Optimal dispatching method and device considering new energy guarantee consumption requirement

Abstract

The application provides an optimization scheduling method and device considering new energy guarantee consumption requirement. The method comprises the following steps: acquiring power related data, wherein the units comprise new energy units and non-new energy units; constructing a power spot market clearing model, the constraint conditions of the power spot market clearing model at least comprising a new energy guarantee consumption constraint, the target function in the power spot market clearing model at least comprising a new energy penalty term, the new energy penalty term being used for punishing a new energy guarantee consumption deficiency, wherein the new energy guarantee consumption constraint is associated with the new energy penalty term; processing the power related data by using the power spot market clearing model to obtain a power optimization scheduling result, and performing power scheduling and power market clearing based on the power optimization scheduling result. The problem that the traditional power market mechanism cannot realize the functions of market-oriented scheduling and guarantee consumption at the same time is solved.

Description

Technical Field

[0001] This application relates to the field of power, and more specifically, to an optimized dispatching method, apparatus, and computer-readable storage medium that takes into account the requirements for ensuring the absorption of new energy sources. Background Technology

[0002] Renewable energy is characterized by randomness, volatility, and unpredictability. A high proportion of renewable energy poses unprecedented challenges to the safe and stable operation of the power grid, and also leads to derivative problems such as limited survival space for traditional thermal power units. Currently, China implements a guaranteed purchase policy for renewable energy, which imposes a mandatory boundary condition on the operation of the domestic electricity market. Traditional electricity market mechanisms clearly cannot simultaneously achieve the functions of market-based dispatch and guaranteed consumption. Therefore, how to achieve guaranteed consumption of renewable energy while ensuring market dispatch and optimal resource allocation is an urgent problem to be solved. Summary of the Invention

[0003] The main purpose of this application is to provide an optimized dispatching method, device and computer-readable storage medium that takes into account the requirements for renewable energy consumption, so as to solve the problem that the traditional power market mechanism in the prior art obviously cannot simultaneously realize the functions of market-based dispatching and guaranteed consumption.

[0004] To achieve the above objectives, according to one aspect of this application, an optimized scheduling method considering the requirements for renewable energy consumption is provided, comprising:

[0005] Obtain power-related data, which includes at least: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information, wherein the generating units include new energy generating units and non-new energy generating units;

[0006] A power spot market clearing model is constructed. The constraints of the power spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the power spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term.

[0007] The electricity spot market clearing model is used to process the electricity-related data to obtain the optimized power dispatching results, and power dispatching and electricity market clearing are carried out based on the optimized power dispatching results.

[0008] In some implementations, the electricity spot market clearing model includes a safety-constrained unit combination sub-model, a safety-constrained economic dispatch sub-model, and a nodal price calculation sub-model. The electricity spot market clearing model is used to process the electricity-related data to obtain optimized power dispatch results. This includes: processing the electricity-related data using the safety-constrained unit combination sub-model to obtain the start-up and shutdown status results of each unit and its first calculated output capacity; and further processing the electricity-related data using the safety-constrained economic dispatch sub-model, combining this with the start-up and shutdown status generated by the safety-constrained unit combination sub-model. As a result, the second calculated output capacity of each unit is obtained, along with the output capacity of the new energy units coupled with the new energy consumption index. The nodal price calculation sub-model is used to process the power-related data, the output capacity of each unit, and the output capacity of the new energy units coupled with the new energy consumption index to obtain the power optimization scheduling result. The power optimization scheduling result includes the output plan of non-new energy units, the marginal electricity price corresponding to the output of non-new energy units in each time period, the output plan of new energy units in the guaranteed consumption period, the output plan of new energy units in the non-guaranteed consumption period, and the corresponding marginal electricity price.

[0009] In some implementations, the constraints in the nodal electricity price calculation sub-model do not include the renewable energy guaranteed consumption constraint and the objective function of the nodal electricity price calculation sub-model does not include a renewable energy penalty term. The constraints in the nodal electricity price calculation sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, renewable energy output constraints, hydropower water level control constraints, and constraints on limiting unit output. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints.

[0010] The constraint limiting the unit output is expressed as follows: Among them, for generator sets whose start-stop status indicates a shutdown status, The value is zero; for the non-priced units among the new energy units that are not shut down, as indicated by the start-stop status result of the unit, the value is zero. The value is the output P of unit i in time period t. i,t For new energy generating units whose start-stop status is indicated by the aforementioned unit, pricing is available. The value can be: Wherein, δ represents the proportion in the nodal pricing calculation sub-model that allows the priceable generating unit to deviate from its output. The minimum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model. The maximum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model.

[0011] In some embodiments, the method further includes: determining a target absorption threshold; if the daily absorption of the new energy unit is less than or equal to the target absorption threshold, determining the new energy unit as a non-pricing unit; if the daily absorption of the new energy unit is greater than the target absorption threshold, determining the new energy unit as a pricing unit.

[0012] In some implementations, the objective functions of the safety-constrained unit combination sub-model, the safety-constrained economic dispatch sub-model, and the nodal price calculation sub-model are respectively expressed as:

[0013]

[0014]

[0015]

[0016] Where N is the total number of units in the target area, T is the total number of time periods, and P is the total number of time periods. i,t For the output of unit i in time period t, C i,t (P i,t Let t be the operating cost of unit i during time period t. The startup cost of unit i during time period t. The minimum technical output cost of unit i in time period t, where M1 is the network flow constraint relaxation penalty factor for clearing optimization. Let l be the positive power flow relaxation variable for line l. Let be the reverse power flow relaxation variable for line l, and NL be the total number of lines for line l. Let be the positive power flow relaxation variable for line s. Let be the reverse power flow relaxation variable for line s, NS be the total number of lines in line s, and M2 be the water level control constraint relaxation penalty factor. For the positive water level control relaxation variable of hydropower h, NH represents the reverse water level control relaxation variable for hydropower h, NH represents the total number of hydropower plants with regulation capacity, and M3 represents the power waste penalty factor for hydropower plants. M5 represents the water wastage capacity of hydropower plant i, and M5 is the penalty factor for insufficient guaranteed consumption of new energy sources. The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0017] In some implementations, the constraints of the safety-constrained unit combination sub-model further include: system load balance constraints, system positive and negative reserve capacity constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, renewable energy output constraints, and hydropower water level control constraints. The constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints, ramp rate constraints, and minimum start-up and shutdown time constraints. The constraints of the safety-constrained economic dispatch sub-model further include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, renewable energy output constraints, and hydropower water level control constraints. The constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints.

