Active power distribution network safety improvement method considering energy storage and demand response

Abstract

The invention discloses an active power distribution network safety improvement method considering energy storage and demand response, and relates to the technical field of smart power grids, the method is divided into a prevention stage, a correction stage and a recovery stage, the calculation burden is effectively reduced, and the complexity is reduced; through serious event modeling, safety index making and available resource utilization, solving is carried out through an IGDT-based robust optimization method, and an optimal solution under an uncertain risk is obtained; the optimization model is divided into two stages: in the first stage, modeling serious events possibly occurring in the ADN, and providing quantitative indexes representing network elasticity and micro-grid safety; in the second stage, the network is decomposed into a plurality of micro-grid clusters, available resources and a network reconstruction technology are utilized to enhance the safety of the ADN, and a robust optimization method is adopted to limit uncertain risks caused by serious events; therefore, the safety of the system is improved by effectively utilizing energy storage and flexible demand response, and the elasticity and reliability of the system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a method for improving the safety of an active distribution network taking energy storage and demand response into consideration. Background Art

[0002] Currently, the infrastructure of the power system remains sensitive to extreme weather events, the destructive forces of which can quickly and extensively damage the power system, potentially causing regional or even large-scale power outages, which further emphasizes the importance of ensuring the security of the power system.

[0003] The future structure of the Active Distribution Network (ADN) will include multiple microgrid clusters. Microgrids with advanced control technology provide a friendly platform for the integration of distributed energy into the active distribution network, while also providing a better energy management model for different energy carriers. Therefore, the rational use of microgrids is an effective measure to ensure reliable power supply to critical loads and improve the safety of the active distribution network in emergency or catastrophic events. At present, the active distribution network ADN that integrates multiple microgrids still has problems with insufficient security and resilience when facing severe events (Extreme Weather Event, EWE). Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for improving the safety of an active distribution network considering energy storage and demand response, comprising the following steps:

[0005] S1. Severe event modeling: Identify various severe events that may affect the active distribution network, and use the system equipment vulnerability curves related to severe events to evaluate the probability of equipment failure under severe events;

[0006] S2. Develop safety indicators: Design indicators to quantify the safety of active distribution networks;

[0007] S3. Preventive preparation before a serious incident occurs: After using the vulnerability curve to determine the parts that need to be strengthened, take preventive measures to maximize the stored energy in the battery;

[0008] S4. Microgrid cluster division after a severe incident: The active distribution network is divided into multiple microgrid clusters based on different factors, including geographical location and power interconnection. Each microgrid cluster contains different types of available resources, including distributed power generation, energy storage, and demand response, and mathematical modeling is performed for each type of available resource.

[0009] S5. Resource optimization configuration: Utilize the robust optimization method based on information gap decision theory to optimize the resource configuration plan for each microgrid cluster.

[0010] The technical solution further defined in the present invention is:

[0011] Furthermore, in step S1, when modeling a severe event, the safety of various devices in the system is comprehensively considered to estimate the tolerance threshold of the system devices; the system device vulnerability curve associated with the severe event is used, including the device vulnerability threshold and the possibility of device failure in the severe event, as shown in the following equation:

[0012]

[0013] Among them, P[ds|S d ] indicates that in a given damage state S d The probability of system damage under the condition of Φ is the standard normal distribution function, ω ds Indicates the standard deviation of the damage state, S d is the current damage status, is the maximum damage state; P F is the total failure rate of the system, P k is the failure rate of the kth independent component, and N is the total number of independent components in the system.

[0014] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S2, the inverse of the system performance loss is used as a metric to define the system's safety against severe events. The system's performance loss after a severe event indicates the efficiency of the system in responding to the severe event, which indicates that the smaller the system performance loss, the greater the system's resilience.

[0015] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S3, potential damage is managed before a serious event occurs, and the stored energy in the battery is maximized through the objective function shown in the following formula:

[0016]

[0017] in, represents the decision variable; represents the charging power of the energy storage system at time t, that is, the power stored in the system during the charging process; represents the discharge power of the energy storage system at time t, that is, the power released from the system during the discharge process; represents the charging power of the natural gas system at time t, that is, the energy stored in the natural gas system; It represents the discharge power of the natural gas system at time t, that is, the energy released from the natural gas system.

