阅读说明：本技术 基于时序生产模拟的直流电网储能配置方法、设备及介质 (Direct-current power grid energy storage configuration method, device and medium based on time sequence production simulation ) 是由 龚贤夫 彭穗 卢洵 刘新苗 彭虹桥 陈鸿琳 万昱娴 李明杨 吴云芸 薛熙臻 艾小 于 2021-09-24 设计创作，主要内容包括：本发明公开了一种基于时序生产模拟的直流电网储能配置方法、设备及介质,该方法包括：结合电力系统全年运行成本、时序生产模拟确定的系统年度运行方案以及第一约束条件,构建内层规划模型；采用滚动求解方式对所述内层规划模型中所述系统年度运行方案进行优化,并在所述内层规划模型计算所述系统全年运行成本后,获取优化后的内层规划模型；将所述优化后的内层规划模型传输至外层规划模型,所述外层规划模型采用粒子群算法进行优化求解,迭代获取储能容量配置和优化布局方案。本发明采用时序生产模拟与粒子群算法,结合内层规划模型与外层规划模型,提高直流电网储能的配置效率。(The invention discloses a direct current power grid energy storage configuration method, equipment and medium based on time sequence production simulation, wherein the method comprises the following steps: constructing an inner layer planning model by combining the annual operation cost of the power system, a system annual operation scheme determined by time sequence production simulation and a first constraint condition; optimizing the annual system operation scheme in the inner-layer planning model by adopting a rolling solving mode, and obtaining the optimized inner-layer planning model after the inner-layer planning model calculates the annual system operation cost; and transmitting the optimized inner layer planning model to an outer layer planning model, wherein the outer layer planning model adopts a particle swarm algorithm to carry out optimization solution, and the energy storage capacity configuration and the optimized layout scheme are obtained in an iterative manner. The method adopts the time sequence production simulation and the particle swarm algorithm, combines the inner layer planning model and the outer layer planning model, and improves the configuration efficiency of the energy storage of the direct current power grid.)

1. A direct current power grid energy storage configuration method based on time sequence production simulation is characterized by comprising the following steps:

constructing an inner layer planning model by combining the annual operation cost of the power system, a system annual operation scheme determined by time sequence production simulation and a first constraint condition;

optimizing the annual system operation scheme in the inner layer planning model by adopting a rolling solving mode, and obtaining the optimized inner layer planning model after the inner layer planning model calculates the annual operation cost of the power system;

and transmitting the optimized inner layer planning model to an outer layer planning model, wherein the outer layer planning model adopts a particle swarm algorithm to carry out optimization solution, and the energy storage capacity configuration and the optimized layout scheme are obtained in an iterative manner.

2. The direct-current power grid energy storage configuration method based on time series production simulation according to claim 1, wherein the outer layer planning model specifically comprises:

and constructing an objective function of the outer layer planning model according to a battery energy storage configuration scheme, the comprehensive cost of the power system and a second constraint condition, wherein the second constraint condition comprises battery energy storage distribution point constraint, and the comprehensive cost of the power system comprises the coal consumption cost of a thermal power unit, the starting and stopping cost of the thermal power unit, the investment cost of battery energy storage, the line loss cost and the wind abandoning cost.

3. The direct-current power grid energy storage configuration method based on time series production simulation of claim 2, wherein an objective function F of the outer layer planning model₂The method specifically comprises the following steps:

minF₂＝C_coal+C_UD+C_inv+C_loss+C_wind；

in the formula, C_coalRepresents the coal consumption cost of the fire-power unit, C_UDRepresenting the start-stop cost of the thermal power generating unit, C_invRepresents the investment cost of energy storage of the battery, C_lossRepresents the line loss cost, C_windRepresenting the wind curtailment cost;

the battery energy storage distribution point constraint is as follows:

in the formula, Q_BS,iRepresenting a positive variable for representing the configuration number of the battery energy storage units of the node i,representing the maximum number of battery storage unit allowed configurations at node i.

4. The direct current power grid energy storage configuration method based on time series production simulation according to claim 3, wherein the inner layer planning model specifically comprises:

and constructing an objective function of the inner layer planning model according to the coal consumption cost of the thermal power generating unit, the start-stop cost of the thermal power generating unit, the line loss cost, the wind abandoning cost and the first constraint condition, wherein the first constraint condition comprises a node power balance constraint, a node voltage constraint, a line capacity constraint, a rotary standby constraint, a unit output constraint, a unit climbing and start-stop power constraint, a start-stop time constraint, a unit start-stop state switching constraint, a battery energy storage power constraint, a battery energy storage energy state constraint, a pumped storage power station power constraint, a pumped storage power station energy state constraint and a rolling cycle end moment thermal power generating unit operation state constraint.

5. The direct current power grid energy storage configuration method based on time series production simulation, according to claim 4, wherein an objective function F of the inner layer planning model₁The method specifically comprises the following steps:

minF₁＝C_coal+C_UD+C_loss+C_wind；

in the formula, C_coalRepresents the coal consumption cost of the fire-power unit, C_UDRepresenting the start-stop cost of the thermal power generating unit, C_lossRepresents the line loss cost, C_windRepresenting the wind curtailment cost;

the node power balance constraint is as follows:

where λ (j) represents the set of all nodes connected to node j in the power system, P_br,ij,tRepresenting the power, P, transferred by node i to node j at time t via the line_wind,j,tIs the wind power grid-connected power P at the node j at the time t_gen,j,tThe output of the thermal power generating unit at the node j at the time t,andrespectively the pumping storage power and the battery energy storage discharge power at the node j at the time t,andrespectively the pumping storage and battery energy storage charging power at the j position of the node at the time t, P_L,j,tIs the load power at the node j at the time t, R_i,jIs the resistance of the line between node i and node j, V_j,tRepresents the voltage at node j at time t, V_i,tRepresents the voltage at node i at time t;

the node voltage constraints are as follows:

