Collaborative optimization method and system for multi-region energy storage and thermal power participation adequacy control

Abstract

Disclosed in the present invention are a collaborative optimization method and system for multi-region energy storage and thermal power participation adequacy preventive control. The method comprises: setting an energy-storage total target outflow electricity quantity of each region; generating a feasible path library of an energy-storage charging and discharging policy of each region; selecting a reference path of each region, and constructing an energy-storage path set; under a given energy-storage path set, optimizing a global thermal power startup and shutdown policy; optimizing the energy-storage charging and discharging policy of each region without changing policies of other regions; using an optimal path of each region to update the energy-storage path set; determining whether the energy-storage path set or the number of iterations meets a preset convergence condition; if the energy-storage path set or the number of iterations meets the preset convergence condition, proceeding to the next step, otherwise, continuing to optimize the global thermal power startup and shutdown policy; and optimizing and outputting an energy-storage charging and discharging and thermal power startup and shutdown policy. The present invention can take power-electricity quantity coupling characteristics and startup and shutdown characteristics into comprehensive consideration to realize collaborative optimization of an energy-storage scheduling plan and a thermal power startup and shutdown plan within a given time scale, thereby effectively preventing and controlling operation risks of a power grid, and reducing scheduling control costs.

Description

Collaborative optimization method and system for multi-region energy storage and thermal power participating in adequacy control Technical Field

[0001] The present invention relates to power system operation and dispatching technology, and in particular to a collaborative optimization method and system for multi-region energy storage and thermal power participation in adequacy control. Background Art

[0002] Building a new power system dominated by renewable energy is becoming increasingly complex and unstable with the high penetration of renewable energy. Conventional power sources must not only adapt to load fluctuations but also balance the output fluctuations of renewable energy. Insufficient coal-fired power generation or regulation rates can lead to power outages, while excessive coal-fired power generation can waste resources by curtailing wind and solar power, posing significant challenges to the balanced coordination and safe and stable operation of the power system.

[0003] The backup system of traditional power systems primarily addresses unilateral demand on the load side, with highly regular load fluctuations and minimal random disturbances. The corresponding backup optimization configuration, while relatively extensive, can still maintain the safe and economical operation of the power grid. With the integration of renewable energy, the system has shifted from unilateral load fluctuations to dual-sided fluctuations on both the power supply and load sides. Due to the intermittent, random, and highly volatile nature of renewable energy output, the fluctuations are less regular and the random disturbances are significant. Traditional backup strategies lack the tools for risk management and decision-making. To coordinate the prevention and control of wind and solar curtailment and the risk of power outages, the existing backup system leverages the advantages of starting and stopping thermal power units in grid operation planning, optimizing the adjustment of backup measures to minimize risk costs. However, the existing backup system does not consider the synergy between energy storage and the start-up and shutdown of thermal power units after participating in backup.

[0004] Summary of the Invention

[0005] Purpose of the invention: The present invention aims to provide a collaborative optimization method and system for multi-region energy storage and thermal power participation in adequacy control, which comprehensively considers energy storage scheduling plans and thermal power start-up and shutdown plans.

[0006] Technical solution: The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control described in the present invention includes the following steps:

[0007] (1) Setting the overall target outflow of energy storage in each region;

[0008] (2) Generate a feasible path library for energy storage charging and discharging strategies in each region based on the initial state of each energy storage measure and the time coupling characteristics of power and electricity;

[0009] (3) Select the benchmark paths in each region and construct the energy storage path set;

[0010] (4) Optimize the global thermal power start-up and shutdown strategy under a given set of energy storage paths;

[0011] (5) Select the energy storage charging and discharging strategy for each region without changing the strategies of other regions;

[0012] (6) updating the energy storage path set using the optimal paths in each region;

[0013] (7) Determine whether the energy storage path set or the number of iteration rounds meets the preset convergence condition; if so, proceed to the next step; otherwise, return to step (4);

[0014] (8) Optimize and output energy storage charging and discharging and thermal power start-up and shutdown strategies.

[0015] Preferably, in step (2), generating a feasible path library of energy storage charging and discharging strategies for each region includes the following steps:

[0016] (2.1) Input the overall target outflow of regional energy storage;

[0017] (2.2) Based on the overall target outflow of regional energy storage, the boundaries of the overall energy storage path planning are established, and the entire scheduling cycle is divided into several scheduling windows;

[0018] (2.3) Move to the starting point of the overall new path of energy storage;

[0019] (2.4) Randomly select a scheduling window and generate the overall energy storage power in the selected scheduling window;

[0020] (2.5) Allocating the overall energy storage power in the selected scheduling window to various energy storage measures and adding them to the path;

[0021] (2.6) Determine whether there is a next window after the selected scheduling window; if so, return to step (2.4); otherwise, add the additional path to the feasible path library;

[0022] (2.7) Determine whether the number of paths in the feasible path library has reached the maximum number of paths; if not, return to step (2.3); otherwise, output the feasible path library of the regional energy storage charging and discharging strategy.

