Multi-energy complementary heat supply system and layered cooperative operation control method thereof

Abstract

The multi-energy complementary heat supply system comprises an external basic heat source, a middle-grade synergistic heat source, a high-grade energy storage heat source, an intelligent heating power mixing center and a central controller, and the intelligent heating power mixing center is used for heating heat source equipment of all levels and operation instructions of associated assemblies according to the operation instructions of the heat source equipment of all the levels and the operation instructions of the associated assemblies. Heat energy of different grades is mixed and allocated, and heat loads meeting the grade requirements of heat consumers are output to the heat consumers; the central controller is used for establishing a multi-target optimization model taking the minimum total operation cost of the system as a core target based on the electricity price prediction data and the different-grade thermal load prediction data, solving the multi-target optimization model and generating a system operation strategy under the condition that preset basic guarantee logic, economic operation logic and peak response and power grid interaction logic are met; and the generated system operation strategy is converted into an operation instruction for the heat source equipment of each level and the associated assembly, so that the reliability and the adaptability of operation control of the heat supply system are effectively improved.

Description

Technical Field

[0001] This invention relates to the fields of integrated energy systems (IES) and energy management technology, and in particular to a multi-energy complementary heating system and its hierarchical coordinated operation control method. Background Technology

[0002] With the increasing urgency of addressing climate change and achieving carbon peaking and neutrality, renewable energy sources, represented by wind and solar power, are developing at an unprecedented pace, with their penetration rate in the power system continuing to climb. However, the inherent intermittency, volatility, and randomness of these new energy sources pose serious challenges to the real-time balance and safe, stable operation of the power system. During periods of abundant wind and solar resources, electricity supply far exceeds demand, leading to frequent occurrences of extremely low "off-peak" electricity prices, or even "negative" electricity prices, creating a huge and urgent problem to be solved in the "consumption" of clean energy.

[0003] At the same time, socio-economic activities generate a continuous, stable, and massive rigid demand for heat energy, especially high-grade heat energy required for industrial production, such as high-temperature and high-pressure steam, and medium- and low-grade heat energy required for urban district heating. Traditional heating systems mainly rely on coal-fired or gas-fired boilers or combined heat and power (CHP) units. These methods not only generate large amounts of carbon emissions and pollutants, running counter to low-carbon development goals, but their operating costs are also primarily determined by fossil fuel prices, largely decoupled from electricity market price fluctuations. Therefore, they cannot utilize the cheap electricity generated by new energy fluctuations in the electricity market.

[0004] Decoupling electrothermal production and promoting the consumption of new energy sources have led to widespread attention to electricity-to-heat technology, particularly large-scale, long-term thermal storage technology. Among these technologies, high-temperature solid thermal storage devices, for example, efficiently convert electrical energy into high-temperature heat energy (up to 800°C) using resistance heating during periods of low electricity prices and store it in a solid medium. During peak heating periods or periods of high electricity prices, the stored heat energy is released to meet user needs in the form of steam, hot water, or hot air. This theoretically achieves the spatial and temporal transfer of electricity and peak shaving / valley filling.

[0005] However, existing technologies have revealed one or more of the following significant drawbacks in practical applications.

[0006] (1) The control strategy is rigid and cannot adapt to the rapidly changing electricity spot market. Currently, the control logic of most operational thermal storage heating systems is still based on the traditional "peak-valley-flat" time-of-use pricing mechanism. The strategy usually sets a fixed off-peak period at night (such as 22:00 to 6:00 the next day) for charging at rated power. This rigid "timed start-stop" mode is completely unsuitable for the electricity spot market with a settlement cycle of 15 minutes or even 5 minutes. In the spot market, electricity prices fluctuate wildly, and the lowest price may occur during the midday peak of photovoltaic power generation or the early morning peak of wind power generation. The rigid control strategy will miss a lot of low-price charging opportunities, and may even continue charging during the second-highest price period, causing its claimed economic benefits to be greatly reduced in the actual market environment.

[0007] (2) The energy cascade utilization efficiency is low, resulting in huge energy loss. In order to produce high-grade industrial steam (e.g., 450°C), a single electric thermal storage system must have its storage medium heated to a temperature much higher (e.g., 750-800°C). When such a system also needs to provide 80°C hot water for district heating, it still needs to be cooled by a high-temperature heat source. This is a typical case of "high-grade but underutilized energy," resulting in huge and irreversible energy quality loss, i.e., energy loss, which seriously reduces the overall energy utilization efficiency of the entire system. It is equivalent to using "good steel" for "poor work," which is extremely unreasonable both economically and thermodynamically.

[0008] (3) The system has a single function and its potential value has not been fully explored. Existing thermal storage systems are often regarded as isolated, one-way energy conversion devices, with the only goal of "charging during off-peak hours and meeting heating needs during peak hours". It has failed to form an organic synergy and coupling with other cheap heat sources that may exist in the region (such as low-temperature waste heat from nuclear power plants, waste heat from data centers, and waste heat from industrial production processes). More importantly, as a huge load with rapidly adjustable power, its potential as a "virtual power plant" participating in grid ancillary services (such as frequency regulation, peak shaving, reserve, and demand-side response) has been completely ignored, and it is unable to create additional economic value through two-way interaction with the grid.

[0009] (4) Poor dynamic response capability to multi-grade heat loads. When a comprehensive energy user (such as an industrial park with residential areas) has multiple heat load demands of different grades and dynamic changes, such as high-temperature steam, medium-temperature process hot water, and low-temperature heating, a single heat source or a simple combination of heat sources is difficult to achieve efficient, flexible, and accurate matching. This usually leads to the need to invest in and construct multiple independent and unrelated heating subsystems, which not only increases the initial investment and land area, but also complicates the operation and scheduling, making it impossible to achieve overall optimization.

[0010] In summary, the reliability and adaptability of the operation and control of heating systems in the relevant technologies are not high. In order to solve the problem of low reliability and adaptability of the operation and control of heating systems in the existing technologies, this solution is proposed. Summary of the Invention

[0011] In order to solve the problems existing in the prior art, this invention innovatively proposes a multi-energy complementary heating system and its hierarchical collaborative operation control method, which effectively solves the problem of low reliability and adaptability of heating system operation control caused by the prior art, and effectively improves the reliability and adaptability of heating system operation control.

[0012] The first aspect of this invention provides a multi-energy complementary heating system, comprising: an external base heat source, a medium-grade efficiency-enhancing heat source, a high-grade energy storage heat source, an intelligent thermal mixing center, and a central controller. The external base heat source is used to meet a preset low-temperature base heat load, the medium-grade efficiency-enhancing heat source is used to meet a preset medium-grade heat load, and the high-grade energy storage heat source is used to meet a preset high-grade heat load. The intelligent thermal mixing center is connected to the outlets of the external base heat source, the medium-grade efficiency-enhancing heat source, and the high-grade energy storage heat source, and to the inlet of at least one heat user. It is used to, according to the operation instructions converted by the central controller for each level of heat source equipment and related components, to mix heat energy of different grades generated by one or more operating heat sources. The system performs mixing, heat exchange, and distribution to output heat loads that meet the grade requirements of heat users. The central controller is used to acquire electricity market time-of-use price forecast data for at least one future scheduling cycle and forecast data of different grades of heat loads served by the heating system. Based on the electricity price forecast data and the forecast data of different grades of heat loads, a multi-objective optimization model is established with the minimum total system operating cost as the core objective. Within one scheduling cycle, under the premise of satisfying the preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, the multi-objective optimization model is solved to generate a system operation strategy. The generated system operation strategy is converted into operation instructions for heat source equipment and related components at each level and sent to the intelligent heat mixing center.

[0013] The second aspect of this invention provides a hierarchical coordinated operation control method for a multi-energy complementary heating system, implemented based on a multi-energy complementary heating system according to the first aspect of this invention, comprising: The central controller acquires electricity market time-of-use price forecasts for at least one future scheduling cycle, as well as forecasts of different grades of heat load served by the heating system. Based on the price forecasts and heat load forecasts, it establishes a multi-objective optimization model with the minimum total system operating cost as the core objective. Within one scheduling cycle, under the premise of satisfying preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, it solves the multi-objective optimization model to generate a system operation strategy. The generated system operation strategy is then converted into operation instructions for heat source equipment and related components at each level and sent to the intelligent thermal mixing center. The intelligent thermal mixing center, based on the operation instructions for each level of heat source equipment and related components converted by the central controller, mixes, exchanges, and distributes heat energy of different grades generated by one or more operating heat sources, and outputs heat loads that meet the grade requirements of heat users.

[0014] The technical solution adopted in this invention has the following technical effects: 1. The central controller of this invention establishes a multi-objective optimization model with the core objective of minimizing the total system operating cost based on electricity price forecast data and heat load forecast data of different grades. Within a scheduling cycle, under the premise of satisfying the preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, the multi-objective optimization model is solved to generate a system operation strategy. Through an intelligent heat mixing center, heat energy of different grades generated by one or more operating heat sources is mixed, exchanged, and distributed to output heat load that meets the grade requirements of heat users. According to the role positioning, technical and economic characteristics, and sensitivity to electricity prices of different heat sources in the energy system, they are divided into three logical levels: basic guarantee layer (external basic heat source), economic operation layer (medium-grade efficiency-enhancing heat source), and peak response and high-grade layer (high-grade energy storage heat source). Each level is assigned a different operating mission, and the operation of the entire system is based on a unified coordinated scheduling of a multi-objective optimization model. This effectively solves the problem of low reliability and adaptability of heating system operation control caused by existing technologies, and effectively improves the reliability and adaptability of heating system operation control.

