Electric-heat-cold flexible mutual-aid RIES cooperative scheduling method, system and device for single heavy load and medium

Abstract

The invention relates to the technical field of comprehensive energy, and discloses an electricity-heat-cold flexible mutual-aid RIES cooperative scheduling method, system and device for a single large load and a medium. The method comprises the following steps: constructing a flexible load transfer strategy, and carrying out prospective energy storage by using time sequence characteristics of heat energy and cold energy; and cross-energy mutual aid is started under an extreme load, and electricity-heat-cold two-way support is realized through energy conversion equipment. The system takes cross-energy mutual aid as a core, and integrates energy production, conversion, storage and load links, and sensor network real-time monitoring and coordination. Establishing a multi-subject collaboration framework covering energy manufacturers, managers and an integrated energy system, setting constraint conditions and optimizing a multi-subject target; and introducing a Great Wall optimization algorithm, solving a system model and optimizing energy pricing to obtain an optimal collaborative scheduling result. The operation toughness, economical efficiency and energy utilization efficiency of the system under a single large load are improved, the operation cost and carbon emission are reduced, and safety and stability of the system are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of integrated energy technology, specifically to a method, system, equipment, and medium for RIES coordinated scheduling of electricity, heat, and cooling with flexible mutual support for a single large load. Background Technology

[0002] As the energy system transformation deepens, the widespread integration of high-proportion renewable energy sources and diversified electricity, heat, and cooling loads into regional integrated energy systems has led to significant challenges in system operation, including randomness, volatility, and multi-energy flow coupling. Against this backdrop, coordinated scheduling of regional integrated energy systems for single large loads has become crucial for improving energy utilization efficiency and operational resilience.

[0003] Current regional integrated energy system scheduling methods suffer from the following main problems: First, existing methods are insufficient in multi-timescale coordination, making it difficult to achieve organic integration of day-ahead, intraday, and real-time scheduling; second, when dealing with extreme single-load scenarios, there is a general lack of effective multi-energy complementarity and cross-energy mutual assistance mechanisms for electricity, heat, and cooling, resulting in a need to improve system resilience; third, commonly used multi-objective optimization algorithms are prone to getting trapped in local optima when dealing with high-dimensional nonlinear scheduling problems, and suffer from slow convergence speed and poor uniformity of solution set distribution; finally, the lack of efficient coordination and interest balancing mechanisms among multiple stakeholders such as energy producers, managers, and users restricts the overall economic efficiency and stability of the system.

[0004] Therefore, existing technologies are insufficient to coordinate the economy, low carbon emissions, and resilience of a system in complex operating environments. An innovative scheduling scheme that can integrate multi-timescale coordination, multi-energy flexible mutual assistance, efficient optimization algorithms, and the balance of interests among multiple stakeholders is needed. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention provides a method, system, device and medium for flexible mutual support of electricity, heat and cold for a single large load, and a coordinated scheduling of RIES.

[0006] Therefore, the technical problem solved by this invention is: how to coordinate the multi-timescale scheduling of multiple energy flows of electricity, heat and cold, establish a flexible load transfer and cross-energy mutual assistance mechanism, and apply an improved multi-objective optimization algorithm to solve the multi-entity collaborative framework, thereby improving the operational resilience and energy utilization efficiency of the regional integrated energy system under a single high-load scenario while ensuring the economic efficiency and reliability of system operation.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a RIES coordinated scheduling method for flexible mutual support between electricity, heat, and cooling for a single large load, comprising, Construct an electric-heat-cooling coupled coordinated scheduling strategy based on flexible load transfer; By employing a collaborative scheduling strategy, the energy conversion and complementarity mechanism of the system under extreme load scenarios is obtained; With cross-energy mutual assistance mechanisms at its core, a regional integrated energy system (RIES) will be constructed. Through the Regional Integrated Energy System (RIES), a multi-stakeholder collaborative framework is obtained, comprising three types of participants: energy producers, energy managers, and integrated energy systems. Based on a multi-agent collaborative framework, constraints for the Regional Integrated Energy System (RIES) are set, and multi-agent objectives are optimized. Based on multi-agent objectives, the Great Wall optimization algorithm is introduced to solve the RIES of the regional integrated energy system, optimize energy pricing parameters, and obtain the optimal coordinated scheduling result.

[0008] As a preferred embodiment of the RIES coordinated scheduling method for flexible mutual support between electricity, heat, and cooling for a single large load as described in this invention, the construction of the electricity-heat-cooling coupled coordinated scheduling strategy based on flexible load transfer includes, The moment with the largest combined deficit within the system scheduling cycle is identified as the critical moment. By utilizing the time-series transfer characteristics of thermal and cold energy, the energy surplus of the preceding period is converted into equivalent electrical energy and stored in advance.

[0009] As a preferred embodiment of the RIES coordinated scheduling method for flexible mutual support between electricity, heat, and cooling for a single large load as described in this invention, the method includes: obtaining an energy conversion and complementarity mechanism for the system under extreme load scenarios through a coordinated scheduling strategy, including: When the self-regulation capacity of a single energy source is insufficient, an inter-energy mutual assistance mechanism is activated, and electricity is used to support the heating or cooling network through energy conversion equipment, forming a two-way mutual assistance network between the electricity-heating-cooling subsystems.

[0010] As a preferred embodiment of the RIES coordinated scheduling method for flexible mutual assistance between electricity, heat, and cooling for a single large load as described in this invention, the method of constructing a regional integrated energy system (RIES) with a cross-energy mutual assistance mechanism as its core includes: The system integrates energy production, conversion, storage, and load processes, and uses sensor networks to acquire real-time data on energy supply and demand and equipment operation status, thereby coordinating renewable energy power generation units.

[0011] As a preferred embodiment of the RIES (Regional Integrated Energy System) coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in this invention, the method involves obtaining a multi-stakeholder collaborative framework through the Regional Integrated Energy System (RIES), comprising energy producers, energy managers, and the integrated energy system itself. Energy producers supply electricity or heat to energy managers; Energy managers formulate and publish transaction prices based on real-time data uploaded by sensors, and coordinate energy flow and the transaction process; The integrated energy system establishes a two-way energy exchange mechanism with the external power grid through the grid connection point.

