Cooperative control method and cooperative controller of integrated energy system

Abstract

The invention provides a cooperative control method and a cooperative controller for an integrated energy system, and the method is based on the day-ahead new energy power supply prediction data and resource supply and demand conditions of a coupling system. In consideration of a plurality of optimization objectives of optimal new energy output, minimum coupling system income change, minimum scheduling scheme prediction error, minimum peak regulation income change and the like, intra-day deviation correction is performed on the day-ahead interaction scheduling scheme, and an intra-day interaction scheduling scheme of the coupling system and the power system is obtained. And performing intra-day cooperative control on the coupling system and the power system. Cooperative optimization control is carried out on the coupling system and the power system in the integrated energy system, multi-energy complementary optimization can be brought into full play, a multi-target optimization decision is introduced into cooperative control, economic benefits can be maximized on the premise of guaranteeing system stability, market demands can be accurately met by considering resource supply and demand conditions, and the economic benefit is improved. According to the invention, through intelligent and dynamic power coordination, the energy utilization rate can be improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system collaborative optimization and control, specifically to a collaborative control method and collaborative controller for integrated energy systems. Background Technology

[0002] With the urgent need for a clean energy transition, the large-scale application of integrated energy systems in industrial parks has become a key trend for achieving efficient and low-carbon development. These systems deeply integrate multiple key components, including renewable energy generation such as water / solar power, grid interconnection, energy storage, water electrolysis for hydrogen production, and hydrogen synthesis for ammonia. However, achieving efficient collaborative operation and optimal global configuration among these heterogeneous energy components remains the core challenge.

[0003] In traditional integrated energy systems, each subsystem often operates independently, making it difficult to leverage the advantages of multi-energy complementarity, resulting in poor energy utilization. Summary of the Invention

[0004] To overcome the shortcomings of traditional integrated energy systems, where subsystems often operate independently, making it difficult to leverage the advantages of multi-energy complementarity and resulting in poor energy utilization, this invention provides a collaborative control method for integrated energy systems. The integrated energy system includes a power-interacting coupled system and a power system. The method includes:

[0005] Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system, the renewable energy output and revenue change of the coupled system are calculated.

[0006] Based on the new energy output, and considering the power interaction security of the coupling system and the power system, calculate the power interaction limit between the coupling system and the power system.

[0007] Based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed to obtain the day-ahead interactive scheduling scheme between the coupled system and the power system.

[0008] Based on the intraday renewable energy power supply forecast data and renewable energy output of the coupled system, with the goal of minimizing the forecast error and peak-shaving revenue changes of the scheduling scheme, intraday deviation correction is performed on the day-ahead interactive scheduling scheme to obtain the intraday interactive scheduling scheme between the coupled system and the power system, and intraday coordinated control is performed on the coupled system and the power system.

[0009] Optionally, based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, and with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system, the calculation of renewable energy output and revenue change in the coupled system includes:

[0010] Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, with the optimal renewable energy output as the optimization objective, the operating power base point of the coupled system is calculated.

[0011] Based on the obtained changes in the interactive power between the coupled system and the power system, and combined with the operating power baseline, the output of new energy and the change in revenue of the coupled system are calculated with the goal of minimizing the change in revenue of the coupled system.

[0012] Optionally, the calculation of the operating power base point of the coupled system based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation, with the optimization objective of maximizing renewable energy production, includes:

[0013] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation costs, and operating costs of the coupled system, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing the utilization of renewable energy.

[0014] Optionally, the optimization objective that maximizes the economic benefits of the coupled system satisfies the following formula:

[0015]

[0016] Where T is the total scheduling period; The selling price of synthetic ammonia; For the cost of synthesized ammonia; J represents the amount of ammonia synthesized. grid,t For grid interaction costs; C pv P represents the unit cost of photovoltaic power generation. pvr,t C represents the actual photovoltaic power. hy Cost of hydroelectric power generation; P hy,t ε represents the actual power output of the hydropower plant. pv The cost of per unit of abandoned light penalty; P pvq,t For abandoned light power; C elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; P el,t This represents the actual power of the electrolytic cell; a h LOH is the power conversion factor for hydrogen production in an electrolyzer; t ε is the state quantity of hydrogen storage at time t; sto is the capacity of the hydrogen storage tank; ε htn The hydrogen-to-ammonia conversion coefficient; t is the current time.

[0017] The optimization objectives of achieving optimal ammonia production and minimizing power system interaction satisfy the following formula:

[0018]

[0019] Where maxF1 is the optimization objective that maximizes ammonia production and minimizes power system interaction, α 11 α 12 f is the weighting coefficient. 11 f 11min f 11max These represent the ammonia production rate, minimum ammonia production rate, and maximum ammonia production rate, respectively. 12 f 12min f 12max These are the power system interaction quantities, the minimum power system interaction quantity, and the maximum power system interaction quantity, respectively.

[0020] Among them, f 11 Satisfy the following formula: f 12 Satisfy the following formula: P gb,t To purchase electricity, P gs,t This refers to the amount of electricity sold.

[0021] Optionally, based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation costs, and operating costs of the coupled system, and with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing renewable energy utilization, the operating power baseline of the coupled system is calculated, including:

[0022] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation cost and operating cost of the coupled system, and combined with the constraints of maximizing economic benefits and the optimal production scenario, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production and maximizing the utilization of renewable energy.

[0023] The constraints for maximizing economic benefits include at least one of the following: internal power balance constraints of the coupled system, hydropower and reservoir capacity constraints, photovoltaic power constraints, interactive power constraints, electrolyzer operation constraints, hydrogen storage constraints, and ammonia synthesis equipment ramp-up constraints.

[0024] The constraints for the optimal production scenario include at least one of the following: power balance constraints of the source-grid hydrogen-ammonia system, synthetic ammonia constraints, hydropower output constraints, hydrogen production constraints, and hydrogen storage constraints.

[0025] Optionally, the step of performing day-ahead peak-shaving optimization scheduling based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data to obtain a day-ahead interactive scheduling scheme between the coupled system and the power system includes:

[0026] Based on the aforementioned revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0027] Optionally, the optimization objective of minimizing net load fluctuation satisfies the following formula:

[0028]

[0029] Where, minf 21 Net load fluctuation f 21 Minimum optimization objective, P net,t For net load, P netave t represents the average net load; t represents the current time; T represents the total scheduling period.

[0030] P net,t Satisfies the following formula: P net,t =P load,t -P w,t -P p,t -P an,t ;

[0031] P netave Satisfy the following formula:

[0032] Among them, P load,t For power system load; P w,t P represents wind power; p,t Photovoltaic power; P an,t Power is supplied to the coupled system.

[0033] Optionally, the optimization objective that minimizes peak-shaving costs satisfies the following formula:

[0034]

[0035] Where, minf 22 For peak shaving cost f 22 Minimum optimization objective, c w The price of wind power generation; P w,t For wind power; c p The price of photovoltaic power generation; P pv,t Photovoltaic power generation; c wq Cost of wind curtailment penalties; Pwre,t Forecast wind power output; c pq The cost of abandoning light penalty; P pre,t For photovoltaic power prediction; P p,t Photovoltaic power; N is the number of thermal power units; f hd,t f3 represents the operating cost of the thermal power unit; f3 represents the change in system revenue; C peak,t To compensate for the cost of deep peak shaving of thermal power units, C peakan,t The peak-shaving compensation cost of the coupled system; t is the current time; T is the total scheduling period; i is the i-th thermal power unit; N is the total number of thermal power units.

[0036] The change in system revenue f3 satisfies the following formula:

[0037]

[0038] Wherein, minf3 is the optimization objective that minimizes the change in system revenue f3, C pv The unit cost of photovoltaic power generation; ΔP pv,t C represents the change in photovoltaic power. hy The cost of hydropower generation; ΔP hy,t ε represents the change in hydropower. pv The cost of per unit of abandoned light penalty; ΔP pvq,t C represents the change in light discard; elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; ΔP el,t For varying power of the electrolytic cell; a h Δg is the power conversion coefficient for hydrogen production in an electrolyzer; t The cost of interactive power variation is t1, and the day-ahead optimized scheduling sampling time is t1.

[0039] Optionally, based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, to obtain a day-ahead interactive scheduling scheme between the coupled system and the power system, including:

[0040] Based on the aforementioned revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, combined with the peak-shaving scenario constraints, day-ahead peak-shaving optimization scheduling is carried out with the optimization objectives of minimizing net load fluctuation and minimizing peak-shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0041] The constraints of the peak shaving scenario include at least one of the following: hydropower power constraints caused by peak shaving, power grid interaction constraints, reservoir capacity change constraints, photovoltaic power constraints, electrolyzer power constraints, hydrogen storage tank status constraints, and system power balance constraints.

[0042] On the other hand, the present invention also provides a cooperative controller for an integrated energy system, characterized in that the integrated energy system includes a power-interacting coupled system and a power system, and the cooperative controller includes:

[0043] The data acquisition module is used to acquire the day-ahead renewable energy power supply forecast data, resource supply and demand status, and day-ahead load forecast data of the coupled system;

[0044] A multi-objective coordinated optimization module is used to calculate the output of new energy and the change in revenue in the coupled system based on the day-ahead renewable energy power supply forecast data and resource supply and demand conditions of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system; based on the renewable energy output, considering the power interaction security of the coupled system and the power system, it calculates the interaction power limit between the coupled system and the power system; based on the change in revenue, the interaction power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, it performs day-ahead peak-shaving optimization scheduling to obtain a day-ahead interaction scheduling scheme between the coupled system and the power system; based on the intraday renewable energy power supply forecast data and the renewable energy output of the coupled system, with the optimization objective of minimizing the forecast error of the scheduling scheme and the change in peak-shaving revenue, it performs intraday deviation correction on the day-ahead interaction scheduling scheme to obtain an intraday interaction scheduling scheme between the coupled system and the power system;

[0045] The collaborative control module is used to perform intraday collaborative control of the coupled system and the power system based on the intraday interactive scheduling scheme.

