Optimization Method and System for the Permeability of Electrical-Water-Biogas EWB Renewable Energy

Abstract

Establish a unified demand behavior model for integrated water flow and energy flow, provide resources with sufficient flexibility to meet the balancing power required for renewable energy, and provide a method and system for improving the penetration rate of electricity-water-biogas (EWB) renewable energy by utilizing the flexibility on the demand side. 【Solution means】The method is to construct a model which is an RO model of LWSP coupling, incorporate the LWSP coupling model into EWB, construct a coordinated scheduling farm distribution network (FDN) framework, construct a coordinated scheduling FDN model including an objective function and constraint conditions based on the coordinated scheduling FDN framework, solve the coordinated scheduling FDN model using a stepwise linear algorithm, obtain the EWB renewable energy optimization parameters, and determine the EWB renewable energy penetration rate based on them.

Description

Technical Field

[0001] The present invention relates to the technical field of optimizing renewable energy, and particularly to a method and system for optimizing the permeability of electricity-water-biogas renewable energy.

Background Art

[0002] With the increasing concern about resource depletion and environmental crisis, cogeneration (CHP) and renewable energy (RES) have been widely introduced worldwide. Cogeneration operates in the form of heat flow, generates electrical energy, and recovers the waste heat of combustion, effectively enhancing energy efficiency. However, the power generation of cogeneration is essentially determined by its heat output, which is determined by the system demand. Due to the lack of operational flexibility, the strong coupling between different energy sectors (electricity, heat systems) often poses a serious obstacle to the adaptability of intermittent renewable energy in an energy system dominated by cogeneration.

[0003] Currently, since there is no technical solution for solving the permeability of renewable energy, it is a great challenge to operate an electric-thermal system (EHES) equipped with renewable energy and cogeneration devices to obtain resources with sufficient flexibility so as to provide the power balance required for integrating renewable energy.

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to provide a method and system for optimizing the permeability of electricity-water-biogas (EWB) renewable energy.

Means for Solving the Problems

[0005] To achieve the above object, the present invention provides an optimization method for the EWB renewable energy transmittance. This method includes: Constructing a livestock water - sewage pump (LWSP) combined model, which is a ranch operation (RO) model (step S1); Integrating the LWSP combined model into the EWB; Coordination Constructing a scheduling farm distribution network (FDN) framework (step S2); Based on the coordinated scheduling FDN framework, constructing a coordinated scheduling FDN model including an objective function and constraint conditions (step S3); Piece - wise linear -ization Using an algorithm to solve the coordinated scheduling FDN model to obtain EWB renewable energy optimization parameters (step S4); Determining the EWB renewable energy transmittance based on the EWB renewable energy optimization parameters (step S5).

[0006] Step S1 may include: Constructing livestock - related models including a livestock water model and a livestock biogas production model for LWSP combination; Constructing energy - related models including an energy production model and an energy consumption model for LWSP combination; Constructing related models for LWSP combination.

[0007] Step S4 may include: Coordination Converting the original MILP problem corresponding to the scheduling FDN model into a strongly convex LP problem (step S41); Performing a dual transformation on the strongly convex LP problem to convert it into a dual function (step S42); Piece - wise linear -ization Using an algorithm to solve the dual function to obtain EWB renewable energy optimization parameters (step S43).

[0008] The equation in step S42 can be selected as follows:

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[0009] Here,

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[0010] The formula in step S41 can also be selected as follows:

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[0011] Here,

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[0012] The present invention further provides an optimization system for the EWB renewable energy transmittance. This system includes an RO model construction module for constructing a model of the LWSP coupling, which is an RO model, and incorporating the model of the LWSP coupling into the EWB, Coordination an FDN framework for constructing the FDN frame of the scheduling Structure construction module, and the above Coordination a cooperative scheduling FDN model construction module for constructing a cooperative scheduling FDN model including an objective function and constraint conditions based on the scheduling FDN framework, and piecewise linear -izationA solution module that solves the collaborative scheduling FDN model using an algorithm to obtain EWB renewable energy optimization parameters; A transmittance calculation module that determines the EWB renewable energy transmittance based on the EWB renewable energy optimization parameters.

