A method for regulating energy storage of multiple CCHP system hybrid sharing

Abstract

The application discloses a kind of multiple CCHP system hybrid sharing energy storage regulation methods, comprising S1: constructing upper layer hybrid sharing energy storage side operation optimization model;S2: constructing lower layer multiple CCHP system side operation optimization model;S3: the solving method of double-layer model optimization problem is established;S4: with whether CCHP system side and hybrid sharing energy storage side is set electric heat conversion device as different precondition, based on the mathematical model of S1, S2 and S3 in Matlab 2020a Calling commercial solver CPLEX 12.9 and YALMIP toolbox, respectively, the position, capacity and storage and release energy power of shared energy storage station, and the cost of CCHP system are optimized.The application can realize the collaborative optimization configuration and scheduling of electric energy storage and heat storage, reduce energy storage station investment cost and system operation cost, shorten payback period.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system operation optimization, specifically relating to a method for regulating and controlling the mixed shared energy storage of multiple CCHP systems containing electrothermal conversion devices. Background Technology

[0002] Integrated Energy Systems (IES) are attracting increasing attention due to their ability to coordinate the use of multiple energy sources, improve energy efficiency, and promote the consumption of renewable energy. Among them, Combined Cooling, Heating, and Power (CCHP) systems, as a typical integrated energy utilization method, have been widely used in industrial parks, industrial buildings, and public buildings. However, due to load fluctuations and the instability of renewable energy output, CCHP systems still face significant challenges in energy dispatch and supply-demand matching. Shared energy storage, as a new energy management model that has emerged in recent years, can improve energy storage utilization and reduce investment and operating costs through centralized construction and unified dispatch.

[0003] The existing technology has the following drawbacks:

[0004] (1) Most studies focus on a single mode of electric energy storage or thermal energy storage, with less research on hybrid shared energy storage (HSES) that combines electric and thermal energy storage and its coupling with multiple CCHP systems.

[0005] (2) As a key connection between electric energy storage and thermal energy storage, electric-to-thermal equipment has an important impact on system investment, economy and carbon emissions, but the lack of systematic analysis limits its engineering application. Summary of the Invention

[0006] The purpose of this invention is to provide a method for regulating and controlling multiple CCHP systems through hybrid shared energy storage, thereby achieving coordinated and optimized configuration and scheduling of electrical energy storage and thermal energy storage, reducing the investment cost and system operating cost of energy storage stations, and shortening the payback period.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: a method for regulating and controlling multiple CCHP systems through hybrid shared energy storage, characterized by comprising: S1: constructing an upper-level hybrid shared energy storage side operation optimization model; S2: constructing a lower-level multiple CCHP system side operation optimization model; S3: establishing a solution method for the two-level model optimization problem; S4: taking the presence or absence of an electro-thermal conversion device on the CCHP system side and the hybrid shared energy storage side as different preconditions, and based on the mathematical models of S1, S2, and S3, calling the commercial solver CPLEX 12.9 and YALMIP toolbox in Matlab 2020a to optimize the location, capacity, energy storage and release power of the shared energy storage station, as well as the cost of the CCHP system.

[0008] In the above scheme, step S4, whether the CCHP system side and the hybrid shared energy storage side are equipped with an electric-to-heat conversion device is divided into four scenarios: Scenario 1: The CCHP system side does not have an electric-to-heat conversion device, and the hybrid shared energy storage side does not have an electric-to-heat conversion device; Scenario 2: The CCHP system side does not have an electric-to-heat conversion device, and the hybrid shared energy storage side has an electric-to-heat conversion device; Scenario 3: The CCHP system side has an electric-to-heat conversion device, and the hybrid shared energy storage side does not have an electric-to-heat conversion device; Scenario 4: The CCHP system side has an electric-to-heat conversion device, and the hybrid shared energy storage side has an electric-to-heat conversion device.

[0009] In the above scheme, in step S1, the upper-layer hybrid shared energy storage side operation optimization model is used to determine the location and capacity of the hybrid shared energy storage station. Its objective function is to maximize the revenue of the hybrid shared energy storage station. The optimization variables are the capacity of the shared energy storage station, the maximum energy storage / release power, and the location of the shared energy storage station. The objective function of the upper-layer hybrid shared energy storage side operation optimization model is calculated by formula (1), which is as follows:

[0010] (1);

[0011] In the formula: This indicates the average daily investment cost of shared electric energy storage and shared thermal energy storage; This refers to the fees charged for shared electric energy storage and shared thermal energy storage when selling energy to the CCHP microgrid system; This refers to the service fees charged to the CCHP microgrid system for shared electric energy storage and shared thermal energy storage; This represents the cost of purchasing energy from the CCHP microgrid system for shared electric energy storage and shared thermal energy storage. and These represent the investment cost and operating cost of the heat transmission pipeline, respectively. This indicates the investment cost of the electric-to-heat conversion equipment;

[0012] The average daily investment cost of the shared energy storage station is calculated using formulas (2) and (3), which are as follows:

[0013] (2);

[0014] (3);

[0015] In the formula: and These represent the maximum energy storage and release capacities of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the capacities of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the cost coefficients for the maximum energy storage / release capacity of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the cost coefficients for shared electrical energy storage and shared thermal energy storage capacities, respectively. The number of days the shared energy storage station operates; and These represent the operation and maintenance costs of shared electrical energy storage and shared thermal energy storage, respectively.

[0016] The cost required for the hybrid shared energy storage station to purchase electricity and heat from CCHP microgrid system users is calculated using formulas (4) and (5), which are as follows:

[0017] (4);

[0018] (5);

[0019] In the formula: and These represent the costs of purchasing electricity and heat at the energy storage station, respectively; N is the number of CCHP microgrid systems; and T is the dispatch cycle. and These represent the electricity price and heat price at which the shared energy storage station purchases energy from the CCHP microgrid system, respectively. and These represent the electricity and heat purchased by the shared energy storage station from users of the CCHP microgrid system, respectively.

[0020] The cost of electricity and heat sold by the hybrid shared energy storage station to each CCHP microgrid system user is calculated using formulas (6) and (7), which are as follows:

[0021] (6);

[0022] (7);

[0023] In the formula: and These represent the fees charged by the shared energy storage station for selling electricity and heat, respectively. and These represent the prices of electricity and heat sold by the shared energy storage station to CCHP microgrid system users, respectively. and These represent the electricity and heat purchased by CCHP microgrid system users from the shared energy storage station, respectively.

[0024] The service fees payable by the CCHP microgrid system when using shared energy storage stations for energy storage and release services are calculated using formulas (8) and (9), which are as follows:

[0025] (8);

[0026] (9);

[0027] In the formula: and These represent the service fees paid by the CCHP microgrid system for using energy storage and thermal storage, respectively. Service fees for shared energy storage stations;

[0028] The shared energy storage station is connected to CCHP microgrid system users via heat transmission pipelines. The investment cost of these heat transmission pipelines... and operating costs The result is obtained through formula (10), which is as follows:

[0029] (10);

[0030] In the formula: C0 is the investment cost per unit length of heat transfer pipeline; p grid The electricity price is the municipal power grid price; EHR is the power consumption to heat transfer ratio of the heat transfer pipeline. The cost coefficient for the electric-to-heat conversion equipment is set at 800 yuan / kWh. The capacity of the electric-to-heat conversion equipment is specified. The heat transfer pipe used in this patent has a diameter of 0.3m, with C0 = 1941 yuan / m and HER = 0.0064.

[0031] In the above scheme, in step S1, in order to ensure the normal operation of the energy storage equipment, the energy storage and release power of the shared energy storage station should meet the upper and lower limit constraints. At the beginning and end of each scheduling cycle, the energy stored in the energy storage station is set to be equal to ensure the sustainability of the scheduling strategy.

