CN113489004B - Method for optimizing economic operation of multi-energy power supply system - Google Patents
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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
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 is a springboard for expanding the national ocean interests, is a main place for ocean economic activities, and is a frontier position for military preparation; the continuous and reliable power supply is a prerequisite condition for maintaining the survival of the army and residents on the residential island, is a basis for ensuring the stable operation of the observation and communication equipment and the informatization platform, is also an important base stone for supporting the offshore island reef to maintain the offshore safety and fulfilling rescue obligations, comprehensively utilizes abundant renewable resources of the island reef and the surrounding sea area thereof, establishes a multi-energy complementary power supply system, gradually becomes the best choice for ensuring the power supply of the island reef, and as most of the island reefs in China establish multi-energy military power supply systems along with the development of science and technology, 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 factors PV (t) wind Power P WT (t) and user load power P Load (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:
in the formula (1), K DE,fuel To fuel consumption rate, P DE (t) is the output power of the diesel generator during the period t, C fuel Is 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:
in the formula (2), P WT (t)、P PV (t) and P DE (t) represents the power of the fan, the photovoltaic and the diesel engine, respectively; k OM,WT 、K OM,PV And K OM,DE Maintaining a cost factor for the operation of each distributed power supply;
2-3) the depreciation cost for constructing the storage battery is as follows:
in the formula (3), C DP,BAT (t)，C OM,BAT (t) is the cost of depreciation and maintenance of the battery, C BAT,rep Is the storage battery replacement cost; e lifetime Is the total charge and discharge energy over the life cycle of the battery; p BAT (t) is the charging and discharging power of the storage battery in the period of t, positive during discharging, negative during charging, K OM,BAT The 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＝C F +C OM +C BAT (4)
in the formula (4), f is the power supply cost of the multi-energy power supply system, C F Is the fuel cost of the diesel generator, C OM Is the distributed power equipment operating maintenance cost, C BAT Which 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:
P Load (t)＝P PV (t)+P WT (t)+P BAT (t)+P DE (t) (5)
in the formula (5), P Load (t)、P PV (t)、P WT (t)、P BAT (t) and P DE (t) respectively representing the power of a load, photovoltaic power generation, wind power generation, an energy storage system and a diesel engine in a t time period;
3-2) constructing the power generation capacity constraint of each unit of the multi-energy power supply system as follows:
P i,min ≤P i (t)≤P i,max (6)
in formula (6), P i (t) represents diesel engine, photovoltaic power generation and wind power generation output power; p is i,max 、P i,min Respectively representing the upper and lower power limits of the power generation unit;
3-3) the climbing restraint of the diesel engine built in the diesel generator is as follows:
in the formula (7), the reaction mixture is,
and &>
Respectively representing the downward slope rate and the upward slope rate of the diesel engine;
3-4) constructing the constraint of the state of charge of the storage battery as follows:
in equation (8), SOC (t) is the state of charge of the energy storage system over a period of t, P BAT (t) is the output power of the energy storage system during a period t, E BAT Is the rated capacity of the energy storage system, Δ t represents the time interval, d is the self-discharge rate, η cha And η discha Respectively representing energy-storage systemsCharge-discharge efficiency, SOC min And SOC max Respectively representing the upper limit and the lower limit of the charge state of the storage battery; SOC 0 And SOC 24 The 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 by taking 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:
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 the maximum iteration number Iter of the self-adaptive weight particle swarm algorithm max Learning factor c 1 、c 2 Maximum velocity of travel V of particles max Randomly 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 individual i And a global optimum fitness value p g ；
4-3) updating and calculating the maximum flight speed of the particles:
in the formula (10), k is the number of iterations, iter max Maximum number of iterations, V max The maximum flight speed of the particles;
4-4) defining a process factor beta epsilon (0,1) if k<β·Iter max Then, calculate a new inertial weight value:
in the formula (11), ω max And ω min Representing maximum and minimum inertial weight, respectively, f i As a function of the fitness function corresponding to the particle, f ave And f min Respectively representing the average value and the minimum value of the fitness function value;
update the particle velocity v i (k + 1) and position x i (k+1)：
v i (k+1)＝ωv i (k)+c 1 r 1 (p i (k)-x i (k))+c 2 r 2 (p g (k)-x i (k)) (12)
x i (k+1)＝x i (k)+v i (k+1),i＝1,2,...,n (13)
If k is not less than beta Iter max When, add the compression factor χ:
updating the particle velocity v i (k + 1) and position x i (k+1)：
v i (k+1)＝χ[v i (k)+c 1 r 1 (p i (k)-x i (k))+c 2 r 2 (p g (k)-x i (k))] (15)
x i (k+1)＝x i (k)+v i (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 Iter max And 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 a 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, so that 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.
