New energy station scheduling method and device and electronic equipment

Abstract

The application discloses a new energy station scheduling method and device and electronic equipment, wherein the method comprises the following steps: constructing a day-ahead optimization model comprising a first target function and day-ahead constraint conditions, and solving to obtain a first scheduling strategy comprising at least day-ahead declaration data of a new energy generator unit, day-ahead declaration data of an energy storage system and an energy storage adjustment margin; constructing a second target function comprising a declaration deviation overrun penalty term and a deviation evaluation term, and a day-ahead power constraint based on the energy storage adjustment margin; and solving the day-ahead optimization model and the day-ahead optimization model based on obtained day-ahead prediction data and day-ahead prediction data, to obtain the first scheduling strategy and the second scheduling strategy, so as to cooperatively schedule the new energy generator unit and the energy storage system. Through the above method, effective coordination between energy time shift efficiency and power deviation suppression reliability can be realized, and the stability of grid-connected power and the practicability of the regulation strategy are improved.

Description

Technical Field

[0001] This application relates to the field of power system dispatching technology, and in particular to a dispatching method, device and electronic equipment for a new energy power station. Background Technology

[0002] With technological advancements, the installed capacity of new energy power generation, represented by wind power and photovoltaics, has continued to grow rapidly, becoming the mainstay of electricity supply. However, new energy power generation inherently possesses intermittency, volatility, and uncertainty, making its output difficult to predict accurately. The deviation between the planned day-ahead power and the actual power generation can lead to power fluctuations at the grid connection point, affecting the safe and stable operation of the power grid. Furthermore, the peak periods of new energy output may mismatch with the peak periods of grid load, increasing the difficulty of system peak shaving. The energy time-shifting and rapid power regulation capabilities of energy storage systems can not only facilitate arbitrage through peak-valley electricity price differences but also smooth out new energy output deviations during real-time operation, reducing assessment costs. Therefore, electrochemical energy storage systems are generally operated in conjunction with new energy power plants, forming an integrated operating entity to participate in the electricity spot market.

[0003] Currently, optimization strategies for the collaborative participation of new energy sources and energy storage in the electricity spot market typically aim to maximize the combined system revenue or minimize operating costs. These strategies involve establishing deterministic or uncertainty-considering optimization models to jointly optimize new energy application strategies and energy storage charging and discharging plans. For example, some schemes employ stochastic programming or robust optimization methods to handle the uncertainties in new energy output and grid load; alternatively, they establish a collaborative and interactive optimization scheduling model for "source-grid-storage" to optimize the scheduling of distribution networks containing distributed new energy sources and energy storage.

[0004] However, the aforementioned collaborative optimization strategy, in its pursuit of maximizing energy time-shift efficiency during the day-ahead planning phase, exhausts energy storage regulation capacity, resulting in insufficient margin to smooth out output deviations during real-time operation and exacerbating power fluctuations at the grid connection point. Furthermore, the adoption of a fixed and conservative reservation strategy cannot simultaneously ensure the reliability of power deviation smoothing and the scheduling efficiency of energy time-shifting throughout the entire time period, often resulting in a mismatch where there is excessive margin in some periods and insufficient margin in others. It is evident that existing technologies have not yet achieved effective coordination between energy time-shift efficiency and the reliability of power deviation smoothing. Simultaneously, it is difficult to stably control power deviations within the over-limit assessment threshold throughout the entire time period; over-limit assessments may be frequently triggered in some periods, resulting in low practicality of the obtained control strategy.

[0005] No effective solution has yet been proposed to address the above issues. Summary of the Invention

[0006] The purpose of this application is to provide a scheduling method, device, and electronic equipment for new energy power plants, which can effectively coordinate energy time-shift efficiency and power deviation smoothing reliability, thereby improving the stability of grid-connected power of new energy power plants and the practicality of control strategies.

[0007] To solve the above-mentioned technical problems, the first aspect of this specification provides a scheduling method for new energy power stations, including: A day-ahead optimization model for constructing new energy power plants is established, including the first objective function and day-ahead constraints. Acquire day-ahead forecast data and solve the day-ahead optimization model based on the day-ahead forecast data to obtain the first scheduling strategy for the day-ahead stage; wherein, the day-ahead forecast data includes the first power forecast value of the renewable energy power station and the transaction forecast value of the electricity market in which the renewable energy power station is located; the first scheduling strategy includes at least the day-ahead application data of the renewable energy generating units of the renewable energy power station, the day-ahead application data of the energy storage system, and the energy storage regulation margin of the energy storage system, wherein the energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday stage in the day-ahead stage; An intraday optimization model for the new energy power station is constructed, including a second objective function and intraday constraints; wherein, the second objective function includes a penalty term for exceeding the reporting deviation limit and a deviation assessment term, and the intraday constraints include at least an intraday power constraint constructed based on the energy storage adjustment margin; Acquire intraday forecast data and solve the intraday optimization model based on the intraday forecast data to obtain the second scheduling strategy for the intraday stage; the intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system; The new energy generator sets and energy storage systems of the new energy power station are coordinated and scheduled based on the first scheduling strategy and the second scheduling strategy.

[0008] In some embodiments of this specification, the energy storage regulation margin includes the energy storage capacity regulation margin and the energy storage power regulation margin; The energy storage capacity adjustment margin is used to constrain the range of daytime state of charge variation of the energy storage system, as well as the capacity adjustment range reserved for intraday phases. The energy storage power regulation margin is used to constrain the range of daytime charge and discharge power variation of the energy storage system, as well as the power regulation range reserved for intraday stages.

[0009] In some embodiments of this specification, the day-ahead optimization model for new energy power plants, including a first objective function and day-ahead constraints, includes: Based on the trading parameters of the new energy power plants and the trading parameters of the corresponding electricity market, a first objective function is constructed. The first objective function includes a new energy grid-connected power term and an energy storage system grid-connected efficiency term. The new energy grid-connected power term is determined based on the day-ahead declaration coefficient and day-ahead declaration parameters of the new energy generating units, as well as the day-ahead forecast real-time electricity price and the day-ahead forecast day-ahead electricity price of the electricity market during the day-ahead period. The energy storage system grid-connected efficiency term is determined based on the day-ahead declaration parameters of the energy storage system and the day-ahead forecast day-ahead electricity price of the electricity market during the day-ahead period. Based on the adjustment range of the day-ahead declaration coefficient of the new energy generator set, a new energy day-ahead declaration constraint for the new energy generator set is constructed; Based on the state of charge and charging / discharging power of the energy storage system, an energy storage output model of the energy storage system is constructed. Based on the day-ahead state of charge adjustment range and energy storage capacity adjustment margin of the energy storage system, the day-ahead capacity constraint of the energy storage system is constructed. Based on the day-ahead charge and discharge power adjustment range, day-ahead state of charge adjustment range, and energy storage power adjustment margin of the energy storage system, the day-ahead power constraint of the energy storage system is constructed. Based on the new energy day-ahead application constraints, the energy storage day-ahead capacity constraints, the energy storage day-ahead power constraints, and the energy storage output model, the day-ahead constraint conditions are constructed.

[0010] In some embodiments of this specification, the first objective function is represented by the following formula: ; Where R represents the system benefits of the renewable energy power station, T represents the day-ahead operating cycle, ε represents the day-ahead power adjustment coefficient of the renewable energy generator unit, and Q t pr P represents the day-ahead predicted output power of the new energy generator unit at time t. t DA,pr P represents the day-ahead forecast electricity price in the electricity market at time t. t RE,pr P represents the day-ahead forecast real-time electricity price in the electricity market at time t. t dis,pr P represents the day-ahead predicted discharge power of the energy storage system at time t. t chr,pr This represents the day-ahead predicted charging power of the energy storage system at time t.

[0011] In some embodiments of this specification, the first objective function further includes an uncertainty penalty term, which is determined based on a preset penalty weight, an uncertainty penalty coefficient, and the energy storage adjustment margin. The uncertainty penalty coefficient is determined based on the prediction error characteristics of the new energy generator set.

[0012] In some embodiments of this specification, the new energy day-ahead declaration constraint in the day-ahead constraint conditions is expressed by the following formula: ; Where ε represents the daily reporting coefficient for new energy generator sets, ε min and ε max These represent the lower and upper limits of the daytime power adjustment coefficient, respectively. The energy storage output model is expressed by the following formula: ; Among them, SOC t and SOC t+1 Let P represent the state of charge of the energy storage system at time t and time t+1, respectively, where δ represents the self-discharge rate of the energy storage system, and P represents the state of charge of the energy storage system at time t and time t+1, respectively. t dis and P t chr Let η represent the discharge power and charging power of the energy storage system at time t, respectively. dis and η chr θ represents the discharge efficiency and charging efficiency of the energy storage system, respectively. dis and θ chr These represent the discharge and charging states of the energy storage system, respectively. 1 indicates the energy storage system is in its current state, and 0 indicates the energy storage system is not in its current state. EES Z represents the capacity of the energy storage system, where Z is an integer. The day-ahead capacity constraint for energy storage in the day-ahead constraints is expressed by the following formula: ; ; Wherein, SOC represents the state of charge of the energy storage system. min and SOC max These represent the lower and upper limits of the state of charge (SOC) of the energy storage system, respectively; α represents the energy storage capacity regulation margin of the energy storage system; SOC1 and SOC T These represent the state of charge of the energy storage system at the initial time and at time T, respectively; The day-ahead power constraint for energy storage in the day-ahead constraints is expressed by the following formula: ; ; Among them, P t dis,pr P represents the day-ahead predicted discharge power of the energy storage system at time t. t chr,pr P represents the day-ahead predicted charging power of the energy storage system at time t. max dis and P max chr P represents the maximum discharge power and maximum charging power of the energy storage system, respectively. ms This indicates the energy storage power regulation margin of the energy storage system.