V_i,min≤V_i,t≤V_i,max；

in the formula, V_i,max、V_i,minRespectively an upper voltage limit and a lower voltage limit at a node i;

the line capacity constraints are as follows:

|P_br,ij,t|≤P_br,ij，max；

in the formula, P_br,ij，maxIs the maximum transmission power of the line between the nodes i, j;

the rotational standby constraint is as follows:

in the formula (I), the compound is shown in the specification,andP _g,tin order to consider the upper limit and the lower limit of the output of the unit g at the time t after climbing,andthe reserve capacity requirements of positive and negative rotation at the moment t of the system, N_gNumber of thermal power generating units, P_g,tRepresenting the output of the unit g at the time t;

the unit output constraints are as follows:

in the formula, P_g,tRepresents the output of the unit g at the time t, I_g,tA variable 0-1 representing the running state of the unit g at the moment t,is the upper limit of the g output of the unit,the lower limit of the g output of the unit;

the unit climbing and starting and stopping power constraint is as follows:

in the formula (I), the compound is shown in the specification,for the limit value of the upward slope of the unit g,in order to limit the climbing of the unit g,maximum power of unit g at start-up and shut-down, I_g,t-10-1 variable, P, representing the operating state of the unit g at time t-1_g,t-1Representing the output of the unit g at the t-1 moment;

the on-off time constraints are as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the minimum continuous operation and the down time of the unit g, I_g,kA variable 0-1 representing the running state of the unit g at the moment k, wherein T represents the hours in a planning single day;

the unit start-stop state switching constraint is as follows:

in the formula u_g,t，v_g,tA variable 0-1, I, representing the start and stop actions of the recording unit g at time t_g,t+1A variable 0-1 representing the running state of the unit g at the moment of t + 1;

the battery energy storage power constraint is as follows:

in the formula (I), the compound is shown in the specification,andrespectively charging and discharging the battery at the characterization node i at the moment tThe 0-1 variation of the electrical state,represents the battery energy storage discharge power at the node i at the time t,representing the battery energy storage charging power at a node i at the time t;

the battery energy storage energy state constraint is as follows:

in the formula eta_BS,cCharging efficiency, η, for storing energy in a battery_BS,dicDischarge efficiency for storing energy in the battery, E_BS,i0Is the initial energy at the node i and,representing the maximum energy at the i-node,represents the battery energy storage discharge power at the node i at the time tau,representing the battery energy storage charging power at a node i at the time tau;

the pumped storage power station power constraint is as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the 0-1 variable of the charging and discharging state of the pumped storage power station at the moment t,in order to extract the maximum generated power of the power station,in order to extract the minimum generated power of the power station,is the maximum water storage power of the pumped storage power station,the minimum water storage power of the pumped storage power station;

the energy state constraint of the pumped storage power station is as follows:

in the formula eta_PS,c、η_PS,dicRespectively charging and discharging efficiency of the energy storage power station, E_PS0For the energy state of the pumped storage power station at the initial moment of the planning period,in order to pump the maximum energy capacity of the power station,representing the pumped charging power at node i at time tau,representing the pumped storage discharge power at a node i at the time tau;

and the thermal power generating unit operation state constraint at the end of the rolling period is as follows:

T_g0≥0；

in the formula I_g0The operating state of the thermal power generating unit at the end of the last rolling period, U_g0For corresponding continued running time, D_g0Is a continuous down time.

6. The direct current power grid energy storage configuration method based on time series production simulation according to claim 1, wherein the system annual operation scheme in the inner layer planning model is optimized by using a rolling solution method, specifically:

taking the unit operation state and the corresponding time at the end of the current rolling period as initial conditions of the unit operation of the next rolling period, performing rollback calculation when time sequence production simulation rolling solution is performed to the kth day, if no solution exists, performing simulation according to the electric power system operation from the kth 1 to the kth day, if no solution exists for M consecutive days, performing continuous rollback and simulating the electric power system operation from the kth-M to the kth day until the time sequence production simulation rolling solution or M is larger than or equal to M, if a solution exists, continuing the time sequence production simulation rolling solution process, and if M is larger than or equal to M, reinitializing the particles until the time sequence production simulation is completed all year round, and obtaining the result of the time sequence production simulation, wherein M represents the maximum number of days allowing rollback;

and optimizing the annual operating scheme of the system containing the stored energy according to the result of the time sequence production simulation.

7. The direct-current power grid energy storage configuration method based on time series production simulation of claim 6, wherein the optimized inner layer planning model is transmitted to an outer layer planning model, and the outer layer planning model is optimized and solved by adopting a particle swarm optimization, specifically:

and the outer layer planning model comprises the steps of calculating the particle fitness of the outer layer planning model by adopting a particle swarm algorithm according to the objective function value calculated by the optimized inner layer planning model, updating the individual historical optimal position of each particle of the model and the group particle historical optimal position of the population according to the particle fitness, and further updating the information of each particle of the model in an iterative manner.

8. The direct-current power grid energy storage configuration method based on time series production simulation of claim 7, wherein the iteratively obtaining the energy storage capacity configuration and optimizing the layout scheme specifically comprises:

and if the current iteration times are more than or equal to the maximum iteration times for which the optimal fitness keeps continuously unchanged, and the iteration times for which the optimal fitness value of the current population keeps continuously unchanged are more than or equal to the maximum iteration times for which the optimal fitness keeps continuously unchanged, acquiring the energy storage capacity configuration and optimized layout scheme.

9. A computer terminal device, comprising:

one or more processors;

a memory coupled to the processor for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-8 for dc grid energy storage configuration based on sequential production simulation.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for configuring dc grid energy storage according to any one of claims 1 to 8 based on a time-series production simulation.

Technical Field

The invention relates to the technical field of electrical engineering, in particular to a direct-current power grid energy storage configuration method, device and medium based on time sequence production simulation.

