Real-time optimization scheduling method and system considering dynamic capacity increase of line in emergency weather

Abstract

The invention discloses a real-time optimization scheduling method considering dynamic capacity increasing of a line in emergency weather. The method comprises the following steps: acquiring data information of a target power system and data information of an overhead line provided with dynamic capacity increasing equipment in real time; calculating to obtain the current-carrying capacity increase rate of the dynamic capacity-increasing circuit; modeling is carried out on the relation between the short-time overcurrent rate and the temperature rise duration of the line; constructing a source network storage real-time optimization scheduling model considering line dynamic capacity increase and corresponding constraint conditions; and solving the constructed model to realize real-time optimization scheduling considering dynamic capacity increase of the line in emergency weather. The invention also discloses a system for realizing the real-time optimization scheduling method considering the dynamic capacity increase of the line in the emergency weather. According to the method, real-time optimization scheduling of the target power system in consideration of dynamic capacity increase of the line in response to sudden weather is realized, the potential of short-time power transmission of the power grid is utilized, the reliability is high, and the accuracy is good.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to a real-time optimization scheduling method and system for considering dynamic capacity increase of lines in response to sudden weather events. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless benefits to people's production and life. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] The increasing penetration of renewable energy is currently changing the traditional power structure of the power system, exacerbating the conflict between ensuring load supply and absorbing renewable energy. Furthermore, due to increasingly severe environmental problems and the frequent occurrence of sudden weather events, the dynamic balance of the power system will be severely challenged when sudden weather events and the structural contradictions of the new power system power supply form a superposition effect. When renewable energy stations experience a sudden drop in output within minutes due to sudden weather events during high-power generation, the net load ramp rate of the grid can reach several times that of conventional scenarios. This is double-edged by the limited regulation rate of thermal power units and the static thermal rating (STR) constraints of transmission lines. This situation is highly likely to cause regional supply and demand imbalances, leading to wind and solar power curtailment or emergency load shedding.

[0004] Under current grid operating conditions, dynamic thermal rating (DTR) technology offers a solution to this problem. This technology dynamically assesses the static capacity limits of power lines by monitoring parameters such as wind speed, sunlight, and ambient temperature in real time. This technology can increase the static transmission capacity of power lines without exceeding the maximum allowable temperature rise specified in existing regulations.

[0005] However, existing research on dynamic capacity expansion solutions for transmission lines has largely focused on the application of DTR technology in short-term optimal scheduling, as well as mid- and long-term optimization planning. Few studies have examined its application in ultra-short-term (real-time) optimal scheduling. Furthermore, existing DTR research focuses solely on the long-term steady-state transmission capacity of transmission lines, ignoring the short-term dynamic transmission potential associated with transient temperature rises. Summary of the Invention

[0006] One of the purposes of the present invention is to provide a real-time optimization scheduling method with high reliability and good accuracy for dealing with sudden weather conditions and considering dynamic capacity increase of lines.

[0007] The second object of the present invention is to provide a system for realizing the real-time optimization scheduling method considering dynamic capacity increase of lines in response to sudden weather events.

[0008] The present invention provides a real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions, comprising the following steps:

[0009] S1. Real-time acquisition of target power system data and overhead line data on equipment equipped with dynamic capacity expansion.

[0010] S2. Based on the data information obtained in step S1, the current carrying capacity increase rate of the dynamic capacity line is calculated;

[0011] S3. Modeling the relationship between the short-term overcurrent rate and the temperature rise duration of the line based on the data information obtained in step S1;

[0012] S4. Build a real-time optimization scheduling model for source, network, and storage that takes into account dynamic line capacity expansion, and establish corresponding constraints.

[0013] S5. Solve the model constructed in step S4 to achieve real-time optimized scheduling considering dynamic capacity expansion of the line in response to sudden weather events.

[0014] The real-time acquisition of data information of the target power system and data information of the overhead line equipped with the dynamic capacity expansion device described in step S1 specifically includes the following steps:

[0015] The acquired data and information of the target power system include the rated operating parameters of thermal power units, rated operating parameters of energy storage power stations, ultra-short-term forecast data of new energy stations, ultra-short-term forecast data of loads, and shelvable load power and capacity data;

[0016] The acquired data information of the overhead line equipped with the dynamic capacity expansion equipment includes the ambient temperature, wind speed, wind direction, and sunshine radiation data information of the overhead line equipped with the dynamic capacity expansion equipment.

[0017] Step S2, based on the data information obtained in step S1, calculates the current carrying capacity increase rate of the dynamic capacity expansion line, and specifically includes the following steps:

[0018] The long-term steady-state current carrying capacity of the dynamic capacity increase line is calculated using the following formula:

[0019]

[0020] In the formula is the heat dissipated by the dynamic capacity expansion circuit through heat conduction at time t; is the heat dissipated by the dynamic capacity expansion line through thermal radiation at time t; is the solar radiation power absorbed by the dynamic capacity expansion line at time t; is the AC resistance of the dynamic capacity expansion circuit at the allowable temperature at time t;

[0021] According to the long-term steady-state current carrying capacity The following formula is used to calculate the current carrying capacity increase rate of the dynamic capacity increase line:

[0022]

[0023] In the formula The static current carrying capacity determined during the design phase for the dynamic capacity expansion line.