[0013] The RO model construction module comprises first model construction means for constructing a livestock-related model including a livestock water use model and a livestock biogas production model in LWSP combination; second model construction means for constructing an energy-related model including an energy production model and an energy consumption model in LWSP combination; and third model construction means for constructing a related model in LWSP combination, and this is selectable.

[0014] The solution module Joule comprises problem conversion means for converting the original MILP problem corresponding to the collaborative scheduling FDN model into a strongly convex LP problem; dual conversion means for performing a dual conversion on the strongly convex LP problem to convert it into a dual function; The stepwise linear -ization parameter solution means for obtaining EWB renewable energy optimization parameters by solving the dual function using an algorithm, and this is selectable.

[0015] It is selectable that the formula for performing a dual conversion on the strongly convex LP problem to convert it into a dual function is as follows:

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[0016] Here,

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[0017] The equation for converting the corresponding original MILP problem of the collaborative scheduling FDN model into a strongly convex LP problem can be selected as follows:

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[0018] Here,[[]]END]]

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Advantages of the Invention

[0019] The present invention establishes a unified demand behavior model for the fusion water flow and energy flow, provides a resource with sufficient flexibility to meet the balancing power required for renewable energy, and utilizes the flexibility on the demand side to improve the penetration rate of EWB renewable energy. Further, it is verified by the solved penetration rate of renewable energy that this model increases the consumption of renewable energy. The method of the present invention is not limited to ranches and can also be applied to farms.

[0020] To more clearly explain the embodiments of the present invention, the drawings are briefly introduced, but these are only some embodiments of the present invention.

Brief Description of the Drawings

[0021]

Figure 1

Figure 2

Figure 3

Modes for Carrying Out the Invention

[0022] Hereinafter, in combination with the drawings of the present invention, the technical means in the embodiments of the present invention will be clearly and completely described. 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 labor belong to the protection scope of the present invention.

[0023] This method can be applied not only to pastures but also to farms. In the following embodiments, all are described by taking pastures as examples. However, the embodiments applicable to farms are similar to them, so no further description will be repeated.

Embodiment

[0024] As shown in FIG. 1, the method for optimizing the EWB renewable energy transmittance is Constructing a model that is an RO model of LWSP coupling (step S1), Integrating the LWSP-coupled model into the EWB, Coordination Constructing a scheduling FDN framework (step S2), The above Coordination Based on the scheduling FDN framework, constructing a cooperative scheduling FDN model including an objective function and constraint conditions (step S3), Stepwise linear -ization Using an algorithm to solve the cooperative scheduling FDN model to obtain EWB renewable energy optimization parameters (step S4), Determining the EWB renewable energy transmittance based on the EWB renewable energy optimization parameters (step S5).

[0025] The present invention utilizes the operational flexibility of RO to cooperate with the application of integrated EWB in the context of renewable energy. The specific collaboration framework is shown in Figure 2. The system considered in the present invention is composed of a power subsystem, a thermal grid subsystem, and a plurality of geographically dispersed pastures, and these pastures are connected to the above two subsystems as multi-energy demands.

[0026] In this configuration, a part of the power and heat of EWB is coupled by a cogeneration device arranged on the power generation side, and RESs are input from the power subsystem. The pastures are distributed to different nodes of the normal EWB as comprehensive energy consumers and biogas producers, and are interconnected via a road network, and are involved in the generation, conversion, transportation, storage, and consumption of various energy carriers.

[0027] Generally, each pasture is composed of three types of infrastructure. That is, gas production and storage facilities, energy conversion and recycling facilities, and resource transportation facilities. The livestock breeding facilities are mainly used to complete the daily production tasks of the pasture. The energy conversion and recycling facilities utilize DERs to generate the electrical / thermal power required for the operation of the pasture, ensuring the reliable operation of a single pasture. In addition, the resource transportation facilities move the fermenters harvested in different pastures and redistribute them between the transport vehicles (such as electric trucks) and the road network, realizing the efficient utilization of the fermenters.

[0028] During the operation period, the ranch depends on on-site power generation units and energy procurement to meet the load demand. By using energy conversion and recycling facilities to manage energy in a coordinated manner among different supply systems, the ranch can be made into a flexible energy user, and the demand model for EWB can be adjusted. In addition, gas production and storage facilities can also be scheduled flexibly, which affects the energy consumption of the ranch and the corresponding DER production volume. Moreover, the resource transportation system allows for strategic control of DER sharing among ranches and the charging demand of electric trucks (ETVs), further enhancing the potential of the operation effect. Therefore, the ranch is expected to provide substantial flexible support, and its revenue can be related to the energy flow and air flow.

