CN113489004A - Optimization method for economic operation of multi-energy power supply system - Google Patents

Optimization method for economic operation of multi-energy power supply system Download PDF

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CN113489004A
CN113489004A CN202110817996.5A CN202110817996A CN113489004A CN 113489004 A CN113489004 A CN 113489004A CN 202110817996 A CN202110817996 A CN 202110817996A CN 113489004 A CN113489004 A CN 113489004A
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power supply
supply system
energy
cost
constructing
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CN113489004B (en
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张国平
王维俊
米红菊
毛龙波
杨文�
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Army Service Academy of PLA
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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
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Abstract

A method for optimizing the economic operation of a multi-energy power supply system comprises the following specific steps: collecting historical data of photovoltaic power generation, wind power generation and user load, and predicting the power of the photovoltaic power generation, the wind power generation and the user load in each small time period in one day by using a self-adaptive weight particle swarm algorithm with process adjusting factors; constructing a power supply cost model of the multi-energy power supply system; constructing operation constraint conditions of all units of the multi-energy power supply system; selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system as optimization decision variables by taking the lowest power supply cost as a target function, randomly generating an initial population by taking the operation constraint conditions of each unit of the multi-energy power supply system as limits, and searching the optimal solution of the target function through an improved adaptive weight particle swarm algorithm; the operation of the multi-dimensional and multi-constraint multi-energy power supply system is optimized, and the economy of the multi-energy power supply system is improved.

Description

Optimization method for economic operation of multi-energy power supply system
Technical Field
The invention relates to the field of multi-energy power supply system regulation and control, in particular to an optimization method for economic operation of a multi-energy power supply system.
Background
The island reef is a springboard for expanding national ocean interests, is a main place for ocean economic activities, and is a leading-edge formation prepared by military warfare; the continuous and reliable power supply is a prerequisite condition for maintaining the survival of the armies and residents of the islands, is a basis for ensuring the stable operation of the observation equipment and the information platform, is also an important base stone for supporting the offshore reefs to maintain the offshore safety and fulfill rescue obligations, comprehensively utilizes abundant renewable resources of the reefs and the surrounding sea areas, establishes a multi-energy complementary power supply system, gradually becomes the best choice for ensuring the power supply of the reefs, and along with the development of scientific technology, most of the reefs in China have established the multi-energy military power supply system, the economic operation optimization of the multi-energy power supply system can effectively improve the energy utilization rate, reduce the power generation cost and the discharge of pollutants, and has important significance for the economic, environmental protection and reliable operation of the system.
Disclosure of Invention
The invention aims to provide a method for optimizing the economic operation of a multi-energy power supply system.
The invention aims to realize the technical scheme that the multi-energy power supply system comprises photovoltaic power generation, wind power generation, a diesel generator set and a storage battery energy storage system, and the method for optimizing the economic operation of the multi-energy power supply system comprises the following specific steps:
1) data acquisition: historical data of photovoltaic power generation, wind power generation and user load are collected, and the photovoltaic power generation power P of each small time period in one day is predicted by utilizing the adaptive weight particle swarm algorithm with process adjusting factorsPV(t) wind Power PWT(t) and user load power PLoad(t)；
2) Respectively constructing a fuel cost model of a diesel generator, an operation maintenance cost model of a distributed power supply in a multi-energy power supply system and a storage battery depreciation cost model to obtain a power supply cost model of the multi-energy power supply system;
3) constructing operation constraint conditions of all units of the multi-energy power supply system;
4) the method comprises the steps of taking the lowest power supply cost as an objective function, selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system as an optimization decision variable X, randomly generating an initial population by taking the operation constraint conditions of all units of a multi-energy power supply system as limits, and searching the optimal solution of the objective function through an improved adaptive weight particle swarm algorithm.