[0015] 2. The heat load information in the technical solution of this invention includes historical sequences of low-temperature heat load, historical sequences of medium-temperature heat load, and historical sequences of high-temperature heat load. Since the energy consumption patterns, sensitivity to weather, and correlation with electricity prices of loads of different grades are different, this prediction method based on grade-differentiated input provides an accurate data foundation for refined hierarchical collaborative optimization based on grade matching in subsequent steps.

[0016] 3. In the technical solution of this invention, based on electricity prices, heat load forecasts, and heat load demand, corresponding basic guarantee logic, economic operation logic, peak response, and grid interaction logic are determined. Through an energy cascade utilization chain of external basic heat source > medium-grade efficiency-enhancing heat source for temperature increase > high-grade energy storage heat source for heat storage and heat generation, the "grade matching" of heat energy supply and heat load demand is achieved. That is, low-temperature heat load is met by basic heat source, medium-grade heat load is efficiently met by medium-grade efficiency-enhancing heat source, and only high-grade heat load is met by high-grade energy storage heat source. This fundamentally avoids the huge energy loss of using an 800°C heat source to heat 80°C water, and the overall energy utilization efficiency of the system (considering primary energy consumption) is greatly improved compared to a single heat source system. Moreover, the operation of different types of heat sources can be flexibly adjusted according to electricity prices and heat load forecasts, improving the reliability and adaptability of the heating system operation control.

[0017] 4. The system operation strategy in the technical solution of this invention specifically includes scheduling and control strategies for different time periods. The operation of different types of heat sources is flexibly adjusted according to the time period of the predicted electricity price, thereby improving the reliability and adaptability of the heating system operation control.

[0018] 5. In the technical solution of this invention, the system operation strategy performs rolling optimization once every preset time period; this cycle repeats to ensure that the heating system always operates on the optimal or suboptimal track that is closest to the current actual situation.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the system structure in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the hierarchical collaborative operation process in the system of Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the heat source hierarchical decision-making logic flow in the system of Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the intelligent thermal mixing center control principle in the system of Embodiment 1 of the present invention; Figure 5This is a schematic diagram of a typical 24-hour scheduling cycle during winter in the system of Embodiment 1 of the present invention. Figure 6 This is a schematic diagram of the rolling optimization control timing in the system of Embodiment 1 of the present invention; Figure 7 This is a flowchart illustrating the method of Embodiment 2 in the present invention.

[0022] Legend: 10: Central controller; 11: Data sensing and prediction module; 12: Optimization decision engine module; 13: Scheduling execution and closed-loop control module; 20: Basic heat source interface; 21: External basic heat source; 30: Medium-grade efficiency-enhancing heat source; 31: High-temperature heat pump system; 40: High-grade energy storage heat source; 41: High-temperature solid heat storage device; 42: High-density heat storage body; 43: Electric heating element group; 44: Energy release heat exchange system; 50: Intelligent thermal mixing center; 51: High-speed electric regulating valve group; 52: Mixing / heat exchange / flash evaporation unit; 60: Electricity market and grid dispatching system; 70: Multi-grade heat user group. Detailed Implementation

[0023] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0024] Example 1 like Figures 1-2As shown, this invention provides a multi-energy complementary heating system, comprising: an external base heat source 21, a medium-grade efficiency-enhancing heat source 30, a high-grade energy storage heat source 40, an intelligent thermal mixing center 50, and a central controller 10. The external base heat source 21 is used to meet a preset low-temperature base heat load, the medium-grade efficiency-enhancing heat source 30 (high-temperature heat pump system) is used to meet a preset medium-grade heat load, and the high-grade energy storage heat source 40 (high-temperature solid heat storage device) is used to meet a preset high-grade heat load. The intelligent thermal mixing center 50 is connected to the outlets of the external base heat source 21, the medium-grade efficiency-enhancing heat source 30, and the high-grade energy storage heat source 40, and the inlet of at least one heat user, respectively. It is used to, according to the operation instructions converted by the central controller for each level of heat source equipment and related components, heat supply one or more... The system mixes, exchanges, and distributes heat energy of different grades generated by operating heat sources to output heat loads that meet the grade requirements of heat users. The central controller 10 is used to acquire electricity market time-of-use price forecast data for at least one future scheduling cycle and heat load forecast data of different grades served by the heating system. Based on the electricity price forecast data and heat load forecast data of different grades, a multi-objective optimization model is established with the minimum total operating cost of the system as the core objective. Within one scheduling cycle, under the premise of satisfying the preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, the multi-objective optimization model is solved to generate a system operation strategy. The generated system operation strategy is converted into operation instructions for heat source equipment and related components at each level and sent to the intelligent heat mixing center.

[0025] Specifically, the heating system may also include: a basic heat source interface 20 for connecting to an external basic heat source 21 (nuclear heating).

[0026] The central controller 10 further includes: a data sensing and prediction module 11, which communicates with the power market platform, meteorological system and user-side load monitoring system (SCADA) through standard communication protocols, and is equipped with a deep prediction model for performing multi-dimensional information acquisition and prediction steps; an optimization decision engine module 12, which has an embedded optimization solver based on mixed integer linear programming (MILP) and nonlinear programming (NLP) for performing hierarchical collaborative optimization decision steps; and a scheduling execution and closed-loop control module 13, which converts the optimization decision results into specific control signals (PWM signals, 4-20mA analog signals) for actuators such as heat pumps, thermal storage device power converters (PCS), electric regulating valves, water pumps, and fans in the intelligent thermal mixing center, and performs closed-loop adjustments based on real-time feedback.

[0027] Among them, such as Figure 2 As shown, the central controller 10 specifically acquires electricity market time-of-use price forecast data for at least one future scheduling cycle and forecast data of different grades of heat load served by the heating system (i.e., step S201) as follows: First, electricity market information, meteorological information, and heat load information are obtained from multiple external sources. The electricity market information includes historical and future electricity price data, the meteorological information includes historical and future meteorological data, and the heat load information includes historical sequences of low-temperature heat load. Historical sequence of medium-temperature load Historical sequence of high temperature load ; The heating system uses a central controller to acquire multi-dimensional information from multiple external sources in real time or periodically through standardized communication interfaces (Modbus / TCP) and APIs. The multi-dimensional information mainly includes electricity market information, meteorological information, and load information.

[0028] Electricity market information refers to obtaining day-ahead market prices for the next 24-48 hours from the electricity trading center, as well as 15-minute or 5-minute price forecasts or real-time price signals for the intraday rolling market and real-time market.

[0029] Meteorological information refers to hourly weather forecasts with high temporal resolution obtained from public meteorological service platforms or self-built meteorological stations for the next few days. Key parameters include ambient temperature, relative humidity, wind speed, and total solar radiation intensity.

[0030] Load information refers to the historical data of various heat loads collected in real time through smart heat meters, flow meters, and temperature sensors installed on the user side.

[0031] Based on the acquired information, the data sensing and prediction module 11 built into the heating system can use deep learning algorithms such as Long Short-Term Memory (LSTM) networks or Gated Recurrent Units (GRUs) for multi-scale prediction. Specifically, to achieve accurate prediction of heat loads of different grades, the data sensing and prediction module 11 distinguishes between data input and processing as follows: Data acquisition and preprocessing: The system independently collects and stores historical data on heat loads of different grades from sensors deployed on the user side. That is, it collects data from the low-temperature heating network system to form a historical sequence of low-temperature loads. Data collected from medium-temperature process hot water systems forms a historical sequence of medium-temperature loads. Data collected from high-temperature steam systems forms historical high-temperature load sequences. .

[0032] Then, using historical heat load data of a certain grade as the output data of the deep prediction model, and using historical electricity price data and historical meteorological data of the same time period as the historical heat load data of that grade as the input data of the deep prediction model, the predicted data of different grades of heat load served by the heating system in the next scheduling cycle are predicted.

[0033] This deep learning algorithm model uses historical heat load data of a certain grade as the output data of the deep prediction model, and historical electricity price data and historical meteorological data of the same time period as the historical heat load data of that grade as the input data of the deep prediction model. It can accurately predict the heat load demand curves of different grades within a future scheduling cycle (24 hours). Represents low-temperature heating load. Represents the hot water load of medium-temperature processes. This represents the trend of high-temperature steam load and electricity price fluctuations.

[0034] Model input and training: Deep prediction models (such as LSTM) are trained individually using their corresponding historical data sequences for each grade of heat load, or used as specific input channels for multi-output models. During training, historical load data for a specific grade (e.g., medium temperature) are used. As output, historical electricity price data, historical meteorological data, and future weather forecast data for the same time period are combined as input features to learn and establish the mapping relationship between the load of this grade and external factors. Since the energy consumption patterns, sensitivity to weather, and correlation with electricity prices of different load grades are all different, this differentiated processing method is crucial.

