A planning method and device for a sewage plant integrated energy system

Abstract

This invention relates to the field of integrated energy system technology, specifically to a planning method and apparatus for an integrated energy system in a wastewater treatment plant. The method includes: firstly, defining the present economic value and carbon emissions within the project period as first and second upper-level objective functions, respectively; then determining first and second constraints, and based on these constraints, integrating the first and second upper-level objective functions into a multi-objective model to generate an upper-level capacity optimization model; next, defining the daily operating cost of the system as a first lower-level objective function, and determining a third constraint; then, based on the first lower-level objective function and the third constraint, generating a lower-level scheduling optimization model; finally, iteratively optimizing the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning result for the integrated energy system. This invention, through a two-level optimization planning and design model, comprehensively considers multiple factors, achieving a balance between the system's economy, environmental friendliness, and reliability.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system technology, specifically to a planning method and apparatus for an integrated energy system of a wastewater treatment plant. Background Technology

[0002] The planning and design of integrated energy systems is a key technology for ensuring the system's economy, environmental friendliness, and reliability. This process involves addressing numerous complex factors, including the intermittency and instability of renewable energy sources, the flexibility and variability of system combination schemes, and the selection of different system operation control strategies. These factors collectively increase the complexity of integrated energy system optimization planning.

[0003] From the perspective of microgrids, they can be divided into grid-connected and stand-alone types based on whether they are connected to the conventional power grid. However, most current technologies only plan and design for one type, lacking a universal technology applicable to both types of microgrids. Regarding the comprehensive optimization of distributed generation in microgrid systems, existing technologies rarely consider the optimization of distributed generation types, which may limit the improvement of system efficiency. Simultaneously, in the optimal scheduling of distributed generation, most technologies adopt fixed strategies, neglecting the optimized operation between distributed generation sources, which also limits the overall system performance. In terms of the economic operation of integrated energy systems, current research mostly focuses only on the economics of system investment and operation, failing to fully consider the impact of grid-side voltage regulation ancillary service strategies on the dynamic economic scheduling of the system. This may lead to the system failing to achieve optimal economic benefits during operation. In terms of planning objectives, existing technologies often focus too solely on economics, neglecting the importance of environmental protection, thus failing to achieve sustainable development.

[0004] Therefore, in the above scheme, the planning and design of the integrated energy system did not take into account multiple factors, and it was impossible to achieve a balance between the system's economy, environmental protection and reliability. Summary of the Invention

[0005] In view of this, the present invention provides a planning method and apparatus for an integrated energy system for a wastewater treatment plant, in order to solve the problem that the planning and design of existing integrated energy systems do not comprehensively consider multiple factors and cannot achieve a balance between the system's economy, environmental protection and reliability.

[0006] In a first aspect, the present invention provides a planning method for an integrated energy system of a wastewater treatment plant, the method comprising:

[0007] The economic present value within the project cycle is defined as the first upper-level objective function, and carbon emissions are defined as the second upper-level objective function.

[0008] Determine the first constraint condition for the first upper-level objective function and the second constraint condition for the second upper-level objective function;

[0009] Based on the first constraint and the second constraint, the first upper-level objective function and the second upper-level objective function are integrated into a multi-objective model to generate an upper-level capacity optimization model.

[0010] Define the daily operating cost of the system as the first lower-level objective function, and determine the third constraint condition of the first lower-level objective function;

[0011] Based on the first lower-level objective function and the third constraint, a lower-level scheduling optimization model is generated;

[0012] The upper-level capacity optimization model and the lower-level scheduling optimization model are iteratively optimized to obtain the optimal planning result of the integrated energy system.

[0013] In one optional implementation, defining the economic present value over the project period as a first upper-level objective function and defining carbon emissions as a second upper-level objective function includes:

[0014] With the goal of minimizing the total life-cycle cost of the integrated energy system, and based on the investment cost, depreciation cost, and annual operating cost of each energy device, the economic present value within the project period is defined as the first upper-level objective function.

[0015] With the goal of minimizing the annual emissions of the integrated energy system, carbon emissions are defined as the second upper-level objective function based on the annual electricity purchase and gas emission coefficients.

[0016] In one alternative implementation, the first upper-level objective function is obtained using the following formula:

[0017] f1 = C inv +C dep +C year ;

[0018] Where f1 represents the total life cycle cost, C inv C represents the investment cost of each of the energy devices. dep C represents the equipment depreciation cost. year This indicates the annual operating cost.

[0019] In one alternative implementation, the second upper-level objective function is obtained using the following formula:

[0020]

[0021] Where f2 represents the annual emission level, w day This represents the average daily electricity consumption. This represents the carbon emission coefficient.

[0022] In one optional implementation, determining the first constraint condition of the first upper-level objective function and the second constraint condition of the second upper-level objective function includes:

[0023] Obtain the first objective optimization interval of the first upper-level objective function and the second objective optimization interval of the second upper-level objective function;

[0024] Based on the first target optimization interval, determine the first constraint condition of the first upper-level objective function;

[0025] Based on the second objective optimization interval, the second constraint condition of the second upper-level objective function is determined.

