Multi-level autonomous operation regulation and control method for medium and low voltage power distribution system

Abstract

The invention discloses a multi-level autonomous operation regulation and control method for a medium-and-low-voltage power distribution system, and the method comprises the steps: building an adjustable potential evaluation model of a transformer area based on the operation characteristics of the adjustable resources of each transformer area in a day-ahead stage, solving an interaction power interval of the transformer area in the next day, and reporting the interaction power interval to a power distribution network dispatching center; and the power distribution network establishes a corresponding day-ahead transformer area safe operation interval planning model according to the interaction power interval, solves the maximum interaction power allowable fluctuation interval value of each transformer area, and transmits the maximum interaction power allowable fluctuation interval value to the transformer areas to provide an interaction power boundary for autonomous operation of the transformer areas. In the intra-day stage, the transformer area carries out autonomous rolling scheduling based on a safe operation interval by taking transformer area economic optimization as a target, and an interaction power curve obtained by scheduling in each time period is transmitted to the power distribution network; and then, the power distribution network formulates a corresponding scheduling strategy based on the interactive power information of each transformer area by taking economical efficiency optimization as a target so as to realize efficient autonomous operation of the whole medium and low voltage power distribution system.

Description

Technical Field

[0001] The present invention relates to the field of power distribution system optimization and dispatching, and in particular to a multi-level autonomous operation and control method for medium and low voltage power distribution systems. Background Art

[0002] With the continuous grid connection and consumption of large-scale distributed renewable energy on the distribution and use side, the current distribution network has evolved from the traditional "passive" distribution network to a new form of "active" distribution network. However, due to the intermittent and volatile nature of renewable energy generation, the source-load mismatch phenomenon of the active distribution network has become increasingly prominent, and its safe and stable operation faces severe challenges.

[0003] Intermittent renewable energy accessed through distributed networks has exacerbated the uncertainty of the operation of active distribution networks from the source side. At the same time, the demand for flexible loads represented by electric vehicles and air conditioners has continued to grow, increasing the uncertainty on the load side. In order to cope with the source-load uncertainty problem and efficiently absorb widely dispersed renewable energy, the best means is to consider the complementarity and mutual assistance between regions and realize the localized regional autonomous operation of sources and loads through the flexible allocation of distributed resources. The large-scale access of multiple source-storage-load devices in the distribution network, coupled with the increasing autonomy and participation of users on the demand side, has made the regulation characteristics of massive controllable resources more complicated and diversified. The distribution network has gradually shown new features such as activeness, intelligent source and load, and network power electronics, which has greatly increased the complexity and control difficulty of the distribution system, and brought huge challenges to the operation and regulation of the distribution network.

[0004] In order to cope with the pressure of latency, loss and security brought by massive data on the operation of traditional centralized distribution networks, edge computing, as a new computing model, deploys edge servers at the edge of the distribution network, allowing data to be directly transmitted to the vicinity of the data source for analysis and processing, which can effectively alleviate the burden of network communication and computing resources in the operation and control of the distribution network, and effectively improve the ability of the substation to independently operate and dispatch. Under this situation, the distributed and flat characteristics of the operation and management of my country's active distribution network have gradually emerged, and a network pattern of partitioned autonomy and cloud-edge collaboration has gradually formed. Faced with the above-mentioned difficulties and changes in the form of active distribution networks, how to build and develop a highly reliable, highly flexible and highly friendly active distribution network management and control system has become an urgent problem to be solved. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a multi-level autonomous operation and control method for medium and low voltage power distribution systems, aiming to give full play to the autonomous optimization capabilities of substations in medium and low voltage power distribution systems, realize multi-level flat scheduling of medium and low voltage power distribution systems, promote the full exploration of adjustable resources of substations and distribution networks and the efficient on-site consumption of distributed energy, and help the safe and efficient operation of medium and low voltage power distribution systems.