Background

With the gradual exhaustion of traditional fossil energy and the increasingly prominent environmental problems, the development of new energy and the reduction of carbon dioxide emission have become common international consensus. The wind power generation technology develops rapidly by virtue of the advantages of mature technology, low power generation cost, wide resource distribution range and the like, and is developed and utilized on a large scale, however, the reverse distribution characteristic of wind power resources and load centers in China and the uncertainty of wind power generation obstruct the large-scale consumption of wind power, the flexible direct current transmission technology is combined with the energy storage technology, and a feasible mode is provided for realizing the large-scale wind power consumption in China. In addition, because the energy storage device is higher in cost at the present stage, when the new energy consumption capability of the power system is improved by configuring the energy storage, the economy of the configuration scheme needs to be considered, and therefore the energy storage configuration in the direct current power grid needs to be reasonably planned.

The loads and the new energy power generation in the power system have obvious seasonal characteristics and day-night characteristics, and the starting and stopping of the thermal power generating unit are related to the running state of the thermal power generating unit in a long period of time. However, the existing energy storage configuration research rarely considers the time sequence operation characteristics of the power system, for example, the current dc power grid energy storage configuration method based on the typical daily curve is difficult to reflect the actual operation condition of the power system because there is no coupling relationship between the typical days, and if the time sequence operation characteristics of the power system are not fully considered, the planning result of the energy storage configuration is likely to have a large deviation from the actual operation and demand condition.

Disclosure of Invention

The invention aims to provide a direct-current power grid energy storage configuration method based on time sequence production simulation, and the direct-current power grid energy storage configuration method is used for solving the problem that in the prior art, a planning result of energy storage configuration has large deviation with actual operation and demand conditions.

In order to achieve the above object, the present invention provides a dc power grid energy storage configuration method based on time sequence production simulation, including:

constructing an inner layer planning model by combining the annual operation cost of the power system, a system annual operation scheme determined by time sequence production simulation and a first constraint condition;

optimizing the annual system operation scheme in the inner-layer planning model by adopting a rolling solving mode, and obtaining the optimized inner-layer planning model after the inner-layer planning model calculates the annual system operation cost;

and transmitting the optimized inner layer planning model to an outer layer planning model, wherein the outer layer planning model adopts a particle swarm algorithm to carry out optimization solution, and the energy storage capacity configuration and the optimized layout scheme are obtained in an iterative manner.

Preferably, the outer layer planning model specifically includes:

and constructing an objective function of the outer layer planning model according to a battery energy storage configuration scheme, the comprehensive cost of the power system and a second constraint condition, wherein the second constraint condition comprises battery energy storage distribution point constraint, and the comprehensive cost of the power system comprises the coal consumption cost of a thermal power unit, the starting and stopping cost of the thermal power unit, the investment cost of battery energy storage, the line loss cost and the wind abandoning cost.

Preferably, the objective function F of the outer layer planning model₂The method specifically comprises the following steps:

minF₂＝C_coal+C_UD+C_inv+C_loss+C_wind；

in the formula, C_coalRepresents the coal consumption cost of the fire-power unit, C_UDRepresenting the start-stop cost of the thermal power generating unit, C_invRepresents the investment cost of energy storage of the battery, C_lossRepresents the line loss cost, C_windRepresenting the wind curtailment cost;

the battery energy storage distribution point constraint is as follows:

in the formula, Q_BS,iRepresenting a positive variable for representing the configuration number of the battery energy storage units of the node i,representing the maximum number of battery storage unit allowed configurations at node i.

Preferably, the inner layer planning model specifically includes:

and constructing an objective function of the inner layer planning model according to the coal consumption cost of the thermal power generating unit, the start-stop cost of the thermal power generating unit, the line loss cost, the wind abandoning cost and the first constraint condition, wherein the first constraint condition comprises a node power balance constraint, a node voltage constraint, a line capacity constraint, a rotary standby constraint, a unit output constraint, a unit climbing and start-stop power constraint, a start-stop time constraint, a unit start-stop state switching constraint, a battery energy storage power constraint, a battery energy storage energy state constraint, a pumped storage power station power constraint, a pumped storage power station energy state constraint and a rolling cycle end moment thermal power generating unit operation state constraint.

Preferably, the objective function F of the inner layer planning model₁The method specifically comprises the following steps:

minF₁＝C_coal+C_UD+C_loss+C_wind；

in the formula, C_coalRepresents the coal consumption cost of the fire-power unit, C_UDRepresenting the start-stop cost of the thermal power generating unit, C_lossRepresents the line loss cost, C_windRepresenting the wind curtailment cost;

the node power balance constraint is as follows:

where λ (j) represents the set of all nodes connected to node j in the power system, P_br,ij,tRepresenting the power, P, transferred by node i to node j at time t via the line_wind,j,tIs the wind power grid-connected power P at the node j at the time t_gen,j,tThe output of the thermal power generating unit at the node j at the time t,andrespectively the pumping storage power and the battery energy storage discharge power at the node j at the time t,andrespectively the pumping storage and battery energy storage charging power at the j position of the node at the time t, P_L,j,tIs the load power at the node j at the time t, R_i,jIs the resistance of the line between node i and node j, V_j,tRepresents the voltage at node j at time t, V_i,tTo representthe voltage at the node i at time t;

the node voltage constraints are as follows:

