Two-stage optimized control method and system for peak shaving and power consumption coordination in green energy parks

Abstract

This invention provides a two-stage optimization control method and system for coordinating peak shaving and absorption in green power parks, relating to the technical field of power system dispatching and control. The control method includes the following steps: acquiring operational data of the green power park; based on the operational data, determining the comprehensive regulation power, assessing peak shaving capacity, and determining the self-absorption rate; classifying the operational mode into mode A or mode B according to the assessment results; responding to mode A, executing a two-stage optimization model: the first stage aims to minimize the peak-valley difference of the comprehensive regulation power; if the optimization result of the first stage meets the peak shaving requirements, executing the second stage aims to maximize the self-absorption rate as the second objective; based on the results of the operational mode classification and the two-stage optimization model, determining the reserve capacity that the external power grid needs to configure. This invention solves the technical problems of the separation of peak shaving and absorption objectives, the lack of adaptability of the coordinated optimization strategy, and the unreasonable method of determining the power grid reserve capacity.

Description

Technical Field

[0001] This invention relates to the technical field of power system dispatching and control, and in particular to a two-stage optimized control method and system for peak shaving and consumption coordination in green power parks. Background Technology

[0002] Against the backdrop of the global energy structure transitioning towards cleaner and lower-carbon energy, green power parks that integrate wind power, photovoltaics, energy storage, and transferable loads can improve local absorption capacity and reduce dependence on external power grids by constructing an integrated source-grid-load-storage system, relying on internal resources to balance load and new energy output fluctuations.

[0003] However, current green energy park regulation technologies suffer from three major pain points: First, peak shaving and consumption targets are disconnected. Existing technologies often optimize a single target independently, lacking coordination. Overemphasizing consumption rate can lead to conflicts between energy storage charging and discharging strategies and load peak-valley characteristics, increasing the burden of peak shaving. On the other hand, unilaterally emphasizing peak shaving may sacrifice the space for renewable energy consumption, violating the essence of low carbon. Second, the determination of grid reserve capacity is not entirely reasonable. It usually relies on static experience values ​​or single scenarios, failing to correlate with the park's own real-time and dynamic regulation capabilities. This can easily lead to an over-allocation of reserves (driving up operating costs) or an under-allocation of reserves (causing grid frequency fluctuations and power outage risks), affecting economic efficiency and reliability. In addition, the operating scenarios of green energy parks change significantly over time, but existing optimization strategies often adopt single-stage or fixed-mode models, making it difficult to flexibly adapt to the different priorities of "ensuring peak shaving capacity" and "improving consumption efficiency" at different times, limiting the regulation and utilization of internal resources.

[0004] Therefore, there is an urgent need for a green energy park control method and system that can achieve coordinated optimization of peak shaving and power consumption, and accurately match the grid reserve capacity. Summary of the Invention

[0005] The purpose of this invention is to provide a two-stage optimization control method and system for peak shaving and consumption coordination in green power parks, which solves the technical problems of the separation of peak shaving and consumption targets, the lack of adaptability of the coordinated optimization strategy, and the unreasonable determination of grid reserve capacity.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a two-stage optimization control method and system for the coordinated peak shaving and absorption of green power parks, comprising the following steps: acquiring green power park operation data, including: user-side base load curves, transferable load curves, wind and solar power prediction curves on the renewable energy side, and initial state data on the energy storage side; based on the green power park operation data, determining the comprehensive regulation power of the green power park, assessing peak shaving capacity, and determining self-absorption rate, and classifying the operation mode into mode A or mode B according to the assessment of peak shaving capacity; responding to mode A, executing a two-stage optimization model: the first stage aims to minimize the peak-valley difference of the comprehensive regulation power, optimizing the power of the energy storage system and transferable loads; if the optimization result of the first stage of the green power park meets the peak shaving requirements, executing the second stage, the second stage aims to maximize the self-absorption rate as the second objective; based on the results of the operation mode classification and the results of the two-stage optimization model, determining the reserve capacity that the external power grid needs to configure.

[0007] Furthermore, the integrated adjustment power P t The calculation formula is: ,in, P t For the comprehensive regulation of power in green energy parks, P load To reduce the load on the park, P bat For energy storage power, P bat The positive and negative values ​​represent charging and discharging, respectively. P shift It is a transferable load.

[0008] Furthermore, assessing peak-shaving capacity includes the following steps: based on the baseline load curve, calculating the peak-to-valley difference Δ of the original net load power when resources are not adjusted. P base Calculate the maximum regulation capacity Δ within the green energy park. P internal_max That is, the maximum energy storage capacity P bat_max With the maximum adjustable power of the transferable load P shift_max The sum of these values ​​sets the acceptable threshold for the peak-to-valley difference in net load power for green energy parks. θ After determining the peak-to-valley difference Δ of the original net load power P base Meets the threshold of not exceeding the net load power peak-to-valley difference. θ With the aforementioned maximum internal adjustment capability Δ P internal_max In the case of the sum, that is If the condition is determined to be mode A, then it is determined to be mode B.