Drawings
The drawings of the present invention are described below.
FIG. 1 is a flow chart of the particle swarm optimization IPSO of the 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 user 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 battery SOC variation curve during one day according to the present 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 factors PV (t) wind Power P WT (t) and user load power P Load (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:
in the formula (17), K DE,fuel To fuel consumption, P DE (t) is the output power of the diesel generator during the period t, C fuel Is 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:
in the formula (18), P WT (t)、P PV (t) and P DE (t) represents the power of the fan, the photovoltaic and the diesel engine, respectively; k OM,WT 、K OM,PV And K OM,DE Maintaining 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:
in the formula (19), C DP,BAT (t)，C OM,BAT (t) is the cost of depreciation and maintenance of the battery, C BAT,rep Is the battery replacement cost; e lifetime Is the total charge and discharge energy over the life cycle of the battery; p BAT (t) is the charging and discharging power of the accumulator in the time period of t, positive when discharging, negative when charging, K OM,BAT The cost coefficient of unit operation and maintenance of the storage battery is obtained;
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:
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 parameter 1 To a 5 Available from battery manufacturers;
thus, at a given DOD, the total charge-discharge energy over the life of the battery is:
E lifetime ＝2E rated ·DOD·N DOD (21)；
2-4) constructing a power supply cost model of the multi-energy power supply system as follows:
f＝C F +C OM +C BAT (22)
in the formula (22), f is the power supply cost of the multi-energy power supply system, C F Is the fuel cost of the diesel generator, C OM Is the maintenance cost of the operation of the distributed power supply equipment, C BAT Which 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:
P Load (t)＝P PV (t)+P WT (t)+P BAT (t)+P DE (t) (23)
in the formula (23), P Load (t)、P PV (t)、P WT (t)、P BAT (t) and P DE (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:
P i,min ≤P i (t)≤P i,max (24)
in formula (24), P i (t) represents diesel engine, photovoltaic power generation and wind power generation output power; p i,max 、P i,min Respectively 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 generator set, in order to consider the operating economy and the system spinning reserve thereof, the maximum and minimum power thereof can be set according to the recommendations of a manufacturer; for an energy storage system, when the battery is discharged, the power is positive, ranging (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:
in the formula (25), the reaction mixture,
and &>
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:
in equation (26), SOC (t) is the state of charge of the energy storage system over a period of t, P BAT (t) is the output power of the energy storage system during a period t, E BAT Is the rated capacity of the energy storage system, Δ t represents the time interval, δ is the self-discharge rate, η cha And η discha Respectively representing the charge-discharge efficiency, SOC of the energy storage system min And SOC max Respectively representing the upper limit and the lower limit of the charge state of the storage battery; SOC 0 And SOC 24 The states of charge of the battery at the initial and final stages of the schedule are shown, respectively.
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:
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-adaptingMaximum iteration number Iter of weight particle swarm optimization max Learning factor c 1 、c 2 Maximum velocity of particle V max Taking 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 individual i And a global optimum fitness value p g ；
4-3) updating and calculating the maximum flight speed of the particles:
in the formula (28), k is the number of iterations, iter max Maximum number of iterations, V max The maximum flight speed of the particles;
4-4) defining a process factor beta epsilon (0,1) if k<β·Iter max Then a new inertial weight value is calculated:
in the formula (29), ω is max And ω min Representing maximum and minimum inertial weight, respectively, f i Is the fitness function value corresponding to the particle, f ave And f min Respectively representing the average value and the minimum value of the fitness function value;
updating the particle velocity v i (k + 1) and position x i (k+1)：
v i (k+1)＝ωv i (k)+c 1 r 1 (p i (k)-x i (k))+c 2 r 2 (p g (k)-x i (k)) (30)
x i (k+1)＝x i (k)+v i (k+1),i＝1,2,...,n (31)
If k is not less than beta. Iter max When, add the compression factor χ:
updating the particle velocity v i (k + 1) and position x i (k+1)：
v i (k+1)＝χ[v i (k)+c 1 r 1 (p i (k)-x i (k))+c 2 r 2 (p g (k)-x i (k))] (33)
x i (k+1)＝x i (k)+v i (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 Iter max And 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, light, firewood and energy 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 stopped in 0-00 a in the morning to avoid noise generated during night firewood 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 2000kWh.