[0006] Technical solution: The multi-level autonomous operation and control method of a medium and low voltage power distribution system described in the present invention comprises the following steps:

[0007] (1) Establishing a daily pre-station regulation potential assessment model based on the operating characteristics of adjustable resources in each substation, and solving the interaction power range of each substation in the next day; the substation regulation potential assessment model is a multi-objective planning model of the interaction power and energy upper and lower limits at the substation grid connection point;

[0008] (2) According to the interactive power intervals of each substation in the next day, a safe operation interval optimization model of the previous substation in the day before is established, and the maximum interactive power safe operation interval of the substation in the day before is solved to maintain the safe and stable operation of the distribution network; the optimization model of the safe operation interval of the previous substation in the day before is a three-layer robust optimization model, the first layer is used to obtain the upper and lower boundary values ​​of the maximum substation power safe operation interval, the second layer obtains the worst interactive power trajectory of the substation for the safe operation of the distribution network from the power safe operation interval, and the third layer verifies whether the distribution network can achieve safe and stable operation under the interactive power trajectory;

[0009] (3) according to the power safety operation range of the substation, with the goal of minimizing the comprehensive daily operation cost of the substation, solving the optimal economic dispatch strategy for each time period within the day, and transmitting the interactive power value of each time period to the distribution network;

[0010] (4) The distribution network calculates the optimal intraday autonomous dispatching strategy based on the interactive power values ​​in each time period with the goal of minimizing the comprehensive cost of the distribution network.

[0011] Furthermore, the operating characteristics of the adjustable resources in step (1) include energy storage operating characteristics and transferable load operating characteristics;

[0012] Energy storage operation characteristics include:

[0013]

[0014] Where Δt is the unit time interval, E is the maximum charging and discharging power value per unit time of energy storage, es,t and and The capacity and charging and discharging power of the E-SOP internal energy storage system at time t, and are the charging efficiency and discharging efficiency of the energy storage system respectively; and are 0-1 variables representing the charging and discharging state of the energy storage at time t. When the energy storage is in the charging state

[0015] Transferable load operation features include:

[0016]

[0017]

[0018] in, and are the load increase or decrease of the substation area based on the scheduling demand of the fusion terminal at time t, and are the maximum load that can be increased or decreased in the substation at time t.

[0019] Furthermore, the method for establishing the current foreland regulation potential assessment model in step (1) is as follows:

[0020] (a) Establish an approximate description model of the substation regulation potential;

[0021]

[0022] Among them, P DSS It is the vector composed of the interactive power at the grid connection point in each period of the substation area; is the interactive power value at the grid connection point at time t in the substation area; is the energy value of the station at time t; and as well as and are the upper and lower limits of the interaction power and energy allowed at the grid connection point in the substation at time t;

[0023] (b) establishing a model for assessing the regulatory potential of the current foreland areas;

[0024]

[0025] The current evaluation model for the regulation potential of the substation is a multi-objective planning model, and its constraints include the regulation potential approximate description model Ω2, the operation constraints of each adjustable resource in the substation, and the power balance constraints of the substation.

[0026] Furthermore, the optimization model of the safe operation range of the current front area in step (2) is:

[0027]

[0028] Among them, Ω n is the set of distribution network nodes, and The upper and lower limits of the power safety operation range at node i are respectively, and as well as and It is the relaxation variable of active power flow and reactive power flow of distribution network;

[0029] The constraints of the current optimization model for the safe operation range of the substation include the regulation potential constraints uploaded by each substation, the operation constraints of each device in the distribution network, and the power balance constraints of the distribution network.

[0030] Furthermore, in step (3), according to the power safety operation range of the substation, with the goal of minimizing the comprehensive daily operation cost of the substation, solving the optimal economic dispatch strategy for each time period during the day includes:

[0031] In the intraday stage, the intraday economic autonomy dispatch model of the substation is established with the goal of minimizing the intraday comprehensive operation cost of the substation:

[0032] minf i DSS =C ess +C DR +C grid

[0033] Among them, f i DSS is the daily comprehensive operating cost of the station located at node i, C ess is the operating cost of the energy storage equipment in the daily stage of the substation, C DR is the dispatch cost of the transferable load, C grid The cost of electricity purchase and sale for the substation and distribution network;

[0034] Solve the intraday economic autonomous dispatch model of the substation to obtain the optimal economic dispatch strategy for each time period within the day.

[0035] Furthermore, in step (4), the method for calculating the optimal intraday autonomous dispatching strategy with the goal of minimizing the comprehensive cost of the distribution network is:

[0036] With the goal of minimizing the comprehensive cost of the distribution network, an intraday economic autonomous dispatch model for the distribution network is established:

[0037]

[0038] Among them, Ω dis is the set of distribution network branches, Ω i is the set of distribution network nodes, C loss,ij is the network loss of the ijth branch of the distribution network during the intraday stage, C ess,i is the daily operation cost of the medium voltage energy storage located at node i, C grid,dis It is the power value of electricity purchased and sold between the distribution network and the upper-level power grid during the day;

[0039] Solving the distribution network's intraday economic autonomous dispatch model obtains the distribution network's optimal intraday autonomous dispatch strategy.