[0018] In the aforementioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S4, it is assumed that the microgrid is composed of distributed power sources and energy storage, and is interconnected; the constraints of the microgrid level, distributed power sources, energy storage, demand response, and active distribution network operation are fully considered;

[0019] The operating constraints at the microgrid level are as follows:

[0020] The power balance equation in the microgrid is:

[0021]

[0022] in, Indicates the upstream power purchase amount, Indicates the amount of electricity from various power sources. represents the power generation of the cogeneration unit, Represents the discharge capacity of the energy storage system, Indicates the power consumption of electrical equipment. Indicates the charge capacity of the energy storage system, For charging efficiency, is the discharge efficiency, η app The efficiency of electrical equipment;

[0023] Thermal balance constraint equation:

[0024]

[0025] in, Heat generated for natural gas applications, The heat energy generated by the consumption of natural gas in the cogeneration unit, Heat release for natural gas equipment, For heat demand, Charge heat for natural gas equipment, Charging efficiency for natural gas equipment, Heat release efficiency for natural gas equipment;

[0026] Equipment constraints for using natural gas to generate heat:

[0027]

[0028] in, is the applied heat energy at time t, η app is the applied thermal energy efficiency at time t, w t is the weight at time t, G t is the natural gas consumption;

[0029] Constraints for combined heat and power units using natural gas for electricity and heat:

[0030]

[0031] in, is the power output of the CHP at time t, η g-e is the efficiency of converting natural gas into electricity, η g-h The efficiency of converting natural gas into thermal energy.

[0032] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S4, the distributed power generation constraint conditions are as follows:

[0033] Active power constraint:

[0034]

[0035] in, represents the minimum active power output of distributed energy source n at time t, represents the maximum active power output of distributed energy source n at time t, ζ nt Indicates the adjustment factor of active power;

[0036] Reactive power constraint:

[0037]

[0038] in, represents the minimum active power output of distributed energy source n at time t, represents the maximum active power output of distributed energy source n at time t;

[0039] If the distributed power source is a controllable distributed power source, the ramp power constraint must also be met:

[0040]

[0041] in, Indicates the active power output of distributed energy at the current moment, Indicates the active power output of distributed energy at the previous moment, Indicates the active power rising slope limit within adjacent time. It indicates the limit of active power decrease slope in adjacent time.

[0042] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S4, the energy storage constraint condition is as follows:

[0043] Energy levels of stored energy per hour:

[0044]

[0045] in, is the charge state at the previous moment, For charging efficiency, For charging and discharging state

[0046] quantity, is the charging power, is the capacity of the energy storage system, is the discharge power;

[0047] Energy storage energy level upper and lower limits:

[0048]

[0049] in, is the minimum state of charge of the energy storage device, is the maximum state of charge of the energy storage device;

[0050] Charge and discharge power upper and lower limits:

[0051]

[0052] in, Indicates the maximum charging power capacity of the energy storage device, Indicates the maximum discharge power capacity of the electrical energy storage device;

[0053] The charging and discharging process of energy storage is linearly modeled with the following constraints:

[0054]

[0055] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S4, direct load control technology is used as a demand response method, that is, the system operator directly reduces or transfers the system power consumption in an emergency to ensure that the total power consumption in the dispatching period remains unchanged after the load is reduced or transferred:

[0056]

[0057] in, is the net demand, which represents the total demand at time t; DR t is demand response, which represents the percentage of internal demand reduced due to demand response measures at time t; is the internal demand, which represents the demand within the system at time t; is the incremental coefficient, which represents the coefficient used for incremental demand at time t; is the incremental demand, which represents the additional demand introduced into the system at time t; is the reduction demand, which means the demand reduced or cut in the system at time t; is the reduction coefficient, which represents the coefficient used to reduce demand at time t;

[0058] Constraints on the speed of load increase and decrease between adjacent time periods:

[0059]

[0060] in, is the incremental demand at the previous moment; D incmax is the maximum value of the incremental demand, indicating the maximum incremental demand that the system can accept; is the reduction demand at the previous moment; D decmax is the minimum value of the incremental demand, indicating the minimum incremental demand that the system can reduce;

[0061] Hourly load response upper limit constraint:

[0062] DR t ≤DR max

[0063] Among them, DR max The maximum percentage of internal demand reduced by demand response measures, set to 1;

[0064] Demand response load does not increase or decrease at the same time constraints:

[0065]

[0066] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S4, the operating constraints of the active distribution network are as follows:

[0067] Power flow constraints:

[0068]

[0069]

[0070] Among them, P i inj is the active power injection amount of node i, is the reactive power injection amount of node i, The voltage amplitude at node i, is the voltage amplitude of node j connected to node i, G ii is the self-admittance of node i, G ij is the admittance value between node i and node j, B ii is the self-susceptance of node i, B ij is the susceptance value between node i and node j, θ ii is the voltage phase angle at node i, θ ij is the voltage phase angle difference between node i and node j;

[0071] During a serious incident, the following conditions must be met:

[0072]

