Energy storage double-layer optimization configuration method and equipment considering flexibility demand matching coefficient

Abstract

The invention discloses an energy storage double-layer optimization configuration method and equipment considering a flexible demand matching coefficient, and provides a flexible demand matching coefficient index from the angle of flexible supply and demand balance. And establishing an energy storage site selection model based on a node importance comprehensive evaluation index in combination with a power flow influence factor, a node betweenness index and an adjacent center index. According to the method, an upper-layer model with the energy storage investment and construction cost and the overall voltage deviation of the system as targets and a lower-layer model with the minimum total operation cost as the target are constructed, a flexible demand matching coefficient constraint is added in a conventional constraint condition, solving is performed by adopting Yamip / CPLEX, and the effectiveness of the method is verified in the improved IEEE 30-node system. The energy storage access position can be selected through the established node importance comprehensive evaluation index, and the problem that the energy storage access position is difficult to select can be solved. By adding the flexibility demand matching coefficient constraint in the configuration model, the system keeps high flexibility in the scheduling period, and the scheduling scheme is more reasonable.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a method and device for optimizing the configuration of a double-layer energy storage system taking into account a flexibility demand matching coefficient. Background Art

[0002] Under the background of the "dual carbon" goal, a high proportion of renewable energy power system will become the basic form of the future power system. It is expected that by 2030, the proportion of global new energy installed capacity will increase to 70%, and the proportion of new energy power generation will reach 50%; in view of the current domestic situation, the proportion of new energy power generation will increase to 60% in 2050.

[0003] The large-scale access of high-proportion renewable energy to the grid will inevitably cause an imbalance in the supply and demand of system flexibility and greatly increase the peak-shaving demand of the power grid. This will bring challenges to the safe and stable operation of the existing power system. Therefore, ensuring sufficient flexibility of the power system is the key to achieving a high proportion of renewable energy consumption and ensuring a reliable supply of electricity.

[0004] As the main resource of flexibility, energy storage, with its power regulation and energy time-shifting capabilities, provides an effective way to promote the consumption of new energy, smooth out new energy fluctuations, and improve the flexibility and economy of the power grid. At present, the investment and construction costs of grid-side energy storage are still relatively high, so it is necessary to optimize the energy storage configuration to reduce the investment and construction costs and give full play to the role of energy storage in regulating the power grid and supporting new energy. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for optimizing the configuration of a double-layer energy storage system taking into account the flexibility demand matching coefficient, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The two-layer optimization configuration method of energy storage considering the flexibility demand matching coefficient includes: According to the flexibility demand matching coefficient index, an energy storage site selection model based on the comprehensive evaluation index of node importance is established; Based on the energy storage site selection model, the flexibility demand matching coefficient constraint is added to the conventional constraint conditions to obtain the energy storage double-layer model, where the energy storage double-layer model includes an upper-layer energy storage capacity optimization model with energy storage investment and construction costs and system overall voltage deviation as targets and a lower-layer energy storage operation strategy optimization model with the minimum total operation cost as the target; The multiple solutions obtained by solving the energy storage double-layer model are used as scheduling solutions when the value of the joint optimal objective function is minimized.

[0007] Furthermore, the flexibility demand matching coefficient index specifically includes flexibility supply resources, flexibility demand resources and flexibility demand matching coefficient .

[0008] Furthermore, the mathematical model of the flexibility supply resource is as follows: (1) (2) (3) (4) (5) (6) Where: and are the upward and downward flexibility supply of thermal power units at time t, respectively; For thermal power units; For thermal power generation units; and They are the upward climbing and downward sliding speeds of the thermal power unit respectively; for a given time interval; and are the upper and lower limits of thermal power unit power respectively; is the maximum charging power of the energy storage; is the charging power of the energy storage at time t; is the rated charging power of the energy storage; is the power of the thermal power unit at time t; and They are t Upward and downward flexibility of energy storage at all times; and are the energy storage charging and energy storage discharging efficiencies respectively; is the unit time interval; is the energy storage charge state; is the minimum value of energy storage capacity; is the maximum value of the energy storage discharge power; It is energy storage t Discharge power at the moment; is the rated discharge power of the energy storage; node Energy storage unit; It is electrical energy storage.