[0009] Furthermore, when the determination result is Mode A, the first stage of peak shaving optimization is performed, and the formula for calculating the minimum comprehensive regulation power peak-to-valley difference is: The constraints include: power balance constraints, equipment and load regulation constraints, transferable load constraints, and peak shaving constraints.

[0010] Furthermore, assuming that the optimization results of the first phase of the green energy park meet the peak-shaving requirements, that is: , where Δ θ To achieve the preset peak-shaving safety margin, the second phase of absorption enhancement is implemented, and the formula for calculating the self-absorption rate η is as follows: ,in, P w ( t ) 、P pv ( t (respectively, wind turbines and photovoltaics) t Forecast output for a given time period P sell ( t )for t The maximum self-absorption rate of excess power returned to the grid by the industrial park during certain periods is [missing information]. .

[0011] Furthermore, determine the reserve capacity. P res This includes: if the determination result is mode A, and the peak-to-valley difference of the optimized integrated regulation power satisfies Φ≤θ, then... P res =0; If the determination result is that the peak-valley difference of the optimized integrated regulation power does not satisfy Φ≤θ, calculate the internal regulation power gap Δ. P gap And based on the power gap Δ P gap and policy bottom line power P policy The principle for acquiring spare capacity is as follows: ,in, P policy This is the minimum reserve capacity value required by policy.

[0012] Furthermore, the first stage of optimization uses a feasible region-restricted particle swarm optimization algorithm to determine the energy storage charging and discharging power and the transferable load adjustment amount, and applies feasible region constraints to limit the search space during the optimization process.

[0013] This invention also provides a two-stage optimization control system for peak shaving and absorption coordination in green power parks, used to implement the method described in any one of the above, comprising: a data import module for acquiring the operating data of the green power park; a calculation module for calculating the comprehensive regulation power, evaluating the peak shaving capacity, and determining the self-absorption rate, and classifying the operating mode into mode A or mode B based on the evaluation of the peak shaving capacity; an optimization decision module for constructing and solving the two-stage optimization model under mode A, and outputting the optimal operating strategy; and a grid reserve assessment module for calculating and outputting the reserve capacity based on the division result of the operating mode and the result of the two-stage optimization.

[0014] Compared with the prior art, the present invention has at least the following beneficial effects: Achieving deep synergy between the dual objectives of "peak shaving and absorption": This invention uses a two-stage optimization model that first minimizes the peak-valley difference of the comprehensive regulation power and then maximizes the self-absorption rate to orderly couple the peak shaving and absorption objectives. This not only ensures the basic peak shaving capacity of the green power park, but also fully taps the potential for local absorption of new energy, avoids system operation imbalance caused by single objective optimization, and improves the efficiency of green power utilization and the stability of park operation.

[0015] More precise and adaptive determination of power grid reserve capacity: This invention changes the traditional method of configuring reserves based on static experience values, and dynamically links the power grid reserve capacity with the real-time adjustment capabilities within the industrial park. Through pre-assessment, the operating modes are precisely divided into Mode A and Mode B. Reserve capacity is determined only in Mode B or when optimization fails to meet standards, based on internal adjustment capacity gaps and policy limits. This ensures that reserve configuration avoids both the increased costs caused by excess and the operational risks caused by insufficiency, improving the targeting and execution efficiency of control strategies.

