Comprehensive energy system scheduling optimization method and system considering water-energy collaboration

Abstract

The invention provides an integrated energy system scheduling optimization method and system considering water-energy collaboration, and the method comprises the steps: constructing a corresponding energy management model, a water resource management model and an electric vehicle charging management model according to the historical operation data of an integrated energy system, and setting operation targets for operation, the method comprises the steps of obtaining an energy balance condition, a water resource balance condition and a power grid balance condition, further fusing a management model to generate a water-energy collaborative optimization scheduling model, decomposing the water-energy collaborative optimization scheduling model by using an alternating direction multiplier method to obtain a plurality of to-be-optimized problems, and performing iterative optimization on each to-be-optimized problem by using an augmented Lagrange function to obtain a plurality of to-be-optimized problems. According to the method, a corresponding optimization scheme is obtained and fed back to a corresponding management model for corresponding balance optimization, and on the premise of respecting independent ownership and management of water resource and energy management departments, the difference between operation is optimized, so that the problems of insufficient collaborative optimization between systems and low resource allocation efficiency are solved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy dispatching technology, and in particular to an integrated energy system dispatching optimization method and system that considers water-energy synergy. Background Technology

[0002] With the development of the energy internet, integrated energy systems have become an important mode of regional energy utilization, improving energy efficiency through the coordinated optimization of multiple energy sources such as electricity, heat, and gas. However, current drinking water and power systems still employ independent modeling and optimization methods, failing to fully consider the water-energy coupling relationship, thus affecting overall operational efficiency. To effectively address this issue, the planning and scheduling of integrated energy systems need to consider the water-energy synergy to optimize and improve overall system efficiency. However, existing optimization and scheduling methods still have significant limitations in terms of multi-objective coupling and cross-system coordination.

[0003] The traditional independent operation model of water-energy systems limits a deeper understanding of their interdependencies. Drinking water production is highly dependent on electricity, involving multiple aspects such as treatment energy consumption and equipment power consumption. However, existing scheduling methods mostly adopt separate economic scheduling, which is difficult to cope with dynamic load fluctuations and multi-objective optimization needs, and lacks coordination and flexibility.

[0004] In integrated energy system dispatching, existing methods have significant shortcomings in terms of privacy protection and distributed management. Because water and electricity systems are operated by different entities, insufficient data sharing hinders collaborative optimization. Traditional centralized dispatching cannot guarantee the privacy and security of each system, nor can it balance conflicting objectives among multiple parties. This problem is particularly prominent in multi-entity collaborative scenarios, ultimately limiting system operating efficiency and practical application value.

[0005] Therefore, the present invention provides a method and system for optimizing the scheduling of a comprehensive energy system that considers water-energy synergy. Summary of the Invention

[0006] This invention provides a method and system for optimizing the scheduling of an integrated energy system that considers water-energy synergy. While respecting the separate ownership and management of water resources and energy management departments, it optimizes the gaps between operations, aiming to solve the problems of insufficient synergistic optimization between systems and low efficiency in resource allocation.

[0007] This invention provides a comprehensive energy system scheduling optimization method considering water-energy synergy, comprising:

[0008] Step 1: Construct corresponding energy management models, water resource management models, and electric vehicle charging management models based on the historical operation data of the integrated energy system;

[0009] Step 2: Set operational objectives for each management model and run them separately to generate the energy balance conditions, water resource balance conditions, and power grid balance conditions of the integrated energy system.

[0010] Step 3: Use the equilibrium condition to fuse the management model to generate a water-energy coordinated optimization scheduling model, and use the alternating direction multiplier method to decompose the water-energy coordinated optimization scheduling model to obtain several optimization problems;

[0011] Step 4: Use the augmented Lagrangian function to iteratively optimize each of the problems to be optimized, obtain the corresponding optimization scheme, and feed it back to the corresponding management model for corresponding balance optimization.

[0012] In one feasible approach

[0013] Step 1 includes:

[0014] Step 11: Obtain the historical operation data of the integrated energy system under different management dimensions, and divide the corresponding historical operation data into training set, validation set and test set according to the dimensional features of each management dimension. Construct the first initial model, second initial model and third initial model corresponding to different management dimensions using several training sets corresponding to each management dimension.

[0015] Step 12: Run each initial model in different evaluation environments to obtain several model performance data corresponding to each initial model. Perform similarity analysis on several model performance data corresponding to the same management dimension to deduce the fitting degree of the corresponding initial model.

[0016] Step 13: Adjust the model parameters of the initial model using the validation set and the degree of fit corresponding to each management dimension, and optimize the performance of the initial model using the test set corresponding to each management dimension to obtain the optimized model corresponding to each management dimension.

[0017] Step 14: Add corresponding dimension functions to the corresponding optimization model according to the dimension attributes of each management dimension to obtain the energy management model, water resource management model and electric vehicle charging management model of the integrated energy system.

[0018] In one feasible approach

[0019] Step 1 includes:

[0020] The management dimensions include: energy dimension, water resource dimension, and electric vehicle charging management dimension;

[0021] The historical operating data corresponding to the energy dimension includes: electricity purchase price, electricity consumption, gas purchase price, gas consumption, electricity purchase power, gas purchase power, renewable energy output, electrical equipment maintenance cost, and electrical equipment operating power.

[0022] The historical operational data corresponding to the water resources dimension includes: water purchase price, water consumption, water equipment maintenance cost, water equipment operating power, and information on water users;

[0023] The historical operational data corresponding to the electric vehicle charging management dimension includes: carbon trading costs, electric vehicle charging demand information, and grid load data.

[0024] In one feasible approach

[0025] Set operational goals for each management model and run them separately, including:

[0026] Minimizing operating costs is considered the operating objective of the energy management model, minimizing water energy consumption is considered the operating objective of the water resource management model, and peak shaving and valley filling are considered the operating objective of the electric vehicle charging management model.

[0027] By running the energy management model, the corresponding power generation response characteristics, energy storage response characteristics and grid interaction response characteristics are obtained, and the supply and demand balance value, energy storage dynamic value and renewable resource stability value of the integrated energy system at different times are derived.

