Source-load interaction optimization scheduling method and system for mining new energy power system

Abstract

The invention discloses a source load interaction optimization scheduling method and system for a mining new energy power system, and belongs to the technical field of power system optimization scheduling. The method comprises the steps of calculating wind abandoning penalty cost, light abandoning penalty cost, gas turbine power generation cost and flexible load adjustment cost, and calculating system power balance constraint, wind power output constraint, photovoltaic output constraint, gas turbine output constraint, energy storage power constraint, energy storage state constraint, energy storage electric quantity constraint and ore heap reserve constraint; constructing an objective function and constraints of the source-load interactive optimization scheduling model; and solving the source-load interaction optimization scheduling model to obtain a new energy output scheduling result, a gas turbine output scheduling result, an energy storage charging and discharging scheduling result and a flexible load power consumption scheduling result. The method can solve the problems that when an existing dispatching technology is used for coping with a mining new energy power system, a source-load interaction mechanism is lacked, equipment operation is flexible and free and is limited, and the contradiction between new energy efficient consumption and system economic operation is unbalanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimization scheduling, in particular to a source-load interaction optimization scheduling method and system for a mine new energy power system. BACKGROUND

[0002] Renewable energy is accelerating its penetration in mine energy systems due to its clean and sustainable advantages, forming a mine new energy power system mainly composed of wind and solar power. However, while high proportion of new energy brings green benefits, it also exposes the shortcomings of existing control methods:

[0003] On the one hand, the dynamic characteristics of different production loads in mines are quite different. The core production link contains both high-power impact load and relatively continuous stable load. The complex and diverse load characteristics, combined with the intermittency and randomness brought by high proportion of new energy generation, make the traditional "source following load" control mode difficult to apply. The complementary regulation capabilities contained in the power supply side and the load side need to be fully tapped and cooperatively scheduled in a unified framework.

[0004] On the other hand, power cost is one of the core costs of mine operation. While mine enterprises increase flexible resources such as energy storage and gas turbines to ensure the continuous and reliable operation of the system, they also face the dual pressure of high energy flow density and low electricity cost flexibility. How to coordinate the resources on the power supply and consumption sides to improve the economic efficiency of the overall system operation under the premise of ensuring mine production capacity is a core problem that needs to be solved in the current mine new energy power system. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provide a source-load interaction optimization scheduling method and system for a mine new energy power system, which can solve the problems of missing "source-load interaction" mechanism, limited flexibility and freedom of equipment operation, and unbalanced contradiction between efficient consumption of new energy and economic operation of the system in the existing scheduling technology when dealing with mine new energy power systems.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] On the one hand, the present application provides a source-load interaction optimization scheduling method for a mine new energy power system, comprising:

[0008] obtaining the operation data of wind power, photovoltaic, gas turbine, energy storage, flexible load and baseline load in the mine new energy power system and the stockpile reserves;

[0009] calculating the wind penalty cost, light penalty cost, gas turbine generation cost and flexible load regulation cost according to the operation data of wind power, photovoltaic, gas turbine, energy storage and flexible load;

[0010] According to the operation data of wind power, photovoltaic, gas turbine, energy storage, flexible load and baseline load and the stockpile reserves, system power balance constraints, wind power output constraints, photovoltaic output constraints, gas turbine output constraints, energy storage power constraints, energy storage state constraints, energy storage power constraints and stockpile reserves are calculated.

[0011] According to the wind curtailment penalty cost, the light curtailment penalty cost, the gas turbine power generation cost and the flexible load adjustment cost, a target function of the source-load interaction optimization scheduling model is constructed.

[0012] According to the system power balance constraints, the wind power output constraints, the photovoltaic output constraints, the gas turbine output constraints, the energy storage power constraints, the energy storage state constraints, the energy storage power constraints and the stockpile reserves, the constraints of the source-load interaction optimization scheduling model are constructed.

[0013] Solving the source-load interaction optimization scheduling model, the new energy output scheduling result, the gas turbine output scheduling result, the energy storage charging and discharging scheduling result and the flexible load power consumption scheduling result are obtained.

