Light-storage coordinated regulation and control optimization method, system and equipment for electricity market and medium

Abstract

The invention relates to a light storage coordinated regulation and control optimization method, system and device for an electricity market and a medium. The method comprises the steps of obtaining light storage operation data; determining power and energy constraints of the optical storage system in multiple time periods according to the optical storage operation data and a preset user side operation model, and obtaining a constraint model; based on the constraint model and the light storage operation data, constructing a distributed energy storage aggregation analysis model taking the electric energy distribution evaluation indexes in the plurality of time periods as a target function; and carrying out optimization solution on the distributed energy storage aggregation analysis model, and generating a light storage coordinated regulation and control strategy under the condition of meeting the constraint model. The method has the effect of accuracy of the power system regulation and control strategy.

Description

Technical Field

[0001] This invention belongs to the technical field of power system operation and dispatch, and in particular relates to a method, system, equipment and medium for the coordinated regulation and optimization of photovoltaic and energy storage in the power market. Background Technology

[0002] Currently, with the increasing penetration rate of photovoltaic (PV) power generation in the power system, the volatility and intermittency of PV output have placed higher demands on grid power balance, voltage stability, and reserve arrangements. In scenarios where multiple electricity market businesses exist simultaneously, such as electricity trading, ancillary services, and demand response, the coupling relationship between PV output and user load, electricity price signals, and operational constraints is becoming increasingly complex. The need for PV and energy storage to operate in synergy while meeting safety constraints and balancing multi-market participation and comprehensive operational performance is becoming increasingly prominent.

[0003] Existing methods for coordinated regulation of photovoltaic (PV) and energy storage in the context of the power market typically employ data-driven strategy learning approaches. These methods autonomously learn PV and energy storage output and market transaction decisions through interaction with the operating environment, enabling the allocation of PV output and the arrangement of energy storage charging and discharging over multiple time periods. However, the modeling process often simplifies different power market trading instruments and their complex rules, adding security and economic constraints as external limitations to the strategy. This results in limited adaptability of the obtained regulation strategies to the actual power market operating mechanism and multiple constraints, making it difficult to achieve stable and ideal system operation results in complex operating scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device and medium for the coordinated regulation and optimization of photovoltaic and energy storage in the power market, so as to solve the technical problem that existing photovoltaic and energy storage coordinated regulation and optimization methods usually unify the multiple power market transaction and operation constraints into fixed external conditions in the strategy solution process, resulting in insufficient adaptability of the obtained regulation strategy to the system operation flexibility and comprehensive performance.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the coordinated regulation of photovoltaic and energy storage in the power market, the method comprising: Acquire photovoltaic storage operation data; Based on the photovoltaic-storage operation data and the preset user-side operation model, the power distribution relationship of photovoltaic output is established and the energy storage discharge is limited to compensate user load only. The power and energy constraints of the photovoltaic-storage system in multiple time periods are determined, and the constraint model is obtained. Based on the photovoltaic and energy storage operation data, the distribution of photovoltaic power on each receiving side is evaluated in separate items to obtain energy distribution evaluation indicators. Based on the constraint model and the photovoltaic and energy storage operation data, a distributed energy storage aggregation analysis model is constructed with the energy distribution evaluation indicators in multiple time periods as the objective function. The distributed energy storage aggregation analysis model is optimized and solved to generate a photovoltaic-storage coordinated control strategy under the constraints of the model.

[0006] By adopting the above technical solutions and acquiring photovoltaic-storage operation data, it is possible to simultaneously grasp multi-source operation information such as photovoltaic output, energy storage status, user load, and electricity price under a unified time reference. This provides complete and reliable basic data for subsequent constraint modeling and strategy optimization, avoiding biases caused by relying on empirical assumptions. By determining the power and energy constraints of the photovoltaic-storage system in multiple time periods based on photovoltaic-storage operation data and a pre-set user-side operation model, it is possible to accurately characterize the photovoltaic output boundary, energy storage power capacity, and energy evolution range at the model level, thereby ensuring that subsequent regulation strategies always fall within safe and feasible physical and operational constraints. Furthermore, by using constraint models and photovoltaic-storage operation data… A distributed energy storage aggregation analysis model is constructed with the evaluation index of power distribution in multiple time periods as the objective function. This model can couple photovoltaic utilization, grid interaction, and energy storage charging and discharging behavior with the power distribution effect in multiple time periods in a unified model, thus providing a quantitative evaluation basis for multi-time period collaborative optimization. By optimizing and solving the distributed energy storage aggregation analysis model and generating photovoltaic-storage collaborative control strategies under the constraints of the model, the optimal photovoltaic-storage output and power distribution scheme in multiple time periods can be obtained while ensuring power balance and energy security. This improves the photovoltaic power consumption level, reduces the impact of grid fluctuations, and enhances the overall operating performance of the photovoltaic-storage system.

[0007] In one example, the present invention can be further configured such that: acquiring the optical storage operation data includes: The photovoltaic power output of a photovoltaic power plant is obtained at multiple preset time periods to obtain photovoltaic power output data; The rated power, rated energy, and energy status of the energy storage power station for a preset period are obtained to obtain energy storage operation data; Obtain the power consumption and electricity consumption records of associated users in multiple preset time periods to obtain user load data; Obtain electricity price information for multiple preset time periods to obtain electricity price operation data; The photovoltaic output data, the energy storage operation data, the user load data, and the electricity price operation data are time-aligned under a unified time reference to obtain the photovoltaic-energy storage operation data.

[0008] By adopting the above technical solutions and acquiring the photovoltaic output of photovoltaic power plants over multiple preset time periods, the intraday fluctuation patterns and multi-period variation characteristics of photovoltaic output can be precisely characterized, thus providing a reliable basis for the subsequent allocation of photovoltaic power among energy storage charging, grid sales, and user self-consumption. By acquiring the rated power, rated energy, and energy status of energy storage power plants over preset time periods, the charging and discharging capabilities and energy margins of energy storage can be accurately reflected during control modeling, thereby avoiding the risk of overcharging and over-discharging due to strategies exceeding the capacity limits of energy storage equipment. Furthermore, by acquiring the power consumption and electricity usage records of associated users over multiple preset time periods, it is possible to... It can reflect user load levels and electricity consumption behavior characteristics, thereby enabling the coordinated regulation of photovoltaics and energy storage to better meet user-side energy demand and improve local absorption capacity; by acquiring electricity price information for multiple preset time periods, it can explicitly introduce electricity market price signals during the regulation process, thus providing a basis for balancing operational objectives and electricity price changes when allocating electricity across multiple time periods; by aligning photovoltaic output data, energy storage operation data, user load data, and electricity price operation data under a unified time benchmark, it can eliminate deviations caused by inconsistent sampling from different data sources, thereby improving the accuracy and stability of subsequent constraint modeling and optimization solutions.

[0009] In one example, the present invention can be further configured as follows: based on the photovoltaic-storage operation data and a preset user-side operation model, establishing the power distribution relationship of photovoltaic output and limiting energy storage discharge to be used only for compensating user loads, determining the power and energy constraints of the photovoltaic-storage system in multiple time periods, and obtaining a constraint model, including: Based on the photovoltaic output data and the user load data, the power allocation relationship of photovoltaic output in each time period among energy storage charging, grid power supply and user self-consumption is established to obtain photovoltaic power allocation constraints. Based on the energy storage operation data, the power relationship between energy storage charging from the grid and discharging to users is established, and the charging and discharging relationship of energy storage in the same time period is limited to obtain energy storage power mutual exclusion constraints. Based on the photovoltaic power allocation constraints and the energy storage power mutual exclusion constraints, a power balance relationship is established between photovoltaic output, energy storage charging and discharging power and user load power in each time period, and the power balance constraints are obtained. Based on adjacent time periods, an energy storage recursive relationship is established between the energy storage energy of the previous time period and the energy storage charging and discharging power of the current time period, and the energy storage energy is limited to a preset rated energy range to obtain energy state constraints; The constraint model is formed by combining the photovoltaic power allocation constraint, the energy storage power mutual exclusion constraint, the power balance constraint, and the energy state constraint.

