A source-load water coordination optimization scheduling method, device, equipment, medium and product

Abstract

The present application relates to sewage treatment technical field, disclose a kind of source load water coordination optimization scheduling method, device, equipment, medium and product, comprising: obtaining and inputting to optimization adjustment model with electricity cost data, photovoltaic output forecast data, inflow forecast data and configuration parameter, solve optimization adjustment objective function in combination with constraint condition, obtain scheduling strategy dataset, and determine the scheduling instruction of source load water sewage treatment system, and scheduling strategy dataset includes the electric power of controllable load unit and the inflow and outflow of reservoir unit;Scheduling instruction is used to control each controllable load unit reservoir unit, the present application is solved by obtaining the forecast data of photovoltaic and water and being brought into optimization adjustment model, obtains the adjustment instruction of each controllable load unit and reservoir unit, so as to utilize the water storage capacity of reservoir to move electricity load from photovoltaic output insufficient period to photovoltaic output abundant period, realize the consumption of photovoltaic power generation, reduce system electricity purchase cost.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a source-load-water coordinated optimization scheduling method, apparatus, equipment, medium, and product. Background Technology

[0002] Wastewater treatment plants, as an important part of urban infrastructure, involve multiple stages in their operation, including influent lifting, aeration reaction, mixing, advanced treatment, and effluent discharge. They have a high overall energy consumption level and are characterized by continuous operation and periodic fluctuations. Wastewater treatment plants utilize electricity generated by photovoltaic (PV) power generation for wastewater treatment. However, PV power output is intermittent and fluctuates, while the wastewater treatment process load is primarily driven by rigid demand. This mismatch in timing limits the absorption of renewable energy, necessitating continued reliance on grid power purchases for supplementary operation, thus increasing operating costs. While introducing electrochemical energy storage systems to alleviate the load mismatch with PV power generation is an option, the investment and maintenance costs are high, and the lifespan is limited, hindering large-scale application in wastewater treatment plants. Summary of the Invention

[0003] This invention provides a source-load-water coordinated optimization scheduling method, device, equipment, medium, and product to solve the problem that wastewater treatment plants use electricity provided by photovoltaic power generation for wastewater treatment, but the photovoltaic power generation is limited and they need to rely on the power grid to purchase electricity, which increases operating costs.

[0004] In a first aspect, the present invention provides a source-load-water coordinated optimization scheduling method, applied to a source-load-water wastewater treatment system, the system comprising a photovoltaic power generation unit, a controllable load unit, and a water storage tank unit, the method comprising: Obtain electricity cost data, photovoltaic output forecast data, water inflow forecast data, and configuration parameters of the source-load wastewater treatment system within the target period; Electricity cost data, photovoltaic output forecast data, water inflow forecast data, and configuration parameters are input into the optimization and regulation model. The optimization and regulation objective function is solved by combining the constraints in the optimization and regulation model to obtain the scheduling strategy dataset of the Yuanheshui wastewater treatment system. The scheduling strategy dataset contains the power of each controllable load unit at different time periods and the water inflow and outflow of the water storage tank unit at different time periods. The optimization and regulation objective function is constructed with the goal of maximizing the sum of the total comprehensive economic benefits at different time periods. The total comprehensive economic benefits are determined based on the power of each controllable load unit at different time periods and the electricity cost data. The constraints are constructed by combining the photovoltaic output forecast data and the configuration parameters of the Yuanheshui wastewater treatment system. Based on the scheduling strategy dataset, the scheduling instructions for the source-load wastewater treatment system are determined. These instructions are used to control the electrical power of each controllable load unit and the inflow and outflow of the water storage tank unit.

[0005] Because photovoltaic power output is fluctuating and intermittent, while wastewater treatment is originally a rigid load that operates continuously based on incoming water, there may be situations where no electricity is needed when photovoltaic power is abundant, and a large amount of electricity is needed when photovoltaic power is scarce. By inputting electricity cost data, photovoltaic forecasts, inflow forecasts, and system configuration parameters into the optimization and adjustment model, and under constraints such as power balance, equipment operating limits, and reservoir capacity, the power consumption of controllable loads in each time period, as well as the storage and release of water in the reservoir, can be planned in advance. The wastewater to be treated is treated as a flexible load that can be moved, and the electricity demand is adapted to photovoltaic power generation, thereby achieving the goal of reducing overall operating costs without increasing battery energy storage.

[0006] In one alternative implementation, the constraints include power balance constraints, reservoir capacity constraints, and process equipment operation constraints.

[0007] Power balance constraints are used to characterize the constraints between photovoltaic output prediction data and the electrical power of each controllable load unit; The reservoir capacity constraint is used to characterize the constraint relationship between the inflow and outflow of the reservoir unit, the electrical power of the controllable load unit, and the reservoir capacity. Process equipment operating constraints are used to characterize the constraint relationship between the electrical power of controllable load units and the total adjustable load capacity.

