation method.
Optionally, because the dimensions of the first and second objective functions are different, the min-max normalization method is used to eliminate the influence of different orders and dimensions between the objective functions, and convert the first and second objective functions, where the min-max normalization method has the expression:
wherein f and f' are respectively the true value and the normalized value of the objective function, f max 、f min The maximum value and the minimum value of the objective function are respectively.
Illustratively, the third objective function expression obtained by the linear weighted sum method is:
in the formula: c 3 Is a third objective function, a 1 Is the weight occupied by the first objective function, C 1max 、C 1min Respectively the maximum and minimum values, C, of the first objective function 2max 、C 2min The maximum value and the minimum value of the second objective function are respectively.
In step 104, based on the constructed single-target optimization model of the third objective function, an immune particle swarm optimization is adopted to perform optimization solution, so as to obtain the optimal configuration power and configuration capacity of the energy storage auxiliary power grid peak shaving.
Illustratively, an algorithm for solving the peak-shaving optimal configuration power and the optimal configuration capacity of the energy storage auxiliary power grid is an immune particle swarm algorithm, and the method comprises the following steps of:
step 1: setting parameters, setting the maximum iteration times k of the algorithm max Particle population size N, particle population dimension nvar, immune memory library capacity K, learning factor c 1 And c 2 Inertia weight ω;
step 2: particle initialization, generating an initial particle population A with a population number N through a random function 0 According to the initial velocity of movement of the particles
And position
Calculating population A 0 All particle fitness values f (x) i ) (i =1,2,3, …, N), and is set as the particle individual extremum P i k (i =1,2,3, …, N) location and fitness value;
and 3, step 3: updating the global extreme value to obtain the current population A k All the individual extreme values and the global extreme value of the medium-sized particles
Comparing and updating the global extreme value;
and 4, step 4: whether the iteration is terminated or not, if the current iteration times k is more than k max If so, terminating the iteration, and taking the fitness function value corresponding to the global extreme value of the particle population obtained by the current iteration as a global optimal solution obtained by the algorithm; otherwise, executing step 5;
and 5: generating new particles, randomly generating M new particles by a vaccination mechanism of an immune particle swarm algorithm, and updating the individual extreme value P of each new particle i k (i = N +1, N +2, …, N + M) and global extremum for particle population
And 6: calculating particle selection probability, and calculating selection probability P (x) of N + M particles in the population based on particle concentration according to formula (4-2) and formula (4-3) i )(i＝1,2,3,…,N+M)；
And 7: creating and updating a memory library, preferentially selecting S particles with the largest fitness value in the current particle population by adopting an elite retention strategy, and preferentially storing the S particles into an immune memory library; then selecting K-S high-quality particles from the rest N + M-S particle populations according to the population particle selection probability and storing the K-S high-quality particles into an immune memory library;
and 8: forming a parent group, and selecting N high-quality particles from the N + M particle groups according to the particle selection probability of the particle groups to form a new particle group B k ；
And step 9: particle renewal for a new particle population B k The speed, the position, the fitness value and the individual extreme value of the particle of all the particles are updated;
step 10: merging the particle groups, and combining K excellent particles in the current immune memory bank with the current particle group B k Merging, eliminating K inferior particles with relatively poor adaptability value in the current merged particle swarm to form a new particle swarm A with the capacity of N k (ii) a And (4) jumping to the step 3, and continuing the algorithm iteration.
Examples
Taking a power grid in a certain area in Hebei as an example, the type of the energy storage system is a lithium iron phosphate battery, the maximum value of the energy storage configuration capacity is 15000kWh, the minimum value of the energy storage configuration capacity is 5000kWh, the maximum value of the energy storage power is 5000kW, the unit energy storage configuration price is 740 yuan/kWh, the energy storage charging efficiency is 0.9, the energy storage discharging efficiency is 0.9, the energy storage operation maintenance price is 0.0832 yuan/kWh, and the energy storage participation peak regulation compensation price is 0.3 yuan/kWh.