[0010] By adopting the above technical solutions, and establishing the power distribution relationship between photovoltaic output and energy storage charging, grid supply, and user self-consumption in different time periods based on photovoltaic output data and user load data, the distribution boundaries of photovoltaic power in different destinations can be explicitly constrained, thereby providing a means of regulation to improve the local photovoltaic consumption ratio and flexibly allocate grid-connected power. By establishing the power relationship between energy storage charging from the grid and discharging to users based on energy storage operation data and limiting the charging and discharging relationship of energy storage in the same time period, the contradictory operating conditions of charging and discharging in the same time period can be avoided, thereby reducing the impact of frequent switching on energy storage equipment and ensuring the consistency and safety of energy storage operation. By establishing the photovoltaic output, energy storage charging and discharging power, and user load in different time periods based on photovoltaic power distribution constraints and energy storage power mutual exclusion constraints, the system can be optimized to ensure the efficient and safe operation of energy storage. The power balance relationship between power sources can ensure the conservation of power on the user side at the model level, thereby avoiding the occurrence of unmet load or false surplus power in the regulation results. By establishing a recursive relationship of energy storage based on the energy storage of the previous period and the energy storage charging and discharging power of the current period, and limiting the energy storage to a preset rated energy range, the evolution process of energy storage over time can be truly reflected and energy over-limit can be prevented, thereby ensuring the continuity of multi-period scheduling and the controllability of energy storage status. By combining photovoltaic power allocation constraints, energy storage power mutual exclusion constraints, power balance constraints and energy status constraints to form a constraint model, various physical and operational constraints can be integrated under a unified framework, thereby improving the matching degree of actual operating conditions and the feasibility of regulation strategies in subsequent optimization solutions.

[0011] In one example, the present invention can be further configured as follows: establishing a recursive relationship between the stored energy of the previous time period and the stored energy charging and discharging power of the current time period based on adjacent time periods, and limiting the stored energy within a preset rated energy range to obtain energy state constraints, includes: Acquire the photovoltaic charging power, grid charging power, and discharge power for each time period; Based on each adjacent time period, the energy balance calculation of the energy storage energy in the previous time period is performed according to the photovoltaic charging power, the grid charging power and the discharge power to obtain the current energy storage energy; The current energy storage capacity is limited according to the preset rated energy range to obtain the current energy storage capacity constraint; The current energy storage constraints of each time period are connected in series to form the energy state constraints of the photovoltaic-storage system in multiple time periods.

[0012] By adopting the above technical solutions, and by acquiring the photovoltaic charging power, grid charging power, and discharge power for each time period, a fine-grained characterization of the energy storage charging and discharging sources and destinations can be achieved at the power level, thus providing accurate input for subsequent energy recursive calculations. By performing energy balance calculations on the energy storage energy of the previous time period based on the photovoltaic charging power, grid charging power, and discharge power for each adjacent time period, the current energy storage energy state can be accurately derived on a discrete time scale, thereby reducing energy estimation errors and enhancing the reliability of energy state descriptions. By limiting the current energy storage energy according to a preset rated energy range, it can be ensured that the energy storage operation is always within a safe energy range, thereby reducing the impact of overcharging and over-discharging on energy storage lifespan and safety. By cascading the current energy storage constraints of each time period to form energy state constraints, the consistency and global feasibility of energy evolution can be maintained across the entire multi-time period time axis, thereby improving the executability and stability of the multi-time period collaborative scheduling scheme in actual operation.

[0013] In one example, the present invention can be further configured as follows: The process involves evaluating the distribution of photovoltaic output across each receiving side based on the photovoltaic-storage operation data to obtain energy distribution evaluation indicators; and, based on the constraint model and the photovoltaic-storage operation data, constructing a distributed energy storage aggregation analysis model with the energy distribution evaluation indicators over multiple time periods as the objective function, including: Based on the photovoltaic power output data, the energy storage operation data, and the user load data, the photovoltaic power output, energy storage power supply, and energy storage power purchase for each time period are calculated to obtain a multi-time period power distribution sequence. The constraint model is used to perform constraint verification on the power allocation sequence. If the power allocation result satisfies the constraint model, a feasible power allocation sequence is determined. Based on the feasible power allocation sequence, a power allocation evaluation index is constructed to characterize photovoltaic utilization, grid interaction, and energy storage charging and discharging utilization. The energy distribution evaluation index is used as the objective function and combined with the constraint model to form the distributed energy storage aggregation analysis model.

[0014] By adopting the above technical solution, and calculating the photovoltaic power output, energy storage power supply, and energy storage purchase volume for each time period based on photovoltaic power output data, energy storage operation data, and user load data, it is possible to quantify the flow of electricity from photovoltaic to energy storage, the grid, and the user side, as well as the energy interaction between energy storage and the grid, over multiple time periods. This provides a precise foundation for analyzing photovoltaic-energy storage collaborative output modes and constructing energy allocation schemes. Furthermore, by performing constraint verification on the energy allocation sequence based on a constraint model and determining feasible energy allocation sequences when the energy allocation results satisfy the constraint model, schemes that violate power and energy constraints can be automatically eliminated when generating candidate allocation schemes, thereby narrowing the optimization search space and improving the solution. The physical feasibility of efficiency and results; by constructing an energy allocation evaluation index based on feasible energy allocation sequences to characterize photovoltaic utilization, grid interaction, and energy storage charging and discharging utilization, the multi-source energy flow and its operational contribution can be transformed into a unified quantitative index, thus facilitating a comprehensive balance of the effects of photovoltaic absorption, grid power purchase and sale, and energy storage participation in regulation during the optimization process; by using the energy allocation evaluation index as the objective function and combining it with the constraint model to form a distributed energy storage aggregation analysis model, the coupling relationship between optimization objectives and physical constraints can be explicitly considered in the same model, thus providing a rigorous mathematical foundation for subsequent solutions to obtain a control strategy that takes into account both operational constraints and allocation performance.

[0015] In one example, the present invention can be further configured as follows: constructing an energy allocation evaluation index to characterize photovoltaic utilization, grid interaction, and energy storage charging / discharging utilization based on the feasible energy allocation sequence includes: The feasible power allocation sequence is classified according to the relevant power of photovoltaic, power grid and energy storage to obtain the sub-items of power; The sub-items of electrical energy are combined with the corresponding electricity price operation data, and the electrical energy allocation evaluation index is constructed based on the allocation of each sub-item of electrical energy in multiple time periods.

[0016] By adopting the above technical solutions, and classifying feasible power allocation sequences according to the relevant power of photovoltaic, grid, and energy storage, the power contributions of photovoltaic, grid, and energy storage sides can be separated from the overall allocation results. This facilitates the analysis of the division of roles of different resources in coordinated regulation and provides a basis for subsequent sub-item optimization. By combining the sub-item power with the corresponding electricity price operation data and constructing power allocation evaluation indicators based on the allocation of various sub-item power in multiple time periods, the comprehensive impact of power flow and price signals can be reflected in the evaluation. This makes power allocation optimization more in line with the electricity market price structure and improves the temporal rationality and comprehensive benefits of power utilization.

[0017] In one example, the present invention can be further configured as follows: optimizing and solving the distributed energy storage aggregation analysis model to generate a photovoltaic-storage coordinated control strategy under the conditions of satisfying the constraint model includes: Decision parameters for characterizing the energy allocation results across multiple time periods are obtained from the distributed energy storage aggregation analysis model. Based on the multiverse optimization theory, the decision parameters are represented as position vectors of individual universes, and multiple individual universes are randomly initialized to obtain an initial universe population. Based on the distributed energy storage aggregation analysis model, the evaluation index of electrical energy distribution corresponding to each of the universe individuals in the initial universe population is calculated to obtain the evaluation results corresponding to each of the universe individuals; Based on the evaluation results, the universe population is iteratively updated using the update mechanism in the multiverse optimization theory to obtain the iteratively optimized population. The final cosmic individual with the best energy distribution evaluation index is selected from the optimized population, and the multi-time period energy distribution sequence corresponding to the final cosmic individual is determined as the photovoltaic-storage synergistic regulation strategy.

