Multi-target control method for photo-thermal integrated solar energy and hot water system

Abstract

The embodiment of the invention discloses a multi-target control method for a photo-thermal integrated solar energy and hot water system, and the method comprises the steps: determining a first prediction time domain according to the fluctuation condition of the output of each energy device in the day-ahead prediction; performing secondary prediction on the output of each energy device in the first prediction time domain by taking economy and emission as optimization targets; determining a second prediction time domain according to the dynamic response of each energy device and the first prediction time domain; with supply and demand balance as a target, predicting parameters of each energy device in the second prediction time domain according to the output of each energy device in the first control time domain in the secondary prediction; according to the prediction parameters of the energy devices in the second control time domain, the energy devices are controlled until the second control time domain ends; returning to the operation of determining the second prediction time domain until the first control time domain ends; and returning the operation of determining the first prediction time domain until the prediction time domain of the day-ahead prediction is ended.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of integrated energy system operation control, and in particular to a multi-objective control method for an integrated solar energy and hot water system. Background Art

[0002] The integrated solar and hot water system is an integrated energy system that integrates multiple energy forms such as electricity, heat, cooling, and gas through energy conversion and storage technology to achieve efficient energy utilization and optimized scheduling. This system can improve energy utilization efficiency and reduce environmental pollution, and is one of the key technologies for achieving sustainable energy development. However, since the integrated energy system involves multiple energy forms and conversion processes, its operation and control face great challenges, especially in predicting and responding to changes in energy demand.

[0003] MPC (Model Predictive Control) is an advanced method for control systems that predicts and optimizes the future behavior of the system to calculate the optimal control strategy at the current moment. The core advantage of MPC is that it can handle multivariable control problems, constraints, and nonlinear systems, making it a powerful tool for dealing with complex integrated energy system control problems. In the prior art, the prediction time domain in MPC is usually fixed, which limits the accuracy of predictive control. Summary of the invention

[0004] The embodiment of the present invention provides a multi-objective control method for an integrated solar energy and water heating system to solve the above problems.

[0005] In a first aspect, an embodiment of the present invention provides a multi-objective control method for a solar-thermal integrated solar energy and water heating system, comprising:

[0006] Obtain the day-ahead forecast results of the output of each energy device in the integrated solar thermal and water heating system;

[0007] According to the fluctuation of the output of each energy device in the day-ahead prediction, a first prediction time domain with the current time as the starting point is determined; and the output of each energy device in the first prediction time domain is secondary predicted with economy and emission as the optimization target; wherein the first prediction time domain is shorter than the prediction time domain of the day-ahead prediction;

[0008] According to the dynamic response of each energy device and the first prediction time domain, determine the second prediction time domain starting from the current time; and with the supply and demand balance as the goal, predict the parameters of each energy device in the second prediction time domain according to the output of each energy device in the first control time domain in the secondary prediction; wherein the first control time domain is a time domain that starts at the same time as the first prediction time domain and is shorter than the first prediction time domain;

[0009] Controlling each energy device according to the predicted parameters of each energy device in a second control time domain starting from the current time until the second control time domain ends; wherein the second control time domain is shorter than the first control time domain;

[0010] Taking the end time of the second control time domain as the new current time domain, returning to the operation of determining the second prediction time domain until the first control time domain ends;

[0011] The end time of the first control time domain is used as the new current time, and the operation of determining the first prediction time domain is returned until the prediction time domain of the day-ahead prediction ends.

[0012] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0013] one or more processors;

[0014] a memory for storing one or more programs,

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-objective control method of the integrated solar thermal and water heating system described in any embodiment.

[0016] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-objective control method of the integrated solar thermal and water heating system described in any embodiment.

[0017] In summary, this embodiment discloses a multi-objective control method for an integrated solar energy and hot water system, and proposes a three-layer control strategy for the operation optimization of the integrated energy system, wherein in the upper day-ahead prediction layer, indicators such as economy and emissions are used as optimization targets to obtain the volatility of equipment output in different time periods; in the middle rolling optimization layer, the rolling prediction time domain of the MPC is adjusted according to the volatility obtained in the upper layer, so as to achieve the best energy matching between load demand and energy equipment output, while considering the game between economy and emissions targets; in the lower dynamic control layer, a short-time dynamic performance optimization strategy is introduced to track the equipment status in real time, and according to the output instructions of the optimization scheduling layer, the dynamic response time of each device at the current moment is accurately calculated, and then the prediction time domain is optimized, which effectively improves the overall dynamic response capability of the system. With supply and demand balance as the optimization target, the prediction time domain of the MPC is adjusted in combination with the dynamic response time of each device, so as to more accurately predict and adjust the supply of energy, and ensure that the system can still maintain efficient, stable and economical operation in the face of demand fluctuations. This method not only significantly improves the dynamic response capability of the integrated energy system through the structural design of the three-layer optimized operation control system, but also better meets the load requirements of the user side, while improving the system's economy and efficiency and meeting the multi-level needs of different entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a schematic diagram of a solar-thermal integrated solar energy and water heating system provided by an embodiment of the present invention;