[0026] In one optional implementation, the upper-layer capacity optimization model is obtained using the following formula:

[0027]

[0028] Where F1 represents the upper-layer capacity optimization result, f 1,max Let f represent the first target optimization interval. 2,min Let λ1 represent the sensitivity corresponding to the first upper-level objective function and λ2 represent the sensitivity corresponding to the second upper-level objective function.

[0029] In one optional implementation, defining the daily operating cost of the system as a first lower-level objective function and determining the third constraint condition of the first lower-level objective function includes:

[0030] With the goal of minimizing the daily operating cost of the integrated energy system, and based on the daily electricity purchase and sale costs and the daily voltage regulation revenue, the daily operating cost of the system is defined as the first lower-level objective function.

[0031] Based on the constraints of electric power balance, thermal power balance, biogas cogeneration device performance, energy storage battery performance, converter performance, photovoltaic power participation in voltage regulation, and wind turbine power generation performance, the third constraint condition of the first lower-level objective function is determined.

[0032] In one alternative implementation, the first lower-level objective function is obtained using the following formula:

[0033] min F2=C e +C t ;

[0034]

[0035] C t =w q,t ΔQ t +β(U t-U ref ) 2 ;

[0036] Where F2 represents the lower-level scheduling optimization result, C e C represents the daily electricity purchase and sale cost. t This represents the daily voltage regulation benefit, where T represents the total time interval, and c represents the total time interval. grid_mic e represents the grid purchase price of electricity. grid_mic.t c represents the system's electricity purchase at time t. mic_grid This indicates the electricity price that the wastewater treatment plant sells to the grid, e mic_grid.t w represents the system's electricity sales at time t. q,t ΔQ represents the voltage regulation compensation electricity price at time t. t U represents the reactive power regulation of the inverter, β represents the cost factor for maintaining voltage stability, and U represents the reactive power regulation of the inverter. t U represents the actual voltage of the microgrid at time t. ref This indicates the reference value of the voltage amplitude at the microgrid connection point.

[0037] In one optional implementation, the iterative optimization process of the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning result of the integrated energy system includes:

[0038] The decision is made through the upper-level capacity optimization model, and the values ​​of the decision variables are obtained;

[0039] The decision variable values ​​are input into the lower-level scheduling optimization model to obtain the lower-level optimization results;

[0040] The lower-level optimization results are returned to the upper-level capacity optimization model for iterative optimization until the optimal planning result of the integrated energy system is obtained.

[0041] Secondly, the present invention provides a planning device for an integrated energy system of a wastewater treatment plant, the device comprising:

[0042] The upper-level objective function acquisition module is used to define the economic present value within the project cycle as the first upper-level objective function and carbon emissions as the second upper-level objective function.

[0043] The upper-level constraint acquisition module is used to determine the first constraint condition of the first upper-level objective function and the second constraint condition of the second upper-level objective function;

[0044] The upper-level capacity optimization model acquisition module is used to perform multi-objective integration on the first upper-level objective function and the second upper-level objective function based on the first constraint and the second constraint to generate an upper-level capacity optimization model;

[0045] The lower-level objective function acquisition module is used to define the daily operating cost of the system as the first lower-level objective function and determine the third constraint condition of the first lower-level objective function;

[0046] The lower-level scheduling optimization model acquisition module is used to generate a lower-level scheduling optimization model based on the first lower-level objective function and the third constraint condition.

[0047] The optimal planning result acquisition module is used to perform iterative optimization processing on the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning result of the integrated energy system.

[0048] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform a planning method and apparatus for an integrated energy system of a wastewater treatment plant as described in the first aspect or any corresponding embodiment.

[0049] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute a planning method and apparatus for an integrated energy system of a wastewater treatment plant according to the first aspect or any corresponding embodiment described above.

[0050] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute a planning method and apparatus for an integrated energy system of a wastewater treatment plant according to the first aspect or any corresponding embodiment described above.

[0051] The technical solution provided by this invention may include the following beneficial effects:

[0052] This invention proposes a two-layer optimization planning and design model applicable to both grid-connected and stand-alone microgrid systems, achieving comprehensive optimization and scheduling optimization of distributed power sources, thus significantly improving system operating efficiency. Secondly, the optimization results of this invention are directly applicable to practical applications such as equipment selection, reducing uncertainties in the selection process. Regarding distributed power source optimization and scheduling, this invention fully considers the coupling relationship between the capacities of various devices, and its algorithm optimization strategy can be flexibly adjusted according to actual conditions to better adapt to economic or environmental performance requirements.

[0053] This invention innovatively combines grid-side voltage regulation ancillary service strategies, using the remaining capacity of distributed photovoltaic grid-connected converters for voltage regulation. This not only makes efficient use of equipment capacity but also enables the microgrid system to obtain economic benefits from voltage regulation ancillary services, providing effective guidance for distributed power sources to participate in voltage regulation. In addition, this invention takes into account both economic efficiency and environmental protection in its planning objectives, balancing multi-objective planning problems through sensitivity calculations, making the system planning more scientific and reasonable.