[0018] By adopting the above technical solution, and by obtaining decision parameters from the distributed energy storage aggregation analysis model to characterize the energy allocation results across multiple time periods, the photovoltaic allocation ratio and energy storage charging and discharging behavior across multiple time periods can be abstracted into a unified parameter set. This facilitates systematic searching and comparison of different control schemes in the optimization algorithm. By representing the decision parameters as position vectors of cosmic individuals based on the multiverse optimization theory and randomly initializing multiple cosmic individuals, widely distributed initial candidate solutions can be generated in the multidimensional decision space, thereby reducing the risk of getting trapped in local optima and improving global search capabilities. By calculating the energy allocation evaluation index corresponding to each cosmic individual in the initial cosmic population according to the distributed energy storage aggregation analysis model, the multi-time period allocation effect of each candidate control scheme can be evaluated. This transforms the results into comparable evaluations, providing a basis for subsequent population updates based on evaluation merits. By iteratively updating the cosmic population using the update mechanism in multiverse optimization theory based on the evaluation results, candidate solutions can be gradually guided to converge toward regions with better energy allocation evaluation indicators while satisfying the constraint model, thereby improving the overall quality of the obtained photovoltaic-storage coordinated control scheme. By selecting the final cosmic individual with the best energy allocation evaluation indicators from the optimized population and determining its corresponding multi-time period energy allocation sequence as the photovoltaic-storage coordinated control strategy, the optimization results can be directly mapped into executable photovoltaic power output allocation and energy storage charging and discharging plans. This effectively improves the photovoltaic absorption level, smooths grid power fluctuations, and enhances the comprehensive adaptability of the photovoltaic-storage system in the electricity market during actual operation.

[0019] In a second aspect, the present invention provides a photovoltaic-storage coordinated regulation and optimization system for the power market, the system comprising: The operation data module is used to acquire optical storage operation data; The constraint modeling module is used to establish the power distribution relationship of photovoltaic output and limit the energy storage discharge to only be used to compensate user load based on the photovoltaic-storage operation data and the preset user-side operation model, and to determine the power and energy constraints of the photovoltaic-storage system in multiple time periods, thereby obtaining the constraint model. The aggregation modeling module is used to evaluate the distribution of photovoltaic power output on each receiving side based on the photovoltaic-storage operation data, obtain the power distribution evaluation index, and construct a distributed energy storage aggregation analysis model with the power distribution evaluation index in multiple time periods as the objective function based on the constraint model and the photovoltaic-storage operation data. The strategy solving module is used to optimize and solve the distributed energy storage aggregation analysis model, and generate a photovoltaic-storage coordinated control strategy under the condition of satisfying the constraints of the model.

[0020] By adopting the above technical solutions and acquiring photovoltaic-storage operation data, it is possible to simultaneously grasp multi-source operation information such as photovoltaic output, energy storage status, user load, and electricity price under a unified time reference. This provides complete and reliable basic data for subsequent constraint modeling and strategy optimization, avoiding biases caused by relying on empirical assumptions. By determining the power and energy constraints of the photovoltaic-storage system in multiple time periods based on photovoltaic-storage operation data and a pre-set user-side operation model, it is possible to accurately characterize the photovoltaic output boundary, energy storage power capacity, and energy evolution range at the model level, thereby ensuring that subsequent regulation strategies always fall within safe and feasible physical and operational constraints. Furthermore, by using constraint models and photovoltaic-storage operation data… A distributed energy storage aggregation analysis model is constructed with the evaluation index of power distribution in multiple time periods as the objective function. This model can couple photovoltaic utilization, grid interaction, and energy storage charging and discharging behavior with the power distribution effect in multiple time periods in a unified model, thus providing a quantitative evaluation basis for multi-time period collaborative optimization. By optimizing and solving the distributed energy storage aggregation analysis model and generating photovoltaic-storage collaborative control strategies under the constraints of the model, the optimal photovoltaic-storage output and power distribution scheme in multiple time periods can be obtained while ensuring power balance and energy security. This improves the photovoltaic power consumption level, reduces the impact of grid fluctuations, and enhances the overall operating performance of the photovoltaic-storage system.

[0021] In a third aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned method for optimizing the coordinated regulation of photovoltaic and energy storage in the power market.

[0022] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned method for optimizing the coordinated regulation of photovoltaic and energy storage in an electricity market. Attached Figure Description

[0023] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a photovoltaic-storage coordinated regulation optimization method for the power market in an embodiment of the present invention; Figure 2 This is a structural block diagram of the photovoltaic-storage coordinated regulation and optimization system for the power market, as described in an embodiment of the present invention. Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0025] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0026] Example 1 like Figure 1 As shown, this invention discloses a method for optimizing the coordinated regulation of photovoltaic and energy storage in the power market, which specifically includes the following steps: S10: Obtain photovoltaic storage operation data.

[0027] Specifically, under a unified time reference, the operation status of photovoltaic power plants, energy storage power plants and related user sides is collected in multiple time periods according to a preset time resolution. The multi-source operation information, including photovoltaic output, energy storage rated capacity and energy status, user power consumption and electricity consumption records, and electricity market trading prices, is time-aligned and formatted. The aligned operation curves and parameter sets are then unified as photovoltaic-energy storage operation data for subsequent power and energy constraint modeling and distributed energy storage aggregation analysis.

[0028] S20: Based on the photovoltaic and energy storage operation data and the preset user-side operation model, establish the power distribution relationship of photovoltaic output and limit the energy storage discharge to be used only to compensate user load, determine the power and energy constraints of the photovoltaic and energy storage system in multiple time periods, and obtain the constraint model.

[0029] Specifically, taking into account the user-side operation characteristics of photovoltaic power generation as a surplus and energy storage not being able to reverse grid connection under current policies, this paper utilizes the correlation between photovoltaic output, rated energy storage power, energy, user load, and electricity price signals to uniformly model the allocation of photovoltaic output among energy storage charging, grid supply, and user self-consumption in different time periods; the power behavior of energy storage charging from the grid and discharging to users; the power balance relationship between photovoltaic output, energy storage charging and discharging power, and user load power; and the evolution of energy storage energy over time. Based on this, multiple types of constraints are extracted, such as photovoltaic power allocation constraints, energy storage power mutual exclusion constraints, power balance constraints, and energy state constraints, and combined according to a unified mathematical form to form a constraint model for subsequent power allocation analysis and strategy optimization solutions.

[0030] S30: Based on the photovoltaic and energy storage operation data, the distribution of photovoltaic power on each receiving side is evaluated separately to obtain energy distribution evaluation indicators. Based on the constraint model and photovoltaic and energy storage operation data, a distributed energy storage aggregation analysis model is constructed with the energy distribution evaluation indicators in multiple time periods as the objective function.

[0031] Specifically, the parameter combination representing photovoltaic power output allocation, energy storage charging and discharging behavior, and grid power purchase and sale behavior is used as decision variables. Within the power balance and energy state range defined by the constraint model, an objective function is constructed in the form of summation or weighting of power allocation evaluation indicators for multiple time periods. This objective function is then combined with power constraints and energy constraints for multiple time periods to form a distributed energy storage aggregation analysis model that can characterize the advantages and disadvantages of different power allocation schemes under given photovoltaic and energy storage operation data, providing a mathematical basis for subsequent optimization solutions.

[0032] S40: Optimize and solve the distributed energy storage aggregation analysis model, and generate a photovoltaic-storage coordinated control strategy under the condition of satisfying the constraints of the model.

