A power supply structure optimization method and system for energy transformation and flexibility improvement

Abstract

The present application relates to the technical field of elastic power system planning optimization, and particularly relates to a power supply structure optimization method and system for energy transformation and elasticity improvement, comprising: constructing and solving a short-term unit commitment model according to collected power system data to obtain unit output and line power flow information; inputting the unit output and line power flow information into a constructed two-stage long-term power system planning model considering extreme events to obtain unit capacity expansion demand and the most serious extreme event scenario of the power system, and inputting the same into the short-term unit commitment operation model to take the same as a standard thermal power unit to be upgraded for unit upgrading to obtain unit expansion results. The present application realizes a unit structure optimization scheme combined with long-term planning and short-term operation by taking into account long-term expansion planning and short-term operation, thereby laying a foundation for subsequent elastic power system planning optimization research.

Description

Technical Field

[0001] This invention relates to the field of resilient power system planning and optimization technology, and in particular to a power structure optimization method and system for energy transition and resilience enhancement. Background Technology

[0002] Power system planning and power system optimization are problems on two different time scales. Power system planning is a long-term planning problem, while power system optimization is a short-term optimization problem. Power system planning includes generation expansion plans and transmission line expansion plans, which are national strategic investments over a long time scale and should be carried out under the guidance of national plans and energy policies. Figure 1 To plan the architecture for the power system.

[0003] The goal of long-term planning expansion is to minimize future power system investment and operating costs, and to better address technical issues such as selecting expansion sites and capacities. With the increasing penetration of renewable energy, power system planning problems including generation expansion plans are particularly important. The uncertainty and volatility of renewable energy pose new requirements for the technical accuracy and operational flexibility involved in long-term planning problems. In the past, power system operation was generally modeled using net load duration curves (LDC), i.e., load blocks, such as... Figure 2 As shown, the modeling method represented by the net load duration curve does not retain the unit's operational sequence information, and therefore cannot capture the characteristics of renewable energy output changing with generation time. LDC modeling only considers the operation within different load blocks; therefore, the more detailed the load blocks considered, the more reliable the unit operation results without considering time sequence. However, detailed partitioning will significantly increase the computational burden. The LDC model's characterization of unit operation cannot meet certain technical accuracy requirements, i.e., the time constraints of the generator units, such as minimum start-up and shutdown time constraints and ramp-up constraints. Therefore, the LDC modeling method cannot meet the requirements of high technical accuracy and flexibility. Long-term planning problems are usually modeled as mixed-integer programming (MIP), where binary decision variables represent investment decisions. The embedded system operation model simulates short-term operational decisions to minimize total investment and total operating costs. To reflect operational flexibility, the embedded system operation model should be a time-series, net load unit combination problem. Under this ideal condition, discrete decisions and unit operating conditions can be modeled, taking into account operational flexibility.

[0004] However, the complexity of the unit combination problem makes it impossible to directly simulate the unit's operating status throughout the year. The introduction of a large number of discrete variables would drastically increase the computation time, causing it to grow exponentially, rendering the embedded unit combination problem meaningless. Therefore, different types of approximation algorithms and simplified embedded operation models have been developed extensively. These include convex relaxation processing of the unit combination problem, using a coarser time resolution while considering unit clustering rather than individual operations, and embedding unit combination operation problems representing weeks or days as short-term simulations. Among these, approximate convex or simplified embedded operation models relax the original mixed integer model, transforming the original MIP problem into a linear programming (LP) problem. The problem is that this method is quite difficult and has certain limitations. Considering clustering to reduce the computational complexity of the embedded operation model is a promising approach. This involves assuming that units within the same group have similar technical parameters such as capacity, heat rate, and heating rate, eliminating heterogeneity within the same cluster, and using binary decision variables for the entire unit. The clustering strategy is crucial in controlling errors caused by approximations. Finding an optimal clustering strategy that minimizes clustering error is itself a difficult combinatorial optimization problem. When considering transmission constraints and similar units in different locations, the value of unit clustering methods is limited. The problem of selecting representative weeks or days for unit combination operation lies in how to choose target representative weeks or days as representative of typical long-term planning scenarios. The operating results obtained from selecting different representative days may differ. However, considering a certain robustness to uncertainties when selecting representative days can effectively solve this problem. That is, the uncertain renewable energy output and uncertain load fluctuation range should encompass as many actual scenarios as possible. Therefore, for long-term planning problems with high technical accuracy and certain technical parameter requirements, robust operation of typical days or weeks can be considered as a combination of long-term planning and short-term operation.

[0005] In summary, to study the unit structure optimization problem for improving power system resilience and energy transition, it is necessary to provide a model framework that combines long-term planning and short-term operation, along with an efficient solution algorithm. Furthermore, it is necessary to consider a more comprehensive power system resilience assessment standard to fully measure the effect of system resilience improvement. Summary of the Invention

[0006] The purpose of this invention is to provide a power structure optimization method and system for energy transition and resilience enhancement, so as to realize a unit structure optimization scheme that combines long-term planning and short-term operation, thereby laying the foundation for subsequent research on flexible power system planning and optimization.

[0007] To address the above technical problems, this invention provides a power structure optimization method and system for energy transition and resilience enhancement.

[0008] In a first aspect, the present invention provides a power structure optimization method for energy transition and resilience enhancement, the method comprising the following steps:

[0009] The objective function is to minimize operating costs, and a short-term unit combination model is constructed based on the collected power system data.

[0010] Solve the short-term unit combination model to obtain unit output and line power flow information;

[0011] A two-stage long-term power system planning model considering extreme events is constructed, and the unit output and line power flow information are input into the two-stage long-term power system planning model to obtain the unit capacity expansion demand and the most severe extreme event scenario of the power system.

[0012] The unit capacity expansion requirements and the most severe extreme event scenario of the power system are input into the short-term unit combination operation model, so as to upgrade the standard thermal power unit to obtain the unit expansion results.