[0018] In some implementations, the constraint on the guaranteed absorption of new energy sources is expressed as: Among them, the total number of NR new energy units, P r,obj The target amount of energy consumption to be consumed on a predetermined operating day under the guaranteed energy consumption target is allocated to the day by the energy consumption target; P r,t This represents the clearing of existing stock of wind, solar, and other new energy generator units. The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0019] In some implementations, the objective function of the safety-constrained unit combination sub-model includes the new energy penalty term, wherein the new energy penalty term is expressed as:

[0020] Among them, M5 is the penalty factor for insufficient guaranteed consumption of new energy; The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0021] According to another aspect of this application, an optimized scheduling device that takes into account the requirements for ensuring the absorption of new energy sources is provided, comprising:

[0022] The acquisition unit is used to acquire power-related data, which includes at least one of the following: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information, wherein the generating units include new energy generating units and non-new energy generating units;

[0023] A construction unit is used to construct a power spot market clearing model. The constraints of the power spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the power spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term.

[0024] The processing unit is used to process the power-related data using the power spot market clearing model to obtain the power optimization scheduling result, and to perform power scheduling and power market clearing based on the power optimization scheduling result.

[0025] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the aforementioned optimized scheduling methods.

[0026] By applying the technical solution of this application, power-related data is acquired, and a power spot market clearing model is constructed. This model is then used to process the power-related data to obtain optimized power dispatching results. Power dispatching and power market clearing are then performed based on these results. Because the power spot market clearing model incorporates renewable energy guarantee consumption constraints and a renewable energy penalty term into its objective function, market-based dispatching is achieved while considering renewable energy guarantee consumption requirements. In other words, power dispatching and power market clearing are realized. Attached Figure Description

[0027] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 A hardware structure block diagram of a mobile terminal for implementing an optimized scheduling method that takes into account the requirements for ensuring the consumption of new energy sources, according to an embodiment of this application, is shown.

[0029] Figure 2 A flowchart illustrating an optimized scheduling method that takes into account the requirements for ensuring the absorption of new energy sources, according to an embodiment of this application, is shown.

[0030] Figure 3 A structural block diagram of an optimized scheduling device that takes into account the requirements for ensuring the absorption of new energy sources is provided according to an embodiment of this application is shown.

[0031] Figure 4 A data design diagram for an IEEE 14-node system based on an embodiment of this application is shown.

[0032] Figure 5 A schematic diagram of the pricing periods for each generating unit provided according to embodiments of this application is shown;

[0033] Figure 6 A schematic diagram of a load power time-period prediction curve provided according to an embodiment of this application is shown;

[0034] Figure 7 A schematic diagram of a new energy power time period prediction curve provided according to an embodiment of this application is shown. Detailed Implementation

[0035] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0038] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Furthermore, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0039] Security-constrained Unit Commitment (SCUC): Under the condition of satisfying the security constraints of the power system, and with the optimization objective of minimizing the system's power purchase cost, a multi-period unit start-up and shutdown plan is formulated.

[0040] Security-constrained Economic Dispatch (SCED) sub-model: Under the condition of satisfying the security constraints of the power system, it formulates a multi-period unit power generation plan with the optimization objective of minimizing the system's power purchase cost.

[0041] The Locational Marginal Price (LMP) sub-model is a spot electricity pricing model. Its rational design combines market forces with system operation, relying on a physical model of the power grid (tidal model), adhering to safety-constrained unit combination and economic dispatch procedures, and pricing based on marginal cost. This achieves both market-based power balance and economic dispatch (minimizing electricity purchase costs).

[0042] Nodal pricing: This refers to the cost incurred or reduced by 1 MW of load at a specific time and location in the market. It reflects the electricity supply and demand relationship at a particular node; a high price indicates a tight electricity supply at that node, while a low price indicates a surplus electricity supply.

[0043] New energy: also known as unconventional energy. It refers to various forms of energy other than traditional energy. It refers to energy that has just begun to be developed and utilized or is under active research and is yet to be promoted, such as solar energy, geothermal energy, wind energy, ocean energy, biomass energy, and nuclear fusion energy.

[0044] Marginal electricity price: In spot electricity trading, the price quoted by the last electricity supplier that satisfies demand, based on the order of bids from lowest to highest, is called the system's "marginal price." Electricity from generators bidding above the marginal price cannot be traded, resulting in a failed bid. Electricity from generators bidding below the marginal price is not settled with the electricity market based on the bid, but rather at the system's marginal price.

[0045] As described in the background section, the traditional power market mechanism in the prior art obviously cannot simultaneously achieve the functions of market-based dispatch and guaranteed absorption. To address the problem that the traditional power market mechanism cannot simultaneously achieve these functions, embodiments of this application provide an optimized dispatch method, apparatus, and computer-readable storage medium that considers the requirements for guaranteed absorption of new energy sources.

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0047] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1This is a hardware structure block diagram of a mobile terminal for an optimized scheduling method that considers the requirements for ensuring the absorption of new energy sources, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0048] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0049] This embodiment provides an optimized scheduling method that considers the requirements for ensuring the consumption of new energy sources and runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0050] According to an embodiment of this application, an optimized scheduling method that takes into account the requirements for ensuring the absorption of new energy sources is provided.

[0051] Figure 2 This is a flowchart of an optimized scheduling method that takes into account the requirements for ensuring the consumption of new energy sources, according to an embodiment of this application.

[0052] like Figure 2 As shown, the method includes the following steps:

[0053] Step S201: Obtain power-related data, which includes at least: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information. The generating units include new energy generating units and non-new energy generating units.

[0054] Step S202: Construct an electricity spot market clearing model. The constraints of the electricity spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the electricity spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term.

[0055] Specifically, the connection between the new energy guaranteed consumption constraint and the new energy penalty item is explained as follows: under the constraint of the new energy guaranteed consumption constraint, the new energy penalty item is combined to achieve market-based scheduling under the premise of taking into account the new energy consumption requirements.

