New energy consumption optimization method for multi-type subject power system and related device

Abstract

The invention belongs to a new energy consumption method, provides a new energy consumption optimization method for a multi-type subject power system and a related device, and aims to solve the technical problems of resource waste and urgent need of consumption of new energy at present, and constructs an optimization clearing model based on multi-type subject participation. And acquiring a new energy 96-point prediction curve and a 96-point baseline load curve, solving the optimization clearing model in combination with the new energy 96-point prediction curve and the 96-point baseline load curve to obtain an optimization strategy, and optimizing the power system by adopting the optimization strategy. Multiple types of subjects are promoted to participate in the market through a market means so as to improve the absorption capacity of new energy and reduce the power abandoning rate of a new energy blocking area.

Description

Technical Field

[0001] The present application relates to a new energy consumption method, and specifically to a new energy consumption optimization method and related devices for multi-type main power systems. Background Art

[0002] The new power system with new energy as the main body is being rapidly promoted, and the installed capacity of wind power, photovoltaic power and other power generation has increased significantly. Renewable resources and load centers are distributed inversely. With the increase in new energy construction, local consumption is difficult, and there are a lot of wind and solar power abandonment phenomena, resulting in resource waste. Therefore, promoting the consumption of new energy has become a top priority. It is necessary to optimize the allocation of power resources efficiently through market means, further expand the types of market players, encourage various types of resources such as source, grid, load and storage to actively participate in the market, and provide new ways for the market-oriented large-scale consumption of new energy. Summary of the invention

[0003] In response to the current technical problems of resource waste and urgent need for consumption of new energy, this application provides a new energy consumption optimization method and related devices for multi-type main power systems.

[0004] In order to achieve the above objectives, this application adopts the following technical solutions: In the first aspect, the present application proposes a new energy consumption optimization method for a multi-type main power system, including: Constructing an optimal clearing model based on the participation of multiple types of entities corresponding to the power system; the optimization goal of the optimal clearing model is to maximize the consumption of new energy; According to the meteorological data corresponding to the power system and the historical new energy power generation curve, a 96-point prediction curve for new energy is obtained; According to the historical demand-side flexible resource load curve corresponding to the power system, a 96-point baseline load curve is obtained; The optimization clearing model is solved by combining the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes the output of multiple types of entities; The optimization strategy is adopted to optimize the power system.

[0005] In the second aspect, the present application proposes a new energy consumption optimization system for multi-type main power systems, including: A model module is used to construct an optimization clearing model corresponding to the power system based on the participation of multiple types of subjects; the optimization goal of the optimization clearing model is to maximize the consumption of new energy; The first curve module is used to obtain a 96-point prediction curve of new energy based on the meteorological data corresponding to the power system and the historical new energy power generation curve; The second curve module is used to obtain a 96-point baseline load curve based on the historical demand-side flexible resource load curve corresponding to the power system; A solution module, used to solve the optimization clearing model by combining the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes the output of multiple types of entities; The optimization module is used to optimize the power system by adopting the optimization strategy.

[0006] In the third aspect, the present application proposes an electronic device, comprising: a memory, and one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned method for optimizing the new energy consumption of multi-type main power systems.

[0007] In a fourth aspect, the present application proposes a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the consumption of new energy for multi-type main power systems are implemented.

[0008] In a fifth aspect, the present application proposes a computer program product, which includes instructions, and when the instructions are executed by a processor, the above-mentioned method for optimizing the consumption of new energy in the power system is implemented.

[0009] Compared with the prior art, this application has the following beneficial effects: This application proposes a new energy consumption optimization method for a multi-type subject power system, constructs an optimization clearing model based on the participation of multiple types of subjects, obtains a 96-point prediction curve for new energy and a 96-point baseline load curve, solves the optimization clearing model in combination with the 96-point prediction curve for new energy and the 96-point baseline load curve, obtains an optimization strategy, and then uses the optimization strategy to optimize the power system. With the vigorous development of new energy, this application promotes the consumption of new energy through market-oriented means, takes the lowest abandonment rate of new energy as the objective function, and constructs a market clearing model with the participation of multiple types of subjects based on multi-type market subject models such as wind power, photovoltaics, hydropower, thermal power, independent energy storage, and adjustable loads. Through market means, it promotes the participation of multiple types of subjects in the market to improve the absorption capacity of new energy and reduce the abandonment rate in new energy blocking areas.

[0010] The present application also proposes a new energy consumption optimization system for multi-type main power systems and a computer program product, which have all the advantages of the new energy consumption optimization method for multi-type main power systems mentioned above. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a first flow chart of the new energy consumption optimization method for multi-type subject power systems of this application; Figure 2 A second flow chart of the new energy consumption optimization method for multi-type subject power systems of this application; Figure 3 A schematic diagram of a new energy consumption optimization system for multiple types of main power systems in this application. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0014] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0015] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0016] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0017] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0018] In the description of the embodiments of the present application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0019] The construction of a new power system with new energy as the main body is advancing at an unprecedented speed. During this transformation process, the installed capacity of clean energy such as wind power and photovoltaics has achieved a substantial increase. A significant problem is the inverse distribution of renewable resources and load centers: that is, areas rich in wind energy, solar energy, etc. are often far away from urban centers with dense populations and high electricity demand. This inverse distribution makes it difficult to absorb new energy power generation locally. In areas with rich wind and solar energy resources, when the power generation exceeds local consumption demand, the excess power is often difficult to effectively transmit to areas with greater demand. As a result, a large amount of wind and solar energy is wasted, that is, the phenomenon of "abandoning wind and solar energy". This not only reduces the return on investment of new energy, but also hinders the healthy development of the new energy industry, and is contrary to the original intention of promoting energy transformation and achieving carbon neutrality. Therefore, promoting the efficient absorption of new energy has become an important task in the development of the current power system. The solution lies in optimizing the allocation of power resources through market mechanisms.

[0020] Expanding the types of market players, especially encouraging the active participation of various types of resources such as sources (power generation side), networks (power transmission and distribution side), loads (power consumption side), and storage (energy storage side), is the key to the market-oriented large-scale absorption of new energy.

[0021] In view of this, this application, through sufficient field research and based on actual needs, proposes an optimization modeling method for promoting the consumption of new energy with the participation of multiple types of entities. The method takes the lowest abandonment rate of new energy as the objective function, and constructs a market clearing model with the participation of multiple types of entities based on models of multiple types of market entities such as wind power, photovoltaics, hydropower, thermal power, independent energy storage, and adjustable loads. Through market means, it promotes the participation of multiple types of entities in the market to improve the consumption capacity of new energy and reduce the abandonment rate in new energy congestion areas.

