Multi-energy complementary based zero-carbon park load collaborative scheduling method and system

Abstract

The application discloses a zero-carbon park load collaborative scheduling method and system based on multi-energy complementarity, which is applied to a park energy system containing new energy supply units, grid interaction units and the like, and the method comprises the following steps: acquiring and processing park operation data to generate scheduling parameters; constructing an electric-thermal-cold multi-energy flow coupling collaborative scheduling model; adding zero-carbon constraints such as carbon budget to form an optimization problem; and solving to obtain scheduling decisions and executing the scheduling decisions. Through multi-energy flow joint optimization, zero-carbon constraint control and online correction, the application realizes new energy preferential consumption, reduces abandoned wind and light and operation cost, and guarantees stable carbon emission to meet the standard.

Description

Technical Field

[0001] This invention relates to the field of energy management and optimization scheduling technology for integrated energy systems in industrial parks, and particularly to a method and system for load coordination scheduling in zero-carbon industrial parks based on multi-energy complementarity. Background Technology

[0002] As the installed capacity of new energy sources such as photovoltaics and wind power increases in industrial parks, commercial parks, and zero-carbon parks, the energy supply of these parks exhibits significant volatility and uncertainty. Meanwhile, the park's load (electricity load, heating load, and cooling load) is highly time-varying and subject to multiple constraints, often leading to a mismatch between the two over time, resulting in the following problems: 1) The peak output of new energy sources is misaligned with the peak load, resulting in wind and solar power curtailment or forced purchase of electricity from the grid, which increases operating costs and brings indirect carbon emissions; 2) Existing industrial parks often schedule electricity, heat and cooling separately without explicitly considering the conversion coupling and cascade utilization between electricity, heat and cooling (e.g., electric heat pump heating, waste heat-driven absorption cooling, heat / cooling energy storage for peak shifting, etc.), resulting in low overall energy efficiency; 3) The lack of a unified constraint and online carbon accounting mechanism for the zero-carbon goal makes it difficult to achieve real-time control and stable compliance of carbon emissions under the fluctuation of new energy sources and load disturbances.

[0003] Therefore, there is an urgent need for a zero-carbon park collaborative scheduling method that can optimize the absorption of new energy under the conditions of multi-energy complementarity and multi-energy flow coupling, taking into account new energy forecasting, load flexibility, energy storage dynamics and time-varying carbon emission factors. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a reliable, flexible, and comprehensive zero-carbon park load coordination scheduling method and system based on multi-energy complementarity that can comprehensively consider supply and load.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows: A zero-carbon park load coordination scheduling method based on multi-energy complementarity is applied to a park energy system comprising a new energy supply unit, a grid interaction unit, an electricity-heat-cooling energy conversion unit, and at least one energy storage unit. The scheduling method includes: S1. Obtain and process the park's operational data within a preset scheduling period; S2. Based on the operational data, scheduling parameters are formed. The scheduling parameters are used to characterize the available supply capacity of new energy sources, the electricity-heat-cooling load demand of the park, and the grid interaction conditions within the scheduling cycle. S3. Construct a cooperative scheduling model to describe the coupling relationship of multiple energy flows such as electricity, heat and cold, wherein the cooperative scheduling model has at least one constraint condition characterizing the coupling characteristics of multiple energy flows; S4. Construct a collaborative scheduling optimization problem with zero-carbon constraints based on the aforementioned collaborative scheduling model; S5. Solve the collaborative scheduling optimization problem to determine the scheduling decision for each time period, and execute the collaborative scheduling of multi-energy flow loads in the park accordingly, so as to improve the consumption of new energy and reduce operating costs while meeting the zero-carbon constraint.

[0006] As one possible implementation, further, in this scheme S1, the operating data includes at least new energy output data, park electricity load data, heat load data, cold load data, energy storage unit status data, and external signal data related to the power grid; In S1, the external signal data includes at least the grid time-of-use electricity price, demand-side response signal and / or grid time-varying carbon emission factor; In S1, the data processing includes at least time alignment and anomaly handling.

[0007] As a preferred implementation option, preferably, in this scheme S1, the data processing further includes performing missing data completion, noise reduction filtering and / or normalization processing on the running data to obtain a scheduling input set with uniform time granularity; In S1, the scheduling period is discretized into multiple time slots; In S2, the scheduling parameters generate corresponding new energy available output prediction sequences and electricity-heat-cooling load prediction sequences at the time slot granularity.