Fig. 2 is a photovoltaic unit output prediction curve, fig. 3 is a wind turbine unit output prediction curve, fig. 4 is a typical daily load curve of a power grid in the area, fig. 5 is a comparison graph of net load curves before and after optimization, and the time-of-use electricity price is shown in table 1:
TABLE 1 time-of-use electricity price of the power grid
Setting the population scale as 100, the generation selection times as 300, solving by using an immune particle swarm algorithm to obtain the optimal energy storage configuration capacity of 15000kWh and the optimal energy storage configuration power of 3323.14kW, and obtaining the peak regulation net profit of 6827.32 yuan from the point of the benefit of energy storage participation peak regulation; from the peak clipping and valley filling effects, the peak-valley difference of the original net load curve is 7.93MW, the standard difference of the original net load curve is 2.54MW, the peak-valley difference of the optimized net load curve is 5.09MW, the standard difference is 1.55MW, the peak-valley difference of the optimized net load curve is reduced by 35.83%, and the standard difference is reduced by 38.98%.
The method comprises the steps of determining a daily load curve according to the daily load curve and a new energy power generation curve by obtaining the daily load curve, the new energy power generation curve and the peak-shaving output maximum value of a traditional hot water power generation unit; constructing a multi-objective optimization model with an economic target of energy storage auxiliary power grid peak shaving as a first objective function and a peak shaving and valley filling effect target as a second objective function; and based on the constructed multi-objective optimization model, converting the first objective function and the second objective function into a single-objective optimization model only containing the third objective function by using a linear weighted sum method, and performing optimization solution by using an immune particle swarm optimization to obtain the optimal configuration power and configuration capacity of the energy storage auxiliary power grid peak regulation.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.
Claims (7)
1. An optimal configuration method for peak shaving of an energy storage auxiliary power grid is characterized by comprising the following steps:
acquiring a daily load curve, a new energy power generation curve and the peak-load-regulation output maximum value of a traditional fire-water power generation unit, and determining a daily net load curve according to the daily load curve and the new energy power generation curve;
constructing a multi-objective optimization model with an economic target of energy storage auxiliary power grid peak shaving as a first objective function and a peak clipping and valley filling effect target as a second objective function based on the daily net load curve data;
based on the constructed multi-objective optimization model, converting the first objective function and the second objective function into a single-objective optimization model only containing a third objective function by utilizing a linear weighted sum method;
and performing optimization solution by adopting an immune particle swarm algorithm based on the constructed single-target optimization model of the third objective function to obtain the optimal configuration power and configuration capacity of the energy storage auxiliary power grid peak regulation.
2. The optimal configuration method for peak shaving of an energy storage auxiliary grid according to claim 1, wherein the maximum output value of the conventional synchronous machine set comprises a maximum output value and a minimum output value.
3. The optimal configuration method for energy storage auxiliary grid peak shaving according to claim 1, wherein the constructing takes an economic objective of energy storage auxiliary grid peak shaving as a first objective function, and comprises:
the economic objective function of the energy storage auxiliary power grid peak shaving refers to that the energy storage auxiliary power grid peak shaving obtains net profits, namely the total gain of the energy storage auxiliary power grid peak shaving subtracts the total peak shaving cost, and the expression of the first objective function is as follows:
C 1 ＝C in -C cost
in the formula: c 1 Is a first objective function; c in For assisting the peak shaving of the grid for energy storage, i.e. the return of the grid from the energy storage station, C cost The peak shaving total cost;
the total peak shaving income of the energy storage auxiliary power grid is determined based on the peak shaving mileage compensation income and the peak shaving capacity compensation income, and the total peak shaving cost is determined based on the auxiliary peak shaving cost of the traditional water-gas power generating unit, the auxiliary peak shaving cost of the new energy generating unit and the auxiliary peak shaving cost of the energy storage system.
4. The method of claim 3, wherein the economic objective function of the energy storage auxiliary grid peak shaving is determined based on a grid power balance constraint, an energy storage power constraint, and an energy storage capacity constraint;
the power grid power balance constraint is determined based on a peak regulation output value of a traditional fire-water power unit, a peak regulation output value of a new energy source unit, an energy storage system output value and a daily load power value; the peak-load regulation output value of the traditional fire-water power generation unit, the peak-load regulation output value of the new energy source unit and the output value of the energy storage system are determined by the physical property constraints of the traditional fire-water power generation unit, the energy storage power constraint is determined based on the charge state of the energy storage system, the properties of the energy storage system and the charge and discharge conditions of the energy storage system, and the energy storage capacity constraint is determined based on the expected configuration conditions of the energy storage system.