[0012] As a preferred embodiment of the RIES (Regional Integrated Energy System) coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in this invention, the method involves: setting constraints on the RIES based on a multi-agent coordinated framework and optimizing multi-agent objectives, including... The constraints of RIES are a multi-level system that covers physical balance, equipment operation and market transactions. The system must strictly follow the instantaneous power balance constraints of the three energy sources of electricity, heat and cooling to ensure that the energy supply at any time can meet the load demand. The output of all equipment must be limited to the upper and lower limits of rated capacity and ramping capability. At the same time, energy storage equipment must also comply with its own energy conservation and charge / discharge state constraints. At the market participant level, transactions between energy producers, managers, and users are subject to strict constraints on the price feasibility domain and communication capacity, which together constitute the complete constraints for ensuring the safe, stable, and efficient operation of the system. The electrical balance constraints of the integrated energy system are as follows: in, For wind power generation capacity, For PV power generation, For GT power generation, For the power generation of fuel cells, Let t be the amount of electricity purchased from the energy management provider. Let be the discharge amount at time t. Let be the electrical load demand at time t. Let be the amount of electricity transferred at time t. This indicates the charging and discharging power of the energy storage system. This refers to the power consumption of the carbon capture device; The heat load balance constraints of the integrated energy system are as follows: in, The power of the gas-fired boiler in terms of the output heat energy. The power of the waste heat boiler's output heat energy. Let be the amount of calories purchased at time t. This refers to the heat release capacity of the thermal storage device. To meet heat load requirements, The power required to charge the thermal storage equipment; The cooling load balance constraints of the integrated energy system are as follows: in, The cooling power of the OP. The refrigeration capacity generated for electric refrigeration units. The total cooling load demand at time t; The power balance constraints for energy producers are as follows: in, Let GWT be the power generation at time t. Let be the power generation of PV at time t. Let t be the amount of electricity generated by GT at time t. Let be the discharge amount of the energy storage device at time t. Let t be the charge level of the energy storage device. This represents the amount of electricity sold by the energy producer to the energy manager at time t. This represents the amount of electricity connected to the grid at time t. This represents the heat output by GT at time t and processed by the waste heat boiler. This indicates the amount of heat released by the thermal storage device. This indicates the amount of heat stored in the thermal storage device. This refers to the amount of heat sold by energy producers to energy managers. Heat sold by energy producers to external power companies; The objective function of the Regional Integrated Energy System (RIES) is shown below: in, To account for overall operating costs, , , Assigning weights to each cost For energy transaction costs, For the purchase cost of natural gas, The cost of penalties when harmful gases are emitted. To cover the cost of maintaining the equipment; The objective function for energy producers is shown below: in, This represents the total operating cost of the integrated energy system. Let be the cost of electricity trading at time t. This represents the cost of heat energy trading at time t. This represents the cost of cold energy trading at time t. This represents the total cost of purchasing gas. This indicates the price at which natural gas is purchased. This represents the amount of natural gas consumed by the gas turbine at time t. This represents the amount of natural gas consumed by the gas-fired boiler at time t. This represents the total natural gas consumption. This indicates the unit price of the penalty for emitting harmful gases. This represents the coefficient for harmful gas emissions. The unit price for purchasing carbon emission rights. The actual carbon emissions at time t The carbon emission allowance allocated at time t.

[0013] As a preferred embodiment of the RIES (Regional Integrated Energy System) coordinated scheduling method for flexible mutual support between electricity, heat, and cooling for a single large load as described in this invention, the method involves: based on multi-agent objectives, introducing the Great Wall optimization algorithm to solve the RIES of the regional integrated energy system, optimizing energy pricing parameters, and obtaining the optimal coordinated scheduling result, including... The Great Wall optimization algorithm mimics the construction hierarchy logic and defense coordination mechanism of the Great Wall by introducing an elite collaborative optimization strategy, congestion distance sorting, and external archiving mechanism. The crowding distance sorting evaluates and compares the distribution density of solutions, and prioritizes retaining sparsely distributed solutions when the archive exceeds the set storage limit; An external archiving mechanism continuously collects and saves high-quality non-dominated solutions found in previous iterations during the algorithm's operation, preventing the loss of excellent solutions. The congestion distance sorting and external archiving mechanism work together to ensure that the final solution set approximates the true Pareto front while obtaining the optimal cooperative scheduling result.

[0014] This invention, by constructing a multi-timescale scheduling and cross-energy mutual assistance mechanism and establishing a multi-entity collaborative framework, can effectively improve the system's resilience to cope with a single large load and optimize operating efficiency, thereby achieving a balance between the economy and reliability of the energy system.

[0015] This invention provides a flexible and mutually supportive RIES (Resources, Environments, and Technologies) coordinated scheduling system for a single large load, comprising: The scheduling strategy construction module constructs an electric-heat-cold coupled coordinated scheduling strategy based on flexible load transfer. It utilizes the time-series transfer characteristics of thermal and cold energy to convert the energy surplus of the preceding period into equivalent electrical energy and make forward-looking reserves, thereby optimizing energy allocation and coping with load peaks. The energy conversion and complementarity mechanism module, through a coordinated scheduling strategy, enables the system to perform energy conversion and complementarity under extreme load scenarios. When the self-regulation capacity of a single energy source is insufficient, a cross-energy mutual assistance mechanism is activated, and the electrical energy is supported by the energy conversion equipment to support the heating network or cooling network, forming a two-way mutual assistance network between the electricity-heating-cooling subsystems. The RIES building module, with cross-energy mutual assistance mechanism as its core, constructs a regional integrated energy system RIES, which integrates energy production, conversion, storage and load links, and collects energy supply and demand and equipment operation status data in real time through sensor network, and coordinates renewable energy power generation units. The multi-stakeholder collaboration framework module establishes a multi-stakeholder collaboration framework based on RIES, involving three types of participants: energy producers, energy managers, and integrated energy systems. The constraint and optimization module, based on a multi-agent collaborative framework, sets constraints on RIES and optimizes multi-agent objectives. The constraints cover physical equilibrium, equipment operation, and market transactions. The solution module is optimized by introducing the Great Wall Optimization Algorithm to solve RIES, optimize energy pricing parameters, and obtain the optimal coordinated scheduling result.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a RIES coordinated scheduling method for flexible mutual support of electricity, heat and cold for a single large load.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a RIES cooperative scheduling method for flexible mutual support of electricity, heat and cold for a single large load are implemented.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs an electric-heat-cold coupled coordinated scheduling strategy based on flexible load transfer. By identifying the moment of maximum joint deficit within the system scheduling cycle, and utilizing the temporal transfer characteristics of thermal and cold energy, the energy surplus of the preceding period is converted into equivalent electrical energy and stored in advance, achieving optimized load allocation in the time dimension. Based on this, the system can activate a cross-energy mutual assistance mechanism under extreme load scenarios, forming a bidirectional support network between the electric-heat-cold subsystems through energy conversion equipment, significantly enhancing the system's operational resilience. By constructing a regional integrated energy system (RIES) with cross-energy mutual assistance as its core, it integrates energy production, conversion, storage, and load links, and utilizes sensor networks to achieve real-time data acquisition and coordination of renewable energy generation units. The system establishes a multi-stakeholder collaborative framework involving energy producers, managers, and the integrated energy system, sets multi-level constraints covering physical balance, equipment operation, and market transactions, and optimizes multi-stakeholder objectives. In terms of solution methodology, an improved Multi-Objective Great Wall Optimization Algorithm (MGWCA) is introduced. Through an elite collaborative optimization strategy, congestion distance ranking, and external archiving mechanism, energy pricing parameters are optimized to effectively solve the two-layer scheduling model and obtain the optimal collaborative scheduling result. This method, through multi-timescale collaborative scheduling and cross-energy flexible mutual assistance mechanisms, combined with efficient optimization algorithms and multi-stakeholder interest balancing, reduces system operating costs, carbon emissions, and gas costs, while maintaining stable operation under different renewable energy penetration scenarios. It provides a reliable solution for energy system management under high-proportion renewable energy integration. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a RIES coordinated scheduling method for flexible mutual support of electricity, heat and cooling for a single large load, provided as an embodiment of the present invention.