[0023] Preferably, the boundary of constructing the overall energy storage path planning in step (2.2) includes:

[0024] The intersection of the allowable output range of each power reserve measure under various constraints is taken as the power feasible domain of the power reserve measure; the boundaries of the feasible domains of all power reserve measures are accumulated to obtain the total upper and lower envelopes, which serve as the boundaries of the overall energy storage path planning.

[0025] Preferably, the step (2.4) of generating the power of the overall energy storage in the selected scheduling window comprises the following steps:

[0026] (2.4.1) Divide the guidance area into positive and negative deviation areas according to the system guidance value;

[0027] The system guide value refers to the total amount of electricity to be discharged from the system in a given area divided by the number of remaining windows in the scheduling period; the guide area refers to the value range of the total energy storage power in the selected scheduling window. The area in the guide area with a value greater than the system guide value is a positive deviation area, and the area in the guide area with a value less than the system guide value is a negative deviation area;

[0028] (2.4.2) extracting a random number to activate a specific power deviation region, and dividing the deviation region into inner and outer guide sub-regions based on a guide region ratio, where the guide region ratio refers to the ratio of the width of the guide region on the parameter coordinate axis to the total width of the parameter coordinate axis;

[0029] (2.4.3) extracting a random number and activating a specific pilot sub-region based on a pilot region probability; the pilot region probability is the ratio of the expected number of times a randomly extracted power value is generated within the pilot region to the total number of times the power value is generated;

[0030] (2.4.4) Extract a random number and extract power within a specific pilot sub-region; output the extracted power, which is the power of the energy storage system in the window.

[0031] Preferably, the method for dividing the deviation area into inner and outer guide sub-areas based on the guide area ratio in step (2.4.2) is:

[0032] p bound =p exp +a*v*|p max -p exp |

[0033] Where p bound is the boundary between the inner and outer guide sub-regions, p exp is the system boot value, p max is the maximum power of the guide sub-area, a takes the value of 1 in the positive deviation area and takes the value of -1 in the negative deviation area, v is the proportion of the guide area, which refers to the ratio of the data width of the guide area to the data width of the total feasible domain, where the total feasible domain is the intersection of the allowable output range under various constraints.

[0034] Preferably, the allocation of the overall energy storage power to various energy storage measures in step (2.5) includes:

[0035] Select energy storage measures that can interact with electricity from the overall energy storage to build an effective measure set;

[0036] According to the sorting index T d , sort the energy storage measures in the effective measures set, and the sorting index T dThe expression is as follows:

[0037] T d =|E s.end -E s | / [(N w -n w +1)*P s.max ]

[0038] Among them, E s.end is the final electricity value of the energy storage measure, E s is the actual power value of the energy storage measure in the current window, N w is the number of remaining windows in the scheduling period, n w is the serial number of the current window, P s.max is the absolute value of the maximum power of the energy storage measure;

[0039] The window power is allocated one by one according to the sorting order. When the remaining power to be allocated is higher than the maximum power of the current energy storage measure, the energy storage measure outputs at the maximum power and enters the next energy storage measure. When the remaining power to be allocated is less than or equal to the maximum power of the current energy storage measure, the energy storage measure outputs at the power to be allocated, and the power allocation is terminated. The power and power changes of each energy storage measure at the current time step are recorded and stored.

[0040] Preferably, the optimization of the global thermal power start-up and shutdown strategy in step (4) specifically includes: obtaining and dynamically optimizing the multi-window thermal power standby for the global thermal power start-up and shutdown strategy based on the output scenarios to be regulated in each region and the regional energy storage paths, updating the configuration plan in each round and region until the configuration plans of two adjacent rounds remain unchanged, and outputting the optimal thermal power start-up and shutdown strategy.

[0041] Preferably, the step (5) of selecting the energy storage charging and discharging strategy for each region specifically includes: optimizing the single-region thermal power reserve for each feasible path and evaluating the cost based on the transmission power curve of the relevant interconnection line in the target region, and selecting the optimal energy storage charging and discharging strategy for the target region.

[0042] Preferably, the preset convergence condition in step (7) includes that the same preferred path combination of each region appears twice in the energy storage path concentration, or the number of iteration rounds reaches a set upper limit.

[0043] Preferably, the optimization and output of the energy storage charging and discharging and thermal power start-up and shutdown strategies in step (8) specifically includes: for the energy storage path set generated by multiple iterative calculations and its corresponding evaluation results, according to the principle of minimizing the global total risk cost in the evaluation results, the energy storage path set at this time and the thermal power start-up and shutdown strategy under the path set are optimized and output.

[0044] The collaborative optimization system for multi-region energy storage and thermal power participating in abundance control described in the present invention includes:

[0045] The target setting module is used to set the overall target outflow of energy storage in each area;

[0046] The module for constructing a feasible path library for energy storage charging and discharging strategies is used to generate a feasible path library for energy storage charging and discharging strategies in each region based on the initial state of each energy storage measure and the time-coupled characteristics of power and electricity. By deducing forward and backward power-electricity limit trajectories, the feasible region of electricity per step for each energy storage measure is calculated. The module also generates an energy storage scheduling plan curve guided by the overall target outflow of energy storage.

