Power distribution network self-healing method based on adjustable interval optimization

Abstract

The invention relates to the technical field of power distribution networks, in particular to a power distribution network self-healing method based on adjustable interval optimization, and the method comprises the following steps: monitoring the real-time operation state in a power distribution network, obtaining the operation parameters of the power distribution network, and determining the adjustable interval of the operation parameters; an optimization model of the power distribution network is established according to the obtained operation parameters and adjustable intervals, and the optimization model comprises constraint conditions of operation of the power distribution network and adjustable intervals of renewable energy generating capacity and load fluctuation; calculating and determining a self-healing control strategy when the power distribution network has a fault; a two-stage optimization algorithm is adopted to decompose the self-healing problem into a main problem and a sub-problem, and the investment decision and operation scheduling of the power distribution network are optimized respectively; and collecting state information of the power distribution network in real time, and executing the self-healing control strategy when a fault occurs according to the investment decision and the operation scheduling of the optimized power distribution network. According to the invention, flexible design and operation scheduling of the micro-grid can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a distribution network self-healing method based on adjustable interval optimization. Background Art

[0002] In the past few years, renewable energy has rapidly become an economically viable solution in developed countries to balance the rapid growth of energy demand and significantly reduce the environmental impact of traditional fossil fuel power generation. However, the dynamic characteristics and diverse control points brought by distributed energy make its management in the distribution system complex and challenging. At the same time, significant advances in control and metering technology have promoted the transformation of traditional distribution networks to modern self-sufficient small-scale regional microgrids.

[0003] A microgrid is a low-voltage or medium-voltage distribution system, usually associated with heterogeneous distributed generation and energy storage systems in a specific geographical area. It has the ability to independently supply power and can provide power to some or all customers in an isolated state in a self-sufficient manner, thereby improving the reliability and resilience of the power system in emergency situations. The self-healing ability of microgrids is particularly outstanding, enabling them to return to normal operation in a short period of time, improving the reliability and stability of the system. Although microgrids have many advantages, their optimal design, control and management remain important challenges for distribution system operators. Especially in the current distribution network, how to effectively divide the traditional distribution system into multiple microgrids with self-healing capabilities and optimize them under the premise of ensuring economy and reliability is an urgent problem to be solved. Most of the existing research focuses on the interconnected operation mode of microgrids, while relatively less attention is paid to the self-healing ability and reliability evaluation in the isolated mode, resulting in the research on the self-healing strategy and optimization method of microgrids under isolated operation conditions is still in its infancy.

[0004] Therefore, how to achieve efficient optimization of the self-healing control strategy in the microgrid island mode, especially the adaptive adjustment of the system operating status in combination with the adjustable interval optimization algorithm, is still one of the key challenges in the intelligent development of distribution networks. Summary of the invention

[0005] In view of at least one of the above technical problems, the present invention provides a distribution network self-healing method based on adjustable interval optimization, which adopts the improvement of the method to achieve flexible design and operation scheduling of microgrids.

[0006] According to a first aspect of the present invention, a distribution network self-healing method based on adjustable interval optimization is provided, comprising the following steps:

[0007] S1: monitor the real-time operating status of the distribution network, obtain the operating parameters of the distribution network, and determine the adjustable range of the operating parameters;

[0008] S2: establishing an optimization model of the distribution network according to the obtained operating parameters and adjustable ranges, wherein the optimization model includes constraints on the operation of the distribution network and adjustable ranges of renewable energy power generation and load fluctuations;

[0009] S3: Based on the adjustable interval optimization algorithm, calculate and determine the self-healing control strategy of the distribution network when a fault occurs;

[0010] S4: A two-stage optimization algorithm is used to decompose the self-healing problem into a main problem and sub-problems, and the investment decision and operation scheduling of the distribution network are optimized respectively;

[0011] S5: collecting the status information of the distribution network in real time, and executing the self-healing control strategy when a fault occurs according to the investment decision and operation scheduling of the optimized distribution network.

[0012] In some embodiments of the present invention, the operating parameters include: distributed energy output, processing cost of distributed resources, upward power capacity reserve price, downward power capacity reserve price, upward power capacity reserve, downward power capacity reserve, number of controllable loads, controllable loads, load commitment status of controllable loads, and load unit quantity of controllable loads;

[0013] The adjustable range of the operating parameters is as follows:

[0014]

[0015] In the formula, f island is the objective function of all microgrid self-healing modes; m is the microgrid index; i is the index of distributed energy; is the output of the ith distributed energy resource in the mth microgrid; is the processing cost of distributed resources; d is the number of heterogeneous distributed generation; is the price of power capacity reserve increased in the dth heterogeneous distributed generation in the mth microgrid; is the price of power capacity reserve in the dth heterogeneous distributed generation in the mth microgrid; is the upward power capacity reserve provided by the dth heterogeneous distributed generation in the mth microgrid; is the downward power capacity reserve provided by the dth heterogeneous distributed generation in the mth microgrid; cl is the number of controllable loads; d cl,m are all controllable loads under the mth microgrid; u cl,m is a binary variable of the load commitment state of the controllable load under the mth microgrid; κ cl,m is the load unit quantity of the controllable load under the mth microgrid.