[0040] Furthermore, in step (1), the boundary node shrinkage algorithm is used to solve the current foreground area regulation potential assessment model.

[0041] Furthermore, in step (2), an iterative algorithm based on a variable range interval constraint is used to solve the current foreground safe operation interval optimization model; the iterative algorithm based on a variable range interval constraint includes:

[0042] The variable-range interval robust optimization problem is transformed into a fixed-range interval robust optimization problem by using decision variables, and the fixed-range interval robust optimization problem is solved by an iterative method based on C&CG idea.

[0043] Furthermore, the method of converting the variable range interval robust optimization problem into the fixed range interval robust optimization problem by using decision variables includes:

[0044] For the area within the variable range Possible interaction power values ​​within By introducing the decision variable ξ i,t And the following constraints to fix the boundaries:

[0045]

[0046] Through the above constraints, the variable range constraints in the second layer of the original robust optimization problem Convert to a bounded constraint ξ i,t ∈[0,1]; where ξ i,t It is the ratio of the worst interactive power value of the substation to its maximum safe operating range, that is, the worst possible interactive power trajectory of the substation.

[0047] Furthermore, the method of solving the fixed range interval robust optimization problem by using the iterative method based on the C&CG concept includes:

[0048] (a) Split the bounded interval robust optimization problem into a main problem and a sub-problem:

[0049] The main question is:

[0050]

[0051] In the formula, x is the first-level decision variable and A collection of; is the decision variable ξ i,t The set of values ​​after the lth iteration is used to characterize the worst interactive power output trajectory in the lower stage of the operating range determined in the first layer; y is the power flow relaxation variable and the set of distribution network flow operation decision variables such as energy storage charging and discharging power, c, b, A, B, E, F, H, J, L are parameter matrices;

[0052] The sub-problems are:

[0053]

[0054] Where b is the parameter matrix of the second-level objective function of the variable range interval robust optimization problem; this subproblem is a max-min double-level optimization problem, which is converted into a single-level max problem through the cone duality transformation strategy for solution;

[0055] (b) Based on the transformed main problem and sub-problems, the following iterative strategy is constructed to solve them:

[0056] 1) Given a set of initial uncertain variable ξ values ​​as the initial scenario, set the number of iterations k = 1, the threshold judgment condition P = +∞ and the algorithm threshold condition δ = 0;

[0057] 2) Calculate the main problem based on the initial scenario ξ and obtain the optimal solution

[0058] 3) The optimal solution Bring it into the sub-problem and solve the objective function of the sub-problem And the corresponding worst scenario interactive power trajectory Update threshold judgment conditions

[0059] 4) Determine whether P≤δ holds. If so, it means that the safe and stable operation of the distribution network can still be guaranteed under the worst operation scenario. In the operating range formed, there is no power flow exceeding the limit; the iteration is terminated and the maximum power safe operating range value of the substation is returned. If not, add variables to the main problem y k+1 And the following constraints:

[0060]

[0061] Let k=k+1 and jump to step b) until the algorithm converges.

[0062] Beneficial effects: Compared with the prior art, the advantages of the present invention are: (1) The present invention proposes a multi-level autonomous operation and control method for medium and low voltage distribution systems. In this strategy, a regulation potential evaluation model for each substation is constructed based on the substation as a unit. Since the model does not need to consider the interaction with other substations, the distribution network has no synchronization requirements for uploading the potential evaluation model of each substation, thereby greatly alleviating the communication pressure during distribution network scheduling; (2) The present invention introduces the optimization of the safe operation range of the substation in the scheduling process. Since the interval is obtained based on the adjustable potential characteristics reported by the substation and comprehensively considering the safe operation constraints of the distribution network, from the perspective of the distribution network, the operation interval reserves a certain regulation margin for the interactive power between the substation and the distribution network scheduling; from the perspective of the substation, by complying with the interactive power interval, the substation does not need to consider the impact of the uncertainty of its interaction with the distribution network power on the safe operation of the distribution network, thereby realizing its own economic autonomous operation. It effectively promotes the safe and efficient operation of medium and low voltage distribution systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the multi-level autonomous operation and control method of the medium and low voltage power distribution system of the present invention;

[0064] Figure 2 A topological diagram of a medium and low voltage power distribution system according to an embodiment of the present invention;

[0065] Figure 3 It is a safe operation range diagram of the four substations obtained by the day-ahead optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0067] like Figure 1 As shown, the multi-level autonomous operation and control method of the medium and low voltage power distribution system includes the following steps.