[0009] Furthermore, the expression of flexibility demand resources is: (7) In the formula, for t The flexibility requirements of the time system; for t Load power at all times; fort Fan power at all times; for t Photovoltaic power at all times.

[0010] Furthermore, the flexibility demand matching coefficient The specific calculation process is as follows: (8) (9) (10) (11) (12) Where: for t The upward flexibility requirements of the system at all times, for t Downward flexibility requirements of the moment system; is the flexibility requirement of the system at time t+1; and Divided into t Moment system flexibility to supply up and down is the scheduling period; , They represent the total flexibility supply of flexibility resources in the system at time t and the total flexibility demand in the system respectively.

[0011] Furthermore, a storage site selection model based on comprehensive evaluation index of node importance is established, which includes pre-selecting a site for the storage energy first, establishing a comprehensive evaluation index of node importance based on the impact factor of node power change on the power flow, node betweenness index and proximity centrality index, and selecting the node with the highest comprehensive evaluation index of node importance as the location for energy storage access.

[0012] Furthermore, the expression of the influence factor of node power change on power flow is: (13) (14) In the formula, is the power transfer distribution factor; is the line-node admittance matrix; Set the reference item of the node admittance matrix to 0; k For the k lines; L is the total number of lines; Is a node The impact factor of power change on power flow.

[0013] Furthermore, the expression of the node betweenness index is: (15) Where: For Node Betweenness of; For Node To Node All shortest paths; For Node To Node When passing through the node The number of shortest paths; is the number of nodes in the network topology.

[0014] Furthermore, the expression of the proximity centrality index is: (16) (17) (18) Where: For Node proximity centrality; For Node The number of neighboring power nodes other than itself; is the number of nodes in the network topology; For Node The sum of the distances to all neighboring power nodes; For Node Its neighboring power nodes The normalized impedance of the transmission line between .

[0015] Furthermore, , and After normalization, we get: (19) (20) (twenty one) Where: , and They are the normalized indices of the influence factor of node power change on power flow, node betweenness and proximity centrality; , and are the minimum values ​​of the node power change impact factor on power flow, node betweenness and proximity centrality, respectively; , and They are the maximum values ​​of the node power change on the power flow impact factor, node betweenness and proximity centrality respectively; Using the weighted summation method, the expression of each node importance evaluation index based on the node importance index (that is, the node location comprehensive evaluation index value) is obtained as follows: (twenty two) Where: is the comprehensive evaluation index value of node location, , and Represents the weight coefficient.

[0016] Furthermore, the energy storage investment and construction cost in the upper-level fixed capacity optimization model of energy storage and the overall node voltage deviation of the system are expressed as follows: (twenty three) Energy storage investment and construction costs The calculation formula is as follows: (twenty four) Overall voltage deviation The calculation formula is as follows: (25) Where: is the objective function of the upper storage capacity optimization model; weight coefficient ; Weight coefficient ; and are the unit power cost and unit capacity cost of the configured energy storage, respectively; and are the power and capacity of energy storage respectively; The expected number of days of use of the configured energy storage; for i Node in t Voltage at the moment; is the voltage rating; A collection of system nodes.

[0017] Furthermore, the capacity and power constraints of the energy storage are configured as follows: (26) (27) The node voltage constraints are as follows: (28) The installation constraints of the candidate nodes are as follows: (29) Where: , , , They are the upper and lower limits of the output power and capacity allowed by the configured energy storage respectively; , are the upper and lower limits of the node voltage respectively; is the pre-selected node; is a binary variable indicating whether energy storage is installed; is the number of energy storage connected.

[0018] Furthermore, the objective function of the energy storage lower-level operation strategy optimization model is: (30) In the formula, is the total cost of system operation; , , They are respectively the system operation cost, line network loss cost and flexibility deficiency penalty cost obtained by running the simulation layer.

[0019] Furthermore, The operating costs of thermal power units and energy storage are as follows: (31) (32) (33) Where: The operating cost of thermal power units; The operating cost of energy storage; For thermal power units g exist t The power at the moment; a, b, c are the coefficients of the operating cost function of the thermal power unit; is the operating cost coefficient of energy storage; , Energy storage t Moment and t- 1 moment capacity.

[0020] Furthermore, The expression is as follows: (34) In the formula, is the unit power loss cost; For the j The branch road t Active power loss at the moment.