Further, the specific steps of constructing the power supply cost model of the multi-energy power supply system in the step 2) are as follows:
2-1) the fuel cost for constructing the diesel generator is as follows:
Figure BDA0003170893370000011
in the formula (1), KDE,fuelTo fuel consumption, PDE(t) is the output power of the diesel generator during the period t, CfuelIs the unit price of the fuel;
2-2) the operation and maintenance cost of the distributed power supply in the multi-energy power supply system is as follows:
Figure BDA0003170893370000021
in the formula (2), PWT(t)、PPV(t) and PDE(t) represents the power of the fan, the photovoltaic and the diesel engine, respectively; kOM,WT、KOM,PVAnd KOM,DEMaintaining a cost factor for the operation of each distributed power supply;
2-3) the depreciation cost for constructing the storage battery is as follows:
Figure BDA0003170893370000022
in the formula (3), CDP,BAT(t)，COM,BAT(t) is the cost of depreciation and maintenance of the accumulator, CBAT,repIs the battery replacement cost; elifetimeIs the total charge and discharge energy over the life cycle of the battery; pBAT(t) is the charging and discharging power of the accumulator in the time period of t, positive when discharging, negative when charging, KOM,BATThe cost coefficient of unit operation and maintenance of the storage battery is obtained;
2-4) constructing a power supply cost model of the multi-energy power supply system as follows:
f＝CF+COM+CBAT (4)
in the formula (4), f is the power supply cost of the multi-energy power supply system, CFIs the fuel cost of the diesel generator, COMIs the maintenance cost of the operation of the distributed power supply equipment, CBATWhich is the depreciation and running cost of the battery.
Further, the specific steps of constructing the operation constraint conditions of each unit of the multi-energy power supply system in the step 3) are as follows:
3-1) constructing a power balance constraint condition of the multi-energy power supply system as follows:
PLoad(t)＝PPV(t)+PWT(t)+PBAT(t)+PDE(t) (5)
in the formula (5), PLoad(t)、PPV(t)、PWT(t)、PBAT(t) and PDE(t) respectively representing the power of the load, the photovoltaic power generation, the wind power generation, the energy storage system and the diesel engine in a t period;
3-2) constructing the power generation capacity constraint of each unit of the multi-energy power supply system as follows:
Pi,min≤Pi(t)≤Pi,max (6)
in the formula (6), Pi(t) represents diesel engine, photovoltaic power generation and wind power generation output power; pi,max、Pi,minRespectively representing the upper and lower power limits of the power generation unit;
3-3) constructing the climbing constraint of a diesel engine in the diesel generator as follows:
Figure BDA0003170893370000023
in the formula (7), the reaction mixture is,
Figure BDA0003170893370000024
and
Figure BDA0003170893370000025
respectively representing the downward and upward climbing rates of the diesel engine;
3-4) constructing the constraint of the state of charge of the storage battery as follows:
Figure BDA0003170893370000031
in the formula (8), SOC (t) is the state of charge of the energy storage system in the period of t, PBAT(t) is the output power of the energy storage system during a period t, EBATIs the rated capacity of the energy storage system, Δ t represents the time interval, d is the self-discharge rate, ηchaAnd ηdischaRespectively representing the charge-discharge efficiency, SOC of the energy storage systemminAnd SOCmaxRespectively representing the upper limit and the lower limit of the charge state of the storage battery; SOC0And SOC24The states of charge of the battery at the initial and final stages of the schedule are shown, respectively.