[0028] By running the water resource management model, the corresponding wastewater treatment process, groundwater extraction process, and system storage-supply process are obtained. The pump energy consumption value, water storage dynamic value, and supply-demand balance value of the integrated energy system at different times are derived.

[0029] By running the electric vehicle charging management model, we can obtain the charging power value of electric vehicles under different charging modes, the power supply value of the integrated energy system to the power grid at different times, and the average load value of the power grid at different times.

[0030] In one feasible approach

[0031] Generating the energy balance conditions, water resource balance conditions, and power grid balance conditions of the integrated energy system includes:

[0032] Based on the supply and demand balance value, the energy storage dynamic value, and the renewable resource stability value corresponding to the energy management model, several energy operation requirements of the integrated energy system under the energy dimension are constructed, and the energy balance conditions of the integrated energy system are constructed based on the constraint and support relationships between different energy operation requirements.

[0033] Based on the pump energy consumption value, water storage dynamic value and supply-demand balance value corresponding to the water resource management model, construct several water source flow paths of the integrated energy system under the water resource dimension, and count the real-time water volume corresponding to each water source flow path to construct the water resource balance conditions of the integrated energy system.

[0034] Based on the charging power values ​​corresponding to electric vehicles in different charging modes, the high-efficiency charging mode and the low-efficiency charging mode of the power grid are derived. Based on the power supply value and the average load value of the power grid at different times, the high-efficiency charging demand corresponding to the high-efficiency charging mode is derived. Based on the power supply value and the average load value of the power grid at different times, the low-efficiency charging defects of the low-efficiency charging mode are derived.

[0035] The inefficient charging defect is optimized to obtain the grid's optimization requirements. Based on the efficient charging requirements and the optimization requirements, the grid balance conditions of the integrated energy system are constructed.

[0036] In one feasible approach

[0037] Step 3 includes:

[0038] Step 31: Based on the system structure of the integrated energy system, identify several model association structures between the energy management model, water resource management model, and electric vehicle charging management model; use the model association structures to fuse the energy management model, water resource management model, and electric vehicle charging management model to generate a water-energy collaborative optimization scheduling model.

[0039] Step 32: Based on the energy balance conditions, identify the first operating characteristic of the energy management model in the water-energy coordinated optimization scheduling model; based on the water resource balance conditions, identify the second operating characteristic of the water resource management model in the water-energy coordinated optimization scheduling model; based on the power grid balance conditions, identify the third operating characteristic of the electric vehicle charging management model in the water-energy coordinated optimization scheduling model.

[0040] Step 33: Couple the operational objectives corresponding to each management model according to the first operational characteristic, the second operational characteristic and the third operational characteristic to obtain the overall optimization objective of the water-energy coordinated optimization scheduling model, and use the overall optimization objective to identify the global optimization problem contained in the water-energy coordinated optimization scheduling model;

[0041] Step 34: Decompose the water-energy collaborative optimization scheduling model using the alternating direction multiplier method. Based on the decomposition results, divide the global optimization problem into corresponding energy management optimization sub-problems, water resource management optimization sub-problems, and electric vehicle optimization sub-problems, and convert them into problems to be optimized for display.

[0042] In one feasible approach

[0043] Step 4 includes:

[0044] Step 41: Determine the predicted energy consumption of water resources and the actual energy consumption of water resources in the integrated energy system according to the water resource management model; determine the predicted energy consumption of charging and the actual energy consumption of charging in the integrated energy system according to the energy management model and the electric vehicle charging management model.

[0045] Step 42: Use formula (1) to establish the augmented Lagrangian function of the integrated energy system;

[0046]

[0047] Among them, L σ Let f represent the augmented Lagrangian function. E Let f represent the first optimal scheduling objective function of the energy management model. W Let f represent the second optimal scheduling objective function of the water resource management model. EV Let λ represent the third optimization scheduling objective function of the electric vehicle charging management model. t This represents the first Lagrange multiplier, μ, generated by the constraints imposed on the integrated energy system by the water resource management model. t This represents the second Lagrange multiplier generated by the constraints of the energy management model and the electric vehicle charging management model on the integrated energy system. t∈T indicates that the time index corresponding to the variable belongs to the time set T within the scheduling period, used to limit the calculation range of the relevant terms at each time point. This indicates the predicted energy consumption of water resources in the integrated energy system. This indicates that the water resource management model determines the actual energy consumption of the integrated energy system based on water resources. This indicates the predicted energy consumption for charging the integrated energy system. ρ represents the actual energy consumption of the integrated energy system during charging, and ρ represents the penalty factor of the augmented Lagrange function.

[0048] Step 43: Use the formula (1) to perform local optimization on each of the problems to be optimized until each of the problems to be optimized shows convergence characteristics, obtain several local optimizations corresponding to each of the problems to be optimized, and generate an optimization scheme.

[0049] Step 44: Feed the optimization scheme back to the energy management model, the water resource management model, and the electric vehicle charging management model for optimization simulation, and obtain and display several balance optimization results of the integrated energy system.

[0050] In one feasible approach

[0051] Also includes:

[0052] According to formulas (2) and (3), the original monitoring residual and dual residual corresponding to each optimization problem are obtained respectively;

[0053]

[0054] in, This represents the original monitoring residual corresponding to the k-th problem to be optimized. This represents the dual residual corresponding to the k-th problem to be optimized;

[0055] When the original monitoring residual and dual residual corresponding to each of the optimization problems tend to zero, the feasibility measure corresponding to each optimization problem is calculated according to formula (4);

[0056]

[0057] Where, ε k This represents the feasibility measure corresponding to the k-th problem to be optimized;

[0058] When the feasibility metric corresponding to each of the optimization problems meets the preset threshold, it is determined that all the optimization problems have completed the balanced optimization, and the optimization scheme is output.

[0059] Conversely, the next iteration of optimization is performed using the formula (1).