[0032] The energy storage equipment of the hybrid shared energy storage power station is constrained by formulas (11) to (14), and the specific formulas (11) to (14) are as follows:

[0033] (11);

[0034] (12);

[0035] (13);

[0036] (14);

[0037] Among them, equations (11) and (12) are the continuity constraints of the energy storage station; equation (13) represents the upper and lower limits of the charging and discharging power of the energy storage station; the same CCHP microgrid system cannot charge or discharge the energy storage station at the same time, and must satisfy the charging and discharging state constraints shown in equation (14). A value of 1 indicates that the state occurs, and a value of 0 indicates that the state does not occur. and respectively This represents the charging and discharging power of a shared energy storage device (SEES) within the interval t. It refers to the electrical energy stored by a shared energy storage device over time t. and These represent the coefficients for charging and discharging, respectively. and These represent the charging and discharging states of the shared energy storage device, respectively, and are 0-1 variables; the scheduling time step... It takes 1 hour; It is the initial stored electrical energy of the shared energy storage device; It is the electrical energy stored at the end of the scheduling cycle; Electricity-to-heat conversion power;

[0038] Similarly, the thermal energy storage equipment of the hybrid shared energy storage station is constrained by formulas (15) to (18), which are as follows:

[0039] (15);

[0040] (16);

[0041] (17);

[0042] (18);

[0043] The meanings of equations (15) to (18) are similar to those of energy storage power stations; and respectively This represents the heat storage and release power of a shared thermal energy storage device (STES) in the interval t. This represents the thermal energy stored in a shared thermal energy storage device over time t. and These represent the storage-release and thermal power coefficients, respectively. and These represent the heat storage and heat release states of the shared thermal energy storage device, respectively, and are 0-1 variables; The initial stored thermal energy for the shared thermal energy storage device; It is the thermal energy stored at the end of the scheduling cycle; This represents the coefficient of the electric-to-heat conversion equipment; This indicates the output thermal power of the electro-thermal conversion equipment;

[0044] In addition, the heating supply of the energy storage heat station is limited by the heat transmission distance, which is constrained by formula (19), as follows:

[0045] (19);

[0046] In the formula: (x0, y0) are the coordinates of the hybrid shared energy storage station, (x0, y0) i ,y i ) represents the coordinate point of the i-th CCHP microgrid system.

[0047] In the above scheme, in step S2, the lower-level multiple CCHP system-side operation optimization model is used to control the operating costs of multiple CCHP microgrid system users. The operating costs include electricity purchase costs. Gas purchase cost The interaction cost with the energy storage station; the operation optimization model of multiple CCHP systems at the lower level takes the minimum annual operating cost of the multiple CCHP microgrid system as the objective function, and the decision variable is the output power of each device in the CCHP microgrid system; the objective function of the operation optimization model of multiple CCHP systems at the lower level is calculated by formula (20), and the specific formula (20) is as follows:

[0048] (20);

[0049] The electricity purchase cost from the municipal power grid for the multi-CCHP microgrid system is calculated using formula (21), which is as follows:

[0050] (twenty one);

[0051] In the formula: For the price of municipal power grid, Electricity supplied to the municipal power grid;

[0052] The gas cost of a multi-CCHP microgrid system is calculated using formula (22), which is as follows:

[0053] (twenty two);

[0054] In the formula: It's the price of natural gas; It is the output power of the prime mover; It is the output thermal power of the gas-fired boiler; It has the low calorific value of natural gas; and These are the efficiencies of the prime mover and the gas boiler, respectively.

[0055] In addition, in order to calculate the total annual cost of the CCHP microgrid system, the investment cost of each device needs to be calculated using formulas (23) and (24), which are as follows:

[0056] (twenty three);

[0057] (twenty four);

[0058] In the formula: R N The installed capacity of each device in the CCHP microgrid system is represented by m; the number of devices in the CCHP microgrid system is represented by I. N Let be the unit initial investment cost of each device in the CCHP microgrid system; j be the annual interest rate, with a value of 0.1; r be the capital recovery factor; n represent the total life cycle of each device in the CCHP microgrid system; it is assumed that all devices in the CCHP microgrid system have the same annual interest rate and life cycle, with a life cycle of 20 years.

[0059] In the above scheme, in step S2, in order to ensure the safe and stable operation of each CCHP microgrid system, the operation of the CCHP microgrid system needs to meet the supply and demand matching between the system and the user load demand, that is, to meet the constraints of power balance, heat balance, cold balance and energy storage and release power balance.

[0060] The power balance between the CCHP microgrid system and users is constrained by formula (25), which is as follows:

[0061] (25);

[0062] In the formula: , and These represent the photovoltaic power, power load demand, and electrical energy consumed by the electro-thermal conversion of the i-th CCHP microgrid system at time t, respectively. These are the dual variables of the equality constraint, and are 0-1 variables; and Let t represent the electrical energy traded between the i-th CCHP microgrid system and the energy storage device at time t.

[0063] The thermal balance between the CCHP microgrid system and users is constrained by formula (26), which is as follows:

[0064] (26);

[0065] In the formula: It is the heat energy provided by the heat exchanger of the i-th CCHP microgrid system at time t; This represents the thermal energy provided by the i-th CCHP microgrid system at time t; This represents the heat load demand of the i-th user at time t;

[0066] Waste heat recovery and thermal energy balance of the CCHP microgrid system are constrained by formula (27), which is as follows:

[0067] (27);

[0068] In the formula: It is the waste heat energy generated by the prime mover of the i-th CCHP microgrid system at time t; It is the cooling power provided by the absorption chiller unit of the i-th CCHP microgrid system at time t; It refers to the efficiency of the heat exchanger; R is the coefficient of performance of the absorption chiller; R is the output thermoelectric ratio of the prime mover. It refers to the efficiency of the waste heat recovery unit; and Let be the heat energy traded between the i-th CCHP microgrid system and the energy storage device at time t;

[0069] The cooling energy balance between the CCHP microgrid system and users is constrained by formula (28), which is as follows:

[0070] (28);

[0071] In the formula: This represents the cooling power provided by the i-th CCHP microgrid system at time t. This represents the cooling load demand of i users at time t;

[0072] Electric-to-heat conversion units cannot operate in both heating and cooling states simultaneously. Therefore, the operation of electric-to-heat conversion units must meet the start-stop constraints of formulas (29) to (34). The specific formulas (29) to (34) are as follows:

[0073] (29);

[0074] (30);

[0075] (31);

[0076] (32);

[0077] (33);

[0078] (34);

[0079] In the formula: and These refer to the cooling and heating power of the electric-to-heat unit, respectively. and These are the maximum cooling and heating capacities of the electric-to-heat unit, respectively. and These represent the operating status of the electric-to-heat unit, and are 0-1 variables;

[0080] The sum of the energy purchased and sold by each CCHP microgrid system through the hybrid shared energy storage station is equal to the energy stored and released by the energy storage station. Therefore, the energy balance between the CCHP microgrid system and the hybrid shared energy storage station is constrained by formulas (35) and (36), which are as follows:

[0081] (35);

[0082] (36);

[0083] To ensure the stable operation of the CCHP microgrid system, the operation of the CCHP microgrid system is limited by the upper and lower limits of the output of each device. The upper and lower limits of each CCHP microgrid system device are constrained by formulas (37) to (42); where formula (37) is the constraint of the prime mover; formula (38) is the constraint of the electric-to-thermal unit; formula (39) is the constraint of the absorption chiller unit; formula (40) is the constraint of the gas boiler; formula (41) is the constraint of the heat exchanger; and formula (42) is the constraint of the power grid purchase. Formulas (37) to (42) are as follows:

