A shared energy storage optimization scheduling method and system considering islanded operation

Abstract

This invention relates to the field of integrated energy optimization scheduling technology, and in particular to a shared energy storage optimization scheduling method and system considering islanded operation. This shared energy storage optimization scheduling method employs a two-layer optimization framework based on a two-layer energy storage optimization model. On the basis of minimizing the operating cost of a multi-region integrated energy system, it introduces islanded operation constraints of regional integrated energy subsystems. This allows the real-time shared energy storage scheduling scheme obtained from the two-layer energy storage optimization model to not only optimize the operating cost of the multi-region integrated energy system based on historical and real-time operating data, but also to meet the emergency energy constraints of islanded operation of regional integrated energy subsystems, address the risk of grid disconnection caused by real-time load changes, improve the power supply reliability of the system under extreme conditions, and thus enhance the robustness of the dynamic correction strategy for energy storage in the multi-region integrated energy system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy optimization scheduling technology, and in particular to a shared energy storage optimization scheduling method and system that takes into account isolated operation. Background Technology

[0002] With the accelerating global energy structure transformation, effectively improving energy efficiency and achieving low-carbon sustainable development have become core issues in the energy sector. Regional Integrated Energy Systems (RIES), as a key technological pathway for promoting energy transformation, horizontally integrate diverse energy subsystems such as electricity, heat, cooling, and gas, and vertically coordinate the synergistic optimization of the entire "source-grid-load-storage" chain. This constructs a multi-energy complementary network with electricity at its core. By breaking down barriers in traditional energy systems and improving the efficiency of energy cascade utilization, RIES provides a systematic solution for regional decarbonization and clean energy consumption. Based on meeting the needs of regional decarbonization and clean energy development, the synergistic optimization of multi-regional integrated energy systems has become an important path to enhance energy complementarity potential and economic viability.

[0003] In the field of optimized scheduling of integrated energy systems, existing research mainly follows the core objective of minimizing operating costs. When coordinating the collaborative operation of shared energy storage operators and multi-regional integrated energy systems, it relies on the assumption of continuous grid interconnection and does not consider the scenario where the integrated energy system is in an islanded mode. Islanded mode refers to a state or operating mode in which a single regional integrated energy system, after being physically disconnected from the upper-level shared energy storage grid, can only rely on its own internal power sources and energy storage devices to independently maintain operation and continuously supply power to its internal critical loads.

[0004] Therefore, improving the adaptability of regional integrated energy systems under the shared energy storage model to extreme imbalances in supply and demand during grid outages, and ensuring the robustness of dynamic correction strategies for energy storage under the isolated model, have become urgent technical problems to be solved in this field. Summary of the Invention

[0005] This invention aims to provide a shared energy storage optimization scheduling method and system involving islanded operation, so as to improve the power supply reliability of the shared energy storage system when entering the islanded operation condition, thereby improving the robustness of the dynamic correction strategy of energy storage in multi-regional integrated energy systems, and solving the technical problem that the existing technology relies on the assumption of continuous grid interconnection and does not consider the integrated energy system in islanded mode.

[0006] To achieve the above objectives, the first aspect of the present invention provides a shared energy storage optimization scheduling method considering islanded operation, comprising the following steps:

[0007] A multi-regional integrated energy system is constructed based on regional power supply networks and regional heating networks. The multi-regional integrated energy system includes a shared energy storage subsystem and several regional integrated energy subsystems. The several regional integrated energy subsystems are electrically connected to the external distribution network through the shared energy storage subsystem.

[0008] Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, a two-layer energy storage optimization model is constructed based on the energy constraint set and the islanded operation constraint set;

[0009] Historical and real-time operational data are acquired based on the aforementioned multi-regional integrated energy system;

[0010] Based on the historical operating data and the real-time operating data, the dual-layer energy storage optimization model is solved to obtain a real-time shared energy storage scheduling scheme.

[0011] The aforementioned shared energy storage optimization scheduling method adopts a two-layer optimization framework based on a two-layer energy storage optimization model. On the basis of minimizing the operating cost of a multi-region integrated energy system, it introduces the islanding operation constraint of the regional integrated energy subsystem. This enables the real-time shared energy storage scheduling scheme obtained by solving the two-layer energy storage optimization model to not only optimize the operating cost of the multi-region integrated energy system based on historical operating data and the real-time operating data, but also to meet the emergency energy constraint requirements of the islanding operation of the regional integrated energy subsystem, cope with the grid disconnection risk caused by real-time load changes, improve the power supply reliability of the system under extreme conditions, and thus enhance the robustness of the dynamic correction strategy of energy storage in the multi-region integrated energy system.

[0012] Furthermore, the step of constructing a two-layer energy storage optimization model based on the energy constraint set and the islanded operation constraint set, with the optimization of the operating cost of the multi-region integrated energy system as the objective function, includes:

[0013] Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, an upper-level energy storage optimization model is constructed based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints.

[0014] Taking the optimization of the operating cost of all the regional integrated energy subsystems as the objective function, a lower-level energy storage optimization model is constructed based on the upper-level energy storage optimization model, the lower-level energy balance constraints, the regional energy storage usage restriction constraints, and the isolated energy storage reservation constraints.

[0015] The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are used as a two-layer energy storage optimization model.

[0016] In this implementation, considering the overall energy supply and demand balance of the multi-region integrated energy system, the management of the shared energy storage subsystem, and the emergency scheduling design of the island mode when each regional integrated energy subsystem is disconnected during the optimization scheduling process, a multi-level master-slave game optimization framework is adopted. On the one hand, the upper-level energy storage optimization model aims to minimize the overall operating cost of the multi-region integrated energy system. Based on the overall energy conservation constraints, energy balance constraints, and island load constraints for reserving energy storage in the island mode, it performs system load prediction and system resource estimation, and formulates a large-scale charging and discharging plan for the shared energy storage of the entire system. On the other hand, In terms of optimization, the lower-level energy storage optimization model aims to minimize the operating cost of each regional integrated energy subsystem. Based on the energy balance constraints of each regional subsystem, the regional energy storage usage restrictions allocated by the upper-level system to the regional subsystems, and the island energy storage reservation constraints for the regional subsystems to reserve energy for island mode, the model performs load forecasting and system resource estimation for each regional subsystem. This refines and corrects the optimization results of the upper-level energy storage optimization model to cope with load changes in the regional integrated energy subsystems. Finally, the robustness of the final shared energy storage scheduling scheme in dealing with the system entering island mode is improved through the game between the optimization objectives of the upper-level and lower-level energy storage optimization models.

[0017] Furthermore, the step of solving the two-layer energy storage optimization model based on the historical operating data and the real-time operating data to obtain a real-time shared energy storage scheduling scheme includes:

[0018] The upper-level energy storage optimization model is solved based on the historical operating data to obtain the day-ahead shared energy storage scheduling scheme;

[0019] Based on the real-time operating data, the dual-layer energy storage optimization model is solved to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme.