[0056] Step S203: The electricity-related data is processed using the electricity spot market clearing model to obtain the electricity optimal dispatching result, and electricity dispatching and electricity market clearing are carried out based on the electricity optimal dispatching result.

[0057] This application presents an optimized dispatching method that considers the requirements for renewable energy consumption. This method acquires relevant power data and constructs a power spot market clearing model. The power data is then processed using this model to obtain optimized power dispatching results. Power dispatching and power market clearing are then performed based on these results. By incorporating renewable energy consumption constraints and a renewable energy penalty term into the objective function of the power spot market clearing model, market-based dispatching is achieved while considering renewable energy consumption requirements. In other words, power dispatching and power market clearing are realized. This means that the overall system output is calculated fairly while considering renewable energy consumption requirements.

[0058] In some embodiments of this application, the electricity spot market clearing model includes a safety-constrained unit combination sub-model, a safety-constrained economic dispatch sub-model, and a nodal price calculation sub-model. The electricity spot market clearing model is used to process the electricity-related data to obtain optimized electricity dispatch results, including:

[0059] The power-related data are processed using the safety-constrained unit combination sub-model to obtain the start-up and shutdown status results of each unit and the first calculated output capacity of each unit.

[0060] The power-related data is reprocessed using the safety-constrained economic dispatch sub-model, and combined with the start-up and shutdown status results generated by the safety-constrained unit combination sub-model, to obtain the second calculated output capacity of each unit, as well as whether the new energy unit output capacity is completed, coupled with the new energy consumption index.

[0061] The nodal price calculation sub-model is used to process the power-related data, the output of each generating unit, and whether the output of the new energy generating unit is completed, coupled with the new energy consumption index, to obtain the power optimization scheduling result. The power optimization scheduling result includes the output plan of non-new energy generating units, the marginal electricity price corresponding to the output of non-new energy generating units in each time period, the output plan of new energy generating units in the guaranteed consumption period, the output plan of new energy generating units in the non-guaranteed consumption period, and the corresponding marginal electricity price.

[0062] That is, by using a safety-constrained unit combination sub-model, a safety-constrained economic dispatch sub-model, and a nodal price calculation sub-model, the accurate determination of the power optimization dispatch results is achieved.

[0063] In some embodiments of this application, the constraints in the nodal electricity price calculation sub-model do not include the renewable energy guaranteed consumption constraint and the objective function of the nodal electricity price calculation sub-model does not include a renewable energy penalty term. The constraints in the nodal electricity price calculation sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, renewable energy output constraints, hydropower water level control constraints, and constraints on limiting unit output. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints.

[0064] The constraint limiting the unit output is expressed as follows: Among them, for generator sets whose start-stop status indicates a shutdown status, The value is zero; for the non-priced units among the new energy units that are not shut down, as indicated by the start-stop status result of the unit, the value is zero. The value is the output P of unit i in time period t. i,tFor new energy generating units whose start-stop status is indicated by the aforementioned unit, pricing is available. The value can be: Wherein, δ represents the proportion in the nodal pricing calculation sub-model that allows the priceable generating unit to deviate from its output. The minimum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model. The maximum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model.

[0065] In this process, both non-priceable and priceable new energy generating units are input into the nodal electricity price calculation sub-model to calculate the pricing of non-priceable and priceable new energy generating units.

[0066] Among them, non-new energy units, i.e. conventional units, are priced according to quantity and price information.

[0067] The constraints in the nodal electricity price calculation sub-model do not include the guaranteed consumption constraint for new energy sources, and the objective function of the nodal electricity price calculation sub-model does not include a penalty term for new energy sources. By removing the constraint on guaranteed consumption and the corresponding penalty function term in the objective function, and adding the constraint on limiting unit output to the SCED dispatch model, the unit output constraint is restricted. This ensures pricing power while avoiding inappropriate negative electricity prices, thus preventing price distortion and ensuring the rationality of the set price.

[0068] This application proposes a method for calculating the marginal electricity price at nodes, which uses capacity constraints to determine whether a renewable energy unit can be used as a pricing unit. When renewable energy does not meet the consumption requirements, it is made into a non-pricing unit, so as not to affect the final result of the power system's optimal dispatch. When the consumption requirements are met, it can participate in the power market's optimal dispatch fairly, just like other unit types, or become a marginal unit.

[0069] In some embodiments of this application, the method further includes:

[0070] Determine the target absorption capacity threshold;

[0071] If the daily consumption of the new energy unit is less than or equal to the target consumption threshold, the new energy unit is determined to be a non-pricing unit.

[0072] If the daily consumption of the new energy unit is greater than the target consumption threshold, the new energy unit is determined to be a priced unit.

[0073] Therefore, when the grid connection capacity is sufficient, new energy and conventional units compete for the same amount of electricity. When the grid connection capacity is insufficient, priority is given to ensuring the completion of guaranteed grid connection. In this case, competition among new energy units can avoid excessively high power generation costs while fulfilling the guaranteed grid connection constraint. In other words, whether a participating new energy unit is dispatchable depends on its daily grid connection. If the daily grid connection is less than or equal to the target grid connection, it is an undispatchable unit; otherwise, it is a dispatchable unit. This approach preserves the dispatchability of new energy units while preventing them from gaining market power through guaranteed grid connection and thus driving up electricity prices.

[0074] In some specific embodiments of this application, the objective functions of the safety-constrained unit combination sub-model, the safety-constrained economic dispatch sub-model, and the nodal price calculation sub-model are respectively expressed as follows:

[0075]

[0076]

[0077]

[0078] Where N is the total number of units in the target area, T is the total number of time periods, and T represents the total number of time periods considered. On day D, each time period is 15 minutes long, considering 96 time periods. On day D+1, two time periods are considered: peak and off-peak load, so T is 98. i,t For the output of unit i in time period t, C i,t (P i,t Let t be the operating cost of unit i during time period t. The startup cost of unit i during time period t. The minimum technical output cost of unit i in time period t, where M1 is the network flow constraint relaxation penalty factor for clearing optimization. Let l be the positive power flow relaxation variable for line l. Let be the reverse power flow relaxation variable for line l, and NL be the total number of lines for line l. Let be the positive power flow relaxation variable for line s. Let be the reverse power flow relaxation variable for line s, NS be the total number of lines in line s, and M2 be the water level control constraint relaxation penalty factor. For the positive water level control relaxation variable of hydropower h, NH represents the reverse water level control relaxation variable for hydropower h, NH represents the total number of hydropower plants with regulation capacity, and M3 represents the power waste penalty factor for hydropower plants. M5 represents the water wastage capacity of hydropower plant i, and M5 is the penalty factor for insufficient guaranteed consumption of new energy sources. The reverse control slack variable for the total absorption capacity of the winning bids for new energy units. Since the guaranteed absorption of new energy is an overall goal, rather than a physical condition that must be met at every time period, it does not need to be expressed as a summation term.