[0022] like Figure 1As shown, it is a first flow chart of a new energy consumption optimization method for a multi-type main power system of the present application, which may include: S101, constructing an optimization clearing model corresponding to the power system based on the participation of multiple types of entities; the optimization goal of the optimization clearing model is to maximize the consumption of new energy.

[0023] S102, obtaining a 96-point prediction curve for new energy based on meteorological data corresponding to the power system and historical new energy power generation curves.

[0024] In practical applications, the 96-point prediction curve of new energy can be used to predict the power generation of new energy (such as wind energy and solar energy) in the future (usually one day, that is, 96 15-minute time points). By collecting meteorological data (such as wind speed, light intensity, etc.) and historical new energy power generation curves, advanced prediction algorithms such as time series analysis and machine learning can be used to generate a 96-point prediction curve for new energy.

[0025] S103, obtaining a 96-point baseline load curve based on the historical demand-side flexible resource load curve corresponding to the power system.

[0026] In practical applications, the load curve of demand-side flexible resources can be obtained by analyzing historical data. This curve reflects the changes in electricity demand of these resources during the day. Similarly, this curve is also divided into 96 points to match the time resolution of the new energy forecast curve. The baseline load curve is an important basis for the optimization clearing model to consider demand-side response and flexibility.

[0027] S104, solving the optimization clearing model in combination with the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes the output of multiple types of entities.

[0028] The new energy forecast curve and the baseline load curve are used as input to solve the constructed optimization clearing model. The solution process can usually use common model solving algorithms to find the optimal solution that maximizes the consumption of new energy while satisfying all constraints. This optimal solution is the optimization strategy, which includes the specific output plans of multiple types of entities (thermal power, hydropower, new energy, energy storage and adjustable load) in a day.

[0029] S105: Optimize the power system using the optimization strategy.

[0030] Applying the obtained optimization strategy to the actual power system can increase the proportion of new energy consumption, reduce the operating cost of the power system, and enhance the stability and flexibility of the power grid.

[0031] This application improves the utilization efficiency and absorption capacity of new energy, can stimulate the enthusiasm of various types of market players to absorb new energy, and promote the healthy development of the new energy industry. It is mainly reflected in the following aspects: (1) Promoting the consumption of new energy: By optimizing the trading mechanism, we can improve the competitiveness and consumption ratio of new energy in the electricity market and reduce the phenomenon of wind and solar power abandonment.

[0032] (2) Improving system supply capacity: By real-time balancing of the entire network, the flexibility and regulation capabilities of the power system can be enhanced to ensure the stability and reliability of power supply.

[0033] (3) Supporting high-quality development of new energy: By setting scientific targets for new energy utilization and improving market mechanisms, we will promote the sustainable development of the new energy industry and reduce the energy costs of the entire society.

[0034] like Figure 2 As shown, it is a second flow chart of a new energy consumption optimization method for a multi-type subject power system of the present application. In this embodiment, the multi-type subjects include thermal power, hydropower, new energy, energy storage and adjustable loads. The optimization method may include: S201, based on meteorological data and historical new energy power generation curves, a 96-point prediction curve for new energy is calculated.

[0035] (1) The longitude and latitude positioning function is used to integrate the meteorological data and power data of wind power and photovoltaic power stations.

[0036] The historical meteorological data (such as wind speed, wind direction, irradiance, temperature, etc.) and corresponding power output data of wind power and photovoltaic power stations can be collected. The longitude and latitude information of the power station is used to ensure that the meteorological data and power data are accurately matched in time and space. The meteorological data and power data are then aligned by timestamp to form a one-to-one corresponding data pair.

[0037] (2) Extract the meteorological and power characteristics of wind power and photovoltaic power stations.

[0038] Select features that have a significant impact on power generation from meteorological data, such as wind speed and irradiance, calculate derived features, such as average wind speed and maximum irradiance period, and consider time features such as date and hour. Then determine the correlation between each feature and power output through statistical analysis methods.

[0039] (3) Construct a sample library to train neural network model parameters.

[0040] In practical applications, data can be divided into training sets and test sets for standardization or normalization. Select a suitable neural network model, such as a long short-term memory network, and optimize the neural network's hyperparameters such as the number of layers, number of neurons, and learning rate through grid search or random search methods. Use the training set data to train the neural network model and evaluate the model performance through cross-validation.

[0041] (4) Predict the power generation of wind power and photovoltaic power stations during specific periods of time through the solidification model.

[0042] S202, based on the historical demand-side flexible resource load curve, calculate a 96-point baseline load curve.

[0043] The following methods can be used: (1) According to the two types of dates, weekends and statutory holidays, the baseline average load for each peak, flat and valley period is determined. If a holiday adjustment occurs, it is determined according to the actual national holiday arrangements; (2) Baseline average load on weekends = average load of users in each peak, flat and valley period of the previous four weekends * (1 + month-on-month growth rate of dispatched electricity consumption in the province last month); (3) The baseline average load of statutory holidays = the average load of the same period of the previous year * K1 + the average load of the user during the peak, flat and valley periods of the previous comparable statutory holiday * K2; (4) The baseline average load on working days = the average load of the user in each peak, flat and valley period in the previous five working days * (1 + the month-on-month growth rate of the province's dispatched electricity consumption in the previous month).

[0044] S203, construct models of various types of market entities based on the operating characteristics of different market entities.

[0045] 1. Thermal power model (1) Upper and lower limits of real-time power generation of the unit: The real-time power generation of thermal power units must be within a certain output range, that is,

[0046] in, Indicates the unit n In the period t The lower limit of output, Indicates the unit n In the period t The output limit of Indicates the unit n of effort.

[0047] (2) Unit climbing (slope) constraints Determined by the actual operating parameters of the thermal power generating unit, there are some constraints on the change of the generating output of the unit in adjacent time periods. The rates of climbing and sliding must meet the following requirements:

[0048] in, Indicates the unit n In the period t of efforts, Indicates the unit n In the period t -1 output, Indicates the unit n The climbing or sliding rate, Indicates the unit n In the period t The start-stop status, Indicates the unit n In the period t -1 start / stop status.

[0049] (3) Constraints on minimum start-up time and minimum shutdown time of the unit The minimum startup time and minimum shutdown time constraints of thermal power units are:

[0050] in, Indicates the unit n In the period The start-stop status, Indicates the unit n Minimum continuous power-on time, Indicates the unit n Minimum continuous downtime.