[0033] Specifically, taking the decision variables in the distributed energy storage aggregation analysis model as the optimization object, within the power and energy feasible domain defined by the constraint model, a search process for multiple candidate photovoltaic-energy storage synergistic control schemes is constructed through the multiverse optimization theory. The obtained optimal energy allocation scheme is mapped to the photovoltaic allocation curve for various uses and the energy storage charging and discharging curve for multiple time periods. This enables the photovoltaic-energy storage synergistic control strategy to achieve the optimal energy allocation evaluation index under the premise of satisfying the power balance and energy state constraints of multiple time periods.

[0034] In one embodiment, step S10, namely acquiring optical storage operation data, includes: S11: Obtain the photovoltaic output of the photovoltaic power station in multiple preset time periods to obtain photovoltaic output data.

[0035] Specifically, taking 96 time periods within a day as an example, the active power output P at each time i is read from the photovoltaic power station's monitoring and control device or historical database according to a fixed sampling interval T. 光,i P 光,i To determine the available power generation of the photovoltaic array under current weather conditions and operating conditions, P at each time point... 光,i The photovoltaic output time series is composed in chronological order, and abnormal missing values ​​can be interpolated or smoothed as needed before being uniformly stored as photovoltaic output data for subsequent power distribution relationship and power distribution calculation.

[0036] S12: Obtain the rated power, rated energy, and energy storage status of the energy storage power station for a preset period to obtain energy storage operation data.

[0037] Specifically, the rated energy storage power P is recorded according to the technical parameters of the energy storage power station. 储 and rated energy storage E 储 This is used as a fixed upper limit parameter in subsequent constraints, and the energy storage energy e corresponding to each time i is obtained or estimated at multiple preset time periods. 储,i or its corresponding state of charge, e 储,i It can be obtained by integrating the historical charge and discharge power of energy storage, or it can be directly measured by a monitoring system. 储 E 储 and e at each moment 储,i The data is stored in association according to the device dimension and the time dimension to form energy storage operation data that describes the rated capacity and initial energy level of energy storage.

[0038] S13: Obtain the power consumption and electricity consumption records of associated users in multiple preset time periods to obtain user load data.

[0039] Specifically, the user's self-consumption power P at each time point i is read from the user's metering device or energy management platform. 用,i And the corresponding electricity consumption record information, including the user's electricity price category, electricity consumption time period division, and the electricity price J agreed with the user. 用,i Wait, P 用,i and J 用,i Organized into a time series by time index, and user load can be denoised, interpolated, or statistically analyzed as needed to obtain user load data that reflects the characteristics of user load changes and electricity price signals, providing a basis for subsequent power balance relationships and energy distribution evaluation.

[0040] S14: Obtain electricity price information for multiple preset time periods to obtain electricity price operation data.

[0041] Specifically, based on the business rules of the electricity market, such as spot trading, agency power purchase, peak shaving and valley filling, and virtual power plants, the price of electricity purchased from the grid at each time point (i) is read from the market operation platform or price database. 买,i and the price J of electricity sold to the power grid 卖,i J 买,i It can correspond to the spot market clearing price or the power purchase agreement price, J 卖,i This can be applied to high-price electricity sales scenarios such as peak-shaving markets, virtual power plants, or mechanism-based electricity pricing, and will... 买,i and J 卖,i Aligned with the same time scale as photovoltaic output and user load, electricity price operation data covering multiple preset time periods is generated, providing price weights for the construction of electricity distribution evaluation indicators.

[0042] S15: Time-align photovoltaic output data, energy storage operation data, user load data, and electricity price operation data under a unified time reference to obtain photovoltaic-energy storage operation data.

[0043] Specifically, a unified time step T and a preset set of time indices for multiple time periods are determined, and the photovoltaic power output time series P is... 光,i Energy storage sequence e 储,i and its rated parameters P 储 E 储 User load sequence P 用,i and price series J 用,i And electricity price operation data J 买,i J 卖,i Resampling or interpolation is performed using the same time index. For data with missing values ​​or different sampling frequencies, the nearest interpolation or interval averaging is used to map it to a unified time base. After alignment, the above multi-source data is organized and stored in the form of a matrix or multi-dimensional array, and the whole is used as the optical storage operation data input into the subsequent constraint modeling and optimization analysis process.

[0044] In one embodiment, in step S20, based on the photovoltaic-storage operation data and a preset user-side operation model, a power distribution relationship for photovoltaic output is established and the energy storage discharge is limited to compensating user loads only. This determines the power and energy constraints of the photovoltaic-storage system over multiple time periods, resulting in a constraint model, including: S21: Based on photovoltaic output data and user load data, establish the power distribution relationship between photovoltaic output in each time period and energy storage charging, grid power supply and user self-consumption, and obtain photovoltaic power distribution constraints.

[0045] Specifically, for each time i, the photovoltaic output P 光,iAccording to the preset allocation ratio α i β i γ i The electricity is allocated to three parts: energy storage charging, grid sales, and user self-consumption, respectively, i.e., let α i P 光,i Corresponding to the power of photovoltaic charging to energy storage, β i P 光,i Corresponding to the grid-connected power of photovoltaic power, γ i P 光,i This corresponds to the photovoltaic power directly supplied to users for their own consumption, while constraining α. i β i γ i All are non-negative real numbers and satisfy α i +β i +γ i =1, to ensure that the photovoltaic output is fully absorbed among the three uses in each period, thereby abstracting the above power distribution relationship into a photovoltaic power distribution constraint for subsequent power balance and power distribution calculations.

[0046] S22: Based on energy storage operation data, establish the power relationship between energy storage charging from the grid and discharging to users, and limit the charging and discharging relationship of energy storage in the same period to obtain energy storage power mutual exclusion constraints.

[0047] Specifically, the energy storage charging and discharging power is expressed as the energy storage rated power P. 储 The dimensionless coefficient form based on i, that is, at each time i let a i P 储 Let b represent the charging power of the energy storage system when it purchases electricity from the grid. i P 储 This represents the power that the energy storage discharges to the user, and is constrained to 0. i b i <1. To limit the actual charging and discharging power to no more than the rated power, and to avoid the physical contradiction of simultaneous charging and discharging within the same time period, a further setting is made. i ×b i The mutual exclusion constraint is set to 0, and α is set in conjunction with the photovoltaic charging behavior. i ×a i =0 and α i ×b i =0 and other relationships are established to ensure that there is no conflict between photovoltaic charging and grid charging behavior when there are scenarios where photovoltaics charge energy storage or energy storage discharges. This results in energy storage power mutual exclusion constraints that describe the relationship between energy storage power purchase and charging and power discharge to users in multiple time periods and reflect the mutual exclusion characteristics of charging and discharging.

[0048] S23: Based on the photovoltaic power allocation constraints and energy storage power mutual exclusion constraints, establish the power balance relationship between photovoltaic output, energy storage charging and discharging power and user load power in each time period, and obtain the power balance constraints.​

[0049] Specifically, for each time i, while ensuring the user load P 用,i Assuming these conditions are met, the agreed-upon direct photovoltaic power supplied to users for their own consumption is γ. i P 光,i The energy storage provides users with a power output of b. i P 储 The energy storage system purchases electricity from the grid and has a charging capacity of a. i P 储 Then the power balance relationship on the user side can be written as γ i P 光,i +b i P 储 ≤P 用,i Meanwhile, the total power purchased from the grid is defined as P. 买,i And let P 买,i =P 用,i -γ i P 光,i -b i P 储 +a i P 储 The power P sold to the grid 卖,i Defined as P 卖,i =β i P 光,i This allows for the construction of a complete power balance relationship between photovoltaic output, energy storage charging and discharging power, grid power purchase and sale power, and user load power at any given moment, and this relationship can be incorporated into the constraint model as a power balance constraint.

[0050] S24: Based on adjacent time periods, establish a recursive relationship between the energy storage energy of the previous time period and the energy storage charging and discharging power of the current time period, and limit the energy storage energy within the preset rated energy range to obtain energy state constraints.