[0013] In some embodiments of this specification, the energy storage system includes at least one first energy storage unit and at least one second energy storage unit, the first energy storage unit and the second energy storage unit having different response characteristics, the response characteristics including at least one of the following: response time, ramp rate, power density, energy density, rated capacity, cycle life, charge / discharge depth tolerance, charge / discharge efficiency, and self-discharge rate; The day-ahead constraints are constructed based on the new energy day-ahead application constraints, the energy storage day-ahead capacity constraints of each energy storage unit, the energy storage day-ahead power constraints of each energy storage unit, the energy storage output model of each energy storage unit, and the collaborative adjustment margin constraints between energy storage units. The coordinated adjustment margin constraint includes the coordinated capacity adjustment margin constraint and the coordinated power adjustment margin constraint. The coordinated capacity adjustment margin constraint is constructed based on the day-ahead capacity constraint of each energy storage unit, the rated capacity of each energy storage unit, and the lower limit of the day-ahead capacity constraint of at least two energy storage units; the coordinated power adjustment margin constraint is constructed based on the day-ahead power constraint of each energy storage unit, the power adjustment margin weight of each energy storage unit, and the lower limit of the day-ahead power constraint of at least two energy storage units; the lower limit of the day-ahead capacity constraint and the lower limit of the day-ahead power constraint are determined based on the prediction error characteristics of the new energy generator set.

[0014] In some embodiments of this specification, the intraday optimization model for the new energy power station, including a second objective function and intraday constraints, is constructed as follows: Based on the day-ahead declared power, actual grid-connected power of the new energy power plants, and the day-ahead and real-time electricity prices of the electricity market, a second objective function is constructed. The declaration deviation penalty term in the second objective function is determined based on the deviation between the day-ahead declared power and the actual grid-connected power, the relationship and deviation between the day-ahead electricity price and the real-time electricity price, and a preset exemption threshold. The deviation assessment term is determined based on the integral of the deviation between the predicted power and the actual power of the new energy generating units within a preset deviation assessment period. Based on the energy storage regulation margin, the response characteristics of the energy storage system, and the intraday regulation power of the energy storage system, an intraday power constraint for the energy storage system is constructed. The intraday power constraint is used to limit the charging and discharging regulation power of the energy storage system during the intraday period from not exceeding the energy storage regulation margin. Based on the state of charge and charging / discharging power of the energy storage system, an energy storage output model of the energy storage system is constructed. Based on the deviation integral and the upper limit of the deviation integral, deviation assessment constraints are constructed; Based on the real-time energy storage power adjustment constraint, the real-time energy storage output model, and the deviation assessment constraint, the intraday constraint conditions are constructed.

[0015] In some embodiments of this specification, the second objective function is represented by the following formula: ; ; ; ; ; ; Where C represents the daily comprehensive cost of the new energy power station, C hs and C kh Q represents the penalty cost for exceeding the reporting limit and the assessment cost for deviation, respectively. t rt P represents the predicted daily power output of the new energy generator unit at time t; t dis,rt P represents the intraday discharge power of the energy storage system at time t; t chr,rt P represents the intraday charging power of the energy storage system at time t; t DA P represents the day-ahead clearing price in the electricity market at time t; t RE,cd D represents the intraday forecast real-time electricity price in the electricity market at time t; ed This indicates the rated power of the new energy generator set; v t P represents the power prediction deviation of the new energy generator set at time t, expressed as the integral of the power quantity; v f represents the unit integral cost; t E represents the score for the unit deviation of electricity consumption at time t; t ε represents the power deviation rate at time t; ε represents the day-ahead power adjustment coefficient of the new energy generator unit; Q t prThis represents the day-ahead predicted output power of the new energy generator unit at time t. This indicates the preset penalty exemption threshold.

[0016] In some embodiments of this specification, the intraday power constraint in the intraday constraint condition is expressed by the following formula: ; ; Among them, P t dis,rt P represents the intraday discharge power of the energy storage system at time t. t chr,rt C represents the intraday charging power of the energy storage system at time t; EES η represents the capacity of the energy storage system. dis and η chr These represent the discharge efficiency and charging efficiency of the energy storage system, respectively, α represents the energy storage capacity adjustment margin of the energy storage system, and P... ms This indicates the energy storage power regulation margin of the energy storage system; The deviation assessment constraint in the intraday power constraint is expressed by the following formula: ; Among them, v t v represents the power prediction deviation of the new energy generator unit at time t, which is the integral of the electricity consumption. max This indicates the upper limit of the deviation integral.

[0017] In some embodiments of this specification, the upper limit of the deviation integral is dynamically determined based on at least one of the following: the prediction error characteristics of the new energy generating unit during the intraday period, meteorological data, the real-time electricity price level of the electricity market, and the grid load level corresponding to the electricity market.

[0018] In some embodiments of this specification, the first objective function includes a collaborative penalty term, which is determined based on an initial deviation integral upper limit, the deviation integral upper limit, and a risk preference coefficient; The day-ahead constraints include risk control constraints, which are constructed based on the energy storage regulation margin, the conversion coefficient corresponding to the energy storage regulation margin, the response characteristics of the energy storage system, the upper limit of the deviation integral, and the lower limit of the preset risk control capability.

[0019] The second aspect of this specification provides a dispatching device for a new energy power station, comprising: The first construction module is used to construct the day-ahead optimization model of the new energy power station, including the first objective function and day-ahead constraints. The first determining module is used to acquire day-ahead forecast data and solve the day-ahead optimization model based on the day-ahead forecast data to obtain a first scheduling strategy for the day-ahead stage; wherein, the day-ahead forecast data includes the first power forecast value of the renewable energy power station and the transaction forecast value of the electricity market in which the renewable energy power station is located; the first scheduling strategy includes at least the day-ahead application data of the renewable energy generating units of the renewable energy power station, the day-ahead application data of the energy storage system, and the energy storage regulation margin of the energy storage system, wherein the energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday stage in the day-ahead stage; The second construction module is used to construct an intraday optimization model for the new energy power station, including a second objective function and intraday constraints; wherein, the second objective function includes a penalty term for exceeding the reporting deviation limit and a deviation assessment term, and the intraday constraints include at least an intraday power constraint constructed based on the energy storage adjustment margin; The second determining module is used to acquire intraday forecast data and solve the intraday optimization model based on the intraday forecast data to obtain the second scheduling strategy for the intraday stage; the intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system; The scheduling module is used to coordinate the scheduling of the new energy generator sets and energy storage systems of the new energy power station based on the first scheduling strategy and the second scheduling strategy.

[0020] A third aspect of this specification provides an electronic device, comprising: a memory and a processor, the processor and the memory being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the steps of the method described in the first aspect above.

[0021] A fourth aspect of this specification provides a computer storage medium storing computer program instructions that, when executed, implement the steps of the method described in the first aspect.

[0022] A fifth aspect of this specification provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0023] The scheduling method, apparatus, and electronic equipment for new energy power stations described in this specification obtain a first scheduling strategy by constructing a day-ahead optimization model that includes a first objective function and day-ahead constraints. The first scheduling includes at least day-ahead application data for new energy generators, day-ahead application data for energy storage systems, and energy storage regulation margin. The energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday phase during the day-ahead phase. A second objective function that includes a penalty term for exceeding the application deviation limit and a deviation assessment term, and an intraday optimization model with intraday power constraints based on the energy storage regulation margin are constructed. The day-ahead optimization model and the intraday optimization model are solved based on the acquired day-ahead forecast data and intraday forecast data, respectively, to obtain the first scheduling strategy for the day-ahead phase and the second scheduling strategy for the intraday phase, so as to coordinate the scheduling of new energy generators and energy storage systems at new energy power stations. By using the above method, the energy storage regulation margin is used as the decision variable in the day-ahead optimization model. It is solved collaboratively with the day-ahead declaration data of new energy generator units and the day-ahead declaration data of energy storage systems. This allows the energy storage regulation capacity in the day-ahead stage to be dynamically optimized and determined based on the predicted data. This achieves an effective balance between energy time-shift efficiency and power deviation smoothing reliability throughout the entire time period, avoiding the mismatch of excessive or insufficient energy storage regulation margin throughout the entire time period. At the same time, by setting a penalty term for declaration deviation exceeding the limit in the second objective function of the intraday optimization model, and using the energy storage regulation margin obtained in the day-ahead stage as the boundary condition of the intraday power constraint, the energy storage power regulation in the intraday stage is always executed within the regulation capacity reserved in the day-ahead stage. This enables cross-time period coupling control between the day-ahead margin decision and the intraday deviation feedback, reducing the risk of power deviation exceeding the limit at the grid connection point. Furthermore, through two-stage coordinated optimization of the day-ahead and intraday phases, the decision-making process for reserving energy storage regulation margin and the penalty mechanism for exceeding power deviation limits are incorporated into a unified optimization framework. This enables the regulation strategy to proactively avoid the risk of exceeding limits from the outset, thereby improving the stability of grid-connected power of new energy power plants and the practicality of the regulation strategy. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 The diagram shown is a schematic representation of a scheduling method for new energy power stations provided in an embodiment of this specification. Figure 2 The diagram shown is a schematic representation of a method for constructing a day-ahead optimization model provided in an embodiment of this specification. Figure 3 The diagram shown is a schematic representation of a method for constructing an intraday optimization model provided in an embodiment of this specification. Figure 4 The diagram shown is a schematic of a method for synergistic optimization of new energy and energy storage in the electricity market provided in the embodiments of this specification. Figure 5 The diagram shown is a schematic representation of the model solving method provided in the embodiments of this specification. Figure 6 The diagram shown is a schematic representation of the spot market electricity price provided in the embodiments of this specification. Figure 7 The diagram shown is a schematic representation of the wind power prediction and actual output provided in the embodiments of this specification. Figure 8 The diagram shown is a schematic representation of a wind power application strategy provided in an embodiment of this specification. Figure 9 The diagram shown is a schematic of an energy storage application strategy provided in an embodiment of this specification. Figure 10 The diagram shown is a comparison of the benefits of various scenarios provided in the embodiments of this specification; Figure 11 The diagram shown is a schematic of a dispatching device for a new energy power station provided in an embodiment of this specification. Figure 12 The diagram shown is a schematic of an electronic device provided in an embodiment of this specification. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0027] It should be noted that the information and data related to users involved in the embodiments of this specification are all information and data authorized by the user or fully authorized by the relevant parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with relevant laws, regulations, and standards, do not violate public order and good morals, and provide corresponding operation entry points for users or relevant parties to choose to authorize or refuse.

[0028] It should also be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that this application has used or necessarily used such a solution.