V_i,min≤V_i,t≤V_i,max；

in the formula, V_i,max、V_i,minRespectively an upper voltage limit and a lower voltage limit at a node i;

the line capacity constraints are as follows:

|P_br,ij,t|≤P_br,ij，max；

in the formula, P_br,ij，maxIs the maximum transmission power of the line between the nodes i, j;

the rotational standby constraint is as follows:

in the formula (I), the compound is shown in the specification,andP _g,tin order to consider the upper limit and the lower limit of the output of the unit g at the time t after climbing,andthe reserve capacity requirements of positive and negative rotation at the moment t of the system, N_gNumber of thermal power generating units, P_g,tRepresenting the output of the unit g at the time t;

the unit output constraints are as follows:

in the formula, P_g,tIndicating the unit g is at tForce exerted by the engraving, I_g,tA variable 0-1 representing the running state of the unit g at the moment t,is the upper limit of the g output of the unit,the lower limit of the g output of the unit;

the unit climbing and starting and stopping power constraint is as follows:

in the formula (I), the compound is shown in the specification,for the limit value of the upward slope of the unit g,in order to limit the climbing of the unit g,maximum power of unit g at start-up and shut-down, I_g,t-10-1 variable, P, representing the operating state of the unit g at time t-1_g,t-1Representing the output of the unit g at the t-1 moment;

the on-off time constraints are as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the minimum continuous operation and the down time of the unit g, I_g,kA variable 0-1 representing the running state of the unit g at the moment k, wherein T represents the hours in a planning single day;

the unit start-stop state switching constraint is as follows:

in the formula u_g,t，v_g,tA variable 0-1, I, representing the start and stop actions of the recording unit g at time t_g,t+1A variable 0-1 representing the running state of the unit g at the moment of t + 1;

the battery energy storage power constraint is as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the 0-1 variable of the charging and discharging state of the battery at the node i at the moment of energy storage t,represents the battery energy storage discharge power at the node i at the time t,representing the battery energy storage charging power at a node i at the time t;

the battery energy storage energy state constraint is as follows:

in the formula eta_BS,cCharging efficiency, η, for storing energy in a battery_BS,dicDischarge efficiency for storing energy in the battery, E_BS,i0Is the initial energy at the node i and,representing the maximum energy at the i-node,represents the battery energy storage discharge power at the node i at the time tau,representing the battery energy storage charging power at a node i at the time tau;

the pumped storage power station power constraint is as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the 0-1 variable of the charging and discharging state of the pumped storage power station at the moment t,in order to extract the maximum generated power of the power station,in order to extract the minimum generated power of the power station,is the maximum water storage power of the pumped storage power station,the minimum water storage power of the pumped storage power station;

the energy state constraint of the pumped storage power station is as follows:

in the formula eta_PS,c、η_PS,dicRespectively charging and discharging efficiency of the energy storage power station, E_PS0For the energy state of the pumped storage power station at the initial moment of the planning period,in order to pump the maximum energy capacity of the power station,representing the pumped charging power at node i at time tau,representing the pumped storage discharge power at a node i at the time tau;

and the thermal power generating unit operation state constraint at the end of the rolling period is as follows:

T_g0≥0；

in the formula I_g0The operating state of the thermal power generating unit at the end of the last rolling period, U_g0For corresponding continued running time, D_g0Is a continuous down time.

Preferably, the system annual operation scheme in the inner layer planning model is optimized by using a rolling solution, specifically:

taking the unit operation state and the corresponding time at the end of the current rolling period as initial conditions of the unit operation of the next rolling period, performing rollback calculation when time sequence production simulation rolling solution is performed to the kth day, if no solution exists, performing simulation according to the electric power system operation from the kth 1 to the kth day, if no solution exists for M consecutive days, performing continuous rollback and simulating the electric power system operation from the kth-M to the kth day until the time sequence production simulation rolling solution or M is larger than or equal to M, if a solution exists, continuing the time sequence production simulation rolling solution process, and if M is larger than or equal to M, reinitializing the particles until the time sequence production simulation is completed all year round, and obtaining the result of the time sequence production simulation, wherein M represents the maximum number of days allowing rollback;

and optimizing the annual operating scheme of the system containing the stored energy according to the result of the time sequence production simulation.

Preferably, the optimized inner layer planning model is transmitted to an outer layer planning model, and the outer layer planning model is optimized and solved by adopting a particle swarm algorithm, specifically:

and the outer layer planning model comprises the steps of calculating the particle fitness of the outer layer planning model by adopting a particle swarm algorithm according to the objective function value calculated by the optimized inner layer planning model, updating the individual historical optimal position of each particle of the model and the group particle historical optimal position of the population according to the particle fitness, and further updating the information of each particle of the model in an iterative manner.

Preferably, the iteratively acquiring the energy storage capacity configuration and the optimized layout scheme specifically includes:

and if the current iteration times are more than or equal to the maximum iteration times for which the optimal fitness keeps continuously unchanged, and the iteration times for which the optimal fitness value of the current population keeps continuously unchanged are more than or equal to the maximum iteration times for which the optimal fitness keeps continuously unchanged, acquiring the energy storage capacity configuration and optimized layout scheme.

The present invention also provides a computer terminal device, comprising:

one or more processors and memory;

a memory coupled to the processor for storing one or more programs;

when the one or more programs are executed by the one or more processors, the one or more processors implement the method for configuring the energy storage of the dc power grid based on the time series production simulation according to any of the embodiments.

The present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for configuring energy storage of a dc power grid based on time series production simulation according to any of the embodiments described above.

Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of aiming at minimizing the comprehensive cost of the power system, fully considering the time sequence operation characteristic of the power system, combining a particle swarm algorithm with time sequence production simulation, and establishing a direct current power grid energy storage configuration double-layer planning model, wherein the inner-layer planning model optimizes an annual operation scheme of the power system containing energy storage in a rolling solving mode, and returns the calculated cost to the outer-layer planning model after calculating the cost of the system operating all the year round so as to iterate for multiple times to obtain an energy storage capacity configuration and an optimized layout scheme.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a dc power grid energy storage configuration method based on time series production simulation according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an exemplary topology of a multi-terminal DC system according to another embodiment of the present invention;

fig. 3 is a schematic flowchart of a dc power grid energy storage configuration method based on time series production simulation according to another embodiment of the present invention;

FIG. 4 is a PSO objective function value convergence curve provided by an embodiment of the present invention;

FIG. 5 is a graph of peak-to-valley difference rankings throughout the year for each single day according to another embodiment of the present invention;

FIG. 6 is a plot of annual single-day wind curtailment rate ranking for a batteryless energy storage configuration according to yet another embodiment of the present invention;

fig. 7 is a schematic structural diagram of a computer terminal device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.

