Intra-day scheduling method and system for new energy station containing energy storage unit

Abstract

The invention relates to an intra-day scheduling method for a new energy station containing an energy storage unit, which is characterized by comprising the following steps of: constructing a new energy station joint scheduling model with the highest participation rate of a new energy unit and the lowest energy storage consumption, and calculating an optimal solution of the model; scheduling tasks of different units and different energy storage in the new energy station are obtained based on the optimal solution of the model, and the scheduling tasks are subjected to forward and reverse sorting based on a sensitivity strategy; and according to the sorting sequence, obtaining the regulating variables of different units and different energy storage so as to complete a preset scheduling target. According to the method, the new energy consumption and stable operation control level is improved through energy storage and new energy field station-level cooperation, and effective support is provided for flexible scheduling of source network load storage multiple resources and improvement of system operation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and more specifically, to a method and system for daily dispatching of a new energy station containing an energy storage unit. Background Art

[0002] In the context of the new power system, there is an increase in flexible and adjustable equipment on the power supply and load sides of the system, and the capacity of various energy storage power stations is growing rapidly. In order to ensure the power supply capacity of the entire network, the operation of the electricity spot market and the green electricity trading needs, the strong randomness and uncertainty brought about by the large-scale new energy grid connection have put forward higher requirements for the refined analysis and response speed of the intraday control system.

[0003] Since the dispatching strategy involved in rapid response to large-scale new energy consumption and transmission is relatively complex, and considering that the total capacity and grid-connected ratio of large-scale new energy stations built in recent years have increased year by year, the scale of station-based energy storage built on demand has also gradually increased. System energy storage units fully demonstrate their advantages in pre-deployment to reduce system fluctuations, emergency response to system accidents during the event, and post-event participation in dispatching to accelerate system recovery, which is very conducive to improving the flexibility of new power systems.

[0004] In the prior art, automatic control software is usually used to distribute power to new energy stations through fixed strategies, and new energy stations with station-equipped energy storage cannot participate in optimal control.

[0005] In the study of technologies for dynamic control of energy storage and new energy stations, "Multi-time scale optimization dispatch of wind, solar and storage microgrids considering flexible loads" proposes to predict wind, solar and storage power and flexible load power based on the extreme learning machine algorithm. From the perspective of time, the day-ahead optimization dispatch is first formulated, and the day-ahead optimization dispatch results are rolled out using the particle swarm algorithm based on the real-time prediction results. The goal of the optimization dispatch is to minimize the cost and the network loss. However, no method is given to maximize the consumption of new energy while considering the optimization dispatch of energy storage within the day, and the optimization target is also relatively limited.

[0006] "A random dynamic optimization method for demand-side resources based on virtual energy storage aggregation modeling" proposes a large-scale virtual energy storage to smooth out the new energy power forecast error optimization scheduling method. The technical solution mentioned sets the new energy power forecast time resolution, statistically analyzes the large-scale virtual energy storage to smooth out the new energy power distribution characteristics, determines the new energy power forecast error distribution characteristics, estimates the new energy power forecast error confidence interval, incorporates the new energy forecast power into the power generation plan according to a certain confidence level, designs large-scale virtual energy storage to smooth out the new energy power forecast error optimization scheduling constraints, constructs a new energy power forecast error optimization scheduling model, and uses the particle swarm algorithm to solve the model optimal solution. This method is more suitable for energy storage to participate in the ancillary service market. However, for dispatching, it is difficult to intuitively and accurately grasp the actual control optimization plan of a "energy storage + new energy" station, and the actual power distribution of the station is not clear enough.

[0007] In view of the above problems, there is an urgent need for a method and system for daily scheduling of new energy stations containing energy storage units. Summary of the invention

[0008] In order to solve the deficiencies in the prior art, the present invention provides a method and system for intraday scheduling of new energy stations containing energy storage units, taking into account the participation of energy storage in new energy scheduling control, introducing a sensitivity analysis method in the optimization calculation process, performing sensitivity evaluation on each optimization variable in the intraday scheduling plan, and reducing invalid optimization calculation processes.

[0009] The present invention adopts the following technical solution.

[0010] A first aspect of the present invention relates to a method for intraday scheduling of a new energy station containing an energy storage unit, the method comprising the following steps: constructing a joint scheduling model for a new energy station with the highest participation rate of new energy units and the lowest energy storage consumption, and calculating the optimal solution of the model; obtaining scheduling tasks for different units and different energy storage in the new energy station based on the optimal solution of the model, and sorting the scheduling tasks in forward and reverse order based on a sensitivity strategy; obtaining adjustment amounts of different units and different energy storage according to the sorting order to achieve a preset scheduling target.