[0029] The new energy power plant dispatching method provided in this application is applicable to integrated operating entities that include new energy generating units (wind power, photovoltaic, or wind-solar hybrid units) and energy storage systems, participating in electricity spot market (including day-ahead market and intraday real-time market) trading and dispatching scenarios. Through a two-stage collaborative optimization framework of day-ahead and intraday, energy storage regulation margin is incorporated as a core decision variable into day-ahead optimization, achieving a dynamic balance between energy time-shift arbitrage and real-time deviation mitigation. Simultaneously, refined deviation control is performed based on reserved margins during the intraday stage, effectively reducing assessment costs and improving overall system profitability and grid connection stability. The dispatching method for new energy power plants provided in this specification will be described in detail below with reference to embodiments.

[0030] Figure 1 The diagram illustrates a scheduling method for a new energy power station provided in an embodiment of this specification. While this specification provides method operation steps or apparatus structures as shown in the following embodiments or figures, the method or apparatus may include more or fewer operation steps or module units, either combined or integrated, based on conventional or non-inventive methods. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure shown in the embodiments or figures of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment). Figure 1 As shown, the method may include: S101: Construct a day-ahead optimization model for new energy power plants, including the first objective function and day-ahead constraints.

[0031] A new energy power station can be a power station consisting of at least one new energy generator set (wind power, photovoltaic, solar thermal, etc.) and a supporting energy storage system. It can participate in electricity market transactions as a single entity in the electricity market, and its grid connection point power can be the algebraic sum of the new energy output and the energy storage charging and discharging power.

[0032] The day-ahead optimization model is an optimization model established before the day-ahead market submission (usually one day in advance) to determine the renewable energy submission power, basic energy storage charging and discharging plans, and the regulation capacity reserved for the day in each time period of the following day. The first objective function can be the optimization objective of the day-ahead optimization model, which can be constructed with the goal of maximizing the revenue of renewable energy power plants in the day-ahead market. The opportunity cost of reserving regulation capacity for the day-ahead market can also be considered in the first objective function. The day-ahead constraints can be the physical constraints and electricity market constraints that the day-ahead optimization model must satisfy, which may include constraints on the scope of renewable energy submissions, renewable energy safe operation constraints, energy storage safe operation constraints, and market submission rule constraints.

[0033] Solving the day-ahead optimization model yields the first scheduling strategy, which may include the decision results corresponding to the multi-dimensional decision vector. It may include at least: the day-ahead application data of the new energy generator units (e.g., the application power or application coefficient for each time period), the day-ahead application data of the energy storage system (e.g., the basic charging and discharging power for each time period), and the energy storage regulation margin of the energy storage system (e.g., the capacity regulation capability and power regulation capability reserved for the intraday stage).

[0034] Energy storage regulation margin can characterize the regulation capacity of the energy storage system reserved for the intraday phase during the day-ahead phase. It can be an optimizable decision variable in the day-ahead optimization model, thereby allowing the sacrifice of some potential energy time-shift efficiency and power during the day-ahead phase in exchange for the ability to smooth out output deviations and reduce assessment costs during the intraday phase.

[0035] In practice, the decision variables of the day-ahead optimization model can be determined first. Then, based on the determined decision variables and optimization objectives, and combined with the operating characteristics of the new energy power plants, the first objective function and day-ahead constraints can be constructed. By combining the first objective function and the day-ahead constraints, the day-ahead optimization model can be obtained.

[0036] In some embodiments of this specification, the energy storage regulation margin may include an energy storage capacity regulation margin and an energy storage power regulation margin; the energy storage capacity regulation margin is used to constrain the range of day-ahead state of charge variation of the energy storage system and to reserve a capacity regulation range for intraday stages; the energy storage power regulation margin is used to constrain the range of day-ahead charge and discharge power variation of the energy storage system and to reserve a power regulation range for intraday stages.

[0037] The embodiments in this specification, based on the energy storage capacity adjustment margin and the energy storage power adjustment margin, constrain the range of changes in the state of charge and charging / discharging power of the energy storage system during the day-ahead period. This allows the capacity adjustment margin to ensure sufficient energy space for long-term deviation mitigation during the day-ahead period, and the power adjustment margin to ensure sufficient power capacity for rapid adjustment on a short-term time scale during the day-ahead period. This enables a comprehensive response to various fluctuations in new energy output. Furthermore, by using the energy storage adjustment margin as a decision variable and solving it collaboratively in the day-ahead optimization model, a dynamic optimal balance between energy time-shift efficiency and power deviation mitigation reliability can be achieved throughout the entire time period. This avoids the mismatch phenomenon where the fixed reserve strategy has excessive margin in some time periods and insufficient margin in others.

[0038] S102: Obtain day-ahead forecast data and solve the day-ahead optimization model based on the day-ahead forecast data to obtain the first scheduling strategy for the day-ahead stage; the day-ahead forecast data includes the first power forecast value of the renewable energy power station in the day-ahead stage and the transaction forecast value of the electricity market in which the renewable energy power station is located.

[0039] The first scheduling strategy includes at least the day-ahead reporting data of the new energy generator units of the new energy power station, the day-ahead reporting data of the energy storage system, and the energy storage regulation margin of the energy storage system. The energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday phase during the day-ahead phase.

[0040] The day-ahead forecast data can be used as input data for the day-ahead optimization model. It can include the first power forecast value (e.g., the output power of new energy generating units for each period of the next day predicted 24 hours in advance) and the transaction forecast value (e.g., the day-ahead electricity price and real-time electricity price of the electricity market for each period of the next day predicted 24 hours in advance).

[0041] In practice, when solving the day-ahead optimization model, the optimization algorithm corresponding to the day-ahead optimization model can be determined first, such as a heuristic algorithm or a genetic algorithm. Then, based on the determined optimization algorithm, the day-ahead optimization model can be solved based on the day-ahead prediction data.

[0042] For example, the day-ahead optimization model in the embodiments of this specification includes integer variables (corresponding to the day-ahead reporting parameters of new energy generator sets and energy storage systems) and continuous decision variables (corresponding to the energy storage adjustment margin). The objective function is a nonlinear function, and the constraints include nonlinear equality constraints such as the recursion of the energy storage state of charge and inequality constraints. Therefore, the solution of the day-ahead optimization model belongs to mixed integer nonlinear programming, which can be solved by an improved genetic algorithm.

[0043] Specifically, based on the value range of each decision variable, an initial population corresponding to the random decision variables can be generated, with each individual containing a complete set of decision variable codes. For each individual in the population, its corresponding fitness value is calculated. The fitness function required for fitness value calculation can be constructed based on the first objective function. For the day-ahead optimization model, the fitness value can be the function value of the first objective function. Furthermore, to handle various constraints in the model, a penalty function method can be used to incorporate the degree of constraint violation into the fitness. For example, for the inequality constraint g(x)≤0, its degree of violation is defined as max(0, g(x)); for the equality constraint h(x)=0, its degree of violation is defined as |h(x)|. The weighted sum of the degree of violation of all constraints is added to the fitness function as a penalty term. Furthermore, to adapt to the characteristics of the large number and significant differences in dimensions of constraints in the day-ahead constraints in the embodiments of this specification, an adaptive penalty strategy can be adopted, that is, the penalty coefficient of the penalty term can be dynamically adjusted with the iteration algebra. In the early stages of iteration, a small penalty coefficient is used to allow for the existence of some infeasible individuals in the population, thus fully exploring the solution space. In the later stages of iteration, the penalty coefficient gradually increases, forcing the population to converge toward the feasible region. After calculating fitness and handling constraints, iterative selection can be performed on the population. During the iterative selection process, crossover and mutation operations can be performed until a preset termination condition is met, at which point the iteration stops, and the final individual obtained can be used as the first scheduling strategy.

[0044] S103: Construct an intraday optimization model for the new energy power station, including a second objective function and intraday constraints.

[0045] The second objective function includes a penalty item for exceeding the reporting deviation limit and a deviation assessment item, and the intraday constraint conditions include at least an intraday power constraint constructed based on the energy storage regulation margin.

[0046] The intraday optimization model can be an optimization model established during the real-time operation phase (e.g., updated every 15 minutes) to determine the real-time charging and discharging power of energy storage within the day-ahead reserved adjustment margin, in order to minimize the overall intraday cost.

[0047] The second objective function is the optimization objective of the intraday optimization model, which can aim to minimize the comprehensive cost of the intraday stage. It can include a penalty for exceeding the reporting deviation limit and a deviation assessment item. The penalty for exceeding the reporting deviation limit can be used to quantitatively assess the degree of deviation between the reported power and the actual grid-connected power of the new energy power plant. Specifically, when the deviation between the actual grid-connected power of the new energy power plant (including the superposition of the actual output of new energy and the regulation power of energy storage) and the reported power exceeds the preset tolerance bandwidth (penalty exemption threshold), the penalty for exceeding the reporting deviation limit will cumulatively penalize the excess deviation amount. Essentially, it is a deviation penalty function with a dead zone. The deviation assessment item can be used to quantitatively assess the total cumulative power deviation of the new energy power plant during the operating cycle. The deviation assessment item obtains the cumulative deviation power integral value for the entire assessment period by integrating (or summing) the deviation between the predicted power and the actual power of the new energy generator units, and calculates the assessment quantity based on this integral value. Essentially, it is an integral evaluation function for cumulative deviation. Based on the penalty for exceeding the reporting deviation limit and the deviation assessment item, the power deviation risk can be evaluated in the second objective function. This allows the intraday optimization model to prevent instantaneous large-scale exceedance risks through the penalty for exceeding the reporting deviation limit, and to control the cumulative deviation throughout the cycle to not exceed the standard through the deviation assessment item. Through the dual-layer deviation control mechanism that combines instantaneous and cumulative deviations, the real-time adjustment strategy of the energy storage system has stronger robustness and practicality.

[0048] Intraday constraints can be constraints that the intraday optimization model must meet. They can include intraday power constraints built based on the day-ahead reserved energy storage regulation margin, ensuring that intraday regulation does not exceed the day-ahead reserved capacity range.

[0049] S104: Obtain intraday forecast data and solve the intraday optimization model based on the intraday forecast data to obtain the second scheduling strategy for the intraday stage; the intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system.