[0024] Step S3, based on the data information obtained in step S1, models the relationship between the short-term overcurrent rate of the line and the temperature rise duration, specifically including the following steps:

[0025] Establish overcurrent rate constraints for short-term operation of dynamic capacity expansion lines considering transient temperature rise effects:

[0026]

[0027] In the formula In order to consider the transient temperature rise characteristics based on The transient increase rate of the kth dynamic capacity increase line at time t; is the increasing rate of the current carrying capacity of the kth dynamic capacity-increasing line at time t; is the first short-time overcurrent rate of the kth dynamic capacity increase line at time t; is the second short-time overcurrent rate of the kth dynamic capacity increase line at time t; is the binary control variable when the kth dynamic capacity increase line is in non-overcurrent operation at time t. If the kth dynamic capacity increase line is in non-overcurrent operation at time t, If the kth dynamic capacity increase line is not in non-overcurrent operation at time t, is the binary control variable of the kth dynamic capacity expansion line operating at the first short-term overcurrent rate at time t. If the kth dynamic capacity expansion line operates at the first short-term overcurrent rate at time t, If the kth dynamic capacity expansion line does not operate at the first short-time overcurrent rate at time t, is the binary control variable of the kth dynamic capacity expansion line operating at the second short-term overcurrent rate at time t. If the kth dynamic capacity expansion line does not operate at the second short-time overcurrent rate at time t,

[0028] Establish constraints between the short-term overcurrent rate and temperature rise duration of the dynamic capacity increase line:

[0029]

[0030] Where ΔTstage2 ΔT is the duration of transient temperature rise corresponding to the kth dynamic capacity increase line when it operates at the first short-time overcurrent rate; stage3 The duration of transient temperature rise corresponding to the kth dynamic capacity increase line when it operates at the second short-time overcurrent rate; δ st To control the kth dynamic capacity increase line to continuously operate at a unique overcurrent rate, the kth dynamic capacity increase line operates at the first short-term overcurrent rate at time t, then δ st =1, the kth dynamic capacity expansion line operates at the second short-time overcurrent rate at time t, then δ st =0;L DTR A collection of dynamically expanded lines.

[0031] The construction of a real-time optimization scheduling model for source, network, and storage considering dynamic line capacity expansion described in step S4 specifically includes the following steps:

[0032] The following formula is used as the objective function of the source-network-storage real-time optimization scheduling model considering dynamic line capacity expansion:

[0033] min C DIS =C GEN +C CURT +C RISK

[0034] Where C DIS is the total cost within the real-time scheduling cycle; C GEN is the operating cost of the thermal power unit, and c g is the unit cost of the output of the g-th thermal power unit, is the output of the g-th thermal power unit at time t, Δt is the scheduling interval, G is the set of thermal power units, and T is the optimal scheduling period; C CURT The cost of emergency load shedding and renewable energy curtailment, and c load is the unit cost of load shedding, is the load shedding value of node i at time t, c res is the unit cost of abandoned electricity from new energy sources, is the amount of renewable energy abandoned by node i at time t, D is the node set; C RISK is the risk cost of short-term overload operation of the dynamic capacity expansion line considering transient temperature rise characteristics, and c r1 is the unit risk cost when the line operates at the first short-time overcurrent rate, c r2 is the unit risk cost when the line operates at the second short-time overcurrent rate, and c r1 <c r2 <c load .

[0035] The construction of the corresponding constraint conditions described in step S4 specifically includes the following steps:

[0036] The following formula is used as the operation constraint of the thermal power unit:

[0037]

[0038] Where p g,min is the minimum output of the g-th thermal power unit; p g,max is the maximum output of the g-th thermal power unit;

[0039] The following formula is used as the judgment condition for the operating status of the thermal power unit:

[0040]

[0041] In the formula is the power state boundary of the g-th thermal power unit that affects the ramp rate; is the maximum output of the g-th thermal power unit at time t; M is a set positive number; It is a binary control variable for judging the operating state of the thermal power unit. When the output of the thermal power unit is greater than the power state boundary of the thermal power unit, When the output of the thermal power unit is less than the power state boundary of the thermal power unit

[0042] The following formula is used as the climbing output range constraint for thermal power units in different operating states:

[0043]

[0044] In the formula It is the ramp output limit of the thermal power unit when it is operating below the power state boundary; It is the ramp output limit of the thermal power unit when it is operating above the power state boundary;

[0045] The following formula is used as the allowable constraint for emergency load shedding and renewable energy curtailment:

[0046]