[0060] In one feasible approach

[0061] Also includes:

[0062] After the optimization scheme is generated, the updated Lagrange multipliers of the integrated energy system are calculated according to formulas (5) and (6);

[0063]

[0064]

[0065] in, This represents the updated first Lagrange multiplier. This represents the updated second Lagrange multiplier;

[0066] The first updated first Lagrange multiplier and the updated second Lagrange multiplier are fed back into the formula (1) for multiplier adjustment.

[0067] This invention provides a comprehensive energy system scheduling optimization system considering water-energy synergy, comprising:

[0068] The model building module is used to build corresponding energy management models, water resource management models, and electric vehicle charging management models based on the historical operating data of the integrated energy system.

[0069] The condition analysis module is used to set operating targets for each management model and run them separately to generate the energy balance conditions, water resource balance conditions and power grid balance conditions of the integrated energy system.

[0070] The problem generation module is used to integrate the management model with the balance condition to generate a water-energy coordinated optimization scheduling model, and to decompose the water-energy coordinated optimization scheduling model with the alternating direction multiplier method to obtain several problems to be optimized.

[0071] The optimization execution module is used to iteratively optimize each of the problems to be optimized using the augmented Lagrangian function, obtain the corresponding optimization scheme, and feed it back to the corresponding management model for corresponding balance optimization.

[0072] The beneficial effects of the above technical solution are as follows: In order to balance the relationship between different energy sources in the integrated energy system, firstly, energy management models, water resource management models, and electric vehicle charging management models are constructed based on historical data. Each model closely matches the actual operating characteristics of its respective system, laying the foundation for subsequent precise management and optimization, and making the management of each system more targeted. Then, corresponding operating objectives are set for each model according to its characteristics. The energy balance conditions, water resource balance conditions, and power grid balance conditions in the integrated energy system are then determined through the operating models, providing clear reference standards for the stable operation of the system and helping to promptly identify potential imbalances in the operation of each system. Furthermore, by constructing a water-energy collaborative optimization scheduling model and decomposing it using the alternating direction multiplier method, the optimization problems in the integrated energy system are determined. Then, the augmented Lagrangian function is used to iteratively optimize the optimization problems, generating optimization schemes to balance and optimize the integrated energy system, forming a dynamic optimization cycle mechanism. This allows each system to continuously adjust according to the actual operating conditions, maintain a stable balance, and enhance the integrated energy system's ability to cope with complex operating conditions and emergencies.

[0073] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0074] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0075] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0076] Figure 1 This is a schematic diagram illustrating the workflow of a comprehensive energy system scheduling optimization method considering water-energy synergy in an embodiment of the present invention.

[0077] Figure 2 This is a schematic diagram of the composition of a comprehensive energy system scheduling optimization system that considers water-energy synergy in an embodiment of the present invention. Detailed Implementation

[0078] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0079] Example 1:

[0080] This embodiment provides a comprehensive energy system scheduling optimization method that considers water-energy synergy, such as... Figure 1 As shown, it includes:

[0081] Step 1: Construct corresponding energy management models, water resource management models, and electric vehicle charging management models based on the historical operation data of the integrated energy system;

[0082] Step 2: Set operational objectives for each management model and run them separately to generate the energy balance conditions, water resource balance conditions, and power grid balance conditions of the integrated energy system.

[0083] Step 3: Use the equilibrium condition to fuse the management model to generate a water-energy coordinated optimization scheduling model, and use the alternating direction multiplier method to decompose the water-energy coordinated optimization scheduling model to obtain several optimization problems;

[0084] Step 4: Use the augmented Lagrangian function to iteratively optimize each of the problems to be optimized, obtain the corresponding optimization scheme, and feed it back to the corresponding management model for corresponding balance optimization.

[0085] In this example, the integrated energy system refers to an energy system that integrates water, wind, and electricity.

[0086] In this example, the energy management model represents the model of renewable energy in the integrated energy system, the water resource management model represents the model of water resource trends in the integrated energy system, and the electric vehicle charging management model represents the model of electricity management for electric vehicle charging in the integrated energy system.

[0087] In this example, the management models represent: energy management model, water resource management model, and electric vehicle charging management model;

[0088] In this example, the problem to be optimized represents the resource balance problem that needs to be solved in the integrated energy system.

[0089] The working principle and beneficial effects of the above technical solution are as follows: In order to balance the relationship between different energy sources in the integrated energy system, energy management models, water resource management models, and electric vehicle charging management models are first constructed based on historical data. This ensures that each model closely matches the actual operating characteristics of its respective system, laying the foundation for subsequent precise management and optimization, and making the management of each system more targeted. Then, corresponding operating objectives are set for each model according to its characteristics. The energy balance conditions, water resource balance conditions, and power grid balance conditions in the integrated energy system are then determined through the operating models, providing clear reference standards for the stable operation of the system and helping to promptly identify potential imbalances in the operation of each system. Furthermore, by constructing a water-energy collaborative optimization scheduling model and decomposing it using the alternating direction multiplier method, the optimization problems in the integrated energy system are determined. Then, the augmented Lagrangian function is used to iteratively optimize the optimization problems, generating optimization schemes to balance and optimize the integrated energy system, forming a dynamic optimization cycle mechanism. This allows each system to continuously adjust according to actual operating conditions, maintain a stable balance, and enhance the integrated energy system's ability to cope with complex operating conditions and emergencies.

[0090] Example 2:

[0091] Based on Example 1, the integrated energy system scheduling optimization method considering water-energy synergy, step 1 includes:

[0092] Step 11: Obtain the historical operation data of the integrated energy system under different management dimensions, and divide the corresponding historical operation data into training set, validation set and test set according to the dimensional features of each management dimension. Construct the first initial model, second initial model and third initial model corresponding to different management dimensions using several training sets corresponding to each management dimension.

[0093] Step 12: Run each initial model in different evaluation environments to obtain several model performance data corresponding to each initial model. Perform similarity analysis on several model performance data corresponding to the same management dimension to deduce the fitting degree of the corresponding initial model.