[0021] Figure 2 This is a structural diagram illustrating the implementation framework of a flexible, mutually supportive RIES (Resources, Environments, and Systems) scheduling method for a single large load, provided by an embodiment of the present invention.

[0022] Figure 3 The MGWCA framework and pseudocode diagram of the RIES coordinated scheduling method for flexible mutual assistance of electricity, heat and cold for a single large load provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 The first embodiment of the present invention provides a RIES coordinated scheduling method for flexible mutual assistance of electricity, heat and cooling for a single large load, including: S1: Construct an electric-heat-cold coupled coordinated scheduling strategy based on flexible load transfer to obtain the energy conversion and complementarity mechanism of the system under extreme load scenarios.

[0025] S2: Constructing a regional integrated energy system (RIES) with cross-energy mutual assistance mechanism as the core.

[0026] S3: Through the Regional Integrated Energy System (RIES), a multi-stakeholder collaborative framework is obtained, involving three types of participants: energy producers, energy managers, and integrated energy systems.

[0027] S4: Based on the multi-agent collaborative framework, set the constraints of the regional integrated energy system (RIES) and optimize the multi-agent objectives.

[0028] S5: Based on multi-agent objectives, the Great Wall optimization algorithm is introduced to solve the RIES of the regional integrated energy system, optimize energy pricing parameters, and obtain the optimal coordinated scheduling result.

[0029] It should be noted that traditional regional integrated energy system scheduling methods have shortcomings in multi-timescale coordination, making it difficult to achieve organic integration of day-ahead, intraday, and real-time scheduling. Furthermore, they lack effective multi-energy complementarity and cross-energy mutual assistance mechanisms for electricity, heat, and cooling when dealing with extreme single-load scenarios, resulting in poor system resilience. Simultaneously, commonly used multi-objective optimization algorithms are prone to getting trapped in local optima, exhibiting slow convergence speeds and poor uniformity of solution distribution when handling high-dimensional nonlinear scheduling problems. Moreover, the lack of efficient coordination and benefit balancing mechanisms among energy producers, managers, and users further restricts the overall economic efficiency and stability of the system. Therefore, developing a flexible, mutually supportive regional integrated energy system scheduling method for single-load scenarios, by constructing a multi-timescale coordination framework, cross-energy mutual assistance mechanisms, improving multi-objective optimization algorithms, and implementing multi-stakeholder collaborative optimization strategies, is also crucial for addressing issues such as improving system resilience, balancing operational economy and low carbon emissions, enhancing optimization algorithm performance, and balancing the interests of multiple stakeholders.

[0030] Therefore, addressing the shortcomings of traditional regional integrated energy systems, such as insufficient coordination in multi-timescale scheduling, inadequate system resilience under extreme load scenarios, poor convergence and distribution of multi-objective optimization algorithms, and difficulty in balancing the interests of multiple stakeholders, this paper proposes a coordinated scheduling strategy based on flexible load transfer, using steps S1-S5. This strategy leverages the time-series transfer characteristics of thermal and cold energy for forward-looking energy storage, improving multi-timescale coordination and system resilience under extreme loads. Furthermore, by constructing a regional integrated energy system (RIES) centered on a cross-energy mutual assistance mechanism, integrating energy production, conversion, storage, and load processes, and using a sensor network to monitor energy supply and demand in real time, this improves the overall energy utilization efficiency and the flexibility of multi-energy complementarity. A multi-stakeholder collaborative framework involving energy producers, energy managers, and integrated energy systems is established. Relying on a dynamic pricing mechanism to coordinate energy flow and trading processes, this improves the balance of interests and the efficiency of coordinated scheduling among multiple stakeholders. By setting constraints such as power balance (electricity, heat, and cooling) and equipment operation limitations for RIES (Resources, Environments, and Systems), and optimizing multi-stakeholder objectives such as minimizing operating costs, the safety, stability, and economy of system operation are improved. Furthermore, by introducing the Great Wall optimization algorithm and employing elite collaborative optimization strategies, congestion distance sorting, and external archiving mechanisms, energy pricing parameters are optimized, improving the convergence speed, solution set uniformity, and solution accuracy of the multi-objective optimization algorithm. This achieves the goal of economic, low-carbon, and resilient collaborative optimization of regional integrated energy systems under single high-load scenarios.

[0031] Example 2, refer to Figure 1 - Figure 3 As an embodiment of the present invention, based on the above embodiment, a RIES coordinated scheduling method for flexible mutual support of electricity, heat and cooling for a single large load is provided.

[0032] In this embodiment of the application, step S1 constructs an electric-thermal-cooling coupled coordinated scheduling strategy based on flexible load transfer to obtain the energy conversion and complementarity mechanism of the system under extreme load scenarios, including the following steps A1-A3: A1: Identify the moment with the largest combined deficit within the system scheduling cycle as the critical moment, and utilize the time-series transfer characteristics of thermal and cold energy to convert the energy surplus of the preceding period into equivalent electrical energy and make forward-looking reserves.

[0033] A2: When the self-regulation capacity of a single energy source is insufficient, a cross-energy mutual assistance mechanism is activated, and electricity is used to support the heating or cooling network through energy conversion equipment, forming a two-way mutual assistance network between the electricity-heating-cooling subsystems.

[0034] Specifically, this involves constructing a flexible load transfer-based electricity-heat-cooling coupled coordinated scheduling strategy. This strategy utilizes a multi-timescale, multi-energy-flow coupled coordinated optimization framework to fully leverage the spatiotemporal transfer potential and complementary energy conversion characteristics of electricity, heat, and cooling loads, thereby achieving a synergistic improvement in system resilience and economy under extreme scenarios. The implementation of this strategy first relies on an accurate assessment of the overall system supply and demand situation and the identification of key bottlenecks. To comprehensively characterize system pressure, a comprehensive electricity, heat, and cooling load joint deficit model needs to be established. This model not only reflects the absolute deficit of each energy subsystem but also introduces weighting coefficients to reflect the differences in scheduling priority, conversion cost, and reliability requirements among different energy sources.

