Distribution robust opportunity constraint low-carbon economic dispatching method considering N-1 safety criterion

Abstract

The invention relates to the technical field of power system optimization scheduling, and discloses a distribution robust opportunity constraint low-carbon economic scheduling method considering an N-1 safety criterion, and the method comprises the steps: building a wind-solar-energy-storage combined power generation system carbon transaction model which considers the bidirectional flowing of a carbon emission right, and taking the minimization of the total operation cost of the wind-solar-energy-storage combined power generation system as a target; establishing a low-carbon economic dispatching model considering the safe operation constraint of the power grid; aggregating wind and light output to establish a fuzzy set based on uncertainty moment information, establishing a distribution robust opportunity constraint economic dispatching model considering wind power uncertainty, and adopting a conditional value-at-risk theory to measure a system operation risk; on the basis, a power transmission line N-1 safety constraint rapid dynamic checking and adding method is provided based on branch circuit breaking distribution factors, and an economic dispatching model considering an N-1 safety criterion is established and solved. The power supply reliability of the system can be ensured under the condition of key line faults, and the solving efficiency and the practicability of the scheduling scheme are improved.

Description

Technical Field

[0001] This invention relates to the field of power system optimization dispatching technology, specifically to a low-carbon economic dispatching method with distributed bar chance constraints that considers the N-1 security criterion. Background Technology

[0002] The proportion of new energy power generation, represented by wind power and photovoltaic power, in the power system continues to increase. It is estimated that by 2030, the installed capacity of new energy will reach 1.05 billion kilowatts, accounting for 36% of the total installed capacity. With the low-carbon transformation of the power system structure, building a new power system with new energy as the core has become a trend. The randomness and volatility of power grid operation are gradually increasing, which puts forward higher requirements for power system dispatch and operation.

[0003] To address the uncertainty of renewable energy output, many scholars have introduced methods such as stochastic optimization, robust optimization, and partial Bruker constraints into dispatching problems. Furthermore, existing research has incorporated chance constraints to balance economy and robustness, but joint modeling of partial Bruker constraints and chance constraints is lacking. In recent years, frequent extreme weather disasters have further amplified uncertainties on both the source and load sides, as well as the operational risks of power grid equipment, leading to numerous large-scale blackouts, causing significant economic losses, and posing challenges to power system supply security. The reliability of power supply and corresponding disaster prevention strategies during the deep decarbonization process of the energy system have received widespread attention. Considering the N-1 safety criterion in economic dispatch is crucial for ensuring the high-quality operation of large power grids and improving the reliability of continuous power supply under line failures. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a low-carbon economic dispatch method based on the N-1 safety criterion and the Bruker chance constraint. This method introduces the Bruker chance constraint into the economic dispatch of wind-solar-storage combined generation systems, comprehensively considering the system's economic efficiency, power supply reliability, and low-carbon operation requirements. It ensures the system's power supply reliability under critical line failure conditions, improves solution efficiency, and enhances the practicality of the dispatch scheme. The technical solution is as follows:

[0005] A low-carbon economic dispatch method considering the N-1 security criterion and leveraging chance constraints includes the following steps:

[0006] Step 1: Quantify the carbon emissions of the power system in terms of economic cost, consider the role of energy storage in the system, and establish a carbon trading model for a wind-solar-storage combined power generation system that considers the bidirectional flow of carbon emission rights.

[0007] Step 2: With the goal of minimizing the total operating cost of the wind-solar-storage combined power generation system, establish a low-carbon economic dispatch model that takes into account the constraints of grid security operation;

[0008] Step 3: Based on the low-carbon economic dispatch model established in Step 2, aggregate wind and solar power output to establish a fuzzy set based on uncertainty moment information, establish a distributed Bruker chance-constrained economic dispatch model that considers wind power uncertainty, and use conditional risk value theory to measure system operation risk;

[0009] Step 4: Based on the low-carbon economic dispatch model established in Step 2 and the multi-branch opportunity-constrained economic dispatch model established in Step 3, and based on the branch interruption distribution factor, a rapid dynamic verification and addition method for the N-1 safety constraints of transmission lines is proposed to establish an economic dispatch model that considers the N-1 safety criterion.

[0010] Step 5: Using optimization software, solve the economic scheduling model considering the N-1 safety criterion established in Step 4 based on the second-order cone theory.

[0011] The beneficial effects of this invention are:

[0012] (1) The present invention provides a low-carbon economic dispatch method with partial Bruker opportunity constraints considering wind and solar uncertainties and the N-1 safety criterion. Compared with existing technologies, this scheme introduces partial Bruker opportunity constraints into the economic dispatch of wind-solar-storage combined power generation systems. By constructing a fuzzy set based on the mean and variance of new energy output to quantify the uncertainty of new energy output, and combining conditional value at risk theory to measure the system operation risk, a carbon tiered trading mechanism is introduced, comprehensively considering the system's economic efficiency, power supply reliability, and the need for low-carbon operation. In addition, the present invention adopts a dynamic verification idea of ​​"verification-addition-re-verification-re-addition" to perform N-1 safety verification on the operation results, ensuring the power supply reliability of the system under critical line failure conditions, which significantly improves the solution efficiency and the practicality of the dispatch scheme compared with traditional methods.

