Shared energy storage optimization scheduling method and system considering island operation

Abstract

The invention relates to the technical field of comprehensive energy optimization scheduling, in particular to a shared energy storage optimization scheduling method and system considering island operation. According to the shared energy storage optimization scheduling method, a double-layer optimization framework of a double-layer energy storage optimization model is adopted, and island operation constraints of regional integrated energy subsystems are introduced on the optimization basis of minimizing the operation cost of a multi-regional integrated energy system; a real-time shared energy storage scheduling scheme obtained by solving a double-layer energy storage optimization model not only can optimize the operation cost of the multi-region integrated energy system based on historical operation data and real-time operation data, but also can meet the island operation emergency energy constraint requirement of a region integrated energy subsystem; the power grid disconnection risk caused by the real-time load change of the system is handled, the power supply reliability of the system under the extreme working condition is improved, and then the robustness of the energy storage dynamic correction strategy of the multi-region integrated energy system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy optimization scheduling, in particular to a shared energy storage optimization scheduling method and system considering island operation. BACKGROUND

[0002] With the continuous acceleration of global energy structure transformation, how to effectively improve energy utilization efficiency and realize low-carbon sustainable development has become the core issue in the current energy field. As a key technical path to promote energy transformation, regional integrated energy system (RIES) integrates horizontal fusion of electricity, heat, cold, gas and other multi-energy subsystems, and vertical coordination of "source-network-load-storage" whole-link collaborative optimization operation, and builds a multi-energy complementary network with electricity as the core. It breaks through the barriers of traditional energy systems, improves the energy cascade utilization efficiency, and provides a systematic solution for regional low-carbonization and clean energy consumption. On the basis of meeting the demand of regional low-carbonization and cleanization, the collaborative optimization of multi-regional integrated energy system becomes an important path to improve the complementary potential and economy of energy.

[0003] In the field of integrated energy system optimization scheduling, existing research mainly follows the core goal of minimizing operation cost, and relies on the assumption of continuous interconnection of power grid when coordinating shared energy storage operators and multi-regional integrated energy system collaborative operation, without considering the case of integrated energy system in island mode. Among them, island mode refers to the state or operation mode in which a single regional integrated energy system is disconnected from the upper shared energy storage power grid in a physical sense, and can only rely on its own internal power supply and energy storage equipment to maintain independent operation and continuously supply energy to the internal key load.

[0004] Therefore, improving the adaptability of regional integrated energy system to the extreme imbalance of supply and demand under the off-grid scenario under the shared energy storage mode, and ensuring the robustness of the dynamic correction strategy of the energy storage under the island mode, have become technical problems to be solved in the field. SUMMARY

[0005] The present application aims to provide a shared energy storage optimization scheduling method and system considering island operation, to improve the power supply reliability of the shared energy storage system under island operation condition, and to improve the robustness of the dynamic correction strategy of the energy storage of the multi-regional integrated energy system, and to solve the technical problem that the existing technology relies on the assumption of continuous interconnection of power grid and does not consider the case of integrated energy system in island mode.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a shared energy storage optimization scheduling method considering island operation, comprising the following steps:

[0007] A multi-region integrated energy system is constructed based on a regional power supply network and a regional heat supply network, the multi-region integrated energy system comprising a shared energy storage subsystem and a plurality of regional integrated energy subsystems, the plurality of regional integrated energy subsystems being electrically connected with an external power distribution network through the shared energy storage subsystem;

[0008] A double-layer energy storage optimization model is constructed based on an energy constraint set and an island operation constraint set, with the operation cost optimization of the multi-region integrated energy system as an objective function;

[0009] Historical operation data and real-time operation data are obtained based on the multi-region integrated energy system;

[0010] The double-layer energy storage optimization model is solved based on the historical operation data and the real-time operation data, so as to obtain a real-time shared energy storage scheduling scheme.

[0011] The above shared energy storage optimization scheduling method adopts a double-layer optimization framework of the double-layer energy storage optimization model. On the basis of optimizing the operation cost of the multi-region integrated energy system to be minimized, the island operation constraint of the regional integrated energy subsystem is introduced, so that the real-time shared energy storage scheduling scheme obtained by solving the double-layer energy storage optimization model can not only optimize the operation cost of the multi-region integrated energy system based on the historical operation data and the real-time operation data, but also meet the island operation emergency energy constraint demand of the regional integrated energy subsystem, cope with the power grid disconnection risk caused by real-time load variation, improve the power supply reliability of the system under extreme working conditions, and further improve the robustness of the energy storage dynamic correction strategy of the multi-region integrated energy system.

[0012] Further, the double-layer energy storage optimization model is constructed based on an energy constraint set and an island operation constraint set, with the operation cost optimization of the multi-region integrated energy system as an objective function, comprising:

[0013] An upper-layer energy storage optimization model is constructed based on an energy conservation constraint, an upper-layer energy balance constraint and an island load constraint, with the operation cost optimization of the multi-region integrated energy system as an objective function;

[0014] A lower-layer energy storage optimization model is constructed based on the upper-layer energy storage optimization model, a lower-layer energy balance constraint, a regional energy storage use limit constraint and an island energy storage reservation constraint, with the operation cost optimization of all the regional integrated energy subsystems as an objective function;

[0015] The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are taken as the double-layer energy storage optimization model.

[0016] In the implementation, in view of the energy supply and demand balance of the multi-region integrated energy system as a whole, the management of the shared energy storage subsystem, and the emergency scheduling design of multiple levels of subject distribution scheduling in the island mode of each regional integrated energy subsystem in the process of optimal scheduling of the multi-region integrated energy system, a double-layer master-slave game optimization framework is adopted. On the one hand, the upper-layer energy storage optimization model takes the minimization of the operation cost of the whole multi-region integrated energy system as the target, performs system load forecasting and system resource estimation based on the energy conservation constraint, the energy balance constraint, and the island load constraint of the reserved energy storage for the island mode, and formulates a large-scale charging and discharging plan of the shared energy storage of the whole system. On the other hand, the lower-layer energy storage optimization model takes the minimization of the operation cost of each regional integrated energy subsystem as the target, performs load forecasting and system resource estimation of each regional subsystem based on the energy balance constraint of each regional subsystem, the regional energy storage use limit constraint distributed to the regional subsystem by the upper-layer system as a whole, and the island energy storage reservation constraint of the reserved energy storage for the island mode of the regional subsystem, and corrects and refines the optimization result of the upper-layer energy storage optimization model to cope with the load change of the regional integrated energy subsystem. Finally, the robustness of the final shared energy storage scheduling scheme in coping with the system entering the island mode is improved through the optimization target game of the upper-layer energy storage optimization model and the lower-layer energy storage optimization model.

[0017] Further, the double-layer energy storage optimization model is solved based on the historical operation data and the real-time operation data, so as to obtain a real-time shared energy storage scheduling scheme, which comprises:

[0018] The upper-layer energy storage optimization model is solved based on the historical operation data, so as to obtain a day-ahead shared energy storage scheduling scheme.

[0019] The double-layer energy storage optimization model is solved based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme.

