Joint scheduling method and device for new energy station and energy storage system, terminal equipment and storage medium

Abstract

The invention discloses a joint scheduling method and device for a new energy station and an energy storage system, terminal equipment and a storage medium, and belongs to the technical field of power grid scheduling. When the joint scheduling model is constructed, not only are new energy maintenance cost, power grid electricity purchasing cost, energy storage loss cost and new energy equipment aging attenuation cost considered, but also carbon emission directly generated by power grid electricity purchasing, carbon emission indirectly generated when new energy equipment is manufactured or recycled and new energy equipment aging attenuation data are considered. The method can quantify the carbon emission and set the minimization target of the carbon emission, achieves the effective control of the carbon emission, can reduce the speed of the service life attenuation of the equipment through considering the aging attenuation of the new energy equipment, and achieves the multi-target balance. Through the implementation of the invention, the problem that multi-target balance is difficult to realize in economy, environmental protection and equipment service life in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of joint dispatching between new energy and energy storage, and particularly to a joint dispatching method, device, terminal device and storage medium for a new energy power station and an energy storage system. Background Technique

[0002] With the global energy structure transforming towards low-carbon, the coordinated operation among photovoltaic power, wind power and energy storage systems can improve the utilization rate of renewable energy and reduce the carbon emissions of the power system. Therefore, joint dispatching between new energy and energy storage can coordinate new energy power generation, energy storage and grid interaction, and give play to the collaborative advantages of multiple parties.

[0003] However, the current optimal dispatching strategies for new energy power generation, energy storage and grid interaction mainly focus on the economic optimization of new energy power stations and energy storage systems, lacking systematic consideration of low-carbon goals. During the coordinated operation process, the impact of carbon emissions cannot be effectively quantified. Traditional joint dispatching methods usually do not consider the impact of carbon emissions, which may increase the environmental burden while pursuing economic benefits. Moreover, frequent charging and discharging to reduce costs may also shorten the lifespan of new energy equipment. Therefore, the existing technology has the problem that it is difficult to obtain an optimal dispatching plan based on achieving multi-objective balance among economy, environmental protection and equipment lifespan, resulting in damaged economic benefits, low energy utilization rate, and inability to effectively control the impact of carbon emissions on the environment. Summary of the Invention

[0004] Embodiments of the present invention provide a joint dispatching method, device, terminal device and storage medium for a new energy power station and an energy storage system, which can simultaneously consider the three goals of minimizing the total cost, minimizing carbon emissions and minimizing the life decay rate, achieving multi-objective balance among economy, environmental protection and equipment lifespan, not only improving economic benefits and energy utilization rate, but also effectively controlling the impact of carbon emissions on the environment, and being able to solve the problems existing in the prior art that it is difficult to achieve multi-objective balance among economy, environmental protection and equipment lifespan and unable to effectively control the impact of carbon emissions on the environment.

[0005] An embodiment of the present invention provides a joint dispatching method for a new energy power station and an energy storage system, including:

[0006] Construct a joint dispatching model with the goals of minimizing the total cost, minimizing carbon emissions and minimizing the life decay rate according to new energy device operation data, energy storage system operation data, new energy maintenance cost data, grid power purchase cost data, energy storage loss cost data, new energy device aging decay data and carbon emission data; wherein, the carbon emission data includes: power purchase carbon emission data used to represent the carbon emissions directly generated by grid power purchase and new energy carbon emission data used to represent the carbon emissions indirectly generated during the manufacture or recycling of new energy devices.

[0007] Under the power balance constraint, state of charge constraint, wind farm output constraint, photovoltaic power station output constraint, grid power purchase constraint, and battery life constraint corresponding to the joint scheduling model, solve the joint scheduling model to generate the first output scheduling value of the wind farm, the second output scheduling value of the photovoltaic power station, and the charge and discharge power scheduling value of the energy storage station;

[0008] According to the first output scheduling value, the second output scheduling value, and the charge and discharge power scheduling value, perform joint scheduling on the wind farm, the photovoltaic power station, and the energy storage station respectively.

[0009] Preferably, the new energy carbon emission data includes: the manufacturing carbon emission data corresponding to the new energy, the production carbon emission data corresponding to the energy storage, and the recycling carbon emission data corresponding to the energy storage; the new energy equipment aging and attenuation data includes: the aging data of the new energy equipment and the life attenuation data of the battery;

[0010] The construction of a joint scheduling model with the objectives of minimizing the total cost, minimizing the carbon emissions, and minimizing the life attenuation rate according to the new energy equipment operation data, energy storage system operation data, new energy maintenance cost data, grid power purchase cost data, energy storage loss cost data, new energy equipment aging and attenuation data, and carbon emission data includes:

[0011] According to the new energy equipment operation data, energy storage system operation data, new energy maintenance cost data, grid power purchase cost data, and energy storage loss cost data, construct a low - economic operation model with the objective of minimizing the total cost;

[0012] According to the power purchase carbon emission data used to characterize the carbon emissions directly generated by grid power purchase, the manufacturing carbon emission data corresponding to the new energy, the production carbon emission data corresponding to the energy storage, and the recycling carbon emission data corresponding to the energy storage, construct a low - carbon operation model with the objective of minimizing the carbon emissions;

[0013] According to the aging data of the new energy equipment and the life attenuation data of the battery, construct a high - life operation model with the objective of minimizing the life attenuation rate;

[0014] According to the low - economic operation model, the low - carbon operation model, and the high - life operation model, construct a joint scheduling model with the objectives of minimizing the total cost, minimizing the carbon emissions, and minimizing the life attenuation rate.

[0015] Preferably, the carbon emissions data for electricity purchase includes: the power purchase data when the grid purchases electricity and the carbon intensity of the grid; the carbon emissions data for manufacturing corresponding to the new energy includes: the carbon emissions for silicon material purification, the carbon emissions for manufacturing solar cells, the carbon emissions for manufacturing photovoltaic brackets, the carbon emissions for manufacturing inverters, the transferred carbon emissions, the carbon emissions for manufacturing the wind turbine tower barrels, and the carbon emissions for wind turbine blades; wherein, the transferred carbon emissions are used to represent the carbon emissions when the carbon emission operations are transferred to other regions through industrial chain division of labor.

[0016] Constructing a low-carbon operation model with the goal of minimizing carbon emissions based on the carbon emissions data for electricity purchase used to represent the carbon emissions directly generated by the grid's electricity purchase, the carbon emissions data for manufacturing corresponding to the new energy, the carbon emissions data for production corresponding to energy storage, and the carbon emissions data for recycling corresponding to energy storage, includes:

[0017] Determine the electricity purchase volume of the grid based on the power purchase data when the grid purchases electricity, and then generate a first carbon emission model for calculating the contribution degree value of the grid's power purchase carbon intensity according to the electricity purchase volume of the grid and the carbon intensity of the grid;

[0018] Generate a second carbon emission model for calculating the contribution degree value of the carbon emissions throughout the life cycle of a photovoltaic power station according to the carbon emissions for silicon material purification, the carbon emissions for manufacturing solar cells, the carbon emissions for manufacturing photovoltaic brackets, the carbon emissions for manufacturing inverters, and the transferred carbon emissions;

[0019] Generate a third carbon emission model for calculating the contribution degree value of the carbon emissions throughout the life cycle of a wind farm according to the carbon emissions for manufacturing the wind turbine tower barrels, the carbon emissions for wind turbine blades, and the transferred carbon emissions;

[0020] Generate a fourth carbon emission model for calculating the contribution degree value of the carbon emissions of energy storage operation according to the carbon emissions data for production corresponding to energy storage and the carbon emissions data for recycling corresponding to energy storage;

[0021] Construct a low-carbon operation model with the goal of minimizing carbon emissions according to the first carbon emission model, the second carbon emission model, the third carbon emission model, and the fourth carbon emission model.