[0050] Intraday forecast data can be used as input data for intraday optimization models. It can include the second power forecast value of renewable energy power plants (which can be ultra-short-term power forecast data), such as the output power of renewable energy generating units predicted 15 minutes in advance for the next 1-4 hours (which can be ultra-short-term power forecast); it can also include transaction forecast values, such as the day-ahead electricity price and real-time electricity price of the electricity market for each time period of the next day predicted 24 hours in advance.

[0051] The second scheduling strategy can be the output of the intraday optimization model, which can include the real-time adjustment power value of the energy storage system in each time period, and can be superimposed on the day-ahead basic charge and discharge plan for execution.

[0052] In practical implementation, the intraday optimization model can be solved using algorithms similar to those used for the day-ahead optimization model, such as heuristic algorithms and genetic algorithms. Specific solution methods can also be referenced in the aforementioned embodiments and will not be elaborated upon here. Alternatively, a rolling optimization method can be employed to solve the intraday optimization model. This involves updating the forecast data every 15 minutes to solve the optimization problem for the next 4 hours (16 time periods), executing only the scheduling instructions for the first time period. This rolling optimization approach fully utilizes the latest forecast information, continuously revising the scheduling strategy and improving the deviation mitigation effect.

[0053] S105: Based on the first scheduling strategy and the second scheduling strategy, the new energy generator sets and energy storage systems of the new energy power station are coordinated and scheduled.

[0054] Coordinated dispatch can issue control commands to new energy generator sets and energy storage systems according to the first and second dispatch strategies, respectively, so that the total power at the grid connection point meets the market application requirements and grid safety requirements.

[0055] In practice, new energy generator sets can control their output according to the day-ahead declared power curve. When there is a deviation between the actual output and the declared power, the energy storage system will make adjustments. The total charging and discharging power of the energy storage system is the sum of the day-ahead base charging and discharging power and the intraday real-time adjustment power, and the total power shall not exceed the rated power of the energy storage system. Specifically, when the actual output of new energy is greater than the day-ahead declared power, the energy storage system will absorb the excess power by charging; when the actual output of new energy is less than the day-ahead declared power, the energy storage system will supplement the insufficient power by discharging. The magnitude of the adjustment is determined by the intraday optimization model and shall not exceed the day-ahead reserved adjustment margin.

[0056] In the embodiments of this specification, the energy storage regulation margin is used as the decision variable of the day-ahead optimization model. It is solved collaboratively with the day-ahead declaration data of new energy generator units and the day-ahead declaration data of energy storage systems. This allows the energy storage regulation capacity in the day-ahead stage to be dynamically optimized and determined based on the predicted data, so as to achieve an effective balance between energy time-shift efficiency and power deviation smoothing reliability throughout the entire time period. This can avoid the mismatch phenomenon of excessive or insufficient energy storage regulation margin throughout the entire time period. At the same time, by setting a declaration deviation exceeding the limit penalty term in the second objective function of the intraday optimization model, and using the energy storage regulation margin obtained in the day-ahead stage as the boundary condition of the intraday power constraint, the energy storage power regulation in the intraday stage is always executed within the regulation capacity reserved in the day-ahead stage. This can realize cross-time period coupling control between the day-ahead margin decision and the intraday deviation feedback, and can reduce the risk of the grid connection point power deviation exceeding the limit. Furthermore, through two-stage coordinated optimization of the day-ahead and intraday phases, the decision-making process for reserving energy storage regulation margin and the penalty mechanism for exceeding power deviation limits are incorporated into a unified optimization framework. This enables the regulation strategy to proactively avoid the risk of exceeding limits from the outset, thereby improving the stability of grid-connected power of new energy power plants and the practicality of the regulation strategy.

[0057] refer to Figure 2 As shown, in some embodiments of this specification, step S101, which constructs a day-ahead optimization model for a new energy power station including a first objective function and day-ahead constraints, may include: S201: Based on the trading parameters of the new energy power station and the trading parameters of the electricity market corresponding to the new energy power station, construct the first objective function.

[0058] The first objective function includes a renewable energy grid-connected power term and an energy storage system grid-connected efficiency term. The renewable energy grid-connected power term is determined based on the day-ahead reporting coefficient and day-ahead reporting parameters of renewable energy generator sets, as well as the day-ahead forecast real-time electricity price and the day-ahead forecast day-ahead electricity price in the electricity market during the day-ahead period. The energy storage system grid-connected efficiency term is determined based on the day-ahead reporting parameters of the energy storage system and the day-ahead forecast day-ahead electricity price in the electricity market during the day-ahead period.

[0059] The renewable energy grid-connected power term is the revenue term related to the grid-connected power of renewable energy generator sets in the first objective function. It can reflect the grid-connected power allocation relationship of renewable energy power plants based on day-ahead electricity prices and real-time electricity prices. The energy storage system grid-connected efficiency term is the revenue term related to the grid-connected power of energy storage systems in the first objective function. It can reflect the net discharge efficiency achieved by the energy storage system through energy time shift.

[0060] In some embodiments of this specification, the first objective function can be expressed by the following formula:

[0061] Where R can represent the system benefits of the renewable energy power station, T can represent the day-ahead operating cycle, ε can represent the day-ahead power adjustment coefficient of the renewable energy generator unit, and Q... t pr P can represent the day-ahead predicted output power of the new energy generator unit at time t. t DA,pr P can represent the day-ahead forecast electricity price in the electricity market at time t. t RE,pr P can represent the day-ahead forecast real-time electricity price in the electricity market at time t. t dis,pr P can represent the day-ahead predicted discharge power of the energy storage system at time t. t chr,pr This can represent the day-ahead predicted charging power of the energy storage system at time t. The first term in the above formula reflects the energy allocation evaluation corresponding to arranging new energy output for grid connection during the day-ahead and intraday periods. The second term reflects the energy transfer in the time dimension achieved by energy storage through low charging and high discharging, i.e., energy time-shift scheduling, and the corresponding net discharge efficiency. By optimizing the solution of the above first objective function, the optimal allocation of new energy output in the day-ahead and real-time periods can be achieved, as well as the optimal energy time-shift scheduling of the energy storage system between peak and valley periods. Furthermore, while pursuing the above efficiency, the constraints also proactively reserve power and capacity adjustment capabilities for the intraday period.

[0062] S202: Based on the adjustment range of the day-ahead declaration coefficient of the new energy generator set, construct the new energy day-ahead declaration constraint of the new energy generator set.

[0063] The day-ahead reporting constraint for new energy sources can be used to limit the adjustment range of the day-ahead reporting coefficient for new energy generating units. In some embodiments of this specification, the day-ahead reporting constraint for new energy sources in the day-ahead constraint conditions can be expressed by the following formula:

[0064] Where ε can represent the daily reporting coefficient for new energy generator sets, ε min and ε max These can represent the lower and upper limits of the daytime power adjustment factor, respectively.

[0065] S203: Based on the state of charge and the charging and discharging power of the energy storage system, construct the energy storage output model of the energy storage system.

[0066] The energy storage output model is a mathematical model that describes the change of the state of charge of an energy storage system with charge and discharge power, and it is the basic physical constraint for the operation of the energy storage system.

[0067] In some embodiments of this specification, the energy storage output model can be represented by the following formula: ; Among them, SOC t and SOC t+1 The states of charge of the energy storage system at time t and t+1 can be represented respectively, δ can represent the self-discharge rate of the energy storage system, and P can represent the state of charge of the energy storage system at time t and t+1 respectively. t dis and P t chr η can represent the discharge power and charging power of the energy storage system at time t, respectively. dis and η chr The discharge efficiency and charging efficiency of the energy storage system can be represented by θ, respectively. dis and θ chr The states of discharge and charge of the energy storage system can be represented respectively. 1 indicates the energy storage system is in its current state, and 0 indicates the energy storage system is not in its current state. EES Z can represent the capacity of the energy storage system, and Z can represent an integer.

[0068] S204: Based on the adjustment range of the day-ahead state of charge of the energy storage system and the energy storage capacity adjustment margin, construct the day-ahead capacity constraint of the energy storage system.

[0069] The day-ahead capacity constraint for energy storage can be a constraint built on the capacity adjustment margin of energy storage, which can limit the range of change of the state of charge (SOC) of energy storage during the day-ahead period.

[0070] In some embodiments of this specification, the day-ahead capacity constraint of energy storage in the day-ahead constraint can be expressed by the following formula:

[0071] Wherein, SOC can represent the state of charge of the energy storage system. min and SOC max These can represent the lower and upper limits of the state of charge (SOC) of the energy storage system, respectively; α can represent the energy storage capacity adjustment margin of the energy storage system; SOC1 and SOC T These can represent the state of charge of the energy storage system at the initial time and at time T, respectively.

[0072] In the embodiments described in this specification, by introducing an energy storage capacity adjustment margin α into the upper and lower limits of the State of Charge (SOC) constraint, the operating range of energy storage during the day-ahead planning phase is actively narrowed. Capacity space is reserved at both the upper and lower ends of the SOC specifically for handling deviations in renewable energy output during the day. When the actual renewable energy output during the day is higher than the day-ahead forecast, the reserved capacity space at the upper end of the SOC can be used to charge and absorb the excess power; when the actual output is lower than the forecast, the reserved capacity space at the lower end of the SOC can be used to discharge and make up the shortfall. Simultaneously, by setting a constraint that the SOC is equal at the beginning and end of the cycle, the energy balance of the energy storage system is ensured.

[0073] Furthermore, after obtaining the energy storage capacity adjustment margin through intraday optimization model, this margin can be further adjusted based on the current health status of the energy storage system. Specifically, a current health status correction factor can be determined based on the current health status of the energy storage battery (specifically, based on the current available capacity). This correction factor can be determined based on a health function characterizing the relationship between the health status and the correction factor. The correction factor is greater than 1 and negatively correlated with the health status of the energy storage system. When the remaining available capacity indicated by the health status is lower than a preset threshold, the correction factor can be increased according to a preset rule to forcibly amplify the corrected energy storage capacity adjustment margin.

[0074] S205: Based on the day-ahead charge and discharge power adjustment range, day-ahead state of charge adjustment range, and energy storage power adjustment margin of the energy storage system, construct the day-ahead power constraint of the energy storage system.

[0075] The day-ahead power constraint for energy storage can be a constraint built based on the energy storage power adjustment margin, which limits the upper limit of the energy storage charging and discharging power during the day-ahead phase.