[0047] In the formula is the load forecast value of node i at time t; is the predicted value of the new energy output of node i at time t;

[0048] The following formula is used as the line power flow constraint considering the effects of DTR and transient temperature rise:

[0049]

[0050] Where θ i,tis the voltage phase angle of node i; θ j,t is the voltage phase angle of node j, and j∈{D,j≠i}; x l is the reactance of the line; is the line power flow variable; is the static capacity value of the line;

[0051] The following formula is used as the upper and lower limit constraints of the node phase angle:

[0052]

[0053] In the formula is the lower limit of the voltage phase angle of the i-th node; is the upper limit of the voltage phase angle of the i-th node;

[0054] The following formula is used as the energy storage operation constraint:

[0055]

[0056] In the formula is a binary variable of the energy storage system discharge state, and when the energy storage system is in the discharge state When the energy storage system is in a non-discharging state is a binary variable of the energy storage system charging state, and when the energy storage system is in the charging state When the energy storage system is not charging Charging power for energy storage system; is the rated power of the energy storage system; η ch The charging efficiency of the energy storage system; is the discharge power of the energy storage system; η dis is the discharge efficiency of the energy storage system; e b,t is the energy of the energy storage system at the operating time t; E b is the rated energy of the energy storage system; e b,T is the energy of the energy storage system at the operating time T; e ini is the energy of the energy storage system at the beginning of the dispatch cycle; e exp is the energy of the energy storage system at the end of the dispatch period;

[0057] The following formula is used as the node power balance constraint:

[0058]

[0059] Where G(i) is the set of serial numbers of thermal power units connected to node i; D(i) is the set of serial numbers of energy storage systems connected to node i; and L(i) is the set of dynamic capacity expansion lines whose head or tail end is node i.

[0060] The present invention also provides a system for realizing the real-time optimization scheduling method for considering dynamic capacity increase of lines in response to sudden weather conditions, comprising a data acquisition module, a current calculation module, an overcurrent modeling module, a scheduling modeling module and an optimization scheduling module; the data acquisition module, the current calculation module, the overcurrent modeling module, the scheduling modeling module and the optimization scheduling module are sequentially connected in series; the data acquisition module is used to acquire data information of the target power system and data information of overhead lines equipped with dynamic capacity increase equipment in real time, and upload the data information to the current calculation module; the current calculation module is used to calculate the dynamic capacity increase according to the received data information and the acquired data information. The current carrying capacity increase rate of the line is calculated, and the data information is uploaded to the overcurrent modeling module; the overcurrent modeling module is used to model the relationship between the short-term overcurrent rate of the line and the temperature rise time according to the received data information, and upload the data information to the scheduling modeling module; the scheduling modeling module is used to construct a source-network-storage real-time optimization scheduling model considering the dynamic capacity increase of the line according to the received data information, and at the same time construct the corresponding constraint conditions, and upload the data information to the optimization scheduling module; the optimization scheduling module is used to solve the constructed model according to the received data information, so as to realize real-time optimization scheduling considering the dynamic capacity increase of the line in response to sudden weather conditions.

[0061] The real-time optimization scheduling method and system provided by the present invention for considering dynamic capacity expansion of lines in response to sudden weather events not only realizes real-time optimization scheduling of the target power system for considering dynamic capacity expansion of lines in response to sudden weather events, but also has higher reliability and better accuracy by acquiring and modeling data information of overhead lines equipped with dynamic capacity expansion equipment and the power systems to which they belong, and considering the conditions and scenarios for dynamic capacity expansion of lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Schematic diagram of the process flow of the present invention.

[0063] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0064] like Figure 1 The figure shows a flow chart of the method of the present invention: the real-time optimization scheduling method for considering dynamic capacity increase of lines in response to sudden weather conditions disclosed by the present invention comprises the following steps:

[0065] S1. Real-time acquisition of target power system data and overhead line data on which dynamic capacity expansion equipment is installed. Specifically, the steps include:

[0066] The acquired data and information of the target power system include the rated operating parameters of thermal power units, rated operating parameters of energy storage power stations, ultra-short-term forecast data of new energy stations, ultra-short-term forecast data of loads, and shelvable load power and capacity data;

[0067] The acquired data information of the overhead line equipped with the dynamic capacity expansion equipment, including the ambient temperature, wind speed, wind direction, and solar radiation data information of the overhead line equipped with the dynamic capacity expansion equipment;

[0068] S2. Based on the data information obtained in step S1, the current carrying capacity increase rate of the dynamic capacity increase line is calculated; specifically comprising the following steps:

[0069] The long-term steady-state current carrying capacity of the dynamic capacity increase line is calculated using the following formula:

[0070]

[0071] In the formula is the heat dissipated by the dynamic capacity expansion circuit through heat conduction at time t; is the heat dissipated by the dynamic capacity expansion line through thermal radiation at time t; is the solar radiation power absorbed by the dynamic capacity expansion line at time t; is the AC resistance of the dynamic capacity expansion circuit at the allowable temperature at time t;