[0029] Step S1 Construct a model of the LWSP combination. Specifically, it comprises the following steps.

[0030] Step S11 Construct a livestock-related model of the LWSP combination, which includes a livestock water model and a livestock production model.

[0031] When describing the livestock water model based on the relationship between the transportation volumes of the fermenters, the formula is as follows.

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[0032] Here,

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[0033] The selected manure is stored in the fermenter until it is exported to the biogas market. The remaining fermenters are transported to the fermenter warehouse and wait for recovery by the local energy system or are moved to other pastures via the transportation system for further utilization.

[0034] Based on the limiting conditions of the virtual storage fermenter / fermenter silo operation, a livestock biogas production model is described, and the formula is as follows.

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[0035] Equations (2a) and (2b) define the time evolution of the gas storage state (SOCS) in CCB and GS considering the intake and exhaust flow rates in the biogas production schedule, among which,

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[0036] Equations (3a) and (3b) limit SOCS based on the associated capacity of CCB / GS. Among them,

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[0037] Equations (4a) and (4b) require that the end - value SOCS during the research period of CCB / GS be equal to its initial value so as to ensure the sustainable operation of the medium - sized pasture. Among them, t 0 represents the initial time, and t N represents the end time.

[0038] Equation (5) ensures that the total export volume of CCB meets the demand of the biogas market

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[0039] Equations (6) and (7) limit the mass flow rates at the inlet and outlet of the fermentation tank silo to within the allowable range. Among them,

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[0040] Step S12 Construct an energy-related model that includes an energy production model and an energy consumption model for LWSP coupling, and couple the gas extraction-related system and the transportation-related system through the BC and ETV charging station (ECS) infrastructure.

[0041] "Energy production" In the present invention, the related energy production facilities include fermentation tank power generation (CFU) and water source heat pump (WSHP). Specifically, the CFU unit generates electricity in the fermentation tank. The WSHP unit utilizes the waste heat of underground spring water to produce high-quality thermal power with low power consumption. Low-quality shale gas extracted from the ranch is used to generate thermal energy. The energy input-output relationship of CFU and WSHP can be expressed as follows by the generalized energy hub model (i.e., the energy production model).

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[0042] Equation (9) is the expansion of (8),

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[0043] Also, the energy output of GFPG / WSHP is limited by its capacity as shown in Equation (10).

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[0044] Actually, the available gas volume

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[0045] "Energy consumption" Belt conveyor (BC) In the pasture, the BC transports the manure produced by consuming electricity from the working surface to the ground. The power consumption of the belt conveyor (i.e., the energy consumption model) may be represented by a broad biogas transportation model. This model is related to the gas extraction and feeding speed.

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[0046] Here,[[]]

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[0047] By adding the following linear constraints to the equation, the linear approximation value of (11) can be obtained.

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[0048] During the operation process, by adjusting the air intake and feeding speed

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[0049] "ETV Charging Stand (ECS)" The introduction of ECS aims to provide charging services for the ETV fleet for DER migration. During operation, the ranch flexibly adjusts the switch state and charging rate of ECS according to the real-time system situation and DER transportation requirements, and an ETV charging load curve is formed. Therefore, the scheduling of ECS brings time flexibility to the ranch's power demand, as shown in (14)-(18).

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[0050] To ensure the safety of the charging process, Equation (14) sets a limit on the charging power of ECS to prevent the charging power of the battery from exceeding the rated capacity. Here,

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[0051] Equation (15) calculates the demand for the ETV charging energy affected by the departure scheduling of DER transportation, and this demand is affected by the vehicle departure scheduling of DER transportation.

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[0052] Equation (16) represents the time evolution of the SOC for each ETV

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[0053] Equation (17) is the supervision and management limit of the SOC of the ETV

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[0054] "Normal power / thermal power demand" The total power / heat consumption (including normal and regulated load demands) is restricted by the power balance of each ranch and is shown as follows.

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[0055] Here,

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[0056] Step S13 Construct a related model of LWSP combination.