[0021] Furthermore, Including penalties for curtailing wind and solar power and load shedding penalties , whose expression is: (35) (36) (37) Where: is the interruptible load power; for t Network loss at any time; is the unit time interval; , are wind power abandonment and solar power abandonment respectively; , , are the penalty coefficients respectively.

[0022] Furthermore, the constraints of the objective function include wind and solar power output constraints, thermal power unit constraints, energy storage unit operation constraints, DC power flow constraints, flexibility demand coefficient constraints and power balance constraints.

[0023] Furthermore, the wind and solar output constraint is the probability density of the wind turbine output in the region Probability density of photovoltaic output They are approximately subject to the two-parameter Weibull distribution and Beta distribution, as shown below: (38) (39) The discrete sequence is obtained by discretizing the continuous variable of wind turbine power, which is recorded as , a probabilistic sequence The length is: (40) Using the known output power probability density function of the wind turbine , through piecewise integration, we can get Specific values ​​of discrete points: (41) During the dispatching process, the output of wind turbines and photovoltaics is less than the expected value calculated, which can be expressed as follows: (42) (43) In the formula, , are the actual wind turbine and PV power respectively; C , K represent the shape parameter and scale parameter of the Weibull distribution respectively; , is the parameter of the Beta distribution; is the actual wind speed; is the cut-in wind speed; is the rated installed capacity of the wind turbine; ; is the known rated wind speed; is the maximum photovoltaic power; is the gamma function; Represents the maximum value of the fan power in the time period; represents the step size of discretization; is the integer of the maximum power value divided by the previous step length; d is the differential; , for t The wind power and photovoltaic power absorbed by the system at all times.

[0024] Furthermore, the constraints of the thermal power unit include output constraints, ramp power constraints, and start-stop time constraints, where the expressions of output constraints and ramp power constraints are as follows: (44) (45) The expression of start and stop time constraint is as follows: (46) (47) Where: , They are the upper and lower limits of thermal power unit power respectively; is the power of the thermal power unit at time t; , They are the downward climbing rate and upward climbing rate of the thermal power unit respectively; is the unit time interval; For thermal power units t The start and stop status at all times; and Respectively represent the thermal power units in t The time it is running and downtime at all times; and Respectively represent the operation and shutdown time of thermal power units.

[0025] Furthermore, the expression of the energy storage unit operation constraint table is as follows: (48) (49) (50) (51) (52) In the formula, , Energy storage t Moment and t- 1 moment capacity; is the capacity of energy storage; For energy storage t The charging and discharging power at the moment; , , are the 24-hour and 0-hour capacities of energy storage respectively; , Energy storage t A binary variable indicating the charging and discharging status at all times; , are the charging and discharging efficiencies of energy storage, respectively.

[0026] Furthermore, the DC power flow constraint is expressed as follows: (53) (54) Where: for t Timeline the power passed; For Line The maximum power allowed to pass; for t Time Node b The voltage phase angle at and are the minimum and maximum values ​​of the voltage phase angle respectively.

[0027] Furthermore, the flexibility requirement coefficient constraint is expressed as follows: (55) Where: is the flexibility demand matching coefficient; is the matching factor for the specified flexibility requirements.

[0028] Furthermore, the power balance constraint is expressed as follows: (56) Where: for t The load in the system at any given moment; For thermal power units; For thermal power generation units; For energy storage t The charging and discharging power at each moment; is the power of the thermal power unit at time t; for tThe wind power and photovoltaic power absorbed by the system at all times.

[0029] Furthermore, the joint optimization objective function is recorded as : (57) In the formula, , are the weight coefficients of the upper and lower layers respectively; The objective function of the upper storage capacity optimization model; Optimize the model objective function for the lower-level operation strategy of energy storage.

[0030] To achieve the above object, the present invention also provides the following technical solutions: The energy storage double-layer optimization configuration equipment taking into account the flexibility demand matching coefficient includes: A construction module is used to establish an energy storage site selection model based on a comprehensive evaluation index of node importance according to the flexibility demand matching coefficient index; A site selection module is used to add a flexibility demand matching coefficient constraint within conventional constraints based on the energy storage site selection model to obtain a two-layer energy storage model, wherein the two-layer energy storage model includes an upper-layer energy storage capacity optimization model with energy storage investment and construction costs and system overall voltage deviation as targets and a lower-layer energy storage operation strategy optimization model with the goal of minimizing total operating costs; The scheduling module is used to solve the energy storage double-layer model to obtain multiple solutions, and use them as scheduling solutions when the value of the joint optimal objective function is the minimum.

