Method for operation optimization of molten salt heat storage coupled combined heat and power unit under steam-electric heating

Abstract

This invention relates to an optimization method for the operation of a steam-electric heating molten salt thermal storage coupled cogeneration unit, belonging to the field of power system optimization and dispatching technology. The method includes the following steps: S1, constructing a cost and benefit mathematical model; S2, establishing a cost model for the thermal storage system; S3, constructing an objective optimization function; and S4, solving the problem using a genetic algorithm under multiple constraints to achieve comprehensive objective optimization of the operation of the steam-electric heating molten salt thermal storage coupled cogeneration unit. This invention obtains the optimal capacity configuration, thermal storage / release operation strategy, and power generation and heating operation scheme of the coupled system through objective function construction, multi-constraint design, and classical real-number encoded genetic algorithm solution. It also yields the optimal thermal storage capacity and hourly thermal storage / release system parameters for the thermal storage system and the power generation and heating operation parameters of the cogeneration unit over 24 hours, alleviating the "heat-driven power generation" constraint contradiction, providing quantitative decision-making basis for actual dispatching, and achieving the optimal balance between economic benefits and carbon emissions.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization and dispatching technology, specifically relating to an optimization method for the operation of a steam-electric heating molten salt thermal storage coupled cogeneration unit. Background Technology

[0002] Combined heat and power (CHP) technology is an important component of the energy supply system and has been widely applied in industrial steam supply and residential heating. It is also a key area of ​​concern regarding energy consumption and carbon emissions. However, CHP units have long been constrained by a "heat-driven power generation" model. Given a fixed heat load, the range of power generation adjustment is strictly limited: during peak electricity price periods, the maximum power generation is limited by the heat load, preventing the generation of higher-priced electricity to increase revenue; during off-peak electricity price periods, the minimum power generation is similarly limited by the heat load, forcing the generation of lower-priced electricity, resulting in significant revenue losses.

[0003] Molten salt thermal energy storage technology, as a highly efficient thermal energy storage solution, boasts outstanding advantages such as high thermal density, strong operational stability, and long service life. This technology uses molten salt as the thermal storage medium and can integrate multiple heat sources, including off-peak electricity and waste heat from generating units, to heat the molten salt for thermal storage. During peak demand periods, it releases heat to supplement heating or drive power generation. Addressing the core pain point of combined heat and power (CHP) systems' "heat-driven power generation" constraint, molten salt thermal energy storage technology can effectively overcome this constraint through a heat storage and release strategy: during off-peak electricity periods, generating units increase their heating power, utilizing surplus heat after meeting the heat load, combined with off-peak electricity to heat the molten salt for thermal storage; during peak electricity periods, the thermal storage system releases heat to replace the generating unit's heating, thereby freeing up the generating unit's power generation capacity and increasing profitability.

[0004] While there have been some research reports on the operation optimization of molten salt thermal energy storage coupled with cogeneration systems, there are still shortcomings in the following areas:

[0005] 1. Existing research mostly focuses on single economic performance, with few optimization methods that take into account both carbon emissions and economic efficiency.

[0006] 2. Existing studies, when considering unit revenue and carbon emissions, only include carbon emissions in the cost accounting scope. However, when the unit revenue is much higher than the carbon emission cost, its carbon emission target will lose its measurement standard.

[0007] 3. Most existing studies involve extracting steam from the unit and purchasing off-peak electricity to heat molten salt thermal storage, thereby achieving thermoelectric decoupling. However, purchasing off-peak electricity is costly, and considering the unit's thermal-to-electricity efficiency, it is difficult to quickly recover energy storage costs, which affects the unit's economic benefits. Summary of the Invention

[0008] The main objective of this invention is to overcome the shortcomings of existing research and provide an optimization method for the operation of a steam-electric heating molten salt thermal storage coupled cogeneration unit. By constructing an objective function, designing multiple constraints, and solving the problem using a classical real-number encoded genetic algorithm, the optimal capacity configuration, thermal storage / release operation strategy, and power generation and heating operation scheme of the coupled system are obtained.

[0009] This invention is achieved through the following technical solution: an operation optimization method for molten salt thermal storage coupled cogeneration units under steam-electric heating, comprising the following steps:

[0010] S1. Establish a mathematical model of fuel consumption cost for molten salt thermal storage coupled cogeneration unit, and construct a mathematical model of power generation and heating revenue.