[0016] In some embodiments of the present invention, the constraints for the operation of the distribution network are as follows:

[0017] The prohibited operation area equation of heterogeneous distributed energy in microgrid:

[0018]

[0019] Heterogeneous distributed generation plus reserve capacity constraint equation:

[0020]

[0021] Heterogeneous distributed generation ramp-up and ramp-down constraint equations:

[0022]

[0023] Heterogeneous distributed generation upward and downward dispatch reserve constraint equations:

[0024]

[0025] The equation for the allowable value of controllable load is:

[0026]

[0027] Power balance constraint equation:

[0028]

[0029] In the formula, are the lower and upper limits of distributed energy output; t is the time index; is the power generation of the dth heterogeneous distributed generation at time t; is the upward power capacity reserve of the dth heterogeneous distributed generation at time t; u d,t is the output binary variable of the dth heterogeneous distributed generation at time t, 1 means that the heterogeneous distributed generation is in the output state, and 0 means that the heterogeneous distributed generation is in the idle state; are the upper and lower limits of the generation capacity of the dth heterogeneous distributed generation; is the downward power capacity reserve of the dth heterogeneous distributed generation at time t; is the power generation of the dth heterogeneous distributed generation at time t-1; RU d is the power generation rise limit of the dth heterogeneous distributed generation; RD d is the power generation reduction limit of the dth heterogeneous distributed generation; is the upper and lower power capacity reserve upper limits of the dth heterogeneous distributed generation; is the upper limit of all controllable loads under the mth microgrid; is the load power of the lth load point in the mth microgrid.

[0030] In some embodiments of the present invention, the proposed deterministic microgrid planning model and its related inequalities and equality constraints are symbolically represented in a standard form in the adjustable interval of renewable energy generation and load fluctuations:

[0031]

[0032] Where F is the objective function, which here represents profit; DV represents the set of decision variable parameters in the entire optimization model, including all adjustable factors; ψ is a subset of decision variables, representing the parameters that need to be optimized in the system; ξ represents uncertain parameters; Profit[ψ,ξ] is the profit under the decision variable ψ and the uncertain parameter ξ;

[0033] Assume that ξ has a lower bound ξ and upper bound If there are uncertain parameters, the minimum and maximum values ​​of the objective function are calculated as follows:

[0034]

[0035] Where F(ψ) represents the objective function when the uncertain parameter ξ is determined; F - (ψ) represents the objective function when ξ takes the minimum value; F + (ψ) represents the objective function when ξ takes the maximum value;

[0036] Simultaneously optimize the average objective F avg (ψ) and the deviation F div (ψ):

[0037] F(ψ)=Max(F avg (ψ)|MinF div (ψ));

[0038] The average value of F(ψ) is expressed as follows:

[0039]

[0040] The deviation of F(ψ) is expressed as follows:

[0041]

[0042] In the formula, F avg (ψ) represents the average value of the objective function in interval optimization; F div (ψ) represents the deviation of the objective function in interval optimization.

[0043] In some embodiments of the present invention, the adjustable interval optimization algorithm includes the case where the interrupted load energy and the average energy are insufficient under the self-healing control strategy, and the expression of the interrupted load energy under the self-healing control strategy is as follows:

[0044]

[0045] The expression of the average energy shortage under the self-healing control strategy is as follows:

[0046]

[0047] In the formula, SD TS is the load interruption situation of the microgrid under the self-healing control strategy; Δ TS is the duration of the microgrid self-healing control strategy; NI is the number of self-healings in one year; D TS is the total power of the self-healing internal load under the microgrid self-healing control strategy; P TS is the power generation in the total available time period under the microgrid self-healing control strategy; IEEI Target is the target value of the interruption energy index of the self-healing control strategy; IEED Target It is the target value of the energy shortage indicator of the self-healing control strategy.

[0048] In some embodiments of the present invention, the self-healing control strategy minimizes the following formula while satisfying the constraints and the adjustable range of the renewable energy power generation and load fluctuations:

[0049]

[0050] In the formula, is the unit load shedding cost; LS t,m is the load shedding amount under the microgrid self-healing control strategy; is the unit renewable energy leakage cost; RS t,m is the leakage of renewable energy under the microgrid self-healing control strategy.

[0051] In some embodiments of the present invention, the two-stage optimization algorithm is a cutting plane method and a branch and bound method, the main problem is an investment problem, the sub-problems are an interconnected operation problem and a self-healing operation problem, and the profit maximization expression in the investment problem is as follows:

[0052] Max Profit=Revenue–Cost;

[0053] Where Max Profit is the objective function that maximizes the profit of the distribution system operator under normal operation mode; Revenue is the revenue obtained during the planning period; Cost is the cost incurred during the planning period;

[0054] in:

[0055]

[0056] In the formula, is the price at which the operator sells electricity to consumers at time t; is the total amount of electricity sold by the operator to consumers at time t; is the price of electricity sold to the electricity market at time t; is a binary variable representing the state of power sold by the microgrid at time t; is the total amount of electricity sold to the electricity market at time t; is the price of electricity purchased from the electricity market at time t; is a binary variable representing the state of the microgrid purchasing electricity at time t; is the total amount of electricity purchased from the electricity market at time t; Z DER is the operating cost and investment cost of its own distributed energy; Z SW It is the operating and investment cost of the remote control system.

[0057] In some embodiments of the present invention, the operating cost of the distributed energy itself is expressed as follows:

[0058] Z DER =IC DER +OC DER ;

[0059] The investment cost expression of the distributed energy itself is as follows:

[0060]

[0061] The annual operation and dispatch cost expression of the microgrid within the planning period is as follows:

[0062]

[0063]

[0064] Where, IC DER is the total investment cost of its own distributed energy device; OC DER is the total operating cost of its own distributed energy device; N DER is the total number of distributed energy devices; x i is a binary variable that determines its own distributed energy investment; ε i It is an integer variable used to determine the type of distributed energy itself; is the investment cost of the ith distributed energy source; is the capacity of the ith distributed energy device; T is the total number of time periods; γ t,m,n is a binary variable showing the status of heterogeneous distributed generation enabled; It is the operating cost per unit output of a single heterogeneous distributed generation in a certain period of time; is the total amount of heterogeneous distributed generation; e is the energy storage device index; It is the operating cost of a single energy storage device over a certain period of time; is a binary variable representing the charge of the energy storage device; is the energy storage charging power; is a binary variable representing the discharge of the energy storage device; is the energy storage discharge power.

