Time sequence production simulation method and device for rural distributed energy system

Abstract

The invention discloses a time sequence production simulation method and device for a rural distributed energy system, and relates to the technical field of comprehensive energy systems. The 8760h of a whole-year time sequence is decomposed into a plurality of sub-periods for distributed asynchronous parallel solving. In the iteration process, clock delay variables are introduced according to different lengths of the sub-periods, asynchronous updating allows all the sub-periods to continuously iterate and solve, and waiting time in the synchronous parallel solving process is avoided. And through orthogonality between conjugate search directions, the convergence speed of iteration is accelerated, the operation time sequence is ensured by utilizing a coupling time period, and meanwhile, the solving speed is accelerated by utilizing parallel calculation.

Description

Technical Field

[0001] The present invention discloses a time sequence production simulation method and device for a rural distributed energy system, and relates to the technical field of integrated energy systems. Background Art

[0002] Considering the diverse nature of multi-energy flows within rural distributed energy systems (RDES), researchers both domestically and internationally typically estimate the operational characteristics of various state variables based on limited known device parameter information. These models then establish operational models for various coupled devices for RDES state estimation, power balance analysis, and optimal scheduling, ultimately achieving unified modeling of multi-energy flows within integrated rural energy systems. Currently, multi-energy coupling approaches include: electricity-hydrogen coupling using hydrogen electrolyzers and hydrogen fuel cells / hybrid hydrogen turbines as coupling elements; electricity-heat coupling using biomass-fired power units as coupling elements; and hydrogen-ammonia coupling using chemical production equipment as coupling elements. These approaches enable bidirectional flows and synergistic interactions among finite energy subsystems. However, existing approaches fail to consider the conversion characteristics and interactions among the multi-energy flows within RDESs, including electricity, hydrogen, ammonia, and heat.

[0003] In the study of multi-energy flow coupled operation optimization of RDES, existing methods generally adopt a strategy of simply superimposing multiple independent energy supply systems. Their control mode is mainly based on load-side demand, including single-energy flow-oriented operation modes such as heat-to-electricity, electricity-to-heat, and electricity-to-hydrogen. They fail to fully consider the synergistic effects between various energy flows from the overall system level, and most of them ignore the impact of the system's internal energy coupling characteristics on overall performance. At the same time, there is a lack of effective utilization of the time scale differences of energy storage technologies such as electric energy storage and hydrogen-ammonia energy storage to achieve decoupling of multiple energy flows, making it difficult to achieve optimal operation and control of the system. Existing research has failed to deeply explore the coordinated operation mechanism of multiple energy supply units to achieve a dynamic balance between supply and demand, which directly affects the improvement of RDES operational flexibility and energy utilization efficiency.

[0004] The integrated RDES (Remote Decomposition Equipment) of electricity, hydrogen, ammonia, and heat features continuous time-series production. Power system production simulations can optimize generator unit production, simulate system scheduling, and evaluate and analyze system operation plans. However, the long time span of time-series production simulations, such as up to 8,760 hours of annual operation, makes them challenging to solve. Therefore, existing literature on solving the time dimension of production simulations primarily categorizes them into sequential, rolling, and time-series dimensionality reduction methods. Specifically, sequential and rolling methods divide the 8,760 hours of a year into subperiods and solve the subproblems of the time-series production simulation within these subperiods. These methods often struggle to obtain global optimal solutions when faced with massive amounts of simulation data, and their serial computational structure limits computational efficiency. Time-series dimensionality reduction methods focus on selecting representative curves within the study period or reducing dimensionality through time series clustering, replacing full medium- and long-term simulations. However, these methods can produce overly optimistic results and introduce significant operational errors. Summary of the Invention

[0005] In order to solve the problems in the prior art, the present invention provides a method and device for simulating the time-series production of a rural distributed energy system. The technical solution adopted is as follows: In a first aspect, a time-series production simulation method for a rural distributed energy system includes: An energy hub model is established based on a rural distributed energy system, the energy hub model including a renewable energy generation input side and a multi-energy load output side; an internal interconnection expansion structure and an energy conversion pathway are established between the renewable energy generation input side and the multi-energy load output side for connection; a steady-state operation mode of the energy hub model includes the production, storage, conversion, and utilization processes of electricity-heat-hydrogen-ammonia energy flows; and a discrete state space multi-energy coupling matrix is ​​established based on the production, storage, conversion, and utilization processes for quantification; Under a full-year time-series production simulation, the economic operating efficiency and multi-energy comprehensive utilization level of the energy hub model are optimized based on a cross-time-scale power and energy balance compensation mechanism. The optimization includes obtaining the annual comprehensive cost of the rural distributed energy system, which includes operation and maintenance costs, penalty costs for curtailed renewable energy generation, and penalty costs for insufficient power supply. The annual time series is decomposed into multiple sub-periods for distributed asynchronous solution, the conjugate gradient direction is determined according to the iteration direction, and the energy hub model is iteratively updated; wherein, in the distributed asynchronous solution process of the sub-periods, the corresponding clock delay variable is introduced according to the length of any sub-period, and the asynchronous solution of the other sub-periods is maintained.