[0011] S2. Determine the operating cost model of the thermal storage system based on the thermal storage capacity and operation and maintenance cost mathematical model;

[0012] S3. Based on steps S1 and S2, construct the objective function that maximizes economic efficiency and minimizes carbon emissions. Determine the comprehensive objective function by coupling Z-Score standardization with weighting coefficients.

[0013] S4. Based on the constraints of the cogeneration unit operation, the thermal storage system, and the thermal power supply and demand balance, and combined with the cost and benefit models established in steps S1-S2, the genetic algorithm is used to solve for the maximum value of the comprehensive objective function, obtain the optimal thermal storage capacity and the hourly operating parameters for 24 time periods, and complete the comprehensive objective optimization of the operation of the molten salt thermal storage coupled cogeneration unit under steam-electric heating.

[0014] Further, in step S1, the mathematical model for fuel consumption cost is:

[0015] ; (1)

[0016] In equation (1), The fuel cost at time t is expressed in yuan. The price is the standard coal unit price, in yuan / ton; This refers to the standard coal consumption rate for power generation. This refers to the standard coal consumption rate for heating, expressed in units of: ; Let be the generating power of the unit at time t. The heating power of the unit at time t is expressed in MW. Duration, in hours (h);

[0017] The mathematical model for the revenue from power generation and heating is as follows:

[0018] ; (2)

[0019] ; (3)

[0020] In equations (2) and (3), Let t be the revenue generated at time t. The heating revenue at time t is expressed in yuan. The electricity price for period t. This is the price for heat, in yuan / ; Let t be the power generation load. The heating load at time t is expressed in MW. Duration, in hours (h).

[0021] Furthermore, in step S2, the operating cost model of the thermal storage system is as follows:

[0022] ; (4)

[0023] ; (5)

[0024] ; (6)

[0025] ; (7)

[0026] In equations (4) to (7), For the rated thermal storage capacity, Let be the thermal storage capacity at time t, in units of: ; The unit time operation and maintenance cost of the thermal storage system is expressed in yuan. This is the thermal energy storage operation and maintenance coefficient, in units of 0.01 yuan / ; Duration, in hours (h); Let be the thermal storage power at time t. The remaining heating power of the thermal power unit after meeting the heating load, This refers to the heating power during off-peak hours. The power generation capacity for heating during off-peak hours of the generating unit is expressed in units of: ; This refers to the electrothermal conversion efficiency.

[0027] Furthermore, in step S3, the comprehensive objective function is:

[0028] ; (8)

[0029] ; (9)

[0030] ; (10)

[0031] ; (11)

[0032] ;(12)

[0033] In equations (8) to (12), Let be the net profit at time t. Let t be the revenue generated at time t. For the heating revenue at time t, Let be the fuel cost at time t. The unit time operation and maintenance cost of the thermal storage system Total net profit for the day, in yuan;

[0034] Let be the carbon emissions at time t; The carbon emission factor (represented by "tCO2 / t"); This refers to the standard coal consumption rate for power generation. This refers to the standard coal consumption rate for heating, expressed in units of: ; Let be the generating power of the unit at time t. The heating power of the unit at time t is expressed in MW. Duration, in hours (h); Total daily carbon emissions (in "t") "express);

[0035] These are the weighting coefficients; for The maximum value after Z-Score standardization for The minimum value after Z-Score standardization.

[0036] Furthermore, in step S4, the operating constraints of the thermal power unit are:

[0037] ; (13)

[0038] ;(14)

[0039] (15)

[0040] ; (16)

[0041] ; (17)

[0042] (18)

[0043] ; (19)

[0044] In equations (13) to (19), These are the minimum and maximum power outputs of the generating unit, respectively. These are the maximum downhill and uphill climbing speeds of the unit, respectively. They are respectively The thermal power of the heat load corresponds to the maximum and minimum electrical power of the unit.