Further, in the step 4), the step of finding the optimal solution of the objective function through the improved adaptive weight particle swarm algorithm with the lowest power supply cost as the objective function is as follows:
4-1) selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system for 24 hours a day as optimization decision variables X:
Figure BDA0003170893370000032
defining each optimization decision variable X as a particle, and defining a fitness function p of the adaptive weight particle swarm algorithm as the lowest power supply cost f of the multi-energy power supply system;
setting the output power parameter of the diesel generator set at the initial momentMaximum iteration number Iter of adaptive weight particle swarm algorithmmaxLearning factor c1、c2Maximum velocity of particle VmaxRandomly generating an initial population by taking the operation constraint condition as a limiting condition, wherein the population scale is N;
4-2) calculating the fitness value of each particle in the initial population through an objective function, finding the optimal value of the fitness in the population, and recording the optimal fitness value p of each individualiAnd a global optimum fitness value pg；
4-3) updating and calculating the maximum flight speed of the particles:
Figure BDA0003170893370000033
in the formula (10), k is the number of iterations, ItermaxMaximum number of iterations, VmaxThe maximum flight speed of the particles;
4-4) defining a process factor beta epsilon (0,1) if k<β·ItermaxThen, calculate a new inertial weight value:
Figure BDA0003170893370000034
in the formula (11), ωmaxAnd ωminRepresenting maximum and minimum inertial weight, respectively, fiAs a function of the fitness function corresponding to the particle, faveAnd fminRespectively representing the average value and the minimum value of the fitness function value;
updating the particle velocity vi(k +1) and position xi(k+1)：
vi(k+1)＝ωvi(k)+c1r1(pi(k)-xi(k))+c2r2(pg(k)-xi(k)) (12)
xi(k+1)＝xi(k)+vi(k+1),i＝1,2,...,n (13)
If k is not less than beta ItermaxWhen, add the compression factor χ:
Figure BDA0003170893370000041
updating the particle velocity vi(k +1) and position xi(k+1)：
vi(k+1)＝χ[vi(k)+c1r1(pi(k)-xi(k))+c2r2(pg(k)-xi(k))] (15)
xi(k+1)＝xi(k)+vi(k+1),i＝1,2,...,n (16)
4-5) calculating a new fitness value of each particle according to the position of each particle, and updating the fitness value of each particle and the optimal fitness value of the population;
4-6) judging whether the current iteration number k reaches the maximum iteration number ItermaxAnd if not, turning to the step 4-3) to continue iteration, otherwise, stopping iteration, and outputting a result as an optimal solution of the optimization decision.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the operation of the multi-dimensional and multi-constraint multi-energy power supply system is optimized, and the economy of the multi-energy power supply system is improved; 2. according to the method, the storage battery depreciation cost is taken as the economic operation cost, the operation condition of the island and reef multi-energy power supply system can be reflected more accurately, the power of each distributed power generation device in an optimized scheduling period is analyzed, and the optimized operation of the multi-energy power generation system is realized; 3. according to the method, the lowest power supply cost is taken as the target function, the optimal solution of the target function is searched through the improved adaptive weight particle swarm algorithm, and the result shows that the improved adaptive weight particle swarm algorithm has higher searching speed and better global optimization capability, and the time of the optimization process is reduced.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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The drawings of the present invention are described below.
Fig. 1 is a flowchart of the particle swarm algorithm IPSO improvement of the present invention.
Fig. 2 is a configuration diagram of a power supply system of the multi-energy power supply system of the invention.
FIG. 3 is a predicted power for photovoltaic, wind turbine and customer loads in accordance with the present invention.
FIG. 4 is a convergence curve of three particle swarm algorithms of the present invention.
Fig. 5 is a power curve of each distributed power source corresponding to the optimal solution obtained by using the improved PSO algorithm according to the present invention.
FIG. 6 is a diagram of the change of the SOC of the storage battery in one day according to the invention.