[0006] In some implementations, based on the electricity-heat-hydrogen-ammonia energy flow, multi-energy flow integration and conversion and storage are performed through the energy supply framework of the rural distributed energy system, including: During short-term power fluctuations, the renewable energy power generation input side provides clean electricity to the hydropower unit and photovoltaic unit to drive the electrolyzer to electrolyze water to produce hydrogen, and compress and store the hydrogen; When the energy is in long-term balance, the hydrogen is converted into electrical energy through a fuel cell to supply power to the rural distributed energy system; According to the ammonia synthesis plant, the hydrogen and nitrogen in the air are processed into green ammonia through the Haber process; Heat recovery is performed by the energy supply framework based on the short-term power fluctuations, the long-term energy balance and the reaction heat generated in the ammonia synthesis plant.

[0007] In some implementations, the energy hub model based on the annual time-series production simulation determines the annual comprehensive cost by including operation and maintenance costs, penalty costs for curtailing wind and solar power generation from renewable energy sources, and penalty costs for insufficient power supply. According to the annual comprehensive cost, renewable energy constraints are imposed on the hydropower units and photovoltaic units by setting reduction coefficients.

[0008] In some implementations, during the sub-period piecewise asynchronous solution process, the convergence rate of the iteration direction is accelerated by using the orthogonality between conjugate search directions, including: Set the clock delay variable and obtain the local optimal solution of the iteration vector through the Lagrangian function; According to the local objective function offset term of the local optimal solution, a dynamic adjustment search is performed through the gradient change information of the Lagrangian function to achieve local curvature matching.

[0009] In some implementations, when the convergence deviation of the iteration direction is greater than the original deviation and the dual deviation, the iteration of the sub-period piecewise asynchronous solution process is stopped.

[0010] In a second aspect, an embodiment of the present invention provides a time-series production simulation device for a rural distributed energy system, comprising: An equipment energy supply module is configured to establish an energy hub model based on a rural distributed energy system, the energy hub model including a renewable energy generation input side and a multi-energy load output side; establish an internal interconnection expansion structure and an energy conversion pathway between the renewable energy generation input side and the multi-energy load output side for connection; the steady-state operation mode of the energy hub model includes the production, storage, conversion, and utilization of electricity-heat-hydrogen-ammonia energy flows; and establish a discrete state space multi-energy coupling matrix based on the production, storage, conversion, and utilization processes for quantification; A production simulation module is used to optimize the economic operating efficiency and multi-energy comprehensive utilization level of the energy hub model under a full-year time-series production simulation based on a cross-time-scale power and energy balance compensation mechanism; the operation optimization includes obtaining the annual comprehensive cost of the rural distributed energy system, wherein the annual comprehensive cost includes operation and maintenance costs, penalty costs for curtailed renewable energy generation, and penalty costs for insufficient power supply; The asynchronous solution module is used to decompose the annual time series into multiple sub-periods for distributed asynchronous solution, determine the conjugate gradient direction according to the iteration direction, and iteratively update the energy hub model; wherein, in the distributed asynchronous solution process of the sub-periods, the corresponding clock delay variable is introduced according to the length of any sub-period, and the asynchronous solution of the other sub-periods is maintained.

[0011] In some implementations, the device energy supply module is configured to integrate, convert, and store multiple energy flows based on the electricity-heat-hydrogen-ammonia energy flow through the energy supply framework of the rural distributed energy system, including: A hydrogen energy conversion unit is used to provide clean electricity from the hydropower unit and photovoltaic unit on the renewable energy power generation input side during short-term power fluctuations, drive the electrolyzer to electrolyze water to produce hydrogen, and compress and store the hydrogen; An electric energy conversion unit, configured to convert the hydrogen into electric energy through a fuel cell during long-term energy balance to supply power to the rural distributed energy system; an energy optimization unit for processing the hydrogen and nitrogen from air into green ammonia by the Haber process according to a synthetic ammonia plant; A heat recovery unit is used to recover heat energy through the energy supply framework according to the short-term power fluctuation, the long-term energy balance and the reaction heat generated in the ammonia synthesis device.

[0012] In some implementations, the production simulation module includes: A cost accounting unit is used to determine the annual comprehensive cost of the energy hub model based on the annual time-series production simulation by using operation and maintenance costs, penalty costs for curtailment of renewable energy power generation, and penalty costs for insufficient power supply; The cost constraint unit is used to constrain the renewable energy of the hydropower unit and the photovoltaic unit by setting a reduction coefficient according to the annual comprehensive cost.