[0022] Preferably, the new energy device operation data includes: the output data of a photovoltaic power station, the power generation performance parameters of a photovoltaic power station, the output data of a wind farm, and the power generation performance parameters of a wind farm; the energy storage system operation data includes: the charging power of the energy storage system, the discharging power of the energy storage system, the preset charging efficiency, the preset discharging efficiency, the preset battery rated capacity, the battery discharge depth, and the number of battery cycle life times.

[0023] The joint dispatching method for the new energy power station and the energy storage system further includes: constructing a power balance constraint in the following manner:

[0024] Construct a photovoltaic power station output model based on the output data of the photovoltaic power station and the power generation performance parameters of the photovoltaic power station;

[0025] Construct a wind farm output model based on the output data of the wind farm and the power generation performance parameters of the wind farm;

[0026] Construct a state of charge model for the energy storage system based on the charging power of the energy storage system, the discharging power of the energy storage system, a preset charging efficiency, a preset discharging efficiency, and a preset battery rated capacity;

[0027] Construct a battery attenuation model for calculating the battery life attenuation value based on the linear relationship between the depth of discharge of the battery and the number of battery cycle life times;

[0028] Construct a power balance constraint based on the photovoltaic power station output model, the wind farm output model, the state of charge model for the energy storage system, the battery attenuation model, the purchased electricity of the power grid, and a preset power grid load power.

[0029] Preferably, the combined dispatching model includes:

[0030] minF = w1minF cost + w2minF carbon + w3minF aging ;

[0031]

[0032] C indirect (t) = C PV-life (t) + C WT-life (t) + C bat-life ;

[0033] Among them, minF is the function value corresponding to the combined dispatching model, and minF cost represents a low-cost operation model with the goal of minimizing the total cost; minF carbon represents a low-carbon operation model with the goal of minimizing carbon emissions; minF aging represents a high-life operation model with the goal of minimizing the life attenuation rate; w1, w2, and w3 are the weight coefficients of the low-cost operation model, the low-carbon operation model, and the high-life operation model respectively; c grid (t) represents the contribution degree value of the purchased electricity carbon intensity of the first carbon emission model for calculating the contribution degree value of the purchased electricity carbon intensity of the power grid at time t, P grid (t) represents the purchased electricity of the power grid at time t, c bat is the time-of-use electricity price of the power grid, L bat (t) is the battery life attenuation value of the battery attenuation model for calculating the battery life attenuation value at time t, cRE The unit aging maintenance cost for new energy, d aging The annual aging attenuation rate of the PV power station among the power generation performance parameters of the PV power station, C total (t) is the total carbon emissions at time t, and the total carbon emissions can be calculated according to the whole life cycle mode or the operation stage mode; The maximum aging attenuation rate of new energy equipment, The maximum life of the battery at the initial time, C indirect (t) is the maximum life attenuation value of the battery at time t, C PV-life (t) is the value of the carbon emission contribution degree of the whole life cycle of the PV power station at time t in the second carbon emission model for calculating the carbon emission contribution degree value of the whole life cycle of the PV power station, C WT-life (t) is the value of the carbon emission contribution degree of the whole life cycle of the wind farm station at time t in the third carbon emission model for calculating the carbon emission contribution degree value of the whole life cycle of the wind farm station, C bat-life (t) is the value of the carbon emission contribution degree of energy storage operation at time t in the fourth carbon emission model for calculating the carbon emission contribution degree value of energy storage operation.

[0034] Preferably, solving the joint dispatch model to generate the first output dispatch value of the wind farm station, the second output dispatch value of the PV power station, and the charge-discharge power dispatch value of the energy storage power station includes:

[0035] Randomly generate a number of particles in the population; wherein, each particle is used to represent the first output dispatch value of the wind farm station, the first output dispatch value of the PV power station, and the charge-discharge power dispatch value of the energy storage power station;

[0036] Initialize each particle of the population to obtain the initial position of each particle, the initial velocity of each particle, and the initial population position;

[0037] Repeat the following target particle determination operation until the current iteration number is the same as the preset iteration number, calculate the function value corresponding to each particle to be processed in the joint dispatch model, and take the particle to be processed with the highest function value among the particles to be processed as the target particle and output the target particle, so that the output target particles all satisfy the power balance constraint, the state of charge constraint, the wind farm station output constraint, the PV power station output constraint, the grid power purchase constraint, and the battery life constraint:

[0038] When the current iteration number is less than the preset iteration number, for each particle, an updated particle velocity corresponding to the particle is generated according to the current position of the particle, the current velocity of the particle, and the current population position; wherein, initially, the initial position of each particle, the initial velocity of each particle, and the initial population position are respectively used as the current position of each particle, the current velocity of each particle, and the current population position;

[0039] For each particle, an updated particle position corresponding to the particle is determined according to the current position of the particle, the updated particle velocity corresponding to the particle, and the current population position;

[0040] An updated population position is obtained according to the updated particle position corresponding to each particle, the updated particle velocity corresponding to each particle, and the function value corresponding to each particle in the joint scheduling model;

[0041] Output a plurality of particles to be processed corresponding to the updated population position;

[0042] The updated iteration number is obtained by adding the preset iteration number increment to the current iteration number value, and the updated iteration number is used as the iteration number for the next execution of the target particle determination operation;

[0043] The updated particle position corresponding to each particle is used as the current position of the particle for the next execution of the target particle determination operation, the updated particle velocity corresponding to each particle is used as the current velocity of the particle for the next execution of the target particle determination operation, and the updated population position is used as the current population position for the next execution of the target particle determination operation.

[0044] Preferably, after constructing a joint scheduling model with the objectives of minimizing the total cost, minimizing the carbon emissions, and minimizing the life attenuation rate, before solving the joint scheduling model, it further includes:

[0045] Input the preset time period, new energy equipment operation data, energy storage system operation data, and carbon emission data into a preset carbon intensity preset model, so that the carbon intensity preset model outputs the target grid carbon intensity after the preset time period;

[0046] Adjust the weight coefficients of the low - economic operation model, the low - carbon operation model, and the high - life operation model in the joint scheduling model respectively according to the target grid carbon intensity, and adjust the power purchase upper limit in the power grid power purchase power constraint and the life threshold in the battery life constraint respectively according to the target grid carbon intensity to generate an updated joint scheduling model;

[0047] The updated joint scheduling model is used as the joint scheduling model to be solved when repeatedly executing the target particle determination operation.

[0048] Based on the above method embodiments, the present invention correspondingly provides apparatus embodiments.

[0049] An embodiment of the present invention provides a joint scheduling apparatus for a new energy power station and an energy storage system, including: a joint scheduling model construction module, a model solving module, and a joint scheduling module;

[0050] The joint scheduling model construction module is configured to construct a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate according to the new energy device operation data, energy storage system operation data, new energy maintenance cost data, grid power purchase cost data, energy storage loss cost data, new energy device aging attenuation data, and carbon emission data; wherein, the carbon emission data includes: power purchase carbon emission data for characterizing the carbon emissions directly generated by grid power purchase and new energy carbon emission data for characterizing the carbon emissions indirectly generated during the manufacturing or recycling of new energy devices;

[0051] The model solving module is configured to solve the joint scheduling model under the power balance constraint, state of charge constraint, wind farm output constraint, photovoltaic power station output constraint, grid power purchase power constraint, and battery life constraint corresponding to the joint scheduling model, and generate a first output scheduling value for the wind farm, a second output scheduling value for the photovoltaic power station, and a charge and discharge power scheduling value for the energy storage power station;

[0052] The joint scheduling module is configured to perform joint scheduling on the wind farm, the photovoltaic power station, and the energy storage power station respectively according to the first output scheduling value, the second output scheduling value, and the charge and discharge power scheduling value.