[0014] Optionally, the wind curtailment penalty cost is represented as:

[0015] ;

[0016] The light curtailment penalty cost is represented as:

[0017] ;

[0018] The gas turbine power generation cost is represented as:

[0019] ;

[0020] The flexible load adjustment cost is represented as:

[0021] ;

[0022] Wherein, , , , The wind curtailment penalty cost of wind power i, the light curtailment penalty cost of photovoltaic j, the gas turbine power generation cost of gas turbine m and the flexible load adjustment cost of flexible load n at the t moment are represented respectively. , The theoretical power and the planned power of wind power i at the t moment are represented respectively. The wind curtailment penalty coefficient of wind power i is represented. , The theoretical power and the planned power of photovoltaic j at the t moment are represented respectively. The light curtailment penalty coefficient is represented. The planned power of gas turbine m at the t moment is represented. , respectively represent the operation cost coefficient of the gas turbine m, the start cost coefficient of the gas turbine m; , respectively represent the start-stop state of the gas turbine m at the t time, the start-stop state of the gas turbine m at the t-1 time; , respectively represent the predicted power of the flexible load n at the t time, the planned power of the flexible load n at the t time; represent the adjustment cost coefficient of the flexible load n.

[0023] Optionally, the system power balance constraint is represented as:

[0024] ;

[0025] wherein, , , , respectively represent the planned power of the wind power i at the t time, the planned power of the photovoltaic j, the planned power of the gas turbine m, the planned power of the flexible load n; , , , respectively represent the number of wind power, the number of photovoltaic, the number of gas turbine, the number of flexible load; represent the baseline load of the system at the t time; , respectively represent the planned charging power of the energy storage at the t time, the planned discharging power of the energy storage at the t time.

[0026] Optionally, the wind power output constraint is represented as:

[0027] ;

[0028] The photovoltaic output constraint is represented as:

[0029] ;

[0030] wherein, , represent the planned power of the wind power i at the t time, the theoretical power of the wind power i at the t time; , respectively represent the planned power of the photovoltaic j at the t time, the theoretical power of the photovoltaic j at the t time.

[0031] Optionally, the gas turbine output constraint is represented as:

[0032] ;

[0033] ;

[0034] wherein, , , These represent the start-up and shutdown states of gas turbine m at time t, time t-1, and time k, respectively. , Let m represent the planned power output of gas turbine m at time t and time t-1, respectively. , These represent the lower and upper limits of the permissible power output of the gas turbine m, respectively. This represents the regulation rate of gas turbine m at time t; , These represent the minimum continuous start-up time and minimum continuous shutdown time of the gas turbine m, respectively.

[0035] Optionally, the energy storage power constraint is expressed as:

[0036] ;

[0037] The energy storage state constraint is expressed as follows:

[0038] ;

[0039] The energy storage capacity constraint is expressed as follows:

[0040] ;

[0041] in, , Let represent the planned charging power and planned discharging power of the energy storage at time t, respectively. , These represent the maximum charging power and maximum discharging power of the energy storage, respectively. , These represent the charging and discharging states of energy storage, respectively. , Let represent the amount of energy stored at time t and time t-1, respectively; Indicates the scheduling time interval; , These represent the lower limit and upper limit of energy storage capacity, respectively.

[0042] Optionally, the ore stockpile constraint is expressed as:

[0043] ;

[0044] ;

[0045] in, , Let represent the ore reserves at time t and time t-1, respectively. This represents the change in the amount of ore reserves caused by the flexible load n at time t. Indicates the scheduling time interval; , These represent the lower limit and upper limit of the ore pile reserves, respectively. This represents the rated feed and discharge rate of the flexible load n; This represents the planned power of the flexible load n at time t; This represents the rated power of the flexible load n at time t; This represents the minimum load percentage at which the flexible load n begins to feed or discharge materials at time t.