[0051] Specifically, a discrete time step T is used to model the energy evolution process of the energy storage. For each time i, the energy stored at the previous time e is... 储,i Based on this, the energy increment α introduced by photovoltaic charging at the current moment is... i P 光,i ×T, Energy increment a introduced by grid charging i P 储 ×T and the energy reduction b caused by discharging to the user i P 储 Adding and subtracting ×T together, we obtain the stored energy e at the next moment. 储,i +1=e 储,i +α i P 光,i ×Tb i P 储 ×T+ai P 储 ×T, and simultaneously apply e 储,i +1 <E 储 and e 储,i Constraints such as +1 not lower than the set lower limit are used to ensure that the energy storage energy is within the preset rated energy range in each time period, thereby forming an energy state constraint that is continuously recursively applied in the time dimension and meets the physical boundary conditions.

[0052] S25: Combine photovoltaic power allocation constraints, energy storage power mutual exclusion constraints, power balance constraints, and energy state constraints to form a constraint model.

[0053] Specifically, the constraint equations corresponding to each time i are uniformly organized in chronological order, and the constraint sets for all time periods are expressed in matrix form. Additional constraints such as electricity price boundaries and transaction declaration capacity limits can be introduced as needed. The above-mentioned multiple power constraints and energy constraints are connected in series and parallel to form a constraint model that characterizes the operating boundary conditions and market participation restrictions of the photovoltaic-storage system in multiple time periods, providing a unified constraint framework for subsequent power allocation analysis and optimization solutions.

[0054] In one embodiment, step S24 involves establishing a recursive relationship between the stored energy of the previous time period and the stored energy charging and discharging power of the current time period based on adjacent time periods, and limiting the stored energy within a preset rated energy range to obtain energy state constraints, including: S241: Obtain the photovoltaic charging power, grid charging power, and discharge power for each time period.

[0055] Specifically, based on the established photovoltaic power allocation constraints and energy storage power mutual exclusion constraints, at each moment i, the photovoltaic output P will be... 光,i With the allocation ratio α i Multiplying them together gives the photovoltaic charging power α. i P 光,i The rated power of energy storage P 储 With grid charging coefficient a i Multiply to obtain the grid charging power a i P 储 , will P 储 With discharge coefficient b i Multiplying them together yields the energy storage and discharge power b. i P 储 The above three types of power quantities can be recorded as photovoltaic charging power sequence, grid charging power sequence and discharge power sequence according to time index, which can be used for subsequent energy balance calculation and energy state constraint generation.

[0056] S242: Based on each adjacent time period, perform energy balance calculation on the energy storage energy of the previous time period according to the photovoltaic charging power, grid charging power and discharge power to obtain the current energy storage energy.

[0057] Specifically, for adjacent times i and i+1, the stored energy e at time i is... 储,i As an initial value, the photovoltaic charging power α is increased within a time step T. i P 光,i and grid charging power a i P 储 The corresponding energy increment α i P 光,i ×T and a i P 储 Accumulate ×T, and simultaneously increase the discharge power b i P 储 The corresponding energy reduction amount b i P 储 ×T from e 储,i Subtracting from the value, we obtain the stored energy e at the current moment. 储,i +1=e 储,i +α i P 光,i ×Tb i P 储 ×T+a i P 储 ×T, by repeating the above energy balance calculation based on power integral over all adjacent time periods, the energy storage sequence at each time point is obtained sequentially, providing a basis for applying energy range constraints.

[0058] S243: Limit the current energy storage capacity according to the preset rated energy range to obtain the current energy storage capacity constraint.

[0059] Specifically, the upper limit of energy storage capacity is determined as E based on the design parameters of the energy storage power station. 储 And set the lower limit to a preset minimum energy or minimum state of charge, and at each time i, adjust the e calculated from the energy balance. 储,i Perform range validation when e 储,i More than E 储 When e 储,i If the value is below the lower limit, it will also be truncated or determined to be infeasible, and the condition that the lower limit ≤ e will be satisfied. 储,i ≤E 储 The energy value of the condition is marked as the feasible state under the current energy storage constraint. By performing this constraint process on all times, a set of current energy storage constraints containing information on the energy feasible intervals of each time period is formed.

[0060] S244: Connect the current energy storage constraints of each time period in series to form the energy state constraints of the photovoltaic-storage system in multiple time periods.

[0061] Specifically, using the time axis as the main line, the energy constraint interval corresponding to each time i is linked to the energy states of the preceding and following time points obtained from the energy balance recursion relationship, while ensuring that the energy recursion equation e of adjacent time points is maintained. 储,i +1=e 储,i +α i P 光,i ×Tb i P 储 ×T+a i P 储 Under the premise that ×T holds, the upper and lower limits of energy at each moment form a continuous time chain. By performing an overall consistency check and sorting of this time chain, energy state constraints covering all preset time periods are obtained, which are used to limit the energy evolution boundary of the photovoltaic-storage system in multi-time period joint operation.

[0062] In one embodiment, in step S30, the distributed electrical energy of photovoltaic output on each receiving side is evaluated based on the photovoltaic-storage operation data to obtain an energy distribution evaluation index. Based on the constraint model and the photovoltaic-storage operation data, a distributed energy storage aggregation analysis model is constructed with the energy distribution evaluation index over multiple time periods as the objective function, including: S31: Based on photovoltaic power output data, energy storage operation data, and user load data, calculate the photovoltaic power output, energy storage power supply, and energy storage power purchase for each time period to obtain a multi-time period power distribution sequence.

[0063] Specifically, the power is quantified using a uniform time step T. For each time i, the power used for energy storage charging, grid sales, and user consumption is determined by photovoltaic power allocation constraints as α. i P 光,i β i P 光,i and γ i P 光,i Multiplying the above power by the time step T yields the corresponding electricity, and the photovoltaic power output can be defined as α. i P 光,i ×T+β i P 光,i ×T+γ i P 光,i ×T, where α i P 光,i ×T and β i P 光,i ×T correspond to the amount of electricity transmitted from photovoltaic power to energy storage and the amount of electricity fed into the grid from photovoltaic power sources, respectively. For the amount of electricity supplied by energy storage and the amount of electricity purchased by energy storage, the coefficient 'a' in the energy storage operation data is used. i b i and rated power P 储 Calculate the power b supplied by the energy storage to the user at each moment. i P储 and the power purchased from the grid a i P 储 Multiplying it by the time step T yields the energy storage power supply b. i P 储 ×T and energy storage purchase volume a i P 储 ×T, and finally, the photovoltaic power output, energy storage power supply and energy storage power purchase at all times are arranged in chronological order to form a multi-time period power allocation sequence, which is used for subsequent constraint verification and evaluation index construction.

[0064] S32: Based on the constraint model, perform constraint verification on the power allocation sequence, and determine the feasible power allocation sequence if the power allocation result satisfies the constraint model.

[0065] Specifically, the photovoltaic allocation coefficient α at each moment corresponding to the multi-time period electrical energy allocation sequence is... i β i γ i and the energy storage charge / discharge coefficient a i b i Substituting the constraints into the photovoltaic power allocation constraints, energy storage power mutual exclusion constraints, power balance constraints, and energy state constraints, we check hourly whether α is satisfied. i +β i +γ i =1、0 i b i <1、a i ×b i =0、γ i P 光,i +b i P 储 ≤P 用,i e 储,i +1=e 储,i +α i P 光,i ×Tb i P 储 ×T+a i P 储 ×T and e 储,i +1 Under the conditions of a preset energy range, the corresponding energy allocation scheme is determined to be infeasible and eliminated when any constraint condition is not met. The energy allocation sequence that satisfies the constraint model at all times is retained and marked as a feasible energy allocation sequence as a candidate scheme set for subsequent evaluation index construction.