[0053] Based on the above method embodiments, the present invention correspondingly provides terminal device embodiments.

[0054] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the joint scheduling method for a new energy power station and an energy storage system described in the above embodiment of the present invention.

[0055] Based on the above method embodiments, the present invention correspondingly provides storage medium embodiments.

[0056] Another embodiment of the present invention provides a storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the joint scheduling method for a new energy power station and an energy storage system described in the above embodiment of the present invention.

[0057] By implementing the present invention, the following beneficial effects are achieved:

[0058] An embodiment of the present invention provides a joint dispatching method, device, terminal device and storage medium for a new energy power station and an energy storage system. When constructing the joint dispatching model, the present invention not only considers the cost factors corresponding to the new energy maintenance cost, the grid power purchase cost, the energy storage loss cost, and the new energy equipment aging attenuation cost, but also considers the new energy equipment aging attenuation data and the carbon emission data. Then, the present invention can construct a joint dispatching model that can achieve multi-objective balance and generate an optimal dispatching strategy by combining the three factors of the total cost, carbon emissions, and life attenuation rate. Specifically, when introducing cost data into the joint dispatching model, the present invention also incorporates carbon emission data, so that the carbon emissions directly generated by grid power purchase and the carbon emissions indirectly generated during the manufacture or recycling of new energy equipment can be considered to quantify carbon emissions and set the minimization goal of carbon emissions, thereby realizing the effective control of carbon emissions. In addition, the present invention also introduces new energy equipment aging attenuation data to set the minimization goal of the life attenuation rate, so that the stable operation of the equipment can be ensured by reducing the speed of equipment life attenuation, thereby improving the operation efficiency of each energy equipment and thus improving the energy utilization rate. Compared with the prior art, the present invention can simultaneously consider the three goals of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate, achieving multi-objective balance in terms of economy, environmental protection, and equipment life, not only improving economic benefits and energy utilization rate, but also effectively controlling the impact of carbon emissions on the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic flow chart of a joint dispatching method for a new energy power station and an energy storage system provided by an embodiment of the present invention.

[0060] Figure 2 is a schematic structural diagram of a joint dispatching device for a new energy power station and an energy storage system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] As Figure 1 shown, in order to solve the problems existing in the prior art that it is difficult to achieve multi-objective balance in terms of economy, environmental protection, and equipment life, and it is impossible to effectively control the impact of carbon emissions on the environment, an embodiment of the present invention provides a joint dispatching method for a new energy power station and an energy storage system, including:

[0063] Step S1: Construct a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate based on the new energy equipment operation data, energy storage system operation data, new energy maintenance cost data, grid power purchase cost data, energy storage loss cost data, new energy equipment aging attenuation data, and carbon emission data; wherein, the carbon emission data includes: power purchase carbon emission data used to represent the carbon emissions directly generated by grid power purchase and new energy carbon emission data used to represent the carbon emissions indirectly generated during the manufacturing or recycling of new energy equipment.

[0064] Step S2: Solve the joint scheduling model under the power balance constraint, state of charge constraint, wind farm output constraint, photovoltaic power station output constraint, grid power purchase power constraint, and battery life constraint corresponding to the joint scheduling model to generate the first output scheduling value of the wind farm, the second output scheduling value of the photovoltaic power station, and the charge and discharge power scheduling value of the energy storage power station.

[0065] Step S3: Perform joint scheduling on the wind farm, photovoltaic power station, and energy storage power station respectively according to the first output scheduling value, the second output scheduling value, and the charge and discharge power scheduling value.

[0066] For step S1, in a preferred embodiment, when constructing the final joint scheduling model, the present invention can first construct three sub-models, namely: a low-economic operation model with the objective of minimizing the total cost, a low-carbon operation model with the objective of minimizing carbon emissions, and a high-life operation model with the objective of minimizing the life attenuation rate.

[0067] Schematically, the new energy carbon emission data collected by the present invention includes the manufacturing carbon emission data corresponding to new energy, the production carbon emission data corresponding to energy storage, and the recycling carbon emission data corresponding to energy storage;

[0068] The new energy equipment aging attenuation data includes: the aging data of new energy equipment and the life attenuation data of the battery.

[0069] Then, when constructing a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate, it specifically includes:

[0070] Construct a low-economic operation model with the objective of minimizing the total cost according to the new energy equipment operation data, energy storage system operation data, new energy maintenance cost data, grid power purchase cost data, and energy storage loss cost data;

[0071] Construct a low-carbon operation model with the goal of minimizing carbon emissions based on the carbon emissions data for power purchase used to characterize the carbon emissions directly generated by the power grid's power purchase, the manufacturing carbon emissions corresponding to new energy, the production carbon emissions corresponding to energy storage, and the recycling carbon emissions corresponding to energy storage;

[0072] Construct a high-life operation model with the goal of minimizing the life attenuation rate based on the aging data of new energy equipment and the life decay data of batteries;

[0073] Construct a joint scheduling model with the goals of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate based on the low-economic operation model, the low-carbon operation model, and the high-life operation model.

[0074] It can be understood that the present invention can decompose the complex joint scheduling problem into three relatively independent sub-models, enabling each sub-model to focus on solving specific problems (such as cost, carbon emissions, life attenuation), thereby improving the modularity and clarity of the model, and can achieve the finally constructed joint scheduling model, which can balance the conflicting requirements of low carbon, economy, and equipment life.

[0075] In a preferred embodiment, the carbon emissions data for power purchase collected by the present invention includes: the power purchase power data when the power grid purchases electricity and the carbon intensity of the power grid;

[0076] The manufacturing carbon emissions corresponding to new energy include: the carbon emissions from silicon material purification, the manufacturing carbon emissions of battery chips, the carbon emissions of photovoltaic brackets, the manufacturing carbon emissions of inverters, the transferred carbon emissions, the manufacturing carbon emissions of wind turbine towers, and the carbon emissions of wind turbine blades; wherein, the transferred carbon emissions are used to characterize the carbon emissions when the carbon emission operation is transferred to other regions through industrial chain division of labor;

[0077] Furthermore, when constructing the low-carbon operation model in the sub-model, the present invention can also construct multiple carbon emission sub-models respectively, then there are:

[0078] The construction of a low-carbon operation model with the goal of minimizing carbon emissions based on the carbon emissions data for power purchase used to characterize the carbon emissions directly generated by the power grid's power purchase, the manufacturing carbon emissions corresponding to new energy, the production carbon emissions corresponding to energy storage, and the recycling carbon emissions corresponding to energy storage includes:

[0079] Determine the electricity purchased by the power grid based on the power purchase power data when the power grid purchases electricity, and then generate a first carbon emission model for calculating the contribution degree value of the power purchase carbon intensity of the power grid according to the electricity purchased by the power grid and the carbon intensity of the power grid;

[0080] Generate a second carbon emission model for calculating the carbon emission contribution degree value of the entire life cycle of a photovoltaic power station based on the carbon emissions from silicon material purification, the carbon emissions from the manufacturing of solar cells, the carbon emissions from photovoltaic brackets, the carbon emissions from the manufacturing of inverters, and the transferred carbon emissions;

[0081] Generate a third carbon emission model for calculating the carbon emission contribution degree value of the entire life cycle carbon emissions of a wind power station based on the carbon emissions from the manufacturing of the wind turbine tower, the carbon emissions from the wind turbine blade, and the transferred carbon emissions;

[0082] Generate a fourth carbon emission model for calculating the carbon emission contribution degree value of the energy storage operation based on the production carbon emission data corresponding to the energy storage and the recycling carbon emission data corresponding to the energy storage;

[0083] Construct a low-carbon operation model with the goal of minimizing carbon emissions based on the first carbon emission model, the second carbon emission model, the third carbon emission model, and the fourth carbon emission model.