[0079] Among them, the penalty factor for insufficient guaranteed consumption of new energy changes with the amount of new energy consumption. Specifically, the larger the amount of new energy consumption, the smaller the penalty factor for insufficient guaranteed consumption of new energy, and the smaller the amount of new energy consumption, the larger the penalty factor for insufficient guaranteed consumption of new energy.

[0080] In other words, for the pricing stage, a nodal pricing mechanism is adopted. In order not to distort the price signal, the objective function of the nodal pricing calculation sub-model only considers the operating cost of the generating units and the congestion component of the line power flow.

[0081] This invention adds a guaranteed consumption constraint to the basic spot electricity market clearing model and adds slack variables. It also adds a penalty function for the amount of wind and solar curtailment to the objective function. The coefficient of the penalty function represents the maximum cost that the system is willing to pay to ensure consumption.

[0082] In other words, this invention first acquires power-related data, and then performs calculations on the improved safety-constrained unit combination sub-model, the safety-constrained economic dispatch sub-model, and the nodal price calculation sub-model. The resulting improvement is that it considers the demand for guaranteed consumption of new energy sources in the traditional model, while ensuring the fairness of the overall system optimization and dispatch.

[0083] Some specific embodiments of this application also include:

[0084] The constraints of the safety-constrained unit combination sub-model also include: system load balance constraints, system positive and negative reserve capacity constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, new energy output constraints, and hydropower water level control constraints. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints, ramp rate constraints, and minimum start-up and shutdown time constraints.

[0085] The constraints of the safety-constrained economic dispatch sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, new energy output constraints, and hydropower water level control constraints. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints.

[0086] Specifically, system load balancing constraints: In the formula, T j,t,O This represents the planned power of external connection line j related to this province during time period t (positive for receiving, negative for sending), where NT is the total number of external connection lines related to this province; D tThis represents the system load of this province during time period t.

[0087] System positive and negative reserve capacity constraints: In the formula, represents the state of unit i in time period t, and α represents the thermal power trading unit. i,t =0 indicates that the unit is shut down, α i,t =1 indicates that the unit is started, and the hydropower trading unit α i,t =0 indicates no positive reserve capability, α i,t =1 indicates that it has positive reserve capability; This indicates the state of unit i during time period t, and represents the thermal power trading unit. With α i,t This indicates agreement; hydropower trading unit This indicates that it lacks negative backup capability. This indicates that it has negative backup capability; The maximum output of trading unit i in time period t. Let i be the minimum output of trading unit i in time period t; For time period t, the coefficient α i,t Network backup limit value, The system's positive reserve capacity requirement for time period t; The system's negative backup capacity requirement for time period t.

[0088] The power flow constraints of the line are:

[0089]

[0090] In the formula, P l max P l min G represents the forward and reverse transmission limits of power flow on line l; l-i G is the generator output power transfer distribution factor from node i to line l; l-j The generator output power transfer distribution factor of the node where the external tie line j is located to line l is denoted as G; NTD is the number of DC tie lines in the region; l-d The generator output power transfer distribution factor of the node where DC tie line d is located in the region to line l. G represents the transmission power of DC tie line d within the region during time period t; K represents the number of load nodes in the system; G l-k D is the generator output power transfer distribution factor from node k to line l; k,t Let be the bus load value of node k in time period t.

[0091] Cross-sectional power flow constraints:

[0092]

[0093] In the formula, G represents the forward and reverse transmission limits of the power flow at section s; s-i G is the generator output power transfer distribution factor from node i to section s; s-j G is the generator output power transfer distribution factor from node j of the external connecting line to section s; s-d G is the generator output power transfer distribution factor from the node where DC tie line d is located to section s within the region; s-k Let be the generator output power transfer distribution factor at node k to section s.

[0094] Constraints on thermal power, gas power, and nuclear power units include upper and lower limit constraints, ramp rate constraints, and minimum start-up and shutdown time constraints:

[0095]

[0096] In the formula, UD represents the duration of the startup process, calculated up to the minimum output; DD represents the duration of the shutdown process, calculated from the minimum output; η and γ are 0-1 variables representing whether the unit switches to startup and shutdown states, respectively; T U T D The minimum continuous start-up time and minimum continuous downtime for a trading unit; The continuous operating time and continuous downtime of unit i during time period t can be represented by the state variable α. i,t (i = 1 to N, t = 1 to T) can be used to represent: Constraints on new energy output: In the formula, The predicted output of the new energy generating unit r during time period t. Hydropower water level control constraints:

[0097]

[0098]

[0099]

[0100] In the formula, The upper and lower limits of the water level in reservoir i of the hydropower station are controlled by scheduling at the end of time period t. Z represents the minimum allowable operating water level and the maximum allowable water level of the hydropower station reservoir. i,0 The initial water level of hydropower station i at midnight the following day is calculated through spot market clearing; h i Let S be the water consumption rate of hydropower station i. i I represents the water surface area corresponding to the current water level of reservoir i at hydropower station i. i,τ P represents the interval flow of hydropower station i during the time period τ.i,τ This represents the output of hydropower station i during time period τ. P represents the flood discharge flow of hydropower station i during a given time period. up(i),τ-s(i) Let up(i) be the power generation flow of the upstream hydropower station i during the time period τ-s(i); Let τ be the flood discharge of the upstream hydropower station up(i) of hydropower station i during the time period τ-s(i). In the variable subscript, up(i) represents the upstream hydropower station of hydropower station i, and s(i) represents the upstream delay time faced by hydropower station i, which is determined in advance.