[0051] (4) Fixed output of the unit The unit operates according to a given power generation plan during a specific period of time, and does not participate in the bidding and clearing during this specific period of time.

[0052]

[0053] in, Indicates the unit n In the period t The active power, Indicates the unit n In the period t Output setting value.

[0054] (5) System power generation reserve constraints In order to ensure the real-time balance of system power, thermal power units must meet a certain adjustment margin and provide sufficient spinning reserve space to ensure the balance of the system by increasing or reducing the power output in real time. The system power reserve constraint is expressed as follows: To increase the standby constraint:

[0055] Lower the standby constraint:

[0056] in, Indicates time period t Raise reserve requirements, Indicates time period t Lower reserve requirements.

[0057] 2. Hydropower model A single reservoir balance model is constructed by establishing reservoir water balance constraints, water topology constraints, reservoir capacity and water level constraints, and water level consumption rate constraints.

[0058] (1) Reservoir water balance constraints To ensure that the water volume of the reservoir remains balanced, the water storage volume of the reservoir in each period is related to the water storage volume of the reservoir in the previous period, the interval water inflow forecast of the reservoir in the current period, the water volume used for power generation in the current period, and the water discharge volume of the reservoir in the current period:

[0059] in, Reservoir t The amount of water stored in the period, Reservoir t The predicted water inflow volume for each time period, Reservoir t The amount of water used for power generation during the period, Reservoir t The amount of water discharged during the period, in m³. Reservoir t -1 water storage capacity in a period.

[0060] By establishing reservoir water balance constraints, the reservoir water volume can be effectively balanced to ensure that the water volume requirements for flood control, irrigation, power generation, shipping, etc. are met.

[0061] (2) Water topology constraints Determination of the inflow and outflow of the reservoir in the current period. For a single reservoir, the inflow of the reservoir in the current period is only the interval water forecast:

[0062] in,w_in ( t ) indicates the current period of the reservoir t The amount of water entering the reservoir, f ( t ) indicates the current period of the reservoir t The predicted water inflow volume for the interval is in m³.

[0063] The outflow volume of the reservoir in the current period is determined by the amount of water used for power generation and the amount of water discharged from the reservoir in the current period. The unit is m³ and is described by the formula:

[0064] in, Reservoir t The amount of water discharged from the reservoir during the period.

[0065] At the same time, considering the upper and lower limits of the reservoir outflow caused by the reservoir's own attributes, the upper and lower limits of the reservoir outflow can be preset according to the reservoir's operating data, requiring the outflow of the reservoir in the current period to be within the preset upper and lower limits of the reservoir outflow to ensure the normal operation of the reservoir. By establishing water topology constraints, the topological structure between reservoirs can be effectively constructed to characterize the spatiotemporal coupling relationship of reservoirs and the reservoir topological structure, similar to network topology.

[0066] (3) Reservoir capacity and water level constraints Characterizes the constraints of the reservoir water level. The water level of the reservoir in the current period is determined by the water storage capacity of the reservoir in the current period and the area of ​​the reservoir, which can be described by the formula:

[0067] in, Reservoir t The water level height of the time period, in m, Reservoir t The water storage capacity of the time period, in m³, Indicates the approved reservoir area, in m 2 .

[0068] At the same time, considering that the reservoir must be able to operate safely, its water level must have corresponding upper and lower limit requirements. Therefore, it is necessary to set the upper and lower limits of the reservoir water level in advance according to the reservoir operation data, and then ensure that the water level of the reservoir in the current period is within the preset upper and lower limits of the reservoir water level to ensure the safe operation of the reservoir.

[0069] (4) Water level consumption rate constraints The key constraint of the combination of hydropower characterizes the relationship between the amount of water used for power generation and the power generation. The water volume data of the reservoir is converted into the electricity volume data of the electricity spot market, so as to realize the participation of cascade hydropower in the electricity spot market. Specifically, the amount of water used for power generation in the current period of the reservoir is determined by the water level consumption rate of the reservoir in the current period and the power generation power of the reservoir in the current period, which can be described by the formula:

[0070] in, Reservoir t The power generated during the period, in kW, Reservoir t The water consumption rate of the time period, in m³ / kWh.

[0071] According to the water level consumption rate constraint, the relationship between the amount of water used for power generation and the power generation capacity is clarified. The amount of water used for power generation can be determined by the power generation capacity, that is, its own operating parameters can be determined according to the demand of the electricity spot market. The power generation capacity can also be determined by the amount of water used for power generation, and the power generation capacity situation can be reported to the electricity spot market to facilitate scheduling by the electricity spot market.

[0072] 3. New energy model (1) Output constraints of new energy units The output level of new energy units should not exceed its predicted level, and the constraints are as follows:

[0073] in, Indicates nk New energy units in t Time output, Indicates nk New energy units in t The expected output for the time period, in MW.

[0074] 4. Energy storage model Energy storage systems are different from traditional thermal power units. They can be used as both power sources and loads in power systems. Chemical energy storage systems also have the characteristics of fast charging and discharging rates. At the same time, they are limited by the power capacity. With the rapid development of energy storage systems in power systems, the power characteristics of energy storage systems should be considered in the dispatch model. In the dispatch model, the power-power constraints of the energy storage system, the charging and discharging power constraints, the ramp constraints, and the upper and lower limits of power in each time period are mainly considered.

[0075] (1) Charge and discharge power constraints The charging and discharging of the energy storage system is affected by factors such as the type of energy storage system and the electric energy value of the energy storage system. The charging and discharging power characteristic curve of the energy storage system is given and can be represented by a segmented polyline.

[0076] (3-1) in, Indicates energy storage si exist t The charging power of the time period, Indicates energy storage si The maximum charging power, Indicates energy storage si exist t The charging status of the time period, Indicates energy storage si exist t The discharge power of the time period, Indicates energy storage si The maximum discharge power, Indicates energy storage si exist t The discharge state of the time period is a value between 0 and 1.

[0077] 2) Power capacity constraints The life of an energy storage system is related to the number of charge and discharge cycles and the depth of charge and discharge. In order to extend the service life of the energy storage system as much as possible, a range of electrical capacity is generally given for the operation of the energy storage system.

[0078] (3-2) in, express t Time storage power station si The capacity status, express t -1 period energy storage power station si The capacity status, Energy storage power station si The charging efficiency, Energy storage power station si The discharge efficiency, Energy storage power station si Capacity status at the end of the operating day, Indicates the energy storage power station in the last period of the declared operation day si The expected value of the capacity state, Energy storage power station si Capacity status at the beginning of the operating day, Indicates the energy storage power station at the initial period of the previous operating day si The capacity status, Energy storage power station si The maximum capacity period.