[0008] By limiting the three types of constraints—power balance, reservoir capacity, and process equipment operation—the optimization and regulation model can coordinate the power of controllable loads and the inflow and outflow sequence of reservoirs, while ensuring real-time power balance, continuous and stable hydraulic operation, and safe equipment operation. Without relying on electrochemical energy storage, it can achieve active matching of electricity load with photovoltaic output, maximize the local photovoltaic consumption rate, reduce grid power purchase costs and curtailment penalties, and thus reduce the system's operating costs.

[0009] In one optional implementation, the configuration parameters include photovoltaic installed capacity data, effective reservoir volume data, and total adjustable load capacity data.

[0010] Photovoltaic installed capacity data is used to confirm the upper limit of photovoltaic output prediction data in the power balance constraint conditions; The effective volume data of the reservoir is used to confirm the upper limit of the reservoir capacity in the reservoir capacity constraint; Total adjustable load capacity data is used to confirm the total adjustable load capacity in the operating constraints of process equipment.

[0011] The effects of photovoltaic installed capacity, effective reservoir volume, and total adjustable load capacity on three types of constraints were determined, providing a physical feasible boundary for optimizing the regulation model and ensuring the accuracy and reliability of dispatch instructions.

[0012] In one optional implementation, the optimization objective function includes a power purchase saving item, a power purchase item, a power curtailment penalty item, and an operating cost item. The power purchase saving item is determined by multiplying the power purchase saving cost data and the equivalent self-consumption power data. The power purchase item is determined by multiplying the power purchase cost data and the grid power purchase data. The power curtailment penalty item is determined by multiplying the power curtailment penalty cost data and the curtailed power data. The equivalent self-consumption power data is determined by the minimum value between the total load data and the photovoltaic output power data. The grid power purchase data is determined by the difference between the total load data and the photovoltaic output power data. The curtailed power data is determined by the difference between the photovoltaic output power data and the total load data.

[0013] In one optional implementation, the method for obtaining configuration parameters includes: Acquire multiple sets of typical electricity cost data, typical photovoltaic output forecast data, typical water inflow forecast data, and planning variable datasets; The typical electricity cost data, typical photovoltaic output prediction data, typical water inflow prediction data, and planning variable datasets of each group are input into the optimization and regulation model. The optimization and regulation objective function is solved in combination with the constraints in the optimization and regulation model to obtain the grid purchase power data and abandoned power data corresponding to each group of planning variable datasets. Input the datasets of each planning variable and the corresponding power grid purchase and abandonment data into the planning optimization model, solve the planning optimization objective function, and obtain the annualized cost data of each planning variable dataset; The parameters in the planning variable dataset with the smallest annualized cost data are used as configuration parameters.

[0014] By calculating the annualized total cost corresponding to each set of planning variable datasets, the set of parameters with the lowest annualized cost is selected as the system configuration parameters. This achieves the optimization of photovoltaic installed capacity, effective reservoir volume, and total adjustable load capacity, ensuring that the finally selected configuration parameters are the optimal parameters in terms of economy.

[0015] In one optional implementation, the method for obtaining configuration parameters further includes: The local photovoltaic (PV) grid connection utilization rate constraint is judged for each planning variable dataset. If the planning variable dataset does not meet the local PV grid connection utilization rate constraint, the current planning variable dataset is removed.

[0016] By introducing the constraint of local photovoltaic power consumption rate, the candidate planning variable dataset is screened, and schemes with consumption rates lower than the preset threshold are eliminated. This avoids extreme situations where a large amount of photovoltaic power is abandoned, such as when the planning model selects too small a reservoir volume or too low an adjustable load capacity in pursuit of cost minimization. At the same time, the economic efficiency and energy utilization efficiency of the system are guaranteed.

[0017] Secondly, this invention provides a source-load-water coordinated scheduling optimization device, applied to a source-load-water wastewater treatment system. The system includes a photovoltaic power generation unit, a controllable load unit, and a water storage tank unit. The device includes: The data acquisition module is used to acquire electricity cost data, photovoltaic output forecast data, water inflow forecast data, and configuration parameters of the source-load wastewater treatment system within the target period. The scheduling calculation module is used to input electricity cost data, photovoltaic output prediction data, water inflow prediction data, and configuration parameters into the optimization and regulation model. It then solves the optimization and regulation objective function based on the constraints in the model, obtaining a scheduling strategy dataset for the Yuanheshui wastewater treatment system. This dataset includes the power output of each controllable load unit at different time periods, as well as the inflow and outflow of the reservoir unit at different time periods. The optimization and regulation objective function is constructed with the goal of maximizing the sum of the total comprehensive economic benefits over different time periods. The total comprehensive economic benefits are determined based on the power output and electricity cost data of each controllable load unit at different time periods. The constraints are constructed by combining the photovoltaic output prediction data and the configuration parameters of the Yuanheshui wastewater treatment system. The instruction acquisition module is used to determine the scheduling instructions of the source-load wastewater treatment system based on the scheduling strategy dataset. The scheduling instructions are used to control the power of each controllable load unit and the inflow and outflow of the water storage tank unit.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform a source-load-water coordinated scheduling optimization method of the first aspect or any corresponding embodiment described above.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute a source-load-water coordinated scheduling optimization method as described in the first aspect or any corresponding embodiment thereof.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute a source-load-water coordinated scheduling optimization method as described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a source-load-water coordinated scheduling optimization method according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a source-load-water coordinated scheduling optimization device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] 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 technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] As an optional application scenario of this invention, such as Figure 1 As shown, the source-load-water coordinated scheduling optimization system may include at least one terminal device and at least one server. Figure 1The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0027] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0028] This invention provides a source-load-water coordinated scheduling optimization method. By obtaining the predicted data of photovoltaic and incoming water and inputting them into the optimization and adjustment model for solution, the adjustment instructions of each controllable load unit and water storage unit are obtained. The water storage capacity of the water storage tank is used to shift the electricity load from the period when the photovoltaic output is insufficient to the period when the photovoltaic output is sufficient, so as to realize the photovoltaic power generation and reduce the system electricity purchase cost.

