Scheduling method for park steam heat supply system containing electric heat accumulator
By establishing an upper-level capacity configuration model and a lower-level recently dispatch optimization model in the steam heating system in the park, and performing the optimal capacity configuration and recent scheduling optimization of the electric heat storage device, the problem of ineffective scheduling of the heating system in the existing technology is solved, and efficient utilization of low-rise electricity and balanced heat load supply and demand is achieved, system flexibility and stability are improved, and operating costs and carbon emissions are reduced.
Patent Information
- Application Number
- CN202510063311.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-15
- Publication Date
- 2025-06-06
AI Technical Summary
In the prior art, the park steam heating system containing electric heat storage lacks an effective scheduling method, and cannot effectively utilize night low-trough electricity and achieve a balance of supply and demand for heat load, resulting in insufficient flexibility and stability of the heating system, high operating costs and large carbon emissions.
By establishing an upper-level capacity configuration model and a lower-level recently dispatch optimization model, and using optimization algorithms to perform optimal capacity configuration and recent scheduling optimization of electric heat storage, the effective utilization of electric heat storage and the intelligent scheduling and control of the heating system are realized.
It realizes effective utilization of low-trough electricity at night, balances the supply and demand of heat load, improves the flexibility and stability of the heating system, saves operating costs, and reduces carbon emissions.
Smart Images
Figure CN120107013A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of park steam heating system scheduling, and in particular relates to a park steam heating system scheduling method containing an electric heat accumulator. Background Art
[0002] The steam heating system is an important subsystem of the park energy system, which can meet the heat demand in the park production. Especially in some large industrial parks, more than 50% of the energy required for industrial production is supplied in the form of thermal energy. Traditional park steam heating systems mainly rely on coal-fired or gas-fired boilers for steam production and heating. These systems have high energy consumption and large carbon emissions. At the same time, the traditional steam heating system is greatly affected by load fluctuations during operation, and cannot flexibly respond to changes in heat demand in the park, which easily leads to problems such as uneven heating, energy waste and increased operating costs. In recent years, with the increasingly stringent environmental protection policies and the continuous improvement of energy conservation and emission reduction requirements, many parks have begun to seek cleaner and more efficient heating solutions.
[0003] As a new type of heat storage device, electric heat accumulators can be charged and stored at a lower electricity price during off-peak hours of the power grid or store heat during low load hours, and release the stored heat during peak hours of the power grid or peak hours of heating demand, thereby achieving a balance between heat load supply and demand, improving the flexibility and stability of the heating system, reducing operating costs and reducing carbon emissions. Electric heat accumulators have significant advantages in utilizing the difference in peak and valley electricity prices of the power grid, and are therefore widely considered to be a key device for optimizing the scheduling of park heating systems. However, in the prior art, heating systems containing electric heat accumulators lack effective scheduling methods. How to connect electric heat accumulators to the park's heating pipe network system and achieve effective utilization, give full play to the advantages of electric heat accumulators, and realize intelligent scheduling and control of the park's heating system is an issue that urgently needs to be solved. Summary of the invention
[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a scheduling method for a park steam heating system containing an electric heat storage device, which can achieve effective utilization of low-peak electricity at night and balance the supply and demand of heat load, improve the flexibility and stability of the heating system, save the operating cost of the steam heating system, reduce carbon emissions, ensure the balance of supply and demand between the user side and the source side, optimize the capacity configuration of the electric heat storage device, and achieve the day-ahead optimal scheduling of the heating system; the optimal capacity configuration and day-ahead scheduling of the electric heat storage device can be achieved through an upper-level capacity configuration model and a lower-level day-ahead scheduling optimization model, wherein the upper-level capacity configuration model optimizes the capacity configuration of the electric heat storage device with the net present value of the total profit over the entire life cycle of the electric heat storage device as the objective function, thereby improving the flexibility and stability of the heating system; the lower-level day-ahead scheduling optimization model combines the optimal heat storage capacity obtained by the upper-level capacity configuration model with the maximum peak shaving and valley filling as the objective function, and optimizes the day-ahead operation scheduling using a self-heuristic optimization algorithm, thereby obtaining an electric heat storage device scheduling scheme, thereby achieving self-optimization decision-making, and improving the accuracy of the day-ahead scheduling of the source, grid, load, and storage of the heating system.