[0079] (3) Power-electricity conversion loss constraints The energy storage system itself has a certain amount of power loss, which can be expressed by the charging energy efficiency coefficient and discharging energy efficiency coefficient of the energy storage system. η d ( i ) to indicate.

[0080] (4) Climbing constraints The power ramp constraint of the energy storage system is similar to that of a thermal power unit, except that its power value can be negative, that is, the energy storage system can operate in a charging state and appear as a system load.

[0081] (3-3) (3-4) in, Energy storage power station si The minimum climbing rate, Energy storage power station si The maximum climbing rate, Energy storage power station si The minimum downhill climbing rate, Energy storage power station si The maximum downhill climbing rate, Energy storage power station si exist t The ramp rate of the time period, Energy storage power station si exist t -1 The ramp rate during the period.

[0082] 5) Backup constraints The backup capacity that the energy storage system can provide is affected by its maximum charging and discharging power value at different time periods on the one hand, and by the electric energy value stored in the energy storage system at different time periods on the other hand. The two together determine the backup capacity value that the energy storage system can provide at different time periods.

[0083] 6) Power Constraints in the Final Period In some cases, it is necessary to establish the energy constraints of the energy storage system in the last period of the planning cycle, that is, the energy storage system is required to have a certain amount of energy storage after the charging and discharging plan is executed. The remaining energy of the energy storage is defined as the state of charge (SOC), which is the ratio of the remaining capacity of the energy storage to the rated capacity. The remaining power must be considered during charging and discharging, so the state of charge constraint is: (3-5) (3-6) in, express t Time storage charge state, expresst The remaining stored energy during the period of storage, Indicates the rated capacity of energy storage, Indicates the minimum state of charge, Indicates the maximum state of charge.

[0084] 5. Adjustable load model Demand-side loads are generally divided into important loads and adjustable loads, while adjustable loads are further divided into transferable loads and interruptible loads.

[0085] For transferable loads: The total power of transferable loads remains unchanged within a scheduling cycle, mainly considering load upper and lower limit constraints and climbing constraints.

[0086] (1) Total power constraint

[0087] in, For transferable load ld exist t The load of the period, For transferable load ld exist t The baseline load for the period, For transferable load ld exist t The amount of load regulation during the period.

[0088] (2) Load upper and lower limit constraints

[0089] in, For transferable load ld exist t The minimum load of the time period, For transferable load ld exist t The maximum load during a period.

[0090] (3) Climbing constraints

[0091] in, For transferable load ld exist t The maximum load active power that can be reduced during the period, For transferable load ld exist t The maximum load active power that can be increased during the period.

[0092] (4) Total adjustment amount constraint

[0093] Wherein, T is a dispatching and adjustment period of the transferable load.

[0094] For interruptible loads: Interruptible loads can reduce electricity consumption according to demand, taking into account the upper limit constraint of the load's winning bid for spare capacity.

[0095] (1) Interruptible quantity constraints

[0096] in, Interruptible load i Declared t Interruption capacity of the period, Interruptible load i exist t The minimum interruption capacity that can be claimed during a period, Interruptible load i exist t The maximum interruption capacity that can be claimed during a time period.

[0097] (2) Total interruption constraint

[0098] in, Interruptible load i exist t 0-1 state variable for the time period, 1 indicates interruptible load i In the bid, 0 means interruptible load i No successful bid.

[0099] S204, construct an optimized clearing model.

[0100] 1. Optimization goals Taking the maximization of new energy consumption as the goal of clearing optimization:

[0101] in, represents the amount of new energy consumption, express Time period, represents the total market clearing time, express The actual grid-connected power of wind turbines in renewable energy during the period, express The predicted grid-connected power of wind turbines in renewable energy sources when the auxiliary service market is not open during the period. express The actual grid-connected power of photovoltaic units in the renewable energy sector during this period, express The predicted grid-connected power of photovoltaic units in renewable energy when the ancillary service market is not opened during the period.

[0102] 2. Constraints In this embodiment, the constraints of the optimization clearing model include supply and demand balance constraints, power purchase constraints, power selling constraints, line flow constraints, section flow constraints and grid operation boundary condition constraints. It should be noted that the supply and demand balance constraint can ensure that the power supply and demand are balanced at any time to avoid power shortage or surplus. The power purchase constraint is used to limit the power purchase amount of each power purchaser (such as power grid company, large users, etc.) to ensure the fairness and stability of the power market. The power selling constraint can constrain the power generation of power sellers (such as thermal power, hydropower, new energy power stations, etc.) to prevent excessive power generation from causing system instability. The line flow constraint considers the physical limitations of the power transmission line to ensure that the power will not exceed the carrying capacity of the line during transmission. The section flow constraint is to set flow restrictions on specific sections of the power network (such as the interface of the cross-regional transmission line) to maintain the overall stability and safety of the power grid. The grid operation boundary condition constraints usually include basic conditions for grid operation such as voltage level and frequency stability to ensure that the grid always remains within a safe and stable operating range during the optimization process.

[0103] 1) Supply and demand balance constraints The system supply and demand balance constraint means that at any time during the system operation, regardless of network losses, the sum of the output of all generators in the system and the sum of the planned power of all tie lines should be equal to the system load demand:

[0104] in:

[0105]

[0106] in, Indicates the number of conventional loads in the power system, express The first A normal load, Indicates the number of generator sets in the power system, express The first The output of each generator set, represents the total number of tie lines in the power system, Indicates contact line exist The planned power period of the time period.

[0107] 2) Constraints on purchased electricity

[0108] in, Indicates the buyer i exist Time period n The winning bid power in the output range, express Time Buyer i No. n Each output range declares electricity, Indicates the buyer i The total number of quotation segments.

[0109] 3) Constraints on electricity sales

[0110] in, express Time Seller No. m Section declaration of electricity, Indicates the number of stages of electricity reported. Indicates the seller The total number of segments in the quote.

[0111] 4) Line flow constraints The power flow constraint means that the line and section power flow should not be greater than the allowable limit of the line power flow:

[0112] in, Indicates the line in the power system l The power transmission limit of Indicates the unit n Node to line l The generator output power transfer distribution factor, express Time unit n of efforts, Indicates contact line tj Node to line l The generator output power transfer distribution factor, express Time contact line tj of efforts, Representation Node k To line l The generator output power transfer distribution factor, Representation Node k In the period The bus load value, , Respectively indicate lines l The forward and reverse power flow relaxation variables are N Indicates the total number of units, K Indicates the total number of nodes.