TABLE 1 operating parameters of Power Generation units
TABLE 2 Battery parameters
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 too early after 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, an adjusting factor beta =0.5 is set, namely, an optimization process is averagely divided into two stages, in the initial optimization stage, the fitness function value searched by the improved algorithm is the largest, which shows that the improved algorithm has a wider search 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, in the better sun illumination, from 10 am to 18 pm, the battery is always charged, and absorbs about 1215kWh of clean electric energy; when the diesel generator is shut down and the load is light in the morning at 0. After discharging for 5 hours in the morning, the SOC of the storage battery is reduced to 0.26 from 0.81 at the highest time and approaches to 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. 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 (2)
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 using a self-adaptive weight particle swarm algorithm with process adjusting factors PV (t) wind Power P WT (t) and user load power P Load (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) 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 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 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:
in the formula (1), K DE,fuel To fuel consumption rate, P DE (t) is the output power of the diesel generator during the period t, C fuel Is 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:
in the formula (2), P WT (t)、P PV (t) and P DE (t) represents the power of the fan, the photovoltaic and the diesel engine, respectively; k OM,WT 、K OM,PV And K OM,DE Maintaining a cost factor for the operation of each distributed power supply;
2-3) the depreciation cost for constructing the storage battery is as follows:
in the formula (3), C DP,BAT (t)，C OM,BAT (t) is the cost of depreciation and maintenance of the battery, C BAT,rep Is the battery replacement cost; e lifetime Is the total charge and discharge energy over the life cycle of the battery; p is BAT (t) is the charging and discharging power of the storage battery in the period of t, positive during discharging, negative during charging, K OM,BAT A cost coefficient for operating and maintaining the storage battery unit;
2-4) constructing a power supply cost model of the multi-energy power supply system as follows:
f＝C F +C OM +C BAT (4)
in formula (4), f is the power supply cost of the multi-energy power supply system, C F Is the fuel cost of the diesel generator, C OM Is the distributed power equipment operating maintenance cost, C BAT The depreciation and running cost of the storage battery are reduced;
in the step 4), the lowest power supply cost is taken as a target function, and the step of finding the optimal solution of the target 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:
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 algorithm max Learning factor c 1 、c 2 Maximum velocity of particle V max Randomly generating an initial population with the population scale of N by taking the operation constraint condition as a limiting condition;
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 individual i And a global optimum fitness value p g ；
4-3) updating and calculating the maximum flight speed of the particles:
in the formula (10), k is the number of iterations, iter max Maximum number of iterations, V max The maximum flight speed of the particles;
4-4) defining a process factor beta epsilon (0,1) if k<β·Iter max Then a new inertial weight value is calculated:
in the formula (11), ω max And ω min Representing maximum and minimum inertial weight, respectively, f i As a function of the fitness function corresponding to the particle, f ave And f min Respectively representing the average value and the minimum value of the fitness function value;
update the particle velocity v i (k + 1) and position x i (k+1)：
If k is not less than beta. Iter max When, add compression factor χ:
updating the particle velocity v i (k + 1) and position x i (k+1)：
v i (k+1)＝χ[v i (k)+c 1 r 1 (p i (k)-x i (k))+c 2 r 2 (p g (k)-x i (k))] (11)
x i (k+1)＝x i (k)+v i (k+1),i＝1,2,...,n (12)
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 Iter max And 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.
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 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:
P Load (t)＝P PV (t)+P WT (t)+P BAT (t)+P DE (t) (13)
in the formula (5), P Load (t)、P PV (t)、P WT (t)、P BAT (t) and P DE (t) represents load, photovoltaic power generation, and wind power generation, respectivelyThe power of the energy storage system and the diesel engine in the t period;
3-2) constructing the power generation capacity constraint of each unit of the multi-energy power supply system as follows:
P i,min ≤P i (t)≤P i,max (14)
in the formula (6), P i (t) represents diesel engine, photovoltaic power generation and wind power generation output power; p i,max 、P i,min Respectively representing the upper and lower power limits of the power generation unit;
3-3) constructing the climbing constraint of the diesel engine in the diesel generator is as follows:
in the formula (7), the reaction mixture is,
and &>
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:
in equation (8), SOC (t) is the state of charge of the energy storage system over a period of t, P BAT (t) is the output power of the energy storage system during a period t, E BAT Is the rated capacity of the energy storage system, Δ t represents the time interval, δ is the self-discharge rate, η cha And η discha Respectively representing the charge-discharge efficiency, SOC of the energy storage system min And SOC max Respectively representing the upper limit and the lower limit of the charge state of the storage battery; SOC 0 And SOC 24 The states of charge of the battery at the initial and final stages of the schedule are shown, respectively.
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