[0046] Optionally, the objective function of the source-load interaction optimization scheduling model is expressed as:

[0047] ;

[0048] in, This represents the objective function of the source-load interaction optimization scheduling model; , , , These represent the number of wind power plants, photovoltaic power plants, gas turbine power plants, and flexible loads, respectively. , , , Let $\frac{i}{t}$ represent the wind curtailment penalty cost of wind power $i$, the solar curtailment penalty cost of solar power $j$, the gas turbine power generation cost of gas turbine $m$, and the flexible load adjustment cost of flexible load $n$ at time $t$, respectively. This represents the total number of moments.

[0049] Optionally, the source-load interaction optimization scheduling model is solved to obtain the scheduling results for new energy power output, gas turbine power output, energy storage charging and discharging, and flexible load power consumption, including:

[0050] The switching of various operating states and start-stop states of different devices is represented as a decision involving multiple binary variables. A mixed-integer linear programming algorithm is used to solve the problem, resulting in the scheduling results of new energy power output, gas turbine power output, energy storage charging and discharging, and flexible load power consumption.

[0051] The switching of various operating states and start / stop states of different devices is the solution result of the source-load interaction optimization scheduling model.

[0052] On the other hand, the present invention provides a computer system comprising:

[0053] Memory, used to store computer instructions;

[0054] A processor is configured to execute the computer instructions to implement the steps of the source-load interaction optimization scheduling method for the mining new energy power system described in the first aspect.

[0055] Compared with the prior art, the present application has the following beneficial effects:

[0056] The present application is aimed at the unique characteristics of mine production load and the demand of "mine-power linkage", and based on fully considering the dynamic characteristics of source and load, taking the principle of guaranteeing efficient consumption of new energy, through the strategy of "flexible consumption of energy storage + efficient power supply of gas turbine", the autonomy and symbiosis of the two are realized, and the elastic boundary of flexible load regulation and control is accurately identified, considering the mine heap reserves as the core constraint condition of mine-power cooperation, realizing the initiative and order of mine load participating in regulation, through overall coordination of power supply and demand side resources, effectively improving the new energy consumption rate and economic operation ability of mine new energy power system. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 Fig. 1 shows the flowchart of the source-load interaction optimization scheduling method of the mine new energy power system in an embodiment of the present application;

[0058] Figure 2 Fig. 2 shows the structural schematic diagram of the mine new energy power system in an embodiment of the present application;

[0059] Figure 3 Fig. 3 shows the schematic diagram of the wind power, photovoltaic and total load predicted power data in an embodiment of the present application;

[0060] Figure 4 Fig. 4 shows the schematic diagram of the determined power supply and demand scheduling result in an embodiment of the present application;

[0061] Figure 5 Fig. 5 shows the schematic diagram of the determined total load scheduling result in an embodiment of the present application;

[0062] Figure 6 Fig. 6 shows the schematic diagram of the predicted power, rated power and planned power data of flexible load 1 in an embodiment of the present application;

[0063] Figure 7 Fig. 7 shows the schematic diagram of the predicted power, rated power and planned power data of flexible load 2 in an embodiment of the present application;

[0064] Figure 8 Fig. 8 shows the schematic diagram of the predicted power, rated power and planned power data of flexible load 3 in an embodiment of the present application;

[0065] Figure 9 Fig. 9 shows the schematic diagram of the determined heap reserves scheduling result in an embodiment of the present application. DETAILED DESCRIPTION

[0066] The technical solutions of the present application will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments and the embodiments can be combined with each other.

[0067] The term "and / or", only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent three cases of A alone, A and B together, and B alone. In addition, the character " / " generally represents that the associated objects before and after are in an "or" relationship.

[0068] Embodiment 1

[0069] As shown in Figure 1 , this embodiment introduces a source-load interaction optimization scheduling method of a mine new energy power system, which includes the following steps:

[0070] Step 1: Establish a mine new energy power system, specifically:

[0071] As shown in Figure 2 , considering the wind power, photovoltaic, gas turbine, energy storage, flexible load and baseline load elements covered by the mine new energy power system, and obtaining the operation data of wind power, photovoltaic, gas turbine, energy storage, flexible load and baseline load in the mine new energy power system and the mine heap reserves.