[0020] In this implementation, during the day-ahead system energy storage optimization and scheduling phase prior to the current day, the main upper-level energy storage optimization model performs load forecasting and resource estimation based on historical operating data of multi-regional integrated energy systems, formulating a large-scale charging and discharging plan that minimizes the overall system operating cost. Furthermore, because the upper-level energy storage optimization model incorporates islanded load constraints to address subsystem disconnections, the formulated scheme can simultaneously meet the emergency energy reserve requirements when any regional integrated energy subsystem enters islanded mode. However, the day-ahead shared energy storage scheduling scheme obtained by the upper-level energy storage optimization model based on historical operating data is not highly adaptable to the dynamic changes in subsystem load on a given day, and cannot cope with changes in energy storage demand caused by daily load variations. Therefore, this invention further introduces a lower-level optimized energy storage model for intraday rolling optimization. Specifically, the dual-layer energy storage optimization model is solved based on the real-time operation data of the day. That is, during the intraday scheduling phase, the lower-layer energy storage optimization model continuously modifies and optimizes the day-ahead shared energy storage scheduling scheme formulated by the upper layer based on the real-time load fluctuations and emergency situations of the regional integrated energy subsystem, so as to cope with the real-time changes in the demand for reserved energy storage in the island mode caused by the real-time load changes of the regional integrated energy subsystem.

[0021] This implementation creates a closed loop between day-ahead decisions based on longer-term historical data and intraday decisions based on shorter-term real-time data: the day-ahead shared energy storage scheduling scheme formulated by the upper-level energy storage optimization model guides the lower-level energy storage optimization model in formulating intraday real-time shared energy storage scheduling schemes, while the intraday real-time shared energy storage scheduling schemes formulated by the lower-level energy storage optimization model can serve as future day-ahead shared energy storage scheduling schemes. The upper-level energy storage optimization model can proactively predict and guide the behavior of the lower-level energy storage optimization model based on historical operating data, while the solution results of the lower-level energy storage optimization model correct the solution results of the upper-level energy storage optimization model. The two continuously engage in a game until a dynamic equilibrium is reached, thereby coordinating decisions based on different time scales and different objectives in a two-layer game, and improving the robustness of the shared energy storage scheduling scheme when the system enters an islanded mode.

[0022] Furthermore, the step of solving the two-layer energy storage optimization model based on the real-time operating data to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme includes:

[0023] Based on the KKT conditions, the lower-level energy storage optimization model is transformed into a lower-level Lagrangian function, and then the lower-level energy storage optimization model is transformed into a lower-level optimization constraint set.

[0024] The upper-layer energy storage optimization model is constrained by the lower-layer optimization constraint set, thereby obtaining a single-layer energy storage optimization model;

[0025] Based on the real-time operating data, the single-layer energy storage optimization model is solved using a preset solver, thereby optimizing the day-ahead shared energy storage scheduling scheme and obtaining the real-time shared energy storage scheduling scheme.

[0026] In this implementation, KKT conditions are introduced to transform the optimization problem of the lower-level energy storage optimization model into constraints for the upper-level energy storage optimization model. KKT conditions are a set of constraints used in solving nonlinear programming problems, linking the optimal solution of the optimization problem to the constraints, thus transforming the optimization problem into a set of constraints. Based on the lower-level energy storage optimization model in the two-level energy storage optimization model, the optimization problem of the regional integrated energy subsystem is transformed into a set of constraints, which are then added to the constraints of the upper-level energy storage optimization model. KKT conditions include the feasibility conditions of the original problem, the dual feasibility conditions, and the complementary relaxation conditions, which can equivalently represent the optimality conditions of the lower-level optimization problem, thereby transforming the two-level optimization problem into a single-level optimization problem, resulting in a single-level energy storage optimization model and reducing computational complexity.

[0027] Furthermore, before the step of solving the single-layer energy storage optimization model using a preset solver based on the real-time operating data to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme, the method further includes:

[0028] The single-layer energy storage optimization model is linearized based on the McCormick envelope method, thereby transforming the nonlinear and bilinear structures in the single-layer energy storage optimization model into linear structures, resulting in a single-layer linear optimization model.

[0029] The single-layer linear optimization model is used as the single-layer energy storage optimization model.

[0030] In this implementation, after applying KKT conditions to transform the optimization problem of the lower-level energy storage optimization model into the constraints of the upper-level energy storage optimization model, the McCormick envelope method is further introduced to linearize the single-level energy storage optimization model, completing the modeling transformation from MINLP (mixed-integer nonlinear programming) to MILP (mixed-integer linear programming). This solves the nonlinear problems caused by control scenarios such as equipment start-up and shutdown in the regional integrated energy subsystem, reduces the deviation between the model solution results and the actual tolerance scenario, and improves the accuracy of shared energy storage optimization scheduling.

[0031] Furthermore, the step of constructing an upper-level energy storage optimization model based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints, with the operating cost optimization of the multi-regional integrated energy system as the objective function, includes:

[0032] Establish the islanded load constraints, including critical load constraints in islanded mode and upper-layer energy storage reservation strategies;

[0033] The expression for the critical load constraint in the islanded mode is shown in the following equation:

[0034] ;

[0035] in, This indicates that the i-th regional integrated energy subsystem is in Once the system enters island mode, the critical load power requirements must be met. This represents the energy storage and discharge capability of the i-th regional integrated energy subsystem. Indicates the preset island operation time;

[0036] The expression for the upper-layer energy storage reservation strategy is shown in the following formula:

[0037] ;

[0038] ;

[0039] in, This represents the current stored energy of the i-th regional integrated energy subsystem. This represents the initial energy storage reservation value issued by the shared energy storage subsystem to the i-th regional integrated energy subsystem. This indicates the minimum stored energy that the regional integrated energy subsystem needs to guarantee after entering islanded mode. This indicates the critical load power that the regional integrated energy subsystem needs to meet after entering islanded mode. This indicates the preset island operation time.

[0040] Furthermore, the construction of the lower-level energy storage optimization model, based on the upper-level energy storage optimization model, lower-level energy balance constraints, regional energy storage usage restrictions, and isolated energy storage reservation constraints, with the operating cost optimization of all the aforementioned regional integrated energy subsystems as the objective function, includes:

[0041] The reserved constraints for the isolated energy storage are established, and their expression is shown in the following formula:

[0042] ;

[0043] in, This represents the current stored energy of the i-th regional integrated energy subsystem. This represents the initial energy storage reservation value issued by the shared energy storage subsystem to the i-th regional integrated energy subsystem. This represents the energy storage and discharge capability of the i-th regional integrated energy subsystem. This indicates a scrolling window with a preset time.

[0044] A second aspect of the present invention provides a shared energy storage optimization scheduling system considering islanded operation, comprising a multi-region integrated energy system construction module, an energy storage optimization model construction module, a data acquisition module, and a shared energy storage scheduling module, wherein:

[0045] The multi-region integrated energy system construction module is used to construct a multi-region integrated energy system based on a regional power supply network and a regional heating network. The multi-region integrated energy system includes a shared energy storage subsystem and several regional integrated energy subsystems. The several regional integrated energy subsystems are electrically connected to the external power distribution network through the shared energy storage subsystem.

[0046] The energy storage optimization model construction module is used to construct a two-layer energy storage optimization model based on the energy constraint set and the island operation constraint set, with the operating cost optimization of the multi-region integrated energy system as the objective function.

[0047] The data acquisition module is used to acquire historical and real-time operating data based on the multi-regional integrated energy system.

[0048] The shared energy storage scheduling module is used to solve the two-layer energy storage optimization model based on the historical operating data and the real-time operating data, thereby obtaining a real-time shared energy storage scheduling scheme.

[0049] Furthermore, the step of constructing a two-layer energy storage optimization model based on the energy constraint set and the islanded operation constraint set, with the optimization of the operating cost of the multi-region integrated energy system as the objective function, includes:

[0050] Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, an upper-level energy storage optimization model is constructed based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints.