[0076] In some embodiments of this specification, the day-ahead power constraint of the energy storage system in the day-ahead constraint can be expressed by the following formula:

[0077] Among them, P t dis,pr P can represent the day-ahead predicted discharge power of the energy storage system at time t. t chr,pr P can represent the day-ahead predicted charging power of the energy storage system at time t. max dis and P max chr P can represent the maximum discharge power and maximum charging power of the energy storage system, respectively. ms This can represent the energy storage power regulation margin of the energy storage system.

[0078] In the embodiments of this specification, by introducing the energy storage power adjustment margin into the upper limit constraint of the charging and discharging power, the charging and discharging power of the energy storage during the daytime phase cannot reach its physical maximum value. A portion of the power adjustment space must be reserved specifically for the rapid response to the deviation of the new energy output during the daytime phase.

[0079] S206: Based on the new energy day-ahead application constraints, the energy storage day-ahead capacity constraints, the energy storage day-ahead power constraints, and the energy storage output model, construct the day-ahead constraint conditions.

[0080] In some embodiments of this specification, the first objective function may further include an uncertainty penalty term, which is determined based on a preset penalty weight, an uncertainty penalty coefficient, and the energy storage adjustment margin. The uncertainty penalty coefficient is determined based on the prediction error characteristics of the new energy generator set.

[0081] Because the output of new energy generating units has inherent uncertainty, there will inevitably be a deviation between the day-ahead power forecast and the actual output during the day. To quantify the impact of this uncertainty on the energy storage regulation margin decision in the day-ahead optimization model, an uncertainty penalty term is also included in the first objective function.

[0082] In practice, the uncertainty penalty term can be determined based on a preset penalty weight, an uncertainty penalty coefficient, and an energy storage adjustment margin. The uncertainty penalty coefficient is determined based on the prediction error characteristics of the new energy generator unit. For example, it can be expressed as: P uncertainty =ω λ(σ forecast ) (α+P ms ), where P uncertainty λ(σ) can represent the cost of uncertainty penalty, ω can represent the preset penalty coefficient, and λ(σ) can represent the cost of uncertainty penalty. forecast σ can represent the error characteristic of day-ahead power prediction for new energy generating units. forecast The uncertainty penalty coefficient.

[0083] Uncertainty penalty coefficient λ(σ) forecast ) and prediction error characteristics σ forecast(For example, the standard deviation, the confidence interval width or variance of the predicted new energy power, etc.) show a positive correlation. When the prediction uncertainty is large, the penalty coefficient increases. If the model attempts to reduce the energy storage regulation margin to pursue energy time-shifting efficiency, it will face higher penalty costs and will therefore be guided to a larger optimal margin value. When the prediction uncertainty is small, the penalty coefficient decreases, reducing the margin is no longer strongly penalized, and the model can more actively engage in energy time-shifting scheduling. Through the uncertainty penalty term, the optimal solution space of the energy storage regulation margin is dynamically shaped by the prediction uncertainty information, which can achieve robust optimization with uncertainty adaptation.

[0084] In some embodiments of this specification, the energy storage system may include at least one first energy storage unit and at least one second energy storage unit, the first energy storage unit and the second energy storage unit having different response characteristics, the response characteristics including at least one of the following: response time, ramp rate, power density, energy density, rated capacity, cycle life, depth of charge / discharge tolerance, charge / discharge efficiency, and self-discharge rate. Response characteristics can characterize the performance of the energy storage system.

[0085] The day-ahead constraints are constructed based on the new energy day-ahead application constraints, the day-ahead capacity constraints of each energy storage unit, the day-ahead power constraints of each energy storage unit, the energy storage output model of each energy storage unit, and the collaborative adjustment margin constraints between energy storage units. The collaborative adjustment margin constraints include collaborative capacity adjustment margin constraints and collaborative power adjustment margin constraints. Specifically, the collaborative capacity adjustment margin constraints are constructed based on the day-ahead capacity constraints of each energy storage unit, the rated capacity of each energy storage unit, and the lower limits of the day-ahead capacity constraints of at least two energy storage units; the collaborative power adjustment margin constraints are constructed based on the day-ahead power constraints of each energy storage unit, the power adjustment margin weights of each energy storage unit, and the lower limits of the day-ahead power constraints of at least two energy storage units; the lower limits of the day-ahead capacity constraints and the lower limits of the day-ahead power constraints are determined based on the prediction error characteristics of the new energy generator sets.

[0086] The day-ahead capacity constraints, day-ahead power constraints, and energy output models of each energy storage unit can be constructed using the corresponding constraint construction methods of the energy storage system in the aforementioned embodiments. The difference lies in using the relevant response characteristic parameters of the corresponding energy storage unit during the construction process.

[0087] In some embodiments of this specification, the first energy storage unit can be a power-type energy storage unit (e.g., a supercapacitor or flywheel energy storage) with a short response time (millisecond level) and a high power density; the second energy storage unit can be an energy-type energy storage unit (e.g., a flow battery) with a long response time (second to minute level) and a high energy density.

[0088] In some embodiments of this specification, the cooperative capacity adjustment margin constraint can be expressed by the following formula:

[0089] Where, α i C can represent the energy storage capacity adjustment margin of the i-th energy storage unit. EES,i E can represent the rated capacity of the i-th energy storage unit. min total It can represent the lower limit of the day-ahead capacity constraint for at least two energy storage units.

[0090] In some embodiments of this specification, the cooperative power regulation margin constraint can be expressed by the following formula:

[0091] Where, γ i P can represent the power regulation margin weight of the i-th energy storage unit. ms,i P can represent the energy storage power regulation margin of the i-th energy storage unit. min total It can represent the lower limit of the day-ahead power constraint for at least two energy storage units.

[0092] The aforementioned lower limits for day-ahead capacity and power constraints can be determined based on the prediction error characteristics of new energy generating units. When the prediction error is large, the aforementioned lower limits will be adjusted upwards accordingly to ensure that the overall reserved adjustment capacity meets the system risk control requirements.

[0093] In the embodiments of this specification, by setting the characteristic parameters and constraint boundaries of each energy storage unit differently, the energy storage units with different response characteristics can achieve synergistic complementarity in the day-ahead reservation stage. Taking power-type energy storage units and energy-type energy storage units as examples, power-type energy storage units reserve a larger power regulation margin, which can give full play to their rapid response advantage; energy-type energy storage units reserve a larger capacity regulation margin, which can undertake the task of continuous energy time shift and deviation smoothing.

[0094] refer to Figure 3 As shown, in some embodiments of this specification, constructing an intraday optimization model for the new energy power station, including a second objective function and intraday constraints, may include: S301: Based on the day-ahead declared power, actual grid-connected power of the new energy power plant, and the day-ahead and real-time electricity prices of the electricity market, construct the second objective function.

[0095] The second objective function is determined based on the deviation between the declared power and the actual on-grid power, the relationship and deviation between the day-ahead electricity price and the real-time electricity price, and a preset exemption threshold. The deviation assessment item is determined based on the integral of the deviation between the predicted power and the actual power of the new energy generator unit within a preset deviation assessment period.

[0096] In some embodiments of this specification, the second objective function can be expressed by the following formula:

[0097] Where C can represent the daily comprehensive cost of a new energy power station, C hs and C kh Q can represent the penalty cost for exceeding the limit in the declaration and the assessment cost for deviation, respectively. t rt P can represent the predicted daily power output of the new energy generator unit at time t; t dis,rt P can represent the intraday discharge power of the energy storage system at time t; t chr,rt P can represent the intraday charging power of the energy storage system at time t; t DA P can represent the day-ahead clearing price of the electricity market at time t; t RE,cd D can represent the intraday forecast real-time electricity price in the electricity market at time t; ed This can represent the rated power of the new energy generator set; v t P can represent the power prediction deviation of the new energy generator set at time t, which is the integral of the electricity consumption. v It can represent the unit integral cost; f t This can represent the score for the unit deviation of electricity consumption at time t; E t ε can represent the power deviation rate at time t; ε can represent the day-ahead power adjustment coefficient of the new energy generator unit; Q t pr This can represent the day-ahead predicted output power of the new energy generator unit at time t. This can represent a preset penalty-free threshold, for example, it can be 5%.

[0098] As can be seen from the above formulas, the penalty for exceeding the reporting limit measures the degree of deviation between the daily reported plan and the actual execution of new energy power plants. The product of the exemption threshold and the rated power is equivalent to a dead zone. Deviations within this dead zone are within the normal prediction error range and are not penalized; deviations exceeding the dead zone are considered abnormal deviations that need to be controlled. Energy storage systems reduce net power deviation by adjusting intraday charging and discharging power, keeping it within the exemption threshold and thus avoiding this penalty. The deviation assessment item measures the cumulative severity of the deviation between the predicted power and the actual power of new energy generator units. Unlike the penalty for exceeding the reporting limit, which focuses on whether the instantaneous deviation between the reported plan and the actual grid connection exceeds the limit, the deviation assessment item focuses on the total continuous deviation between the predicted output and the actual output. The evaluation objects and triggering mechanisms of the two are different.

[0099] In the example provided in this manual, the energy storage system is guided to prioritize power adjustment to avoid instantaneous over-limit when unfavorable electricity prices may trigger over-limit, by declaring a penalty for exceeding the limit. The deviation assessment item guides the energy storage system to control the cumulative deviation throughout the entire cycle from a global perspective, avoiding excessive accumulation of deviations in other periods due to excessive focus on over-limit avoidance in a certain period. The synergistic effect of these two measures can realize a two-layer power deviation management mechanism that combines instantaneous over-limit prevention and cumulative deviation control.

[0100] In the embodiments of this specification, the energy storage regulation margin obtained by solving the first objective function becomes the dynamic boundary of the intraday power constraint in the second objective function; by optimizing the energy storage regulation power within the boundary through the second objective function, and by feeding back the deviation control effect through the reporting deviation over-limit penalty item and the deviation assessment item, a closed-loop control mechanism of day-ahead reservation, intraday execution, and deviation feedback can be realized.

[0101] S302: Based on the energy storage regulation margin, the response characteristics of the energy storage system, and the intraday regulation power of the energy storage system, an intraday power constraint for the energy storage system is constructed. The intraday power constraint is used to limit the charging and discharging regulation power of the energy storage system during the intraday period from not exceeding the energy storage regulation margin.