[0072] When implementing it specifically, and The calculation can be made by referring to GB 50545-2010 "110KV ~ 750KV Overhead Transmission Line Design Specification";

[0073] According to the long-term steady-state current carrying capacity The following formula is used to calculate the current carrying capacity increase rate of the dynamic capacity increase line:

[0074]

[0075] In the formula The static current carrying capacity determined during the design phase for the dynamic capacity expansion line;

[0076] S3. Modeling the relationship between the short-term overcurrent rate and the temperature rise duration of the line based on the data information obtained in step S1; specifically comprising the following steps:

[0077] Establish overcurrent rate constraints for short-term operation of dynamic capacity expansion lines considering transient temperature rise effects:

[0078]

[0079] In the formula In order to consider the transient temperature rise characteristics based on The transient increase rate of the kth dynamic capacity increase line at time t; is the increasing rate of the current carrying capacity of the kth dynamic capacity-increasing line at time t; is the first short-time overcurrent rate of the kth dynamic capacity increase line at time t; is the second short-time overcurrent rate of the kth dynamic capacity increase line at time t; is the binary control variable when the kth dynamic capacity increase line is in non-overcurrent operation at time t. If the kth dynamic capacity increase line is in non-overcurrent operation at time t, If the kth dynamic capacity increase line is not in non-overcurrent operation at time t, is the binary control variable of the kth dynamic capacity expansion line operating at the first short-term overcurrent rate at time t. If the kth dynamic capacity expansion line operates at the first short-term overcurrent rate at time t, If the kth dynamic capacity expansion line does not operate at the first short-time overcurrent rate at time t, is the binary control variable of the kth dynamic capacity expansion line operating at the second short-term overcurrent rate at time t. If the kth dynamic capacity expansion line does not operate at the second short-time overcurrent rate at time t,

[0080] Establish constraints between the short-term overcurrent rate and temperature rise duration of the dynamic capacity increase line:

[0081]

[0082] Where ΔT stage2 ΔT is the duration of transient temperature rise corresponding to the kth dynamic capacity increase line when it operates at the first short-time overcurrent rate; stage3 The duration of transient temperature rise corresponding to the kth dynamic capacity increase line when it operates at the second short-time overcurrent rate; δ st To control the kth dynamic capacity increase line to continuously operate at a unique overcurrent rate, the kth dynamic capacity increase line operates at the first short-term overcurrent rate at time t, then δ st =1, the kth dynamic capacity expansion line operates at the second short-time overcurrent rate at time t, then δ st =0;L DTR A collection of lines for dynamic capacity expansion;

[0083] The modeling significance is as follows: When the line short-term overcurrent runs in [1,ε stage2 ] range, it means it has passed ΔT stage2The maximum allowable temperature rise will be reached, so the short-term overcurrent operation duration of the line during the real-time scheduling phase must not exceed this period to ensure operational safety; st For the binary control variable introduced, the auxiliary modeling overcurrent rate is within a unique range. Because the transient temperature rise process of the line after continuous step current in a short time is relatively complex and lacks experimental verification, the present invention restricts this scenario.

[0084] It should be noted that the duration of the transient temperature rise of the line is related to the real-time environmental parameters. If the environmental parameters deteriorate during the forecast period, the duration will gradually decrease. In this case, the overcurrent rate of the line needs to be dynamically reduced to prevent the line temperature from reaching the allowable value prematurely. To this end, the present invention directly considers the worst-case environmental parameter scenario during the forecast period to ensure that the overcurrent operation duration during the scheduling period does not exceed its most conservative duration.

[0085] S4. Build a real-time optimization scheduling model for source, network, and storage that takes into account dynamic line capacity expansion, and establish corresponding constraints.

[0086] In specific implementation, the following formula is used as the objective function of the source-network-storage real-time optimization scheduling model considering dynamic line capacity expansion:

[0087] min C DIS =C GEN +C CURT +C RISK

[0088] Where C DIS is the total cost within the real-time scheduling cycle; C GEN is the operating cost of the thermal power unit, and c g is the unit cost of the output of the g-th thermal power unit, is the output of the g-th thermal power unit at time t, Δt is the scheduling interval, G is the set of thermal power units, and T is the optimal scheduling period; C CURT The cost of emergency load shedding and renewable energy curtailment, and c load is the unit cost of load shedding, is the load shedding value of node i at time t, c res is the unit cost of abandoned electricity from new energy sources, is the amount of renewable energy abandoned by node i at time t, D is the node set; C RISK is the risk cost of short-term overload operation of the dynamic capacity expansion line considering transient temperature rise characteristics, and c r1 is the unit risk cost when the line operates at the first short-time overcurrent rate, c r2 is the unit risk cost when the line operates at the second short-time overcurrent rate, and c r1<c r2 <c load , which means that in the real-time scheduling phase, load shedding should be avoided first, and a larger overcurrent rate should be avoided on the DTR line as much as possible;