[0094] Step 13: Adjust the model parameters of the initial model using the validation set and the degree of fit corresponding to each management dimension, and optimize the performance of the initial model using the test set corresponding to each management dimension to obtain the optimized model corresponding to each management dimension.

[0095] Step 14: Add corresponding dimension functions to the corresponding optimization model according to the dimension attributes of each management dimension to obtain the energy management model, water resource management model and electric vehicle charging management model of the integrated energy system.

[0096] In this example, the management dimensions include: energy dimension, water resource dimension, and power grid consumption dimension. Since the maximum energy-consuming load of the power grid in this integrated energy system is electric vehicles, the power grid consumption dimension can also be the electric vehicle charging dimension.

[0097] In this example, the first initial model represents a preliminary energy management system built based on the training set, the second initial model represents a preliminary water resource management system built based on the training set, and the third initial model represents a preliminary electric vehicle charging management system built based on the training set.

[0098] In this example, the model represents the data presented in the initial model;

[0099] In this example, the purpose of adjusting the model parameters of the initial model using the validation set and the corresponding fit is to avoid overfitting.

[0100] In this example, the dimension function represents the function that the optimization model must implement under different management dimensions. For example, the water resource management model can perform water resource flow direction analysis.

[0101] The working principle and beneficial effects of the above technical solution are as follows: By classifying historical data and splitting it into corresponding training, validation, and test sets, the model can be continuously adjusted and optimized at each stage of model building. This avoids model bias caused by data misuse or single-use, providing a data foundation for reliable model building. Furthermore, the generated model is optimized in terms of parameters and functions to ensure that the generated model is consistent with the functions of the integrated energy system. This provides a high-quality basic model for the subsequent fusion of various models and the construction of a water-energy coordinated optimization scheduling model, which helps to improve the scheduling and optimization effect of the entire integrated energy system.

[0102] Example 3:

[0103] Based on Example 2, the integrated energy system scheduling optimization method considering water-energy synergy further includes:

[0104] The management dimensions include: energy dimension, water resource dimension, and electric vehicle charging management dimension;

[0105] The historical operating data corresponding to the energy dimension includes: electricity purchase price, electricity consumption, gas purchase price, gas consumption, electricity purchase power, gas purchase power, renewable energy output, electrical equipment maintenance cost, and electrical equipment operating power.

[0106] The historical operational data corresponding to the water resources dimension includes: water purchase price, water consumption, water equipment maintenance cost, water equipment operating power, and information on water users;

[0107] The historical operational data corresponding to the electric vehicle charging management dimension includes: carbon trading costs, electric vehicle charging demand information, and grid load data.

[0108] Example 4:

[0109] Based on Example 1, the integrated energy system scheduling optimization method considering water-energy synergy sets operational targets for each management model and runs them separately, including:

[0110] Minimizing operating costs is considered the operating objective of the energy management model, minimizing water energy consumption is considered the operating objective of the water resource management model, and peak shaving and valley filling are considered the operating objective of the electric vehicle charging management model.

[0111] By running the energy management model, the corresponding power generation response characteristics, energy storage response characteristics and grid interaction response characteristics are obtained, and the supply and demand balance value, energy storage dynamic value and renewable resource stability value of the integrated energy system at different times are derived.

[0112] By running the water resource management model, the corresponding wastewater treatment process, groundwater extraction process, and system storage-supply process are obtained. The pump energy consumption value, water storage dynamic value, and supply-demand balance value of the integrated energy system at different times are derived.

[0113] By running the electric vehicle charging management model, we can obtain the charging power value of electric vehicles under different charging modes, the power supply value of the integrated energy system to the power grid at different times, and the average load value of the power grid at different times.

[0114] In this example, minimizing operating costs includes: b1 minimizing the operating costs of a combined heat and power system that integrates power-to-gas conversion with carbon capture and storage technologies;

[0115] b2 Minimize the operating costs of micro gas-fired boilers;

[0116] b3 Minimize the operating costs of energy storage systems, including both electrical and thermal energy storage;

[0117] b4 Minimizes grid interaction costs and optimizes electricity purchase and sales strategies;

[0118] b5 Minimize the cost of renewable energy losses and improve the utilization rate of wind and solar energy;

[0119] b6 minimizes carbon trading costs and optimizes carbon emission and carbon capture strategies;

[0120] b7 minimizes demand response incentive costs and optimizes the allocation of adjustable loads on the user side;

[0121] In this example, minimizing water energy consumption includes: c1 minimizing the overall energy consumption of the wastewater treatment unit, the groundwater treatment unit, and the water storage unit;

[0122] c2 optimizes the pump operation strategy to reduce power consumption during pumping and water delivery;

[0123] c3 ensures a balance between water supply and demand in the water supply system, avoiding water waste and insufficient water supply;

[0124] C4 optimizes the start-up and shutdown strategy of the water treatment unit, improves water treatment efficiency, and reduces operating costs;

[0125] In this example, peak shaving and valley filling represent the strategy of balancing peak and valley periods in the power grid. The essential operational goal of the electric vehicle charging management model is: d1 to optimize the allocation of charging time and achieve power grid load balance while ensuring user charging needs.

[0126] The d2 optimizes the switching strategy between fast and slow charging modes, reducing peak load impact and improving charging economy;

[0127] Based on the operating characteristics of microgrids, d3 dynamically adjusts the charging power to improve the capacity for renewable energy absorption.

[0128] The working principle and beneficial effects of the above technical solution are as follows: Based on the specific management dimensions of each management model, corresponding operational goals are set, providing a clear and aligned guidance for the operation of each model with its core functions. This avoids blind operation and allows for targeted solutions to key problems in each system. Furthermore, the operational data presented in each management model can be accurately derived through the operational model. This data forms a crucial foundation for the subsequent construction of a water-energy coordinated optimization scheduling model. Model fusion based on this precise data allows for more accurate alignment of coordinated optimization with the actual conditions of each system, further enhancing the synergy between systems and improving the overall operational efficiency of the integrated energy system.