[0102] In some embodiments of this specification, the intraday power constraint in the intraday constraint conditions can be expressed by the following formula:

[0103] Among them, P t dis,rt P can represent the intraday discharge power of the energy storage system at time t. t chr,rt C can represent the intraday charging power of the energy storage system at time t; EESη can represent the capacity of the energy storage system. dis and η chr The discharge efficiency and charging efficiency of the energy storage system can be represented respectively, α can represent the energy storage capacity adjustment margin of the energy storage system, and P can represent the energy storage capacity adjustment margin of the energy storage system. ms This can represent the energy storage power regulation margin of the energy storage system.

[0104] In the embodiments of this specification, the charging power and discharging power of the energy storage system during the daytime are limited by both the energy regulation space corresponding to the capacity regulation margin reserved before the daytime and the power regulation margin reserved before the daytime. The combination of the two constitutes the rigid constraint boundary of the daytime decision on the intraday execution, ensuring that the real-time adjustment is always carried out within the acceptable range.

[0105] S303: Based on the state of charge and charging / discharging power of the energy storage system, construct the energy storage output model of the energy storage system.

[0106] In practice, the energy storage output model for the intraday phase can have the same structure as the energy storage output model for the day-ahead phase, but the variables will use the real-time values ​​of the intraday phase.

[0107] S304: Based on the deviation integral and the upper limit of the deviation integral, construct deviation assessment constraints.

[0108] In some embodiments of this specification, the deviation assessment constraint in the intraday power constraint can be expressed by the following formula:

[0109] Among them, v t The power prediction deviation of the new energy generator set at time t can be represented by the integral of the power consumption, v. max It can represent the upper limit of the deviation integral.

[0110] The system's tolerance for power deviation varies under different operating conditions, therefore, the upper limit of the deviation integral can be determined based on different operating conditions. Specifically, the upper limit of the deviation integral can be dynamically determined based on at least one of the following: the prediction error characteristics of the new energy generating units during the daytime period, meteorological data, the real-time electricity price level of the electricity market, and the grid load level corresponding to the electricity market.

[0111] Prediction error characteristics can include the standard deviation, maximum error, and error distribution of renewable energy power forecasts, reflecting the uncertainty level of renewable energy output. Meteorological data can include meteorological elements such as wind speed, solar intensity, cloud cover, and temperature, and their real-time changes, directly affecting the fluctuations in renewable energy output. Real-time electricity price levels can include real-time electricity prices in the electricity market, reflecting the supply and demand relationship and value of electricity. Grid load levels can include the total load demand of the grid, reflecting the grid's operating status and tolerance for power deviations.

[0112] For example, when the prediction error characteristics of new energy generating units indicate an increase in prediction uncertainty, the upper limit of the deviation integral is dynamically adjusted upward according to preset rules, giving the optimization model greater adjustment freedom; when the prediction error characteristics indicate a decrease in prediction uncertainty, the upper limit of the deviation integral is dynamically adjusted downward, forcing the model to more strictly track the day-ahead declared plan. When the real-time electricity price level in the electricity market is at its peak or the grid load level is high, the upper limit of the deviation integral is dynamically adjusted downward, forcing the system to track the plan more accurately to ensure grid security; when the real-time electricity price is at its low point or the grid load level is low, the upper limit of the deviation integral is dynamically adjusted upward, giving the system greater adjustment flexibility. Through this adaptive adjustment mechanism, the upper limit of the deviation integral is no longer a passively accepted rigid parameter, but a dynamic control boundary that is coordinated with the system's operating state.

[0113] In some embodiments of this specification, the first objective function may further include a cooperative penalty term, which is determined based on an initial upper limit of the deviation integral, the upper limit of the deviation integral, and a risk preference coefficient. The day-ahead constraints may include risk control constraints, which are constructed based on the energy storage regulation margin, the conversion coefficient corresponding to the energy storage regulation margin, the response characteristics of the energy storage system, the upper limit of the deviation integral, and a preset lower limit of risk control capability.

[0114] That is, the upper limit of the deviation integral can be used as one of the decision variables of the day-ahead optimization model and solved in conjunction with the energy storage regulation margin.

[0115] In some embodiments of this specification, the cooperative penalty term can be expressed by the following formula: P coordinate =β1 (v max base -v max ) λ risk

[0116] Among them, v max base This can represent the upper limit of the initial deviation integral (e.g., a default value specified by electricity market rules), v maxβ can represent the upper limit of the integral of the actual decision deviation, and λ can represent the weighting coefficient. risk It can represent the risk preference coefficient. (v) max base -v max The risk preference coefficient λ can represent the tightening of the upper limit of the deviation integral; a positive value indicates tightening, and a negative value indicates loosening. risk Used to characterize the degree of aversion of an operating entity to deviation risk. When λ risk When λ is a larger value, the model tends to tighten the upper limit of the bias integral to reduce the risk of exceeding the limit; when λ is a larger value, the model tends to tighten the upper limit of the bias integral to reduce the risk of exceeding the limit. risk When a smaller value is taken, the model tends to relax the upper limit of the bias integral in order to release more energy storage capacity to participate in energy time-shift scheduling.

[0117] Furthermore, the day-ahead constraints also include risk control constraints, which are constructed based on the energy storage regulation margin, the conversion coefficient corresponding to the energy storage regulation margin, the response characteristics of the energy storage system, the upper limit of the deviation integral, and the preset lower limit of the risk control capability. Specifically, this constraint is expressed by the following formula: v max +β2 (α+P ms ) C EES η sys ≥S min system

[0118] Where β2 can represent the conversion coefficient for converting energy storage regulation margin into equivalent integral deduction, and η sys S can represent the comprehensive response characteristic coefficient of an energy storage system. min system It can represent the lower limit of the preset risk control capability.

[0119] In the embodiments of this specification, the energy storage regulation margin (α+P) ms ) and the upper limit of the deviation integral v max Together, these constitute the system's risk control capabilities. The larger the energy storage regulation margin, the stronger the ability to smooth out deviations in real time; therefore, the allowable upper limit for the integral deviation can be tightened (v). max The smaller the energy storage regulation margin, the more relaxed the upper limit of the deviation integral needs to be. The sum of the two must meet the minimum risk control requirements of the system. Through this joint optimization mechanism, the model automatically finds the optimal matching point between deviation tolerance and regulation capacity reserve, which can realize the fine allocation of risk control resources.

[0120] S305: Based on the real-time energy storage power regulation constraint, the real-time energy storage output model, and the deviation assessment constraint, construct the intraday constraint conditions.

[0121] In some embodiments of this specification, a method for synergistic optimization of new energy sources and energy storage in the electricity market is provided, with reference to... Figure 4 As shown, in the day-ahead market, energy storage is used to transfer the output of new energy sources, and the benefits are calculated through the electricity price difference. At the same time, in order to cope with the uncertainty of new energy output, a portion of the adjustment capacity (power margin and capacity margin) of energy storage is reserved in the day-ahead decision. In the real-time market, the reserved margin of energy storage is used to reduce the deviation of real-time output of new energy sources, thereby reducing the deviation assessment.

[0122] Specifically, refer to Figure 5 As shown, a new energy and energy storage day-ahead trading optimization model (i.e., day-ahead optimization model) and an energy storage real-time scheduling optimization model (i.e., intraday optimization model) can be constructed, and a heuristic algorithm can be used for optimization. In the day-ahead phase, the new energy and energy storage day-ahead trading optimization model can be optimized based on the input basic parameters to obtain the new energy day-ahead declaration curve, the energy storage day-ahead declaration curve, and the margin coefficient (i.e., energy storage adjustment margin) in the decision variables. Some decision results from the day-ahead phase, such as the day-ahead clearing result and the margin coefficient, can be used as partial inputs for the real-time phase (i.e., intraday phase). In the real-time phase, the energy storage real-time scheduling optimization model can be optimized based on the input basic parameters and the day-ahead decision results to obtain the energy storage real-time charging and discharging curves.

[0123] To verify the effectiveness of the model, simulation analysis was conducted on the model established in this application based on actual data from a certain new energy power plant. The simulation period was 24 hours, and the time granularity was 15 minutes. The spot price before the current day and the real-time price are as follows: Figure 6 As shown, the day-ahead power forecast and actual output of new energy sources are as follows: Figure 7 As shown.

[0124] Based on the above data and the model constructed using the embodiments in this specification, a genetic algorithm is called to solve the problem, resulting in the new energy day-ahead market reporting strategy as follows: Figure 8 As shown. By Figure 8 It can be seen that the application strategy is optimized based on power forecasting and margin coefficient constraints, combined with electricity price trends and energy storage regulation capabilities. When the day-ahead electricity price is higher than the real-time electricity price, the application curve is raised to allocate more forecasted output to the grid during the day-ahead period; when the day-ahead electricity price is lower than the real-time electricity price, the application curve is lowered to allocate more forecasted output to the grid during the real-time period, thereby achieving the optimal allocation of renewable energy power generation in both the day-ahead and real-time periods. In addition, during periods of lower electricity prices, the application curve is further lowered to store surplus electricity through the energy storage system; during periods of higher electricity prices, the application curve is raised to release the stored electricity from the energy storage system and connect it to the grid, realizing the transfer and scheduling of electrical energy in the time dimension.

[0125] Energy storage day-ahead and real-time market reporting strategies, such as Figure 9 As shown. By Figure 9 It can be seen that the day-ahead declaration for energy storage is based on a peak-valley arbitrage + margin reserve strategy. During periods of low electricity prices, charging power is declared, but the charging power does not reach the maximum limit of energy storage, reserving a 15% power margin and a 5% capacity margin. During periods of high electricity prices, discharging power is declared to cooperate with wind power to increase market electricity sales revenue, also reserving adjustment margins while ensuring overall balance between charging and discharging. In the real-time market, energy storage uses the day-ahead reserved margin for dynamic adjustment. When the actual output of wind power is lower than the predicted value, it discharges to supplement power; when the actual output is higher than the predicted value, it charges to absorb the excess output, achieving precise mitigation of real-time power deviations.

[0126] To further verify the effectiveness of the model constructed in the embodiments of this specification, the following three scenarios are set up: Scenario 1: New energy sources participate independently in the electricity market; Scenario 2: New energy and energy storage participate in the market in a coordinated manner, without considering the reserve margin of energy storage; Scenario 3: New energy and energy storage participate in the market in a coordinated manner, with consideration given to reserving margin for energy storage.