[0035] After training, the deep prediction model can take the latest historical data and future prediction data as input and output the low-temperature heating load prediction curve for future scheduling cycles in parallel or independently. Medium-temperature process hot water load prediction curve High-temperature steam load prediction curve .

[0036] This prediction method, which differentiates inputs by grade, provides an accurate data foundation for refined hierarchical collaborative optimization based on grade matching in subsequent steps.

[0037] In the central controller 10, a multi-objective optimization model based on electricity price forecast data and heat load forecast data of different grades is established, with the core objective of minimizing the total system operating cost. This model serves as a precise mathematical optimization model to solve for the operating strategies of all controllable equipment within a future scheduling cycle (e.g., 24 hours, 96 time points). Figure 2 (Step S202). This is a typical multi-objective, multi-constraint optimization problem, modeled as a mixed-integer linear programming (MILP) or mixed-integer nonlinear programming (MINLP) problem.

[0038] The multi-objective optimization model is as follows:

[0039] Where J is the function value of the multi-objective optimization model; T is the total duration of the optimization scheduling cycle; The total cost of electricity purchased in time period t depends on the power consumption of the heat pump and the thermal storage device; The system start-up, shutdown, operation, and maintenance costs during time period t; The revenue gained from participating in grid ancillary services (such as peak shaving) during time period t; Among them, the total electricity purchase cost during time period t The calculation method is as follows:

[0040] in, The real-time or predicted electricity price for time period t; Input electrical power for medium-grade heat source enhancement; The charging power for high-grade energy storage heat sources; The duration of time period t or the time difference from the previous time period; Among them, the system start-up, shutdown, operation and maintenance costs during time period t The calculation method is as follows:

[0041] in, It is a variable signal (0-1 variable) for starting and stopping the heat source device i. It is a heat source device The cost of a single start-stop action, It is a heat source device The unit power operation and maintenance cost coefficient, It is a heat source device The operating power during time period t; I is the set of devices used to iterate through the set of devices. Each device in it; Among them, the revenue obtained from participating in grid ancillary services during time period t The calculation method is as follows:

[0042] in, It refers to the market price of grid ancillary services in time period t; It is the auxiliary service power provided by the heating system to the power grid during time period t.

[0043] Solving the multi-objective optimization model also requires satisfying preset constraints, including thermal power balance constraints, heat source operation constraints, and energy storage system constraints; among these, Among them, the heat power balance constraint (the heat power balance constraint refers to the requirement that heat production must meet the load at each grade g and each time period t) is specifically as follows:

[0044] in, The heat generated by the external basic heat source during time period t; For the heat production of the grade-enhanced heat source during time period t; For the heat generation of high-grade energy storage heat source during time period t; The heat load demand at grade g during time period t; Represents a set of grades; Among them, the heat source operation constraints (heat source operation constraints refer to the fact that the output of each heat source cannot exceed its rated power, and the ramp rate is limited) are as follows:

[0045] in, It is a variable (0-1 variable) indicating whether heat source device i is in operation during time period t. For heat source equipment Operating power during time period t; Let i be the minimum operating power of heat source device i during time period t. The maximum operating power of heat source device i during time period t; Specifically, the constraints of the energy storage system include the state transition equation, the charge-discharge mutual exclusion relationship, and the upper and lower limits of the stored energy (SOC): State transition equation:

[0046] Charge and discharge mutual exclusion relationship:

[0047] Upper and lower limits of stored energy (SOC):

[0048] in, The heat storage capacity of a high-grade energy storage heat source during time period t; The heat storage capacity of a high-grade energy storage heat source in the previous time period or the current time period t; The charging efficiency of a high-grade energy storage heat source in time period t; The charging power of a high-grade energy storage heat source during time period t; The heat release efficiency of a high-grade energy storage heat source in time period t; The heat production power of a high-grade energy storage heat source during time period t; Whether the high-grade energy storage heat source is in a charging state during time period t; Whether the high-grade energy storage heat source is in a discharge state during time period t; This represents the minimum heat storage capacity of a high-grade energy storage heat source during time period t. This represents the maximum heat storage capacity of a high-grade energy storage heat source during time period t.

[0049] It is a binary variable (0-1 variable) used to represent the charging state of the high-grade energy storage heat source in time period t. When its value is 1, it means that the energy storage device is in the charging state in time period t; when its value is 0, it means that the energy storage device is not in the charging state in time period t. It is also a binary variable (0-1 variable) used to represent the discharge state of the high-grade energy storage heat source in time period t. When its value is 1, it means that the energy storage device is in the discharge state in time period t; when its value is 0, it means that the energy storage device is not in the discharge state in time period t.

[0050] Preferably, it also includes heat pump performance constraints (heat pump performance constraints refer to the heat output power of the heat pump). and power consumption The relationship between them is determined by their performance curves. The decision (and ancillary service constraints) refers to the requirement to reserve corresponding spare capacity if participating in ancillary services. Specifically, the heat pump performance constraints are:

[0051] in, The heat output power of the heat pump during time period t; The power consumption of the heat pump during time period t; The temperature of the heat source during time period t (i.e., the low-temperature heat source temperature at which the heat pump absorbs heat). The heat sink temperature during time period t (i.e., the temperature of the return water from the heating system on the output side of the heat pump). The energy efficiency ratio of a medium-grade enhanced heat source is given by the heat sink temperature and heat source temperature during time period t. Specifically, the basic guarantee logic is as follows: External basic heat source 21 is prioritized for scheduling, using its maximum available capacity or economic boundary to meet the basic heat load matching its grade. The economic operation logic is as follows: When the heating system has a medium-grade heat load demand, the multi-objective optimization model determines whether the equivalent heat production cost generated by starting the medium-grade efficiency-enhancing heat source 30 in time period t meets the preset first economic decision condition. If the preset first economic decision condition is met, the medium-grade efficiency-enhancing heat source 30 is scheduled to operate to meet the medium-grade heat load. The peak response and grid interaction logic is as follows: In the multi-objective optimization model, the electricity price in time period t meets the preset second economic decision condition. If the preset second economic decision condition is met, the high-grade energy storage heat source 40 is scheduled for heat storage. When the heating system has a high-grade heat load demand, or the total heat load exceeds the supply capacity of external basic heat source 21 and medium-grade efficiency-enhancing heat source 30, or in response to the grid peak shaving command, the high-grade energy storage heat source 40 is scheduled to release heat.

[0052] The core idea of ​​this method is to move away from treating all heat sources as a single entity for extensive management. Instead, based on their role in the energy system, techno-economic characteristics, and sensitivity to electricity prices, they are creatively divided into three logical levels: a basic security layer, an economic operation layer, and a peak response and high-quality layer. Each level is assigned a different operational mission and economic decision-making conditions strictly linked to electricity market prices. The entire system is operated by a central controller based on a sophisticated, economy-oriented multi-objective optimization model for unified and coordinated scheduling. Specifically, the heat sources within the system are logically divided into three levels.

[0053] like Figure 3 As shown, the first layer is the Base Load Layer. This layer consists of stable, inexpensive external base heat sources that are typically not directly related to electricity price fluctuations, such as low-temperature heating from nuclear power plants and stable waste heat from large industrial enterprises. Its main mission is to act as the "ballast" of the system, continuously and stably meeting the most basic heat load in the system (district heating and domestic hot water <90°C) at extremely low marginal costs.

[0054] The second level is the Economic Operation Layer. The core equipment at this level is highly efficient electrically driven enhancement equipment, typically a high-temperature heat pump. It uses the low-temperature hot water from the first level as a heat source, employing electricity to drive a compressor to raise its heat energy quality to a medium temperature (90°C-150°C). Its operational decisions are strictly linked to the first economic decision condition. This condition is defined as the equivalent heat production cost of starting the heat pump must be lower than a preset economic threshold (medium-temperature heat value threshold). Mathematically, this is expressed as:

[0055] in, Is it a real-time or predicted electricity price? It is the overall energy efficiency ratio of the heat pump under current operating conditions (usually between 2 and 5). This is the benchmark for the value of medium-temperature heat. This condition ensures that the heat pump only operates during periods of moderate to low electricity prices, making it the "main force" for reducing costs in the daily operation of the system.

[0056] The operating conditions include source / sink temperatures. The source temperature refers to the temperature of the circulating medium (such as water) on the input side (i.e., the heat absorption side) of the heat pump; the sink temperature refers to the temperature of the circulating medium (such as water) on the output side (i.e., the heat release side) of the heat pump, which represents the target temperature to which the heat pump needs to raise the heat.

[0057] The third tier is the Peak Response & High-Grade Layer. The core equipment in this tier is electrothermal energy storage devices, typically high-temperature solid thermal storage devices or molten salt thermal storage systems. It serves a dual purpose: first, to meet the system's highest-grade heat load; and second, to provide rapid peak shaving and demand response services when the grid requires them. Its thermal storage decisions are strictly linked to the second economic decision condition, namely, real-time or forecasted electricity prices. It must be below a very low threshold. (Low electricity price threshold). This ensures that it absorbs energy only during the "valley" period when the grid has the most surplus and cheapest electricity, such as during periods of high wind power generation at night or when electricity prices are negative, serving as a "strategic reserve" and "sponge" for the heating system. Its heat release decision is jointly determined by the demand for high-grade heat load, the overall thermal balance of the system, and grid dispatch instructions.