[0046] Optional, multi-objective coordinated optimization module, specifically used for:

[0047] Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, the operating power base point of the coupled system is calculated with the optimal renewable energy output as the optimization objective.

[0048] Based on the obtained changes in the interactive power between the coupled system and the power system, and combined with the operating power baseline, the output of new energy and the change in revenue of the coupled system are calculated with the goal of minimizing the change in revenue of the coupled system.

[0049] Optional, multi-objective coordinated optimization module, specifically used for:

[0050] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation costs, and operating costs of the coupled system, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing the utilization of renewable energy.

[0051] Optionally, the optimization objective that maximizes the economic benefits of the coupled system satisfies the following formula:

[0052]

[0053] Where T is the total scheduling period; The selling price of synthetic ammonia; For the cost of synthesized ammonia; J represents the amount of ammonia synthesized. grid,t For grid interaction costs; C pv P represents the unit cost of photovoltaic power generation. pvr,t C represents the actual photovoltaic power. hy Cost of hydroelectric power generation; P hy,t ε represents the actual power output of the hydropower plant. pv The cost of per unit of abandoned light penalty; P pvq,t For abandoned light power; C elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; P el,t This represents the actual power of the electrolytic cell; a h LOH is the power conversion factor for hydrogen production in an electrolyzer; t ε is the state quantity of hydrogen storage at time t; sto is the capacity of the hydrogen storage tank; ε htn The hydrogen-to-ammonia conversion coefficient; t is the current time.

[0054] The optimization objectives of achieving optimal ammonia production and minimizing power system interaction satisfy the following formula:

[0055]

[0056] Where maxF1 is the optimization objective that maximizes ammonia production and minimizes power system interaction, α 11 α 12 f is the weighting coefficient. 11 f 11min f 11max These represent the ammonia production rate, minimum ammonia production rate, and maximum ammonia production rate, respectively. 12 f 12min f 12max These are the power system interaction quantities, the minimum power system interaction quantity, and the maximum power system interaction quantity, respectively.

[0057] Among them, f 11 Satisfy the following formula: f 12 Satisfy the following formula: P gb,t To purchase electricity, P gs,t This refers to the amount of electricity sold.

[0058] Optional, multi-objective coordinated optimization module, specifically used for:

[0059] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation cost and operating cost of the coupled system, and combined with the constraints of maximizing economic benefits and the optimal production scenario, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production and maximizing the utilization of renewable energy.

[0060] The constraints for maximizing economic benefits include at least one of the following: internal power balance constraints of the coupled system, hydropower and reservoir capacity constraints, photovoltaic power constraints, interactive power constraints, electrolyzer operation constraints, hydrogen storage constraints, and ammonia synthesis equipment ramp-up constraints.

[0061] The constraints for the optimal production scenario include at least one of the following: power balance constraints of the source-grid hydrogen-ammonia system, synthetic ammonia constraints, hydropower output constraints, hydrogen production constraints, and hydrogen storage constraints.

[0062] Optional, multi-objective coordinated optimization module, specifically used for:

[0063] Based on the aforementioned revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0064] Optionally, the optimization objective of minimizing net load fluctuation satisfies the following formula:

[0065]

[0066] Where, minf 21 Net load fluctuation f 21 Minimum optimization objective, P net,t For net load, P netave t represents the average net load; t represents the current time; T represents the total scheduling period.

[0067] P net,t Satisfies the following formula: P net,t =P load,t -P w,t -P p,t -P an,t ;

[0068] P netave Satisfy the following formula:

[0069] Among them, P load,t For power system load; P w,t P represents wind power; p,t Photovoltaic power; P an,tPower is supplied to the coupled system.

[0070] Optionally, the optimization objective that minimizes peak-shaving costs satisfies the following formula:

[0071]

[0072] Where, minf 22 For peak shaving cost f 22 Minimum optimization objective, c w For wind power generation price; P w,t For wind power; c p The price of photovoltaic power generation; P pv,t Photovoltaic power generation; c wq Cost of wind curtailment penalties; P wre,t Forecast wind power output; c pq The cost of abandoning light penalty; P pre,t For photovoltaic power prediction; P p,t Photovoltaic power; N is the number of thermal power units; f hd,t f3 represents the operating cost of the thermal power unit; f3 represents the change in system revenue; C peak,t To compensate for the cost of deep peak shaving of thermal power units, C peakan,t The peak-shaving compensation cost of the coupled system; t is the current time; T is the total scheduling period; i is the i-th thermal power unit; N is the total number of thermal power units.

[0073] The change in system revenue f3 satisfies the following formula:

[0074]

[0075] Wherein, minf3 is the optimization objective that minimizes the change in system revenue f3, C pv The unit cost of photovoltaic power generation; ΔP pv,t C represents the change in photovoltaic power. hy Cost of hydropower generation; ΔP hy,t ε represents the change in hydropower. pv The cost of per unit of light curtailment penalty; ΔP pvq,t C represents the change in light discard; elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; ΔP el,t The varying power of the electrolytic cell; a h Δg is the power conversion coefficient for hydrogen production in the electrolyzer; t The cost of interactive power variation is t1, and the day-ahead optimized scheduling sampling time is t1.

[0076] Optional, multi-objective coordinated optimization module, specifically used for:

[0077] Based on the aforementioned revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, combined with the peak-shaving scenario constraints, day-ahead peak-shaving optimization scheduling is carried out with the optimization objectives of minimizing net load fluctuation and minimizing peak-shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0078] The constraints of the peak shaving scenario include at least one of the following: hydropower power constraints caused by peak shaving, power grid interaction constraints, reservoir capacity change constraints, photovoltaic power constraints, electrolyzer power constraints, hydrogen storage tank status constraints, and system power balance constraints.

[0079] On the other hand, the present invention also provides a computer device, characterized in that it includes: one or more processors;

[0080] The processor is used to store one or more programs;

[0081] When the one or more programs are executed by the one or more processors, the method for constructing the capacity optimization two-layer model described in any one of the above-mentioned methods is implemented, or the method for coordinated control of the integrated energy system described in any one of the above-mentioned methods is implemented.

[0082] On the other hand, the present invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon, wherein when the computer program is executed, it implements the method for constructing the capacity optimization two-layer model as described in any one of the above, or implements the collaborative control method for the integrated energy system as described in any one of the above.

[0083] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0084] This invention provides a collaborative control method and controller for an integrated energy system. The method calculates the output and revenue changes of the coupled system based on the day-ahead renewable energy power supply forecast data and resource supply and demand conditions, with the optimization objectives being optimal renewable energy output and minimal changes in the coupled system's revenue. Based on the renewable energy output, and considering the power interaction security of the coupled system and the power system, the interactive power limit between the coupled system and the power system is calculated. Based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak-shaving optimization scheduling is performed to obtain a day-ahead interactive scheduling scheme between the coupled system and the power system. Based on the intraday renewable energy power supply forecast data and renewable energy output of the coupled system, with the optimization objective of minimizing the forecast error of the scheduling scheme and the change in peak-shaving revenue, intraday deviation correction is applied to the day-ahead interactive scheduling scheme to obtain an intraday interactive scheduling scheme between the coupled system and the power system, and intraday collaborative control is performed on the coupled system and the power system. This invention integrates the coupled system and the power system in the comprehensive energy system for coordinated optimization control, which can give full play to the optimization of multi-energy complementarity. Furthermore, the introduction of multi-objective optimization decision-making in the coordinated control can maximize economic benefits while ensuring system stability. By considering resource supply and demand, it can accurately meet market demand. In other words, this invention can improve energy utilization through intelligent and dynamic power coordination. Attached Figure Description

[0085] Figure 1 This is a flowchart illustrating the collaborative control method for the integrated energy system of the present invention.

[0086] Figure 2 This is a schematic diagram of the two-stage peak-shaving collaborative optimization process of the present invention;

[0087] Figure 3 This is a schematic diagram of the structure of the collaborative controller of the source-grid-hydrogen-ammonia integrated energy system of the present invention;

[0088] Figure 4 This is a schematic diagram of the software flow of the collaborative controller of the present invention;

[0089] Figure 5 This is a schematic diagram of the architecture of the collaborative controller of the integrated energy system of the present invention;

[0090] Figure 6 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0091] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0092] Example 1:

[0093] This invention provides a collaborative control method flow for an integrated energy system, which includes a power-interacting coupled system and a power system, such as... Figure 1 As shown, the method flow includes:

[0094] Step 101: Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system, calculate the renewable energy output and revenue change in the coupled system.

[0095] Step 102: Based on the output of new energy sources, and considering the power interaction security of the coupled system and the power system, calculate the power interaction limit between the coupled system and the power system;

[0096] Step 103: Based on the revenue change, interactive power limit, day-ahead renewable energy power supply forecast data and the obtained day-ahead load forecast data, perform day-ahead peak shaving optimization scheduling to obtain the day-ahead interactive scheduling scheme between the coupled system and the power system;

[0097] Step 104: Based on the intraday renewable energy power supply forecast data and renewable energy output of the coupled system, with the optimization objective of minimizing the forecast error of the dispatch scheme and the change in peak-shaving revenue, intraday deviation correction is performed on the day-ahead interactive dispatch scheme to obtain the intraday interactive dispatch scheme between the coupled system and the power system, and intraday coordinated control is performed on the coupled system and the power system.