[0065] In some embodiments of the present invention, the investment decision includes a sectionalizing switch investment decision and a tie switch investment decision, and the total investment cost expression of the remote control system installed on the distribution network cluster, which is a group of interconnected microgrid systems, is as follows:

[0066] Z SW =IC SSW +IC TSW ;

[0067] Among them, a sectionalizing switch and a connecting switch are installed in the remote control system. The sectionalizing switch is installed on the network line, and the connecting switch is installed between different algorithm feeds. The investment cost expression of the sectionalizing switch and the connecting switch is as follows:

[0068]

[0069] Where, IC SSW is the investment cost of the sectionalizer installed on the distribution network line; IC TSW is the investment cost of the tie switch installed between different feeders; sw is the section switch index; tw is the tie switch index; τ sw is a binary variable for the segmented switch investment decision; k sw is the investment cost of the sectionalizer; tw is a binary variable for tie switch investment decision; k tw It is the investment cost of the tie switch.

[0070] In some embodiments of the present invention, the self-healing control strategy comprises the following steps:

[0071] S51: setting the lower bound, upper bound and tolerance of the optimization model, and initializing the optimization model;

[0072] S52: Optimize the main problem, update the lower bound, and calculate the optimal solution;

[0073] S53: Optimize the sub-problem, update the set upper bound according to the optimal solution, and update the main problem through the two-stage optimization algorithm;

[0074] S54: Determine whether to end the optimization process based on the optimality gap. If it is satisfied, end the optimization process; if not, return to step S51 and continue iterating until it is satisfied.

[0075] The beneficial effects of the present invention are as follows: the present invention successfully combines the optimization algorithm with the self-healing control strategy through the distribution network self-healing method based on the adjustable interval optimization algorithm, realizes the rapid recovery of the distribution network after a fault, and effectively optimizes the investment decision and operation scheduling of the distribution network. Compared with the prior art, the present invention not only enhances the adaptability, flexibility and self-healing ability of the distribution network, but also improves the economy, reliability and stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0077] Figure 1 It is a flow chart of a distribution network self-healing method based on adjustable interval optimization in an embodiment of the present invention;

[0078] Figure 2 Schematic diagram of the process of the self-healing control strategy in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0081] like Figure 1 to Figure 2 The distribution network self-healing method based on adjustable interval optimization shown includes the following steps:

[0082] S1: monitor the real-time operating status of the distribution network, obtain the operating parameters of the distribution network, and determine the adjustable range of the operating parameters;

[0083] S2: Based on the obtained operating parameters and adjustable ranges, an optimization model of the distribution network is established. The optimization model includes the constraints of the distribution network operation and the adjustable ranges of renewable energy power generation and load fluctuations;

[0084] S3: Based on the adjustable interval optimization algorithm, calculate and determine the self-healing control strategy of the distribution network when a fault occurs;

[0085] S4: A two-stage optimization algorithm is used to decompose the self-healing problem into a main problem and sub-problems, and the investment decision and operation scheduling of the distribution network are optimized respectively;

[0086] S5: Collect the status information of the distribution network in real time, and execute the self-healing control strategy when a fault occurs based on the investment decision and operation scheduling of the optimized distribution network.

[0087] like Figure 1 As shown, in step S1, intelligent sensors and data acquisition technology can be used to reflect the operation of the distribution network in real time. In step S2, the optimization model can fully consider the operating characteristics of the distribution network, and incorporate the uncertainty brought by renewable energy fluctuations and distributed generation, thereby effectively providing theoretical support for the formulation of self-healing control strategies. In step S3, the advantage of the adjustable interval algorithm is that it can dynamically adjust the parameter range involved in the optimization process, so that the system can respond flexibly under different working conditions and achieve a more efficient self-healing strategy. In step S4, the two-stage optimization algorithm is used to decompose the self-healing problem into the main problem and sub-problems, and the investment decision and operation scheduling of the distribution network are optimized respectively. In step S5, this step ensures that an immediate response can be made when a fault occurs, ensuring that the distribution network quickly returns to normal. This method can optimize the self-healing function of the microgrid, ensure that even in the island mode, the power supply can be restored in the shortest time, enhance the resilience and reliability of the system, and greatly improve the adaptability of the distribution network to changing working conditions.

[0088] In the above embodiments, the present invention successfully combines the optimization algorithm with the self-healing control strategy through the distribution network self-healing method based on the adjustable interval optimization algorithm, realizes the rapid recovery of the distribution network after the failure, and effectively optimizes the investment decision and operation scheduling of the distribution network. Compared with the prior art, the present invention not only enhances the adaptability, flexibility and self-healing ability of the distribution network, but also improves the economy, reliability and stability of the distribution network.

[0089] In an embodiment of the present invention, the operating parameters include: distributed energy output, processing cost of distributed resources, upward power capacity reserve price, downward power capacity reserve price, upward power capacity reserve, downward power capacity reserve, number of controllable loads, controllable loads, load commitment status of controllable loads, and load unit quantity of controllable loads;

[0090] The adjustable range of operating parameters is as follows:

[0091]