[0031] To achieve the above object, the present invention also provides the following technical solutions: A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0032] To achieve the above object, the present invention also provides the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods described above.

[0033] Compared with the prior art, the present invention has the following beneficial effects: In this invention, in order to solve the problem of insufficient flexibility of new power systems with a high proportion of new energy grid-connected, this paper proposes a two-layer optimization configuration method for energy storage that takes into account the flexibility demand matching coefficient. First, from the perspective of flexibility supply and demand balance, a flexibility demand matching coefficient index is proposed. Secondly, combining the flow impact factor, node betweenness index, and proximity center index, an energy storage site selection model based on the comprehensive evaluation index of node importance is established. Then, on this basis, an upper-level model with the energy storage investment and construction cost and the overall system voltage deviation as the target and a lower-level model with the minimum total operating cost as the target are constructed, and the flexibility demand matching coefficient constraint is added within the conventional constraints, and Yamip / CPLEX is used to solve it. Finally, the effectiveness of the method is verified in the improved IEEE 30-node system.

[0034] This invention studies the energy storage configuration problem considering flexibility and proposes a two-layer optimization configuration method for energy storage considering flexibility. The example verification results show that the energy storage access location can be selected by establishing a comprehensive evaluation index of node importance, which can solve the problem of difficulty in selecting the energy storage access location. On the other hand, by adding flexibility demand matching coefficient constraints to the configuration model, the system maintains a high degree of flexibility during the scheduling cycle, and the scheduling plan is more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart for solving the energy storage double-layer optimization configuration model according to an embodiment of the present invention.

[0036] Figure 2 This is a topology diagram of an IEEE 30-node system according to an embodiment of the present invention.

[0037] Figure 3 This is a typical load scenario diagram of an embodiment of the present invention.

[0038] Figure 4 This is a typical daily output curve of wind power according to an embodiment of the present invention.

[0039] Figure 5 This is a typical photovoltaic daily power curve diagram of an embodiment of the present invention.

[0040] Figure 6 It is a weighted value diagram of the comprehensive evaluation index of the importance of each node in the embodiment of the present invention.

[0041] Figure 7 This is a diagram showing the output of each unit in a spring scenario according to an embodiment of the present invention.

[0042] Figure 8 This is a diagram showing the output of each unit in a summer scenario according to an embodiment of the present invention.

[0043] Fig. 9 This is a diagram showing the output of each unit in an autumn scenario according to an embodiment of the present invention.

[0044] Fig.10 This is a diagram showing the output of each unit in a winter scenario according to an embodiment of the present invention.

[0045] Fig.11 This is a system model diagram of the method of the present invention.

[0046] Fig.12 It is a diagram of the internal structure of the computer device of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figures 1 to 12 , the present invention provides a technical solution: In order to solve the problem of insufficient flexibility of new power systems with a high proportion of renewable energy grid-connected, this paper proposes a two-level optimization configuration method for energy storage that takes into account the flexibility demand matching coefficient. First, from the perspective of flexibility supply and demand balance, a flexibility demand matching coefficient index is proposed. Secondly, a site selection model for energy storage based on the comprehensive evaluation index of node importance is established by combining the flow impact factor, node betweenness index, and proximity center index. Then, on this basis, an upper model with energy storage investment and construction cost and system overall voltage deviation as the target (i.e., the energy storage upper capacity optimization model described below) and a lower model with the minimum total operating cost as the target (i.e., the energy storage lower operation strategy optimization model described below) are constructed, and the flexibility demand matching coefficient constraint is added within the conventional constraint conditions, and Yamip / CPLEX is used to solve it. Finally, the effectiveness of this method is verified in the improved IEEE 30-node system.

[0049] 1 Flexibility demand matching coefficient 1.1 Flexibility in supplying resources In the power system, any resource that can adjust its own output can be regarded as a flexibility resource. The flexibility resources of the power system are the basis for responding to flexibility needs within the system and are also the foundation for the quantitative indicators of flexibility.