[0013] In some implementations, the asynchronous solution module is configured to accelerate the convergence speed of the iteration direction by using the orthogonality between conjugate search directions during the sub-period piecewise asynchronous solution process, including: Vector iteration unit, used to set the clock delay variable and obtain the local optimal solution of the iteration vector through the Lagrangian function; A vector optimization unit is used to dynamically adjust and search based on the local objective function offset term of the local optimal solution through the gradient change information of the Lagrangian function to achieve local curvature matching.

[0014] In some implementations, the asynchronous solution module further includes: The iterative constraint unit is used to stop iterating the sub-period piecewise asynchronous solution process when the convergence deviation of the iterative direction is greater than the original deviation and the dual deviation.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein when the one or more computer instructions are executed by the processor, the method described in the first aspect above is implemented. In a fourth aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the method described in the first aspect.

[0016] One or more embodiments of the present invention can bring at least the following beneficial effects: The method proposed in this paper constructs a RDES hub model based on the synergistic effect of multiple energy sources, characterizing the system's internal interconnection topology and energy conversion paths from renewable energy generation input to multiple energy load output. By establishing a discrete state-space multi-energy coupling matrix, the production, storage, conversion, and utilization of multiple energy flows (electricity, hydrogen, ammonia, and heat) in the RDES under steady-state operation are quantified. The method of the present invention establishes an electricity-hydrogen-ammonia-heat coordinated operation optimization model based on an 8760-hour time series production simulation throughout the year. This model can fully utilize the complementary characteristics of electricity and heterogeneous energy flows such as hydrogen, ammonia, and biomass on long and short time scales. Through a cross-time scale power and energy balance compensation mechanism, it optimizes the economic operation efficiency of RDES and the level of comprehensive utilization of multiple energy sources. The method of the present invention proposes a conjugate asynchronous distributed solution method, which decomposes the 8760h of the annual time series into multiple sub-periods for distributed asynchronous parallel solution. During the iterative process, the clock delay variable is introduced according to the different lengths of the sub-periods. Asynchronous updates allow continuous iterative solving across all sub-periods, avoiding the waiting time associated with synchronous parallel solving. The orthogonality between conjugate search directions accelerates iterative convergence, while coupled periods ensure runtime order and parallel computing accelerates solution speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 This is a flow chart of a method for simulating sequential production of a rural distributed energy system provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of an energy supply framework of a rural distributed energy system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0020] Example 1: Figure 1 A flow chart of a time-series production simulation method for a rural distributed energy system is shown in FIG. Figure 1 As shown, the time-series production simulation method for a rural distributed energy system provided in this embodiment includes: Step 1: Modeling the rural distributed energy system (RDES) energy supply framework and equipment: An energy hub model is established based on a rural distributed energy system, the energy hub model including a renewable energy generation input side and a multi-energy load output side; an internal interconnection expansion structure and an energy conversion pathway are established between the renewable energy generation input side and the multi-energy load output side for connection; a steady-state operation mode of the energy hub model includes the production, storage, conversion, and utilization processes of electricity-heat-hydrogen-ammonia energy flows; and a discrete state space multi-energy coupling matrix is ​​established based on the production, storage, conversion, and utilization processes for quantification; Specifically include: Step 1.1: Establish an RDES energy supply framework containing electricity, heat, hydrogen and ammonia; like Figure 2 The RDES energy supply structure framework shown in the figure integrates renewable energy generation with multi-energy conversion and storage technologies, achieving synergistic optimization between electricity and multiple energy carriers such as hydrogen, ammonia, and heat, thereby meeting the diverse energy needs of sectors such as chemical production and transportation. On the renewable energy generation input side, the RDES primarily relies on hydroelectric generating units (HGUs) and photovoltaic (PV) systems to provide clean electricity. This electricity drives electrolyzers (ELZs) to electrolyze water to produce hydrogen. The generated hydrogen is compressed and stored in hydrogen storage tanks (HSTs) for long-term use. Specifically, during short-term power fluctuations, the renewable energy generation input side uses the hydroelectric generating units and PV units to provide clean electricity, driving the electrolyzers to electrolyze water to produce hydrogen, which is then compressed and stored. Compared to battery energy storage (BES), hydrogen / ammonia energy storage systems are more suitable for scenarios with low discharge frequencies and long / extremely long-term energy storage requirements.