[0051] Taking the optimization of the operating cost of all the regional integrated energy subsystems as the objective function, a lower-level energy storage optimization model is constructed based on the upper-level energy storage optimization model, the lower-level energy balance constraints, the regional energy storage usage restriction constraints, and the isolated energy storage reservation constraints.

[0052] The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are used as a two-layer energy storage optimization model.

[0053] Furthermore, the step of solving the two-layer energy storage optimization model based on the historical operating data and the real-time operating data to obtain a real-time shared energy storage scheduling scheme includes:

[0054] The upper-level energy storage optimization model is solved based on the historical operating data to obtain the day-ahead shared energy storage scheduling scheme;

[0055] Based on the real-time operating data, the dual-layer energy storage optimization model is solved to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating a shared energy storage optimization scheduling method considering isolated operation, provided by an embodiment of the present invention.

[0057] Figure 2 This is a schematic diagram of the structure of a multi-region integrated energy system provided in an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the structure of a regional integrated energy subsystem provided in an embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram illustrating the relationship between an upper-layer energy storage optimization model and a lower-layer energy storage model provided in an embodiment of the present invention.

[0060] Figure 5 This is a schematic diagram of a process for solving a two-layer energy storage optimization model provided by an embodiment of the present invention;

[0061] Figure 6 This is a schematic diagram of a shared energy storage optimization scheduling system that takes into account isolated operation, provided by an embodiment of the present invention. Detailed Implementation

[0062] In the field of optimal scheduling of multi-regional integrated energy systems, existing research mainly follows the core objective of minimizing operating costs. However, existing integrated energy optimal scheduling methods have not fully considered the differences in optimization objectives between shared energy storage operators and multi-regional integrated energy systems when coordinating their collaborative operation. In addition, existing research is mostly based on a static day-ahead scheduling framework with fixed forecast data, lacking the ability to dynamically respond to real-time fluctuations in renewable energy output and load, resulting in insufficient adaptability of scheduling strategies in actual operation.

[0063] It is worth noting that the shared energy storage mechanism, by aggregating dispersed energy storage resources into a shared pool, separates "ownership" from "usage rights," thereby reducing user investment costs and improving resource utilization, providing a new path to solve the aforementioned coordination problems. Existing research has proposed collaborative strategies based on cooperative game theory and auction mechanisms to address the allocation of shared energy storage resources. However, current research also has certain limitations: firstly, insufficient coupling of time scales, with the disconnect between day-ahead planning and intraday rolling adjustments leading to deviations in energy storage capacity reservations from actual demand; secondly, a lack of emergency response, as existing models rely on the assumption of continuous grid interconnection and lack robustness guarantees for dynamic correction strategies for energy storage under islanded modes.

[0064] In recent years, game theory methods have demonstrated unique advantages in coordinating conflicts of interest among multiple stakeholders and improving the synergistic efficiency of integrated energy systems. Among these, Stackelberg game theory has attracted considerable attention due to its ability to effectively characterize the master-slave decision-making relationship between energy suppliers and users. (Some technologies...) ] To address the dynamic pricing problem of photovoltaic (PV) producers and consumers within microgrids, a two-level optimization model based on the Stackelberg-Nash game theory was constructed. In this model, retailers act as leaders, setting electricity pricing strategies, while PV producers and consumers act as followers, adjusting their energy trading strategies through non-cooperative game theory. This research, by introducing demand response and carbon trading mechanisms, designed a distributed iterative algorithm to achieve a balance between real-time scheduling and privacy protection, validating the effectiveness of the master-slave game theory in coordinating global cost optimization and individual economic objectives. Building upon this foundation, some technologies further extend the Stackelberg game theory to shared energy storage scenarios, constructing a two-level decision-making framework between integrated energy system operators and user alliances. In this framework, operators guide user-side electric vehicle clusters to form shared energy storage resource pools through carbon trading mechanisms, and an improved Shapley value method is used for cost sharing within the alliance. Research shows that this game structure can simultaneously improve the system's renewable energy absorption capacity and the fairness of multi-stakeholder revenue distribution. However, all of the above frameworks neglected integer variables and nonlinear constraints related to equipment start-up and shutdown (such as unit start-up and shutdown states, energy storage charging and discharging logic, etc.) in their design, leading to significant deviations between the models and actual engineering scenarios. At the same time, it does not take into account the situation where the integrated energy system is in an isolated mode, and lacks adaptability to extreme imbalances in supply and demand under the scenario of grid outage.

[0065] Based on this, to address the shortcomings of existing technologies, this paper designs a "day-ahead-intra-day" collaborative rolling mechanism within a collaborative scheduling framework based on a two-layer Stackelberg master-slave game. The upper-layer energy storage optimization model formulates an overall charging and discharging plan for shared energy storage devices by considering the energy consumption of the global multi-region integrated energy system and the reserve capacity under islanded conditions. The lower-layer energy storage optimization model combines the day-ahead shared energy storage scheduling scheme of the upper-layer energy storage optimization model and aims to minimize the operating cost of the regional integrated energy subsystem itself, continuously optimizing its own optimal operating plan in real time within the day. Furthermore, a dynamic response mechanism for islanded modes is introduced, adjusting the reserve capacity allocation strategy through real-time rolling optimization within the day, significantly improving the power supply reliability of the system under extreme conditions. This provides theoretical and methodological support for the optimization of multi-entity collaborative multi-region integrated energy systems, and constructs state persistence constraints to ensure the temporal continuity of multi-scale strategies. This addresses the problem of insufficient time-scale coupling between day-ahead planning and intra-day rolling adjustment.

[0066] To achieve the above objectives, see Figure 1The first aspect of this invention provides a shared energy storage optimization scheduling method considering islanded operation, comprising the following steps:

[0067] S1. Construct a multi-regional integrated energy system based on regional power supply network and regional heating network. The multi-regional integrated energy system includes a shared energy storage subsystem and several regional integrated energy subsystems. The several regional integrated energy subsystems are electrically connected to the external power distribution network through the shared energy storage subsystem.

[0068] S2. Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, a two-layer energy storage optimization model is constructed based on the energy constraint set and the island operation constraint set.

[0069] S3. Obtain historical and real-time operating data based on the multi-regional integrated energy system;

[0070] S4. Solve the two-layer energy storage optimization model based on the historical operation data and the real-time operation data to obtain a real-time shared energy storage scheduling scheme.

[0071] The aforementioned shared energy storage optimization scheduling method adopts a two-layer optimization framework based on a two-layer energy storage optimization model. On the basis of minimizing the operating cost of a multi-region integrated energy system, it introduces the islanding operation constraint of the regional integrated energy subsystem. This enables the real-time shared energy storage scheduling scheme obtained by solving the two-layer energy storage optimization model to not only optimize the operating cost of the multi-region integrated energy system based on historical operating data and the real-time operating data, but also to meet the emergency energy constraint requirements of the islanding operation of the regional integrated energy subsystem, cope with the grid disconnection risk caused by real-time load changes, improve the power supply reliability of the system under extreme conditions, and thus enhance the robustness of the dynamic correction strategy of energy storage in the multi-region integrated energy system.

[0072] Specifically, see Figure 2 The multi-region integrated energy system described in step S1 is as follows: Figure 2 As shown in the diagram, shared energy storage represents a shared energy storage subsystem, IES_1 represents the first regional integrated energy subsystem, IES_2 represents the second regional integrated energy subsystem, and IES_n represents the nth regional integrated energy subsystem. Each regional integrated energy subsystem includes multiple types of energy storage and discharge devices, and each regional integrated energy subsystem is responsible for providing the electricity and heat load for users in one area.

