CN109449973B - Energy optimization method for rail transit power supply system containing photovoltaic and energy storage - Google Patents
Energy optimization method for rail transit power supply system containing photovoltaic and energy storage Download PDFInfo
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Abstract
The application provides a track traffic power supply system energy optimization method containing photovoltaic and energy storage, which comprises the following steps: step 1: data initialization mainly comprises an energy optimization model of a rail transit traction power supply system and various parameters related to an improved particle swarm algorithm; step 2: establishing a rail transit energy optimization model which comprises a photovoltaic power generation model, an energy storage model and a rail transit traction power supply system model, and setting an optimization objective function and corresponding constraint conditions by taking the optimal economy, namely the lowest running cost as a target; and step 3: an energy management model of the system is solved by adopting an improved particle swarm algorithm, and the optimal output point of photovoltaic power generation, energy storage and a large power grid is found out through iterative optimization.
Description
Technical Field
The invention relates to the technical field of rail transit electrification and automation, in particular to an energy optimization method for a rail transit power supply system containing photovoltaic and energy storage.
Background
In recent years, energy crisis and environmental pollution are increasing, so renewable energy technologies such as wind power generation and photovoltaic power generation are being researched and applied in a large quantity. Among them, clean, reliable, safe and maintenance-free photovoltaic power generation is one of the new energy sources with highest acceptance and acceptance.
Meanwhile, the scale of the rail transit construction in China is rapidly increased, and the scale is expected to increase to 4.5 kilometers by 2030. In the aspect of urban rail transit, more than 40 cities in China are approved to build urban rail transit. It is expected that the rail transit in China will still be in a rapid development period in the next decade. The rail transit is used as a high-energy consumption unit for high-speed development, and the energy efficiency level of the rail transit is urgently required to be improved, so that the aims of energy conservation and emission reduction are fulfilled.
The rail transit power supply system is distributed along the driving line, daily load is relatively stable, and favorable conditions are provided for access of distributed renewable energy sources such as photovoltaic and the like and energy storage. Photovoltaic and energy storage are connected into the rail transit system, so that the on-site consumption of distributed energy sources is facilitated, and the energy consumption of rail transit is reduced. In view of the above, the invention provides an energy optimization method for a rail transit power supply system with photovoltaic and energy storage access, and aims to ensure economic and green operation of the rail transit power supply system.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an energy optimization method for a rail transit power supply system containing photovoltaic and energy storage.
A rail transit power supply system energy optimization method containing photovoltaic and energy storage comprises the following steps:
step 1: initializing data, wherein the initialized data comprises: parameters related to an energy optimization model of the rail transit traction power supply system in the step 2 and parameters related to the improved particle swarm algorithm in the step 3; wherein, the parameters related to the step 2 comprise: the system comprises a temperature coefficient, illumination intensity under standard test conditions, photovoltaic cell array temperature and maximum output power value, maximum and minimum energy storage capacity of an energy storage system, photovoltaic power generation unit operation maintenance coefficient and regression parameters; the parameters involved in step 3 include: the method comprises the following steps of (1) counting particles, particle space dimensions, iteration times, inertia weight values, learning factors and initial particle positions;
step 2: establishing a photovoltaic power generation model, an energy storage model and a rail transit traction power supply system model, and setting an optimization objective function and corresponding constraint conditions by taking the optimal economy, namely the lowest running cost as a target;
the method specifically comprises the following steps:
the calculation formula of the output power of the photovoltaic array is as follows:
wherein, PPV-tThe output power of the photovoltaic cell array is G (t) when the illumination intensity is G (t); gSTC、TSTC、PSTCRespectively the illumination intensity, the photovoltaic cell array temperature and the maximum output power value under the standard test condition; τ is the temperature coefficient, and T (t) is the surface temperature of the photovoltaic array at time t;
the energy management model of the energy storage system is as follows:
wherein E isS(0) Is the initial storage capacity, P, of the energy storage systemS(k) The charging and discharging power of the energy storage system in a period k (k is more than or equal to 1 and less than or equal to t); pS(t) is the charge and discharge power of the energy storage system during the period t,
maximum and minimum energy storage capacities of the energy storage system, respectively; etaC、ηDRespectively representing the charging efficiency and the discharging efficiency of the energy storage system;
the calculation formula of the rail transit traction load power is as follows:
Pload-t＝a1+b2Xt+εt (4)
wherein, Pload-tTraction load power of rail transit at time t，XtThe passenger flow at the time t; epsilontThe load fluctuation caused by other factors is subjected to normal distribution; a is1、b2Is a regression parameter, a1＝-0.271，b2＝1.781；