[0084] (37);

[0085] (38);

[0086] (39);

[0087] (40);

[0088] (41);

[0089] (42);

[0090] In the formula: and These represent the upper and lower limits of the power output of the prime mover in the CCHP microgrid system, respectively. and These represent the upper and lower limits of the output of the electric-to-heat unit, respectively. and These represent the upper and lower limits of the output of the absorption chiller unit, respectively. and These represent the upper and lower limits of the output of the gas-fired boiler, respectively. and These represent the upper and lower limits of the heat exchanger's output, respectively. This indicates the maximum output value of the municipal power grid; The dual variable of the inequality constraint is a 0-1 variable;

[0091] The upper and lower limits of energy trading between the hybrid shared energy storage station and the CCHP microgrid system are constrained by formulas (43) to (48), which are as follows:

[0092] (43);

[0093] (44);

[0094] (45);

[0095] (46);

[0096] (47);

[0097] (48);

[0098] in, and These represent the maximum trading energy between the hybrid shared energy storage station and the CCHP microgrid system, respectively. and These are the dual variables of the equality constraints and inequality constraints, respectively, and are 0-1 variables; and The auxiliary binary variable is a 0-1 variable.

[0099] In the above scheme, when establishing the solution method for the two-layer model optimization problem in step S3, there are coupling constraints between the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, which are difficult to solve directly. Considering that the two-layer model includes two mathematical processes, it can be simplified to formula (49), and formula (49) is as follows:

[0100] (49);

[0101] In the formula: and These are the objective functions of the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, respectively. and These represent the sets of equality constraints for the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, respectively. and These represent the sets of inequalities for the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, respectively.

[0102] In the above scheme, in step S3, for the lower-level multiple CCHP system side operation optimization model with a convex objective function and a non-empty feasible solution set, its first-order necessary condition can be given by the Karush-Kuhn-Tucker (KKT) condition, and the lower-level multiple CCHP system side operation optimization model can be replaced by the KKT relaxation condition; by constructing the Lagrangian function of the lower-level multiple CCHP system side operation optimization model, the lower-level multiple CCHP system side operation optimization model is transformed into a supplementary condition for a single-layer optimization problem, resulting in a mathematical programming problem with equilibrium constraint (MPEC); therefore, the two-layer model optimization problem is transformed into a single-layer nonlinear optimization problem, expressed by formula (50), which is as follows:

[0103] (50);

[0104] In the formula: and Lagrange multipliers representing the equality and inequality constraints of the lower-level CCHP system side-run optimization equations and inequalities, respectively; complementary relaxation constraints. It can be linearized into a constraint using Big-M. and Where M is a sufficiently large positive number. It is a binary 0-1 variable.

[0105] The beneficial effects of the present invention are as follows: (1) The present invention targets a regional integrated energy system with multiple CCHP microgrids, with the optimization objectives of minimizing the operating cost of the microgrid system and maximizing the benefits of the hybrid shared energy storage station, and provides a new control method for a system coupled with multiple microgrids and shared energy storage. (2) The present invention optimizes the location, capacity and energy storage and release power of the shared energy storage station and the cost of the CCHP system by considering whether the CCHP system side is equipped with an electric heat transfer device and whether the hybrid shared energy storage side is equipped with an electric heat transfer device. It studies the impact of configuring electric heat transfer equipment on the performance of the shared energy storage station and the CCHP system, and provides technical support for the economy and sustainability of multiple CCHP systems and regional integrated energy systems. Attached Figure Description

[0106] Figure 1 This is a schematic diagram of a hybrid shared energy storage station.

[0107] Figure 2 This is a schematic diagram of the CCHP microgrid system architecture.

[0108] Figure 3 This is a schematic diagram of a two-level optimization problem and its calculation method.

[0109] Figure 4 This is to meet user load demand and photovoltaic power during the cooling season.

[0110] Figure 5 The operating power and stored energy of the hybrid shared energy storage under Case 4.

[0111] Figure 6 The time-by-time storage and release characteristics of each CCHP system under Case 4.

[0112] Figure 7 The cost of the CCHP system is given under four scenarios.

[0113] Figure 8 The figures represent the carbon emissions from the CCHP system under four scenarios. Detailed Implementation

[0114] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0115] A method for hybrid shared energy storage regulation of multiple CCHP systems, in one specific embodiment, includes the following steps:

[0116] (1): Combination Figure 1 and Figure 2 As shown, an operational optimization model for the upper-layer hybrid shared energy storage side is constructed.

[0117] (1.1): The upper-layer hybrid shared energy storage side operation optimization model mainly determines the location and capacity of the hybrid shared energy storage station. Its objective function is to maximize the revenue of the hybrid shared energy storage station. The optimization variables are the capacity of the shared energy storage station, the maximum energy storage / release power, and the location of the shared energy storage station. The objective function of the upper-layer optimization problem is calculated by formula (1), which is as follows:

[0118] (1);

[0119] In the formula This indicates the average daily investment cost of shared electric energy storage and shared thermal energy storage; This refers to the fees charged for shared electric energy storage and shared thermal energy storage when selling energy to the CCHP microgrid system; This refers to the service fees charged to the CCHP microgrid system for shared electric energy storage and shared thermal energy storage; This represents the cost of purchasing energy from the CCHP microgrid system for shared electric energy storage and shared thermal energy storage. and These represent the investment cost and operating cost of the heat transmission pipeline, respectively. This indicates the investment cost of the electric-to-heat conversion equipment.

[0120] The average daily investment cost of the shared energy storage station is calculated using formulas (2) and (3), which are as follows:

[0121] (2);

[0122] (3);

[0123] In the formula and These represent the maximum energy storage and release capacities of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the capacities of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the cost coefficients for the maximum energy storage / release capacity of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the cost coefficients for shared electrical energy storage and shared thermal energy storage capacities, respectively. The number of days the shared energy storage station operates; and These represent the operation and maintenance costs of shared electrical energy storage and shared thermal energy storage, respectively.

[0124] The cost required for the hybrid shared energy storage station to purchase electricity and heat from CCHP microgrid system users is calculated using formulas (4) and (5), which are as follows:

[0125] (4);

[0126] (5);

[0127] In the formula and These represent the costs of purchasing electricity and heat at the energy storage station, respectively; N is the number of CCHP microgrid systems; and T is the dispatch cycle. and These represent the electricity price and heat price at which the shared energy storage station purchases energy from the CCHP microgrid system, respectively. and These represent the electricity and heat purchased by the shared energy storage station from users of the CCHP microgrid system, respectively.

[0128] The cost of electricity and heat sold by the hybrid shared energy storage station to each CCHP microgrid system user is calculated using formulas (6) and (7), which are as follows:

[0129] (6);

[0130] (7);

[0131] In the formula and These represent the fees charged by the shared energy storage station for selling electricity and heat, respectively. and These represent the prices of electricity and heat sold by the shared energy storage station to CCHP microgrid system users, respectively. and These represent the electricity and heat purchased by CCHP microgrid system users from the shared energy storage station, respectively.

[0132] The service fees payable by the CCHP microgrid system when using shared energy storage stations for energy storage and release services are calculated using formulas (8) and (9), which are as follows:

[0133] (8);

[0134] (9);

[0135] In the formula and These represent the service fees paid by the CCHP microgrid system for using energy storage and thermal storage, respectively. The service fee price for shared energy storage stations.

[0136] The shared energy storage station is connected to CCHP microgrid system users via heat transmission pipelines. The investment cost of these heat transmission pipelines... and operating costs The result is obtained through formula (10), which is as follows:

[0137] (10);

[0138] In the formula, C0 represents the investment cost per unit length of heat transfer pipeline; p grid The electricity price is the municipal power grid price; EHR is the power consumption to heat transfer ratio of the heat transfer pipeline. The cost coefficient for the electric-to-heat conversion equipment is set at 800 yuan / kWh. The capacity of the electric-to-heat conversion equipment is given. The heat transfer pipeline used in this chapter has a diameter of 0.3m, with C0 = 1941 yuan / m and HER = 0.0064.