[0047] The collaborative optimization module for energy storage and thermal power backup resources is used to optimize the global thermal power start-up and shutdown strategy under a given set of energy storage paths. Without changing the strategies of other regions, it selects the energy storage charging and discharging strategies for each region and updates the energy storage path set with the preferred paths for each region. It determines whether the energy storage path set or the number of iteration rounds meets the preset convergence conditions. If so, it outputs the energy storage charging and discharging and thermal power start-up and shutdown strategies.

[0048] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: 1. It can comprehensively consider the power-electricity coupling characteristics and the start-up and shutdown characteristics, realize the coordinated optimization of the energy storage scheduling plan and the thermal power start-up and shutdown plan within a given time scale, effectively prevent and control the operation risks of the power grid, and reduce the scheduling control cost; 2. Set up a guidance area, and adjust the divergence of the energy storage electricity path by modifying the relevant parameters of the guidance area to cope with different needs under various risk scenarios and efficiently perform random power extraction; 3. Establish a power allocation algorithm for power reserve, analyze the distribution relationship between the total power of energy storage measures and the power of individual energy storage measures, and also take into account factors such as charging and discharging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] FIG1 is a flow chart of the energy storage and thermal power coordinated optimization method of the present invention;

[0050] FIG2 is a schematic diagram of the feasible range of power consumption of the present invention;

[0051] FIG3 is a schematic diagram of the guide area arrangement of the present invention;

[0052] FIG4 is a power distribution flow chart of the present invention;

[0053] FIG5 is a flow chart of generating a feasible energy storage charging and discharging path library according to the present invention;

[0054] FIG6 is a schematic diagram of the optimal configuration of thermal power reserve resources according to the present invention;

[0055] FIG7 is a flow chart of an algorithm for optimizing the configuration of multi-region thermal power reserve resources according to the present invention;

[0056] FIG8 is a flowchart of the multi-window thermal power reserve dynamic optimization of the present invention;

[0057] FIG9 is a flow chart showing an optimal target area energy storage charging and discharging strategy according to the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0059] As shown in FIG1 , the collaborative optimization method for multi-region energy storage and thermal power participating in abundance control according to the present invention includes the following steps:

[0060] Step 1: Set the overall target outflow of energy storage in each region.

[0061] The overall target outflow of energy storage refers to the net outflow of electricity from all energy storage measures in the area as a whole within a scheduling cycle.

[0062] Step 2: Generate a feasible path library for energy storage charging and discharging strategies in each region, as shown in Figure 5. This includes the following steps:

[0063] 2.1 Input the overall target outflow of regional energy storage;

[0064] 2.2 Construct the boundaries of the overall energy storage path planning, including:

[0065] First, the intersection of the allowable output range of each power reserve measure under various constraints is taken as the power feasible domain of the power reserve measure; then the feasible domain boundaries of all power reserve measures are accumulated to obtain the total upper and lower envelopes, which serve as the boundaries of the overall energy storage path planning.

[0066] The various constraints of the power reserve measures include objective operation constraints, subjective operation constraints and implicit operation constraints.

[0067] a) Objective operational constraints

[0068] The process of generating the power path is subject to the efficiency limitations of the power reserve measures themselves, such as energy storage output constraints and energy storage power constraints.

[0069] Energy storage output constraints, that is, the range constraints of the power difference between each time step:

[0070] P s.min ≤P s ≤P s.max

[0071] Among them, P s is the actual output of energy storage, P s.max is the upper limit of energy storage output, P s.min It is the lower limit of energy storage output.

[0072] Energy storage capacity constraints require that the capacity of each backup measure involved in the energy path must not be lower than the minimum capacity nor exceed the set maximum capacity:

[0073] E s.min ≤E s ≤E s.max

[0074] Where, E s is the energy storage capacity, E s.min 、E s.max are the minimum and maximum energy storage capacity respectively.

[0075] b) Subjective operational constraints

[0076] In addition to inherent output and power constraints, each power reserve measure in the set also has independent operational requirements:

[0077] E s.end.min ≤E s.end ≤E s.end.max

[0078] Among them, E s.end E is the end-of-term power reserve measure power, s.end.max E is the upper limit of power at the end of the power reserve measure period, s.end.min The lower limit of the end-of-period energy reserve capacity. If not specified in the actual algorithm, the end-of-period energy constraint is the energy storage capacity constraint.

[0079] In actual operation, routine maintenance and other situations may result in the network not being connected throughout the entire cycle. Depending on the overall arrangement, the network may be connected in the following situations:

[0080] Among them, P s is the actual output of energy storage, P s.max is the upper limit of energy storage output, P s.min is the lower limit of energy storage output, t is the time step number at the current moment, t start is the time for power backup measures to be connected to the grid, t end The off-grid time for the power reserve measure. If not specified in the actual algorithm, the power reserve measure is assumed to be always connected to the grid.