[0011] Preferably, a new energy station joint dispatching model with the highest new energy unit participation rate and the lowest energy storage consumption is constructed, including: the joint dispatching model is:

[0012]

[0013] Where t is any time in the scheduling period [0, T], T is the end time of a single scheduling,

[0014] j is the number of the new energy unit, n new is the total number of new energy units,

[0015] i is the number of the energy storage unit deployed in the new energy station, n s is the total energy storage,

[0016] P′ new,j (t) is the capacity of the j-th renewable energy unit that does not participate in the power consumption at time t,

[0017] Δt is the time between two consecutive moments,

[0018] SOC i (t) is the charge state percentage of the i-th energy storage unit at time t,

[0019] E s,i (t) is the remaining power of the i-th energy storage unit at time t.

[0020] Preferably, the optimal solution of the calculation model includes: based on the joint dispatch model, calculating the optimal state of all new energy units and all energy storage at the time t to be dispatched In the formula, is the optimal capacity of the new energy unit that does not participate in power consumption, is the optimal remaining power of energy storage; based on the actual power P of all new energy units at the previous time t-1 new，i (t-1) Calculate the optimal dispatching power of the new energy unit as follows:

[0021] ΔP new，j (t) = P new，j，N -P′ new，j (t)-P new，i (t-1)

[0022] P new，j，N is the rated power of the new energy unit;

[0023] Based on the actual remaining power E of all energy storage at the previous time t-1 s，i (t-1) Calculate the optimal dispatch adjustment of energy storage:

[0024] ΔE s，i (t) = E s，i (t)-E s，i (t-1).

[0025] Preferably, the dispatching tasks of different units and different energy storage in the new energy station are obtained based on the optimal solution of the model, and the dispatching tasks are sorted in positive and reverse order based on the sensitivity strategy, including: the optimal dispatching power ΔP of all new energy units is calculated. new，j (t), the optimal dispatching power ΔE of the energy storage s，i (t) are sorted; all ΔP new，j (t) and ΔE s，iThe items greater than 0 in (t) are sorted to obtain the forward scheduling tasks. All ΔP new，j (t) and ΔE s，i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks. And there is as well as Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

[0026] Preferably, the dispatching tasks of different units and different energy storages in the new energy station are obtained based on the optimal solution of the model, and the dispatching tasks are sorted in forward and reverse order based on the sensitivity strategy, including: calculating the sensitivity S of each new energy unit and each energy storage, wherein the sensitivity S(R, C) is related to the regulation cost R of the unit and the regulation margin C of the unit; dividing the sensitivity of all new energy units and energy storage into intervals I[S(R, C)], and setting a weight W for the sensitivity falling into the same interval I[S(R，C)] , based on the weights, the optimal dispatching power W of all weighted new energy units j ΔP new，j (t), the optimal dispatch adjustment amount W of the energy storage i ΔE s，i (t) is sorted; W j ΔP new，j (t) and W i ΔE s，i (t) Order items greater than 0 to obtain forward scheduling tasks All ΔP new，j (t) and ΔE s，i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks. And there is as well as Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

[0027] Preferably, the dispatching tasks of different units and different energy storage in the new energy station are obtained based on the optimal solution of the model, and the dispatching tasks are sorted in forward and reverse order based on the sensitivity strategy, including: calculating the sensitivity S of each new energy unit and each energy storage in the section or branch, and the sensitivity S (V) is related to the regulation rate V of the section or branch; all ΔP new,j (t) and ΔE s,i The items greater than 0 in (t) are sorted to obtain the forward scheduling tasks.

[0028] All ΔP new,j (t) and ΔEs,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks. And there is as well as Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

[0029] Preferably, the adjustment amounts of different units and different energy storage are obtained according to the sorting order to complete the preset scheduling target, including: extracting scheduling tasks from positive scheduling tasks and negative scheduling tasks based on the size of the numbers and implementing scheduling until the total scheduling amount meets the preset scheduling target; the preset scheduling target is based on eliminating the heavy overload of the current network.

