Energy system configuration ratio smooth control and dynamic constraint method

Abstract

The application provides an energy system configuration proportion smooth control and dynamic constraint method, relates to the technical field of energy system configuration, and comprises the following steps: identifying the optimal configuration proportion of each type of technology in the energy system in the terminal year through forward optimization; initializing the year anchor point and the configuration proportion anchor point; obtaining the optimal configuration proportion of each year from the initial year to the previous year of the terminal year by iteratively performing the following steps: constructing the upper and lower limit intervals of the configuration proportion of each type of technology in a year based on the optimal configuration proportion; solving the optimal configuration proportion of the year based on the upper and lower limit intervals of the configuration proportion of the year; and updating the year anchor point and the configuration proportion anchor point. The application has the advantages of realizing the energy system configuration with stage reachability and continuity of the technology path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy system configuration, in particular to an energy system configuration proportion smooth control and dynamic constraint method. BACKGROUND

[0002] In order to ensure the reliability of energy technology configuration, it is necessary to periodically perform energy system configuration for a period of time.

[0003] The prior art often determines the configuration strategy of energy through the objective function on the multi-period time sequence. This mode has the following defects: lack of continuity constraint of technology evolution path, that is, the model optimization result often appears a phenomenon that a certain technology suddenly appears in a dominant position in a certain year, completely ignoring the actual constraints of sudden technology replacement; also ignoring the engineering implementability of technology penetration, for example, such a jump result may be the optimal solution in the model, but it is difficult to implement in reality, which may lead to distortion, infrastructure lag, unstable system operation and other serious consequences; and the dynamic change boundary of technology configuration proportion is not considered.

[0004] Therefore, it is urgent to provide a modeling mechanism that combines future carbon constraint targets with real system evolution capabilities, so that the optimization result of the energy system not only meets the final carbon emission requirement, but also has stage accessibility and continuity of technology path. SUMMARY

[0005] The purpose of the present application is to provide an energy system configuration proportion smooth control and dynamic constraint method, which realizes an energy system configuration method with stage accessibility and continuity of technology path.

[0006] The present application is realized by the following technical scheme:

[0007] An energy system configuration proportion smooth control and dynamic constraint method comprises the following steps:

[0008] Identifying the optimal configuration proportion of the a-type technology in the energy system in the final year through forward optimization , A is the set of all types of technology;

[0009] Assigning the initial value N to Y to realize the initialization year anchor point , and the initialization configuration proportion anchor point is the optimal configuration proportion of each type of technology in the final year , and the following operations are executed in a loop until the optimal configuration proportion of each year from the initial year to the year before the final year is obtained:

[0010] Based on the optimal configuration ratio, various types of technologies are constructed in the year. Configuration ratio upper and lower limits range;

[0011] Based on year The upper and lower limits of the configuration ratio, and the solution for the year. Optimal configuration ratio ;

[0012] Anchor the year by subtracting 1 from the value of Y. Updated to And update the configuration ratio anchor point to , .

[0013] Preferably, the method for identifying the optimal configuration ratio of various types of technologies in the energy system in the final year through positive optimization is as follows:

[0014] Establish an optimization model:

[0015] ;

[0016] in, and The final year The unit annualized consumption and new planned capacity of type a technology in China and The final year Unit operation and maintenance consumption and available capacity of type a technology;

[0017] Construct multiple constraints;

[0018] Solving based on constraints and the optimization model yields the following results: optimal solution , ;

[0019] Obtain the final year respectively Optimal configuration ratio of type a technology :

[0020] .

[0021] Preferably, the multiple constraints include carbon emission constraints, energy demand constraints, pollutant emission demand constraints, equipment retirement constraints, and technology allocation share constraints.

[0022] Preferably, the techniques for constructing various types of [technology] are [specifically] in [year]. The method for determining the upper and lower limits of the configuration ratio is as follows:

[0023] Year of Construction The annual variation boundary;

[0024] Based on the optimal configuration ratio and year of the anchor point. The annual variation boundary, deriving the year The upper and lower limits of the configuration ratio described in the type a technology , .

[0025] Preferably, the year of construction The method for determining the annual change boundary is as follows:

[0026] For the year The maximum annual growth rate of technology promotion for type a technology with unequal numerical settings Maximum annual deceleration rate of technology elimination , .