Fig. 7 is a cost distribution corresponding to the optimal operation scheme of the multi-energy power supply system of the invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
The method for optimizing the economic operation of the multi-energy power supply system comprises the following steps of:
1) data acquisition: historical data of photovoltaic power generation, wind power generation and user load are collected, and the photovoltaic power generation power P of each small time period in one day is predicted by utilizing the adaptive weight particle swarm algorithm with process adjusting factorsPV(t) wind Power PWT(t) and user load power PLoad(t)；
2) Respectively constructing a fuel cost model of a diesel generator, an operation maintenance cost model of a distributed power supply in a multi-energy power supply system and a storage battery depreciation cost model to obtain a power supply cost model of the multi-energy power supply system, and specifically comprising the following steps:
2-1) the fuel cost for constructing the diesel generator is as follows:
Figure BDA0003170893370000051
in the formula (17), KDE,fuelTo fuel consumption, PDE(t) is the output power of the diesel generator during the period t, CfuelIs the unit price of the fuel;
2-2) the operation and maintenance cost of the distributed power supply in the multi-energy power supply system is as follows:
Figure BDA0003170893370000052
in the formula (18), PWT(t)、PPV(t) and PDE(t) represents the power of the fan, the photovoltaic and the diesel engine, respectively; kOM,WT、KOM,PVAnd KOM,DEMaintaining a cost factor for the operation of each distributed power supply;
2-3) depreciation cost of the battery system is one of the optimization targets because the severe natural environment of the island accelerates the aging of the battery. Meanwhile, frequent charging and discharging may shorten the service life of the storage battery, thereby indirectly increasing the economic cost of the system. By converting the replacement cost of the storage battery into the operation cost, the influence of the service life of the storage battery on the operation cost can be more accurately reflected. Therefore, a battery depreciation cost model considering the replacement cost of the storage battery is designed as follows:
Figure BDA0003170893370000053
in the formula (19), CDP,BAT(t)，COM,BAT(t) is the cost of depreciation and maintenance of the accumulator, CBAT,repIs the battery replacement cost; elifetimeIs the total charge and discharge energy over the life cycle of the battery; pBAT(t) is the charging and discharging power of the accumulator in the time period of t, positive when discharging, negative when charging, KOM,BATCost system for operating and maintaining storage battery unitCounting;
generally, the total amount of charging and discharging energy cyclically used over the life cycle of a battery is substantially constant; the relationship between the total number of cycles before failure of the battery and the depth of discharge DOD can be described by a bi-exponential function:
Figure BDA0003170893370000061
in equation (20), NDOD is the total number of cycles before failure of the battery, DOD is the depth of discharge of the battery, and a parameter1To a5Available from battery manufacturers;
thus, at a given DOD, the total charge-discharge energy over the life of the battery is:
Elifetime＝2Erated·DOD·NDOD (21)；
2-4) constructing a power supply cost model of the multi-energy power supply system as follows:
f＝CF+COM+CBAT (22)
in the formula (22), f is the power supply cost of the multi-energy power supply system, CFIs the fuel cost of the diesel generator, COMIs the maintenance cost of the operation of the distributed power supply equipment, CBATWhich is the depreciation and running cost of the battery.
3) The method comprises the following steps of constructing operation constraint conditions of each unit of the multi-energy power supply system, and specifically comprising the following steps:
3-1) constructing a power balance constraint condition of the multi-energy power supply system as follows:
PLoad(t)＝PPV(t)+PWT(t)+PBAT(t)+PDE(t) (23)
in the formula (23), PLoad(t)、PPV(t)、PWT(t)、PBAT(t) and PDE(t) respectively representing the power of the load, the photovoltaic power generation, the wind power generation, the energy storage system and the diesel engine in a t period;
3-2) constructing the power generation capacity constraint of each unit of the multi-energy power supply system as follows:
Pi,min≤Pi(t)≤Pi,max (24)
in the formula (24), Pi(t) represents diesel engine, photovoltaic power generation and wind power generation output power; pi,max、Pi,minRespectively representing the upper and lower power limits of the power generation unit, wherein the minimum output power is zero and the maximum output power is the installed capacity of the photovoltaic power generation unit and the wind power generation unit; for a diesel generating set, in order to consider the operation economy and the system rotation standby, the maximum power and the minimum power can be set according to the recommendation of a manufacturer; for an energy storage system, when the battery is discharged, the power is positive, ranging from (0, P)max) When the battery is charged, the power is negative and ranges from (-P)max，0)；
3-3) constructing the climbing constraint of a diesel engine in the diesel generator as follows:
Figure BDA0003170893370000062
in the formula (25), the reaction mixture,
Figure BDA0003170893370000063
and
Figure BDA0003170893370000064
respectively representing the downward and upward climbing rates of the diesel engine;
3-4) constructing the constraint of the state of charge of the storage battery as follows:
Figure BDA0003170893370000071
in equation (26), SOC (t) is the state of charge of the energy storage system over time t, PBAT(t) is the output power of the energy storage system during a period t, EBATIs the rated capacity of the energy storage system, Δ t represents the time interval, δ is the self-discharge rate, ηchaAnd ηdischaRespectively representing the charge-discharge efficiency, SOC of the energy storage systemminAnd SOCmaxRespectively representing the upper limit and the lower limit of the charge state of the storage battery; SOC0And SOC24Respectively indicating the initial stages of schedulingAnd end-stage battery state-of-charge.