With the goal of optimal economy, i.e. lowest running cost, an optimization objective function is set, the objective function is shown as formulas (5) and (6), and the corresponding constraint conditions are set as shown in (7) to (12):
Fbuy-t＝fPbuy-t (6)
wherein, minF is the lowest cost of system operation, delta is the operation maintenance coefficient of the photovoltaic power generation unit, and delta is 0.0095 yuan/kWh, PPV-tThe active power output of photovoltaic power generation in the t hour is N, N is a time unit, N is 24 hours, Fbuy-tIt is the cost required for purchasing electricity from the large power grid at the t hour, FbatFor energy storage system reset costs, FmagIs the system operation monitoring and management cost, f is the real-time unit electricity price of the large power grid, Pbuy-tPurchasing power to the large power grid at the tth hour;
the constraint conditions comprise a power balance constraint formula (7), a photovoltaic power generation unit generated energy constraint formula (8) and an energy storage system charge state constraint formula (9-11); a photovoltaic power generation and large power grid transmission capacity constraint formula (12); pDG-tIs the generated power P of the photovoltaic andor energy storage system in any time periodPV-tIs the generated power of the photovoltaic power generation unit at the moment t, Pbuy-tIs the output power of the large power grid, Pload-tFor load power, PS(t) is the charging and discharging power of the energy storage unit at the moment t,
respectively the minimum and maximum charge-discharge power of the energy storage unit,
respectively the minimum and maximum power generation output of the photovoltaic power generation unit, ES(t) is the capacity of the energy storage unit at time t,
respectively the minimum and maximum energy storage capacity, SOC of the energy storage systemt+1And SOCtIs the state of charge, P, of the energy storage system at two adjacent momentsS-tIs the output power of the energy storage system, Pline(t) is the interactive power of the photovoltaic power generation system and the power grid at the moment t,
the upper and lower limit values of the interaction power of the photovoltaic power generation system and the large power grid are set;
and step 3: solving formulas (5) and (6) by adopting the initialization data obtained in the step (1) to obtain the minimum running cost minF in the state of the initialization data; the corresponding output power of the large power grid and the output power of the energy storage system are respectively obtained by the formulas (7) and (11), and under the condition that the constraint conditions (7) to (12) are met, the particle speed and the particle position are updated according to the formulas (17) to (19);
w＝wmax-(wmax-wmin)*t/MaxDt (19)
and 4, step 4: and (4) re-executing the step (3) to perform iterative solution by making the iteration number k equal to k +1 until the iteration number reaches the set iteration upper limit number, namely k equal to kmaxThen, the minimum value minF of the objective function is obtained, and the output power P of the large power grid in the state is obtainedbuy-tAnd the output power P of the energy storage systemS-tNamely the optimal output point of photovoltaic power generation, energy storage and a large power grid.
Has the advantages that:
a multi-source multi-storage type rail transit power supply system formed by connecting various distributed renewable energy sources and energy storage devices into a rail transit power supply system is an important path for future development of the rail transit system and is also a core means for saving energy and reducing consumption of the system. According to the energy optimization method for the rail transit power supply system, under the working condition that photovoltaic and energy storage are connected into the rail transit power supply system, the energy optimization regulation strategy of cooperation of renewable energy sources, energy storage and electric locomotives is formulated, the fluctuation of electric locomotive loads, the randomness of the renewable energy sources and the variability of the operating condition of the energy storage system can be effectively adapted, and the economy of the rail transit power supply system is improved. Meanwhile, the nearby consumption of renewable energy sources is realized, and guidance is provided for planning and operating the access of various distributed energy sources and energy storage devices to the rail transit power supply system.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a flow chart of the algorithm of the present invention;
FIG. 3 is a topology diagram of a test system of the present invention;
FIG. 4 is a graph illustrating photovoltaic power generation output and rail transit traction load prediction according to the present invention;
FIG. 5 is the output power of the large power grid and energy storage system of the present invention;
FIG. 6 shows the difference between the photovoltaic power generation and the rail transit traction load according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to the method, a photovoltaic power generation and energy storage device is connected into a rail transit traction power supply system, energy management modeling is carried out on a photovoltaic power generation part, then rail transit traction load is predicted by using a regression equation, then the optimal economy is taken as a target, and constraint conditions are set by considering elements such as power balance, the power generation amount of a photovoltaic power generation unit, the charge state of the energy storage system, photovoltaic power generation and the transmission capacity of a large power grid. And finally, performing energy optimization solution on the system by adopting an improved particle swarm algorithm, finding out an optimal output point and matching capacity, and obtaining an optimal output scheme and corresponding operation cost.