[0008] As a possible implementation, further, in this scheme S3, the collaborative scheduling model adopts an energy hub model to describe the multi-energy flow coupling relationship. The energy hub model includes the energy flow distribution relationship between new energy supply, grid interaction, energy storage charging and discharging and energy conversion. In S3, the constraints include at least the electric side energy balance constraints, the hot side energy balance constraints, and the cold side energy balance constraints, as well as the state update constraints of the energy storage unit and the performance constraints of the energy conversion unit.

[0009] As a preferred implementation option, the energy conversion unit of this solution preferably includes at least one of the following: heat pump, electric boiler, electric chiller, absorption chiller and / or waste heat recovery unit; the performance constraints at least include conversion efficiency constraints, performance coefficient constraints and / or upper and lower limits of output constraints. The energy storage unit includes at least one of the following: electrical energy storage, thermal energy storage, and / or cold energy storage; the state update constraints include the dynamic update relationship between the energy storage state of charge, thermal energy storage, and cold energy storage, as well as their upper and lower limit constraints.

[0010] As a possible implementation, further, in this scheme S4, the zero-carbon constraint is determined by carbon emission characterization parameters related to the power grid, and is used to limit the amount of carbon emissions or carbon intensity within the scheduling cycle to meet preset conditions.

[0011] The zero-carbon constraint includes any one or a combination of the following: a) Carbon budget constraint: Limiting the cumulative carbon emissions within the scheduling period to not exceed a preset carbon budget threshold; b) Net-zero carbon constraint: Limiting the cumulative carbon emissions during the scheduling period to no more than zero after deductions; c) Real-time carbon intensity constraint: Limiting the carbon intensity in at least some time slots to not exceed a preset upper limit for carbon intensity; The cumulative carbon emissions are calculated at least based on the product of the power purchased from the grid and the time-varying carbon emission factor of the grid, and may further include fuel consumption emissions, purchased steam emissions and / or green electricity deductions.

[0012] As a possible implementation, further, in this scheme S4, the optimization objective of the collaborative scheduling optimization problem includes at least one or a combination of the following: minimum operating cost, minimum wind and solar curtailment, minimum carbon emissions, minimum carbon intensity, and / or minimum load adjustment cost; In S5, the scheduling decision includes at least the grid power purchase / grid connection power, energy storage charging and discharging power, and the output allocation of the electric-heat-cold energy conversion unit.

[0013] As a preferred implementation option, preferably, in this scheme S5, the scheduling decision also includes the adjustment amount of the adjustable load of the park, wherein the adjustable load includes at least the peak-shifting load and / or the load that can be reduced; wherein the peak-shifting load satisfies the energy conservation constraint, and the load that can be reduced satisfies the reduction upper limit constraint; The adjustment amount of the adjustable load is determined based on preset comfort constraints and / or production capacity constraints, the comfort constraints including indoor temperature allowable range constraints and / or thermal inertia constraints; In S5, the priority strategy of the scheduling decision satisfies the following: first, new energy sources are used to meet the electrical load; second, energy storage is charged and / or the electric-heat-cold energy conversion unit is driven to form heat / cold supply and / or the thermal / cold energy storage is charged; and the remaining part is used for grid connection or for other convertible loads.

[0014] As a possible implementation, further, in this solution S5, solving the cooperative scheduling optimization problem includes: generating a day-ahead baseline scheduling plan, and using rolling time-domain optimization to perform online correction of the day-ahead baseline scheduling plan during execution; The rolling time-domain optimization includes: updating the energy storage unit state and load deviation in each rolling control cycle, reconstructing and solving the collaborative scheduling optimization problem, and outputting only the control quantity of the current time slot for execution.

[0015] As a possible implementation, the method described in this scheme further includes: a real-time carbon emission accounting and adaptive adjustment step: calculating the real-time carbon emission amount based on the real-time power purchase and the real-time time-varying carbon emission factor of the power grid; when it is detected that the carbon budget is about to be exhausted or the carbon intensity exceeds the limit, adjusting the objective function weight and / or constraint boundary in the collaborative scheduling optimization problem to suppress high-carbon power purchase and improve the consumption of new energy.