[0020] In the implementation, in the day-ahead system energy storage optimization scheduling phase before the day, the upper energy storage optimization model of the subject formulates a large-scale charging and discharging plan of minimizing the overall system operation cost based on the historical operation data of the multi-region integrated energy system. And because the upper energy storage optimization model is provided with an island load constraint for coping with the disconnection of the subsystem, the formulated scheme can meet the emergency energy reservation demand when any region integrated energy subsystem enters the island mode. However, the day-ahead shared energy storage scheduling scheme obtained by the upper energy storage optimization model based on the historical operation data has poor adaptability to the dynamic change of the load of the subsystem on the day, and cannot cope with the change of the energy storage demand caused by the change of the load on the day, so the present application further introduces the lower optimization energy storage model for intraday rolling optimization. Specifically, the double-layer energy storage optimization model is solved based on the real-time operation data on the day, that is, the lower energy storage optimization model continuously corrects and optimizes the day-ahead shared energy storage scheduling scheme formulated by the upper model based on the real-time load fluctuation and emergency situation of the region integrated energy subsystem in the intraday scheduling phase, and copes with the real-time change of the reserved energy storage demand for the island mode caused by the real-time load change of the region integrated energy subsystem.

[0021] Through the implementation, the day-ahead decision based on the historical data of a long period and the intraday short-period decision based on real-time data form a closed loop: the day-ahead shared energy storage scheduling scheme formulated by the upper energy storage optimization model provides guidance for the intraday real-time shared energy storage scheduling scheme formulated by the lower energy storage optimization model, and the intraday real-time shared energy storage scheduling scheme formulated by the lower energy storage optimization model can be used as the future day-ahead shared energy storage scheduling scheme. The upper energy storage optimization model can prospectively predict and guide the behavior of the lower energy storage optimization model according to the historical operation data, and the solution of the lower energy storage optimization model corrects the solution of the upper energy storage optimization model, and the two continuously game until a dynamic equilibrium is reached, so as to coordinate the decisions of different time scales and different subject targets in the double-layer game, and improve the robustness of the shared energy storage scheduling scheme in coping with the system entering the island mode.

[0022] Further, the solving the double-layer energy storage optimization model based on the real-time operation data, thereby optimizing the day-ahead shared energy storage scheduling scheme to obtain a real-time shared energy storage scheduling scheme, comprises:

[0023] transforming the lower energy storage optimization model into a lower Lagrange function according to the KKT condition, and further transforming the lower energy storage optimization model into a lower optimization constraint set;

[0024] constraining the upper energy storage optimization model based on the lower optimization constraint set, thereby obtaining a single-layer energy storage optimization model;

[0025] Solving the single-layer energy storage optimization model by using a preset solver based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme.

[0026] In this implementation, the KKT condition is introduced to convert the optimization problem of the lower-layer energy storage optimization model into a constraint of the upper-layer energy storage optimization model. The KKT condition is a set of constraint conditions used in the solving process of a nonlinear programming problem, which links the optimal solution of the optimization problem with the constraint condition and converts the optimization problem into a set of constraints. Based on the lower-layer energy storage optimization model in the double-layer energy storage optimization model, the optimization problem of the regional integrated energy subsystem is converted into a set of constraints, which is added to the constraints of the upper-layer energy storage optimization model. The KKT condition includes the feasibility condition of the original problem, the dual feasibility condition and the complementary slackness condition, which can equivalently represent the optimality condition of the lower-layer optimization problem, so as to convert the double-layer optimization problem into a single-layer optimization problem, obtain the single-layer energy storage optimization model and reduce the calculation complexity.

[0027] Further, before the single-layer energy storage optimization model is solved by using a preset solver based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme, the method further comprises:

[0028] linearizing the single-layer energy storage optimization model based on the McCormick envelope method, so as to convert the nonlinear structure and bilinear structure in the single-layer energy storage optimization model into linear structure and obtain 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 the KKT condition is applied to convert the optimization problem of the lower-layer energy storage optimization model into a constraint of the upper-layer energy storage optimization model, the McCormick envelope method is further introduced to linearize the single-layer energy storage optimization model, complete the modeling conversion from MINLP (mixed integer nonlinear programming) to MILP (mixed integer linear programming), solve the nonlinear problem caused by the start-stop control scene of the equipment in the regional integrated energy subsystem, reduce the deviation of the model solving result from the actual tolerance scene, and improve the accuracy of the shared energy storage optimization scheduling.

[0031] Further, the upper-layer energy storage optimization model is constructed based on the energy conservation constraint, the upper-layer energy balance constraint and the islanded load constraint, with the operation cost optimization of the multi-region integrated energy system as the objective function, comprising:

[0032] The islanded load constraint is established, including the key load constraint in the islanded mode and the upper-layer energy storage reservation strategy.

[0033] The expression of the key load constraint in the island mode is as follows:

[0034]

[0035] Wherein, represents the key load power that needs to be met by the i-th regional integrated energy subsystem after entering the island mode at t, represents the energy storage discharge capability of the i-th regional integrated energy subsystem, T island represents the preset island operation time;

[0036] The expression of the upper energy storage reservation strategy is as follows:

[0037]

[0038] Wherein, represents the current storage energy of the i-th regional integrated energy subsystem, represents the initial energy storage reservation value of the shared energy storage subsystem issued to the i-th regional integrated energy subsystem, SOC island (t) represents the minimum storage energy that needs to be guaranteed by the regional integrated energy subsystem after entering the island mode, P criticalload (τ) represents the key load power that needs to be met by the regional integrated energy subsystem after entering the island mode, T island represents the preset island operation time.

[0039] Further, the operation cost of all the regional integrated energy subsystems is taken as the objective function, and a lower energy storage optimization model is constructed based on the upper energy storage optimization model, the lower energy balance constraint, the regional energy storage use restriction constraint and the island energy storage reservation constraint, including:

[0040] The island energy storage reservation constraint is established, and its expression is as follows:

[0041]

[0042] Wherein, represents the current storage energy of the i-th regional integrated energy subsystem, represents the initial energy storage reservation value of the shared energy storage subsystem issued to the i-th regional integrated energy subsystem, represents the energy storage discharge capability of the i-th regional integrated energy subsystem, T forecast represents the preset time rolling window.

[0043] The second aspect of the application provides a shared energy storage optimization scheduling system considering island 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:

[0044] 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 comprising a shared energy storage subsystem and a plurality of regional integrated energy subsystems, the plurality of regional integrated energy subsystems being electrically connected with an external power distribution network through the shared energy storage subsystem;

[0045] The energy storage optimization model construction module is used to construct a double-layer energy storage optimization model based on an energy constraint set and an island operation constraint set, with the operation cost optimization of the multi-region integrated energy system as an objective function;

[0046] The data acquisition module is used to acquire historical operation data and real-time operation data based on the multi-region integrated energy system;

[0047] The shared energy storage scheduling module is used to solve the double-layer energy storage optimization model based on the historical operation data and the real-time operation data, thereby obtaining a real-time shared energy storage scheduling scheme.

[0048] Further, the double-layer energy storage optimization model is constructed based on an energy constraint set and an island operation constraint set, with the operation cost optimization of the multi-region integrated energy system as an objective function, comprising:

[0049] An upper-layer energy storage optimization model is constructed based on an energy conservation constraint, an upper-layer energy balance constraint and an island load constraint, with the operation cost optimization of the multi-region integrated energy system as an objective function;

[0050] A lower-layer energy storage optimization model is constructed based on the upper-layer energy storage optimization model, a lower-layer energy balance constraint, a regional energy storage use limit constraint and an island energy storage reservation constraint, with the operation cost optimization of all the regional integrated energy subsystems as an objective function;

[0051] The upper-layer energy storage optimization model and the lower-layer energy storage optimization model are taken as the double-layer energy storage optimization model.