[0073] in, is the active power output of the distributed generation unit, is the reactive power output of the distributed generation unit, is the discharge power of the energy storage system, is the charging power of the energy storage system, is the active power injected into the distribution network, is the reactive power injected into the distribution network, is the active power of node i at time t, is the reactive power of node i at time t, n represents the number of nodes connected to node i, and e represents the number of adjacent lines;

[0074] Transmission constraints of active / reactive power on feeders:

[0075] -FP l max ≤P l flw ≤FP l max

[0076]

[0077] in, is the maximum permissible active power flow, is the maximum allowable reactive power flow, P l flw is the active power flow on the distribution network lines, is the reactive power flow on the distribution network lines;

[0078] Network node voltage constraints:

[0079] V i min ≤V it ≤V i max

[0080] in, is the lowest voltage allowed for network nodes, is the maximum voltage allowed by the network node, V it is the voltage value of node i at time t.

[0081] In the above-mentioned method for improving the safety of an active distribution network considering energy storage and demand response, in step S5, the robust optimization method based on information gap decision theory specifically includes the following sub-steps:

[0082] S5.1. Setting the objective function:

[0083]

[0084] in, is the objective function, Indicates that at a specific voltage and disturbance The system response, is a disturbance or interference signal;

[0085] S5.2. Introducing parameter deviation and constraints on uncertainty parameters:

[0086]

[0087] in, and represents the final value of the state at time t; α is the uncertainty radius, which represents the allowable deviation range of the system;

[0088] S5.3. Determine the acceptable level of objective function value:

[0089]

[0090] in, is the threshold, representing the reliability that the system needs to meet;

[0091] S5.4. The robust optimization algorithm based on information gap decision theory is used to determine the upper limit of the uncertainty radius while ensuring that the minimum objective function value is not less than the threshold:

[0092]

[0093] Among them, β RA represents the robustness estimate, is the benchmark value of the objective function under certain conditions.

[0094] The beneficial effects of the present invention are:

[0095] (1) In the present invention, when improving the resilience of the active distribution network, the coupled decision-making of planning and economic operation is very complex. The present invention divides the problem into prevention, correction and recovery stages, effectively reducing the computational burden and complexity; by modeling severe events, formulating safety indicators and utilizing available resources, the robust optimization method based on IGDT is used to solve the problem, and the optimal solution under uncertain risks is obtained, thereby significantly improving the resilience and reliability of the system;

[0096] (2) In the present invention, the optimization model is divided into two stages: the first stage models the severe events that may occur in ADN and proposes quantitative indicators to characterize network resilience and microgrid security; the second stage decomposes the network into multiple microgrid clusters, utilizes available resources and network reconstruction technology to enhance the security of ADN, and adopts a robust optimization method to limit the uncertainty risks brought by severe events, thereby improving the security of the system by effectively utilizing energy storage and flexible demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0098] Figure 2 Schematic diagram of the robust optimization method based on IGDT in an embodiment of the present invention;

[0099] Figure 3 It is a topological diagram of a 33-node test system improved in an embodiment of the present invention;

[0100] Figure 4 A comparison chart of safety performance indicators in different cases after a serious incident occurs in an embodiment of the present invention;

[0101] Figure 5 A comparison diagram of load shedding conditions in different cases in an embodiment of the present invention;

[0102] Figure 6 This is a comparison chart of fault recovery time in different cases in an embodiment of the present invention;

[0103] Figure 7 A schematic diagram of the power generation time of the distributed power source and energy storage in an embodiment of the present invention;

[0104] Figure 8 4 is a comparison diagram of voltage curves of different nodes in an embodiment of the present invention. DETAILED DESCRIPTION

[0105] This embodiment provides a method for improving the safety of an active distribution network considering energy storage and demand response, which is used to solve the problem of insufficient safety and flexibility of an active distribution network (ADN) integrating multiple microgrids when facing extreme weather events (EWE), such as Figure 1 As shown, the method of this embodiment includes the following steps:

[0106] S1. Severe event modeling: Identify various severe events that may affect the active distribution network, such as natural disasters, power outages, etc., and use the system equipment vulnerability curves related to severe events to evaluate the probability of equipment failure under severe events.

[0107] When modeling severe events, it is necessary to comprehensively consider the safety of various devices in the system to estimate the tolerance threshold of the system equipment; taking the storm as a representative of severe events, the system equipment vulnerability curve associated with the storm is used, including the equipment vulnerability threshold and the possibility of equipment failure in severe events, as shown in the following equation:

[0108]

[0109] Among them, P[ds|S d ] indicates that in a given damage state S d The probability of system damage under the condition of Φ is the standard normal distribution function, ω ds Indicates the standard deviation of the damage state, S d is the current damage status, is the maximum damage state; P F is the total failure rate of the system, P k is the failure rate of the kth independent component, and N is the total number of independent components in the system.