[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:
[0006] A method for scheduling a park steam heating system containing an electric heat storage device comprises the following steps:
[0007] S1. Based on the heat load impact data of the heat users of the heating units, the characteristic analysis of the heat load data on the load side of the park and the prediction of the heat load supply and demand of the park are realized;
[0008] S2. Based on the characteristic analysis results of the heat load data on the load side of the park in step S1, with the goal of maximizing the net present value of the total profit over the entire life cycle of the electric heat accumulator, and with the maximum designable capacity and the maximum designable charging and discharging heat power of the electric heat accumulator as constraints, an upper-level capacity configuration model is constructed, and the upper-level capacity configuration model is solved based on an optimization algorithm to obtain the optimal capacity configuration for realizing the electric heat accumulator;
[0009] S3, based on the optimal capacity configuration of the electric heat accumulator in step S2, determining relevant parameters of the electric heat accumulator, and obtaining a parameter optimization scheme for the electric heat accumulator under the optimal capacity configuration;
[0010] S4. Combining the parameter optimization scheme of the electric heat storage device under the optimal capacity configuration obtained in S3 and the prediction result of the heat load supply and demand of the park in step S1, with the goal of maximizing peak shaving and valley filling, and taking the charging and discharging power, capacity and continuity of the electric heat storage device as constraints, a lower-level day-ahead scheduling optimization model is constructed, and the lower-level day-ahead scheduling optimization model is solved based on the optimization algorithm to obtain the operation scheduling optimization scheme.
[0011] In the above technical solution, further, the park steam heating system containing electric heat storage device specifically includes a heating unit, a municipal power grid, a controller, an electric heat storage device, a heat user, a desalted water tank, an electric ball valve and a user inlet regulating valve.
[0012] Furthermore, the heating unit includes: coal-fired boiler, gas boiler, oil boiler, electric boiler, heat pump system, biomass boiler; the electric heat accumulator includes: solid heat accumulator, steam heat accumulator, eutectic salt heat accumulator;
[0013] Among them, the heat storage process of the electric heat accumulator includes: the excess heat is exchanged through a heat exchanger or heated by an electric heating element, the heat is transferred to the heat storage medium, the temperature of the heat storage medium gradually increases and stores heat; the heat release process includes: the heat storage medium begins to release the stored heat, for the steam heat accumulator, the steam is directly transported to the heating system, for the solid or eutectic salt heat accumulator, the heat is transferred to the steam through heat conduction or radiation and then transported to the heating system.
[0014] Furthermore, the heating unit is connected in parallel with the electric heat accumulator, and the hot steam of the electric heat accumulator enters the heating main pipeline to meet the heat demand of users in the park. A part of the hot steam of the heating unit enters the heating main pipeline to meet the heat demand of users in the park, and the other part enters the heating branch pipeline and enters the electric heat accumulator to realize steam storage; an electric ball valve is provided at the connection between the steam outlet of the heating unit and the heating main pipeline, and at the connection between the steam outlet of the heating unit and the heating branch pipeline, and at the steam outlet of the electric heat accumulator, for controlling the outlet steam volume of the heating unit and the electric heat accumulator; a user inlet regulating valve is provided at the connection between the heat user and the heating main pipeline, for regulating the flow of steam to the heat user. The amount of steam delivered by the user; the desalted water tank is connected to the electric heat accumulator, and the desalted water in the desalted water tank enters the electric heat accumulator through the water supply pipe to replenish the water volume inside the electric heat accumulator; the municipal power grid is connected to the electric heater in the electric heat accumulator, and the electricity of the municipal power grid is transmitted to the electric heater through the power line to meet the power demand of the electric heat accumulator; the controller is respectively connected to the connection between the steam outlet of the heating unit and the heating main pipeline, and the connection between it and the heating branch pipeline, and the electric ball valve at the steam outlet of the electric heat accumulator, and the control instructions of the controller are transmitted to the above three electric ball valves through the information flow channel to achieve optimized control of the heating unit and the electric heat accumulator.
[0015] Furthermore, in step S1, the heat load impact data of the heat users of the heating unit specifically include: source side heat load impact data, including but not limited to source side historical heating data, heating cost historical change data; load side heat load impact data, including but not limited to user historical heat load data, user production plan data; and meteorological data including temperature and humidity that have a significant impact on heat load demand.
[0016] Furthermore, in step S1, the characteristic analysis of the heat load data on the load side of the park is specifically performed by: using a clustering algorithm to perform cluster analysis on the heat load characteristics of the park users based on the heat load data of multiple historical time points of the heat users, and determining the heat load characteristics of the users;
[0017] The specific method for predicting the supply and demand of the heat load in the park is as follows: for the source side, based on the historical heating data of the source side, the historical change data of the heating cost, and the meteorological data including temperature and humidity that have a significant impact on the heat load demand, a machine learning algorithm is used to make a short-term prediction of the heat supply in each time period of the next day on the source side; for the load side, based on the user's historical heat load data, the user's production plan data, and the meteorological data including temperature and humidity that have a significant impact on the heat load demand, a machine learning algorithm is used to make a short-term prediction of the heat load demand of the park users in each time period of the next day.