[0020] Figure 2 It is a flow chart of a multi-objective control method of a solar-thermal integrated solar energy and water heating system provided by an embodiment of the present invention;

[0021] Figure 3 It is an implementation framework diagram of a three-layer control strategy provided by an embodiment of the present invention;

[0022] Figure 4 It is an execution flow chart of a three-layer control strategy provided by an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0025] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0026] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] An embodiment of the present invention provides a multi-objective control method for a solar-thermal integrated solar energy and water heating system. To illustrate the method, a solar-thermal integrated solar energy and water heating system to which the method is applied is first introduced. Figure 1 Schematic diagram of a solar-thermal integrated solar and water heating system provided by an embodiment of the present invention. Figure 1 As shown, the system mainly includes the following energy equipment: gas turbine, waste heat boiler, absorption chiller, gas boiler, photovoltaic thermal collector, air source heat pump, electric chiller, heat storage equipment and cold storage equipment. The connection method and structure of each device are described in detail below.

[0028] The electric energy generated by the gas turbine is transmitted through electric wires to an external power grid for supplying electricity to users. The heat energy generated by the gas turbine is transmitted to the waste heat boiler and the absorption chiller through pipelines. The heat generated by the waste heat boiler is transmitted to the heating system through pipelines for supplying heat to users, and the heating system is equipped with a heat storage device. The cold energy generated by the absorption chiller is transmitted to the cooling system through another pipeline for supplying cold energy to users, and the cooling system is equipped with a cold storage device. The cold energy generated by the electric chiller is also transmitted to the cooling system. The heat generated by the gas boiler is directly transmitted to the heating system. The photovoltaic thermal collector is used to collect heat, exchange heat with the medium therein, and transmit it to the heating system. The heat generated by the air source heat pump is also transmitted to the heating system.

[0029] Specifically, the gas turbine generates heat or electricity by consuming natural gas. The electricity generated by the gas turbine is transmitted through wires to an external power grid for powering users, providing electrical load for the system. The system can also meet the system's electricity needs by purchasing electricity from the power grid. The heat energy generated by the gas turbine is transmitted to the waste heat boiler and absorption chiller through pipelines, in which it is transmitted to the waste heat boiler in winter and to the absorption chiller in summer.

[0030] The waste heat boiler generates heat by utilizing hot flue gas to provide heat load for the system, and the generated heat is transported to the heating pipeline for heating users. The absorption chiller generates cold energy by utilizing hot flue gas to provide cooling load, and the generated cold energy is transported to the cooling pipeline for cooling users.

[0031] PV / T absorbs solar energy through the collector plate, and transfers heat to the heating pipeline through the circulating medium (usually ethylene glycol). Air source heat pump consumes electrical energy to provide heat load, and the generated heat is transferred to the heating pipeline that provides heating for users.

[0032] Gas boilers generate heat by consuming natural gas, and the heat generated is transported to the heating pipeline that provides heating for users. Electric refrigerators are connected to the power grid, and the cold generated by the electric refrigerators is transported to the cooling pipeline.

[0033] The heat storage tank (i.e., heat storage equipment) and the cold storage tank (i.e., cold storage equipment) store or release energy respectively, and transport heat or cold to the heating pipeline or the cooling pipeline.

[0034] It should be noted that Figure 1 This is only one example of an integrated solar thermal and hot water system. Any cooling and heating system involving solar energy and hot water in actual applications can be regarded as the integrated solar thermal and hot water system in this embodiment, and the control method provided in the embodiment of the present invention can be used for scheduling optimization.

[0035] For Figure 1 Take the system as an example, Figure 2This is a flowchart of a multi-objective control method for a solar-thermal integrated solar energy and water heating system provided by an embodiment of the present invention. Based on a single-layer MPC, this method designs three levels of optimization control strategies, which are respectively called the day-ahead prediction layer, the optimization scheduling layer, and the dynamic control layer. The three levels of optimization control strategies will be described below in conjunction with specific steps. This control method can be executed by a controller of a solar-thermal integrated solar energy and water heating system, or other electronic devices, such as Figure 2 As shown, the method specifically includes:

[0036] S110, obtaining a day-ahead forecast result of the output of each energy device in the integrated solar-thermal solar energy and hot water system.