[0110] S2. Develop safety indicators: Design indicators for quantifying the safety of active distribution networks. Use the inverse of system performance loss as a metric to define the system's safety against severe events. The system's performance loss after a severe event indicates its efficiency in responding to severe events, indicating that the smaller the system performance loss, the greater the system's resilience.

[0111] S3. Preventive preparation before a serious event occurs: After using the vulnerability curve to determine the parts that need to be strengthened, preventive measures need to be taken to effectively manage potential damage before an accident occurs; the objective function is shown in the following formula, which aims to maximize the stored energy in the battery, so that when a serious event EWE occurs, the critical load can be powered by battery discharge, thereby reducing the potential losses caused by the accident:

[0112]

[0113] in, represents the decision variable; represents the charging power of the energy storage system at time t, that is, the power stored in the system during the charging process; represents the discharge power of the energy storage system at time t, that is, the power released from the system during the discharge process; represents the charging power of the natural gas system at time t, that is, the energy stored in the natural gas system; It represents the discharge power of the natural gas system at time t, that is, the energy released from the natural gas system.

[0114] S4. Microgrid cluster division after a serious incident occurs: Based on factors such as geographical location and power interconnection, the active distribution network is divided into multiple microgrid clusters. Each microgrid cluster contains available resources such as distributed energy resources (DER), energy storage and demand response, and mathematical modeling is performed for each type of available resource.

[0115] It is assumed that the microgrid consists of distributed power sources (such as wind / photovoltaic power stations and cogeneration units) and energy storage, and is interconnected; the constraints at the microgrid level, distributed power sources, energy storage, demand response, and active distribution network operation are fully considered.

[0116] The operating constraints at the microgrid level are as follows:

[0117] The power balance equation in the microgrid is:

[0118]

[0119] The left side of the formula represents the electricity generated by upstream power purchases, various power generation sources, and energy storage discharge, while the right side of the formula represents the power consumption generated by power utilization and energy storage charging; Indicates the upstream power purchase amount, Indicates the amount of electricity from various power sources. Indicates the power generation of the combined heat and power (CHP) device, Represents the discharge capacity of the energy storage system, Indicates the power consumption of electrical equipment. Indicates the charge capacity of the energy storage system, For charging efficiency, is the discharge efficiency, η app The efficiency of electrical equipment.

[0120] Thermal balance constraint equation:

[0121]

[0122] Among them, the left side of the formula represents the generation of heat energy, and the right side of the formula represents the consumption of heat energy; Heat generated for natural gas applications, The heat energy generated by the consumption of natural gas in the cogeneration unit, Heat release for natural gas equipment, For heat demand, Charge heat for natural gas equipment, Charging efficiency for natural gas equipment, Heat release efficiency for natural gas equipment.

[0123] Equipment constraints for using natural gas to generate heat:

[0124]

[0125] in, is the applied heat energy at time t, η app is the applied thermal energy efficiency at time t, w t is the weight at time t, G t The natural gas consumption.

[0126] Constraints for combined heat and power units using natural gas for electricity and heat:

[0127]

[0128] in, is the power output of the CHP at time t, η g-e is the efficiency of converting natural gas into electricity, η g-h The efficiency of converting natural gas into thermal energy.

[0129] The distributed power generation constraints are as follows:

[0130] Active power constraint:

[0131]

[0132] in, represents the minimum active power output of distributed energy source n at time t, represents the maximum active power output of distributed energy source n at time t, ζ nt It indicates the adjustment factor of active power, which is related to specific grid or market conditions.

[0133] Reactive power constraint:

[0134]

[0135] in, represents the minimum active power output of distributed energy source n at time t, Represents the maximum active power output of distributed energy source n at time t.

[0136] For controllable distributed power sources, the ramp power constraint must also be met:

[0137]

[0138] in, Indicates the active power output of distributed energy at the current moment, Indicates the active power output of distributed energy at the previous moment, Indicates the active power rising slope limit within adjacent time. It indicates the limit of active power decrease slope in adjacent time.

[0139] The energy storage constraints are as follows:

[0140] Energy levels of stored energy per hour:

[0141]

[0142] in, is the charge state at the previous moment, For charging efficiency, For charging and discharging state

[0143] quantity, is the charging power, is the capacity of the energy storage system, is the discharge power.

[0144] Energy storage energy level upper and lower limits:

[0145]

[0146] in, is the minimum state of charge of the energy storage device, It is the maximum state of charge of the energy storage device.