[0054] Furthermore, this invention addresses the actual operational needs of wastewater treatment plants by utilizing biogas generated from sludge fermentation for combined heat and power (CHP), achieving efficient resource utilization, reducing electricity purchase costs, and significantly decreasing indirect carbon emissions, thus providing strong support for the sustainable development of wastewater treatment plants. It also demonstrates significant advantages and broad application prospects in the field of microgrid system planning and design. Attached Figure Description

[0055] 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.

[0056] Figure 1 This is a flowchart illustrating a planning method for an integrated energy system of a wastewater treatment plant according to an embodiment of the present invention.

[0057] Figure 2 This is a flowchart illustrating another planning method for an integrated energy system for a wastewater treatment plant according to an embodiment of the present invention.

[0058] Figure 3 This is a structural block diagram of a planning device for an integrated energy system of a wastewater treatment plant according to an embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0060] 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.

[0061] It should be noted that this invention is a planning and design model for a wastewater treatment plant integrated energy system based on bi-level optimization. Bi-level planning is an optimization problem with two levels of systems. The upper-level model and the lower-level model each have their own objective functions and constraints. The upper-level model makes decisions first and passes the decision variable values ​​of the upper-level model to the lower-level model. The lower-level model determines the feasible region based on the upper-level model, performs optimization, and obtains the optimal value of the objective function. The optimization results of the lower level are returned to the upper level, and the optimal solution and its corresponding optimal value are obtained through iteration.

[0062] According to an embodiment of the present invention, a planning method for an integrated energy system of a wastewater treatment plant 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.

[0063] This embodiment provides a planning method for an integrated energy system of a wastewater treatment plant. Figure 1 This is a flowchart of a planning method for an integrated energy system of a wastewater treatment plant according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0064] Step S101: Define the present value of the economy within the project period as the first upper-level objective function, and define carbon emissions as the second upper-level objective function.

[0065] Furthermore, in this embodiment, the economic and environmental performance of the wastewater treatment plant's integrated energy system within the project cycle are selected as indicators for evaluating the system's merits. Therefore, this embodiment takes minimizing the economic present value within the cycle as the first objective and minimizing carbon emissions as the second objective, constructing a first upper-level objective function and a second upper-level objective function.

[0066] Step S102: Determine the first constraint condition of the first upper-level objective function and the second constraint condition of the second upper-level objective function.

[0067] Furthermore, since the system is a multi-objective optimization, in order to ensure that the system optimization objective is within an acceptable range, this embodiment restricts the system objective function to a certain range, thereby generating multi-objective optimization constraints, namely the first constraint and the second constraint.

[0068] Step S103: Based on the first constraint and the second constraint, perform multi-objective integration on the first upper-level objective function and the second upper-level objective function to generate an upper-level capacity optimization model.

[0069] Furthermore, in order to ensure that the economic efficiency and environmental friendliness are within an acceptable range, this embodiment transforms the multi-objective problem into a single-objective problem, and takes into account the different attributes of the two objectives to obtain the final upper-level capacity optimization model.

[0070] Step S104: Define the daily operating cost of the system as the first lower-level objective function, and determine the third constraint condition of the first lower-level objective function.

[0071] Furthermore, in order to achieve dynamic economic scheduling of the system, this embodiment takes the minimum daily operating cost of the system as the objective of the lower-level model, constructs a first lower-level objective function, and comprehensively considers constraints such as power balance, thermal power balance and equipment performance to determine the third constraint condition corresponding to the first lower-level objective function. The actual operating limitations of the equipment are taken into account to ensure that the optimization results are feasible in practical applications.

[0072] Step S105: Based on the first lower-level objective function and the third constraint, generate a lower-level scheduling optimization model.

[0073] Furthermore, in this embodiment, the first lower-level objective function is constrained by a third constraint condition to generate a lower-level scheduling optimization model, thereby realizing dynamic economic scheduling of the system.

[0074] Step S106: Iterative optimization processing is performed on the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning result of the integrated energy system.

[0075] Furthermore, this embodiment combines upper-level capacity optimization and lower-level scheduling optimization to form a two-layer optimization structure, which can ensure that the system achieves optimal performance in terms of economy and environmental protection, and generate the optimal planning result of the integrated energy system.

[0076] In summary, this embodiment proposes a two-layer optimization planning and design model applicable to both grid-connected and stand-alone microgrid systems, achieving comprehensive optimization and scheduling optimization of distributed power sources, greatly improving the system's operating efficiency. The optimization results are directly applicable to practical applications such as equipment selection, reducing uncertainties in the selection process.

[0077] This embodiment provides another planning method for an integrated energy system of a wastewater treatment plant. Figure 2 This is a flowchart of another planning method for an integrated energy system for a wastewater treatment plant according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0078] Step S201: Taking the minimum life-cycle cost of the integrated energy system as the optimization objective, the economic present value within the project cycle is defined as the first upper-level objective function based on the investment cost, depreciation cost, and annual operating cost of each energy device.