[0018] Furthermore, in step S2, the specific method of constructing the upper layer capacity configuration model is:
[0019] The characteristic analysis results of the heat load data on the load side of the park include the production plans of the park's heat users and changes in heat demand;
[0020] The upper capacity configuration model takes maximizing the net present value of total profit over the entire life cycle of the electric heat storage device as its objective function, wherein the net present value of total profit over the entire life cycle of the electric heat storage device is calculated by the electric heat storage device cost model and the electric heat storage device income model. The electric heat storage device cost model accounts for the initial installation cost and operation and maintenance cost of the electric heat storage device, and the electric heat storage device income model accounts for the balanced heat load income and the off-peak electricity price heat storage income.
[0021] Since electric heat storage devices generally have a long operating life, depreciation costs can be ignored. The cost of electric heat storage devices mainly includes initial installation cost, operating cost and maintenance cost. The initial installation cost is proportional to the capacity of the electric heat storage device, and the operating and maintenance cost is the cumulative value of the annual operating and maintenance cost after considering the discount rate.
[0022] The cost model of electric heat storage is expressed as:
[0023] C=C 1 +C 2
[0024] C 1 =c cap ·SOC
[0025]
[0026] Where C is the cost of the electric heat storage device over its entire life cycle; C 1 is the initial installation cost; C 2 is the operation and maintenance cost;cap is the price per unit heat storage capacity; c o,m is the proportion of annual maintenance cost to initial installation investment; SOC is the heat storage capacity of the electric heat storage device; n is the service life of the electric heat storage device; r is the annual discount rate;
[0027] The income model of electric heat storage is expressed as:
[0028] I=I 1 +I 2
[0029]
[0030] Where I is the income of the electric heat storage device during its entire life cycle; I 1 To balance the heat load income; I 2 is the income from low-cost electricity and heat storage; w is the unit heat price; w e is the off-peak electricity price; a is the annual production days, which is determined according to the production plan of the park's heat users; ΔQ is the annual heat loss reduced after adding the electric heat storage device; Q is the heat storage capacity of the electric heat storage device, which is related to the change in heat demand; η h is the electric heating efficiency of the electric heat storage device; η s is the heat storage efficiency of the electric heat storage device; η e is the heat release efficiency of the electric heat storage device;
[0031] Then the objective function of the upper-layer capacity configuration optimization model is expressed as:
[0032] max O=IC
[0033] Among them, O is the present value of the total profit of the electric heat storage device during its entire life cycle;
[0034] The maximum designable capacity and the maximum designable charging and discharging power of the electric heat storage are constrained. The specific constraints are:
[0035] 0≤SOC≤SOC max
[0036]
[0037] Among them, SOC max The minimum capacity of the electric heat storage device that can be designed in theory is affected by the specific production scenario of the park; is the designed heat storage power of the electric heat storage device; is the minimum heat storage power of the electric heat storage device that can be designed theoretically; is the maximum heat storage power of the electric heat storage device that can be designed theoretically; is the designed heat release power of the electric heat storage device; is the minimum heat release power of the electric heat storage device that can be designed theoretically; It is the maximum heat release power of the electric heat storage device that can be designed theoretically.
[0038] Furthermore, in step S3, the relevant parameters of the electric heat accumulator are designed according to the optimal electric heat accumulator capacity and the heat storage and release power of the electric heat accumulator determined by the upper capacity configuration optimization model, including: the minimum and maximum heat storage power of the electric heat accumulator, and the minimum and maximum heat release power of the electric heat accumulator. According to the actual needs of the project, the heat storage medium volume of the electric heat accumulator, the heating power of the electric heat accumulator, the internal pipeline structure of the electric heat accumulator, and the insulation structure of the electric heat accumulator can also be designed.
[0039] Furthermore, in step S4, the lower layer day-ahead scheduling optimization model is constructed by:
[0040] After the electric heat storage device is installed, the heat load demand curve (i.e., the curve constructed based on the forecast results of the heat load demand of users in the park at each time period of the next day) can be smoothed, heat storage can be achieved during low-peak heat consumption, and heat release can be achieved during peak heat consumption, thus reducing the heat waste caused by variable load operation of the boiler; the cheap electricity during the valley period can also be converted into heat and stored for use during subsequent peak heat consumption periods.
[0041] Considering that the main scheduling scenario of electric heat storage is to use electric heat storage to store heat at low temperatures and release heat at peak temperatures (smoothing the heat load curve), the goal of scheduling strategy optimization is to achieve maximum peak shaving and valley filling (i.e., smoothing the heat load demand curve). The charging and discharging power, capacity, and continuity of the electric heat storage are used as constraints to construct a lower-level day-ahead scheduling optimization model.