[0101] In some specific embodiments of this application, the constraint on the guaranteed consumption of new energy sources is expressed as follows:

[0102] Among them, the total number of NR new energy units, P r,obj The target amount of energy consumption to be consumed on a predetermined operating day under the guaranteed energy consumption target is allocated to the day by the energy consumption target; P r,t This represents the clearing of existing stock of wind, solar, and other new energy generator units. The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0103] In some specific embodiments of this application, the objective function of the safety-constrained unit combination sub-model includes the new energy penalty term, wherein the new energy penalty term is expressed as:

[0104] Among them, M5 is the penalty factor for insufficient guaranteed consumption of new energy; SL r -e is the inverse control slack variable for the total consumption of new energy units in the bid.

[0105] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0106] This application also provides an optimized scheduling device that considers the requirements for renewable energy supply and consumption. It should be noted that this optimized scheduling device can be used to execute the optimized scheduling method provided in this application that considers the requirements for renewable energy supply and consumption. The optimized scheduling device considering the requirements for renewable energy supply and consumption provided in this application will be described below.

[0107] Figure 3 This is a schematic diagram of an optimized scheduling device that takes into account the requirements for ensuring the consumption of new energy sources, according to an embodiment of this application.

[0108] like Figure 3As shown, the device includes:

[0109] The acquisition unit 10 is used to acquire power-related data, which includes at least one of the following: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information, wherein the generating units include new energy generating units and non-new energy generating units.

[0110] Construction unit 20 is used to construct a power spot market clearing model. The constraints of the power spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the power spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term.

[0111] The processing unit 30 is used to process the power-related data using the power spot market clearing model to obtain the power optimization scheduling result, and to perform power scheduling and power market clearing based on the power optimization scheduling result.

[0112] This application presents an optimized dispatching method that considers the requirements for renewable energy consumption. The method involves an acquisition unit to obtain electricity-related data, a construction unit to build an electricity spot market clearing model, and a processing unit to process the electricity-related data using the electricity spot market clearing model to obtain optimized power dispatching results. Based on these results, power dispatching and electricity market clearing are then performed. By incorporating renewable energy consumption guarantees into the electricity spot market clearing model and adding a renewable energy penalty term to the objective function, market-based dispatching is achieved while considering renewable energy consumption guarantees. In other words, power dispatching and electricity market clearing are realized.

[0113] In some embodiments of this application, the electricity spot market clearing model includes a safety-constrained unit combination sub-model, a safety-constrained economic dispatch sub-model, and a nodal price calculation sub-model, and the processing unit includes:

[0114] The first processing module is used to process the power-related data using the safety-constrained unit combination sub-model to obtain the start-up and shutdown status results of each unit and the first calculated output capacity of each unit.

[0115] The second processing module is used to reprocess the power-related data using the safety-constrained economic dispatch sub-model, and combine the start-up and shutdown status results generated by the safety-constrained unit combination sub-model to obtain the second calculated output capacity of each unit, as well as whether the new energy unit output capacity is completed, coupled with the new energy consumption index.

[0116] The third processing module is used to process the power-related data, the output of each of the generating units, and whether the output of the new energy generating units has been completed, coupled with the new energy consumption index, using the nodal electricity price calculation sub-model to obtain the power optimization scheduling result. The power optimization scheduling result includes the output plan of non-new energy generating units, the marginal electricity price corresponding to the output of non-new energy generating units in each time period, the output plan of new energy generating units in the guaranteed consumption period, the output plan of new energy generating units in the non-guaranteed consumption period, and the corresponding marginal electricity price.

[0117] In some embodiments of this application, the apparatus further includes a first determining unit, a second determining unit, and a third determining unit.

[0118] The first determining unit is used to determine the target absorption capacity threshold.

[0119] The second determining unit is used to determine that the new energy unit is a non-pricing unit when the daily consumption of the new energy unit is less than or equal to the target consumption threshold.

[0120] The third determining unit is used to determine that the new energy unit is a priced unit when the daily consumption of the new energy unit is greater than the target consumption threshold.

[0121] Therefore, when the grid connection capacity is sufficient, new energy and conventional units compete for the same amount of electricity. When the grid connection capacity is insufficient, priority is given to ensuring the completion of guaranteed grid connection. In this case, competition among new energy units can avoid excessively high power generation costs while fulfilling the guaranteed grid connection constraint. In other words, whether a participating new energy unit is dispatchable depends on its daily grid connection. If the daily grid connection is less than or equal to the target grid connection, it is an undispatchable unit; otherwise, it is a dispatchable unit. This approach preserves the dispatchability of new energy units while preventing them from gaining market power through guaranteed grid connection and thus driving up electricity prices.

[0122] In some specific embodiments of this application, the objective functions of the safety-constrained unit combination sub-model, the safety-constrained economic dispatch sub-model, and the nodal price calculation sub-model are respectively expressed as follows:

[0123]

[0124]

[0125]

[0126] Where N is the total number of units in the target area, T is the total number of time periods, and P is the total number of time periods. i,t For the output of unit i in time period t, C i,t (P i,tLet t be the operating cost of unit i during time period t. The startup cost of unit i during time period t. The minimum technical output cost of unit i in time period t, where M1 is the network flow constraint relaxation penalty factor for clearing optimization. Let l be the positive power flow relaxation variable for line l. Let be the reverse power flow relaxation variable for line l, and NL be the total number of lines for line l. Let be the positive power flow relaxation variable for line s. Let be the reverse power flow relaxation variable for line s, NS be the total number of lines in line s, and M2 be the water level control constraint relaxation penalty factor. For the positive water level control relaxation variable of hydropower h, NH represents the reverse water level control relaxation variable for hydropower h, NH represents the total number of hydropower plants with regulation capacity, and M3 represents the power waste penalty factor for hydropower plants. M5 represents the water wastage capacity of hydropower plant i, and M5 is the penalty factor for insufficient guaranteed consumption of new energy sources. The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0127] Some specific embodiments of this application also include:

[0128] The constraints of the safety-constrained unit combination sub-model also include: system load balance constraints, system positive and negative reserve capacity constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, new energy output constraints, and hydropower water level control constraints. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints, ramp rate constraints, and minimum start-up and shutdown time constraints.

