Layered planning method and system for comprehensive energy system comprising steam heat storage tank

Abstract

The invention provides a hierarchical planning method and system for a comprehensive energy system comprising a steam heat storage tank. The method comprises the steps that an upper capacity configuration layer searches an equipment capacity configuration set omega; the middle capacity configuration layer takes omega as a fixed constraint, introduces a variable operation cost correction term caused by the heat supply equipment to compensate the steady-state cost, constructs a mixed integer linear programming model, and solves a scheduling instruction set theta; and the lower capacity configuration layer predicts an output track for the equipment by adopting an MPC method according to the omega and the theta, calculates a variable operation cost correction item and returns the variable operation cost correction item to the middle layer, so that the middle layer returns the minimum value which can be reached by the variable operation cost under the omega to the upper layer. According to the method, a complex comprehensive energy system decision problem is divided into three sub-problem layers which are closely associated and relatively independent, a collaborative optimization mechanism of decomposition from top to bottom and feedback from bottom to top is formed among the layers, the actual operation economy of the system can be remarkably improved, the dynamic adjustment energy consumption is reduced, and the method has very high actual application value.

Description

Technical Field

[0001] This invention relates to the field of electrothermal coupling technology, specifically to a hierarchical planning method for an integrated energy system including a steam storage tank and a hierarchical planning system for an integrated energy system including a steam storage tank. Background Technology

[0002] In current research and engineering practice on the optimal configuration of integrated energy systems, most existing models use the steady-state assumption as the theoretical basis for single-layer optimization.

[0003] While this modeling method offers significant advantages in terms of computational complexity, real-world operating systems often involve a wide variety of chemical processes and thermal equipment with substantial dynamic characteristics (such as steam accumulators, heat pump systems, and electric boilers). Their thermal inertia, fluid dynamics, and phase change processes result in system response times ranging from minutes to hours. When using a steady-state model for single-layer capacity configuration, a "static-dynamic" model mismatch inevitably arises. Specifically, this manifests as a significant time delay and amplitude deviation between the actual equipment output curve and the scheduling expectation (measured data shows a maximum dynamic deviation of up to 25%). This deviation, accumulated and amplified at the system level, ultimately leads to a 10-15% discrepancy between actual operating economic indicators and optimized calculation results, severely impacting the reliability and practicality of the planning scheme. Summary of the Invention

[0004] To address the technical problems of low reliability and practicality of the aforementioned integrated energy system planning schemes, the present invention provides a hierarchical planning method for an integrated energy system including a steam storage tank in its first aspect embodiment.

[0005] A second aspect of the present invention provides a hierarchical planning system for an integrated energy system including a steam storage tank.

[0006] The technical solution adopted in this invention is as follows:

[0007] A first aspect of the present invention proposes a hierarchical planning method for an integrated energy system including a steam storage tank, comprising the following steps: An upper-level capacity configuration layer uses the equipment capacity configuration set composed of the capacities of various devices in the electrothermal coupling park as decision variables, and the system's full life-cycle economic evaluation index as the objective function, searching for an equipment capacity configuration set Ω that enables the system to achieve optimal economic performance; a middle-level capacity configuration layer uses the equipment capacity configuration set Ω passed from the upper-level capacity configuration layer as a fixed constraint, the operating variables of each device in the system as decision variables, and introduces a variable operating cost correction term caused by the heating equipment to compensate for the steady-state cost, constructing a mixed-integer linear programming model to solve for the scheduling instruction set Θ, wherein the heating equipment includes: Air source heat pumps, electric heating boilers, and steam storage tanks; the lower capacity configuration layer uses the MPC (Model Predictive Control) method to predict the output trajectory of the equipment based on the equipment capacity configuration set Ω and the scheduling instruction set Θ transmitted from the middle capacity configuration layer. Based on the output trajectory, the system's actual output tracks the scheduling instructions by adjusting the actuator actions in real time, and calculates a variable operating cost correction term to return to the middle capacity configuration layer. This allows the middle capacity configuration layer to return the minimum achievable variable operating cost under the equipment capacity configuration set Ω based on the variable operating cost correction term transmitted from the lower capacity configuration layer, and then return to the upper capacity configuration layer.

[0008] The hierarchical planning method for an integrated energy system including a steam storage tank described above in this invention also has the following additional technical features:

[0009] According to one embodiment of the present invention, the upper capacity configuration layer adopts a planning time scale of years, the middle capacity configuration layer adopts a scheduling time scale of minutes, and the lower capacity configuration layer adopts a control time scale of seconds.