[0016] Phased optimization to fully activate internal resource potential: This invention designs an adaptive two-stage optimization framework tailored to the operational characteristics and target priorities of green power parks at different times. The first stage prioritizes the use of energy storage and transferable loads to ensure basic peak-shaving capacity; the second stage, while meeting peak-shaving requirements, further optimizes strategies to tap into absorption potential. This model can fully release the regulation potential of flexible resources within the park, reduce dependence on the external power grid, and improve adaptability and economy in multiple scenarios. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is an overall architecture diagram of the control system provided in this embodiment; Figure 2 This is the overall operation logic diagram of the control system provided in this embodiment; Figure 3 The algorithm flow of the two-stage optimization model of the control method provided in this embodiment; Figure 4 This is an overall architecture diagram of the green energy park provided in this embodiment; Figure 5 A power curve comparison analysis diagram of the control method provided in this embodiment; Figure 6 An optimized diagram of the energy storage charge and discharge plan for the control method provided in this embodiment; Figure 7 The SOC change trajectory diagram of the regulation method provided in this embodiment; Figure 8 This is a load transfer plan diagram for the control method provided in this embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0021] This embodiment provides a two-stage optimization and control method for the coordinated peak shaving and absorption of green power parks, including the following steps: acquiring green power park operation data, which includes: user-side base load curves, transferable load curves, wind and solar power prediction curves on the renewable energy side, and initial state data on the energy storage side; based on the green power park operation data, determining the comprehensive regulation power of the green power park, assessing peak shaving capacity, and determining self-absorption rate, and classifying the operation mode into mode A or mode B according to the assessment of peak shaving capacity; responding to mode A, executing a two-stage optimization model: the first stage aims to minimize the peak-valley difference of the comprehensive regulation power, optimizing the power of the energy storage system (specifically including optimizing energy storage charging and discharging) and transferable loads; if the optimization result of the first stage of the green power park meets the peak shaving requirements, executing the second stage, which aims to maximize the self-absorption rate as the second objective; based on the results of the operation mode classification and the results of the two-stage optimization model, determining the reserve capacity that the external power grid needs to configure. Specifically, this technical solution constructs a full-process control method of "data acquisition - capacity assessment - mode division - phased optimization - reserve determination". First, it acquires multi-dimensional basic operation data, including user-side (basic load curve, transferable load curve, maintenance plan), new energy side (wind and solar power prediction curve), and energy storage side (initial state data), to provide data support for subsequent analysis. Then, based on the above data, it calculates the comprehensive regulation power, assesses peak-shaving capacity, and determines the self-consumption rate. It evaluates the park's peak-shaving capacity from the power dimension, and then divides it into mode A, which has the potential to emphasize peaking, and mode B, which has insufficient regulation capacity. The core advantage of this solution is that, through the intelligent judgment of the operation mode and the orderly connection of the two-stage objectives, it maximizes the exploitation potential of local new energy consumption while ensuring the stability of the grid interface power (autonomous peak shaving). At the same time, it dynamically binds the determination of reserve capacity with the results of the aforementioned optimization process, so that the reserve configuration is transformed from a static experience value to a dynamic capacity matching value, systematically improving the economy, independence, and synergy of green power park operation.

[0022] In this embodiment, the core variable "comprehensive regulation power" is specifically defined. P t The calculation method is as follows: ,in, P t For the comprehensive regulation of power in green energy parks, P load To reduce the load on the park, P bat For energy storage power, P bat The positive and negative values ​​represent charging and discharging, respectively. P shift It is a transferable load.

[0023] Specifically, the scheme integrates three core elements: park load, energy storage, and transferable load. It quantifies the net load level of the park's internal adjustable resources, providing a unified, clear, and calculable power benchmark for subsequent peak-valley difference calculation, peak-shaving capacity assessment, and optimization model construction. It is the foundation for the entire method to achieve quantitative regulation.

[0024] This embodiment specifies the steps for peak-shaving capacity assessment and operation mode classification: Based on the aforementioned base load curve, calculate the peak-to-valley difference Δ of the original net load power when resources are not adjusted. P base : Calculate the maximum regulation capacity Δ within the green energy park. P internal_max That is, the maximum energy storage capacity P bat_max With the maximum adjustable power of the transferable load P shift_max The sum is calculated using the following formula: Set a qualified threshold for the peak-to-valley difference of net load power in green power parks. θ : ,in, δ This is the peak-valley difference coefficient. The criterion for determining the division mode is: after determining the peak-valley difference Δ of the original net load power. P base Meets the threshold of not exceeding the net load power peak-to-valley difference. θ With the aforementioned maximum internal adjustment capability Δ P internal_max In the case of the sum, that is If the condition is determined to be mode A, then it is determined to be mode B.

[0025] Specifically, peak shaving refers to green power parks relying solely on their internal energy storage, load transferable resources, and renewable energy generation to achieve peak shaving and valley filling without depending on the external power grid. The acceptable threshold for peak-valley load difference in green power parks is... θ Set at 15% to 20% of the park's average daily load, when max ( P t ) -min ( P t )< θ At that time, it was determined that the green power park could achieve independent peak regulation without the need for grid backup.

[0026] Figure 2 This diagram illustrates the overall system operation logic of an embodiment of the present invention. The process starts from the beginning node and first enters the pre-evaluation phase. The calculation module calculates the original peak-valley difference under unadjusted conditions based on the baseline load curve. Simultaneously, by integrating the maximum regulating power of energy storage with the maximum regulating amount of transferable load, the maximum internal regulating capacity is obtained. .