[0021] When renewable energy output is insufficient, stored hydrogen can be converted back into electricity via fuel cells (FCs), ensuring system power supply reliability. Furthermore, hydrogen can react with nitrogen from the air via the Haber process in an ammonia synthesis plant to produce green ammonia, which can then be used as a chemical feedstock in urea synthesis, serving local agricultural production. Biomass energy is generated through biomass generators, achieving cogeneration to meet electricity and heat load requirements. Notably, the reaction heat generated during the electricity-hydrogen-ammonia conversion process (e.g., electrolysis for hydrogen production and ammonia synthesis) can be effectively recovered and utilized, significantly improving overall energy efficiency. Regarding operational strategies, given the high energy losses associated with the electricity-hydrogen-electricity conversion pathway, the RDES prioritizes the use of BES to address short-term power fluctuations, while utilizing FCs as a means of regulating long-term energy balance. This coordinated management mechanism for multiple energy flows not only enhances system operational flexibility but also ensures the security and affordability of multi-energy supply in rural areas through cascaded energy utilization.

[0022] Step 1.2, establish the RDES hub model; According to this step, an energy hub modeling method is developed to describe the electricity-heat-hydrogen-ammonia production, conversion, storage, and supply process under steady-state operation of renewable energy power generation systems. Specifically, a discrete state space matrix X is constructed, which encapsulates the interconnected topology and energy conversion paths. In X, / is the efficiency of hydrogen production / heat generation by water electrolysis; / is the charge / discharge efficiency of BES; / is the storage / release hydrogen efficiency of the HST; / is the hydrogen-to-electricity / heat production efficiency of FC; is the operating efficiency of the compressor; / is the hydrogen production / heat production efficiency of the ammonia tower; / is the storage / release efficiency of the ammonia tank; / is the efficiency of burning biomass to generate electricity / heat. In addition, the state vector Represents the steady-state energy input and output of the RDES device in each operating cycle. middle / is the output power of HGUs / PVs at time t; It is the purchase of electricity from the grid; is the calorific value of biomass that can be used for power generation at time t; and is the input electrolysis power and hydrogen production rate of the electrolyzer stack at time t; / is the charging / discharging power of BES; is the compression power of the compressor; is the output electrical power of FC; / is the power generation / heating power of the biomass fuel unit at time t; / is the hydrogen storage / release rate of HST; / is the ammonia storage / release rate of the ammonia storage tank; / / are the power of electricity-to-hydrogen / electricity-to-ammonia / hydrogen-to-electricity heat recovery at time t respectively; / / / is the system's output electrical power / hydrogen / ammonia / thermal power, as shown below, (1) (2) (3) (4) Step 1.3, electricity-heat-hydrogen-ammonia conversion storage unit model; Among them, including step 1.3.1, establish the energy storage unit model; considering the state of charge (SOC) and charge and discharge rate, the energy storage dynamics of BES can be formulated as follows: (5) (6) (7) (8) (9) (10) Where, is the self-discharge rate of BES; , Θ and Γ are fitting parameters based on experimental data; and It is a binary variable that indicates the working status of BES.

[0023] Step 1.3.2, establish a hydrogen energy storage unit model; The generalized hydrogen energy storage model includes the hydrogen production, storage, and utilization stages. The operation model of the electrolyzer stack, HST, FC, and electric compressor, taking into account the electricity-to-hydrogen conversion, can be expressed as follows: (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (twenty one) Where, is the electrolysis power of the electrolytic stack at time t; is the hydrogen production rate of the electrolytic stack at time t; is the heat value generated during the electrolysis of water at time t; represents the state of energy (SOE) of HST at time t; is the internal pressure of HST; is the rated capacity of the HST; represents the molecular weight of hydrogen; and is a binary variable that represents the charging and discharging state of the HST at time t. Specifically, 1 indicates that the HST is in the charging state or the discharging state, and 0 indicates that the HST is not in the working state.

[0024] Step 1.3.3: Establish the ammonia energy system unit model The ammonia energy system unit model includes ammonia synthesis, ammonia storage, ammonia decomposition and ammonia power generation. It can be expressed as follows: (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) Where, is the ammonia production power of the ammonia tower at time t; is the ammonia production rate of the ammonia tower at time t; is the electricity consumption coefficient per ton of ammonia; is the hydrogen rate input to the ammonia tower at time t; is the conversion coefficient between hydrogen consumption rate and ammonia production rate; It is the switching process of different quasi-steady states of synthetic ammonia; is the quasi-steady-state condition of synthetic ammonia; K is the index set of the quasi-steady-state condition; is the heat power released by the ammonia tower at time t. The ammonia production process is an exothermic reaction, so its heat can be used to meet the heat load demand; is the ammonia storage capacity at time t; It is the rated capacity of the ammonia storage tank (AST).