[0052] Further, the double-layer energy storage optimization model is solved based on the historical operation data and the real-time operation data, thereby obtaining a real-time shared energy storage scheduling scheme, comprising:

[0053] The upper-layer energy storage optimization model is solved based on the historical operation data, thereby obtaining a day-ahead shared energy storage scheduling scheme;

[0054] The two-layer energy storage optimization model is solved based on the real-time operation data, thereby optimizing the day-ahead shared energy storage scheduling plan and obtaining a real-time shared energy storage scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 1 is a flow chart of a method for optimizing the scheduling of shared energy storage taking island operation into account, provided by an embodiment of the present invention;

[0056] Figure 2 This is a schematic structural diagram of a multi-region integrated energy system provided by an embodiment of the present invention;

[0057] Figure 3 This is a schematic structural diagram of a regional integrated energy subsystem provided by an embodiment of the present invention;

[0058] Figure 4 Schematic diagram of the relationship between an upper-layer energy storage optimization model and a lower-layer energy storage model provided by an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram of a process for solving a double-layer energy storage optimization model provided by an embodiment of the present invention;

[0060] Figure 6 This is a structural diagram of a shared energy storage optimization scheduling system taking island operation into account provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] Existing research on the optimal scheduling of multi-regional integrated energy systems primarily focuses on minimizing operating costs. However, existing integrated energy optimization scheduling methods fail to fully consider the differences in optimization objectives between shared energy storage operators and multi-regional integrated energy systems when coordinating their coordinated operations. Furthermore, existing research often relies on static day-ahead scheduling frameworks based on fixed forecast data, lacking the ability to dynamically respond to real-time fluctuations in renewable energy output and load, resulting in scheduling strategies that are insufficiently adaptable in actual operations.

[0062] It is worth noting that shared energy storage mechanisms, by aggregating dispersed energy storage resources to form a shared pool, separate "ownership" from "usage rights," thereby reducing user investment costs and improving resource utilization, providing a new path to addressing the aforementioned coordination issues. Existing research on the allocation of shared energy storage resources has proposed collaborative strategies based on cooperative games and auction mechanisms. However, current research also has certain limitations. First, there is insufficient coupling of time scales. The separation between day-ahead planning and intraday rolling adjustments leads to energy storage capacity reservations deviating from actual demand. Second, there is a lack of emergency response. Existing models rely on the assumption of continuous grid interconnection, lacking the robustness of dynamic adjustment strategies for energy storage in isolated island modes.

[0063] In recent years, game theory methods have shown unique advantages in coordinating multi-agent interest conflicts and improving the collaborative efficiency of integrated energy systems. Among them, the Stackelberg game is concerned because it can effectively depict the master-slave decision-making relationship between energy suppliers and users. Some technologies focus on the dynamic pricing problem of photovoltaic producers and consumers in microgrids, and construct a two-level optimization model based on Stackelberg-Nash game. In this model, retailers act as leaders to develop pricing strategies, and photovoltaic producers and consumers act as followers to adjust energy trading strategies through non-cooperative game. This study introduces demand response and carbon trading mechanisms to achieve real-time scheduling and privacy protection balance, and verifies the effectiveness of the master-slave game in coordinating global cost optimization and individual economic goals. On this basis, some technologies further extend the Stackelberg game to the shared energy storage scenario, and construct a two-level decision-making framework between integrated energy system operators and user alliances. The operator guides the user-side electric vehicle cluster to form a shared energy storage resource pool through the carbon trading mechanism, and uses the improved Shapley value method for cost allocation within the alliance. Research shows that this game structure can simultaneously improve the system's renewable energy consumption capacity and the fairness of multi-agent income distribution. However, the above frameworks ignore the integer variables and nonlinear constraints related to device start-stop (such as unit start-stop state, energy storage charging and discharging logic, etc.), resulting in a significant deviation between the model and the actual engineering scenario. At the same time, it lacks adaptability to the extreme imbalance between supply and demand in the island mode.

[0064] Based on the above, to solve the problems of the prior art, a "day-ahead-day-ahead" collaborative rolling mechanism is designed based on the collaborative scheduling framework of the two-level Stackelberg master-slave game. The upper energy storage optimization model formulates the overall charging and discharging plan of the shared energy storage device by considering the global multi-region integrated energy system energy consumption and the standby capacity in island mode. The lower energy storage optimization model combines the day-ahead shared energy storage scheduling scheme of the upper energy storage optimization model and optimizes the best operation plan of its own device in real-time rolling in the day-ahead. At the same time, a dynamic response mechanism in island mode is further introduced to significantly improve the power supply reliability of the system in extreme working conditions by adjusting the standby capacity allocation strategy in real-time rolling in the day-ahead. It provides theoretical and methodological support for multi-agent collaborative multi-region integrated energy system optimization, and constructs state persistence constraints to ensure the time sequence continuity of multi-scale strategies. It solves the problem of insufficient coupling between day-ahead planning and day-ahead rolling adjustment of time scales.

[0065] To achieve the above purpose, referring to Figure 1 The first aspect of the embodiment of the present application provides a shared energy storage optimization scheduling method considering island operation, comprising the following steps:

[0066] S1, constructing a multi-region integrated energy system based on a regional power supply network and a regional heat supply network, the multi-region integrated energy system comprising a shared energy storage subsystem and a plurality of regional integrated energy subsystems, the plurality of regional integrated energy subsystems being electrically connected with an external power distribution network through the shared energy storage subsystem;

[0067] S2, constructing a double-layer energy storage optimization model based on an energy constraint set and an island operation constraint set, with the operation cost of the multi-region integrated energy system as an objective function;

[0068] S3, obtaining historical operation data and real-time operation data based on the multi-region integrated energy system;

[0069] S4, solving the double-layer energy storage optimization model based on the historical operation data and the real-time operation data, thereby obtaining a real-time shared energy storage scheduling scheme.

[0070] The above shared energy storage optimization scheduling method adopts a double-layer optimization framework of a double-layer energy storage optimization model. On the basis of optimizing the operation cost of the multi-region integrated energy system to be minimized, the island operation constraint of the regional integrated energy subsystem is introduced, so that the real-time shared energy storage scheduling scheme obtained by solving the double-layer energy storage optimization model can not only optimize the operation cost of the multi-region integrated energy system based on the historical operation data and the real-time operation data, but also meet the island operation emergency energy constraint demand of the regional integrated energy subsystem, cope with the power grid disconnection risk brought by real-time load changes, improve the power supply reliability of the system under extreme working conditions, and further improve the robustness of the energy storage dynamic correction strategy of the multi-region integrated energy system.

[0071] Specifically, referring to Figure 1 , the multi-region integrated energy system of step S1 is as shown in Figure 1 . In the figure, shared energy storage represents the 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 comprises a plurality of types of energy storage discharge devices, and each regional integrated energy subsystem is responsible for providing the electric and heat load of a region.

[0072] Referring to Figure 2The structure of the regional integrated energy subsystem described in step S1 includes a photovoltaic power generation group, a collector, a hot water storage tank, an electric energy storage device, a water pump, a ground source heat pump and a control system. Among them, the hot water storage tank can exchange heat with the regional soil heat source. The regional integrated energy subsystem is responsible for the local user heat load and the user electricity load. Multiple regional integrated energy subsystems are connected to a shared energy storage subsystem to store and use electric energy. When the renewable energy equipment generates insufficient power and the shared energy storage subsystem's storage capacity is insufficient to meet the system's power load demand, the system will respond by purchasing electricity from the distribution network; when the equipment production capacity of each regional integrated energy subsystem is sufficient to meet the power load requirements and there is surplus power, the surplus power will be stored in the shared energy storage subsystem, which can not only avoid the situation where the renewable energy equipment generates insufficient power and the power is completely purchased from the distribution network when the electricity price is peak, but also can transmit the power generated in the region with more power to other regional integrated energy subsystems with power shortage, thereby realizing energy mutual assistance among regions.