It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1, an embodiment of the present invention provides a dc grid energy storage configuration method based on time sequence production simulation, including the following steps:

s101: and constructing an inner layer planning model by combining the annual operation cost of the power system, the annual operation scheme of the system determined by the time sequence production simulation and the first constraint condition.

Specifically, the time sequence production simulation is carried out based on historical data of load and new energy output, the data comprises time sequence correlation of the load and the new energy output, therefore, fine modeling can be carried out, coupling constraints such as thermal power unit start-stop and climbing power constraint in system operation are considered, the simulation result is more detailed due to the improvement of the model complexity, and the time sequence operation characteristic of the power system can be reflected. Based on the above advantages, the sequential production simulation technology is increasingly applied to the reliability, flexibility and new energy consumption capability assessment of the operation of the power system.

Constructing an objective function of an inner layer planning model according to the coal consumption cost, the start-stop cost, the line loss cost, the wind abandoning cost and a first constraint condition of the thermal power unit, wherein the first constraint condition comprises node power balance constraint, node voltage constraint, line capacity constraint, rotation standby constraint, unit output constraint, unit climbing and start-stop power constraint, start-stop time constraint, unit start-stop state switching constraint, battery energy storage power constraint, battery energy storage state constraint, pumped storage power station power constraint, pumped storage power station energy state constraint and thermal power unit operation state constraint at the end of a rolling period, and the objective function F of the inner layer planning model is₁The following are:

minF₁＝C_coal+C_UD+C_loss+C_wind；

in the formula, C_coalRepresents the coal consumption cost of the fire-power unit, C_UDIndicating the start-stop cost of the thermal power generating unit, C_lossRepresents the line loss cost, C_windRepresenting the wind abandoning cost, wherein the coal consumption cost of the thermal power generating unit, the start-stop cost of the thermal power generating unit, the line loss cost and the wind abandoning cost are calculated as follows:

in the formula, N_dayFor typical days in the planning cycle, T for hours in a planning single day, N_gNumber of thermal power generating units, N_nodeNumber of system nodes, N_brIs the number of system lines, N_wIs the number of wind power nodes, F_g,CIs the coal consumption cost function of the unit g, P_g,t,dThe output level of the unit g at the time of d days and t days, F_g,CFunction value and P_g,t,dIn a quadratic relationship, C_UDIn order to reduce the start-stop cost of the thermal power generating unit,andthe starting and stopping costs, u, of the unit g, respectively_g,t,dAnd v_g,t,dRespectively a variable 0-1, k recording the starting and stopping actions of the unit g at d days and t moments_lossFor the line loss penalty factor,is the line loss, V, of the line i at the time t of d days_{i_h,t,d}For the voltage of the i-head-end node of the line, V_{i_e,t,d}For the line i end node voltage, R_{i_h,i_e}Is a wireResistance of way i, k_windIn order to make the wind abandon penalty factor,the wind abandoning time of the wind power plant i at d days t is shown.

Node power balance constraints are as follows:

where λ (j) represents the set of all nodes connected to node j in the power system, P_br,ij,tRepresenting the power, P, transferred by node i to node j at time t via the line_wind,j,tIs the wind power grid-connected power P at the node j at the time t_gen,j,tThe output of the thermal power generating unit at the node j at the time t,andrespectively the pumping storage power and the battery energy storage discharge power at the node j at the time t,andrespectively the pumping storage and battery energy storage charging power at the j position of the node at the time t, P_L,j,tIs the load power at the node j at the time t, R_i,jIs the resistance of the line between node i and node j, V_j,tRepresents the voltage at node j at time t, V_i,tRepresenting the voltage at node i at time t.

Node voltage constraints are as follows:

V_i,min≤V_i,t≤V_i,max；

in the formula, V_i,max、V_i,minRespectively, the upper and lower voltage limits at node i.

Line capacity constraints are as follows:

|P_br,ij,t|≤P_br,ij，max；

in the formula, P_br,ij，maxIs the maximum transmission power of the line between nodes i, j.

Rotational standby constraints, as follows:

in the formula (I), the compound is shown in the specification,andP _g,tin order to consider the upper limit and the lower limit of the output of the unit g at the time t after climbing,andthe reserve capacity requirements of positive and negative rotation at the moment t of the system, N_gNumber of thermal power generating units, P_g,tAnd the output of the unit g at the moment t is shown.

The unit output constraints are as follows:

in the formula, P_g,tRepresents the output of the unit g at the time t, I_g,tA variable 0-1 representing the running state of the unit g at the moment t,is the upper limit of the g output of the unit,the lower limit of the g output of the unit.

The unit climbing and starting and stopping power constraint is as follows:

in the formula (I), the compound is shown in the specification,for the limit value of the upward slope of the unit g,in order to limit the climbing of the unit g,maximum power of unit g at start-up and shut-down, I_g,t-10-1 variable, P, representing the operating state of the unit g at time t-1_g,t-1And the output of the unit g at the moment t-1 is shown.

The on-off time constraints are as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the minimum continuous operation and the down time of the unit g, I_g,kAnd a variable 0-1 representing the running state of the unit g at the moment k, and T represents the hours in a planning single day.

The switching constraint of the start and stop states of the unit is as follows:

in the formula u_g,t，v_g,tA variable 0-1, I, representing the start and stop actions of the recording unit g at time t_g,t+1And the variable is 0-1 representing the running state of the unit g at the moment of t + 1.

Battery stored energy power constraints are as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the 0-1 variable of the charging and discharging state of the battery at the node i at the moment of energy storage t,represents the battery energy storage discharge power at the node i at the time t,and represents the battery energy storage charging power at the node i at the time t.