[0025] Step 1.3.4: Establish a thermal system unit model Taking an agricultural base in a southern city as an example, the crop straw and sugarcane grown there are abundant biomass resources that can be used as fuel for biomass combustion generators. However, due to the seasonality of crop harvests, the calorific value of the harvested crop straw is calculated on an annual basis, as shown below: (31) (32) (33) Where, is the calorific value of biomass that can be used for power generation at time t; is the total mass of biomass consumed in a year; is the calorific value per unit mass; is the electric energy available for power generation at time t; The heat energy converted from biomass power generation at time t; and It is the electrical conversion efficiency and thermal conversion efficiency of biomass.

[0026] According to step 2, under the full-year time-series production simulation, the economic operation efficiency and the level of comprehensive utilization of multiple energy sources of the energy hub model are optimized based on the cross-time-scale power and energy balance compensation mechanism; the optimization includes obtaining the annual comprehensive cost of the rural distributed energy system; wherein the annual comprehensive cost includes operation and maintenance costs, penalty costs for curtailed renewable energy generation, and penalty costs for insufficient power supply; Specifically include: Step 2.1, determine the objective function The RDES operation model based on full-year sequential production simulation aims to minimize the annual comprehensive cost of the system , specifically as follows, (34) Where, Including operation and maintenance costs , Penalty costs for curtailing wind and solar power generation from renewable energy sources , Penalty costs for insufficient power supply .

[0027] Specifically, Including the operation and maintenance costs of the power unit , Hydrogen energy unit operation and maintenance costs , ammonia unit operation and maintenance costs and hydrogen unit operation and maintenance costs ,as follows, (35) (36) (37) (38) (39) Where, / / / / / It is the operation and maintenance cost coefficient of BES / HST / electrolysis stack / FC / ammonia tower / ammonia storage tank / biomass fuel unit.

[0028] If the electricity generated by hydropower and solar power cannot be fully consumed, it will lead to energy waste. Therefore, penalty measures are taken for the curtailment of wind and solar power. The penalty cost of such energy curtailment can be calculated as follows, (40) Step 2.2, establish constraints; Step 2.2.1, Renewable Energy Constraints: Since RDES complies with the sustainable development policy, the curtailment constraints for hydropower and solar power are as follows, (41) Where, In the range of [0,1], it represents the curtailment coefficient of hydropower and solar power generation.

[0029] Step 2.2.2, Operation Constraints: The electrolyzer and FC are both key power-hydrogen coupling devices in the system. Their input / output operating power cannot exceed their rated power and the two devices cannot be in operation at the same time. (42) (43) (44) Where, and are binary variables representing the operating status of the electrolyzer stack and FC at time t. Specifically, 0 indicates that the device is in the shutdown state, and 1 indicates that the device is in the operating state.

[0030] At the same time, the electrolyzer and FC also need to consider the climbing constraints during operation, as shown below: (45) (46) Where, and are the climbing limits of electrolyzer and FC respectively.

[0031] Step 2.2.3, Balance Constraints: The electricity-hydrogen-ammonia-heat balance constraints of RDES are as follows, (47) (48) (49) (50) Step 2.2.4, comprehensive energy efficiency constraints: To ensure the efficient use of multiple energy sources in RDES, the constraints are as follows: (51) According to step 3, the annual time series is decomposed into multiple sub-periods for distributed asynchronous solution, the conjugate gradient direction is determined according to the iteration direction, and the energy hub model is iteratively updated; wherein, in the distributed asynchronous solution process of the sub-periods, the corresponding clock delay variable is introduced according to the length of any of the sub-periods, and the asynchronous solution of the other sub-periods is maintained.

[0032] This example proposes a conjugate asynchronous distributed parallel solution algorithm that requires only limited information exchange. It divides the long, 8760-hour annual time period of a time-series production simulation into multiple smaller, continuous sub-periods for separate solutions. During the solution process, the concept of associated time periods is introduced, and local operations on the generalized Lagrangian function are performed within each sub-period. This algorithm considers time-series state variables and the constraints of continuous equipment operation, ensuring the sequential nature of the entire 8760-hour time-series production simulation problem. The iteration direction is the conjugate gradient direction, ensuring optimality of the iterative solution direction.

[0033] First, the time series state variables SOC, SOE, SOA and HST internal pressure are Decoupling and introducing auxiliary variables 、 、 、 、 、 、 and To decouple the original SOC, SOE, SOA and , then the daily consistency constraint can be rewritten as, (52) Where, is a collection of time series; is the set of coupling time points.

[0034] Introducing slack variables via augmented Lagrangian method = [ , ]T, transform the inequality constraint of equipment operation continuity into an equality constraint as shown below: (53) Generalized Lagrangian function The local optimization problem of the corresponding i-th sub-period system can be rewritten as: (54) Where, =[ ]T and =[ ]T are the system SOC, SOE, SOA and Auxiliary vector of ; =[ , , , , ]T are the Lagrange multiplier vectors of the kth decoupling point; = , , , , ]T are the penalty factor vectors for the timing constraints at the kth decoupling point.