[0113] 5) Section flow constraints Considering the flow constraint of the key section, the constraint can be described as:

[0114] in, Indicates the power system section s The lower limit of power transmission, Indicates the unit n Node section s The generator output power transfer distribution factor, Indicates contact line tj Node section s The generator output power transfer distribution factor, Representation Node k Cross-section s The generator output power transfer distribution factor, Representation of cross section s The upper limit of power transmission, , Respectively represent cross sections s The forward and reverse power flow relaxation variables.

[0115] 6) Power grid operation boundary condition constraints

[0116] in, Indicates the unit n In the period t The collection of various boundary conditions, including the transmission curve of the interconnection line formed by the medium- and long-term transactions between provinces, the units that must be turned on or off due to safety constraints, heating for the people's livelihood or government requirements, Indicates the unit n In the period t The output meets various boundary conditions of the day-ahead market.

[0117] The planned values ​​and forecast values ​​of new energy market players can be obtained. The current new energy consumption situation can be calculated through the planned values ​​and forecast values ​​of new energy stations to determine whether the market needs to be opened.

[0118] S205, perform clearing calculations in real time.

[0119] In practical applications, the above optimization can be used to clear the model expression, import it into a cplex input file, and solve it with the help of cplex software.

[0120] S206, the clearing results are released.

[0121] The results of solving the optimized clearing model can be released to buyer and seller market entities after security verification.

[0122] like Figure 3 As shown, it is a schematic diagram of a new energy consumption optimization system for a multi-type main power system of the present application, which may include: Model module, used to construct an optimization clearing model based on the participation of multiple types of subjects; the optimization goal of the optimization clearing model is to maximize the consumption of new energy, and the constraints of the optimization clearing model include supply and demand balance constraints, power purchase constraints, power sales constraints, line flow constraints, section flow constraints and grid operation boundary condition constraints; the multiple types of subjects include thermal power, hydropower, new energy, energy storage and adjustable loads; The first curve module is used to obtain a 96-point prediction curve of new energy based on meteorological data and historical new energy power generation curves; The second curve module is used to obtain a 96-point baseline load curve based on the historical demand-side flexible resource load curve; A solution module, used to solve the optimization clearing model by combining the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes the output of multiple types of entities; The optimization module is used to optimize the power system by adopting the optimization strategy.

[0123] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the multi-type subjects may include thermal power, hydropower, new energy, energy storage and adjustable loads.

[0124] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the optimization clearing model includes:

[0125] in, represents the amount of new energy consumption, express Time period, represents the total market clearing time, express The actual grid-connected power of wind turbines in renewable energy during the period, express The predicted grid-connected power of wind turbines in renewable energy sources when the auxiliary service market is not open during the period. express The actual grid-connected power of photovoltaic units in the renewable energy sector during this period, express The predicted grid-connected power of photovoltaic units in renewable energy when the ancillary service market is not opened during the period.

[0126] In some embodiments of the new energy consumption optimization system of the multi-type main power system of the present application, the constraints of the optimization clearing model include supply and demand balance constraints, power purchase constraints, power sale constraints, line flow constraints, section flow constraints and grid operation boundary condition constraints.

[0127] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the supply and demand balance constraint includes:

[0128] in, Indicates the number of conventional loads in the power system, express The first A normal load, Indicates the number of generator sets in the power system, express The first The output of each generator set, represents the total number of tie lines in the power system, Indicates contact line exist The planned power for the time period.

[0129] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the power purchase constraint includes:

[0130] in, Indicates the buyer i exist Time period n The winning bid power in the output range, express Time Buyer i No. n Each output range declares electricity, Indicates the buyer i The total number of quotation segments.

[0131] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the power selling constraints include:

[0132] in, express Time Seller No. m Section declaration of electricity, Indicates the number of stages of electricity reported. Indicates the seller The total number of segments of the quotation; In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the line flow constraint includes:

[0133] in, Indicates the line in the power system l The power transmission limit of Indicates the unit n Node to line l The generator output power transfer distribution factor, express Time unit n of efforts, Indicates contact line tj Node to line l The generator output power transfer distribution factor, express Contact time Line tj of efforts, Representation Node k To line l The generator output power transfer distribution factor, Representation Node k In the period The bus load value, , Respectively indicate lines l The forward and reverse power flow relaxation variables are N Indicates the total number of units, K Indicates the total number of nodes.

[0134] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the section flow constraint includes:

[0135] in, Indicates the power system section s The lower limit of power transmission, Indicates the unit n Node section s The generator output power transfer distribution factor, Indicates contact line tj Node section s The generator output power transfer distribution factor, Representation Node k Cross-section s The generator output power transfer distribution factor, Representation of cross sections The upper limit of power transmission, , Respectively represent cross sections s The forward and reverse power flow relaxation variables.

[0136] In some embodiments of the new energy consumption optimization system for multi-type subject power systems of the present application, the grid operation boundary condition constraints include:

[0137] in, Indicates the unit n In the period t The output meets the various boundary conditions of the day-ahead market. Indicates the unit n In the period t A collection of various boundary conditions.

[0138] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the thermal power in the multi-type subjects include the upper and lower limit constraints of the real-time power generation power of the unit, the climbing or landslide constraints of the unit, the minimum start-up time and minimum shutdown time constraints of the unit, the fixed output constraints of the unit, the system power generation upward reserve constraints and the system power generation downward reserve constraints.

[0139] In some embodiments of the new energy consumption optimization system for multi-type subject power systems of the present application, the upper and lower limit constraints of the real-time power generation power of the unit include:

[0140] in, Indicates the unit n In the period t The lower limit of output, Indicates the unit n In the period t The output limit of Indicates the unit n of effort.

[0141] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the unit climbing or sliding constraint includes:

[0142] in, Indicates the unit n In the period t of efforts, Indicates the unit n In the period t -1 output, Indicates the unit nThe climbing or sliding rate, Indicates the unit n In the period t The start-stop status, Indicates the unit n In the period t -1 start / stop status.

[0143] In some embodiments of the new energy consumption optimization system for multi-type subject power systems of the present application, the minimum startup time and minimum shutdown time constraints of the unit include:

[0144] in, Indicates the unit n In the period The start-stop status, Indicates the unit n Minimum continuous power-on time, Indicates the unit n Minimum continuous downtime.

[0145] In some embodiments of the new energy consumption optimization system of the multi-type main power system of the present application, the fixed output constraint of the unit includes:

[0146] in, Indicates the unit n In the period t The active power, Indicates the unit n In the period t Output setting value.