[0129] Example 5:

[0130] Based on Example 4, the integrated energy system scheduling optimization method considering water-energy synergy generates the energy balance conditions, water resource balance conditions, and power grid balance conditions of the integrated energy system, including:

[0131] Based on the supply and demand balance value, the energy storage dynamic value, and the renewable resource stability value corresponding to the energy management model, several energy operation requirements of the integrated energy system under the energy dimension are constructed, and the energy balance conditions of the integrated energy system are constructed based on the constraint and support relationships between different energy operation requirements.

[0132] Based on the pump energy consumption value, water storage dynamic value and supply-demand balance value corresponding to the water resource management model, construct several water source flow paths of the integrated energy system under the water resource dimension, and count the real-time water volume corresponding to each water source flow path to construct the water resource balance conditions of the integrated energy system.

[0133] Based on the charging power values ​​corresponding to electric vehicles in different charging modes, the high-efficiency charging mode and the low-efficiency charging mode of the power grid are derived. Based on the power supply value and the average load value of the power grid at different times, the high-efficiency charging demand corresponding to the high-efficiency charging mode is derived. Based on the power supply value and the average load value of the power grid at different times, the low-efficiency charging defects of the low-efficiency charging mode are derived.

[0134] The inefficient charging defect is optimized to obtain the grid's optimization requirements. Based on the efficient charging requirements and the optimization requirements, the grid balance conditions of the integrated energy system are constructed.

[0135] In this example, the constraint relationship represents the relationship that exists when different energy operation needs restrict each other, while the support relationship represents the relationship that exists when different energy operation needs have basic cooperation.

[0136] In this example, energy operating demand represents the operational basis when the integrated energy system is in normal operating condition;

[0137] In this example, the high-efficiency charging mode means that the electric vehicle can be charged quickly in this mode, while the low-efficiency charging mode means that the electric vehicle can only be charged slowly in this mode.

[0138] In this example, the water flow path represents all the flow paths of water resources in the integrated energy system.

[0139] The working principle and beneficial effects of the above technical solution are as follows: By analyzing the operation process of the energy management model, water resource management model and electric vehicle charging management model, the actual operation process corresponding to different energy sources in the integrated energy system is analyzed. This provides a unified constraint framework for the subsequent water-energy coordinated optimization scheduling model, avoids systemic imbalance caused by single-dimensional optimization, and fundamentally ensures the feasibility and efficiency of multi-system coordination.

[0140] Example 6:

[0141] Based on Example 1, the integrated energy system scheduling optimization method considering water-energy synergy, step 3 includes:

[0142] Step 31: Based on the system structure of the integrated energy system, identify several model association structures between the energy management model, water resource management model, and electric vehicle charging management model; use the model association structures to fuse the energy management model, water resource management model, and electric vehicle charging management model to generate a water-energy collaborative optimization scheduling model.

[0143] Step 32: Based on the energy balance conditions, identify the first operating characteristic of the energy management model in the water-energy coordinated optimization scheduling model; based on the water resource balance conditions, identify the second operating characteristic of the water resource management model in the water-energy coordinated optimization scheduling model; based on the power grid balance conditions, identify the third operating characteristic of the electric vehicle charging management model in the water-energy coordinated optimization scheduling model.

[0144] Step 33: Couple the operational objectives corresponding to each management model according to the first operational characteristic, the second operational characteristic and the third operational characteristic to obtain the overall optimization objective of the water-energy coordinated optimization scheduling model, and use the overall optimization objective to identify the global optimization problem contained in the water-energy coordinated optimization scheduling model;

[0145] Step 34: Decompose the water-energy collaborative optimization scheduling model using the alternating direction multiplier method. Based on the decomposition results, divide the global optimization problem into corresponding energy management optimization sub-problems, water resource management optimization sub-problems, and electric vehicle optimization sub-problems, and convert them into problems to be optimized for display.

[0146] In this example, the overall optimization objectives include: e1 By incorporating the energy consumption of water resource management and electric vehicle charging management into the net load calculation of energy dispatch, they can be indirectly regulated during the optimization process to reduce power distribution costs and achieve coordinated optimization of water, electricity, and electric vehicle charging loads;

[0147] e2 comprehensively considers factors such as electricity purchase cost, carbon emission cost, energy storage cost, water treatment energy consumption, and charging load to minimize the overall operating cost of the integrated energy system;

[0148] e3 optimizes the resource scheduling of water-energy synergy through a global optimization objective function, thereby improving the system's economy and stability.

[0149] In this example, the model association structure represents the relationships between the energy management model, the water resource management model, and the electric vehicle charging management model;

[0150] In this example, the first operational feature represents the characteristics of the energy management model when the water-energy coordinated optimization scheduling model is running; the second operational feature represents the characteristics of the water resource management model when the water-energy coordinated optimization scheduling model is running; and the third operational feature represents the characteristics of the electric vehicle charging management model when the water-energy coordinated optimization scheduling model is running.

[0151] In this example, the global optimization problem represents the problem that needs to be optimized in the integrated energy system.

[0152] The working principle and beneficial effects of the above technical solution are as follows: By analyzing the system structure of the integrated energy system, the energy management model, water resource management model, and electric vehicle charging management model are integrated to accurately capture the inherent relationship between the three, making the generated water-energy coordinated optimization scheduling model more in line with the actual operating logic of the integrated energy system. Then, the operating characteristics of each management model in the water-energy coordinated optimization scheduling model are identified, and the key operating rules originally hidden in the model are mined out. Furthermore, by coupling the operating objectives of different management models, the overall optimization objective is determined and the global optimization problem contained in the water-energy coordinated optimization scheduling model is identified, ensuring that the optimization direction always points to the global optimum of the integrated energy system. Finally, the global optimization problem is divided into independent problems under each dimension through the decomposition method, so that the decomposed sub-problems can be optimized independently and can also return to the coordinated framework through the constraints of the global objective. This achieves the effect of local optimization supporting global optimization, improving the stability and efficiency of integrated energy system scheduling.