[0073] See Figure 3The structure of the regional integrated energy subsystem described in step S1 includes photovoltaic generators, solar collectors, hot water storage tanks, electric energy storage devices, water pumps, ground source heat pumps, and a control system. The hot water storage tanks exchange heat with the regional soil heat source. The regional integrated energy subsystem is responsible for the local user heat load and user electricity load. Multiple regional integrated energy subsystems are connected to a shared energy storage subsystem for energy storage and utilization. When renewable energy generation is insufficient and the shared energy storage subsystem's capacity is insufficient to meet the system's electricity load demand, the system will purchase electricity from the distribution network. When the capacity of each regional integrated energy subsystem is sufficient to meet the electricity load requirements and there is a surplus, the surplus electricity will be stored in the shared energy storage subsystem. This not only avoids situations where renewable energy generation is insufficient and electricity prices peak, requiring complete purchase from the distribution network, but also allows regions with excess power generation to supply electricity to other regional integrated energy subsystems with power shortages, achieving energy mutual assistance between regions.

[0074] In the regional integrated energy subsystem, photovoltaic (PV) power generation is one of the main renewable energy sources. Although many factors influence PV power generation efficiency and output, they are primarily determined by two factors: first, the characteristics of the PV array itself, such as its area and conversion efficiency; and second, the surrounding environment, such as solar radiation intensity and temperature. The mathematical expression for a photovoltaic power generation unit is:

[0075]

[0076] In the formula, The output power of the photovoltaic generator set during time period t, in MW; This refers to the rated power of the photovoltaic generator set; These represent the solar irradiance of the photovoltaic generator at the current time period and the solar irradiance at 25 degrees Celsius, respectively. This is the converted temperature coefficient under the current ambient temperature; These are the current ambient temperature and the standard ambient temperature, respectively. The conversion efficiency of light energy into electrical energy; Effective solar irradiance area of ​​photovoltaic power generation unit .

[0077] In the regional integrated energy subsystem, the solar collector proposed in this embodiment is a flat-plate solar collector. The main task of the solar collector is to convert solar radiation energy into heat energy and heat the circulating fluid (such as water or air), then transfer the heat to the hot water storage tank. Assuming the collector operates under steady-state conditions and its performance does not degrade with operating time, the heat balance equation of the collector can be expressed as:

[0078]

[0079] in: The effective heat output of a solar collector, measured in W. The thermal efficiency of a solar collector; The effective light-receiving area of ​​a solar collector, in units of , The intensity of solar radiation is expressed in units of 1. The heat loss of the solar collector is expressed in W, and it is determined by the overall heat loss coefficient of the solar collector.

[0080] Among them, the thermal efficiency of the solar collector The photothermal conversion efficiency and heat loss are affected, and can be expressed by the following equation:

[0081]

[0082] in: The photothermal conversion efficiency under conditions of no heat loss; This is the primary heat loss coefficient, in units of... ; This is the secondary heat loss coefficient, with units of... ; This refers to the temperature of the fluid at the collector outlet, in Kelvin (K). Ambient temperature, in Kelvin (K).

[0083] In the regional integrated energy subsystem, the water tank involved is a hot water storage tank. To facilitate simulation, the number of water tank nodes is artificially determined to control the stratification of the water tank, and it is assumed that each node of equal volume is isothermal, and that only vertical temperature changes exist within the water tank. In the water tank model, the water tank temperature TS is a function of time and space. Therefore:

[0084]

[0085] Simplified to:

[0086]

[0087] During system operation, heat transfer from the water tank is controlled by the tank's volume. and heat exchange area The basic formula for calculating heat storage in a water tank can be expressed as:

[0088]

[0089] in: The heat stored in the water tank, measured in J; The mass of water, in units of The quality of water can be determined by the volume of the water tank. calculate:

[0090]

[0091] in The density of water is usually taken as... .

[0092] in, Specific heat capacity of water, in units of , usually take ; The change in water temperature, measured in K, is a weighted average of the temperatures between different layers in a water tank.

[0093] Hot water storage tanks are divided into Layers, the temperature of each layer is determined by the node temperature within the layer. This indicates that the temperature is uniform within each layer, but there are temperature gradients between adjacent layers. Based on the law of conservation of energy, the... The heat balance equation for the layer is as follows:

[0094]

[0095] In the formula: For the first Quality of layer water Specific heat capacity of water, in units of ; The temperature of the hot water entering the water tank The water temperature at the bottom of the tank, in units of... Ambient temperature, in K; This is the heat loss coefficient of the water tank, in units of... The outer surface area of ​​the water tank, in units of .

[0096] in, The water flow control factor is defined as follows:

[0097]

[0098]

[0099] In the regional integrated energy subsystem, fixed-frequency pumps are used, operating at a constant flow rate within a specified loop. In this closed-loop circulation system, the pumps maintain a constant outlet mass flow rate, and pressure drop is ignored during startup and shutdown. The pump control signal is set so that the pump is on when the input value is greater than or equal to 0.5, and off when the input value is less than 0.5. The pump process efficiency calculation formula is shown below:

[0100]

[0101] In the formula: The total efficiency of the water pump; For motor efficiency.

[0102] Because the motor generates heat, the fluid absorbs this heat, causing the water outlet temperature to be higher than the inlet temperature. The corresponding calculation formula is shown below:

[0103]

[0104] In the formula: The temperature of the fluid at the pump outlet, in units of... The inlet fluid temperature of the water pump, in units of... The heat generated by the motor transferred to the fluid, in units of This refers to the fluid mass flow rate, measured in units of... This refers to the specific isobaric heat capacity of the fluid, in units of... The pump shaft work is calculated using the following formula:

[0105]

[0106] In the formula: Shaft power required for the pumping process, in units of ; Rated power of the water pump, in units of .

[0107] In the regional integrated energy subsystem, the ground source heat pump (GSHP), as a core electrothermal coupling device, possesses highly efficient electrical and thermal energy conversion capabilities and is a crucial supporting technology for achieving cross-system coordinated optimization of energy. This paper utilizes the ground source heat pump to improve the overall system energy efficiency and optimize the coordinated scheduling of electricity and heat energy in the optimization scheduling model.

[0108] In heating mode, the heat transfer of a ground source heat pump can be expressed as:

[0109]

[0110] in: This indicates the amount of heat provided by the hot spring, measured in kW. This indicates the electrical power consumed by the heat pump, measured in kW.

[0111] This indicates the coefficient of performance (COP) of a heat pump.

[0112] The COP (Coefficient of Performance) of a heat pump is affected by ambient temperature, ground temperature, and heat exchange efficiency, and can generally be expressed by an empirical formula:

[0113]

[0114] in: This refers to the condenser temperature, measured in K, which is the heating water temperature. Evaporator temperature, in units of This refers to the fluid temperature in the underground heat exchange circuit.

[0115] Ground source heat pumps exchange heat through underground pipes, and their heat exchange capacity is described by the following equation:

[0116]

[0117] in: This refers to underground heat exchange, measured in kW. This is the underground heat transfer coefficient, in units of... It is related to the soil thermal conductivity and the structure of the buried pipe; The heat exchange area of ​​the buried pipe is expressed in units of... ; Temperature of the fluid inside the buried pipe, in Kelvin (K). Soil temperature, in Kelvin (K).