[0084] Schematically, the present invention can calculate the direct carbon emissions and indirect carbon emissions in the coordinated operation and coordinated scheduling of the new energy and energy storage systems. The direct carbon emissions are related to the carbon intensity contribution of grid power purchase, and the indirect carbon emissions are related to the entire life cycle carbon emissions of the photovoltaic power station, the entire life cycle carbon emissions of the wind power station, and the entire life cycle carbon emissions during the operation of the energy storage system. Therefore, in addition to considering the carbon emission impact directly generated when the grid purchases electricity, the present invention also incorporates the implicit carbon emissions in the equipment manufacturing, transportation, and recycling stages into the optimization goal by constructing the entire life cycle carbon emission models of the new energy power stations and the energy storage system, which can further improve the scheduling accuracy of the new energy and obtain a scheduling plan that meets the multi-objective balance.

[0085] The present invention can combine direct carbon emissions and indirect carbon emissions, not only considering the real-time carbon emissions of grid power purchase, such as the direct emissions during the operation stage, but also incorporating the implicit carbon emissions in the entire life cycle of photovoltaic and wind power equipment manufacturing, transportation, recycling, etc., such as the indirect emissions from silicon material purification, tower production, etc., as well as the production and recycling carbon emissions of the energy storage system, so as to achieve the entire life cycle carbon emission coverage of the new energy and energy storage batteries.

[0086] Furthermore, by considering the transferred carbon emissions, the present invention can quantify the regional transfer of carbon emission operations in the industrial chain division of labor (for example, production in place A and use in place B), prevent carbon emissions from being underestimated or transferred to regions with lax supervision, and thus avoid the carbon emission calculation loopholes caused by "carbon transfer", thereby achieving a comprehensive consideration of carbon emissions.

[0087] In a preferred embodiment, the first carbon emission model for calculating the carbon intensity contribution degree value of grid power purchase is:

[0088] C grid C(t) = P grid (t)·λ grid (t);

[0089] Among them, C grid (t) is the direct carbon emission generated by purchasing electricity from the power grid, P grid (t) is the electricity quantity purchased from the power grid, and λ grid (t) is the real-time carbon intensity of the power grid.

[0090] In a preferred embodiment, the second carbon emission model for calculating the carbon emission contribution degree value of the entire life cycle of a photovoltaic power station may be:

[0091]

[0092] Among them, C PV-life (t) is the carbon emission of the entire life cycle of the photovoltaic power station at time t, C cell is the carbon emission of silicon material purification and cell manufacturing, C frame is the carbon emission of bracket and inverter manufacturing, C trans is the transfer carbon emission, T life is the photovoltaic life, is the rated power of the photovoltaic.

[0093] In a preferred embodiment, the third carbon emission model for calculating the carbon emission contribution degree value of the entire life cycle of a wind farm may be:

[0094]

[0095] Among them, C WT-life (t) is the carbon emission of the entire life cycle of the wind power station, C tower is the carbon emission of tower barrel manufacturing, C blade is the carbon emission of blade manufacturing.

[0096] Furthermore, by associating the embodied carbon emissions of battery production and recycling with real-time losses to dynamically reflect the indirect impact of energy storage operation on carbon emissions, the fourth carbon emission model for calculating the carbon emission contribution degree value of energy storage operation may be:

[0097]

[0098] Among them, C prod is the carbon emission of battery production (lithium ore mining, cathode material processing), C recycle is the carbon emission of recycling and treatment, is the total life cycle attenuation limit of the battery.

[0099] Then, the present invention can incorporate implicit carbon emissions into real-time optimization, avoid the one-sidedness of "zero carbon in the operation stage", and achieve the low-carbon goal of the whole life cycle.

[0100] In a preferred embodiment, the objective function of the combined scheduling model of the present invention is:

[0101] minF = w1minF cost + w2minF carbon + w3minF aging ;

[0102]

[0103] C indirect (t) = C PV-life (t) + C WT-life (t) + C bat-life (t);

[0104] Among them, minF is the function value corresponding to the combined scheduling model, and minF cost represents the low-economic operation model aiming to minimize the total cost; minF carbon represents the low-carbon operation model aiming to minimize carbon emissions; minF aging represents the high-life operation model aiming to minimize the life decay rate; w1, w2, and w3 are the weight coefficients of the low-economic operation model, the low-carbon operation model, and the high-life operation model respectively; c grid (t) represents the power purchase carbon intensity contribution degree value of the first carbon emission model for calculating the power purchase carbon intensity contribution degree value of the power grid at time t, P grid (t) represents the purchased power of the power grid at time t, c bat is the time-of-use electricity price of the power grid, L bat (t) is the battery life decay value of the battery decay model for calculating the battery life decay value at time t, c RE is the unit aging maintenance cost of new energy, d aging is the annual aging decay rate of the photovoltaic power station in the power generation performance parameters of the photovoltaic power station, C total (t) is the total carbon emission at time t, and the total carbon emission can be calculated according to the whole life cycle mode or the operation stage mode; is the maximum aging decay rate of new energy equipment, is the maximum life of the battery at the initial time, C indirect (t) is the maximum life decay value of the battery at time t, C PV-life (t) is the carbon emission contribution degree value of the second carbon emission model for calculating the carbon emission contribution degree value of the whole life cycle of the photovoltaic power station at time t, C WT-life(t) is the carbon emission contribution value of the wind farm's full life cycle carbon emissions at time t by the third carbon emission model for calculating the carbon emission contribution value of the wind farm's full life cycle carbon emissions, C bat-life (t) is the carbon emission contribution value of the energy storage operation at time t by the fourth carbon emission model for calculating the carbon emission contribution value of the energy storage operation.

[0105] It can be understood that by introducing the low economic operation model, the low carbon operation model, and the high life operation model, and setting the corresponding weight coefficients, the present invention can achieve a trade-off and optimization among the economic cost, carbon emissions, and equipment life, solve the one-sidedness problem of the traditional scheduling method, and achieve the collaborative optimization of multiple objectives.

[0106] Illustratively, from the above objective function, it can be seen that the present invention can perform dynamic adjustment according to information such as the grid purchase power, electricity price, and battery life attenuation value at different times, so as to achieve a more accurate scheduling decision.

[0107] Moreover, the high life operation model of the present invention can take the annual aging attenuation rate d of the photovoltaic power station aging and the battery life attenuation value L bat (t) and other factors into the objective function, which can avoid the problem of shortened life caused by overusing the equipment, thereby reducing the equipment replacement and maintenance costs, and improving the long-term reliability and economy of the system.

[0108] Illustratively, the total carbon emissions C total (t) can be calculated according to the full life cycle mode or the operation stage mode, so that the objective function can adapt to different operation scenarios and evaluation requirements. For example, in some cases, more attention is paid to the carbon emissions in the operation stage, while in other cases, the carbon emissions in the full life cycle need to be considered, thus realizing the automatic switching of the accounting mode.