[0029] According to an embodiment of the present invention, a source-load-water coordinated scheduling optimization method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a source-load-water coordinated scheduling optimization method. Figure 2 This is a flowchart of a source-load-water coordinated scheduling optimization method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain electricity cost data, photovoltaic output forecast data, water inflow forecast data, and configuration parameters of the source-load wastewater treatment system within the target period.

[0031] The target period refers to the length of the scheduling cycle. Within the target period, there are multiple time periods of equal length. For example, the target period could be 24 hours, and the length of each time period could be 15 minutes; there are no restrictions. Electricity cost data refers to the price parameters used to calculate the system's economic benefits. For example, electricity cost data can include the cost savings from self-consumption of photovoltaic power, the cost of purchasing electricity from the grid, and the penalty cost for photovoltaic curtailment. Photovoltaic output forecast data refers to the expected power output of the photovoltaic power generation units in each time period within the target period. For example, photovoltaic output forecast data can be calculated by inputting weather forecasts and photovoltaic installed capacity into a photovoltaic power generation forecasting model. Influent forecast data refers to the expected influent flow rate of the wastewater treatment plant in each time period within the target period. For example, influent forecast data can be calculated using a wastewater influent forecasting model. Configuration parameters refer to the key design parameters of the source-load wastewater treatment system, including photovoltaic installed capacity data, effective reservoir volume data, and total adjustable load capacity data.

[0032] Step S202: Input the electricity cost data, photovoltaic output prediction data, water inflow prediction data, and configuration parameters into the optimization and regulation model. Solve the optimization and regulation objective function based on the constraints in the optimization and regulation model to obtain the scheduling strategy dataset of the source-load wastewater treatment system. The scheduling strategy dataset contains the power of each controllable load unit at different time periods and the water inflow and outflow of the reservoir unit at different time periods. The optimization and regulation objective function is constructed with the goal of maximizing the sum of the total comprehensive economic benefits at different time periods. The total comprehensive economic benefits are determined based on the power of each controllable load unit at different time periods and the electricity cost data. The constraints are constructed by combining the photovoltaic output prediction data and the configuration parameters of the source-load wastewater treatment system.

[0033] An optimization and regulation model is a mathematical model used to solve for the optimal scheduling strategy. It includes an optimization and regulation objective function and constraints, aiming to maximize the system's economic benefits. Under the premise of satisfying constraints such as power balance, equipment operation, and reservoir capacity, it determines the control commands for each controllable load unit and reservoir unit. Constraints refer to the restrictions that must be met during the optimization solution process, including power balance constraints, reservoir capacity constraints, and process equipment operation constraints, used to limit the feasible range of values ​​for decision variables. The optimization and regulation objective function is a mathematical expression aimed at maximizing the total comprehensive economic benefits of the system within the scheduling cycle. The scheduling strategy dataset refers to the set of control commands output after solving the optimization and regulation model, containing the power data of each controllable load unit at different time periods, as well as the influent and effluent data of each reservoir unit at different time periods. A controllable load unit refers to the electrical equipment in a wastewater treatment plant whose operating status or power level can be adjusted according to scheduling commands. As an example, controllable load units can include peak-shaving equipment, adjustable load equipment, and equipment operable during designated windows. Peak-shaving equipment refers to equipment whose energy consumption and operating cycle are relatively fixed, but whose operating time can be shifted forward or backward to achieve peak shaving and valley filling. Examples include inlet and outlet pumps, which must complete their pumping and drainage within a certain cycle, but are not required to complete it at a specific moment. Therefore, they can be operated when photovoltaic output is high, reducing electricity purchases. Adjustable load equipment refers to equipment with a relatively fixed operating time, but whose operating power can be adjusted within a certain range. Examples include blowers and mixers, which require continuous aeration or mixing, but whose operating power can be adjusted through frequency converter control. The load adjustment range can be 50%–100%. Window-operable equipment refers to equipment that must operate within a specific time window, but whose operating point can be selected within that time window. Examples include filtration and disinfection equipment, which must be executed once a day, but can be operated at any time in the morning or afternoon to coordinate with peak photovoltaic periods. A water storage tank unit refers to a water storage facility set between wastewater treatment process sections, used to temporarily store wastewater. By adjusting the inflow and outflow, the power consumption sequence of subsequent process sections is changed, achieving a temporal shift of the power load. Inflow refers to the volume of sewage flowing into the reservoir unit per unit time, and is a decision variable in the optimization and regulation model. Outflow refers to the volume of sewage flowing out of the reservoir unit per unit time, and is also a decision variable in the optimization and regulation model.