[0118] The mathematical model of the shared energy storage subsystem is expressed as follows:

[0119] ;

[0120] In the formula, Indicates the maximum capacity of the battery, in units of ; Indicates the battery's maximum discharge rate; This indicates the battery's maximum power, in units of... ; This represents the battery's stored energy in time period t, in units of... This represents the charging power in time period t, in units of... ; This represents the discharge power in time period t, in units of... ; These represent the charging and discharging efficiencies of the energy storage device, respectively. All are taken as 0.98; These represent the charging and discharging states of the energy storage device, respectively. Both are variables of 0-1, ensuring that the charging and discharging of the shared energy storage subsystem cannot occur simultaneously.

[0121] It should be noted that the most commonly used batteries are lead-acid batteries and lithium iron phosphate batteries. While lead-acid batteries have a moderate cost, they have a lower rated power and shorter cycle life. In contrast, lithium iron phosphate batteries have advantages such as large capacity, high efficiency, and long cycle life. Therefore, the shared energy storage subsystem in this embodiment uses lithium iron phosphate batteries.

[0122] For details, please refer to Figure 4 , Figure 4This is a schematic diagram illustrating the relationship between an upper-level energy storage optimization model and a lower-level energy storage model provided in an embodiment of the present invention. In the optimization scheduling process of a multi-region integrated energy system (IES), the energy supply and demand balance of each regional integrated energy subsystem, the management of shared energy storage subsystems, and the emergency scheduling in islanded mode all involve the distribution of interests among multiple stakeholders. To effectively coordinate energy interaction between regional integrated energy subsystems while ensuring the rational use of shared energy storage, this paper adopts a two-layer master-slave game optimization framework to construct a coordinated optimization scheduling strategy for the integrated energy system. Under the two-layer game framework, it is necessary to ensure that intraday adjustments are consistent with the day-ahead plan. During the day-ahead optimization phase, the upper-level energy storage optimization model (leader) formulates a large-scale charging and discharging plan for the overall shared energy storage equipment based on load forecasting and resource estimation. The upper-level energy storage optimization objective is to minimize the operating cost of the entire multi-region integrated energy system while meeting the 72-hour emergency energy reserve requirement in the islanded mode of the regional integrated energy subsystems, ensuring the rational and efficient configuration of energy storage equipment among the multi-region integrated energy systems. The lower-level energy storage optimization model (follower) modifies and optimizes the shared energy storage plan formulated by the upper-level energy storage optimization model during the intraday scheduling phase based on real-time load fluctuations and emergency situations, addressing real-time load changes while considering the reliability constraints of islanded operation. The goal of the lower-level energy storage optimization model is to minimize the operating costs of the integrated energy subsystems in each region and maintain local energy balance.

[0123] Furthermore, the step of constructing a two-layer energy storage optimization model based on the energy constraint set and the islanded operation constraint set, with the optimization of the operating cost of the multi-region integrated energy system as the objective function, includes:

[0124] Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, an upper-level energy storage optimization model is constructed based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints.

[0125] Taking the optimization of the operating cost of all the regional integrated energy subsystems as the objective function, a lower-level energy storage optimization model is constructed based on the upper-level energy storage optimization model, the lower-level energy balance constraints, the regional energy storage usage restriction constraints, and the isolated energy storage reservation constraints.

[0126] The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are used as a two-layer energy storage optimization model.

[0127] Specifically, the steps for constructing the upper-level energy storage optimization model are as follows:

[0128] Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, the expression is as follows:

[0129]

[0130] The function objective is to minimize the overall system operating cost. ,in:

[0131] Electricity purchase cost ;

[0132] :time The electricity purchase price;

[0133] :time Power purchased from the power grid;

[0134] Energy storage loss cost:

[0135]

[0136] in, It is a shared energy storage subsystem in Charging power at any time It is a shared energy storage subsystem in Discharge power at any given time This is the loss factor of the shared energy storage subsystem (usually calculated based on charge and discharge efficiency). This item represents the additional cost incurred by the battery and hot water tank due to losses during charge and discharge.

[0137] The constraints of the upper-layer energy storage optimization model include:

[0138] Energy conservation constraint:

[0139] For the entire system, all energy supply and demand must be satisfied:

[0140]

[0141] in, It is powered by renewable energy; It is an energy storage device in The charging power at any given time; It is an energy storage device in Discharge power at any given moment; The system's total load demand and energy storage device power constraints: The charging and discharging power of energy storage devices (including batteries and hot water tanks) at any given time must be limited by the device's rated power.

[0142]

[0143] in, This is the maximum charging power of the energy storage device. This refers to the maximum discharge power and the state of energy (SOC) constraint of the energy storage device. It also refers to the stored energy capacity of the energy storage device. It needs to be between the upper and lower limits:

[0144]

[0145] And it is subject to energy balance constraints:

[0146]

[0147] in, and These are the charging and discharging efficiencies. This formula ensures that the charging and discharging of the energy storage will not exceed the allowable range of the equipment, guaranteeing a portion of the energy storage capacity as emergency backup (72 hours in islanded mode).

[0148] Establish the islanded load constraints, including critical load constraints in islanded mode and upper-layer energy storage reservation strategies;

[0149] The expression for the critical load constraint in the islanded mode is shown in the following equation:

[0150] ;

[0151] in, This indicates that the i-th regional integrated energy subsystem is in Once the system enters island mode, the critical load power requirements must be met. This represents the energy storage and discharge capability of the i-th regional integrated energy subsystem. Indicates the preset island operation time;

[0152] The expression for the upper-layer energy storage reservation strategy is shown in the following formula, which is used to ensure that at any time when entering islanding mode, there are at least [missing information]. As a backup energy source:

[0153] ;

[0154] ;

[0155] in, This represents the current stored energy of the i-th regional integrated energy subsystem. This represents the initial energy storage reservation value issued by the shared energy storage subsystem to the i-th regional integrated energy subsystem. This indicates the minimum stored energy that the regional integrated energy subsystem needs to guarantee after entering islanded mode. This indicates the critical load power that the regional integrated energy subsystem needs to meet after entering islanded mode. This indicates the preset island operation time.

[0156] Specifically, the steps for constructing the lower-level energy storage optimization model are as follows:

[0157] The objective function for the lower-level regional integrated energy subsystem is to minimize the sum of the operating costs of the regional integrated energy subsystem based on the electrical interaction between the regional integrated energy subsystem and the shared energy storage subsystem. Taking the nth regional integrated energy subsystem as an example, its expression is as follows:

[0158]

[0159] in, .

[0160] Minimize the operating cost of the nth regional integrated energy subsystem, given variables such as natural gas power purchase strategy and energy storage charging and discharging power.

[0161] in:

[0162]

[0163] Electricity purchase cost: ;

[0164] in, The purchase price of electricity from the grid. This refers to the amount of electricity purchased from the power grid for this region.

[0165] Energy storage usage costs:

[0166]

[0167] It is the shared energy storage usage cost set by the upper layer.

[0168] Equipment operating costs:

[0169]

[0170] in, Equipment for regional integrated energy subsystems Operating time (e.g., ground source heat pumps, circulating heat pumps), It is the cost per unit of equipment operating time, calculated by multiplying the operating power of each piece of equipment by the electricity price at the current operating time.