[0109] In a preferred embodiment, the power balance constraint corresponding to the joint scheduling model of the present invention is:

[0110] P PV (t)+P WT (t)+P dis (t)-P ch (t)+P grid (t)=P load (t);

[0111] Among them, P PV (t) is the output of the photovoltaic power station at time t, P WT (t) is the output of the wind farm at time t, P dis (t) is the discharge power of the energy storage system, P ch (t) is the charging power of the energy storage system, Pgrid (t) is the purchased power of the power grid, P load (t) is the preset power grid load power.

[0112] Then, the embodiments of the present invention can construct power balance constraints in the following manner:

[0113] First, obtain the output data of the photovoltaic power station, the power generation performance parameters of the photovoltaic power station, the output data of the wind farm, and the power generation performance parameters of the wind farm in the operation data of the new energy device;

[0114] Obtain the charging power of the energy storage system, the discharging power of the energy storage system, the preset charging efficiency, the preset discharging efficiency, the preset battery rated capacity, the battery discharge depth, and the number of battery cycle life times in the operation data of the energy storage system;

[0115] Next, construct a photovoltaic power station output model according to the output data of the photovoltaic power station and the power generation performance parameters of the photovoltaic power station;

[0116] Construct a wind farm output model according to the output data of the wind farm and the power generation performance parameters of the wind farm;

[0117] Construct a state of charge model of the energy storage according to the charging power of the energy storage system, the discharging power of the energy storage system, the preset charging efficiency, the preset discharging efficiency, and the preset battery rated capacity;

[0118] Construct a battery decay model for calculating the battery life decay value according to the linear relationship between the battery discharge depth and the number of battery cycle life times;

[0119] Finally, construct power balance constraints according to the photovoltaic power station output model, the wind farm output model, the state of charge model of the energy storage, the battery decay model, the purchased power of the power grid, and the preset power grid load power.

[0120] It can be understood that in the process of constructing power balance constraints in the present invention, the output characteristics of new energy, the dynamic characteristics of the energy storage system, and the interaction constraints of the power grid are mainly considered, such as the correlation between the purchased power and the time-of-use electricity price and the rigid constraint of the preset power grid load power. By comprehensively considering the new energy output, the behavior of the energy storage system, and the power grid purchased power, it can be ensured that the load demand of the power grid can be met at any time and the power balance can be maintained.

[0121] Furthermore, the above photovoltaic power station output model is:

[0122] P PV (t) = η PV ·A·G(t)·(1 - α·(T(t) - T SIC ))·(1 - d aging );

[0123] Among them, P PV (t) is the output power of the photovoltaic at time t, G(t) is the real-time irradiance, T(t) is the temperature of the photovoltaic panel, and T SIC is the standard test temperature, η PV is the photoelectric conversion efficiency, A is the total area of the photovoltaic array, α is the temperature coefficient, and d aging is the annual aging attenuation rate.

[0124] Furthermore, the output power model of the wind farm is:

[0125]

[0126] Among them, P WT (t) is the output power of the wind power station at time t, v(t) is the real-time wind speed, v cut-in and v cut-out are the cut-in and cut-out wind speeds respectively, v rated is the rated wind speed, ρ is the air density, C p is the wind energy utilization coefficient, A rotor is the swept area of the wind turbine, η WT is the mechanical and electrical efficiency, is the rated power of the wind farm.

[0127] Furthermore, the state of charge model of the energy storage is:

[0128]

[0129] Among them, SOC(t) and SOC(t + 1) are the state of charge of the energy storage at times t and t + 1 respectively, P ch (t) and P dis (t) are the charge and discharge powers respectively, η ch and η dis are the charge and discharge efficiencies respectively, and E bat is the rated capacity of the battery.

[0130] Furthermore, the battery attenuation model is:

[0131]

[0132] That is, L bat (t) is the battery life attenuation value, is the depth of discharge of the k-th cycle, and N cycle (DoD k ) is the cycle life number corresponding to DoD.

[0133] Therefore, through the output power model of the photovoltaic power station, considering factors such as real-time irradiance, photovoltaic panel temperature, photoelectric conversion efficiency, total area of the photovoltaic array, temperature coefficient, and annual aging attenuation rate, the output power variation of the photovoltaic power station under different environmental conditions can be accurately simulated.

[0134] Through the output power model of the wind farm, considering factors such as real-time wind speed, cut-in and cut-out wind speeds, rated wind speed, air density, wind energy utilization coefficient, swept area of the wind turbine, mechanical and electrical efficiency, and rated power of the wind farm, the output power variation of the wind farm under different wind speed conditions can be further accurately simulated.

[0135] By calculating the state of charge of the energy storage system in real time through the state of charge model of the energy storage, considering factors such as charge and discharge power, charge and discharge efficiency, and rated capacity of the battery, it is ensured that the charge and discharge process conforms to physical laws; and through the battery attenuation model, based on the non-linear relationship between the depth of discharge (DoD) and the number of cycles, the attenuation of the battery capacity is quantified, avoiding the shortening of the service life caused by frequent deep charge and discharge.

[0136] Then, in the power balance constraint, all decision variables can be constrained to satisfy the real-time power balance, ensuring the physical feasibility of the optimization result. So that in the optimization process, if the output power of photovoltaic / wind power is insufficient, it can be supplemented by energy storage discharge or grid power purchase; if the output power is excessive, energy storage charging can be carried out or power purchase can be reduced. Thus, under the balance of various objectives and the power balance constraint, the first output power scheduling value of the wind farm, the second output power scheduling value of the photovoltaic power station, and the charge and discharge power scheduling value of the energy storage power station can be obtained finally.

[0137] In a preferred embodiment, the joint scheduling model of the present invention also corresponds to state of charge constraint, wind farm output power constraint, photovoltaic power station output power constraint, grid power purchase power constraint, and battery life constraint.

[0138] Schematically, the state of charge constraint is used to characterize the safe range of SOC, then there is:

[0139] 0.2 ≤ SOC(t) ≤ 0.9;

[0140] Wherein, SOC(t) is the state of charge of the energy storage at time t, then the safe range of the energy storage state of charge can be limited, avoiding irreversible damage to the battery caused by overcharging (too high SOC) or over-discharging (too low SOC), extending the service life of the energy storage device, and at the same time ensuring that the energy storage system always has the charge and discharge ability during the scheduling process, ensuring the reliability of energy supply.

[0141] Further, the wind farm output power constraint and the photovoltaic power station output power constraint are:

[0142]

[0143] Among them, and are the maximum power generation of the photovoltaic device and the maximum power generation of the wind power generation device respectively. Then, setting the power upper limit based on the actual power generation capacity of the photovoltaic and wind power devices can prevent the dispatching scheme from exceeding the physical limit of the devices, avoid equipment damage caused by over-dispatching, ensure stable and safe power generation of the new energy power station, and improve power generation efficiency and equipment utilization rate at the same time.

[0144] Furthermore, the power purchase power constraint of the power grid is used to achieve low-carbon constraints, then there is:

[0145]

[0146] Among them, is the maximum power upper limit of power grid power purchase, and λ grid (t) is the carbon intensity of the power grid. is the maximum value of the power grid carbon intensity. Then, the present invention can associate the power purchase power with the power grid carbon intensity, reduce power purchase when the power grid carbon intensity is high, and reasonably increase power purchase when the carbon intensity is low, so as to reduce the overall carbon emission.

[0147] Furthermore, the battery life constraint is used to achieve the life balance of the equipment, then there is:

[0148]

[0149] Among them, is the initial maximum life of the battery, is the maximum aging degree that the battery can withstand. Then, the present invention can achieve the balance of the life attenuation degree of the energy storage battery and the new energy equipment, avoid the rapid aging of the energy storage due to overuse, ensure the matching of the life cycles of each device in the system, and reduce the equipment replacement cost and resource waste.