[0027] Based on the acceptable peak-to-valley difference threshold θ, if the original peak-to-valley difference does not exceed the sum of θ and the internal maximum adjustment capacity, i.e. If the park has strong self-regulating peak-shaving potential, it is judged as Mode A and enters the two-stage optimization stage; otherwise, if the internal adjustment capacity is insufficient, it is judged as Mode B and directly jumps to the standby capacity calculation stage.

[0028] This pre-assessment mechanism not only considers the peak-valley difference Δ of the park's original net load power. P base It also took into account the qualified threshold. θ And the park's own maximum adjustment capacity Δ P internal_max This design can accurately identify parks that, although currently experiencing large peak-to-valley differences, have the potential to adjust and meet standards (Model A), and guide them towards refined two-stage optimization. For parks with significantly insufficient potential (Model B), it skips complex optimization and directly calculates backup needs, improving the efficiency and intelligence of the overall decision-making process and avoiding unnecessary consumption of computing resources.

[0029] In this embodiment, when the determination result is Mode A, the first stage of peak shaving optimization is performed, and the calculation formula for the minimum comprehensive regulation power peak-valley difference is: The constraints include: power balance constraints, equipment and load regulation constraints, transferable load constraints, and peak shaving constraints.

[0030] Specifically, the power balance constraint is as follows: , ,in, P cha ( t )and P dis ( t These represent the charging and discharging power of energy storage, respectively. Power balance constraints ensure a dynamic balance between new energy output, energy storage regulation, transferable load, and load demand. The equipment and load regulation constraints are... Where SOC(t) is the state of charge of the stored energy; ;in, η For the charging and discharging efficiency of energy storage, Δ t E represents 1 hour, and E represents the rated capacity of the park's energy storage. The load transferability adjustment constraint is: , Among them, transferable load Pshift The adjustment strategy is as follows: load is shifted from peak hours to off-peak hours, with the total amount shifted throughout the day ensuring that the sum of the total shifts is zero, and the upper and lower limits of the shifts not exceeding 10% of the base load power; peak-shaving constraints limit the absolute value of the interaction power between the park and the power grid to not exceeding a threshold. ε And when autonomous peak shaving is possible =0, that is ,in, ε The permissible inter-network interaction power threshold; and when the overall adjustment power peak-valley difference... hour, =0.

[0031] This technical solution provides a safe and reliable operational guarantee for the two-stage optimization through multi-dimensional and refined constraint design. Constraints related to the energy storage system prevent damage to equipment due to overcharging and over-discharging, extending equipment lifespan; load transfer constraints ensure the rationality of load regulation and guarantee user electricity experience; and inter-grid interaction power constraints enhance the park's peak-shaving capacity and reduce dependence on the external power grid. These constraints work together to ensure that the optimization results not only meet peak-shaving and power consumption targets but also comply with equipment operating specifications and user electricity needs, improving the feasibility and safety of the control strategy.

[0032] In this embodiment, if the optimization results of the first stage of the green power park meet the peak-shaving requirements, that is: , where Δ θ To achieve the preset peak-shaving safety margin, the second phase of absorption enhancement will be implemented, increasing the self-absorption rate. η The calculation formula is: ,in, P w ( t ) 、P pv ( t (respectively, wind turbines and photovoltaics) t Forecast output for a given time period P sell ( t )for t The maximum self-absorption rate of excess power returned to the grid by the industrial park during certain periods is [missing information]. .

[0033] Specifically, the self-consumption rate represents the ratio of the amount of renewable energy generated by the green energy park itself to the total renewable energy generated in the park. This indicator reflects the degree of self-consumption of renewable energy in the green energy park; a peak-shaving safety margin Δ is introduced. θThis is a key design feature of the scheme, reserving a buffer space for the stability of power supply and ensuring that the optimization of power consumption does not affect the basic power supply. With the peak-shaving target already achieved, the focus of optimization is shifted to maximizing self-consumption. By fine-tuning the strategies for resources such as energy storage, the renewable energy generation is used for local load as much as possible without significantly affecting the peak-shaving effect. This improves the economic and environmental benefits of the park, increases the utilization rate of renewable energy sources such as wind power and photovoltaics, reduces the curtailment rate, and meets the needs of low-carbon development.

[0034] Figure 3 The algorithm flow of the two-stage optimization model according to an embodiment of the present invention is shown, as follows: Figure 3 As shown, under Mode A, the goal of "prioritizing peak shaving and then improving power consumption" will be achieved in stages.

[0035] Phase 1: Autonomous Peak Shaving Optimization. This phase aims to minimize the peak-to-valley difference in comprehensive power regulation. The optimization decision module invokes a particle swarm optimization algorithm with a limited feasible region to determine the energy storage charging and discharging power. and transferable load adjustment Among them, the comprehensive regulation power is defined as follows: Positive values ​​represent charging, negative values ​​represent discharging, and the optimization objective is: .