[0066] S33: Based on the feasible energy allocation sequence, construct an energy allocation evaluation index to characterize photovoltaic utilization, grid interaction, and energy storage charging and discharging utilization.

[0067] ​Specifically, at each time i, the grid-connected photovoltaic power P obtained according to the feasible electrical energy allocation sequence. 卖,i Power purchased from the grid P 买,i and user load power P 用,i Combined with the corresponding electricity price operation data J 卖,i J 买,i And user-side price J 用,i Construct a single-period electrical energy distribution evaluation index S i =P 卖,i ×J 卖,i +P 用,i ×J 用,i -P 买,i ×J 买,i Then, within the entire preset time range, the S values ​​for each time period are... i By summing or weighted aggregation, a total evaluation index or multi-dimensional evaluation vector is obtained to comprehensively measure the utilization level of photovoltaic power, the intensity of power purchase and sale interaction with the grid, and the contribution of energy storage charging and discharging. By comparing the magnitude of this power allocation evaluation index, the advantages and disadvantages of different power allocation schemes at the technical and economic levels can be reflected, providing a basis for the objective function for optimization.

[0068] S34: Combine the evaluation index of electrical energy distribution as the objective function with the constraint model to form a distributed energy storage aggregation analysis model.

[0069] Specifically, the cumulative value or weighted sum of the overall evaluation index over all preset time periods is used as the objective function to be maximized. Photovoltaic power allocation constraints, energy storage power mutual exclusion constraints, power balance constraints, and energy state constraints are used as equality or inequality constraints. The photovoltaic allocation ratio α is... i β i γ i and the energy storage charge / discharge coefficient a i b i The multi-time parameter set is used as the decision variable to be optimized, and a high-dimensional, nonlinear optimization problem is constructed in the decision space, thus forming a distributed energy storage aggregation analysis model. This model can characterize the advantages and disadvantages of different photovoltaic-storage coordinated control schemes under the condition of given photovoltaic-storage operation data and market price signals, providing a basis for subsequent solution using the multivariate universe optimization algorithm.

[0070] In one embodiment, step S33 involves constructing an energy allocation evaluation index to characterize photovoltaic utilization, grid interaction, and energy storage charging and discharging utilization based on a feasible energy allocation sequence, including: S331: Classify the feasible electrical energy allocation sequence according to the relevant electrical energy of photovoltaic, power grid and energy storage to obtain the sub-item electrical energy.

[0071] Specifically, for each time i, the amount of electricity α that the photovoltaic power will charge the energy storage is extracted from the feasible electrical energy allocation sequence. i P 光,i ×T, Photovoltaic grid-connected electricity β i P 光,i ×T and photovoltaic power are directly supplied to users for their own consumption. i P 光,i ×T, the above three parts are classified into photovoltaic-related electrical energy components according to their sources; at the same time, the amount of electricity b supplied to users by energy storage is extracted. i P 储 ×T and the amount of electricity purchased from the grid for energy storage charging a i P 储 ×T, and categorized into energy storage-related electrical energy components according to their application; further, it will be combined with P purchased from the grid. 买,i And selling electricity to the grid P 卖,i The corresponding electricity is classified as grid-related electrical energy components. By traversing the above classification process over all time periods, the feasible electrical energy allocation sequence is decomposed into photovoltaic-related, grid-related, and energy storage-related sub-item electrical energy sets, providing a basis for constructing detailed evaluation indicators in conjunction with electricity price information.

[0072] S332: Combine the sub-items of electrical energy with the corresponding electricity price operation data, and construct an evaluation index for electrical energy allocation based on the distribution of various sub-items of electrical energy in multiple time periods.

[0073] Specifically, for the photovoltaic-related electrical energy portion, the photovoltaic grid-connected electricity β will be... i P 光,i ×T and J 卖,i Multiplication, photovoltaic self-consumption γ i P 光,i ×T and J 用,i Multiplying to reflect the value contribution of photovoltaics in market electricity sales and user self-consumption, for the energy storage-related electrical energy portion, the energy storage power supply b is... i P 储 ×T and J 用,i Multiplying the energy storage to reflect its role in peak shaving and valley filling and improving the value of electricity consumption on the user side, the amount of electricity purchased using energy storage is 'a'. i P 储 ×T and J 买,i Multiplying the components to reflect the cost of energy storage charging, for the grid-related electrical energy portion, will be done by multiplying the electricity purchased from the grid by J. 买,i Electricity sold to the grid and J 卖,i By combining the costs and benefits of grid interaction behavior, and then accumulating or statistically analyzing the weighted results over multiple time periods, an energy allocation evaluation index is constructed that can reflect photovoltaic utilization, grid interaction level, and energy storage charging and discharging regulation effect. This index can be used as the objective function or a component of the optimization solution.

[0074] In one embodiment, step S40 involves optimizing the distributed energy storage aggregation analysis model to generate a photovoltaic-energy storage coordinated control strategy under the constraints of the model, including: S41: Obtain decision parameters from the distributed energy storage aggregation analysis model to characterize the energy allocation results across multiple time periods.

[0075] Specifically, the photovoltaic allocation ratio α in the model i β i γ i and the energy storage charge / discharge coefficient a i b i The values ​​taken in all preset time periods are arranged and combined in a fixed order to form a parameter vector of dimension d, where d is equal to the product of the number of decision variables and the number of time steps. This parameter vector is regarded as the decision parameters that fully describe the energy distribution results of a candidate photovoltaic-storage coordinated control scheme in multiple time periods, and is used as the position representation of an individual universe in the multiverse optimization algorithm.

[0076] S42: Based on the multiverse optimization theory, the decision parameters are represented as position vectors of individual universes, and multiple individual universes are randomly initialized to obtain the initial universe population.

[0077] Specifically, each candidate's combination of decision parameters is considered as a d-dimensional vector x=[x1,x2,…,x...]. d ] T Multiple vectors of this type are stacked in rows or columns to form a cosmic population matrix U, where the nth row (or nth column) of U corresponds to the position vector of the nth cosmic individual. For the initialization process, given the upper and lower bounds of each dimension, initial values ​​are generated for each dimension in a uniform or pseudo-random manner within the feasible range, so that the initial positions of all cosmic individuals are distributed in the global search space. At the same time, the initial individuals can be simply screened for feasibility by combining the constraint model, so as to obtain the initial cosmic population that meets the basic constraint conditions, and provide the starting solution set for subsequent selection and movement operations.

[0078] S43: Based on the distributed energy storage aggregation analysis model, the evaluation index of electrical energy distribution corresponding to each individual universe in the initial universe population is calculated to obtain the evaluation results corresponding to each individual universe.

[0079] Specifically, the position vector of each individual in the universe is interpreted as a specific set of α. i β i γ i a i b iThe parameters are substituted into the constraint model and the power distribution calculation formula. First, the corresponding multi-time period power distribution sequence and energy storage sequence are calculated based on the power balance relationship and the energy state recursion relationship. Then, the S at each time moment is calculated according to the definition of the power distribution evaluation index. i The overall evaluation result is obtained by summarizing multiple time periods. At the same time, a feasibility penalty is imposed on the individual universe based on whether it meets all power and energy constraints, or infeasible individuals are eliminated. Finally, a normalized or unnormalized evaluation result is obtained for each individual universe, which is used for white hole, black hole and wormhole operations in subsequent multiverse optimization.

[0080] S44: Based on the evaluation results, the cosmic population is iteratively updated using the update mechanism in the multiverse optimization theory to obtain the optimized population after iteration.