[0010] According to one embodiment of the present invention, the upper capacity configuration layer searches for the set of device configurations Ω that enable the system to achieve optimal economic performance using the following formula:

[0011]

[0012] Where Ω represents the device capacity configuration set, ω j For the capacity configuration of device j, PV stands for photovoltaic equipment, BO for electric heating boiler, HSS for steam storage tank, SOC for battery, EC for air source heat pump, and C... LCC (Ω) represents the total annual cost of the integrated energy system over its entire life cycle under Ω, C INV (Ω) represents the annual investment cost of the integrated energy system over its entire life cycle under Ω, and C FO (Ω) represents the annual fixed operating cost of the integrated energy system over its entire life cycle under Ω, min[CVO [Ω] represents the minimum achievable variable operating cost under Ω, where Ω min and Ω max These are the maximum and minimum values ​​of the constraints that the equipment capacity configuration needs to satisfy, respectively.

[0013] According to one embodiment of the present invention, the middle-layer capacity configuration layer specifically constructs a mixed-integer linear programming model based on the following formula:

[0014]

[0015] Among them, J1(Ω) and C VO (Ω) represents the variable operating cost under the equipment capacity configuration set Ω, Θ represents the scheduling instruction set, t represents the index of each scheduling cycle under the mid-level operation scheduling time scale, and C VO,t (Θ,Ω) represents the variable operating cost at time t given the scheduling instruction set Θ and the equipment capacity configuration set Ω. This is a variable operating cost adjustment term for heating equipment under a given set of scheduling instructions Θ and a set of equipment capacity configurations Ω.

[0016] According to one embodiment of the present invention, the lower capacity configuration layer specifically uses the following formula to predict the output trajectory:

[0017]

[0018] Among them, f cor To configure the set Ω based on the equipment capacity, the set of scheduling instructions Θ, and the set of control variable sequences. A function for calculating the variable operating cost adjustment term; J 2,j Output trajectory, The optimal set of control variable sequences that minimizes dynamic characteristics; Δ U j To control the incremental sequence; Y j To predict the output sequence; R j To track the target sequence; This is a diagonal matrix used to adjust the control effect.

[0019] A second aspect of the present invention proposes a hierarchical planning system for an integrated energy system including a steam thermal storage tank, comprising: an upper capacity configuration layer, used as the equipment capacity configuration set composed of the capacity of each device in the electrothermal coupling park as the decision variable, and the system's full life-cycle economic evaluation index as the objective function, to search for the equipment capacity configuration set Ω that enables the system to achieve optimal economic performance; and a middle capacity configuration layer, used as the equipment capacity configuration set Ω passed from the upper capacity configuration layer as a fixed constraint, the operating variables of each device in the system as the decision variables, and introducing a variable operating cost correction term caused by the heating equipment to compensate for the steady-state cost, constructing a mixed integer linear programming model, and solving the tuning... The heating equipment includes an air source heat pump, an electric heating boiler, and a steam storage tank. The lower capacity configuration layer is used to predict the output trajectory of the equipment using the MPC method based on the equipment capacity configuration set Ω and the scheduling instruction set Θ transmitted from the middle capacity configuration layer. Based on the output trajectory, the system's actual output tracks the scheduling instructions by adjusting the actuator actions in real time. A variable operating cost correction term is calculated and returned to the middle capacity configuration layer, so that the middle capacity configuration layer returns the minimum achievable variable operating cost under the equipment capacity configuration set Ω based on the variable operating cost correction term transmitted from the lower capacity configuration layer, and then returns it to the upper capacity configuration layer.

[0020] The hierarchical planning system for the integrated energy system including a steam storage tank described above in this invention also has the following additional technical features:

[0021] According to one embodiment of the present invention, the upper capacity configuration layer adopts a planning time scale of years, the middle capacity configuration layer adopts a scheduling time scale of minutes, and the lower capacity configuration layer adopts a control time scale of seconds.

[0022] According to one embodiment of the present invention, the upper capacity configuration layer searches for the set of device configurations Ω that enable the system to achieve optimal economic performance using the following formula:

[0023]

[0024] Where Ω represents the device capacity configuration set, ω j For the capacity configuration of device j, PV stands for photovoltaic equipment, BO for electric heating boiler, HSS for steam storage tank, SOC for battery, EC for air source heat pump, and C... LCC (Ω) represents the total annual cost of the integrated energy system over its entire life cycle under Ω, C INV (Ω) represents the annual investment cost of the integrated energy system over its entire life cycle under Ω, and C FO (Ω) represents the annual fixed operating cost of the integrated energy system over its entire life cycle under Ω, min[C VO[Ω] represents the minimum achievable variable operating cost under Ω, where Ω min and Ω max These are the maximum and minimum values ​​of the constraints that the equipment capacity configuration needs to satisfy, respectively.

[0025] According to one embodiment of the present invention, the middle-layer capacity configuration layer specifically constructs a mixed-integer linear programming model based on the following formula:

[0026]

[0027] Among them, J1(Ω) and C VO (Ω) represents the variable operating cost under the equipment capacity configuration set Ω, Θ represents the scheduling instruction set, t represents the index of each scheduling cycle under the mid-level operation scheduling time scale, and C VO,t (Θ,Ω) represents the variable operating cost at time t given the scheduling instruction set Θ and the equipment capacity configuration set Ω. This is a variable operating cost adjustment term for heating equipment under a given set of scheduling instructions Θ and a set of equipment capacity configurations Ω.