4) The method comprises the following steps of selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system as an optimization decision variable X by taking the lowest power supply cost as a target function, randomly generating an initial population by taking the operation constraint conditions of each unit of a multi-energy power supply system as limits, and searching the optimal solution of the target function through an improved adaptive weight particle swarm algorithm, wherein the method specifically comprises the following steps:
4-1) selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system for 24 hours a day as optimization decision variables X:
Figure BDA0003170893370000072
defining each optimization decision variable X as a particle, and defining a fitness function p of the adaptive weight particle swarm algorithm as the lowest power supply cost f of the multi-energy power supply system;
setting the output power parameter of the diesel generating set at the initial moment, and self-adapting the maximum iteration number Iter of the weight particle swarm algorithmmaxLearning factor c1、c2Maximum velocity of particle VmaxTaking the operation constraint condition as a limiting condition,
randomly generating an initial population, wherein the population scale is N;
4-2) calculating the fitness value of each particle in the initial population through an objective function, finding the optimal value of the fitness in the population, and recording the optimal fitness value p of each individualiAnd a global optimum fitness value pg；
4-3) updating and calculating the maximum flight speed of the particles:
Figure BDA0003170893370000073
in the formula (28), k is the number of iterations, ItermaxMaximum number of iterations, VmaxThe maximum flight speed of the particles;
4-4) defining a process factor beta epsilon (0,1) if k<β·ItermaxThen, calculate a new inertial weight value:
Figure BDA0003170893370000074
in the formula (29), ω ismaxAnd ωminRepresenting maximum and minimum inertial weight, respectively, fiAs a function of the fitness function corresponding to the particle, faveAnd fminRespectively representing the average value and the minimum value of the fitness function value;
updating the particle velocity vi(k +1) and position xi(k+1)：
vi(k+1)＝ωvi(k)+c1r1(pi(k)-xi(k))+c2r2(pg(k)-xi(k)) (30)
xi(k+1)＝xi(k)+vi(k+1),i＝1,2,...,n (31)
If k is not less than beta ItermaxWhen, add the compression factor χ:
Figure BDA0003170893370000081
updating the particle velocity vi(k +1) and position xi(k+1)：
vi(k+1)＝χ[vi(k)+c1r1(pi(k)-xi(k))+c2r2(pg(k)-xi(k))] (33)
xi(k+1)＝xi(k)+vi(k+1),i＝1,2,...,n (34)
4-5) calculating a new fitness value of each particle according to the position of each particle, and updating the fitness value of each particle and the optimal fitness value of the population;
4-6) judging whether the current iteration number k reaches the maximum iteration number ItermaxAnd if not, turning to the step 4-3) to continue iteration, otherwise, stopping iteration, and outputting a result as an optimal solution of the optimization decision.
By adopting the method, taking an island wind-solar-diesel-storage multi-energy military power supply system as an example, the optimization model considers a day-ahead economic operation plan taking 1 hour as a time interval, and requires that the diesel generator is shut down in the morning between 0:00 and 5:00 so as to avoid noise generated during night diesel engine operation from influencing official rest. The operating parameters of each distributed power generation device are shown in table 1, the upward and downward climbing rates of the diesel generator are both 50kW, the storage battery parameters are shown in table 2, and the capacity of the energy storage system is 2000 kWh.