[0034] Step S203: Based on the scheduling strategy dataset, determine the scheduling instructions for the source-load wastewater treatment system. The scheduling instructions are used to control the electrical power of each controllable load unit and the inflow and outflow of the water storage tank unit.

[0035] Dispatch instructions are specific commands generated based on the dispatch strategy dataset to control the operation of various equipment in a wastewater treatment plant. As an example, dispatch instructions may include the target power value or start / stop status of each controllable load unit within a set time period, as well as the target influent and effluent of each water storage tank unit within a set time period.

[0036] For example, obtaining dispatch instructions may also include a rolling optimization phase, in which distributed generation and water inflow prediction data are reacquired according to a preset step size and input into the optimization and regulation model for calculation to obtain new dispatch instructions, thereby making the dispatch scheme more in line with actual operating conditions.

[0037] This embodiment provides a source-load-water coordinated scheduling optimization method. Since photovoltaic power output is fluctuating and intermittent, while sewage treatment is originally a rigid load that operates continuously based on incoming water, there may be situations where no electricity is needed when photovoltaic power is abundant, and a large amount of electricity is needed when photovoltaic power is scarce. By inputting electricity cost data, photovoltaic forecasts, inflow forecasts, and system configuration parameters into the optimization and adjustment model, and under constraints such as power balance, equipment operating limits, and reservoir capacity, the power consumption of controllable loads in each time period and the storage and release of water in the reservoir are planned in advance. The sewage to be treated is treated as a flexible load that can be moved, and the electricity demand is adapted to photovoltaic power generation, thereby achieving the goal of reducing overall operating costs without increasing battery energy storage.

[0038] In an optional embodiment, the constraints include power balance constraints, reservoir capacity constraints, and process equipment operation constraints. When performing step S202 to solve the optimization and regulation objective function in conjunction with the constraints in the optimization and regulation model: Power balance constraints are used to characterize the constraints between photovoltaic power output prediction data and the electrical power of each controllable load unit.

[0039] The power balance constraint is used to describe that, within any scheduling period, the sum of the actual output power of the photovoltaic power generation unit and the power purchased from the grid must be equal to the sum of the power consumption of all controllable load units and the power consumption of the unadjustable base loads within that period.

[0040] For example, the power balance constraint can be determined by the following formula:

[0041] in, Let t be the photovoltaic output power during time period t. Let t be the power of the distribution network during time period t. For non-adjustable base load, Classification for dispatchable load management This represents the electrical power of the i-th type of load during time period t.

[0042] The reservoir capacity constraint is used to characterize the constraint relationship between the inflow and outflow of the reservoir unit, the electrical power of the controllable load unit, and the reservoir capacity.

[0043] The water storage capacity constraint describes that the water volume of each section of the water storage tank must be between the preset lower limit and upper limit, and the change of water volume over time must follow the water balance relationship.

[0044] For example, the capacity limit of the reservoir capacity constraint can be determined by the following formula:

[0045] in, This represents the lower limit of the capacity of the j-th segment of the reservoir. This represents the upper limit of the capacity of the j-th segment of the reservoir. Let t be the water volume in the reservoir at the end of the j-th segment of time period t.

[0046] The water balance of this section of the reservoir under capacity constraints can be determined using the following formula:

[0047] in, Let be the water volume in the reservoir at the end of time period t for segment j. for The water volume in the reservoir at the end of the j-th period. Let be the inflow of water into the reservoir during the j-th segment of time period t. Let be the water outflow from the reservoir during the j-th segment of time period t.

[0048] The continuous coupling of water volume under reservoir capacity constraints can be determined using the following formula:

[0049] in, For time period t, the first Water inflow into the reservoir section Let be the water outflow from the reservoir during the j-th segment of time period t.

[0050] The continuous operation process section with reservoir capacity constraints can be determined using the following formula:

[0051] in, Let j be the maximum flow rate that the j-th load segment can handle during time period t. for Unit energy consumption processing coefficient, Let be the actual electrical power of the adjustable load in segment j. The effluent must not exceed the already treated capacity, which can be expressed as: ,in, Let J be the water outflow from the reservoir during time period t. Let be the maximum flow rate that the j-th load segment can handle during time period t.

[0052] Process equipment operating constraints are used to characterize the constraint relationship between the electrical power of controllable load units and the total adjustable load capacity.