An excellent energy management system can greatly improve the working efficiency of each unit and is important for realizing economic, efficient and reliable operation of the system. The system composition structure diagram of the invention is shown in figure 1, the invention considers that the photovoltaic power generation unit is preferentially used for supplying power to the rail transit traction power supply system, and when the photovoltaic power generation is residual, the redundant electric quantity is stored in the energy storage device; when the photovoltaic power generation can not meet the requirement of a traction power supply system, the energy storage system is preferentially used for discharging; and when the energy storage system is discharged to the lower limit but cannot meet the traction power supply requirement, purchasing power to the large power grid to ensure the normal operation of the system.
Next, the photovoltaic power generation model, the energy storage model, and the rail transit traction power supply system model in fig. 1 are described in detail first.
Photovoltaic power generation model
Among various new energy sources, solar energy is one of the most accepted and approved clean energy sources. The photovoltaic power generation which is inexhaustible and inexhaustible, and integrates cleanness, reliability, safety, universality, long service life, maintenance-free property and economy has an important position in a long-term energy strategy.
The photovoltaic power generation utilizes the photovoltaic effect of the solar panel, and under a certain voltage, the output power of the photovoltaic power generation array is increased along with the increase of illumination intensity. According to statistical data, the illumination intensity is distributed from Beta within a certain time, and the calculation formula of the output power of the photovoltaic array is as follows:
wherein, PPV-tThe output power of the photovoltaic cell array is G (t) when the illumination intensity is G (t); gSTC、TSTC、PSTCRespectively, the standard test conditions (1000W/m)2Illumination intensity at 25 ℃, photovoltaic cell array temperature and maximum output power value; tau is temperature coefficient, tau is-0.45; and T (t) is the surface temperature of the photovoltaic array at time t.
Energy storage model
Because the rail transit traction load has an obvious peak valley period, the power balance is ensured in the peak period, and only the power output of a power grid can be increased under the condition that the load cannot be thrown; during the valley period, no excess electric energy is wasted, so that a system (an energy storage device or a peak load device) needs to be established for managing the energy. In comparison, the investment cost of the energy storage system is lower than the construction cost of the peak load device. Among various energy storage devices, the energy storage system not only has high safety and small maintenance amount, but also has low cost. Therefore, the energy storage system is selected to store energy. The energy management model of the energy storage system is as follows:
wherein E isS(0) Is the initial storage capacity, P, of the energy storage systemS(k) The charging and discharging power of the energy storage system in a period k (k is more than or equal to 1 and less than or equal to t); pS(t) is the charge and discharge power of the energy storage system during the period t,
maximum and minimum energy storage capacities of the energy storage system, respectively; etaC、ηDRespectively representing the charge and discharge efficiency of the energy storage system.
Rail transit traction power supply system model
In recent years, the rapid development of rail transit construction has greatly increased the consumption of electric energy. The rail transit power supply system is subjected to energy management, so that the safe operation of the rail transit power supply system can be ensured, the system efficiency is improved, electric energy can be reasonably utilized, the resource waste is reduced, and the operation cost is saved. Energy management is carried out on a rail transit power supply system, and reasonable prediction needs to be carried out on rail transit traction load.