[0036] The optimization process needs to meet several constraints: power balance constraints ensure the dynamic balance between renewable energy output, energy storage regulation, transferable load, and load demand. ; Autonomous peak shaving constraints limit the power threshold of inter-network interaction varepsilon ,when At that time, it is completely independent of the external power grid. Otherwise, only emergency offline replenishment is permitted. varepsilon Take 5% to 10% of the maximum load; state of charge within the range Internally, and updated according to the charge / discharge logic: ; The load transfer constraint requires that the total transfer volume for the whole day be zero, and the transfer volume for a single transaction not exceed 10% of the base load, in order to avoid significant impact on users' electricity consumption.

[0037] The particle swarm optimization algorithm is used for iterative solution. The inertia weight is set to 0.8, the learning factors c1=c2=2, the maximum number of iterations is 50, and the number of particles is 30. The output is the optimization result for stage one. , and the corresponding peak-to-valley difference .

[0038] Phase Two: Self-Accommodation and Improvement Based on the Satisfaction of Phase One Results Under the premise of this, we will enter the second phase of optimization, with the goal of increasing the self-consumption rate of new energy sources. maximize.

[0039] The constraints in this stage are basically the same as in the first stage, but the autonomous peak-shaving constraints are appropriately relaxed, and a new lower limit constraint on the absorption rate is added. By optimizing the decision-making module and solving the problem, the regulation strategies for energy storage and transferable loads are modified while ensuring peak-shaving effectiveness, ultimately outputting the desired results. , This will enhance the internal absorption capacity of green energy parks.

[0040] In this embodiment, the reserve capacity is determined. P res This includes: if the determination result is mode A, and the peak-to-valley difference of the optimized integrated regulation power satisfies Φ≤θ, then... P res =0; If the determination result is that the peak-valley difference of the optimized integrated regulation power does not satisfy Φ≤θ, calculate the internal regulation power gap Δ. P gap And based on the power gap Δ P gap and policy bottom line power P policy To acquire reserve capacity, the principle for acquiring reserve capacity is to take the minimum reserve capacity value between the internal adjustment capacity gap and the policy requirements. P policy The larger value in, that is: ,in, P policy This is the minimum reserve capacity value required by policy.

[0041] Specifically, Δ P gap This represents the difference between the unadjusted initial net load peak-to-valley difference and the maximum internal regulating capacity. ,in, and These are the upper limit for energy storage capacity and the upper limit for transferable load, respectively. For energy storage utilization rate, For prediction error coefficients, When the "original net load peak-valley difference" exceeds the "internal maximum regulation potential", the difference represents the power gap that the park itself cannot cover through peak regulation, and the external power grid needs to provide backup capacity to supplement it.

[0042] This solution enables dynamic and precise allocation of reserve capacity. First, when the industrial park achieves fully autonomous peak shaving, there is no need for grid reserves, avoiding waste of grid resources. Second, when grid support is required, the reserve demand stems directly from the "gap" (Δ) between the park's own regulation capacity and load fluctuations. P gap Finally, by taking the larger of the policy bottom line, the compliance of the plan was ensured. This approach not only met the emergency power supply needs of the industrial park but also complied with policy regulations, effectively balancing the sufficiency and economy of grid reserves, and reducing the power supply risks caused by insufficient reserves and the resource waste caused by excess reserves.

[0043] In this embodiment, the specific algorithm used to solve the complex optimization problem in the first stage is specified as the feasible region restricted particle swarm optimization algorithm, which determines the energy storage charging and discharging power and the transferable load adjustment amount, and applies feasible region constraints to limit the search space during the optimization process.

[0044] Specifically, the particle swarm is encoded, initialized, and subjected to feasibility constraints: the position of each particle is defined as... The feasible region constraint is: in, The particle position vector, its first half components ( =1,…,T) represents the first… The charging and discharging power of a time-of-use energy storage system is defined by positive values ​​representing charging and negative values ​​representing discharging; the latter half of the component... Representing the Power regulation of load that can be transferred during a specific time period.

[0045] The feasible region constraints specifically include energy storage state of charge constraints and transferable load constraints: In the energy storage state of charge constraints, among which, SOC 0 represents the initial state of charge of the energy storage system. E The rated capacity of the energy storage is given by Δt, where Δt is the dispatch time interval. and These represent the charging efficiency and discharging efficiency of the energy storage system, respectively. The formula uses... The item extracts the charging power and uses The discharge power is extracted to accurately calculate the cumulative change in stored energy. This constraint ensures that the energy storage system maintains a preset minimum state of charge at any given time. With maximum state of charge Prevent the device from being overcharged or over-discharged.