[0042] The objective function of the lower-level day-ahead scheduling optimization model is:
[0043] minY=Y 1 +Y 2
[0044]
[0045] Among them, Y 1 It is an indicator for evaluating valley filling capacity; Y 2 It is an indicator for evaluating the peak shaving capability; P in (t i ) is t i The heat storage power of the electric heat storage device at that moment; P out (t i ) is t i The heat release power of the electric heat storage device at time t i ) is the predicted t i User heat load at any moment; P h (t i ) is the predicted t i The heating power of the park heating network at the moment; Δt is t i and ti+1 the time interval between
[0046] The constraints of the lower-level day-ahead scheduling optimization model are:
[0047] Q(t i+1 )=Q(t i )-[P out (t i )·B out (t i )-P in (t i )·B in (t i )·η s ]·Δt
[0048] 0≤Q(t i )≤SOC
[0049] P in,min ≤P in (t i )≤P in,max
[0050] P out,min ≤P out (t i )≤P out,max
[0051] Among them, Q(t i )Q(t i ) and Q(t i+1 ) are t i and t i+1 The heat storage of the electric heat storage device at that moment; B out (t i ) and B in (t i ) is t i The heat release and storage state of the electric heat storage device at this moment is a Boolean variable; η s is the heat storage efficiency of the electric heat storage device; P in,min and P in,max is the minimum and maximum heat storage power of the electric heat storage device; P out,min and P out,max It is the minimum and maximum heat release power of the electric heat storage device.
[0052] Furthermore, based on the obtained operation scheduling optimization plan, the park steam heating system containing electric heat storage is scheduled. The specific method is as follows:
[0053] By connecting the output of the lower-level day-ahead optimization scheduling model with the controller, the controller issues control instructions to the electric heat storage and the heating unit according to the operation scheduling optimization plan obtained after calculation by the lower-level day-ahead optimization scheduling model.
[0054] The beneficial effects of the present invention are:
[0055] (1) The present invention analyzes the characteristics of the heat load data on the load side of the park, predicts the heat load demand of the park, and predicts the heat output of the heating unit (i.e., the heat supply on the source side). Through the heat load characteristics on the load side and the supply and demand prediction of the source load, the basic conditions for the subsequent electric heat storage capacity configuration and system scheduling optimization are established;
[0056] (2) The present invention establishes an upper-level capacity configuration optimization model: based on the characteristic analysis results of the heat load data on the load side of the park, combined with the heating cost of the heating unit, the night valley electricity price, and the installation and operation and maintenance cost of the electric heat storage device, the total profit net present value of the electric heat storage device under different capacities in the entire life cycle is obtained. The goal is to maximize the total profit net present value of the electric heat storage device in the entire life cycle. With the maximum designable capacity and the maximum designable charging and discharging power of the electric heat storage device as constraints, the optimal electric heat storage device capacity is calculated based on the heuristic optimization algorithm. This can not only ensure the effective use of night valley electricity, but also maximize the economic benefits after installing the electric heat storage device, saving energy costs;
[0057] (3) The present invention establishes a lower-level system scheduling optimization model: combining the parameter optimization scheme of the electric heat storage device under the optimal capacity configuration with the heat load obtained by the source load supply and demand prediction, under the premise of realizing peak shaving and valley filling to the greatest extent, taking the charging and discharging power, capacity and continuity of the electric heat storage device as constraints, the lower-level system scheduling optimization model is established, and the daily scheduling strategy of the electric heat storage device is calculated based on the heuristic optimization algorithm to realize the day-ahead scheduling optimization, which can not only ensure the smoothest heat load curve to the greatest extent and achieve the balance of heat supply and demand, but also save the system operation cost and reduce carbon emissions, and ensure that the electric heat storage device can realize the maximum utilization value in the heating system;
[0058] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.
[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0060] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0061] Figure 1 This is a flow chart of a method for scheduling a park steam heating system containing an electric heat storage device according to the present invention;
[0062] Figure 2 The schematic diagram of the structure of the park steam heating system containing the electric heat storage device of the present invention.
[0063] In the figure, 1- desalted water tank; 2- heating unit; 3- electric heat storage device; 4- electric ball valve; 5- heat user; 6- user inlet regulating valve; 7- information flow channel; 8- power line; 9- heating main line; 10- heating branch pipeline. DETAILED DESCRIPTION
[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0065] Example 1
[0066] like Figure 1 As shown, this embodiment 1 provides a method for scheduling a park steam heating system containing an electric heat storage device.
[0067] Combined with Figure 2 The park steam heating system with electric heat storage device shown in the figure specifically illustrates the above method. The park steam heating system with electric heat storage device specifically includes a heating unit, a municipal power grid, a controller, an electric heat storage device, a heat user, a desalted water tank, an electric ball valve, and a user inlet regulating valve.