[0035] A3: The core of the coordinated scheduling strategy is to identify the most vulnerable critical moments of the system, utilize the time-series transfer characteristics of thermal and cold energy, convert the energy surplus of the preceding period into equivalent electrical energy and make forward-looking reserves, thereby realizing the shift of load in the time dimension; when the self-regulation capacity of a single energy source is insufficient, the cross-energy mutual assistance mechanism is activated to support the heating or cooling network through conversion equipment, ultimately forming a hierarchical and progressively responsive system defense scheduling system.

[0036] The core indicators reflecting this reliability include: Weighted total load deficit: in, This indicates the weighted total load deficit. , , These represent the weighting coefficients for electricity, heat, and cold, respectively. , and These represent the load demands for electricity, heat, and cooling, respectively. , and These represent the supply capacity for electricity, heat, and cold, respectively.

[0037] The weighted total load deficit is a core indicator that measures the overall supply and demand pressure of the system by assigning different weighting coefficients to the deficits in electricity, heat, and cooling.

[0038] At this moment T, the system is at its most vulnerable point within the scheduling cycle, and this is the target that the coordinated scheduling strategy needs to address. Maximum joint deficit moment. express: in, is a mathematical operator, meaning the independent variable that maximizes the objective function. In this invention, it represents the search for the variable that maximizes the objective function. The moment when the maximum value is reached. .

[0039] After identifying the critical period The core of the strategy is to use the resources of its previous period for "forward-looking" load shifting. The theoretical basis is the inertia characteristics and energy storage potential in the thermodynamic system, that is, heat energy and cold energy can be transferred and stored at a relatively low cost in the time dimension. To achieve unified scheduling across different energy sources, the electrical, thermal, and cold energy surpluses at each moment t < T need to be converted into a comparable reference value.

[0040] Among them, represents the equivalent electrical energy surplus, , , respectively represent the net surplus amounts of electricity, heat, and cold energy for each energy source, and are the conversion efficiency coefficients from electrical energy to heat energy and cold energy. This definition enables the homogenization of multiple heterogeneous energy sources in the scheduling decision-making.

[0041] To ensure that the load transfer process does not induce new supply-demand imbalances in the previous period, the distribution of the transfer amount must follow the principles of systematicness and proportionality. Therefore, the load amount transferred from the moment to the critical moment is determined by the following formula: Among them, is the transferred load amount, represents the equivalent electrical energy surplus, Δ represents the weighted total load deficit, is the energy storage loss coefficient. This distribution mechanism ensures that the transferred load borne by each period is proportional to its energy margin, thus achieving the optimal allocation of system resources in the time dimension and risk sharing.

[0042] To achieve the complementary process, first, it is necessary to determine the remaining flexibility available for invocation after meeting the scheduling requirements of electrical energy itself. Subsequently, through coupled devices such as combined heat and power, heat pumps, and electric chillers, electrical energy is converted into heat or cold energy as needed. The conversion amount is simultaneously limited by the available electrical power, conversion efficiency, and actual demand gap. The conversion formula is: <00​​​​​​​This refers to the conversion efficiency from electrical energy to thermal energy. Essentially, this mechanism constructs a two-way mutual support network between electrical, thermal, and cooling subsystems. When one subsystem is under extreme pressure, other subsystems can provide effective flexibility support, thereby significantly enhancing the overall robustness of the integrated energy system.

[0043] In an alternative implementation, a complementary energy conversion mechanism for the system under extreme load scenarios can be obtained based on the identification of critical moments of electrical load deficit and a unidirectional energy conversion strategy.

[0044] Specifically, the system first identifies the moment of maximum electrical load deficit within the system scheduling cycle as the critical moment, ignoring the combined impact of thermal and cold energy deficits. In terms of energy transfer, the system utilizes the time-series transfer characteristics of electrical energy to directly store the electrical surplus from the preceding period. The cross-energy mutual assistance mechanism allows electrical energy to be converted to the heating network, and electrical energy is used to support the heating network through energy conversion equipment. During the load transfer process, the transfer amount is determined based on the electrical margin of the preceding period.

[0045] In another alternative implementation, a complementary energy conversion mechanism for the system under extreme load scenarios can be obtained based on a strategy of mutual assistance between fixed-ratio energy transfer and unified conversion efficiency.

[0046] Specifically, when identifying the moment of maximum combined deficit within the system scheduling cycle, a simple summation of the deficits in electricity, heat, and cooling loads is used; during the energy transfer phase, the surplus of electricity, heat, and cooling energy in the preceding period is converted into equivalent electrical energy at a fixed ratio and stored; when the cross-energy mutual assistance mechanism is activated, the amount of electrical energy converted to the heating network or cooling network is calculated based on a preset fixed conversion efficiency; and the allocation of load transfer volume is adjusted using a unified ratio.

[0047] In this embodiment of the invention, step S2, centered on a cross-energy mutual assistance mechanism, constructs a regional integrated energy system (RIES), including: The system integrates energy production, conversion, storage, and load processes, and uses sensor networks to acquire real-time data on energy supply and demand and equipment operation status, thereby coordinating renewable energy power generation units.

[0048] Specifically, the construction of the Regional Integrated Energy System (RIES) model: This framework constructs a regional integrated energy system with multi-energy complementarity and multi-entity collaboration as its core. Its structure revolves around energy production, conversion, storage and load links, and real-time status perception is achieved through a sensor network deployed at each key node.

[0049] The operation of the regional integrated energy system revolves around users' energy needs. Internally, a sensor network deployed at key nodes acquires real-time data on energy supply and demand, as well as equipment operating status. This coordinates renewable energy generation units such as wind and solar power with traditional energy supply equipment like gas turbines, gas boilers, and electric chillers to meet diverse load demands, including electricity, heat, and cooling. The addition of energy storage enhances the system's regulation capabilities. Leveraging its time-shifting energy characteristics, it mitigates the impact of random fluctuations in renewable energy, enabling the redistribution and efficient utilization of energy over time.

[0050] In an alternative implementation, a single energy-dominated scheme can be used as the core to construct a Regional Integrated Energy System (RIES).

[0051] Specifically, in the construction of a regional integrated energy system, the main focus is on electric energy. The system covers the electricity production, storage and load links, while heat and cooling loads are directly provided by independent equipment. Sensor networks are deployed at key power nodes to monitor the power supply and demand status.

[0052] In another alternative implementation, a simplified coordination scheme can be used as the core to construct a Regional Integrated Energy System (RIES).

[0053] Specifically, in the construction of regional integrated energy systems, a multi-energy complementary structure is retained, and energy production, conversion, storage and load processes operate based on preset rules or fixed schedules, with renewable energy power generation units and traditional energy supply equipment operating independently.

[0054] In this embodiment of the invention, step S3 obtains a multi-stakeholder collaborative framework involving three types of participants—energy producers, energy managers, and the integrated energy system—through the Regional Integrated Energy System (RIES), including the following steps B1-B3: B1: Energy producers supply electricity or heat to energy managers; B2: Energy managers formulate and publish transaction prices based on real-time data uploaded by sensors, and coordinate energy flow and transaction processes; B3: The integrated energy system establishes a two-way energy exchange mechanism with the external power grid through the grid connection point.