[0016] Based on the above, this solution also proposes a multi-energy complementary zero-carbon park load coordination and scheduling system, which includes: The data acquisition unit is used to acquire and process the park's operational data within a preset scheduling period. The scheduling parameter generation unit is used to generate scheduling parameters based on the operating data. The scheduling parameters are used to characterize the available supply capacity of new energy sources, the electricity-heat-cooling load demand of the park, and the grid interaction conditions during the scheduling cycle. The model building unit is used to build a cooperative scheduling model for describing the coupling relationship of multiple energy flows such as electricity, heat and cold, wherein the cooperative scheduling model has at least one constraint condition characterizing the coupling characteristics of multiple energy flows; The problem construction unit is used to construct a cooperative scheduling optimization problem with zero carbon constraints based on the cooperative scheduling model. The decision planning unit is used to solve the collaborative scheduling optimization problem to determine the scheduling decision for each time period, and accordingly execute the collaborative scheduling of multi-energy flow loads in the park, so as to improve the consumption of new energy and reduce operating costs while meeting the zero-carbon constraint.

[0017] Compared with the prior art, the present invention has the following advantages by adopting the above technical solution: This solution achieves priority consumption of new energy output in the path of direct load supply - driving energy conversion - energy storage charging - grid connection by unifying modeling and joint optimization of multi-energy flow coupling of electricity, heat and cold, thereby reducing wind and solar curtailment; In addition, this solution also achieves peak shifting and valley filling and cascade utilization of electricity, heat and cold loads through the synergy of load flexibility feasible domain and energy storage dynamic constraints, thereby improving comprehensive energy efficiency and reducing operating costs; This solution also constructs zero-carbon constraints such as carbon budget / net zero / carbon intensity by introducing time-varying carbon emission factors of the power grid, and combines online rolling correction and carbon accounting feedback to achieve real-time carbon emission control and stable achievement of zero-carbon goals. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a simplified implementation flowchart of the scheduling method in this scheme; Figure 2 This is a schematic diagram of the unit module connections of the scheduling system in this scheme. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figure 1 As shown in the figure, this embodiment proposes a zero-carbon park load coordination scheduling method based on multi-energy complementarity. It is applied to a park energy system comprising a new energy supply unit, a grid interaction unit, an electricity-heat-cooling energy conversion unit, and at least one energy storage unit. The scheduling method includes: S1. Obtain the park's operational data within a preset scheduling cycle and process the data. The operational data includes at least new energy output data, park electricity load data, heat load data, cold load data, energy storage unit status data, and external signal data related to the power grid. The data processing includes at least: performing missing data completion, anomaly removal processing, and time alignment on the operational data to obtain a scheduling input set with a unified time granularity. S2. Based on the operational data, scheduling parameters are formed. The scheduling parameters are used to characterize the available supply capacity of new energy sources, the electricity-heat-cooling load demand of the park, and the grid interaction conditions within the scheduling cycle. S3. Construct a cooperative scheduling model to describe the coupling relationship of multiple energy flows such as electricity, heat and cold. The cooperative scheduling model has at least one constraint condition characterizing the coupling characteristics of multiple energy flows. The constraint condition includes at least the energy balance constraint on the electric side, the energy balance constraint on the heat side, and the energy balance constraint on the cold side, and includes the state update constraint of the energy storage unit and the performance constraint of the energy conversion unit. S4. Construct a collaborative scheduling optimization problem with zero carbon constraints based on the aforementioned collaborative scheduling model; the zero carbon constraints are determined by carbon emission characterization parameters related to the power grid, and are used to limit the amount of carbon emissions or carbon intensity within the scheduling cycle to meet preset conditions; wherein, the zero carbon constraints include any one or a combination of the following: a) Carbon budget constraint: Limiting the cumulative carbon emissions within the scheduling period to not exceed a preset carbon budget threshold; b) Net-zero carbon constraint: Limiting the cumulative carbon emissions during the scheduling period to no more than zero after deductions; c) Real-time carbon intensity constraint: Limiting the carbon intensity in at least some time slots to not exceed a preset upper limit for carbon intensity; The cumulative carbon emissions are calculated at least based on the product of the grid power purchase and the grid time-varying carbon emission factor, and may further include fuel consumption emissions, purchased steam emissions and / or green electricity deductions. S5. Solve the collaborative scheduling optimization problem to determine the scheduling decision for each time period. The scheduling decision includes at least the grid power purchase / grid power, energy storage charging and discharging power, and output allocation of the electric-heat-cold energy conversion unit. Based on this, perform collaborative scheduling of multi-energy flow loads in the park to improve the absorption of new energy and reduce operating costs while meeting the zero-carbon constraint.