[0139] (1.2): To ensure the normal operation of energy storage equipment, the storage and release power of shared energy storage stations should meet the upper and lower limits. In addition, at the beginning and end of each scheduling cycle, the energy stored in the energy storage station should be equal to ensure the sustainability of the scheduling strategy.

[0140] The energy storage equipment of the hybrid shared energy storage power station is constrained by formulas (11) to (14), and the specific formulas (11) to (14) are as follows:

[0141] (11);

[0142] (12);

[0143] (13);

[0144] (14);

[0145] Equations (11) to (12) represent the continuity constraints of the energy storage station; Equation (13) represents the upper and lower limits of the charging and discharging power of the energy storage station; the same CCHP microgrid system cannot charge or discharge the energy storage station simultaneously, therefore it must satisfy the charging and discharging state constraints shown in Equation (14), where 1 indicates that the state occurs, and 0 indicates that the state does not occur. and respectively This represents the charging and discharging power of a shared energy storage device (SEES) within the interval t. It refers to the electrical energy stored by a shared energy storage device over time t. and These represent the coefficients for charging and discharging, respectively. and These represent the charging and discharging states of the shared energy storage device, respectively, and are 0-1 variables; the scheduling time step... It takes 1 hour; It is the initial stored electrical energy of the shared energy storage device; It is the electrical energy stored at the end of the scheduling cycle; This refers to the power converted from electricity to heat.

[0146] Similarly, the thermal energy storage equipment of the hybrid shared energy storage station is constrained by formulas (15) to (18), which are as follows:

[0147] (15);

[0148] (16);

[0149] (17);

[0150] (18);

[0151] The meanings of equations (15) to (18) are similar to those of energy storage power stations. In the equations... and respectively This represents the heat storage and release power of a shared thermal energy storage device (STES) in the interval t. This represents the thermal energy stored in a shared thermal energy storage device over time t. and These represent the storage-release and thermal power coefficients, respectively. and These represent the heat storage and heat release states of the shared thermal energy storage device, respectively, and are 0-1 variables; The initial stored thermal energy for the shared thermal energy storage device; It is the thermal energy stored at the end of the scheduling cycle; For the coefficient of the electric-to-heat conversion equipment; This refers to the output thermal power of the electro-thermal conversion equipment.

[0152] In addition, the heating supply of the energy storage heat station is limited by the heat transmission distance, which is constrained by formula (19), as follows:

[0153] (19);

[0154] In the formula (x 0, y0) is the coordinate point of the hybrid shared energy storage station, (x i, y i ) represents the coordinate point of the i-th CCHP microgrid system.

[0155] (2): Combination Figure 1 and Figure 2 As shown, a multi-layered CCHP system-side operation optimization model is constructed.

[0156] (2.1): The lower-level model mainly controls the operating costs of users in a multi-CCHP microgrid system, which is an operation optimization problem. Operating costs mainly include electricity purchase costs. Gas purchase cost The interaction cost with the energy storage station. The lower-level optimization problem takes the minimum annual operating cost of the multi-CCHP microgrid system as the objective function, and the decision variable is the output power of each device in the CCHP microgrid system. The objective function of the lower-level optimization problem is calculated by formula (20), which is as follows:

[0157] (20);

[0158] The electricity purchase cost from the municipal power grid for the multi-CCHP microgrid system is calculated using formula (21), which is as follows:

[0159] (twenty one);

[0160] In the formula For the price of municipal power grid, Electricity supplied to the municipal power grid.

[0161] The gas cost of a multi-CCHP microgrid system is calculated using formula (22), which is as follows:

[0162] (twenty two);

[0163] In the formula It's the price of natural gas; It is the output power of the prime mover; It is the output thermal power of the gas-fired boiler; It has the low calorific value of natural gas; and These are the efficiencies of the prime mover and the gas boiler, respectively.

[0164] In addition, in order to calculate the total annual cost of the CCHP microgrid system, the investment cost of each device needs to be calculated using formulas (23) and (24), which are as follows:

[0165] (twenty three);

[0166] (twenty four);

[0167] In the formula R N The installed capacity of each device in the CCHP microgrid system is represented by m; the number of devices in the CCHP microgrid system is represented by I. NLet be the unit initial investment cost of each device in the CCHP microgrid system; j be the annual interest rate, with a value of 0.1; r be the capital recovery factor; n represent the total life cycle of each device in the CCHP microgrid system; it is assumed that all devices in the CCHP microgrid system have the same annual interest rate and life cycle, with a life cycle of 20 years.

[0168] (2.2): In order to ensure the safe and stable operation of each CCHP microgrid system, the operation of the CCHP microgrid system needs to meet the supply and demand matching of the system and the user load demand, that is, to meet the constraints of power balance, heat balance, cold balance and energy storage and release power balance.

[0169] The power balance between the CCHP microgrid system and users is constrained by formula (25), which is as follows:

[0170] (25);

[0171] In the formula , and These represent the photovoltaic power, power load demand, and electrical energy consumed by the electro-thermal conversion of the i-th CCHP microgrid system at time t, respectively. These are the dual variables of the equality constraint, and are 0-1 variables; and Let t represent the electrical energy traded between the i-th CCHP microgrid system and the energy storage device at time t.

[0172] The thermal balance between the CCHP microgrid system and users is constrained by formula (26), which is as follows:

[0173] (26);

[0174] In the formula It is the heat energy provided by the heat exchanger of the i-th CCHP microgrid system at time t; This represents the thermal energy provided by the i-th CCHP microgrid system at time t; This represents the heat load demand of the i-th user at time t.

[0175] Waste heat recovery and thermal energy balance of the CCHP microgrid system are constrained by formula (27), which is as follows:

[0176] (27);

[0177] In the formula It is the waste heat energy generated by the prime mover of the i-th CCHP microgrid system at time t; It is the cooling power provided by the absorption chiller unit of the i-th CCHP microgrid system at time t; It refers to the efficiency of the heat exchanger; R is the coefficient of performance of the absorption chiller; R is the output thermoelectric ratio of the prime mover. It refers to the efficiency of the waste heat recovery unit; and Let be the heat energy traded between the i-th CCHP microgrid system and the energy storage device at time t.

[0178] The cooling energy balance between the CCHP microgrid system and users is constrained by formula (28), which is as follows:

[0179] (28);

[0180] In the formula This represents the cooling power provided by the i-th CCHP microgrid system at time t. This represents the cooling load demand of i users at time t.

[0181] Electric-to-heat units cannot operate in both heating and cooling states simultaneously. Therefore, the operation of electric-to-heat units must meet the start-stop constraints of formulas (29) to (34). The specific formulas (29) to (34) are as follows:

[0182] (29);

[0183] (30);

[0184] (31);

[0185] (32);

[0186] (33);

[0187] (34);

[0188] In the formula and These refer to the cooling and heating power of the electric-to-heat unit, respectively. and These are the maximum cooling and heating capacities of the electric-to-heat unit, respectively. and These represent the operating status of the electric-to-heat unit, and are 0-1 variables.