[0068] (1) Based on the operating characteristics of the adjustable resources in each substation, a substation regulation potential assessment model is established to obtain the interactive power range of each substation in the next day.

[0069] The operating characteristics of adjustable resources in the substation mainly include the operating characteristics of energy storage and the operating characteristics of transferable loads, as follows:

[0070] (a) Energy storage operation characteristics

[0071]

[0072]

[0073] E es,0 =E es,24

[0074] Where Δt is the unit time interval, E is the maximum charging and discharging power value per unit time of energy storage, es,t and and The capacity and charging and discharging power of the E-SOP internal energy storage system at time t, and are the charging efficiency and discharging efficiency of the energy storage system respectively; and are 0-1 variables representing the charging and discharging state of the energy storage at time t. When the energy storage is in the charging state

[0075] (b) Transferable load operation characteristics

[0076]

[0077]

[0078] in, and are the load increase or decrease of the substation area based on the scheduling demand of the fusion terminal at time t, and are the maximum load that can be increased or decreased in the substation at time t.

[0079] The method for establishing the current model for evaluating the regulatory potential of the foreshore area is as follows:

[0080] (a) Establishing an approximate descriptive model of the substation regulation potential

[0081] The regulation potential Ω2 of the substation can be regarded as the mapping of the power operation domain of each adjustable resource in the substation at each moment at the grid connection point on the basis of satisfying the substation network constraints and the adjustable resource operation constraints. At the same time, considering that the above-mentioned adjustable resource operation characteristics in the substation can be expressed by power limit constraints and energy limit constraints, the following energy storage-like model can be established to achieve an approximate expression of the regulation potential of the substation at the grid connection point at each moment:

[0082]

[0083] Among them, P DSS It is the vector composed of the interactive power at the grid connection point in each period of the substation area; is the interactive power value at the grid connection point at time t in the substation area; is the energy value of the station at time t; and as well as and They are respectively the upper and lower limits of the interaction power and energy allowed at the grid connection point in the substation at time t.

[0084] (b) Establishing the current foreland regulation potential assessment model

[0085]

[0086] The current evaluation model for the regulation potential of the substation is a multi-objective planning model. Its constraints include the operation constraints of the adjustable resources in the substation and the power balance constraints of the substation in addition to the regulation potential approximate description model Ω2. and Together, they constitute the potential boundary of the substation regulation considering time coupling. By solving this model, the maximum interactive power and energy range that the substation can reach at each moment can be obtained, thereby realizing the description of the adjustable potential of the substation.

[0087] (2) According to the collected interactive power ranges of each substation in the distribution network and based on the safe operation constraints of the distribution network, an optimization model for the safe operation range of the substation is established to obtain the maximum safe operation range of the substation that can maintain the safe and stable operation of the distribution network.

[0088] The current optimization model for the safe operation range of the front area is:

[0089]

[0090] The current optimization model for the safe operation range of the foreshore area is a three-stage robust optimization model, where Ω n is the set of distribution network nodes, and are the upper and lower limits of the power safety operation range at node i obtained by optimizing the distribution network, and as well as and It is the relaxation variable of active power flow and reactive power flow of distribution network.

[0091] The constraints mainly include the regulation potential constraints uploaded by each substation, the operation constraints of energy storage, photovoltaic inverters and other equipment in the distribution network, and the power balance constraints of the distribution network.

[0092] In this three-layer robust optimization model, the minimization problem of the first layer aims to obtain the possible maximum safe operating range of the substation interaction power by solving a mixed integer linear programming problem, which determines the uncertainty set of the substation interaction power fluctuations; the maximization problem of the second layer aims to obtain the substation interaction power value that will make the distribution network operate the worst from the interaction power uncertainty set obtained in the first layer; and the last layer of the model aims to solve an optimal power flow problem to verify whether the distribution network can operate safely and stably under the worst substation interaction power scenario.

[0093] The solved safe operation range of the substation will be passed to each substation to provide a reference for its daily operation. For the substation, as long as the interactive power of its grid-connected point is maintained within the range, there is no need to consider the impact of its operation scheduling on the safety of the distribution network, thereby effectively improving the efficiency and economy of the substation autonomy.

[0094] (3) The safe operation range of the substation transmitted by the base distribution network. Each substation takes economic optimization as the goal and constructs a corresponding intraday economic autonomous dispatch model to solve the optimal economic dispatch strategy for each time period within the day. At the same time, the interactive power value of each time period is transmitted to the distribution network.