[0150] For step S2, in a preferred embodiment, after constructing a joint dispatching model with the objectives of minimizing the total cost, minimizing the carbon emission, and minimizing the life attenuation rate, before solving the joint dispatching model, the present invention further includes:

[0151] Inputting the preset time period, the operation data of the new energy device, the operation data of the energy storage system, and the carbon emission data into a preset carbon intensity preset model, so that the carbon intensity preset model outputs the target power grid carbon intensity after the preset time period;

[0152] Adjusting the weight coefficients of the low-economic operation model, the low-carbon operation model, and the high-life operation model in the joint dispatching model respectively according to the target power grid carbon intensity, and adjusting the power purchase upper limit in the power purchase power constraint of the power grid and the life threshold in the battery life constraint respectively according to the target power grid carbon intensity, to generate an updated joint dispatching model;

[0153] Use the updated joint scheduling model as the joint scheduling model to be solved when repeatedly executing the target particle determination operation.

[0154] Illustratively, the embodiment of the present invention can predict the grid carbon intensity in the next 24 hours through the LSTM model, and then dynamically adjust the objective function weight and constraint threshold according to the predicted grid carbon intensity.

[0155] For the adjustment of the objective function weight, the low-carbon objective F carbon The formula for dynamically adjusting the corresponding weight w2 is:

[0156]

[0157] Where Is the basic weight, which is defaulted to 0.3 here.

[0158] Furthermore, for the economic objective F cost And the life objective F aging The formulas for dynamically adjusting the corresponding weights w1 and w3 are:

[0159]

[0160] w3(t) = 1 - w1(t) - w2(t);

[0161] Where c grid (t) is the real-time electricity price, Is the historical highest electricity price.

[0162] For the adjustment of the constraint threshold, there is:

[0163] Dynamically adjust the upper limit of the grid power purchase according to the following formula:

[0164]

[0165] Where Is the basic upper limit of the power purchase.

[0166] Adjust the life threshold in the battery life constraint according to the following formula:

[0167]

[0168] Where δ base Is the basic deviation value.

[0169] Therefore, the present invention can adjust the weight of the economic objective according to the predicted grid carbon intensity, optimize energy utilization and reduce operating costs while ensuring carbon emission control. After the weight of the low-carbon operation model is dynamically adjusted according to the grid carbon intensity, the dispatching intensity of low-carbon energy can be increased and carbon emissions can be reduced when the grid carbon intensity is high. By adjusting the life threshold in the battery life constraint, the life of the energy storage device can be extended and the equipment replacement and maintenance costs can be reduced while ensuring the stable operation of the grid. Then, by predicting the future grid carbon intensity through the LSTM model in the embodiment of the present invention, the weight coefficient and constraint threshold in the joint dispatching model can be dynamically adjusted according to the actual situation, making the dispatching strategy more flexible and better adapting to the changes in grid operation.

[0170] Further, in a preferred embodiment, the solving of the joint dispatching model to generate the first output dispatching value of the wind farm, the second output dispatching value of the photovoltaic power station, and the charge and discharge power dispatching value of the energy storage power station includes:

[0171] Randomly generate a number of particles in the population; where each particle is used to represent the first output dispatching value of the wind farm, the first output dispatching value of the photovoltaic power station, and the charge and discharge power dispatching value of the energy storage power station;

[0172] Initialize each particle in the population to obtain the initial position of each particle, the initial velocity of each particle, and the initial population position;

[0173] Repeat the following target particle determination operation until the current iteration number is the same as the preset iteration number, calculate the function value corresponding to each particle to be processed in the joint dispatching model, and use the particle to be processed with the highest function value as the target particle and output the target particle, so that the output target particles all satisfy the power balance constraint, the state of charge constraint, the wind farm output constraint, the photovoltaic power station output constraint, the grid power purchase constraint, and the battery life constraint:

[0174] When the current iteration number is less than the preset iteration number, for each particle, generate the updated particle velocity corresponding to the particle according to the current position of the particle, the current velocity of the particle, and the current population position; where initially, the initial position of each particle, the initial velocity of each particle, and the initial population position are respectively used as the current position of each particle, the current velocity of each particle, and the current population position;

[0175] For each particle, determine the updated particle position corresponding to the particle according to the current position of the particle, the updated particle velocity corresponding to the particle, and the current population position;

[0176] Based on the updated particle positions corresponding to each particle, the updated particle velocities corresponding to each particle, and the function values corresponding to each particle in the joint scheduling model, an updated population position is obtained;

[0177] Output a number of particles to be processed corresponding to the updated population position;

[0178] Add the preset iteration number increment to the current iteration number value to obtain the updated iteration number, and use the updated iteration number as the iteration number for the next execution of the target particle determination operation;

[0179] Use the updated particle position corresponding to each particle as the current position of the particle for the next execution of the target particle determination operation, use the updated particle velocity corresponding to each particle as the current velocity of the particle for the next execution of the target particle determination operation, and use the updated population position as the current population position for the next execution of the target particle determination operation.

[0180] Then, in the embodiments of the present invention, by repeatedly executing the target particle determination operation and continuously updating the positions and velocities of the particles, the entire particle swarm will gradually converge to the vicinity of the global optimal solution, so as to ensure that the finally obtained scheduling scheme is globally optimal or approximately globally optimal.

[0181] In a preferred embodiment, the present invention can update the particle velocity of the particles according to the following formula:

[0182]

[0183] Where: and are the velocities of the i-th particle at the (k + 1)-th and k-th iterations respectively, ω is the inertia weight, c1 and c2 are learning factors, and the value ranges are between 1.5 and 2.0, which are used to adjust the step sizes for the particle to learn from its own optimal position and the global optimal position. r1 and r2 are random numbers between 0 and 1, which introduce randomness for each iteration. pbest i The historical optimal position of the i-th particle, is the position of the i-th particle at the k-th iteration, and gbest is the global optimal position currently found by the entire particle swarm.

[0184] It can be understood that by introducing the inertia weight, the present invention enables the particle to retain a part of the velocity at the previous moment when updating the velocity, so that it can maintain a certain degree of exploration in the search space and avoid falling into the local optimal solution prematurely. The learning factors c1 and c2 respectively adjust the step sizes for the particle to learn from its own optimal position and the global optimal position, so that the particle can comprehensively consider its own experience and group experience when updating the position, thereby realizing a more efficient search.

[0185] The present invention thus adopts an improved multi-objective particle swarm optimization algorithm to achieve global search for the optimal solution, and introduces an adaptive mutation mechanism to update the velocity of the particles, thereby avoiding falling into a local optimum.

[0186] For step S3, in a preferred embodiment, since the present invention quantifies the expenditures of power grid power purchase, energy storage loss, and equipment maintenance through cost data, each scheduling value obtained by solving can achieve a low-cost scheduling combination. For example, it preferentially absorbs photovoltaic or wind power, reduces power purchase during high electricity price periods, and reduces the overall operating cost.

[0187] The present invention can also optimize the objective of minimizing carbon emissions based on carbon emission data. When performing joint scheduling according to the finally obtained first output scheduling value, second output scheduling value, and charge-discharge power scheduling value, it is possible to reduce power grid power purchase during high carbon intensity periods, increase energy storage discharge, and reduce carbon emissions.

[0188] Moreover, the present invention also utilizes aging attenuation data. When performing joint scheduling according to the finally obtained first output scheduling value, second output scheduling value, and charge-discharge power scheduling value, it is possible to balance the equipment usage intensity, such as avoiding overcharging and discharging of the energy storage, controlling the output fluctuations of new energy equipment, and extending the service life of the equipment.