[0171] The constraints of the lower-level energy storage optimization model include:

[0172] Regional energy balance constraints: For the first Each regional integrated energy subsystem must meet the following requirements:

[0173]

[0174] in, The region's renewable energy output; Power consumption of equipment such as ground source heat pumps; and It is energy storage Discharge power;

[0175] Upper-level energy storage usage constraints:

[0176] Upper-level energy storage usage restrictions:

[0177]

[0178] here The upper-level multi-region integrated energy system determines the amount of energy storage used by the lower-level regional integrated energy subsystems.

[0179] The lower layer dynamically adjusts the energy storage reservation constraints for islanding mode: Since the trigger time of islanding mode is uncertain, the lower layer needs to adjust the energy storage reservation during intraday rolling optimization to ensure that islanding occurs at the appropriate times. Previously, sufficient energy storage was always available. Once islanding mode is entered, energy storage usage must be replanned to maintain power supply to critical loads. Real-time changes are reserved for islanding mode:

[0180]

[0181] in, This represents the current stored energy of the i-th regional integrated energy subsystem. This represents the initial energy storage reservation value issued by the shared energy storage subsystem to the i-th regional integrated energy subsystem. This represents the energy storage and discharge capability of the i-th regional integrated energy subsystem. This indicates a scrolling window with a preset time. Lower-level optimizations are in place. During the daytime optimization process, the energy storage discharge is corrected to ensure... In the scrolling window The internal system always meets the energy supply needs of isolated islands.

[0182] Furthermore, the step of solving the two-layer energy storage optimization model based on the historical operating data and the real-time operating data to obtain a real-time shared energy storage scheduling scheme includes:

[0183] The upper-level energy storage optimization model is solved based on the historical operating data to obtain the day-ahead shared energy storage scheduling scheme;

[0184] Based on the real-time operating data, the dual-layer energy storage optimization model is solved to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme.

[0185] Specifically, the decisions made at the higher level in the previous day serve as boundary conditions and reference schemes for intraday optimization. Intraday rolling optimization flexibly adjusts details while adhering to the overall strategy of the higher level. In this way, longer-term decisions (pre-day) and short-term decisions (intraday) form a closed loop: the plans formulated by the previous day's scheduling provide guidance for intraday decisions, and the feedback from intraday scheduling can be used to revise the plans for the next period or the next day. The higher level can guide the behavior of the lower level based on forward-looking predictions, while the actual reactions of the lower level revise the higher level's cognition. The two continue to play a game until a dynamic equilibrium is reached, thereby coordinating decisions with different time scales and different subject objectives in a two-level game.

[0186] Furthermore, the step of solving the two-layer energy storage optimization model based on the real-time operating data to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme includes:

[0187] Based on the KKT conditions, the lower-level energy storage optimization model is transformed into a lower-level Lagrangian function, and then the lower-level energy storage optimization model is transformed into a lower-level optimization constraint set.

[0188] The upper-layer energy storage optimization model is constrained by the lower-layer optimization constraint set, thereby obtaining a single-layer energy storage optimization model;

[0189] Based on the real-time operating data, the single-layer energy storage optimization model is solved using a preset solver, thereby optimizing the day-ahead shared energy storage scheduling scheme and obtaining the real-time shared energy storage scheduling scheme.

[0190] Furthermore, before the step of solving the single-layer energy storage optimization model using a preset solver based on the real-time operating data to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme, the method further includes:

[0191] The single-layer energy storage optimization model is linearized based on the McCormick envelope method, thereby transforming the nonlinear and bilinear structures in the single-layer energy storage optimization model into linear structures, resulting in a single-layer linear optimization model.

[0192] The single-layer linear optimization model is used as the single-layer energy storage optimization model.

[0193] Specifically, see Figure 5 , Figure 5This is a flowchart illustrating a method for solving a two-layer energy storage optimization model according to an embodiment of the present invention. The optimization model established by the optimization objectives and strategies of both sides in the game, as well as their respective constraints, as described above, is a two-layer optimization problem with integer variables, i.e., a mixed-integer nonlinear programming problem. The upper and lower layers are nested and exhibit rolling optimization characteristics; therefore, a solution method capable of handling integer variables, optimizing nonlinear terms, and solving at different time scales is required.

[0194] After combining the upper-level and lower-level energy storage optimization models to obtain the two-layer energy storage optimization model (MINLP), KKT conditions are introduced to transform the lower-level optimization problem into constraints. KKT conditions are a set of constraints used in solving nonlinear programming problems, linking the optimal solution of the optimization problem to the constraints, thus transforming the optimization problem into a set of constraints. Based on the lower-level energy storage optimization model in the two-layer energy storage optimization model, the optimization problem of the lower-level energy storage optimization model is transformed into a set of constraints, which are then added to the constraints of the upper-level energy storage optimization model's optimization problem. KKT conditions include the feasibility conditions of the original problem, the dual feasibility conditions, and the complementary relaxation conditions, which can equivalently represent the optimality conditions of the lower-level optimization problem. The KKT functions are directly called using the YALMIP toolbox to transform the lower-level optimization problem into upper-level constraints, thereby forming a single-layer optimization problem and reducing computational complexity. Based on the KKT conditions, a Lagrangian function is established based on the relevant functions and constraints of the lower-level energy storage optimization model.

[0195]

[0196] in, Corresponding energy balance constraints Corresponding energy storage usage restrictions Energy storage reserved for islanded mode

[0197] The first derivative condition introduced by KKT:

[0198] Purchased power Differentiate:

[0199]

[0200] Energy storage charging power Differentiate:

[0201]

[0202] Energy storage discharge power Differentiate:

[0203]

[0204] Differentiate with respect to the energy storage SOC:

[0205]

[0206] This first-order derivative condition is part of the KKT conditions and represents the necessary condition for optimality. Taking the derivative with respect to each decision variable is equivalent to constructing the gradient condition of the Lagrangian function ℒL, that is, finding the point that makes the objective function optimal under all constraints. This is a key step in transforming the model from the original nonlinear two-layer structure into a single-layer optimization problem.

[0207] The original feasibility constraints introduced by KKT:

[0208]

[0209] Dual feasibility constraints introduced by KKT:

[0210]

[0211] Complementary relaxation conditions introduced by KKT:

[0212]

[0213] The KKT conditions of the transformed lower-level optimization model are merged with the constraints of the upper-level model, transforming the two-level optimization model into a single-level optimization model.

[0214] Furthermore, strong duality theory is introduced to linearize the bilinear terms: in the lower-level optimization objective, the regional integrated energy subsystem needs to optimally utilize shared energy storage and optimize local equipment scheduling based on the constraints of the upper level. This is due to SOC state updates. With equipment start / stop variables The presence of bilinear terms in the constraints leads to nonlinear problems. Strong duality theory and the McCormick envelope method are used to linearize the bilinear terms, transforming the nonlinear MINLP into a mixed-integer linear programming (MILP) problem, ensuring efficient solver processing.

[0215] It should be noted that after applying KKT conditions to transform the lower-level problem into constraints, in order to avoid introducing strong duality theory into the bilinear structure formed by multipliers and variables, the optimal value expression in the original objective function can be replaced with its Lagrange dual expression. Under the dual function form, dual variables are introduced to construct an equivalent linear lower bound expression, further promoting structural linearization. On this basis, for the product terms of continuous variables and integer variables retained in scenarios such as energy storage state updates and equipment start-up and shutdown control in the original model, the McCormick envelope method is further introduced for linearization, completing the modeling transformation from a single-layer energy storage optimization model to a single-layer linear optimization model.