[0027] Preferably, the year is derived. The upper and lower limits of the configuration ratio described in the type a technology The method is as follows:

[0028] definition:

[0029] ;

[0030] ;

[0031] ;

[0032] in, and Years The lower limit and upper limit of the experience-based technology configuration ratio for type a technology in China. and These are intermediate parameters.

[0033] Preferably, the method for obtaining the maximum annual growth rate of technology promotion and the maximum annual deceleration rate of technology obsolescence is as follows:

[0034] The results were obtained using a multi-factor linear regression method.

[0035] ;

[0036] ;

[0037] in, and Years The maximum annual growth rate of technology promotion and the maximum annual deceleration rate of technology obsolescence are not equal for type a technology. , The total number of years for the end year , , , , , and are the learning rate, policy incentive level, supply chain scalability, acceptance and resource availability of the a-type technology in the year , and are weights, k=1,2,…,5.

[0038] Preferably, the method for solving the optimal configuration ratio of the year is:

[0039] Establish the objective function:

[0040] ;

[0041] wherein, and are the unit annual consumption and newly planned capacity of the a-type technology in the year , and are the unit operation and maintenance consumption and available capacity of the a-type technology in the year ;

[0042] Establish multiple constraints;

[0043] According to the multiple constraints and the optimization model, the optimal solution of the year is obtained, and the optimal solution is located within the upper and lower limit intervals of the configuration ratio of the a-type technology in the year ;

[0044] The optimal configuration ratio of each type of technology is obtained respectively:

[0045] ;

[0046] wherein, is the optimal configuration ratio of the a-type technology in the year .

[0047] Preferably, the multiple constraints include supply and demand balance constraints, emission limit constraints, budget constraints and equipment retirement constraints in the year .

[0048] The technical solution of the present application has at least the following advantages and beneficial effects:

[0049] ​The application controls the deployment speed of various energy technologies by constructing the upper and lower limit intervals of the configuration proportion of each year, effectively prevents the phenomenon of cliff changes or step replacement in the technology configuration proportion in the model output result, guarantees the continuity, accessibility and deployment logic rationality of the system structure change, and meets the engineering implementation rhythm;

[0050] The application introduces the expected deployment boundary based on the target, scenario assumption or experience judgment, and crosses and fuses the dynamic interval obtained by reversing the configuration proportion change rate to construct the technology deployment interval, which improves the flexibility and robustness of the path control, and enhances the credibility and real adaptability of the simulation result.

[0051] The application models the promotion (popularization) rate and the exit (elimination) rate of technology deployment respectively, reflects the physical inertia and institutional lag existing in the technology replacement process, is suitable for describing the double-track system evolution process in which the rapid expansion of renewable energy and the slow exit of fossil energy coexist, and makes the path result more in line with the objective law of energy system evolution.

[0052] The application takes the final configuration result as an anchor point, adopts the cooperative process of nested reverse deduction and forward fitting year by year, determines the configuration proportion and system deployment behavior of each year in turn, avoids the instability and path incoherence caused by one-time long-period global optimization, is beneficial to generate an energy transformation path with clear stages and clear logic, and is convenient for policy landing and stage evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The flowchart of the energy system configuration proportion smooth control and dynamic constraint method provided for embodiment 1 of the application is shown.

[0054] Figure 2 The flowchart of step S2 provided for embodiment 1 of the application is shown.

[0055] Figure 3 The principle diagram of the energy system configuration proportion smooth control and dynamic constraint method based on the coordinate axis display provided for embodiment 1 of the application is shown. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. The components of the embodiments of the application described and shown in the drawings here can be arranged and designed in various different configurations.

[0057] Embodiment 1

[0058] The embodiment provides a method for configuring proportionally smooth control and dynamic constraints of an energy system, which comprises the following steps Figure 1 , and specifically comprises the following steps:

[0059] Step S1: identifying optimal configuration proportions of each type of technology in the energy system in the terminal year through forward optimization, that is, optimal configuration proportions of the a-th type of technology in the terminal year . A is a set of all types of technology.