[0013] (2) The scheduling method proposed in this invention can flexibly adjust the risk parameter ε according to the load demand and renewable energy output characteristics of different time periods, thereby realizing a refined scheduling strategy for different time periods. This not only improves the economic efficiency of the system during peak, off-peak, and low-peak periods, but also effectively reduces the risk of load loss and enhances power supply stability. In addition, compared with existing technologies, this scheme fully leverages the role of energy storage in smoothing renewable energy fluctuations and peak shaving and valley filling through the coordinated optimization scheduling of the energy storage system, significantly improving the renewable energy penetration rate and system operational flexibility. Attached Figure Description

[0014] Figure 1 This is a structural diagram of the wind-solar-storage combined power generation system of the present invention.

[0015] Figure 2 This is the N-1 security verification flowchart of the present invention.

[0016] Figure 3These are the predicted values ​​of load and new energy output for the calculation examples of this invention.

[0017] Figure 4 This is the optimized scheduling result of Example 1 of the present invention.

[0018] Figure 5 Example 2 of this invention illustrates the impact of different risk parameters ε on the total system cost.

[0019] Figure 6 The charging / discharging results of the energy storage device in Example 3 of this invention are shown.

[0020] Figure 7 The optimized scheduling result is shown in Example 4 of this invention.

[0021] Figure 8 This is the result of a typical intraday carbon trading arrangement in Example 4 of this invention.

[0022] Figure 9 Example 4 of this invention illustrates the impact of different carbon base prices on carbon trading.

[0023] Figure 10 This is the power flow iteration of line 2-25 after a fault in line 13-14 in Example 5 of this invention.

[0024] Figure 11 This is the power flow iteration situation of section 2 after the fault in line 13-14 in example 5 of the present invention. Detailed Implementation

[0025] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0026] A wind-solar-storage combined power generation system comprises conventional thermal power units, wind farms, photovoltaic power stations, and energy storage devices, achieving coordinated dispatch through a load center. The energy storage system effectively mitigates fluctuations in renewable energy demand. During off-peak hours, the energy storage is in a charging state, preventing wind and solar curtailment. During peak hours, the energy storage system discharges, playing a role in peak shaving and valley filling, reducing the probability of load shedding, and improving power supply stability. The wind-solar-storage combined power generation system plays a crucial role in promoting renewable energy consumption and improving power system stability. The system structure diagram is shown below. Figure 1 As shown.

[0027] The technical solution adopted in this invention is:

[0028] A low-carbon economic dispatch method considering wind and solar uncertainties and the N-1 safety criterion with opportunity constraints is proposed. This invention addresses the uncertainty of renewable energy output by introducing carbon cascade trading into the economic dispatch problem of wind-solar-storage combined power generation systems. Furthermore, it considers the N-1 safety criterion to ensure system power supply reliability in the event of critical line failures. The method includes the following steps:

[0029] Step 1: Quantify the carbon emissions of the power system based on economic costs, consider the role of energy storage in the system, and establish a carbon trading model for a wind-solar-storage combined power generation system that considers the two-way flow of carbon emission rights.

[0030] The model of a combined wind, solar, and energy storage power generation system considering carbon trading is shown below:

[0031] Currently, the commonly used quota method in China is to allocate free quotas based on the installed capacity of thermal power units. The carbon emission quotas for thermal power units are:

[0032] ;

[0033] In the formula, Free carbon emission allowances for all thermal power units in the system; This refers to the free quota coefficient; Let g be the maximum active power of thermal power unit g at time t; This represents the number of thermal power units.

[0034] The essence of carbon trading is to incentivize power generation companies to reduce carbon emissions through economic costs. Carbon emission rights can flow in both directions in the carbon trading market. Units with higher carbon emissions need to purchase carbon emission allowances when they exceed their free carbon emission allowances. Units with lower carbon emissions can sell carbon emission rights to generate revenue.

[0035] This invention uses the baseline method to determine the actual carbon emissions of thermal power units during operation:

[0036] ;

[0037] ;

[0038] In the formula, Let t be the actual carbon emissions of the thermal power unit at time t; and These are the carbon emission coefficients for high-carbon emission and low-carbon emission units, respectively. and The numbers of high-carbon emission and low-carbon emission units are respectively; Let g be the active power output of thermal power unit g at time t; This refers to the actual carbon emissions generated by participating in the carbon trading market after taking into account carbon emission allowances.

[0039] In the tiered carbon trading cost model, the length of the carbon emission tiers should be reasonably determined, and a price penalty factor should be set. and reward factors It can encourage businesses to reduce carbon emissions. Carbon trading costs. It can be represented as:

[0040] ;

[0041] In the formula, and These represent the basic prices for carbon trading penalties and rewards; L is the ladder length. For carbon ladder interval index; Price penalty factor; This is a reward factor.

[0042] Step 2: With the goal of minimizing the total system operating cost, establish a low-carbon economic dispatch model that considers power balance constraints, thermal power unit output constraints, start-up and shutdown constraints, ramping constraints, and energy storage device constraints, among other constraints on grid safety operation.