And (3) restraining the energy storage state of the battery as follows:

in the formula eta_BS,cCharging efficiency, η, for storing energy in a battery_BS,dicDischarge efficiency for storing energy in the battery, E_BS,i0Is the initial energy at the node i and,representing the maximum energy at the i-node,represents the battery energy storage discharge power at the node i at the time tau,representing the battery energy storage charging power at node i at time τ.

The pumped storage power plant power constraints are as follows:

in the formula (I), the compound is shown in the specification,andrespectively representing the 0-1 variable of the charging and discharging state of the pumped storage power station at the moment t,in order to extract the maximum generated power of the power station,in order to extract the minimum generated power of the power station,is the maximum water storage power of the pumped storage power station,the minimum water storage power of the pumped storage power station.

And (3) constraint of energy state of the pumped storage power station, as follows:

in the formula eta_PS,c、η_PS,dicRespectively charging and discharging efficiency of the energy storage power station, E_PS0For pumping the energy of the power station at the initial moment of the planning periodThe status of the mobile station is,in order to pump the maximum energy capacity of the power station,representing the pumped charging power at node i at time tau,indicating the pumped-up discharge power at node i at time τ.

And (3) constraining the operation state of the thermal power generating unit at the end of the rolling period as follows:

T_g0≥0；

in the formula I_g0The operating state of the thermal power generating unit at the end of the last rolling period, U_g0For corresponding continued running time, D_g0Is a continuous down time.

S102: and optimizing the annual system operation scheme in the inner layer planning model by adopting a rolling solving mode, and obtaining the optimized inner layer planning model after the inner layer planning model calculates the annual operation cost of the power system.

Specifically, in order to avoid the situations of slow solving speed and no solution, the inner-layer planning model adopts a rolling mode to solve so as to improve the solving speed, and provides a solution-free automatic rollback mechanism to deal with the situation of no solution in the rolling solving process.

Further, definition k represents the number of days corresponding to the current time series production simulation, and m represents the number of days for rollback due to no solution. Taking the unit operation state and the corresponding time at the end of the current rolling period as initial conditions of the unit operation of the next rolling period, performing rollback calculation when time sequence production simulation rolling solution is performed to the kth day, if no solution exists, performing simulation according to the electric power system operation from the kth 1 to the kth day, if no solution exists for M consecutive days, performing continuous rollback and simulating the electric power system operation from the kth-M to the kth day until the time sequence production simulation rolling solution or M is larger than or equal to M, if a solution exists, continuing the time sequence production simulation rolling solution process, and if M is larger than or equal to M, reinitializing the particles until the time sequence production simulation is completed all year round, and obtaining the result of the time sequence production simulation, wherein M represents the maximum number of days allowing rollback; and optimizing the annual operation scheme of the system containing the stored energy according to the result of the time sequence production simulation.

Please refer to fig. 2, in an embodiment, 3000MW, 1500MW, and 3000MW wind power grid connections are respectively provided at nodes 2, 3, and 5 of the dc system, the load and thermal power generating units are connected to the dc power grid through nodes 1, 6, and 7, the load scales are 3000MW, and 1500MW, and the pumped storage power station with a rated power of 1500MW at node 4 is the inherent energy storage of the system. The nodes of the direct current system are allowed to be configured with battery energy storage, the voltage fluctuation range of each node is 0.98-1.02pu, and the voltage reference value is 500 kV.

Firstly, collecting technical parameters of each element in a multi-terminal direct-current system, wherein each element of the multi-terminal direct-current system comprises a thermal power generating unit, a wind power generating unit, an energy storage device and a direct-current line, and specifically, the technical parameters of each element specifically comprise the following steps:

1) and the allowable fluctuation range of the rated voltage and the node voltage of the system.

2) Numbering of nodes where thermal power generating units are located and upper and lower output limits P of thermal power generating units_maxAnd P_minMinimum continuous running time T^onMinimum continuous downtime T^offMaximum upward and downward climbing rate P^upAnd P^dnAnd coal consumption parameters.

3) Maximum charging power of pumped storage power stationMinimum charging powerMaximum discharge powerMinimum discharge powerCharging efficiency η_PS,dicDischarge efficiency eta_PS,cAnd initial energy level E_PS0。

4) Power capacity P of battery energy storage unit_BSEnergy capacity E of battery energy storage unit_BSCharging efficiency eta_BS,dicDischarge efficiency eta_BS,cAnd initial energy level E_BS0。

5) The number of the direct current power grid lines, the serial numbers of the end nodes of the lines, the line resistance and the line capacity.

In this embodiment, the operation parameters, the coal consumption parameters, the pumping storage parameters, the battery energy storage unit parameters, and the line parameters of the thermal power generating unit of the system are shown in tables 1, 2, 3, 4, and 5, respectively:

TABLE 1 thermal power generating unit operation parameters of system

TABLE 2 coal consumption parameters of thermal power generating units

TABLE 3 pumped storage parameters

TABLE 4 Battery energy storage Unit parameters

TABLE 5 line parameters

S103: and transmitting the optimized inner layer planning model to an outer layer planning model, wherein the outer layer planning model adopts a particle swarm algorithm to carry out optimization solution, and the energy storage capacity configuration and the optimized layout scheme are obtained in an iterative manner.

Specifically, an objective function of the outer layer planning model is constructed according to a battery energy storage configuration scheme, the comprehensive cost of the power system and a second constraint condition, wherein the second constraint condition comprises battery energy storage distribution point constraint, and the comprehensive cost of the power system comprises the coal consumption cost of the thermal power unit, the starting and stopping cost of the thermal power unit, the battery energy storage investment cost, the line loss cost and the wind abandoning cost.