[0058] Preferably, the threshold value of medium-temperature heat corresponding to the first economic decision condition. The low electricity price threshold corresponding to the second economic decision condition It is not a fixed value, but is dynamically and adaptively adjusted by the central controller 10 according to the external market environment, alternative energy costs and the internal operating status of the system, so as to ensure that the economic operation method is always on the optimal economic track.

[0059] For the medium-temperature calorific value threshold The central controller 10 periodically queries external energy market data, calculates the current heating cost of the gas-fired boiler, and sets it near this cost value. When natural gas prices fluctuate significantly, adjustments are immediately triggered. This mechanism ensures that the high-temperature heat pump operates only when its equivalent heat production cost is consistently lower than the current lowest market alternative, achieving true economic optimization. Its relational expression is as follows:

[0060] in, This is an adjustable coefficient, typically set to a value slightly less than 1 (e.g., 0.95 to 0.99). This coefficient is designed to ensure that heat pump operation is not only more economical than gas boilers, but also retains a certain cost advantage margin. This is the unit heating cost of the natural gas boiler on day d (yuan / kWh).

[0061] For low electricity price threshold The benchmark value is typically set at a low percentile of recent historical electricity prices (e.g., the 5th percentile), and is temporarily adjusted upwards when a future surplus of renewable energy generation is predicted or when instructions are received from the grid to promote consumption. A threshold is set to encourage energy storage systems to begin storing heat when prices are relatively high, thus more actively absorbing green electricity. This threshold is temporarily lowered when energy storage devices have sufficient heat storage capacity and future demand for high-grade heat is low. A threshold is set so that it only accumulates heat during the most extreme periods of low prices, further improving economic efficiency. Its dynamic adjustment relationship expression is as follows:

[0062] in, It is a benchmark value, usually taken as a low percentile of the recent (e.g., the past 30 days) spot market electricity price. This is the grid signal offset. It's a dynamic term; when the central controller receives a clear signal from the grid's demand response (DR) or to promote renewable energy integration, this offset is a positive adjustment increment, temporarily raising the signal level. This threshold encourages the thermal storage device to charge more actively. It is zero when there is no signal. This is the system state offset. It is a fine-tuning item based on the internal state. When the thermal state of the high-grade energy storage source is below the safety threshold or a large increase in future high-grade heat load demand is predicted, this offset is a positive adjustment increment; when the SOC is very high and the demand forecast is stable, this offset can be a negative adjustment decrement.

[0063] Through the aforementioned dynamic adjustment mechanism, the system described in this invention can adapt to complex and ever-changing external environments, thereby achieving global optimization of operational economy, renewable energy absorption rate, and grid interaction capability.

[0064] The optimization solver CPLEX (the optimization decision engine module 12 has an embedded optimization solver based on mixed integer linear programming (MILP) and nonlinear programming (NLP) to perform hierarchical collaborative optimization decision steps) solves the multi-objective optimization model based on preset constraints, under the premise of satisfying preset basic guarantee logic, economic operation logic, peak response and grid interaction logic. The result is a set of optimal scheduling plans, that is, in the next 24 hours, what state (on / off / standby) each heat source device should be in each scheduling cycle, and at what power.

[0065] The result of the optimization decision, i.e., the system operation strategy, is a series of digitized instructions (i.e., step S203). The scheduling execution module is responsible for translating these instructions into signals that the physical world can understand and execute, and sending them to the PLCs or direct drives of various heat source devices via industrial fieldbuses (such as Profibus, CAN). For example, controlling the output frequency of the frequency converter to regulate the water pump flow, and controlling the output of the power converter (PCS) to precisely control the charging power of the heat storage device.

[0066] Meanwhile, a key physical component—the intelligent thermal mixing center 50—is responsible for "refining" the final thermal product. It is a sophisticated system composed of multiple high-speed electric regulating valves, plate heat exchangers, flash evaporators, mixing tanks, and online sensors (temperature, pressure, flow rate). Following instructions from the central controller 10, it precisely proportions, mixes, or exchanges the low-temperature water from the base heat source, the medium-temperature water from the heat pump, and the high-temperature steam / thermal oil from the thermal storage unit in real time. This enables the simultaneous and stable delivery of final thermal products that fully meet the specific temperature, pressure, and flow rate requirements of different users, achieving "on-demand customized heating."

[0067] The scheduling execution and closed-loop control module 13 is used to convert the optimization decision results into specific control signals (PWM signals, 4-20mA analog signals) for the actuators such as the power converter (PCS) of the heat pump, the high-grade energy storage heat source 40, the electric regulating valve, water pump, and fan in the intelligent thermal mixing center, and to perform closed-loop adjustment based on real-time feedback.

[0068] like Figure 4As shown, the Intelligent Thermal Mixing Center 50 is a highly integrated energy station. It integrates three heat media: 90°C hot water from a nuclear energy interface (external basic heat source), 140°C hot water from a heat pump (medium-grade efficiency-enhancing heat source), and 450°C steam from a thermal storage device (high-grade energy storage heat source). Internally, it contains dozens of high-speed electric regulating valves, multi-stage plate heat exchangers, a flash tank, and a set of desuperheating and pressure-reducing devices, forming a mixing / heat exchange / flash unit. Through precise control by a central controller, it can produce on demand and stably output to user groups: supplying 75°C heating circulating water to residential areas; supplying 130°C process hot water to food processing enterprises; and supplying 450°C, 4.0MPa superheated steam to chemical enterprises.

[0069] The intelligent thermal mixing center 50's control principle comprises a three-layer core mechanism. The intelligent thermal mixing center 50 receives three heat source inputs. First, 90°C low-temperature water from nuclear waste heat is connected via a low-temperature pipeline, serving as an external basic heat source 21. Second, 140°C high-temperature water from the heat pump system is connected via a medium-temperature pipeline, serving as a medium-grade efficiency-enhancing heat source 30. Finally, 450°C superheated steam from the heat storage device is connected via a high-temperature pipeline, serving as a high-grade energy storage heat source 40. The central controller 10 sends commands to the high-speed electric regulating valve group, with a response time of <1 second, employing PID precise control to achieve rapid adjustment requirements. The three heat transfer media output from the valve group enter the mixing / heat exchange / flash evaporation unit for grade matching. The mixing tank processes the nuclear waste low-temperature water, eliminating pressure fluctuations. The plate heat exchanger processes the heat pump high-temperature water (140°C), maintaining stable medium-temperature thermal energy. The flash evaporation tank processes the heat storage superheated steam (450°C), achieving grade reduction utilization of the high-temperature steam through pressure reduction. The mixing center outlet is equipped with a real-time sensor array (including temperature sensors, pressure sensors, and flow meters) to continuously monitor output parameters and feed them back to the central controller 10.

[0070] Based on feedback data, the central controller 10 dynamically corrects the valve opening, forming a closed-loop control to ensure multi-grade output. (It has been upgraded from a passive, single-loop PID controller to an intelligent decision-making and control system integrating "decoupling calculation," "feedforward compensation," and "optimized allocation" functions. Through algorithms, it proactively resolves system coupling, anticipates and counteracts disturbances, and faithfully executes economic scheduling strategies, thereby ensuring high precision and stability of multi-grade heat energy output under complex operating conditions. This is the key underlying guarantee for achieving the final technical effect of this invention.) Residential heating water is maintained at a constant temperature of 75±0.5℃ (derived from the mixing tank output, meeting accuracy requirements). Food processing hot water is 130℃ (derived from the plate heat exchanger output, meeting industrial process needs). Chemical industrial steam is 450℃ (derived from the flash tank output, directly utilizing the high-temperature characteristics of the heat storage device).

[0071] This control mechanism solves the problem of multi-source coupling fluctuations through a data feedback closed loop (sensor → central controller → valve group → mixing unit), supporting the on-demand customization of multi-grade thermal energy.

[0072] Connectivity and Communication: All devices are connected to the central controller via industrial Ethernet. The controller communicates in real time with the power market and grid dispatching system, the National Meteorological Information Center, and the SCADA systems deployed at various user sites via API interfaces.

[0073] The system operation strategy specifically includes scheduling control strategies for different time periods, which are as follows: During periods of low electricity prices, the real-time electricity price meets the second economic decision-making condition, and the equivalent heat production cost meets the first economic decision-making condition. The dispatch control strategy is to ensure that the external basic heat source 21 continues to operate at full capacity to undertake the basic heating load; the high-grade energy storage heat source 40 receives a charging instruction and stores high-grade heat energy converted from cheap electricity at maximum power; the medium-grade efficiency-enhancing heat source 30 is forcibly shut down. Specifically, during the off-peak electricity price period (00:00-06:00), when electricity prices are at their lowest point of the day, the system operation control strategy is as follows: the external base heat source 21 operates continuously at full capacity, undertaking the base heating load; the high-grade energy storage heat source 40 receives charging instructions and stores high-grade heat energy converted from cheap electricity at maximum power; the medium-grade efficiency-enhancing heat source 30 (high-temperature heat pump) is forcibly shut down during this period because storing extremely cheap electricity directly at its highest grade has a far greater economic value and system flexibility benefit than converting it into medium-grade heat energy. Electricity price analysis is as follows: Figure 5 As shown in the figure, with time as the horizontal axis, this graph illustrates the dynamic synergy between the predicted electricity price curve, heat load curves of different grades, operating power curves of heat sources, and the state of thermal storage (SOC) curve of high-grade energy storage heat sources. During this period, the electricity price is at its lowest point of the day, especially between 02:00 and 05:00, when the price is below 0.1 yuan / kWh, fully meeting the second economic decision-making condition. Load analysis shows that residential heating load is high, while industrial load is essentially zero.