[0053] Process equipment operating constraints are used to describe the physical and technological limitations that each controllable load unit in a wastewater treatment plant must meet during operation.

[0054] For example, the adjustable upper and lower limits of the load for process equipment operating constraints can be determined using the following formula:

[0055] in, The minimum allowable power for Class i loads. The maximum allowable power for Class i loads. Let be the electrical power of the i-th type of load during time period t.

[0056] The load operation time requirements within the scheduling time window for process equipment operating constraints can be determined using the following formula:

[0057] in, Indicates the adjustable load time window. For start-stop decision variables, This represents the minimum number of time periods that must be run within the window.

[0058] The minimum continuous operating time requirement for process equipment operating constraints can be determined using the following formula:

[0059] in, The current continuous runtime, This is the minimum allowed runtime.

[0060] The minimum continuous downtime requirement for process equipment operation constraints can be determined using the following formula:

[0061] in, This represents the current continuous downtime. This is the minimum allowable downtime.

[0062] This embodiment provides a source-load-water coordinated scheduling optimization method. By limiting three types of constraints—power balance, reservoir capacity, and process equipment operation—the optimization and regulation model can coordinate the power of controllable loads and the inflow and outflow sequence of reservoirs under the premise of ensuring real-time power balance, continuous and stable hydraulic operation, and safe equipment operation. Without relying on electrochemical energy storage, it can achieve active matching of electricity load with photovoltaic output, maximize the local photovoltaic consumption rate, reduce grid power purchase costs and curtailment penalties, and thus reduce the system's operating costs.

[0063] In an optional embodiment, the configuration parameters include photovoltaic installed capacity data, effective reservoir volume data, and total adjustable load capacity data. In step S202: Photovoltaic installed capacity data is used to confirm the upper limit of photovoltaic output prediction data in the power balance constraint conditions.

[0064] Photovoltaic installed capacity data refers to the rated installed power of photovoltaic power generation units, which represents the maximum electrical power that a photovoltaic system can output under standard conditions. In the optimization and regulation model, it is used to limit the upper limit of photovoltaic output prediction data.

[0065] The effective volume data of the reservoir is used to determine the upper limit of the reservoir capacity in the reservoir capacity constraint.

[0066] The effective volume data of the water storage tank refers to the effective volume of each section of the water storage tank in the sewage treatment plant that can be used to regulate water volume. In the optimization and regulation model, it is used to limit the upper limit of the water storage capacity of the water storage tank to prevent overflow.

[0067] Total adjustable load capacity data is used to confirm the total adjustable load capacity in the operating constraints of process equipment.

[0068] Total adjustable load capacity refers to the total power that can be adjusted upwards or downwards by all controllable load units in a wastewater treatment plant. In the optimization and regulation model, it is used to limit the power adjustment range of each controllable load unit.

[0069] This embodiment provides a source-load-water coordinated scheduling optimization method, which determines the limiting effects of photovoltaic installed capacity, effective reservoir volume, and total adjustable load capacity on three types of constraints, providing a physical feasible boundary for the optimized regulation model and ensuring the accuracy and reliability of scheduling instructions.

[0070] In an optional embodiment, the optimization adjustment objective function in step S202 includes a power purchase saving item, a power purchase item, a power curtailment penalty item, and an operating cost item. The power purchase saving item is determined based on the product of the power purchase saving cost data and the equivalent self-consumption power data. The power purchase item is determined based on the product of the power purchase cost data and the grid power purchase data. The power curtailment penalty item is determined based on the product of the power curtailment penalty cost data and the power curtailment power data. The equivalent self-consumption power data is determined based on the minimum value between the total load data and the photovoltaic output power data. The grid power purchase data is determined based on the difference between the total load data and the photovoltaic output power data. The power curtailment power data is determined based on the difference between the photovoltaic output power data and the total load data.

[0071] The "savings in electricity purchase costs" item refers to the savings in grid purchase costs resulting from the wastewater treatment plant's own consumption of photovoltaic power generation, as defined in the revenue objective function. The data on saved electricity purchase costs refers to the grid purchase costs replaced by self-consumption of photovoltaic power. The data on equivalent self-consumption refers to the actual amount of electricity generated by photovoltaic power generation that is consumed by the wastewater treatment plant within a single dispatch period.

[0072] The electricity purchase item refers to the cost incurred in purchasing electricity from the grid to meet the electricity demand of the wastewater treatment plant, as defined in the revenue objective function. Electricity purchase cost data refers to the cost of purchasing electricity from the grid. Grid purchase volume data refers to the actual amount of electricity purchased by the wastewater treatment plant from the grid during a single dispatch period.

[0073] The curtailment penalty term refers to the penalty cost incurred in the revenue objective function due to the forced abandonment of photovoltaic power generation because it is not consumed locally. Curtailment penalty cost data refers to the penalty cost incurred per unit of curtailed electricity. Curtailed electricity data refers to the amount of photovoltaic power generation that is forced to be abandoned by wastewater treatment plants within a single dispatch period.