[0028] According to one embodiment of the present invention, the lower capacity configuration layer specifically uses the following formula to predict the output trajectory:

[0029]

[0030] Among them, f cor To configure the set Ω based on the equipment capacity, the set of scheduling instructions Θ, and the set of control variable sequences. A function for calculating the variable operating cost adjustment term; J 2,j Output trajectory, The optimal set of control variable sequences that minimizes dynamic characteristics; Δ U j To control the incremental sequence; Y j To predict the output sequence; R j To track the target sequence; This is a diagonal matrix used to adjust the control effect.

[0031] The beneficial effects of this invention are:

[0032] This invention innovatively proposes a hierarchical planning method integrating "configuration-scheduling-control," which divides the complex integrated energy system decision-making problem into three closely related yet relatively independent sub-problem levels. A collaborative optimization mechanism of "top-down decomposition and bottom-up feedback" is formed between each level. The middle level (scheduling level) optimizes the generation of steady-state economic scheduling commands, while the lower level (control level) uses the MPC algorithm to accurately track dynamic outputs. Simultaneously, the correction value of variable operating costs under the influence of dynamic characteristics is fed back to the upper planning level in real time, forming a closed-loop optimization mechanism. This hierarchical architecture not only retains the high computational efficiency of the steady-state model but also effectively improves the engineering applicability of the planning scheme through a dynamic compensation mechanism. This significantly improves the actual operating economy of the system and reduces dynamic energy consumption, demonstrating high practical application value.

[0033] Through the closed-loop feedback of dynamic cost adjustment items, the impact of equipment dynamic characteristics on economics is accurately transmitted to the planning level.

[0034] Different capacity configuration layers use different time scales. By decoupling through time scales, each layer can focus on the optimization problem within its own time scale, thereby reducing the complexity of the problem. Attached Figure Description

[0035] Figure 1 This is a flowchart of a hierarchical planning method for an integrated energy system including a steam storage tank according to an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the structure of a hierarchical planning system for an integrated energy system including a steam storage tank, according to an embodiment of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Integrated energy systems, as a key development direction in the current energy sector, integrate various advanced technologies such as renewable energy power generation, system energy storage and high-efficiency conversion, and multi-energy complementarity to construct an efficient, flexible, and sustainable energy supply system. This system not only significantly improves energy utilization efficiency and reduces energy waste but also effectively promotes the large-scale development and utilization of renewable energy, providing crucial technological support for achieving sustainable energy supply development. Introducing steam storage tanks as a key component of integrated energy systems allows for full utilization of low-priced electricity during off-peak hours and clean electricity generated from renewable energy sources such as photovoltaics and wind power. Through power conversion devices such as electric boilers, electrical energy is efficiently converted into heat energy and stored in steam storage tanks, enabling flexible conversion and optimized allocation of electrical and thermal loads.

[0039] Figure 1 This is a flowchart of a hierarchical planning method for an integrated energy system including a steam storage tank according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0040] S1, the upper capacity configuration layer uses the equipment capacity configuration set composed of the capacity of each device in the electrothermal coupling park as the decision variable, and the system's full life cycle economic evaluation index as the objective function to search for the equipment capacity configuration set Ω that enables the system to achieve the optimal economic performance.

[0041] Specifically, the integrated energy system mainly consists of core equipment such as photovoltaic (PV) equipment, electric heating boilers (BO), steam storage tanks (HSS), battery systems (SOC), and air source heat pumps (EC). In terms of system operation, the PV equipment serves as the primary power source, responsible for meeting the basic electrical load demand within the microgrid. When PV power generation cannot meet the real-time electrical load demand, the system supplements it in two ways: first, by purchasing electricity from the main grid; and second, by discharging the stored energy in the batteries. Conversely, if there is surplus PV power generation, the excess energy is preferentially stored in the batteries for later use; when the batteries are fully charged, the remaining energy can also be sold to the grid, achieving economical energy utilization. The air source heat pump, as another important component of the system, can flexibly switch operating modes according to seasonal changes. During non-summer periods, the air source heat pump unit mainly operates in heating mode, producing domestic hot water and delivering it to areas requiring heat, such as washrooms. The public power grid plays a crucial supporting role in the entire system. It not only fills the load gap that photovoltaic and battery storage cannot meet, but also works in conjunction with electric steam storage tanks during off-peak electricity periods to convert low-cost electricity into heat energy for storage, providing a stable and reliable heat supply to the heat load when needed. This multi-energy complementary operation mode not only improves the system's economics but also significantly enhances the reliability and flexibility of energy supply.