[0074] Optimizing decision-making outputs firstly ensures the continuous and stable operation of the nuclear heat source in the basic guarantee layer, directly meeting the basic heating load of most residents through an intelligent thermal mixing center. Secondly, it ensures the binary operation of the high-temperature solid thermal storage device in the peak response layer. Set to 1 and charged at a maximum power of 100MW (see Figure 5 Solid red line indicates thermal storage capacity (Positive value). Its heat storage SOC curve (orange line) rises rapidly and linearly from the previous day's low. The high-temperature heat pump in the final economic operating layer remains off during this period. Because it stores heat energy directly as the highest grade using such inexpensive electricity, its opportunity cost is lowest and its strategic value is greatest.

[0075] During peak electricity price periods, if the real-time electricity price does not meet the second economic decision-making condition and the equivalent heat production cost does not meet the first economic decision-making condition, the dispatch control strategy is to forcibly shut down all electrically driven heat sources, including the medium-grade efficiency-enhancing heat source 30; the external basic heat source 21 independently undertakes the basic heat load; and the high-grade energy storage heat source 40 controls the heat release power according to the high-grade heat load demand to meet the peak load. Specifically, during peak electricity price periods (07:00-11:00, 16:00-22:00), the system operation control strategy involves forcibly shutting down all electrically driven heat sources (medium-grade efficiency-enhancing heat source 30) to strictly avoid purchasing electricity at high prices; the external basic heat source 21 independently bears the basic heat load; and the high-grade energy storage heat source 40 precisely controls its heat release power according to the high-grade heat load demand to meet peak load requirements. Electricity price analysis shows that the electricity price rapidly climbs to its highest point of the day, far exceeding all economic thresholds. Load analysis shows that the combined effect of residential heating load and the start-up of steam load from chemical plants results in the total heat load reaching its first peak during the day.

[0076] The optimized decision-making process begins with the complete shutdown of all power-consuming equipment (heat pumps and thermal storage devices charging) to avoid purchasing electricity during peak pricing periods. Secondly, the basic safety layer ensures that the nuclear heat source continues to provide basic heating. Finally, the peak response layer activates the high-temperature solid thermal storage device to release heat. =1), The curve turns negative, indicating the production of 450°C industrial steam via the intelligent thermo-mixing center. Its SOC curve then begins to decline steadily.

[0077] During the second lowest electricity price period, the real-time electricity price does not meet the second economic decision-making condition, while the equivalent heat production cost meets the first economic decision-making condition. The dispatch control strategy is to keep the external basic heat source 21 running continuously. The medium-grade efficiency-enhancing heat source 30 is activated when the equivalent heat production cost is lower than the alternative energy threshold, producing medium-grade heat energy. The high-grade energy storage heat source 40 reduces its heat release power.

[0078] Specifically, during the second lowest electricity price period (11:00-15:00), the system operation control strategy involves the continuous operation of the external basic heat source 21; the medium-grade efficiency-enhancing heat source 30 becomes the core of the dispatch, and its equivalent heat production cost is immediately activated once it falls below the alternative energy threshold, utilizing nuclear heat source as a low-grade heat source to efficiently produce medium-grade heat energy; the high-grade energy storage heat source 40 reduces its heat release power because some medium-temperature load has been shared by heat pumps, and its heat storage state is preserved to cope with subsequent peak periods. Electricity price analysis shows that due to the large amount of photovoltaic power generation in the region, the electricity price has significantly fallen to a medium-low level. This price is higher than the low electricity price threshold. However, this does not meet the second economic decision condition; yet, for a heat pump, the equivalent heat production cost at this point is... far below ($0.22 yuan), thus meeting the first economic decision-making condition. Load analysis shows that residential heating load has decreased, but the demand for medium-temperature hot water from food processing enterprises has started.

[0079] Optimize decision-making output by first starting up the high-temperature heat pump in the economic operation layer (see...). Figure 5 Solid green line for heat pump power Utilizing waste heat from nuclear power and the relatively inexpensive electricity available at that time, 140°C hot water is efficiently produced and supplied to food companies through a mixing center. The nuclear heat source and the high-temperature heat pump are in a synergistic relationship of series coupling and cascade utilization. The nuclear heat source serves as the foundation and stable heat source of the system, continuously outputting low-temperature steam at 90°C. This steam operation is divided into two steps. In the first step, the steam goes directly to the intelligent thermal mixing center to meet the basic low-grade heating load. Then, the steam releases heat and becomes condensate, which serves as the input heat source for the high-temperature heat pump. The nuclear heat source provides a stable and inexpensive heat source for the efficient operation of the high-temperature heat pump, which is a key prerequisite for it to achieve a high COP value. The high-temperature heat pump consumes inexpensive electricity from the grid, and through the work of the compressor, it raises the grade of the low-grade heat of the steam condensate to 140°C. Essentially, it is an electrical value-added process for the heat output from the nuclear heat source, producing higher-grade heat energy. Secondly, the peak-response layer solid thermal storage device continues to release heat on demand to meet steam requirements, but its heat release power can be appropriately reduced because some of the medium-temperature load has been economically shared by the heat pump. Finally, since the electricity price has not reached the low electricity price threshold... The heat storage device does not charge.

[0080] Preferably, the system operation strategy performs rolling optimization once every preset time period (i.e., Figure 2 Step S204); the control timing of the rolling optimization specifically includes: An initialization strategy based on day-ahead forecast data of electricity market time-of-use pricing and day-ahead forecast data of different grades of heat load served by the heating system is executed as the initial benchmark for rolling optimization. Once the scheduling cycle begins, real-time data is acquired, and future time-of-use electricity price forecasts and heat load forecasts for different grades of heat served by the heating system are dynamically updated based on the current data. Starting from the current moment, every preset time period, a re-optimization is triggered based on the updated future electricity market time-of-use price forecast data and the heat load forecast data of different grades served by the heating system, and the optimal system operation strategy for the next scheduling cycle is generated on a rolling basis; wherein, the scheduling cycle includes multiple preset time periods; Through the command issuance interface, only the control command for the next preset time period is output. The results of the executed time periods are used as historical data to update the future electricity market time-of-use price forecast data and the forecast data of different grades of heat load served by the heating system.

[0081] Within the optimized scheduling cycle, at a time frequency much smaller than the scheduling cycle (15 minutes), the latest actual electricity market prices, measured heat load data, and updated short-term forecast data are used to recalculate and dynamically adjust the rolling time domain operation strategy for the next few hours, and the decision instructions for the first time period after adjustment are issued for execution.

[0082] To address forecasting errors (such as inaccurate load forecasts due to sudden weather changes) and abrupt changes in the market environment (such as real-time electricity price spikes due to grid failures), this method employs a rolling optimization strategy. Within a given scheduling cycle, the system operates cyclically at a relatively short time frequency (a preset time period, e.g., every 15 minutes or 1 hour). This involves acquiring the latest real-time market and load data, using this new data to revise the forecasting model, and then recalculating the operational plan for the next few hours (e.g., 4-6 hours) starting from the current moment. Then, only the scheduling instructions for the first time step (the next 15 minutes) of the calculation results are executed. This process is repeated continuously to ensure that the system always operates on an optimal or near-optimal track that most closely approximates the current actual situation.

[0083] Throughout the 24-hour operation, the central controller performs rolling optimizations every 15 minutes. For example, at 10:00 AM, the central controller detects that the actual steam load is 10% higher than predicted, while the real-time electricity price is 5% lower than the daytime price. It immediately uses this new data as input to re-optimize the operating plan for the next four hours (10:00 AM to 2:00 PM). The new result might be: immediately slightly increase the heat release capacity of the thermal storage unit and plan to start the heat pump at 11:15 AM. This dynamic fine-tuning ensures that the system always operates on a path optimal for the current conditions.

[0084] The scheduling execution and closed-loop control module 13 converts all the above decisions into millisecond-level control commands. When the decision requires the thermal storage device to charge at a power of 50MW, the scheduling execution module will send a corresponding power command signal to its PCS; when the decision requires the supply of 75°C hot water to the residential area, the module will accurately calculate and control the opening of the 90°C hot water valve from the nuclear heat source and the opening of the return water mixing valve in the intelligent thermal mixing center 50, and ensure that the outlet temperature is accurately and stably maintained at 75±0.5°C through PID closed-loop control.

[0085] like Figure 6 As shown, the rolling optimization control timing is dynamically executed according to the following process.

[0086] In the initialization phase of step 1, the system first executes the initialization strategy generated based on the day-ahead forecast data (24-hour forward forecast of electricity price / load / weather) as the initial benchmark for rolling optimization.