[0073] Photovoltaic power generation is one of the main renewable energy sources in the regional integrated energy subsystem. Although there are many factors that affect the efficiency and power generation of photovoltaic power generation, they are mainly determined by two factors: the characteristics of the photovoltaic array itself, such as the area of ​​the photovoltaic array and the conversion efficiency; and the environment, such as the sunlight intensity and temperature. The mathematical expression of the photovoltaic generator set is:

[0074]

[0075] Where, is the output power of the photovoltaic generator set in time period t, in MW; p pv,r is the rated power of the photovoltaic generator set; L t ,L ST The light intensity of the photovoltaic generator set in the current time period and the light intensity at 25 degrees Celsius are respectively 1000W / m 2 ; k is the conversion temperature coefficient at the current ambient temperature; T t ,T ST are the current ambient temperature and the standard ambient temperature respectively; γ is the conversion efficiency of light energy into electrical energy; S is the effective illumination area of ​​the photovoltaic generator set in m 2 .

[0076] In the regional integrated energy subsystem, the collector proposed in this embodiment is a flat-plate solar thermal collector. The main task of the solar thermal collector is to convert solar radiation energy into thermal energy, heat the circulating fluid (such as water or air), and then transfer the heat to the hot water storage tank. Assuming that the collector operates under steady-state conditions and its performance does not decay with the operating time of the equipment, the heat balance equation of the collector can be expressed as:

[0077] Q collector =ηcollector A collector G solar -Q loss

[0078] wherein: Q collector is the effective output heat of the solar collector, in W, η collector is the thermal efficiency of the solar collector; A collector is the effective light receiving area of the solar collector, in m 2 , G solar is the solar radiation intensity, in W / m 2 ; Q loss is the heat loss of the collector, in W, which is determined by the comprehensive heat loss coefficient of the collector.

[0079] wherein the thermal efficiency η collector of the collector is affected by the light-heat conversion efficiency and heat loss, and is expressed by the following equation:

[0080] η collector = η0- a1(T fluid - T ambient )- a2(T fluid - T ambient ) 2

[0081] wherein: η0is the light-heat conversion efficiency without heat loss; a1is the first-order heat loss coefficient, in W / m 2 · K; a2is the second-order heat loss coefficient, in W / m 2 · K 2 ; T fluid is the outlet fluid temperature of the collector, in K; T ambient is the ambient temperature, in K.

[0082] wherein, in the regional comprehensive energy subsystem, the water tank involved is a heat storage water tank, in order to facilitate the simulation, the number of water tank nodes is artificially specified to control the degree of stratification of the water tank and it is assumed that each equal-volume node is isothermal and only vertical temperature variation exists in the water tank, in the water tank model, the water tank temperature TSis a function of time and space, then:

[0083] T S = T S (x, y, z, t)

[0084] simplified as:

[0085] T S = T S (x, t)

[0086] During system operation, the heat transfer of the water tank is controlled by the water tank volume V and the heat exchange area A. The basic heat calculation formula and the heat storage in the water tank can be expressed as:

[0087] Q stored =m water c p ΔT

[0088] Where: Q stored is the heat stored in the water tank, in J; m water is the mass of water in kg, which can be calculated from the volume V of the water tank:

[0089] m water =ρV

[0090] Where ρ is the density of water, usually 1000 kg / m 3 .

[0091] Among them, c p is the specific heat capacity of water, in J / kg·K, usually taken as 4186 J / kg·K; ΔT is the change in water temperature, in K. For a water tank, it is the weighted average of the temperatures between different layers.

[0092] The hot water tank is divided into N layers, and the temperature of each layer is determined by the node temperature T j Indicates that the temperature in each layer is uniform, but there is a temperature gradient between adjacent layers. Based on the law of conservation of energy, the heat balance equation for the jth layer is as follows:

[0093]

[0094] In the formula: m j is the mass of the jth layer of water in kg, c p is the specific heat capacity of water, in J / kg·K; T h T is the temperature of hot water entering the water tank, in K; L is the water temperature at the bottom of the water tank, in K; T a is the ambient temperature, in K; U loss is the heat loss coefficient of the water tank, in W / m 2 K, A are the outer surface area of ​​the water tank, in m 2 .

[0095] Among them, F j ,G j is the water flow control factor, and the control function is defined as

[0096]

[0097] In the regional integrated energy subsystem, the water pump uses a fixed frequency water pump to run at a specified flow rate. In the closed circulation water loop, the water pump can maintain the outlet mass flow rate at a constant set value, and the effect of pressure drop is ignored when it is turned on and off. The control signal of the water pump is set to be in the on state when the input value is greater than or equal to 0.5, and in the off state when the input value is less than 0.5. The water pump process efficiency calculation formula is shown in the following formula:

[0098]

[0099] Due to the heat generated by the motor, the fluid absorbs the heat generated by the motor, so that the outlet temperature of the water spring is higher than the inlet temperature. The corresponding calculation formula is shown in the following formula:

[0100]

[0101] In the formula: T out is the outlet fluid temperature of the water pump, in ℃; T in is the inlet fluid temperature of the water pump, in ℃; Q is the heat generated by the motor transferred to the fluid, in kJ / h; m is the mass flow rate of the fluid, in kg / h; c p is the specific constant pressure heat capacity of the fluid, in kJ / (kg·K). The shaft power of the water pump is calculated according to the following formula:

[0102] P shaft = P rated η motor

[0103] In the formula: P shaft is the shaft power required by the pumping process, in kJ / h; P rated is the rated power of the water pump, in kJ / h.

[0104] In the regional integrated energy subsystem, the ground source heat pump (GSHP) is used as the core electric-thermal coupling device, which has high efficient electric and thermal energy conversion capability, and is an important supporting technology for realizing energy cross-system collaborative optimization. In the optimization scheduling model, the ground source heat pump is used to improve the overall energy efficiency of the system and optimize the coordinated scheduling of electric power and thermal energy.

[0105] In the heating mode, the heat transfer of the ground source heat pump can be represented as:

[0106] Q heat = COP heating · P elec

[0107] In the formula: Q heat represents the heat provided by the hot spring, in kW; P elec represents the electric power consumed by the heat pump, in kW;

[0108] COP heating COP represents the heating performance coefficient of the heat pump;

[0109] The COP (performance coefficient) of the heat pump is affected by the ambient temperature, the underground temperature and the heat exchange efficiency, and can generally be described by an empirical formula:

[0110]

[0111] Wherein: T cond is the condenser temperature, in units of (K), i.e. the heating water temperature; T evap is the evaporator temperature, in units of K, i.e. the underground heat exchange loop fluid temperature.

[0112] The ground source heat pump exchanges heat through underground buried pipes, and its heat exchange capacity can be described by the following equation:

[0113] Q ground = U ground A pipe (T fluid -T ground )

[0114] Wherein: Q ground is the underground heat exchange capacity, in units of kW; U ground is the underground heat exchange coefficient, in units of W / m 2 ·K, related to the soil thermal conductivity and the buried pipe structure; A pipe is the buried pipe heat exchange area, in units of m 2 ; T fluid is the fluid temperature inside the buried pipe, in units of K; T ground is the soil temperature, in units of K.