[0089] The following formula is used as the operation constraint of the thermal power unit:

[0090]

[0091] Where p g,min is the minimum output of the g-th thermal power unit; p g,max is the maximum output of the g-th thermal power unit;

[0092] Traditional thermal power unit ramping models ignore the impact of the unit's operating status on ramping capability: the unit's ramping limit at low load is much smaller than at high load. Ignoring this characteristic will result in an unrealistic operating strategy. Therefore, the following formula is used as the criterion for determining the thermal power unit's operating status:

[0093]

[0094] In the formula is the power state boundary of the g-th thermal power unit that affects the ramp rate; is the maximum output of the g-th thermal power unit at time t; M is a set positive number; It is a binary control variable for judging the operating state of the thermal power unit. When the output of the thermal power unit is greater than the power state boundary of the thermal power unit, When the output of the thermal power unit is less than the power state boundary of the thermal power unit

[0095] The following formula is used as the climbing output range constraint for thermal power units in different operating states:

[0096]

[0097] In the formula It is the ramp output limit of the thermal power unit when it is operating below the power state boundary; It is the ramp output limit of the thermal power unit when it is operating above the power state boundary;

[0098] The following formula is used as the allowable constraint for emergency load shedding and renewable energy curtailment:

[0099]

[0100] In the formula is the load forecast value of node i at time t; is the predicted value of the new energy output of node i at time t;

[0101] The following formula is used as the line power flow constraint considering the effects of DTR and transient temperature rise:

[0102]

[0103] Where θ i,t is the voltage phase angle of node i; θ j,t is the voltage phase angle of node j, and j∈{D,j≠i}; x l is the reactance of the line; is the line power flow variable; is the static capacity value of the line;

[0104] The following formula is used as the upper and lower limit constraints of the node phase angle:

[0105]

[0106] In the formula is the lower limit of the voltage phase angle of the i-th node; is the upper limit of the voltage phase angle of the i-th node;

[0107] The following formula is used as the energy storage operation constraint:

[0108]

[0109] In the formula is a binary variable of the energy storage system discharge state, and when the energy storage system is in the discharge state When the energy storage system is in a non-discharging state is a binary variable of the energy storage system charging state, and when the energy storage system is in the charging state When the energy storage system is not charging Charging power for energy storage system; is the rated power of the energy storage system; η ch The charging efficiency of the energy storage system; is the discharge power of the energy storage system; η dis is the discharge efficiency of the energy storage system; e b,t is the energy of the energy storage system at the operating time t; E b is the rated energy of the energy storage system; e b,T is the energy of the energy storage system at the operating time T; e ini is the energy of the energy storage system at the beginning of the dispatch cycle; e exp is the energy of the energy storage system at the end of the dispatch period;

[0110] The following formula is used as the node power balance constraint:

[0111]

[0112] Where G(i) is the set of thermal power units connected to node i; D(i) is the set of energy storage systems connected to node i; L(i) is the set of dynamic capacity expansion lines with node i as the beginning or end of the line.

[0113] S5. Solve the model constructed in step S4 to achieve real-time optimized scheduling that takes into account dynamic capacity expansion of power lines in response to sudden weather events; ultimately calculate the real-time output plan of thermal power units, the new energy curtailment and emergency load shedding plan, the real-time dynamic capacity expansion strategy of transmission lines, and the real-time charging and discharging strategy of energy storage.

[0114] The method of the present invention is further described below with reference to an embodiment:

[0115] The case analysis is based on a 3-machine, 9-node IEEE-9 standard test system with a reference voltage of 345kV. The thermal power units are connected to nodes 1 and 3, with installed capacities of 250MW and 350MW respectively. Their operating efficiency shall not be less than 30%. When the operating efficiency is 50% and above and in the range of 30%-50%, the ramp rates are 4% and 2% of the rated capacity / minute respectively. The photovoltaic power station and the energy storage power station are connected to nodes 2 and 5, with installed capacities of 400MW and 200MW / 300MWh respectively. The dispatch starting energy of the energy storage power station is 60WMh. When the DTR overhead line is in emergency operation at an overload rate of 120% and 150%, the allowable temperature rise time is 30min and 20min. The settings of the DTR line are shown in Table 1:

[0116] Table 1 Schematic diagram of DTR equipment layout for overhead lines

[0117]

[0118] To simplify the calculation, the DTR current capacity increase rate, ignoring the effects of transient temperature rise, was uniformly set to 1.2. Regarding costs, the unit operating cost of thermal power units was 300 yuan / MWh, the unit cost of load shedding was 800 yuan / MWh, and the cost of PV curtailment was 450 yuan / MWh. The risk cost coefficients for line overload operation were 500 yuan / hour and 600 yuan / hour, respectively. A sudden drop in PV output during the afternoon in late October 2024 in a southern city was used as a simulated weather emergency scenario. The load and PV power station output curves were scaled based on actual data from 10:00 a.m. to 12:55 p.m. in the northern part of the city on that day. After scaling, the maximum load of 630 MW occurred at 12:05 p.m., and the PV output dropped by 190.13 MW from 11:40 a.m. to 12:25 p.m. A real-time optimal scheduling simulation analysis was performed.