[0046] In the transferable load constraint, For the first The park's base load power during a given time period. Inequality The load adjustment range within a single time period is limited to no more than 10% of the current base load to ensure a normal power consumption experience for users; equation The total amount of load that can be transferred during the entire scheduling cycle is limited to zero, which ensures that the load only shifts on the time axis, while the total electricity consumption of users remains energy-conserving.

[0047] The fitness function is: in, Aiming to quantify particles The scheduling strategy represented by this parameter has a smoothing effect on the power curve of green energy parks. The function expression contains... The term represents the base load at time t. The energy storage charging and discharging power corresponding to the current particle solution and transferable load adjustment The resulting integrated regulation power of the park is achieved through superposition.

[0048] The fitness function directly reflects the peak-to-valley difference level under the current scheduling strategy by calculating the difference between the maximum and minimum values ​​of the above-mentioned integrated regulation power sequence within the entire scheduling period T. During the iterative optimization process of the feasible region-constrained particle swarm optimization algorithm, the system's optimization objective is to find the particle position that minimizes the fitness function value, thereby achieving the first-stage regulation objective of "minimizing the peak-to-valley difference of the integrated regulation power."

[0049] This technology applies the feasible-domain-constrained particle swarm optimization algorithm to the first stage of peak shaving optimization. Compared with traditional optimization algorithms, the particle encoding method makes the algorithm more closely aligned with actual control scenarios. The inclusion of constraints avoids ineffective searches and significantly improves solution efficiency. The fitness function ensures that the algorithm focuses on the core peak shaving target and can quickly find the optimal energy storage and transferable load operation strategy. This technical solution guarantees that the entire method can stably and efficiently calculate high-quality scheduling strategies when facing complex scenarios in actual industrial parks, laying the foundation for the subsequent second stage of absorption optimization and improving the technical feasibility and optimization effect of the entire control method.

[0050] This invention also provides a two-stage optimization control system for green power park peak shaving and absorption coordination, used to implement the method described in any one of the above, comprising: a data import module for acquiring the operating data of the green power park; a calculation module for calculating the comprehensive regulation power, evaluating the peak shaving capacity, and determining the self-absorption rate, and classifying the operating mode into mode A or mode B based on the evaluation of the peak shaving capacity; an optimization decision module for constructing and solving the two-stage optimization model under mode A, and outputting the optimal operating strategy; and a power grid reserve assessment module for calculating and outputting the reserve capacity based on the division result of the operating mode and the result of the two-stage optimization.

[0051] Figure 1 A system framework diagram of an embodiment of the present invention is shown, as follows: Figure 1 As shown, the system mainly consists of a data import module, a calculation module, an optimization decision-making module, and a grid reserve assessment module. These modules work together to achieve data acquisition, capacity assessment, strategy optimization, and reserve decision-making. Before actual operation, basic data needs to be collected through the data acquisition and import module, including: the time-series load curve and transferable load curve of the green energy park; the wind power output and photovoltaic power output curves within the green energy park; and the initial state of charge, rated capacity, maximum charging and discharging power, and charging and discharging efficiency of the energy storage within the green energy park. Simultaneously, control parameters such as the grid-side policy-limited reserve value and peak-valley difference coefficient are imported.

[0052] The modular system constructed by this technical solution is highly compatible with the aforementioned control methods, clearly mapping the core steps of the methods and realizing the automated and systematic operation of the control process. The data acquisition module ensures the timeliness and accuracy of data acquisition, the calculation and evaluation module improves the efficiency of capacity assessment and mode classification, the two-stage optimization decision-making module enables rapid solution of optimization strategies, and the grid reserve capacity determination module ensures the accuracy of reserve configuration. Compared with traditional manual control or distributed systems, this modular system significantly improves the efficiency and accuracy of control, reduces the cost of manual intervention, and provides reliable hardware and software support for intelligent control in green energy parks.

[0053] Figure 4 This diagram illustrates the overall system architecture of a green energy park according to an embodiment of the present invention. The system uses the park's AC busbar as its core hub. The left side connects to the external power grid via a main interface, while the right side connects to various internal resources: photovoltaic arrays and wind turbines provide new energy output, which is connected to the busbar via lines; energy storage systems, transferable loads, and base loads are all connected to the busbar via lines, forming an integrated source-load-storage network. The Energy Management System (EMS) is located at the bottom of the architecture, connecting the photovoltaic, wind power, energy storage, and load units via dashed control lines, and is responsible for data acquisition, strategy calculation, and command issuance.