[0057] Part of the DER (e.g., fermentation tank) generated during gas extraction is transferred and dispersed to other geographical locations via ETV and can be further used for power / heat production through an energy recovery device. Since the DER transfer energy flow is linked to transportation scheduling, the operation management of ETV provides spatial flexibility to the energy demand of LWSP. As shown in formulas (21) to (25).

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[0058] Formula (21) defines the operating state of the ETV vehicle fleet and confirms that each ETV can only be in one of four states (departure, charging, moving, standby) at the same time. Among them,

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[0059] Equation (22) requires that the total number of ETVs in the state of charge at ranch k does not exceed the number of available charging facilities of the ECS [Number] for this requirement. [Number] represents the index of the electric truck at ranch k.

[0060] The logical constraints for the transition between the departure state and the in-motion state of the ETV are shown in (23)-(24). These are integer constraints established based on vehicle operation logic to ensure the closed-loop operation of the vehicle. For example, the ETV departs from the affiliated ranch every time it transports, transports the DER to the designated ranch location, and finally returns to the original ranch which is the destination. Specifically, Equation (23) indicates that when the ETV departs at time t and heads towards route [Number] it will be in the in-motion state within the next [Number] time period. Equation (24) indicates that if an ETV is neither in the in-motion state nor in the departure state, due to the effect of the delay time, it cannot enter the in-motion state within the subsequent time period. Among them, [Number] represents the in-motion state variable of the electric truck.

[0061] Equation (25) associates the starting state with the loading capacity limit of a single ETV unit. Among them,

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[0062] Step S2 As shown in Figure 3, incorporate the LWSP coupling model into the EWB, Coordination and construct the FDN framework for scheduling.

[0063] 「Problem description」 To incorporate the potential of flexibility, an integrated operation framework of DR and optimization coordination was constructed in the LWSP cooperation management.

[0064] This framework is designed to separate the devices, and the infrastructure therein includes the power and heat subsystems. The power and heat subsystems are operated by a single independent system operator (ISO), and the infrastructure is managed by the ranch operator (ranch).

[0065] During operation, the ISO operates the optimal thermoelectric current model to determine scheduling in the integrated energy market. The ISO responsible for power / heat supply can improve performance and RES availability by leveraging the flexibility of energy operations. As compensation, the farm meets the demand for energy procurement with adjusted energy and biogas flow rates according to the ISO's system requirements. In practice, there are conflicts of interest between the ISO and the farm, so the interaction during operation forms a hierarchical decision-making process, and the goals of the two entities are sequentially optimized in a solution set that depends on each other. Also, as mentioned above, the introduction of demand flexibility has brought uncertainty to the operation of the system. Therefore, it is necessary to resolve the impact of uncertainty and effectively integrate the proposed method based on the framework.

[0066] Based on the above considerations, the present invention proposes a two-layer stochastic planning scenario (FDN) considering the uncertainty in the ISO-farm interaction and decision-making to create a coordinated scheduling problem.

[0067] "Overview of the FDN Framework" Figure 3 shows the structure of the proposed FDN framework for coordinated scheduling, and shows the decision-making procedure by stakeholders in the model in a time chart. As shown in Figure 3, the upper-level model represents the cost-minimization decision-making when the ISO executes with flexible energy demand. Due to the existence of uncertainty factors, the problem faced by the ISO corresponds to a two-stage stochastic program. To offset the potential risks caused by uncertainty, the problem faced by the ISO is expressed as a two-stage stochastic program with a risk constraint method using conditional value at risk (CVaR).

[0068] First, the OEF calculation implemented by the ISO determines the energy scheduling per hour of the system and the necessary load adjustment capacity required from the farm within the one-day planning scope. These determinations are made based on the system demand predicted by the farm and the previous-day prediction of renewable energy generation and operation restrictions. Due to the influence of the uncertainty of prediction errors, these errors are related to the RES output and DER production volume of the farm. The conditional value at risk (CVaR) is used to limit the variation of the expected profit and is also incorporated into risk aversion. It is denoted as step (1) in Figure 3.