[0068] In this embodiment, the heating unit 2 includes: a coal-fired boiler, a gas boiler, an oil boiler, an electric boiler, a heat pump system, and a biomass boiler; the electric heat accumulator 3 includes: a solid heat accumulator, a steam heat accumulator, and a eutectic salt heat accumulator;
[0069] The heat storage process of the electric heat accumulator 3 includes: the excess heat is exchanged by a heat exchanger or heated by an electric heating element, the heat is transferred to the heat storage medium, the temperature of the heat storage medium gradually increases and stores the heat; the heat release process includes: the heat storage medium begins to release the stored heat, for the steam heat accumulator, the steam is directly transported to the heating system, for the solid or eutectic salt heat accumulator, the heat is transferred to the steam by heat conduction or radiation and then transported to the heating system.
[0070] In this embodiment, the heating unit 2 in the park steam heating system containing an electric heat accumulator is connected in parallel with the electric heat accumulator 3, and the hot steam of the electric heat accumulator enters the heating main pipeline 9 to meet the heat demand of park users. A part of the hot steam of the heating unit enters the heating main pipeline 9 to meet the heat demand of park users, and the other part enters the heating branch pipeline 10 and enters the electric heat accumulator 3 to realize steam storage; an electric ball valve 4 is provided at the connection between the steam outlet of the heating unit 2 and the heating main pipeline 9, and at the connection between the steam outlet of the heating unit 2 and the heating branch pipeline 10, and at the steam outlet of the electric heat accumulator 3, which is used to control the outlet steam volume of the heating unit 2 and the electric heat accumulator 3; a user inlet regulating valve is provided at the connection between the heat user 5 and the heating main pipeline 9 6, used to adjust the amount of steam delivered to the heat user 5; the desalted water tank 1 is connected to the electric heat accumulator 3, and the desalted water in the desalted water tank 1 enters the electric heat accumulator 3 through the water supply pipe, and is used to supplement the water volume inside the electric heat accumulator 3; the municipal power grid is connected to the electric heater in the electric heat accumulator 3, and the power of the municipal power grid is transmitted to the electric heater through the power line 8 to meet the power demand of the electric heat accumulator 3; the controller is respectively connected to the connection between the steam outlet of the heating unit 2 and the heating main pipeline 9, and the connection between it and the heating branch pipeline 10, and the electric ball valve 4 at the steam outlet of the electric heat accumulator 3, and the control instructions of the controller are transmitted to the electric ball valves 4 at the above three locations through the information flow channel 7 to achieve optimized control of the heating unit 2 and the electric heat accumulator 3.
[0071] In this embodiment, the method for scheduling a park steam heating system containing an electric heat storage device specifically includes the following steps:
[0072] S1. Based on the heat load impact data of the heat users of the heating units, the characteristic analysis of the heat load data on the load side of the park and the prediction of the heat load supply and demand of the park are realized;
[0073] S2. Based on the characteristic analysis results of the heat load data on the load side of the park in step S1, with the goal of maximizing the net present value of the total profit over the entire life cycle of the electric heat accumulator, and with the maximum designable capacity and the maximum designable charging and discharging heat power of the electric heat accumulator as constraints, an upper-level capacity configuration model is constructed, and the upper-level capacity configuration model is solved based on an optimization algorithm to obtain the optimal capacity configuration for realizing the electric heat accumulator;
[0074] S3, based on the optimal capacity configuration of the electric heat accumulator in step S2, determining relevant parameters of the electric heat accumulator, and obtaining a parameter optimization scheme for the electric heat accumulator under the optimal capacity configuration;
[0075] S4. Combining the parameter optimization scheme of the electric heat storage device under the optimal capacity configuration obtained in S3 and the prediction result of the heat load supply and demand of the park in step S1, with the goal of maximizing peak shaving and valley filling, and taking the charging and discharging power, capacity and continuity of the electric heat storage device as constraints, a lower-level day-ahead scheduling optimization model is constructed, and the lower-level day-ahead scheduling optimization model is solved based on the optimization algorithm to obtain the operation scheduling optimization scheme.
[0076] In this embodiment, the heat load impact data of the heat users of the heating unit in step S1 specifically include: source side heat load impact data, including but not limited to source side historical heating data and heating cost historical change data; load side heat load impact data, including but not limited to user historical heat load data and user production plan data; and meteorological data including temperature and humidity that have a significant impact on heat load demand.
[0077] The characteristic analysis of the heat load data on the load side of the park in step S1 is specifically performed by using a clustering algorithm to perform cluster analysis on the heat load characteristics of the park users based on the heat load data of multiple historical time points of the heat users, so as to determine the heat load characteristics of the users.