[0095] In the intraday stage, each substation will operate autonomously based on the substation safe operation range issued by the distribution network the day before, with the goal of economic optimization. The model is:

[0096] minf i DSS =C ess +C DR +C grid

[0097] Among them, f i DSS is the daily comprehensive operating cost of the station located at node i, C ess is the operating cost of the energy storage equipment in the daily stage of the substation, C DR is the dispatch cost of the transferable load, C grid is the electricity purchase and sales cost of the substation and distribution network. The specific calculation formulas for each cost are as follows:

[0098]

[0099] Among them, τ is the scheduling time interval, T is the total time step of the scheduling stage within the day, and ρ ess , ρ FL , ρ but and ρ sell are the unit power costs of energy storage, transferable load, and power purchase and sale during the daily dispatch phase of the substation, P t ch and P t dis are the charging and discharging power values ​​of the energy storage day stage respectively, and are the amount of increase and decrease of the load in the area during period t, P t buy and P t sellare the power values ​​of electricity purchased and sold from the substation to the distribution network. In addition to the above cost calculation constraints, the optimization model should also include interactive power limit constraints at the grid connection point, operation constraints of adjustable resources such as energy storage, and power balance constraints in the substation.

[0100] By solving this model, the autonomous operation strategy of the substation in the daily stage can be obtained. At the same time, the optimized interactive power value of each period at the grid connection point needs to be transmitted to the distribution network in real time to facilitate the distribution network to carry out autonomous scheduling within the day, thereby realizing the safe and economical operation of the entire system.

[0101] (4) Based on the collected intraday interactive power curves of each substation, the distribution network will formulate corresponding intraday autonomous dispatching strategies with the goal of optimizing economic efficiency, thereby ensuring the safe operation of the distribution network while improving the economic efficiency of the entire medium and low voltage distribution system.

[0102] The intraday economic autonomous dispatch model of distribution network is:

[0103]

[0104] Among them, Ω dis is the set of distribution network branches, Ω i is the set of distribution network nodes, C loss,ij is the network loss of the ijth branch of the distribution network during the intraday stage, C ess,i is the daily operation cost of the medium voltage energy storage located at node i, C grid,dis is the power value of the power purchased and sold by the distribution network and the upper power grid during the day. By solving this model, the economic dispatch strategy with the optimal comprehensive cost of the distribution network can be obtained under the condition of deterministic area interaction power, thereby effectively improving the economic efficiency of the operation of the entire medium and low voltage distribution system.

[0105] In step (1), the boundary node contraction algorithm is used to solve the current frontier area regulation potential assessment model, and in step (2), the iterative solution algorithm based on the variable range interval constraint is used to solve the current frontier area safe operation interval optimization model. The models in steps (3) and (4) are mixed integer linear programming problems, which can be directly solved by calling commercial solvers.

[0106] The iterative solution algorithm based on variable range interval constraints in step (2) mainly includes the following two steps:

[0107] (A) New variables are introduced to fix the variable range interval, thereby converting the original variable range interval robust optimization problem into a fixed range interval robust optimization problem;

[0108] (B) Referring to the C&CG idea, a corresponding iterative method is constructed to solve the transformed fixed-range interval robust optimization problem.

[0109] Furthermore, the method for fixing the variable range interval in step (1) of the above iterative solution algorithm based on the variable range interval constraint is as follows:

[0110] For the area within the variable range Possible interaction power values ​​within By introducing a new decision variable ξ i,t And the following constraints are applied to fix the boundaries.

[0111]

[0112] Through the above constraints, the variable range constraints in the second layer of the original robust optimization problem can be converted to a bounded constraint ξ i,t ∈[0,1]. Among them, ξ i,t It can be understood as the ratio of the worst interactive power value of the substation to its maximum safe operating range, that is, the worst possible interactive power trajectory of the substation.

[0113] Furthermore, the iterative solution method in step (B) of the above iterative solution algorithm based on variable range interval constraints is specifically as follows:

[0114] (a) Split the converted fixed-scope robust optimization problem into a main problem and a subproblem

[0115] The main question is:

[0116]

[0117] In the formula, x is the first-level decision variable and A collection of; is the decision variable introduced in the above variable range constraint conversion step after the lth iteration i,t is a set of values ​​used to characterize the worst interactive power output trajectory in the lower zone of the operating range determined in the first layer; y is the power flow relaxation variable As well as the set of distribution network flow operation decision variables such as energy storage charging and discharging power, c, b, A, B, E, F, H, J, L are parameter matrices.