[0060] In the embodiment, the method for identifying optimal configuration proportions of each type of technology in the energy system in the terminal year through forward optimization is as follows:

[0061] Step S1.1: establishing an optimization model

[0062] ;

[0063] wherein, and respectively represent unit annual consumption and newly planned capacity of the a-th type of technology in the terminal year , and respectively represent unit operation and maintenance consumption and available capacity of the a-th type of technology in the terminal year ;

[0064] Step S1.2: constructing multiple constraint conditions

[0065] Step S1.3: solving according to the constraint conditions and the optimization model to obtain optimal solutions of , ;

[0066] Step S1.4: obtaining optimal configuration proportions of the a-th type of technology in the terminal year :

[0067] .

[0068] On this basis, the multiple constraint conditions comprise carbon emission constraints, energy demand constraints, pollutant emission demand constraints, equipment retirement constraints and technology configuration share constraints.

[0069] It is particularly stated that the optimal configuration proportions of each type of technology obtained herein do not refer to sales or market income shares, but physical supply proportions of each type of technology in the system energy supply structure, and the configuration result is transmitted to step S2 as a numerical value used in the first iteration in the iterative calculation of step S2, that is, as a starting point of recursion.

[0070] ​Step S2: Assign an initial value N to Y to initialize the year anchor point. And the initial configuration ratio anchor point is the optimal configuration ratio of each type of technology in the final year. The operation is repeated, that is, iterated, until the result is obtained from the initial year. The year before the end of the term Optimal allocation ratio for each year:

[0071] Step S21: Based on the optimal configuration ratio, construct the various types of technologies in the year. The purpose of this step is to establish upper and lower limits for the configuration ratio of each technology in different years, so as to ensure that the deployment path forward from the final goal is physically achievable and technically smooth, and to avoid system infeasibility problems caused by leapfrog replacements or abrupt changes.

[0072] As a preferred embodiment, the techniques for constructing various types of [technology] are described in [year]. The method for determining the upper and lower limits of the configuration ratio is as follows:

[0073] Year of Construction The annual variation boundary, which is to set two asymmetric deployment rate parameters for each technology, aims to reflect the differences in construction cycle, substitution inertia and other factors in the physical deployment of different technologies. For example, renewable power generation technology is promoted quickly but phased out slowly, while coal-fired units are phased out slowly and are difficult to withdraw quickly.

[0074] Next, based on the optimal configuration ratio and year of the anchor point, The annual variation boundary, deriving the year The upper and lower limits of the configuration ratio described in the type a technology , .

[0075] Based on the above scheme, the construction year The method for determining the annual change boundary is as follows:

[0076] For the year The maximum annual growth rate of technology promotion for type a technology with unequal numerical settings Maximum annual deceleration rate of technology elimination , .

[0077] Then, derive the year. The upper and lower limits of the configuration ratio described in the type a technology The method is as follows:

[0078] definition:

[0079] ;

[0080] ;

[0081] ;

[0082] wherein, and are the lower and upper bounds of the experienced technology deployment ratio of the a-th type of technology in year , and are intermediate parameters.

[0083] On the other hand, the maximum annual growth rate of the technology promotion and the maximum annual deceleration rate of the technology elimination are constructed as a dynamic step, which is determined by a multi-factor function, and the specific acquisition method is as follows:

[0084] The multi-factor linear regression method is used for acquisition:

[0085] ;

[0086] ;

[0087] wherein, and are the maximum annual growth rate and the maximum annual deceleration rate of the a-th type of technology in year , , is the total number of years from the final year , , , , and are the learning rate, the policy incentive level, the supply chain expandability, the acceptance and the resource availability of the a-th type of technology in year , and are weights, k = 1, 2, …, 5.

[0088] Step S22: based on the upper and lower limit interval of the deployment ratio in year , the optimal deployment ratio in year is solved.

[0089] Step S23: the year anchor point is updated to by reducing the value of Y by 1, and the deployment ratio anchor point is updated to , .

[0090] In the embodiment, the method for solving the optimal configuration ratio of the year is as follows:

[0091] The objective function is established as follows:

[0092]

[0093] wherein, and are the unit annual consumption and the newly planned capacity of the a-type technology in the year , and are the unit operation and maintenance consumption and the available capacity of the a-type technology in the year ;

[0094] As another optional solution, the objective function of the total consumption and the total emission weighted by the weights and for weighted summation can also be established, for example:

[0095]

[0096] The multiple constraint conditions are established;

[0097] According to the multiple constraint conditions and the optimization model, the optimal solution of the year , is obtained, and the optimal solution is located within the upper and lower limit interval of the configuration ratio of the a-type technology in the year ;

[0098] The optimal configuration ratio of each type of technology is obtained respectively as follows:

[0099]

[0100] wherein, is the optimal configuration ratio of the a-type technology in the year .