[0050] Different flexible resources have different output regulation characteristics and regulation speeds, so it is necessary to conduct a specific analysis of specific flexible resources and establish their corresponding flexibility mathematical models. At present, common flexible supply resources in the power system include thermal power units, pumped storage, energy storage, and transferable loads. However, in the power grid studied in this article, hydropower accounts for a small proportion, and there is no unified standard for demand-side response. Therefore, only thermal power units and energy storage are considered as flexible supply resources, and their mathematical models are as follows: (1) (2) (3) (4) (5) (6) Where: and are the upward and downward flexibility supply of thermal power units at time t, respectively; For thermal power generation units; and They are the upward climbing and downward sliding speeds of the thermal power unit respectively; for a given time interval; and are the upper and lower limits of thermal power unit power respectively; is the force / power of the thermal power unit at time t; and They are t Upward and downward flexibility of energy storage at all times; and They are respectively the energy storage charging and discharging efficiency; and are the maximum charging and discharging power of energy storage respectively; The unit time interval.

[0051] 2.3.3 Flexibility Demand Resources The flexibility supply resource meets the flexibility demand in the system through its own power conversion. When large-scale new energy is connected to the power system, the volatility and uncertainty of new energy are the main flexibility demands in the system. The expression of flexibility demand is: (7) In the formula, for t The flexibility requirements of the time system; for t Load power at all times; for tFan power at all times; for t Photovoltaic power at all times.

[0052] 2.4 Flexibility demand matching coefficient The relationship between flexibility supply and flexibility demand determines the flexibility of the system. From the perspective of the flexibility characteristics of the power system and the balance of flexibility supply and demand, the flexibility of the system is evaluated by the ratio between flexibility demand and total supply. This ratio is defined as the flexibility demand matching coefficient. The specific calculation process is as follows: (8) (9) (10) (11) (12) Where: for t Upward flexibility requirements of the (power / grid / test) system at all times, for t Downward flexibility requirements of the moment system.

[0053] and for t The flexibility of the power system to supply upwards and downwards; , They represent the total flexibility supply of flexibility resources in the power system at time t and the total flexibility demand in the system respectively.

[0054] 2 Energy storage site selection model based on node importance index In a network topology with multiple nodes, if all nodes are searched for energy storage site selection, the calculation variable dimension is very high, the model optimization is difficult, and the result is easy to be a local optimal solution. Therefore, this paper first pre-selects the site for energy storage and proposes an energy storage site selection model based on the comprehensive evaluation index of node importance. This model uses the node importance comprehensive evaluation index established based on the influence factor of node power change on the flow, the node betweenness index, and the proximity centrality index, and selects the node with the highest node importance comprehensive evaluation index as the location for energy storage access.

[0055] 1) Factors affecting power flow due to node power changes The influence factor of node power change on power flow reflects the influence of power change on the node on the overall power flow in the network. The larger the value, the more important the node is. The expression is: (13) (14) In the formula, is the power transfer distribution factor; is the line-node admittance matrix; Set the node admittance matrix reference term to 0.

[0056] 2) Node betweenness index In graph theory, information transmission between nodes depends on the shortest path between nodes. Therefore, if a node exists in multiple shortest paths, it means that the node is more important. In graph theory, the concept of node betweenness is used to quantify the importance of nodes. Betweenness is defined as: (15) Where: For Node To Node All shortest paths; For Node To Node When passing through the node The number of shortest paths.

[0057] 3) Proximity centrality index Proximity centrality is also an indicator of the importance of nodes in a graph. Its physical meaning is the distance between a node and other nodes. Therefore, the proximity centrality in graph theory is defined as the reciprocal of the sum of the average shortest paths between the node and other nodes in the network. The proximity centrality of for: (16) (17) (18) Where: For Node The number of neighboring power nodes other than itself; is the number of nodes in the network topology; For Node The sum of the distances to all neighboring power nodes; For Node Its neighboring power nodes The normalized impedance of the transmission line between .

[0058] 4) Comprehensive evaluation index of node importance In order to better evaluate the impact of each indicator on node selection, each indicator is first normalized to obtain: (19) (20) (twenty one) Where: , and They are the normalized indices of the influence factor of node power change on power flow, node betweenness and proximity centrality; , and are the minimum values ​​of the node's influence factor on power flow, node betweenness, and proximity centrality, respectively; , and They are the maximum values ​​of the node's influence factor on power flow, node betweenness and proximity centrality respectively.