[0013] Secondly, the present invention provides a power structure optimization system for energy transition and resilience enhancement, the system comprising:

[0014] The short-term model building module is used to construct a short-term unit combination model based on collected power system data, with the objective function of minimizing operating costs.

[0015] The short-term model calculation module is used to solve the short-term unit combination model to obtain unit output and line power flow information;

[0016] The long-term model building module is used to construct a two-stage power system long-term planning model that considers extreme events. The unit output and line power flow information are input into the two-stage power system long-term planning model to obtain the unit capacity expansion demand and the most severe extreme event scenario of the power system.

[0017] The unit upgrade module is used to input the unit capacity expansion requirements and the most severe extreme event scenario of the power system into the short-term unit combined operation model, so as to use it as the standard thermal power unit to be upgraded and obtain the unit expansion result.

[0018] Thirdly, the present invention also provides a computer device, including a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the computer device performs the steps of implementing the above-described method.

[0019] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0020] This invention provides a power structure optimization method and system for energy transition and resilience enhancement. The method divides the overall power structure optimization model into two iterative solutions. The first part is the overall long-term planning part, which uses an improved LDC model to determine the minimum unit capacity required for expansion at different locations. This long-term planning part also uses a two-stage model to reduce system load shedding under extreme events, while taking into account the impact of long-term energy transition policies on power structure expansion. The second part initially treats the solutions for the corresponding expansion units as standard thermal power units to be upgraded, and then upgrades the units in the expansion section based on this. This achieves a technical solution for unit structure optimization that combines long-term planning with short-term operation, laying the foundation for subsequent research on resilient power system planning and optimization. Compared with existing technologies, this method takes into account both long-term expansion planning and short-term operation, enabling power structure upgrade optimization with high technical accuracy and a long-term planning scale. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the power system planning architecture provided by the background technology of this invention;

[0022] Figure 2 This is a schematic diagram of the continuous net load curve provided in the background technology of this invention;

[0023] Figure 3 This is a schematic diagram of a power structure optimization method for energy transition and resilience enhancement provided by an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the C&CG algorithm provided in an embodiment of the present invention;

[0025] Figure 5 This is a block diagram of a power structure optimization system for energy transition and resilience enhancement provided by an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0028] refer to Figure 3 This invention provides a power structure optimization method for energy transition and resilience enhancement, such as... Figure 3 As shown, the method includes the following steps:

[0029] S1. The objective function is to minimize operating costs, and a short-term unit combination model is constructed based on the collected power system data.

[0030] In this embodiment, the power system data includes uncertain renewable energy output, the expected forced outage rate (EFOR) considering extreme events and unit output, minimum operating capacity, rated operational capacity, minimum start-up and shutdown time, carbon emissions per unit output, and ramp rate. This power system data differentiates between different types of generating units. Depending on the variations in the power system data, the units considered may include wind turbines, photovoltaic units, nuclear power units, hydropower units, natural gas units, coal-fired power units, and critical coal-fired units equipped with carbon capture devices. The considered resilience indicators include the system offset rate, i.e., robustness RM. devi System recovery rate RM adpt With recovery time t r , respectively defined as and t r = t4-t1, where t1 is the time when the extreme event begins, t2 is the time when the system suffers the maximum loss, and P min That is, the system function value at the moment of maximum loss, P. 0(t) Let P(t) be the target system function function under normal operation, t3 be the actual system function function, t4 be the time when the system can restore the load to the maximum extent by restarting the unit and quickly starting the unit without dispatching personnel for maintenance, and t4 be the time when the system returns to normal operation. It should be noted that although most of the elasticity assessment indicators used are post-simulation assessment indicators, that is, they cannot be included in the model as objective functions, the system elasticity can be improved by determining measures. The system robustness and deviation rate can be improved by expanding a certain number of units in the long-term planning model under the consideration of the impact of extreme events. The system recovery rate can be improved by selecting units with faster ramp-up rates while meeting the rated operating capacity standards. Selecting units with faster start-up and shutdown times can shorten the recovery time to a certain extent.

[0031] S2. Solve the short-term unit combination model to obtain unit output and line power flow information.

[0032] In one embodiment, the objective function of the short-term unit combination model is to minimize operating costs, including thermal power unit operating costs, minimum cost of maintaining unit operation, and minimum start-up and shutdown costs, specifically:

[0033]

[0034] The constraints of the short-term unit combination model include:

[0035] Node power balance constraints:

[0036]

[0037] Reference node and line DC power flow constraints:

[0038]

[0039] Line capacity constraints:

[0040]

[0041] Unit output and segment output constraints:

[0042]

[0043]

[0044] Renewable energy output constraints:

[0045]

[0046] Minimum start-up and stop time constraints:

[0047]

[0048]

[0049] Unit start-up and shutdown cost constraints:

[0050]

[0051]

[0052] Minimum ramp constraint:

[0053]

[0054]