[0069] Once the amount of electric energy required for the energy balance at each time period is known, the farm needs to optimally respond to the ISO requirements by controlling production and the operation of internal facilities, and provide flexibility with minimal supervision and management costs. This constitutes the sub-problem of the FDN model. The sub-decision-making is based on complete information regarding the prediction of DER availability at each farm, and the generation of RES is uncertain. It is shown in step (2) of Figure 3.

[0070] The response of the farm is composed of the actual energy regulation power supplied from each farm, which is returned to the upper-level ISO. The upper-level ISO finally makes a decision on the relief measures through real-time operation of the previous-day scheduling deviation and the system energy balance constraints. Within the real-time horizon, these decisions are obtained based on complete information regarding RES generation and demand. It is shown in step (3) in Figure 3.

[0071] In the actual scenario, equations (1) to (25) are realized by a distributed optimization method and perform an iterative solution method. The final calculation result gives the optimal plan for scheduling. This model takes into account the progressive uncertainty in the hierarchical decision-making structure based on multi-agent and has the following advantages.

[0072] The present invention provides a practical framework for the ISO and manages operation decisions in cooperation with farms in a distributed manner. Thereby, when the interests of different stakeholders conflict, the purpose of increasing the penetration rate of renewable energy can be achieved.

[0073] By introducing risk avoidance measures based on random planning and CVaR, the ISO can perform strategic management to adjust the uncertainty of potential and its impact on operation, and provide executable solutions to meet practical needs.

[0074] Since the supervisor's decision variables are only applied to the sub-problems of the ISO and the ranch, the information to be exchanged between the two entities in decision-making is limited. Therefore, the model is given privacy protection characteristics.

[0075] "Determination of Uncertainty" Similar to the conventional random planning, the realization of the uncertain parameters in this FDN problem is represented by using a set of limited discrete scenarios. In the present invention, first, the Monte Carlo simulation with a roulette mechanism is used to generate a set of possible scenarios according to the relevant PDFs of the uncertainties. Then, the technique of shortening the look-ahead scenarios is applied to reduce the size of the scenario set and relieve the computational burden of the model.

[0076] By the above process, each random scene s represents a vector in which the upper-level uncertain variable (wind power generation) is realized, and there are different wind power generation values for different scenes, and its occurrence probability is

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[0077] Step S3 CoordinationConstruct a collaborative scheduling FDN model that includes an objective function and constraints based on the FDN framework of scheduling.

[0078] The objective function is as follows.

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[0079] Here,

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[0080] The constraint conditions are as follows.

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[0081] Here,

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[0082] This model includes the upper-level problems (26)-(40) and a set of lower-level problems (41)-(42), and each problem is related to the available DER scenarios of the ranch

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[0083] The upper objective function (26) is minimized in the ISO's decision-making set and consists of three terms. The first term represents the total expected cost of the ISO operating in the electricity and heat market on a certain day. This is obtained from the sum of the power generation costs of the CG / CHP units, the penalty for renewable energy reduction, and the demand response cost. Due to the uncertainties in the renewable energy production volume and DER production volume, there may be potential imbalances between real-time energy supply and demand during the operation period. Therefore, the second term represents the cost for the system to obtain the necessary balancing power in the real-time market and correct this energy deviation when considering the regulation of both electricity and heat quantity to realize scenario s. The last term represents multiplying by the weight coefficient

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[0084] ​The upper-level optimization is restricted by the operation constraints (27)-(30) of components, the power / heat supply system constraints (31)-(38), and the CVaR constraint (41). Equations (27)-(29) represent the renewable energy generation based on the predicted capacity factor and its feasible operation region, and the output limits of the power generation units and cogeneration units. Also, as in (30), the power / heat energy to be procured should not exceed the capacity limit of the farm. Equations (31) and (32) represent the power and thermal power balance of the system respectively. Equation (33) limits the transmission capacity per transmission line. Equation (34) limits the voltage amplitude and angle of the node within its allowable pipeline capacitance value range. Equation (35) represents the relationship between the heat output and mass flow rate of the corresponding DH node. According to the law of conservation of energy, when multiple mass flows converge, the mixing temperature at the convergence point can be calculated from Equation (36). Equation (37) simulates the heat transfer characteristics of the thermal grid. Due to heat loss in the pipeline, the temperature decreases exponentially according to the mass flow rate. Also, to ensure the reliable operation of the thermal system, Equation (38) limits the pipeline temperature of the return water network. Equation (39) is the constraint limit of CVaR. Also, the actual energy control adjustment of the farm in scenario s [Number] and [Number] is given by Equation (40) and is equal to the expected value of the actual energy control amount of the farm in scenario [Number] .