[0059] Using the weighted summation method, the expression of each node importance evaluation index based on the node importance index is obtained as follows: (twenty two) Where: is the comprehensive evaluation index value of node location, , and Represents the weight coefficient.

[0060] 3. Double-layer energy storage model 3.1 Energy storage upper layer fixed capacity optimization model 3.1.1 Objective Function The objective function of the upper-level fixed capacity optimization model for energy storage includes the investment and construction cost of energy storage Deviation from the overall node voltage of the system , the expression is: (twenty three) 1) Energy storage investment and construction costs

[0061] The capacity and power of energy storage configuration determine the investment and construction cost of energy storage, and the investment and construction cost of the same energy storage is linearly related to its rated power and maximum capacity. The calculation formula is as follows: (twenty four) Where: and The unit power cost and unit capacity cost of the configured energy storage; and are the power and capacity of the configured energy storage respectively; The expected number of days of use of the configured energy storage.

[0062] 2) Overall voltage deviation

[0063] (25) In the formula, for i Node in t Voltage at the moment; is the voltage rating; A collection of system nodes.

[0064] 3.1.2 Constraints 1) Energy storage power and capacity constraints The constraints of the energy storage configuration decision layer are the capacity and power constraints of the configured energy storage: (26) (27) Where: , , , They are respectively the upper and lower limits of the output power and capacity allowed by the configured energy storage.

[0065] 2) Node voltage constraints (28) In the formula, , are the upper and lower limits of the node voltage respectively.

[0066] 3) Installation constraints of the selected nodes (29) Where: is the pre-selected node; is a binary variable indicating whether energy storage is installed; is the number of energy storage connected.

[0067] 3.2 Energy storage lower-level operation strategy optimization model Based on the energy storage access location and rated power capacity determined by the upper model, the energy storage configuration is further combined with system scheduling to optimize the energy storage device operation strategy. According to the typical daily load curve, the minimum line loss cost and system operation cost are sought. and the cost of inflexibility penalty The energy storage operation strategy is optimized.

[0068] 3.2.1 Objective Function The objective function of optimizing the lower-level operation strategy of energy storage is: (30) In the formula, is the total cost of system operation; , , They are respectively the system operation cost, line network loss cost and flexibility deficiency penalty cost obtained by running the simulation layer.

[0069] 1) System operating costs

[0070] The system operating costs include the operating costs of thermal power units and energy storage, as shown below: (31) (32) (33) Where: The operating cost of thermal power units; The operating cost of energy storage; For thermal power units g exist t The power at the moment; a, b, c are the coefficients of the operating cost function of the thermal power unit; is the operating cost coefficient of energy storage.

[0071] 2) Line network loss / loss cost

[0072] (34) In the formula, is the unit power loss cost; For the j The branch road t Active power loss at the moment.

[0073] 3) Penalty costs for insufficient flexibility

[0074] The penalty costs for insufficient system flexibility include penalties for wind and solar power curtailment and load shedding penalties , whose expression is: (35) (36) (37) Where: is the interruptible load power; , are wind power abandonment and solar power abandonment respectively; , , are the penalty coefficients respectively.

[0075] 3.2.2 Constraints 1) Wind and solar power output constraints Probability density of wind turbine and photovoltaic output in the region and They approximately obey the two-parameter Weibull distribution and Beta distribution, respectively, as shown in equations (38)-(39).

[0076] (38) (39) In the formula, , are the actual wind turbine and PV output / power respectively; C , K represent the shape parameter and scale parameter of the Weibull distribution respectively; , is the parameter of the Beta distribution; is the actual wind speed; is the cut-in wind speed; is the rated installed capacity of the wind turbine; ; is the maximum photovoltaic power.

[0077] According to the probability density functions of wind turbines and photovoltaics, this paper uses sequence operation theory to generate the expected value of wind and photovoltaic power output as the basic data for scheduling. Since the conversion process is similar, only the wind turbine output probability density function processing process is shown below.