[0147] Charge and discharge power upper and lower limits:

[0148]

[0149] in, Indicates the maximum charging power capacity of the energy storage device, Indicates the maximum discharge power capacity of an energy storage device.

[0150] The charging and discharging process of energy storage is linearly modeled with the following constraints:

[0151]

[0152] The demand response constraints are as follows:

[0153] Direct load control is a way to implement demand response programs, whereby system operators directly reduce or divert system electricity consumption in an emergency to ensure that total electricity consumption remains unchanged during the dispatch period after load reduction or diversion:

[0154]

[0155] in, is the net demand, which represents the total demand at time t; DR tis demand response, which represents the percentage of internal demand reduced due to demand response measures at time t; is the internal demand, which represents the demand within the system at time t, before considering external influences or demand responses; is the incremental coefficient, which represents the coefficient used for incremental demand at time t; is the incremental demand, which represents the additional demand introduced into the system at time t; is the reduction demand, which means the demand reduced or cut in the system at time t; is the reduction coefficient, which represents the coefficient used to reduce demand at time t.

[0156] Constraints on the speed of load increase and decrease between adjacent time periods:

[0157]

[0158] in, is the incremental demand at the previous moment; D incmax is the maximum value of the incremental demand, indicating the maximum incremental demand that the system can accept; is the reduction demand at the previous moment; D decmax It is the minimum value of incremental demand, indicating the minimum incremental demand that the system can reduce.

[0159] Hourly load response upper limit constraint:

[0160] DR t ≤DR max

[0161] Among them, DR max The maximum percentage of internal demand that can be reduced by a demand response measure, set to 1.

[0162] Demand response load does not increase or decrease at the same time constraints:

[0163]

[0164] The operating constraints of the active distribution network are as follows:

[0165] Power flow constraints:

[0166]

[0167] Among them, P i inj is the active power injection amount of node i, is the reactive power injection amount of node i, The voltage amplitude at node i, is the voltage amplitude of node j connected to node i, G ii is the self-admittance of node i, Gij is the admittance value between node i and node j, B ii is the self-susceptance of node i, B ij is the susceptance value between node i and node j, θ ii is the voltage phase angle at node i, θ ij is the voltage phase difference between node i and node j.

[0168] During a serious incident, the following conditions must be met:

[0169]

[0170] in, is the active power output of the distributed generation unit, is the reactive power output of the distributed generation unit, is the discharge power of the energy storage system, is the charging power of the energy storage system, is the active power injected into the distribution network, is the reactive power injected into the distribution network, is the active power of node i at time t, is the reactive power of node i at time t, n represents the number of nodes connected to node i, and e represents the number of adjacent lines.

[0171] Transmission constraints of active / reactive power on feeders:

[0172] -FP l max ≤P l flw ≤FP l max

[0173]

[0174] in, is the maximum permissible active power flow, is the maximum allowable reactive power flow, P l flw is the active power flow on the distribution network lines, is the reactive power flow on the distribution network lines.

[0175] Network node voltage constraints:

[0176] V i min ≤V it ≤V i max

[0177] in, is the lowest voltage allowed for network nodes, is the maximum voltage allowed by the network node, V it is the voltage value of node i at time t.

[0178] S5. Optimal resource allocation: Using a robust optimization method based on Information Gap Decision Theory (IGDT), we optimize the resource allocation plan for each microgrid cluster to improve the recovery speed and stability of ADN in the face of severe events, consider the synergy between different resources, and ensure the overall economic operation of the system.

[0179] The application of robust optimization methods can enable the system to better cope with uncertainty, thereby further improving the security and robustness of advanced distribution networks (ADNs), such as Figure 2 As shown in FIG. 1 , the robust optimization method based on information gap decision theory specifically includes the following steps:

[0180] S5.1. Setting the objective function:

[0181]

[0182] in, is the objective function, Indicates that at a specific voltage and disturbance The system response, It is a disturbance or interference signal.

[0183] S5.2. Introducing parameter deviation. Robust optimization methods usually take parameter deviation into account, that is, uncertainty is considered as the range of parameter variation during the optimization process. The objective function value can be set to a value that remains unchanged within the parameter deviation range, or an acceptable minimum performance level can be set to ensure that this level can be achieved under uncertainty conditions.

[0184] Constraints on uncertainty parameters:

[0185]

[0186] in, and represents the final value of the state at time t; α is the uncertainty radius, which represents the allowable deviation range of the system.

[0187] S5.3. Determine the acceptable level of objective function value:

[0188]

[0189] in, is the threshold, representing the reliability that the system needs to meet.