[0037] The day-ahead prediction refers to the prediction of the energy equipment output in the next 24 hours in advance. This step can directly obtain the known day-ahead prediction results, or calculate the day-ahead prediction results through the day-ahead prediction layer, which is not specifically limited in this embodiment.

[0038] Specifically, the day-ahead prediction layer can optimize the optimal energy matching between the load demand and the output of energy equipment in the next 24 hours based on the predicted values ​​of the system load demand and light intensity changes in the next 24 hours, through optimized scheduling, with indicators such as economy and equivalent carbon dioxide emissions as comprehensive optimization goals. In a specific implementation, the day-ahead prediction may include the following steps:

[0039] Step 1: Build a static model of the system. The objective functions of the static model include an economic objective function and an equivalent carbon dioxide emission objective function. Specifically, the sampling time interval and optimization time interval of the system can be set, for example, 15 minutes or shorter, and the prediction time domain is 24 hours. The economic objective function of the day-ahead prediction layer is determined by formulas (1)-(5):

[0040]

[0041] C pur =C gri +C gas (2)

[0042]

[0043]

[0044] Among them, C is the total daily operating cost of the system, yuan; T is the daily operating time, h; C pur is the cost of purchasing electricity and gas, RMB; C opt is the equipment operation and maintenance cost, yuan; C gri is the electricity purchase cost, yuan; C gas is the gas purchase cost, yuan; is the electricity price at time t, yuan / kWh; is the power of electricity purchased and sold, RMB / kWh; c gas is the unit price of natural gas, yuan / m 3 ; is the natural gas flow rate, m 3 / s; Δt is the sampling time, s; is the gas turbine operation and maintenance cost, RMB / kWh; is the operation and maintenance cost of the electric chiller, RMB / kWh; is the operation and maintenance cost of absorption chiller, RMB / kWh; is the gas boiler operation and maintenance cost, RMB / kWh; is the operation and maintenance cost of the waste heat boiler, RMB / kWh; is the PV / T operation and maintenance cost, RMB / kWh; is the operation and maintenance cost of the air source heat pump, RMB / kWh; is the operation and maintenance cost of cold storage equipment, RMB / kWh; is the operation and maintenance cost of the thermal storage equipment, RMB / kWh; is the gas turbine output power at time t, kW; is the cooling power of the electric refrigerator at time t, kW; is the cooling power of the absorption chiller at time t, kW; is the heating power of the gas boiler at time t, kW; is the heating power of the waste heat boiler at time t, kW; is the heating power of PV / T equipment at time t, kW; is the heating power of the air source heat pump at time t, kW; is the charging and discharging power of the cold storage equipment at time t, kW; is the charging and discharging power of the heat storage device at time t, kW.

[0045] The emission objective function of the day-ahead forecast layer is determined by formula (6):

[0046]

[0047] Among them, EF ele is the equivalent carbon dioxide emissions corresponding to the amount of electricity, kg CO2 / kWh; EF is the power of electricity purchased or sold, RMB / kW; gas is the equivalent carbon dioxide emissions corresponding to natural gas, kg CO2 / m 3 ; is the natural gas flow rate, m 3 / s; Δt is the sampling time, s.

[0048] Step 2: According to the capacity of each energy device and the energy conservation condition, the constraint conditions of the static model are constructed, that is, the custom constraint set of MPC is constructed:

[0049]

[0050] in, is the user's electricity load at time t, kW; is the power consumed by the electric refrigerator at time t, kW; is the power consumed by the air source heat pump at time t, kW; is the cooling load of the user at time t, kW; is the energy discharging power of the cold storage tank at time t, kW; is the energy storage power of the cold storage tank at time t, kW; is the user heat load at time t, kW; is the energy discharging power of the heat storage tank at time t, kW; is the energy storage power of the heat storage tank at time t, kW.

[0051] 0≤P i t ≤P i max (10)

[0052]

[0053] Among them, P i t is the electrical power of device i at time t, kW; P i max is the rated power of the corresponding device i, kW; is the cooling power or heating power of equipment i at time t, kW; is the rated power of the corresponding device i, kW; is the minimum energy storage power of the energy storage device, kW; is the maximum energy storage power of the energy storage device, kW; is the minimum energy release power of the energy storage device, kW; is the maximum energy release power of the energy storage device, kW. Among them, device i refers to the energy device with index i in the system.

[0054]

[0055] Where Δz j,min (τ) and Δz j,max (τ) are the minimum and maximum increments of the j-th input variable at the τ-th moment; Δz j (k+τ-1|k) is the increment of the j-th input variable predicted at time t=k at the τ-th time; ε kis the slack variable; and They are the optimization weight coefficients used to soften the minimum and maximum input increment constraints, which are non-negative values. Users can set them in a personalized way and control the strictness of the constraints by adjusting the coefficients. When the coefficient increases, the rule becomes more flexible; when the coefficient is set to 0, the rule becomes an absolute constraint and must not be violated.