[0030] The second aspect of the present invention relates to a daily scheduling system for a new energy station containing an energy storage unit; the system is implemented using the method in the first aspect of the present invention; the system includes a solution module, a sorting module, and a scheduling module; wherein the solution module is used to construct a joint scheduling model for new energy stations with the highest participation rate of new energy units and the lowest energy storage consumption, and calculate the optimal solution of the model; the sorting module is used to obtain the scheduling tasks of different units and different energy storage in the new energy station based on the optimal solution of the model, and to sort the scheduling tasks in forward and reverse order based on the sensitivity strategy; the scheduling module is used to obtain the adjustment amount of different units and different energy storage according to the sorting order to complete the preset scheduling target. The third aspect of the present invention relates to a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method described in the first aspect of the present invention.

[0031] A fourth aspect of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect of the present invention.

[0032] The beneficial effect of the present invention lies in that, compared with the prior art, the method and system for intraday scheduling of new energy stations containing energy storage units in the present invention considers the participation of energy storage in new energy scheduling control, introduces a sensitivity analysis method in the optimization calculation process, performs sensitivity evaluation on each optimization variable in the intraday scheduling plan, and reduces invalid optimization calculation processes.

[0033] The beneficial effects of the present invention also include:

[0034] 1. Establish a daily dispatch model for new energy stations considering energy storage, conduct sensitivity evaluation on each optimization variable in the daily dispatch plan through sensitivity analysis method, identify invalid optimization calculation objects and remove them according to the sensitivity evaluation results, and based on the optimization variables after removing invalid optimization calculation objects, during the daily dispatch operation, identify the key sections of the power grid operation corresponding to the occurrence of congestion through the daily dispatch model of new energy stations considering energy storage, and assist in dispatch decision-making. The present invention explores the improvement of the level of new energy consumption and stable operation control through the synergy between energy storage and new energy stations, and provides effective support for the flexible dispatch of multiple resources of source, grid, load and storage and the improvement of system operation efficiency.

[0035] 2. In order to ensure the safe and stable operation of the power grid after a high proportion of new energy is connected to the grid and reduce the potential operational risks caused by the obstruction of new energy transmission, the transmission channel resources are analyzed to determine the transmission capacity of key sections. The present invention proposes a method for intraday scheduling decision-making of new energy stations containing energy storage units. The method generates an intraday scheduling strategy for "energy storage + new energy" stations based on the optimization algorithm, serves for subsequent optimization calculation and scheduling control, and provides technical support for reducing the scale of network constraints in the optimization solution of intraday regulation auxiliary decision-making.

[0036] 3. The method of the present invention takes into account the actual demand and operation of the new energy station distribution storage, establishes an "energy storage + new energy" station optimization control model, and improves the deficiency of new energy dispatch control that rarely considers the participation of energy storage in dispatch. At the same time, in order to improve the calculation efficiency, the present invention introduces a sensitivity analysis method in the optimization calculation process, performs sensitivity evaluation on each optimization variable in the intraday dispatch plan, identifies variables with less impact on the objective function, and fixes or ignores the interference in the subsequent optimization process, effectively optimizing the calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of a method for daily dispatching of a new energy station including an energy storage unit according to the present invention;

[0038] Figure 2 The schematic diagram of the structure of a daily dispatching system of a new energy station containing an energy storage unit according to the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention clearer and more accurate, the technical solution of the present invention is described in detail below through multiple specific implementation methods. The embodiments adopted by the present invention are only used to explain the present invention and are not used to limit the content of the present invention.

[0040] Figure 1 The figure is a flow chart of a method for daily dispatching of a new energy station containing an energy storage unit according to the present invention. Figure 1As shown, the first aspect of the present invention relates to a method for intraday scheduling of a new energy station containing an energy storage unit, and the method includes steps 1 to 3.

[0041] Step 1: construct a new energy station joint dispatch model with the highest new energy unit participation rate and the lowest energy storage consumption, and calculate the optimal solution of the model.

[0042] A daily dispatch optimization model for "energy storage + new energy" stations is constructed with the goal of ensuring a high proportion of new energy transmission and safe and economical operation of energy storage, taking into account operating conditions such as energy storage capacity, charging and discharging rates, and new energy transmission sections.