[0055] Specifically, the Regional Integrated Energy System (RIES) relies on energy managers as the central hub, coordinating energy producers and users, and under the guidance of a dynamic pricing mechanism, achieves flexible mutual support and efficient dispatch of various energy sources such as electricity, heat, and cooling, ultimately forming a resilient and economical closed-loop system capable of bidirectional energy exchange with the external power grid.

[0056] like Figure 2As shown, this system architecture primarily involves three types of participants: energy producers, energy managers, and integrated energy systems. Energy producers, mainly large-scale distributed power sources and high-capacity energy supply units, are responsible for providing electricity or heat to energy managers. Energy managers, acting as an intermediary coordinating layer, formulate and publish transaction prices based on real-time data uploaded by sensors. They purchase energy from producers and distribute energy to lower-level integrated energy systems, regulating energy flow and trading processes within the system through a real-time pricing mechanism. Furthermore, the system establishes a two-way collaborative mechanism with external power grid companies through metering sensors installed at grid connection points. When sensors detect energy shortages within the region, the external grid can supplement power to ensure load demand is met; when the system generates excess power, the surplus can be uploaded to the grid, thus forming a two-way energy flow. The external grid therefore simultaneously plays the role of energy supplier and consumer, helping to improve system reliability and promote the economical use of energy.

[0057] In an alternative implementation, a multi-stakeholder collaborative framework can also be derived based on the RIES framework of fixed price and periodic coordination, involving three types of participants: energy producers, energy managers, and integrated energy systems.

[0058] Specifically, energy producers provide electricity or heat to energy managers; energy managers set fixed transaction prices based on historical data or preset rules, and regularly publish price information to coordinate energy flow and transaction processes, with a relatively long price update cycle; the integrated energy system establishes a two-way energy exchange mechanism with the external power grid through the grid connection point, and the exchange operation is based on a predefined schedule or manually triggered.

[0059] In another alternative implementation, a multi-stakeholder collaborative framework can be derived based on the RIES framework of decentralized trading and manual decision-making, involving three types of participants: energy producers, energy managers, and integrated energy systems.

[0060] Specifically, energy producers directly supply electricity or heat to the integrated energy system; the transaction price is determined by the energy producers and the integrated energy system through bilateral negotiations, which rely on regular meetings or written communication; the integrated energy system establishes a two-way energy exchange mechanism with the external power grid through the grid connection point, and the exchange decision is manually executed by the local operator based on non-real-time information.

[0061] In this embodiment of the application, step S4, based on a multi-agent collaborative framework, sets constraints for the Regional Integrated Energy System (RIES) and optimizes the multi-agent objectives, including the following steps C1-C4. Specifically, the constraints and multi-stakeholder optimization objectives of the Regional Integrated Energy System (RIES) are set: the operation of the system is subject to strict physical constraints and differentiated economic objectives. At the constraint level, real-time power balance of various energy sources such as electricity, heat, and cooling must be met at all times, and the output limits, ramp rates, and energy storage capacity limits of various equipment must be strictly observed. At the objective level, energy producers within the system pursue the maximization of the difference between energy sales revenue and operation and maintenance costs, energy managers strive to maximize profits through the purchase and sale price difference, while the integrated energy system focuses on minimizing its total operating costs. Thus, under the premise of ensuring the safe and stable operation of the system, the interests of multiple stakeholders are balanced through a two-layer optimization framework.

[0062] C1: Mathematical model of Regional Integrated Energy Systems (RIES): The photovoltaic (PV) model shows that environmental factors and temperature have a significant impact on PV power generation. The model is as follows: in, for The power generation capacity of the PV unit at any given time, expressed in kilowatts (kW). Rated output power under standard test conditions. This is a coefficient representing the effect of temperature on actual power. For ambient temperature, Light intensity, express Actual light intensity at any given time Indicates the light intensity under standard test conditions. This indicates the temperature under standard test conditions.

[0063] The wind turbine model is shown below: in, for The actual power of the WTG unit at any given time, in kilowatts. Rated output power, for The actual wind speed measured by the wind speed sensor at all times. The starting wind speed, For the rated wind speed, The cut-off wind speed.

[0064] C2: The constraints of RIES are a multi-level system covering physical balance, equipment operation and market transactions. The system must strictly follow the instantaneous power balance constraints of the three energy sources of electricity, heat and cooling to ensure that the energy supply at any time can meet the load demand. The output of all equipment must be limited to the upper and lower limits of rated capacity and ramping capability. At the same time, energy storage equipment must also comply with its own energy conservation and charge / discharge state constraints. C3: At the market entity level, transactions between energy producers, managers and users are subject to strict constraints on the price feasibility domain and communication power, which together constitute the complete constraints to ensure the safe, stable and efficient operation of the system. The electrical balance constraints of the integrated energy system are as follows: in, For wind power generation capacity, For PV power generation, For GT power generation, For the power generation of fuel cells, Let t be the amount of electricity purchased from the energy management provider. Let be the discharge amount at time t. Let be the electrical load demand at time t. Let be the amount of electricity transferred at time t. This indicates the charging and discharging power of the energy storage system. This refers to the power consumption of the carbon capture device; The heat load balance constraints of the integrated energy system are as follows: in, The power of the gas-fired boiler in terms of the output heat energy. The power of the waste heat boiler's output heat energy. Let be the amount of calories purchased at time t. This refers to the heat release capacity of the thermal storage device. To meet heat load requirements, The power required to charge the thermal storage equipment; The cooling load balance constraints of the integrated energy system are as follows: in, The cooling power of the OP. The refrigeration capacity generated for electric refrigeration units. The total cooling load demand at time t; The power balance constraints for energy producers are as follows: in, Let GWT be the power generation at time t. Let be the power generation of PV at time t. Let t be the amount of electricity generated by GT at time t. Let be the discharge amount of the energy storage device at time t. Let t be the charge level of the energy storage device. This represents the amount of electricity sold by the energy producer to the energy manager at time t. This represents the amount of electricity connected to the grid at time t. This represents the heat output by GT at time t and processed by the waste heat boiler. This indicates the amount of heat released by the thermal storage device. This indicates the amount of heat stored in the thermal storage device. This refers to the amount of heat sold by energy producers to energy managers. Heat sold by energy producers to external power companies; C4: The objective function of the Regional Integrated Energy System (RIES) is shown below: in, To account for overall operating costs, , , Assigning weights to each cost For energy transaction costs, For the purchase cost of natural gas, The cost of penalties when harmful gases are emitted. To cover the cost of maintaining the equipment; The objective function for energy producers is shown below: in, This represents the total operating cost of the integrated energy system. Let be the cost of electricity trading at time t. This represents the cost of heat energy trading at time t. This represents the cost of cold energy trading at time t. This represents the total cost of purchasing gas. This indicates the price at which natural gas is purchased. This represents the amount of natural gas consumed by the gas turbine at time t. This represents the amount of natural gas consumed by the gas-fired boiler at time t. This represents the total natural gas consumption. This indicates the unit price of the penalty for emitting harmful gases. This represents the coefficient for harmful gas emissions. The unit price for purchasing carbon emission rights. The actual carbon emissions at time t The carbon emission allowance allocated at time t.