[0189] The sum of the energy purchased and sold by each CCHP microgrid system through the hybrid shared energy storage station is equal to the energy stored and released by the energy storage station. Therefore, the energy balance between the CCHP microgrid system and the hybrid shared energy storage station is constrained by formulas (35) and (36), which are as follows:

[0190] (35);

[0191] (36);

[0192] To ensure the stable operation of the CCHP microgrid system, its operation is limited by the upper and lower limits of the output of each device. The upper and lower limits of each CCHP microgrid system device are constrained by formulas (37) to (42). Formula (37) is the constraint of the prime mover; formula (38) is the constraint of the electric-to-thermal unit; formula (39) is the constraint of the absorption chiller; formula (40) is the constraint of the gas boiler; formula (41) is the constraint of the heat exchanger; and formula (42) is the constraint of the power grid purchase. Formulas (37) to (42) are as follows:

[0193] (37);

[0194] (38);

[0195] (39);

[0196] (40);

[0197] (41);

[0198] (42);

[0199] In the formula and These represent the upper and lower limits of the power output of the prime mover in the CCHP microgrid system, respectively. and These represent the upper and lower limits of the output of the electric-to-heat unit, respectively. and These represent the upper and lower limits of the output of the absorption chiller unit, respectively. and These represent the upper and lower limits of the output of the gas-fired boiler, respectively. and These represent the upper and lower limits of the heat exchanger's output, respectively. This indicates the maximum output value of the municipal power grid; The dual variable of the inequality constraint is a 0-1 variable.

[0200] The upper and lower limits of energy trading between the hybrid shared energy storage station and the CCHP microgrid system are constrained by formulas (43) to (48), which are as follows:

[0201] (43);

[0202] (44);

[0203] (45);

[0204] (46);

[0205] (47);

[0206] (48);

[0207] in, and These represent the maximum trading energy between the hybrid shared energy storage station and the CCHP microgrid system, respectively. and These are the dual variables of the equality constraints and inequality constraints, respectively, and are 0-1 variables; and The auxiliary binary variable is a 0-1 variable.

[0208] (3): Based on Figure (3), establish a solution method for the two-layer model optimization problem.

[0209] (3.1): In the bi-layer optimization model established in this patent, there are coupling constraints between the upper-layer model and the lower-layer model, making it difficult to solve directly. The bi-layer model includes two mathematical processes, which can be simplified to formula (49), as follows:

[0210] (49);

[0211] In the formula and These are the objective functions for the upper-level optimization problem and the lower-level optimization problem, respectively; and These represent the sets of equality constraints for the upper-level optimization problem and the lower-level optimization problem, respectively. and These represent the sets of inequalities for the upper-level optimization problem and the lower-level optimization problem, respectively.

[0212] (3.2): For the lower-level optimization problem with a convex objective function and a non-empty feasible solution set, the first-order necessary conditions can be given by the Karush-Kuhn-Tucker (KKT) conditions, and the lower-level optimization problem can be replaced by the KKT relaxation conditions. By constructing the Lagrangian function of the lower-level optimization problem, the lower-level optimization problem is transformed into a supplementary condition for a single-level optimization problem, resulting in a mathematical programming problem with equilibrium constraints (MPEC). Therefore, the two-level optimization problem is transformed into a single-level nonlinear optimization problem, expressed by formula (50), which is as follows:

[0213] (50);

[0214] In the formula and These represent the Lagrange multipliers for the equality and inequality constraints of the lower-level optimization problem, respectively. Complementary relaxation constraints. It can be linearized into a constraint using Big-M. and Where M is a sufficiently large positive number. It is a binary 0-1 variable.

[0215] First, the Lagrangian function of the lower-level model is established using formula (51), which is as follows:

[0216] (51);

[0217] Therefore, the KKT conditions satisfied by the lower-level model are expressed by formula (52), which is as follows:

[0218] (52);

[0219] The equality constraints of the lower-level optimization problem remain unchanged, while the inequality constraints can be transformed using KKT complementary relaxation conditions. Thus, the bi-level optimization problem can be transformed into an MPEC problem with attachment constraints, expressed by formulas (53) to (62), which are as follows:

[0220] (53);

[0221] (54);

[0222] (55);

[0223] (56);

[0224] (57);

[0225] (58);

[0226] (59);

[0227] (60);

[0228] (61);

[0229] (62);

[0230] Among them, formulas (55) to (62) are complementary relaxation conditions for inequality constraints. Based on the Big-M method, by introducing 0-1 variables, the transformed nonlinear complementary relaxation conditions can be further transformed into mixed integer linear constraints. This patent uses the upper and lower limit constraints of the prime mover (formula 55) and the inequality constraints of the hybrid shared energy storage station (formula 62) as examples for illustration. The method of converting nonlinear constraints into mixed integer linear constraints is expressed by formulas (63) and (64), which are as follows:

[0231] (63);

[0232] (64);

[0233] In the formula and It is a sufficiently large positive number. and These are 0-1 variables. The transformation methods for other inequality constraints are similar, and will not be elaborated upon in this patent.

[0234] (4): Based on four scenarios: Case 1: CCHP system side is not charged and heat transfer is not charged; Case 2: CCHP system side is not charged and heat transfer is not charged; hybrid shared energy storage side is charged and heat transfer is not charged; Case 3: CCHP system side is charged and heat transfer is not charged; hybrid shared energy storage side is not charged and heat transfer is not charged; Case 4: CCHP system side is charged and heat transfer is not charged; hybrid shared energy storage side is charged and heat transfer is not charged. The location, capacity and energy storage and release power of the shared energy storage station, as well as the cost of the CCHP system, are optimized respectively.

[0235] (4.1) Based on four scenarios: Case 1: CCHP system side is not charged and heat transfer is not charged; Case 2: CCHP system side is not charged and heat transfer is not charged; hybrid shared energy storage side is charged and heat transfer is not charged; Case 3: CCHP system side is charged and heat transfer is not charged; hybrid shared energy storage side is not charged and heat transfer is not charged; Case 4: CCHP system side is charged and heat transfer is not charged; combined with Figure 4 As shown in Tables 1 and 2, the location of hybrid shared energy storage stations is optimized.

[0236] The specific solution process is as follows: Figure 3 As shown, the upper-level model in the two-layer model constructed in this paper includes integer variables and continuous variables, and there are nonlinear constraints. The lower-level model is a mixed-integer linear programming problem. There is a coupling relationship between the upper and lower-level models, which is difficult to solve directly. By constructing the Lagrangian function of the lower-level model and based on the KKT complementary relaxation conditions of the lower-level model, the lower-level model is converted into the constraint conditions of the upper-level model. The converted single-layer nonlinear model is shown in Appendix Equations (53)-(62). Then, the nonlinear terms in the converted single-layer nonlinear model are linearized using the Big-M method to form a single-layer mixed-integer linear programming problem. The specific solution and conversion process is shown in the conversion of Equations (51) to (64) in the instruction manual. The mixed-integer linear programming problem is solved by calling the commercial solver CPLEX 12.9 and YALMIP toolbox in Matlab2020a, and the optimization results are obtained.

[0237] First, the Lagrangian function of the lower-level model is established using formula (51), which is as follows:

[0238] (51);

[0239] Therefore, the KKT conditions satisfied by the lower-level model are expressed by formula (52), which is as follows:

[0240] (52);

[0241] The equality constraints of the lower-level optimization problem remain unchanged, while the inequality constraints can be transformed using KKT complementary relaxation conditions. Thus, the bi-level optimization problem can be transformed into an MPEC problem with attachment constraints, expressed by formulas (53) to (62), which are as follows:

[0242] (53);

[0243] (54);

[0244] (55);

[0245] (56);

[0246] (57);

[0247] (58);

[0248] (59);

[0249] (60);

[0250] (61);

[0251] (62);

[0252] Among them, formulas (55) to (62) are complementary relaxation conditions for inequality constraints. Based on the Big-M method, by introducing 0-1 variables, the transformed nonlinear complementary relaxation conditions can be further transformed into mixed integer linear constraints. This patent uses the upper and lower limit constraints of the prime mover (formula 55) and the inequality constraints of the hybrid shared energy storage station (formula 62) as examples for illustration. The method of converting nonlinear constraints into mixed integer linear constraints is expressed by formulas (63) and (64), which are as follows:

[0253] (63);

[0254] (64);

[0255] In the formula and It is a sufficiently large positive number. and These are 0-1 variables. The transformation methods for other inequality constraints are similar, and will not be elaborated upon in this patent.