[0074] Operating costs refer to the operating losses incurred due to equipment scheduling and adjustment in the revenue objective function.

[0075] For example, the optimization objective function can be determined according to the following formula:

[0076] in, To save on electricity purchase cost data, namely, replacing the cost of purchasing electricity with self-consumption of photovoltaic power, This is equivalent to self-consumption electricity data. For electricity purchase cost data, For electricity purchase data from the power grid, Data on the cost of power curtailment penalties. This is data on the amount of electricity wasted. For the operating cost function of process equipment, The duration of a single segment within the scheduling cycle. To save on electricity purchase costs, For electricity purchase items, This is a penalty for abandoning electricity. This is an operating cost item.

[0077] For example, the electricity purchase data of the power grid can be confirmed according to the following formula:

[0078] in, Let t be the photovoltaic output power during time period t. This refers to the power of the distribution network during time period t, i.e., the electricity purchased by the power grid. For non-adjustable base load, Classification for dispatchable load management This represents the electrical power of the i-th type of load during time period t.

[0079] The equivalent self-consumption electricity data can be determined using the following formula:

[0080] in, This is equivalent to self-consumption electricity data. Let t be the photovoltaic output power during time period t. For non-adjustable base load, Classification for dispatchable load management The duration is the length of the time period. This represents the electrical power of the i-th type of load during time period t.

[0081] The amount of abandoned electricity can be determined using the following formula:

[0082] in, This is data on the amount of electricity wasted. Let t be the photovoltaic output power during time period t. For non-adjustable base load, Classification for dispatchable load management The duration is the length of the time period. This represents the electrical power of the i-th type of load during time period t.

[0083] In an optional embodiment, step S201 involves obtaining electricity cost data, photovoltaic output prediction data, influent prediction data, and configuration parameters of the source-load wastewater treatment system for the target period. The method for obtaining the configuration parameters specifically includes: Step a1: Obtain multiple sets of typical electricity cost data, typical photovoltaic power output prediction data, typical water inflow prediction data, and planning variable datasets.

[0084] Typical electricity cost data refers to electricity cost data used during the planning phase to represent typical days under different weather conditions. As an example, this can include electricity purchase cost savings, grid purchase cost, and curtailment penalty cost, used to calculate the typical daily operating cost under each planning scheme. Typical photovoltaic output forecast data refers to photovoltaic output curve data used during the planning phase to represent typical days under different weather conditions, used to simulate the time-series distribution of photovoltaic power generation under each planning scheme. Typical influent forecast data refers to sewage influent flow curve data used during the planning phase to represent typical days under different weather conditions, used to simulate the time-series distribution of sewage treatment load under each planning scheme. Planning variable datasets refer to the system configuration parameters that need to be optimized and determined during the planning phase, including photovoltaic installed capacity data, reservoir effective volume data, and total adjustable load capacity data, serving as boundary constraint inputs for the optimization and regulation model. Multiple sets of planning variable datasets are used in subsequent calculations to evaluate the annualized total cost under different configuration schemes, thereby selecting the optimal configuration.

[0085] Step a2: Input the typical electricity cost data, typical photovoltaic output prediction data, typical water inflow prediction data, and planning variable datasets of each group into the optimization and regulation model. Solve the optimization and regulation objective function in combination with the constraints in the optimization and regulation model to obtain the grid purchase power data and abandoned power data corresponding to each group of planning variable datasets.

[0086] The process of inputting typical electricity cost data, typical photovoltaic output prediction data, typical water inflow prediction data, and planning variable datasets into the optimization and regulation model, and solving the optimization and regulation objective function in combination with the constraints in the optimization and regulation model, is the same as step S202, and will not be repeated here.

[0087] Step a3: Input the datasets of each planning variable and the corresponding power grid purchase and abandonment data into the planning optimization model, solve the planning optimization objective function, and obtain the annualized cost data of each planning variable dataset.

[0088] The planning optimization model is a mathematical model used to determine the optimal system configuration parameters. It includes a planning optimization objective function, aiming to minimize the annualized total cost. By evaluating the performance of each set of planning variable datasets under different typical daily scenarios, the optimal combination of photovoltaic installed capacity, effective reservoir volume, and total adjustable load capacity is ultimately selected. Annualized cost data refers to the total cost obtained by uniformly converting investment and operating costs to annual values. This data is used to compare the economics of different planning schemes, and the scheme with the lowest annualized cost is the optimal planning scheme.

[0089] For example, the objective function for planning optimization can be determined according to the following formula:

[0090] in, For photovoltaic installed capacity, The effective volume of the reservoir, Total adjustable load capacity The annualized cost per unit capacity of photovoltaic power. The annualized cost per unit volume of the reservoir. The annualized cost coefficient for load control upgrades is given, where d is the index for a single typical day. Electricity purchased from the grid on day d This represents the amount of solar power wasted on day d. The unit price of electricity purchased from the power grid. Let E[·] be the unit price of the light abandonment penalty, and E[·] be the average of the D scenes, which is approximately the annualized expectation. This represents the minimum annualized total cost.