[0065] In an alternative implementation, the multi-agent collaborative framework can be simplified by setting constraints for the Regional Integrated Energy System (RIES) and optimizing multi-agent objectives.

[0066] Specifically, the photovoltaic model only considers the impact of solar irradiance on power generation, while the wind turbine model uses a piecewise linear function. Based on historical average wind speed data, it sets power balance constraints, and equipment operation constraints include an output limit. It considers the power balance constraints of energy producers, omits the transaction behavior constraints between energy managers and users, and includes energy transaction costs and equipment maintenance costs in the objective function. It omits natural gas purchase costs and emission penalty costs. In the multi-agent optimization, it optimizes the objectives of energy producers and ignores the objectives of energy managers and the integrated energy system.

[0067] In another alternative implementation, a static and step-by-step optimized multi-agent collaborative framework can be used to set constraints for the Regional Integrated Energy System (RIES) and optimize multi-agent objectives.

[0068] Specifically, the photovoltaic and wind turbine models use daily average output power values. The photovoltaic model ignores actual changes in sunlight intensity and adopts fixed standard test conditions. The power balance constraints for electricity, heat, and cooling are based on daily average load demand. The equipment operation constraints only consider rated capacity limits. The market transaction constraints use fixed prices and fixed interconnection power values. The heat balance of thermal storage equipment is omitted in the energy producer constraints, and only electricity trading is considered. The objective function adopts a step-by-step optimization method, first optimizing the energy producer objective independently, and then optimizing the total system operating cost. Carbon emission quotas and harmful gas emission penalty costs are omitted in the objective function.

[0069] In this embodiment of the application, step S5, based on a multi-agent objective, introduces the Great Wall optimization algorithm to solve the Regional Integrated Energy System (RIES), optimizes energy pricing parameters, and obtains the optimal coordinated scheduling result, including the following steps D1-D3. D1: The Great Wall optimization algorithm imitates the construction hierarchy logic and defense coordination mechanism of the Great Wall by introducing an elite collaborative optimization strategy, congestion distance sorting and external archiving mechanism. The crowding distance sorting evaluates and compares the distribution density of solutions, and prioritizes retaining sparsely distributed solutions when the archive exceeds the set storage limit; An external archiving mechanism continuously collects and saves high-quality non-dominated solutions found in previous iterations during the algorithm's operation, preventing the loss of excellent solutions. The congestion distance sorting and external archiving mechanism work together to ensure that the final solution set approximates the true Pareto front while obtaining the optimal cooperative scheduling result.

[0070] Specifically, the improved Multi-objective Great Wall Construction Algorithm (MGWCA) is used to solve the two-level scheduling model of the regional integrated energy system. The MGWCA framework and pseudocode are as follows: Figure 3As shown, this algorithm introduces an elite collaborative optimization strategy, crowding distance sorting, and an external archiving mechanism. The external archiving mechanism continuously collects and saves high-quality non-dominated solutions found in each iteration during the algorithm's operation, preventing the loss of excellent solutions. Crowding distance sorting is used to evaluate and compare the distribution density of these solutions, prioritizing the retention of sparsely distributed solutions when the archive is too large. The two mechanisms work together to ensure that the final solution set approximates the true Pareto front while also possessing good distribution uniformity and diversity. This effectively balances global exploration and local exploitation capabilities, significantly improving search efficiency, convergence performance, and solution set distribution uniformity in high-dimensional nonlinear multi-objective optimization problems. In specific applications, the upper-level scheduling utilizes MGWCA to dynamically optimize energy pricing parameters, while the lower-level system combines the Gurobi solver to achieve fine-grained scheduling of equipment output and energy trading. This allows for a balance of economy, low carbon emissions, and operational resilience in complex scheduling scenarios, providing efficient and stable algorithmic support for collaborative optimization across multiple time scales.

[0071] D2: In the original Great Wall algorithm, the fixed movement of Labor makes it prone to getting trapped in local optima, making it the fundamental driving force for the entire algorithm to converge efficiently to the Pareto optimal front. This easily leads to premature convergence of the population and a lack of effective global exploration. To alleviate these shortcomings, this invention proposes an elite collaborative optimization strategy to optimize the position update of Labor. In the improved Multi-Objective Great Wall Construction Algorithm (MGWCA) of this invention, Labor is the core abstract entity of this metaheuristic algorithm, mimicking the Great Wall construction process. It represents the basic individual exploring in the search space, i.e., a potential solution to the optimization problem. Each Labor, like a worker building the Great Wall, continuously moves and adjusts its working position (corresponding to the coordinates of the solution in the decision variable space) to find a better region. Specifically, the position update mechanism of Labor integrates the guidance of elite individuals (learning from the current best solution) and its own historical memory, thereby balancing the algorithm's global exploration and local development capabilities. This design effectively overcomes the fixed movement of Labor in the original algorithm, and its formula is: in, Guiding the weight of the elite, For individual memory components, and These are the elite guidance weight and the individual memory weight, respectively. For the first During the nth iteration The position of a worker For the first The optimal position for workers in the next iteration. For the first During the nth iteration The position of the engineer, Li(t+1) is the position of the engineer in the (t+1)th iteration. The position of a worker As a dimension, This indicates that a 1×dim matrix is ​​randomly generated.

[0072] By incorporating an elite collaborative optimization strategy, the algorithm's global search capability and local exploitation capability are balanced, and the algorithm's convergence speed is also significantly improved.

[0073] D3: The Multi-Objective Great Wall Optimization Algorithm (MGWCA) introduces crowding distance sorting and an external archive mechanism to construct a multi-objective Great Wall optimization algorithm. First, all non-dominated solutions are selected from the population to form an external archive, as shown in the following formula: in, For a file consisting of non-dominated solutions, b≻a denotes a candidate solution. Better than candidate solutions , It is the original population.

[0074] Next, the crowding distance is calculated and the quality of solutions in the solution space is evaluated, as shown in the following formula: in, For the first The crowded distance of each solution, and The first The maximum and minimum values ​​of the objective function. The total number of non-dominated solutions. and They are respectively Two adjacent individuals in the solution are in the first... The values ​​on the objective function.

[0075] Finally, a solution is selected based on the non-dominated ranking and the crowding distance. Solutions with higher non-dominated rankings are given priority. When comparing solutions of the same ranking, solutions with smaller crowding distances are retained, as shown in the following formula: in, To select a solution The probability, For the first Non-dominated ranking of solutions Indicates the first The crowded distance between individuals.