[0098] In this embodiment of the invention, the coupled system and the power system in the integrated energy system are coordinated and optimized, which can give full play to the optimization of multi-energy complementarity. Furthermore, multi-objective optimization decision-making is introduced into the coordinated control, which can maximize economic benefits while ensuring system stability. Moreover, by considering the supply and demand of resources, it can accurately meet market demand. In other words, the present invention can improve energy utilization through intelligent and dynamic power coordination.

[0099] The integrated energy system in this embodiment of the invention includes a coupling system and a power system. This integrated energy system encompasses multiple energy components, such as, but not limited to, "source, grid, storage, hydrogen, ammonia," or "source-grid-hydrogen-ammonia." The specific multiple energy components can be divided according to the optimization needs of the actual scenario, and no limitation is imposed here. For example, the collaborative control method for the integrated energy system proposed in this embodiment of the invention is used in the collaborative control scenario of a source-grid-hydrogen-ammonia integrated energy system in an industrial park, such as the collaborative control scenario of an industrial distributed control system or a station-level power generation control system. Here, "source" refers to renewable energy sources such as water and solar power; "grid" refers to the power grid; "storage" refers to energy storage; and "hydrogen-ammonia" includes hydrogen production through water electrolysis and hydrogen synthesis into ammonia.

[0100] To achieve efficient collaborative operation among heterogeneous energy components in an integrated energy system and improve cross-component collaboration efficiency, this invention proposes a two-stage peak-shaving collaborative optimization strategy (day-ahead and intraday). This strategy addresses the shortcomings of current technologies where subsystems often operate independently or only achieve simple coupling, making it difficult to leverage the advantages of multi-energy complementarity, thereby improving collaboration efficiency. Furthermore, considering the weak dynamic response capability of current technologies to fluctuations in electricity and chemical product (such as hydrogen and ammonia) market prices, which prevents flexible and agile adjustments to production strategies and energy flows based on real-time price signals, leading to missed market opportunities or increased operational risks, this invention also considers resource supply and demand to reflect market fluctuations. In addition, recognizing the limitations of current technologies in effectively supporting multi-objective optimization decision-making and the insufficient ability to simultaneously consider complex requirements such as economy, energy efficiency, and system stability, this invention also provides multiple optimization objectives. Through these improvements, this invention enables efficient collaborative operation and globally optimal configuration among these heterogeneous energy components, maximizing energy utilization efficiency, economic benefits, and system stability. These improvements will be explained in detail later.

[0101] The collaborative control method for integrated energy systems proposed in this invention can be applied to collaborative controllers. The collaborative controller is based on a modular design concept, with its core consisting of a data acquisition and preprocessing module, a multi-objective optimization algorithm module, a strategy generation and distribution module, and a secure communication interface. The data acquisition and preprocessing module is responsible for collecting real-time data and historical parameters from the source, grid, storage, and load sides. After cleaning and formatting, it provides accurate and timely data input for the optimization algorithm. The multi-objective optimization algorithm module uses particle swarm optimization as its core, supplemented by an adaptive weight allocation mechanism, ensuring that the importance of each objective can be flexibly adjusted during the multi-objective optimization process to cope with market changes and system state variations. The strategy generation and distribution module generates specific operation instructions based on the optimization results and seamlessly connects with the park's production control network and power grid dispatching system through the secure communication interface to achieve rapid instruction distribution and execution.

[0102] The resource supply and demand situation of the embodiments of the present invention will be described below. In order to respond flexibly and agilely to the market resource supply and demand situation, the present invention can build a real-time market data analysis module in the collaborative controller. This module integrates an actual transaction index fusion mechanism. By continuously tracking the market transaction index of industrial products such as hydrogen and ammonia, such as spot prices and futures trends, it provides decision-making assistance for the collaborative controller to dynamically adjust the power allocation and production strategy inside the system, in order to maximize economic benefits and respond to market fluctuations.

[0103] The actual trading index integration mechanism includes the following stages:

[0104] 1) Real-time Market Data Acquisition and Analysis: First, establish an efficient data acquisition system integrated into the collaborative controller. This system can automatically retrieve the latest trading indices for products such as hydrogen and ammonia from various market information platforms, exchanges, and industry reports in real time. These trading indices reflect resource supply and demand, including but not limited to spot prices, futures prices, trading volume, and supply-demand ratios. For example, but not limited to, using API (Application Programming Interface), RSS (Really Simple Syndication Subscription), web crawling, and other technologies to ensure the real-time nature and accuracy of the data.

[0105] 2) Data Preprocessing and Standardization: The raw market data collected, i.e., resource supply and demand information, often has problems such as heterogeneity, missing values, and noise. Therefore, preprocessing and standardization are required. This includes data cleaning, missing value imputation, outlier detection and removal, and data normalization or standardization to ensure data consistency and comparability, laying the foundation for subsequent analysis and integration.

[0106] 3) Mapping Model Between Market Indices and Production Strategies: Construct a flexible mapping model to connect market transaction indices with the system's internal production strategies. This model needs to comprehensively consider factors such as product prices, production costs, and inventory levels to predict expected returns under different production strategies, thereby guiding the system to dynamically adjust its production plans.

[0107] 4) Dynamic Power Regulation and Production Optimization: Based on the prediction results of the mapping model, the collaborative controller can dynamically adjust the power allocation of each process segment. For example, when the hydrogen market price is high and expected to remain so for some time, the system can automatically increase the power allocation for hydrogen production via water electrolysis, while optimizing the charging and discharging strategy of the energy storage system to ensure sufficient electrical energy is stored for hydrogen production during periods of lower cost. Conversely, if the market is unfavorable, the system may reduce hydrogen production and instead optimize other production processes or energy storage strategies to reduce costs.

[0108] 5) Real-time feedback and adaptive adjustment: Establish a real-time feedback mechanism to monitor the impact of changes in the market transaction index on the execution effect of the production strategy, and adaptively adjust the model parameters based on the deviation between the actual return and the expected target, so as to continuously optimize its own strategy from each decision.

[0109] 6) Safety Margin and Risk Control: While pursuing economic benefits, consider setting a safety margin to prevent production risks caused by drastic market fluctuations. This can be achieved by setting price fluctuation thresholds, establishing a risk early warning system, and developing emergency response strategies to ensure the system maintains basic stable operation and economic viability even under extreme market conditions.

[0110] Phases 1) to 3) introduce resource supply and demand information, and phases 4) to 6) combine with the collaborative control process to not only effectively improve the economic performance of the integrated energy system, but also enhance its responsiveness and adaptability to market changes, providing strong support for achieving a sustainable energy production and consumption model.

[0111] The following describes the multi-objective optimization decision-making process of this invention. The multiple optimization objectives proposed in this invention include, but are not limited to, maximizing the absorption of new energy sources, maximizing grid revenue, and maximizing ammonia revenue. Each objective function is quantified through a mathematical model and corresponding constraints are set, such as reservoir capacity constraints for hydropower generation, safe storage ranges for hydrogen and ammonia storage facilities, and upper and lower limits of the operating power of equipment in each section, ensuring the practical feasibility and safety of the optimization process. In constructing the multi-objective optimization scheme of this invention, each key objective needs to be considered and transformed into a quantifiable mathematical expression for easy solution through optimization algorithms.

[0112] The optimization objectives include, but are not limited to, one or more of the following: optimal new energy output, minimum change in coupled system revenue, minimum prediction error of dispatch scheme, minimum change in peak shaving revenue, minimum net load fluctuation, and minimum peak shaving cost; optimal new energy output includes, but is not limited to, one or more of the following: maximum economic benefits of coupled system, optimal ammonia production, and maximum utilization of renewable energy.

[0113] In the optimization objective that maximizes the economic benefits of the coupled system, the coupled system comprehensively considers the power generation cost, grid interaction cost, electrolyzer operating cost, hydrogen storage cost, ammonia production cost, etc. For example, the economic benefit maximization function is: ammonia sales revenue - grid interaction cost - power generation cost - electrolyzer operating cost - hydrogen storage cost; the function for this optimization objective is established according to maximizing ammonia sales revenue, which satisfies the following formula (1):

[0114]

[0115] Where T is the total scheduling period; The selling price of synthetic ammonia; For the cost of synthesized ammonia; J represents the amount of ammonia synthesized. grid,t For grid interaction costs; C pv P represents the unit cost of photovoltaic power generation. pvr,t C represents the actual photovoltaic power. hy Cost of hydroelectric power generation; P hy,t ε represents the actual power output of the hydropower plant. pv The cost of per unit of abandoned light penalty; P pvq,t For abandoned light power; C elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&mFor hydrogen storage costs; P el,t This represents the actual power of the electrolytic cell; a h LOH is the power conversion factor for hydrogen production in an electrolyzer; t ε is the state quantity of hydrogen storage at time t; sto is the capacity of the hydrogen storage tank; ε htn t represents the hydrogen-to-ammonia conversion coefficient; t represents the current time.