[0092] In the formula, f island is the objective function of all microgrid self-healing modes; m is the microgrid index; i is the index of distributed energy; is the output of the ith distributed energy resource in the mth microgrid; is the processing cost of distributed resources; d is the number of heterogeneous distributed generation; is the price of power capacity reserve increased in the dth heterogeneous distributed generation in the mth microgrid; is the price of power capacity reserve in the dth heterogeneous distributed generation in the mth microgrid; is the upward power capacity reserve provided by the dth heterogeneous distributed generation in the mth microgrid; is the downward power capacity reserve provided by the dth heterogeneous distributed generation in the mth microgrid; cl is the number of controllable loads; d cl,m are all controllable loads under the mth microgrid; u cl,m is a binary variable of the load commitment state of the controllable load under the mth microgrid; κ cl,m It is the load unit quantity of the controllable load under the mth microgrid. Distributed energy output refers to the output power of each distributed energy in the microgrid, such as solar energy, wind energy, energy storage system, etc. The power capacity reserve price reflects the economic cost of the microgrid in providing backup power. Controllable load refers to the load device that can be controlled under specific requirements, and the change of load directly affects the power supply stability of the microgrid. The objective function takes into account the processing cost of each distributed energy under different outputs, that is, the economic cost required for each distributed resource under a given output. The formula involves the price and capacity of backup power provided by different types of heterogeneous distributed generation, which will play a vital role in regulating when a microgrid fails. The objective function also takes into account the enabled state of the controllable load and its contribution to the load unit quantity of the power grid. By adjusting the enabled state and load capacity of the load, the microgrid can flexibly respond to load fluctuations and optimize the stability and reliability of power supply. In this embodiment, by defining specific operating parameters and their adjustable intervals, and combining the optimization algorithm to perform self-healing control on the microgrid, the system can be effectively restored in the event of a failure, and the economy, reliability and stability of the system can be optimized. Its core technology lies in how to use the adjustable interval optimization algorithm to coordinate and optimize multiple parameters in the microgrid, improve the self-healing function of the microgrid, and ensure that the distribution network can flexibly respond to various faults and load changes in a complex and dynamic environment.

[0093] In an embodiment of the present invention, the constraints for the operation of the distribution network are as follows:

[0094] The prohibited operation area equation of heterogeneous distributed energy in microgrid:

[0095]

[0096] This constraint ensures that the output of distributed energy is within the specified upper and lower limits. The output of each distributed energy must operate between its maximum and minimum power allowed. This constraint prevents distributed energy from operating beyond the capacity of its equipment, ensuring that the energy supply will not be overloaded or insufficient, and improving the safety of the system.

[0097] Heterogeneous distributed generation plus reserve capacity constraint equation:

[0098]

[0099] This constraint ensures that the reserve capacity does not exceed the actual output capacity of the power generation equipment, avoiding system instability or overload caused by reserve capacity adjustment.

[0100] Heterogeneous distributed generation ramp-up and ramp-down constraint equations:

[0101]

[0102] This constraint condition avoids the impact of rapid power fluctuations on the power grid by limiting the rate of power generation ramp-up and ramp-down, reduces system instability, and helps maintain the smooth operation of the distribution network.

[0103] Heterogeneous distributed generation upward and downward dispatch reserve constraint equations:

[0104]

[0105] The reserve capacity must be within the adjustable upper and lower limits. This constraint ensures that the reserve capacity will not exceed the capacity of distributed generation, while avoiding the stability of the distribution network due to insufficient or excessive reserve power supply.

[0106] The equation for the allowable value of controllable load is:

[0107]

[0108] The load of each controllable load must be greater than or equal to 0 and less than or equal to its maximum allowable value. This constraint condition limits the upper and lower limits of the load to ensure that there will be no overload or underload during load adjustment, thereby ensuring the rationality of the load control of the microgrid.

[0109] Power balance constraint equation:

[0110]

[0111] This constraint ensures that the total power of all distributed energy resources, loads, and controllable loads in the microgrid is balanced within each time step. That is, the total power generation must be equal to the total load. This ensures that the power supply matches the demand, avoids system overload or power shortage, and helps maintain the stability and reliability of the power system.

[0112] In the formula, are the lower and upper limits of distributed energy output; t is the time index; is the power generation of the dth heterogeneous distributed generation at time t; is the upward power capacity reserve of the dth heterogeneous distributed generation at time t; u d,t is the output binary variable of the dth heterogeneous distributed generation at time t, 1 means that the heterogeneous distributed generation is in the output state, and 0 means that the heterogeneous distributed generation is in the idle state; are the upper and lower limits of the generation capacity of the dth heterogeneous distributed generation; is the downward power capacity reserve of the dth heterogeneous distributed generation at time t; is the power generation of the dth heterogeneous distributed generation at time t-1; RU d is the power generation rise limit of the dth heterogeneous distributed generation; RD d is the power generation reduction limit of the dth heterogeneous distributed generation; is the upper and lower power capacity reserve upper limits of the dth heterogeneous distributed generation; is the upper limit of all controllable loads under the mth microgrid; is the load power of the first load point in the mth microgrid. These constraints provide comprehensive control over distributed generation, load scheduling, and power balance in the microgrid, ensuring that the microgrid can operate smoothly in self-healing mode. Each constraint not only helps to ensure the stability of the system, but also optimizes the recovery capability of the microgrid under different fault conditions. Through the effective constraints of these conditions, the microgrid can respond quickly and restore normal power supply while ensuring economy and reliability when a fault occurs.

[0113] In an embodiment of the present invention, the proposed deterministic microgrid planning model and its related inequalities and equality constraints are symbolically represented in a standard form in the adjustable range of renewable energy generation and load fluctuations:

[0114]

[0115] Where F is the objective function, which here represents profit; DV represents the set of decision variable parameters in the entire optimization model, including all adjustable factors; ψ is a subset of decision variables, representing the parameters that need to be optimized in the system; ξ represents uncertain parameters; Profit[ψ,ξ] is the profit under the decision variable ψ and the uncertain parameter ξ;

[0116] Assume that ξ has a lower bound ξ and upper bound If there are uncertain parameters, the minimum and maximum values ​​of the objective function are calculated as follows:

[0117]

[0118] Where F(ψ) represents the objective function when the uncertain parameter ξ is determined; F - (ψ) represents the objective function when ξ takes the minimum value; F + (ψ) represents the objective function when ξ takes the maximum value;

[0119] Through the above formula, the objective function values ​​in the worst and optimal cases can be obtained, thus providing a basis for subsequent optimization decisions.