[0076] In an alternative implementation, the regional integrated energy system RIES can also be solved based on the optimization of the basic genetic algorithm to optimize the energy pricing parameters and obtain the optimal coordinated scheduling result.

[0077] Specifically, a basic genetic algorithm is used to optimize the energy pricing parameters of the regional integrated energy system. This algorithm simulates the natural selection process, including selection, crossover, and mutation operations. The process involves initializing the population and randomly generating a set of energy pricing parameters as initial solutions; calculating the multi-objective fitness value (e.g., economic efficiency, low carbon emissions) for each solution; using a selection operation (e.g., roulette wheel selection) to select parent individuals from the current population; performing a crossover operation on the parent individuals to generate offspring individuals; performing a mutation operation on the offspring individuals to introduce random perturbations; repeating the iterative process to update the population until the maximum number of iterations is reached; and extracting the solution set from the final population as the optimization result.

[0078] In another alternative implementation, the regional integrated energy system RIES can be solved based on the single-objective transformation of weighted particle swarm optimization to optimize energy pricing parameters and obtain the optimal coordinated scheduling result.

[0079] Specifically, the Particle Swarm Optimization (PSO) algorithm is employed to transform a multi-objective problem into a single-objective problem by using fixed weights, thereby optimizing energy pricing parameters. The process involves initializing the particle swarm, with each particle representing a set of energy pricing parameters; assigning preset weights to each objective function, merging the multi-objective functions into a single-objective fitness function; calculating the fitness value (weighted sum) of each particle; updating the individual historical best position and global best position of each particle; adjusting the particle positions according to the velocity update formula; repeating the iterative process until a termination condition is met; and outputting the solution corresponding to the global best position as the optimization result.

[0080] In summary, this invention constructs an electricity-heat-cooling coupled coordinated scheduling strategy based on flexible load transfer to achieve spatiotemporal load transfer and energy complementarity across multiple time scales, thereby enhancing the system's operational resilience in extreme scenarios. Building upon this, a Regional Integrated Energy System (RIES) centered on cross-energy mutual assistance is established, forming a multi-stakeholder collaborative framework encompassing energy producers, managers, and the integrated energy system. By setting system constraints and optimization objectives, a balance of interests among all parties and safe, stable operation are achieved. Furthermore, an improved Multi-Objective Great Wall Construction Algorithm (MGWCA) is introduced to efficiently solve the system model, optimize energy pricing and equipment scheduling strategies, and obtain the optimal coordinated scheduling scheme. This effectively enhances the resilience, economy, and environmental friendliness of the regional integrated energy system under single high-load scenarios, significantly reducing operating costs, carbon emissions, and gas consumption, and providing a reliable management solution for energy systems with a high proportion of renewable energy integration.

[0081] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a RIES coordinated scheduling system for flexible mutual support of electricity, heat, and cooling for a single large load, including: The scheduling strategy construction module constructs an electric-heat-cold coupled coordinated scheduling strategy based on flexible load transfer. It utilizes the time-series transfer characteristics of thermal and cold energy to convert the energy surplus of the preceding period into equivalent electrical energy and make forward-looking reserves, thereby optimizing energy allocation and coping with load peaks. The energy conversion and complementarity mechanism module, through a coordinated scheduling strategy, enables the system to perform energy conversion and complementarity under extreme load scenarios. When the self-regulation capacity of a single energy source is insufficient, a cross-energy mutual assistance mechanism is activated, and the electrical energy is supported by the energy conversion equipment to support the heating network or cooling network, forming a two-way mutual assistance network between the electricity-heating-cooling subsystems. The RIES building module, with cross-energy mutual assistance mechanism as its core, constructs a regional integrated energy system RIES, which integrates energy production, conversion, storage and load links, and collects energy supply and demand and equipment operation status data in real time through sensor network, and coordinates renewable energy power generation units. The multi-stakeholder collaboration framework module establishes a multi-stakeholder collaboration framework based on RIES, involving three types of participants: energy producers, energy managers, and integrated energy systems. The constraint and optimization module, based on a multi-agent collaborative framework, sets constraints on RIES and optimizes multi-agent objectives. The constraints cover physical equilibrium, equipment operation, and market transactions. The solution module is optimized by introducing the Great Wall Optimization Algorithm to solve RIES, optimize energy pricing parameters, and obtain the optimal coordinated scheduling result.

[0082] This embodiment also provides an electronic device suitable for RIES coordinated scheduling of a single large load with flexible mutual support between electricity, heat and cold, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the RIES coordinated scheduling method for a single large load with flexible mutual support between electricity, heat and cold as proposed in the above embodiment.

[0083] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as proposed in the above embodiments.

[0084] The storage medium proposed in this embodiment belongs to the same inventive concept as the RIES coordinated scheduling method for realizing flexible mutual support between electricity, heat and cold for a single large load proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0085] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load, characterized by: include, Construct an electric-heat-cooling coupled coordinated scheduling strategy based on flexible load transfer; By employing a collaborative scheduling strategy, the energy conversion and complementarity mechanism of the system under extreme load scenarios is obtained; With cross-energy mutual assistance mechanisms at its core, a regional integrated energy system (RIES) will be constructed. Through the Regional Integrated Energy System (RIES), a multi-stakeholder collaborative framework is obtained, comprising three types of participants: energy producers, energy managers, and integrated energy systems. Based on a multi-agent collaborative framework, constraints for the Regional Integrated Energy System (RIES) are set, and multi-agent objectives are optimized. Based on multi-agent objectives, the Great Wall optimization algorithm is introduced to solve the RIES of the regional integrated energy system, optimize energy pricing parameters, and obtain the optimal coordinated scheduling result.

2. The RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in claim 1, characterized in that: The construction of the electricity-heat-cooling coupled coordinated scheduling strategy based on flexible load transfer includes, The moment with the largest combined deficit within the system scheduling cycle is identified as the critical moment. By utilizing the time-series transfer characteristics of thermal and cold energy, the energy surplus of the preceding period is converted into equivalent electrical energy and stored in advance.

3. The RIES coordinated scheduling method for flexible mutual assistance of electricity, heat, and cooling for a single large load as described in claim 2, characterized in that: The aforementioned energy conversion and complementarity mechanism for the system under extreme load scenarios, obtained through a collaborative scheduling strategy, includes: When the self-regulation capacity of a single energy source is insufficient, an inter-energy mutual assistance mechanism is activated, and electricity is used to support the heating or cooling network through energy conversion equipment, forming a two-way mutual assistance network between the electricity-heating-cooling subsystems.