[0216] Specifically, the following nonlinear bilinear structure still exists in the transformed single-layer optimization model:

[0217] Items in the energy storage status update:

[0218]

[0219] Equipment start-up / shutdown and operating power coupling terms:

[0220]

[0221] McCormick envelope technique procedure:

[0222] For each pair of product terms Construct four sets of linear inequalities such that the product variables The value is taken from the convex hull formed by its upper and lower bounds. As shown below:

[0223] set up Introducing new variables Then add the following constraints:

[0224]

[0225] In the model we use Replace the original bilinear terms with auxiliary variables.

[0226] The specific process is as follows: First, let:

[0227] ;

[0228] Based on the above equation, the original energy balance formula in the upper-layer energy storage optimization system of the single-layer energy storage optimization model is transformed into:

[0229]

[0230] Then add McCormick envelope constraints to the new variables.

[0231] Set upper and lower power limits: The above power upper and lower bounds are for This adds constraints to the model:

[0232]

[0233] Similarly, based on Constraints are established for the model:

[0234]

[0235] The second term is similarly defined:

[0236]

[0237] set up Add McCormick linear envelope to the model:

[0238]

[0239] Then use Replace all of the original single-layer energy storage optimization model formulas This allows the nonlinear and bilinear structures in the single-layer energy storage optimization model to be transformed into linear structures, resulting in a single-layer linear optimization model.

[0240] The step of using the real-time operating data and a preset solver to solve the single-layer energy storage optimization model to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme includes: using the Gurobi solver to solve the single-layer linear optimization model problem: after KKT transformation and strong dual linearization, the final optimization problem is transformed into a single-layer linear optimization model (Mixed-Integer Linear Programming). The Gurobi solver is called using the YALMI toolbox for optimization. During the solution process, Gurobi uses the branch-and-cut method to ensure that the optimal solution can converge quickly.

[0241] The above process includes:

[0242] Step 1: Preprocess the model. The purpose is to reduce the problem size, remove invalid or redundant information, and improve the overall solution efficiency.

[0243] Specifically, remove redundant constraints (such as identities that are always satisfied) from the single-layer linear optimization model; fix the variables of the single-layer linear optimization model (inferring unique values ​​through constraints or variable limits); normalize constraints and tighten variable limits; detect infeasibility or unboundedness (such as upper bound of variables < lower bound); merge linear dependency constraints in the single-layer linear optimization model and transform it into a simpler structure; identify the problem structure of the single-layer linear optimization model.

[0244] Step 2, Root LP: Relax all integer variables of the single-layer linear optimization model into continuous variables to obtain the relaxed linear problem:

[0245]

[0246] Solving relaxed linear problems is a core step in understanding single-level linear optimization models; the solution provides a lower bound on the global optimum (for minimization problems); if the solution satisfies all integer constraints, then the solution is the optimal integer solution; otherwise, the branch and bound procedure must be entered.

[0247] Step 3: Use the Gurobi solver to perform branch and bound on the single-layer linear optimization model: Starting from the root node of the single-layer linear optimization model, select a non-integer solution variable. Construct two subproblems: ;

[0248] Each subproblem forms a search tree node; each node solves a relaxed linear subproblem, ultimately updating the upper and lower bounds. If a node is infeasible, it is pruned; if a node satisfies integer property and is better than the current optimal solution, the model's optimal solution is updated; if the node's optimal value is lower than the upper bound of the current optimal solution, the search is continued; if the node's optimal value is worse than the upper bound, the node is pruned.

[0249] Step 4: Implementation of the Cutting Planes method.

[0250] When solving mixed integer programming problems, even if a feasible solution exists for its linear programming (LP) relaxation problem, the relaxation solution may not satisfy the requirements for integer variable values, resulting in slow convergence of the solution process based on the branch-and-bound framework. To overcome this technical problem, in this embodiment, the optimization solver automatically generates and adds cutting plane constraints.

[0251] The cutting plane constraint is used to remove regions from the feasible region of the current LP relaxation problem that do not contain feasible integer solutions, thereby gradually tightening (shrinking) the feasible region and improving the efficiency of subsequent branch and bound or LP solving. The types of cutting planes automatically generated by the solver include, but are not limited to:

[0252] Gomory Cuts: A type of cutting plane derived from the Simplex Tableau for linear programming, and is a commonly used type of cutting plane in integer programming.

[0253] Cover inequalities: particularly suitable for handling knapsack-like constraint structures containing binary variables of 0 and 1;

[0254] Clique Cuts: Suitable for identifying and handling mutually exclusive relationships between variables (e.g., at most one of multiple Boolean decision variables can be true).

[0255] Flow Cover Cuts: Enhances the ability to identify and process network flow structure constraints;

[0256] Lift-and-Project Cuts: Enhance the approximation of the integer polyhedron structure through lifting and projection operations.

[0257] The core technical effect of the cutting plane method is that by iteratively adding cutting plane constraints, the feasible region of the continuous LP relaxation problem is continuously cut (trimmed), and the part that does not contain any feasible integer solutions is excluded, thereby effectively reducing the search space, accelerating the discovery of integer feasible solutions and the convergence of the overall problem.

[0258] Step 5: Implementation of heuristics.

[0259] In solving mixed integer programming problems, even with branch and bound and cutting plane methods, it may be difficult to find feasible integer solutions in the early stages of the search. To address this technical problem and accelerate the acquisition of better integer feasible solutions, in this embodiment, the Gurobi optimization solver integrates and enables multiple heuristic search strategies.

[0260] The main technical objectives of the heuristic strategy include:

[0261] To quickly obtain high-quality feasible integer solutions, which can be used to improve the quality of the global lower bound;

[0262] This reduces the time required for the entire solution process to converge to the global optimum or the optimum that satisfies the specified tolerance.

[0263] The heuristic strategies applied by the solver specifically include:

[0264] Feasibility Pump: This strategy iteratively searches between continuous LP relaxed solutions (satisfying linear constraints but potentially not integer requirements) and integer points (satisfying integer requirements but potentially violating linear constraints). Its core process alternately: a) rounding the current solution to the nearest integer point (projecting it into integer space); b) solving a linear programming problem with the objective of minimizing the distance to the previous LP solution, while maintaining integer integrity. By repeatedly performing this "pumping" process, the aim is to find feasible solutions that simultaneously satisfy both linear constraints and integer requirements.

[0265] Relaxation-Induced Neighborhood Search (RINS): This strategy utilizes information from the currently found best continuous relaxation solution (typically containing scores) and the currently found best integer feasible solution. It defines a neighborhood around the currently best integer feasible solution, bounded by variables that take fixed values ​​(typically 0 or 1) in the continuous relaxation solutions. The solver then solves a sub-MIP problem within this bounded neighborhood, hoping to find a higher-quality integer feasible solution.

[0266] Local Branching: This strategy aims to perform a refined search of the neighborhood of the currently known integer feasible solution. Its core is to introduce a linear constraint (local branching constraint) that limits the number of binary variables (or general integer variables) that change between the new solution and the current reference solution (i.e., the currently known feasible solution) to no more than a preset upper limit (k). By solving the local sub-MIP problem defined under this constraint, potential better solutions can be explored in the neighborhood of the current solution.

[0267] By comprehensively applying the above heuristic algorithms, the optimized solver can more effectively discover feasible integer solutions in the early stages or during the branch and bound tree search, and improve the quality of the found solutions, thereby significantly improving the overall solution efficiency.