[0120] Simultaneously optimize the average objective F avg (ψ) and the deviation F div (ψ):

[0121] F(ψ)=Max(F avg (ψ)|MinF div (ψ));

[0122] The average value of F(ψ) is expressed as follows:

[0123]

[0124] The deviation of F(ψ) is expressed as follows:

[0125]

[0126] In the formula, F avg (ψ) represents the average value of the objective function in interval optimization; F div (ψ) represents the deviation of the objective function in interval optimization. Both provide more accurate optimization basis for decision-making by quantifying the uncertainty (deviation) and expected value (average value) of the objective function. In this embodiment, by introducing a decision model based on adjustable interval optimization, the complex optimization problem caused by renewable energy fluctuations and load fluctuations in the microgrid is solved. By minimizing and maximizing the value of the objective function, optimizing the average value and deviation, a powerful tool for adapting to changes in uncertainty is provided, thereby effectively improving the economy, stability and self-healing ability of the microgrid.

[0127] In an embodiment of the present invention, the adjustable interval optimization algorithm includes the case where the interrupted load energy and the average energy are insufficient under the self-healing control strategy. The expression of the interrupted load energy under the self-healing control strategy is as follows:

[0128]

[0129] The core of this formula is to calculate the load interruption energy during each self-healing process and compare it with the target value, thereby ensuring that the microgrid will not have excessive load interruptions during the self-healing process and reach the maximum value tolerated by the system. Its technical effect is to ensure that the microgrid self-healing process does not have excessive negative impact on users by constraining the load interruption energy.

[0130] The expression of average energy shortage under the self-healing control strategy is as follows:

[0131]

[0132] The formula calculates the difference between the power generation and the load demand (i.e. the energy deficit) during each self-healing process, and calculates the average energy deficit per year based on these differences. Target The maximum energy that the microgrid cannot meet the load demand due to insufficient power generation during the self-healing process each year can be limited.

[0133] In the formula, SD TS is the load interruption situation of the microgrid under the self-healing control strategy; Δ TS is the duration of the microgrid self-healing control strategy; NI is the number of self-healings in one year; D TS is the total power of the self-healing internal load under the microgrid self-healing control strategy; P TS is the power generation in the total available time period under the microgrid self-healing control strategy; IEEI Target is the target value of the interruption energy index of the self-healing control strategy; IEED Target is the target value of the energy shortage index of the self-healing control strategy. In this embodiment, by quantifying and optimizing the load interruption energy and energy shortage of the microgrid during the self-healing process, a refined self-healing control strategy is provided, so that the microgrid can reasonably balance the load and power generation resources when a fault occurs, minimize the impact on users, and ensure stable and efficient recovery of the system.

[0134] In an embodiment of the present invention, the self-healing control strategy minimizes the following formula while satisfying the constraints and the adjustable range of renewable energy generation and load fluctuations:

[0135]

[0136] In the formula, is the unit load shedding cost; LS t,m is the load shedding amount under the microgrid self-healing control strategy; is the unit renewable energy leakage cost; RS t,mis the amount of renewable energy leakage under the microgrid self-healing control strategy. In this embodiment, by defining the objective function f island The various cost factors of the microgrid in the self-healing process are quantified, and the operation of the microgrid is optimized by minimizing the objective function. This method can achieve economic optimization of the system, not only reduce operating costs, but also improve the utilization efficiency of renewable energy and ensure the balance and reliability of the system during fault recovery.

[0137] In the embodiment of the present invention, the two-stage optimization algorithm is the cutting plane method and the branch and bound method, the main problem is the investment problem, and the sub-problems are the interconnection operation problem and the self-healing operation problem. The profit maximization expression in the investment problem is as follows:

[0138] Max Profit=Revenue–Cost;

[0139] Where Max Profit is the objective function that maximizes the profit of the distribution system operator under normal operation mode; Revenue is the revenue obtained during the planning period; Cost is the cost incurred during the planning period;

[0140] in:

[0141]

[0142] In the formula, is the price at which the operator sells electricity to consumers at time t; is the total amount of electricity sold by the operator to consumers at time t; is the price of electricity sold to the electricity market at time t; is a binary variable representing the state of power sold by the microgrid at time t; is the total amount of electricity sold to the electricity market at time t; is the price of electricity purchased from the electricity market at time t; is a binary variable representing the state of the microgrid purchasing electricity at time t; is the total amount of electricity purchased from the electricity market at time t; Z DER is the operating cost and investment cost of its own distributed energy; Z SWis the operating and investment cost of the remote control system. The operating and investment costs of the distributed energy itself cover the construction, operation, maintenance and other related costs of all distributed energy in the microgrid; the operating and investment costs of the remote control system cover the construction and operating costs of the remote control system of the microgrid, ensuring the stable operation and effective management of the system. The goal of the main problem is to maximize the profit of the microgrid within a certain planning period. The core of this problem is how to make the best decision in the investment stage, such as investing in distributed energy and remote control systems. The optimization goal of this part is to determine the optimal investment configuration by calculating the difference between income and cost. In the daily operation of the microgrid, the sub-problems involve the interconnected operation mode, that is, the interaction between the microgrid and the power market, and the self-healing operation mode, that is, the recovery ability of the microgrid when a fault occurs. These two sub-problems need to be further optimized to determine the behavior of the microgrid under different operation modes to ensure that the profit is maximized while meeting the load demand. What we need to understand here is that the cutting plane method approximates the integer solution by gradually adding linear constraints. The steps are: solve the relaxation problem, ignore the integer constraints, and solve the linear programming problem; check the integer property, if the solution is an integer, stop; otherwise, go to the next step; generate a cutting plane, add a new linear constraint, cut off some non-integer solution areas, but do not affect the integer solution; repeat, continue to solve new linear programming problems until an integer solution is found. The branch and bound rule gradually narrows the solution space through branching and bounding strategies. The steps are: solve the relaxation problem, ignore the integer constraints, and solve the linear programming problem; branch, if the solution is not an integer, select a variable to divide it into two small problems; bound, calculate the upper and lower bounds of each small problem, and cut off the small problems that cannot contain the optimal solution; repeat, continue branching and bounding until the optimal integer solution is found. Combining the cutting plane method with the branch and bound method can make dynamic adjustments in the face of changing market prices and demands, so that the microgrid can make the best decision in the shortest time, ensuring the stable operation of the system and maximizing profits. In optimizing electricity sales and purchases, the model considers how microgrids adjust electricity sales and purchases under different operating modes. and Microgrids can balance demand and supply between the market and consumers to maximize profits. In reducing operating costs, both investment and operating costs are considered, and Z is reduced by adjusting distributed energy investment and the deployment of remote control systems. DER and Z SW By rationally allocating investment, the microgrid can achieve higher operating benefits and lower costs. In this embodiment, by combining income, cost, market transactions, distributed energy investment and self-healing control strategy, a model based on a two-stage optimization algorithm is proposed, so that the microgrid can maximize profits while satisfying constraints.