TABLE 1 operating parameters of Power Generation units
Figure BDA0003170893370000082
TABLE 2 Battery parameters
Figure BDA0003170893370000083
The photovoltaic power generation power, the wind power generation power and the user load power in the multi-energy power supply system are predicted by using the adaptive weight particle swarm algorithm with the process adjusting factors, as shown in fig. 3.
The method comprises the steps of respectively solving an operation optimization model of the island and reef multi-energy military power supply system by respectively adopting 3 different particle swarm algorithms, namely a standard particle swarm algorithm PSO, an adaptive weight particle swarm algorithm AWPSO and an improved particle swarm algorithm IPSO provided by the application, and obtaining fitness function value convergence curves of the different algorithms as shown in figure 4. It can be seen that, in the early stage, although the standard PSO algorithm has a certain optimizing capability, the standard PSO algorithm finally falls into a local extremum prematurely through 114 iterations, which indicates that the global searching capability of the standard PSO algorithm is poor; the Adaptive Weight Particle Swarm Optimization (AWPSO) shows stronger searching capability in the early searching stage, meanwhile, the convergence and the robustness are greatly superior to those of the standard PSO, the middle two times of the AWPSO falls into a local extreme value, the AWPSO can successfully jump out after multiple iterations, but finally falls into local optimum when the AWPSO is iterated for the 460 th time; in the improved particle swarm algorithm IPSO, a regulating factor beta is set to be 0.5, namely, the optimizing process is averagely divided into two stages, in the initial optimizing stage, the fitness function value searched by the improved algorithm is the largest, which shows that the improved algorithm has a wider searching range, although the convergence speed in the initial stage is not as good as that of the adaptive weight particle swarm algorithm AWPSO, the searched fitness function value is smaller than that of the AWPSO algorithm after 100 iterations, and the fitness function value is continuously reduced along with the increase of the iteration times, so that better convergence and robustness are shown; and entering the later optimization stage, the algorithm starts to perform local search, and still can find a smaller fitness function value. The comparison result shows that: for the operation optimization problem of a multi-dimensional and multi-constrained multi-energy system, the improved particle swarm algorithm IPSO shows better global optimization capability, the search speed is high, and the robustness of the convergence process is stronger.
The power curve of each distributed power supply corresponding to the optimal scheme obtained by using the improved particle swarm algorithm IPSO is shown in FIG. 5; as can be seen from the figure, when the sun is well illuminated, i.e. from 10 am to 18 pm, the storage battery is always in a charged state, and absorbs about 1215kWh of clean electric energy; when the diesel generator stops running and the load is light in the morning at 0:00-5:00, the storage battery releases 1210kWh of electric energy totally, and the electric energy absorbed and released by the storage battery is basically consistent all day long. After 5 hours of discharging in the morning, the SOC of the storage battery is reduced from 0.81 at the highest time to 0.26 and approaches the lower limit of the SOC of the storage battery, then the diesel generator is started, the load is continuously increased until the peak value in the morning is reached, the output of the diesel generator is gradually increased along with the increase of the load, the discharging power of the storage battery is gradually reduced until the SOC of the storage battery reaches the minimum value of 0.22, the storage battery stops discharging, and the storage battery starts to enter a charging state. In the morning, the charging power of the storage battery is increased along with the gradual increase of the photovoltaic output, the output of the diesel generator is gradually reduced, when the sunlight is maximum, from 12 noon to 15 pm, the photovoltaic power generation is used as a main power supply, and the diesel engine operates at the minimum power. In the afternoon, as the load gradually increases until the late peak is reached, the renewable energy output is reduced, and the power generated by the firewood is increased until the maximum output power is reached, so as to meet the electric energy demand of the late peak of the load. In order to ensure that the storage battery has enough energy to independently bear the load between 0:00 and 5:00 in the morning, the storage battery is charged when renewable resources are good, and the storage battery is also charged by using the diesel engine when the load is low at night. As shown in fig. 6, the SOC of the battery at the beginning and the end of an optimal scheduling period is substantially equal, and the SOC value satisfies the SOC constraint condition.