[0256] The optimization results are shown in Table 3. As can be seen from Table 3, the location of the shared energy storage station undergoes only minor changes, and the changes in the location of the shared energy storage station and its distance from each CCHP system are also relatively small. Therefore, the electric-to-thermal conversion equipment has a relatively small impact on the location of the hybrid shared energy storage station.

[0257] Table 1 Technical parameters of CCHP microgrid system:

[0258] ;

[0259] Table 2 Energy Trading Prices [RMB / kWh]:

[0260] ;

[0261] Table 3. Location optimization results for hybrid shared energy storage stations:

[0262] ;

[0263] Note: (x0, y0) are the coordinates of the energy storage station, and d1~d3 are the distances from the hybrid shared energy storage station to each microgrid system user.

[0264] (4.2) Based on four scenarios: Case 1: CCHP system side without power transfer to heat, hybrid shared energy storage side without power transfer to heat; Case 2: CCHP system side without power transfer to heat, hybrid shared energy storage side with power transfer to heat; Case 3: CCHP system side with power transfer to heat, hybrid shared energy storage side without power transfer to heat; Case 4: CCHP system side with power transfer to heat, hybrid shared energy storage side with power transfer to heat, combined with Figure 4 As shown in Tables 1 and 2, the capacity and energy storage and release power of the hybrid shared energy storage station were optimized, and the capacity optimization results are shown in Table 4.

[0265] Table 4. Capacity optimization of energy storage devices:

[0266] ;

[0267] In Case 4, the optimal operating power and energy storage capacity of the energy storage device under the hybrid shared energy storage operation mode are as follows: Figure 5 As shown, from Figure 5 It can be seen that, influenced by energy prices, the "low storage, high release" characteristics of energy storage equipment, and user load demand characteristics, the changes in the stored electricity and heat capacity of shared energy storage stations are similar. Shared electrical energy storage is characterized by storing excess electricity during off-peak hours (2:00 to 8:00, 13:00 to 16:00) and releasing it back into the combined cooling, heating, and power (CCHP) system during peak hours (9:00 to 12:00, 17:00 to 21:00). Similarly, shared thermal energy storage stores heat between 2:00 and 8:00 and releases it later, thereby reducing energy waste and improving energy utilization. Furthermore, from... Figure 5 It can also be seen that during the period from 2:00 to 8:00, the hybrid shared energy storage side electric-to-thermal equipment operates, converting some of the excess electrical energy into thermal energy storage, thereby reducing the capacity of shared electrical energy storage and increasing the capacity of shared thermal energy storage.

[0268] Figure 6 The diagram shows the synchronous operation characteristics of energy storage and release in a hybrid shared energy storage station when multiple CCHP systems participate in energy sharing. Figure 6(a) shows a shared energy storage system. From 2:00 to 7:00, due to lower electricity demand from CCHP system users in office and residential buildings, the microgrid system charges the shared energy storage station. However, hotel users have higher electricity demand and therefore purchase energy from the storage station. Similarly, from 13:00 to 16:00, hotel and residential users have lower electricity demand, and excess electricity is used to charge the shared energy storage station, while office users have higher electricity demand and need to purchase electricity from the storage station. Conversely, from 9:00 to 12:00 and from 17:00 to 21:00, due to higher electricity demand from CCHP system users in all three building types, the CCHP system purchases electricity from the shared energy storage station. From 22:00 to 1:00, residential and hotel buildings have insufficient electricity and need to purchase electricity from the storage station, while office buildings have sufficient electricity, and the electricity supply between the various CCHP systems is completely complementary. Similarly, as... Figure 6 As shown in (b), the operating characteristics of shared thermal energy storage are similar to those of shared electrical energy storage. Due to the different load demands of each user, the CCHP system of multiple users can achieve synchronous energy storage and release with the shared energy storage station, thereby significantly reducing the capacity of shared energy storage.

[0269] (4.3) Based on four scenarios: Case 1: CCHP system side without power transfer to heat, hybrid shared energy storage side without power transfer to heat; Case 2: CCHP system side without power transfer to heat, hybrid shared energy storage side with power transfer to heat; Case 3: CCHP system side with power transfer to heat, hybrid shared energy storage side without power transfer to heat; Case 4: CCHP system side with power transfer to heat, hybrid shared energy storage side with power transfer to heat, combined with Figure 4 Tables 1 and 2 show the calculations for the cost and carbon emissions of the CCHP microgrid system. The calculation results are as follows: Figure 7 , Figure 8As shown in the figure, Case 2 exhibits a significant advantage from a total cost perspective, with the lowest total cost of RMB 189,526.33. This result indicates that configuring electric-to-heat (EHH) equipment on the hybrid shared energy storage side helps CCHP systems achieve more optimized energy utilization and cost-effectiveness. Although the electricity purchase cost of Case 2 is relatively high, the significant reduction in gas purchase cost and interaction cost is sufficient to offset the increase in electricity purchase cost and drive down the total cost. In contrast, Case 1 has the highest total cost, reaching RMB 196,588.86. This is mainly because, without EHH equipment, the system relies more on gas turbines to meet users' electricity and heat energy demands, leading to a significant increase in gas purchase cost. The total costs of Case 3 and Case 4 fall between Case 1 and Case 2. The total cost of Case 3 is RMB 192,950.24, slightly lower than Case 1 but higher than Case 2. This is because, although the gas purchase cost decreases after the CCHP system is converted to heat, the increase in electricity purchase cost and investment cost affects the total cost. The total cost of Case 4 is 195,606.82 yuan, close to that of Case 1. This indicates that when both the CCHP system and the hybrid shared energy storage side are equipped with heat conversion equipment, although the gas purchase cost and interaction cost decrease, the increase in electricity purchase cost and investment cost significantly offsets the overall cost.

[0270] The economic optimization results of the hybrid shared energy storage station are shown in Table 5. From the perspective of investment cost, the configuration of the electric-to-thermal (ETG) equipment significantly reduces the initial investment of the system. In Case 2, where only the ETG equipment is configured on the hybrid shared energy storage side, the investment cost is reduced by 21.39% compared to the baseline scenario Case 1. This reduction indicates that the ETG technology effectively reduces redundant equipment investment by optimizing the energy storage system configuration. When the CCHP system side is also configured with ETG equipment, as shown in Case 4, although the investment cost is slightly higher than Case 2, it is still 22.42% lower than Case 1, demonstrating the advantage of joint configuration in cost control. Similarly, in terms of revenue, the configuration of ETG equipment also brings significant improvement. In Case 2, configuring ETG equipment only on the energy storage side increases revenue by 12.43% compared to Case 1. This is mainly due to the participation of ETG equipment in electricity market peak shaving and demand response mechanisms, as well as the additional revenue brought by the reduction in energy storage capacity. In Case 4, configuring electro-thermal conversion equipment on both the CCHP system side and the shared energy storage side increased revenue by 12.93% compared to Case 1 and by 0.60% compared to Case 2, indicating a synergistic effect in revenue growth when both sides are equipped with electro-thermal conversion equipment. Furthermore, payback period is a crucial indicator of investment economics. In Case 2, configuring electro-thermal conversion equipment only on the energy storage side shortened the payback period by 29.96% compared to Case 1, demonstrating that electro-thermal conversion equipment significantly improves the economic efficiency of the energy storage side. In Case 4, configuring electro-thermal conversion equipment on both sides shortened the payback period by 31.40% compared to Case 1 and by 1.94% compared to Case 2, further proving the economic advantages of combined configuration.