[0147] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the system power generation upward reserve constraint includes: .

[0148] In some embodiments of the new energy consumption optimization system for multi-type subject power systems of the present application, the system power generation down-regulation reserve constraint includes:

[0149] in, Indicates time period t Raise reserve requirements, Indicates time period t Lower reserve requirements.

[0150] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the hydropower in the multi-type subjects include reservoir water balance constraints, water topology constraints, reservoir capacity water level constraints and water level consumption rate constraints.

[0151] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the reservoir water balance constraint includes:

[0152] in, Reservoir t The amount of water stored in the period, Reservoir t The predicted water inflow volume for each time period, Reservoir t The amount of water used for power generation during the period, Reservoir t The amount of water discharged during the period, Reservoir t -1 water storage capacity in a period.

[0153] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the water topology constraint includes:

[0154] in, Reservoir t The amount of water discharged from the reservoir during the period.

[0155] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the reservoir water level constraint includes:

[0156] in, Reservoir t The water level during the period, Reservoir t The amount of water stored in the period, Indicates the approved reservoir area.

[0157] In some embodiments of the new energy consumption optimization system for multi-type subject power systems of the present application, the water level water consumption rate constraint includes:

[0158] in, Reservoir t The power generation during the period, Reservoir t Water level consumption rate during a period of time.

[0159] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the new energy in the multi-type subjects include: Output constraints of new energy units:

[0160] in, Indicates nk New energy units in t Time output, Indicates nk New energy units in t Expected forecast output for the time period.

[0161] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the energy storage in the multi-type subjects include charging and discharging power constraints, electric energy capacity constraints, power-electric energy conversion loss constraints, energy storage climbing constraints, standby constraints and final period electric energy constraints.

[0162] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the charging and discharging power constraints include:

[0163] in, Indicates energy storage si exist t The charging power of the time period, Indicates energy storage si The maximum charging power, Indicates energy storage si exist t The charging status of the time period, Indicates energy storage si exist t The discharge power of the time period, Indicates energy storage si The maximum discharge power, Indicates energy storage si exist t The discharge state of the time period is a value between 0 and 1.

[0164] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the electric energy capacity constraint includes:

[0165] in, express t Time storage power station si The capacity status, express t-1 period energy storage power station si The capacity status, Energy storage power station si The charging efficiency, Energy storage power station si The discharge efficiency, Energy storage power station si Capacity status at the end of the operating day, Indicates the energy storage power station in the last period of the declared operation day si The expected value of capacity state, Energy storage power station si Capacity status at the beginning of the operating day, Indicates the energy storage power station at the initial period of the previous operating day si The capacity status, Energy storage power station si The maximum capacity.

[0166] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the energy storage ramp constraint includes:

[0167]

[0168] in, Energy storage power station si The minimum climbing rate, Energy storage power station si The maximum climbing rate, Energy storage power station si The minimum downhill climbing rate, Energy storage power station si The maximum downhill climbing rate, Energy storage power station si exist t The ramp rate of the time period, Energy storage power station si exist t -1 The ramp rate during the period.

[0169] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the last period power constraint includes:

[0170]

[0171] in, express t Time storage charge state, express tThe remaining stored energy during the period of storage, Indicates the rated capacity of energy storage, Indicates the minimum state of charge, Indicates the maximum state of charge.

[0172] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the constraints corresponding to the adjustable loads in the multi-type subjects include total power constraints, load upper and lower limit constraints, adjustable load climbing constraints and total adjustment amount constraints.

[0173] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the total power constraint includes: ; in, For transferable load ld exist t The load of the period, For transferable load ld exist t The baseline load for the period, For transferable load ld exist t The amount of load regulation during the period.

[0174] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the load upper and lower limit constraints include: ; in, For transferable load ld exist t The minimum load of the time period, For transferable load ld exist t The maximum load during a period.

[0175] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the adjustable load climbing constraint includes: ; in, For transferable load ld exist t The maximum load active power that can be reduced during the period, For transferable load ld exist t The maximum load active power that can be increased during the period is: For transferable load ld exist t -1 period of load.

[0176] In some embodiments of the new energy consumption optimization system of the multi-type subject power system of the present application, the total adjustment amount constraint includes: . It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The modules described as separate components may or may not be physically separated, and the components displayed as modules may be one physical unit or multiple physical units, that is, they may be located in one place, or they may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0177] In addition, each module in each embodiment of the present invention may be integrated into a processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0178] The application embodiment also provides an electronic device, which may include one or more processors, a memory, and a communication interface.

[0179] The memory, the communication interface and the processor are coupled, for example, the memory, the communication interface and the processor may be coupled together via a bus.

[0180] The communication interface is used for data transmission with other devices. The memory stores computer program code. The computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned new energy consumption optimization method for multi-type main power systems.

[0181] Wherein, the processor can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the present disclosure. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and the like. The processor can be used to support electronic devices to execute the method steps provided in the above embodiments.

[0182] The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The above bus may be divided into an address bus, a data bus, a control bus, etc.

[0183] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the consumption of new energy by multi-type main power systems are implemented.

[0184] The computer-readable storage medium involved in the present application includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field.

[0185] The present application also proposes a computer program product, which includes instructions, and when the instructions are executed by a processor, the steps in the above-mentioned embodiment of the method for optimizing the consumption of new energy sources in a multi-type power system are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned system embodiment are implemented.

[0186] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in a memory and executed by the processor to accomplish the present invention.

[0187] The carrier for implementing the above-mentioned computer program product may be a computer device, or the computer program product may be stored in a computer storage medium.

[0188] The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory.

[0189] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0190] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0191] If the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0192] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A new energy consumption optimization method for a multi-type main power system, characterized in that: include: Constructing an optimal clearing model based on the participation of multiple types of entities corresponding to the power system; the optimization goal of the optimal clearing model is to maximize the consumption of new energy; According to the meteorological data corresponding to the power system and the historical new energy power generation curve, a 96-point prediction curve for new energy is obtained; According to the historical demand-side flexible resource load curve corresponding to the power system, a 96-point baseline load curve is obtained; The optimization clearing model is solved by combining the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes the output of multiple types of entities; The optimization strategy is adopted to optimize the power system.

2. The new energy consumption optimization method for multi-type main power system according to claim 1 is characterized in that: The multiple types of entities include thermal power, hydropower, new energy, energy storage and adjustable loads.