[0055] In the formula, c i,k This represents the output cost of the k-th segment of the generator set; Indicates the output variable of the unit in sections; co i This refers to the cost of putting the unit into operation, or the minimum cost of maintaining the unit's operation. This represents the variable indicating whether unit i is in operation within time period t, and characterizes whether the unit is in operation. denoted by and , respectively, the minimum start-up and shutdown costs of unit i within time period t; the superscript b indicates the preliminary basic solution of the short-term unit combination model; Indicates the magnitude of the power flow along the line; This represents the unit output variable in the short-term unit combination model; This means that the wind power output variable in the basic solution should be less than its predicted value. represents the load output variable; k represents the unit segment output index; l, m, i, w, and d are the indices of lines, power nodes, units, wind power, and loads, respectively; L(m) represents the set of lines connected to node m; U(m) represents the set of conventional units connected to node m; W(m) represents the set of wind turbine units connected to node m; D(m) represents the set of loads connected to node m. This represents the phase of power node m in the fundamental solution; x represents the phase of power node n in the fundamental solution; mn This represents the line reactance of line mn; P represents the reference phase in the fundamental solution. l max Indicates the upper limit of line transmission capacity; P i min Indicates the lower limit of unit output; P i max Indicates the upper limit of the generator unit's output; This represents the segmented output in the basic solution, where i, k, and t are the indices of the generator, the output segment, and the time, respectively. Indicates the upper limit of the output of each section of the unit; This represents the predicted output value of renewable energy, which is considered to be the predicted output value under the above constraints. T represents the start-up time variable of unit i during the time period (t-1); i on This indicates the minimum startup time of the generator set, with the superscript "on" indicating that the generator set is enabled, and "i" being the generator set index. T represents the shutdown time variable of unit i during the time period (t-1); i off This represents the minimum shutdown time for the generator set, where i is the generator set index and off is the shutdown index; st i sd i Indicates the start-up and shutdown costs of unit i; UR i Indicates the unit's ramp-up capacity; DR i This indicates the unit's ramp-down capacity; the superscript b indicates the preliminary basic solution of the short-term unit combination model.

[0056] This embodiment considers power system data such as minimum operating capacity, rated operating capacity, minimum start-up and shutdown time, and ramp rate when calculating the short-term unit combination model. When calculating line power flow, either AC or DC power flow can be used to ensure a certain level of calculation accuracy. The power output of each unit obtained from the above unit combination model is... With line power flow size This will be used to construct an uncertainty set in the next long-term planning phase, taking into account unit output and forced outage rates due to extreme events, as well as the probability of line interruption.

[0057] S3. Construct a two-stage long-term planning model for the power system that considers extreme events, and input the unit output and line power flow information into the two-stage long-term planning model to obtain the unit capacity expansion demand and the most severe extreme event scenario of the power system.

[0058] This embodiment uses an improved Net Load Duration Curve (LDC) model to construct a two-stage long-term power system planning model that considers extreme events. Although the LDC model does not involve unit operation sequence information, it can better characterize the long-term capacity planning problem of units. In capacity planning, the impact of extreme events on the power system is considered. An uncertainty set is constructed by considering the forced outage rate related to unit output and extreme events in the unit combination model. At the same time, an uncertainty set considering the probability of line interruption is constructed to simulate the impact of extreme events on the power system.

[0059] In one embodiment, the two-stage power system long-term planning model includes a first-stage long-term planning model. This first-stage long-term planning model does not consider the impact of extreme events on the power system, and its objective function is to minimize facility expansion costs and equipment operating costs, specifically:

[0060] min TC=IC+OC

[0061]

[0062]

[0063] In the formula, TC represents the total investment cost for power system expansion; IC represents the investment cost for unit and line expansion; OC represents the system operating cost; tl represents the long-term planning time period index of the system; ge represents the generating unit; pl represents the line; c ge,tl Indicates the investment cost for unit expansion; e ge,tl c represents a binary discrete variable representing the state of unit expansion; pl,tl Indicates the investment cost for line expansion; e pl,tl A binary discrete variable representing the status of line expansion; ac tl Λ represents the annualized operating cost for each planning period t; dIndicates the duration of load block d; f gec,d,tl This represents the unit operating cost function; The output variable `gec` represents the traditional unit output variable in the first stage; `gec` represents the traditional unit index that satisfies...

[0064] The constraints of the first-stage long-term planning model include:

[0065] Facility status constraints:

[0066]

[0067]

[0068] Capacity constraints:

[0069]

[0070] Unit output constraints:

[0071]

[0072]

[0073] Line power constraints and DC power flow constraints:

[0074]

[0075]

[0076]

[0077] Power balance constraints:

[0078]

[0079] Maximum limit on the number of facilities to be constructed:

[0080]

[0081] Carbon emission policy constraints:

[0082]

[0083]

[0084]

[0085]

[0086] In the formula, e gec,tl p represents a binary discrete variable representing the GEC expansion status of a traditional unit;gec,max Indicates the upper limit of GEC output of traditional generator sets; e ger,tl This indicates the expansion status of renewable energy units, where ger and tl are indices of the renewable energy unit and the planning time, respectively; p ger,re Indicates the predicted value for renewable energy units; D tl,max represents the maximum load demand; r represents the reserve ratio, a higher reserve ratio results in higher system reliability but increases costs; p gec,min This indicates the lower limit of GEC output for conventional generator sets; This represents the output of renewable energy units. 0 is the index of the first-stage variable, and ger, d, and tl are the indices of the renewable energy unit, load block, and planning time, respectively. Indicates the power flow of the first-stage line; p pl,max This indicates the upper limit of power flow on the line, where pl is the index of the line; This represents the first phase planning time tl, and the phase of node A within load block d; Indicates the first-stage planning time tl, and the phase of node A′ within load block d; x AA′ This represents the line reactance of line AA′; M represents a constant. In the first stage of the traditional unit planning phase tl, the output variable corresponding to the load block d is represented; A represents the bus in the system; φ A,d,tl This represents the load corresponding to load block d within the planning stage tl at bus i; G A This represents the set of units connected to bus A; PF A PT represents the set of routes originating from bus A. A Represents the set of routes leading to bus A; RT lb Indicates the lower limit of the renewable energy installed capacity target; D tl,max Indicates the maximum load demand; PC tl tce represents the percentage of non-fossil energy generation at time tl; tl Indicates carbon emissions; F gec,tl (p gec,d,tl () indicates carbon-containing fuel consumption; CEF gec,tl p represents the emission factor. gec,d,tl This indicates the power generation of the load block d within the unit's planning phase tl; represents carbon emission intensity; g represents the maximum number of expansion facilities across all phases.

[0087] In this embodiment, the facility status constraints describe facility status quantities that are essentially non-reduction quantities; the power balance constraints describe the power balance at each bus of the system; it should be noted that this embodiment can also add a lower limit constraint on the proportion of regional renewable energy consumption, which will not be elaborated here.