[0081] Specifically, the normalized expansion rate NI(U) is calculated based on the evaluation results of each individual universe. i Then, using a roulette wheel mechanism, objects are transferred between white holes and black holes. For the j-th dimension variable in an individual universe, when the random number r1 <NI(U i When ), replace it with the value x of the same dimension of a selected individual in the universe. j,k Otherwise, it remains unchanged to simulate the exchange of objects between white holes and black holes; based on this, without considering the magnitude of the expansion rate, wormholes are constructed between all individual universes and the current optimal universe, and the position of each individual universe in the j-th dimension is determined according to the formula x. j,i =X j +TDR×(ubj-lbj)×r3 or x j,i =X j -TDR×(ubj-lbj)×r3 towards the optimal cosmic position X j The offset is calculated, where WEP is the probability of the wormhole's existence, TDR is the travel distance, and WEP increases with the iteration number l according to the formula WEP = WEP. min +(WEP max -WEP min The TDR increases linearly by 1 - (l / L)1 / p, and decreases by TDR = 1 - (l / L)1 / p. Through the update mechanism that combines the orbits of white holes / black holes with the movement of wormholes, a new cosmic population is generated in each iteration. After the update, infeasible individuals are eliminated again according to the constraint model, thus obtaining an iteratively optimized population that gradually approaches the optimal solution region.

[0082] S45: Select the final cosmic individual with the best energy distribution evaluation index from the optimized population, and determine the multi-time period energy distribution sequence corresponding to the final cosmic individual as the photovoltaic-storage synergistic regulation strategy.

[0083] Specifically, after a preset maximum number of iterations or convergence condition for the evaluation index, the cosmic individual with the largest electrical energy allocation evaluation index value under the premise of satisfying all power constraints and energy state constraints is selected from the current optimization population. The position vector of this cosmic individual is then restored to a specific multi-time period photovoltaic allocation ratio α. i β i γ i and the energy storage charge / discharge coefficient a i b i Based on these parameters, the corresponding multi-period power distribution sequence and energy storage trajectory are recalculated. The power curves of photovoltaic power for energy storage charging, grid power sales and user self-use, as well as the power curves of energy storage power purchase from the grid for charging and discharging to users, given in the sequence are used together as the execution instructions of the photovoltaic-energy storage coordinated control strategy, providing a basis for the coordinated control of photovoltaic power plants, energy storage power plants and user loads in actual operation.

[0084] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a photovoltaic-storage coordinated regulation and optimization system for the power market, comprising: The operation data module is used to acquire optical storage operation data; The constraint modeling module is used to establish the power distribution relationship of photovoltaic output based on photovoltaic and energy storage operation data and a preset user-side operation model, and to limit the energy storage discharge to be used only to compensate user load. It determines the power and energy constraints of the photovoltaic and energy storage system in multiple time periods and obtains the constraint model. The aggregation modeling module is used to evaluate the distribution of photovoltaic power output on each receiving side based on photovoltaic and energy storage operation data, obtain energy distribution evaluation indicators, and construct a distributed energy storage aggregation analysis model with energy distribution evaluation indicators in multiple time periods as the objective function based on the constraint model and photovoltaic and energy storage operation data. The strategy solving module is used to optimize and solve the distributed energy storage aggregation analysis model, and generate a photovoltaic-storage coordinated control strategy under the condition of satisfying the constraints of the model.

[0085] Optionally, the running data module includes: The photovoltaic output submodule is used to obtain the photovoltaic output of the photovoltaic power station in multiple preset time periods and obtain photovoltaic output data; The energy storage operation submodule is used to obtain the rated power, rated energy, and energy storage status of the energy storage power station during a preset period, and to obtain energy storage operation data. The user load submodule is used to obtain the power consumption and electricity consumption records of associated users in multiple preset time periods to obtain user load data; The electricity price acquisition submodule is used to acquire electricity price information for multiple preset time periods to obtain electricity price operation data; The time alignment submodule is used to align photovoltaic power output data, energy storage operation data, user load data, and electricity price operation data under a unified time reference to obtain photovoltaic and energy storage operation data.

[0086] Optional, the constraint modeling module includes: The photovoltaic distribution submodule is used to establish the power distribution relationship between photovoltaic output in different time periods and energy storage charging, grid power supply and user self-consumption based on photovoltaic output data and user load data, so as to obtain photovoltaic power distribution constraints. The energy storage mutual exclusion submodule is used to establish the power relationship between energy storage charging from the grid and discharging to users based on energy storage operation data, and to limit the charging and discharging relationship of energy storage in the same period to obtain energy storage power mutual exclusion constraints. The power balance submodule is used to establish the power balance relationship between photovoltaic output, energy storage charging and discharging power and user load power in each time period based on photovoltaic power allocation constraints and energy storage power mutual exclusion constraints, so as to obtain power balance constraints. The energy recursion submodule is used to establish a recursive relationship between the energy storage energy of the previous period and the energy storage charging and discharging power of the current period based on adjacent time periods, and to limit the energy storage energy within a preset rated energy range to obtain energy state constraints. The constraint combination submodule is used to combine photovoltaic power allocation constraints, energy storage power mutual exclusion constraints, power balance constraints, and energy state constraints to form a constraint model.

[0087] Optional, the energy recursion submodule includes: The power acquisition unit is used to acquire the photovoltaic charging power, grid charging power and discharge power for each time period; The energy calculation unit is used to perform energy balance calculations on the energy storage energy of the previous period based on the photovoltaic charging power, grid charging power and discharging power for each adjacent time period, so as to obtain the current energy storage energy. An energy limiting unit is used to limit the current energy storage energy according to a preset rated energy range, thereby obtaining the current energy storage energy constraint. The energy series unit is used to connect the current energy storage constraints of each time period in series to form the energy state constraints of the photovoltaic-storage system in multiple time periods.

[0088] Optionally, the aggregation modeling module includes: The power calculation submodule is used to calculate the photovoltaic power output, energy storage power supply and energy storage power purchase for each time period based on photovoltaic power output data, energy storage operation data and user load data, so as to obtain the power distribution sequence for multiple time periods. The feasibility verification submodule is used to perform constraint verification on the power allocation sequence based on the constraint model. If the power allocation result satisfies the constraint model, a feasible power allocation sequence is determined. The index construction submodule is used to construct an evaluation index for energy allocation that characterizes photovoltaic utilization, grid interaction, and energy storage charging and discharging utilization based on feasible energy allocation sequences. The model combination submodule is used to combine the power distribution evaluation index as the objective function with the constraint model to form a distributed energy storage aggregation analysis model.

[0089] Optionally, the metric construction submodule includes: The power classification unit is used to classify the feasible power allocation sequence according to the relevant power of photovoltaic, grid and energy storage to obtain the sub-item power; The indicator generation unit is used to combine the sub-items of electrical energy with the corresponding electricity price operation data, and construct the electrical energy allocation evaluation index based on the distribution of various sub-items of electrical energy in multiple time periods.

[0090] Optionally, the strategy solving module includes: The parameter acquisition submodule is used to obtain decision parameters from the distributed energy storage aggregation analysis model to characterize the power allocation results over multiple time periods. The population initialization submodule is used to represent decision parameters as position vectors of cosmic individuals based on the multiverse optimization theory, and to randomly initialize multiple cosmic individuals to obtain the initial cosmic population. The index calculation submodule is used to calculate the evaluation index of electrical energy distribution for each individual universe in the initial universe population based on the distributed energy storage aggregation analysis model, and obtain the evaluation result corresponding to each individual universe. The population update submodule is used to iteratively update the cosmic population based on the evaluation results through the update mechanism in the multiverse optimization theory, so as to obtain the optimized population after iteration. The strategy determination submodule is used to select the final cosmic individual with the best energy allocation evaluation index from the optimized population, and to determine the multi-time period energy allocation sequence corresponding to the final cosmic individual as the photovoltaic-storage synergistic regulation strategy.