The dispatching of the traction power supply system is carried out by information fed back by the traction load of the rail transit in real time. The rail transit traction load size is closely related to factors such as train length, passenger flow volume and weather, and a linear regression analysis method can be generally adopted to predict the rail transit traction load, and the specific mathematical expression is as follows:
Pload-t＝a1+b2Xt+εt (4)
wherein, Pload-tIs the track traffic traction load power at time t, XtThe passenger flow at the time t; epsilontDue to other causesLoad fluctuations due to elements whose values follow a normal distribution; a is1、b2Is a regression parameter, a1＝-0.271，b2＝1.781。
Energy optimization method of photovoltaic traction power supply system based on improved particle swarm optimization
The main purpose of energy optimization is to ensure safe, stable, reliable and economic operation of the system and realize energy scheduling and optimized operation. Due to the fact that the photovoltaic power generation and energy storage device is connected into the rail transit power supply system, the structure is complex, controllability requirements are high, the system optimization problem is complicated, and solving is difficult. Therefore, electric power balance is one of the first issues to be considered for energy optimization.
However, photovoltaic power generation has instability, and power generation accidents such as system power imbalance and the like can be caused. Therefore, for optimal energy scheduling of a rail transit power supply system containing photovoltaic and energy storage, the output characteristics of photovoltaic and energy storage devices, the requirement of electric energy quality, the management information of the rail transit power supply system and the like must be comprehensively considered, economic scheduling and environmental protection are taken as targets, photovoltaic power generation is reasonably utilized, the balance of operating power in the system is guaranteed, continuous and stable power supply of loads is guaranteed, and therefore optimal configuration of stable operation of the system is achieved.
Firstly, sequentially introducing an objective function, a constraint condition and an improved particle swarm algorithm related to the energy optimization method, then giving specific steps of the energy optimization method of the rail transit power supply system based on the improved particle swarm algorithm, and finally carrying out simulation checking calculation on the proposed method by adopting an IEEE9 node system.
Objective function
The present invention targets economic dispatch to minimize operating costs. The cost related to photovoltaic power generation mainly comprises the operation and maintenance cost of a renewable power generation unit, the price trading cost between large power grids and the resetting cost of an energy storage system. If the power generation power, the load, the interaction power of the micro-grid and the large grid of the distributed power generation unit in unit time are constant, the objective function of the lowest operation cost is as follows:
Fbuy-t＝fPbuy-t (6)
wherein, minF is the lowest cost of system operation, delta is the operation maintenance coefficient of the photovoltaic power generation unit, and delta is 0.0095 yuan/kWh, PPV-tThe active power output of photovoltaic power generation in the t hour is N, N is a time unit, N is 24 hours, Fbuy-tIt is the cost required for purchasing electricity from the large power grid at the t hour, FbatFor energy storage system reset costs, FmagIs the system operation monitoring and management cost, f is the real-time unit electricity price of the large power grid, Pbuy-tAnd purchasing electric power to the large power grid at the tth hour.
Constraint conditions
The method comprises the following steps that formula (7) is power balance constraint, formula (8) is photovoltaic power generation unit power generation amount constraint, and formula (9-11) is energy storage system charge state constraint; formula (12) photovoltaicAnd power generation and large power grid transmission capacity constraint. The relevant variables are explained below: pDG-tThe generated power of the distributed units (photovoltaic and energy storage systems) in any time period; pPV-tIs the generated power of the photovoltaic power generation unit at the moment t, Pbuy-tIs the output power of the large power grid, Pload-tFor load power, PS(t) is the charging and discharging power of the energy storage unit at the moment t,
respectively the minimum and maximum charge-discharge power of the energy storage unit,
respectively the minimum and maximum power generation output of the photovoltaic power generation unit, ES(t) is the capacity of the energy storage unit at time t,
respectively the minimum and maximum energy storage capacity, SOC of the energy storage systemt+1And SOCtIs the state of charge, P, of the energy storage system at two adjacent momentsS-tIs the output power of the energy storage system, Pline(t) is the interactive power of the photovoltaic power generation system and the power grid at the moment t,
the upper and lower limit values of the interactive power of the photovoltaic power generation system and the large power grid are obtained.