[0088] In a further implementation, prior to the second phase of constructing the two-stage power system long-term planning model, an uncertainty set is constructed to account for the most severe impacts of extreme events on the power system.

[0089] In this embodiment, the impact of extreme events on the power system can be divided into the impact on the output of renewable energy units, the impact on traditional units, and the impact on power lines. Renewable energy units include wind power, photovoltaic units, and other units whose output is uncontrolled. Therefore, uncertainty sets are constructed to represent their extreme conditions, resulting in uncertainty sets including the renewable energy output uncertainty set, the unit damage uncertainty set, and the power line interruption uncertainty set. For renewable energy units, the output under extreme events differs significantly from that under normal conditions. The predicted value of power generation under extreme events is unknown, and the fluctuation is difficult to determine. In addition, the probability of extreme events is low. Therefore, the historical data and information on unit output under these conditions are much less than under normal conditions. Based on the above characteristics, the renewable energy output uncertainty set is constructed as follows based on the expected value fuzzy set and the fluctuation value fuzzy set:

[0090]

[0091] In the formula, This represents the predicted value of renewable energy under extreme event ex; This represents the fluctuation value of renewable energy under extreme event ex; This represents the minimum average output of renewable energy sources when the extreme event ex occurs; This represents the average maximum output of renewable energy sources when the extreme event ex occurs; This represents the minimum value of renewable energy output fluctuation when the extreme event ex occurs; This represents the maximum value of renewable energy output fluctuation when the extreme event ex occurs.

[0092] Therefore, the maximum output p of renewable energy can be obtained. ger,r Uncertainty set:

[0093]

[0094] in, It reflects the severity of the extreme events to be considered, and its size can be determined based on historical data.

[0095] In practice, it is difficult to determine which extreme event will have the most severe impact on the power system over a long timescale. Therefore, multiple extreme events should be considered during the planning phase, and the event with the worst impact should be selected. This allows for consideration of the impact of different extreme events *e* on the power system. The resulting uncertainty set can be extended to consider other extreme events, introducing discrete variables I. exRepresents renewable energy output and fluctuation values:

[0096]

[0097] In the formula, Indicates the output value of renewable energy; This indicates the volatility of renewable energy.

[0098] Therefore, the set of uncertainties can be expressed as:

[0099]

[0100] To reflect the severity of the most severe extreme event considered, the impact of the extreme event on the unit can be mitigated by constructing an uncertainty set considering the expected forced shutdown probability (EFOR) of the unit. The expected forced shutdown probability is related to factors such as its load current (i.e., load capacity), unit lifespan, and the extreme event considered. When the unit load is less than a certain value, its expected forced shutdown probability can be considered a constant, with the corresponding load current being the unit's rated current. When the load exceeds this value, the expected shutdown probability can be considered an exponential function. The relationship between the unit's expected forced shutdown probability and the load current is shown below:

[0101]

[0102] Where EFOR is p gecdb,tl The function. This indicates that when the non-renewable energy unit's GEC operating current is at the rated value... The time corresponds to the output force, and When the operating current of the corresponding unit is at the trip current limit Corresponding output, EFOR 0 The calculation formula is Where λ and μ represent the unit outage rate and unit repair rate, respectively. This indicator is a standard for power system reliability and can be derived from historical data. The calculation formulas for a and b are as follows: Given the service age T0 of the unit, its EFOR in the i-th planning cycle can be evaluated as follows:

[0103]

[0104] Where F(t) represents the aging probability distribution of the selected equipment; in reality, the expected forced shutdown probability of the equipment is influenced by the load current and the aging period. Aging equipment is more sensitive to different operating conditions and is therefore more susceptible to extreme events. The modified EFOR function is as follows:

[0105]

[0106] a age With b age The calculation method is the same as under normal circumstances.

[0107] Therefore, the set of uncertainties regarding unit damage under extreme events is specifically as follows:

[0108]

[0109] In the formula, Z represents a function negatively correlated with EFOR; GE p represents the set of uncertainties regarding unit damage; EFOR represents the expected forced outage probability of the unit; gec,d,tl This represents the output variable of a traditional generator unit (GEC); z gec,d,tl This indicates the interruption status of a traditional generator unit (GEC) within time tl and load block d; T0 represents the service life of the line; k GE This indicates that extreme scenarios need to be considered.

[0110] The higher the expected downtime rate, the lower the function value, meaning a higher likelihood of unit failure. Conversely, the lower the expected downtime rate, the higher the function value, indicating that unit failures with lower downtime rates are more accidental and constitute a larger portion of the uncertainty budget. In the extreme case, when the downtime rate approaches zero, the corresponding function value should be sufficiently large, causing z to be significantly larger within a finite uncertainty budget. gec,d,tl A value of 0 indicates that the unit cannot be damaged. Therefore, the typical function to choose is the [-log()] function. GE Reflecting the need to consider severe extreme events, the similarity principle, namely the line interruption probability function, can also be applied to the set of uncertainties regarding line interruption.

[0111] The specific set of uncertainties regarding line interruption is as follows:

[0112]

[0113] In the formula, Z PL z represents the set of uncertainties regarding line interruption; pl,d,tl This indicates the interruption status of line pl within time tl and load block d; P pl This represents the interruption probability function of line p1, with a range of 0-1; p pl,d,tl ξ represents the line power flow; ξ represents the extreme event under consideration; k PL This indicates the extent to which the extreme events considered will affect the system circuitry.

[0114] In one embodiment, the two-stage power system long-term planning model includes a second-stage robust planning model, the objective function of which is:

[0115]

[0116] In the formula, f A,d,tl Lc is a function representing the economic loss corresponding to the load shedding amount. A,d,tl This represents the load shear rate at bus A, time tl, under load block d; RM devi The robustness index of the target system is typically set as a percentage of the total load, requiring that the load shedding level under the most severe extreme event does not exceed the set resilience index; R ES This represents the set of uncertainties in renewable energy output.