[0189] Therefore, the present invention can simultaneously consider the three objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life decay rate, achieving a multi-objective balance in terms of economy, environmental protection, and equipment life, not only improving economic benefits and energy utilization efficiency, but also effectively controlling the impact of carbon emissions on the environment.

[0190] As Figure 2 shown, based on the above embodiments of the joint scheduling method for various new energy power stations and energy storage systems, the present invention correspondingly provides an apparatus embodiment;

[0191] An embodiment of the present invention provides a joint scheduling apparatus for a new energy power station and an energy storage system, including: a joint scheduling model construction module, a model solving module, and a joint scheduling module;

[0192] The joint scheduling model construction module is configured to construct a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life decay rate according to new energy device operation data, energy storage system operation data, new energy maintenance cost data, power grid power purchase cost data, energy storage loss cost data, new energy device aging attenuation data, and carbon emission data; wherein, the carbon emission data includes: power purchase carbon emission data used to represent the carbon emissions directly generated by power grid power purchase and new energy carbon emission data used to represent the carbon emissions indirectly generated during the manufacturing or recycling of new energy devices.

[0193] The model solving module is used to solve the joint scheduling model under the power balance constraint, state of charge constraint, wind farm output constraint, photovoltaic power station output constraint, grid power purchase constraint and battery life constraint corresponding to the joint scheduling model, and generate the first output scheduling value of the wind farm, the second output scheduling value of the photovoltaic power station and the charge and discharge power scheduling value of the energy storage power station;

[0194] The joint scheduling module is used to perform joint scheduling on the wind farm, photovoltaic power station and energy storage power station respectively according to the first output scheduling value, the second output scheduling value and the charge and discharge power scheduling value.

[0195] It should be noted that the device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative work.

[0196] Those skilled in the art can clearly understand that for the convenience and conciseness, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0197] Based on the foregoing embodiments of the joint scheduling method for various new energy power stations and energy storage systems, the present invention correspondingly provides embodiments of a terminal device.

[0198] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a joint scheduling method for a new energy power station and an energy storage system according to any method embodiment of the present invention.

[0199] The terminal device may be a desktop computer, a notebook, a palm computer, a cloud server and other computing terminal devices. The terminal device may include, but is not limited to, a processor and a memory.

[0200] The so-called processor may be a Central Processing Unit (CPU), or 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, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, and connects various parts of the entire terminal device through various interfaces and lines.

[0201] The memory can be used to store the computer program. The processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0202] Based on the above embodiments of the joint scheduling method for various new energy power stations and energy storage systems, the present invention correspondingly provides an embodiment of a storage medium.

[0203] An embodiment of the present invention provides a storage medium. The storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a joint scheduling method for a new energy power station and an energy storage system according to any one of the method embodiments of the present invention.

[0204] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0205] The above are the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art in the technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A joint dispatching method for a new energy power station and an energy storage system, characterized in that Including: Construct a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate based on the operation data of new energy equipment, the operation data of energy storage systems, the maintenance cost data of new energy, the power purchase cost data of the power grid, the energy storage loss cost data, the aging attenuation data of new energy equipment, and the carbon emission data. Among them, the carbon emission data includes: the power purchase carbon emission data used to represent the carbon emissions directly generated by power grid power purchase and the new energy carbon emission data used to represent the carbon emissions indirectly generated during the manufacturing or recycling of new energy equipment. Under the power balance constraint, state of charge constraint, wind farm output constraint, photovoltaic power station output constraint, power grid power purchase power constraint, and battery life constraint corresponding to the joint scheduling model, solve the joint scheduling model to generate the first output scheduling value of the wind farm, the second output scheduling value of the photovoltaic power station, and the charge and discharge power scheduling value of the energy storage power station. Perform joint scheduling on the wind farm, photovoltaic power station, and energy storage power station respectively according to the first output scheduling value, the second output scheduling value, and the charge and discharge power scheduling value.

2. The joint dispatching method of a new energy power station and an energy storage system according to claim 1, characterized in that The new energy carbon emission data includes: the manufacturing carbon emission data corresponding to new energy, the production carbon emission data corresponding to energy storage, and the recycling carbon emission data corresponding to energy storage. The aging attenuation data of the new energy equipment includes: the aging data of the new energy equipment and the life attenuation data of the battery. The constructing a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate based on the operation data of new energy equipment, the operation data of energy storage systems, the maintenance cost data of new energy, the power purchase cost data of the power grid, the energy storage loss cost data, the aging attenuation data of new energy equipment, and the carbon emission data includes: Construct a low-economic operation model with the objective of minimizing the total cost based on the operation data of new energy equipment, the operation data of energy storage systems, the maintenance cost data of new energy, the power purchase cost data of the power grid, and the energy storage loss cost data. Construct a low-carbon operation model with the objective of minimizing carbon emissions based on the power purchase carbon emission data used to represent the carbon emissions directly generated by power grid power purchase, the manufacturing carbon emission data corresponding to new energy, the production carbon emission data corresponding to energy storage, and the recycling carbon emission data corresponding to energy storage. Construct a high-life operation model with the objective of minimizing the life attenuation rate based on the aging data of new energy equipment and the life attenuation data of the battery. Construct a joint scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate based on the low-economic operation model, the low-carbon operation model, and the high-life operation model.

3. The joint scheduling method of a new energy power station and an energy storage system according to claim 2, characterized in that, The power purchase carbon emission data includes: the power purchase power data when the power grid purchases electricity and the carbon intensity of the power grid. The manufacturing carbon emission data corresponding to new energy includes: the carbon emissions of silicon material purification, the manufacturing carbon emissions of battery chips, the carbon emissions of photovoltaic brackets, the manufacturing carbon emissions of inverters, the transferred carbon emissions, the manufacturing carbon emissions of wind turbine towers, and the carbon emissions of wind turbine blades. Among them, the transferred carbon emissions are used to represent the carbon emissions during the regional transfer of carbon emission operations through industrial chain division of labor. Construct a low-carbon operation model with the goal of minimizing carbon emissions based on the electricity purchase carbon emission data used to characterize the carbon emissions directly generated by the power grid's electricity purchase, the manufacturing carbon emission data corresponding to new energy, the production carbon emission data corresponding to energy storage, and the recycling carbon emission data corresponding to energy storage, including: Determine the electricity purchased by the power grid based on the electricity purchase power data when the power grid purchases electricity, and then generate a first carbon emission model for calculating the contribution degree value of the electricity purchase carbon intensity of the power grid according to the electricity purchased by the power grid and the carbon intensity of the power grid; Generate a second carbon emission model for calculating the contribution degree value of the carbon emissions throughout the life cycle of a photovoltaic power station based on the carbon emissions of silicon material purification, the manufacturing carbon emissions of solar cells, the carbon emissions of photovoltaic brackets, the manufacturing carbon emissions of inverters, and the transferred carbon emissions; Generate a third carbon emission model for calculating the contribution degree value of the carbon emissions throughout the life cycle of a wind farm based on the manufacturing carbon emissions of the wind turbine tower, the carbon emissions of the wind turbine blade, and the transferred carbon emissions; Generate a fourth carbon emission model for calculating the contribution degree value of the energy storage operation to carbon emissions based on the production carbon emission data corresponding to energy storage and the recycling carbon emission data corresponding to energy storage; Construct a low-carbon operation model with the goal of minimizing carbon emissions according to the first carbon emission model, the second carbon emission model, the third carbon emission model, and the fourth carbon emission model.