[0056] Step 3: Obtain the predicted values ​​of the load demand and light intensity change in the prediction time domain predicted by the system a day ago, that is, obtain the predicted values ​​of the load demand and light intensity change in the next 24 hours.

[0057] Step 4: Based on the acquired data, the static model and the constraints, the output of each energy device in the next 24 hours is predicted using a game theory method with economy and emissions as optimization targets. Optionally, a sequential quadratic programming algorithm can be used to achieve optimization solution calculation.

[0058] S120. Determine another prediction time domain with the current time as the starting point based on the fluctuation of the output of each energy device in the day-ahead prediction; and make a secondary prediction of the output of each energy device in the other prediction time domain with economy and emission as optimization goals; wherein the other prediction time domain is shorter than the prediction time domain of the day-ahead prediction.

[0059] This step corresponds to the control strategy of the scheduling optimization layer, which optimizes the prediction time domain of the short-term rolling prediction (i.e., the secondary prediction) according to the results of the day-ahead prediction, and performs short-term rolling prediction according to the optimized prediction time domain, and coordinates the economy and emission targets through the Stackberg game to achieve the best energy match between load demand and energy equipment output. In order to distinguish it from the prediction time domain of the day-ahead prediction, this embodiment refers to the other prediction time domain of the short-term rolling prediction as the first prediction time domain.

[0060] Specifically, short-term prediction refers to predicting and controlling the output of equipment in a shorter time domain in the future based on changes in load demand and light intensity in a shorter time domain in the future; after a short-term prediction is completed, the prediction time domain is advanced forward along the time axis to perform the next short-term prediction, thereby realizing a short-term rolling prediction. Since the prediction time domain of the day-ahead prediction is relatively long and cannot respond to real-time changes in load demand and light intensity in a timely manner, the short-term rolling prediction can improve the ability to respond to real-time changes in the system, thereby improving the accuracy of prediction and control. In order to avoid confusion, the time domain, duration and moment in this embodiment are distinguished here. Among them, moment refers to a certain point in time; time domain refers to a certain time interval, which includes a start time and an end time; and duration refers to the length of a certain time domain, which is independent of the start time and the end time.

[0061] In a specific implementation, determining the first prediction time domain starting from the current time (i.e., the optimization process of the first prediction time domain) according to the fluctuation of the output of each energy device in the day-ahead prediction may include the following steps:

[0062] Step 1: Taking the current time as the starting point, intercept a time domain of the same length as the first prediction time domain of the previous round of secondary prediction as the initial first prediction time domain of this round of secondary prediction. From the previous introduction to short-term rolling prediction and the subsequent step S160, it can be seen that the secondary prediction is performed in a cycle, and each rolling corresponds to a round of secondary prediction. Although the prediction time domain of this round of secondary prediction has rolled forward a certain distance compared to the prediction time domain of the previous round of secondary prediction, the lengths of the two first prediction time domains remain the same.

[0063] Step 2: Calculate the RMS value of the output of each energy device in the day-ahead forecast within the initial first forecast time domain. Specifically, the RMS value can be calculated according to the following formula:

[0064]

[0065] Wherein, L represents the duration of the initial first prediction time domain, in hours (h); represents the average output of device i at each moment in the initial first prediction time domain obtained by the day-ahead prediction layer, in kW; P τ,i It represents the output of device i at time τ within the initial first prediction time domain obtained by the day-ahead prediction layer, in kW; τ0 represents the current time, that is, the starting time of the initial first prediction time domain; N is the total number of energy devices in the system.

[0066] Step 2: According to the RMS value and the RMS value of each energy device in the day-ahead forecast within the first forecast time domain of the previous round of secondary forecast, optimize the duration of the first forecast time domain of the current round of secondary forecast. As mentioned above, the RMS value is calculated in each round of short-term rolling forecast. For the convenience of distinction and description, the RMS value calculated in the current round of secondary forecast is referred to as the first RMS value, and the RMS value calculated in the previous round of secondary forecast is referred to as the second RMS value. Optionally, based on the difference between the two RMS values, this step may include the following situations:

[0067] Case 1: The absolute value of the difference between the first RMS value s1 and the second RMS value s0 |s1-s0| is less than the set threshold Δs min , which indicates that the fluctuation of the equipment output in the prediction time domain of the current round of secondary prediction is smaller than that of the equipment output in the prediction time domain of the previous round of secondary prediction, so the initial first prediction time domain can be kept unchanged.