[0042] The upper capacity configuration layer uses the capacity configuration set Ω, composed of the capacities of each device, as the decision variable, and the system's full life-cycle economic evaluation index (the total annual cost C over the entire life-cycle). LCC Let be the objective function, including investment cost C. INV Fixed operating costs C FO and variable operating costs C VO Searching for equipment capacity that allows the system to achieve optimal economic performance under proper scheduling and control can be performed using a multi-objective particle swarm optimization algorithm for global optimization.

[0043] The above economic performance indicators are all converted to annual values ​​during calculation to facilitate a unified assessment from the perspective of the entire project life cycle. That is, the planning time scale adopted by the upper capacity configuration layer is annual.

[0044] Investment costs include the initial investment costs of all equipment in the system, as well as the replacement costs of equipment with a lifespan shorter than the system's lifespan. Annual fixed operating costs are also related to equipment capacity. Both investment costs and fixed operating costs can be directly calculated from equipment capacity, specifically using three cost or benefit items from the system's economic indicators: annual operating cost, annual investment cost, and annual variable operating cost. Among these, investment costs and fixed operating costs are directly determined by the equipment capacity configuration set Ω, while variable operating costs are related to the control of the intermediate and lower capacity configuration layers described below. The influence of the equipment capacity configuration set Ω on these costs is reflected in the operating boundary constraints.

[0045] In a specific embodiment of the present invention, the upper capacity configuration layer can use the following formula to search for the set of device configurations Ω that enable the system to achieve optimal economic performance:

[0046]

[0047] Where Ω represents the device capacity configuration set, ω j For the capacity configuration of device j, PV stands for photovoltaic equipment, BO for electric heating boiler, HSS for steam storage tank, SOC for battery, EC for air source heat pump, and C... LCC (Ω) represents the total annual cost of the integrated energy system over its entire life cycle under Ω, C INV (Ω) represents the annual operating cost of the integrated energy system over its entire life cycle under Ω, C FO (Ω) represents the annual investment cost of the integrated energy system over its entire life cycle under Ω, min[C VO [Ω] represents the minimum achievable variable operating cost under Ω, where Ω min and Ω max These are the maximum and minimum values ​​of the constraints that the equipment capacity configuration needs to satisfy, respectively.

[0048] S2, the middle capacity configuration layer takes the equipment capacity configuration set Ω passed from the upper capacity configuration layer as a fixed constraint, the operating variables of each device in the system as decision variables, and introduces the variable operating cost correction term caused by the heating equipment to compensate for the steady-state cost, constructs a mixed integer linear programming model, and solves the scheduling instruction set Θ. The heating equipment includes: air source heat pump, electric heating boiler and steam storage tank.

[0049] Specifically, for power supply equipment such as photovoltaic devices, energy storage tanks, and the power grid in the system, the dynamic response process is relatively short, generally lasting on the order of seconds. However, for heating equipment, the adjustment transition time varies from several minutes to tens of minutes. For equipment with such a long adjustment transition time, the dynamic process cannot be ignored. Therefore, this invention does not consider the dynamic characteristics of power supply equipment, but focuses on the dynamic characteristics of heating equipment and their impact on the optimization of the integrated energy system configuration.

[0050] like Figure 2 As shown, after the upper-level capacity configuration layer determines the capacity configuration Ω, the middle layer treats it as a given constant and focuses on solving the steady-state operation scheduling problem for the given capacity configuration to obtain the minimum variable operating cost. The steady-state scheduling problem is constructed as a MILP (Mixed-integer linear programming) problem, with the decision variables being the operating variables of each device in the system, i.e., variables representing the real-time operating power, storage charging and discharging status, etc., of each device. However, due to the introduction of heating equipment with slower dynamic characteristics into the integrated energy system including steam storage tanks, the heating equipment needs a transition time after receiving the scheduling command to reach the expected working state, causing a deviation between the actual economic indicators of the system and the optimization results of the steady-state scheduling problem. Under this premise, the variable operating cost correction term caused by the heating equipment... This is used to describe the correction value of variable operating costs considering the deviation caused by dynamic characteristics under a given capacity configuration set Ω and scheduling instruction set Θ. The lower capacity configuration layer returns to the life cycle economic evaluation index as the objective function to search for the equipment capacity configuration set Ω that enables the system to achieve the optimal economic performance.

[0051] In a specific embodiment of the present invention, the middle-layer capacity configuration layer can be constructed according to the following formula:

[0052]

[0053] Among them, J1(Ω) and C VO (Ω) represents the variable operating cost under the equipment capacity configuration set Ω, Θ represents the scheduling instruction set, t represents the index of each scheduling cycle under the mid-level operation scheduling time scale, and C VO,t(Θ,Ω) represents the variable operating cost at time t given the scheduling instruction set Θ and the equipment capacity configuration set Ω. This is a variable operating cost adjustment term for heating equipment under a given set of scheduling instructions Θ and a set of equipment capacity configurations Ω.