[0101] On the other hand, the multiple constraints include the supply and demand balance constraint, the emission limitation constraint, the budget constraint and the equipment retirement constraint in the year .

[0102] In short, referring to Figure 3 ,the embodiment first identifies the optimal configuration ratio of each type of technology in the terminal year of the energy system by forward optimization , and assigns the initial value N to Y to realize the initialization year anchor point , at this time, the year ​​​​​​​​the upper and lower limit intervals of the configuration proportion of the a-th type of technology, and then forward optimization identifies the optimal configuration proportion of each type of technology in the energy system in year. By updating the value of Y, the above steps are repeatedly performed to obtain the optimal configuration proportion of the a-th type of technology in all periods.

[0103] The embodiment adopts the nested "end-period configuration anchoring-reverse interval derivation-forward path solving" structure year by year, realizes the dynamic constraint and path optimization of the evolution process of the energy technology configuration proportion, and solves the key problems such as technology path jumping and deployment infeasibility in traditional models. The embodiment includes the following three core stages, and advances year by year in a nested iterative manner. The scheme of the embodiment can be roughly divided into three stages:

[0104] The first stage starts from the target year in the long term (such as 2060), determines the optimal configuration proportion of each type of energy technology under the constraint conditions of carbon emission, energy supply safety, equipment capacity and the like based on the solving result of the traditional energy system optimization model. The "configuration proportion" here refers to the energy supply proportion of each energy technology in the system to undertake terminal services, reflecting its functional role in the long-term structure, rather than market sales or income share, and has a clear physical system meaning. The result of this stage will be the technology anchoring target of the entire path planning, which is passed to the next derivation process. In short, the first stage mainly identifies the optimal configuration proportion of each type of technology in the energy system in the terminal year , as the starting point of subsequent recursion.

[0105] The second stage and the third stage are iterative steps. Among them, the second stage is the reverse derivation stage of the deployment interval, which is based on the terminal technology configuration result obtained in the first stage, combines the maximum annual evolution rate of each technology in the real deployment, and reversely derives from the target year to the history year by year to form the upper and lower limit intervals of the acceptable configuration proportion of each technology in the last year. Ensure smooth and continuous technology evolution, avoid unrealistic drastic changes in share in the middle years, and enhance accessibility. This stage introduces an asymmetric modeling mechanism of promotion rate and elimination rate to enhance the expression ability of different technology development pace. Δs can be a fixed value, or can be dynamically set according to technology learning rate, policy support strength, social acceptance, resource availability and supply chain construction capacity and the like, to ensure that the deployment interval has realistic feasibility and adaptability. The second stage of the embodiment is to derive the configuration proportion interval of the year based on the optimal configuration proportion of the year .

[0106] The third stage is a forward energy system deployment optimization stage, which uses the annual share interval obtained in the second stage as a hard constraint condition, combines a multi-period dynamic optimization model, and gradually deduces forward from the starting year, comprehensively considers investment cost, operation cost, energy demand and carbon emission constraints, solves the new investment amount, capacity update and operation strategy of each technology in the multi-period energy system, and obtains the annual optimized configuration proportion. Then, the result is used as a new anchor point to re-enter the second stage and the third stage, and the target year is sequentially decreased to gradually advance year by year until the full period path optimization is completed. In this embodiment, the third stage is to calculate the optimal configuration proportion of the year based on the configuration proportion interval of the year deduced. The second stage and the third stage can be repeated to continue to deduce the optimal configuration proportion of the previous year as soon as the optimal configuration proportion of a year is calculated, until the optimal configuration proportion of all years is obtained, that is, a sequence of all optimal configuration proportions from the initial year to the terminal year is formed.