[0087] Step 2: Real-time Data Acquisition and Model Correction. Upon entering the execution cycle, the real-time data acquisition module obtains measured data: fluctuations in the grid spot electricity price; measured user heat load; and real-time changes in meteorological parameters. This measured data is synchronously input into the prediction correction algorithm, which dynamically updates the prediction model parameters through an LSTM / GRU neural network, thus resolving the static prediction lag problem described in the manual.

[0088] Step 3: Rolling optimization window execution. Starting from the current moment, a rolling optimization window is initiated, with a fixed duration of 4 hours to cover short-term fluctuation cycles. The time step is 15 minutes, and the solution mechanism triggers a re-optimization every 15 minutes. Based on the corrected prediction data, the optimization engine uses a mixed integer linear programming (MILP) model to continuously generate the optimal strategy for the next 4 hours. Step 4: Step-by-step instruction issuance. Through the instruction issuance interface, only control instructions for the next time period are output. Strategies for unexecuted time periods are retained until the next round of rolling updates. Results from executed time periods are fed back as historical data to the model correction stage.

[0089] To illustrate this solution more clearly, we will take a specific example of a smart heating project applied to a nuclear power plant, industrial park, and residential area in northern China.

[0090] This embodiment is deployed in a coastal area of ​​northern China, which has a large nuclear power plant and is surrounded by an industrial park encompassing fine chemicals, food processing, and other industries, as well as a large residential community. The goal of this project is to utilize the clean thermal energy from nuclear power, combined with the fluctuating power supply from the grid, to provide high- and medium-grade process heat energy for the industrial park and winter heating for the residential area.

[0091] like Figure 1 As shown, the specific components of the smart heating system in this embodiment include: Central Controller 10: Employs a redundant Siemens or Rockwell high-performance industrial control computer (IPC) running custom-developed control software embedded with the method of this invention. This software includes a data sensing and prediction module 11, an optimization decision engine module 12 based on the Gurobi solver, and a scheduling execution and closed-loop control module 13.

[0092] Basic heat source interface 20: Safely connected to the secondary loop non-radioactive steam of the external basic heat source 21 (nuclear power plant) via a large isolated heat exchange station, obtaining low-temperature hot water with a stable flow rate and a temperature of 90°C through heat exchange. This hot water supply is continuous and extremely low-cost, serving as the basic guarantee layer of the system.

[0093] Medium-grade heat source 30: Three ammonia-water absorption high-temperature heat pump systems 31 are deployed in parallel. This heat pump uses 90°C hot water provided by nuclear power as the driving heat source and part of the low-grade heat source, consuming a small amount of electricity, and can stably output high-temperature, high-pressure hot water at 140°C on the condensing side. Its COP curves under different operating conditions are accurately modeled and stored in the central controller 10, forming the economic operation layer of the system.

[0094] High-grade energy storage heat source 40: A large-scale high-temperature solid thermal storage device 41 was constructed. This device uses high-density magnesia bricks as the high-density heat storage body 42, and incorporates nickel-chromium alloy electric heating element groups 43. Its rated charging power is 100MW, the total heat storage capacity is 1200MWh, and the maximum heat storage temperature can reach 850°C. Its energy release heat exchange system 44 directly produces superheated steam at 450°C and 4.0MPa in the heat exchange coils by introducing high-pressure water. This device constitutes the system's peak response and high-grade layer. Figure 3 As shown, the present invention adopts a multi-grade heat source collaborative architecture, including: an external basic heat source 21 connected to a low-temperature heating network, a high-temperature heat pump system 31 connected to a medium-temperature heating network, and a high-temperature solid heat storage device 41 connected to a high-temperature steam pipeline network; the three heat sources are connected to an intelligent thermal mixing center 50 through independent pipelines.

[0095] like Figure 3 The heat source hierarchical decision-making logic flow of the present invention shown is executed by the central controller 10, and specifically includes the following core steps: Firstly, by collecting real-time electricity prices from the power grid. User heat load The step of determining the operating status of the heat source provides an input basis for decision-making.

[0096] Secondly, the basic guarantee layer prioritizes the activation of external basic heat source 21 (nuclear heating system) to meet the low-temperature basic heat load. Only when the real-time load exceeds the maximum power supply of nuclear energy will the shortfall load be transmitted to the economic layer, thereby achieving quantitative control of the basic load undertaken by nuclear energy and ensuring the stability of residential heating.

[0097] Again, based on the economic operation level, the equivalent heat production cost of the high-temperature heat pump system 31 is calculated. .like Below the preset threshold It meets the conditions for starting the heat pump system and outputs medium-temperature heat to meet the medium-grade heat load. This enables the activation of medium-grade heat sources based on electricity price thresholds.

[0098] Then, the peak response and high-grade level determination involve a dual triggering mechanism. The first trigger is the electricity price trigger, which occurs when the real-time electricity price... Below the low electricity price threshold At the same time, the high-temperature solid thermal storage device 41 is activated for charging. The second step is command triggering. When there is a sudden increase in high-grade heat load or when a peak shaving command is received from the power grid, the high-grade energy storage heat source 40 is forcibly started to release heat, so that the thermal storage system can participate in the grid demand response.

[0099] Finally, a valve control command is sent to the intelligent thermal mixing center 50, and this output is directly connected to... Figure 4 The hybrid control process forms a decision-making-execution closed loop.

[0100] Intelligent Thermal Mixing Center 50: This is a highly integrated energy station. It integrates three heat media: 90°C hot water from a nuclear power interface, 140°C hot water from a heat pump, and 450°C steam from a thermal storage device. Internally, it contains a mixing / heat exchange / flash unit 52 consisting of dozens of high-speed electric regulating valve groups 51, a multi-stage plate heat exchanger, a flash tank, and a desuperheating and pressure reducing device. Through precise control by the central controller 10, it can produce on demand and stably output heat to multiple user groups 70: supplying 75°C heating circulating water to residential areas; supplying 130°C process hot water to food processing enterprises; and supplying 450°C, 4.0MPa superheated steam to chemical enterprises.

[0101] like Figure 4 The control principle of the intelligent thermal mixing center shown includes a three-layer core mechanism. The intelligent thermal mixing center 50 receives three heat source inputs. First, 90°C low-temperature water from nuclear waste heat is connected through a low-temperature pipeline as the basic heat source. Second, 140°C high-temperature water from the heat pump system is connected through a medium-temperature pipeline as an efficiency-enhancing heat source. Finally, 450°C superheated steam from the heat storage device is connected through a high-temperature pipeline as an energy storage heat source. The central controller 10 sends commands to the high-speed electric regulating valve group 51 with a response time of <1 second and uses PID precise control to achieve rapid adjustment requirements. The three heat media output from the high-speed electric regulating valve group 51 enter the mixing / heat exchange / flash evaporation unit 52 for grade matching. The mixing tank processes the nuclear low-temperature water (90°C) to eliminate pressure fluctuations. The plate heat exchanger processes the heat pump high-temperature water (140°C) to maintain stable medium-temperature heat energy. The flash evaporation tank processes the heat storage superheated steam (450°C), achieving grade reduction utilization of high-temperature steam through pressure reduction. The mixing center outlet is equipped with a real-time sensor array (including temperature sensors, pressure sensors, and flow meters) to continuously monitor output parameters and feed them back to the central controller 10.

[0102] Based on feedback data, the central controller 10 dynamically corrects valve openings, forming a closed-loop control to ensure multi-grade output. (It has been upgraded from a passive, single-loop PID controller to an intelligent decision-making and control system integrating "decoupling calculation," "feedforward compensation," and "optimized allocation" functions. Through algorithms, it proactively resolves system coupling, anticipates and counteracts disturbances, and faithfully executes economic scheduling strategies, thereby ensuring high-precision, high-stability, and "on-demand" customization of multi-grade heat energy output even under complex operating conditions.) Residential heating water is maintained at a constant temperature of 75±0.5℃ (derived from the mixing tank output, meeting accuracy requirements). Food processing hot water is provided at 130℃ (derived from the plate heat exchanger output, meeting industrial process needs). Chemical plants provide industrial steam at 450℃ (derived from the flash tank output, directly utilizing the high-temperature characteristics of the heat storage device).

[0103] Connectivity and Communication: All devices are connected to the central controller 10 via industrial Ethernet. The central controller 10 communicates in real time with the power market and grid dispatching system 60, the National Meteorological Information Center, and the SCADA systems deployed at various user sites via API interfaces.

[0104] The operation of this heating system strictly follows... Figure 2 The process is shown below. Taking a typical winter workday as an example, the 24-hour operational decision-making process is described as follows: Figure 5 As shown.

[0105] (1) Step S201 Information acquisition and prediction (15:00 the previous afternoon), the data perception and prediction module 11 of the central controller 10 automatically performs the following tasks.

[0106] 1) Obtain the day-ahead market-clearing price curve for the next 24 hours (00:00 to 24:00 the next day) from the power trading platform (e.g., Figure 5 (As shown by the blue dashed line).

[0107] 2) Get the weather forecast for the next 24 hours (the nighttime low temperature is expected to be -12°C, the daytime low temperature is expected to be -3°C, and there will be strong sunlight at noon).