[0268] See Figure 6 A second aspect of the present invention provides a shared energy storage optimization scheduling system considering isolated operation, comprising a multi-region integrated energy system construction module 100, an energy storage optimization model construction module 200, a data acquisition module 300, and a shared energy storage scheduling module 400, wherein:

[0269] The multi-region integrated energy system construction module 100 is used to construct a multi-region integrated energy system based on a regional power supply network and a regional heating network. The multi-region integrated energy system includes a shared energy storage subsystem and several regional integrated energy subsystems. The several regional integrated energy subsystems are electrically connected to the external power distribution network through the shared energy storage subsystem.

[0270] The energy storage optimization model construction module 200 is used to construct a two-layer energy storage optimization model based on the energy constraint set and the island operation constraint set, with the operation cost optimization of the multi-region integrated energy system as the objective function.

[0271] The data acquisition module 300 is used to acquire historical and real-time operating data based on the multi-regional integrated energy system.

[0272] The shared energy storage scheduling module 400 is used to solve the two-layer energy storage optimization model based on the historical operating data and the real-time operating data, thereby obtaining a real-time shared energy storage scheduling scheme.

Claims

1. A shared energy storage optimization scheduling method considering islanded operation, characterized in that, include: A multi-regional integrated energy system is constructed based on regional power supply networks and regional heating networks. The multi-regional integrated energy system includes a shared energy storage subsystem and several regional integrated energy subsystems. The several regional integrated energy subsystems are electrically connected to the external distribution network through the shared energy storage subsystem. Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, a two-layer energy storage optimization model is constructed based on the energy constraint set and the islanded operation constraint set, specifically: Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, an upper-level energy storage optimization model is constructed based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints. Taking the optimization of the operating cost of all the regional integrated energy subsystems as the objective function, a lower-level energy storage optimization model is constructed based on the upper-level energy storage optimization model, the lower-level energy balance constraints, the regional energy storage usage restriction constraints, and the isolated energy storage reservation constraints. The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are used as a two-layer energy storage optimization model; Historical and real-time operational data are acquired based on the aforementioned multi-regional integrated energy system; Based on the historical and real-time operational data, the two-layer energy storage optimization model is solved to obtain a real-time shared energy storage scheduling scheme, specifically: The upper-level energy storage optimization model is solved based on the historical operating data to obtain the day-ahead shared energy storage scheduling scheme; Based on the real-time operating data, the dual-layer energy storage optimization model is solved to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme.

2. The shared energy storage optimization scheduling method considering islanded operation according to claim 1, characterized in that, The step of solving the two-layer energy storage optimization model based on the real-time operating data to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme includes: Based on the KKT conditions, the lower-level energy storage optimization model is transformed into a lower-level Lagrangian function, and then the lower-level energy storage optimization model is transformed into a lower-level optimization constraint set. The upper-layer energy storage optimization model is constrained by the lower-layer optimization constraint set, thereby obtaining a single-layer energy storage optimization model; Based on the real-time operating data, the single-layer energy storage optimization model is solved using a preset solver, thereby optimizing the day-ahead shared energy storage scheduling scheme and obtaining the real-time shared energy storage scheduling scheme.

3. The shared energy storage optimization scheduling method considering islanded operation according to claim 2, characterized in that, Before optimizing the day-ahead shared energy storage scheduling scheme and obtaining the real-time shared energy storage scheduling scheme by solving the single-layer energy storage optimization model using a preset solver based on the real-time operating data, the process further includes: The single-layer energy storage optimization model is linearized based on the McCormick envelope method, thereby transforming the nonlinear and bilinear structures in the single-layer energy storage optimization model into linear structures, resulting in a single-layer linear optimization model. The single-layer linear optimization model is used as the single-layer energy storage optimization model.

4. The shared energy storage optimization scheduling method considering islanded operation according to claim 1, characterized in that, The objective function for optimizing the operating cost of the multi-regional integrated energy system is to construct an upper-level energy storage optimization model based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints, including: Establish the islanded load constraints, including critical load constraints in islanded mode and upper-layer energy storage reservation strategies; The expression for the critical load constraint in the islanded mode is shown in the following equation: ； in, This indicates that the i-th regional integrated energy subsystem is in Once the system enters island mode, the critical load power requirements must be met. This represents the energy storage and discharge capability of the i-th regional integrated energy subsystem. Indicates the preset island operation time; The expression for the upper-layer energy storage reservation strategy is shown in the following formula: ； ； in, This represents the current stored energy of the i-th regional integrated energy subsystem. This represents the initial energy storage reservation value issued by the shared energy storage subsystem to the i-th regional integrated energy subsystem. This indicates the minimum stored energy that the regional integrated energy subsystem needs to guarantee after entering islanded mode. This indicates the critical load power that the regional integrated energy subsystem needs to meet after entering islanded mode. This indicates the preset island operation time.

5. The shared energy storage optimization scheduling method considering islanded operation according to claim 1, characterized in that, The objective function is to optimize the operating costs of all the aforementioned regional integrated energy subsystems. Based on the upper-level energy storage optimization model, lower-level energy balance constraints, regional energy storage usage restrictions, and isolated energy storage reservation constraints, a lower-level energy storage optimization model is constructed, including: The reserved constraints for the isolated energy storage are established, and their expression is shown in the following formula: ； in, This represents the current stored energy of the i-th regional integrated energy subsystem. This represents the initial energy storage reservation value issued by the shared energy storage subsystem to the i-th regional integrated energy subsystem. This represents the energy storage and discharge capability of the i-th regional integrated energy subsystem. This indicates a scrolling window with a preset time.

6. A shared energy storage optimized scheduling system considering islanded operation, characterized in that, It includes a multi-region integrated energy system construction module, an energy storage optimization model construction module, a data acquisition module, and a shared energy storage scheduling module, among which: The multi-region integrated energy system construction module is used to construct a multi-region integrated energy system based on a regional power supply network and a regional heating network. The multi-region integrated energy system includes a shared energy storage subsystem and several regional integrated energy subsystems. The several regional integrated energy subsystems are electrically connected to the external power distribution network through the shared energy storage subsystem. The energy storage optimization model construction module is used to construct a two-layer energy storage optimization model based on the energy constraint set and the islanded operation constraint set, with the operating cost optimization of the multi-region integrated energy system as the objective function. Specifically: Taking the optimization of the operating cost of the multi-region integrated energy system as the objective function, an upper-level energy storage optimization model is constructed based on energy conservation constraints, upper-level energy balance constraints, and islanded load constraints. Taking the optimization of the operating cost of all the regional integrated energy subsystems as the objective function, a lower-level energy storage optimization model is constructed based on the upper-level energy storage optimization model, the lower-level energy balance constraints, the regional energy storage usage restriction constraints, and the isolated energy storage reservation constraints. The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are used as a two-layer energy storage optimization model; The data acquisition module is used to acquire historical and real-time operating data based on the multi-regional integrated energy system. The shared energy storage scheduling module is used to solve the two-layer energy storage optimization model based on the historical operating data and the real-time operating data, thereby obtaining a real-time shared energy storage scheduling scheme, specifically: The upper-level energy storage optimization model is solved based on the historical operating data to obtain the day-ahead shared energy storage scheduling scheme; Based on the real-time operating data, the dual-layer energy storage optimization model is solved to optimize the day-ahead shared energy storage scheduling scheme and obtain the real-time shared energy storage scheduling scheme.

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