[0115] Wherein, the mathematical model of the shared energy storage subsystem is expressed as follows:

[0116]

[0117] In the formula, represents the maximum capacity of the battery, in units of kW; β represents the maximum discharge rate of the battery; represents the maximum power of the battery, in units of kW; Q ess (t) represents the storage capacity of the battery at the tth time period, in units of kW; p ess,c (t) represents the charging power at the tth time period, in units of kW; p ess，d (t) represents the discharging power at the tth time period, in units of kW; η c , η d represent the charging and discharging efficiencies of the electric energy storage device, respectively; η c , η d are both 0.98;

[0118] Uc (t),U d (t) respectively represent the charging and discharging states of the electric energy storage device, both of which are 0-1 variables, ensuring that the charging and discharging of the shared energy storage subsystem cannot occur at the same time.

[0119] It should be noted that the commonly used batteries at present are mainly lead-acid batteries and lithium iron phosphate batteries. Although the lead-acid battery has moderate cost, its rated power is low and its cycle life is short. In comparison, the lithium iron phosphate battery has the advantages of large capacity, high efficiency and long cycle life, and therefore the shared energy storage subsystem of the embodiment selects the lithium iron phosphate battery.

[0120] Specifically, please refer to Figure 4 , Figure 4 is a relationship diagram of an upper energy storage optimization model and a lower energy storage model provided by the embodiment of the application. In the optimization scheduling process of a multi-regional integrated energy system (IES), the energy supply and demand balance of each regional integrated energy subsystem, the management of the shared energy storage subsystem and the emergency scheduling of the island mode all involve the benefit distribution of multiple subjects. In order to effectively coordinate the energy interaction between the regional integrated energy subsystems and at the same time ensure the reasonable use of the shared energy storage, a double-layer master-slave game optimization framework is adopted in this paper to construct a coordinated optimization scheduling strategy for the integrated energy system. Under the double-layer game framework, it is necessary to ensure that the intra-day adjustment is consistent with the day-ahead plan. The subject (leader) of the upper energy storage optimization model formulates a large-scale charging and discharging plan for the overall shared energy storage equipment based on load forecasting and resource estimation in the day-ahead optimization stage. The upper energy storage optimization objective is to minimize the operation cost of the entire multi-regional integrated energy system while meeting the 72-hour emergency energy reservation demand in the island mode of the regional integrated energy subsystem, and to ensure that the energy storage equipment is reasonably and efficiently configured among the multi-regional integrated energy systems. The subject (follower) of the lower energy storage optimization model then corrects and optimizes the shared energy storage plan formulated by the upper energy storage optimization model in the intra-day scheduling stage based on real-time load fluctuations and emergency situations, so as to cope with real-time load changes and take into account the reliability constraints of island operation. The objective of the lower energy storage optimization model is to minimize the operation cost of each regional integrated energy subsystem and maintain local energy balance.

[0121] Further, the double-layer energy storage optimization model is constructed based on an energy constraint set and an island operation constraint set with the operation cost optimization of the multi-regional integrated energy system as the objective function, which includes:

[0122] The upper energy storage optimization model is constructed based on the energy conservation constraint, the upper energy balance constraint and the island load constraint with the operation cost optimization of the multi-regional integrated energy system as the objective function;

[0123] Taking the optimization of the operating costs of all the regional integrated energy subsystems as the objective function, a lower-layer energy storage optimization model is constructed based on the upper-layer energy storage optimization model, the lower-layer energy balance constraint, the regional energy storage usage restriction constraint, and the island energy storage reservation constraint;

[0124] The upper layer energy storage optimization model and the lower layer energy storage optimization model are used as a double layer energy storage optimization model.

[0125] Specifically, the steps for constructing the upper-layer energy storage optimization model are as follows:

[0126] Taking the operation cost optimization of the multi-region integrated energy system as the objective function, the expression is as follows:

[0127]

[0128] The function goal is to minimize the total system operating cost C total ,in:

[0129] Power purchase cost C grid (t) = p grid (t)·P grid (t);

[0130] p grid (t): electricity purchase price at time t;

[0131] P grid (t): power purchased from the grid at time t;

[0132] Energy storage loss cost:

[0133] C storage (t) = α storage ·(P storage,charge (t)+P storage,discharge (t))

[0134] Among them, P storage，charge (t) is the charging power of the shared energy storage subsystem at time t, P storage，discharge (t) is the discharge power of the shared energy storage subsystem at time t, α storage is the loss factor of the shared energy storage subsystem (usually calculated based on the charge and discharge efficiency). This item represents the additional cost caused by the loss of batteries and hot water tanks during the charging and discharging process.

[0135] The constraints of the upper-level energy storage optimization model include:

[0136] Energy conservation constraints:

[0137] For the entire system, all energy supply and demand must meet:

[0138] P grid (t)+Prenewable (t) + P storage, discharge (t) = P load (t) + P storage, charge (t)

[0139] where P renewable (t) is the renewable energy output; P storage，charge (t) is the charging power of the energy storage device at time t; P storage，discharge (t) is the discharging power of the energy storage device at time t; P load (t) is the total load demand of the system

[0140] Energy storage device power constraint: the charging and discharging power of the energy storage device (including batteries and thermal storage tanks) at each time must be limited by the rated power of the device:

[0141]

[0142] where P is the maximum charging power of the energy storage device, is the maximum discharging power of the energy storage device, and

[0143] The storage capacity SOC of the energy storage device storage (t) needs to be between the upper and lower limits:

[0144] SOC min (t) ≤ SOC storage (t) ≤ SOC max (t)

[0145] and is subject to the energy balance constraint:

[0146]

[0147] where η charge and η discharge are the charging and discharging efficiencies, respectively. This formula ensures that the charging and discharging of the energy storage device does not exceed the allowed range of the device and guarantees that part of the energy storage capacity is reserved as an emergency backup (72 hours of emergency backup in island mode)

[0148] The island load constraint is established, including the key load constraint in island mode and the upper energy storage reservation strategy.

[0149] The expression of the key load constraint in island mode is as follows:

[0150]

[0151] where P represents the critical load power that the i-th regional integrated energy subsystem needs to meet after entering the island mode at time t, represents the energy storage discharge capacity of the i-th regional integrated energy subsystem, T island represents the preset island operation time;

[0152] The expression of the upper-layer energy storage reservation strategy is as follows, which is used to ensure that at any time when the island mode is entered, there is at least SOC island as backup energy:

[0153]

[0154] wherein, represents the current storage energy of the i-th regional integrated energy subsystem, represents the initial energy storage reservation value of the i-th regional integrated energy subsystem issued by the shared energy storage subsystem, SOC island (t) represents the minimum storage energy that needs to be guaranteed after the regional integrated energy subsystem enters the island mode, P criticalload (τ) represents the critical load power that the regional integrated energy subsystem needs to meet after entering the island mode, T island represents the preset island operation time.

[0155] Specifically, the steps of constructing the lower-layer energy storage optimization model are as follows:

[0156] The objective function of the lower-layer regional integrated energy subsystem side is based on the power interaction between the regional integrated energy subsystem and the shared energy storage subsystem, and the sum of the operation costs of the regional integrated energy subsystem is minimized. Taking the n-th regional integrated energy subsystem as an example, the expression is as follows:

[0157]

[0158] wherein, τ GGP represents the electricity purchase strategy or electricity purchase plan.

[0159] Under the given variables (such as natural gas electricity purchase strategy, energy storage charging and discharging power), the operation cost of the n-th regional integrated energy subsystem is minimized.

[0160] wherein:

[0161]

[0162] Electricity purchase cost: wherein, p grid (t) is the grid electricity purchase price, is the power purchased by the region from the grid.

[0163] Energy storage use cost:

[0164]

[0165] where p storage (t) is the sharing energy storage usage cost set by the upper layer.