[0117] Secondly, the facility status in the first-stage model is changed. When the facility is attacked or not expanded, its corresponding status is 0. The second-stage constraints are as follows, corresponding to the first-stage constraints.

[0118] The constraints of the second-stage robust programming model are:

[0119] p gec,min e gec,tl (1-z gec,d,tl )≤p gec,d,tl ≤p gec,max e gec,tl (1-z gec,d,tl )

[0120] 0≤p ger,d,tl ≤p ger,r

[0121]

[0122]

[0123] -p pl,max e pl,tl (1-z pl,d,tl )≤p pl,d,tl ≤p pl,max e pl,tl (1-z pl,d,tl )

[0124]

[0125] In the formula, p gec,min This indicates the lower limit of the output of a conventional generator set; p ger,d,tl This indicates the output of the renewable energy unit ger within time tl and load block d; p ger,r θ represents the maximum output that a renewable energy unit (ger) can obtain; A,d,tl Indicates; PE A This represents the set of routes leading to node A.

[0126] like Figure 4As shown, the two-stage power system long-term planning model in this embodiment can be solved by the C&CG (Column-constraint Generation) algorithm.

[0127] S4. Input the unit capacity expansion requirements and the most severe extreme event scenario of the power system into the short-term unit combined operation model, so as to upgrade the standard thermal power unit to obtain the unit expansion results.

[0128] This embodiment inputs the obtained capacity expansion demand results along with the scenarios of the most severe unit damage, line interruption, and renewable energy output into the short-term unit combination operation model, and uses it as the standard thermal power unit to be upgraded. It evaluates the sensitivity of the minimum load shedding to different unit ramp rates and minimum start-up / shutdown times, and upgrades the unit with the highest sensitivity within a certain budget. Simultaneously, it inputs the basic solution of the first unit combination computer unit commissioning results.

[0129] Short-term operation models consider the impact of minimum operating capacity, rated operational capacity, minimum start-up and shutdown time, and ramp rate on resilience indicators and recovery time. Using units with shorter start-up and shutdown times and faster ramp rates can effectively improve system recovery rate and shorten recovery time. In determining which unit start-up and shutdown times to reduce and increase their ramp rates to maximize system recovery rate and shorten recovery time, it is necessary to determine the sensitivity of the unit load reduction (i.e., the recovery rate) to the ramp rate and start-up and shutdown times of different units.

[0130] Step S4 involves upgrading the expanded generating units. When the number of expanded generating units is small, an enumeration method can be used to determine which newly expanded standard thermal power units to upgrade. When the number of units is large, the system ramp rate increment and the reduction in system start-up and shutdown time can be used as variables in the problem of minimizing load shedding. The objective function includes minimizing unit output cost, unit start-up and shutdown cost, unit operation maintenance cost, and minimizing load shedding. The established model is shown below, and the meanings of the relevant variables are the same as in the short-term unit combination model in step S1. For fast-start units, if the unit can still start when it is damaged, its start-up and shutdown time is ignored. For non-fast-start units, their restart and shutdown time is considered, and the start-up and shutdown times corresponding to the expanded generating units are upgraded.

[0131] In one embodiment, the objective function and constraints for the unit upgrade specifically include:

[0132]

[0133] Bus power balance constraints for load shedding:

[0134]

[0135] Line constraints in extreme scenarios:

[0136]

[0137]

[0138] The most severe output constraint for renewable energy:

[0139]

[0140] Post-disaster constraints are considered only for the restart of units requiring rapid start-up:

[0141]

[0142]

[0143] When a unit that relies on a rapid start-up system completes its initial recovery phase and still has the ability to start up is started, the following constraints must be met:

[0144]

[0145] If the unit is undamaged and already in operation, startup is not required; if the unit is attacked and forced to stop operation but still has startup capability, or if the unit is undamaged and not in operation, startup must satisfy the following constraints:

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] In the formula, This indicates the output variables of the thermal power units to be upgraded according to the standard; This represents the variable indicating the operational status of the standard thermal power unit i to be upgraded within time period t; γ represents the minimum start-up and shutdown costs required for the standard thermal power unit i to be upgraded within time period t; A ·Lc A,t The penalty amount for load shear at busbar A; γ A This represents the penalty value per unit load shedding. This indicates the output of the standard thermal power unit to be upgraded; This represents the output variable of the standard thermal power unit i to be upgraded; This represents the wind power output variable considering both generator failure and line interruption scenarios; P d,t The load output variable is represented by l, m, i, w, and d, which are the indices of the line, power node, standard thermal power unit to be upgraded, wind power, and load, respectively; L(m) represents the set of lines connected to node m; U(m) represents the set of traditional units connected to node m; W(m) represents the set of wind turbine units connected to node m; and D(m) represents the set of loads connected to node m. This represents the phase of power node m in the fundamental solution; This represents the phase of power node n in the fundamental solution; Indicates the reference phase in the fundamental solution; x mn Represents the line reactance, where m and n are the node indices; P l max Indicates the upper limit of line transmission capacity; This represents the magnitude of the piecewise output force in the fundamental solution; T represents the start-up time variable of unit i during the time period (t-1); i on This indicates the minimum start-up time for unit i; T represents the shutdown time variable of unit i during the time period (t-1); i off Indicates the minimum shutdown time of unit i; st i sd i Indicates the start-up and shutdown costs of unit i; UR i Indicates the unit's ramp-up capacity; This indicates the most severe line outage scenario; This indicates extreme power output scenarios for renewable energy. This indicates the most severe scenario of unit damage; Representing discrete variables, UT characterizes the selection of start-up and shutdown times for different types of generating units; seg UD seg These represent different reductions in startup and shutdown times, respectively. Representing discrete variables, UP represents the selection of ramp rate for different types of generator units; seg DP seg Indicates the increase in rate for different uphill and downhill climbs; UD i Indicates the rate of ... i This indicates the direct operational capacity of the fast-start unit i.