Improved particle swarm algorithm
The particle swarm optimization is a swarm-based optimization algorithm, and an optimal solution is finally searched by starting from a random solution and through a continuous iteration process. The velocity and coordinates of each particle in the D-dimensional space at time t are shown in equations (13) and (14), respectively. At time t + 1, the particle updates its velocity and position by equations (15) and (16).
vi(t+1)＝vi(t)+c1·r1(pi(t)-xi(t))+c2·r2(pg(t)-xi(t))(15)
xi(t+1)＝xi(t)+vi(t+1) (16)
Because the invention considers more constraint conditions, in order to improve the optimization performance, the invention adopts the improved particle swarm optimization, and in the invention, the particle speed is the charge state of the energy storage system. The particle swarm algorithm speed and position updating formula is as follows:
w＝wmax-(wmax-wmin)*t/MaxDt (19)
where w is the inertial weight value, c1And c2Is a learning factor, rand is [0,1 ]]The values of the two groups are randomly selected,
the velocity and position of the particle i in K-dimensional space over n iterations;
the individual optimal solution and the global optimal solution from the particle i to the k generation.
The method steps of the present invention are described in detail below with reference to fig. 2:
step 1: data initialization mainly comprises the following steps: in the step 2, various parameters related to the energy optimization model of the rail transit traction power supply system are as follows: the system comprises a temperature coefficient, illumination intensity under standard test conditions, photovoltaic cell array temperature and maximum output power value, maximum and minimum energy storage capacity of an energy storage system, photovoltaic power generation unit operation maintenance coefficient and regression parameters; and 3, improving various parameters related to the particle swarm algorithm, namely: the method comprises the following steps of (1) counting particles, particle space dimensions, iteration times, inertia weight values, learning factors and initial particle positions;
step 2: establishing a rail transit energy optimization model, wherein the photovoltaic power generation model is shown as a formula (1), the energy storage model is shown as formulas (2) to (3), the rail transit traction power supply system model is shown as a formula (4), an optimization objective function is set as formulas (5) to (6) and corresponding constraint conditions are shown as formulas (17) to (19) by taking the optimal economy, namely the lowest running cost as a target;
and step 3: an improved particle swarm algorithm is adopted to solve a rail transit energy optimization model, and the optimal output point of photovoltaic power generation, energy storage and a large power grid is found out through iterative optimization. Comprises two steps, which are specifically set forth as follows:
step A: solving formulas (5) and (6) by adopting the initialization data obtained in the step (1) to obtain the minimum running cost minF in the state of the initialization data; the corresponding large grid output power and the output power of the energy storage system are determined by the equations (7) and (11), respectively. Updating the particle speed and the position according to equations (17) to (19) when the constraint conditions (7) to (12) are satisfied;
and B: and (5) carrying out iterative solution by making the iteration number k equal to k +1, and when the iteration number reaches the set iteration upper limit number, namely k equal to kmaxAnd then, finishing the algorithm, obtaining the minimum value (minF) of the target function, and solving the output power P of the large power grid in the statebuy-tAnd the output power P of the energy storage systemS-tThe optimal output point of the large power grid and the energy storage system is obtained, and the result is output; otherwise, returning to execute the step A.
System operation parameter selection and setting
The improved particle swarm optimization is adopted for optimization solution, the iteration times and the swarm size of the improved particle swarm optimization have obvious influence on the optimization result, and the power generation capacity matching of each unit is also important for stable and economic operation of the system. Therefore, when performing the simulation verification, it is necessary to set these parameters reasonably, as shown in table 1 and table 2, where table 1 shows the selected values of the basic parameters for improving the particle swarm optimization, and table 2 shows the power setting of each unit.
In addition, the invention selects an IEEE9 node system to perform simulation checking calculation on the proposed method, and the system structure diagram is shown in FIG. 3. The node 1 is connected with a large power grid, the node 2 is connected with a photovoltaic power generation unit, and the node 3 is connected with an energy storage system; the nodes 5, 6 and 8 are all connected with rail transit traction loads.
TABLE 1 improved particle swarm algorithm parameter settings
TABLE 2 Unit parameter settings
Photovoltaic power generation output and load prediction
The invention takes the No. 2 line of a metropolis subway as an example, carries out statistical prediction on the passenger flow in summer at a certain day, utilizes a linear regression prediction method to carry out predictive statistics on the traction load, and carries out predictive calculation on the generated output of photovoltaic power generation according to the weather condition of the day. The results are shown in FIG. 4.