[0045] Among them, the generating power of the unit at time t during the high heating power range for:

[0046] ; (20)

[0047] Generating power of the unit at time t during the low heating power range for:

[0048] ; (twenty one)

[0049] The constraints of the thermal storage system are:

[0050] ; (twenty two)

[0051] ; (twenty three)

[0052] In equations (22) and (23), Let t be the thermal storage capacity at time t, in units of: The initial capacity is 10%. ; Let be the thermal storage power at time t. The heat output at time t is expressed in units of: ; For heat release efficiency;

[0053] The thermoelectric supply and demand balance constraint is:

[0054] ; (twenty four)

[0055] Off-peak electricity hours:

[0056] ; (25)

[0057] ; (26)

[0058] Peak-valley transition period:

[0059] ;(27)

[0060] Peak power periods:

[0061] ; (28)

[0062] ; (29)

[0063] In equations (24) to (29), for The thermal power of the heat load corresponds to the minimum electrical power of the unit. for The thermal power of the thermal load corresponds to the maximum electrical power of the unit.

[0064] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on the economic-carbon emission mathematical model of molten salt thermal storage coupled cogeneration units, energy balance, unit operating characteristics, thermal storage system capacity and other constraints, this invention uses a genetic algorithm to solve for the maximum value of the comprehensive objective function that maximizes market revenue and minimizes carbon emissions. By eliminating the dual objective requirements of dimensional differences and weight coefficient balance through Z-Score standardization, the optimal thermal storage capacity and the hourly thermal storage / heat release of the thermal storage system and the power generation and heating operation parameters of the cogeneration unit are obtained for 24 time periods. This alleviates the contradiction of "heat-determined power generation" constraint, provides a quantitative decision-making basis for actual scheduling, and achieves the optimal balance between economic benefits and carbon emissions. Attached Figure Description

[0065] Figure 1 This is a flowchart of the present invention;

[0066] Figure 2 Optimize the flowchart for the genetic algorithm;

[0067] Figure 3 This is a graph showing the thermoelectric operation curve;

[0068] Figure 4 A graph showing the power generation of the generating unit and the heat storage system's charge and discharge power over 24 hours.

[0069] Figure 5 This is a real-time curve showing the change in the heat storage capacity of the thermal storage system over 24 hours.

[0070] Figure 6 A graph showing the real-time revenue and carbon emission changes of the unit's heating power over a 24-hour period. Detailed Implementation

[0071] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0072] In this embodiment, the operation mode of the molten salt thermal storage coupled cogeneration system is as follows: During off-peak electricity prices, the cogeneration units increase their heating power, and the minimum power output of the units increases synchronously with the increase in heating power. The surplus heat supplied after the units meet the heat load demand is used in conjunction with the electricity generated during off-peak electricity periods to heat the thermal storage medium, thus avoiding revenue loss caused by selling electricity at low prices during off-peak periods. During peak electricity prices (including peak periods), the thermal storage system releases heat to supplement the heat load demand gap, and the heating power of the units decreases accordingly. The maximum power output increases synchronously with the decrease in heating power, thereby maximizing electricity sales revenue. During transition periods, the thermal storage system is maintained in stable operation through appropriate thermal storage / release regulation.

[0073] like Figure 1 and Figure 2 The method for optimizing the operation of a steam-electric heating molten salt thermal storage coupled cogeneration unit, as shown, is implemented in a Python environment and includes the following steps:

[0074] S1. Construct a cost and benefit mathematical model: Establish a mathematical model of fuel consumption cost for molten salt thermal storage coupled cogeneration unit, and construct a mathematical model of power generation and heating benefits.

[0075] The mathematical model for fuel consumption cost is as follows:

[0076] ; (1)

[0077] In equation (1), The fuel cost at time t is expressed in yuan. The price is the standard coal unit price, in yuan / ton; This refers to the standard coal consumption rate for power generation. This refers to the standard coal consumption rate for heating, expressed in units of: ; Let be the generating power of the unit at time t. The heating power of the unit at time t is expressed in MW. Duration, in hours (h);

[0078] The mathematical model for the revenue from power generation and heating is as follows:

[0079] ; (2)

[0080] ; (3)

[0081] In equations (2) and (3), Let t be the revenue generated at time t. The heating revenue at time t is expressed in yuan. The electricity price for period t. This is the price for heat, in yuan / ; Let t be the power generation load. The heating load at time t is expressed in MW. Duration, in hours (h);

[0082] S2. Establish a cost model for the thermal storage system: Based on the thermal storage capacity and operation and maintenance cost mathematical model of the thermal storage system, determine the operating cost model of the thermal storage system;

[0083] The operating cost model for a thermal energy storage system is as follows:

[0084] ; (4)

[0085] ; (5)

[0086] ; (6)

[0087] ; (7)