[0119] In order to verify the advantages of the method of the present invention, five examples are set up for comparative analysis, as shown in Table 2:

[0120] Table 2 Schematic diagram of comparative example settings

[0121] Calculation example Consider DTR Considering transient temperature rise effect Consider energy storage resources Method 1 × × × Method 2 √ × × Method 3 √ √ × Method 4 × × √ Method 5 (the present invention) √ √ √

[0122] Example 1: Disregarding DTR and its transient temperature rise effects, and excluding the participation of energy storage in supply and demand balancing; Example 2: Considering DTR, excluding its transient temperature rise effects and the participation of energy storage; Example 3: Considering DTR and its transient temperature rise effects, excluding the participation of energy storage in balancing; Example 4: excluding DTR and its transient temperature rise effects, but including the participation of energy storage in balancing; Example 5: Considering DTR and its transient temperature rise effects, but including the participation of energy storage in balancing. These examples were solved using a commercial solver on a standard laptop computer with an Intel i7-8750H processor to verify whether the model's solution rate can meet the requirements of real-time scheduling.

[0123] Table 3 shows the calculation results of different methods:

[0124] Table 3 Comparison of calculation results

[0125]

[0126] As can be seen from Table 3, the average solution time of the method of the present invention is 14.2 seconds. The solution rate of the method of the present invention can meet the needs of real-time optimization scheduling. In order to effectively compare the contribution of thermal power units to the supply and demand balance under different methods, a peak-shaving depth index of thermal power units was constructed. Its model is as follows:

[0127]

[0128] A comparison of Methods 1 and 2 shows that considering dynamic line capacity expansion can unlock flexibility in line transmission, increasing the average line load factor by 4.11%, significantly improving the peak-shaving capacity of thermal power units and reducing load shedding and curtailment. A comparison of Methods 2 and 3 shows that considering the transient temperature rise effect of the line further unlocks the line's short-term transmission capacity, thereby improving power flow distribution, reducing load shedding and curtailment, and improving operational efficiency. This also reflects the limited peak-shaving capacity of thermal power units, resulting in the same peak-shaving depth indicators under both methods. A comparison of Methods 3 and 5 shows that incorporating energy storage systems can achieve coordinated optimization of the thermal power and transmission networks, minimizing emergency load shedding and curtailment. Furthermore, the transmission substitution effect of energy storage systems further improves power flow distribution and optimizes line load conditions. A comparison of the peak-shaving depth indicators of thermal power units reveals that the rapid ramping capability of energy storage facilitates optimized ramping output of thermal power units. Under Method 5, thermal power units also have a 32.46% peak-shaving margin, enabling them to withstand even more severe weather emergencies. Comparing the charging / discharging conditions of the energy storage system in Method 4 and Method 5, it can be seen that considering the dynamic capacity increase of the line and its transient temperature rise effect can further release the flexible adjustment capability of the energy storage system.

[0129] Therefore, the method of the present invention can effectively deal with the real-time supply and demand balance problem under sudden weather conditions in power systems with a high proportion of renewable energy, and realize the coordinated optimization of the flexible adjustment capabilities of the source, network and storage in the scenario of a sudden drop in renewable energy output. By considering the dynamic capacity expansion of the transmission lines and their transient temperature rise characteristics, the ability of the power grid to transmit electricity can be greatly released in a short period of time to improve the power flow distribution and then optimize the ramp output strategy of thermal power units and energy storage systems. The real-time scheduling strategy solved by the method of the present invention can minimize the occurrence of emergency load shedding and abandonment of renewable energy power, and can effectively solve the problem of safe supply of the power system in the face of sudden weather events.

[0130] like Figure 2The figure shows a functional module diagram of the system of the present invention: the system disclosed by the present invention for realizing the real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions comprises a data acquisition module, a current calculation module, an overcurrent modeling module, a scheduling modeling module and an optimization scheduling module; the data acquisition module, the current calculation module, the overcurrent modeling module, the scheduling modeling module and the optimization scheduling module are sequentially connected in series; the data acquisition module is used to acquire data information of the target power system and data information of overhead lines equipped with dynamic capacity expansion equipment in real time, and upload the data information to the current calculation module; the current calculation module is used to, based on the received data information, The current carrying capacity increase rate of the dynamically increased capacity line is calculated and the data information is uploaded to the overcurrent modeling module; the overcurrent modeling module is used to model the relationship between the short-term overcurrent rate and the temperature rise duration of the line based on the received data information and the acquired data information, and upload the data information to the scheduling modeling module; the scheduling modeling module is used to construct a source-network-storage real-time optimization scheduling model considering the dynamic capacity increase of the line based on the received data information, and at the same time construct the corresponding constraint conditions, and upload the data information to the optimization scheduling module; the optimization scheduling module is used to solve the constructed model based on the received data information to realize real-time optimization scheduling considering the dynamic capacity increase of the line in response to sudden weather conditions.