[0043] The low-carbon economic dispatch model considering the constraints of power grid safety operation is shown below:

[0044] Objective function:

[0045] ;

[0046] In the formula, This represents the total scheduling duration for a typical day. denoted as the number of thermal power units; a, b, and c are the operating cost coefficients of thermal power units. Let g be the output power of the thermal power unit at time t; and These represent the start-up / shutdown costs of thermal power unit g, respectively. and These represent the unit positive and negative standby costs for thermal power units, respectively. and These represent positive and negative reserve capacities, respectively. The load depletion penalty factor; The load loss at time t; Depreciation cost per unit of energy storage; and These represent the discharge and charge amounts of the energy storage device at time t, respectively. The charging and discharging efficiency of energy storage devices.

[0047] The model's constraints include system power balance constraints, thermal power unit start / stop cost constraints, unit output constraints, ramping constraints, minimum start / stop time constraints, wind and solar power output constraints, emergency load shedding constraints, reserve capacity constraints, and energy storage equipment constraints.

[0048] System power balance constraints:

[0049] ;

[0050] In the formula, and These are the numbers of wind power and photovoltaic units, respectively. and These represent the active power outputs of the wind power and photovoltaic units at time t, respectively. Let be the power flow of transmission line l at time t; and These are the transmitting bus (node) and receiving bus (node) of transmission line l, respectively. Let t be the predicted system load value at time t.

[0051] Cost constraints for starting / stopping thermal power units:

[0052] ;

[0053] In the formula, and These are the start-up / shutdown cost coefficients, respectively.

[0054] Thermal power unit output constraints:

[0055] ;

[0056] In the formula, and These represent the start-up and stop states of thermal power unit g at times t and t-1, respectively, with 1 indicating start-up and 0 indicating otherwise. and These represent the upper and lower limits of the output of the thermal power unit at time t.

[0057] Thermal power unit ramping constraints:

[0058] ;

[0059] In the formula, and These represent the maximum upward and downward ramp rates of the thermal power unit, respectively.

[0060] Minimum start-up and shutdown time constraints for thermal power units:

[0061] ;

[0062] In the formula, and These represent the operating and shutdown times of unit g at time t, respectively. and These are the minimum start-up and shutdown times for unit g, respectively.

[0063] Wind and solar power output constraints:

[0064] ;

[0065] In the formula, and These are the predicted values ​​for wind and solar power output.

[0066] Emergency loss of load constraint:

[0067] ;

[0068] In the formula, This is the upper limit of the load shedding capacity; This is the maximum unload factor.

[0069] Reserve capacity constraints:

[0070] ;

[0071] In the formula, and These are the maximum upward and downward reserve capacities that unit g can provide, respectively, and are related to the unit's ramp-up capability. and These are the minimum coefficients for the system's required upward and downward reserve capacity, respectively.

[0072] Constraints of energy storage devices:

[0073] ;

[0074] In the formula, and The variables are state variables, representing the charging and discharging states of the energy storage device at time t. A value of 1 indicates that the energy storage is in a charging / discharging state. and These represent the maximum charging and discharging power of the energy storage device at time t; Let be the capacity at time t; and These represent the maximum and minimum capacity limits, respectively. and These represent the initial and final capacities of the energy storage device, respectively.

[0075] Step 3: Based on the low-carbon economic dispatch model established in Step 2, aggregate wind and solar power output to establish a fuzzy set based on uncertainty moment information, establish a distributed Bruker chance-constrained economic dispatch model that considers wind power uncertainty, and use conditional risk value theory to measure system operation risk;

[0076] After considering wind power uncertainty, based on the economic dispatch model in step 2, establish the distributed bar chance constraint model considering wind power uncertainty in step 3:

[0077] This invention aggregates wind and solar power output to establish a fuzzy set. Although the mean and variance of new energy power output can be obtained from a large amount of historical data, the accurate values ​​obtained based on historical conditions are too absolute and difficult to measure all possibilities in actual operation. Therefore, by setting upper and lower limits for the mean and variance, the uncertainty of new energy can be described more accurately, thus constructing a fuzzy set based on moment information. , Let be the probability distribution function.

[0078] ;

[0079] In the formula, This refers to the set of all distributions of new energy power output after considering uncertainties. and These represent the mean and variance of the new energy output, respectively. , , and These are the upper and lower limits for the mean and variance, respectively. It is the probability distribution function; Indicates the probability distribution The initial fuzzy set, It is an uncertain quantity; The probability distribution is represented as The expected value.

[0080] Strict power balancing can lead to excessively high system operating costs. To balance system economy and reliability, this invention introduces a chance constraint to relax the system power balance relationship, defining the confidence level as 1-ε, where ε is an adjustable risk parameter. By adjusting ε, the reliability of the system can be controlled. This invention sets the ε value according to the time-of-use pricing concept. During peak hours, electricity consumption is high, so ε is set to a lower value, allowing for a very low probability that power generation will fall below load demand. During off-peak hours, electricity consumption is low, so ε is set to a higher value, allowing for some power imbalance to improve system economy. During neutral hours, ε is set to an appropriate value.

[0081] To facilitate model transformation, the general form of the chance constraint is introduced as follows:

[0082] ;

[0083] In the formula, For decision variables; It is an uncertain quantity.