[0081] In addition, the power reserve measure has an upper limit on the number of charging times. That is, the number of charging times of the power reserve measure is limited within a cycle. When the power reserve measure goes from the charging state to the discharging state and then changes to the charging state, the number of charging times of the power reserve measure is increased by one:

[0082] n char ge ≤nchar ge.max

[0083] Among them, nchar ge The current charging times of the power reserve measure are nchar ge.max, which is the upper limit of the charging times of the power reserve measure cycle.

[0084] c) Implicit operational constraints

[0085] Since the system specifies a total outflow value for the period, the end-of-period power consumption is a given value. There are infinite paths from the initial value to the end-of-period value. However, due to the maximum and minimum output constraints of the generator, a path may ultimately be infeasible due to excessively large / small slopes. A feasible path must be within the boundary, but not all paths within the boundary are feasible. Therefore, the intersection of the allowable output ranges of each power reserve measure under various constraints is the power feasible domain of that power reserve measure. As shown in Figure 2, for any power reserve measure, given the end-of-period power consumption, it is subject to implicit upper and lower bounds of the feasible domain.

[0086] The implicit upper bound formula is as follows:

[0087] E u.j =E s.end -(N w *N T -j)*P s.max / ηdischar ge

[0088] Where, E u.j is the upper limit of energy storage capacity of the power reserve measure at the jth time step within the cycle, E s.end is the final value of the power reserve measure, N w is the number of windows in one cycle, N T is the number of time steps in a window, P s.max is the upper limit of the energy storage output of the power reserve measure, ηdischar ge is the discharge efficiency of the energy storage of the power reserve measure;

[0089] The implicit lower bound formula is as follows:

[0090] E l.j =E s.end -(N w *N T -j)*P s.max *η char ge

[0091] Where, E l.j is the lower limit of energy storage capacity of the power reserve measure at the jth time step within the cycle, E s.end is the final value of the power reserve measure, N w is the number of windows in one cycle, N T is the number of time steps in a window, P s.maxis the upper limit of energy storage output for power reserve measures, η char ge Charging efficiency of energy storage for power backup measures.

[0092] 2.3 Divide the entire scheduling cycle into several scheduling windows, each scheduling window has the same number of time steps.

[0093] The scheduling window refers to a portion of continuous time periods that constitute a scheduling cycle.

[0094] 2.4 Randomly select a dispatch window and generate the overall energy storage power in the selected dispatch window. Specifically including:

[0095] 2.4.1 Divide the guidance area into positive and negative deviation areas according to the system guidance value;

[0096] The system guide value refers to the total amount of electricity to be discharged from the system in a given area divided by the number of remaining windows in the scheduling period; the guide area refers to the value range of the overall energy storage power in the selected scheduling window. The area in the guide area with a value greater than the system guide value is the positive deviation area, and the area in the guide area with a value less than the system guide value is the negative deviation area, as shown in Figure 3.

[0097] 2.4.2 Draw a random number to activate a specific power deviation area; divide the deviation area into inner and outer guide sub-areas based on the proportion of the guide area;

[0098] The inner and outer guide sub-areas refer to the inner and outer areas of the guide area in the deviation area. The division formula of the inner and outer guide sub-areas is as follows:

[0099] p bound =p exp +a*v*|p max -p exp |

[0100] Where p bound is the boundary between the inner and outer guide sub-regions, p exp is the system boot value, p max is the maximum power of the guide sub-area, a takes the value of 1 in the positive deviation area and takes the value of -1 in the negative deviation area, v is the guide area ratio, which refers to the ratio of the data width of the guide area to the data width of the total feasible region, where the total feasible region is the intersection of the allowable output range under various constraints;

[0101] 2.4.3 Draw a random number and activate a specific guide sub-area;

[0102] The method for activating a specific guide sub-area is to extract a random number between 0 and 1. If the random number is less than the guide area probability p, the inner guide sub-area is activated; otherwise, the outer guide sub-area is activated. The guide area probability p refers to the ratio of the expected number of times the generated output, i.e., the randomly extracted power, is within the guide area to the total number of times the generated output is generated, i.e., the probability that the generated power falls within the guide area.

[0103] 2.4.4 Extract random numbers and extract power within a specific pilot sub-area;

[0104] 2.4.5 Output the extracted power, which is the overall power of the energy storage in the selected scheduling window.

[0105] By modifying the input guide area probability p and guide area proportion v, adjusting the guide area probability and guide area proportion can affect the relevant characteristics of the generated power path, such as target deviation and path divergence. Target deviation is the ratio of the error between the actual and target outflow of the path to the target outflow value. Path divergence is the ratio of the average variance of the generated path at that time step to the average variance of the generated path at the most extreme time step.

[0106] 2.5 Allocate the overall energy storage power in the selected dispatch window to various energy storage measures and add them to the path;

[0107] As shown in Figure 4, first, off-grid energy storage measures are excluded from all measures in the overall energy storage set, and energy storage measures that can interact with electricity are screened into the effective measure set.