[0159] The above model considers the commissioning of units that can be quickly started after a disaster and the restart of some units that still have the ability to start. Although the above formula does not emphasize the order of time, there is a sequence of unit startups after a disaster and corresponding recovery strategies. The objective function of the above model considers both the speed and economy of recovery. When economic operation is not considered, it means that the units will recover quickly at all costs. Economy is applied to the startup process of units that can be quickly started after the units that can be quickly started and the units that still have the ability to start.

[0160] This embodiment integrates the sensitivity of different units to ramp rate increments and start-stop time reductions to upgrade capacity expansion. When there is no corresponding unit for upward capacity upgrade, it can be split into multiple smaller units to improve system resilience and output upgrade plan. If there are higher accuracy requirements for expansion planning, the unit output and line power flow under this scenario can be calculated, iterated to step S3 to generate uncertainty set, and the most severe extreme event scenario is input into step S4 for re-selection calculation of unit upgrade.

[0161] Specifically, this embodiment uses a simple calculation example to illustrate the implementation process of this method:

[0162] The example can consider the impact of hurricanes on the power system. Based on the proposed power system resilience enhancement and energy transition framework, the short-term unit combination model without considering extreme events is first calculated to obtain the unit operating output and line power flow information under normal conditions. This information is then used as input to the long-term planning model to generate an uncertainty set considering the unit forced outage rate, line power flow load, and extreme events. The minimum unit capacity requirement for expansion and the unit damage and line interruption information with a certain accuracy are then solved and used as input to the next step. In step S4, two recovery phases are considered as the basis for unit upgrade optimization. In the first phase, only the start-up recovery of some important loads by fast-start units are considered. At this time, newly expanded units that have not been attacked are optimized, and their ramp rate is upgraded with multiple options. In the second phase, non-fast-start units that have been attacked but still have the ability to start are considered. Their minimum start-up and shutdown time upgrades and ramp rate upgrades are considered. Finally, by combining the ramp rate upgrade options and the unit start-up and shutdown time optimization options, the unit that is closest to it and has the potential for upward capacity upgrade is determined. When the upgraded unit capacity is small, select two or more units of the same type to be expanded as the final expansion result. When higher accuracy is required, attention should be paid to the matching of the net load curve with the load in the unit combination problem, and the upgraded result should be used as input to calculate the unit combination problem under normal conditions, and a new round of iterative solution should be carried out.

[0163] This embodiment provides a power structure optimization method for energy transition and resilience enhancement. By considering the different mechanisms by which various power technology characteristics affect power system resilience, this method proposes efficient methods to improve power system resilience, quantitatively evaluates the optimization effect, and simultaneously meets the needs of renewable energy expansion and unit upgrades for energy transition, ultimately forming a complete power system power structure upgrade framework. Compared to existing methods, this embodiment can more comprehensively and maximally improve power system resilience while meeting energy transition needs, and can qualitatively analyze the mechanisms by which related measures enhance power system resilience.

[0164] It should be noted that the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0165] In one embodiment, such as Figure 5 As shown, a power structure optimization system for energy transition and resilience enhancement is provided, the system comprising:

[0166] The short-term model building module 101 is used to build a short-term unit combination model based on the collected power system data, with the objective function of minimizing operating costs.

[0167] The short-term model calculation module 102 is used to solve the short-term unit combination model to obtain unit output and line power flow information.

[0168] The long-term model building module 103 is used to construct a two-stage power system long-term planning model that considers extreme events, and inputs the unit output and line power flow information into the two-stage power system long-term planning model to obtain the unit capacity expansion demand and the most severe extreme event scenario of the power system.

[0169] The unit upgrade module 104 is used to input the unit capacity expansion requirements and the most severe extreme event scenario of the power system into the short-term unit combined operation model, so as to use it as the standard thermal power unit to be upgraded for unit upgrade, and obtain the unit expansion result.

[0170] For specific limitations regarding a power structure optimization system for energy transition and resilience enhancement, please refer to the above-described limitations regarding a power structure optimization method for energy transition and resilience enhancement, which will not be repeated here. Those skilled in the art will recognize that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] This embodiment provides a power structure optimization system for energy transition and resilience enhancement. The system, through its various modules, simultaneously considers long-term expansion planning and short-term operation to form a complete power system power structure upgrade framework. Compared with existing technologies, this application achieves power structure upgrade optimization with high technical accuracy and long-term planning scale, enabling a more comprehensive and maximally responsive improvement of power system resilience while meeting energy transition needs. It also allows for qualitative analysis of the mechanisms by which related measures enhance power system resilience.

[0172] Figure 6 This invention provides a computer device including a memory, a processor, and a transceiver, which are connected to each other via a bus. The memory is used to store a set of computer program instructions and data, and can transmit the stored data to the processor. The processor can execute the program instructions stored in the memory to perform the steps of the above method.

[0173] In one embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0174] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0175] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed, it can include the processes of the embodiments of the above methods.