[0127] Benefit comparison in different scenarios Figure 10 As shown. By Figure 10 It can be seen that Scenario 3 has the best overall operational efficiency, followed by Scenario 2, and Scenario 1 has the lowest. Specifically, the power allocation strategies of wind power have basically the same efficiency in the three scenarios, but Scenario 1 has a high deviation assessment cost due to the large deviation in renewable energy output. Scenario 2 has achieved high day-ahead efficiency through day-ahead energy time-shift scheduling of energy storage, but due to the lack of reserved adjustment margin, it cannot cope with real-time output deviation, resulting in high levels of penalty for exceeding the deviation limit and deviation assessment. Scenario 3 has achieved a dynamic balance between energy time-shift efficiency and power deviation smoothing reliability through day-ahead two-stage collaborative optimization. It actively reserves energy storage adjustment margin in the day-ahead stage to provide power deviation smoothing capability in the intraday stage, effectively reducing the amount of exceeding the deviation limit in the real-time stage and the amount of cumulative deviation assessment. It has achieved a dynamic balance between energy time-shift efficiency and power deviation smoothing reliability, and ultimately improved the stability of renewable energy power grid connection and overall operational efficiency, effectively verifying the effectiveness of the technical solution proposed in this invention.

[0128] Based on the above-described scheduling method for new energy power stations, one or more embodiments of this specification also provide a scheduling device for new energy power stations. The device may include apparatus (including distributed systems), software (applications), modules, plug-ins, servers, clients, etc., using the methods described in the embodiments of this specification, combined with necessary implementation hardware. Based on the same innovative concept, the devices in one or more embodiments provided in this specification are as described in the following embodiments. Since the implementation schemes and methods for solving the problem by the devices are similar, the implementation of specific devices in the embodiments of this specification can refer to the implementation of the aforementioned methods, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated. Figure 11 The diagram shown is a schematic representation of a dispatching device for a new energy power station provided in an embodiment of this specification. Figure 11 As shown, the dispatching device 1100 of the new energy power station may include: The first construction module 1101 is used to construct a day-ahead optimization model for new energy power plants, including a first objective function and day-ahead constraints.

[0129] The first determining module 1102 is used to acquire day-ahead forecast data and solve the day-ahead optimization model based on the day-ahead forecast data to obtain a first scheduling strategy for the day-ahead stage; wherein, the day-ahead forecast data includes the first power forecast value of the renewable energy power station and the transaction forecast value of the electricity market in which the renewable energy power station is located in the day-ahead stage; the first scheduling strategy includes at least the day-ahead application data of the renewable energy generating units of the renewable energy power station, the day-ahead application data of the energy storage system, and the energy storage regulation margin of the energy storage system, wherein the energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday stage in the day-ahead stage.

[0130] The second construction module 1103 is used to construct an intraday optimization model for the new energy power station, including a second objective function and intraday constraints; wherein, the second objective function includes a penalty term for exceeding the reporting deviation limit and a deviation assessment term, and the intraday constraints include at least an intraday power constraint constructed based on the energy storage adjustment margin.

[0131] The second determining module 1104 is used to acquire intraday forecast data and solve the intraday optimization model based on the intraday forecast data to obtain a second scheduling strategy for the intraday stage; the intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system.

[0132] The scheduling module 1105 is used to coordinate the scheduling of the new energy generator sets and energy storage systems of the new energy power station based on the first scheduling strategy and the second scheduling strategy.

[0133] The descriptions and functions of the above modules can be understood by referring to the section on scheduling methods for new energy power plants, and will not be repeated here.

[0134] This application also provides an electronic device, such as... Figure 12 As shown, the electronic device may include a processor 1201 and a memory 1202, wherein the processor 1201 and the memory 1202 may be connected via a bus or other means. Figure 12 Taking the example of a connection between China and Israel via a bus.

[0135] Processor 1201 may be a central processing unit (CPU). Processor 1201 may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0136] The memory 1202, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the scheduling method for new energy power stations in the embodiments of the present invention. The processor 1201 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 1202, thereby realizing the scheduling method for new energy power stations in the above method embodiments.

[0137] The memory 1202 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 1201, etc. Furthermore, the memory 1202 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1202 may optionally include memory remotely located relative to the processor 1201, and these remote memories may be connected to the processor 1201 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0138] The one or more modules are stored in the memory 1202, and when executed by the processor 1201, they perform the following scheduling method for new energy power stations: A day-ahead optimization model for a renewable energy power station is constructed, including a first objective function and day-ahead constraints. The first scheduling strategy includes at least the day-ahead reporting data of the renewable energy generating units at the power station, the day-ahead reporting data of the energy storage system, and the energy storage regulation margin of the energy storage system. The energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday phase during the day-ahead phase. Day-ahead forecast data is obtained, and the day-ahead optimization model is solved based on the day-ahead forecast data to obtain the first scheduling strategy for the day-ahead phase. The day-ahead forecast data includes the first power forecast value of the renewable energy power station during the day-ahead phase and the transaction forecast value of the electricity market where the renewable energy power station is located. A package... An intraday optimization model is defined, comprising a second objective function and intraday constraints. The second objective function includes a penalty term for exceeding reporting deviation limits and a deviation assessment term. The intraday constraints include at least an intraday power constraint constructed based on the energy storage adjustment margin. Intraday forecast data is acquired, and the intraday optimization model is solved based on the intraday forecast data to obtain a second scheduling strategy for the intraday stage. The intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system. The new energy generator units and energy storage system of the new energy power station are coordinated and scheduled based on the first scheduling strategy and the second scheduling strategy.

[0139] The specific details of the aforementioned electronic device can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0140] This specification also provides a computer storage medium storing computer program instructions, which, when executed, implement the steps of the above-described scheduling method for new energy power stations.

[0141] This specification also provides a computer program product, which includes a computer program that, when executed, implements the steps of the above-described scheduling method for new energy power stations.

[0142] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0143] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. The focus of each embodiment is to describe the differences from other embodiments.

[0144] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0145] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0146] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute certain parts of the methods of various embodiments of this application.

[0147] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0148] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0149] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to the embodiments described herein by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

Claims

1. A scheduling method for new energy power stations, characterized in that, include: A day-ahead optimization model for constructing new energy power plants is established, including the first objective function and day-ahead constraints. Acquire day-ahead forecast data and solve the day-ahead optimization model based on the day-ahead forecast data to obtain the first scheduling strategy for the day-ahead stage; wherein, the day-ahead forecast data includes the first power forecast value of the renewable energy power station and the transaction forecast value of the electricity market in which the renewable energy power station is located; the first scheduling strategy includes at least the day-ahead application data of the renewable energy generating units of the renewable energy power station, the day-ahead application data of the energy storage system, and the energy storage regulation margin of the energy storage system, wherein the energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday stage in the day-ahead stage; An intraday optimization model for the new energy power station is constructed, including a second objective function and intraday constraints; wherein, the second objective function includes a penalty term for exceeding the reporting deviation limit and a deviation assessment term, and the intraday constraints include at least an intraday power constraint constructed based on the energy storage adjustment margin; Acquire intraday forecast data and solve the intraday optimization model based on the intraday forecast data to obtain the second scheduling strategy for the intraday stage; the intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system; Based on the first scheduling strategy and the second scheduling strategy, the new energy generator sets and energy storage systems of the new energy power station are coordinated and scheduled. The second objective function is expressed by the following formula: ； ； ； ； ； ； Where C represents the daily comprehensive cost of the new energy power station, C hs and C kh Q represents the penalty cost for exceeding the reporting limit and the assessment cost for deviation, respectively. t rt P represents the predicted daily power output of the new energy generator unit at time t; t dis,rt P represents the intraday discharge power of the energy storage system at time t; t chr,rt P represents the intraday charging power of the energy storage system at time t; t DA P represents the day-ahead clearing price in the electricity market at time t; t RE,cd D represents the intraday forecast real-time electricity price in the electricity market at time t; ed This indicates the rated power of the new energy generator set; v t P represents the power prediction deviation of the new energy generator set at time t, expressed as the integral of the power quantity; v f represents the unit integral cost; t E represents the score for the unit deviation of electricity consumption at time t; t ε represents the power deviation rate at time t; ε represents the day-ahead power adjustment coefficient of the new energy generator unit; Q t pr This represents the day-ahead predicted output power of the new energy generator unit at time t. This indicates the preset penalty exemption threshold.

2. The scheduling method for new energy power stations according to claim 1, characterized in that, Energy storage regulation margin includes energy storage capacity regulation margin and energy storage power regulation margin; The energy storage capacity adjustment margin is used to constrain the range of daytime state of charge variation of the energy storage system, as well as the capacity adjustment range reserved for intraday phases. The energy storage power regulation margin is used to constrain the range of daytime charge and discharge power variation of the energy storage system, as well as the power regulation range reserved for intraday stages.

3. The scheduling method for new energy power stations according to claim 1, characterized in that, The day-ahead optimization model for new energy power plants, including the first objective function and day-ahead constraints, is constructed as follows: Based on the trading parameters of the new energy power plants and the trading parameters of the corresponding electricity market, a first objective function is constructed. The first objective function includes a new energy grid-connected power term and an energy storage system grid-connected efficiency term. The new energy grid-connected power term is determined based on the day-ahead declaration coefficient and day-ahead declaration parameters of the new energy generating units, as well as the day-ahead forecast real-time electricity price and the day-ahead forecast day-ahead electricity price of the electricity market during the day-ahead period. The energy storage system grid-connected efficiency term is determined based on the day-ahead declaration parameters of the energy storage system and the day-ahead forecast day-ahead electricity price of the electricity market during the day-ahead period. Based on the adjustment range of the day-ahead declaration coefficient of the new energy generator set, a new energy day-ahead declaration constraint for the new energy generator set is constructed; Based on the state of charge and charging / discharging power of the energy storage system, an energy storage output model of the energy storage system is constructed. Based on the day-ahead state of charge adjustment range and energy storage capacity adjustment margin of the energy storage system, the day-ahead capacity constraint of the energy storage system is constructed. Based on the day-ahead charge and discharge power adjustment range, day-ahead state of charge adjustment range, and energy storage power adjustment margin of the energy storage system, the day-ahead power constraint of the energy storage system is constructed. Based on the new energy day-ahead application constraints, the energy storage day-ahead capacity constraints, the energy storage day-ahead power constraints, and the energy storage output model, the day-ahead constraint conditions are constructed.