[0190] S5.4. The robust optimization algorithm based on information gap decision theory is used to determine the upper limit of the uncertainty radius while ensuring that the minimum objective function value is not less than the threshold:

[0191]

[0192] Among them, β RA represents the robustness estimate, is the benchmark value of the objective function under certain conditions.

[0193] In this embodiment, an improved 33-node distribution network is used to verify the effectiveness of the proposed model. The severe event types considered are storms and tornadoes. It is assumed that the necessary data can be obtained through an online meteorological information communication system. At the same time, the hurricane vulnerability curve, technical and economic parameters of distributed power sources and energy storage are referenced through data.

[0194] like Figure 3 As shown in the figure, the improved topology of 33-node ADN and the layout of various power sources are demonstrated; in the simulation, it is assumed that the accident occurs at 4 a.m. and the system operator is notified in advance; four cases are designed for research and analysis: Case 1, no microgrid and resources are used; Case 2, only distributed energy is available; Case 3, energy storage equipment is added on the basis of Case 2; Case 4, demand-side response is added on the basis of Case 3.

[0195] Step 1: Severe event modeling using hurricane vulnerability curves.

[0196] Step 2: Develop security indicators: Use the inverse of system performance loss as a metric to define the system's security against events. The amount of system performance loss after an event indicates its efficiency in responding to severe events, indicating that the smaller the performance loss, the greater the system's resilience.

[0197] like Figure 4 As shown in the figure, the system safety indicators under various cases after a serious incident occur. It can be clearly seen from the figure that the system safety index dropped significantly in the 4th hour after the incident; the system safety index of Case 1 was only 32.83%, while the system safety index of Case 2 reached 52.96%, which was 61.29% higher than that of Case 1. This improvement is mainly attributed to the division of the distribution network into autonomous microgrids; the use of network partitioning technology and the inherent functions of microgrids can not only improve the response time of the system, but also reduce the occurrence of load interruptions, thereby improving the adaptability and safety of the system in serious incidents.

[0198] like Figures 5 and 6As shown, the technical indicators of different cases are displayed, including load shedding value, load recovery value and recovery period. Case 4 considers energy storage and demand response technologies, and its load shedding amount is reduced from 8035.49kWh in Case 1 to 2581.99kWh, a reduction of about 67.87%; in addition, compared with Cases 1 to 3, the recovery time of Case 4 is shortened by 61.45%, 53.26% and 30.26%, respectively, which shows that when storage and demand response technologies are comprehensively considered, the system can better cope with severe events and recover from them faster.

[0199] Step 3: Preventive preparations before serious incidents occur:

[0200] like Figure 7 The figure shows the generation dispatch of DER and energy storage in the system preparation phase. The system operator optimizes the dispatch of various power sources to strengthen the network's preparedness for severe events, thereby improving its resilience and safety. Distributed Generation (DG) representing DER will reserve a portion of its generation capacity to activate lines and reactivate unactivated local generation, enabling controllable power sources to balance power in island mode and maintain voltage levels in each area, ensuring system frequency stability.

[0201] The output power of wind / photovoltaic power stations is closely related to natural conditions and system safety limitations, which limit their maximum power generation capacity; although these energy sources have lower economic costs, they cannot generate electricity at maximum capacity and supply power to load locations after severe events due to network congestion; however, energy storage devices have fast charging and discharging capabilities and can store excess renewable energy to a large extent. Therefore, as a preventive measure, optimizing the integration of energy storage devices in distribution network planning can significantly improve system safety.

[0202] Step 4: Microgrid cluster division after a serious incident:

[0203] As shown in Table 1 below, the network division results after the incident are listed; the ADN adopted is divided into seven microgrid areas, and the switching status is optimized to ensure reliable power supply to users and promote power restoration in areas where independent power generation is not possible; dividing the radial distribution network into multiple microgrid areas not only reduces the power outage time for users, but also allows corrective measures to be implemented simultaneously in each area, thereby greatly shortening the power outage time.

[0204] Table 1 Results of network partitions after severe events

[0205]

[0206] Step 5: Optimize resource configuration:

[0207] like Figure 8As shown in the figure, the voltage curves of different nodes in various cases are further illustrated; by comparing the results of the four cases, it can be found that with the increase of available resources in the distribution network, the voltage level of the node has also been significantly improved. For example, Case 4, which integrates DER, energy storage and demand response, shows the smallest voltage fluctuation.

[0208] As shown in Table 2 below, the impact of the uncertainty of the occurrence and termination time of the storm on network decisions is listed. It can be seen that compared with the expected scenario, the worst scenario leads to a 19.70% reduction in load recovery, while the best scenario increases load recovery by 27.43%; these comparative results show that if the system can be fully prepared according to the predicted conditions, the losses caused can be reduced and the system's recovery capacity can be effectively improved.