[0078] The specific method for predicting the supply and demand of the park's heat load in step S1 is as follows: for the source side, based on the historical heating data of the source side, the historical change data of the heating cost, and the meteorological data including temperature and humidity that have a significant impact on the heat load demand, a machine learning algorithm is used to make a short-term prediction of the heat supply for each time period of the next day on the source side; for the load side, based on the user's historical heat load data, the user's production plan data, and the meteorological data including temperature and humidity that have a significant impact on the heat load demand, a machine learning algorithm is used to make a short-term prediction of the heat load demand of the park users in each time period of the next day.
[0079] In this embodiment, the specific method of constructing the upper layer capacity configuration model in step S2 is:
[0080] The characteristic analysis results of the heat load data on the load side of the park include the production plans of the park's heat users and changes in heat demand;
[0081] The upper capacity configuration model takes maximizing the net present value of total profit over the entire life cycle of the electric heat storage device as its objective function, wherein the net present value of total profit over the entire life cycle of the electric heat storage device is calculated by the electric heat storage device cost model and the electric heat storage device income model. The electric heat storage device cost model accounts for the initial installation cost and operation and maintenance cost of the electric heat storage device, and the electric heat storage device income model accounts for the balanced heat load income and the off-peak electricity price heat storage income.
[0082] Since electric heat storage devices generally have a long operating life, depreciation costs can be ignored. The cost of electric heat storage devices mainly includes initial installation cost, operating cost and maintenance cost. The initial installation cost is proportional to the capacity of the electric heat storage device, and the operating and maintenance cost is the cumulative value of the annual operating and maintenance cost after considering the discount rate.
[0083] The cost model of electric heat storage is expressed as:
[0084] C=C 1 +C 2
[0085] C 1 =c cap ·SOC
[0086]
[0087] Where C is the cost of the electric heat storage device over its entire life cycle; C 1 is the initial installation cost; C 2 is the operation and maintenance cost; cap is the price per unit heat storage capacity; c o,m is the proportion of annual maintenance cost to initial installation investment; SOC is the heat storage capacity of the electric heat storage device; n is the service life of the electric heat storage device; r is the annual discount rate;
[0088] The income model of electric heat storage is expressed as:
[0089] I=I 1 +I 2
[0090]
[0091] Where I is the income of the electric heat storage device during its entire life cycle; I 1 To balance the heat load income; I 2 is the income from low-cost electricity and heat storage; w is the unit heat price; w e is the off-peak electricity price; a is the annual production days, which is determined according to the production plan of the park's heat users; ΔQ is the annual heat loss reduced after adding the electric heat storage device; Q is the heat storage capacity of the electric heat storage device, which is related to the change in heat demand; η h is the electric heating efficiency of the electric heat storage device; η s is the heat storage efficiency of the electric heat storage device; η e is the heat release efficiency of the electric heat storage device;
[0092] Then the objective function of the upper-layer capacity configuration optimization model is expressed as:
[0093] max O=IC
[0094] Among them, O is the present value of the total profit of the electric heat storage device during its entire life cycle;
[0095] The maximum designable capacity and the maximum designable charging and discharging power of the electric heat storage are constrained. The specific constraints are:
[0096] 0≤SOC≤SOC max
[0097]
[0098] Among them, SOC max The minimum capacity of the electric heat storage device that can be designed in theory is affected by the specific production scenario of the park; is the designed heat storage power of the electric heat storage device; is the minimum heat storage power of the electric heat storage device that can be designed theoretically; is the maximum heat storage power of the electric heat storage device that can be designed theoretically; is the designed heat release power of the electric heat storage device; is the minimum heat release power of the electric heat storage device that can be designed theoretically; It is the maximum heat release power of the electric heat storage device that can be designed theoretically.
[0099] In this embodiment, the relevant parameters of the electric heat accumulator are designed according to the optimal electric heat accumulator capacity and the heat storage and release power of the electric heat accumulator determined by the upper capacity configuration optimization model, including: the minimum and maximum heat storage power of the electric heat accumulator, and the minimum and maximum heat release power of the electric heat accumulator. According to the actual needs of the project, the heat storage medium volume of the electric heat accumulator, the heating power of the electric heat accumulator, the internal pipeline structure of the electric heat accumulator, and the insulation structure of the electric heat accumulator can also be designed.
[0100] In this embodiment, the lower layer day-ahead scheduling optimization model is constructed in step S4, and the specific method is as follows:
[0101] After the electric heat storage device is installed, the heat load demand curve (i.e., the curve constructed based on the forecast results of the heat load demand of users in the park at each time period of the next day) can be smoothed, heat storage can be achieved during low-peak heat consumption, and heat release can be achieved during peak heat consumption, thus reducing the heat waste caused by variable load operation of the boiler; the cheap electricity during the valley period can also be converted into heat and stored for use during subsequent peak heat consumption periods.