[0072] Step 2: Determine the optimization scheduling objective function, specifically:

[0073] Determine the objective function of the source-load interaction optimization scheduling model, the first choice is the lowest system operation cost, the system operation cost includes wind power penalty cost, light penalty cost, gas turbine power generation cost and flexible load adjustment cost.

[0074] According to the operation data of wind power, photovoltaic, gas turbine, energy storage and flexible load, calculate the wind power penalty cost, light penalty cost, gas turbine power generation cost and flexible load adjustment cost.

[0075] The objective function of the source-load interaction optimization scheduling model is represented as:

[0076] ;

[0077] Wherein, represents the objective function of the source-load interaction optimization scheduling model; , , , respectively represent the number of wind power, the number of photovoltaic, the number of gas turbine and the number of flexible load; , , , Let $\frac{i}{t}$ represent the wind curtailment penalty cost of wind power $i$, the solar curtailment penalty cost of solar power $j$, the gas turbine power generation cost of gas turbine $m$, and the flexible load adjustment cost of flexible load $n$ at time $t$, respectively. This represents the total number of moments.

[0078] The cost of wind curtailment penalties is expressed as follows:

[0079] ;

[0080] The cost of light abandonment penalty is expressed as:

[0081] ;

[0082] The cost of gas turbine power generation is expressed as follows:

[0083] ;

[0084] The cost of flexible load adjustment is expressed as:

[0085] ;

[0086] in, , Let represent the theoretical power and planned power of wind power i at time t, respectively; This represents the wind curtailment penalty coefficient for wind power i; , Let represent the theoretical power and planned power of photovoltaic j at time t, respectively; Indicates the light-wasting penalty coefficient; This represents the planned power output of gas turbine m at time t; , Let represent the operating cost coefficient and start-up cost coefficient of gas turbine m, respectively; , Let represent the start-up and shutdown states of gas turbine m at time t and time t-1, respectively. If gas turbine m is on, then... ,otherwise ; , Let represent the predicted power and planned power of the flexible load n at time t, respectively; This represents the adjustment cost coefficient for the flexible load n.

[0087] Step 3: Determine the optimal scheduling constraints, specifically:

[0088] The constraints of the source-load interaction optimization scheduling model include system power balance constraints, wind power output constraints, photovoltaic power output constraints, gas turbine output constraints, energy storage power constraints, energy storage status constraints, energy storage power constraints, and mineral storage capacity constraints.

[0089] According to the operation data of wind power, photovoltaic, gas turbine, energy storage, flexible load, baseline load, and stockpile reserves, the system power balance constraint, wind power output constraint, photovoltaic output constraint, gas turbine output constraint, energy storage power constraint, energy storage state constraint, energy storage power constraint, and stockpile reserves constraint are calculated.

[0090] System power balance constraint: during system operation, the sum of power generation of power supply and discharge power of energy storage at any time should be equal to the sum of power consumption of load and charging power of energy storage, that is:

[0091] ;

[0092] wherein, represents the baseline load of the system at the t th time; , respectively represent the planned charging power and planned discharging power of the energy storage at the t th time.

[0093] The wind power output constraint is represented as:

[0094] ;

[0095] wherein, the theoretical power of wind power i at the t th time should be the smaller value of the predicted output of wind power i at the t th time and the rated capacity.

[0096] The photovoltaic output constraint is represented as:

[0097] ;

[0098] wherein, the theoretical power of photovoltaic j at the t th time should be the smaller value of the predicted output of photovoltaic j at the t th time and the rated capacity.

[0099] The gas turbine output constraint is represented as:

[0100] ;

[0101] wherein, represents the start-stop state of the gas turbine m at the k th time; represents the planned power of the gas turbine m at the t-1 th time; , respectively represent the lower limit of the power and the upper limit of the power allowed by the gas turbine m; represents the adjustment rate of the gas turbine m at the t th time;

[0102] Considering the operation characteristics of the gas turbine, the minimum continuous on / off time requirement should be met during the start-stop state switching process, and the specific constraint description is as follows:

[0103] ;

[0104] wherein, , respectively represent the minimum continuous start-up time and the minimum continuous shutdown time of the gas engine m.