Objective function F of outer planning model₂The method specifically comprises the following steps:

minF₂＝C_coal+C_UD+C_inv+C_loss+C_wind；

in the formula, C_coalRepresents the coal consumption cost of the fire-power unit, C_UDIndicating the start-stop cost of the thermal power generating unit, C_invRepresents the investment cost of battery energy storage, C_lossRepresents the line loss cost, C_windAnd representing the wind abandoning cost, wherein the solving of the coal consumption cost of the thermal power unit, the starting and stopping cost of the thermal power unit, the battery energy storage investment cost, the line loss cost and the wind abandoning cost is as follows:

in the formula, N_dayFor typical days in the planning cycle, T for hours in a planning single day, N_gNumber of thermal power generating units, N_nodeNumber of system nodes, N_brIs the number of system lines, N_wIs the number of wind power nodes, F_g,CIs the coal consumption cost function of the unit g, P_g,t,dThe output level of the unit g at the time of d days and t days, F_g,CFunction value and P_g,t,dIn a quadratic relationship, C_UDIn order to reduce the start-stop cost of the thermal power generating unit,andthe starting and stopping costs, u, of the unit g, respectively_g,t,dAnd v_g,t,dAre respectively recording unitsg variable 0-1, Q, for starting and stopping actions at time t on d days_BS,jConfiguring the number of battery energy storage units, C, for the node j_BSEquivalent single-day investment cost, η, for a battery energy storage unit_PCost per unit power capacity, η, of the battery energy storage unit_ECost per energy capacity, P, of the battery energy storage unit_BSFor the rated power capacity of the battery energy storage unit, E_BSFor the rated energy capacity, T, of the battery energy storage unit_lifeLife expectancy of a battery energy storage unit and expressed in days, k_lossFor the line loss penalty factor,is the line loss, V, of the line i at the time t of d days_{i_h,t,d}For the voltage of the i-head-end node of the line, V_{i_e,t,d}For the line i end node voltage, R_{i_h,i_e}Is line i resistance, k_windIn order to make the wind abandon penalty factor,the wind abandoning time of the wind power plant i at d days t is shown.

And (3) battery energy storage distribution constraint, as follows:

in the formula, Q_BS,iRepresenting a positive variable for representing the configuration number of the battery energy storage units of the node i,representing the maximum number of battery storage unit allowed configurations at node i.

The outer layer planning model comprises the steps of calculating the particle fitness of the outer layer planning model by adopting a particle swarm algorithm according to the objective function value calculated by the optimized inner layer planning model, updating the individual historical optimal position of each particle of the model and the group particle historical optimal position of the group according to the particle fitness, and further updating the information of each particle of the model in an iterative mode. And if the current iteration times are more than or equal to the maximum iteration times for which the optimal fitness keeps continuously unchanged, and the iteration times for which the optimal fitness value of the current population keeps continuously unchanged are more than or equal to the maximum iteration times for which the optimal fitness keeps continuously unchanged, acquiring the energy storage capacity configuration and optimized layout scheme.

Referring to fig. 3, in the corresponding outer-layer planning, I represents the current iteration number, J represents the iteration number of the current population where the optimal fitness value is continuously kept unchanged, I represents the maximum iteration number, and J represents the maximum iteration number of the optimal fitness value is continuously kept unchanged; in the inner-layer planning, K represents the number of days corresponding to the current time sequence production simulation, n represents the number of days included in each rolling cycle, M represents the number of days for rolling back due to no solution, K represents the total number of days of the planning cycle, and M represents the maximum number of days for which rolling back is allowed. The detailed solving steps are as follows:

A. inputting system parameters and PSO parameters.

B. And initializing the position and speed information of the particles to generate an initial population.

C. And providing an energy storage configuration scheme for inner layer planning based on the particle position information, starting time sequence production simulation, and taking the unit operation state at the end of the rolling period and corresponding time as the initial conditions for the unit operation in the next rolling period after the unit initial operation state is obtained through system parameters.

D. And C, when the time sequence production simulation rolling solution is carried out until the kth day, if no solution condition occurs, carrying out rolling calculation, simulating the operation of the power system from the kth to the kth day together, if no solution condition occurs for M times continuously, carrying out rolling continuously, simulating the operation of the power system from the kth to the kth day together until a solution exists or M is larger than or equal to M, continuing the solution flow if a solution exists, if M is larger than or equal to M, indicating that the value of the particle deviates from the solution space, and entering the step C after the particle is reinitialized.

E. When the annual time sequence production simulation is completed, calculating an inner layer planning objective function value according to a simulation operation result, transmitting the inner layer planning objective function value to an outer layer scale model, calculating and comparing the particle fitness by an outer layer particle swarm algorithm, and updating the individual historical optimal position P of each particle according to a fitness comparison result_best,iGroup particle history optimal position G with population_best。

F. Based on updated P_best,iAnd G_bestAnd updating the information of each particle.

G. And repeating the steps C-F until the termination condition is met.

In order to further explain the direct current power grid energy storage configuration method based on time sequence production simulation provided by the invention, a multi-terminal direct current system in fig. 2 is subjected to simulation verification, 3000MW, 1500MW and 3000MW wind power grid connection are respectively arranged at the nodes 2, 3 and 5 of the direct current system, a load and thermal power generating unit is connected to the direct current power grid through the nodes 1, 6 and 7, the load scales are 3000MW, 3000MW and 1500MW respectively, and a pumped storage power station with the rated power of 1500MW at the node 4 is the inherent energy storage of the system. The nodes of the direct current system are allowed to be configured with battery energy storage, the voltage fluctuation range of each node is 0.98-1.02pu, and the voltage reference value is 500 kV.

Please refer to the simulation result of fig. 2 and the convergence of the objective function value corresponding to the outer-layer particle swarm algorithm of fig. 4: in this embodiment, the direct current power grid energy storage configuration model based on the time sequence production simulation is solved, the convergence condition of the objective function value corresponding to the outer-layer particle swarm algorithm is solved, and the battery energy storage configuration result obtained by solving the double-layer planning model is shown in table 6.