[0054] In this invention, the scheduling time scale used by the middle capacity configuration layer is minutes, and specifically, the scheduling time scale (scheduling cycle interval) used by the middle capacity configuration layer can be 15 minutes.

[0055] S3, the lower capacity configuration layer uses the MPC method to predict the output trajectory of the equipment based on the equipment capacity configuration set Ω and scheduling instruction set Θ passed by the middle capacity configuration layer. Based on the output trajectory, the system's actual output tracks the scheduling instructions by adjusting the actions of the actuators in real time. It also calculates the variable operating cost correction item and returns it to the middle capacity configuration layer. This allows the middle capacity configuration layer to return the minimum achievable variable operating cost under the equipment capacity configuration set Ω based on the variable operating cost correction item passed by the lower capacity configuration layer and then return it to the upper capacity configuration layer.

[0056] Specifically, the lower-level capacity configuration layer adopts the MPC method, which uses the standard form of the MPC problem to solve the device output tracking problem. The lower-level capacity configuration layer can predict the output trajectory using the following formula:

[0057]

[0058] Among them, f cor To configure the set Ω based on the equipment capacity, the set of scheduling instructions Θ, and the set of control variable sequences. A function for calculating the variable operating cost adjustment term; J 2,j Output trajectory, The optimal set of control variables to minimize dynamic characteristics; ΔU j To control the incremental sequence; Y j To predict the output sequence; R j To track the target sequence; This is a diagonal matrix used to adjust the control effect.

[0059] Furthermore:

[0060]

[0061] ΔU j ={Δu j (τ),Δu j (τ+1),…,Δu j (τ+N c -1)}

[0062] Y j ={y j (τ),yj (τ+1),…y j (τ+N P -1)}

[0063]

[0064] N P and N c These represent the prediction time domain and control time domain of the lower capacity configuration layer, respectively; τ is the control time scale (sampling period) adopted by the lower capacity configuration layer, which can be on the order of seconds. For example, the sampling period time interval Δτ can be 30 seconds, and Δu j To control the incremental sequence ΔU j The element, y j To predict the output sequence Y j The element, r j To track the target sequence R j Element.

[0065] The scheduling instruction set Θ is passed from the middle-level capacity configuration layer to the lower-level capacity configuration layer as the target trajectory for control. On one hand, the lower-level capacity configuration layer controls the equipment to track the target trajectory, reducing the deviation between the actual output curve and the scheduling instructions during the dynamic process; on the other hand, the lower-level capacity configuration layer calculates variable operating costs based on the dynamic deviation. It is important to note that although the control of both the middle-level and lower-level capacity configuration layers optimizes the system operation process, their time scales differ. Once the scheduling instruction issued by the middle-level capacity configuration layer arrives, the lower layer begins tracking the equipment output trajectory within that scheduling cycle until a new scheduling instruction arrives in the next scheduling cycle.

[0066] The entire hierarchical planning method can be understood as Figure 2 As shown, firstly, the upper-level capacity configuration layer performs capacity configuration, using a particle swarm optimization algorithm to optimize the set of device capacity configurations. Since variable operating costs are related to operational decisions, the set of device capacity configurations Ω that enables the system to achieve optimal economic performance is passed to the middle-level capacity configuration layer to obtain the optimal scheduling instruction. Then, the middle-level capacity configuration layer performs operational scheduling, solving the steady-state operational scheduling MILP problem under a given capacity configuration. However, considering that the actual output of the devices during the dynamic transition process may not meet the expectations of the scheduling instruction, a variable operating cost correction term is introduced. The deviation of economic indicators caused by dynamic performance is represented, and the capacity configuration set Ω and scheduling command Θ are further passed to the lower capacity configuration layer. After obtaining Ω and Θ, the lower capacity configuration layer uses the MPC method to track the scheduling command for the equipment, performs control tracking, evaluates the dynamic deviation during the actual transient process, and calculates the variable operating cost correction item. Returning to the middle layer, the middle capacity configuration layer adjusts the variable operating cost based on the variable operating cost passed from the lower capacity configuration layer. Return the minimum achievable variable operating cost min[C] under the device capacity configuration set Ω. VO (Ω)] Return to the upper capacity configuration layer, which determines the minimum value min[C] that can be achieved based on the variable operating cost. VO The search (Ω) sets the set of equipment capacity configurations Ω that enable the system to achieve optimal economic performance, thereby obtaining the equipment capacity configuration with the optimal cost for the system under different scenarios, forming a closed-loop optimization mechanism.