3. The new energy consumption optimization method for multi-type main power system according to claim 1 is characterized in that: The optimized clearing model includes: in, represents the amount of new energy consumption, express Time period, represents the total market clearing time, express The actual grid-connected power of wind turbines in renewable energy during the period, express The predicted grid-connected power of wind turbines in renewable energy sources when the auxiliary service market is not open during the period. express The actual grid-connected power of photovoltaic units in the renewable energy sector during this period, express The predicted grid-connected power of photovoltaic units in renewable energy when the ancillary service market is not opened during the period.

4. The new energy consumption optimization method for multi-type main power system according to claim 1 is characterized in that: The constraints of the optimization clearing model include supply and demand balance constraints, power purchase constraints, power sale constraints, line flow constraints, section flow constraints and grid operation boundary condition constraints.

5. The new energy consumption optimization method for multi-type main power system according to claim 4 is characterized in that: The supply and demand balance constraints include: in, Indicates the number of conventional loads in the power system, express The first A normal load, Indicates the number of generator sets in the power system, express The first The output of each generator set, represents the total number of tie lines in the power system, Indicates contact line exist The planned power for the time period.

6. The new energy consumption optimization method for multi-type main power system according to claim 4 is characterized in that: The power purchase constraints include: in, Indicates the buyer i exist Time period n The winning bid power in the output range, express Time Buyer i No. n Each output range declares electricity, Indicates the buyer i The total number of quotation segments.

7. The new energy consumption optimization method for a multi-type main power system according to claim 4 is characterized in that: The electricity selling constraints include: in, express Time Seller No. m Section declaration of electricity, Indicates the number of stages of electricity reported. Indicates the seller The total number of segments in the quote.

8. The new energy consumption optimization method for multi-type main power system according to claim 4 is characterized in that: The line power flow constraints include: in, Indicates the line in the power system l The power transmission limit of Indicates the unit n Node to line l The generator output power transfer distribution factor, express Time unit n of effort, Indicates contact line tj Node to line l The generator output power transfer distribution factor, express Contact time Line tj of effort, Representation Node k To line l The generator output power transfer distribution factor, Representation Node k In the period The bus load value, , Respectively indicate lines l The forward and reverse power flow relaxation variables are N Indicates the total number of units. K Indicates the total number of nodes.

9. The new energy consumption optimization method for multi-type main power system according to claim 4 is characterized in that: The section flow constraints include: in, Indicates the power system section s The lower limit of power transmission, Indicates the unit n Node section s The generator output power transfer distribution factor, Indicates contact line tj Node section s The generator output power transfer distribution factor, Representation Node k Cross-section s The generator output power transfer distribution factor, Representation of cross section s The upper limit of power transmission, , Respectively represent cross sections s The forward and reverse power flow relaxation variables.

10. The new energy consumption optimization method for multi-type main power system according to claim 4 is characterized in that: The grid operation boundary condition constraints include: in, Indicates the unit n In the period t The output meets the various boundary conditions of the day-ahead market. Indicates the unit n In the period t A collection of various boundary conditions.

11. The new energy consumption optimization method for multi-type main power system according to claim 2 is characterized in that: The constraints corresponding to thermal power in multiple types of entities include upper and lower limit constraints on the real-time power generation of the unit, climbing or sliding constraints on the unit, minimum start-up time and minimum shutdown time constraints on the unit, fixed output constraints on the unit, system power generation upward reserve constraints and system power generation downward reserve constraints.

12. The new energy consumption optimization method for multi-type main power system according to claim 11 is characterized in that: The upper and lower limits of the real-time power generation of the unit include: in, Indicates the unit n In the period t The lower limit of output, Indicates the unit n In the period t The output limit of Indicates the unit n of effort.

13. The new energy consumption optimization method for multi-type main power system according to claim 11 is characterized in that: The unit climbing or sliding constraints include: in, Indicates the unit n In the period t of effort, Indicates the unit n In the period t -1 output, Indicates the unit n The climbing or sliding rate, Indicates the unit n In the period t The start-stop status, Indicates the unit n In the period t -1 start / stop status.

14. The new energy consumption optimization method for multi-type main power system according to claim 11 is characterized in that: The minimum startup time and minimum shutdown time constraints of the unit include: in, Indicates the unit n In the period The start and stop status, Indicates the unit n Minimum continuous power-on time, Indicates the unit n Minimum continuous downtime.

15. The new energy consumption optimization method for multi-type main power system according to claim 11 is characterized in that: The fixed output constraint of the unit includes: in, Indicates the unit n In the period t The active power, Indicates the unit n In the period t Output setting value.

16. The new energy consumption optimization method for multi-type main power system according to claim 11 is characterized in that: The system power generation upward reserve constraint includes: 。 17. The new energy consumption optimization method for multi-type main power system according to claim 11 is characterized in that: The system power generation down-regulation reserve constraint includes: in, Indicates time period t Raise reserve requirements, Indicates time period t Lower reserve requirements.

18. The new energy consumption optimization method for multi-type main power system according to claim 2 is characterized in that: The constraints corresponding to the hydropower in the multi-type entities include reservoir water balance constraints, water topology constraints, reservoir capacity and water level constraints, and water level and water consumption rate constraints.

19. The new energy consumption optimization method for multi-type main power system according to claim 18 is characterized in that: The reservoir water balance constraints include: in, Reservoir t The amount of water stored during the period, Reservoir t The predicted water inflow volume for each time period, Reservoir t The amount of water used for power generation during the period, Reservoir t The amount of water discharged during the period, Reservoir t -1 water storage capacity in a period.

20. The new energy consumption optimization method for multi-type main power system according to claim 18 is characterized in that: The water topology constraints include: in, Reservoir t The amount of water discharged from the reservoir during the period.

21. The new energy consumption optimization method for multi-type main power system according to claim 18 is characterized in that: The reservoir capacity and water level constraints include: in, Reservoir t The water level during the period, Reservoir t The amount of water stored during the period, Indicates the approved reservoir area.

22. The new energy consumption optimization method for multi-type main power system according to claim 18 is characterized in that: The water level consumption rate constraint includes: in, Reservoir t The power generation during the period, Reservoir t Water level consumption rate during a period of time.

23. The new energy consumption optimization method for multi-type main power system according to claim 2 is characterized in that: The constraints corresponding to the new energy sources in the multi-type entities include: Output constraints of new energy units: in, Indicates n New energy units in t Time output, Indicates n New energy units in t Expected forecast output for the time period.

24. The new energy consumption optimization method for multi-type main power system according to claim 2 is characterized in that: The constraints corresponding to the energy storage in the multi-type subjects include charging and discharging power constraints, electric energy capacity constraints, power-electric energy conversion loss constraints, energy storage ramp constraints, standby constraints and final period electric energy constraints.