[0068] Case 2: The absolute value of the difference between the first root mean square value s1 and the second root mean square value s0 is greater than or equal to a set threshold, and the first root mean square value is greater than the second root mean square value, that is, |s1 - s0| ≥ Δs min And s1 > s0, which indicates that the fluctuation of the device output power within the prediction time domain of this round of secondary prediction is greater than that within the prediction time domain of the previous round of secondary prediction. Then, the duration of the first prediction time domain of the previous round of secondary prediction can be shortened and used as the duration of the first prediction time domain of this round of secondary prediction. Exemplarily, if the final duration of the first prediction time domain L0 = 3h in the previous round of secondary prediction, then the final duration of the first prediction time domain L1 = L0 - 1 = 2h in this round of secondary prediction.

[0069] Case 3: The difference between the first root mean square value and the second root mean square value is greater than or equal to a set threshold, and the first root mean square value is less than the second root mean square value, that is, |s1 - s0| < Δs min And s1 < s0, which indicates that the fluctuation of the device output power within the prediction time domain of this round of secondary prediction is smaller than that within the prediction time domain of the previous round of secondary prediction. Then, the duration of the first prediction time domain of the previous round of secondary prediction can be lengthened and used as the duration of the first prediction time domain of this round of secondary prediction. Exemplarily, if the final duration of the first prediction time domain L0 = 3h in the previous round of secondary prediction, then the final duration of the first prediction time domain L1 = L0 + 1 = 4h in this round of secondary prediction.

[0070] Of course, for the first round of secondary prediction, the first prediction time domain of the previous round of secondary prediction does not exist, and there is no need to perform comparison and optimization. The above optimization operations of the first prediction time domain are all executed starting from the second round of secondary prediction.

[0071] After the first prediction time domain is optimized, this round of secondary prediction can be performed according to the optimized first prediction time domain. In a specific embodiment, the process of the secondary prediction may include the following steps:

[0072] Step 1: Build a static model of the system. The objective function of the static model includes an economic objective function and an equivalent carbon dioxide emission objective function; at the same time, according to the capacity of each energy device and the energy conservation condition, construct the constraint conditions of the static model. Specifically, if the above operations have been performed by the day-ahead prediction layer in S110, then the static model and constraint conditions in S110 can be reused here. If the above operations have not been performed in S110, then the static model and constraint conditions are constructed in this step.

[0073] Step 2: After the model and constraints are constructed, the load demand and light intensity changes of the integrated solar thermal and water heating system in the first prediction time domain of this round are obtained. For example, if the optimized first prediction time domain is 3 hours, the load demand and light intensity changes in the next 3 hours are obtained.

[0074] Step 4: Based on the acquired data, the static model and the constraints, with economy and emissions as the optimization goals, a game theory method is used to predict the output of each energy device in the first prediction time domain. Similarly, a sequential quadratic programming algorithm can be used to achieve optimization solution calculation. The specific process is a prior art and will not be repeated here.

[0075] S130. Determine a second prediction time domain with the current moment as the starting point based on the dynamic response of each energy device and the first prediction time domain; and with supply and demand balance as the goal, predict the parameters of each energy device in the second prediction time domain based on the output of each energy device in a control time domain in the secondary prediction; wherein the control time domain is a time domain that starts at the same time as the first prediction time domain and is shorter than the first prediction time domain.

[0076] This step corresponds to the control strategy of the dynamic control layer, which introduces the short-term dynamic performance optimization strategy of the MPC algorithm, tracks the device status in real time, and adjusts the prediction time domain of the MPC according to the dynamic response time of each device at the current moment according to the output instructions provided by the optimization scheduling layer, so as to realize the accurate dynamic response of energy equipment to load demand. In order to distinguish it from the prediction time domain of the above-mentioned day-ahead prediction and short-term rolling prediction (i.e., secondary prediction), the prediction time domain of the dynamic control layer is called the second prediction time domain in this embodiment.

[0077] Specifically, the prediction control object of the short-term rolling prediction is the output of each energy device, while the prediction control object of the dynamic control layer is the parameters of each energy device, also known as input variables, such as natural gas flow, etc. This step predicts the specific parameters of each device based on the device output predicted by the optimization scheduling layer, so that the actual output of each device under the predicted parameters is as close as possible to the device output predicted by the optimization scheduling layer. In addition, although the accuracy of the secondary prediction results is higher than the day-ahead prediction, as time goes by, due to changes in equipment performance and the surrounding environment, the accuracy of the secondary prediction results will gradually decrease. Therefore, in the dynamic control layer, only the device output in the first period of the first prediction time domain is used to guide the control of device parameters, and the medical results in the subsequent time domains are not used. The time domain used is called the control time domain of the optimization scheduling layer.