[0108] Then construct the sorting index T d , sort the energy storage measures in the effective measures set, and the expression is as follows:

[0109] T d =|E s.end -E s | / [(N w -n w +1)*P s.max ]

[0110] Among them, E s.end is the final electricity value of the energy storage measure, E s is the actual power value of the energy storage measure in the current window, N w is the number of remaining windows in the scheduling period, n w is the serial number of the current window, P s.max is the absolute value of the maximum power of the energy storage measure. Sorting index T d The meaning of is the minimum number of windows required to meet the end-of-period power value. In order to maximize the end-of-period power requirements of each energy storage measure, T dSmaller energy storage measures have higher priority and are ranked higher. It's worth noting that the number of remaining windows in a scheduling cycle isn't necessarily equal to the total number of windows in the scheduling cycle minus the current window number. This is because the impact of off-grid time cannot be ignored. The number of remaining windows in a scheduling cycle should be equal to the number of remaining on-grid windows.

[0111] Finally, the window power is allocated one by one according to the ranking. If the remaining power to be allocated is higher than the maximum power of the current energy storage measure, the energy storage measure outputs at the maximum power and moves on to the next energy storage measure. If the remaining power to be allocated is less than or equal to the maximum power of the current energy storage measure, the measure outputs at the power to be allocated, and power allocation ends. The power and power changes of each energy storage measure at that time step are recorded and stored.

[0112] 2.6 Determine whether the next window exists; if so, return to step 2.4; otherwise, proceed to the next step;

[0113] 2.7 Add the additional path to the feasible path library of the energy storage charging and discharging strategy;

[0114] 2.8 Determine whether the number of paths in the feasible path library has reached the maximum number of paths; if not, return to step 2.2; otherwise, proceed to the next step;

[0115] 2.9 Output a library of feasible paths for regional energy storage charging and discharging strategies.

[0116] In this process, the influence of charge and discharge efficiency needs to be considered.

[0117] The power of the power reserve measure is defined as a negative number when charging and a positive number when discharging. Under the same scheduling window power, the charging and discharging efficiency of the power reserve measure is different, and the change in its power is also different. When the power is positive, that is, when the power reserve measure is discharging, the formula for the window power change is as follows:

[0118] delta E =-power*delta t *n T / ηdischar ge

[0119] Among them, delta E is the change in power reserve, power is the scheduling window power, delta t The step length for each time step, N T is the number of time steps in a scheduling window, and ηdischar ge is the discharge efficiency of the power reserve measure.

[0120] When the power is negative, that is, when the power reserve measure is charging, the formula for the change in power in the scheduling window is as follows:

[0121] delta E=-power*delta t *n T *η char ge

[0122] Among them, delta E is the change in power reserve, power is the scheduling window power, delta t The step length for each time step, N T is the number of time steps in a scheduling window, η char ge Charging efficiency for power backup measures.

[0123] Ideally, the charge and discharge efficiency is 1. However, in practice, the losses during charge and discharge of reserve power must be taken into account, resulting in a charge and discharge efficiency less than 1. Different reserve power types experience different losses, and therefore different charge and discharge efficiencies. For example, the efficiency of pumped hydropower storage differs from that of electrochemical energy storage, which varies depending on the type and is also affected by external factors such as the environment and temperature.

[0124] Step 3: Select the benchmark paths in each region and construct the energy storage path set;

[0125] The energy storage path is a curve formed by the sum of the charge and discharge power of all energy storage measures in a given area during each time period. The baseline path is an energy storage path with a value of 0 in all time periods. The energy storage path set is the set of preferred energy storage paths for each area.

[0126] Step 4: Optimize the global thermal power start-up and shutdown strategy under a given set of energy storage paths;

[0127] The global thermal power generation startup and shutdown strategy optimization process involves dynamically optimizing the global thermal power generation startup and shutdown strategy through multiple windows by reading in the output scenarios to be regulated in each region and the energy storage paths in that region. The configuration plan is updated on a round-by-round and region-by-region basis until the configuration plan remains unchanged between two consecutive rounds. The optimal thermal power generation startup and shutdown strategy is then output, as shown in Figure 7. Specifically, multi-region thermal power generation backup optimization is performed under the predefined energy storage paths in each region, ultimately generating the optimal thermal power generation backup resource configuration plan. The optimal global cost is then calculated and stored in the optimal path history library, corresponding to the path sequence group.

[0128] The multi-window thermal power reserve dynamic optimization method partially refers to the method proposed in the applicant's prior patent CN202210007806.8 or CN202210633325.8. Specifically, it includes:

[0129] A day or a cycle is divided into multiple windows, and the same number of time steps is set in each window. For capacity backup resources, an initial sorting table is first generated according to the empirical sequence method, and then the cost-performance ratio index is calculated for secondary sorting. The optimal backup measure is selected to achieve the goal of minimizing the total risk cost in the region and verify it.

[0130] Since backup resources involve various situations, including but not limited to start-up and shutdown costs, start-up and shutdown time, price, type, output limit, output cost, whether to participate in deep regulation and deep regulation cost, it is necessary to establish a specific unit economic cost indicator to achieve the most economical backup resource allocation.