25. The new energy consumption optimization method for multi-type main power system according to claim 24 is characterized in that: The charge and discharge power constraints include: in, Indicates energy storage si exist t The charging power of the time period, Indicates energy storage si The maximum charging power, Indicates energy storage si exist t The charging status of the time period, Indicates energy storage si exist t The discharge power of the time period, Indicates energy storage si The maximum discharge power, Indicates energy storage si exist t The discharge state of the time period is a value between 0 and 1.

26. The new energy consumption optimization method for multi-type main power system according to claim 24 is characterized in that: The electric energy capacity constraint includes: in, express t Time storage power station si The capacity status, express t -1 period energy storage power station si The capacity status, Energy storage power station si The charging efficiency, Energy storage power station si The discharge efficiency, Energy storage power station si Capacity status at the end of the operating day, Indicates the energy storage power station in the last period of the declared operation day si The expected value of capacity state, Energy storage power station si Capacity status at the beginning of the operating day, Indicates the energy storage power station at the initial period of the previous operating day si The capacity status, Energy storage power station si The maximum capacity.

27. The new energy consumption optimization method for multi-type main power system according to claim 24 is characterized in that: The energy storage ramp constraint includes: in, Energy storage power station si The minimum climbing rate, Energy storage power station si The maximum climbing rate, Energy storage power station si The minimum downhill climbing rate, Energy storage power station si The maximum downhill climbing rate, Energy storage power station si exist t The ramp rate of the time period, Energy storage power station si exist t -1 The ramp rate during the period.

28. The new energy consumption optimization method for multi-type main power system according to claim 24 is characterized in that: The power constraints of the last period include: in, express t Time storage charge state, express t The remaining stored energy during the period of storage, Indicates the rated capacity of energy storage, Indicates the minimum state of charge, Indicates the maximum state of charge.

29. The new energy consumption optimization method for multi-type main power system according to claim 2 is characterized in that: The constraints corresponding to the adjustable loads in the multi-type subjects include total power constraints, load upper and lower limit constraints, adjustable load climbing constraints and total adjustment amount constraints.

30. The new energy consumption optimization method for multi-type main power system according to claim 29 is characterized in that: The total power constraint includes: ； in, For transferable load ld exist t The load of the period, For transferable load ld exist t The baseline load for the period, For transferable load ld exist t The amount of load regulation during the period.

31. The new energy consumption optimization method for multi-type main power system according to claim 29 is characterized in that: The load upper and lower limit constraints include: ； in, For transferable load ld exist t The minimum load of the time period, For transferable load ld exist t The maximum load during a period.

32. The new energy consumption optimization method for multi-type main power system according to claim 29 is characterized in that: The adjustable load climbing constraint includes: ； in, For transferable load ld exist t The maximum load active power that can be reduced during the period, For transferable load ld exist t The maximum load active power that can be increased during the period is: For transferable load ld exist t -1 period of load.

33. The new energy consumption optimization method for multi-type main power system according to claim 29 is characterized in that: The total adjustment amount constraint includes: 。 34. A new energy consumption optimization system for multi-type main power systems, characterized in that: include: A model module is used to construct an optimization clearing model corresponding to the power system based on the participation of multiple types of subjects; the optimization goal of the optimization clearing model is to maximize the consumption of new energy; The first curve module is used to obtain a 96-point prediction curve of new energy based on the meteorological data corresponding to the power system and the historical new energy power generation curve; The second curve module is used to obtain a 96-point baseline load curve based on the historical demand-side flexible resource load curve corresponding to the power system; A solution module, used to solve the optimization clearing model by combining the 96-point prediction curve of new energy and the 96-point baseline load curve to obtain an optimization strategy; the optimization strategy includes the output of multiple types of entities; The optimization module is used to optimize the power system by adopting the optimization strategy.

35. The new energy consumption optimization system for multi-type main power systems according to claim 34 is characterized in that: The multiple types of entities include thermal power, hydropower, new energy, energy storage and adjustable loads.

36. The new energy consumption optimization system for multi-type main power systems according to claim 34 is characterized in that: The optimized clearing model includes: in, represents the amount of new energy consumption, express Time period, represents the total market clearing time, express The actual grid-connected power of wind turbines in renewable energy during the period, express The predicted grid-connected power of wind turbines in renewable energy sources when the auxiliary service market is not open during the period. express The actual grid-connected power of photovoltaic units in the renewable energy sector during this period, express The predicted grid-connected power of photovoltaic units in renewable energy when the ancillary service market is not opened during the period.

37. The new energy consumption optimization system for multi-type main power systems according to claim 34 is characterized in that: The constraints of the optimization clearing model include supply and demand balance constraints, power purchase constraints, power sale constraints, line flow constraints, section flow constraints and grid operation boundary condition constraints.

38. The new energy consumption optimization system for multi-type main power systems according to claim 34 is characterized in that: The constraints corresponding to thermal power in multiple types of entities include upper and lower limit constraints on the real-time power generation of the unit, climbing or sliding constraints on the unit, minimum start-up time and minimum shutdown time constraints on the unit, fixed output constraints on the unit, system power generation upward reserve constraints and system power generation downward reserve constraints.

39. The new energy consumption optimization system for multi-type main power systems according to claim 34 is characterized in that: The constraints corresponding to the hydropower in the multi-type entities include reservoir water balance constraints, water topology constraints, reservoir capacity and water level constraints, and water level and water consumption rate constraints.

40. The new energy consumption optimization system for multi-type main power systems according to claim 34 is characterized in that: The constraints corresponding to the energy storage in the multi-type subjects include charging and discharging power constraints, electric energy capacity constraints, power-electric energy conversion loss constraints, energy storage ramp constraints, standby constraints and final period electric energy constraints.

41. The new energy consumption optimization system for multi-type main power systems according to claim 34, characterized in that: The constraints corresponding to the adjustable loads in the multi-type subjects include total power constraints, load upper and lower limit constraints, adjustable load climbing constraints and total adjustment amount constraints.

42. An electronic device, characterized in that: include: A memory and one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the new energy consumption optimization method for a multi-type main power system as described in any one of claims 1 to 33.

43. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the new energy consumption optimization method for a multi-type power system as described in any one of claims 1 to 33.

44. A computer program product, characterized in that: The computer program product includes instructions, which, when executed by a processor, implement the method for optimizing the consumption of new energy in the power system according to any one of claims 1 to 33.

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