[0022] As one possible implementation example, further in this scheme S1, the external signal data includes at least the grid time-of-use electricity price, demand-side response signal, and / or grid time-varying carbon emission factor. Additionally, the data processing also includes denoising filtering and / or normalization processing of the operating data.

[0023] In S2, the scheduling parameters generate corresponding new energy available output prediction sequences and electricity-heat-cooling load prediction sequences at the time slot granularity, wherein the scheduling period is discretized into multiple time slots.

[0024] As a possible implementation, further, in this scheme S3, the collaborative scheduling model adopts an energy hub model to describe the multi-energy flow coupling relationship. The energy hub model includes the energy flow distribution relationship between new energy supply, grid interaction, energy storage charging and discharging and energy conversion. As a preferred implementation option, the energy conversion unit of this solution preferably includes at least one of the following: heat pump, electric boiler, electric chiller, absorption chiller and / or waste heat recovery unit; the performance constraints at least include conversion efficiency constraints, performance coefficient constraints and / or upper and lower limits of output constraints. The energy storage unit includes at least one of the following: electrical energy storage, thermal energy storage, and / or cold energy storage; the state update constraints include the dynamic update relationship between the energy storage state of charge, thermal energy storage, and cold energy storage, as well as their upper and lower limit constraints.

[0025] As a possible implementation, further, in this scheme S4, the optimization objective of the collaborative scheduling optimization problem includes at least one or a combination of the following: minimum operating cost, minimum wind and solar curtailment, minimum carbon emissions, minimum carbon intensity, and / or minimum load adjustment cost.

[0026] As a preferred implementation option, preferably, in this scheme S5, the scheduling decision also includes the adjustment amount of the adjustable load of the park, wherein the adjustable load includes at least the peak-shifting load and / or the load that can be reduced; wherein the peak-shifting load satisfies the energy conservation constraint, and the load that can be reduced satisfies the reduction upper limit constraint; The adjustment amount of the adjustable load is determined based on preset comfort constraints and / or production capacity constraints, the comfort constraints including indoor temperature allowable range constraints and / or thermal inertia constraints; In S5, the priority strategy of the scheduling decision satisfies the following: first, new energy sources are used to meet the electrical load; second, energy storage is charged and / or the electric-heat-cold energy conversion unit is driven to form heat / cold supply and / or the thermal / cold energy storage is charged; and the remaining part is used for grid connection or for other convertible loads.

[0027] As a possible implementation, further, in this solution S5, solving the cooperative scheduling optimization problem includes: generating a day-ahead baseline scheduling plan, and using rolling time-domain optimization to perform online correction of the day-ahead baseline scheduling plan during execution; The rolling time-domain optimization includes: updating the energy storage unit state and load deviation in each rolling control cycle, reconstructing and solving the collaborative scheduling optimization problem, and outputting only the control quantity of the current time slot for execution.

[0028] As a possible implementation, the method described in this scheme further includes: a real-time carbon emission accounting and adaptive adjustment step: calculating the real-time carbon emission amount based on the real-time power purchase and the real-time time-varying carbon emission factor of the power grid; when it is detected that the carbon budget is about to be exhausted or the carbon intensity exceeds the limit, adjusting the objective function weight and / or constraint boundary in the collaborative scheduling optimization problem to suppress high-carbon power purchase and improve the consumption of new energy.

[0029] As an example of implementation, the following example will be used to elaborate on this solution: In one embodiment, the park energy system includes at least: (1) New energy supply unit: at least one of photovoltaic power generation unit and wind power generation unit; (2) Power grid interaction unit: used for purchasing electricity and connecting to the grid; (3) Electric-heat-cold energy conversion unit: including at least one of heat pump, electric boiler, electric chiller, absorption chiller, and waste heat recovery unit; (4) Energy storage unit: at least one of electrical energy storage, thermal energy storage, and cold energy storage; (5) Load and execution end: including electrical load, heating load, cooling load, and adjustable load execution end (peak shifting, reduction or flexible control).

[0030] In this example, the scheduling period is discretized into T There are 1 time slot, and the length of each time slot is 1. The time slot index is .