[0084] Considering the risk maximization scenario, risk can be measured using CVaR (Conditional Value at Risk):

[0085] ;

[0086] Step 4: Based on the models established in Steps 2 and 3, a rapid dynamic verification and addition method for N-1 safety constraints of transmission lines is proposed based on the branch interruption distribution factor, and an economic dispatch model considering the N-1 safety criterion is established.

[0087] Based on the models established in steps 2 and 3, a rapid dynamic verification and addition method for N-1 safety constraints of transmission lines is proposed based on the branch interruption distribution factor. An economic dispatch model considering the N-1 safety criterion is established. The economic dispatch model considering the N-1 safety criterion in step 4 is as follows:

[0088] Frequent extreme weather events pose new challenges to the reliability of power grid supply. To improve the power system's ability to cope with line outage faults, this invention proposes a novel method for rapid dynamic verification and addition of N-1 safety constraints for transmission lines based on the Line Outage Distribution Factor (LODF). The LODF characterizes the power flow transfer features after a faulty branch is disconnected and depends only on the system's grid structure and line parameters, independent of the system's current operating state. Therefore, the system's LODF can be pre-calculated offline. During subsequent fault verification, the power flow distribution of the remaining lines in the fault scenario can be solved online in one step. The efficiency of N-1 safety constraint verification is improved by two orders of magnitude compared to traditional methods.

[0089] Based on the DC power flow method and compensation principle, the LODF expression can be derived as follows:

[0090] ;

[0091] In the formula, For branch j after the faulty branch q is disconnected; and Let J represent the self-impedance and mutual impedance between node pairs of branch j and branch q. and Let X be the node-branch correlation vectors of branches j and q, respectively, and let X be the branch reactance matrix, which is obtained by inverting the susceptance matrix B. (where n is the number of nodes excluding the balancing node). and These are the reactances corresponding to branches j and q, respectively.

[0092] After obtaining the LODF, the new power flow calculation formula for the remaining normal branches j after a branch q fails can be obtained from the above formula:

[0093] ;

[0094] In the formula, The new power flow in branch j after branch q is disconnected; and These represent the power flow before the faults in branches j and q, respectively.

[0095] Based on the above formula, the power flow transfer of the remaining branches after N-1 occurs is obtained. It is then determined whether the power flow of the line and cross-section meets the safety constraints. If it does, the current solution is the optimal solution considering the N-1 principle. If it does not meet the safety constraints, new power flow safety constraints are added to the original optimization model for iterative solution until the power flow meets the safety constraints. The new line and cross-section power flow safety constraints are:

[0096] ;

[0097] In the formula, The set of lines included in the key cross-section; and These represent the upper and lower limits of the power flow at the critical section s, respectively.

[0098] like Figure 2 The diagram shown is an N-1 security verification flowchart, which includes the following steps:

[0099] Step 1) Initialization: First, calculate the LODF based on the network parameters and topology.

[0100] Step 2) Solve the split-bar chance constraint model proposed in this invention.

[0101] Step 3) Select the critical path and set the fault, and calculate the new power flow of the remaining branches after the fault in branch q based on LODF.

[0102] Step 4) Determine whether the branch power flow / section power flow exceeds the limit (i.e. whether it meets the safety constraints). If the power flow exceeds the limit (does not meet the safety constraints), add the new power flow safety constraints of the line / section to the original optimized scheduling model and return to Step 2) Iterative solution; if the power flow does not exceed the limit (meets the safety constraints), output the optimal solution that satisfies the N-1 safety check.

[0103] Step 5: The partial Bruker chance constraint model considering the N-1 safety criterion established in Step 4 was solved using mature optimization software combined with second-order cone theory. The effectiveness of the model was verified by using improved IEEE-39 node data for example analysis.

[0104] The solution process combines mature optimization software with second-order cone theory to solve the bibru bar chance constraint model considering the N-1 safety criterion established in step 4 using MATLAB 2022b. The specific solution process is as follows:

[0105] Step 3 considers the risk maximization scenario, and uses CvaR to measure the risk as follows:

[0106] ;

[0107] The internal maximization problem is difficult to solve directly, so variables are introduced. In the worst probability distribution The mean value is expressed in integral form as follows:

[0108] ;

[0109] In the formula, for The set of nonnegative Bernoulli measures; It is an auxiliary variable of a second-order cone. Indicates in The integral of r within the range.

[0110] Based on duality theory, by introducing dual variables v, s, τ, and z, the above equation is derived as follows:

[0111] ;

[0112] Considering the effects of uncertainty, the above formula can be equivalent to:

[0113] ;

[0114] because and Both are positive. Equivalent to Maximizing the problem based on the duality principle This can be transformed into a minimization problem:

[0115] ;

[0116] In the formula, and The dual variable introduced.

[0117] Considering only the first-order moment information in the fuzzy set yields a highly conservative solution. Typically, the conservatism of the solution is reduced by constraining the distribution of the fuzzy set to a unimodal function. This invention introduces a unimodal factor. To control the conservatism of the system. Based on the fuzzy set of uncertainty moment information, let Therefore, single-peak information should be taken into account based on the above formula. The subsequent DR-CC reconstruction, containing bilinear variables, was transformed using second-order cone theory into:

[0118]

[0119] Finally, the results obtained are the total system operating cost, thermal power output, and carbon trading results under different calculation scenario settings.