[0166] Device operation cost:

[0167]

[0168] where, is the operation time of device i (such as ground source heat pump, circulating heat pump) in the regional integrated energy subsystem, β i is the cost per unit operation time of the device, which is calculated by multiplying the operation power of each device by the current electricity price.

[0169] The constraints of the lower energy storage optimization model include:

[0170] Regional energy balance constraint:

[0171] For the nth regional integrated energy subsystem, it must satisfy:

[0172]

[0173] where, : the renewable energy output of the region; : the power consumption of devices such as ground source heat pumps; and is the charging and discharging power of the energy storage;

[0174] Upper layer allocated energy storage usage limit constraint:

[0175] Upper layer allocated energy storage usage limit:

[0176]

[0177] Here Decided by the upper multi-regional integrated energy system, limit the energy storage usage of the lower regional integrated energy subsystem.

[0178] Lower layer dynamic adjustment of island mode energy storage reservation constraint:

[0179] Since the triggering time of island mode is uncertain, the lower layer needs to correct the energy storage reservation when rolling optimization within the day to ensure that there is always enough energy storage available before the time point t when island mode may occur. Once in island mode, energy storage usage must be re-planned to maintain critical load power supply. Real-time changes in island mode reservation:

[0180]

[0181] wherein, represents the current stored energy of the i-th said regional integrated energy subsystem, represents the initial stored energy reservation value of the i-th said regional integrated energy subsystem issued by the shared energy storage subsystem, represents the capability of the i-th said regional integrated energy subsystem to discharge stored energy, T forecast represents a preset time rolling window. When the lower layer optimization runs the intraday optimization at time t, the stored energy discharge is corrected to make always meet the island energy supply demand within the rolling window T forecast .

[0182] Further, the double-layer energy storage optimization model is solved based on the historical operation data and the real-time operation data, so as to obtain a real-time shared energy storage scheduling scheme, which comprises:

[0183] solving the upper-layer energy storage optimization model based on the historical operation data, so as to obtain a day-ahead shared energy storage scheduling scheme;

[0184] solving the double-layer energy storage optimization model based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme.

[0185] Specifically, the day-ahead upper-layer decision serves as a boundary condition and a reference scheme for the intraday optimization. The intraday rolling optimization flexibly adjusts details under the premise of following the upper-layer overall strategy. In this way, the long-period decision (day-ahead) and the short-period decision (intraday) form a closed loop: the plan made by the day-ahead scheduling provides guidance for the intraday, and the feedback of the intraday scheduling can be used to correct the plan of the next period or the next day. The upper layer can guide the behavior of the lower layer according to the forward-looking prediction, while the actual response of the lower layer corrects the cognition of the upper layer. Both of them continue to game until a dynamic equilibrium is reached, so as to coordinate the decisions of different time scales and different subject targets in the double-layer game.

[0186] Further, the double-layer energy storage optimization model is solved based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme, which comprises:

[0187] transforming the lower-layer energy storage optimization model into a lower-layer Lagrange function according to the KKT condition, and then transforming the lower-layer energy storage optimization model into a lower-layer optimization constraint set;

[0188] constraining the upper-layer energy storage optimization model based on the lower-layer optimization constraint set, so as to obtain a single-layer energy storage optimization model;

[0189] Solving the single-layer energy storage optimization model by using a preset solver based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme.

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

[0191] linearizing the single-layer energy storage optimization model based on a McCormick envelope method, so as to convert the nonlinear structure and bilinear structure in the single-layer energy storage optimization model into linear structure and obtain a single-layer linear optimization model;

[0192] taking the single-layer linear optimization model as the single-layer energy storage optimization model.

[0193] Specifically, referring to Figure 5 , Figure 5 is a flowchart of solving a double-layer energy storage optimization model provided by an embodiment of the present application. The optimization operation model established by the optimization objectives and strategies of the two parties in the game and the related constraint conditions of each party is a double-layer optimization problem with integer variables, that is, a mixed integer nonlinear programming problem. The upper and lower layer models are nested with each other and have the characteristics of rolling optimization, so a solving method capable of processing integer variables, optimizing nonlinear terms and solving at different time scales needs to be selected.

[0194] After the upper and lower layer energy storage optimization models are combined to obtain the double-layer energy storage optimization model (MINLP), the KKT condition is introduced to convert the lower layer optimization problem into a constraint: the KKT condition is a set of constraint conditions used in the solving process of a nonlinear programming problem, which links the optimal solution of the optimization problem with the constraint condition and converts the optimization problem into a set of constraints. Based on the lower layer energy storage optimization model in the double-layer energy storage optimization model, the optimization problem of the lower layer energy storage optimization model is converted into a set of constraints, which is added to the constraints of the optimization problem of the upper layer energy storage optimization model. The KKT condition includes the feasibility condition of the original problem, the dual feasibility condition and the complementary slackness condition, which can equivalently represent the optimality condition of the lower layer optimization problem. The KKT function is directly called by using the YALMIP toolbox to convert the lower layer optimization problem into an upper layer constraint, so as to form a single-layer optimization problem and reduce the calculation complexity. According to the KKT condition, the Lagrange function is established based on the related function and constraint of the lower layer energy storage optimization model:

[0195]

[0196] Where λ1 corresponds to energy balance constraint, λ2, λ3 corresponds to energy storage usage limit, λ4 corresponds to island mode energy storage reservation

[0197] The first derivative condition introduced by KKT:

[0198] Taking the derivative of the purchased power Taking the derivative:

[0199]

[0200] Taking the derivative of the energy storage charging power Taking the derivative:

[0201]

[0202] Taking the derivative of the energy storage discharging power Taking the derivative:

[0203]

[0204] Taking the derivative of the energy storage SOC:

[0205]

[0206] This part of the first derivative condition is part of the KKT condition, which represents the necessary condition for optimality. Taking the derivative of each decision variable is equivalent to constructing the Lagrangian function The gradient condition of L, i.e. finding the point that makes the objective function optimal under all constraints, is the key step for the model to transform from the original nonlinear double-layer structure to a single-layer optimization problem.

[0207] The original feasibility constraint introduced by KKT:

[0208]

[0209] The dual feasibility constraint introduced by KKT:

[0210] λ2, λ3, λ4 ≥ 0

[0211] The complementary slackness condition introduced by KKT:

[0212]

[0213] Combine the KKT conditions of the converted lower-level optimization model with the constraint conditions of the upper-level model to transform the double-layer optimization model into a single-layer optimization model.

[0214] Further, introduce the strong duality theory, linearize the bilinear term: in the lower-level optimization objective, the regional integrated energy subsystem needs to optimally use the shared energy storage, and optimize the local device scheduling based on the constraints of the upper level. Since the SOC state update and the device start-stop variable The bilinear terms in the equality constraints lead to nonlinear problems. The strong duality theory and McCormick envelope method are used to linearize the bilinear terms, transforming the nonlinear MINLP into a mixed integer linear programming (MILP) that can be efficiently solved by solvers.

[0215] It should be noted that after applying the KKT condition to convert the lower problem into constraints, in order to avoid the bilinear structure formed by the multipliers and variables, the strong duality theory can be introduced, and the optimal value expression in the original objective function can be replaced by its Lagrangian dual expression. In the dual function form, a dual variable is introduced to construct an equivalent linear lower bound expression, which further promotes the linearization of the structure. On this basis, for the product terms of continuous variables and integer variables remaining in the original model in the scenarios of energy storage state update, device start-stop control, etc., further linearization processing is carried out by introducing McCormick envelope method, and the modeling transformation from single-layer energy storage optimization model to single-layer linear optimization model is completed.