[0031] Based on this, the solution includes the following steps: S1 Operational Data Acquisition and Status Estimation S1.1 Time Alignment and Equivalent Time Slotting In this step, when the collected data is a power sequence (Continuous time), to ensure energy consistency, it is mapped to time-slot equivalent power, defined as follows:

[0032]

[0033] in, For time slot index, The original power sequence, For slot equivalent power, This refers to time-slot energy.

[0034] S1.2 Missing and Exception Handling The power and load sequences are processed for missing data completion, anomaly removal, and filtering. Anomaly detection can be achieved by combining physical boundaries and statistical thresholds.

[0035] For example, the definition for judging photovoltaic power is as follows:

[0036] in, Time-slot photovoltaic power, Rated power of photovoltaic power; S1.3 Taking batteries as an example, energy storage SOC state estimation (1) Derivation of the SOC state equation Based on the battery's rated energy capacity Define SOC as the percentage of energy consumed. The charging energy within a time slot is: The discharge energy is: Then we can define the following formula:

[0037] in, In time Battery SOC, , These represent charging and discharging power, respectively. , These represent charging and discharging efficiencies, respectively. For battery capacity, This is the process disturbance term.

[0038] (2) Measurement equation and Kalman update If BMS estimated SOC measurement values ​​can be obtained Then it can be defined as follows:

[0039] in, To measure the noise, based on the aforementioned derivation of the SOC state equation, we can obtain initial values ​​for subsequent optimization. That is, the SOC estimate.

[0040] This step outputs a unified time-slot dataset and SOC estimates, which serve as the training input for S2 prediction and the initial state for S4 / S5 energy storage constraints.

[0041] S2 Multi-Time Domain Forecasting (New Energy Sources / Load / External Signals) S2.1 Photovoltaic power output prediction (physical mapping + correction) In one embodiment, based on irradiance The basic predictions obtained from the component efficiency model are defined as follows:

[0042] The efficiency temperature correction is defined as follows:

[0043] If error correction is used, let the error be... The first-order decay prediction is defined as follows:

[0044] The final prediction result is defined as follows:

[0045] in, , These are the basic photovoltaic forecast and the correction forecast, respectively. For effective area, Irradiance, For component efficiency, For component temperature, For efficiency reference, For temperature coefficient, For reference temperature, For error estimation, This is the attenuation coefficient.

[0046] S2.2 Wind Power Output Prediction (Power Curve Mapping) According to wind speed Based on the wind turbine power curve, define the following function:

[0047] It can be defined in a segmented form, which includes the following:

[0048] in, For wind power forecasting, To predict wind speed, , , These are the wind turbine cut-in, rated, and cut-out wind speeds, respectively. This refers to the rated power of the wind power.

[0049] S2.3 Load Forecasting (ARX Regression Derivation) Taking electrical load as an example, its formula is defined as follows:

[0050] The historical samples are written in matrix form, and their definition is as follows:

[0051] The least squares objective is defined as follows:

[0052] The normal equation is defined as follows:

[0053] If it is invertible, then it is defined as follows:

[0054] in, For electricity load forecasting, As a historical burden, As an exogenous characteristic, it can be determined by temperature and day type. , , To adjust the parameters, , For the order and characteristic number, For the target vector, For the characteristic matrix, For parameter vectors, This is the noise term.

[0055] Furthermore, by referring to the load forecast in this step, a heat load forecast can be obtained. and cooling load forecast .

[0056] Output of this step , , , , As the input of load demand for S3 flexible boundary baseline and S5 optimization.

[0057] S3 Adjustable Load Flexible Modeling and Feasibility Domain Generation S3.1 Peak-Shifting Load Model In one embodiment, based on the predicted baseline

[0058] Peak shifting satisfies the conservation of total energy, which is defined as follows:

[0059] And satisfy the boundary conditions:

[0060] in, To adjust the electrical load, For peak shifting amount, , These are the upper and lower limits for peak shifting.

[0061] S3.2 Load Reduction Model Introducing reduction Its definition is as follows:

[0062]

[0063] in, This is the maximum reduction limit. , These are the baseline load and the adjustment load, respectively.