[0120] The forecast data within the dispatch cycle includes predicted load and wind and solar power output; the low-carbon economic dispatch model considering carbon trading includes system power balance constraints, thermal power unit start / stop cost constraints, unit output constraints, ramping constraints, minimum start / stop time constraints, wind and solar power output constraints, emergency load shedding constraints, reserve capacity constraints, and energy storage equipment constraints; model-related parameters include the maximum and minimum output power of the energy storage system, the charging and discharging efficiency of energy storage equipment, the carbon trading ladder length, the carbon trading reward and penalty factor, and the carbon emission quota coefficient; relevant price data include the depreciation cost of energy storage equipment, the carbon trading base price, the start / stop cost of thermal power units, the thermal power unit operating cost coefficient, and the load shedding penalty coefficient.

[0121] This invention uses a method that combines mature optimization software with second-order cone theory to solve the model of this invention.

[0122] The example uses a modified IEEE-39 node system, with a photovoltaic power station connected at node 4 and a wind farm connected at node 16. It includes 10 thermal power units, of which units 2, 6, 7, 8, and 10 are high-carbon emission units with a carbon emission factor of 0.95, and the rest are low-carbon emission units with a carbon emission factor of 0.8. The carbon emission quota factor is 0.581 t / MW, the carbon ladder length is 200 t, the carbon trading base price is 150 yuan / t, and the reward / penalty factor is uniformly set to 0.2. Figure 3 The load and renewable energy output forecasts shown are based on a 24-hour scheduling time, a 1-hour resolution, and a positive and negative reserve ratio of 5%. Relevant parameters for the energy storage equipment are shown in Table 1.

[0123] Table 1. Relevant parameters of energy storage equipment .

[0124] This invention uses five examples as shown in Table 2 for comparative analysis.

[0125] Table 2. Example Execution Scheme Settings .

[0126] Table 3 shows the system operating costs for Examples 1-5, including the results of solving for thermal power costs, off-load costs, energy storage depreciation costs, carbon trading costs, and total system operating costs.

[0127] Table 3 System Operating Costs for Scenarios 1-6 .

[0128] The optimization scheduling results of Example 1 are as follows: Figure 4As shown, considering the uncertainty of wind and solar power output, the system experiences load shedding during the peak load period of 20:00-23:00, with a total load shedding of 444.65MW. Example 2, based on Example 1, considers that different confidence levels of power generation exceeding load levels imply different degrees of slack in power balance; higher confidence levels reduce the likelihood of power exceeding limits but also increase costs. Therefore, ε is set for different time periods to pursue economic efficiency. The dispatch costs under different risk parameters are as follows: Figure 5 As shown, the cost variation is greatest when the risk parameter ε fluctuates between 0 and 0.05. When ε increases from 0 to 0.05, the total system cost decreases by 1.90%, while when ε > 0.2, the impact of ε on cost remains essentially unchanged. Therefore, further reducing ε to excessively pursue economic benefits is not meaningful. Thus, based on Example 1, using the concept of time-of-use pricing, the risk parameter ε is set in different time periods during the actual system scheduling time. During peak load periods (10:00-12:00 and 19:00-21:00), ε is set to 0.05; during off-peak periods (8:00-9:00, 13:00-18:00 and 22:00-24:00), ε is set to 0.1; and during off-peak periods (1:00-7:00), ε is set to 0.2. After setting ε in different time periods, the scheduling cost is 5,407,929.05 yuan. Compared to Example 1, Example 2 further improves the economic efficiency of system operation.

[0129] Example 3, based on Example 2, considers the role of energy storage in absorbing new energy sources and improving power supply stability. The total operating cost of the system is 5,152,831.86 yuan, of which the depreciation cost of energy storage is 185,898.10 yuan. The charging / discharging results of the energy storage system are as follows: Figure 6 As shown, the energy storage device is charging during off-peak hours (4:00-6:00, 17:00, and 24:00) and discharging during peak hours (1:00 and 19:00-23:00) to meet load demand. In comparison to Example 1, Example 3 shows that during peak load hours (20:00-23:00), when power is insufficient, the energy storage device discharges a total of 379.99MW to supplement the power gap, preventing load shedding. During the charging of the energy storage device from 4:00-6:00, thermal power unit 1 maintains maximum power output within its efficient operating range, effectively reducing the short-term power difference in unit 2, avoiding increased fuel consumption due to rapid ramp-up, and improving system flexibility. Therefore, the investment in energy storage not only increases the penetration rate of new energy sources but also prevents load shedding and improves power supply stability.