[0035] In each sub-period, the algorithm iterates through the conjugate gradient direction and The solution is used to accelerate convergence, which can effectively reduce the number of iterations of the distributed solution sub-period local optimal solution. The key iterative process of the algorithm is summarized as follows: Step 3.1, Update and :exist When the +1 iteration is executed, the global clock No longer unified, introduce clock delay variables , for example, in period i The adjacent sub-period information received during the iteration may be outdated, so the received is the delayed version of time period j. Similarly, the Lagrange multiplier vector will also be delayed, which is , the system is used in sub-periods i and j The obtained 、 and , substitute the generalized Lagrangian function to find the local optimal solution and get and , as shown below, (55) (56) Step 3.2, update the iteration direction and : Using Lagrangian function The gradient change information of , dynamically adjusts the search direction to match the local curvature of the objective function, as shown below, (57) (58) (59) (60) Where, is the offset term of the local objective function; and are the iterative gradient direction vector and the conjugate gradient direction vector respectively; is the conjugate gradient parameter, which is derived from the Polak-Ribiere formula.

[0036] Step 3.3, Update :Use the system in sub-periods i and j The conjugate gradient direction is updated at the iteration and calculate , as shown below, (61) (62) (63) Where, and is the momentum set.

[0037] Step 3.4, convergence check: When the original residual and the dual residual Less than the allowed convergence deviation and The iteration stops when , as shown below, (64) (65) In this embodiment, the problem of sequential production simulation considering the complementary energy of electricity, hydrogen, ammonia and heat is essentially a high-dimensional, multi-constraint, multi-objective system optimization problem. Traditional operation models often lead to low comprehensive energy utilization efficiency of RDES and difficult to solve the model by adopting simple modeling; traditional solution methods usually adopt rolling solution or use typical scenarios for production simulation. For renewable energy with strong sequential characteristics, the disadvantage of this method is that it can only consider the sequentiality within a limited time, and the solved operation results are often local optimal rather than global optimal. The above problems put forward higher requirements for the sequential production simulation model and solution algorithm of RDES with the complementary energy of electricity, hydrogen, ammonia and heat.

[0038] In response to the above-mentioned defects and difficulties, the present invention constructs an RDES hub model based on the synergistic effect of multiple energy sources, which characterizes the internal interconnection topology and energy conversion path of the system from renewable energy power generation input to multiple energy load output. By establishing a discrete state space multi-energy coupling matrix, the production, storage, conversion and utilization process of multiple energy flows such as electricity, hydrogen, ammonia and heat in the steady-state operation mode of RDES is quantified; an electricity-hydrogen-ammonia-heat synergistic operation optimization model based on 8760-hour time series production simulation is established, which can make full use of the complementary characteristics of heterogeneous energy flows such as electricity, hydrogen, ammonia, and biomass on long and short time scales, and optimize the economic operation efficiency and comprehensive utilization level of multiple energy sources of RDES through a power and energy balance compensation mechanism across time scales; a conjugate asynchronous distributed solution method is proposed, which decomposes the 8760 hours of the year into multiple sub-periods for distributed asynchronous parallel solution. During the iteration process, clock delay variables are introduced according to the different lengths of the sub-periods. Asynchronous updates allow continuous iterative solving across all sub-periods, avoiding the waiting time associated with synchronous parallel solving. The orthogonality between conjugate search directions accelerates iterative convergence, while coupled periods ensure runtime order and parallel computing accelerates solution speed.

[0039] Example 2: An embodiment of the present invention provides a time-series production simulation device for a rural distributed energy system, comprising: An equipment energy supply module is configured to establish an energy hub model based on a rural distributed energy system, the energy hub model including a renewable energy generation input side and a multi-energy load output side; establish an internal interconnection expansion structure and an energy conversion pathway between the renewable energy generation input side and the multi-energy load output side for connection; the steady-state operation mode of the energy hub model includes the production, storage, conversion, and utilization of electricity-heat-hydrogen-ammonia energy flows; and establish a discrete state space multi-energy coupling matrix based on the production, storage, conversion, and utilization processes for quantification; A production simulation module is used to optimize the economic operating efficiency and multi-energy comprehensive utilization level of the energy hub model under a full-year time-series production simulation based on a cross-time-scale power and energy balance compensation mechanism; the operation optimization includes obtaining the annual comprehensive cost of the rural distributed energy system, wherein the annual comprehensive cost includes operation and maintenance costs, penalty costs for curtailed renewable energy generation, and penalty costs for insufficient power supply; The asynchronous solution module is used to decompose the annual time series into multiple sub-periods for distributed asynchronous solution, determine the conjugate gradient direction according to the iteration direction, and iteratively update the energy hub model; wherein, in the distributed asynchronous solution process of the sub-periods, the corresponding clock delay variable is introduced according to the length of any sub-period, and the asynchronous solution of the other sub-periods is maintained.