[0043] In one embodiment, a new energy station joint dispatch model with the highest new energy unit participation rate and the lowest energy storage consumption is constructed, including: the joint dispatch model is:

[0044]

[0045] Where t is any time in the scheduling period [0, T], T is the end time of a single scheduling,

[0046] j is the number of the new energy unit, n new is the total number of new energy units,

[0047] i is the number of the energy storage unit deployed in the new energy station, n s is the total energy storage,

[0048] P′ new,j (t) is the capacity of the j-th renewable energy unit that does not participate in the power consumption at time t,

[0049] Δt is the time between two consecutive moments,

[0050] SOC i (t) is the charge state percentage of the i-th energy storage unit at time t,

[0051] E s,i (t) is the remaining power of the i-th energy storage unit at time t.

[0052] The constraints include conventional unit constraints, new energy unit constraints, power balance constraints, thermal power unit ramp constraints, energy storage constraints, new energy consumption ratio constraints, section load rate constraints and grid stability constraints. The specific contents of each constraint are as follows:

[0053] The expression of the conventional unit constraint is as follows:

[0054] P F,i,min ≤P F,i (t)≤P F,i,ma

[0055] P F,i,min With P F,i,max are the upper and lower limits of the power generation of thermal power unit i,

[0056] P F,i (t) is the power generation of thermal power unit i at time t;

[0057] For wind turbines, the expression of wind turbine constraints is as follows:

[0058] 0≤P W,i (t)≤P W,i,N

[0059] P W,i (t) is the power generation of wind turbine i at time t,

[0060] P W，i，N is the rated power of wind turbine i.

[0061] The constraint expressions of other new energy units are also expressed in a similar way.

[0062] The expression of power balance constraint is:

[0063] P F (t)+P New (t)+P S (t) = P L (t)

[0064] Where P F (t) is the total power of the thermal power unit at time t,

[0065] P New (t), P S (t) and P L (t) are the total power of the new energy units and energy storage power station and the total system load respectively.

[0066] The expression of the thermal power unit climbing constraint is as follows:

[0067]

[0068] In the formula, is the rated ramp rate of the thermal power unit.

[0069] The expression of energy storage constraint is as follows:

[0070] SOC min ≤SOC i (t)≤SOC max

[0071] -P S，i,max ≤P S，i (t)≤P S，i,max

[0072]

[0073] E s，i (t) = P s，i (t)gΔt

[0074] In the formula, SOC i (t) is the energy storage charge state, SOC min With SOC max are the maximum and minimum charge states, respectively;

[0075] P S，i (t) is the energy storage power, P S，i,max is the maximum charge and discharge power;

[0076] E s，i,min With E s，i,max They are the maximum and minimum remaining power of energy storage respectively;

[0077] Δt is a plurality of scheduling intervals under the same scheduling, and g is a conversion parameter between energy storage power and remaining power. In the embodiment, g=1 within the unit time Δt.

[0078] The expression of the new energy consumption ratio constraint is:

[0079]

[0080] 0≤η new ≤1

[0081] P′ new，j (t) = P new，j，N -P new,j (t)

[0082] 0≤P new，j (t)≤P new，j，N

[0083] 0≤P′ new，j (t)≤P new，j，N

[0084] Where η new is the consumption ratio of new energy units, P new，j，N is the rated power of the new energy unit,

[0085] Section load factor constraint The expression is:

[0086]

[0087] Where n line is the number of lines in the section, I i is the current operating value of the ith line, Ti is the thermal stability limit of the ith circuit, sec j Number the section.

[0088] In addition, grid stability constraints include stability indicators such as voltage, frequency and phase angle.

[0089] By considering the intraday dispatch model of new energy stations with energy storage, the key sections of power grid operation corresponding to the occurrence of congestion are identified. In the step of assisting dispatch decision-making, an optimization strategy combining any one or more of mixed integer linear programming and heuristic algorithms is used to assist decision-making analysis; the heuristic algorithms include genetic algorithms and particle swarm optimization algorithms.

[0090] Calculate the optimal solution of the model, including:

[0091] Based on the joint dispatch model, the optimal state of all new energy units and all energy storage at the dispatching time t is calculated.

[0092] In the formula, is the optimal capacity of the new energy unit that does not participate in power consumption, The optimal remaining power for energy storage;

[0093] Based on the actual power P of all new energy units at the previous time t-1 new,i (t-1) Calculate the optimal dispatching power of the new energy unit as follows:

[0094] ΔP new，j (t) = P new，j，N -P′ new，j (t)-P new,i (t-1)

[0095] P new，j，N is the rated power of the new energy unit;

[0096] Based on the actual remaining power E of all energy storage at the previous time t-1 s，i (t-1) Calculate the optimal dispatch adjustment of energy storage:

[0097] ΔE s，i (t) = E s，i (t)-E s，i (t-1).