[0085] The lower-level objective function (41) minimizes the operation adjustment cost of the farm by implementing participation and demand response. These three terms are respectively the cost of procuring energy [Number] and [Number] It represents the carbon tax paid for the power generation emissions at the ranch site and the penalty cost for DER disposal. In reality, the DER disposal penalty cost may not be the actual cost that the ISO has to pay, but rather a way to artificially simulate promoting the use of DER in the collaborative operation.

[0086] The sub - problems are solved within the decision - making set of the ranch and are restricted to the model created in Section 2. Also, to ensure the efficient operation of the LWSP, the actual values

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[0087] The above - mentioned collaborative scheduling FDN model can transform the problem to be solved into a mixed - integer linear programming (MILP) problem and solve it using a centralized algorithm. However, the ISO and the ranch do not want to share

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[0088]

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[0089] This variable

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[0090] Here,

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[0091] Step S4 Solve the coordinated scheduling FDN model using a piecewise linearization algorithm to obtain the transmittance of EWB renewable energy. Specifically, it comprises the following steps.

[0092] Step S41 Convert the original MILP problem corresponding to the coordinated scheduling FDN model into a strongly convex LP problem. The specific formula is as follows.

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[0093] Here,

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[0094] Step S42 Perform a dual transformation on the strongly convex LP problem to convert it into a dual function. The specific formula is as follows.

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[0095] Here,

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[0096] Update the pasture decision variable according to Equation (49).

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[0097] Here,

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[0098] Update the ISO decision variable according to Equation (50).

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[0099] Here,

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[0100] Update the dual variable according to Equation (51).

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[0101] Update the primal residual and dual residual according to Equations (52)-(53).

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[0102] The main idea of AOP is,

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[0103] Step S431 Initialize the parameters,

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[0104] Step S432 Determine whether the convergence condition is satisfied. If the convergence condition is satisfied, the cooperative scheduling FDN model converges, and the EWB renewable energy optimization parameters of the last iteration output are output. If the convergence condition is not satisfied, it indicates that the cooperative scheduling FDN model has not converged,

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[0105] The EWB renewable energy optimization parameters of the last iteration output are the

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[0106] The convergence condition updates the primal residual and the dual residual according to equations (52)-(53), and determines that the primal problem convergence accuracy

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[0107] For all the above equations, please refer to the following explanations for specific sets or indexes (it is not specified one by one in the text).

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[0108] The present invention provides an integrated framework for maximizing the flexibility of RO operation and supporting the integration of EWB in the context of renewable energy. Therefore, a unified demand behavior model that first fuses the ranch flow and the energy flow is constructed. This model not only considers traditional load shifting but also clearly captures the potential of spatial demand regulation caused by geographically distributed renewable energy (DER) in the transportation system. LWSP can comprehensively consider control solutions in multiple fields such as gas extraction task scheduling, spatial regulation, and multi-energy management, and has high operational flexibility to fully explore its potential in EWB applications. Incorporating the flexible demand of LWSP into the operation of EWB will cause a problem of double optimization. That is, the system operator needs to determine the optimal energy schedule to balance the power demand, and the ranch determines the demand response of LWSP based on the system demand. In this paper, such a hierarchical decision-making problem is described as a flexible double-time optimization problem (FDN). This model comprehensively considers the impact of uncertainty factors from both EWB (i.e., the availability of renewable energy) and LWSP (i.e., the demand compliance of the ranch) on the decision-making of the ISO. In addition, a risk-limiting strategy based on the concept of conditional value at risk (CVaR) is adopted to enhance the robustness of the -EHES scheduling. To effectively solve the obtained FDN model, a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) is introduced to achieve rapid market clearing on the premise of protecting the privacy of different stakeholders. The present invention improves the penetration rate of renewable energy by utilizing the flexibility on the demand side and further improves the accommodation of renewable energy.