[0102] Considering that the main scheduling scenario of electric heat storage is to use electric heat storage to store heat at low temperatures and release heat at peak temperatures (smoothing the heat load curve), the goal of scheduling strategy optimization is to achieve maximum peak shaving and valley filling (i.e., smoothing the heat load demand curve). The charging and discharging power, capacity, and continuity of the electric heat storage are used as constraints to construct a lower-level day-ahead scheduling optimization model.
[0103] The objective function of the lower-level day-ahead scheduling optimization model is:
[0104] minY=Y 1 +Y2
[0105]
[0106] Among them, Y 1 It is an indicator for evaluating valley filling capacity; Y 2 It is an indicator for evaluating the peak shaving capability; P in (t i ) is t i The heat storage power of the electric heat storage device at that moment; P out (t i ) is t i The heat release power of the electric heat storage device at time t i ) is the predicted t i User heat load at any moment; P h (t i ) is the predicted t i The heating power of the park heating network at the moment; Δt is t i and t i+1 the time interval between
[0107] The constraints of the lower-level day-ahead scheduling optimization model are:
[0108] Q(t i+1 )=Q(t i )-[P out (t i )·B out (t i )-P in (t i )·B in (t i )·η s ]·Δt
[0109] 0≤Q(t i )≤SOC
[0110] P in,min ≤P in (t i )≤P in,max
[0111] P out,min ≤P out (t i )≤P out,max
[0112] Among them, Q(t i )Q(t i ) and Q(t i+1 ) are t i and t i+1 The heat storage of the electric heat storage device at that moment; B out (ti ) and B in (t i ) is t i The heat release and storage state of the electric heat storage device at this moment is a Boolean variable; η s is the heat storage efficiency of the electric heat storage device; P in,min and P in,max is the minimum and maximum heat storage power of the electric heat storage device; P out,min and P out,max It is the minimum and maximum heat release power of the electric heat storage device.
[0113] In this embodiment, the park steam heating system containing the electric heat storage is scheduled based on the obtained operation scheduling optimization plan. The specific method is as follows:
[0114] By connecting the output of the lower-level day-ahead optimization scheduling model with the controller, the controller issues control instructions to the electric heat storage and the heating unit according to the operation scheduling optimization plan obtained after calculation by the lower-level day-ahead optimization scheduling model.
[0115] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.
Claims
1. A method for scheduling a park steam heating system containing an electric heat storage device, characterized in that: The steps include: S1. Based on the heat load impact data of the heat users of the heating units, the characteristic analysis of the heat load data on the load side of the park and the prediction of the heat load supply and demand of the park are realized; S2. Based on the characteristic analysis results of the heat load data on the load side of the park in step S1, with the goal of maximizing the net present value of the total profit over the entire life cycle of the electric heat accumulator, and with the maximum designable capacity and the maximum designable charging and discharging heat power of the electric heat accumulator as constraints, an upper-level capacity configuration model is constructed, and the upper-level capacity configuration model is solved based on an optimization algorithm to obtain the optimal capacity configuration for realizing the electric heat accumulator; S3, based on the optimal capacity configuration of the electric heat accumulator in step S2, determining relevant parameters of the electric heat accumulator, and obtaining a parameter optimization scheme for the electric heat accumulator under the optimal capacity configuration; S4. Combining the parameter optimization scheme of the electric heat storage device under the optimal capacity configuration obtained in S3 and the prediction result of the heat load supply and demand of the park in step S1, with the goal of maximizing peak shaving and valley filling, and taking the charging and discharging power, capacity and continuity of the electric heat storage device as constraints, a lower-level day-ahead scheduling optimization model is constructed, and the lower-level day-ahead scheduling optimization model is solved based on the optimization algorithm to obtain the operation scheduling optimization scheme.
2. The method for scheduling a park steam heating system with an electric heat storage device according to claim 1, characterized in that: In step S1, the heat load impact data of the heat users of the heating unit specifically include: historical heating data on the source side, historical change data on the heating cost on the source side, meteorological data including temperature and humidity that have a significant impact on the heat load demand, and historical heat load data of users and production plan data of users; The characteristic analysis of the heat load data on the load side of the park is specifically performed by: using a clustering algorithm to perform cluster analysis on the heat load characteristics of the park users based on the heat load data of multiple historical time points of the heat users, and determining the heat load characteristics of the users; The specific method for predicting the supply and demand of the heat load in the park is as follows: for the source side, based on the historical heating data of the source side, the historical change data of the heating cost, and the meteorological data including temperature and humidity that have a significant impact on the heat load demand, a machine learning algorithm is used to make a short-term prediction of the heat supply in each time period of the next day on the source side; for the load side, based on the user's historical heat load data, the user's production plan data, and the meteorological data including temperature and humidity that have a significant impact on the heat load demand, a machine learning algorithm is used to make a short-term prediction of the heat load demand of the park users in each time period of the next day.