[0088] In equations (4) to (7), For the rated thermal storage capacity, Let be the thermal storage capacity at time t, in units of: ; The unit time operation and maintenance cost of the thermal storage system is expressed in yuan. This is the thermal energy storage operation and maintenance coefficient, in units of 0.01 yuan / ; Duration, in hours (h); Let be the thermal storage power at time t. The remaining heating power of the thermal power unit after meeting the heating load, This refers to the heating power during off-peak hours. The power generation capacity for heating during off-peak hours of the generating unit is expressed in units of: ; For electrothermal conversion efficiency;

[0089] S3. Construct the target optimization function: Based on steps S1 and S2, construct the target function that maximizes economic efficiency and minimizes carbon emissions. Determine the comprehensive target function by coupling Z-Score standardization with weight coefficients.

[0090] The overall objective function is:

[0091] ; (8)

[0092] ; (9)

[0093] ; (10)

[0094] ; (11)

[0095] ;(12)

[0096] In equations (8) to (12), Let be the net profit at time t. Let t be the revenue generated at time t. For the heating revenue at time t, Let be the fuel cost at time t. The unit time operation and maintenance cost of the thermal storage system Total net profit for the day, in yuan;

[0097] Let be the carbon emissions at time t; The carbon emission factor (represented by "tCO2 / t"); This refers to the standard coal consumption rate for power generation. This refers to the standard coal consumption rate for heating, expressed in units of: ; Let be the generating power of the unit at time t. The heating power of the unit at time t is expressed in MW. Duration, in hours (h); Total daily carbon emissions (in "t") "express);

[0098] These are the weighting coefficients; for The maximum value after Z-Score standardization for Minimum value after Z-Score standardization;

[0099] S4. Solving with Genetic Algorithm under Multiple Constraints: Based on the constraints of the cogeneration unit operation, the thermal storage system, and the thermal power supply and demand balance, and combined with the cost and benefit model established in steps S1-S2, the genetic algorithm is used to solve for the maximum value of the comprehensive objective function, obtain the optimal thermal storage capacity and the hourly operating parameters for 24 time periods, and complete the comprehensive objective optimization of the operation of the molten salt thermal storage coupled cogeneration unit under steam-electric heating.

[0100] The operating constraints of the thermal power unit are:

[0101] ; (13)

[0102] ;(14)

[0103] (15)

[0104] ; (16)

[0105] ; (17)

[0106] (18)

[0107] ; (19)

[0108] In equations (13) to (19), These are the minimum and maximum power outputs of the generating unit, respectively. These are the maximum downhill and uphill climbing speeds of the unit, respectively. They are respectively The thermal power of the heat load corresponds to the maximum and minimum electrical power of the unit.

[0109] Thermoelectric operation curve diagram as follows Figure 3 As shown, the unit's power generation at time t is within the high heating power range (segment AB). for:

[0110] ; (20)

[0111] Generating power of the unit at time t in the low heating power range (section BC) for:

[0112] ; (twenty one)

[0113] The constraints of the thermal storage system are:

[0114] ; (twenty two)

[0115] ; (twenty three)

[0116] In equations (22) and (23), Let t be the thermal storage capacity at time t, in units of: The initial capacity is 10%. ; Let be the thermal storage power at time t. The heat output at time t is expressed in units of: ; For heat release efficiency;

[0117] The thermoelectric supply and demand balance constraint is:

[0118] ; (twenty four)

[0119] Off-peak electricity hours:

[0120] ; (25)

[0121] ; (26)

[0122] Peak-valley transition period:

[0123] ;(27)

[0124] Peak power periods:

[0125] ; (28)

[0126] ; (29)

[0127] In equations (24) to (29), for The thermal power of the heat load corresponds to the minimum electrical power of the unit. for The thermal power of the thermal load corresponds to the maximum electrical power of the unit.

[0128] This embodiment aims to maximize the comprehensive objective function, using off-peak electricity heating power, thermal storage / heat release power, and unit power generation / heat supply power as decision variables, and employs a genetic algorithm (500 iterations) to solve the problem. The solution results include: optimal thermal storage capacity, unit operating parameters for each of the 24 time periods, total daily revenue, and total daily carbon emissions.

[0129] The following is a specific example of the present invention.

[0130] 1. The parameters of the unit and thermal storage system are shown in Table 1.