[0118] It is worth noting that, unlike conventional robust optimization algorithms, in the main problem of this algorithm, the power flow relaxation variables in the model will be directly set to 0 to ensure that under the operating trajectory delivered by the sub-problem, the maximum interactive power operating range obtained can maintain the safe and stable operation of the distribution network.

[0119] The sub-problems are:

[0120]

[0121] Where b is the parameter matrix of the second-level objective function of the original robust optimization problem. This subproblem is a typical max-min two-level optimization problem, which can be transformed into a single-level max problem for solution through strategies such as cone duality transformation.

[0122] (b) Based on the transformed main problem and sub-problems, the following iterative strategy is constructed to solve them:

[0123] 1) Given a set of initial uncertain variable ξ values ​​as the initial scenario, set the number of iterations k = 1, the threshold judgment condition P = +∞ and the algorithm threshold condition δ = 0;

[0124] 2) Based on the initial scenario ξ, calculate the above main problem and obtain the optimal solution

[0125] 3) The optimal solution Bring it into the above sub-problem and solve the objective function of the sub-problem And the corresponding worst scenario interactive power trajectory Update threshold judgment conditions

[0126] 4) Determine whether P≤δ holds. If so, it means that the safe and stable operation of the distribution network can still be guaranteed under the worst operation scenario. There is no power flow exceeding the limit in the operating range formed. The iteration ends and the maximum power safe operating range value of the substation is returned. If not, add variables to the main problem y k+1 And the following constraints

[0127]

[0128] Let k=k+1 and jump to step 2) until the algorithm converges.

[0129] After the above calculations, we can obtain the maximum power safe operation range that the distribution network can accept while ensuring its own safe operation, and this range will be passed to each substation. In the subsequent dispatching stage, each substation only needs to comply with the power range constraint to ensure the safe and stable operation of the distribution network, which greatly reduces the complexity of substation operation decision-making and effectively promotes the efficient and autonomous operation of the substation.

[0130] The method of the present invention is described below with reference to specific implementation cases.

[0131] In order to verify the effectiveness of the method described in this invention, this embodiment adopts Figure 2 The medium and low voltage distribution system shown in the figure is tested. The network has 33 nodes, 37 branches and four substations.

[0132] Figure 3 The power safety operation intervals of the four substations obtained by optimizing under this embodiment. It can be seen that before 9:00, due to the low overall power generation of distributed power sources in the distribution network and the high load output, in order to avoid the problem of node voltage exceeding the lower limit caused by the increase in load demand at the distribution network nodes, the power safety operation interval of each substation will tend to be closer to its lower limit to ensure the safe and stable operation of the distribution network. During the noon period, as the photovoltaic power in the distribution network reaches its peak, the power safety operation interval of the substation will gradually approach its upper limit to cope with the overvoltage problem caused by the excess active power of the distribution network. In addition, after 17:00, as the load in each distribution network and substation increases, the distribution network will once again limit the power safety operation interval of the substation to its lower limit to avoid the problem of node voltage exceeding the lower limit.

[0133] In addition, in order to verify the reliability of the proposed method, based on the intraday forecast data, the present invention randomly generated 1000 distributed power supply output and load scenarios from 21:00 to 22:00 through Monte Carlo sampling based on the error intervals of ±5% and ±10% to simulate real-time uncertainty.

[0134] In order to verify the performance of the present invention in operation and scheduling, the following two methods are introduced for comparison. In terms of the intraday source-load output scenario setting, the present invention is based on the source-load predicted output in the day-ahead stage, and randomly generates 500 sets of source-load output data of the substation and distribution network through Monte Carlo sampling within the error range of ±10% as the intraday operation scenario set of the distribution network and substation, and then selects 10% of the photovoltaic output scenarios in the distribution network and substation respectively, and sets their output values ​​to 50% of the original values ​​during 12:00-13:00 to simulate photovoltaic ramp-up events such as cloud crossing.

[0135] Method 1: Centralized economic dispatch strategy. This method aims to minimize the total operating cost of medium and low voltage distribution systems and does not consider the impact of the substation regulation potential.

[0136] Method 2: Optimal dispatching strategy for medium and low voltage distribution systems based on the substation resource aggregation model, in which the interactive power curve of each low voltage substation is directly specified by the medium voltage distribution network based on the resource aggregation model reported by each substation and its own economic requirements.

[0137] Table 1 gives the distribution network operation and dispatching results under three methods.