[0067] In summary, the hierarchical planning method for an integrated energy system including a steam storage tank, according to embodiments of the present invention, innovatively proposes a three-in-one hierarchical collaborative planning method of "configuration-scheduling-control". Through time scale decoupling and functional layering, the steady-state economic scheduling instructions are optimized and generated at the middle layer (scheduling layer) based on a 15-minute time resolution. At the lower layer (control layer), a model predictive control (MPC) algorithm with a 30-second time step is used to achieve accurate tracking of dynamic output. At the same time, by establishing a quantitative correlation model between dynamic deviation and economic cost, the correction value of variable operating cost under the influence of dynamic characteristics is fed back to the upper planning layer in real time, forming a closed-loop optimization mechanism. This layered architecture not only retains the high computational efficiency of steady-state models, but also effectively improves the engineering applicability of planning schemes through dynamic compensation mechanisms. It can significantly improve the actual operating economy of the system and reduce energy consumption through dynamic adjustment, thus possessing high practical application value. This invention accurately transmits the impact of equipment dynamic characteristics on economy to the planning layer through closed-loop feedback of dynamic cost correction terms. Different capacity configuration layers of this invention adopt different time scales. Through time scale decoupling, each layer focuses on the optimization problem under its own time scale, reducing the complexity of the problem.

[0068] Corresponding to the hierarchical planning method for an integrated energy system including a steam storage tank described above, this invention also proposes a hierarchical planning system for an integrated energy system including a steam storage tank. Since the system embodiments of this invention correspond to the method embodiments described above, details not disclosed in the system embodiments can be found in the method embodiments described above, and will not be repeated here.

[0069] Figure 2 This is a schematic diagram of the hierarchical planning system of an integrated energy system including a steam storage tank according to an embodiment of the present invention, as shown below. Figure 2 As shown, the hierarchical planning system of the integrated energy system including the steam storage tank includes: an upper capacity configuration layer, a middle capacity configuration layer, and a lower capacity configuration layer.

[0070] The upper capacity configuration layer uses the equipment capacity configuration set composed of the capacity of each device in the electrothermal coupling park as the decision variable and the system's full life cycle economic evaluation index as the objective function to search for the equipment capacity configuration set Ω that enables the system to achieve optimal economic performance. The middle capacity configuration layer uses the equipment capacity configuration set Ω passed from the upper capacity configuration layer as a fixed constraint, the operating variables of each device in the system as the decision variable, and introduces a variable operating cost correction term caused by the heating equipment to compensate for the steady-state cost. It constructs a mixed integer linear programming model to solve the scheduling instruction set Θ. The heating equipment includes: air source heat pumps, electric heating boilers, and steam storage tanks. The lower capacity configuration layer uses the MPC method to predict the output trajectory of the equipment based on the equipment capacity configuration set Ω and the scheduling instruction set Θ passed from the middle capacity configuration layer. Based on the output trajectory, it adjusts the actions of the actuators in real time to make the actual output of the system track the scheduling instructions, and calculates the variable operating cost correction term to return to the middle capacity configuration layer. This allows the middle capacity configuration layer to return to the upper capacity configuration layer with the minimum achievable variable operating cost under the equipment capacity configuration set Ω based on the variable operating cost correction term passed from the lower capacity configuration layer.

[0071] According to one embodiment of the present invention, the upper capacity configuration layer adopts a planning time scale of years, the middle capacity configuration layer adopts a scheduling time scale of minutes, and the lower capacity configuration layer adopts a control time scale of seconds.

[0072] According to one embodiment of the present invention, the upper capacity configuration layer searches for the set of device configurations Ω that enable the system to achieve optimal economic performance using the following formula:

[0073]

[0074] Where Ω represents the device capacity configuration set, ω j For the capacity configuration of device j, PV stands for photovoltaic equipment, BO for electric heating boiler, HSS for steam storage tank, SOC for battery, EC for air source heat pump, and C... LCC (Ω) represents the total annual cost of the integrated energy system over its entire life cycle under Ω, C INV (Ω) represents the annual investment cost of the integrated energy system over its entire life cycle under Ω, and C FO (Ω) represents the annual fixed operating cost of the integrated energy system over its entire life cycle under Ω, min[C VO [Ω] represents the minimum achievable variable operating cost under Ω, where Ω min and Ω max These are the maximum and minimum values ​​of the constraints that the equipment capacity configuration needs to satisfy, respectively.

[0075] According to one embodiment of the present invention, the middle-layer capacity configuration layer specifically constructs a mixed-integer linear programming model based on the following formula:

[0076]

[0077] Among them, J1(Ω) and C VO (Ω) represents the variable operating cost under the equipment capacity configuration set Ω, Θ represents the scheduling instruction set, t represents the index of each scheduling cycle under the mid-level operation scheduling time scale, and C VO,t (Θ,Ω) represents the variable operating cost at time t given the scheduling instruction set Θ and the equipment capacity configuration set Ω. This is a variable operating cost adjustment term for heating equipment under a given set of scheduling instructions Θ and a set of equipment capacity configurations Ω.