[0108] 3) Run the built-in LSTM deep prediction model, combine historical data and weather forecasts to generate prediction curves for residential heating load, food enterprise hot water load, and chemical enterprise steam load at a resolution of 15 minutes for the next 24 hours (merged into...). Figure 5 The total heat load is shown in the medium gray area.

[0109] (2) Step S202 Hierarchical collaborative optimization decision (executed by optimization decision engine module 12 at 16:00 the previous afternoon).

[0110] The optimization decision engine module 12 is activated. Based on the acquired prediction data, it solves the aforementioned MILP optimization model, with the goal of minimizing the total operating cost over the next 24 hours. The key thresholds for the preset economic decision conditions in the multi-objective optimization model are as follows.

[0111] The threshold of medium-temperature thermal value corresponding to the first economic decision-making condition. The cost is 0.22 yuan / kWh (this cost is slightly lower than the cost of natural gas heating in the region).

[0112] The low electricity price threshold corresponding to the second economic decision-making condition The price is 0.10 yuan / kWh (this electricity price is the 5th percentile of historical electricity price data, representing an extremely low price).

[0113] Through the specific implementation of this invention, the joint smart heating project successfully achieved perfect tracking and utilization of electricity spot market price signals, efficiently and in a tiered manner converting low-priced, even negative-priced, wind and solar power generated at night and during midday into high-value industrial and residential heat energy that meets diverse needs. Preliminary calculations show that compared to the alternative solution of "gas-fired boiler + traditional electric thermal storage boiler," its annual comprehensive operating cost is reduced by approximately 60%, and it provides 100MW of high-quality peak-shaving resources to the local power grid, achieving significant economic, environmental, and social benefits.

[0114] This invention achieves exceptional operational economics. Through a hierarchical collaborative optimization method based on real-time electricity prices, it can accurately capture and utilize low-priced electricity across all time periods in the electricity spot market, rather than being limited to fixed nighttime hours. High-temperature heat pumps utilize the second-lowest priced electricity, while high-temperature thermal storage utilizes the lowest priced electricity. Compared to the traditional timed charging model based on peak-valley-flat pricing, this refined energy utilization strategy, according to calculations, can reduce annual electricity purchase costs by 30% to 70% in a typical electricity spot market environment, resulting in significant economic benefits.

[0115] The technical solution of this invention can maximize the absorption of fluctuating renewable energy sources. The system as a whole constitutes a large-scale, rapidly responsive, and intelligently adjustable flexible load. It can proactively respond to grid dispatch signals and market price signals, deeply and fully absorbing potentially wasted electricity during periods of high wind and solar power generation (whenever they occur). This not only efficiently transforms unstable, low-value electricity into high-value, stable, multi-grade thermal energy products, but also effectively improves the overall utilization rate of renewable energy, providing strong technical support for the construction of new power systems.

[0116] This invention enables efficient, tiered energy utilization, significantly reducing energy loss. The proposed energy tiered utilization chain—basic heat source > heat pump heating > high-grade heat storage—achieves a "grade match" between heat supply and heat load demand. Low-temperature loads are met by the basic heat source, medium-temperature loads by the efficient heat pump, and only the highest-temperature loads utilize high-temperature heat storage. This fundamentally avoids the enormous energy loss of heating 800°C water with an 800°C heat source, resulting in a significant improvement in the overall energy utilization efficiency (considering primary energy consumption) compared to a single heat source system.

[0117] The technical solution of this invention enhances the stability and flexibility of the power grid, creating additional value: this system is not only a passive electricity load, but also a "virtual power plant" that can provide a variety of high-value ancillary services to the power grid. When the grid frequency or voltage fluctuates, it can quickly adjust its charging power; when the system needs peak shaving, it can act as an interruptible load or a rapid response load. By participating in the ancillary services market, the system can obtain considerable economic benefits, achieving a fundamental shift from "one-way demand" to "two-way interaction" with the power grid, making it a typical grid-friendly asset.

[0118] The technical solution of this invention provides strong heating assurance capabilities and high service quality. The combination of multiple heat sources forms a natural mutual backup; the basic heat source, heat pump, and heat storage device can support each other in extreme situations, greatly improving the reliability and safety of heating. Simultaneously, the intelligent thermal mixing center, like a "thermal router," can flexibly, quickly, and accurately respond to the diverse and dynamically changing heat load demands of users, achieving "on-demand heating" and "precision heating," significantly improving the energy experience and service quality for end users.

[0119] In summary, the technical solution of this invention establishes a multi-objective optimization model based on electricity price forecast data and heat load forecast data of different grades, with the core objective of minimizing the total system operating cost. Within a scheduling cycle, under the premise of satisfying the preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, the multi-objective optimization model is solved to generate a system operation strategy. Through an intelligent heat mixing center, heat energy of different grades generated by one or more operating heat sources is mixed, exchanged, and distributed to output heat loads that meet the grade requirements of heat users. According to the role positioning, technical and economic characteristics, and sensitivity to electricity prices of different heat sources in the energy system, they are divided into three logical levels: the basic guarantee layer (external basic heat source), the economic operation layer (medium-grade efficiency-enhancing heat source), and the peak response and high-grade layer (high-grade energy storage heat source). Each level is assigned a different operational mission, and the operation of the entire system is based on a unified coordinated scheduling of a multi-objective optimization model. This effectively solves the problem of low reliability and adaptability of heating system operation control caused by existing technologies, and effectively improves the reliability and adaptability of heating system operation control.

[0120] The heat load information in the technical solution of this invention includes historical sequences of low-temperature heat load, historical sequences of medium-temperature heat load, and historical sequences of high-temperature heat load. Since the energy consumption patterns, sensitivity to weather, and correlation with electricity prices of loads of different grades are all different, this prediction method that distinguishes input by grade provides an accurate data foundation for the refined hierarchical collaborative optimization by grade matching in subsequent steps.

[0121] The technical solution of this invention determines the corresponding basic guarantee logic, economic operation logic, peak response and grid interaction logic based on electricity price, heat load forecast, and heat load demand. Through an energy cascade utilization chain of external basic heat source > medium-grade efficiency-enhancing heat source for temperature increase > high-grade energy storage heat source for heat storage and generation, it achieves "grade matching" between heat energy supply and heat load demand. That is, low-temperature heat load is met by the basic heat source, medium-grade heat load is efficiently met by the medium-grade efficiency-enhancing heat source, and only high-grade heat load is met by the high-grade energy storage heat source. This fundamentally avoids the huge energy loss of using an 800°C heat source to heat 80°C water, significantly improving the overall energy utilization efficiency of the system (considering primary energy consumption) compared to a single heat source system. Furthermore, it allows for flexible adjustment of the operation of different types of heat sources based on electricity price and heat load forecast, improving the reliability and adaptability of the heating system's operation control.

[0122] The system operation strategy in the technical solution of this invention specifically includes scheduling and control strategies for different time periods. The operation of different types of heat sources is flexibly adjusted according to the time period of the predicted electricity price, thereby improving the reliability and adaptability of the heating system operation control.

[0123] In the technical solution of this invention, the system operation strategy performs rolling optimization every preset time period; this cycle repeats to ensure that the heating system always operates on the optimal or suboptimal track that is closest to the current actual situation.

[0124] Example 2 like Figure 7 As shown, the present invention also provides a hierarchical coordinated operation control method for a multi-energy complementary heating system, implemented based on a multi-energy complementary heating system in Embodiment 1, comprising: S1, the central controller acquires at least the electricity market time-of-use price forecast data for the next scheduling cycle and the heat load forecast data of different grades served by the heating system; based on the price forecast data and the heat load forecast data of different grades, it establishes a multi-objective optimization model with the minimum total system operating cost as the core objective; within one scheduling cycle, under the premise of satisfying the preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, it solves the multi-objective optimization model to generate a system operation strategy; the generated system operation strategy is converted into operation instructions for heat source equipment and related components at each level and sent to the intelligent thermal mixing center; S2, the intelligent thermal mixing center, based on the operation instructions for each level of heat source equipment and related components converted by the central controller, mixes, exchanges, and distributes heat energy of different grades generated by one or more operating heat sources, and outputs heat loads that meet the grade requirements of heat users.

[0125] The technical effects in this embodiment are the same as those in Embodiment 1, and will not be described again here.

[0126] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-energy complementary heating system, characterized in that, include: The system comprises an external base heat source, a medium-grade enhanced heat source, a high-grade energy storage heat source, an intelligent thermal mixing center, and a central controller. The external base heat source is used to meet a preset low-temperature base heat load; the medium-grade enhanced heat source is used to meet a preset medium-grade heat load; and the high-grade energy storage heat source is used to meet a preset high-grade heat load. The intelligent thermal mixing center is connected to the outlets of the external base heat source, the medium-grade enhanced heat source, and the high-grade energy storage heat source, as well as the inlet of at least one heat user. Based on the operational instructions from the central controller to the heat source equipment and related components at each level, it mixes, exchanges, and distributes the heat energy of different grades generated by one or more operating heat sources, distributing it to... Heat users output heat loads that meet their grade requirements; the central controller is used to acquire electricity market time-of-use price forecast data for at least one future scheduling cycle and heat load forecast data of different grades served by the heating system; based on the electricity price forecast data and heat load forecast data of different grades, a multi-objective optimization model is established with the minimum total system operating cost as the core objective; within one scheduling cycle, under the premise of satisfying the preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, the multi-objective optimization model is solved to generate a system operation strategy; the generated system operation strategy is converted into operation instructions for heat source equipment and related components at each level and sent to the intelligent thermal mixing center.