[0098] In an embodiment, an optimization strategy combining mixed integer linear programming (MILP) and a heuristic algorithm (such as a genetic algorithm, a particle swarm optimization algorithm, etc.) may be used to perform auxiliary decision analysis.

[0099] Step 2: Obtain the dispatching tasks of different units and different energy storage in the new energy station based on the optimal solution of the model, and sort the dispatching tasks in forward and reverse order based on the sensitivity strategy.

[0100] Based on the sensitivity algorithm analysis, the operating relationship between system equipment is obtained, the operating status of the power generation capacity and transmission system of the new energy station is mastered, and the energy storage regulation capacity results are obtained. By establishing sensitivity strategy knowledge such as the scheduling decision with the smallest regulation amount, the least regulation equipment, and the fastest regulation rate, the system objects involved in regulation are compressed before the optimization calculation, thereby reducing the calculation amount of the overall optimization calculation and meeting the demand for fast and accurate calculation under the fluctuation conditions of new energy.

[0101] The algorithm aims to minimize the adjustment amount. It sorts the units in positive and negative order according to their sensitivity to the section / branch. The units are selected in the order of sensitivity from large to small in positive and negative directions. The adjustment amounts of the units are calculated in pairs to ensure that the total amount of active power increase and decrease is consistent, until the heavy overload is eliminated to the target value or all adjustment measures are exhausted.

[0102] Based on the optimal solution of the model, the dispatching tasks of different units and different energy storage in the new energy station are obtained, and the dispatching tasks are sorted in positive and reverse order based on the sensitivity strategy, including:

[0103] The optimal dispatching power ΔP of all new energy units new,j (t), the optimal dispatching power ΔE of the energy storage s,i (t) sorting;

[0104] All ΔP new,j (t) and ΔE s,i The items greater than 0 in (t) are sorted to obtain the forward scheduling tasks. All ΔP new,j (t) and ΔE s,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks.

[0105] And there is as well as

[0106] Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

[0107] The algorithm aims to adjust the minimum number of devices, assigns corresponding weights to units with different sensitivity intervals, and sorts the units by sensitivity and adjustment margin, ensuring that units with greater sensitivity are adjusted first, and units with greater adjustment margin are selected first among units with the same sensitivity interval. Units are selected in descending order of weighted values, and the adjustment amount of the units is calculated in pairs until the heavy overload is eliminated to the target value or the adjustment means are exhausted.

[0108] Based on the optimal solution of the model, the dispatching tasks of different units and different energy storage in the new energy station are obtained, and the dispatching tasks are sorted in positive and reverse order based on the sensitivity strategy, including:

[0109] Calculate the sensitivity S of each new energy unit and each energy storage, where the sensitivity S(R,C) is related to the regulation cost R and regulation margin C of the unit;

[0110] The sensitivity of all new energy units and energy storage is divided into intervals I[S(R,C)], and the weight W is set for the sensitivity falling into the same interval I[S(R,C)] , based on the weights, the optimal dispatching power W of all weighted new energy units j ΔP new,j (t), the optimal dispatch adjustment amount W of the energy storage i ΔE s,i (t) sorting;

[0111] W j ΔP new,j (t) and W i ΔE s,i (t) Order items greater than 0 to obtain forward scheduling tasks All ΔP new,j (t) and ΔE s,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks.

[0112] And there is as well as

[0113] Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

[0114] The algorithm aims at the fastest adjustment rate. The computer group calculates the sensitivity of the section / branch and classifies it into positive and negative directions. The units are selected in the order of adjustment rate from large to small in terms of positive and negative sensitivity. The adjustment amounts are calculated in pairs to ensure that the units with fast adjustment rates are adjusted first, until the heavy overload is eliminated to the target value or all adjustment methods are exhausted.

[0115] Based on the optimal solution of the model, the dispatching tasks of different units and different energy storage in the new energy station are obtained, and the dispatching tasks are sorted in positive and reverse order based on the sensitivity strategy, including:

[0116] Calculate the sensitivity S of each new energy unit and each energy storage in the section or branch, where the sensitivity S(V) is related to the regulation rate V of the section or branch;

[0117] All ΔPnew,j (t) and ΔE s,i The items greater than 0 in (t) are sorted to obtain the forward scheduling tasks. All ΔP new,j (t) and ΔE s,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks.