5. The optimal configuration method according to claim 1, wherein the constructing takes a peak clipping and valley filling effect target of the energy storage auxiliary grid peak shaving as a second objective function, and comprises:
the peak clipping and valley filling effect target function of the energy storage auxiliary power grid peak shaving refers to a standard deviation of net load power of the power grid after the energy storage auxiliary power grid peak shaving, and an expression of the second target function is as follows:
in the formula: c 2 In order to be the second objective function of the system,
is a net load value of t period, P grid.av Is the mean of the net load.
6. The method according to claim 1, wherein the transforming the first and second objective functions into a single-objective optimization model containing only the third objective function by using a linear weighted sum method comprises:
because the dimensions of the first objective function and the second objective function are different, the min-max standardization method is adopted to eliminate the influence of different orders and dimensions between the objective functions and convert the first objective function and the second objective function, and the expression of the min-max standardization method is as follows:
in the formula: f. f' is the true value, normalized value of the objective function, respectively max 、f min Respectively the maximum value and the minimum value of the objective function;
the third objective function expression obtained by the linear weighted sum method is:
in the formula: c 3 Is a third objective function, a 1 Weight taken up by the first objective function, C 1max 、C 1min Respectively, the maximum and minimum values, C, of the first objective function 2max 、C 2min The maximum value and the minimum value of the second objective function are respectively.
7. The optimal configuration method according to claim 1, wherein the algorithm for solving the peak shaving optimal configuration power and the optimal configuration capacity of the energy storage auxiliary power grid is an immune particle swarm algorithm, and comprises the following steps:
step 1: setting parameters, setting the maximum iteration number k of the algorithm max Particle population size N, particle population dimension nvar, immune memory library capacity K, learning factor c 1 And c 2 Inertia weight ω;
step 2: particle initialization, generating an initial particle population A with the population number N through a random function 0 According to the initial moving speed of the particles
And position
Calculating population A 0 All particle fitness values f (x) i ) (i =1,2,3, …, N), and is set as the particle individual extremum P i k (i =1,2,3, …, N) location and fitness value;
and step 3: updating the global extreme value to obtain the current population A k All the individual extreme values and the global extreme value of the medium-sized particles
Comparing and updating the global extreme value;
and 4, step 4: whether to terminate the iteration, if the current iteration number k is more than k max If so, terminating the iteration, and taking the fitness function value corresponding to the global extreme value of the particle population obtained by the current iteration as a global optimal solution obtained by the algorithm; otherwise, executing step 5;
and 5: generating new particles, randomly generating M new particles through a vaccination mechanism of immune particle swarm optimization, and updating individual extreme value P of each new particle i k (i = N +1, N +2, …, N + M) and global extremum for particle population
Step 6: calculating particle selection probability, and calculating selection probability P (x) of N + M particles in the population based on particle concentration according to formula (4-2) and formula (4-3) i )(i＝1,2,3,…,N+M)；
And 7: creating and updating a memory library, preferentially selecting S particles with the largest fitness value in the current particle population by adopting an elite retention strategy, and preferentially storing the S particles into an immune memory library; then selecting K-S high-quality particles from the rest N + M-S particle populations according to the population particle selection probability and storing the K-S high-quality particles into an immune memory library;
and 8: forming a parent group, and selecting N high-quality particles from the N + M particle groups according to the particle selection probability of the particle groups to form a new particle group B k ；
And step 9: particle renewal for a new particle population B k The speed, the position, the fitness value and the individual extreme value of the particle of all the particles are updated;
step 10: merging the particle groups, and combining K excellent particles in the current immune memory bank with the current particle group B k Merging, eliminating K inferior particles with relatively poor adaptability value in the current merged particle swarm to form a new particle swarm A with the capacity of N k (ii) a And (4) jumping to the step 3, and continuing the algorithm iteration.
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| CN115549091A (en) * | 2022-11-03 | 2022-12-30 | 国网山东省电力公司经济技术研究院 | Multi-target control method and system for energy storage system |
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