[0116] When the photovoltaic-hydropower generation of the coupled system is insufficient to support the electrolysis of water to produce hydrogen and synthesize ammonia, the cost of purchasing electricity from the grid is incurred. When the photovoltaic-hydropower generation has a surplus power to supply the electrolyzers for hydrogen synthesis and ammonia production, the revenue generated by selling electricity to the grid is recorded. The grid interaction cost J is also mentioned. grid,t The following formula (2) is satisfied:

[0117] J grid,t =δ gb,t C gb,t P gb,t +δ gs,t C gs P gs,t (2)

[0118] Where, δ gb,t C represents the power grid purchase status (0-1 variable); gb,t For electricity purchase price; P gb,t For electricity purchase; δ gs,t For the power grid's electricity sales status (0-1 variables); C gs (<0) represents the electricity price; P gs,t This refers to the amount of electricity sold.

[0119] The optimization objective of maximizing ammonia production and minimizing power system interaction is applicable to the scenario of optimal production. Considering sub-objective function 1, which is to maximize ammonia production, and sub-objective 2, which is to maximize the utilization of renewable energy, we perform normalization and assign corresponding weights to obtain the total objective ammonia production ratio weight and renewable energy utilization ratio weight, thereby obtaining the expression of the optimization objective.

[0120] The sub-objective function 1 satisfies the following formula (3):

[0121]

[0122] Among them, f 11 Let t represent the ammonia production rate, t represent the current time, and T represent the total scheduling period, i.e., the optimized scheduling period (e.g., 24 hours). This represents the amount of ammonia synthesized.

[0123] The sub-objective function 2 satisfies the following formula (4):

[0124]

[0125] Among them, f 12For power system interaction quantities, the minimum grid interaction is used to maximize the utilization of renewable energy, P gb,t To purchase electricity, P gs,t This refers to the amount of electricity sold.

[0126] Normalize the objective functions represented by formulas (3) and (4), set corresponding weights for each objective function, and obtain the total objective function, which satisfies the following formula (5):

[0127]

[0128] Where maxF1 is the optimization objective that maximizes ammonia production and minimizes power system interaction, α 11 α 12 For weighting coefficients (such as the weighting of the overall target ammonia production ratio and the weighting of the renewable energy utilization ratio mentioned above), f 11 f 11min f 11max These represent the ammonia production rate, minimum ammonia production rate, and maximum ammonia production rate, respectively. 12 f 12min f 12max These are the power system interaction quantities, the minimum power system interaction quantity, and the maximum power system interaction quantity, respectively.

[0129] In peak-shaving scenarios, the coupled system connects to the power system and participates in peak-shaving ancillary services. Changes in the power interaction between the coupled system and the grid are shared by photovoltaic (PV), hydropower, and electrolytic cells within the coupled system. The goal is to minimize the revenue change caused by the power interaction with the grid, thus obtaining the revenue change of the coupled system, which represents the cost of the coupled system's participation in peak shaving. Based on the power system's renewable energy forecast curve, system load forecast curve, and the power interaction limit between the coupled system and the grid, an objective function is established to minimize the grid net load variance and system operating cost. This yields the power interaction between the coupled system and the grid, as well as the power of each device, under peak-shaving conditions, and is then transferred to the intraday stage. The intraday optimization scheduling timescale is 15 minutes. Based on the PV power forecast deviation and considering economic factors, the intraday scheduling results of the coupled system are obtained, completing the two-stage scheduling under peak-shaving scenarios. (See [link to relevant documentation]). Figure 2 As shown.

[0130] exist Figure 2In the illustrated process, the first-stage scheduling (day-ahead 1-hour economic optimization) includes the calculation process of the power base point of the multi-coupling system, the calculation process of the peak-shaving cost of the coupling system, and the peak-shaving optimization scheduling process of the power system. In one implementation, step 1 includes: calculating the operating power base point of the coupling system based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation, with the optimization objective of maximizing renewable energy output; and calculating the renewable energy output and revenue change in the coupling system based on the obtained interaction power change between the coupling system and the power system, combined with the operating power base point, with the optimization objective of minimizing the revenue change of the coupling system. Renewable energy output may include ammonia synthesis. The interaction power change between the coupling system and the power system can be obtained by dynamically cointegrating the power allocation of each section based on the prediction results of the above mapping model.

[0131] For example, the day-ahead renewable energy power supply forecast data for the coupled system in step 1 above includes the photovoltaic forecast data of the coupled system (which may be, but is not limited to, the day-ahead photovoltaic forecast curve based on 1 hour), and the resource supply and demand situation includes ammonia sales revenue, renewable energy power generation cost, and operating cost. The process of calculating the operating power base point of the coupled system based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation, with the optimization objective of maximizing renewable energy production, includes: calculating the operating power base point of the coupled system based on the day-ahead photovoltaic forecast data, ammonia sales revenue, renewable energy power generation cost, and operating cost, with the optimization objectives of maximizing the economic benefits of the coupled system, maximizing ammonia production, and maximizing renewable energy utilization.

[0132] The optimization objective that maximizes the economic benefits of the coupled system can satisfy the above formula (1), and the optimization objectives that maximize ammonia production and renewable energy utilization can satisfy the above formula (5).

[0133] Based on the selected optimization objectives and system state, constraints can be introduced into the calculation process. For example, based on the day-ahead photovoltaic forecast data, ammonia sales revenue, renewable energy generation costs, and operating costs of the coupled system, the process of calculating the operating power baseline of the coupled system, with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing renewable energy utilization, includes:

[0134] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation costs and operating costs of the coupled system, and combined with the constraints of maximizing economic benefits and the optimal production scenario, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing the utilization of renewable energy.

[0135] Among them, the constraints for maximizing economic benefits include at least one of the following: internal power balance constraints of the coupled system, hydropower and reservoir capacity constraints, photovoltaic power constraints, interactive power constraints, electrolyzer operation constraints, hydrogen storage constraints, and ammonia synthesis equipment ramp-up constraints.

[0136] The power balance constraint inside the coupled system satisfies the following formula (6):

[0137]

[0138] Among them, P pvr,t P represents the actual photovoltaic power. hy,t P represents the actual power output of the hydropower plant. gb,t For purchasing electricity; P el,t This represents the actual power of the electrolytic cell; Power consumption for ammonia synthesis; P gs,t This refers to the amount of electricity sold.

[0139] In the constraints of hydropower power and reservoir capacity, the hydropower power meets the limit requirements. The water consumed during power generation causes changes in the hydropower reservoir capacity. At the same time, the water coming from above the hydropower station provides stability to the reservoir capacity. The constraints satisfy the following formula (7):

[0140]

[0141] Among them, P hymin P represents the minimum value for hydropower generation. hy,t P represents the actual power output of the hydropower plant. hymax V represents the maximum hydropower generation value. hy,t V hy,t-1 V is the state quantity of the hydropower reservoir. hymin V represents the minimum state quantity of the hydropower reservoir. hymax V is the maximum value of the reservoir capacity state quantity; α is the reservoir capacity; V is the hydropower power conversion coefficient; hyb t represents the inflow of water into the reservoir; t represents the current time; t-1 represents the previous time; and t1 represents the day-ahead optimized scheduling sampling time (e.g., 1 hour).

[0142] In photovoltaic power constraints, photovoltaic power is fluctuating, and under the action of the objective function, there may be curtailment. The actual photovoltaic power and curtailment should satisfy the following formula (8):

[0143]

[0144] Among them, P pv,for,t For photovoltaic power prediction; P pvq,t P represents the power of abandoned light. pvr,t This represents the actual power output of the photovoltaic system.

[0145] In the interactive power constraint, the power purchase / sale of the coupled system (with the grid) cannot be carried out simultaneously within the same time period, and each must meet the limit requirements, as shown in the following formula (9):

[0146]

[0147] Where, δgb,t For the power grid purchase status, δ gs,t P represents the power grid's electricity sales status. gbmin Minimum purchase volume; P gb,t For purchasing electricity; P gbmax Maximum purchase volume; P gsmin To achieve the minimum electricity sales volume, P gs,t For electricity sales, P gsmax This represents the maximum electricity sales volume.

[0148] The electrolytic cell operating constraints require the electrolytic cell operating power to meet the limit constraints and ramp constraints, as shown in the following formula (10):

[0149]

[0150] Among them, P elmin P is the minimum operating power of the electrolytic cell. el,t P el,t-1 P represents the actual power of the electrolytic cell. elmax ΔP represents the maximum operating power of the electrolytic cell. elmin This represents the minimum change in the operating power of the electrolytic cell; ΔP elmax This represents the maximum variation in the operating power of the electrolytic cell.

[0151] In the hydrogen storage constraint, the electrolyzer generates hydrogen during operation and ammonia synthesis consumes hydrogen. The hydrogen storage state quantity constraint in the hydrogen storage tank satisfies the following formula (11):

[0152]

[0153] Among them, LOH t LOH t-1 P is the state quantity of hydrogen storage. el,t This represents the actual power of the electrolytic cell; LOH min This represents the minimum value of the hydrogen storage state quantity; LOH max a represents the maximum value of the hydrogen storage state quantity. h denoted as the hydrogen production power conversion factor of the electrolyzer, sto as the hydrogen storage tank capacity, and t1 as the day-ahead optimized scheduling sampling time (1 hour). ε represents the amount of ammonia synthesized. htn The hydrogen-to-ammonia conversion coefficient.