[0078] According to one embodiment of the present invention, the lower capacity configuration layer specifically uses the following formula to predict the output trajectory:

[0079]

[0080] Among them, f cor To configure the set Ω based on the equipment capacity, the set of scheduling instructions Θ, and the set of control variable sequences. A function for calculating the variable operating cost adjustment term; J 2,j Output trajectory, The optimal set of control variables to minimize dynamic characteristics; ΔU j To control the incremental sequence; Y j To predict the output sequence; R j To track the target sequence; This is a diagonal matrix used to adjust the control effect.

[0081] In summary, the hierarchical planning system for an integrated energy system including a steam storage tank, according to embodiments of the present invention, innovatively proposes a three-in-one hierarchical planning method of "configuration-scheduling-control". This method divides the complex integrated energy system decision-making problem into three closely related yet relatively independent sub-problem levels. A collaborative optimization mechanism of "top-down decomposition and bottom-up feedback" is formed between each level. The middle level (scheduling level) optimizes the generation of steady-state economic scheduling commands, while the lower level (control level) uses the MPC algorithm to accurately track dynamic outputs. Simultaneously, it addresses the variable operating costs under the influence of dynamic characteristics. The correction values ​​are fed back to the upper planning layer in real time, forming a closed-loop optimization mechanism. This layered architecture not only retains the high computational efficiency of the steady-state model, but also effectively improves the engineering applicability of the planning scheme through a dynamic compensation mechanism. This significantly improves the actual operating economy of the system and reduces energy consumption through dynamic adjustment, making it highly valuable for practical applications. Through the closed-loop feedback of the dynamic cost correction item, the impact of the equipment's dynamic characteristics on the economy is accurately transmitted to the planning layer. Different capacity configuration layers use different time scales. Through time scale decoupling, each layer focuses on the optimization problem within its own time scale, reducing the complexity of the problem.

[0082] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0083] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples and features of different embodiments or examples described in this specification without contradiction. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of the different embodiments or examples, without contradiction.

[0084] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0086] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0087] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0088] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0089] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A hierarchical planning method for an integrated energy system including a steam storage tank, characterized in that, Includes the following steps: The upper capacity configuration layer uses the equipment capacity configuration set composed of the capacity of each device in the electrothermal coupling park as the decision variable and the system's full life cycle economic evaluation index as the objective function to search for the equipment capacity configuration set Ω that enables the system to achieve the optimal economic performance. The middle capacity configuration layer takes the equipment capacity configuration set Ω passed by the upper capacity configuration layer as a fixed constraint, the operating variables of each device in the system as decision variables, and introduces a variable operating cost correction term caused by the heating equipment to compensate for the steady-state cost. A mixed integer linear programming model is constructed to solve the scheduling instruction set Θ. The heating equipment includes: air source heat pump, electric heating boiler and steam storage tank. The lower capacity configuration layer uses the MPC method to predict the output trajectory of the equipment based on the equipment capacity configuration set Ω and the scheduling instruction set Θ passed from the middle capacity configuration layer. Based on the output trajectory, it adjusts the actuator actions in real time to make the actual system output track the scheduling instructions, and calculates a variable operating cost correction term and returns it to the middle capacity configuration layer. This allows the middle capacity configuration layer to return the minimum achievable variable operating cost under the equipment capacity configuration set Ω based on the variable operating cost correction term passed from the lower capacity configuration layer and return it to the upper capacity configuration layer.

2. The hierarchical planning method for an integrated energy system including a steam storage tank according to claim 1, characterized in that, The upper capacity configuration layer uses a planning time scale of years, the middle capacity configuration layer uses a scheduling time scale of minutes, and the lower capacity configuration layer uses a control time scale of seconds.

3. The hierarchical planning method for an integrated energy system including a steam storage tank according to claim 1, characterized in that, The upper capacity configuration layer uses the following formula to search for the set of device configurations Ω that enables the system to achieve optimal economic performance: stΩ min ≤Ω≤Ω max Where Ω represents the device capacity configuration set, ω j For the capacity configuration of device j, PV stands for photovoltaic equipment, BO for electric heating boiler, HSS for steam storage tank, SOC for battery, EC for air source heat pump, and C... LCC (Ω) represents the total annual cost of the integrated energy system over its entire life cycle under Ω, C INV (Ω) represents the annual investment cost of the integrated energy system over its entire life cycle under Ω, and C FO (Ω) represents the annual fixed operating cost of the integrated energy system over its entire life cycle under Ω, min[C VO [Ω] represents the minimum achievable variable operating cost under Ω, where Ω min and Ω max These are the maximum and minimum values ​​of the constraints that the equipment capacity configuration needs to satisfy, respectively.

4. The hierarchical planning method for an integrated energy system including a steam storage tank according to claim 3, characterized in that, The middle-level capacity configuration layer is specifically constructed using a mixed-integer linear programming model based on the following formula: Among them, J1(Ω) and C VO (Ω) represents the variable operating cost under the equipment capacity configuration set Ω, Θ represents the scheduling instruction set, t represents the index of each scheduling cycle under the mid-level operation scheduling time scale, and C VO,t (Θ,Ω) represents the variable operating cost at time t given the scheduling instruction set Θ and the equipment capacity configuration set Ω. This is a variable operating cost adjustment term for heating equipment under a given set of scheduling instructions Θ and a set of equipment capacity configurations Ω.