4. The scheduling method for new energy power stations according to claim 1 or 3, characterized in that, The first objective function is expressed by the following formula: ； Where R represents the system benefits of the renewable energy power station, T represents the day-ahead operating cycle, ε represents the day-ahead power adjustment coefficient of the renewable energy generator unit, and Q t pr P represents the day-ahead predicted output power of the new energy generator unit at time t. t DA,pr P represents the day-ahead forecast electricity price in the electricity market at time t. t RE,pr P represents the day-ahead forecast real-time electricity price in the electricity market at time t. t dis,pr P represents the day-ahead predicted discharge power of the energy storage system at time t. t chr,pr This represents the day-ahead predicted charging power of the energy storage system at time t.

5. The scheduling method for new energy power stations according to claim 1 or 3, characterized in that, The first objective function also includes an uncertainty penalty term, which is determined based on a preset penalty weight, an uncertainty penalty coefficient, and the energy storage adjustment margin. The uncertainty penalty coefficient is determined based on the prediction error characteristics of the new energy generator set.

6. The scheduling method for new energy power stations according to claim 3, characterized in that, The new energy vehicle day-ahead declaration constraint in the aforementioned day-ahead constraints is expressed by the following formula: ； Where ε represents the daily reporting coefficient for new energy generator sets, ε min and ε max These represent the lower and upper limits of the daytime power adjustment coefficient, respectively. The energy storage output model is expressed by the following formula: ； Among them, SOC t and SOC t+1 Let P represent the state of charge of the energy storage system at time t and time t+1, respectively, where δ represents the self-discharge rate of the energy storage system, and P represents the state of charge of the energy storage system at time t and time t+1, respectively. t dis and P t chr Let η represent the discharge power and charging power of the energy storage system at time t, respectively. dis and η chr θ represents the discharge efficiency and charging efficiency of the energy storage system, respectively. dis and θ chr These represent the discharge and charging states of the energy storage system, respectively. 1 indicates the energy storage system is in its current state, and 0 indicates the energy storage system is not in its current state. EES Z represents the capacity of the energy storage system, where Z is an integer. The day-ahead capacity constraint for energy storage in the day-ahead constraints is expressed by the following formula: ； ； Wherein, SOC represents the state of charge of the energy storage system. min and SOC max These represent the lower and upper limits of the state of charge (SOC) of the energy storage system, respectively; α represents the energy storage capacity regulation margin of the energy storage system; SOC1 and SOC T These represent the state of charge of the energy storage system at the initial time and at time T, respectively; The day-ahead power constraint for energy storage in the day-ahead constraints is expressed by the following formula: ； ； Among them, P t dis,pr P represents the day-ahead predicted discharge power of the energy storage system at time t. t chr,pr P represents the day-ahead predicted charging power of the energy storage system at time t. max dis and P max chr P represents the maximum discharge power and maximum charging power of the energy storage system, respectively. ms This indicates the energy storage power regulation margin of the energy storage system.

7. The scheduling method for new energy power stations according to claim 3, characterized in that, The energy storage system includes at least one first energy storage unit and at least one second energy storage unit. The first energy storage unit and the second energy storage unit have different response characteristics, which include at least one of the following: response time, ramp rate, power density, energy density, rated capacity, cycle life, charge / discharge depth tolerance, charge / discharge efficiency, and self-discharge rate. The day-ahead constraints are constructed based on the new energy day-ahead application constraints, the energy storage day-ahead capacity constraints of each energy storage unit, the energy storage day-ahead power constraints of each energy storage unit, the energy storage output model of each energy storage unit, and the collaborative adjustment margin constraints between energy storage units. The coordinated adjustment margin constraint includes the coordinated capacity adjustment margin constraint and the coordinated power adjustment margin constraint. The coordinated capacity adjustment margin constraint is constructed based on the day-ahead capacity constraint of each energy storage unit, the rated capacity of each energy storage unit, and the lower limit of the day-ahead capacity constraint of at least two energy storage units; the coordinated power adjustment margin constraint is constructed based on the day-ahead power constraint of each energy storage unit, the power adjustment margin weight of each energy storage unit, and the lower limit of the day-ahead power constraint of at least two energy storage units; the lower limit of the day-ahead capacity constraint and the lower limit of the day-ahead power constraint are determined based on the prediction error characteristics of the new energy generator set.

8. The scheduling method for new energy power stations according to claim 1, characterized in that, The intraday optimization model for the aforementioned new energy power station, including a second objective function and intraday constraints, is constructed as follows: Based on the day-ahead declared power, actual grid-connected power of the new energy power plants, and the day-ahead and real-time electricity prices of the electricity market, a second objective function is constructed. The declaration deviation penalty term in the second objective function is determined based on the deviation between the day-ahead declared power and the actual grid-connected power, the relationship and deviation between the day-ahead electricity price and the real-time electricity price, and a preset exemption threshold. The deviation assessment term is determined based on the integral of the deviation between the predicted power and the actual power of the new energy generating units within a preset deviation assessment period. Based on the energy storage regulation margin, the response characteristics of the energy storage system, and the intraday regulation power of the energy storage system, an intraday power constraint for the energy storage system is constructed. The intraday power constraint is used to limit the charging and discharging regulation power of the energy storage system during the intraday period from not exceeding the energy storage regulation margin. Based on the state of charge and charging / discharging power of the energy storage system, an energy storage output model of the energy storage system is constructed. Based on the deviation integral and the upper limit of the deviation integral, deviation assessment constraints are constructed; Based on the real-time energy storage power adjustment constraint, the real-time energy storage output model, and the deviation assessment constraint, the intraday constraint conditions are constructed.

9. The scheduling method for new energy power stations according to claim 1 or 8, characterized in that, The intraday power constraint in the intraday constraint conditions is expressed by the following formula: ； ； Among them, P t dis,rt P represents the intraday discharge power of the energy storage system at time t. t chr,rt C represents the intraday charging power of the energy storage system at time t; EES η represents the capacity of the energy storage system. dis and η chr These represent the discharge efficiency and charging efficiency of the energy storage system, respectively, α represents the energy storage capacity adjustment margin of the energy storage system, and P... ms This indicates the energy storage power regulation margin of the energy storage system; The deviation assessment constraint in the intraday power constraint is expressed by the following formula: ； Among them, v t v represents the power prediction deviation of the new energy generator unit at time t, which is the integral of the electricity consumption. max This indicates the upper limit of the deviation integral.

10. The scheduling method for new energy power stations according to claim 8, characterized in that, The upper limit of the deviation integral is dynamically determined based on at least one of the following: the prediction error characteristics of the new energy generating units during the intraday period, meteorological data, the real-time electricity price level of the electricity market, and the grid load level corresponding to the electricity market.

11. The scheduling method for new energy power stations according to claim 8, characterized in that, The first objective function includes a collaborative penalty term, which is determined based on the initial deviation integral upper limit, the deviation integral upper limit, and the risk preference coefficient; The day-ahead constraints include risk control constraints, which are constructed based on the energy storage regulation margin, the conversion coefficient corresponding to the energy storage regulation margin, the response characteristics of the energy storage system, the upper limit of the deviation integral, and the lower limit of the preset risk control capability.

12. A dispatching device for a new energy power station, characterized in that, include: The first construction module is used to construct the day-ahead optimization model of the new energy power station, including the first objective function and day-ahead constraints. The first determining module is used to acquire day-ahead forecast data and solve the day-ahead optimization model based on the day-ahead forecast data to obtain a first scheduling strategy for the day-ahead stage; wherein, the day-ahead forecast data includes the first power forecast value of the renewable energy power station and the transaction forecast value of the electricity market in which the renewable energy power station is located; the first scheduling strategy includes at least the day-ahead application data of the renewable energy generating units of the renewable energy power station, the day-ahead application data of the energy storage system, and the energy storage regulation margin of the energy storage system, wherein the energy storage regulation margin represents the regulation capacity of the energy storage system reserved for the intraday stage in the day-ahead stage; The second construction module is used to construct an intraday optimization model for the new energy power station, including a second objective function and intraday constraints; wherein, the second objective function includes a penalty term for exceeding the reporting deviation limit and a deviation assessment term, and the intraday constraints include at least an intraday power constraint constructed based on the energy storage adjustment margin; The second determining module is used to acquire intraday forecast data and solve the intraday optimization model based on the intraday forecast data to obtain the second scheduling strategy for the intraday stage; the intraday forecast data includes the second power forecast value of the new energy power station for the intraday stage, and the second scheduling strategy includes at least the intraday power data of the energy storage system; The scheduling module is used to coordinate the scheduling of the new energy generator sets and energy storage systems of the new energy power station based on the first scheduling strategy and the second scheduling strategy. The second objective function is expressed by the following formula: ； ； ； ； ； ； Where C represents the daily comprehensive cost of the new energy power station, C hs and C kh Q represents the penalty cost for exceeding the reporting limit and the assessment cost for deviation, respectively. t rt P represents the predicted daily power output of the new energy generator unit at time t; t dis,rt P represents the intraday discharge power of the energy storage system at time t; t chr,rt P represents the intraday charging power of the energy storage system at time t; t DA P represents the day-ahead clearing price in the electricity market at time t; t RE,cd D represents the intraday forecast real-time electricity price in the electricity market at time t; ed This indicates the rated power of the new energy generator set; v t P represents the power prediction deviation of the new energy generator set at time t, expressed as the integral of the power quantity; v f represents the unit integral cost; t E represents the score for the unit deviation of electricity consumption at time t; t ε represents the power deviation rate at time t; ε represents the day-ahead power adjustment coefficient of the new energy generator unit; Q t pr This represents the day-ahead predicted output power of the new energy generator unit at time t. This indicates the preset penalty exemption threshold.

13. An electronic device, characterized in that, include: A memory and a processor, the processor and the memory being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to implement the steps of the method according to any one of claims 1 to 11.

14. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed, implement the steps of the method according to any one of claims 1 to 11.

15. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 11.

Images