[0105] Energy storage power constraint:

[0106] The charging and discharging power of the energy storage should not exceed the power limit value, and is also limited by the State Of Charge (SOC) at the corresponding moment. The energy storage power constraint is represented as:

[0107] ;

[0108] At the same moment, the energy storage can only be in a charging state, a discharging state, or a state of neither charging nor discharging. The energy storage state constraint is represented as:

[0109] ;

[0110] The energy storage power at the current moment is determined by the energy storage power at the previous moment and the energy storage charging and discharging power at the current moment. At the same time, the energy storage power has upper and lower limit values. The energy storage power constraint is represented as:

[0111] ;

[0112] wherein, , respectively represent the maximum charging power and the maximum discharging power of the energy storage; , respectively represent the charging state and the discharging state of the energy storage; , respectively represent the energy storage power at the tth moment and the energy storage power at the (t-1)th moment; represents the scheduling time interval, which is used to describe the time interval between adjacent two scheduling moments. For example, a 24h scheduling plan needs to be made, the total time interval T=96, and the scheduling time interval is 0.25h; , respectively represent the lower limit of the energy storage power and the upper limit of the energy storage power.

[0113] Mine pile storage constraint: In the mine new energy power system, the mine pile is the coupling hub of the power supply side and the load side. When the power supply side has sufficient power, the mine pile storage is maintained at a high level. When the power supply side is insufficient, the load side can reduce the flexible load according to the level of the mine pile storage to realize "power failure but not production stop".

[0114] The change of the stockpile storage mainly relates to different types of flexible load power, which can be divided into feeding type load, discharging type load and other load. The feeding type load is the stockpile feeding in normal production state, which increases the stockpile storage; the discharging type load is the stockpile discharging in normal production state, which reduces the stockpile storage; and the flexible load unrelated to the stockpile storage is the other load. Similar to the energy storage, the stockpile storage should satisfy the upper and lower limit constraints, i.e.

[0115]

[0116] wherein, denote the stockpile storage at the tth moment and the t-1th moment respectively; denote the lower limit of the stockpile storage and the upper limit of the stockpile storage respectively; denotes the change of the stockpile storage caused by the flexible load n at the tth moment, which is expressed as:

[0117]

[0118] wherein, denotes the rated feeding and discharging amount of the flexible load n, is the feeding, is the discharging, and the flexible load n has no effect on the stockpile storage; denotes the rated power of the flexible load n at the tth moment; denotes the minimum load percentage of the flexible load n starting feeding and discharging at the tth moment.

[0119] Step four: input the boundary data and model parameters, specifically:

[0120] wind power: number of power stations, rated power, power prediction data, wind curtailment penalty coefficient;

[0121] photovoltaic: number of power stations, rated power, power prediction data, light curtailment penalty coefficient;

[0122] gas turbine: number of gas turbines, rated power, power upper and lower limits, power regulation rate, minimum continuous start-up time, minimum continuous shutdown time, operation cost coefficient and start-up cost coefficient;

[0123] energy storage: rated power, power upper and lower limits, maximum charging and discharging power;

[0124] load: baseline load plan data, number of flexible loads, flexible load plan data, flexible load regulation upper and lower limits, flexible load regulation cost coefficient.

[0125] Step five: solve by using a mixed integer linear programming algorithm, specifically: ​​​​

[0126] Solving the source-load interaction optimization scheduling model involves switching of various operating states and start-stop states of different devices, therefore, the switching of various operating states and start-stop states of different devices is represented as decisions of multiple binary variables, a mixed integer linear programming algorithm is used for solving, and new energy output scheduling results, gas turbine output scheduling results, energy storage charging and discharging scheduling results, and flexible load power consumption scheduling results are obtained.

[0127] Step six: determining new energy output plan, gas turbine output plan, energy storage charging and discharging plan, and flexible load power consumption plan, specifically:

[0128] The new energy output scheduling result is the new energy output plan, the gas turbine output scheduling result is the gas turbine output plan, the energy storage charging and discharging scheduling result is the energy storage charging and discharging plan, and the flexible load power consumption scheduling result is the flexible load power consumption plan.