[0143] In an embodiment of the present invention, the operating cost of the distributed energy itself is expressed as follows:

[0144] Z DER =IC DER +OC DER ;

[0145] The investment cost of distributed energy itself is expressed as follows:

[0146]

[0147] In this formula, the investment cost of the microgrid depends on the investment decision and capacity size of each distributed energy device. The more energy devices invested, the greater the total investment cost.

[0148] The annual operation and dispatch cost expression of the microgrid within the planning period is as follows:

[0149]

[0150] This formula calculates the operating costs of all enabled heterogeneous distributed generation devices at each time, which depends on the output and unit operating cost of each device.

[0151] Where, IC DER is the total investment cost of its own distributed energy device; OC DER is the total operating cost of its own distributed energy device; N DER is the total number of distributed energy devices; x i is a binary variable that determines its own distributed energy investment; ε i It is an integer variable used to determine the type of distributed energy itself; is the investment cost of the ith distributed energy source; is the capacity of the ith distributed energy device; T is the total number of time periods; γ t,m,n is a binary variable showing the status of heterogeneous distributed generation enabled; It is the operating cost per unit output of a single heterogeneous distributed generation in a certain period of time; is the total amount of heterogeneous distributed generation; e is the energy storage device index; It is the operating cost of a single energy storage device over a certain period of time; is a binary variable representing the charge of the energy storage device; is the energy storage charging power; is a binary variable representing the discharge of the energy storage device; is the energy storage discharge power. By rationally planning investment and operation strategies, microgrids can minimize total costs while ensuring reliability, which includes not only equipment investment, but also the activation of distributed generation and energy storage devices in daily operations. And by defining binary variables for the activation state, the system can flexibly adjust which energy devices need to be turned on or off, thereby optimizing the overall power generation efficiency and cost. In this embodiment, by involving distributed generation devices, energy storage systems, and their operating strategies, the system can accurately calculate the cost of each device, providing strong support for the economic decision-making of the microgrid, ensuring the optimal cost-effectiveness in investment and operation, and thus providing a more flexible and sustainable economic operation solution for the microgrid.

[0152] In an embodiment of the present invention, the investment decision includes a sectionalizing switch investment decision and a tie switch investment decision, and the total investment cost expression of the remote control system installed on the distribution network cluster, which is a group of interconnected microgrid systems, is as follows:

[0153] Z SW =IC SSW +IC TSW ;

[0154] Among them, the sectionalizing switch and the tie switch are installed in the remote control system. The sectionalizing switch is installed on the network line, and the tie switch is installed between different algorithm feeds. The investment cost expression of the sectionalizing switch and the tie switch is as follows:

[0155] Z SW =(∑ sw τ sw κ sw )+(∑ tw ζ tw κ tw );

[0156] Where, IC SSW is the investment cost of the sectionalizer installed on the distribution network line; IC TSW is the investment cost of the tie switch installed between different feeders; sw is the section switch index; tw is the tie switch index; τ sw is a binary variable for the segmented switch investment decision; κ sw is the investment cost of the sectionalizer; is a binary variable for tie switch investment decision; twis the investment cost of the tie switch. The sectionalizer is installed on the line of the distribution network. It is used for segmented management of the power grid to ensure fault isolation and maintainability of the power grid. The investment cost of the sectionalizer is calculated based on its installation decision and the investment cost of a single device. The tie switch is installed between different feeders and is mainly used to connect different parts of the microgrid so that when necessary, power can be transferred from one part to another through the tie switch to improve the flexibility and reliability of the power grid. The investment decision of the sectionalizer and tie switch is determined by using binary variables to decide whether to invest in each specific switch. This decision-making method enables the entire system to make the best choice based on demand and economy. By reasonably selecting the investment plan for the sectionalizer and tie switch, the overall investment cost of the microgrid can be minimized while ensuring the stability and flexibility of the power grid.

[0157] Based on the above embodiments, Figure 2 As shown, the self-healing control strategy includes the following steps:

[0158] S51: setting the lower bound, upper bound and tolerance of the optimization model, and initializing the optimization model;

[0159] S52: Optimize the main problem, update the lower bound, and calculate the optimal solution;

[0160] S53: Optimize the sub-problem, update the set upper bound according to the optimal solution, and update the main problem through the two-stage optimization algorithm;