[0079] In one alternative implementation, the first upper-level objective function is obtained using the following formula:

[0080] f1 = C inv +C dep +C year ;

[0081]

[0082]

[0083]

[0084] C year =365C day,aver；

[0085] Where f1 represents the total life cycle cost, C inv C represents the investment cost of each energy device. dep C represents the equipment depreciation cost. year Let α represent the annual operating cost, k represent the total number of devices, and α represent the annual operating cost. i E represents the investment recovery factor of equipment i. i c represents the configured capacity of device i. i inv τ represents the unit capacity installation cost of device i. i ρ represents the investment interest rate of equipment i, N represents the planning period of the integrated energy system of the wastewater treatment plant, T represents the total time, and ρ represents the total investment period of equipment i. i P represents the depreciation factor for device i. i,t C represents the output of device i at time t on a typical scheduling day. day，aver This represents the operating cost of a typical scheduling day in the planned scenario.

[0086] Furthermore, this embodiment selects the economic and environmental performance of the wastewater treatment plant integrated energy project as indicators for evaluating the system's merits. Firstly, with the goal of minimizing the total life-cycle cost, including the investment cost of each energy device, equipment depreciation cost, and annual operating cost, a first upper-level objective function is constructed.

[0087] Step S202: Taking the minimum annual emission level of the integrated energy system as the optimization objective, carbon emission is defined as the second upper-level objective function based on the annual electricity purchase and gas emission coefficient.

[0088] In one alternative implementation, the second upper-level objective function is obtained using the following formula:

[0089]

[0090] Where f2 represents the emission level for that year, w day This represents the average daily electricity consumption. This represents the carbon emission coefficient.

[0091] Furthermore, the carbon emissions from wastewater treatment plants are mainly the indirect carbon emissions generated from purchasing electricity from the power grid during the wastewater treatment and sludge disposal processes. The annual emission level of a wastewater treatment plant is equal to the annual electricity purchased multiplied by the gas emission coefficient.

[0092] Step S203: Determine the first constraint condition of the first upper-level objective function and the second constraint condition of the second upper-level objective function.

[0093] In one optional implementation, step S203 includes:

[0094] Obtain the first objective optimization interval of the first upper-level objective function and the second objective optimization interval of the second upper-level objective function;

[0095] Based on the first objective optimization interval, the first constraint condition of the first upper-level objective function is determined;

[0096] Based on the second objective optimization interval, the second constraint condition of the second upper-level objective function is determined.

[0097] Furthermore, since the system involves multi-objective optimization, this embodiment limits the system objective function to a certain range to ensure that the system optimization objectives remain within an acceptable range, namely:

[0098] f1 <f 1,max ;

[0099] f2 <f 2,max ;

[0100] Among them, f 1,max f represents the first objective optimization interval. 2,min This represents the optimization interval for the second objective.

[0101] Step S204: Based on the first constraint and the second constraint, perform multi-objective integration on the first upper-level objective function and the second upper-level objective function to generate an upper-level capacity optimization model.

[0102] In one alternative implementation, the upper-level capacity optimization model is obtained using the following formula:

[0103]

[0104] Where F1 represents the upper-layer capacity optimization result, f 1,max f represents the first objective optimization interval. 2,minLet λ1 represent the sensitivity corresponding to the first upper-level objective function and λ2 represent the sensitivity corresponding to the second upper-level objective function.

[0105] Furthermore, this embodiment uses the weighted coefficient method to transform the multi-objective problem into a single-objective problem, and takes into account the different attributes of the two objectives to obtain the final objective function. The sensitivity value is determined by sensitivity analysis. From the perspective of quantitative analysis, the influence of the changes in the first and second upper-level objective functions on the upper-level capacity optimization model is studied. In essence, the method of changing the values ​​of relevant variables one by one is used to explain the law of the influence of these factors on the key indicators, so as to determine the sensitivity.

[0106] Step S205: Taking the minimum daily operating cost of the integrated energy system as the optimization objective, the daily operating cost of the system is defined as the first lower-level objective function based on the daily electricity purchase and sale cost and the daily voltage regulation revenue.

[0107] In one alternative implementation, the first lower-level objective function is obtained using the following formula:

[0108] minF2=C e +C t ;

[0109]

[0110] C t =w q,t ΔQ t +β(U t -U ref ) 2 ;

[0111] Where F2 represents the lower-level scheduling optimization result, C e C represents the electricity purchase and sale cost for that day. t This represents the daily voltage regulation profit, T represents the total time, and c represents the total time. grid_mic e represents the grid purchase price of electricity. grid_mic.t c represents the system's electricity purchase at time t. mic_grid This indicates the electricity price that the wastewater treatment plant sells to the grid, e mic_grid.t w represents the system's electricity sales at time t. q,t This represents the voltage regulation compensation price issued by the DSO at time t, which is the voltage regulation strategy required by the grid side, ΔQ. t U represents the reactive power regulation of the inverter, β represents the cost factor for maintaining voltage stability, and U represents the reactive power regulation of the inverter. ref U represents the reference value of voltage amplitude at the microgrid connection point. t The voltage at time t represents the actual voltage of the microgrid, and the difference between the two represents the voltage regulation effect of the microgrid under voltage regulation compensation on the DSO side.