[0129] Step seven: plan checking and issuing, specifically:

[0130] The new energy output plan, gas turbine output plan, energy storage charging and discharging plan, and flexible load power consumption plan are safety checked, after meeting the system normal operation conditions, each plan is issued to the active power control system of each module, including wind and solar power generation automatic control system, gas turbine coordinated control system, energy storage energy management system, and load side coordinated control system, to realize closed-loop control.

[0131] Embodiment 2

[0132] Based on embodiment 1, this embodiment introduces a test example of a source-load interaction optimization scheduling method of a mine-used new energy power system:

[0133] Wind power: 1 power station, rated power 200MW, wind power prediction data as shown in Figure 3 , in this embodiment, the prediction data of 5 consecutive days is selected as an example, and the wind curtailment penalty coefficient is 0.5 yuan / kWh;

[0134] Photovoltaic: 1 power station, rated power 300MW, photovoltaic power prediction data as shown in Figure 3 , in this embodiment, the prediction data of 5 consecutive days is selected as an example, and the light curtailment penalty coefficient is 0.5 yuan / kWh;

[0135] Gas turbine: rated power 80MW, power upper limit 80MW, power lower limit 0MW, power regulation rate 4MW / min, minimum continuous start-up time 1h, minimum continuous shutdown time 1h, operation cost coefficient 4 yuan / kWh, start-up cost coefficient 200 yuan / time;

[0136] Energy storage: rated power 1120 MWh, upper limit 1008 MWh (90%), lower limit 172.5 MWh (15%), maximum charge-discharge power 225 MW;

[0137] The total load prediction data is as shown in Figure 3 In this embodiment, the prediction data of 5 consecutive days is selected as an example.

[0138] Table 1: Load-related parameters

[0139] Load type Predicted power (MW) Rated power (MW) Regulation cost (Yuan / kWh) Minimum load percentage (%) for start-up and shut-down Rated in-out material quantity (t / h) Baseline load 67.19 - - - - Flexible load 1 Figure 6 Figure 6 5.6 0 0 Flexible load 2 Figure 7 Figure 7 7.7 80% 0.43 Flexible load 3 Figure 8 Figure 8 10 80% -0.33

[0140] The load-related parameters are shown in Table 1. The solution of the source-load interaction optimization scheduling model involves the switching of multiple operating states and start-stop states of different devices, which is represented as the decision of multiple binary variables in the mathematical model. Therefore, a mixed integer linear programming algorithm is used for solution to obtain the new energy output plan, gas turbine output plan, energy storage charge-discharge plan, and flexible load power consumption plan.

[0141] Figure 4 、 Figure 5 The determined power generation and consumption scheduling plan and the total load scheduling plan are shown. Under the premise of priority to meet the load power consumption demand, the output of energy storage and gas turbine is scheduled to adapt to the output fluctuation of new energy.

[0142] During the day, when the wind and light resources are sufficient, the system stops the gas turbine in time, and adjusts the energy storage to the charging state to cope with the situation of insufficient power at night. If there is still surplus wind and light, the load is increased based on the predicted load power, while ensuring that the adjusted planned load power is within the allowable range of the rated power, thereby promoting high proportion of new energy consumption.

[0143] At night, the photovoltaic output is 0. In windy weather, the system prioritizes calling energy storage to smooth power fluctuations based on wind power prediction. If there is a power shortage, the gas turbine is started in time to supplement power. When energy storage and gas turbine cannot meet the power consumption of the system, the load is reduced based on the predicted load power to obtain the adjusted planned load power.

[0144] Figure 6 、 Figure 7 、 Figure 8 The prediction power, rated power, and planned power data of flexible load 1, the prediction power, rated power, and planned power data of flexible load 2, and the prediction power, rated power, and planned power data of flexible load 3 are shown. Figure 9 The corresponding yard storage scheduling plan data is shown.