[0154] In the ramp-up constraint of ammonia synthesis equipment, the raw materials for ammonia synthesis are hydrogen and nitrogen. The reaction process is a reversible process, which is carried out by the Haber process under the action of a catalyst. The nitrogen required for ammonia synthesis is usually obtained from the air separation unit. The compressor compresses the inlet gas. Therefore, the main power-consuming equipment for ammonia synthesis is the air separation unit and the compressor. The power consumption of ammonia synthesis satisfies the following formula (12):

[0155]

[0156] Where, k asu and k ac These are the power consumption coefficients of the air separation unit and the compressor, respectively. This represents the amount of ammonia synthesized. The electricity consumed in ammonia synthesis.

[0157] The ammonia synthesis equipment is set to operate at only one load level at any given time, and the constraint is expressed as follows: (13)

[0158]

[0159] The ramp rate for different stages is set according to the load level of ammonia synthesis, and the constraint satisfies the following formula (14):

[0160]

[0161] In the formula: μ1, μ2, and μ3 represent the state of the ammonia synthesis equipment (0-1 variables), and a value of 1 indicates that the ammonia synthesis is in a low, medium, or high load state; This represents the amount of ammonia synthesized. These represent the lower and upper limits of the ammonia synthesis rate under low loading, respectively. These represent the lower and upper limits of the ammonia synthesis rate under medium loading, respectively. These are the lower and upper limits of the ammonia synthesis rate under high load, respectively. This represents the maximum amount of ammonia synthesized.

[0162] The ammonia synthesis plan curve is obtained by solving the objective function and constraints. At this point, the power of the interaction between the coupled system and the power grid is used as the operating power base point of the source hydrogen ammonia coupled system.

[0163] The constraints for the optimal production scenario include at least one of the following: power balance constraints of the source-grid hydrogen-ammonia system, ammonia synthesis constraints, hydropower output constraints, hydrogen production constraints, and hydrogen storage constraints.

[0164] The power balance constraint of the source-grid hydrogen-ammonia system satisfies the following formula (15):

[0165]

[0166] Among them, P grid,b To purchase optimized electricity volume, P PV,real P represents the optimized amount of actual photovoltaic power generation. Hydro To optimize hydropower output, P el To optimize the power of the electrolyzer for economic purposes, k is the coefficient for calculating the ammonia rate, and P... grid,s To optimize the amount of electricity sold, To optimize the rate of ammonia synthesis.

[0167] The constraints for ammonia synthesis satisfy the following formula (16):

[0168]

[0169] in, This represents the minimum amount of ammonia synthesized. This represents the maximum amount of ammonia synthesized. To optimize the rate of ammonia synthesis.

[0170] The ammonia synthesis equipment is set to operate at only one load level at any given time, and the constraint is expressed as follows: (17)

[0171]

[0172] Among them, the ramp rate of different stages is set according to the load level of ammonia synthesis, and the constraint satisfies the following formula (18):

[0173]

[0174] Where μ1, μ2, and μ3 represent the status of the ammonia synthesis equipment (0-1 variables), and a value of 1 indicates that the ammonia synthesis is in a low, medium, or high load state. This represents the amount of ammonia synthesized. These represent the lower and upper limits of the ammonia synthesis rate under low loading, respectively. These represent the lower and upper limits of the ammonia synthesis rate under medium loading, respectively. These are the lower and upper limits of the ammonia synthesis rate under high load, respectively. This represents the maximum amount of ammonia synthesized.

[0175] The power output constraint of hydropower satisfies the following formula (19):

[0176] P Hydromin ≤P Hydro ≤P Hydromax (19)

[0177] Among them, P Hydromin P represents the minimum hydroelectric output. Hydro To optimize hydropower output, P Hydromax This represents the maximum output power of the hydroelectric power plant.

[0178] The hydrogen production constraint satisfies the following formula (20):

[0179]

[0180] in, P represents the amount of hydrogen produced. el,t This represents the actual power of the electrolytic cell. The power conversion factor for hydrogen production in the electrolyzer is... For the optimized amount of ammonia to be synthesized, ε htn The hydrogen-to-ammonia conversion coefficient.

[0181] The hydrogen storage capacity constraint satisfies the following formula (21):

[0182]

[0183] Among them, LOH min LOH represents the minimum hydrogen storage capacity. max The maximum hydrogen storage capacity is given by P, where T is the day-ahead sampling time, LOH is the hydrogen storage capacity, and P is the maximum hydrogen storage capacity. el To optimize the power of the electrolyzer, The power conversion factor for hydrogen production in the electrolyzer is... For the optimized ammonia synthesis rate, sto is the hydrogen storage tank capacity, and ε htn t represents the hydrogen-to-ammonia conversion coefficient, and t represents the current time.

[0184] Based on actual scheduling needs, the power interaction security considerations in step 102 above may include an optimization objective that maximizes the power interaction between the coupled system and the power system, or an optimization objective that minimizes the power interaction between the coupled system and the power system.

[0185] return Figure 2 For example, the first-stage peak shaving scenario includes optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost. In one implementation, in step 103 above, based on the revenue change, interactive power limit, day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0186] Net load is the difference between the power system load and the power output of equipment with uncertain output. In this system, the output of wind and solar power and the coupled system has uncertainties, so the net load is the load borne by thermal power plants. Frequent power fluctuations will shorten the service life of thermal power units. To avoid excessive power fluctuations in thermal power units, a minimum net load fluctuation objective function f is established. 21 The optimization objective of minimizing net load fluctuation satisfies the following formula (22):

[0187]

[0188] Where, minf 21 Net load fluctuation f 21 Minimum optimization objective, P net,t For net load, P netave t represents the average net load, t represents the current time, and T represents the total scheduling cycle.

[0189] P net,t Satisfying the following formula (23): P net,t =P load,t -P w,t -Pp,t -P an,t (twenty three);

[0190] P netave The following formula (24) is satisfied:

[0191] Among them, P load,t For power system load; P w,t P represents wind power; p,t Photovoltaic power; P an,t Power is supplied to the coupled system.

[0192] Taking into account the costs of wind power and photovoltaic power generation, the cost of wind and solar curtailment penalties, the operating costs of thermal power units, the connection costs of coupled systems, the cost of thermal power peak-shaving ancillary services, and the cost of coupled system peak-shaving subsidies, a system cost optimization function f is established with the goal of minimizing the total system cost. 22 The optimization objective of minimizing peak shaving cost satisfies the following formula (25):

[0193]

[0194] Where, minf 22 For peak shaving cost f 22 Minimum optimization objective, c w The price of wind power generation; P w,t For wind power; c p The price of photovoltaic power generation; P pv,t Photovoltaic power generation; c wq Cost of wind curtailment penalties; P wre,t Forecast wind power output; c pq The cost of abandoning light penalty; P pre,t For photovoltaic power prediction; P p,t Photovoltaic power; N is the number of thermal power units; f hd,t f3 represents the operating cost of the thermal power unit; f3 represents the change in system revenue; C peak,t To compensate for the cost of deep peak shaving of thermal power units, C peakan,t The peak-shaving compensation cost of the coupled system; t is the current time; T is the total scheduling period; i is the i-th thermal power unit; and N is the total number of thermal power units.

[0195] Based on the obtained ammonia synthesis plan curve, disregarding economic factors, the objective function is to maximize the power exchange between the coupled system and the power grid. The obtained maximum power value represents the maximum power that the coupled system can connect to the power system, and this maximum power value is used as a limit input to the system. When the power connected to the power grid changes, the revenue of the coupled system changes; this change represents the cost of connecting the coupled system to the power system. The change in power exchange between the power grid and the grid is ΔP. grid,tPhotovoltaics, hydropower, and electrolytic cells share the interactive power variation. To minimize the change in system revenue, the sub-objective function f3 is obtained. The optimization objective of minimizing the change in system revenue satisfies the following formula (26):

[0196]

[0197] Wherein, minf3 is the optimization objective that minimizes the change in system revenue f3, C pv The unit cost of photovoltaic power generation; ΔP pv,t C represents the change in photovoltaic power. hy The cost of hydropower generation; ΔP hy,t ε represents the change in hydropower. pv The cost of per unit of abandoned light penalty; ΔP pvq,t C represents the change in light discard; elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; ΔP el,t For varying power of the electrolytic cell; a h Δg is the power conversion coefficient for hydrogen production in an electrolyzer; t The cost of interactive power variation is t1, and the day-ahead optimized scheduling sampling time is t1.

[0198] By weighting and normalizing the objective functions with different dimensions, and using the entropy weight method to determine the weights, the system objective function minF2 in the peak shaving scenario is obtained, which satisfies the following formula (27).

[0199]

[0200] Where F1 and F2 are weighting coefficients, f 21 f 21min f 21max These represent net load, minimum net load, and maximum net load, respectively. 22 f 22min f 22max These are the peak-shaving cost, minimum peak-shaving cost, and maximum peak-shaving cost, respectively.

[0201] By applying the system objective function and constraints, the day-ahead optimal scheduling results of the operating power of each device in the power system and the participation of the coupled system in grid peak shaving are obtained. For example, the process of obtaining the day-ahead interactive scheduling scheme between the coupled system and the power system, based on revenue changes, interactive power limits, day-ahead renewable energy supply forecast data, and acquired day-ahead load forecast data, with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, includes: based on revenue changes, interactive power limits, day-ahead renewable energy supply forecast data, and acquired day-ahead load forecast data, combined with peak shaving scenario constraints, day-ahead optimal scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in the day-ahead interactive scheduling scheme between the coupled system and the power system.

[0202] The constraints for peak shaving scenarios include at least one of the following: hydropower power constraints caused by peak shaving, power grid interaction constraints, reservoir capacity change constraints, photovoltaic power constraints, electrolyzer power constraints, hydrogen storage tank status constraints, and system power balance constraints.