[0216] Specifically, in the converted single-layer optimization model, the following nonlinear bilinear structures still exist:

[0217] Term in energy storage state update:

[0218]

[0219] Device start-stop and running power coupling term:

[0220] τ i (t)·P i (t)

[0221] McCormick envelope method process:

[0222] For each pair of product terms z = x·y, four sets of linear inequalities are constructed to make the product variable z take value in the convex hull formed by its upper and lower bounds. As follows:

[0223] Let x∈[x min ,x max ], y∈0y min ,y max 1, introduce new variable z = x·y, then add the following constraints:

[0224]

[0225] In the model, we replace the original bilinear terms with auxiliary variables z ch ,z dis ,z run , respectively.

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

[0227]

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

[0229]

[0230] Then add McCormick envelope constraints for new variables.

[0231] Set the upper and lower bounds of power:

[0232]

[0233] The above upper and lower bounds of power are for Therefore, constraints are added to the model:

[0234]

[0235] Similarly, based on Constraints are established for the model:

[0236]

[0237] The second term is the same: define:

[0238]

[0239] Let P i (t)∈[0,P i max ], McCormick linear envelope is added to the model:

[0240]

[0241] Then replace all in the original single-layer energy storage optimization model formula with Further, the nonlinear structure and bilinear structure in the single-layer energy storage optimization model are converted into linear structure, and a single-layer linear optimization model is obtained.

[0242] The single-layer energy storage optimization model is solved by using a preset solver based on the real-time operation data, so as to optimize the day-ahead shared energy storage scheduling scheme and obtain a real-time shared energy storage scheduling scheme, including solving a single-layer linear optimization model problem by using a Gurobi solver; after KKT conversion and strong dual linearization, the final optimization problem is converted into a single-layer linear optimization model (Mixed-Integer Linear Programming); the Gurobi solver is called by using a YALMI tool box for optimization, and the Branch-and-Cut method is used by the Gurobi in the solving process, so that the optimal solution can be quickly converged.

[0243] The above process includes:

[0244] Step 1, pre-processing the model, which aims to reduce the problem size, remove invalid or redundant information, and improve the overall solving efficiency.

[0245] Specifically, the redundant constraints (such as the identity that is always satisfied) of the single-layer linear optimization model are removed; the variables of the single-layer linear optimization model are fixed (the unique value is inferred by constraints or variable bounds); the constraints are normalized and the variable bounds are tightened; the infeasibility or unboundedness (such as variable upper bound < lower bound) is detected; the linear dependent constraints in the single-layer linear optimization model are merged and converted into a simpler structure; the problem structure of the single-layer linear optimization model is identified.

[0246] Step 2, root LP solving, all integer variables of the single-layer linear optimization model are relaxed to continuous variables to obtain a relaxed linear problem:

[0247]

[0248] Solving the relaxed linear problem is the core step of understanding the single-layer linear optimization model; the solution provides a lower bound of the global optimal value (for the minimization problem); if this solution already satisfies all integer constraints, then the solution is the optimal integer solution; otherwise, the branch-and-bound procedure needs to be entered.

[0249] Step 3, using the Gurobi solver to perform branch-and-bound on the single-layer linear optimization model:

[0250] Starting from the root node of the single-layer linear optimization model, a non-integer solution variable is selected Two sub-problems are constructed:

[0251]

[0252] Where each sub-problem forms a search tree node; each node solves a relaxed linear sub-problem and updates the upper and lower bounds. If a node is infeasible, the node is pruned; if a node is integer-feasible and better than the current best solution, the model best solution is updated; if a node is optimal and better than the current upper bound, the search is preserved; if a node is optimal and worse than the current upper bound, the node is pruned.

[0253] Step 4: Implementation of Cutting Planes.

[0254] In solving mixed integer programming problems, even if the linear programming (LP) relaxation problem has a feasible solution, the relaxed solution can not satisfy the integer variable value requirement, resulting in slow convergence of the solution process based on the Branch-and-Bound framework. To overcome this technical problem, in the present embodiment, the optimization solver automatically generates and adds cutting plane constraints.

[0255] The cutting plane constraints are used to remove areas 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:

[0256] Gomory cuts: a type of cutting plane derived from the linear programming simplex tableau, commonly used in integer programming;

[0257] Cover inequalities: particularly suitable for handling Knapsack-type constraint structures containing 0-1 binary variables;

[0258] Clique cuts: suitable for identifying and handling mutual exclusive relationships between variables (e.g. at most one of multiple Boolean decision variables can be true);

[0259] Flow cover cuts: enhance the ability to identify and handle constraints with network flow structure;

[0260] Lift-and-project cuts: strengthen the approximation of the structure of the integer polyhedron through lifting and projection operations.

[0261] 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) to exclude the part that does not contain any feasible integer solution, thereby effectively reducing the search space and accelerating the discovery of integer feasible solutions and the convergence of the overall problem.

[0262] Step 5: Implementation of Heuristics.

[0263] In the process of solving mixed integer programming, even if the branch and bound method and the cutting plane method are applied, it may be difficult to find a feasible integer solution in the early stage of the search. To solve this technical problem and accelerate the acquisition of a better integer feasible solution, in the present embodiment, the Gurobi optimization solver integrates and enables multiple heuristic search strategies.

[0264] The main technical purposes of the heuristic strategies include:

[0265] Quickly obtain a good quality feasible integer solution to improve the quality of the global lower bound;

[0266] Shorten the time required for the entire solving process to converge to the global optimal solution or the optimal solution that meets the specified tolerance.

[0267] The heuristic strategies applied by the solver specifically include:

[0268] Feasibility Pump: This strategy iteratively searches between a continuous LP relaxation solution (satisfies linear constraints but may not satisfy integer requirements) and an integer point (satisfies integer requirements but may violate linear constraints). Its core process is to alternately: a) round the current solution to the nearest integer point (project it to the integer space); b) solve a linear programming problem whose objective is to minimize the distance from the LP solution in the last step, while ensuring integer. By repeatedly executing this "pumping" process, it aims to find a feasible solution that satisfies both linear constraints and integer requirements.

[0269] Relaxation Induced Neighborhood Search (RINS): This strategy uses the information of the optimal continuous relaxation solution (usually containing fractional values) found and the best integer feasible solution found. It defines a neighborhood around the current best integer feasible solution, which is defined by variables that take fixed values (usually 0 or 1) in the continuous relaxation solution. Then, the solver solves a sub-MIP problem within this defined neighborhood in the hope of finding a better quality integer feasible solution.

[0270] Local Branching: This strategy aims to refine the search in the neighborhood of the current known integer feasible solution. The core is to introduce a linear constraint (local branching constraint) to limit the number of binary (or general integer) variables that change between the new solution and the current reference solution (i.e. the current known feasible solution) to no more than a pre-set 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.

[0271] By comprehensively applying the above heuristic algorithms, the optimization solver can more effectively find feasible integer solutions in the early or process of the branch and bound tree search, and improve the quality of the found solutions, thereby significantly improving the overall solving efficiency.

[0272] Referring to Figure 6 The second aspect of the embodiment of the present application provides a shared energy storage optimization scheduling system considering island 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:

[0273] 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 comprising a shared energy storage subsystem and a plurality of regional integrated energy subsystems, and the plurality of regional integrated energy subsystems are electrically connected with an external power distribution network through the shared energy storage subsystem;

[0274] The energy storage optimization model construction module 200 is used to construct a double-layer energy storage optimization model based on an energy constraint set and an island operation constraint set, with the operation cost optimization of the multi-region integrated energy system as an objective function;

[0275] The data acquisition module 300 is used to acquire historical operation data and real-time operation data based on the multi-region integrated energy system;

[0276] The shared energy storage scheduling module 400 is used to solve the double-layer energy storage optimization model based on the historical operation data and the real-time operation data, thereby obtaining a real-time shared energy storage scheduling scheme.