[0129] The constraints of the safety-constrained economic dispatch sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, new energy output constraints, and hydropower water level control constraints. The constraints on thermal power, gas power, and nuclear power units include upper and lower limit constraints and ramp rate constraints. The constraints of the above-mentioned safety-constrained economic dispatch sub-model are similar to those of the above-mentioned safety-constrained unit combination sub-model, except that the safety-constrained economic dispatch sub-model does not consider system reserve capacity constraints and minimum start-up and shutdown time constraints for units.

[0130] Specifically, system load balancing constraints: In the formula, T j,t,O This represents the planned power of external connection line j related to this province during time period t (positive for receiving, negative for sending), where NT is the total number of external connection lines related to this province; D t This represents the system load of this province during time period t.

[0131] System positive and negative reserve capacity constraints: In the formula, represents the state of unit i in time period t, and α represents the thermal power trading unit. i,t =0 indicates that the unit is shut down, α i,t =1 indicates that the unit is started, and the hydropower trading unit α i,t =0 indicates no positive reserve capability, α i,t =1 indicates that it has positive reserve capability; This indicates the state of unit i during time period t, and represents the thermal power trading unit. With α i,t This indicates agreement; hydropower trading unit This indicates that it lacks negative backup capability. This indicates that it has negative backup capability; The maximum output of trading unit i in time period t. Let i be the minimum output of trading unit i in time period t; For time period t, the coefficient α i,t Network backup limit value, The system's positive reserve capacity requirement for time period t; The system's negative backup capacity requirement for time period t.

[0132] The power flow constraints of the line are:

[0133]

[0134] In the formula, P l max P l min G represents the forward and reverse transmission limits of power flow on line l; l-i G is the generator output power transfer distribution factor from node i to line l; l-j The generator output power transfer distribution factor of the node where the external tie line j is located to line l is denoted as G; NTD is the number of DC tie lines in the region; l-d The generator output power transfer distribution factor of the node where DC tie line d is located in the region to line l. G represents the transmission power of DC tie line d within the region during time period t; K represents the number of load nodes in the system; G l-k D is the generator output power transfer distribution factor from node k to line l; k,t Let be the bus load value of node k in time period t.

[0135] Cross-sectional power flow constraints:

[0136]

[0137] In the formula, G represents the forward and reverse transmission limits of the power flow at section s; s-i G is the generator output power transfer distribution factor from node i to section s; s-j G is the generator output power transfer distribution factor from node j of the external connecting line to section s; s-d G is the generator output power transfer distribution factor from the node where DC tie line d is located to section s within the region; s-k Let be the generator output power transfer distribution factor at node k to section s.

[0138] Constraints on thermal power, gas power, and nuclear power units include upper and lower limit constraints, ramp rate constraints, and minimum start-up and shutdown time constraints:

[0139]

[0140] In the formula, UD represents the duration of the startup process, calculated up to the minimum output; DD represents the duration of the shutdown process, calculated from the minimum output; η and γ are 0-1 variables representing whether the unit switches to startup and shutdown states, respectively; T U T D The minimum continuous start-up time and minimum continuous downtime for a trading unit; The continuous operating time and continuous downtime of unit i during time period t can be represented by the state variable α. i,t (i = 1 to N, t = 1 to T) can be used to represent: Constraints on new energy output: In the formula, The predicted output of the new energy generating unit r during time period t. Hydropower water level control constraints:

[0141]

[0142]

[0143]

[0144] In the formula, The upper and lower limits of the water level in reservoir i of the hydropower station are controlled by scheduling at the end of time period t. Z represents the minimum allowable operating water level and the maximum allowable water level of the hydropower station reservoir. i,0 The initial water level of hydropower station i at midnight the following day is calculated through spot market clearing; h i Let S be the water consumption rate of hydropower station i. i I represents the water surface area corresponding to the current water level of reservoir i at hydropower station i. i,τ P represents the interval flow of hydropower station i during the time period τ. i,τ This represents the output of hydropower station i during time period τ. P represents the flood discharge flow of hydropower station i during a given time period. up(i),τ-s(i) Let up(i) be the power generation flow of the upstream hydropower station i during the time period τ-s(i); Let τ be the flood discharge of the upstream hydropower station up(i) of hydropower station i during the time period τ-s(i). In the variable subscript, up(i) represents the upstream hydropower station of hydropower station i, and s(i) represents the upstream delay time faced by hydropower station i, which is determined in advance.

[0145] In some specific embodiments of this application, the constraint on the guaranteed consumption of new energy sources is expressed as follows:

[0146] Among them, the total number of NR new energy units, P r,obj The target amount of energy consumption to be consumed on a predetermined operating day under the guaranteed energy consumption target is allocated to the day by the energy consumption target; P r,t This represents the clearing of existing stock of wind, solar, and other new energy generator units. The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0147] In some specific embodiments of this application, the objective function of the safety-constrained unit combination sub-model includes the new energy penalty term, wherein the new energy penalty term is expressed as:

[0148] Among them, M5 is the penalty factor for insufficient guaranteed consumption of new energy; The reverse control slack variable for the total consumption of the new energy units won in the bid.

[0149] The optimized scheduling device that takes into account the requirements for ensuring the consumption of new energy sources includes a processor and a memory. The aforementioned building units are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0150] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, an optimized scheduling method that takes into account the requirements for ensuring the absorption of renewable energy sources can be implemented.

[0151] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0152] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device where the computer-readable storage medium is located to execute the optimized scheduling method that considers the requirements for ensuring the consumption of new energy sources.

[0153] This invention provides a processor for running a program, wherein the program executes the optimized scheduling method that considers the requirements for ensuring the absorption of new energy sources.

[0154] This invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: Step S101, acquiring power-related data, wherein the power-related data includes at least: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information, wherein the generating units include new energy generating units and non-new energy generating units.

[0155] Step S102: Construct an electricity spot market clearing model. The constraints of the electricity spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the electricity spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term.

[0156] Step S103: The electricity-related data is processed using the electricity spot market clearing model to obtain the electricity optimal dispatching result, and electricity dispatching and electricity market clearing are carried out based on the electricity optimal dispatching result.

[0157] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0158] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: Step S101, acquiring power-related data, wherein the power-related data includes at least: declared quantity and price information, declared generating unit information, power load forecast information, and line network structure information, wherein the generating units include new energy generating units and non-new energy generating units;

[0159] Step S102: Construct an electricity spot market clearing model. The constraints of the electricity spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the electricity spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term.