[0203] The constraints on hydropower power, power grid interaction, and reservoir capacity change caused by peak shaving indicate that the hydropower power after the change meets the constraints of the hydropower power limit, and the change is less than the change in power grid interaction. The resulting change in reservoir capacity meets the reservoir capacity constraint, as shown in the following formula (28):

[0204]

[0205] Where, ΔP grid,t P represents the change in power exchange between the power grids. hy,t Hydropower output; ΔP hy,t P represents the change in hydropower. hymin P hymax V represents the lower and upper limits of hydropower output; hymin V represents the minimum state quantity of the hydropower reservoir. hymax V represents the maximum value of the reservoir capacity state quantity; hy,t-1 V represents the reservoir capacity state quantity, α is the conversion coefficient, and V hyb t1 represents the reservoir's inflow volume, and t1 represents the day-ahead optimized scheduling sampling time.

[0206] In the photovoltaic power constraint, the change in photovoltaic power is less than the change in grid interaction power, and the changed photovoltaic power is less than the photovoltaic predicted power, satisfying the following formula (29):

[0207]

[0208] Where, ΔP pv,t Let ΔP be the change in photovoltaic power. grid,t P represents the change in power exchange between the power grids. pv,t For photovoltaic power, P pvfor,tFor photovoltaic power prediction.

[0209] The constraints on electrolyzer power and hydrogen storage tank status, considering the changes in hydrogen production caused by changes in electrolyzer power, and the constraints on hydrogen storage tank status, satisfy the following formula (30):

[0210]

[0211] Among them, LOH min This represents the minimum value of the hydrogen storage state quantity; LOH max P represents the maximum value of the hydrogen storage state quantity. el,t ΔP represents the actual power of the electrolytic cell. el,t The change in electrolyzer power is represented by sto; the hydrogen storage tank capacity is represented by sto. LOH is the power conversion factor for hydrogen production in an electrolyzer. t-1 This refers to the state properties of hydrogen storage. ε represents the amount of ammonia synthesized. htn t1 represents the hydrogen-to-ammonia conversion coefficient, and t1 represents the day-ahead optimized scheduling sampling time.

[0212] The system power balance constraint serves as the system operation constraint condition, satisfying the following formulas (31) and (32):

[0213] P w,t +P pv,t +P hd,t +P an,t =P load,t (31)

[0214]

[0215] Where, P w,t For wind power output, P pv,t To contribute to photovoltaic power, P hd,t For the thermal power unit to output power, P an,t For the power input to the coupled system, P load,t For the power system load, s w For wind power prices, s pv For the price of photovoltaics, c w For the price of wind power generation, c p For the price of photovoltaic power generation, C peakw,t To compensate for the cost of deep peak shaving of wind power, C peakpv,t To compensate for the cost of deep peak shaving in photovoltaic systems, c wq To account for the cost of wind curtailment penalties, c pq To account for the cost of penalties for abandoning light, P wre,t For wind power forecast, P pre,t This represents the predicted power output of wind power.

[0216] Based on the above objective function and constraints, the cost of the coupled system participating in system peak shaving, as well as the day-ahead interactive scheduling scheme, can be obtained.

[0217] For example Figure 2 As shown, step 104 can obtain the intraday interactive scheduling scheme, i.e. the final scheduling result of the coupled system participating in peak shaving, by correcting the intraday (15min) deviation.

[0218] It is understood that the solution algorithm used in any step of the embodiment of the present invention is not limited, such as particle swarm optimization algorithm, or linear weighted method, hierarchical sequence method or neural network algorithm, etc.

[0219] Figure 3 A schematic block diagram of a collaborative controller for a source-grid-hydrogen-ammonia integrated energy system is shown, comprising a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and an FPGA (Field-Programmable Gate Array). Through an external data acquisition and control command receiving communication interface, it can acquire the data required for coordinated control. By calling a real-time operating system, optimizing control algorithms, data processing modules, and a simulation production module, it can generate scheduling results. Through a production control communication module, it can achieve coordinated control of the coupled system and the power system. Using a heterogeneous control unit of CPU, GPU, and FPGA, the FPGA undertakes real-time high-speed control commands and data processing, the GPU undertakes online real-time analysis and optimization algorithms, and the CPU handles external interface tasks. This satisfies the comprehensive capabilities of real-time performance, online analysis, and high-speed data processing and control, thus meeting real-time requirements and enabling broader application scenarios.

[0220] Figure 4A schematic diagram of the workflow of a collaborative controller is shown. The data required for coordinated control includes: optimization algorithm configuration parameters, system task configuration data, real-time production acquisition data, scheduling instruction data, new energy forecast data, and electricity market data. This data is fused and processed. Using an online simulation analysis module, optimized simulation results and suggested solutions (e.g., but not limited to day-ahead interactive scheduling schemes) can be generated. Then, the online optimization control module determines whether to adjust the scheduling scheme. If adjustment is needed, it adjusts and issues control instructions for hydrogen and ammonia production (an example of an intraday interactive scheduling scheme). The process then returns to the online optimization control module for further adjustment judgment and control. This data processing process can include the utilization of historical data and the construction of performance indicators. For example, firstly, through big data analysis technology, historical operating data is comprehensively mined, and key indicators related to system performance are extracted based on the optimization objectives, such as the historical power generation efficiency of hydropower and photovoltaic power plants, grid load fluctuation characteristics, and the energy consumption ratio of the hydrogen and ammonia production process, serving as a basic reference for algorithm iterative optimization.

[0221] Figure 3 and Figure 4 The issuance of commands relies on the secure and efficient system integration and control command issuance mechanism within the controller. The controller strictly adheres to international standards such as IEC 61850 and IEC 104 to ensure secure and efficient communication with the park's internal control system and power grid dispatching system. Encryption technology ensures data transmission security, while mechanisms such as double confirmation and fault isolation enhance the reliability and response speed of command execution. Upon receiving a power grid dispatching command or a system status change signal, the controller can quickly calculate a new power allocation plan and issue the command to the execution units of each section through a secure and reliable channel, ensuring the efficient and coordinated operation of the entire park's energy system.

[0222] As can be seen, this invention significantly improves the collaborative efficiency of multiple energy links (source, grid, storage, hydrogen, and ammonia) by designing an advanced optimization decision-making and control architecture; provides powerful multi-objective collaborative optimization management capabilities to achieve optimal overall system operating efficiency; and ensures that the system has a good modular architecture and configuration flexibility to adapt to integrated energy systems in industrial parks of different scales and energy structures, meeting future expansion needs. Furthermore, it can significantly improve the operating efficiency and economy of integrated energy systems in industrial parks, promoting the effective utilization of new energy sources and the optimization and upgrading of the energy structure.

[0223] This invention provides an efficient and intelligent solution for integrated energy management of industrial parks, encompassing energy sources, grid, hydrogen, and ammonia, through a collaborative controller and its collaborative control method. This is significant for promoting green and low-carbon transformation and optimizing the energy structure. By employing multi-dimensional coordinated control strategies, a high-efficiency controller, and a software platform, it effectively integrates various stages of the energy source-grid-hydrogen-ammonia process. The introduction of a particle swarm optimization algorithm during the strategy optimization phase improves computational efficiency, achieving multi-objective optimization functions such as maximizing new energy consumption, maximizing grid revenue, and maximizing ammonia production revenue under multiple constraints.

[0224] The embodiments of this invention realize the efficient collaborative operation of the integrated energy system in industrial parks, significantly improving energy utilization; through intelligent and dynamic power coordination, it optimizes the economic and environmental benefits of hydrogen and ammonia production; multi-objective optimization capabilities maximize economic benefits while ensuring system stability; it accurately meets market demands, enhancing the flexibility and competitiveness of the industrial chain; and by utilizing modern optimization algorithms, it improves the computational efficiency and implementation effectiveness of control strategies.

[0225] Example 2:

[0226] Based on the same inventive concept, this invention also provides a cooperative controller for an integrated energy system, which includes a power-interacting coupled system and a power system. A schematic diagram of the cooperative controller structure is shown below. Figure 5 As shown, it includes:

[0227] The data acquisition module is used to acquire the day-ahead renewable energy power supply forecast data, resource supply and demand status, and day-ahead load forecast data of the coupled system;

[0228] The multi-objective coordinated optimization module is used to calculate the output and revenue changes of new energy sources in the coupled system based on the day-ahead renewable energy power supply forecast data and resource supply and demand conditions of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system. Based on the renewable energy output, considering the power interaction security of the coupled system and the power system, the module calculates the interaction power limit between the coupled system and the power system. Based on the revenue change, interaction power limit, day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, the module performs day-ahead peak-shaving optimization scheduling to obtain the day-ahead interaction scheduling scheme between the coupled system and the power system. Based on the intraday renewable energy power supply forecast data and renewable energy output of the coupled system, with the optimization objectives of minimizing the forecast error of the scheduling scheme and the change in peak-shaving revenue, the module performs intraday deviation correction on the day-ahead interaction scheduling scheme to obtain the intraday interaction scheduling scheme between the coupled system and the power system.

[0229] The collaborative control module is used to perform intraday collaborative control of the coupled system and the power system based on the intraday interactive scheduling scheme.

[0230] In one specific implementation, the multi-objective coordination optimization module is specifically used for:

[0231] Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, the operating power base point of the coupled system is calculated with the optimal renewable energy output as the optimization objective.