[0131] Table 1 shows the specific parameters in this example.

[0132]

[0133] 2. Electricity pricing mechanism and load data.

[0134] Time period division:

[0135] Off-peak electricity hours (thermal storage): 00:00-07:00, 11:00-13:00, electricity price 375.2 yuan / MWh;

[0136] Transitional periods (heat preservation): 07:00-08:00, 13:00-17:00, 23:00-24:00, electricity price 587.3 yuan / MWh;

[0137] Peak electricity hours (heat release): 08:00-11:00, 17:00-23:00, electricity price 820.5 yuan / MWh;

[0138] Peak hours: 18:00-20:00, electricity price: 984.6 yuan / MWh.

[0139] 24-hour heating load (unit: MW): 210, 205, 200, 216, 232, 254, 298, 332, 348, 354, 357, 337, 326, 332, 337, 343, 348, 357, 357, 357, 348, 321, 276;

[0140] Total daily power generation load: 6000MWh.

[0141] 3. Solve the problem.

[0142] Operating cycle: 24 hours, duration Δt=1h (T=23);

[0143] Algorithm: Genetic algorithm, 500 iterations;

[0144] Decision variables: off-peak electricity thermal storage power, thermal storage / release power, and generator power / heat supply power.

[0145] 4. Explanation of optimization results.

[0146] The optimal thermal storage capacity operation optimization results obtained through algorithm solution are shown in Table 2. The core output data are:

[0147] (1) Hourly parameters for 24-hour period, thermal storage power Heat dissipation power Generating capacity of the unit Heating power Power generation load ;

[0148] (2) Economic indicators, hourly returns Daily total revenue ;

[0149] (3) Environmental indicators, hourly carbon emissions Daily total carbon emissions .

[0150] Table 2 Optimization Results

[0151]

[0152] Figure 4The curves showing the changes in generator power and thermal storage / release power of the thermal storage system over 24 hours under optimal thermal storage capacity are presented. During off-peak hours (00:00-07:00, 11:00-13:00), the thermal storage power is maintained in the range of approximately 130-150MW, while the generator load drops to approximately 150-160MW. During peak hours (08:00-11:00, 17:00-23:00), the thermal release power is approximately 130MW, while the generator power increases to 270-280MW. During peak hours (18:00-20:00), both thermal release power and generator power reach their peak values, maximizing the capture of high-priced electricity revenue.

[0153] Figure 5 The system demonstrates the real-time changes in the heat storage capacity of the thermal energy storage system. During off-peak hours, the heat storage capacity gradually increases from an initial 10% to 95%; during the transition period, it is finely adjusted to 100% full capacity; during peak hours, the heat storage capacity decreases rapidly, with about 30% remaining after the peak period ends. It continues to release heat during subsequent peak periods, and by the end of 24 hours, the heat storage capacity returns to the initial 10%, meeting the requirements for heat storage / release balance.

[0154] Figure 6 The system displays the unit's heating power, real-time revenue, and carbon emissions over 24 hours. During peak electricity price periods, the thermal storage system releases heat to replace part of the unit's heating load, reducing the unit's heating power and thus increasing the unit's power generation, thereby increasing the unit's revenue. During off-peak electricity price periods, the unit increases its heating power while simultaneously reducing its power generation, utilizing the unit's surplus heating capacity combined with off-peak electricity to heat molten salt for thermal storage. Revenue is relatively low during this period.

[0155] In summary, by coordinating the storage and release of heat during peak and off-peak periods, overall revenue can be improved while carbon emissions are reduced by reasonably decreasing power generation, ultimately achieving the dual optimization goals of increased revenue and carbon emission reduction.

[0156] This invention addresses the operation and scheduling of molten salt thermal energy storage coupled with combined heat and power (CHP) systems. An economic-carbon emission target optimization model is established, and the optimal operating parameters are obtained through a genetic algorithm. The optimized scheme fully utilizes time-of-use electricity price differences to achieve arbitrage, increasing revenue while reducing carbon emissions by decreasing power generation. This provides a basis for decision-making in actual operation, ensuring that the system achieves optimal economic benefits and carbon emissions while meeting heating requirements.