Claims

1. A real-time optimization scheduling method considering dynamic capacity expansion of lines in response to sudden weather conditions, comprising the following steps: S1. Real-time acquisition of target power system data and overhead line data on equipment equipped with dynamic capacity expansion equipment; S2. Based on the data information obtained in step S1, the current carrying capacity increase rate of the dynamic capacity line is calculated; S3. Modeling the relationship between the short-term overcurrent rate and the temperature rise duration of the line based on the data information obtained in step S1; S4. Build a real-time optimization scheduling model for source, network, and storage that takes into account dynamic line capacity expansion, and establish corresponding constraints. S5. Solve the model constructed in step S4 to achieve real-time optimized scheduling considering dynamic capacity expansion of the line in response to sudden weather events.

2. The real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions according to claim 1 is characterized in that The real-time acquisition of data information of the target power system and data information of the overhead line equipped with the dynamic capacity expansion device in step S1 specifically includes the following steps: The acquired data and information of the target power system include the rated operating parameters of thermal power units, rated operating parameters of energy storage power stations, ultra-short-term forecast data of new energy stations, ultra-short-term forecast data of loads, and shelvable load power and capacity data; The acquired data information of the overhead line equipped with the dynamic capacity expansion equipment includes the ambient temperature, wind speed, wind direction, and sunshine radiation data information of the overhead line equipped with the dynamic capacity expansion equipment.

3. The real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions according to claim 2 is characterized in that Step S2, based on the data information obtained in step S1, calculates the current carrying capacity increase rate of the dynamic capacity expansion line, and specifically includes the following steps: The long-term steady-state current carrying capacity of the dynamic capacity increase line is calculated using the following formula: In the formula is the heat dissipated by the dynamic capacity expansion circuit through heat conduction at time t; is the heat dissipated by the dynamic capacity expansion line through thermal radiation at time t; is the solar radiation power absorbed by the dynamic capacity expansion line at time t; is the AC resistance of the dynamic capacity expansion circuit at the allowable temperature at time t; According to the long-term steady-state current carrying capacity The following formula is used to calculate the current carrying capacity increase rate of the dynamic capacity increase line: In the formula The static current carrying capacity determined during the design phase for the dynamic capacity expansion line.

4. The real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions according to claim 3 is characterized in that Step S3, based on the data information obtained in step S1, models the relationship between the short-term overcurrent rate of the line and the temperature rise duration, specifically including the following steps: Establish overcurrent rate constraints for short-term operation of dynamic capacity expansion lines considering transient temperature rise effects: In the formula In order to consider the transient temperature rise characteristics based on The transient increase rate of the kth dynamic capacity increase line at time t; is the increasing rate of the current carrying capacity of the kth dynamic capacity-increasing line at time t; is the first short-time overcurrent rate of the kth dynamic capacity increase line at time t; is the second short-time overcurrent rate of the kth dynamic capacity increase line at time t; is the binary control variable when the kth dynamic capacity increase line is in non-overcurrent operation at time t. If the kth dynamic capacity increase line is in non-overcurrent operation at time t, If the kth dynamic capacity increase line is not in non-overcurrent operation at time t, is the binary control variable of the kth dynamic capacity expansion line operating at the first short-term overcurrent rate at time t. If the kth dynamic capacity expansion line operates at the first short-term overcurrent rate at time t, If the kth dynamic capacity expansion line does not operate at the first short-time overcurrent rate at time t, is the binary control variable of the kth dynamic capacity expansion line operating at the second short-term overcurrent rate at time t. If the kth dynamic capacity expansion line does not operate at the second short-time overcurrent rate at time t, Establish constraints between the short-term overcurrent rate and temperature rise duration of the dynamic capacity increase line: Where ΔT stage2 The duration of the transient temperature rise corresponding to the kth dynamic capacity increase line when it operates at the first short-time overcurrent rate; ΔT stage3 The duration of transient temperature rise corresponding to the kth dynamic capacity increase line when it operates at the second short-time overcurrent rate; δ st To control the kth dynamic capacity increase line to continuously operate at a unique overcurrent rate, the kth dynamic capacity increase line operates at the first short-term overcurrent rate at time t, then δ st =1, the kth dynamic capacity expansion line operates at the second short-time overcurrent rate at time t, then δ st =0;L DTR A collection of dynamically expanded lines.