[0153] Example 7:

[0154] Based on Example 1, the integrated energy system scheduling optimization method considering water-energy synergy, step 4 includes:

[0155] Step 41: Determine the predicted energy consumption of water resources and the actual energy consumption of water resources in the integrated energy system according to the water resource management model; determine the predicted energy consumption of charging and the actual energy consumption of charging in the integrated energy system according to the energy management model and the electric vehicle charging management model.

[0156] Step 42: Use formula (1) to establish the augmented Lagrangian function of the integrated energy system;

[0157]

[0158] Among them, L σ Let f represent the augmented Lagrangian function. E Let f represent the first optimal scheduling objective function of the energy management model. W Let f represent the second optimal scheduling objective function of the water resource management model. EVLet λ represent the third optimization scheduling objective function of the electric vehicle charging management model. t This represents the first Lagrange multiplier, μ, generated by the constraints imposed on the integrated energy system by the water resource management model. t This represents the second Lagrange multiplier generated by the constraints of the energy management model and the electric vehicle charging management model on the integrated energy system. t∈T indicates that the time index corresponding to the variable belongs to the time set T within the scheduling period, used to limit the calculation range of the relevant terms at each time point. This indicates the predicted energy consumption of water resources in the integrated energy system. This indicates that the water resource management model determines the actual energy consumption of the integrated energy system based on water resources. This indicates the predicted energy consumption for charging the integrated energy system. ρ represents the actual energy consumption of the integrated energy system during charging, and ρ represents the penalty factor of the augmented Lagrange function.

[0159] Step 43: Use the formula (1) to perform local optimization on each of the problems to be optimized until each of the problems to be optimized shows convergence characteristics, obtain several local optimizations corresponding to each of the problems to be optimized, and generate an optimization scheme.

[0160] Step 44: Feed the optimization scheme back to the energy management model, the water resource management model, and the electric vehicle charging management model for optimization simulation, and obtain and display several balance optimization results of the integrated energy system.

[0161] In this example, a corresponding objective function is set for each type of problem to be optimized:

[0162] The objective function for the energy management optimization sub-problem is as follows:

[0163]

[0164] In the formula, the subproblem is solved by applying {P} E,water ,P E,EV The update minimizes the objective f of the energy management system. E At the same time, ensure consistency with the water resource management system and the electric vehicle charging management system;

[0165] The objective function for the water resources management optimization sub-problem is as follows:

[0166]

[0167] In the formula, the subproblem is solved by applying f EV The update minimizes the water resource management system objective f W At the same time, ensure consistency with the energy management system;

[0168] The objective function for the electric vehicle optimization subproblem is:

[0169]

[0170] In the formula, the subproblem is solved by applying P V,EV The update minimizes the target f of the electric vehicle charging management system. EV At the same time, ensure consistency with the energy management system.

[0171] The working principle and beneficial effects of the above technical solution are as follows: By predicting and determining the energy consumption of water resources and electric vehicles in advance, the energy consumption contradiction in the integrated energy system is identified. Furthermore, the augmented Lagrangian function is used to locally optimize the problem until convergence is achieved. This fully leverages the function's targeted optimization capability for local problems and ensures the stability of the optimization results through convergence conditions. Finally, the obtained optimization scheme is used to balance and optimize the integrated energy system. This reduces the energy consumption deviation of water resources and the charging system, lowers the overall system operating cost through multi-objective collaboration, ensures grid load stability, and ultimately significantly improves the operational accuracy and economy of the integrated energy system.

[0172] Example 8:

[0173] Based on Example 7, the integrated energy system scheduling optimization method considering water-energy synergy further includes:

[0174] According to formulas (2) and (3), the original monitoring residual and dual residual corresponding to each optimization problem are obtained respectively;

[0175]

[0176]

[0177] in, This represents the original monitoring residual corresponding to the k-th problem to be optimized. This represents the dual residual corresponding to the k-th problem to be optimized;

[0178] When the original monitoring residual and dual residual corresponding to each of the optimization problems tend to zero, the feasibility measure corresponding to each optimization problem is calculated according to formula (4);

[0179]

[0180] Where, ε k This represents the feasibility measure corresponding to the k-th problem to be optimized;

[0181] When the feasibility metric corresponding to each of the optimization problems meets the preset threshold, it is determined that all the optimization problems have completed the balanced optimization, and the optimization scheme is output.

[0182] Conversely, the next iteration of optimization is performed using the formula (1).

[0183] In this example, the preset threshold is a value set in advance by the management based on the energy balance requirements. The preset threshold is set by the system designer in combination with the accuracy requirements and engineering tolerances. It is used to balance the reliability and convergence efficiency of optimization. When the feasibility metric meets the preset threshold, it means that all kinds of residuals have converged to an acceptable range, and it can be considered that all problems to be optimized have been balanced and optimized.

[0184] The working principle and beneficial effects of the above technical solution are as follows: by calculating the monitoring original residual and dual residual corresponding to each problem to be optimized, the optimization of each problem to be optimized is analyzed from the side. After completion, the optimization scheme is generated in a timely manner, which facilitates the subsequent actual optimization of the integrated energy system.

[0185] Example 9:

[0186] Based on Example 7, the integrated energy system scheduling optimization method considering water-energy synergy further includes:

[0187] After the optimization scheme is generated, the updated Lagrange multipliers of the integrated energy system are calculated according to formulas (5) and (6);

[0188]

[0189]

[0190] in, This represents the updated first Lagrange multiplier. This represents the updated second Lagrange multiplier;

[0191] The first updated first Lagrange multiplier and the updated second Lagrange multiplier are fed back into the formula (1) for multiplier adjustment.

[0192] The working principle and beneficial effects of the above technical solution are as follows: by continuously optimizing the Lagrange multipliers, the consistency between the augmented Lagrange function and the integrated energy system is ensured, thus ensuring the effectiveness of each subsequent energy balance optimization.

[0193] Example 10

[0194] This embodiment provides a comprehensive energy system scheduling optimization system that considers water-energy synergy, such as... Figure 2 As shown, it includes:

[0195] The model building module is used to build corresponding energy management models, water resource management models, and electric vehicle charging management models based on the historical operating data of the integrated energy system.