[0091] Step a4: Use the parameters from the planning variable dataset with the smallest annualized cost data as configuration parameters.

[0092] During the planning phase, multiple sets of different planning variable datasets are substituted into the planning optimization model for calculation to obtain the annualized total cost for each set. Since the smaller the annualized total cost, the better the scheme, the set with the smallest annualized cost is selected from the multiple sets of different planning variable datasets. The photovoltaic installed capacity, effective volume of the water storage tank, and total adjustable load capacity in the selected planning variable dataset are used as the configuration parameters for the final construction and renovation of the sewage treatment plant for subsequent daily scheduling and operation.

[0093] This embodiment provides a source-load-water coordinated scheduling optimization method. By calculating the annualized total cost corresponding to each set of planning variable datasets, the method selects the set of parameters with the lowest annualized cost as the system configuration parameters. This achieves optimization of photovoltaic installed capacity, effective reservoir volume, and total adjustable load capacity, ensuring that the finally selected configuration parameters are the optimal parameters in terms of economy.

[0094] In an optional embodiment, the method for obtaining configuration parameters further includes: Step b1: Perform a photovoltaic local consumption utilization rate constraint judgment on each planning variable dataset. If the planning variable dataset does not meet the photovoltaic local consumption utilization rate constraint condition, then remove the current planning variable dataset.

[0095] The planning optimization model also includes a constraint on local consumption and utilization rate. The local consumption and utilization rate constraint refers to the indicator that must be met when screening candidate planning variable datasets during the planning stage. It means that the average proportion of photovoltaic power generation that is locally consumed by wastewater treatment plants in typical daily scenarios throughout the year is not less than a preset threshold.

[0096] For example, the constraint on local photovoltaic grid connection utilization rate can be determined according to the following formula:

[0097] in, This represents the available photovoltaic power generation on day d. This represents the amount of solar power wasted on day d. This represents the total available photovoltaic power generation on day d. To improve the local utilization rate of photovoltaic power, For example, the total number of typical daytime scenarios. The value can be No restrictions are imposed here.

[0098] This embodiment provides a source-load-water coordinated scheduling optimization method. By introducing the constraint of photovoltaic local consumption utilization rate, the candidate planning variable dataset is screened and schemes with consumption rates lower than the preset threshold are eliminated. This avoids the extreme situation where a large amount of photovoltaic power is abandoned, such as the planning model choosing too small a reservoir volume or too low an adjustable load capacity in pursuit of cost minimization. At the same time, it ensures the economy and energy utilization efficiency of the system.

[0099] This embodiment also provides a source-load-water coordinated scheduling optimization device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0100] This embodiment provides a source-load-water coordinated scheduling optimization device, such as... Figure 3 As shown, it includes: Data acquisition module 301 is used to acquire electricity cost data, photovoltaic output prediction data, water inflow prediction data and configuration parameters of the source load wastewater treatment system within the target period; The scheduling calculation module 302 is used to input electricity cost data, photovoltaic output prediction data, water inflow prediction data, and configuration parameters into the optimization and regulation model. It then solves the optimization and regulation objective function based on the constraints in the model, obtaining a scheduling strategy dataset for the source-load wastewater treatment system. This dataset includes the power output of each controllable load unit at different times, as well as the inflow and outflow of the reservoir unit at different times. The optimization and regulation objective function is constructed with the goal of maximizing the sum of the total comprehensive economic benefits over different time periods. The total comprehensive economic benefits are determined based on the power output and electricity cost data of each controllable load unit at different times. The constraints are constructed by combining the photovoltaic output prediction data and the configuration parameters of the source-load wastewater treatment system. The instruction acquisition module 303 is used to determine the scheduling instructions of the source-load wastewater treatment system based on the scheduling strategy dataset. The scheduling instructions are used to control the power of each controllable load unit and the inflow and outflow of the water storage tank unit.

[0101] The source-load-water coordinated scheduling optimization device provided in this embodiment of the invention can execute the source-load-water coordinated scheduling optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0102] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0103] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0104] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0105] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the source-load-water coordinated scheduling optimization method of the embodiments of the present invention.

[0106] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0107] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the source-load-water coordinated scheduling optimization method shown in the above embodiments.

[0108] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0109] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A source-load-water coordinated scheduling optimization method, applied to a source-load-water wastewater treatment system, characterized in that, The system includes a photovoltaic power generation unit, a controllable load unit, and a water storage tank unit; the method includes: Obtain electricity cost data, photovoltaic output forecast data, water inflow forecast data, and configuration parameters of the source-load wastewater treatment system within the target period; The electricity cost data, photovoltaic output prediction data, water inflow prediction data, and configuration parameters are input into the optimization and regulation model. The optimization and regulation objective function is solved by combining the constraints in the optimization and regulation model to obtain the scheduling strategy dataset of the source-load wastewater treatment system. The scheduling strategy dataset includes the power of each controllable load unit at different time periods and the water inflow and outflow of the water storage tank unit at different time periods. The optimization and regulation objective function is constructed with the goal of maximizing the sum of the total comprehensive economic benefits at different time periods. The total comprehensive economic benefits are determined based on the power of each controllable load unit at different time periods and the electricity cost data. The constraints are constructed by combining the photovoltaic output prediction data and the configuration parameters of the source-load wastewater treatment system. Based on the scheduling strategy dataset, scheduling instructions for the source-load wastewater treatment system are determined. These instructions are used to control the electrical power of each controllable load unit and the inflow and outflow of the water storage tank unit.