[0161] S54: Determine whether to end the optimization process according to the optimality gap. If it is satisfied, the optimization process is ended; if it is not satisfied, return to step S51 and continue to iterate until it is satisfied and stop. In step S51, the lower bound refers to the minimum value of the objective function, which indicates the possible minimum range of the objective value in the optimization problem; the upper bound refers to the maximum value of the objective function, which indicates the possible maximum range of the objective value in the optimization problem; the tolerance is used to set the error range allowed in the optimization process, which can help control the accuracy of the calculation and the stop condition of the iteration. In addition, this step also includes the initialization of the optimization model, that is, setting the initial conditions or initial solutions, which provides a basis for subsequent optimization iterations. In step S52, the optimization algorithm first focuses on solving the main problem, that is, the overall optimization model, and performs the core process of optimization according to the set upper and lower bounds; during the optimization process, the solution of the main problem will be continuously iterated, and each time a new possible solution is found, the lower bound will be updated according to the optimal solution to more accurately reflect the estimated value of the current optimal solution. The optimal solution is calculated through the optimization step of the algorithm, which can be the solution with the minimum or maximum objective function value, depending on the optimization goal. In step S53, the optimization algorithm solves the sub-problem, that is, optimizes some local parts or subsets of the main problem. The optimal solution of the sub-problem may affect the solution space of the entire optimization model. Therefore, according to the results obtained from the sub-problem, the upper bound is updated, and the upper bound is used to limit the maximum possible solution of the entire system. The two-stage optimization algorithm is an iterative process, in which the main problem and the sub-problem influence and feedback each other. By updating the upper bound and the lower bound, the two-stage optimization algorithm can gradually approach the global optimal solution. In step S54, the optimality gap refers to the gap between the current solution and the optimal solution, which is the criterion for judging whether the optimization process is over. If the optimality gap is less than the preset tolerance, the optimization process ends. If the optimality gap is greater than the tolerance, it means that the current solution is not good enough and the optimization process needs to continue. Therefore, the algorithm returns to step S51, resets the lower bound, upper bound and tolerance, and performs a new round of optimization iteration. In this embodiment, the above process is a two-stage optimization algorithm under the self-healing control strategy, which optimizes the main problem and the sub-problem through continuous iteration, updates the upper and lower bounds, and finally judges whether the optimization process is over by the optimality gap. The goal of this process is to find an optimal solution that meets the conditions to achieve the best operating state of the microgrid under the self-healing control strategy.

[0162] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A distribution network self-healing method based on adjustable interval optimization, characterized in that: The following steps are involved: S1: monitor the real-time operating status of the distribution network, obtain the operating parameters of the distribution network, and determine the adjustable range of the operating parameters; S2: establishing an optimization model of the distribution network according to the obtained operating parameters and adjustable ranges, wherein the optimization model includes constraints on the operation of the distribution network and adjustable ranges of renewable energy power generation and load fluctuations; S3: Based on the adjustable interval optimization algorithm, calculate and determine the self-healing control strategy of the distribution network when a fault occurs; S4: A two-stage optimization algorithm is used to decompose the self-healing problem into a main problem and sub-problems, and the investment decision and operation scheduling of the distribution network are optimized respectively; S5: collecting the status information of the distribution network in real time, and executing the self-healing control strategy when a fault occurs according to the investment decision and operation scheduling of the optimized distribution network.

2. The distribution network self-healing method based on adjustable interval optimization according to claim 1 is characterized in that: The operating parameters include: distributed energy output, processing cost of distributed resources, upward power capacity reserve price, downward power capacity reserve price, upward power capacity reserve, downward power capacity reserve, number of controllable loads, controllable loads, load commitment status of controllable loads and load unit quantity of controllable loads; The adjustable range of the operating parameters is as follows: In the formula, f island is the objective function of all microgrid self-healing modes; m is the microgrid index; i is the index of distributed energy; is the output of the ith distributed energy resource in the mth microgrid; is the processing cost of distributed resources; d is the number of heterogeneous distributed generation; is the price of power capacity reserve increased in the dth heterogeneous distributed generation in the mth microgrid; is the price of power capacity reserve in the dth heterogeneous distributed generation in the mth microgrid; is the upward power capacity reserve provided by the dth heterogeneous distributed generation in the mth microgrid; is the downward power capacity reserve provided by the dth heterogeneous distributed generation in the mth microgrid; cl is the number of controllable loads; d cl,m are all controllable loads under the mth microgrid; u cl,m is a binary variable of the load commitment state of the controllable load under the mth microgrid; κ cl,m is the load unit quantity of the controllable load under the mth microgrid.

3. The distribution network self-healing method based on adjustable interval optimization according to claim 2 is characterized in that: The constraints for the operation of the distribution network are as follows: The prohibited operation area equation of heterogeneous distributed energy in microgrid: Heterogeneous distributed generation plus reserve capacity constraint equation: Heterogeneous distributed generation ramp-up and ramp-down constraint equations: Heterogeneous distributed generation upward and downward dispatch reserve constraint equations: The equation for the allowable value of controllable load is: Power balance constraint equation: In the formula, It is the lower and upper limits of distributed energy output; t is the time index; is the power generation of the dth heterogeneous distributed generation at time t; is the upward power capacity reserve of the dth heterogeneous distributed generation at time t; u d,t is the output binary variable of the dth heterogeneous distributed generation at time t, 1 means that the heterogeneous distributed generation is in the output state, and 0 means that the heterogeneous distributed generation is in the idle state; are the upper and lower limits of the generation capacity of the dth heterogeneous distributed generation; is the downward power capacity reserve of the dth heterogeneous distributed generation at time t; is the power generation of the dth heterogeneous distributed generation at time t-1; RU d is the power generation rise limit of the dth heterogeneous distributed generation; RD d is the power generation reduction limit of the dth heterogeneous distributed generation; is the upper and lower power capacity reserve upper limits of the dth heterogeneous distributed generation; is the upper limit of all controllable loads under the mth microgrid; is the load power of the lth load point in the mth microgrid.