4. The joint scheduling method of a new energy power station and an energy storage system according to claim 3, characterized in that, The new energy device operation data includes: the output data of the photovoltaic power station, the power generation performance parameters of the photovoltaic power station, the output data of the wind farm, and the power generation performance parameters of the wind farm; the energy storage system operation data includes: the charging power of the energy storage system, the discharging power of the energy storage system, the preset charging efficiency, the preset discharging efficiency, the preset battery rated capacity, the battery discharge depth, and the number of battery cycle life times; The method for the joint scheduling of the new energy station and the energy storage system further includes: constructing a power balance constraint in the following manner: Construct a photovoltaic power station output model according to the output data of the photovoltaic power station and the power generation performance parameters of the photovoltaic power station; Construct a wind farm output model according to the output data of the wind farm and the power generation performance parameters of the wind farm; Construct an energy storage state of charge model according to the charging power of the energy storage system, the discharging power of the energy storage system, the preset charging efficiency, the preset discharging efficiency, and the preset battery rated capacity; Construct a battery attenuation model for calculating the battery life attenuation value according to the linear relationship between the battery discharge depth and the number of battery cycle life times; Construct a power balance constraint according to the photovoltaic power station output model, the wind farm output model, the energy storage state of charge model, the battery attenuation model, the electricity purchased by the power grid, and the preset power grid load power.

5. The combined dispatching method of a new energy power station and an energy storage system according to claim 4, characterized in that, The joint scheduling model includes: minF = w1minF cost + w2minF carbon + w3minF aging ; C indirect y(t) = C PV-life x(t)+C WT-life z(t)+C bat-life w(t); Among them, minF is the function value corresponding to the joint scheduling model, minF cost represents a low - economic operation model aiming to minimize the total cost; minF carbon represents a low - carbon operation model aiming to minimize carbon emissions; minF aging represents a high - life operation model aiming to minimize the life - decay rate; w1, w2, and w3 are the weight coefficients of the low - economic operation model, low - carbon operation model, and high - life operation model respectively; c grid (t) represents the power - purchase carbon - intensity contribution value of the first carbon - emission model for calculating the power - purchase carbon - intensity contribution degree of the power grid at time t, P grid (t) represents the power grid's purchased electricity at time t, c bat is the time - of - use electricity price of the power grid, L bat (t) is the battery life - decay value of the battery - decay model for calculating the battery life - decay value at time t, c RE is the unit aging maintenance cost of new energy, d aging is the annual aging decay rate of the photovoltaic power station in the power - generation performance parameters of the photovoltaic power station, C total (t) is the total carbon emissions at time t, and the total carbon emissions can be calculated according to the whole - life - cycle mode or the operation - stage mode; is the maximum aging decay rate of new - energy equipment, is the maximum life of the battery at the initial time, C indirect (t) is the maximum life - decay value of the battery at time t, C PV-life (t) is the carbon - emission contribution value of the second carbon - emission model for calculating the carbon - emission contribution degree of the whole - life - cycle of the photovoltaic power station at time t, C WT-life (t) is the carbon - emission contribution value of the third carbon - emission model for calculating the carbon - emission contribution degree of the whole - life - cycle carbon emissions of the wind farm at time t, C bat-life (t) is the carbon - emission contribution value of the fourth carbon - emission model for calculating the carbon - emission contribution degree of energy - storage operation at time t.

6. The joint dispatching method of a new energy power station and an energy storage system according to claim 5, characterized in that, Solving the joint scheduling model to generate a first output scheduling value of the wind farm, a second output scheduling value of the photovoltaic power station, and a charge-discharge power scheduling value of the energy storage power station, including: Randomly generate a number of particles in the population; where each particle is used to represent the first output scheduling value of the wind farm, the first output scheduling value of the photovoltaic power station, and the charge-discharge power scheduling value of the energy storage power station; Initialize each particle of the population to obtain the initial position of each particle, the initial velocity of each particle, and the initial population position; Repeat the following target particle determination operation until the current iteration number is the same as the preset iteration number. Calculate the function value corresponding to each particle to be processed in the joint scheduling model, and take the particle to be processed with the highest function value as the target particle and output the target particle, so that the output target particles all satisfy the power balance constraint, the state of charge constraint, the wind farm output constraint, the photovoltaic farm output constraint, the grid power purchase constraint, and the battery life constraint: When the current iteration number is less than the preset iteration number, for each particle, generate the updated particle velocity corresponding to the particle according to the current position of the particle, the current velocity of the particle, and the current population position; initially, take the initial position of each particle, the initial velocity of each particle, and the initial population position as the current position of each particle, the current velocity of each particle, and the current population position, respectively; For each particle, determine the updated particle position corresponding to the particle according to the current position of the particle, the updated particle velocity corresponding to the particle, and the current population position; Obtain the updated population position according to the updated particle position corresponding to each particle, the updated particle velocity corresponding to each particle, and the function value corresponding to each particle in the joint scheduling model; Output several particles to be processed corresponding to the updated population position; Add the preset iteration number increment to the current iteration number value to obtain the updated iteration number, and take the updated iteration number as the iteration number when the target particle determination operation is executed next time; Take the updated particle position corresponding to each particle as the current position of the particle when the target particle determination operation is executed next time, take the updated particle velocity corresponding to each particle as the current velocity of the particle when the target particle determination operation is executed next time, and take the updated population position as the current population position when the target particle determination operation is executed next time.

7. The combined dispatching method of a new energy power station and an energy storage system according to claim 6, wherein After constructing a joint scheduling model with the objectives of minimizing the total cost, minimizing the carbon emissions, and minimizing the life attenuation rate, before solving the joint scheduling model, it further includes: Input the preset time period, new energy equipment operation data, energy storage system operation data, and carbon emission data into a preset carbon intensity preset model, so that the carbon intensity preset model outputs the target grid carbon intensity after the preset time period; Adjust the weight coefficients of the low-economic operation model, the low-carbon operation model, and the high-life operation model in the joint scheduling model respectively according to the target grid carbon intensity, and adjust the power purchase upper limit in the grid power purchase constraint and the life threshold in the battery life constraint respectively according to the target grid carbon intensity to generate an updated joint scheduling model; Take the updated joint scheduling model as the joint scheduling model to be solved when repeating the target particle determination operation.

8. A combined dispatching device for a new energy power station and an energy storage system, characterized in that It includes: A joint scheduling model construction module, a model solving module, and a joint scheduling module; The combined scheduling model construction module is used to construct a combined scheduling model with the objectives of minimizing the total cost, minimizing carbon emissions, and minimizing the life attenuation rate based on the operation data of new energy equipment, the operation data of the energy storage system, the maintenance cost data of new energy, the grid power purchase cost data, the energy storage loss cost data, the aging attenuation data of new energy equipment, and the carbon emission data. Among them, the carbon emission data includes: the power purchase carbon emission data used to represent the carbon emissions directly generated by the grid power purchase, and the new energy carbon emission data used to represent the carbon emissions indirectly generated during the manufacturing or recycling of new energy equipment. The model solving module is used to solve the combined scheduling model under the power balance constraint, state of charge constraint, wind farm output constraint, photovoltaic power station output constraint, grid power purchase power constraint, and battery life constraint corresponding to the combined scheduling model, and generate the first output scheduling value of the wind farm, the second output scheduling value of the photovoltaic power station, and the charge and discharge power scheduling value of the energy storage power station. The combined scheduling module is used to perform combined scheduling on the wind farm, the photovoltaic power station, and the energy storage power station respectively according to the first output scheduling value, the second output scheduling value, and the charge and discharge power scheduling value.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a combined scheduling method for a new energy power station and an energy storage system according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute a combined scheduling method for a new energy power station and an energy storage system according to any one of claims 1 to 7.

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