[0131] To solve the above problems, the CPR indicator, i.e., the ratio of the risk control cost of the standby unit to the effective power it can provide under the risk scenario, is increased to take into account the deep regulation cost to improve the indicator accuracy. The improved CPR formula is:

[0132] Among them, C cap,i To control the capacity cost of i, C st,i To control the start-stop cost of i, C risk,i,s,t To control the risk electricity cost of i, ΔR i is the replacement cost of removing the control object i.

[0133] The replacement cost is to evaluate the performance of the control object by using the alternative control cost after removing the control object. The formula is:

[0134] ΔR i =R up,i -(R dn,P_min -R dn,P_min-P_min,i )

[0135] Among them, R up,i To remove the upward cost of the control object i, R dn,P_min The minimum output P before removing the measures min Calculate the downgrade cost, R dn,P_min-P_min,i The downward adjustment cost is calculated by subtracting the minimum output after removing the measure i from the minimum output after removing the measure i.

[0136] Among them, u up,i is the expected load rate of control measure i, P max,i To control the maximum output of i, T is the window length, price up,i Increase the risk cost for the unit that controls i.

[0137] Among them, R dn,i is the downward adjustment cost of control measure i, P min,iis the minimum output of control measure i, P min is the minimum output sum of all selected measures in the previous round, R dn represents the average total cost of downward adjustment risk in the previous round, I dn,i It represents the benefit obtained by controlling object i from participating in deep adjustment.

[0138] As shown in Figure 6, at each time step, flexible units are categorized based on whether they are selected. Selected units can be removed by calculating the CPR index, while unselected units can be activated by calculating the CPR index. Within the same window, all time steps use the backup resource startup plan generated by the decision-making process within that window. The time steps are adjusted to take into account factors such as the start and stop times of the units, ultimately achieving optimal allocation of backup resources.

[0139] The thermal power reserve resource optimization algorithm divides the optimization process into rounds, as shown in Figure 8. Each large cycle is divided into several windows, with the first window serving as the starting point for determining the round. If this is the first round of optimization, the desired output line to be regulated for that scenario is generated. The reserve measures are then sorted using an empirical ranking method: the set of available measures in that window is selected and sorted from smallest to largest based on the dispatch order. If the dispatch order is the same, the measures are sorted from largest to smallest based on maximum output. After sorting, the sum of the capacities of all measures is sufficient to cover the desired output line to be regulated.

[0140] If it is not the first round of optimization, the CPR prioritization method is used after updating the local and remote action sets to arrange the backup resources according to the CPR indicators.

[0141] When a certain round faces the first window, the control start and stop state selects the state corresponding to the same window in the previous round. When the window is greater than 1, the start and stop state of the previous window is introduced and the capacity reserve is considered to be increased or decreased. The process is as follows: the CPR index of all unselected available measures is calculated at the specified power level. If there are measures with a CPR value less than 1, they are updated to the new power level. The cycle is repeated until there are no measures with a CPR value less than 1 under the new power standard. If there are measures with a CPR value greater than 1, the measures are removed and the power level is updated. The cycle is repeated until there are no measures with a CPR value greater than 1 in the selected units.

[0142] After completing the set of control measures, the deep peak-shaving cost will be evaluated when any output scenario to be regulated is lower than the lower output limit within the window. The deep peak-shaving evaluation includes an evaluation of the total cost of deep-shaving risks and the deep-shaving benefits of each deep-shaving measure. When the deep-shaving measures cannot meet the demand, the reference cost of abandoned wind and solar power is used for estimation. The window is then moved back, and the control measures in the window are re-prioritized in turn until the last window is prioritized, and the backup resource configuration plan for this round of optimization is obtained. Enter the next round of optimization until multiple rounds of iterative optimization meet the convergence conditions, and finally obtain the resource configuration plan for thermal power standby.

[0143] Step 5: Select the energy storage charging and discharging strategy for each region without changing the strategies of other regions;

[0144] The preferred energy storage charging and discharging strategy for each region is determined by inputting the transmission power curves of the target region's relevant tie lines, optimizing the thermal power reserve for each feasible path in the region, and evaluating the costs. This ultimately outputs the preferred energy storage charging and discharging strategy for the target region, as shown in Figure 9. The total cost evaluation for a single region still involves several aspects, such as capacity reserve cost and power reserve cost. Each power path in each region is independently evaluated to select the optimal energy storage power path for that region. The serial numbers corresponding to the optimal paths in each region are individually selected and grouped into the optimal path history library.

[0145] Step 6: Update the energy storage path set with the optimal path in each area;

[0146] Updating the energy storage path set refers to forming a new energy storage path set from the optimal energy storage paths evaluated in each region.

[0147] Step 7: Determine whether the energy storage path set or the number of iteration rounds meets the preset convergence conditions; if so, proceed to the next step; otherwise, return to step 4;

[0148] The preset convergence condition refers to when the same preferred path combination of each area appears twice in the energy storage path concentration, or the number of iteration rounds reaches a set upper limit.