[0196] The condition analysis module is used to set operating targets for each management model and run them separately to generate the energy balance conditions, water resource balance conditions and power grid balance conditions of the integrated energy system.

[0197] The problem generation module is used to integrate the management model with the balance condition to generate a water-energy coordinated optimization scheduling model, and to decompose the water-energy coordinated optimization scheduling model with the alternating direction multiplier method to obtain several problems to be optimized.

[0198] The optimization execution module is used to iteratively optimize each of the problems to be optimized using the augmented Lagrangian function, obtain the corresponding optimization scheme, and feed it back to the corresponding management model for corresponding balance optimization.

[0199] In this example, the integrated energy system refers to an energy system that integrates water, wind, and electricity.

[0200] In this example, the energy management model represents the model of renewable energy in the integrated energy system, the water resource management model represents the model of water resource trends in the integrated energy system, and the electric vehicle charging management model represents the model of electricity management for electric vehicle charging in the integrated energy system.

[0201] In this example, the management models represent: energy management model, water resource management model, and electric vehicle charging management model;

[0202] In this example, the problem to be optimized represents the resource balance problem that needs to be solved in the integrated energy system.

[0203] The working principle and beneficial effects of the above technical solution are as follows: In order to balance the relationship between different energy sources in the integrated energy system, energy management models, water resource management models, and electric vehicle charging management models are first constructed based on historical data. This ensures that each model closely matches the actual operating characteristics of its respective system, laying the foundation for subsequent precise management and optimization, and making the management of each system more targeted. Then, corresponding operating objectives are set for each model according to its characteristics. The energy balance conditions, water resource balance conditions, and power grid balance conditions in the integrated energy system are then determined through the operating models, providing clear reference standards for the stable operation of the system and helping to promptly identify potential imbalances in the operation of each system. Furthermore, by constructing a water-energy collaborative optimization scheduling model and decomposing it using the alternating direction multiplier method, the optimization problems in the integrated energy system are determined. Then, the augmented Lagrangian function is used to iteratively optimize the optimization problems, generating optimization schemes to balance and optimize the integrated energy system, forming a dynamic optimization cycle mechanism. This allows each system to continuously adjust according to actual operating conditions, maintain a stable balance, and enhance the integrated energy system's ability to cope with complex operating conditions and emergencies.

[0204] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A comprehensive energy system scheduling optimization method considering water-energy synergy, characterized in that, include: Step 1: Construct corresponding energy management models, water resource management models, and electric vehicle charging management models based on the historical operation data of the integrated energy system; Step 2: Set operational objectives for each management model and run them separately to generate the energy balance conditions, water resource balance conditions, and power grid balance conditions of the integrated energy system. Step 3: Use the equilibrium condition to fuse the management model to generate a water-energy coordinated optimization scheduling model, and use the alternating direction multiplier method to decompose the water-energy coordinated optimization scheduling model to obtain several optimization problems; Step 4: Use the augmented Lagrangian function to iteratively optimize each of the problems to be optimized, obtain the corresponding optimization scheme, and feed it back to the corresponding management model for corresponding balance optimization.

2. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 1, characterized in that, Step 1 includes: Step 11: Obtain the historical operation data of the integrated energy system under different management dimensions, and divide the corresponding historical operation data into training set, validation set and test set according to the dimensional features of each management dimension. Construct the first initial model, second initial model and third initial model corresponding to different management dimensions using several training sets corresponding to each management dimension. Step 12: Run each initial model in different evaluation environments to obtain several model performance data corresponding to each initial model. Perform similarity analysis on several model performance data corresponding to the same management dimension to deduce the fitting degree of the corresponding initial model. Step 13: Adjust the model parameters of the initial model using the validation set and the degree of fit corresponding to each management dimension, and optimize the performance of the initial model using the test set corresponding to each management dimension to obtain the optimized model corresponding to each management dimension. Step 14: Add corresponding dimension functions to the corresponding optimization model according to the dimension attributes of each management dimension to obtain the energy management model, water resource management model and electric vehicle charging management model of the integrated energy system.

3. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 2, characterized in that, Also includes: The management dimensions include: energy dimension, water resource dimension, and electric vehicle charging management dimension; The historical operating data corresponding to the energy dimension includes: electricity purchase price, electricity consumption, gas purchase price, gas consumption, electricity purchase power, gas purchase power, renewable energy output, electrical equipment maintenance cost, and electrical equipment operating power. The historical operational data corresponding to the water resources dimension includes: water purchase price, water consumption, water equipment maintenance cost, water equipment operating power, and information on water users; The historical operational data corresponding to the electric vehicle charging management dimension includes: carbon trading costs, electric vehicle charging demand information, and grid load data.

4. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 1, characterized in that, Set operational goals for each management model and run them separately, including: Minimizing operating costs is considered the operational objective of the energy management model, minimizing water energy consumption is considered the operational objective of the water resource management model, and peak shaving and valley filling are considered the operational objective of the electric vehicle charging management model. By running the energy management model, the corresponding power generation response characteristics, energy storage response characteristics and grid interaction response characteristics are obtained, and the supply and demand balance value, energy storage dynamic value and renewable resource stability value of the integrated energy system at different times are derived. By running the water resource management model, the corresponding wastewater treatment process, groundwater extraction process, and system storage-supply process are obtained, and the pump energy consumption value, water storage dynamic value, and supply-demand balance value of the integrated energy system at different times are derived. By running the electric vehicle charging management model, we can obtain the charging power value of electric vehicles under different charging modes, the power supply value of the integrated energy system to the power grid at different times, and the average load value of the power grid at different times.

5. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 4, characterized in that, Generating the energy balance conditions, water resource balance conditions, and power grid balance conditions of the integrated energy system includes: Based on the supply and demand balance value, the energy storage dynamic value, and the renewable resource stability value corresponding to the energy management model, several energy operation requirements of the integrated energy system under the energy dimension are constructed, and the energy balance conditions of the integrated energy system are constructed based on the constraint and support relationships between different energy operation requirements. Based on the pump energy consumption value, water storage dynamic value and supply-demand balance value corresponding to the water resource management model, construct several water source flow paths of the integrated energy system under the water resource dimension, and count the real-time water volume corresponding to each water source flow path to construct the water resource balance conditions of the integrated energy system. Based on the charging power values ​​corresponding to electric vehicles in different charging modes, the high-efficiency charging mode and the low-efficiency charging mode of the power grid are derived. Based on the power supply value and the average load value of the power grid at different times, the high-efficiency charging demand corresponding to the high-efficiency charging mode is derived. Based on the power supply value and the average load value of the power grid at different times, the low-efficiency charging defects of the low-efficiency charging mode are derived. The inefficient charging defect is optimized to obtain the grid's optimization requirements. Based on the efficient charging requirements and the optimization requirements, the grid balance conditions of the integrated energy system are constructed.

6. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 1, characterized in that, Step 3 includes: Step 31: Based on the system structure of the integrated energy system, identify several model association structures between the energy management model, water resource management model, and electric vehicle charging management model; use the model association structures to fuse the energy management model, water resource management model, and electric vehicle charging management model to generate a water-energy collaborative optimization scheduling model. Step 32: Based on the energy balance conditions, identify the first operating characteristic of the energy management model in the water-energy coordinated optimization scheduling model; based on the water resource balance conditions, identify the second operating characteristic of the water resource management model in the water-energy coordinated optimization scheduling model; based on the power grid balance conditions, identify the third operating characteristic of the electric vehicle charging management model in the water-energy coordinated optimization scheduling model. Step 33: Couple the operational objectives corresponding to each management model according to the first operational characteristic, the second operational characteristic and the third operational characteristic to obtain the overall optimization objective of the water-energy coordinated optimization scheduling model, and use the overall optimization objective to identify the global optimization problem contained in the water-energy coordinated optimization scheduling model; Step 34: Decompose the water-energy collaborative optimization scheduling model using the alternating direction multiplier method. Based on the decomposition results, divide the global optimization problem into corresponding energy management optimization sub-problems, water resource management optimization sub-problems, and electric vehicle optimization sub-problems, and convert them into problems to be optimized for display.

7. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 1, characterized in that, Step 4 includes: Step 41: Determine the predicted energy consumption of water resources and the actual energy consumption of water resources in the integrated energy system according to the water resource management model; determine the predicted energy consumption of charging and the actual energy consumption of charging in the integrated energy system according to the energy management model and the electric vehicle charging management model. Step 42: Use formula (1) to establish the augmented Lagrangian function of the integrated energy system; Among them, L σ Let f represent the augmented Lagrangian function. E Let f represent the first optimal scheduling objective function of the energy management model. W Let f represent the second optimal scheduling objective function of the water resource management model. EV Let λ represent the third optimization scheduling objective function of the electric vehicle charging management model. t This represents the first Lagrange multiplier, μ, generated by the constraints imposed on the integrated energy system by the water resource management model. t This represents the second Lagrange multiplier generated by the constraints of the energy management model and the electric vehicle charging management model on the integrated energy system. t∈T indicates that the time index corresponding to the variable belongs to the time set T within the scheduling period. This indicates the predicted energy consumption of water resources in the integrated energy system. This indicates that the water resource management model determines the actual energy consumption of the integrated energy system based on water resources. This indicates the predicted energy consumption for charging the integrated energy system. ρ represents the actual energy consumption of the integrated energy system during charging, and ρ represents the penalty factor of the augmented Lagrange function. Step 43: Use the formula (1) to perform local optimization on each of the problems to be optimized until each of the problems to be optimized shows convergence characteristics, obtain several local optimizations corresponding to each of the problems to be optimized, and generate an optimization scheme. Step 44: Feed the optimization scheme back to the energy management model, the water resource management model, and the electric vehicle charging management model for optimization simulation, and obtain and display several balance optimization results of the integrated energy system.

8. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 7, characterized in that, Also includes: According to formulas (2) and (3), the original monitoring residual and dual residual corresponding to each optimization problem are obtained respectively; in, This represents the original monitoring residual corresponding to the k-th problem to be optimized. This represents the dual residual corresponding to the k-th problem to be optimized; When the original monitoring residual and dual residual corresponding to each of the optimization problems tend to zero, the feasibility measure corresponding to each optimization problem is calculated according to formula (4); Where, ε k This represents the feasibility measure corresponding to the k-th problem to be optimized; When the feasibility metric corresponding to each of the optimization problems meets the preset threshold, it is determined that all the optimization problems have completed the balanced optimization, and the optimization scheme is output. Conversely, the next iteration of optimization is performed using the formula (1).

9. The integrated energy system scheduling optimization method considering water-energy synergy as described in claim 7, characterized in that, Also includes: After the optimization scheme is generated, the updated Lagrange multipliers of the integrated energy system are calculated according to formulas (5) and (6); in, This represents the updated first Lagrange multiplier. This represents the updated second Lagrange multiplier; The first updated first Lagrange multiplier and the updated second Lagrange multiplier are fed back into the formula (1) for multiplier adjustment.

10. A comprehensive energy system scheduling optimization system considering water-energy synergy, characterized in that, include: The model building module is used to build corresponding energy management models, water resource management models, and electric vehicle charging management models based on the historical operating data of the integrated energy system. The condition analysis module is used to set operating targets for each management model and run them separately to generate the energy balance conditions, water resource balance conditions and power grid balance conditions of the integrated energy system. The problem generation module is used to integrate the management model with the balance condition to generate a water-energy coordinated optimization scheduling model, and to decompose the water-energy coordinated optimization scheduling model with the alternating direction multiplier method to obtain several problems to be optimized. The optimization execution module is used to iteratively optimize each of the problems to be optimized using the augmented Lagrangian function, obtain the corresponding optimization scheme, and feed it back to the corresponding management model for corresponding balance optimization.

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