[0118] And there is as well as

[0119] Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

[0120] The sensitivity analysis method is used to evaluate the sensitivity of each optimization variable in the intraday scheduling plan, and invalid optimization calculation objects are identified and removed based on the sensitivity evaluation results.

[0121] The optimization algorithm may not be limited to linear or nonlinear optimization tools. In one embodiment, first, the mixed integer linear programming MILP method is used to preliminarily solve the scheduling model to obtain a better initial solution. Then, based on the sensitivity analysis results, the scheduling model solution is simplified and invalid optimization calculation objects are removed. Finally, a heuristic algorithm is used to fine-tune the simplified model to further improve the quality of the solution. The upper and lower limits of the energy storage SOC operating range are between [0.1~0.9] or [0.2~0.8]. Avoiding excessive charging and discharging can effectively extend the battery life and increase the investment and operation economy.

[0122] Step 3, obtain the adjustment amount of different units and different energy storage according to the sorting order to achieve the preset scheduling target.

[0123] The adjustment amounts of different units and different energy storage are obtained according to the sorting order to complete the preset scheduling target, including: extracting scheduling tasks from positive scheduling tasks and negative scheduling tasks based on the size of the numbers and implementing scheduling until the total scheduling amount meets the preset scheduling target; the preset scheduling target is based on eliminating the heavy overload of the current network.

[0124] By establishing a sensitivity strategy that generates the smallest adjustment amount, the least adjustment equipment and the fastest adjustment rate, the optimization calculation objects are sorted, and invalid optimization calculation objects are identified and removed.

[0125] The present invention takes into account the participation of energy storage in the dispatching and control of new energy, introduces a sensitivity analysis method in the optimization calculation process, performs sensitivity evaluation on each optimization variable in the intraday dispatching plan, cuts down on invalid optimization calculation processes, thereby reducing the amount of calculation, and strengthens the regulation and control capabilities of energy storage, thereby improving the control level of new energy.

[0126] Figure 2 The schematic diagram of the structure of a new energy station intraday dispatching system containing an energy storage unit of the present invention. Figure 2 As shown, the present invention relates to a daily scheduling system for a new energy station containing an energy storage unit; the system is implemented using the method described in the first aspect of the present invention; the system includes a solution module, a sorting module, and a scheduling module; wherein the solution module is used to construct a new energy station joint scheduling model with the highest new energy unit participation rate and the lowest energy storage consumption, and calculate the optimal solution of the model; the sorting module is used to obtain the scheduling tasks of different units and different energy storage in the new energy station based on the optimal solution of the model, and sort the scheduling tasks in forward and reverse order based on the sensitivity strategy; the scheduling module is used to obtain the adjustment amounts of different units and different energy storage according to the sorting order to complete the preset scheduling target.

[0127] A third aspect of the present invention relates to a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method described in the first aspect of the present invention.

[0128] A fourth aspect of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect of the present invention.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention is described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention still include contents that can modify or replace the specific embodiments of the present invention. Any modification or replacement that does not depart from the spirit and scope of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A method for daily dispatching of a new energy station containing an energy storage unit, characterized in that: The method comprises the following steps: Construct a new energy station joint dispatch model with the highest new energy unit participation rate and the lowest energy storage consumption, and calculate the optimal solution of the model; Based on the optimal solution of the model, the dispatching tasks of different units and different energy storage in the new energy station are obtained, and the dispatching tasks are sorted in forward and reverse order based on the sensitivity strategy; According to the sorting order, the adjustment amount of different units and different energy storage is obtained to achieve the preset scheduling target.

2. The method for daily dispatching of a new energy station containing an energy storage unit according to claim 1, characterized in that: The structure is a new energy station joint dispatching model with the highest new energy unit participation rate and the lowest energy storage consumption, including: The joint scheduling model is: Where t is any time in the scheduling period [0, T], T is the end time of a single scheduling, j is the number of the new energy unit, n new is the total number of new energy units, i is the number of the energy storage unit deployed in the new energy station, n s is the total energy storage, P n ′ ew,j (t) is the capacity of the j-th renewable energy unit that does not participate in the power consumption at time t, Δt is the time between two consecutive moments, SOC i (t) is the charge state percentage of the i-th energy storage unit at time t, E s,i (t) is the remaining power of the i-th energy storage unit at time t.