4. The distribution network self-healing method based on adjustable interval optimization according to claim 3 is characterized in that: In the adjustable range of renewable energy generation and load fluctuations, the proposed deterministic microgrid planning model and its related inequalities and equality constraints are symbolized in standard form: In the formula, F is the objective function, which here represents profit; DV represents the set of decision variable parameters in the entire optimization model, including all adjustable factors; ψ is a subset of decision variables, representing the parameters that need to be optimized in the system; ξ represents the uncertain parameters; Profit[ψ,ξ] is the profit under the decision variable ψ and the uncertain parameter ξ; Assume that ξ has a lower bound ξ and upper bound If there are uncertain parameters, the minimum and maximum values ​​of the objective function are calculated as follows: Where F(ψ) represents the objective function when the uncertain parameter ξ is determined; F - (ψ) represents the objective function when ξ takes the minimum value; F + (ψ) represents the objective function when ξ takes the maximum value; Simultaneously optimize the average objective F avg (ψ) and the deviation F div (ψ): F(ψ)=Max(F avg (ψ)|MinF div (ψ)); The average value of F(ψ) is expressed as follows: The deviation of F(ψ) is expressed as follows: In the formula, F avg (ψ) represents the average value of the objective function in interval optimization; F div (ψ) represents the deviation of the objective function in interval optimization.

5. The distribution network self-healing method based on adjustable interval optimization according to claim 4 is characterized in that: The adjustable interval optimization algorithm includes the case where the interrupted load energy and the average energy are insufficient under the self-healing control strategy. The expression of the interrupted load energy under the self-healing control strategy is as follows: The expression of the average energy shortage under the self-healing control strategy is as follows: In the formula, SD TS is the load interruption situation of the microgrid under the self-healing control strategy; Δ TS is the duration of the microgrid self-healing control strategy; NI is the number of self-healing within one year; D TS is the total power of the self-healing internal load under the microgrid self-healing control strategy; P TS is the power generation in the total available time period under the microgrid self-healing control strategy; IEEI Target is the target value of the interruption energy index of the self-healing control strategy; IEED Target It is the target value of the energy shortage indicator of the self-healing control strategy.

6. The distribution network self-healing method based on adjustable interval optimization according to claim 5 is characterized in that: The self-healing control strategy minimizes the following formula while satisfying the constraints and the adjustable range of the renewable energy power generation and load fluctuations: In the formula, is the unit load shedding cost; LS t,m is the load shedding amount under the microgrid self-healing control strategy; is the unit renewable energy leakage cost; RS t,m is the leakage of renewable energy under the microgrid self-healing control strategy.

7. The distribution network self-healing method based on adjustable interval optimization according to claim 1 is characterized in that: The two-stage optimization algorithm is the cutting plane method and the branch and bound method. The main problem is the investment problem. The sub-problems are the interconnection operation problem and the self-healing operation problem. The profit maximization expression in the investment problem is as follows: Max Profit=Revenue–Cost; Where Max Profit is the objective function that maximizes the profit of the distribution system operator under normal operation mode; Revenue is the revenue obtained during the planning period; Cost is the cost incurred during the planning period; in: Cost=Z DER +Z SW ; In the formula, is the price at which the operator sells electricity to consumers at time t; is the total amount of electricity sold by the operator to consumers at time t; is the price of electricity sold to the electricity market at time t; is a binary variable representing the state of power sold by the microgrid at time t; is the total amount of electricity sold to the electricity market at time t; is the price of electricity purchased from the electricity market at time t; is a binary variable representing the state of the microgrid purchasing electricity at time t; is the total amount of electricity purchased from the electricity market at time t; Z DER is the operating cost and investment cost of its own distributed energy; Z SW It is the operating and investment cost of the remote control system.

8. The distribution network self-healing method based on adjustable interval optimization according to claim 7 is characterized in that: The operating cost expression of the distributed energy itself is as follows: Z DER =IC DER +OC DER ; The investment cost expression of the distributed energy itself is as follows: The annual operation and dispatch cost expression of the microgrid within the planning period is as follows: Where, IC DER is the total investment cost of its own distributed energy device; OC DER is the total operating cost of its own distributed energy device; N DER is the total number of distributed energy devices; x i is a binary variable that determines its own distributed energy investment; ε i It is an integer variable used to determine the type of distributed energy itself; is the investment cost of the ith distributed energy source; is the capacity of the ith distributed energy device; T is the total number of time periods; γ t,m,n is a binary variable showing the status of heterogeneous distributed generation enabled; It is the operating cost per unit output of a single heterogeneous distributed generation in a certain period of time; is the total amount of heterogeneous distributed generation; e is the energy storage device index; It is the operating cost of a single energy storage device over a certain period of time; is a binary variable representing the charge of the energy storage device; is the energy storage charging power; is a binary variable representing the discharge of the energy storage device; is the energy storage discharge power.

9. The distribution network self-healing method based on adjustable interval optimization according to claim 8, characterized in that: The investment decision includes the investment decision of the section switch and the investment decision of the tie switch. The total investment cost expression of the remote control system installed on the distribution network cluster, which is a group of interconnected microgrid systems, is as follows: Z SW =IC SSW +IC TSW ; Among them, a sectionalizing switch and a connecting switch are installed in the remote control system. The sectionalizing switch is installed on the network line, and the connecting switch is installed between different algorithm feeds. The investment cost expression of the sectionalizing switch and the connecting switch is as follows: Where, IC SSW is the investment cost of the sectionalizer installed on the distribution network line; IC TSW is the investment cost of the tie switch installed between different feeders; sw is the section switch index; tw is the tie switch index; τ sw is a binary variable for the segmented switch investment decision; κ sw is the investment cost of the sectionalizer; is a binary variable for tie switch investment decision; tw It is the investment cost of the tie switch.

10. The distribution network self-healing method based on adjustable interval optimization according to any one of claims 1 to 9, characterized in that: The self-healing control strategy includes the following steps: S51: setting the lower bound, upper bound and tolerance of the optimization model, and initializing the optimization model; S52: Optimize the main problem, update the lower bound, and calculate the optimal solution; S53: Optimize the sub-problem, update the set upper bound according to the optimal solution, and update the main problem through the two-stage optimization algorithm; S54: judging whether to end the optimization process according to the optimality gap, and if satisfied, ending the optimization process; If not, return to step S51 and continue iterating until it is satisfied.

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