[0232] Based on the obtained changes in the interactive power between the coupled system and the power system, and combined with the operating power baseline, the output and revenue changes of the coupled system are calculated with the goal of minimizing the change in the revenue of the coupled system.

[0233] In one specific implementation, the multi-objective coordination optimization module is specifically used for:

[0234] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation costs, and operating costs of the coupled system, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing the utilization of renewable energy.

[0235] In one specific implementation, the optimization objective that maximizes the economic benefits of the coupled system satisfies the following formula:

[0236]

[0237] Where T is the total scheduling period; The selling price of synthetic ammonia; For the cost of synthesized ammonia; J represents the amount of ammonia synthesized. grid,t For grid interaction costs; C pv P represents the unit cost of photovoltaic power generation. pvr,t C represents the actual photovoltaic power. hy Cost of hydroelectric power generation; P hy,t ε represents the actual power output of the hydropower plant. pv The cost of per unit of abandoned light penalty; P pvq,t For abandoned light power; C elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; P el,t This represents the actual power of the electrolytic cell; a h LOH is the power conversion factor for hydrogen production in an electrolyzer; t ε is the state quantity of hydrogen storage at time t; sto is the capacity of the hydrogen storage tank; ε htn The hydrogen-to-ammonia conversion coefficient; t is the current time.

[0238] The optimization objectives of maximizing ammonia production and minimizing power system interaction satisfy the following formula:

[0239]

[0240] Where maxF1 is the optimization objective that maximizes ammonia production and minimizes power system interaction, α 11 α 12 f is the weighting coefficient. 11 f 11min f 11max These represent the ammonia production rate, minimum ammonia production rate, and maximum ammonia production rate, respectively. 12 f 12min f 12max These are the power system interaction quantities, the minimum power system interaction quantity, and the maximum power system interaction quantity, respectively.

[0241] Among them, f 11 Satisfy the following formula: f 12 Satisfy the following formula: P gb,t To purchase electricity, P gs,t This refers to the amount of electricity sold.

[0242] In one specific implementation, the multi-objective coordination optimization module is specifically used for:

[0243] Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation cost and operating cost of the coupled system, and combined with the constraints of maximizing economic benefits and the optimal production scenario, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production and maximizing the utilization of renewable energy.

[0244] The constraints for maximizing economic benefits include at least one of the following: internal power balance constraints of the coupled system, hydropower and reservoir capacity constraints, photovoltaic power constraints, interactive power constraints, electrolyzer operation constraints, hydrogen storage constraints, and ammonia synthesis equipment ramp-up constraints.

[0245] The constraints for the optimal production scenario include at least one of the following: power balance constraints of the source-grid hydrogen-ammonia system, ammonia synthesis constraints, hydropower output constraints, hydrogen production constraints, and hydrogen storage constraints.

[0246] In one specific implementation, the multi-objective coordination optimization module is specifically used for:

[0247] Based on revenue changes, interactive power limits, day-ahead renewable energy power supply forecast data, and acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is carried out with the optimization objectives of minimizing net load fluctuations and minimizing peak shaving costs, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0248] In one specific implementation, the optimization objective of minimizing net load fluctuation satisfies the following formula:

[0249]

[0250] Where, minf 21 Net load fluctuation f 21 Minimum optimization objective, P net,t For net load, P netave t represents the average net load; t represents the current time; T represents the total scheduling period.

[0251] P net,t Satisfies the following formula: P net,t =P load,t -P w,t -P p,t -P an,t ;

[0252] P netave Satisfy the following formula:

[0253] Among them, P load,t For power system load; P w,t P represents wind power; p,t Photovoltaic power; P an,t Power is supplied to the coupled system.

[0254] In one specific implementation, the optimization objective of minimizing peak shaving costs satisfies the following formula:

[0255]

[0256] Where, minf 22 For peak shaving cost f 22 Minimum optimization objective, c w The price of wind power generation; P w,t For wind power; c p The price of photovoltaic power generation; P pv,t Photovoltaic power generation; c wq Cost of wind curtailment penalties; P wre,t Forecast wind power output; c pq The cost of abandoning light penalty; P pre,t For photovoltaic power prediction; P p,t Photovoltaic power; N is the number of thermal power units; f hd,t f3 represents the operating cost of the thermal power unit; f3 represents the change in system revenue; C peak,t To compensate for the cost of deep peak shaving of thermal power units, C peakan,t The peak-shaving compensation cost of the coupled system; t is the current time; T is the total scheduling period; i is the i-th thermal power unit; N is the total number of thermal power units.

[0257] The change in system revenue f3 satisfies the following formula:

[0258]

[0259] Wherein, minf3 is the optimization objective that minimizes the change in system revenue f3, C pv The unit cost of photovoltaic power generation; ΔP pv,t C represents the change in photovoltaic power. hy The cost of hydropower generation; ΔP hy,t ε represents the change in hydropower. pv The cost of per unit of abandoned light penalty; ΔP pvq,t C represents the change in light discard; elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; ΔP el,t For varying power of the electrolytic cell; a h Δg is the power conversion coefficient for hydrogen production in an electrolyzer; t The cost of interactive power variation is t1, and the day-ahead optimized scheduling sampling time is t1.

[0260] In one specific implementation, the multi-objective coordination optimization module is specifically used for:

[0261] Based on the revenue change, interactive power limit, day-ahead renewable energy power supply forecast data and the acquired day-ahead load forecast data, combined with the peak shaving scenario constraints, day-ahead peak shaving optimization scheduling is carried out with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

[0262] The constraints for peak shaving scenarios include at least one of the following: hydropower power constraints caused by peak shaving, power grid interaction constraints, reservoir capacity change constraints, photovoltaic power constraints, electrolyzer power constraints, hydrogen storage tank status constraints, and system power balance constraints.

[0263] The data acquisition module connects to the market transaction index analysis module via the internet, acquiring real-time market data through various methods to support the controller's core algorithm module in optimizing the economic performance of the operating scheme. It also connects to the new energy power supply forecasting module, using forecast data to support production scheduling plans for new energy consumption and production control strategies in response to grid dispatch. Furthermore, it connects to the multi-objective coordination and optimization module of the power production control area, calculating efficient and stable operating schemes for the integrated energy system of source-grid-hydrogen-ammonia based on safety and with economic and flexibility objectives. The module adjusts the operating scheme based on data feedback from basic control equipment, reserving adjustment margins for uncertain system scenarios.

[0264] The coordination controller is configured with a network architecture featuring horizontal security isolation devices. This architecture enables the controller to connect to the Internet to obtain market data required by the trading index fusion mechanism, to the power production control area to obtain dispatch instructions, and to the industrial production control network to issue power control instructions and receive feedback information on instruction execution. The network architecture design has the following characteristics to achieve network access at different levels and with varying security requirements:

[0265] 1) Modular design: The controller is designed to consist of multiple modules. The core consists of a market trading index analysis module, a multi-objective coordinated optimization algorithm module, an instruction issuance and feedback detection module, and a secure communication interface, so that different modules can run independently on servers with different security levels, and also facilitate maintenance and upgrades.

[0266] 2) Data Acquisition Module Connects to Market Transaction Index Analysis Module: This module is responsible for acquiring real-time market data from the internet, including but not limited to spot prices, futures trends, and trading volumes of industrial products such as hydrogen and ammonia. This module is deployed in a non-security control area and can freely access the internet, but it has clear physical isolation from the main coordination control module to prevent potential threats from external networks from penetrating into the power system.

[0267] 3) Multi-objective Coordination and Optimization Module: Located within the power security zone, this module directly interfaces with the park's production control network and power grid dispatching system to ensure rapid receipt and execution of instructions. This module receives information processed by the market transaction index analysis module and power grid dispatching instructions, then generates specific control instructions based on the optimization algorithm's calculation results to perform real-time regulation of various energy links within the park.

[0268] 4) Secure communication interface: Located between the market transaction index analysis module and the multi-objective coordinated optimization algorithm module, this interface can be called by the data acquisition module to undertake the task of secure data exchange. Through data transcription, encryption and other technologies, this interface ensures the security and integrity of data during transmission.

[0269] The data acquisition module integrates a trading index fusion mechanism from the real-time market data analysis module. This mechanism includes: a real-time data acquisition system that automatically captures market trading indices via API interfaces and other technologies; a data preprocessing and standardization process that ensures data quality through data cleaning and outlier removal; a market index-production strategy mapping model that links market information with internal production strategies; dynamic power adjustment in the production process that automatically adjusts power allocation based on market price changes; and a real-time feedback and adaptive adjustment mechanism that monitors the impact of market changes on strategies and makes adaptive adjustments.

[0270] As a key component of the controller, the multi-objective coordinated optimization module can configure different quantitative objective functions and corresponding constraints for various objectives, such as maximizing new energy consumption, maximizing grid revenue, and maximizing ammonia revenue. These constraints include equipment power range and safety limits for hydrogen and ammonia storage. The system can easily add new optimization objectives and corresponding objective functions. Utilizing the particle swarm optimization algorithm, the system automatically adjusts the weights of optimization objectives while considering system state and market dynamics, enabling free switching between multiple objectives and maintaining stable system operation. Furthermore, dynamic adjustment strategies under different operating conditions are considered, such as increasing consumption during periods of high photovoltaic production, reducing electricity purchases during peak electricity prices, and adjusting energy storage charging and discharging strategies based on peak and off-peak electricity prices at different times. This ensures that the system can dynamically adjust in complex and ever-changing operating environments to maximize overall economic benefits.