[0130] Furthermore, Case 4 introduces a tiered carbon trading mechanism based on Case 3, allowing carbon emission rights to flow bidirectionally in the carbon trading market. The system operating cost is 5,608,871.67 yuan, and the carbon trading cost is 444,928.22 yuan. The system scheduling results are as follows: Figure 7As shown, when considering carbon trading during system operation, power generation companies adjust unit output to minimize carbon trading costs. Comparing with Example 3, high-emission unit 2 experiences a significant output reduction from 3:00 to 7:00, while low-emission unit 4 increases its output; from 12:00 to 14:00, unit 2's output decreases, while low-emission unit 5 operates at full load, meeting load demand while reducing carbon trading costs. Typical intraday carbon trading results are shown below. Figure 8 As shown, during the off-peak period from 1:00 to 7:00, the carbon emissions of thermal power units are relatively low, and the remaining carbon emission rights can be sold on the carbon trading market, generating revenue of 271,181.89 yuan. After 7:00, the carbon emissions of thermal power units exceed the carbon emission quota, requiring the purchase of carbon emission rights from the carbon trading market at a cost of 716,110.11 yuan. The typical intraday carbon trading cost is 444,928.22 yuan.

[0131] Furthermore, the impact of varying carbon ladder lengths on carbon trading was analyzed, and the results are as follows: Figure 9 As shown, with the increase in the carbon base price, carbon trading costs increase while carbon emissions decrease. This is because power generation companies, in order to maximize profits, reduce the output of high-carbon emission units, with the power difference being borne by low-carbon emission units. When the carbon base price increases from 80 yuan / t to 250 yuan / t, the carbon trading cost rises from 239,954.77 yuan to 705,347.09 yuan. To maximize profits, companies reduce carbon emissions from 2,813.51 tons to 2,665.77 tons, a reduction of 4.14%, achieving the goal of energy conservation and emission reduction.

[0132] To ensure the reliability of the power supply system, Case 5 considers the N-1 safety check of the lines based on Case 4. According to the power flow conditions, a fault is selected on critical lines 13-14, resulting in a total system operating cost of 5,645,174.45 yuan. To meet the N-1 safety constraint, the unit output needs to be adjusted to improve system reliability, an increase of 36,302.78 yuan compared to Case 4. Furthermore, some high-emission units reduce their output to avoid line overload, while low-emission units bear more load, thus reducing carbon trading costs. After the N-1 safety check, the energy storage system needs to charge and discharge more frequently to balance power fluctuations. In fault scenarios, it needs to discharge rapidly to alleviate line overload or charge to absorb excess power, leading to an increase in depreciation costs of 0.67 million yuan.

[0133] Based on LODF, the power flow transfer situation of other lines can be obtained. Taking line 2-25 as an example, the power flow iteration situation after the fault is as follows: Figure 10As shown, after a fault occurred on line 13-14, line 2-25 experienced power flow exceeding limits during time periods 1:00, 4:00, 7:00-9:00, 12:00, 18:00-19:00, and 21:00-22:00. After the second iteration, power flow exceeding limits still occurred during time periods 3:00, 5:00, 10:00, and 24:00. After the third iteration, the power flow of line 2-25 met the safety constraints. The transmission section components and power limits are shown in Table 4.

[0134] Table 4. Cross-sectional Composition of Circuits and Power Constraints .

[0135] A fault is set in line 13-14, causing power flow exceeding limits in both section 1 and section 2. Taking section 2 as an example, the power flow iteration is as follows: Figure 11 As shown, after the fault in line 13-14, the power flow in section 2 exceeded the limit in many time periods, requiring iterative solution. After three iterations, the power flow in section 2 exceeded the limit only in time period 2:00. After the fourth iteration, the power flow in section 2 met the safety constraints in all scheduling time periods.

[0136] This invention addresses the uncertainties in renewable energy output, the low-carbon transformation of the power system, and the cascading failures caused by system outages under extreme weather conditions. It designs a two-way carbon tiered trading mechanism, considers the uncertainty of wind power output, relaxes the power balance relationship using opportunity constraints, and introduces CVaR theory to measure system operation risk. A low-carbon economic dispatch method with opportunity constraints, considering wind and solar uncertainties and the N-1 safety criterion, is established. The objective function is to minimize the total system operating cost. The following conclusions are drawn:

[0137] (1) Based on the idea of ​​tiered electricity pricing, the risk parameter ε is set, and the total operating cost of the system is reduced by RMB 3,341.2 compared with the fixed parameter of 0.05. This realizes the refined scheduling strategy of time periods, which not only improves the economic efficiency of the system during peak, valley and low periods, but also effectively reduces the risk of load loss and improves the stability of power supply.

[0138] (2) The use of a combined wind, solar and energy storage power generation system effectively absorbs new energy sources, stabilizes the output of thermal power units and eliminates load loss through energy storage charging and discharging, which proves the role of energy storage system in improving the penetration rate of new energy sources and the reliability of power supply.

[0139] (3) Introduce two-way carbon tiered trading, change the impact of carbon base price on carbon trading costs, promote enterprises to reduce carbon emissions by 4.14%, and achieve the goal of energy conservation and emission reduction.

[0140] (4) Based on the idea of ​​“verification-addition-re-verification-re-addition”, line N-1 safety verification is added, which avoids a lot of redundant constraints and ensures that the power flow of other lines and key sections does not exceed the limit under the fault of important line disconnection, thus improving the power supply reliability of the system under fault.