[0078] In a specific implementation, the second prediction time domain may be determined according to the following formula:

[0079]

[0080] Among them, k is the current scheduling time, that is, the current time; p k is the duration of the second prediction time domain at time t=k, min; is the dynamic response time of device i at time t = k, s; is the natural frequency of device i at time t = k, rad / s; is the damping ratio of device i at time t = k; The scheduling layer is optimized to predict the time domain length at time t=k.

[0081] After the second prediction time domain is determined, the parameters of each energy device in the second prediction time domain can be predicted. Optionally, the prediction process may include the following steps:

[0082] Step 1: Build a dynamic model of the device. Specifically, set the sampling time interval of the dynamic control layer, for example, 1 minute. The objective function of the dynamic model is:

[0083]

[0084] Where J represents the supply and demand balance indicator; n represents the number of output variables of energy equipment; Γ τ,j represents the weight coefficient of the output variable with index j at the k+τth time; y j (k+τ|k) represents the output variable value with index j at time k+τ predicted at time k; r j (k+τ|k) is the user's cooling, heating, and electricity demands at time k+τ predicted at time k.

[0085] At the same time, the constraints of the model are constructed as shown in formulas (7)-(13).

[0086] Step 2: predict the parameters of each energy device in the second prediction time domain based on the device dynamic model and constraints, and the output of each energy device in the first control time domain in the secondary prediction.

[0087] S140. Control each energy device according to the predicted parameters of each energy device in another control time domain starting from the current moment until the other control time domain ends; wherein the other control time domain is shorter than the one control time domain.

[0088] This step corresponds to the actual control of the dynamic control layer, and each energy device is actually controlled according to the device parameters predicted in S130. Due to the uncertainty in device performance and environment, it is difficult for the actual operating state of the device to be completely consistent with the predicted state. As time accumulates, this inconsistency will become larger and larger. Therefore, in this step, the device is controlled according to the predicted parameters only in the first time domain of the second prediction time domain. This time domain is called the control time domain of the dynamic control layer.

[0089] For the convenience of distinction and description, in this embodiment, the control time domain used to control the output of the device in S130 is referred to as the first control time domain, and the other control time domain used to control the device parameters is referred to as the second control time domain. Optionally, the duration of the first control time domain may be 1 hour, and the duration of the second control time domain may be 10 minutes.

[0090] S150: Taking the end time of the second control time domain as the new current time domain, and returning to the operation of determining the second prediction time domain until the first control time domain ends.

[0091] As described above, after each prediction of the device parameters, the dynamic control layer only controls the device according to the predicted parameters in the second control time domain. After the second control time domain ends, the dynamic control layer uses the end time as the new current time and returns to S130. Based on the dynamic response of each energy device at the new current time and the first prediction time domain, a new second prediction time domain with the new current time as the starting point is determined. With the goal of balancing supply and demand, the parameters of each energy device in the new second prediction time domain are predicted based on the output of each energy device in the first control time domain in the secondary prediction, and S140-S150 are executed again. This cycle repeats until the first control time domain ends.

[0092] It should be emphasized that in the above-mentioned cycle of the dynamic control layer, the first control time domain and the first prediction time domain remain unchanged; each loop iteration updates the current device control moment, the second control time domain and the second prediction time domain, that is, rolling forward the length of a second control time domain (this length is usually fixed).

[0093] S160: Taking the end time of the first control time domain as the new current time, and returning to the operation of determining the first prediction time domain until the prediction time domain of the day-ahead prediction ends.

[0094] After the cycle of S150 ends, the first control time domain of the optimization scheduling layer ends. The optimization scheduling layer takes the end time as the new current time, returns to S120, and continues to determine the new first prediction time domain with the new current time as the starting point based on the fluctuation of the output of each energy device in the day-ahead prediction, and takes economy and emissions as optimization goals, and performs a secondary prediction of the output of each energy device in the new first prediction time domain, and executes S130-S160 again... This cycle repeats until the prediction time domain of the day-ahead prediction ends. Of course, you can also return to S110 at this time to start the next 24 hours of prediction control.

[0095] The working principles and interrelationships of the three-layer control strategies in the above method can also be combined with Figure 3 and Figure 4 Understand. Figure 3 The implementation framework of the three-layer control strategy in the method is presented. Figure 3 The prediction time domain in the optimized scheduling layer is the first prediction time domain, and the control time domain in the prediction time domain is the first control time domain; the "prediction time domain after optimization in the lower layer" in the dynamic control layer is the second prediction time domain, and the "lower layer control time domain" is the second control time domain; and the length of the first prediction time domain > the length of the first control time domain > the length of the second prediction time domain > the length of the second control time domain. Figure 4 The execution flow of the three-layer control strategy is shown, where k is the index of the first control time domain, t=k represents the start time of the first control time domain with index k, and Δt' is the duration of the second control time domain.