[0054] Combination Figure 1The closed-loop logic of the power grid reserve assessment module calculates the reserve capacity based on the pre-assessment and optimization results: if the second-stage result under mode A satisfies P t If the peak-valley difference is ≤ θ, the park can independently regulate peak loads with a reserve capacity of 0. If the peak-valley difference does not meet the standard under mode A, or under mode B, the internal peak-regulation capacity gap is calculated. The difference between the original peak-valley difference and the maximum internal regulation capacity is taken as a non-negative value. Combined with the prediction error coefficient and the policy bottom line, the larger value among the product of the gap and the error coefficient and the policy bottom line is taken as the reserve capacity to ensure that the external power grid supplements the peak-regulation demand that the park cannot cover.

[0055] During system operation, EMS will... Figure 4 The architecture coordinates various units in real time: real-time output and load data of photovoltaic and wind power are aggregated to the EMS via the bus; the calculation module dynamically tracks P t Peak-valley difference and absorption rate; the optimization decision module fine-tunes the two-stage strategy based on the deviation; the reserve assessment module corrects the reserve capacity based on the deviation between the actual peak-valley difference and θ. Through this dynamic regulation, the internal resource potential is fully tapped to achieve stable, economical, and low-carbon operation of the green power park.

[0056] The present invention also provides the following specific embodiments: For example, Figures 5 to 8 The diagram shows the optimized scheduling results of green energy parks after implementing the method described in this invention, as provided in an embodiment of the invention.

[0057] Figure 5 A comparative analysis of power curves is presented. The power demand curve before optimized scheduling is called the original load curve, exhibiting typical daily load characteristics. The peak value occurs in the 13th time period, reaching 240.00 kW, and the valley value occurs in the 4th time period, reaching 95.00 kW, with a peak-valley difference of 145.00 kW. The power generation curve of the solar photovoltaic system is called the photovoltaic power generation curve, generating electricity in the 6th to 19th time periods, with a peak power generation of 175.00 kW in the 13th time period. After optimized scheduling using the method of this invention, the actual grid load curve after energy storage system regulation and load transfer optimization is called the net load curve. The net load curve is significantly smoothed, with a peak value of only 97.09 kW, a valley value of 96.70 kW, and a peak-valley difference reduced to 0.39 kW. The critical value θ for determining the system operating mode is set at 35.66 kW. After scheduling, the peak-valley difference is far below this threshold, successfully achieving mode A operation, indicating an optimized operating mode where the peak-valley difference is controlled within the preset threshold.

[0058] Figure 6The diagram shows an optimized energy storage charging and discharging plan. The energy storage system, an electrochemical device for storing and releasing electrical energy, exhibits a reasonable distribution of charging and discharging power over 24 hours. The maximum charging power occurs in period 16, reaching 75.92 kW, while the maximum discharging power occurs in period 8, reaching 62.99 kW. The total charging capacity is 787.7 kWh, and the total discharging capacity is 621.8 kWh. The energy storage cycle efficiency, the ratio of discharging energy to charging energy, reaches 78.9%. The energy storage capacity utilization rate, the ratio of actual used capacity to rated capacity, is 196.9%. The energy storage system, operating on a "daytime charging, peak discharge" model, effectively achieves peak shaving and valley filling, indicating full utilization.

[0059] Figure 7 The graph shows the SOC (State of Charge) trajectory, which dynamically changes from an initial 25.0% during the optimized scheduling process. The lowest SOC value occurred in period 8 at 16.4%, and the highest value occurred in period 17 at 85.9%, both within the set constraint range (15.0%-95.0%). The final SOC was 37.2%, meeting the constraint requirement of not less than 30.0%, thus ensuring the safe operation of the energy storage system and its subsequent scheduling capabilities.

[0060] Figure 8 The load shifting plan is shown. Load shifting refers to the optimization strategy of transferring certain adjustable loads from peak hours to off-peak hours, totaling 441.7 kWh, accounting for 11.36% of the total daily load. The maximum load shifted out occurred in the 12th period at 27.72 kW, and the maximum load shifted in occurred in the 13th period at 10.98 kW.

[0061] Comprehensive performance indicators show that the self-consumption rate reaches 100.00%, realizing the complete on-site utilization of wind and solar power; the peak-valley difference improvement rate reaches 99.73%, significantly improving the stability of grid operation; the system successfully achieves mode A operation, proving the excellent performance of the method of this invention in the optimized scheduling of green power parks.

[0062] Through the coordinated presentation of the above four images, the microgrid optimization scheduling method of this invention has achieved significant optimization effects in multiple dimensions such as peak shaving and valley filling, photovoltaic consumption, energy storage scheduling and load transfer, providing an effective technical solution for the intelligent operation of green power parks.