[0176] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A power structure optimization method for energy transition and resilience enhancement, characterized in that, Includes the following steps: The objective function is to minimize operating costs, and a short-term unit combination model is constructed based on collected power system data; wherein, the objective function of the short-term unit combination model is: in, Indicates the unit output variable. This represents the wind power output variable in the basic solution. Indicates the magnitude of the power flow along the line. This represents the output cost of the k-th segment of the generator set. This represents the output variable of the unit in sections. This indicates the unit's commissioning cost. This represents the variable indicating the operational status of unit i within time period t. , Let represent the minimum start-up and shutdown costs of unit i within time period t, respectively. i, k, t These are indices for the generator unit, output segment, and time, respectively. Solve the short-term unit combination model to obtain unit output and line power flow information; A two-stage long-term power system planning model considering extreme events is constructed, and the unit output and line power flow information are input into the two-stage long-term power system planning model to obtain the unit capacity expansion demand and the most severe extreme event scenario of the power system. The two-stage power system long-term planning model includes a first-stage long-term planning model, which takes minimizing facility expansion costs and equipment operating costs as its objective function, specifically: In the formula, TC represents the total investment cost for power system expansion; IC represents the investment cost for unit and line expansion; and OC represents the system operating cost. tl This represents the index of the system's long-term planning time period; ge represents the generator unit; pl Indicates the route; This indicates the investment cost for expanding the generating unit; A binary discrete variable representing the status of unit expansion; This indicates the investment cost for the line expansion; A binary discrete variable representing the status of line expansion; This represents the annualized operating cost for each planning period t; Indicates the duration of load block d; This represents the unit operating cost function; The output variable `gec` represents the traditional unit output variable in the first stage; `gec` represents the traditional unit index that satisfies... ; The two-stage power system long-term planning model includes a second-stage robust programming model, the objective function of which is: In the formula, A function representing the economic loss corresponding to the load shedding amount; This indicates that at busbar A, tl Load shedding amount under load block at time d; Indicators representing the robustness of the target system; Represents the set of uncertainties in renewable energy output; The unit capacity expansion requirements and the most severe extreme event scenario of the power system are input into the short-term unit combination operation model, so as to upgrade the standard thermal power unit to obtain the unit expansion results.

2. The power structure optimization method for energy transition and resilience enhancement as described in claim 1, characterized in that: The power system data includes the unit's minimum operating capacity, rated operating capacity, minimum start-up and shutdown time, and ramp rate.

3. The power structure optimization method for energy transition and resilience enhancement as described in claim 1, characterized in that, The constraints of the short-term unit combination model include: Node power balance constraints: Reference node and line DC power flow constraints: Line capacity constraints: Unit output and segment output constraints: Renewable energy output constraints: Minimum start-up and stop time constraints: Unit start-up and shutdown cost constraints: Minimum ramp constraint: In the formula, Indicates the load output variable; l m, i, w, and d are the indices for lines, power nodes, generator units, wind power, and loads, respectively. This represents the set of lines connected to node m; This represents the set of traditional units connected to node m; This represents the set of wind turbine units connected to node m; This represents the set of loads connected to node m; This represents the phase of power node m in the fundamental solution; This represents the phase of power node n in the fundamental solution; This represents the line reactance of line mn; Indicates the reference phase in the fundamental solution; Indicates the upper limit of line transmission capacity; Indicates the lower limit of the unit's output; Indicates the upper limit of the generator unit's output; This represents the piecewise output in the fundamental solution; Indicates the upper limit of the output of each section of the unit; This indicates the projected output value of renewable energy. This represents the start-up timing variable of unit i during the time period (t-1); This indicates the minimum startup time of the generator set, with the superscript "on" indicating that the generator set is enabled, and "i" being the generator set index. This represents the shutdown timing variable for unit i during the time period (t-1); This represents the minimum shutdown time for the generator set, where i is the generator set index and off is the shutdown index. , This represents the start-up and shutdown costs of unit i; Indicates the unit's ramp-up capacity; This indicates the unit's ramp-down capacity; the superscript b indicates the preliminary basic solution of the short-term unit combination model.

4. The power structure optimization method for energy transition and resilience enhancement as described in claim 1, characterized in that: The two-stage power system long-term planning model includes a first-stage long-term planning model. The constraints of the first-stage long-term planning model include: Facility status constraints: Capacity constraints: In the formula, A binary discrete variable representing the GEC expansion status of a traditional unit; This indicates the upper limit of GEC output for traditional generator sets; This indicates the expansion status of renewable energy units, with ger and tl being the indices of the renewable energy units and the planning time, respectively. This represents the projected value for renewable energy units; This indicates the maximum load demand. Indicates the reserve ratio; Unit output constraints: In the formula, This indicates the lower limit of GEC output for conventional generator sets; This indicates the output of renewable energy units. The superscript 0 is the index of the first-stage variable, and ger, d, and tl are the indices of renewable energy units, load blocks, and planning time, respectively. Line power constraints and DC power flow constraints: In the formula, This indicates the power flow of the first phase of the line; This indicates the upper limit of power flow on the line, where pl is the index of the line; This represents the first phase planning time tl, and the phase of node A within load block d; This represents the planning time tl for the first phase and the nodes within the load block d. Phase; Indicates the line The line reactance; Represents a constant; Power balance constraints: In the formula, ge represents the output variable of the traditional unit in the first stage; A represents the bus in the system; This indicates the planning stage of busbar A. tl The load corresponding to the internal load block d; This represents the set of units connected to bus A; This represents the set of routes originating from bus A; This represents the set of routes leading to bus A; Maximum limit on the number of facilities to be constructed: In the formula, g represents the maximum number of expansion facilities in all phases; Carbon emission policy constraints: In the formula, This indicates the lower limit of the renewable energy installation target; This indicates the maximum load demand. express tl Percentage of non-fossil energy generation at any given time; Indicates carbon emissions; Indicates the consumption of carbon-containing fuels; Indicates emission factor; Indicates the amount of electricity generated; Indicates carbon emission intensity.