[0096] Furthermore, in order to verify the effectiveness of the above method, this embodiment takes the load demand of a hotel in Shanghai as an example, and performs scheduling optimization verification on the cooling and heating system built on the APROS simulation software. Figure 1; at the same time, the design excitation data is input to each device, the output data is obtained, and the transfer function model of each device in the integrated energy system is constructed through the system identification toolbox in MATLAB according to the input and output data. Considering that the dynamic response process of the power supply part is relatively short compared with the cooling and heating parts, this embodiment focuses on scheduling optimization of the cooling and heating parts. In the optimization scheduling layer, the sampling time interval is set to 15 minutes and the control time domain length is 1 hour. In the dynamic control layer, in order to ensure that the calculation speed meets the needs of real-time control, the sampling time interval is 1 minute, and according to the output plan of the optimization scheduling layer and the prediction model of the controlled device, the dynamic response time of each device is recalculated every minute, where the maximum value is selected as the prediction time domain length of the dynamic control layer, and the minimum prediction time domain length is set to 15min, and its control time domain is set to 10 minutes. Then, the objective function and constraints are constructed according to the method of this embodiment, and the simulation system is run, and the sequential quadratic programming algorithm is used to realize the optimization solution calculation. It has been verified that the method provided in this embodiment has a high dynamic response capability, and can well meet the user-side load requirements, while taking into account performance such as economy and efficiency, and can meet the multi-level requirements of different entities.

[0097] In summary, this embodiment discloses a multi-objective control method for a solar-thermal integrated solar energy and hot water system, and proposes a three-layer control strategy for the operation optimization of the integrated energy system, wherein in the upper day-ahead prediction layer, a static model of the equipment is built, and indicators such as economy and emissions are used as optimization targets to obtain the volatility of the equipment output in different time periods; in the middle rolling optimization layer, the rolling prediction time domain of the MPC is adjusted according to the volatility obtained in the upper layer, so as to achieve the best energy matching between the load demand and the output of the energy equipment, while considering the game between the economy and emissions targets; in the lower dynamic control layer, a dynamic model of the equipment is built, and a short-time dynamic performance optimization strategy is introduced to track the equipment status in real time, and according to the output instructions of the optimization scheduling layer, the dynamic response time of each device at the current moment is accurately calculated, and then the prediction time domain is optimized, which effectively improves the overall dynamic response capability of the system. With supply and demand balance as the optimization target, the prediction time domain of the MPC is adjusted in combination with the dynamic response time of each device, so as to more accurately predict and adjust the supply of energy, and ensure that the system can still maintain efficient, stable and economical operation in the face of demand fluctuations. This method not only significantly improves the dynamic response capability of the integrated energy system through the structural design of the three-layer optimized operation control system, but also better meets the load requirements of the user side, while improving the system's economy and efficiency and meeting the multi-level needs of different entities.

[0098] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 5As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 5 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0099] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the multi-objective control method of the integrated solar-thermal solar energy and hot water system in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the multi-objective control method of the integrated solar-thermal solar energy and hot water system.

[0100] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0101] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0102] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-objective control method of a solar-thermal integrated solar energy and water heating system of any embodiment.

[0103] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0104] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0105] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0106] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective control method for a solar-thermal integrated solar and hot water system, characterized in that: include: Obtain the day-ahead forecast results of the output of each energy device in the integrated solar thermal and water heating system; According to the fluctuation of the output of each energy device in the day-ahead prediction, a first prediction time domain with the current time as the starting point is determined; and the output of each energy device in the first prediction time domain is secondary predicted with economy and emission as the optimization target; wherein the first prediction time domain is shorter than the prediction time domain of the day-ahead prediction; According to the dynamic response of each energy device and the first prediction time domain, determine the second prediction time domain starting from the current time; and with the supply and demand balance as the goal, predict the parameters of each energy device in the second prediction time domain according to the output of each energy device in the first control time domain in the secondary prediction; wherein the first control time domain is a time domain that starts at the same time as the first prediction time domain and is shorter than the first prediction time domain; Controlling each energy device according to the predicted parameters of each energy device in a second control time domain starting from the current time until the second control time domain ends; wherein the second control time domain is shorter than the first control time domain; Taking the end time of the second control time domain as the new current time domain, returning to the operation of determining the second prediction time domain until the first control time domain ends; The end time of the first control time domain is used as the new current time, and the operation of determining the first prediction time domain is returned until the prediction time domain of the day-ahead prediction ends.