The cost distribution corresponding to the optimal operation scheme of the multi-energy power supply system is shown in fig. 7, and the fuel cost of the diesel generator accounts for 64.81 percent of the total operation expenditure; once the fuel price rises or the transport distance increases, the fuel cost will rise further, which will result in a further increase in the proportion of the fuel cost to the power generation operation cost. Therefore, abundant renewable resources such as wind energy, solar energy and the like are utilized on the spot at the offshore island to construct a multi-energy power supply system, so that the power supply cost can be reduced and the power supply reliability can be improved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (4)

1. The method for optimizing the economic operation of the multi-energy power supply system comprises the following steps of:
1) data acquisition: historical data of photovoltaic power generation, wind power generation and user load are collected, and the photovoltaic power generation power P of each small time period in one day is predicted by utilizing the adaptive weight particle swarm algorithm with process adjusting factorsPV(t) wind Power PWT(t) and user load power PLoad(t)；
2) Respectively constructing a fuel cost model of a diesel generator, an operation maintenance cost model of a distributed power supply in a multi-energy power supply system and a storage battery depreciation cost model to obtain a power supply cost model of the multi-energy power supply system;
3) constructing operation constraint conditions of all units of the multi-energy power supply system;
4) the method comprises the steps of taking the lowest power supply cost as an objective function, selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system as an optimization decision variable X, randomly generating an initial population by taking the operation constraint conditions of all units of a multi-energy power supply system as limits, and searching the optimal solution of the objective function through an improved adaptive weight particle swarm algorithm.
2. The method for optimizing the economic operation of the multi-energy power supply system according to claim 1, wherein the specific steps of constructing the power supply cost model of the multi-energy power supply system in the step 2) are as follows:
2-1) the fuel cost for constructing the diesel generator is as follows:
Figure FDA0003170893360000011
in the formula (1), KDE,fuelTo fuel consumption, PDE(t) is the output power of the diesel generator during the period t, CfuelIs the unit price of the fuel;
2-2) the operation and maintenance cost of the distributed power supply in the multi-energy power supply system is as follows:
Figure FDA0003170893360000012
in the formula (2), PWT(t)、PPV(t) and PDE(t) represents the power of the fan, the photovoltaic and the diesel engine, respectively; kOM,WT、KOMPV and KOM,DEMaintaining a cost factor for the operation of each distributed power supply;
2-3) the depreciation cost for constructing the storage battery is as follows:
Figure FDA0003170893360000013
in the formula (3), CDP,BAT(t)，COM,BAT(t) is the cost of depreciation and maintenance of the accumulator, CBAT,repIs the battery replacement cost; elifetimeIs the total charge and discharge energy over the life cycle of the battery; pBAT(t) is the charging and discharging power of the accumulator in the time period of t, positive when discharging, negative when charging, KOM,BATThe cost coefficient of unit operation and maintenance of the storage battery is obtained;
2-4) constructing a power supply cost model of the multi-energy power supply system as follows:
f＝CF+COM+CBAT (4)
in the formula (4), f is the power supply cost of the multi-energy power supply system, CFIs the fuel cost of the diesel generator, COMIs the maintenance cost of the operation of the distributed power supply equipment, CBATWhich is the depreciation and running cost of the battery.