5. A power structure optimization method for energy transition and resilience enhancement as described in claim 1, characterized in that: Before constructing the second phase of the two-stage power system long-term planning model, an uncertainty set is constructed to account for the most severe impacts of extreme events on the power system. The uncertainty set includes the renewable energy output uncertainty set, the unit damage uncertainty set, and the line interruption uncertainty set; The specific set of uncertainties in renewable energy output is as follows: In the formula, This represents the predicted value of renewable energy under extreme event ex; This represents the fluctuation value of renewable energy under extreme event ex; This represents the minimum average output of renewable energy sources when the extreme event ex occurs; This represents the average maximum output of renewable energy sources when the extreme event ex occurs; This represents the minimum value of renewable energy output fluctuation when the extreme event ex occurs; This represents the maximum value of renewable energy output fluctuation when the extreme event ex occurs; The set of uncertainties regarding unit damage under extreme events is specifically as follows: In the formula, A function representing a negative correlation with EFOR; This represents the set of uncertainties regarding unit damage; EFOR This represents the expected probability of a forced shutdown of the generator unit; This represents the output variable of a traditional generator unit (GEC). This indicates the interruption status of the traditional unit's GEC within the planned time tl and load block d; Indicates the service life of the line; This indicates that extreme scenarios need to be considered. The specific set of uncertainties regarding line interruption is as follows: In the formula, Represents the set of uncertainties regarding line interruptions; This indicates the interruption status of line pl within the planned time tl and load block d; The interruption probability function of line pl; Indicates the power flow of the line; Indicates the extreme event being considered; This indicates the extent to which the extreme events considered will affect the system circuitry.

6. The power structure optimization method for energy transition and resilience enhancement as described in claim 1, characterized in that: The constraints of the second-stage robust programming model are: In the formula, This indicates the lower limit of GEC output for conventional generator sets; This indicates the output of the renewable energy unit ger within the planned time tl and load block d; This indicates the maximum output capacity that renewable energy generating units can achieve. This represents the set of routes leading to node A.

7. A power structure optimization method for energy transition and resilience enhancement as described in claim 1, characterized in that, The objective function and constraints for the unit upgrade specifically include: In the formula, This indicates the output variables of the thermal power units to be upgraded according to the standard; This represents the variable indicating the operational status of the standard thermal power unit i to be upgraded within time period t; , These represent the minimum start-up and shutdown costs required for the standard thermal power unit i to be upgraded within time period t, respectively. This represents the penalty for the load sheared at busbar A; This represents the penalty value per unit load shedding. This indicates the output of the standard thermal power unit to be upgraded, where u is the index that distinguishes it from the basic solution; This represents the output variable of the standard thermal power unit i to be upgraded; This represents the wind power output variables considering both generator failure and line interruption scenarios. Indicates the load output variable; l m, i, w, and d are the indices for lines, power nodes, thermal power units, wind power, and loads, respectively. This represents the set of lines connected to node m; This represents the set of traditional units connected to node m; This represents the set of wind turbine units connected to node m; This represents the set of loads connected to node m; This represents the phase of power node m in the fundamental solution; This represents the phase of power node n in the fundamental solution; Indicates the reference phase in the fundamental solution; This represents the line reactance, where m and n are the node indices, respectively. Indicates the upper limit of line transmission capacity; This represents the magnitude of the segmented output force in the fundamental solution; This represents the start-up timing variable of unit i during the time period (t-1); This indicates the minimum start-up time of unit i; This represents the shutdown timing variable for unit i during the time period (t-1); This represents the minimum shutdown time for unit i; , This represents the start-up and shutdown costs of unit i; Indicates the unit's ramp-up capacity; This indicates the most severe line outage scenario; This indicates extreme power output scenarios for renewable energy. This indicates the most severe scenario of unit damage; Represents discrete variables, characterizing the selection of start-up and shutdown times for different types of generating units; , These represent different reductions in startup and shutdown times, respectively. , These represent discrete variables, characterizing the selection of ramp rate for different types of generator units; , Indicates the increase in different uphill and downhill rates; Indicates the rate of ... This indicates the direct operational capacity of the fast-start unit i.

8. A power structure optimization system for energy transition and resilience enhancement, characterized in that, The system includes: A short-term model construction module is used to construct a short-term unit combination model based on collected power system data, with the objective function of minimizing operating costs; wherein, the objective function of the short-term unit combination model is: in, Indicates the unit output variable. This represents the wind power output variable in the basic solution. Indicates the magnitude of the power flow along the line. This represents the output cost of the k-th segment of the generator set. This represents the output variable of the unit in sections. This indicates the unit's commissioning cost. This represents the variable indicating the operational status of unit i within time period t. , Let represent the minimum start-up and shutdown costs of unit i within time period t, respectively. i, k, t These are indices for the generator unit, output segment, and time, respectively. The short-term model calculation module is used to solve the short-term unit combination model to obtain unit output and line power flow information; The long-term model building module is used to construct a two-stage long-term planning model for the power system considering extreme events. The unit output and line power flow information are input into the two-stage long-term planning model to obtain the unit capacity expansion demand and the most severe extreme event scenario for the power system. The two-stage long-term planning model includes a first-stage long-term planning model, which uses minimizing facility expansion costs and equipment operating costs as its objective function. Specifically: In the formula, TC represents the total investment cost for power system expansion; IC represents the investment cost for unit and line expansion; and OC represents the system operating cost. tl This represents the index of the system's long-term planning time period; ge represents the generator unit; pl Indicates the route; This indicates the investment cost for expanding the generating unit; A binary discrete variable representing the status of unit expansion; This indicates the investment cost for the line expansion; A binary discrete variable representing the status of line expansion; This represents the annualized operating cost for each planning period t; Indicates the duration of load block d; This represents the unit operating cost function; The output variable `gec` represents the traditional unit output variable in the first stage; `gec` represents the traditional unit index that satisfies... ; The two-stage power system long-term planning model includes a second-stage robust programming model, the objective function of which is: In the formula, A function representing the economic loss corresponding to the load shedding amount; This indicates that at busbar A, tl Load shedding amount under load block at time d; Indicators representing the robustness of the target system; Represents the set of uncertainties in renewable energy output; The unit upgrade module is used to input the unit capacity expansion requirements and the most severe extreme event scenario of the power system into the short-term unit combined operation model, so as to use it as the standard thermal power unit to be upgraded and obtain the unit expansion result.

9. A computer device, characterized in that: The device includes a processor and a memory, the processor being connected to the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to cause the computer device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 7.

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