2. The multi-energy complementary heating system according to claim 1, characterized in that, Specifically, the acquisition of electricity market time-of-use price forecasts for at least one future scheduling cycle and forecasts of different grades of heat load served by the heating system includes: Electricity market information, meteorological information, and heat load information are obtained from multiple external sources. The electricity market information includes historical and future electricity price data, the meteorological information includes historical and future meteorological data, and the heat load information includes historical low-temperature heat load sequences. Historical sequence of medium-temperature load Historical sequence of high temperature load ; Using historical heat load data of a certain grade as the output data of the deep prediction model, and using historical electricity price data and historical meteorological data of the same time period as the historical heat load data of that grade as the input data of the deep prediction model, the predicted data of different grades of heat load served by the heating system in the next scheduling cycle are predicted.

3. The multi-energy complementary heating system according to claim 1, characterized in that, The multi-objective optimization model with the core objective of minimizing the total system operating cost is as follows: Where J is the function value of the multi-objective optimization model; T is the total duration of the optimization scheduling cycle; The total cost of electricity purchase during time period t; The system start-up, shutdown, operation, and maintenance costs during time period t; The revenue gained from participating in grid ancillary services during time period t; Among them, the total electricity purchase cost during time period t The calculation method is as follows: in, The real-time or predicted electricity price for time period t; Input electrical power for medium-grade heat source enhancement; The charging power for high-grade energy storage heat sources; The duration of time period t or the time difference from the previous time period; Among them, the system start-up, shutdown, operation and maintenance costs during time period t The calculation method is as follows: ， in, It is a variable signal for starting and stopping the heat source device. It is a heat source device The cost of a single start-stop action, It is a heat source device The unit power operation and maintenance cost coefficient, It is a heat source device Operating power during time period t; I represents the set of devices; Among them, the revenue obtained from participating in grid ancillary services during time period t The calculation method is as follows: in, It refers to the market price of grid ancillary services in time period t; It is the auxiliary service power provided by the heating system to the power grid during time period t.

4. The multi-energy complementary heating system according to claim 1, characterized in that, The basic guarantee logic is as follows: Prioritize scheduling external basic heat sources to meet the basic heat load matching the grade of the external basic heat sources, using their maximum available capacity or economic boundary. The economic operation logic is as follows: When the heating system has a medium-grade heat load demand, the multi-objective optimization model determines whether the equivalent heat production cost generated by starting a medium-grade efficiency-enhancing heat source in time period t meets the preset first economic decision condition. If the preset first economic decision condition is met, the medium-grade efficiency-enhancing heat source is scheduled to operate to meet the medium-grade heat load. The peak response and grid interaction logic is as follows: In the multi-objective optimization model, it determines whether the electricity price in time period t meets the preset second economic decision condition. If the preset second economic decision condition is met, a high-grade energy storage heat source is scheduled for heat storage. When the heating system has a high-grade heat load demand, or the total heat load exceeds the supply capacity of the external basic heat source and the medium-grade efficiency-enhancing heat source, or in response to the grid peak shaving command, a high-grade energy storage heat source is scheduled to release heat.

5. A multi-energy complementary heating system according to claim 4, characterized in that, The first economic decision-making condition is specifically: real-time or predicted electricity price. Divide by the energy efficiency ratio of the medium-grade heat source under current operating conditions The resulting equivalent heat production cost is lower than the mesothermal heat value threshold. ; The second economic decision-making condition specifically refers to: real-time or predicted electricity prices. Below the low electricity price threshold ; Among them, the medium-temperature heat value threshold The specific adjustment method is as follows: in, This is an adjustable coefficient; It is the unit heating cost of the natural gas boiler on the dth natural day; Among them, the low electricity price threshold The specific method for determining it is as follows: in, It is the benchmark value for electricity prices; It is the offset of the power grid signal. It is the state offset of the heating system.

6. A multi-energy complementary heating system according to claim 1, characterized in that, Solving the multi-objective optimization model also requires satisfying preset constraints, including thermal power balance constraints, heat source operation constraints, and energy storage system constraints; among these, Specifically, the thermal power balance constraint is as follows: in, The heat generated by the external basic heat source during time period t; For the heat production of the grade-enhanced heat source during time period t; For the heat generation of high-grade energy storage heat source during time period t; The heat load demand at grade g during time period t; Represents a set of grades; Specifically, the operating constraints of the heat source are as follows: in, It is a variable indicating whether heat source device i is in operation during time period t; For heat source equipment Operating power during time period t; Let i be the minimum operating power of heat source device i during time period t. The maximum operating power of heat source device i during time period t; Specifically, the constraints on energy storage systems are as follows: in, The heat storage capacity of a high-grade energy storage heat source during time period t; The heat storage capacity of a high-grade energy storage heat source in the previous time period or the current time period t; The charging efficiency of a high-grade energy storage heat source in time period t; The charging power of a high-grade energy storage heat source during time period t; The heat release efficiency of a high-grade energy storage heat source in time period t; The heat production power of a high-grade energy storage heat source during time period t; Whether the high-grade energy storage heat source is in a charging state during time period t; Whether the high-grade energy storage heat source is in a discharge state during time period t; This represents the minimum heat storage capacity of a high-grade energy storage heat source during time period t. This represents the maximum heat storage capacity of a high-grade energy storage heat source during time period t.

7. A multi-energy complementary heating system according to claim 1, characterized in that, The intelligent thermal mixing center is used to proportion, mix, or exchange low-temperature water from an external basic heat source, medium-temperature water from a medium-grade enhanced heat source, and high-temperature steam from a high-grade energy storage heat source, according to operating instructions, and output heat loads that meet the grade requirements of heat users.

8. A multi-energy complementary heating system according to claim 5, characterized in that, The system operation strategy specifically includes scheduling and control strategies for different time periods, which are as follows: During periods of low electricity prices, the real-time electricity price meets the second economic decision-making condition, and the equivalent heat production cost meets the first economic decision-making condition. The dispatch and control strategy ensures that the external basic heat source continues to operate at full capacity to bear the basic heating load. The high-grade energy storage heat source receives a charging instruction and stores the high-grade heat energy converted from cheap electricity at maximum power. The medium-grade efficiency-enhancing heat source is forcibly shut down. During peak electricity price periods, if the real-time electricity price does not meet the second economic decision-making condition and the equivalent heat production cost does not meet the first economic decision-making condition, the dispatch control strategy is to forcibly shut down all electrically driven heat sources, including medium-grade efficiency-enhancing heat sources; external basic heat sources independently bear the basic heat load; and high-grade energy storage heat sources control their heat release power according to the high-grade heat load demand to meet peak load. During the second lowest electricity price period, the real-time electricity price does not meet the second economic decision-making condition, while the equivalent heat production cost meets the first economic decision-making condition. The dispatch control strategy is to keep the external basic heat source running continuously. The medium-grade efficiency-enhancing heat source is activated when the equivalent heat production cost is lower than the alternative energy threshold, producing medium-grade heat energy. The high-grade energy storage heat source reduces its heat release power.

9. A multi-energy complementary heating system according to claim 1, characterized in that, The system operation strategy performs rolling optimization every preset time period; The control timing for the rolling optimization specifically includes: An initialization strategy based on day-ahead forecast data of electricity market time-of-use pricing and day-ahead forecast data of different grades of heat load served by the heating system is executed as the initial benchmark for rolling optimization. Once the scheduling cycle begins, real-time data is acquired, and future time-of-use electricity price forecasts and heat load forecasts for different grades of heat served by the heating system are dynamically updated based on the current data. Starting from the current moment, every preset time period, a re-optimization is triggered based on the updated future electricity market time-of-use price forecast data and the heat load forecast data of different grades served by the heating system, and the optimal system operation strategy for the next scheduling cycle is generated on a rolling basis; wherein, the scheduling cycle includes multiple preset time periods; Through the command issuance interface, only the control command for the next preset time period is output. The results of the executed time periods are used as historical data to update the future electricity market time-of-use price forecast data and the forecast data of different grades of heat load served by the heating system.

10. A hierarchical coordinated operation control method for a multi-energy complementary heating system, characterized in that, Based on the multi-energy complementary heating system described in any one of claims 1-9, it includes: The central controller acquires electricity market time-of-use price forecasts for at least one future scheduling cycle, as well as forecasts of different grades of heat load served by the heating system. Based on the price forecasts and heat load forecasts, it establishes a multi-objective optimization model with the minimum total system operating cost as the core objective. Within one scheduling cycle, under the premise of satisfying preset basic guarantee logic, economic operation logic, peak response and grid interaction logic, it solves the multi-objective optimization model to generate a system operation strategy. The generated system operation strategy is then converted into operation instructions for heat source equipment and related components at each level and sent to the intelligent thermal mixing center. The intelligent thermal mixing center, based on the operation instructions for each level of heat source equipment and related components converted by the central controller, mixes, exchanges, and distributes heat energy of different grades generated by one or more operating heat sources, and outputs heat loads that meet the grade requirements of heat users.

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