[0112] Furthermore, in this embodiment, the lower-level scheduling optimization model selects 24 hours as the scheduling scale to realize dynamic economic scheduling of the system. The objective function is to minimize the daily operating cost of the system, including the daily electricity purchase and sale cost and the daily voltage regulation revenue.

[0113] In this embodiment, the distributed system operator (DSO) plays a crucial role in ensuring the safe operation of the entire distribution network and guiding microgrid voltage regulation, thereby maintaining the supporting role of the voltage regulation service market in the grid operation. Therefore, the DSO needs to maintain voltage stability and guide microgrids to participate in voltage regulation during its participation in voltage regulation. The DSO dynamically formulates voltage regulation compensation for the producer-consumer side based on the operation of the distribution network and the voltage regulation situation of distributed power sources.

[0114] Step S206: Based on the constraints of electric power balance, thermal power balance, biogas cogeneration device performance, energy storage battery performance, converter performance, photovoltaic power participation in voltage regulation, and wind turbine power generation performance, determine the third constraint condition of the first lower-level objective function.

[0115] Furthermore, the power balance constraint is obtained using the following formula:

[0116] P G +P PV +P WT +P DG +P CON =P L +P S +P SOC ;

[0117] Among them, P G P represents the power purchased by the power grid. PV Photovoltaic power generation, P WT P represents the power generation capacity of the wind turbine. DG P represents the power output of a combined heat and power (CHP) generator. CON P represents the converter power; rectification is negative, inversion is positive. L P represents the load power. S P represents the power output sold by the power grid. SOC This indicates the energy storage capacity; charging is positive and discharging is negative.

[0118] The thermal power balance constraint is obtained using the following formula:

[0119]

[0120] Among them, P HX,w(t) η represents the output heating power of the heat exchanger in the microgrid of a typical wastewater treatment plant during time period t; HX Indicates the efficiency of the heat exchanger; Q G,w(t)η represents the energy efficiency ratio of the absorption heater in the microgrid of a typical wastewater treatment plant during time period t; G P represents the efficiency of a heating engine. G,w(t) γ represents the output power of the combined heat and power unit in the wastewater treatment plant microgrid during time period t on a typical day; G Indicates the heat-to-power ratio of a combined heat and power (CHP) unit; η WH This indicates the efficiency of a hot boiler.

[0121] The performance constraints of the biogas cogeneration unit are obtained using the following formula:

[0122] P DG,min u DG ≤P DG ≤P DGmax u DG ;

[0123] Among them, P DG P represents the active power output of a combined heat and power (CHP) generator. DG,min and P DG,max These represent the minimum and maximum active power (kW) output of the cogeneration generator, respectively. DG This is a binary variable that indicates whether the diesel generator is on or off (0 indicates off, 1 indicates on).

[0124] The performance constraints of the energy storage battery are obtained using the following formula:

[0125] To extend the lifespan of a battery, its state of charge (SOC) should meet the following requirements:

[0126] SOC min ≤SOC t ≤SOC max ;

[0127] Among them, SOC min SOC max These represent the minimum and maximum states of charge (SOC) of the battery, respectively. t This indicates the state of charge of the battery at time t.

[0128] The converter performance constraints are obtained using the following formula:

[0129]

[0130] Among them, P B,c P B,d These represent the charging and discharging power of the battery, η. B,c η B,d These represent the battery charging and discharging efficiencies, η and η, respectively. CON,rec η CON,inv These represent the rectification and inversion efficiencies of the converter, respectively.

[0131] The power constraint for photovoltaic participation in voltage regulation is obtained using the following formula:

[0132] The total active and reactive power output of a photovoltaic grid-connected converter must meet capacity constraints:

[0133]

[0134] Among them, P pv,t Q represents the photovoltaic power generation at time t. 0,t S represents the initial reactive power at the wastewater treatment plant's grid connection node before photovoltaic voltage regulation at time t, with the injection into the grid considered positive; inv This indicates the capacity of the photovoltaic grid-connected converter at the grid connection node of the wastewater treatment plant.

[0135] The power generation performance constraints of wind turbines are obtained using the following formula:

[0136]

[0137] Where v represents the actual wind speed at the height of the wind turbine hub, v ci v co The cut-in and cut-out wind speeds represent the wind speeds. When the actual wind speed is lower or higher than the actual wind speed, the fan will not work. P(v) represents the fan output within the normal wind speed range, which is obtained by linear interpolation of the wind speed-power curve.

[0138] Step S207: Based on the first lower-level objective function and the third constraint, generate a lower-level scheduling optimization model.