[0040] Furthermore, the device energy supply module is used to integrate, convert and store multiple energy flows based on the electricity-heat-hydrogen-ammonia energy flow through the energy supply framework of the rural distributed energy system, including: A hydrogen energy conversion unit is used to provide clean electricity from the hydropower unit and photovoltaic unit on the renewable energy power generation input side during short-term power fluctuations, drive the electrolyzer to electrolyze water to produce hydrogen, and compress and store the hydrogen; An electric energy conversion unit, configured to convert the hydrogen into electric energy through a fuel cell during long-term energy balance to supply power to the rural distributed energy system; an energy optimization unit for processing the hydrogen and nitrogen from air into green ammonia by the Haber process according to a synthetic ammonia plant; A heat recovery unit is used to recover heat energy through the energy supply framework according to the short-term power fluctuation, the long-term energy balance and the reaction heat generated in the ammonia synthesis device.

[0041] Furthermore, the production simulation module includes: A cost accounting unit is used to determine the annual comprehensive cost of the energy hub model based on the annual time-series production simulation by using operation and maintenance costs, penalty costs for curtailment of renewable energy power generation, and penalty costs for insufficient power supply; The cost constraint unit is used to constrain the renewable energy of the hydropower unit and the photovoltaic unit by setting a reduction coefficient according to the annual comprehensive cost.

[0042] Furthermore, the asynchronous solution module is used to accelerate the convergence speed of the iteration direction by using the orthogonality between conjugate search directions during the sub-period segmented asynchronous solution process, including: Vector iteration unit, used to set the clock delay variable and obtain the local optimal solution of the iteration vector through the Lagrangian function; A vector optimization unit is used to dynamically adjust and search based on the local objective function offset term of the local optimal solution through the gradient change information of the Lagrangian function to achieve local curvature matching.

[0043] Furthermore, the asynchronous solution module further includes: The iterative constraint unit is used to stop iterating the sub-period piecewise asynchronous solution process when the convergence deviation of the iterative direction is greater than the original deviation and the dual deviation.

[0044] Example 3: This embodiment further provides an electronic device, including a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method of embodiment 1; In practical applications, the processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller unit (MCU), a microprocessor or other electronic components to execute the methods in the above embodiments.

[0045] The method implemented in this embodiment is as described in the content of the first embodiment.

[0046] Example 4: This embodiment further provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the method of embodiment 1 is implemented; Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0047] The method implemented in this embodiment is as described in the content of the first embodiment.

[0048] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed system and method can also be implemented in other ways. The above-described system and method embodiments are merely illustrative.

[0049] It should be noted that, in this document, the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0050] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A time-series production simulation method for a rural distributed energy system, characterized in that: include: An energy hub model is established based on a rural distributed energy system, the energy hub model including a renewable energy generation input side and a multi-energy load output side; an internal interconnection expansion structure and an energy conversion pathway are established between the renewable energy generation input side and the multi-energy load output side; the steady-state operation mode of the energy hub model includes the production, storage, conversion, and utilization of electricity-heat-hydrogen-ammonia energy flows; According to the production, storage, conversion and utilization process, a discrete state space multi-energy coupling matrix is ​​established for quantification; Under a full-year time-series production simulation, the economic operating efficiency and multi-energy comprehensive utilization level of the energy hub model are optimized based on a cross-time-scale power and energy balance compensation mechanism. The optimization includes obtaining the annual comprehensive cost of the rural distributed energy system, which includes operation and maintenance costs, penalty costs for curtailed renewable energy generation, and penalty costs for insufficient power supply. The annual time series is decomposed into multiple sub-periods for distributed asynchronous solution, the conjugate gradient direction is determined according to the iteration direction, and the energy hub model is iteratively updated; wherein, in the distributed asynchronous solution process of the sub-periods, the corresponding clock delay variable is introduced according to the length of any sub-period, and the asynchronous solution of the other sub-periods is maintained.

2. The method according to claim 1, characterized in that Based on the electricity-heat-hydrogen-ammonia energy flow, multi-energy flow integration and conversion and storage are performed through the energy supply framework of the rural distributed energy system, including: During short-term power fluctuations, the renewable energy power generation input side provides clean electricity to the hydropower unit and photovoltaic unit to drive the electrolyzer to electrolyze water to produce hydrogen, and compress and store the hydrogen; When the energy is in long-term balance, the hydrogen is converted into electrical energy through a fuel cell to supply power to the rural distributed energy system; According to the ammonia synthesis plant, the hydrogen and nitrogen in the air are processed into green ammonia through the Haber process; Heat recovery is performed by the energy supply framework based on the short-term power fluctuations, the long-term energy balance and the reaction heat generated in the ammonia synthesis plant.