[0145] Since the change of the flexible load 1 has no effect on the change of the stockpile reserve, when the load needs to be added or reduced, the predicted power of the flexible load 1 is adjusted in priority. The flexible load 4 is a discharge type load, and the change of the load size of the flexible load 4 causes the change of the stockpile discharge, which directly affects whether the downstream process can be normally produced, so in the embodiment, the planned power of the flexible load 4 is ensured to be consistent with the rated power as much as possible, so as to maintain the stable discharge amount. The flexible load 2 is a feed type load, and when participating in the load adding and reducing, the stockpile reserve also needs to be ensured to be within a reasonable upper and lower limit range, when the stockpile reserve is too low, the planned power of the flexible load 2 is adjusted to be above 80% of the rated power through the means of avoiding reducing and preferentially loading, so as to restore the stockpile reserve, and when the stockpile reserve is too high, the load is preferentially reduced to prevent the stockpile reserve from being excessive.

[0146] The new energy output plan, the gas turbine output plan, the energy storage charging and discharging plan, and the flexible load power consumption plan obtained by solving are safety checked, after meeting the system normal operation conditions, each plan is issued to the active power control system of each module, including the wind and light power generation automatic control system, the gas turbine coordinated control system, the energy storage energy management system, and the load side coordinated control system, to realize closed-loop control.

[0147] Embodiment 3

[0148] The embodiment introduces a computer system, comprising:

[0149] a memory for storing computer instructions;

[0150] a processor for executing the computer instructions to realize the steps of the source-load interaction optimization scheduling method of the mine new energy power system in embodiment 1.

[0151] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0152] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0153] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0154] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0155] The embodiments of the present application described above are merely intended to illustrate the principles of the present application, and the present application is not limited to the above-described embodiments. The above-described embodiments are merely illustrative, and are not intended to limit the present application. Those skilled in the art can make many modifications without departing from the spirit and scope of the present application, and these modifications are also intended to fall within the scope of the present application.

Claims

1. A source-load interaction optimization scheduling method for a mining new energy power system, characterized in that, include: To acquire operational data of wind power, photovoltaic, gas turbine, energy storage, flexible load, and baseline load in mining new energy power systems, as well as mine stockpile reserves; Based on the operating data of wind power, photovoltaic power, gas turbine power, energy storage, and flexible load, calculate the cost of wind curtailment penalty, the cost of solar curtailment penalty, the cost of gas turbine power generation, and the cost of flexible load adjustment; Based on the operating data of wind power, photovoltaic, gas turbine, energy storage, flexible load, baseline load, and mineral stockpile reserves, calculate the system power balance constraints, wind power output constraints, photovoltaic power output constraints, gas turbine output constraints, energy storage power constraints, energy storage status constraints, energy storage capacity constraints, and mineral stockpile reserves constraints. Based on the costs of wind curtailment penalties, solar curtailment penalties, gas turbine power generation costs, and flexible load adjustment costs, an objective function for the source-load interaction optimization scheduling model is constructed. Based on system power balance constraints, wind power output constraints, photovoltaic power output constraints, gas turbine output constraints, energy storage power constraints, energy storage status constraints, energy storage capacity constraints, and mineral storage capacity constraints, the constraints of the source-load interaction optimization scheduling model are constructed. Solving the source-load interaction optimization scheduling model yields the scheduling results for new energy power output, gas turbine power output, energy storage charging and discharging, and flexible load power consumption.

2. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The cost of wind curtailment penalty is expressed as follows: ； The cost of light abandonment penalty is expressed as: ； The cost of gas turbine power generation is expressed as follows: ； The cost of flexible load adjustment is expressed as follows: ； in, , , , Let $\frac{i}{t}$ represent the wind curtailment penalty cost of wind power $i$, the solar curtailment penalty cost of solar power $j$, the gas turbine power generation cost of gas turbine $m$, and the flexible load adjustment cost of flexible load $n$ at time $t$, respectively. , Let represent the theoretical power and planned power of wind power i at time t, respectively; This represents the wind curtailment penalty coefficient for wind power i; , Let represent the theoretical power and planned power of photovoltaic j at time t, respectively; Indicates the light-wasting penalty coefficient; This represents the planned power output of gas turbine m at time t; , Let represent the operating cost coefficient and start-up cost coefficient of gas turbine m, respectively; , These represent the start-up and shutdown states of gas turbine m at time t and time t-1, respectively. , Let represent the predicted power and planned power of the flexible load n at time t, respectively; This represents the adjustment cost coefficient for the flexible load n.

3. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The system power balance constraint is expressed as follows: ； in, , , , Let represent the planned power of wind power i, photovoltaic power j, gas turbine m, and flexible load n at time t, respectively. , , , These represent the number of wind power plants, photovoltaic power plants, gas turbine power plants, and flexible loads, respectively. This represents the baseline load of the system at time t; , Let represent the planned charging power and planned discharging power of the energy storage at time t, respectively.

4. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The wind power output constraint is expressed as follows: ； The photovoltaic output constraint is expressed as follows: ； in, , Represents the planned power and theoretical power of wind power i at time t; , Let represent the planned power and theoretical power of photovoltaic j at time t, respectively.

5. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The gas turbine output constraint is expressed as follows: ； ； in, , , These represent the start-up and shutdown states of gas turbine m at time t, time t-1, and time k, respectively. , Let m represent the planned power output of gas turbine m at time t and time t-1, respectively. , These represent the lower and upper limits of the permissible power output of the gas turbine m, respectively. This represents the regulation rate of gas turbine m at time t; , These represent the minimum continuous start-up time and minimum continuous shutdown time of the gas turbine m, respectively.

6. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The energy storage power constraint is expressed as follows: ； The energy storage state constraint is expressed as follows: ； The energy storage capacity constraint is expressed as follows: ； in, , Let represent the planned charging power and planned discharging power of the energy storage at time t, respectively. , These represent the maximum charging power and maximum discharging power of the energy storage, respectively. , These represent the charging and discharging states of energy storage, respectively. , Let represent the amount of energy stored at time t and time t-1, respectively; Indicates the scheduling time interval; , These represent the lower limit and upper limit of energy storage capacity, respectively.

7. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The ore reserve constraint is expressed as follows: ； ； in, , Let represent the ore reserves at time t and time t-1, respectively. This represents the change in the amount of ore reserves caused by the flexible load n at time t. Indicates the scheduling time interval; , These represent the lower limit and upper limit of the ore pile reserves, respectively. This represents the rated feed and discharge rate of the flexible load n; This represents the planned power of the flexible load n at time t; This represents the rated power of the flexible load n at time t; This represents the minimum load percentage at which the flexible load n begins to feed or discharge materials at time t.

8. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, The objective function of the source-load interaction optimization scheduling model is expressed as: ； in, This represents the objective function of the source-load interaction optimization scheduling model; , , , These represent the number of wind power plants, photovoltaic power plants, gas turbine power plants, and flexible loads, respectively. , , , Let $\frac{i}{t}$ represent the wind curtailment penalty cost of wind power $i$, the solar curtailment penalty cost of solar power $j$, the gas turbine power generation cost of gas turbine $m$, and the flexible load adjustment cost of flexible load $n$ at time $t$, respectively. This represents the total number of moments.

9. The source-load interaction optimization scheduling method for a mining new energy power system according to claim 1, characterized in that, Solving the source-load interaction optimization scheduling model yields the scheduling results for new energy power output, gas turbine power output, energy storage charging and discharging, and flexible load power consumption, including: The switching of various operating states and start-stop states of different devices is represented as a decision involving multiple binary variables. A mixed-integer linear programming algorithm is used to solve the problem, resulting in the scheduling results of new energy power output, gas turbine power output, energy storage charging and discharging, and flexible load power consumption. The switching of various operating states and start / stop states of different devices is the solution result of the source-load interaction optimization scheduling model.

10. A computer system, characterized in that, include: Memory, used to store computer instructions; A processor is configured to execute the computer instructions to implement the steps of the source-load interaction optimization scheduling method for a mining new energy power system as described in any one of claims 1-9.

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