As can be seen from FIG. 4, the traction load is substantially zero from 00:00 to 06: 00; the generating power of the photovoltaic module is basically zero. And when the six points are over, the traction load rises rapidly, the early peak phenomenon occurs, and the photovoltaic power generation amount can not meet the requirement of the traction load at the moment. Therefore, at this time, a large power grid is needed to supply power to the traction power supply system so as to maintain the power balance of the system and ensure the normal operation of the train.
At noon, the traction load is reduced, the photovoltaic module generates excessive power, and the part of electric quantity can be stored by the energy storage system. At the beginning of five pm, the traction load will have a late peak, but the illumination is gradually weakened, so that electricity needs to be purchased from a large power grid to meet the demand. The output power curves of the large power grid and the energy storage system in the MATLAB simulation environment are shown in FIG. 5, and the difference curve between the photovoltaic power generation output and the traction load is shown in FIG. 6.
As can be seen from the interactive power in fig. 6, the rail transit power supply system including photovoltaic and energy storage provided by the invention has two power demand peaks in the morning and in the evening according to the traveling situation of people. From 11:00 to 16:00, the difference between the traction load demand and the photovoltaic power generation output is small; at around 12:00 am and 16:00 pm, even photovoltaic power generation output above load demand occurs.
As can be seen from fig. 5, during the period from 00:00 to 06:00, due to the transmission capacity constraint between the power grid and the photovoltaic power generation, the power grid still outputs electric energy to the rail transit power supply system, and the power demand of the rail transit power supply system is very small at this time. According to the economic principle, the part of the electric energy input by the power grid to the rail transit power supply system can be stored by the energy storage system during the period. At 06:00 to 23: in the 00 period, the subway is in a normal operation stage, and the photovoltaic power generation amount cannot meet the requirement of a traction load in real time. At this time, the power grid is used as a hot standby power supply to ensure the normal operation of the system.
The invention relates to the current development situation and the trend of a distributed energy and rail transit power supply system, and provides an energy optimization method for connecting a photovoltaic power generation and energy storage device into a rail transit power supply system. According to the method, energy management models of the photovoltaic power generation, the energy storage device and the traction power supply system are respectively established, the minimum running cost is taken as a target function, constraint conditions such as power balance, the generating capacity of a photovoltaic power generation unit, the charge state of a storage battery, the transmission capacity of photovoltaic power generation and a large power grid are considered, and then an improved particle swarm algorithm is adopted for optimization solution. And finally, the optimal output point of the photovoltaic power generation system, the energy storage system and the large power grid is obtained. The result is analyzed, and the method not only ensures the continuous and stable operation of the rail transit power supply system, but also makes full use of photovoltaic power generation, and simultaneously reduces the power supply pressure of a large power grid, and achieves the purpose of energy optimization.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will 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; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (1)
1. A rail transit power supply system energy optimization method containing photovoltaic and energy storage is characterized by comprising the following steps:
step 1: initializing data, wherein the initialized data comprises: parameters related to an energy optimization model of the rail transit traction power supply system in the step 2 and parameters related to the improved particle swarm algorithm in the step 3; wherein, the parameters related to the step 2 comprise: the system comprises a temperature coefficient, illumination intensity under standard test conditions, photovoltaic cell array temperature and maximum output power value, maximum and minimum energy storage capacity of an energy storage system, photovoltaic power generation unit operation maintenance coefficient and regression parameters; the parameters involved in step 3 include: the method comprises the following steps of (1) counting particles, particle space dimensions, iteration times, inertia weight values, learning factors and initial particle positions;
step 2: establishing a photovoltaic power generation model, an energy storage model and a rail transit traction power supply system model, and setting an optimization objective function and corresponding constraint conditions by taking the optimal economy, namely the lowest running cost as a target;