[0139] Step S208: Iterative optimization processing is performed on the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning result of the integrated energy system.

[0140] In one optional implementation, step S208 includes:

[0141] The decision is made through this upper-level capacity optimization model, and the values ​​of the decision variables are obtained;

[0142] Input the value of the decision variable into the lower-level scheduling optimization model to obtain the lower-level optimization results;

[0143] The lower-level optimization result is returned to the upper-level capacity optimization model for iterative optimization until the optimal planning result of the integrated energy system is obtained.

[0144] In summary, the technical solution provided in this embodiment can include the following beneficial effects:

[0145] Enhanced System Adaptability: Most existing technologies only optimize stand-alone or grid-connected microgrids individually, while this invention, in conjunction with distribution network voltage regulation ancillary service policy requirements, proposes a two-layer optimization planning and design model applicable to both grid-connected and stand-alone microgrid systems. This design not only meets the needs of different types of microgrids but also better adapts to grid-side voltage regulation ancillary service strategies, making the system more flexible and efficient.

[0146] The optimization results are more practical: Existing technologies rarely consider the optimization of distributed power source types in microgrid systems, while the optimization results of this invention can be directly applied to practical applications such as equipment selection. This optimization method, which is directly geared towards practical applications, greatly improves the practicality and economy of the system.

[0147] More flexible scheduling strategies: In the optimization and scheduling of distributed power sources, existing technologies rarely consider the optimized operation between distributed power sources. However, this invention, by representing the strong coupling relationship between the capacities of each device, can adjust the optimization strategy according to the actual situation, giving more consideration to economic performance or environmental performance to meet different engineering needs.

[0148] More Efficient Voltage Regulation: Addressing the issue that existing technologies rarely consider the impact of grid-side voltage regulation ancillary service strategies on the dynamic economic dispatch of the system, this invention utilizes the remaining capacity of grid-connected converters in distributed photovoltaic systems for voltage regulation. This not only efficiently utilizes the limited capacity of the equipment but also allows the planned entities to obtain benefits from voltage regulation ancillary services. Simultaneously, by reusing power electronic devices, it reduces significant investment in reactive power equipment for addressing voltage exceedance issues, thereby improving grid voltage quality.

[0149] The planning objectives are more comprehensive: Existing technologies often only consider economic efficiency as a planning objective, while this invention weights economic efficiency and environmental protection, and uses computational sensitivity to more accurately balance multi-objective programming problems. This comprehensive approach to planning objectives makes system planning more scientific and rational, and meets the requirements of sustainable development.

[0150] More efficient resource utilization: Existing technologies rarely consider the actual operational needs of wastewater treatment plants in conjunction with microgrid operation. This invention, however, utilizes biogas obtained from the fermentation of wastewater sludge as an input energy source for combined heat and power (CHP), which not only improves resource utilization efficiency but also reduces electricity purchase costs and indirect carbon emissions. This innovative resource utilization method provides a new approach to the sustainable development of wastewater treatment plants.

[0151] Therefore, this invention demonstrates significant advantages in the field of microgrid system planning and design. It not only possesses stronger system adaptability, more practical optimization results, more flexible scheduling strategies, more efficient voltage regulation methods, and more comprehensive planning objectives, but also achieves more efficient resource utilization. These advantages make this invention more competitive in practical applications and better able to meet the needs of power systems.

[0152] This embodiment also provides a planning device for an integrated energy system of a wastewater treatment plant. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs 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.

[0153] This embodiment provides a planning device for an integrated energy system of a wastewater treatment plant, such as... Figure 3 As shown, it includes:

[0154] The upper-level objective function acquisition module 301 is used to define the economic present value within the project cycle as the first upper-level objective function and carbon emissions as the second upper-level objective function.

[0155] The upper-level constraint condition acquisition module 302 is used to determine the first constraint condition of the first upper-level objective function and the second constraint condition of the second upper-level objective function;

[0156] The upper-level capacity optimization model acquisition module 303 is used to perform multi-objective integration on the first upper-level objective function and the second upper-level objective function based on the first constraint and the second constraint, and generate an upper-level capacity optimization model.

[0157] The lower-level objective function acquisition module 304 is used to define the daily operating cost of the system as the first lower-level objective function and determine the third constraint condition of the first lower-level objective function;

[0158] The lower-level scheduling optimization model acquisition module 305 is used to generate a lower-level scheduling optimization model based on the first lower-level objective function and the third constraint condition.

[0159] The optimal planning result acquisition module 306 is used to perform iterative optimization processing on the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning result of the integrated energy system.

[0160] In some optional implementations, the upper-level constraint acquisition module 302 is further configured to:

[0161] With the goal of minimizing the total life-cycle cost of the integrated energy system, and based on the investment cost, depreciation cost, and annual operating cost of each energy device, the economic present value within the project period is defined as the first upper-level objective function.

[0162] With the goal of minimizing the annual emissions of the integrated energy system, carbon emissions are defined as the second upper-level objective function based on the annual electricity purchase and gas emission coefficient.