[0271] Table 5. Economic viability of hybrid shared energy storage stations:

[0272] ;

[0273] Based on the technical solution of the present invention, the above-described embodiments are only one application of the present invention. All other embodiments obtained by those skilled in the art without departing from the core content of the present invention and without creative effort are within the scope of protection of the present invention.

Claims

1. A method for hybrid shared energy storage regulation of multiple CCHP systems, characterized in that, include: S1, Construct an upper-layer hybrid shared energy storage side operation optimization model; S2, constructing multiple lower-level CCHP system-side operation optimization models; S3, Establishing a solution method for two-level model optimization problems; S4 uses the presence or absence of an electro-thermal conversion device on the CCHP system side and the hybrid shared energy storage side as different preconditions. Based on the mathematical models of S1, S2 and S3, the commercial solver CPLEX 12.9 and YALMIP toolbox are called in Matlab 2020a to optimize the location, capacity and energy storage and release power of the shared energy storage station, as well as the cost of the CCHP system.

2. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 1, characterized in that, In step S4, whether the CCHP system side and the hybrid shared energy storage side are equipped with an electric-to-heat conversion device is divided into four scenarios: Scenario 1: The CCHP system side does not have an electric-to-heat conversion device, and the hybrid shared energy storage side does not have an electric-to-heat conversion device; Scenario 2: The CCHP system side does not have an electric-to-heat conversion device, and the hybrid shared energy storage side has an electric-to-heat conversion device; Scenario 3: The CCHP system side has an electric-to-heat conversion device, and the hybrid shared energy storage side does not have an electric-to-heat conversion device; Scenario 4: The CCHP system side has an electric-to-heat conversion device, and the hybrid shared energy storage side has an electric-to-heat conversion device.

3. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 1, characterized in that, In step S1, the upper-layer hybrid shared energy storage side operation optimization model is used to determine the location and capacity of the hybrid shared energy storage station. Its objective function is to maximize the revenue of the hybrid shared energy storage station. The optimization variables are the capacity of the shared energy storage station, the maximum energy storage / release power, and the location of the shared energy storage station. The objective function of the upper-layer hybrid shared energy storage side operation optimization model is calculated by formula (1), which is as follows: （1）； In the formula: This indicates the average daily investment cost of shared electric energy storage and shared thermal energy storage; This refers to the fees charged for shared electric energy storage and shared thermal energy storage when selling energy to the CCHP microgrid system; This refers to the service fees charged to the CCHP microgrid system for shared electric energy storage and shared thermal energy storage; This represents the cost of purchasing energy from the CCHP microgrid system for shared electric energy storage and shared thermal energy storage. and These represent the investment cost and operating cost of the heat transmission pipeline, respectively. This indicates the investment cost of the electric-to-heat conversion equipment; The average daily investment cost of the shared energy storage station is calculated using formulas (2) and (3), which are as follows: （2）； （3）； In the formula: and These represent the maximum energy storage and release capacities of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the capacities of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the cost coefficients for the maximum energy storage / release capacity of shared electrical energy storage and shared thermal energy storage, respectively. and These represent the cost coefficients for shared electrical energy storage and shared thermal energy storage capacities, respectively. The number of days the shared energy storage station operates; and These represent the operation and maintenance costs of shared electrical energy storage and shared thermal energy storage, respectively. The cost required for the hybrid shared energy storage station to purchase electricity and heat from CCHP microgrid system users is calculated using formulas (4) and (5), which are as follows: （4）； （5）； In the formula: and These represent the costs of purchasing electricity and heat at the energy storage station, respectively; N is the number of CCHP microgrid systems; and T is the dispatch cycle. and These represent the electricity price and heat price at which the shared energy storage station purchases energy from the CCHP microgrid system, respectively. and These represent the electricity and heat purchased by the shared energy storage station from users of the CCHP microgrid system, respectively. The cost of electricity and heat sold by the hybrid shared energy storage station to each CCHP microgrid system user is calculated using formulas (6) and (7), which are as follows: （6）； （7）； In the formula: and These represent the fees charged by the shared energy storage station for selling electricity and heat, respectively. and These represent the prices of electricity and heat sold by the shared energy storage station to CCHP microgrid system users, respectively. and These represent the electricity and heat purchased by CCHP microgrid system users from the shared energy storage station, respectively. The service fees payable by the CCHP microgrid system when using shared energy storage stations for energy storage and release services are calculated using formulas (8) and (9), which are as follows: （8）； （9）； In the formula: and These represent the service fees paid by the CCHP microgrid system for using energy storage and thermal storage, respectively. Service fees for shared energy storage stations; The shared energy storage station is connected to CCHP microgrid system users via heat transmission pipelines. The investment cost of these heat transmission pipelines... and operating costs The result is obtained through formula (10), which is as follows: （10）； In the formula: C0 is the investment cost per unit length of heat transfer pipeline; p grid The electricity price is the municipal power grid price; EHR is the power consumption to heat transfer ratio of the heat transfer pipeline. The cost coefficient for the electric-to-heat conversion equipment is set at 800 yuan / kWh. For the capacity of the electric-to-heat conversion equipment, the heat transfer pipe diameter used in this patent is 0.3m, with C0=1941 yuan / m and HER=0.0064.

4. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 3, characterized in that, In step S1, to ensure the normal operation of the energy storage equipment, the energy storage and release power of the shared energy storage station should meet the upper and lower limit constraints. At the beginning and end of each scheduling cycle, the energy stored in the energy storage station is set to be equal to ensure the sustainability of the scheduling strategy. The energy storage equipment of the hybrid shared energy storage power station is constrained by formulas (11) to (14), and the specific formulas (11) to (14) are as follows: （11）； （12）； （13）； （14）； Among them, equations (11) and (12) are the continuity constraints of the energy storage station; equation (13) represents the upper and lower limits of the charging and discharging power of the energy storage station; the same CCHP microgrid system cannot charge or discharge the energy storage station at the same time, and must satisfy the charging and discharging state constraints shown in equation (14). A value of 1 indicates that the state occurs, and a value of 0 indicates that the state does not occur. and respectively This represents the charging and discharging power of a shared energy storage device (SEES) within the interval t. It refers to the electrical energy stored by a shared energy storage device over time t. and These represent the coefficients for charging and discharging, respectively. and These represent the charging and discharging states of the shared energy storage device, respectively, and are 0-1 variables; the scheduling time step... It takes 1 hour; It is the initial stored electrical energy of the shared energy storage device; It is the electrical energy stored at the end of the scheduling cycle; Electricity to heat conversion power; Similarly, the thermal energy storage equipment of the hybrid shared energy storage station is constrained by formulas (15) to (18), which are as follows: （15）； （16）； （17）； （18）； The meanings of equations (15) to (18) are similar to those of energy storage power stations; and respectively This represents the heat storage and release power of a shared thermal energy storage device (STES) in the interval t. This represents the thermal energy stored in a shared thermal energy storage device over time t. and These represent the storage-release and thermal power coefficients, respectively. and These represent the heat storage and heat release states of the shared thermal energy storage device, respectively, and are 0-1 variables; The initial stored thermal energy for the shared thermal energy storage device; It is the thermal energy stored at the end of the scheduling cycle; This represents the coefficient of the electric-to-heat conversion equipment; This indicates the output thermal power of the electro-thermal conversion equipment; In addition, the heating supply of the energy storage heat station is limited by the heat transmission distance, which is constrained by formula (19), as follows: （19）； In the formula: (x0, y0) are the coordinates of the hybrid shared energy storage station, (x0, y0) i ,y i ) represents the coordinate point of the i-th CCHP microgrid system.

5. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 1, characterized in that, In step S2, the lower-level multiple CCHP system-side operation optimization model is used to control the operating costs of multiple CCHP microgrid system users. The operating costs include electricity purchase costs. Gas purchase cost The interaction cost with the energy storage station; the operation optimization model of the lower-level multiple CCHP system side takes the minimum annual operating cost of the multiple CCHP microgrid system as the objective function, and the decision variable is the output power of each device in the CCHP microgrid system; the objective function of the operation optimization model of the lower-level multiple CCHP system side is calculated by formula (20), and the specific formula (20) is as follows: （20）； The electricity purchase cost from the municipal power grid for the multi-CCHP microgrid system is calculated using formula (21), which is as follows: （21）； In the formula: For the price of municipal power grid, Electricity supplied to the municipal power grid; The gas cost of a multi-CCHP microgrid system is calculated using formula (22), which is as follows: （22）； In the formula: It's the price of natural gas; It is the output power of the prime mover; It is the output thermal power of the gas-fired boiler; It has the low calorific value of natural gas; and These are the efficiencies of the prime mover and the gas boiler, respectively. In addition, in order to calculate the total annual cost of the CCHP microgrid system, the investment cost of each device needs to be calculated using formulas (23) and (24), which are as follows: （23）； （24）； In the formula: R N The installed capacity of each device in the CCHP microgrid system is represented by m; the number of devices in the CCHP microgrid system is represented by I. N Let be the unit initial investment cost of each device in the CCHP microgrid system; j be the annual interest rate, with a value of 0.1; r be the capital recovery factor; n represent the total life cycle of each device in the CCHP microgrid system; it is assumed that all devices in the CCHP microgrid system have the same annual interest rate and life cycle, with a life cycle of 20 years.

6. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 5, characterized in that, In step S2, in order to ensure the safe and stable operation of each CCHP microgrid system, the operation of the CCHP microgrid system needs to meet the supply and demand matching between the system and the user load demand, that is, to meet the constraints of power balance, heat balance, cold balance and energy storage and release power balance. The power balance between the CCHP microgrid system and users is constrained by formula (25), which is as follows: （25）； In the formula: , and These represent the photovoltaic power, power load demand, and electrical energy consumed by the electro-thermal conversion of the i-th CCHP microgrid system at time t, respectively. These are the dual variables of the equality constraint, and are 0-1 variables; and Let be the electrical energy traded between the i-th CCHP microgrid system and the energy storage device at time t; The thermal balance between the CCHP microgrid system and users is constrained by formula (26), which is as follows: （26）； In the formula: It is the heat energy provided by the heat exchanger of the i-th CCHP microgrid system at time t; This represents the thermal energy provided by the i-th CCHP microgrid system at time t; This represents the heat load demand of the i-th user at time t; Waste heat recovery and thermal energy balance of the CCHP microgrid system are constrained by formula (27), which is as follows: （27）； In the formula: It is the waste heat energy generated by the prime mover of the i-th CCHP microgrid system at time t; It is the cooling power provided by the absorption chiller unit of the i-th CCHP microgrid system at time t; It refers to the efficiency of the heat exchanger; R is the coefficient of performance of the absorption chiller; R is the output thermoelectric ratio of the prime mover. It refers to the efficiency of the waste heat recovery unit; and Let be the heat energy traded between the i-th CCHP microgrid system and the energy storage device at time t; The cooling energy balance between the CCHP microgrid system and users is constrained by formula (28), which is as follows: （28）； In the formula: This represents the cooling power provided by the i-th CCHP microgrid system at time t. This represents the cooling load demand of i users at time t; Electric-to-heat conversion units cannot operate in both heating and cooling states simultaneously. Therefore, the operation of electric-to-heat conversion units must meet the start-stop constraints of formulas (29) to (34). The specific formulas (29) to (34) are as follows: （29）； （30）； （31）； （32）； （33）； （34）； In the formula: and These refer to the cooling and heating power of the electric-to-heat unit, respectively. and These are the maximum cooling and heating capacities of the electric-to-heat unit, respectively. and These represent the operating status of the electric-to-heat unit, and are 0-1 variables; The sum of the energy purchased and sold by each CCHP microgrid system through the hybrid shared energy storage station is equal to the energy stored and released by the energy storage station. Therefore, the energy balance between the CCHP microgrid system and the hybrid shared energy storage station is constrained by formulas (35) and (36), which are as follows: （35）； （36）； In order to ensure the stable operation of the CCHP microgrid system, the operation of the CCHP microgrid system is limited by the upper and lower limits of the output of each device. The upper and lower limits of each CCHP microgrid system device are constrained by formulas (37) to (42). Formula (37) represents the constraint of the prime mover; Formula (38) represents the constraint of the electric-to-heat unit; Formula (39) represents the constraint of the absorption chiller unit; Formula (40) represents the constraint of the gas boiler; Formula (41) represents the constraint of the heat exchanger; Formula (42) represents the constraint of the power grid purchase; Formulas (37) to (42) are as follows: （37）； （38）； （39）； （40）； （41）； （42）； In the formula: and These represent the upper and lower limits of the power output of the prime mover in the CCHP microgrid system, respectively. and These represent the upper and lower limits of the output of the electric-to-heat unit, respectively. and These represent the upper and lower limits of the output of the absorption chiller unit, respectively. and These represent the upper and lower limits of the output of the gas-fired boiler, respectively. and These represent the upper and lower limits of the heat exchanger's output, respectively. This indicates the maximum output value of the municipal power grid; The dual variable of the inequality constraint is a 0-1 variable; The upper and lower limits of energy trading between the hybrid shared energy storage station and the CCHP microgrid system are constrained by formulas (43) to (48), which are as follows: （43）； （44）； （45）； （46）； （47）； （48）； in, and These represent the maximum trading energy between the hybrid shared energy storage station and the CCHP microgrid system, respectively. and These are the dual variables of the equality constraints and inequality constraints, respectively, and are 0-1 variables; and The auxiliary binary variable is a 0-1 variable.

7. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 1, characterized in that, In step S3, when establishing the solution method for the two-layer model optimization problem, there are coupling constraints between the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, which are difficult to solve directly. Considering that the two-layer model includes two mathematical processes, it can be simplified to formula (49), which is as follows: （49）； In the formula: and These are the objective functions of the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, respectively. and These represent the sets of equality constraints for the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, respectively. and These represent the sets of inequalities for the upper-layer hybrid shared energy storage side operation optimization model and the lower-layer multiple CCHP system side operation optimization model, respectively.

8. The method for hybrid shared energy storage regulation of multiple CCHP systems according to claim 7, characterized in that, In step S3, for the lower-level multiple CCHP system side-run optimization model with a convex objective function and a non-empty feasible solution set, its first-order necessary conditions can be given by the Karush-Kuhn-Tucker (KKT) conditions, and the lower-level multiple CCHP system side-run optimization model can be replaced by the KKT relaxation conditions; by constructing the Lagrangian function of the lower-level multiple CCHP system side-run optimization model, the lower-level multiple CCHP system side-run optimization model is transformed into a supplementary condition for a single-level optimization problem, resulting in a mathematical programming problem with equilibrium constraint (MPEC); therefore, the two-level model optimization problem is transformed into a single-level nonlinear optimization problem, expressed by formula (50), which is as follows: （50）； In the formula: and Lagrange multipliers representing the equality and inequality constraints of the lower-level CCHP system side operation optimization equations and inequalities, respectively; complementary relaxation constraints. It can be linearized into a constraint using Big-M. and Where M is a sufficiently large positive number. It is a binary 0-1 variable.

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