3. The method for optimizing the economic operation of the multi-energy power supply system according to claim 1, wherein the specific steps of constructing the operation constraint conditions of the units of the multi-energy power supply system in the step 3) are as follows:
3-1) constructing a power balance constraint condition of the multi-energy power supply system as follows:
PLoad(t)＝PPV(t)+PWT(t)+PBAT(t)+PDE(t) (5)
in the formula (5), PLoad(t)、PPV(t)、PWT(t)、PBAT(t) and PDE(t) respectively representing the power of the load, the photovoltaic power generation, the wind power generation, the energy storage system and the diesel engine in a t period;
3-2) constructing the power generation capacity constraint of each unit of the multi-energy power supply system as follows:
Pi,min≤Pi(t)≤Pi,max (6)
in the formula (6), Pi(t) represents diesel engine, photovoltaic power generation and wind power generation output power; pi,max、Pi,minRespectively representing the upper and lower power limits of the power generation unit;
3-3) constructing the climbing constraint of a diesel engine in the diesel generator as follows:
Figure FDA0003170893360000021
in the formula (7), the reaction mixture is,
Figure FDA0003170893360000022
and
Figure FDA0003170893360000023
respectively representing the downward and upward climbing rates of the diesel engine;
3-4) constructing the constraint of the state of charge of the storage battery as follows:
Figure FDA0003170893360000024
in the formula (8), SOC (t) is the state of charge of the energy storage system in the period of t, PBAT(t) is the output power of the energy storage system during a period t, EBATIs the rated capacity of the energy storage system, Δ t represents the time interval, d is the self-discharge rate, ηchaAnd ηdischaRespectively representing the charge-discharge efficiency, SOC of the energy storage systemminAnd SOCmaxRespectively representing the upper limit and the lower limit of the charge state of the storage battery; SOC0And SOC24The states of charge of the battery at the initial and final stages of the schedule are shown, respectively.
4. The method for optimizing the economic operation of the multi-energy power supply system according to claim 1, wherein in the step 4), the power supply cost is the lowest as an objective function, and the step of finding the optimal solution of the objective function through the improved adaptive weight particle swarm algorithm comprises the following steps:
4-1) selecting the output power of a diesel engine generator and the charge and discharge power of an energy storage system for 24 hours a day as optimization decision variables X:
Figure FDA0003170893360000031
defining each optimization decision variable X as a particle, and defining a fitness function p of the adaptive weight particle swarm algorithm as the lowest power supply cost f of the multi-energy power supply system;
setting the output power parameter of the diesel generating set at the initial moment, and self-adapting the maximum iteration number Iter of the weight particle swarm algorithmmaxLearning factor c1、c2Maximum velocity of particle VmaxRandomly generating an initial population by taking the operation constraint condition as a limiting condition, wherein the population scale is N;
4-2) calculating the fitness value of each particle in the initial population through an objective function, finding the optimal value of the fitness in the population, and recording the optimal fitness value p of each individualiAnd a global optimum fitness value pg；
4-3) updating and calculating the maximum flight speed of the particles:
Figure FDA0003170893360000032
in the formula (10), k is the number of iterations, ItermaxMaximum number of iterations, VmaxThe maximum flight speed of the particles;
4-4) defining a process factor beta epsilon (0,1) if k<β·ItermaxThen, calculate a new inertial weight value:
Figure FDA0003170893360000033
in the formula (11), ωmaxAnd ωminRepresenting maximum and minimum inertial weight, respectively, fiAs a function of the fitness function corresponding to the particle, faveAnd fminRespectively representing the average value and the minimum value of the fitness function value;
updating the particle velocity vi(k +1) and position xi(k+1)：
vi(k+1)＝ωvi(k)+c1r1(pi(k)-xi(k))+c2r2(pg(k)-xi(k)) (12)
xi(k+1)＝xi(k)+vi(k+1),i＝1,2,...,n (13)
If k is not less than beta ItermaxWhen, add the compression factor χ:
Figure FDA0003170893360000034
updating the particle velocity vi(k +1) and position xi(k+1)：
vi(k+1)＝χ[vi(k)+c1r1(pi(k)-xi(k))+c2r2(pg(k)-xi(k))] (15)
xi(k+1)＝xi(k)+vi(k+1),i＝1,2,...,n (16)
4-5) calculating a new fitness value of each particle according to the position of each particle, and updating the fitness value of each particle and the optimal fitness value of the population;
4-6) judging whether the current iteration number k reaches the maximum iteration number ItermaxAnd if not, turning to the step 4-3) to continue iteration, otherwise, stopping iteration, and outputting a result as an optimal solution of the optimization decision.
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