3. The method for daily dispatching of a new energy station containing an energy storage unit according to claim 2, characterized in that: The optimal solution of the calculation model includes: Based on the joint dispatch model, the optimal state of all new energy units and all energy storage at the dispatching time t is calculated. In the formula, is the optimal capacity of the new energy unit that does not participate in power consumption, The optimal remaining power for energy storage; Based on the actual power P of all new energy units at the previous time t-1 new,i (t-1) Calculate the optimal dispatching power of the new energy unit as follows: ΔP new,j (t)=P new,j,N -P n ′ ew,j (t)-P new,i (t-1) P new,j,N is the rated power of the new energy unit; Based on the actual remaining power E of all energy storage at the previous time t-1 s,i (t-1) Calculate the optimal dispatch adjustment of energy storage: ΔE s,i (t)=E s,i (t)-E s,i (t-1)。 4. The method for daily dispatching of a new energy station containing an energy storage unit according to claim 3 is characterized in that: The method of obtaining the dispatching tasks of different units and different energy storage in the new energy station based on the optimal solution of the model and sorting the dispatching tasks in a positive and reverse order based on the sensitivity strategy includes: The optimal dispatching power ΔP of all new energy units new,j (t), the optimal dispatching power ΔE of the energy storage s,i (t) sorting; All ΔP new,j (t) and ΔE s,i The items greater than 0 in (t) are sorted to obtain the forward scheduling tasks. All ΔP new,j (t) and ΔE s,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks. And there is as well as Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

5. The method for daily dispatching of a new energy station containing an energy storage unit according to claim 3, characterized in that: The method of obtaining the dispatching tasks of different units and different energy storage in the new energy station based on the optimal solution of the model and sorting the dispatching tasks in a positive and reverse order based on the sensitivity strategy includes: Calculate the sensitivity S of each new energy unit and each energy storage, where the sensitivity S(R,C) is related to the regulation cost R and regulation margin C of the unit; The sensitivity of all new energy units and energy storage is divided into intervals I[S(R,C)], and the weight W is set for the sensitivity falling into the same interval I[s(R,C)] , based on the weights, the optimal dispatching power W of all weighted new energy units j ΔP new,j (t), the optimal dispatch adjustment amount W of the energy storage i ΔE s,i (t) sorting; W j ΔP new,j (t) and W i ΔE s,i (t) Order items greater than 0 to obtain forward scheduling tasks All ΔP new,j (t) and ΔE s,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks. And there is as well as Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

6. The method for daily dispatching of a new energy station containing an energy storage unit according to claim 3, characterized in that: The method of obtaining the dispatching tasks of different units and different energy storage in the new energy station based on the optimal solution of the model and sorting the dispatching tasks in a positive and reverse order based on the sensitivity strategy includes: Calculate the sensitivity S of each new energy unit and each energy storage in the section or branch, where the sensitivity S(V) is related to the regulation rate V of the section or branch; All ΔP new,j (t) and ΔE s,i The items greater than 0 in (t) are sorted to obtain the forward scheduling tasks. All ΔP new,j (t) and ΔE s,i The items less than 0 in (t) are sorted to obtain the reverse scheduling tasks. And there is as well as Where m and n are the numbers of the scheduled tasks, M and N are the total number of positive scheduled tasks and the total number of negative scheduled tasks, respectively.

7. A method for daily dispatching of a new energy station containing an energy storage unit according to claims 4, 5 and 6, characterized in that: The adjusting amounts of different units and different energy storage are obtained according to the sorting order to achieve the preset dispatching target, including: Extract the scheduling tasks from the positive scheduling tasks and negative scheduling tasks based on the number size and implement the scheduling until the total scheduling amount meets the preset scheduling target; The preset scheduling target is preset based on eliminating the heavy overload of the current network.

8. A new energy station intraday dispatching system containing an energy storage unit; characterized in that: The system is implemented by the method described in any one of claims 1 to 7; the system includes a solution module, a sorting module, and a scheduling module; wherein, The solution module is used to construct a new energy station joint dispatch model with the highest new energy unit participation rate and the lowest energy storage consumption, and calculate the optimal solution of the model; The sorting module is used to obtain the scheduling tasks of different units and different energy storage in the new energy station based on the optimal solution of the model, and to sort the scheduling tasks in the forward and reverse directions based on the sensitivity strategy; The scheduling module is used to obtain the adjustment amounts of different units and different energy storage according to the sorting order to achieve the preset scheduling target.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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