[0064] S3.3 Discretization Derivation of Building Thermal Inertia (Comfort Constraints) A first-order RC model is adopted, which is defined as follows:

[0065] The forward Euler discretization is defined as follows:

[0066] The comfort zone is defined as follows:

[0067] in, , Indoor and outdoor temperatures, respectively. For equivalent thermal resistance, For equivalent heat capacity, The equivalent heat power for heating; For equivalent cooling power, , For coefficients, , These are the upper and lower limits for comfort, respectively.

[0068] This step outputs information about the adjusted loads for electricity, heat, and cooling. , , The feasible region and boundary will be used as the demand side term of the S4 balance constraint and will be entered into the S5 decision.

[0069] S4 Electric-Heat-Cold Coupled Energy Hub Model S4.1 Relationship between renewable energy consumption and curtailment Let the available renewable energy power be:

[0070] Introducing actual consumption With abandoned energy :

[0071] in, For usable new energy sources, For the curtailment of wind and solar power, For actual consumption.

[0072] S4.2 Electrical Side Balance Constraints

[0073] in, , For electricity purchase and grid connection power, , For battery charging and discharging power, For heat pump electrical input, For electric cooling, the electrical input is provided.

[0074] S4.3 Energy Conversion Performance Constraints Heat pump heating:

[0075] Electric refrigeration:

[0076] Absorption refrigeration (thermal drive):

[0077] in, , , These are heating capacity, heat pump coefficient of performance, and cooling capacity, respectively. , , , These are the coefficient of performance for electric cooling, the cooling capacity supplied by absorption cooling, the input heat power, and the coefficient of performance for absorption cooling.

[0078] S4.4 Hot / Cold Side Balance (Reflecting cascade utilization and energy storage for peak shifting) Hot side:

[0079] Cold side:

[0080] in, To provide heating for electric boilers, , These are heat storage charging / discharging, respectively. For waste heat recovery, To adjust the heat load, , These are respectively cold storage charging / discharging. To adjust the cooling load.

[0081] S4.5 Energy Storage Dynamic Constraints (Taking thermal storage as an example, the same applies to batteries)

[0082] It satisfies:

[0083] in, In a heat storage state, For heat storage capacity, , These represent the charge / discharge efficiency, respectively.

[0084] The electrical / thermal / cold balance and equipment / energy storage constraints formed in this step serve as the constraint set for the S5 optimization problem.

[0085] S5 Cooperative Scheduling Optimization Modeling and Solution with Zero-Carbon Constraints The goal of this step is to achieve the overall optimal balance of cost, energy curtailment, and carbon emissions while satisfying multi-energy flow balance, equipment safety, and comfort constraints, and to output an executable scheduling plan.

[0086] S5.1 Decision Variables and Objective Function Define the time slot scheduling decision set: power grid purchase / grid access, energy storage charging and discharging, input / output of each energy conversion unit, peak shifting / reduction amount, abandoned energy, etc.

[0087] Construct the comprehensive objective function: The operating cost item is defined as follows:

[0088] The energy forfeiture penalty term is defined as follows:

[0089] The demand response cost term is defined as follows:

[0090] The energy storage loss term is defined as follows:

[0091] The overall objective is defined as follows:

[0092] in, , These are the electricity purchase price and the grid connection price, respectively. , , , All are weighting coefficients.

[0093] S5.2 Carbon Emission Accounting Model and Zero Carbon Constraint Time-slot power purchase capacity is The time-varying carbon emission factor of the power grid is The indirect carbon emissions in the time slot are:

[0094] Cumulative carbon emissions over time:

[0095] in, Emissions can be from optional fuels or purchased steam.

[0096] Zero carbon constraints can take any of the following forms: (1) Carbon budget constraint:

[0097] (2) Net zero constraint:

[0098] (3) Real-time carbon intensity constraint:

[0099] in, The time-varying carbon factor; Electricity purchased for time slots; For cyclical emissions; This is the carbon budget threshold (which can be set to 0 to indicate strict zero carbon). For deduction amount; Carbon strength; For service energy (which can be defined as electric / heating / cooling); This represents the upper limit of carbon intensity.