Table 6 battery energy storage configuration results obtained by the double-layer planning model

In fig. 4, the numerical value of the objective function value corresponding to the outer layer plan is decreased at a high speed in the initial iteration process, and then the decrease speed is gradually decreased, and the calculation is terminated after the 11 th iteration reaches the convergence condition, so that the optimization and solution characteristics of the particle swarm algorithm are met.

In the energy storage configuration scheme shown in table 6, the battery energy storage unit is configured only at the node 5, the peak load pressure distribution condition of the system all the year is represented by the peak-to-valley difference of each single day all the year, the time sequence production simulation in the scene of no battery energy storage configuration is performed, the wind curtailment rate of each single day in the extraction result is used for measuring the wind power consumption condition of the system all the year, and the difference of the two schemes in the battery energy storage configuration capacity is analyzed from the angle of the peak load pressure and the wind power consumption of the system all the year.

Referring to fig. 5 and 6, the peak-to-valley difference data of each single day of the whole year and the wind curtailment rate data of each single day of the whole year when there is no battery energy storage configuration are counted and sorted, only a small number of single day scenes in the whole year have a high peak-to-valley difference, the system faces a large peak-to-valley pressure only in a small part of the time of the whole year, and meanwhile, under the condition of no battery energy storage configuration, the wind curtailment rate in most single day scenes of the whole year is 0, that is, most time systems in the year can completely absorb wind power, so that compared with a mode of selecting a typical day based on the maximum peak-to-valley difference, a planning result obtained by a mode of performing time sequence production simulation based on wind power and load data of the whole year has a lower demand on configuring battery energy storage, and has better economy while reflecting the time sequence operation characteristic of a power system.

In addition, the air curtailment, line loss and annual total cost of the system in the battery-free and battery-free energy storage configurations were compared, and the results are shown in tables 7 and 8. Compared with the result that battery energy storage is not configured, after the energy storage is configured, the annual wind abandoning rate and the line loss rate of the system are reduced through the power regulation effect of the battery energy storage, and meanwhile, the wind abandoning, the starting and stopping, the line loss and the coal consumption cost of the system are all reduced.

TABLE 7 wind abandon and line loss conditions of the system with and without battery energy storage configuration

TABLE 8 annual combined cost comparison results

Compared with the prior art, the technical scheme of the invention can obtain the following beneficial effects:

1. the method combines the particle swarm algorithm and the time sequence production simulation, establishes a direct-current power grid energy storage configuration double-layer planning model based on the time sequence production simulation, adopts the time sequence production simulation in an inner layer to enable an operation result to fully consider the time sequence operation characteristic of the power system, adopts the particle swarm algorithm to optimize an energy storage configuration scheme in outer layer planning, and considers the solution speed and the result optimality.

2. In the double-layer planning model established by the invention, the outer layer planning is solved by adopting a particle swarm algorithm, compared with a genetic algorithm, the particle swarm algorithm does not need to carry out cross and variation operation, the search is carried out through the particle speed, the update of the particle position in the iteration process is only guided by the particle position and the optimal position of the population, the information is pertinently inherited, the search efficiency is higher, in the particle swarm algorithm, the historical optimal position of the particle population can be memorized and transmitted to the particles in the population, the memory has better learning capacity, the update of the particle speed in the iteration initial stage of the algorithm has stronger randomness, the optimization can be found in a larger search range, the parallel characteristic is good, the operations of all the particles are mutually independent in the same iteration, and the solution time can be shortened through the parallel operation.

3. In the double-layer planning model established by the invention, the inner-layer planning is based on a time sequence production simulation planning system annual operation scheme and the cost is calculated. Compared with random production simulation, the time sequence production simulation input data comprises the time sequence correlation of the load and the new energy output, so that fine modeling can be performed, coupling constraints between periods such as starting and stopping of a thermal power generating unit, climbing power constraint and the like in system operation are considered, the simulation result is more detailed due to the improvement of the model complexity, and the time sequence operation characteristic of the power system can be reflected. Compared with a method based on a typical daily curve, the time sequence production simulation introduces the time sequence operation characteristic of the power system, and the technical problem that the actual operation condition of the power system is difficult to reflect due to no coupling relation among typical days in the typical daily method is solved;

4. the time sequence production simulation method adopted by the invention has overlong input sequence, avoids the situations of low solving speed and no solution, adopts a rolling mode to solve, shortens the single planning period and simultaneously improves the solving speed, and in addition, provides a solution-free automatic rollback mechanism to deal with the situation of no solution in the rolling solving process, thereby effectively solving the problems of low solving speed and easy solution of medium-and-long time sequence production simulation.

5. The invention provides a direct-current power grid energy storage configuration method based on time sequence production simulation, which has better economy while reflecting the time sequence operation characteristics of a power system based on the time sequence production simulation, adopts a particle swarm algorithm to give consideration to the optimality of solving speed and results, and adopts a rolling solving mode and a solution-free automatic rollback mechanism to solve the problems of low solving speed and easy solution-free.

Referring to fig. 7, a dc power grid energy storage configuration based on time sequence production simulation according to an embodiment of the present invention provides a terminal device, including:

one or more processors;

a memory coupled to the processor for storing one or more programs;

when executed by the one or more processors, the one or more programs cause the one or more processors to implement the method for configuring energy storage for a dc power grid based on time series production simulation as described above.

The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the direct current power grid energy storage configuration method based on the time sequence production simulation. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.

In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to perform the above dc grid energy storage configuration method based on time series simulation, and achieve technical effects consistent with the above method.

In another exemplary embodiment, a computer readable storage medium is further provided, which includes program instructions, which when executed by a processor, implement the steps of the dc grid energy storage configuration method based on time series production simulation in any of the above embodiments. For example, the computer-readable storage medium may be the above-mentioned memory including program instructions, and the program instructions may be executed by a processor of a computer terminal device to implement the above-mentioned dc power grid energy storage configuration method based on time series production simulation, and achieve the technical effects consistent with the above-mentioned method.