5. The hierarchical planning method for an integrated energy system including a steam storage tank according to claim 4, characterized in that, The lower capacity configuration layer uses the following formula to predict the output trajectory: Among them, f cor To configure the set Ω based on the equipment capacity, the set of scheduling instructions Θ, and the set of control variable sequences. A function for calculating the variable operating cost adjustment term; J 2,j Output trajectory, The optimal set of control variable sequences that minimizes dynamic characteristics; Δ U j To control the incremental sequence; Y j To predict the output sequence; R j To track the target sequence; This is a diagonal matrix used to adjust the control effect.

6. A hierarchical planning system for an integrated energy system including a steam storage tank, characterized in that, include: The upper capacity configuration layer is used to search for the equipment capacity configuration set Ω that enables the system to achieve the optimal economic performance, with the equipment capacity configuration set composed of the capacity of each device in the electrothermal coupling park as the decision variable and the economic evaluation index of the entire system life cycle as the objective function. The middle capacity configuration layer is used to take the equipment capacity configuration set Ω passed by the upper capacity configuration layer as a fixed constraint, the operating variables of each device in the system as decision variables, and introduce a variable operating cost correction term caused by the heating equipment to compensate for the steady-state cost. A mixed integer linear programming model is constructed to solve the scheduling instruction set Θ. The heating equipment includes: air source heat pump, electric heating boiler and steam storage tank. The lower capacity configuration layer is used to predict the output trajectory of the equipment using the MPC method based on the equipment capacity configuration set Ω and the scheduling instruction set Θ passed from the middle capacity configuration layer. Based on the output trajectory, the system's actual output tracks the scheduling instructions by adjusting the actions of the actuators in real time. The lower capacity configuration layer calculates a variable operating cost correction term and returns it to the middle capacity configuration layer. This allows the middle capacity configuration layer to return the minimum achievable variable operating cost under the equipment capacity configuration set Ω based on the variable operating cost correction term passed from the lower capacity configuration layer and then return it to the upper capacity configuration layer.

7. The hierarchical planning system for an integrated energy system including a steam storage tank according to claim 6, characterized in that, The upper capacity configuration layer uses a planning time scale of years, the middle capacity configuration layer uses a scheduling time scale of minutes, and the lower capacity configuration layer uses a control time scale of seconds.

8. The hierarchical planning system for an integrated energy system including a steam storage tank according to claim 6, characterized in that, The upper capacity configuration layer uses the following formula to search for the set of device configurations Ω that enables the system to achieve optimal economic performance: stΩ min ≤Ω≤Ω max Where Ω represents the device capacity configuration set, ω j For the capacity configuration of device j, PV stands for photovoltaic equipment, BO for electric heating boiler, HSS for steam storage tank, SOC for battery, EC for air source heat pump, and C... LCC (Ω) represents the total annual cost of the integrated energy system over its entire life cycle under Ω, C INV (Ω) represents the annual investment cost of the integrated energy system over its entire life cycle under Ω, and C FO (Ω) represents the annual fixed operating cost of the integrated energy system over its entire life cycle under Ω, min[C VO [Ω] represents the minimum achievable variable operating cost under Ω, where Ω min and Ω max These are the maximum and minimum values ​​of the constraints that the equipment capacity configuration needs to satisfy, respectively.

9. The hierarchical planning system for an integrated energy system including a steam storage tank according to claim 8, characterized in that, The middle-level capacity configuration layer is specifically constructed using a mixed-integer linear programming model based on the following formula: Among them, J1(Ω) and C VO (Ω) represents the variable operating cost under the equipment capacity configuration set Ω, Θ represents the scheduling instruction set, t represents the index of each scheduling cycle under the mid-level operation scheduling time scale, and C VO,t (Θ,Ω) represents the variable operating cost at time t given the scheduling instruction set Θ and the equipment capacity configuration set Ω. This is a variable operating cost adjustment term for heating equipment under a given set of scheduling instructions Θ and a set of equipment capacity configurations Ω.

10. The hierarchical planning system for an integrated energy system including a steam storage tank according to claim 9, characterized in that, The lower capacity configuration layer uses the following formula to predict the output trajectory: Among them, f cor To configure the set Ω based on the equipment capacity, the set of scheduling instructions Θ, and the set of control variable sequences. A function for calculating the variable operating cost adjustment term; J 2,j Output trajectory, The optimal set of control variable sequences that minimizes dynamic characteristics; Δ U j To control the incremental sequence; Y j To predict the output sequence; R j To track the target sequence; This is a diagonal matrix used to adjust the control effect.

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