[0078] The continuous variable of wind turbine power is discretized to obtain a discrete sequence, which is recorded as . The length of this probabilistic sequence is: (40) Where: Represents the maximum value of the fan output / power in the time period t, represents the discretization step size, The maximum power value is the integer of the previous step length result, and the probability sequence of the fan output after discretization is as follows: Table 1 Fan output and corresponding probability

[0079] Using the known output power probability density function of the wind turbine , through piecewise integration, we can get Specific values ​​of discrete points: (41) During the dispatching process, the output of wind turbines and photovoltaics is less than the expected value calculated, which can be expressed as follows: (42) (43) In the formula, , for t The wind power and photovoltaic output / power absorbed by the system at any given moment.

[0080] 2) Constraints on thermal power units Thermal power unit constraints include output constraints, ramp power constraints, and start / stop time constraints: (44) (45) Where: , They are the upper and lower limits of the output / power of thermal power units; , They are the downward climbing rate and upward climbing rate of the thermal power unit respectively.

[0081] The start and stop time constraints of thermal power units are: (46) (47) Where: For thermal power units t The start and stop status at all times; and Respectively represent the thermal power units in t The time of operation and downtime in each period; and Respectively represent the operation and shutdown time of thermal power units.

[0082] 3) Energy storage unit operation constraints The storage capacity constraint of energy storage is: (48) (49) (50) The charging and discharging power constraints of energy storage are: (51) The charge and discharge state constraints of energy storage are: (52) In the formula, For energy storage t The charging and discharging power at each moment; , is a binary variable indicating the energy storage charging and discharging state at time t; , are the charging and discharging efficiencies of energy storage, respectively.

[0083] 4) DC power flow constraints (53) (54) Where: for t Timeline the power passed; For Line The maximum power allowed to pass; for t Time Node b The voltage phase angle at and are the minimum and maximum values ​​of the voltage phase angle respectively.

[0084] 5) Flexibility demand coefficient constraint The system needs to meet certain flexibility during operation. The flexibility requirement matching coefficient defined in this article is used as the system flexible operation constraint: (55) Where: is the matching factor for the specified flexibility requirements.

[0085] 6) Power balance constraints At each moment, the power in the system needs to be balanced: (56) Where: for t The load in the system at any time.

[0086] 3.3 Joint Optimization Objective Function In order to determine the optimal solution from multiple solutions obtained by solving the two-layer model, we now set the joint optimal objective function , when the value of the joint optimal function is the smallest, the optimization scheme is considered to be the optimal scheme, as follows: (57) In the formula, , are the weight coefficients of the upper and lower layers respectively.

[0087] 4. Solution process of the double-layer energy storage model 4.1 Constrained Linearization The operating cost function of the thermal power unit is a nonlinear quadratic function. Therefore, in order to optimize the calculation process, the quadratic function of the thermal power unit is piecewise linearized. The quadratic cost function of the thermal power unit is divided into k The segment is determined according to the output power of the thermal power unit. The function in the segment interval is expressed by a linear function. Linearizing equation (32) is equivalent to equation (58): (58) Where: and are the slope and intercept corresponding to each line segment; for t At this moment, the thermal power unit is k Output / power value on the segment; and Respectively represent the thermal power units in k Minimum and maximum output / power values ​​on the segment; is a binary variable.

[0088] 4.2 Model solution process The upper layer of the model in this paper is the energy storage configuration decision layer. The objective function includes multiple objectives such as energy storage investment cost and system voltage deviation. By using the linear weighting method, the weights are obtained using the hierarchical analysis method. After the multi-objective model is converted into a single objective, it is solved using CPLEX; the lower operation simulation layer is converted into a linear model after Section 4.1, and is also solved using CPLEX. The specific flow chart of the algorithm is shown in the figure. Figure 1 shown.

[0089] The specific steps of the algorithm are as follows: 1) Obtain the initial power flow distribution of the original network through power flow calculation.

[0090] 2) Pre-select the energy storage site based on the node importance index to obtain the initial energy storage access location.

[0091] 3) Perform upper-level optimization of energy storage site selection and capacity determination, use CPLEX to solve the upper-level model, and obtain the initial energy storage capacity and power variables.

[0092] 5) Input the initial energy storage data into the inner operation and scheduling simulation layer, and use CPLEX to solve the output of energy storage, wind and solar power units with the minimum system operation cost, and iterate the upper and lower modules.