[0163] In some alternative implementations, the first upper-level objective function is obtained using the following formula:

[0164] f1 = C inv +C dep +C year ;

[0165] Where f1 represents the total life cycle cost, C inv C represents the investment cost of each energy device. dep C represents the equipment depreciation cost. year This indicates the annual operating cost.

[0166] In some alternative implementations, the second upper-level objective function is obtained using the following formula:

[0167]

[0168] Where f2 represents the emission level for that year, w day This represents the average daily electricity consumption. This represents the carbon emission coefficient.

[0169] In some optional implementations, the upper-level constraint acquisition module 302 is further configured to:

[0170] Obtain the first objective optimization interval of the first upper-level objective function and the second objective optimization interval of the second upper-level objective function;

[0171] Based on the first objective optimization interval, the first constraint condition of the first upper-level objective function is determined;

[0172] Based on the second objective optimization interval, the second constraint condition of the second upper-level objective function is determined.

[0173] In some optional implementations, the upper-level capacity optimization model is obtained using the following formula:

[0174]

[0175] Where F1 represents the upper-layer capacity optimization result, f 1,max f represents the first objective optimization interval. 2,min Let λ1 represent the sensitivity corresponding to the first upper-level objective function and λ2 represent the sensitivity corresponding to the second upper-level objective function.

[0176] In some optional implementations, the lower-level objective function acquisition module 304 is further configured to:

[0177] With the goal of minimizing the daily operating cost of the integrated energy system, and based on the daily electricity purchase and sale costs and the daily voltage regulation revenue, the daily operating cost of the system is defined as the first lower-level objective function.

[0178] Based on the constraints of electric power balance, thermal power balance, biogas cogeneration unit performance, energy storage battery performance, converter performance, photovoltaic power participation in voltage regulation, and wind turbine power generation performance, the third constraint condition of the first lower-level objective function is determined.

[0179] In some alternative implementations, the first lower-level objective function is obtained using the following formula:

[0180] min F2=C e +C t ;

[0181]

[0182] C t =w q,t ΔQ t +β(U t -U ref ) 2 ;

[0183] Where F2 represents the lower-level scheduling optimization result, C e C represents the electricity purchase and sale cost for that day. t This represents the daily voltage regulation profit, T represents the total time, and c represents the total time. grid_mic e represents the grid purchase price of electricity. grid_mic.t c represents the system's electricity purchase at time t. mic_grid This indicates the electricity price that the wastewater treatment plant sells to the grid, e mic_grid.t w represents the system's electricity sales at time t. q,t ΔQ represents the voltage regulation compensation electricity price at time t. t U represents the reactive power regulation of the inverter, β represents the cost factor for maintaining voltage stability, and U represents the reactive power regulation of the inverter. t U represents the actual voltage of the microgrid at time t. ref This indicates the reference value of the voltage amplitude at the microgrid connection point.

[0184] In some alternative implementations, the optimal planning result acquisition module 306 is further configured to:

[0185] The decision is made through this upper-level capacity optimization model, and the values ​​of the decision variables are obtained;

[0186] Input the value of the decision variable into the lower-level scheduling optimization model to obtain the lower-level optimization results;

[0187] The lower-level optimization result is returned to the upper-level capacity optimization model for iterative optimization until the optimal planning result of the integrated energy system is obtained.

[0188] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0189] This invention also provides a computer device having the above-described features. Figure 3 The diagram shows a planning device for an integrated energy system in a wastewater treatment plant.

[0190] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0191] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0192] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0193] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0194] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0195] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0196] 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 over 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, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0197] 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.

[0198] 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 method for planning an integrated energy system for a wastewater treatment plant, characterized in that, The method comprises: defining the economic present value in the project cycle as a first upper-level objective function and defining carbon emissions as a second upper-level objective function; determining first constraint conditions of the first upper-level objective function and second constraint conditions of the second upper-level objective function; based on the first constraint conditions and the second constraint conditions, performing multi-objective integration on the first upper-level objective function and the second upper-level objective function to generate an upper-level capacity optimization model; defining system daily operation cost as a first lower-level objective function and determining third constraint conditions of the first lower-level objective function; based on the first lower-level objective function and the third constraint conditions, generating a lower-level scheduling optimization model; performing iterative optimization processing on the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain optimal planning results of the comprehensive energy system; wherein the definition of the system daily operation cost as the first lower-level objective function and the determination of the third constraint conditions of the first lower-level objective function comprise: defining the system daily operation cost as the first lower-level objective function based on daily purchase and sale of electricity cost and daily pressure regulation income, with the lowest daily operation cost of the comprehensive energy system as the optimization target; determining the third constraint conditions of the first lower-level objective function based on electric power balance constraints, thermal power balance constraints, biogas combined heat and power device performance constraints, energy storage battery performance constraints, converter performance constraints, photovoltaic participation in pressure regulation power constraints, and wind turbine generator performance constraints; the first lower-level objective function is obtained through the following formula: ； ； ； wherein F2 represents the lower layer scheduling optimization result, C e represents the daily purchase and sale of electricity costs, C t represents the daily voltage regulation income, T represents the total time, represents the grid purchase price, represents the system purchase quantity at t time, represents the sewage plant to the grid electricity price, represents the system sale quantity at t time, represents the voltage regulation compensation price at t time, represents the inverter reactive power regulation amount, represents the cost coefficient for maintaining voltage stability, represents the actual voltage of the micro-grid at t time, represents the voltage amplitude reference value of the micro-grid access point.