[0160] Step S103: The electricity-related data is processed using the electricity spot market clearing model to obtain the electricity optimal dispatching result, and electricity dispatching and electricity market clearing are carried out based on the electricity optimal dispatching result.

[0161] This embodiment relates to a specific optimized scheduling method that considers the requirements for ensuring the absorption of new energy sources, as detailed below.

[0162] This implementation example is based on data from an IEEE 14-node system. New energy generating units are located at node 1, while conventional generating units are distributed across nodes 2, 3, 6, and 8. The system contains multiple load nodes, with nodes 4 and 5 experiencing heavier loads. Figure 4 As shown.

[0163] The embodiments are analyzed based on three scenarios, and the comparison results illustrate the advantages of the present invention.

[0164] Scenario 1: The system scheduling optimization results are obtained according to the relevant model of this invention. New energy units participate in the market through electricity bidding and ensure that new energy wins at least the guaranteed consumption of electricity. In this example, the total predicted amount of new energy is set at 90%.

[0165] Scenario 2: According to the traditional clearing model, the predicted output of new energy units is used as a fixed boundary and all of them are absorbed.

[0166] Scenario 3: According to the relevant model of this invention, new energy units participate in the market by submitting bids based on their volume, but the guaranteed consumption constraint is removed from the model constraints.

[0167] The relevant parameter settings are shown in Table 1 below. Basic parameters for conventional generating units are also shown in Table 1. Pricing for each unit is as follows: Figure 5 The market is divided into 15-minute time slots, with 96 time slots throughout the day. Load forecasts are as follows: Figure 6 Considering the zero marginal cost of new energy units, and assuming a low price of 20 yuan / MWh, the predicted power output for each time period is as follows: Figure 7 .

[0168] Table 1 Basic Parameters of Conventional Units

[0169]

[0170] (2) Analysis of Implementation Results

[0171] Table 2. Power generation costs for various scenarios

[0172]

[0173]

[0174] Table 3. Winning Bid Electricity and Renewable Energy Consumption in Various Scenarios (Electricity Unit: MWh)

[0175]

[0176] The total power generation costs in Scenario 1 and Scenario 3 are 66.3% and 60.0% of the total power generation costs in Scenario 2, respectively. The difference in total power generation costs mainly stems from incremental costs and start-up costs. In Scenario 2, to ensure full absorption of renewable energy, when renewable energy output is high and load is low, low-cost generating units experience reduced output, leading to forced shutdowns. These units must then be restarted in subsequent periods to meet peak loads, resulting in significant start-up and shutdown costs. Considering the volatility and randomness of renewable energy output, this strategy for dealing with short-term fluctuations in renewable energy output is uneconomical.

[0177] Therefore, the model proposed in this invention corresponding to Scenario 1 relaxes the requirements for renewable energy consumption, discarding renewable energy output within an acceptable range to reduce the adverse impact of renewable energy on the total power generation cost of the system.

[0178] Under the clearing model of this invention, when the absorption capacity is sufficient, new energy and conventional units compete for the same amount of electricity and can be dispatched; when the absorption capacity is insufficient, priority is given to ensuring the completion of guaranteed absorption, in which case new energy units compete for the same amount of electricity and cannot be dispatched. Examples demonstrate that the constructed model can avoid excessively high power generation costs while fulfilling the guaranteed absorption constraints.

[0179] The method proposed in this invention can, on the one hand, fully consider the relevant national support strategies and guaranteed consumption development policies for the new energy industry and set the target consumption volume to be completed on the operating day; on the other hand, setting the target consumption volume instead of full consumption can relax the requirements for new energy consumption to a certain extent, and discard the output of new energy within an acceptable range, so as to reduce the adverse impact of forcibly consuming new energy on the total power generation cost of the system.

[0180] Under the scheduling optimization and scheduling model of this invention, when the absorption capacity is sufficient, new energy and conventional units compete for the same amount of electricity and can be scheduled; when the absorption capacity is insufficient, priority is given to ensuring the completion of guaranteed absorption, in which case new energy units compete for the same amount of electricity and cannot be scheduled. Examples demonstrate that the constructed model can avoid excessively high power generation costs while fulfilling the guaranteed absorption constraints.

[0181] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0183] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0184] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0185] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0186] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0187] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0188] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0189] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An optimized scheduling method considering the requirements for ensuring the absorption of new energy sources, characterized in that, include: Obtain power-related data, which includes at least: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information, wherein the generating units include new energy generating units and non-new energy generating units; A power spot market clearing model is constructed. The constraints of the power spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the power spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term. The electricity spot market clearing model is used to process the electricity-related data to obtain the optimized power dispatching results, and power dispatching and electricity market clearing are carried out based on the optimized power dispatching results. The electricity spot market clearing model includes a safety-constrained unit combination sub-model, a safety-constrained economic dispatch sub-model, and a nodal price calculation sub-model. The electricity spot market clearing model is used to process the relevant electricity data to obtain optimized electricity dispatch results, including: The power-related data are processed using the safety-constrained unit combination sub-model to obtain the start-up and shutdown status results of each unit and the first calculated output capacity of each unit. The power-related data is reprocessed using the safety-constrained economic dispatch sub-model, and combined with the start-up and shutdown status results generated by the safety-constrained unit combination sub-model, to obtain the second calculated output capacity of each unit, as well as the output capacity of the new energy units coupled with whether the new energy consumption index has been met. The nodal price calculation sub-model is used to process the power-related data, the output of each generating unit, and the output of the new energy generating units coupled with whether the new energy consumption index has been met, to obtain the power optimization scheduling result. The power optimization scheduling result includes the output plan of non-new energy generating units, the marginal electricity price corresponding to the output of non-new energy generating units in each time period, the output plan of new energy generating units in the guaranteed consumption period, the output plan of new energy generating units in the non-guaranteed consumption period, and the corresponding marginal electricity price. The constraints in the nodal electricity price calculation sub-model do not include the renewable energy guaranteed consumption constraint and the objective function of the nodal electricity price calculation sub-model does not include a renewable energy penalty term. The constraints in the nodal electricity price calculation sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, renewable energy output constraints, hydropower water level control constraints, and constraints on limiting unit output. The constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints. The constraint limiting the unit output is expressed as follows: Among them, for generator sets whose start-stop status indicates a shutdown status, , The value is zero; for the non-priced units among the new energy units that are not shut down, as indicated by the start-stop status result of the unit, the value is zero. , Value is the unit During the period contribution For the new energy generating units that are not shut down and whose start-stop status indicates that the units are priced, , The value can be: , , ,in, This represents the proportion of the output of the priced generating unit that is allowed to deviate from the unit's output in the nodal pricing calculation sub-model. The minimum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model. The maximum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model.