the method specifically comprises the following steps:
the calculation formula of the output power of the photovoltaic array is as follows:
wherein, PPV-tThe output power of the photovoltaic cell array is G (t) when the illumination intensity is G (t); gSTC、TSTC、PSTCRespectively the illumination intensity, the photovoltaic cell array temperature and the maximum output power value under the standard test condition; τ is the temperature coefficient, and T (t) is the surface temperature of the photovoltaic array at time t;
the energy management model of the energy storage system is as follows:
wherein E isS(0) Is the initial storage capacity, P, of the energy storage systemS(k) The charging and discharging power of the energy storage system in a period k (k is more than or equal to 1 and less than or equal to t); pS(t) is the charge and discharge power of the energy storage system during the period t,
maximum and minimum energy storage capacities of the energy storage system, respectively; etaC、ηDRespectively representing the charging efficiency and the discharging efficiency of the energy storage system;
the calculation formula of the rail transit traction load power is as follows:
Pload-t＝a1+b2Xt+εt (4)
wherein, Pload-tIs the track traffic traction load power at time t, XtThe passenger flow at the time t; epsilontThe load fluctuation caused by other factors is subjected to normal distribution; a is1、b2Is a regression parameter, a1＝-0.271，b2＝1.781；
With the goal of optimal economy, i.e. lowest running cost, an optimization objective function is set, the objective function is shown as formulas (5) and (6), and the corresponding constraint conditions are set as shown in (7) to (12):
Fbuy-t＝fPbuy-t (6)
wherein min F is the lowest cost of system operation, delta is the operation maintenance coefficient of the photovoltaic power generation unit, and delta is 0.0095 yuan/kWh, PPV-tThe active power output of photovoltaic power generation in the t hour is N, N is a time unit, N is 24 hours, Fbuy-tIt is the cost required for purchasing electricity from the large power grid at the t hour, FbatFor energy storage system reset costs, FmagIs the system operation monitoring and management cost, f is the real-time unit electricity price of the large power grid, Pbuy-tPurchasing power to the large power grid at the tth hour;
the constraint conditions comprise a power balance constraint formula (7), a photovoltaic power generation unit generated energy constraint formula (8) and an energy storage system charge state constraint formula (9-11); a photovoltaic power generation and large power grid transmission capacity constraint formula (12); pDG-tIs the generated power P of the photovoltaic andor energy storage system in any time periodPV-tIs the generated power of the photovoltaic power generation unit at the moment t, Pbuy-tIs the output power of the large power grid, Pload-tFor load power, PS(t) is the charging and discharging power of the energy storage unit at the moment t,
respectively the minimum and maximum charge-discharge power of the energy storage unit,
respectively the minimum and maximum power generation output of the photovoltaic power generation unit, ES(t) is the capacity of the energy storage unit at time t,
respectively the minimum and maximum energy storage capacity, SOC of the energy storage systemt+1And SOCtIs the state of charge, P, of the energy storage system at two adjacent momentsS-tIs the output power of the energy storage system, Pline(t) is the interactive power of the photovoltaic power generation system and the power grid at the moment t,
the upper and lower limit values of the interaction power of the photovoltaic power generation system and the large power grid are set;
and step 3: solving formulas (5) and (6) by adopting the initialization data obtained in the step (1) to obtain the minimum running cost minF in the state of the initialization data; the corresponding output power of the large power grid and the output power of the energy storage system are respectively obtained by the formulas (7) and (11), and under the condition that the constraint conditions (7) to (12) are met, the particle speed and the particle position are updated according to the formulas (17) to (19);
w＝wmax-(wmax-wmin)*t/MaxDt (19)
where w is the inertial weight value, c1And c2Is a learning factor, rand is [0,1 ]]The values of the two groups are randomly selected,
the velocity and position of the particle i in K-dimensional space over n iterations;
the individual optimal solution and the global optimal solution from the particle i to the k generation are obtained;
and 4, step 4: and (4) re-executing the step (3) to perform iterative solution by making the iteration number k equal to k +1 until the iteration number reaches the set iteration upper limit number, namely k equal to kmaxThen, the minimum value minF of the objective function is obtained, and the output power P of the large power grid in the state is obtainedbuy-tAnd the output power P of the energy storage systemS-tNamely the optimal output point of photovoltaic power generation, energy storage and a large power grid.
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| CN111242420B (en) * | 2019-12-30 | 2023-11-28 | 湖南大学 | Comprehensive performance multidimensional evaluation method |
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