2. The method of claim 1, wherein, the definition of the economic present value in the project cycle as the first upper-level objective function and the definition of carbon emissions as the second upper-level objective function comprise: defining the economic present value in the project cycle as the first upper-level objective function based on the investment cost, the equipment depreciation cost and the annual operation cost of each energy equipment, with the minimum life cycle cost of the comprehensive energy system as the optimization target; defining carbon emissions as the second upper-level objective function based on annual electricity purchase and gas emission coefficient, with the minimum annual emission level of the comprehensive energy system as the optimization target.

3. The method of claim 2, wherein, the first upper-level objective function is obtained through the following formula: ； wherein f1 represents the total life cycle cost, C inv represents the investment cost of each energy device, C dep represents the device depreciation cost, C year represents the annual operation cost.

4. The method of claim 2, wherein, the second upper-level objective function is obtained through the following formula: ； wherein f2 represents the annual emission level, w day represents the average daily electricity consumption, represents the carbon emission coefficient.

5. The method of claim 1, wherein, the determination of the first constraint conditions of the first upper-level objective function and the second constraint conditions of the second upper-level objective function comprises: obtaining a first target optimization interval of the first upper-level objective function and a second target optimization interval of the second upper-level objective function; determining the first constraint conditions of the first upper-level objective function based on the first target optimization interval; determining the second constraint conditions of the second upper-level objective function based on the second target optimization interval.

6. The method of claim 5, wherein, the upper-level capacity optimization model is obtained through the following formula: ； Wherein, F1 represents the upper layer capacity optimization result, f 1,max represents the first target optimization interval, f 2,min represents the second target optimization interval, λ1 represents the sensitivity corresponding to the first upper layer target function, and λ2 represents the sensitivity corresponding to the second upper layer target function.

7. The method according to any one of claims 1 to 6, characterized in that, the iterative optimization processing on the upper-level capacity optimization model and the lower-level scheduling optimization model to obtain the optimal planning results of the comprehensive energy system comprises: making a decision through the upper-level capacity optimization model and obtaining a decision variable value; inputting the decision variable value into the lower-level scheduling optimization model to obtain a lower-level optimization result; The lower layer optimization result is returned to the upper layer capacity optimization model for iterative optimization until an optimal planning result of the comprehensive energy system is obtained.

8. A planning device for an integrated energy system of a sewage plant, characterized by The device comprises: An upper layer objective function acquisition module is configured to define an economic present value in a project period as a first upper layer objective function and define carbon emission as a second upper layer objective function; An upper layer constraint condition acquisition module is configured to determine a first constraint condition of the first upper layer objective function and a second constraint condition of the second upper layer objective function; An upper layer capacity optimization model acquisition module is configured to perform multi-objective integration on the first upper layer objective function and the second upper layer objective function based on the first constraint condition and the second constraint condition to generate an upper layer capacity optimization model; A lower layer objective function acquisition module is configured to define a system daily operation cost as a first lower layer objective function and determine a third constraint condition of the first lower layer objective function; A lower layer scheduling optimization model acquisition module is configured to generate a lower layer scheduling optimization model based on the first lower layer objective function and the third constraint condition; An optimal planning result acquisition module is configured to perform iterative optimization processing on the upper layer capacity optimization model and the lower layer scheduling optimization model to obtain an optimal planning result of the comprehensive energy system; The lower layer objective function acquisition module is specifically configured to: Define the system daily operation cost as the first lower layer objective function based on daily purchase and sale of electricity cost and daily voltage regulation income with the lowest daily operation cost of the comprehensive energy system as an optimization target; Determine the third constraint condition of the first lower layer objective function based on an electric power balance constraint, a thermal power balance constraint, a biogas combined heat and power device performance constraint, an energy storage battery performance constraint, a converter performance constraint, a photovoltaic participation in voltage regulation power constraint and a wind turbine generator performance constraint; and Obtain the first lower layer objective function through the following formula: ； ； ； wherein F2 represents the lower layer scheduling optimization result, C e represents the daily purchase and sale of electricity costs, C t represents the daily voltage regulation income, T represents the total time, represents the grid purchase price, represents the system purchase quantity at time t, represents the sewage plant to the grid electricity price, represents the system sale quantity at time t, represents the voltage regulation compensation price at time t, represents the inverter reactive power regulation amount, represents the cost coefficient for maintaining voltage stability, represents the actual voltage of the micro-grid at time t, represents the voltage amplitude reference value of the micro-grid access point.

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