[0209] Table 2 Probability statistics of the objective function in the model

[0210]

[0211] This embodiment confirms the effectiveness and feasibility of the active distribution network safety improvement method considering energy storage and demand response proposed by the present invention. The method divides the problem into prevention, correction and recovery stages, effectively reducing the computational burden and complexity. By modeling severe events, formulating safety indicators and utilizing available resources, the robust optimization method based on IGDT is used to solve the problem and obtain the optimal solution under uncertain risks. Experimental results show that the method of this embodiment can significantly improve the resilience and reliability of the system.

[0212] In addition to the above embodiments, the present invention may also have other implementation modes. Any technical solution formed by equivalent replacement or equivalent transformation falls within the protection scope required by the present invention.

Claims

1. A method for improving the safety of an active distribution network considering energy storage and demand response, characterized in that: The following steps are involved: S1. Severe event modeling: Identify various severe events that may affect the active distribution network, and use the system equipment vulnerability curves related to severe events to evaluate the probability of equipment failure under severe events; S2. Develop safety indicators: Design indicators to quantify the safety of active distribution networks; S3. Preventive preparation before a serious incident occurs: After using the vulnerability curve to determine the parts that need to be strengthened, take preventive measures to maximize the stored energy in the battery; S4. Microgrid cluster division after a severe incident: The active distribution network is divided into multiple microgrid clusters based on different factors, including geographical location and power interconnection. Each microgrid cluster contains different types of available resources, including distributed power generation, energy storage, and demand response, and mathematical modeling is performed for each type of available resource. S5. Resource optimization configuration: Utilize the robust optimization method based on information gap decision theory to optimize the resource configuration plan for each microgrid cluster.

2. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 1, characterized in that: In step S1, when modeling a severe event, the safety of various devices in the system is comprehensively considered to estimate the tolerance threshold of the system devices; the system device vulnerability curve related to the severe event is used, including the device vulnerability threshold and the possibility of device failure of the severe event, as shown in the following equation: Among them, P[ds|S d ] indicates that in a given damage state S d The probability of system damage under the condition of Φ is the standard normal distribution function, ω ds Indicates the standard deviation of the damage state, S d is the current damage status, is the maximum damage state; P F is the total failure rate of the system, P k is the failure rate of the kth independent component, and N is the total number of independent components in the system.

3. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 1, characterized in that: In step S2, the inverse of the system performance loss is used as a metric to define the system's security against severe events. The system performance loss after a severe event indicates the efficiency of the system in responding to the severe event, which indicates that the smaller the system performance loss, the greater the system's resilience.

4. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 1, characterized in that: In step S3, potential damage is managed before a serious event occurs, and the stored energy in the battery is maximized through the objective function shown in the following formula: in, represents the decision variable; represents the charging power of the energy storage system at time t, that is, the power stored in the system during the charging process; represents the discharge power of the energy storage system at time t, that is, the power released from the system during the discharge process; represents the charging power of the natural gas system at time t, that is, the energy stored in the natural gas system; It represents the discharge power of the natural gas system at time t, that is, the energy released from the natural gas system.

5. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 1, characterized in that: In step S4, it is assumed that the microgrid is composed of distributed power sources and energy storage, and is interconnected; the constraints of the microgrid level, distributed power sources, energy storage, demand response, and active distribution network operation are fully considered; The operating constraints at the microgrid level are as follows: The power balance equation in the microgrid is: in, Indicates the upstream power purchase amount, Indicates the amount of electricity from various power sources. represents the power generation of the cogeneration unit, Represents the discharge capacity of the energy storage system, Indicates the power consumption of electrical equipment. Indicates the charge capacity of the energy storage system, For charging efficiency, is the discharge efficiency, η app The efficiency of electrical equipment; Thermal balance constraint equation: in, Heat generated for natural gas applications, The heat energy generated by the consumption of natural gas in the cogeneration unit, Heat release for natural gas equipment, For heat demand, Charge heat for natural gas equipment, Charging efficiency for natural gas equipment, Heat release efficiency for natural gas equipment; Equipment constraints for using natural gas to generate heat: in, is the applied heat energy at time t, η app is the applied thermal energy efficiency at time t, w t is the weight at time t, G t is the natural gas consumption; Constraints for combined heat and power units using natural gas for electricity and heat: in, is the power output of the CHP at time t, η g-e is the efficiency of converting natural gas into electricity, η g-h The efficiency of converting natural gas into thermal energy.

6. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 5, characterized in that: In step S4, the distributed power supply constraint conditions are as follows: Active power constraint: in, represents the minimum active power output of distributed energy source n at time t, represents the maximum active power output of distributed energy source n at time t, ζ nt Indicates the adjustment factor of active power; Reactive power constraint: in, represents the minimum active power output of distributed energy source n at time t, represents the maximum active power output of distributed energy source n at time t; If the distributed generation is a controllable distributed generation, the ramp power constraint must also be met: in, Indicates the active power output of distributed energy at the current moment, Indicates the active power output of distributed energy at the previous moment, Indicates the active power rising slope limit within adjacent time. It indicates the limit of active power decrease slope within adjacent time.

7. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 5, characterized in that: In step S4, the energy storage constraint conditions are as follows: Energy levels of stored energy per hour: in, is the charge state at the previous moment, For charging efficiency, is the charge and discharge state quantity, is the charging power, is the capacity of the energy storage system, is the discharge power; Energy storage energy level upper and lower limits: in, is the minimum state of charge of the energy storage device, is the maximum state of charge of the energy storage device; Charge and discharge power upper and lower limits: in, Indicates the maximum charging power capacity of the energy storage device, Indicates the maximum discharge power capacity of the electrical energy storage device; The charging and discharging process of energy storage is linearly modeled with the following constraints:

8. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 5, characterized in that: In step S4, direct load control technology is used as a demand response method, that is, the system operator directly reduces or transfers system power consumption in an emergency to ensure that the total power consumption in the dispatching period remains unchanged after the load is reduced or transferred: in, is the net demand, which represents the total demand at time t; DR t is demand response, which represents the percentage of internal demand reduced due to demand response measures at time t; is the internal demand, which represents the demand within the system at time t; is the incremental coefficient, which represents the coefficient used for incremental demand at time t; is the incremental demand, which represents the additional demand introduced into the system at time t; is the reduction demand, which means the demand reduced or cut in the system at time t; is the reduction coefficient, which represents the coefficient used to reduce demand at time t; Constraints on the speed of load increase and decrease between adjacent time periods: in, is the incremental demand at the previous moment; D incmax is the maximum value of the incremental demand, indicating the maximum incremental demand that the system can accept; is the reduction demand at the previous moment; D decmax is the minimum value of the incremental demand, indicating the minimum incremental demand that the system can reduce; Hourly load response upper limit constraint: DR t ≤DR max Among them, DR max The maximum percentage of internal demand reduced by demand response measures, set to 1; Demand response load does not increase or decrease at the same time constraints:

9. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 5, characterized in that: In step S4, the operating constraints of the active distribution network are as follows: Power flow constraints: in, is the active power injection amount of node i, is the reactive power injection amount of node i, The voltage amplitude at node i, is the voltage amplitude of node j connected to node i, G ii is the self-admittance of node i, G ij is the admittance value between node i and node j, B ii is the self-susceptance of node i, B ij is the susceptance value between node i and node j, θ ii is the voltage phase angle at node i, θ ij is the voltage phase angle difference between node i and node j; During a serious incident, the following conditions must be met: in, is the active power output of the distributed generation unit, is the reactive power output of the distributed generation unit, is the discharge power of the energy storage system, is the charging power of the energy storage system, is the active power injected into the distribution network, is the reactive power injected into the distribution network, is the active power of node i at time t, is the reactive power of node i at time t, n represents the number of nodes connected to node i, and e represents the number of adjacent lines; Transmission constraints of active / reactive power on feeders: in, is the maximum permissible active power flow, is the maximum allowed reactive power flow, is the active power flow on the distribution network lines, is the reactive power flow on the distribution network lines; Network node voltage constraints: in, is the lowest voltage allowed for network nodes, is the maximum voltage allowed by the network node, V it is the voltage value of node i at time t.

10. The method for improving the safety of an active distribution network considering energy storage and demand response according to claim 1, characterized in that: In step S5, the robust optimization method based on information gap decision theory specifically includes the following sub-steps: S5.

1. Setting the objective function: in, is the objective function, Indicates that at a specific voltage and disturbance The system response, is a disturbance or interference signal; S5.

2. Introducing parameter deviation and constraints on uncertainty parameters: in, and represents the final value of the state at time t; α is the uncertainty radius, which represents the allowable deviation range of the system; S5.

3. Determine the acceptable level of objective function value: in, is the threshold, representing the reliability that the system needs to meet; S5.

4. The robust optimization algorithm based on information gap decision theory is used to determine the upper limit of the uncertainty radius while ensuring that the minimum objective function value is not less than the threshold: Among them, β RA represents the robustness estimate, is the benchmark value of the objective function under certain conditions.

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