2. The method according to claim 1, characterized in that The method of obtaining the day-ahead forecast results of the output of each energy device in the integrated solar-thermal solar energy and hot water system includes: Obtain the load demand and light intensity changes of the integrated solar thermal and water heating system in the forecast time domain predicted by the day-ahead forecast; With economy and emission as optimization goals, the output of each energy device in the system is predicted on the day-ahead basis according to the load demand and light intensity changes.

3. The method according to claim 1, characterized in that Determining the first prediction time domain starting from the current time according to the fluctuation of the output of each energy device in the day-ahead prediction includes: Taking the current time as the starting point, a time domain with the same length as the first prediction time domain of the previous round of secondary prediction is intercepted as the initial first prediction time domain of this round of secondary prediction; Calculate a first root mean square value of the output of each energy device in the day-ahead forecast within the initial first forecast time domain; The duration of the first prediction time domain of the current round of secondary prediction is optimized according to the first root mean square value and the second root mean square value of the output of each energy device in the day-ahead prediction within the first prediction time domain of the previous round of secondary prediction.

4. The method according to claim 3, characterized in that The calculating of the first root mean square value of the output of each energy device in the day-ahead prediction within the initial first prediction time domain includes: According to the following formula, the first RMS value of the output of each energy device in the day-ahead forecast within the initial first forecast time domain is calculated: Wherein, L represents the duration of the initial first prediction time domain, represents the average output of the energy equipment indexed by i in the day-ahead forecast at each moment in the initial first forecast time domain, P τ,i represents the output of the energy device with index i in the day-ahead prediction at the time with index τ in the initial first prediction time domain, τ0 represents the current time, and N represents the total number of energy devices in the system.

5. The method according to claim 3, characterized in that: The optimizing the duration of the first prediction time domain of the current round of secondary prediction according to the first root mean square value and the second root mean square value of the output of each energy device in the first prediction time domain of the previous round of secondary prediction in the day-ahead prediction includes: The root mean square value of the output of each energy device in the day-ahead prediction within the first prediction time domain of the previous round of secondary prediction is used as the second root mean square value; If the absolute value of the difference between the first root mean square value and the second root mean square value is less than a set threshold, keeping the duration of the first prediction time domain of the current round of secondary prediction equal to the duration of the first prediction time domain of the previous round of secondary prediction; If the absolute value of the difference between the first root mean square value and the second root mean square value is greater than or equal to a set threshold, and the first root mean square value is greater than the second root mean square value, shortening the duration of the first prediction time domain of the previous round of secondary prediction to serve as the duration of the first prediction time domain of the current round of secondary prediction; If the absolute value of the difference between the first root mean square value and the second root mean square value is greater than or equal to the set threshold, and the first root mean square value is less than the second root mean square value, the duration of the first prediction time domain of the previous round of secondary prediction is lengthened as the duration of the first prediction time domain of the current round of secondary prediction.

6. The method according to claim 1, characterized in that The second prediction of the output of each energy device within the first prediction time domain with economy and emission as optimization goals includes: Building a static model of the system, wherein the objective function of the static model includes an economic objective function and an equivalent carbon dioxide emission objective function; According to the capacity of each energy device and the energy conservation condition, the constraint conditions of the static model are constructed; Obtaining load demand and light intensity of the system in the first prediction time domain; According to the acquired data and the static model and constraints, with economy and emissions as optimization goals, a game theory method is used to perform a secondary prediction of the output of each energy device within the first prediction time domain.

7. The method according to claim 1, characterized in that The step of determining a second prediction time domain starting from the current moment according to the dynamic response of each energy device and the first prediction time domain comprises: According to the following formula, the duration p of the second prediction time domain starting at the current time k is determined: k : in, and They represent the dynamic response time, natural frequency and damping ratio of the energy device with index i at time k, is the first prediction time domain.

8. The method according to claim 1, characterized in that The method of taking supply and demand balance as the goal and predicting the parameters of each energy device in the second prediction time domain according to the output of each energy device in the first control time domain in the secondary prediction includes: A dynamic model of the system is constructed, and the objective function of the dynamic model is: Among them, J represents the supply and demand balance indicator, p k represents the duration of the second prediction time domain, n represents the number of output variables of the energy equipment; Γ τ,j represents the weight coefficient of the output variable with index j at the k+τth time, y j (k+τ|k) represents the output variable value with index j at time k+τ predicted at time k; r j (k+τ|k) is the user's cooling, heating and electricity demand at time k+τ predicted at time k; According to the dynamic model and the output of each energy device in the first control time domain in the secondary prediction, the parameters of each energy device in the second prediction time domain are predicted.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-objective control method of the integrated solar energy and water heating system according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the multi-objective control method of the integrated solar energy and water heating system according to any one of claims 1 to 8 is implemented.

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