5. The real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions according to claim 4 is characterized in that The construction of a real-time optimization scheduling model for source, network, and storage considering dynamic line capacity expansion described in step S4 specifically includes the following steps: The following formula is used as the objective function of the source-network-storage real-time optimization scheduling model considering dynamic line capacity expansion: my C DIS =C GEN +C CURT +C RISK Where C DIS is the total cost within the real-time scheduling cycle; C GEN is the operating cost of the thermal power unit, and c g is the unit cost of the output of the g-th thermal power unit, is the output of the g-th thermal power unit at time t, Δt is the scheduling interval, G is the set of thermal power units, and T is the optimal scheduling period; C CURT The cost of emergency load shedding and renewable energy curtailment, and c load is the unit cost of load shedding, is the load shedding value of node i at time t, c res is the unit cost of abandoned electricity from new energy sources, is the amount of renewable energy abandoned by node i at time t, D is the node set; C RISK is the risk cost of short-term overload operation of the dynamic capacity expansion line considering transient temperature rise characteristics, and c r1 is the unit risk cost when the line operates at the first short-time overcurrent rate, c r2 is the unit risk cost when the line operates at the second short-time overcurrent rate, and c r1 <c r2 <c load .

6. The real-time optimization scheduling method for considering dynamic capacity expansion of lines in response to sudden weather conditions according to claim 5 is characterized in that The construction of the corresponding constraint conditions described in step S4 specifically includes the following steps: The following formula is used as the operation constraint of the thermal power unit: Where p g,min is the minimum output of the g-th thermal power unit; p g,max is the maximum output of the g-th thermal power unit; The following formula is used as the judgment condition for the operating status of the thermal power unit: In the formula is the power state boundary of the g-th thermal power unit that affects the ramp rate; is the maximum output of the g-th thermal power unit at time t; M is a set positive number; It is a binary control variable for judging the operating state of the thermal power unit. When the output of the thermal power unit is greater than the power state boundary of the thermal power unit, When the output of the thermal power unit is less than the power state boundary of the thermal power unit The following formula is used as the climbing output range constraint for thermal power units in different operating states: In the formula It is the ramp output limit of the thermal power unit when it is operating below the power state boundary; It is the ramp output limit of the thermal power unit when it is operating above the power state boundary; The following formula is used as the allowable constraint for emergency load shedding and renewable energy curtailment: In the formula is the load forecast value of node i at time t; is the predicted value of new energy output of node i at time t; The following formula is used as the line power flow constraint considering the effects of DTR and transient temperature rise: Where θ i,t is the voltage phase angle of node i; θ j,t is the voltage phase angle of node j, and j∈{D,j≠i}; x l is the reactance of the line; is the line power flow variable; is the static capacity value of the line; The following formula is used as the upper and lower limit constraints of the node phase angle: In the formula is the lower limit of the voltage phase angle of the i-th node; is the upper limit of the voltage phase angle of the i-th node; The following formula is used as the energy storage operation constraint: In the formula is a binary variable of the energy storage system discharge state, and when the energy storage system is in the discharge state When the energy storage system is in a non-discharging state It is a binary variable of the energy storage system charging state, and when the energy storage system is in the charging state When the energy storage system is not charging Charging power for energy storage system; is the rated power of the energy storage system; η ch The charging efficiency of the energy storage system; is the discharge power of the energy storage system; η dis is the discharge efficiency of the energy storage system; e b,t is the energy of the energy storage system at the operating time t; E b is the rated energy of the energy storage system; e b,T is the energy of the energy storage system at the operating time T; e ini is the energy of the energy storage system at the beginning of the dispatch cycle; e exp is the energy of the energy storage system at the end of the dispatch period; The following formula is used as the node power balance constraint: Where G(i) is the set of thermal power units connected to node i; D(i) is the set of energy storage systems connected to node i; and L(i) is the set of dynamic capacity expansion lines whose head or tail end is node i.

7. A system for implementing the real-time optimization scheduling method for considering dynamic capacity increase of lines in response to sudden weather events as described in any one of claims 1 to 6, characterized in that It includes a data acquisition module, a current calculation module, an overcurrent modeling module, a dispatch modeling module and an optimized dispatch module; the data acquisition module, the current calculation module, the overcurrent modeling module, the dispatch modeling module and the optimized dispatch module are connected in series in sequence; the data acquisition module is used to obtain data information of the target power system and data information of the overhead line equipped with dynamic capacity expansion equipment in real time, and upload the data information to the current calculation module; The current calculation module is used to calculate the current carrying capacity increase rate of the dynamic capacity increase line according to the received data information and the acquired data information, and upload the data information to the current modeling module; The overcurrent modeling module is used to model the relationship between the short-term overcurrent rate of the line and the temperature rise duration based on the received data information, and upload the data information to the scheduling modeling module; The scheduling modeling module is used to build a real-time optimization scheduling model for source, network, and storage based on the received data information, taking into account the dynamic capacity expansion of the line, and at the same time establish corresponding constraints and upload the data information to the optimization scheduling module; The optimization scheduling module is used to solve the constructed model based on the received data information to achieve real-time optimization scheduling considering dynamic capacity expansion of the line in response to sudden weather events.

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