[0149] Step 8: Optimize and output energy storage charging and discharging and thermal power start-up and shutdown strategies;

[0150] Specifically, for the energy storage pathways generated by multiple iterative calculations and their corresponding evaluation results, the optimal energy storage pathway and the thermal power generation start-up and shutdown strategies under that pathway are selected and output based on the principle of minimizing the total risk cost across the entire region. The regional total risk cost includes, but is not limited to, energy storage kilowatt-hour costs, thermal power unit optimization costs, risk costs, and other costs; thermal power unit optimization costs include start-up and shutdown costs, capacity costs, power regulation costs, and power regulation costs; operational risk costs include power outage costs and power curtailment costs; and other costs include stabilization costs, network loss costs, and frequency regulation costs.

[0151] The present invention optimizes the combination of electricity paths in each region and calculates the final global cost result through a multi-region thermal power reserve resource algorithm in the next cycle. This can obtain an optimal path history library after multiple rounds of cycles, in which each data contains the electricity path of each regional energy storage and the global cost of coordination with thermal power reserve resources under this path combination; the data in the optimal path history library are sorted from small to large according to the global cost, and the sequence number of the energy storage path combination with the lowest cost is obtained, thereby completing the optimal configuration of coordinated energy storage and thermal power reserve.

[0152] The collaborative optimization system for multi-region energy storage and thermal power participating in abundance control described in the present invention includes:

[0153] The target setting module is used to set the overall target outflow of energy storage in each area;

[0154] The module for constructing a feasible path library for energy storage charging and discharging strategies is used to generate a feasible path library for energy storage charging and discharging strategies in each region based on the initial state of each energy storage measure and the time-coupled characteristics of power and electricity. By deducing forward and backward power-electricity limit trajectories, the feasible region of electricity per step for each energy storage measure is calculated. The module also generates an energy storage scheduling plan curve guided by the overall target outflow of energy storage.

[0155] The collaborative optimization module for energy storage and thermal power backup resources is used to optimize the global thermal power start-up and shutdown strategy under a given set of energy storage paths. Without changing the strategies of other regions, it selects the energy storage charging and discharging strategies for each region and updates the energy storage path set with the preferred paths for each region. It determines whether the energy storage path set or the number of iteration rounds meets the preset convergence conditions. If so, it outputs the energy storage charging and discharging and thermal power start-up and shutdown strategies.

Claims

1. A collaborative optimization method for multi-region energy storage and thermal power participating in adequacy control, characterized in that: The following steps are involved: (1) Setting the overall target outflow of energy storage in each region; (2) Generate a feasible path library for energy storage charging and discharging strategies in each region based on the initial state of each energy storage measure and the time coupling characteristics of power and electricity; (3) Select the benchmark paths in each region and construct the energy storage path set; (4) Optimize the global thermal power start-up and shutdown strategy under a given set of energy storage paths; (5) Select the energy storage charging and discharging strategy for each region without changing the strategies of other regions; (6) Updating the energy storage path set using the optimal paths in each region; (7) determining whether the energy storage path set or the number of iteration rounds meets a preset convergence condition; if so, proceeding to the next step; Otherwise, return to step (4); (8) Optimize and output energy storage charging and discharging and thermal power start-up and shutdown strategies.

2. The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control according to claim 1 is characterized in that: In step (2), generating a feasible path library for energy storage charging and discharging strategies in each region includes the following steps: (2.1) Input the overall target outflow of regional energy storage; (2.2) According to the overall target outflow of regional energy storage, the boundary of the overall energy storage path planning is constructed, and the entire scheduling cycle is divided into several scheduling windows; (2.3) Move to the starting point of the overall new path of energy storage; (2.4) randomly selecting a scheduling window and generating the power of the energy storage system in the selected scheduling window; (2.5) allocating the power of the energy storage as a whole in the selected scheduling window to various energy storage measures and adding them to the path; (2.6) Determine whether there is a next window after the selected scheduling window; if so, return to step (2.4); otherwise, add the additional path to the feasible path library; (2.7) Determine whether the number of paths in the feasible path library has reached the maximum number of paths; if not, return to step (2.3); otherwise, output the feasible path library of the regional energy storage charging and discharging strategy.

3. The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control according to claim 2 is characterized in that: The boundaries of the overall energy storage path planning described in step (2.2) include: The intersection of the allowable output range of each power reserve measure under various constraints is used as the power feasible region of the power reserve measure; The feasible domain boundaries of all power reserve measures are accumulated to obtain the total upper and lower envelopes, which serve as the boundaries of the overall energy storage path planning.