3. The method according to claim 2, characterized in that The energy hub model, based on a full-year time-series production simulation, determines the annual comprehensive cost through operation and maintenance costs, penalty costs for curtailment of renewable energy generation, and penalty costs for insufficient power supply; According to the annual comprehensive cost, renewable energy constraints are imposed on the hydropower units and photovoltaic units by setting reduction coefficients.

4. The method according to claim 1, wherein In the sub-period piecewise asynchronous solution process, the convergence speed of the iteration direction is accelerated by the orthogonality between the conjugate search directions, including: Set the clock delay variable and obtain the local optimal solution of the iteration vector through the Lagrangian function; According to the local objective function offset term of the local optimal solution, a dynamic adjustment search is performed through the gradient change information of the Lagrangian function to achieve local curvature matching.

5. The method according to claim 4, characterized in that When the convergence deviation of the iteration direction is greater than the original deviation and the dual deviation, the iteration of the sub-period piecewise asynchronous solution process is stopped.

6. A time-series production simulation device for a rural distributed energy system, characterized in that: include: An equipment energy supply module is configured to establish an energy hub model based on a rural distributed energy system, the energy hub model including a renewable energy generation input side and a multi-energy load output side; an internal interconnection expansion structure and an energy conversion pathway are established between the renewable energy generation input side and the multi-energy load output side; and a steady-state operation mode of the energy hub model includes the production, storage, conversion, and utilization of electricity-heat-hydrogen-ammonia energy flows; According to the production, storage, conversion and utilization process, a discrete state space multi-energy coupling matrix is ​​established for quantification; A production simulation module is used to optimize the economic operating efficiency and multi-energy comprehensive utilization level of the energy hub model under a full-year time-series production simulation based on a cross-time-scale power and energy balance compensation mechanism; the operation optimization includes obtaining the annual comprehensive cost of the rural distributed energy system, wherein the annual comprehensive cost includes operation and maintenance costs, penalty costs for curtailed renewable energy generation, and penalty costs for insufficient power supply; The asynchronous solution module is used to decompose the annual time series into multiple sub-periods for distributed asynchronous solution, determine the conjugate gradient direction according to the iteration direction, and iteratively update the energy hub model; wherein, in the distributed asynchronous solution process of the sub-periods, the corresponding clock delay variable is introduced according to the length of any sub-period, and the asynchronous solution of the other sub-periods is maintained.

7. The device according to claim 6, characterized in that The device energy supply module is used to integrate, convert and store multiple energy flows based on the electricity-heat-hydrogen-ammonia energy flow through the energy supply framework of the rural distributed energy system, including: A hydrogen energy conversion unit is used to provide clean electricity from the hydropower unit and photovoltaic unit on the renewable energy power generation input side during short-term power fluctuations, drive the electrolyzer to electrolyze water to produce hydrogen, and compress and store the hydrogen; An electric energy conversion unit, configured to convert the hydrogen into electric energy through a fuel cell during long-term energy balance to supply power to the rural distributed energy system; an energy optimization unit for processing the hydrogen and nitrogen from air into green ammonia by the Haber process according to a synthetic ammonia plant; A heat recovery unit is used to recover heat energy through the energy supply framework according to the short-term power fluctuation, the long-term energy balance and the reaction heat generated in the ammonia synthesis device.

8. The device according to claim 7, characterized in that The production simulation module includes: A cost accounting unit is used to determine the annual comprehensive cost of the energy hub model based on the annual time-series production simulation by using operation and maintenance costs, penalty costs for curtailment of renewable energy power generation, and penalty costs for insufficient power supply; The cost constraint unit is used to constrain the renewable energy of the hydropower unit and the photovoltaic unit by setting a reduction coefficient according to the annual comprehensive cost.

9. The device according to claim 6, characterized in that The asynchronous solution module is used to accelerate the convergence speed of the iteration direction by using the orthogonality between conjugate search directions during the sub-period segmented asynchronous solution process, including: Vector iteration unit, used to set the clock delay variable and obtain the local optimal solution of the iteration vector through the Lagrangian function; A vector optimization unit is used to dynamically adjust and search based on the local objective function offset term of the local optimal solution through the gradient change information of the Lagrangian function to achieve local curvature matching.

10. The device according to claim 9, characterized in that The asynchronous solution module further includes: The iterative constraint unit is used to stop iterating the sub-period piecewise asynchronous solution process when the convergence deviation of the iterative direction is greater than the original deviation and the dual deviation.

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