2. The method according to claim 1, characterized in that, The method further includes: Determine the target absorption capacity threshold; If the daily consumption of the new energy unit is less than or equal to the target consumption threshold, the new energy unit is determined to be a non-pricing unit. If the daily consumption of the new energy unit is greater than the target consumption threshold, the new energy unit is determined to be a priced unit.

3. The method according to claim 1, characterized in that, The objective functions of the safety-constrained unit combination sub-model, the safety-constrained economic dispatch sub-model, and the nodal price calculation sub-model are respectively expressed as follows: ； ； ； in, The total number of units in the target area. This represents the total number of time periods. For the unit During the period of efforts, For the unit During the period Operating costs, For the unit During the period Startup costs, For the unit During the period Minimum technical output cost, To clear the network flow constraint relaxation penalty factor for optimization, For the line Positive current slack variable, For the line The reverse current slack variable, For the line The total number of lines; For the line Positive current slack variable, For the line The reverse current slack variable, For the line The total number of lines, The penalty factor for relaxing water level control constraints. For hydropower Positive water level control relaxation variable, For hydropower Reverse water level control slack variable, This represents the total number of hydropower plants with regulation capabilities. Penalty factor for water wastage power at hydropower plants; For hydropower plant The power of water discharge, Penalty factor for insufficient guaranteed consumption of new energy sources; This is a reverse control slack variable for the total consumption of new energy units that have won the bid.

4. The method according to claim 1, characterized in that, Also includes: The constraints of the safety-constrained unit combination sub-model also include: system load balance constraints, system positive and negative reserve capacity constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, new energy output constraints, and hydropower water level control constraints. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints, ramp rate constraints, and minimum start-up and shutdown time constraints. The constraints of the safety-constrained economic dispatch sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, new energy output constraints, and hydropower water level control constraints. Among them, the constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints.

5. The method according to any one of claims 1 to 4, characterized in that, The constraint on the guaranteed absorption of new energy sources is expressed as follows: Where NR represents the total number of new energy generating units. The target amount of energy consumption to be completed on a predetermined operating day under the guaranteed energy consumption target is allocated to the day by the energy consumption target. This is to clear out the spot inventory of wind and solar power units. This is a reverse control slack variable for the total consumption of new energy units that have won the bid.

6. The method according to any one of claims 1 to 4, characterized in that, The objective function of the safety-constrained unit combination sub-model includes the new energy penalty term, wherein the new energy penalty term is expressed as: ,in, Penalty factor for insufficient guaranteed consumption of new energy sources; This is a reverse control slack variable for the total consumption of new energy units that have won the bid.

7. An optimized scheduling device that considers the requirements for ensuring the absorption of new energy sources, characterized in that, include: The acquisition unit is used to acquire power-related data, which includes at least one of the following: declared quantity and price information, declared generating unit information, power load forecast information, and power grid structure information, wherein the generating units include new energy generating units and non-new energy generating units; A construction unit is used to construct a power spot market clearing model. The constraints of the power spot market clearing model include at least a renewable energy guaranteed consumption constraint. The objective function of the power spot market clearing model includes at least a renewable energy penalty term. The renewable energy penalty term is used to penalize insufficient renewable energy guaranteed consumption. The renewable energy guaranteed consumption constraint is associated with the renewable energy penalty term. The processing unit is used to process the power-related data using the power spot market clearing model to obtain power optimization scheduling results, and to perform power scheduling and power market clearing based on the power optimization scheduling results; The electricity spot market clearing model includes a safety-constrained unit combination sub-model, a safety-constrained economic dispatch sub-model, and a nodal price calculation sub-model. The processing unit includes: The first processing module is used to process the power-related data using the safety-constrained unit combination sub-model to obtain the start-up and shutdown status results of each unit and the first calculated output capacity of each unit. The second processing module is used to reprocess the power-related data using the safety-constrained economic dispatch sub-model, and combine the start-up and shutdown status results generated by the safety-constrained unit combination sub-model to obtain the second calculated output capacity of each unit, as well as the output capacity of the new energy units coupled with whether the new energy consumption index has been completed. The third processing module uses the node price calculation sub-model to process the power-related data, the output of each generating unit, and the output of the new energy generating units coupled with whether the new energy consumption index has been completed, to obtain the power optimization scheduling result. The power optimization scheduling result includes the output plan of non-new energy generating units, the marginal electricity price corresponding to the output of non-new energy generating units in each time period, the output plan of new energy generating units in the guaranteed consumption period, the output plan of new energy generating units in the non-guaranteed consumption period, and the corresponding marginal electricity price. The constraints in the nodal electricity price calculation sub-model do not include the renewable energy guaranteed consumption constraint and the objective function of the nodal electricity price calculation sub-model does not include a renewable energy penalty term. The constraints in the nodal electricity price calculation sub-model also include: system load balance constraints, line power flow constraints, cross-sectional power flow constraints, constraints on thermal power, gas power, and nuclear power units, renewable energy output constraints, hydropower water level control constraints, and constraints on limiting unit output. The constraints on thermal power, gas power, and nuclear power units include: upper and lower limit constraints and ramp rate constraints. The constraint limiting the unit output is expressed as follows: Among them, for generator sets whose start-stop status indicates a shutdown status, , The value is zero; for the non-priced units among the new energy units that are not shut down, as indicated by the start-stop status result of the unit, the value is zero. , Value is the unit During the period contribution For the new energy generating units that are not shut down and whose start-stop status indicates that the units are priced, , The value can be: , , ,in, This represents the proportion of the output of the priced generating unit that is allowed to deviate from the unit's output in the nodal pricing calculation sub-model. The minimum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model. The maximum output of the unit is obtained by processing the safety-constrained economic scheduling sub-model.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the optimized scheduling method according to any one of claims 1 to 6.

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