2. The method according to claim 1, characterized in that, The constraints include power balance constraints, reservoir capacity constraints, and process equipment operation constraints. The power balance constraint is used to characterize the constraint between the photovoltaic output prediction data and the electrical power of each controllable load unit. The reservoir capacity constraint is used to characterize the constraint relationship between the inflow and outflow of the reservoir unit, the electrical power of the controllable load unit, and the reservoir capacity. The process equipment operating constraints are used to characterize the constraint relationship between the electrical power of the controllable load unit and the total adjustable load capacity.

3. The method according to claim 2, characterized in that, The configuration parameters include photovoltaic installed capacity data, effective reservoir volume data, and total adjustable load capacity data. The photovoltaic installed capacity data is used to confirm the upper limit of the photovoltaic output prediction data in the power balance constraint conditions. The effective volume data of the water storage tank is used to confirm the upper limit of the water storage tank capacity in the water storage tank capacity constraint; The total adjustable load capacity data is used to confirm the total adjustable load capacity in the operating constraints of the process equipment.

4. The method according to claim 1, characterized in that, The optimization and adjustment objective function includes a power purchase saving item, a power purchase item, a power curtailment penalty item, and an operating cost item. The power purchase saving item is determined by multiplying the power purchase saving cost data and the equivalent self-consumption power data. The power purchase item is determined by multiplying the power purchase cost data and the grid power purchase data. The power curtailment penalty item is determined by multiplying the power curtailment penalty cost data and the curtailed power data. The equivalent self-consumption power data is determined by the minimum value between the total load data and the photovoltaic output power data. The grid power purchase data is determined by the difference between the total load data and the photovoltaic output power data. The curtailed power data is determined by the difference between the photovoltaic output power data and the total load data.

5. The method according to claim 1, characterized in that, The method for obtaining the configuration parameters includes: Acquire multiple sets of typical electricity cost data, typical photovoltaic output forecast data, typical water inflow forecast data, and planning variable datasets; The typical electricity cost data, typical photovoltaic output prediction data, typical water inflow prediction data, and planning variable datasets of each group are input into the optimization and regulation model. The optimization and regulation objective function is solved in combination with the constraints in the optimization and regulation model to obtain the grid purchase power data and abandoned power data corresponding to each group of planning variable datasets. The planning variable datasets and the corresponding power grid purchase and abandonment data are input into the planning optimization model, and the planning optimization objective function is solved to obtain the annualized cost data of each planning variable dataset. The parameters in the planning variable dataset with the smallest annualized cost data are used as configuration parameters.

6. The method according to claim 5, characterized in that, The method for obtaining the configuration parameters further includes: The photovoltaic local consumption and utilization rate constraint is judged for each of the planning variable datasets. If the planning variable dataset does not meet the photovoltaic local consumption and utilization rate constraint, the current planning variable dataset is removed.

7. A source-load-water coordinated scheduling optimization device, applied to a source-load-water wastewater treatment system, characterized in that, The system includes a photovoltaic power generation unit, a controllable load unit, and a water storage tank unit; the device includes: The data acquisition module is used to acquire electricity cost data, photovoltaic output prediction data, water inflow prediction data, and configuration parameters of the source-load wastewater treatment system within the target period. The scheduling calculation module is used to input the electricity cost data, photovoltaic output prediction data, water inflow prediction data, and configuration parameters into the optimization and regulation model. It then solves the optimization and regulation objective function based on the constraints in the optimization and regulation model to obtain the scheduling strategy dataset of the source-load wastewater treatment system. This dataset includes the power output of each controllable load unit at different time periods, as well as the water inflow and outflow of the reservoir unit at different time periods. The optimization and regulation objective function is constructed with the goal of maximizing the sum of the total comprehensive economic benefits over different time periods. The total comprehensive economic benefits are determined based on the power output and electricity cost data of each controllable load unit at different time periods. The constraints are constructed by combining the photovoltaic output prediction data and the configuration parameters of the source-load wastewater treatment system. The instruction acquisition module is used to determine the scheduling instructions of the source-load wastewater treatment system based on the scheduling strategy dataset. The scheduling instructions are used to control the power of each controllable load unit and the inflow and outflow of the water storage tank unit.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the source-load-water coordinated scheduling optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the source-load-water coordinated scheduling optimization method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute a source-load-water coordinated scheduling optimization method according to any one of claims 1 to 6.

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