Example

[0109] An optimization system for the EWB renewable energy penetration rate, comprising an RO model construction module for constructing an LWSP coupling model, An FDN framework construction module that incorporates the LWSP combination model into EWB and constructs an FDN framework for coordinated scheduling, and A coordinated scheduling FDN model construction module that constructs a coordinated scheduling FDN model including an objective function and constraint conditions based on the FDN framework for coordinated scheduling, and Stepwise linear -ization A solution module that solves the coordinated scheduling FDN model using an algorithm to obtain EWB renewable energy optimization parameters, and A transmittance calculation module that determines the EWB renewable energy transmittance based on the EWB renewable energy optimization parameters, and a system comprising the same.

[0110] The RO model construction module includes A first model construction means for constructing a livestock-related model including a livestock water model and a livestock biogas production model combined with LWSP, and A second model construction means for constructing an energy-related model including an energy production model and an energy consumption model combined with LWSP, and A third model construction means for constructing an LWSP-combined related model, and comprises the same.

[0111] The solution module includes A problem conversion means for converting the original MILP problem corresponding to the coordinated scheduling FDN model into a strongly convex LP problem, and A dual conversion means for performing a dual conversion on the strongly convex LP problem and converting it into a dual function, and Stepwise linear -ization A parameter solution means for obtaining EWB renewable energy optimization parameters by solving the dual function using an algorithm, and comprises the same.

[0112] For parts similar to Example 1, please refer to Example 1. The description here is omitted.

[0113] Each embodiment in this specification is described in a step-by-step procedure. In each embodiment, the differences from other embodiments are mainly described. For the same or similar parts of each embodiment, reference may be made to each other. The system disclosed in the embodiment corresponds to the method disclosed in the embodiment. For related information, reference may be made to the part of the method description.

Claims

1. 1. An electricity-water-biogas renewable energy transmission optimization system, comprising: a farm operation model construction module for constructing a model, the model being a farm operation model of a livestock water-sewage pump combination; a farm grid framework construction module for incorporating the livestock water-sewage pump coupling model into an electricity-water-biogas model to construct a farm grid framework for cooperative scheduling; A cooperative scheduling farm power grid model construction module for constructing a cooperative scheduling farm power grid model including an objective function and a constraint condition based on the cooperative scheduling farm power grid framework; a solving module for solving the cooperatively scheduled farm grid model by minimizing the objective function under the constraints to obtain optimized parameters for electricity-water-biogas renewable energy; and a transmittance calculation module for determining an electricity-water-biogas renewable energy transmittance based on the optimization parameters of the electricity-water-biogas renewable energy; A system, wherein the objective function is a function representing the operating cost of the system, and the constraints consist of operating constraints of components of the system, power / heat supply system constraints, and condition risk value constraints.

2. The farm operation model construction module includes: A first model construction means for constructing a livestock-related model including a livestock water model and a livestock biogas production model for a livestock water-sewage pump combination; A second model construction means for constructing an energy-related model including an energy production model and an energy consumption model of the livestock water-sewage pump combination; and a third model building means for building a relational model of the livestock water-sewage pump combination.

3. The solution module: A problem conversion means for converting a primitive MILP problem corresponding to the farm distribution network model of the cooperative scheduling into a strongly convex LP problem; a duality transformation means for transforming the strongly convex LP problem into a dual function; and a parameter solver for solving the dual function using a stepwise linearization algorithm to obtain electricity-water-biogas renewable energy optimization parameters.

4. The equation for converting the strongly convex LP problem into a dual function is as follows: [0018] Where: [0019] is the dual function to which the ISO cost function corresponds, [0020] is the ISO cost function, λt is the dual variable, ##EQU00021## is the ISO judgment variable, [0022] is the coupling variable the farm is looking for, [0023] is the coupling variable required by ISO, [0024] is the penalty parameter of the ADMM, [0025] is the dual function of the farm cost function, [0026] is the farm cost function, [0027] is the farm determination variable, [0028] 4. The system of claim 3, wherein: t is a dual variable and T is a time period index.

5. The formula for converting the primitive MILP problem corresponding to the ranch distribution network model of the cooperative scheduling into a strongly convex LP problem is as follows: [0029] Where: [0030] is the ISO cost function, [0031] is the farm cost function, [0032] is the ISO judgment variable, [Equation 33] is the farm determination variable, [0034] The system of claim 3 , wherein: is an index of farms.

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