[0107] Through the bidirectional modeling mechanism of the nested year-by-year loop, the scheme of the embodiment not only avoids the problems such as sudden technology replacement and fault path switching in the traditional energy system optimization model, but also significantly enhances the engineering landing nature of the path result, the physical rationality of the system scheduling and the explainability of the policy making. The method maintains the generality of the model while embedding dynamic reachability and evolutionary continuity constraints, and is suitable for energy system modeling and decision support in various low-carbon transformation scenarios, providing a scientific, efficient and operable path design tool for achieving the long-term carbon neutralization strategy. The multi-period path output by the model not only meets the carbon constraints and energy supply balance, but also can be accepted by policy makers and the industry in practice, and will not mislead the actual deployment due to the idealized assumptions of the model. Moreover, the model output can be seamlessly connected to existing linear programming models, and the complexity of large-scale reconstruction of the model is not increased, and the model is enhanced only by the constraint condition of the share interval without changing the existing optimal investment solving framework.

[0108] The above is only a preferred embodiment of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.​

Claims

1. A method for smooth control and dynamic constraint of energy system configuration ratio, characterized in that, The method comprises the following steps: Identifying the terminal year in an energy system by forward optimization Optimal configuration ratio of technology of type a in year t , A is the set of all types of technology Y is given an initial value N, which realizes the initialization of the year anchor point , and the configuration proportion anchor point of each type of technology in the terminal year is initialized as the optimal configuration proportion , the following operations are performed in a loop until the optimal configuration proportion of each year from the initial year to the year before the terminal year is obtained : Based on the optimal configuration ratio, construct the year The upper and lower limit intervals of the configuration ratio Based on the upper and lower limit intervals of the configuration ratio of the year , the optimal configuration ratio of the year is solved ; The year anchor is updated by reducing the value of Y by 1 to and the configuration scale anchor is updated to , ; The construction of each type of technology in the year The method for configuring the upper and lower limit intervals of the proportion is as follows: year of construction annual change boundaries; according to the optimal configuration proportion of the year of the anchor point and the year deriving the year of the annual change boundary the configuration proportion upper and lower limit interval of the a-type technology of the year , ; the construction year The method for the annual change boundary is: for the year of the a-type technology is set to a value unequal to the maximum annual growth rate of technology diffusion and the maximum annual deceleration rate of technology obsolescence , ; derivation year the configuration proportion upper and lower limit interval of the a-type technology of the derivation year The method is: Definition: ； ； ； wherein, and are the lower and upper bounds of the experienced technology configuration ratio for type a technology in year and are intermediate parameters;​ The acquisition method of the maximum annual growth rate of the technology popularization and the maximum annual deceleration rate of the technology elimination is: The acquisition is performed by using a multi-factor linear regression method: ； ； where, and are the maximum annual growth rate and the maximum annual deceleration rate of technology diffusion, respectively, , is the total number of years from the initial year to the final year, , , , and are the learning rate, the policy incentive level, the supply chain scalability, the acceptance, and the resource availability of the a-th type technology in year , and are the weights, k = 1, 2, …, 5.​ 2. The method of claim 1, wherein, The method for identifying the optimal configuration proportion of each type of technology in the energy system in the terminal year through forward optimization is: An optimization model is established: ； wherein, and are the unit annual consumption and the newly planned capacity of the a-th type technology in the terminal year respectively, and are the unit operation and maintenance consumption and the available capacity of the a-th type technology in the terminal year respectively. A plurality of constraint conditions are constructed; According to the constraint condition and the optimization model, a solution is obtained of the optimal solution , ; acquiring the terminal year optimal configuration ratio of the a-type technology : 。 3. The method of claim 2, wherein, The plurality of constraint conditions include carbon emission constraints, energy demand constraints, pollutant emission demand constraints, equipment retirement constraints, and technology configuration share constraints.

4. The method of claim 1, wherein, The solving year The optimal configuration ratio The method is: An objective function is established: ； wherein, and are the unit annual consumption and the newly planned capacity of the a-th type technology in year respectively, and are the unit operation and maintenance consumption and the available capacity of the a-th type technology in year respectively. A plurality of constraint conditions are constructed; The optimal solution of the configuration proportion of the a-type technology in the year is obtained according to solving under various constraint conditions and optimization models , The optimal solution of the configuration proportion of the a-type technology in the year is obtained according to solving under various constraint conditions and optimization models ​​​ The optimal configuration proportion of each type of technology is acquired respectively: ； wherein is the year the optimal configuration ratio of the a-type technology in the year 5. The method of claim 4, wherein, The plurality of constraints includes year supply and demand balance constraints, emission limit constraints, budget constraints, and equipment retirement constraints.

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