[0138] Table 1 Simulation operation and dispatching results of medium voltage distribution system

[0139]

[0140] As can be seen from Table 1, by constructing a substation regulation potential assessment model, the distribution network dispatching center can effectively tap the flexible and adjustable resources in the substation without significantly increasing the communication pressure, thereby improving the economic efficiency of the operation of the medium and low voltage distribution system. In addition, by comparing method 2 and the method of the present invention, it can be seen that method 2 cannot effectively guarantee the safe and stable operation of the medium and low voltage system in some intraday test scenarios. The reason is that the substation interactive power in method 2 is directly specified by the distribution network, which will greatly reduce the ability of the substation to cope with its own intraday source and load fluctuations. In contrast, the method of the present invention provides the distribution network with a safe operation range of the substation. On the one hand, the distribution network can provide a certain interactive power adjustment margin for the substation to help the substation cope with its own source and load fluctuations and enhance the power support capacity of the distribution network to the substation. On the other hand, by maintaining its own interactive power within the specified operating range, the substation greatly reduces the possibility of the distribution network flow over-limit event caused by the intraday interactive power of the substation, thereby effectively enhancing the safety of the entire medium and low voltage distribution system during operation. Thereby effectively improving the autonomous operation capability of the distribution network and the substation and the efficiency of the operation of the entire medium and low voltage distribution system.

Claims

1. A multi-level autonomous operation and control method for a medium and low voltage power distribution system, characterized in that: The steps include: (1) Establishing a daily pre-station regulation potential assessment model based on the operating characteristics of adjustable resources in each substation, and solving the interaction power range of each substation in the next day; the substation regulation potential assessment model is a multi-objective planning model of the interaction power and energy upper and lower limits at the substation grid connection point; (2) According to the interactive power range of each substation in the next day, an optimization model of the safe operation range of the substation in the future is established to solve the maximum substation interactive power safe operation range that maintains the safe and stable operation of the distribution network; The current station safe operation interval optimization model is a three-layer robust optimization model, the first layer is used to obtain the upper and lower boundary values ​​of the maximum station power safe operation interval, the second layer obtains the worst station interactive power trajectory for the safe operation of the distribution network from the power safe operation interval, and the third layer verifies whether the distribution network can achieve safe and stable operation under the interactive power trajectory; (3) according to the power safety operation range of the substation, with the goal of minimizing the comprehensive daily operation cost of the substation, solving the optimal economic dispatch strategy for each time period within the day, and transmitting the interactive power value of each time period to the distribution network; (4) The distribution network calculates the optimal intraday autonomous dispatching strategy based on the interactive power values ​​in each time period with the goal of minimizing the comprehensive cost of the distribution network.

2. The multi-level autonomous operation and control method for medium and low voltage power distribution systems according to claim 1 is characterized in that: The operating characteristics of the adjustable resources in step (1) include energy storage operating characteristics and transferable load operating characteristics; Energy storage operation characteristics include: AND es,0 =And es,24 Where Δt is the unit time interval, E is the maximum charging and discharging power value per unit time of energy storage, es,t and and The capacity and charging and discharging power of the E-SOP internal energy storage system at time t, and are the charging efficiency and discharging efficiency of the energy storage system respectively; and are 0-1 variables representing the charging and discharging state of the energy storage at time t. When the energy storage is in the charging state Transferable load operation features include: in, and are the load increase or decrease of the substation area based on the scheduling demand of the fusion terminal at time t, and are the maximum load that can be increased or decreased in the substation at time t.

3. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 1 is characterized in that: The method for establishing the current foreshore regulation potential assessment model in step (1) is as follows: (a) Establish an approximate description model of the substation regulation potential; Among them, P DSS It is the vector composed of the interactive power at the grid connection point in each period of the substation area; is the interactive power value at the grid connection point at time t in the substation area; is the energy value of the station at time t; and as well as and are the upper and lower limits of the interaction power and energy allowed at the grid connection point in the substation at time t; (b) establishing a model for assessing the regulatory potential of the current foreland areas; The current evaluation model for the regulation potential of the substation is a multi-objective planning model, and its constraints include the regulation potential approximate description model Ω2, the operation constraints of each adjustable resource in the substation, and the power balance constraints of the substation.

4. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 1 is characterized in that: The current optimization model for the safe operation range of the foreshore area described in step (2) is: Among them, Ω n is the set of distribution network nodes, and The upper and lower limits of the power safety operation range at node i are respectively, and as well as and It is the relaxation variable of active power flow and reactive power flow of distribution network; The constraints of the current optimization model for the safe operation range of the substation include the regulation potential constraints uploaded by each substation, the operation constraints of each device in the distribution network, and the power balance constraints of the distribution network.

5. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 1 is characterized in that: In step (3), according to the power safety operation range of the substation, with the goal of minimizing the comprehensive daily operation cost of the substation, solving the optimal economic dispatch strategy for each time period within the day includes: In the intraday stage, the intraday economic autonomy dispatch model of the substation is established with the goal of minimizing the intraday comprehensive operating cost of the substation: minf i DSS =C ess +C DR +C grid Among them, f i DSS is the daily comprehensive operating cost of the station located at node i, C ess is the operating cost of the energy storage equipment in the daily stage of the substation, C DR is the dispatch cost of the transferable load, C grid The cost of electricity purchase and sale between the substation and the distribution network; Solve the intraday economic autonomous dispatch model of the substation to obtain the optimal economic dispatch strategy for each time period within the day.

6. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 1 is characterized in that: In step (4), the method for calculating the optimal intraday autonomous dispatching strategy with the goal of minimizing the comprehensive cost of the distribution network is: With the goal of minimizing the comprehensive cost of the distribution network, an intraday economic autonomous dispatch model for the distribution network is established: Among them, Ω dis is the set of distribution network branches, Ω i is the set of distribution network nodes, C loss,ij is the network loss of the ijth branch of the distribution network during the intraday stage, C ess,u is the daily operation cost of the medium voltage energy storage located at node i, C grid,dis It is the power value of electricity purchased and sold between the distribution network and the upper-level power grid during the day; Solving the distribution network's intraday economic autonomous dispatch model obtains the distribution network's optimal intraday autonomous dispatch strategy.

7. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 1 is characterized in that: In step (1), the boundary node shrinkage algorithm is used to solve the current frontier area regulation potential assessment model.

8. The multi-level autonomous operation and control method for medium and low voltage power distribution systems according to claim 1 is characterized in that: In step (2), an iterative algorithm based on a variable range interval constraint is used to solve the current foreground safe operation interval optimization model; the iterative algorithm based on a variable range interval constraint includes: The variable-range interval robust optimization problem is transformed into a fixed-range interval robust optimization problem by using decision variables, and the fixed-range interval robust optimization problem is solved by an iterative method based on C&CG idea.

9. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 8 is characterized in that: The method of converting the variable range interval robust optimization problem into the fixed range interval robust optimization problem by using decision variables includes: For the area within the variable range Possible interaction power values ​​within By introducing the decision variable ξ i,t And the following constraints to fix the boundaries: Through the above constraints, the variable range constraints in the second layer of the original robust optimization problem Convert to a bounded constraint ξ i,t ∈[0,1]; where ξ i,t It is the ratio of the worst interactive power value of the substation to its maximum safe operating range, that is, the worst possible interactive power trajectory of the substation.

10. The multi-level autonomous operation and control method of a medium and low voltage power distribution system according to claim 9 is characterized in that: The method of solving the fixed range interval robust optimization problem by using the iterative method based on the C&CG concept includes: (a) Split the bounded interval robust optimization problem into a main problem and a sub-problem: The main question is: In the formula, x is the first-level decision variable and A collection of; is the decision variable ξ i,t The set of values ​​after the lth iteration is used to characterize the worst interactive power output trajectory in the lower stage of the operating range determined in the first layer; y is the power flow relaxation variable and the set of distribution network flow operation decision variables such as energy storage charging and discharging power, c, b, A, B, E, F, H, J, L are parameter matrices; The sub-problems are: Where b is the parameter matrix of the second-level objective function of the variable range interval robust optimization problem; this subproblem is a max-min double-level optimization problem, which is converted into a single-level max problem through the cone duality transformation strategy for solution; (b) Based on the transformed main problem and sub-problems, the following iterative strategy is constructed to solve them: 1) Given a set of initial uncertain variable ξ values ​​as the initial scenario, set the number of iterations k = 1, the threshold judgment condition P = +∞ and the algorithm threshold condition δ = 0; 2) Calculate the main problem based on the initial scenario ξ and obtain the optimal solution 3) The optimal solution Bring it into the sub-problem and solve the objective function of the sub-problem And the corresponding worst scenario interactive power trajectory Update threshold judgment conditions 4) Determine whether P≤δ holds. If so, it means that the safe and stable operation of the distribution network can still be guaranteed under the worst operation scenario. In the operating range formed, there is no power flow exceeding the limit; the iteration is terminated and the maximum power safe operating range value of the substation is returned. If not, add variables to the main problem y k+1 And the following constraints: Let k=k+1 and jump to step b) until the algorithm converges.

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