4. The RIES coordinated scheduling method for flexible mutual assistance of electricity, heat, and cooling for a single large load as described in claim 3, characterized in that: The aforementioned regional integrated energy system (RIES), centered on a cross-energy mutual assistance mechanism, includes: The system integrates energy production, conversion, storage, and load processes, and uses sensor networks to acquire real-time data on energy supply and demand and equipment operation status, thereby coordinating renewable energy power generation units.

5. The RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in claim 4, characterized in that: The aforementioned Regional Integrated Energy System (RIES) establishes a multi-stakeholder collaborative framework involving three main stakeholders: energy producers, energy managers, and the integrated energy system itself. include, Energy producers supply electricity or heat to energy managers; Energy managers formulate and publish transaction prices based on real-time data uploaded by sensors, and coordinate energy flow and the transaction process; The integrated energy system establishes a two-way energy exchange mechanism with the external power grid through the grid connection point.

6. The RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in claim 5, characterized in that: The aforementioned multi-agent collaborative framework sets constraints for the Regional Integrated Energy System (RIES) and optimizes multi-agent objectives, including: The constraints of RIES are a multi-level system that covers physical balance, equipment operation and market transactions. The system must strictly follow the instantaneous power balance constraints of the three energy sources of electricity, heat and cooling to ensure that the energy supply at any time can meet the load demand. The output of all equipment must be limited to the upper and lower limits of rated capacity and ramping capability. At the same time, energy storage equipment must also comply with its own energy conservation and charge / discharge state constraints. At the market participant level, transactions between energy producers, managers, and users are subject to strict constraints on the price feasibility domain and communication capacity, which together constitute the complete constraints for ensuring the safe, stable, and efficient operation of the system. The electrical balance constraints of the integrated energy system are as follows: in, For wind power generation capacity, For PV power generation, For GT power generation, For the power generation of fuel cells, Let t be the amount of electricity purchased from the energy management provider. Let be the discharge amount at time t. Let be the electrical load demand at time t. Let be the amount of electricity transferred at time t. This indicates the charging and discharging power of the energy storage system. This refers to the power consumption of the carbon capture device; The heat load balance constraints of the integrated energy system are as follows: in, The power of the gas-fired boiler in terms of the output heat energy. The power of the waste heat boiler's output heat energy. Let be the amount of calories purchased at time t. This refers to the heat release capacity of the thermal storage device. To meet heat load requirements, The power required to charge the thermal storage equipment; The cooling load balance constraints of the integrated energy system are as follows: in, The cooling power of the OP. The cooling capacity generated for electric refrigeration units. The total cooling load demand at time t; The power balance constraints for energy producers are as follows: in, Let GWT be the power generation at time t. Let be the power generation of PV at time t. Let t be the amount of electricity generated by GT at time t. Let be the discharge amount of the energy storage device at time t. Let t be the charge level of the energy storage device. This represents the amount of electricity sold by the energy producer to the energy manager at time t. This represents the amount of electricity connected to the grid at time t. This represents the heat output by GT at time t and processed by the waste heat boiler. This indicates the amount of heat released by the thermal storage device. This indicates the amount of heat stored in the thermal storage device. This refers to the amount of heat sold by energy producers to energy managers. Heat sold by energy producers to external power companies; The objective function of the Regional Integrated Energy System (RIES) is shown below: in, To account for overall operating costs, , , Assigning weights to each cost For energy transaction costs, For the purchase cost of natural gas, The cost of penalties when harmful gases are emitted. To cover the cost of maintaining the equipment; The objective function for energy producers is shown below: in, This represents the total operating cost of the integrated energy system. Let be the cost of electricity trading at time t. This represents the cost of heat energy trading at time t. This represents the cost of cold energy trading at time t. This represents the total cost of purchasing gas. This indicates the price at which natural gas is purchased. This represents the amount of natural gas consumed by the gas turbine at time t. This represents the amount of natural gas consumed by the gas-fired boiler at time t. This represents the total natural gas consumption. This indicates the unit price of the penalty for emitting harmful gases. This represents the coefficient for harmful gas emissions. The unit price for purchasing carbon emission rights. The actual carbon emissions at time t The carbon emission allowance allocated at time t.

7. The RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in claim 6, characterized in that: Based on multi-agent objectives, the Great Wall optimization algorithm is introduced to solve the Regional Integrated Energy System (RIES), optimize energy pricing parameters, and obtain the optimal coordinated scheduling result. include, The Great Wall optimization algorithm mimics the construction hierarchy logic and defense coordination mechanism of the Great Wall by introducing an elite collaborative optimization strategy, congestion distance sorting, and external archiving mechanism. The crowding distance sorting evaluates and compares the distribution density of solutions, and prioritizes retaining sparsely distributed solutions when the archive exceeds the set storage limit; An external archiving mechanism continuously collects and saves high-quality non-dominated solutions found in previous iterations during the algorithm's operation, preventing the loss of excellent solutions. The congestion distance sorting and external archiving mechanism work together to ensure that the final solution set approximates the true Pareto front while obtaining the optimal cooperative scheduling result.

8. A RIES coordinated scheduling system for flexible mutual support of electricity, heat, and cooling for a single large load, comprising the RIES coordinated scheduling method for flexible mutual support of electricity, heat, and cooling for a single large load as described in any one of claims 1 to 7, characterized in that, include: The scheduling strategy construction module constructs an electric-heat-cold coupled coordinated scheduling strategy based on flexible load transfer. It utilizes the time-series transfer characteristics of thermal and cold energy to convert the energy surplus of the preceding period into equivalent electrical energy and make forward-looking reserves, thereby optimizing energy allocation and coping with load peaks. The energy conversion and complementarity mechanism module, through a coordinated scheduling strategy, enables the system to perform energy conversion and complementarity under extreme load scenarios. When the self-regulation capacity of a single energy source is insufficient, a cross-energy mutual assistance mechanism is activated, and the electrical energy is supported by the energy conversion equipment to support the heating network or cooling network, forming a two-way mutual assistance network between the electricity-heating-cooling subsystems. The RIES building module, with cross-energy mutual assistance mechanism as its core, constructs a regional integrated energy system RIES, which integrates energy production, conversion, storage and load links, and collects energy supply and demand and equipment operation status data in real time through sensor network, and coordinates renewable energy power generation units. The multi-stakeholder collaboration framework module establishes a multi-stakeholder collaboration framework based on RIES, involving three types of participants: energy producers, energy managers, and integrated energy systems. The constraint and optimization module, based on a multi-agent collaborative framework, sets constraints on RIES and optimizes multi-agent objectives. The constraints cover physical equilibrium, equipment operation, and market transactions. The solution module is optimized by introducing the Great Wall Optimization Algorithm to solve RIES, optimize energy pricing parameters, and obtain the optimal coordinated scheduling result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the RIES cooperative scheduling method for flexible mutual support of electricity, heat and cold for a single large load, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the RIES cooperative scheduling method for flexible mutual support of electricity, heat and cold for a single large load as described in any one of claims 1 to 7.

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