4. The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control according to claim 2 is characterized in that: The step (2.4) of generating the power of the overall energy storage in the selected scheduling window comprises the following steps: (2.4.1) Divide the guidance area into positive and negative deviation areas according to the system guidance value; The system guide value refers to the total amount of electricity to be discharged from the system in a given area divided by the number of remaining windows in the scheduling period; the guide area refers to the value range of the overall energy storage power in the selected scheduling window, and the area in the guide area with a value greater than the system guide value is a positive deviation area, and the area in the guide area with a value less than the system guide value is a negative deviation area; (2.4.2) extracting a random number, activating a specific power deviation region, and dividing the deviation region into inner and outer guide sub-regions based on a guide region ratio, wherein the guide region ratio refers to a ratio of a width of the guide region on a parameter coordinate axis to a total width of the parameter coordinate axis; (2.4.3) extracting a random number and activating a specific guide sub-area based on a guide area probability; the guide area probability refers to the ratio of the expected number of times the generated output, i.e., the randomly extracted power size, occurs in the guide area to the total number of times the generated output occurs; (2.4.4) Extract a random number and extract power in a specific pilot sub-area; output the extracted power, which is the power of the energy storage system in the window.

5. The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control according to claim 4 is characterized in that: The method for dividing the deviation area into inner and outer guide sub-areas based on the guide area ratio described in step (2.4.2) is: bound =p exp +a*v*|p max -p exp | In the formula, p bound is the boundary between the inner and outer guide sub-regions, p exp is the system boot value, p max is the maximum power of the guide sub-area, a takes a value of 1 in the positive deviation area and a value of -1 in the negative deviation area, v is the guide area ratio, which refers to the ratio of the data width of the guide area to the data width of the total feasible domain, where the total feasible domain is the intersection of the allowable output range under various constraints.

6. The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control according to claim 2 is characterized in that: The allocation of the total energy storage power to various energy storage measures described in step (2.5) includes: Select energy storage measures that can interact with electricity from the overall energy storage to build an effective measure set; According to the sorting index T d , sort the energy storage measures in the effective measures set, and the sorting index T d The expression is as follows: T d =|E s.end -E s | / [(N w -n w +1)*P s.max ] Among them, E s.end is the final electricity value of the energy storage measure, E s is the actual power value of the energy storage measure in the current window, N w is the number of remaining windows in the scheduling period, n w is the serial number of the current window, P s.max is the absolute value of the maximum power of the energy storage measure; The window power is allocated one by one according to the order. When the remaining power to be allocated is higher than the maximum power of the current energy storage measure, the storage The energy storage measure outputs at the maximum power and enters the next energy storage measure; when the remaining power to be allocated is less than or equal to the maximum power of the current energy storage measure, the energy storage measure outputs at the power to be allocated, the power allocation is terminated, and the power and power changes of each energy storage measure at the current time step are recorded and stored.

7. The collaborative optimization method for multi-region energy storage and thermal power participating in adequacy control according to claim 1 is characterized in that: The optimization of the global thermal power start-up and shutdown strategy in step (4) specifically includes: The multi-window thermal power reserve is dynamically optimized for the global thermal power start-up and shutdown strategy based on the output scenarios to be regulated in each region and the regional energy storage path. The configuration plan is updated round by round and region by region until the configuration plans of two adjacent rounds remain unchanged, and the optimal thermal power start-up and shutdown strategy is output.

8. The collaborative optimization method for multi-region energy storage and thermal power participating in the abundance control according to claim 1 is characterized in that: The step (5) of selecting the energy storage charging and discharging strategy for each region specifically includes: According to the transmission power curves of the relevant interconnection lines in the target area, the single-region thermal power reserve is optimized and the cost is evaluated for each feasible path, and the optimal energy storage charging and discharging strategy for the target area is selected.

9. The collaborative optimization method for multi-region energy storage and thermal power participating in adequacy control according to claim 1, characterized in that: The preset convergence conditions in step (7) include that the same preferred path combination of each area appears twice in the energy storage path set, or the number of iteration rounds reaches a set upper limit.

10. The collaborative optimization method for multi-region energy storage and thermal power participating in adequacy control according to claim 1, characterized in that: The optimization and output of energy storage charging and discharging and thermal power start-up and shutdown strategies in step (8) specifically include: It refers to the energy storage path set generated by multiple iterative calculations and its corresponding evaluation results. According to the principle of minimizing the total risk cost of the entire domain in the evaluation results, the energy storage path set at this time and the thermal power start-up and shutdown strategy under this path set are selected and output.

11. A collaborative optimization system for multi-region energy storage and thermal power participating in adequacy control, characterized in that: include: The target setting module is used to set the overall target outflow of energy storage in each area; The feasible path library construction module of energy storage charging and discharging strategy is used to generate the feasible path library of energy storage charging and discharging strategy in each region according to the initial state of each energy storage measure and the time coupling characteristics of power-electricity. By deducing the forward and backward power-electricity limit trajectory, the feasible domain of each step of each energy storage measure is calculated; under the guidance of the overall target outflow of energy storage, the energy storage scheduling plan curve is generated; The collaborative optimization module of energy storage and thermal power reserve resources is used to optimize the global thermal power start-up and shutdown strategy under a given energy storage path set; select the energy storage charging and discharging strategy of each region without changing the strategies of other regions, and update the energy storage path set with the preferred path of each region; determine whether the energy storage path set or the number of iteration rounds meets the preset convergence conditions; if so, select and output the energy storage charging and discharging and thermal power start-up and shutdown strategies.

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