3. The method for scheduling a park steam heating system with an electric heat storage device according to claim 1, characterized in that: In step S3, the characteristic analysis results of the heat load data on the load side of the park include the production plans and heat demand changes of the heat users in the park; The specific method of constructing the upper layer capacity configuration model is: The objective function is to maximize the total net present value of the profit over the entire life cycle of the electric heat storage device, specifically: max O=IC C=C1+C2 C1=c cap ·SOC Among them, O is the present value of the total profit of the electric heat storage device in the whole life cycle; C is the cost of the electric heat storage device in the whole life cycle; C1 is the initial installation cost; C2 is the operation and maintenance cost; c cap is the price per unit heat storage capacity; c o,m is the proportion of annual maintenance cost to initial installation investment; SOC is the heat storage capacity of the electric heat storage; n is the service life of the electric heat storage; r is the annual discount rate; I is the income of the electric heat storage during the whole life cycle; I1 is the income of balanced heat load; I2 is the income of low-cost electric heat storage; w is the unit heat price; w e is the off-peak electricity price; a is the annual production days, which is determined according to the production plan of the heat users in the park; ΔQ is the annual heat loss reduced after adding the electric heat storage device, which is related to the change in heat demand; Q is the heat storage capacity of the electric heat storage device; η h is the electric heating efficiency of the electric heat storage device; η s is the heat storage efficiency of the electric heat storage device; η e is the heat release efficiency of the electric heat storage device; Taking the maximum designable capacity and the maximum designable charging and discharging power of the electric heat storage as constraints, it is specifically expressed as: 0≤SOC≤SOC max Among them, SOC max The minimum capacity of the electric heat storage device that can be designed in theory is affected by the specific production scenario of the park; is the designed heat storage capacity of the electric heat storage device; is the minimum heat storage power of the electric heat storage device that can be designed theoretically; is the maximum heat storage power of the electric heat storage device that can be designed theoretically; is the designed heat release power of the electric heat storage device; is the minimum heat release power of the electric heat storage device that can be designed theoretically; It is the maximum heat release power of the electric heat storage device that can be designed theoretically.
4. The method for scheduling a park steam heating system with an electric heat storage device according to claim 1, characterized in that: In step S3, the relevant parameters of the electric heat accumulator to be determined specifically include: the minimum and maximum heat storage powers of the electric heat accumulator, and the minimum and maximum heat release powers of the electric heat accumulator.
5. The method for scheduling a park steam heating system with an electric heat storage device according to claim 1, characterized in that: The specific method of constructing the lower-layer day-ahead scheduling optimization model is as follows: The goal is to maximize the peak shaving and valley filling. The specific objective function is expressed as: minY=Y1+Y2 Among them, Y1 is the index for evaluating valley filling capability; Y2 is the index for evaluating peak shaving capability; P in (t i ) is t i The heat storage power of the electric heat storage device at that moment; P out (t i ) is t i The heat release power of the electric heat storage device at time t i ) is the predicted t i User heat load at any moment; P h (t i ) is the predicted t i The heating power of the park heating network at the moment; Δt is t i and t i+1 the time interval between The charging and discharging power, capacity and continuity of the electric heat storage device are constrained. The specific constraints are: Q(t i+1 )=Q(t i )-[P out (t i )·B out (t i )-P in (t i )·B in (t i )·η s ]·Δt 0≤Q(t i )≤SOC P in,min ≤P in (t i )≤P in,max P out,min ≤P out (t i )≤P out,max Among them, Q(t i ) and Q(t i+1 ) are t i and t i+1 The heat storage of the electric heat storage device at that moment; B out (t i ) and B in (t i ) is t i The heat release and storage state of the electric heat storage device at this moment is a Boolean variable; η s is the heat storage efficiency of the electric heat storage device; P in,min and P in,max is the minimum and maximum heat storage power of the electric heat storage device; P out,min and P out,max It is the minimum and maximum heat release power of the electric heat storage device.
6. The method for scheduling a park steam heating system with an electric heat storage device according to claim 1, characterized in that: Based on the obtained operation scheduling optimization plan, the park steam heating system with electric heat storage is scheduled. The specific method is as follows: By connecting the output of the lower-level day-ahead optimization scheduling model with the controller, the controller issues control instructions to the electric heat storage and the heating unit according to the operation scheduling optimization plan obtained after calculation by the lower-level day-ahead optimization scheduling model.