[0063] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-stage optimized control method for coordinated peak shaving and power consumption in green energy parks, characterized in that, Includes the following steps: Obtain operational data of green energy parks, including: basic load curve and transferable load curve on the user side, wind and solar power prediction curve on the new energy side, and initial state data on the energy storage side; Based on the operational data of the green power park, the comprehensive regulation power of the green power park is determined, the peak-shaving capacity is assessed, and the self-consumption rate is determined. Based on the assessment of the peak-shaving capacity, the operation mode is divided into mode A or mode B. In response to Mode A, a two-stage optimization model is executed: the first stage aims to minimize the peak-valley difference of the integrated regulation power and optimizes the power of the energy storage system and the transferable load; if the optimization result of the first stage in the green power park meets the peak-shaving requirements, the second stage is executed, with the second stage aiming to maximize the self-consumption rate as the second objective. Based on the results of the operation mode division and the results of the two-stage optimization model, the required reserve capacity of the external power grid is determined.

2. The two-stage optimized control method for peak shaving and power consumption coordination in green energy parks according to claim 1, characterized in that, The comprehensive adjustment power P t The calculation formula is: in, P t For the comprehensive regulation of power in green energy parks, P load To reduce the load on the park, P bat For energy storage power, P bat The positive and negative values ​​represent charging and discharging, respectively. P shift It is a transferable load.

3. The two-stage optimized control method for peak shaving and power consumption coordination in green energy parks according to claim 1, characterized in that, Assessing peak-shaving capacity includes the following steps: Based on the aforementioned base load curve, calculate the peak-to-valley difference Δ of the original net load power when resources are not adjusted. P base ; Calculate the maximum regulation capacity Δ within the green energy park. P internal_max That is, the maximum energy storage capacity P bat_max With the maximum adjustable power of the transferable load P shift_max sum; Set a qualified threshold for the peak-to-valley difference of net load power in green power parks. θ ; After determining the peak-valley difference Δ of the original net load power P base Meets the threshold of not exceeding the net load power peak-to-valley difference. θ With the aforementioned maximum internal adjustment capability Δ P internal_max In the case of the sum of, that is: If it is determined to be mode A, otherwise it is determined to be mode B.

4. The two-stage optimized control method for peak shaving and power consumption coordination in green energy parks according to claim 3, characterized in that, When the determination result is Mode A, the first stage of peak shaving optimization is performed. The formula for calculating the minimum comprehensive regulation power peak-to-valley difference is: The constraints include: power balance constraints, equipment and load regulation constraints, transferable load constraints, and peak shaving constraints.

5. The two-stage optimized control method for peak shaving and power consumption coordination in green energy parks according to claim 4, characterized in that, If the optimization results of the first phase of the green energy park meet the peak-shaving requirements, that is: Where, Δ θ To ensure the preset peak-shaving safety margin, the second phase of absorption enhancement is implemented. The formula for calculating the self-absorption rate η is as follows: in, P w ( t ) 、P pv ( t (respectively, wind turbines and photovoltaics) t Forecast output for a given time period P sell ( t )for t The maximum self-absorption rate of excess power returned to the grid by the industrial park during certain periods is [missing information]. .

6. The two-stage optimized control method for peak shaving and power consumption coordination in green energy parks according to claim 1, characterized in that, Determine the reserve capacity P res include: If the determination result is mode A, and the peak-to-valley difference of the optimized integrated regulation power satisfies Φ≤θ, then P res =0; If the determination result is that the peak-to-valley difference of the optimized integrated regulation power does not satisfy Φ≤θ, the internal regulation power gap Δ is calculated. P gap And based on the power gap Δ P gap and policy bottom line power P policy The principle for acquiring spare capacity is as follows: in, P policy This is the minimum reserve capacity value required by policy.

7. The two-stage optimized control method for peak shaving and power consumption coordination in green energy parks according to claim 1, characterized in that, The first stage of optimization uses a feasible region-restricted particle swarm optimization algorithm to determine the energy storage charging and discharging power and the transferable load adjustment amount, and applies feasible region constraints to limit the search space during the optimization process.

8. A two-stage optimized control system for coordinated peak shaving and power consumption in green energy parks, used to implement the method described in any one of claims 1-7, characterized in that, include: The data import module is used to acquire the operational data of the green energy park. The calculation module is used to calculate the comprehensive regulation power, evaluate the peak-shaving capacity and determine the self-absorption rate, and classify the operation mode into mode A or mode B based on the evaluation of the peak-shaving capacity. The optimization decision module is used to construct and solve the two-stage optimization model under the mode A, and output the optimal operating strategy. The power grid reserve assessment module is used to calculate and output the reserve capacity based on the division results of the operation mode and the results of the two-stage optimization.

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