[0100] S5.3 Constraint Sets and Solution Output Under the conditions of satisfying the feasible region constraint of adjustable load (S3) and the energy hub constraint (S4), the objective shown in equation (39) is solved and the zero-carbon constraint in the previous equation is satisfied, thereby obtaining the optimal scheduling plan for each time slot and the planning parameter set. D The definition is as follows:

[0101] Online rolling correction and closed-loop carbon feedback To enhance adaptability to prediction errors and disturbances, this invention optionally employs rolling time-domain optimization: in each rolling control cycle, the S1 data and initial SOC value are updated, S2 to S5 are re-executed, the current time slot control quantity is output and issued for execution; simultaneously, the actual carbon emissions are calculated online, and if the cumulative emissions are detected to be approaching the threshold or the carbon intensity exceeds the limit, the weight of energy curtailment penalty or the weight of high-carbon power purchase cost are increased, and / or the load adjustment boundary is tightened, thereby suppressing high-carbon power purchase, improving the consumption of new energy sources, and ensuring the stability of zero-carbon constraint compliance.

[0102] Based on the above, this solution also proposes a multi-energy complementary zero-carbon park load coordination and scheduling system, which includes: The data acquisition unit is used to acquire and process the park's operational data within a preset scheduling period. The scheduling parameter generation unit is used to generate scheduling parameters based on the operating data. The scheduling parameters are used to characterize the available supply capacity of new energy sources, the electricity-heat-cooling load demand of the park, and the grid interaction conditions during the scheduling cycle. The model building unit is used to build a cooperative scheduling model for describing the coupling relationship of multiple energy flows such as electricity, heat and cold, wherein the cooperative scheduling model has at least one constraint condition characterizing the coupling characteristics of multiple energy flows; The problem construction unit is used to construct a cooperative scheduling optimization problem with zero carbon constraints based on the cooperative scheduling model. The decision planning unit is used to solve the collaborative scheduling optimization problem to determine the scheduling decision for each time period, and accordingly execute the collaborative scheduling of multi-energy flow loads in the park, so as to improve the consumption of new energy and reduce operating costs while meeting the zero-carbon constraint.

[0103] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity, characterized in that, The scheduling method, applicable to a park energy system comprising a new energy supply unit, a grid interaction unit, an electricity-heat-cooling energy conversion unit, and at least one energy storage unit, includes: S1. Obtain and process the park's operational data within a preset scheduling period; S2. Based on the operational data, scheduling parameters are formed. The scheduling parameters are used to characterize the available supply capacity of new energy sources, the electricity-heat-cooling load demand of the park, and the grid interaction conditions within the scheduling cycle. S3. Construct a cooperative scheduling model to describe the coupling relationship of multiple energy flows such as electricity, heat and cold, wherein the cooperative scheduling model has at least one constraint condition characterizing the coupling characteristics of multiple energy flows; S4. Construct a collaborative scheduling optimization problem with zero-carbon constraints based on the aforementioned collaborative scheduling model; S5. Solve the collaborative scheduling optimization problem to determine the scheduling decision for each time period, and execute the collaborative scheduling of multi-energy flow loads in the park accordingly, so as to improve the consumption of new energy and reduce operating costs while meeting the zero-carbon constraint.

2. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 1, characterized in that, In S1, the operating data includes at least new energy output data, park electricity load data, heat load data, cold load data, energy storage unit status data, and external signal data related to the power grid; In S1, the external signal data includes at least the grid time-of-use electricity price, demand-side response signal and / or grid time-varying carbon emission factor; In S1, the data processing includes at least time alignment and anomaly handling.

3. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 2, characterized in that, In S1, the data processing further includes performing missing data completion, noise reduction filtering and / or normalization on the running data to obtain a scheduling input set with a unified time granularity; In S1, the scheduling period is discretized into multiple time slots; In S2, the scheduling parameters generate corresponding new energy available output prediction sequences and electricity-heat-cooling load prediction sequences at the time slot granularity.

4. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 1, characterized in that, In S3, the collaborative scheduling model uses an energy hub model to describe the multi-energy flow coupling relationship. The energy hub model includes the energy flow distribution relationship between new energy supply, grid interaction, energy storage charging and discharging, and energy conversion. In S3, the constraints include at least the electric side energy balance constraints, the hot side energy balance constraints, and the cold side energy balance constraints, as well as the state update constraints of the energy storage unit and the performance constraints of the energy conversion unit.

5. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 4, characterized in that, The energy conversion unit includes at least one of the following: heat pump, electric boiler, electric chiller, absorption chiller and / or waste heat recovery unit; the performance constraints include at least conversion efficiency constraints, performance coefficient constraints and / or upper and lower output limits constraints. The energy storage unit includes at least one of the following: electrical energy storage, thermal energy storage, and / or cold energy storage; the state update constraints include the dynamic update relationship between the energy storage state of charge, thermal energy storage, and cold energy storage, as well as their upper and lower limit constraints.

6. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 1, characterized in that, In S4, the zero-carbon constraint is determined by carbon emission characterization parameters related to the power grid, and is used to limit the amount of carbon emissions or carbon intensity within the scheduling cycle to meet preset conditions. The zero-carbon constraint includes any one or a combination of the following: a) Carbon budget constraint: Limiting the cumulative carbon emissions within the scheduling period to not exceed a preset carbon budget threshold; b) Net-zero carbon constraint: Limiting the cumulative carbon emissions during the scheduling period to no more than zero after deductions; c) Real-time carbon intensity constraint: Limiting the carbon intensity in at least some time slots to not exceed a preset upper limit for carbon intensity; The cumulative carbon emissions are calculated at least based on the product of the power purchased from the grid and the time-varying carbon emission factor of the grid, and may further include fuel consumption emissions, purchased steam emissions and / or green electricity deductions.

7. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 1, characterized in that, In S4, the optimization objective of the coordinated scheduling optimization problem includes at least one or a combination of the following: minimum operating cost, minimum wind and solar curtailment, minimum carbon emissions, minimum carbon intensity, and / or minimum load adjustment cost; In S5, the scheduling decision includes at least the grid power purchase / grid connection power, energy storage charging and discharging power, and the output allocation of the electric-heat-cold energy conversion unit.

8. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 7, characterized in that, In S5, the scheduling decision also includes the adjustment amount of the adjustable load in the park, the adjustable load includes at least peak-shifting load and / or load that can be reduced; wherein, the peak-shifting load satisfies the energy conservation constraint, and the load that can be reduced satisfies the reduction upper limit constraint; The adjustment amount of the adjustable load is determined based on preset comfort constraints and / or production capacity constraints, the comfort constraints including indoor temperature allowable range constraints and / or thermal inertia constraints; In S5, the priority strategy of the scheduling decision satisfies the following: first, new energy sources are used to meet the electrical load; second, energy storage is charged and / or the electric-heat-cold energy conversion unit is driven to form heat / cold supply and / or the thermal / cold energy storage is charged; and the remaining part is used for grid connection or for other convertible loads.

9. The load coordination scheduling method for zero-carbon industrial parks based on multi-energy complementarity as described in claim 1, characterized in that, In S5, solving the cooperative scheduling optimization problem includes: generating a day-ahead baseline scheduling plan, and using rolling time-domain optimization to perform online correction of the day-ahead baseline scheduling plan during execution; The rolling time-domain optimization includes: updating the energy storage unit status and load deviation in each rolling control cycle, reconstructing and solving the collaborative scheduling optimization problem, and outputting only the control quantity of the current time slot for execution. The method further includes: a real-time carbon emission accounting and adaptive adjustment step: calculating real-time carbon emissions based on real-time power purchase and real-time grid time-varying carbon emission factors; and adjusting the objective function weights and / or constraint boundaries in the collaborative scheduling optimization problem when the carbon budget is detected to be depleted or the carbon intensity exceeds the limit, in order to suppress high-carbon power purchase and improve the consumption of new energy.

10. A zero-carbon industrial park load coordination and scheduling system based on multi-energy complementarity, characterized in that: It includes: The data acquisition unit is used to acquire and process the park's operational data within a preset scheduling period. The scheduling parameter generation unit is used to generate scheduling parameters based on the operating data. The scheduling parameters are used to characterize the available supply capacity of new energy sources, the electricity-heat-cooling load demand of the park, and the grid interaction conditions during the scheduling cycle. The model building unit is used to build a cooperative scheduling model for describing the coupling relationship of multiple energy flows such as electricity, heat and cold, wherein the cooperative scheduling model has at least one constraint condition characterizing the coupling characteristics of multiple energy flows; The problem construction unit is used to construct a cooperative scheduling optimization problem with zero carbon constraints based on the cooperative scheduling model. The decision planning unit is used to solve the collaborative scheduling optimization problem to determine the scheduling decision for each time period, and accordingly execute the collaborative scheduling of multi-energy flow loads in the park, so as to improve the consumption of new energy and reduce operating costs while meeting the zero-carbon constraint.

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