[0157] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for operation optimization of molten salt heat storage coupled combined heat and power unit under steam-electric heating, characterized in that, Includes the following steps: S1. Establish a mathematical model of fuel consumption cost for molten salt thermal storage coupled cogeneration unit, and construct a mathematical model of power generation and heating revenue. S2. Determine the operating cost model of the thermal storage system based on the thermal storage capacity and operation and maintenance cost mathematical model; S3. Based on steps S1 and S2, construct the objective function that maximizes economic efficiency and minimizes carbon emissions. Determine the comprehensive objective function by coupling Z-Score standardization with weighting coefficients. S4. Based on the constraints of the cogeneration unit operation, the thermal storage system, and the thermal power supply and demand balance, and combined with the cost and benefit models established in steps S1-S2, the genetic algorithm is used to solve for the maximum value of the comprehensive objective function, obtain the optimal thermal storage capacity and the hourly operating parameters for 24 time periods, and complete the comprehensive objective optimization of the operation of the molten salt thermal storage coupled cogeneration unit under steam-electric heating.

2. The method of claim 1, wherein, In step S1, The mathematical model for fuel consumption cost is as follows: ； （1） In formula (1), is the fuel cost at time t, with unit of yuan; is the unit price of standard coal, with unit of yuan / ton; is the power generation standard coal consumption rate, is the heating standard coal consumption rate, with unit of: ; is the unit price of standard coal, with unit of yuan / ton; is the unit price of standard coal, with unit of yuan / ton; is the time length, with unit of h; The mathematical model for the revenue from power generation and heating is as follows: ； （2） ； （3） In formula (2), formula (3), is the power generation income at time t, is the heat supply income at time t, in yuan; is the electricity price in the period t, is the heat price, in yuan / MWh; ; is the power generation load at time t, is the heat supply load at time t, in MW; is the time length, in h.

3. The method according to claim 1, characterized in that, In step S2, the operating cost model of the thermal storage system is as follows: ； （4） ； （5） ； （6） ； （7） In equations (4) to (7), For the rated thermal storage capacity, Let be the thermal storage capacity at time t, in units of: ; The unit time operation and maintenance cost of the thermal energy storage system is expressed in yuan. This is the thermal energy storage operation and maintenance coefficient, in units of 0.01 yuan / ; Duration, in hours (h); Let be the thermal storage power at time t. The remaining heating power of the thermal power unit after meeting the heating load, This refers to the heating power during off-peak hours. The power generation capacity for heating during off-peak hours of the generating unit is expressed in units of: ; This refers to the electrothermal conversion efficiency.

4. The method according to claim 1, characterized in that, In step S3, the comprehensive objective function is: ； （8） ； （9） ； （10） ； （11） ； （12） In equations (8) to (12), Let be the net profit at time t. Let t be the revenue generated at time t. For the heating revenue at time t, Let be the fuel cost at time t. The unit time operation and maintenance cost of the thermal storage system Total net profit for the day, in yuan; Let be the carbon emissions at time t; Carbon emission factor; This refers to the standard coal consumption rate for power generation. This refers to the standard coal consumption rate for heating, expressed in units of: ; Let be the generating power of the unit at time t. The heating power of the unit at time t is expressed in MW. Duration, in hours (h); Total daily carbon emissions; These are the weighting coefficients; for The maximum value after Z-Score standardization for The minimum value after Z-Score standardization.

5. The method according to claim 1, characterized in that, In step S4, The operating constraints of the thermal power unit are: ； （13） ； （14） （15） ； （16） ； （17） （18） ； （19） In equations (13) to (19), These are the minimum and maximum power outputs of the generating unit, respectively. These are the maximum downhill and uphill climbing speeds of the unit, respectively. They are respectively The thermal power of the heat load corresponds to the maximum and minimum electrical power of the unit. Among them, the generating power of the unit at time t during the high heating power range for: ； （20） Generating power of the unit at time t during the low heating power range for: ； （21） The constraints of the thermal storage system are: ； （22） ； （23） In equations (22) and (23), Let t be the thermal storage capacity at time t, in units of: The initial capacity is 10%. ; Let be the thermal storage power at time t. The heat output at time t is expressed in units of: ; For heat release efficiency; The thermoelectric supply and demand balance constraint is: ； （24） Off-peak electricity hours: ； （25） ； （26） Peak-valley transition period: ； （27） Peak power hours: ； （28） ； （29） In equations (24) to (29), for The thermal power of the heat load corresponds to the minimum electrical power of the unit. for The thermal power of the thermal load corresponds to the maximum electrical power of the unit.

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