Claims

1. A shared energy storage optimization scheduling method taking island operation into account, characterized in that: include: A multi-region integrated energy system is constructed based on a regional power supply network and a regional heating network, wherein the multi-region integrated energy system includes a shared energy storage subsystem and a plurality of regional integrated energy subsystems, wherein the plurality of regional integrated energy subsystems are electrically connected to an external power distribution network through the shared energy storage subsystem; Taking the operation cost optimization 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; Acquiring historical operating data and real-time operating data based on the multi-region integrated energy system; The double-layer energy storage optimization model is solved based on the historical operation data and the real-time operation data, thereby obtaining a real-time shared energy storage scheduling solution.

2. A shared energy storage optimization scheduling method taking island operation into account according to claim 1, characterized in that: The objective function is to optimize the operating cost of the multi-region integrated energy system and to construct a two-layer energy storage optimization model based on an energy constraint set and an island operation constraint set, including: Taking the operation cost optimization of the multi-region integrated energy system as the objective function, an upper-layer energy storage optimization model is constructed based on energy conservation constraints, upper-layer energy balance constraints, and island load constraints; Taking the optimization of the operating costs of all the regional integrated energy subsystems as the objective function, a lower-layer energy storage optimization model is constructed based on the upper-layer energy storage optimization model, the lower-layer energy balance constraint, the regional energy storage usage restriction constraint, and the island energy storage reservation constraint; The upper layer energy storage optimization model and the lower layer energy storage optimization model are used as a double-layer energy storage optimization model.

3. The method for optimizing the shared energy storage scheduling taking island operation into account according to claim 2, characterized in that: Solving the double-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 solution includes: Solving the upper-layer energy storage optimization model based on the historical operating data to obtain a day-ahead shared energy storage scheduling plan; The two-layer energy storage optimization model is solved based on the real-time operation data, thereby optimizing the day-ahead shared energy storage scheduling plan and obtaining a real-time shared energy storage scheduling plan.

4. The method for optimizing the shared energy storage scheduling taking island operation into account according to claim 2, characterized in that: Solving the two-layer energy storage optimization model based on the real-time operation data, thereby optimizing the day-ahead shared energy storage scheduling plan and obtaining a real-time shared energy storage scheduling plan, includes: Converting the lower-layer energy storage optimization model into a lower-layer Lagrangian function according to the KKT condition, and then converting the lower-layer energy storage optimization model into a lower-layer optimization constraint set; Constraining the upper-layer energy storage optimization model based on the lower-layer optimization constraint set, thereby obtaining a single-layer energy storage optimization model; Based on the real-time operation data, a preset solver is used to solve the single-layer energy storage optimization model, thereby optimizing the day-ahead shared energy storage scheduling plan and obtaining a real-time shared energy storage scheduling plan.

5. The method for optimizing the shared energy storage scheduling taking island operation into account according to claim 4, characterized in that: Before solving the single-layer energy storage optimization model based on the real-time operation data using a preset solver to optimize the day-ahead shared energy storage scheduling plan and obtain the real-time shared energy storage scheduling plan, the method further includes: The single-layer energy storage optimization model is linearized based on the McCormick envelope method, thereby converting the nonlinear structure and bilinear structure in the single-layer energy storage optimization model into a linear structure to obtain a single-layer linear optimization model; The single-layer linear optimization model is used as the single-layer energy storage optimization model.

6. The method for optimizing the shared energy storage scheduling taking island operation into account according to claim 3, characterized in that: The operation cost optimization of the multi-region integrated energy system is used as the objective function, and an upper-layer energy storage optimization model is constructed based on energy conservation constraints, upper-layer energy balance constraints, and island load constraints, including: Establishing the island load constraints, including key load constraints in island mode and upper-layer energy storage reservation strategies; The expression of the key load constraint in the island mode is as follows: in, It represents the critical load power that needs to be met after the i-th regional integrated energy subsystem enters the island mode at time t, represents the energy storage and discharge capacity of the ith regional integrated energy subsystem, T island Indicates the preset island operation time; The upper-layer energy storage reservation strategy is expressed as follows: in, represents the current stored energy of the i-th regional integrated energy subsystem, Indicates the initial energy storage reserve value sent by the shared energy storage subsystem to the i-th regional integrated energy subsystem, SOC island (t) represents the minimum storage energy that needs to be guaranteed after the regional integrated energy subsystem enters the island mode, P criticalload (τ) represents the critical load power that the regional integrated energy subsystem needs to meet after entering the island mode, T island Indicates the preset island operation time.

7. The method for optimizing the shared energy storage scheduling taking island operation into account according to claim 3, characterized in that: The objective function is to optimize the operating costs of all the regional integrated energy subsystems, and to construct a lower-layer energy storage optimization model based on the upper-layer energy storage optimization model, the lower-layer energy balance constraint, the regional energy storage usage restriction constraint, and the island energy storage reservation constraint, including: The island energy storage reservation constraint is established, and its expression is shown in the following formula: in, represents the current stored energy of the i-th regional integrated energy subsystem, represents the initial energy storage reserve value sent by the shared energy storage subsystem to the i-th regional integrated energy subsystem, represents the energy storage and discharge capacity of the ith regional integrated energy subsystem, T forecast Indicates a preset time rolling window.

8. A shared energy storage optimization scheduling system taking island operation into account, 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-regional integrated energy system construction module is used to construct a multi-regional integrated energy system based on the regional power supply network and the regional heating network. The multi-regional integrated energy system includes a shared energy storage subsystem and a plurality of regional integrated energy subsystems. The plurality of regional integrated energy subsystems are electrically connected to an 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 island operation constraint set, taking the operation cost optimization of the multi-region integrated energy system as the objective function; The data acquisition module is used to acquire historical operation data and real-time operation data based on the multi-region integrated energy system; The shared energy storage scheduling module is used to solve the double-layer energy storage optimization model based on the historical operation data and the real-time operation data, so as to obtain a real-time shared energy storage scheduling solution.

9. The shared energy storage optimization scheduling system taking island operation into account according to claim 8, characterized in that: The objective function is to optimize the operating cost of the multi-region integrated energy system and to construct a two-layer energy storage optimization model based on an energy constraint set and an island operation constraint set, including: Taking the operation cost optimization of the multi-region integrated energy system as the objective function, an upper-layer energy storage optimization model is constructed based on energy conservation constraints, upper-layer energy balance constraints, and island load constraints; Taking the optimization of the operating costs of all the regional integrated energy subsystems as the objective function, a lower-layer energy storage optimization model is constructed based on the upper-layer energy storage optimization model, the lower-layer energy balance constraint, the regional energy storage usage restriction constraint, and the island energy storage reservation constraint; The upper layer energy storage optimization model and the lower layer energy storage optimization model are used as a double-layer energy storage optimization model.

10. A shared energy storage optimization scheduling system taking island operation into account according to claim 9, characterized in that: Solving the double-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 solution includes: Solving the upper-layer energy storage optimization model based on the historical operating data to obtain a day-ahead shared energy storage scheduling plan; The two-layer energy storage optimization model is solved based on the real-time operation data, thereby optimizing the day-ahead shared energy storage scheduling plan and obtaining a real-time shared energy storage scheduling plan.

Images