[0091] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for realizing a method for optimizing the coordinated regulation of photovoltaic and energy storage in the power market; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0092] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the photovoltaic-storage coordinated regulation optimization method of the power market in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0093] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0094] At least one processor 102 may be a Central Processing Unit (CPU), or 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. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0095] The memory 101 in the electronic device 100 stores multiple instructions to implement a photovoltaic-storage coordinated regulation optimization method for the power market, and the processor 102 can execute multiple instructions to achieve the following: Acquire photovoltaic storage operation data; Based on the photovoltaic-storage operation data and the preset user-side operation model, the power distribution relationship of photovoltaic output is established and the energy storage discharge is limited to compensate user load only. The power and energy constraints of the photovoltaic-storage system in multiple time periods are determined, and the constraint model is obtained. Based on the photovoltaic and energy storage operation data, the distribution of photovoltaic power on each receiving side is evaluated in separate items to obtain energy distribution evaluation indicators. Based on the constraint model and photovoltaic and energy storage operation data, a distributed energy storage aggregation analysis model is constructed with the energy distribution evaluation indicators in multiple time periods as the objective function. The distributed energy storage aggregation analysis model is optimized and solved to generate a photovoltaic-storage coordinated control strategy under the constraints of the model.

[0096] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the coordinated regulation of photovoltaic and energy storage in the power market, characterized in that, The method comprises: acquiring photovoltaic storage operation data; establishing a power distribution relationship of photovoltaic output and limiting discharging of storage energy to only compensate for user load according to the photovoltaic storage operation data and a preset user-side operation model, determining power and energy constraints of the photovoltaic storage system in multiple time periods, and obtaining a constraint model; evaluating distribution energy of photovoltaic output at each receiving side according to the photovoltaic storage operation data to obtain an electric energy distribution evaluation index, constructing a distributed storage aggregation analysis model with the electric energy distribution evaluation index in multiple time periods as an objective function based on the constraint model and the photovoltaic storage operation data; optimizing the distributed storage aggregation analysis model to generate a photovoltaic storage collaborative regulation strategy under the condition of meeting the constraint model. 2.The method of claim 1, wherein, The photovoltaic storage operation data is acquired by: acquiring photovoltaic output of a photovoltaic power station in a preset plurality of time periods to obtain photovoltaic output data; acquiring rated power, rated energy and storage energy state of a storage power station in a preset time period to obtain storage operation data; acquiring power and power consumption records of associated users in a preset plurality of time periods to obtain user load data; acquiring electricity price information in a preset plurality of time periods to obtain electricity price operation data; aligning the photovoltaic output data, the storage operation data, the user load data and the electricity price operation data in time under a unified time reference to obtain the photovoltaic storage operation data.

3. The method of claim 2, wherein, The constraint model is obtained by establishing a power distribution relationship of photovoltaic output and limiting discharging of storage energy to only compensate for user load according to the photovoltaic storage operation data and a preset user-side operation model, determining power and energy constraints of the photovoltaic storage system in multiple time periods, and comprising: based on the photovoltaic output data and the user load data, establishing a power distribution relationship of photovoltaic output in each time period between storage charging, grid power supply and user self-use to obtain a photovoltaic power distribution constraint; based on the storage operation data, establishing a power relationship of storage charging from the grid and discharging to the user, and limiting the charging and discharging relationship of the storage in the same time period to obtain a storage power mutual exclusion constraint; according to the photovoltaic power distribution constraint and the storage power mutual exclusion constraint, establishing a power balance relationship between photovoltaic output, storage charging and discharging power and user load power in each time period to obtain a power balance constraint; based on adjacent time periods, establishing a storage energy recursive relationship of storage energy in the last time period and storage charging and discharging power in the current time period, and limiting the storage energy within a preset rated energy range to obtain an energy state constraint; combining the photovoltaic power distribution constraint, the storage power mutual exclusion constraint, the power balance constraint and the energy state constraint to form the constraint model.

4. The method of claim 3, wherein, The storage energy recursive relationship is obtained by: acquiring photovoltaic charging power, grid charging power and discharging power in each time period; performing energy balance calculation on the energy storage energy of a previous time period according to the photovoltaic charging power, the grid charging power and the discharging power based on each adjacent time period, to obtain current energy storage energy; limiting the current energy storage energy according to the preset rated energy range, to obtain current energy storage energy constraints; serially connecting the current energy storage energy constraints of each time period to form the energy state constraints of the energy storage system within multiple time periods.

5. The method of claim 2, wherein, the distribution of the electrical energy of each receiving side is evaluated according to the photovoltaic output based on the operation data of the photovoltaic and energy storage system, to obtain an electrical energy distribution evaluation index, and a distributed energy storage aggregation analysis model is constructed based on the constraint model and the operation data of the photovoltaic and energy storage system, taking the electrical energy distribution evaluation index within multiple time periods as a target function, including: based on the photovoltaic output data, the energy storage operation data and the user load data, the photovoltaic output power, the energy storage power supply and the energy storage power purchase of each time period are calculated to obtain a multi-time period electrical energy distribution sequence; based on the constraint model, the electrical energy distribution sequence is constrained and verified, and in the case that the electrical energy distribution result meets the constraint model, a feasible electrical energy distribution sequence is determined; based on the feasible electrical energy distribution sequence, an electrical energy distribution evaluation index is constructed to represent the utilization of photovoltaic, the interaction of the grid and the utilization of energy storage charging and discharging; the electrical energy distribution evaluation index is taken as a target function and is associated with the constraint model to form the distributed energy storage aggregation analysis model.

6. The method of claim 5, wherein, based on the feasible electrical energy distribution sequence, an electrical energy distribution evaluation index is constructed to represent the utilization of photovoltaic, the interaction of the grid and the utilization of energy storage charging and discharging, including: the feasible electrical energy distribution sequence is classified according to the related electrical energy of photovoltaic, grid and energy storage to obtain sub-item electrical energy; the sub-item electrical energy is combined with the corresponding electricity price operation data, and the electrical energy distribution evaluation index is constructed according to the distribution of each type of sub-item electrical energy within multiple time periods.

7. The method of claim 5, wherein, the distributed energy storage aggregation analysis model is optimized and solved, and a photovoltaic and energy storage collaborative regulation strategy is generated under the condition of meeting the constraint model, including: decision parameters representing the electrical energy distribution result within multiple time periods are obtained from the distributed energy storage aggregation analysis model; based on the multi-universe optimization theory, the decision parameters are represented as position vectors of universe individuals, and the initial universe population is randomly initialized to obtain an initial universe population; based on the distributed energy storage aggregation analysis model, the electrical energy distribution evaluation index corresponding to each universe individual in the initial universe population is calculated to obtain an evaluation result corresponding to each universe individual; based on the evaluation result, the universe population is iteratively updated through the updating mechanism in the multi-universe optimization theory to obtain an optimized population after iteration; the final universe individual with the optimal electrical energy distribution evaluation index is selected from the optimized population, and the multi-time period electrical energy distribution sequence corresponding to the final universe individual is determined as the photovoltaic and energy storage collaborative regulation strategy.

8. A photovoltaic-storage coordinated regulation and optimization system for the power market, characterized in that, the system includes: an operation data module configured to obtain photovoltaic and energy storage operation data; A constraint modeling module is configured to establish a power distribution relationship of photovoltaic output according to the photovoltaic storage operation data and a preset user-side operation model, limit discharging of the storage energy to only compensate for user load, determine power and energy constraints of the photovoltaic storage system in multiple time periods, and obtain a constraint model; An aggregation modeling module is configured to perform itemized evaluation on distribution of electric energy of photovoltaic output at each receiving side according to the photovoltaic storage operation data, obtain an electric energy distribution evaluation index, and construct a distributed storage aggregation analysis model with the electric energy distribution evaluation index in multiple time periods as an objective function based on the constraint model and the photovoltaic storage operation data; A strategy solving module is configured to perform optimization solving on the distributed storage aggregation analysis model, and generate a photovoltaic storage collaborative regulation strategy under the condition of meeting the constraint model.

9. An electronic device, comprising: A processor and a memory are included, and the processor is configured to execute a computer program stored in the memory to implement the steps of the photovoltaic storage collaborative regulation optimization method of the power market in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor to implement the steps of the photovoltaic storage collaborative regulation optimization method of the power market in any one of claims 1 to 7.

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