Claims

1. A low-carbon economic dispatch method considering the N-1 security criterion and based on chance constraints, characterized in that, Includes the following steps: Step 1: Quantify the carbon emissions of the power system in terms of economic cost, consider the role of energy storage in the system, and establish a carbon trading model for a wind-solar-storage combined power generation system that considers the bidirectional flow of carbon emission rights. Step 2: With the goal of minimizing the total operating cost of the wind-solar-storage combined power generation system, establish a low-carbon economic dispatch model that takes into account the constraints of grid security operation; Step 3: Based on the low-carbon economic dispatch model established in Step 2, aggregate wind and solar power output to establish a fuzzy set based on uncertainty moment information, establish a distributed Bruker chance-constrained economic dispatch model that considers wind power uncertainty, and use conditional risk value theory to measure system operation risk; Step 4: Based on the low-carbon economic dispatch model established in Step 2 and the multi-branch opportunity-constrained economic dispatch model established in Step 3, and based on the branch interruption distribution factor, a rapid dynamic verification and addition method for the N-1 safety constraints of transmission lines is proposed to establish an economic dispatch model that considers the N-1 safety criterion. Step 5: Using optimization software, solve the economic scheduling model considering the N-1 safety criterion established in Step 4 based on the second-order cone theory to obtain the total system operating cost, thermal power output, and carbon trading results under different calculation scenario settings.

2. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 1, characterized in that, In step 1, the carbon trading model for the combined wind, solar, and energy storage power generation system is as follows: The carbon emission quotas for thermal power units are: ； In the formula, Free carbon emission allowances for all thermal power units in the wind-solar-storage combined power generation system; This refers to the free quota coefficient; Let g be the maximum active power of thermal power unit g at time t; This refers to the number of thermal power units. The actual carbon emissions during the operation of thermal power units were determined using the baseline method as follows: ； ； In the formula, Let t be the actual carbon emissions of the thermal power unit at time t; and These are the carbon emission coefficients for high-carbon emission and low-carbon emission units, respectively. and The numbers of high-carbon emission and low-carbon emission units are respectively; Let g be the active power output of thermal power unit g at time t; This refers to the actual carbon emissions generated by participating in the carbon trading market after taking into account carbon emission allowances. Carbon trading costs Represented as: ； In the formula, and These represent the basic prices for carbon trading penalties and rewards; L is the ladder length. Price penalty factor; As a reward factor; This is a carbon ladder interval index.

3. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 2, is characterized in that, The low-carbon economic dispatch model considering power grid security operation constraints in step 2 is shown below: Objective function: ； In the formula, The total scheduling time is a typical day; a, b, and c are the operating cost coefficients of thermal power units. and These represent the start-up / shutdown costs of thermal power unit g, respectively. and These represent the unit positive and negative standby costs for thermal power units, respectively. and These represent positive and negative reserve capacities, respectively. The load depletion penalty factor; The load loss at time t; Depreciation cost per unit of energy storage; and These represent the discharge and charge amounts of the energy storage device at time t, respectively. The charging and discharging efficiency of energy storage devices.

4. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 3, is characterized in that... The constraints on the safe operation of the power grid include system power balance constraints, thermal power unit start / stop cost constraints, unit output constraints, ramp-up constraints, and minimum start / stop time constraints; as detailed below: System power balance constraints: ； In the formula, and These are the numbers of wind power and photovoltaic units, respectively. and These represent the active power outputs of the wind power and photovoltaic units at time t, respectively. Let be the power flow of transmission line l at time t; and These are the transmitting bus and receiving bus of transmission line l, respectively; The system load forecast value at time t; This represents the total number of transmission lines. Cost constraints for starting / stopping thermal power units: ； In the formula, and These are the start / stop cost coefficients, respectively. and These represent the start-up and stop states of thermal power unit g at times t and t-1, respectively, with 1 indicating start-up and 0 indicating otherwise. Thermal power unit output constraints: ； In the formula, and These represent the upper and lower limits of the output of the thermal power unit at time t, respectively. Thermal power unit ramping constraints: ； In the formula, and These represent the maximum upward and downward ramp rates of the thermal power unit, respectively. Minimum start / stop time constraints for thermal power units: ； In the formula, and These represent the operating and shutdown times of thermal power unit g at time t, respectively. and These represent the minimum start-up and shutdown times for thermal power unit g, respectively.

5. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 4, characterized in that, The constraints on the safe operation of the power grid include constraints on wind and solar power output, emergency load shedding, reserve capacity, and energy storage devices, as detailed below: Wind and solar power output constraints: ； In the formula, and These are the predicted values ​​for wind and solar power output; Emergency loss of load constraint: ； In the formula, This is the upper limit of the load shedding capacity; The maximum unload factor; Reserve capacity constraints: ； In the formula, and These are the maximum upward and downward reserve capacities that thermal power unit g can provide, respectively, and are related to the unit's ramp-up capability. and These are the minimum coefficients for the system's required upward and downward reserve capacity, respectively. Constraints of energy storage devices: ； In the formula, and , are state variables, representing the charging and discharging states of the energy storage device at time t, respectively. When 1 is taken, it indicates that the energy storage is in a charging / discharging state; and These represent the maximum charging and discharging power of the energy storage device at time t; Let be the capacity of the energy storage device at time t; and These represent the maximum and minimum capacity limits for energy storage devices, respectively. and These represent the initial and final capacities of the energy storage device, respectively.

6. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 5, is characterized in that, In step 3, the establishment of the fuzzy set based on uncertainty moment information is specifically as follows: By setting upper and lower limits for the mean and variance, the uncertainty of new energy sources can be described more accurately, and a fuzzy set based on moment information is constructed. The set of all distributions of new energy power output after considering uncertainties. and fuzzy sets It is expressed as follows: ； In the formula, and These represent the mean and variance of the new energy output, respectively. , , and These are the upper and lower limits for the mean and variance, respectively. It is the probability distribution function; Indicates the probability distribution The initial fuzzy set, It is an uncertain quantity; The probability distribution is denoted as The expected value.

7. The low-carbon economic dispatch method considering the N-1 safety criterion with distributed bar chance constraints according to claim 6, characterized in that, In step 3, the specific steps for establishing the distributed bar opportunity-constrained economic scheduling model are as follows: Opportunity constraints are introduced to relax the system power balance relationship. The confidence level is defined as 1-ε, where ε is an adjustable risk parameter. The reliability of the control system is controlled by adjusting ε; as follows: ； The general form of introducing opportunity constraints is: ； In the formula, For decision variables; , These represent the coefficients of the deterministic and uncertain decision variables, respectively.

8. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 7, is characterized in that, In step 3, the operational risks of the measurement system are specifically as follows: Considering the risk maximization scenario, risk can be measured using Conditional Value at Risk (CVaR): ； In the formula, The conditional value-at-risk parameter is introduced. The set of natural numbers; This represents the supremum, i.e., the boundary in the worst-case probability distribution. Down; Denotes the infimum, i.e., finding the minimum loss. .

9. The low-carbon economic dispatch method considering the N-1 safety criterion with distributed bar chance constraints according to claim 8, characterized in that, In step 4, the economic scheduling model considering the N-1 safety criterion is as follows: Based on the DC power flow method and compensation principle, the expression for the branch interruption distribution factor is derived as follows: ； In the formula, The branch disconnection distribution factor for branch j after the faulty branch q is disconnected; and Let J represent the self-impedance and mutual impedance between node pairs of branch j and branch q. and Let J and Q be the node-branch association vectors for branches j and q, respectively, and X be the branch reactance matrix. and These are the reactances corresponding to branches j and q, respectively; T is the transpose symbol. Based on the above formula, the new power flow calculation formula for the remaining normal branches j after branch q fails is: ； In the formula, The new power flow in branch j after branch q is disconnected; and These represent the power flow before the faults in branches j and q, respectively. Based on the above formula, the power flow of the remaining branches after N-1 occurs is obtained. It is then determined whether the power flow of the line and cross-section meets the safety constraints. If it does, the current solution is the optimal solution considering the N-1 principle. If it does not meet the safety constraints, new power flow safety constraints are added to the optimization model for iterative solution until the power flow meets the safety constraints. The new line and cross-section power flow safety constraints are: ； In the formula, The set of lines included in the key cross-section; and These are the upper and lower limits of the power flow at the critical section s, respectively; and These are the upper and lower limits of the power flow of transmission line l at time t, respectively.

10. The low-carbon economic dispatch method considering the N-1 safety criterion with distributed bar chance constraints according to claim 9, characterized in that, In step 5, the specific solution process is as follows: In step 3, considering the risk maximization scenario, variables are introduced when measuring risk using Conditional Value at Risk (CVaR). In the worst probability distribution The mean value is expressed in integral form as follows: ； In the formula, for The set of nonnegative Bernoulli measures; It is an auxiliary variable of a second-order cone. Indicates in The integral of r within the range; Based on duality theory, by introducing dual variables v, s, τ, and z, the above equation is derived as follows: ； Considering the impact of uncertainty, the above equation is equivalent to: ； because and Both are positive. Equivalent to According to the duality principle, the maximization problem is to... Transform it into a minimization problem: ； In the formula, and For the introduction of dual variables; Introducing a single-peak factor To control system conservatism, a fuzzy set based on uncertainty moment information is used, allowing... Therefore, based on the above formula, single-peak information is taken into account. The subsequent DR-CC reconstruction, containing bilinear variables, was transformed using second-order cone theory into: ； Finally, the total system operating cost, thermal power output, and carbon trading results were obtained under different calculation scenario settings.

11. The low-carbon economic dispatch method considering the N-1 safety criterion and based on the chance constraints of the distributed bar as described in claim 10, characterized in that, The forecast data within the dispatch cycle includes the predicted values ​​of load and wind and solar power output; the relevant parameters of the economic dispatch model considering the N-1 safety criterion include the maximum and minimum output power of the energy storage system, the charging and discharging efficiency of the energy storage equipment, the carbon trading ladder length, the carbon trading reward and penalty factor, and the carbon emission quota coefficient; the relevant price data includes the depreciation cost of energy storage equipment, the carbon trading base price, the start-up and shutdown cost of thermal power units, the operating cost coefficient of thermal power units, and the load shedding penalty coefficient.

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