[0271] In addition, the coordination controller may also include an online production simulation and verification module design module, which is used to form a digital twin correspondence between the production simulation model and the production object. Through the full-process simulation of the power system and industrial production system, it can simulate dispatching instructions, power market transaction constraints, production scheduling plans, control strategies, etc., and connect and run them with the simulation model, providing a reliable preliminary verification means for optimizing control objectives under the premise of ensuring production safety.

[0272] Example 3:

[0273] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0274] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the capacity optimization two-layer model construction method in the above embodiments, or the steps of the integrated energy system collaborative control method in the above embodiments.

[0275] Example 4:

[0276] Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the capacity optimization two-layer model construction method in the above embodiments, or the steps of the collaborative control method of an integrated energy system in the above embodiments.

[0277] 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.

[0278] 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 device that provides the functions specified in one or more boxes.

[0279] 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 instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0280] 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.

[0281] 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A collaborative control method for an integrated energy system, characterized in that, The integrated energy system includes a power-interacting coupled system and a power system, and the method includes: Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system, the renewable energy output and revenue change of the coupled system are calculated. Based on the new energy output, and considering the power interaction security of the coupling system and the power system, calculate the power interaction limit between the coupling system and the power system. Based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed to obtain the day-ahead interactive scheduling scheme between the coupled system and the power system. Based on the intraday renewable energy power supply forecast data and renewable energy output of the coupled system, with the goal of minimizing the forecast error and peak-shaving revenue changes of the scheduling scheme, intraday deviation correction is performed on the day-ahead interactive scheduling scheme to obtain the intraday interactive scheduling scheme between the coupled system and the power system, and intraday coordinated control is performed on the coupled system and the power system.

2. The method as described in claim 1, characterized in that, Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing changes in the revenue of the coupled system, the calculation of renewable energy output and revenue changes in the coupled system includes: Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, with the optimal renewable energy output as the optimization objective, the operating power base point of the coupled system is calculated. Based on the obtained changes in the interactive power between the coupled system and the power system, and combined with the operating power baseline, the output of new energy and the change in revenue of the coupled system are calculated with the goal of minimizing the change in revenue of the coupled system.

3. The method as described in claim 1 or 2, characterized in that, Based on the day-ahead renewable energy power supply forecast data and resource supply and demand situation of the coupled system, and with the optimization objective of maximizing renewable energy production, the operating power base point of the coupled system is calculated, including: Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation costs, and operating costs of the coupled system, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing the utilization of renewable energy.

4. The method as described in claim 3, characterized in that, The optimization objective that maximizes the economic benefits of the coupled system satisfies the following formula: Where T is the total scheduling period; The selling price of synthetic ammonia; Cost of synthesized ammonia; J represents the amount of ammonia synthesized. grid,t For grid interaction costs; C pv P represents the unit cost of photovoltaic power generation. pvr,t C represents the actual photovoltaic power. hy Cost of hydroelectric power generation; P hy,t ε represents the actual power output of the hydropower plant. pv The cost of per unit of abandoned light penalty; P pvq,t For abandoned light power; C elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; P el,t This represents the actual power of the electrolytic cell; a h LOH is the power conversion factor for hydrogen production in an electrolyzer; t ε is the state quantity of hydrogen storage at time t; sto is the capacity of the hydrogen storage tank; ε htn The hydrogen-to-ammonia conversion coefficient; t is the current time. The optimization objectives of achieving optimal ammonia production and minimizing power system interaction satisfy the following formula: Where maxF1 is the optimization objective that maximizes ammonia production and minimizes power system interaction, α 11 α 12 f is the weighting coefficient. 11 f 11min f 11max These represent the ammonia production rate, minimum ammonia production rate, and maximum ammonia production rate, respectively. 12 f 12min f 12max These are the power system interaction quantities, the minimum power system interaction quantity, and the maximum power system interaction quantity, respectively. Among them, f 11 Satisfy the following formula: f 12 Satisfy the following formula: P gb,t To purchase electricity, P gs,t This refers to the amount of electricity sold.

5. The method as described in claim 3, characterized in that, Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, renewable energy generation costs, and operating costs of the coupled system, and with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production, and maximizing renewable energy utilization, the operating power baseline of the coupled system is calculated, including: Based on the day-ahead photovoltaic forecast data, ammonia sales revenue, new energy power generation cost and operating cost of the coupled system, and combined with the constraints of maximizing economic benefits and the optimal production scenario, the operating power base point of the coupled system is calculated with the optimization objectives of maximizing the economic benefits of the coupled system, optimizing ammonia production and maximizing the utilization of renewable energy. The constraints for maximizing economic benefits include at least one of the following: internal power balance constraints of the coupled system, hydropower and reservoir capacity constraints, photovoltaic power constraints, interactive power constraints, electrolyzer operation constraints, hydrogen storage constraints, and ammonia synthesis equipment ramp-up constraints. The constraints for the optimal production scenario include at least one of the following: power balance constraints of the source-grid hydrogen-ammonia system, synthetic ammonia constraints, hydropower output constraints, hydrogen production constraints, and hydrogen storage constraints.

6. The method as described in claim 1, characterized in that, The day-ahead peak shaving optimization scheduling is performed based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data to obtain a day-ahead interactive scheduling scheme between the coupled system and the power system, including: Based on the aforementioned revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system.

7. The method as described in claim 6, characterized in that, The optimization objective of minimizing net load fluctuation satisfies the following formula: Where, minf 21 Net load fluctuation f 21 Minimum optimization objective, P net,t For net load, P netave t represents the average net load; t represents the current time; T represents the total scheduling period. P net,t Satisfies the following formula: P net,t =P load,t -P w,t -P p,t -P an,t ; P netave Satisfy the following formula: Among them, P load,t For power system load; P w,t P represents wind power; p,t Photovoltaic power; P an,t Power is supplied to the coupled system.

8. The method as described in claim 6 or 7, characterized in that, The optimization objective of minimizing peak-shaving costs satisfies the following formula: Where, minf 22 For peak shaving cost f 22 Minimum optimization objective, c w The price of wind power generation; P w,t For wind power; c p The price of photovoltaic power generation; P pv,t Photovoltaic power generation; c wq Cost of wind curtailment penalties; P wre,t Forecast wind power output; c pq The cost of abandoning light penalty; P pre,t For photovoltaic power prediction; P p,t Photovoltaic power; N is the number of thermal power units; f hd,t f3 represents the operating cost of the thermal power unit; f3 represents the change in system revenue; C peak,t To compensate for the cost of deep peak shaving of thermal power units, C peakan,t The peak-shaving compensation cost of the coupled system; t is the current time; T is the total scheduling period; i is the i-th thermal power unit; N is the total number of thermal power units. The change in system revenue f3 satisfies the following formula: Wherein, minf3 is the optimization objective that minimizes the change in system revenue f3, C pv The unit cost of photovoltaic power generation; ΔP pv,t C represents the change in photovoltaic power. hy The cost of hydropower generation; ΔP hy,t ε represents the change in hydropower; pv The cost of per unit of abandoned light penalty; ΔP pvq,t C represents the change in light discard; elo&m For the operation and maintenance costs of the electrolytic cell; C Ho&m For hydrogen storage costs; ΔP el,t For varying power of the electrolytic cell; a h Δg is the power conversion coefficient for hydrogen production in the electrolyzer; t The cost of interactive power variation is t1, and the day-ahead optimized scheduling sampling time is t1.

9. The method as described in claim 6, characterized in that, Based on the revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, day-ahead peak shaving optimization scheduling is performed with the optimization objectives of minimizing net load fluctuation and minimizing peak shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system, including: Based on the aforementioned revenue change, the interactive power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, combined with the peak-shaving scenario constraints, day-ahead peak-shaving optimization scheduling is carried out with the optimization objectives of minimizing net load fluctuation and minimizing peak-shaving cost, resulting in a day-ahead interactive scheduling scheme between the coupled system and the power system. The constraints of the peak shaving scenario include at least one of the following: hydropower power constraints caused by peak shaving, power grid interaction constraints, reservoir capacity change constraints, photovoltaic power constraints, electrolyzer power constraints, hydrogen storage tank status constraints, and system power balance constraints.

10. A collaborative controller for an integrated energy system, characterized in that, The integrated energy system includes a power-interacting coupled system and a power system, and the cooperative controller includes: The data acquisition module is used to acquire the day-ahead renewable energy power supply forecast data, resource supply and demand status, and day-ahead load forecast data of the coupled system; A multi-objective coordinated optimization module is used to calculate the output of new energy and the change in revenue in the coupled system based on the day-ahead renewable energy power supply forecast data and resource supply and demand conditions of the coupled system, with the optimization objectives of maximizing renewable energy output and minimizing the change in revenue of the coupled system; based on the renewable energy output, considering the power interaction security of the coupled system and the power system, it calculates the interaction power limit between the coupled system and the power system; based on the change in revenue, the interaction power limit, the day-ahead renewable energy power supply forecast data, and the acquired day-ahead load forecast data, it performs day-ahead peak-shaving optimization scheduling to obtain a day-ahead interaction scheduling scheme between the coupled system and the power system; based on the intraday renewable energy power supply forecast data and the renewable energy output of the coupled system, with the optimization objective of minimizing the forecast error of the scheduling scheme and the change in peak-shaving revenue, it performs intraday deviation correction on the day-ahead interaction scheduling scheme to obtain an intraday interaction scheduling scheme between the coupled system and the power system; The collaborative control module is used to perform intraday collaborative control of the coupled system and the power system based on the intraday interactive scheduling scheme.

Images