[0093] 5) Determine whether the optimal solution is obtained when the iteration terminates. If the optimal solution is obtained, the calculation ends and the optimal solution is output; otherwise, the energy storage power dispatch value obtained by the lower layer is fed back to the upper layer model, and the iterative solution is continued according to the process until the optimal solution is obtained.

[0094] 5 Example Analysis This paper uses an improved IEEE 30-node example for simulation verification. The network topology diagram is shown in Figure 2 As shown. In this example, thermal power units are connected at nodes 1, 5, 8, 11, and 13, and wind power and photovoltaic units are connected at nodes 10 and 3, with capacities of 500MW and 100MW respectively. The energy storage operation parameters are shown in Table 3. Figure 3-Figure 5 They are load and wind and solar data respectively.

[0095] Table 3 Energy storage parameters

[0096] 5.1 Energy storage access solution According to the network topology data in the IEEE 30-node system, the power flow impact factor, node betweenness and proximity centrality index of each node in the example system are calculated. The calculation results are shown in Table 4.

[0097] Table 4 Node importance index

[0098] Figure 6 is the weighted value of the comprehensive evaluation index of each node’s importance. It can be seen that the weighted value of node 12 is the highest. Therefore, the energy storage is connected at node 12.

[0099] 5.2 Energy storage configuration solutions in different scenarios Using the model proposed in this paper, the energy storage configuration schemes under different scenario probabilities are calculated. The configuration and cost analysis of each scenario are detailed in Table 5, and the output power of the system units under different scenarios is shown in Table 5. Figures 7 to 10 shown.

[0100] Table 5 Energy storage configuration and cost results under different scenarios

[0101] Combining Table 5 and Figures 7 to 10 It can be concluded that after incorporating the flexibility demand matching coefficient constraint into the model, the system maintains a high flexibility, usually around 0.990. In addition, since the load fluctuations in each scenario are similar, the output power of the thermal power unit remains consistent in different scenarios. The changes in flexibility demand mainly come from changes in wind turbine and photovoltaic output, and these changes in demand are borne by the charging and discharging of energy storage.

[0102] 5.3 Comparative analysis of energy storage configuration options In order to verify the algorithm proposed in this paper, this paper sets up three different schemes, as follows: Case 1: Without considering flexibility, the traditional multi-objective particle swarm optimization method is used to solve the energy storage two-layer optimization configuration model.

[0103] Case 2: Using CPLEX solver to solve the energy storage two-layer optimization configuration model without considering flexibility.

[0104] Case 3: In In the following, the CPLEX solver is used to solve the proposed energy storage two-layer optimization configuration model, which takes flexibility into account.

[0105] Table 6 Cost comparison of different solutions

[0106] As shown in Table 6, the capacity and power configurations of energy storage in Case 1 and Case 2 are very similar, while the energy storage capacity and power configured in Case 3 are significantly higher and have the highest flexibility demand matching coefficient. This shows that after adding the flexibility demand matching coefficient constraint to the model, the system needs to meet a certain flexibility. Therefore, the energy storage configuration in Case 3 is higher than other schemes, showing poor economic efficiency but greater flexibility. In addition, this paper adopts a linear weighted method to process the multi-objective function of the upper layer, and the results are similar to those of the traditional multi-objective particle swarm optimization algorithm, but significantly reduces the calculation time, so it is more suitable for practical applications.

[0107] In addition, the voltage test results under different conditions are shown in Table 7. The average voltage deviation is the average value of the voltage deviation across 30 nodes in 24 cycles; the maximum and minimum voltage slopes refer to the maximum and minimum voltage changes at each node in the same cycle.

[0108] Table 7 Voltage test results under different scenarios

[0109] It can be seen from Table 7 that the results of Case 1 and Case 2 are almost consistent, which further confirms the accuracy of the solution algorithm proposed in this paper. In addition, compared with Case 1 and Case 2, the optimized voltage deviation in Case 3 is significantly reduced to only 0.0138.

[0110] 6 Conclusion This paper studies the problem of energy storage configuration considering flexibility and proposes a two-layer optimization configuration method for energy storage considering flexibility. The example verification results show that the node importance comprehensive evaluation index established in this paper can select the energy storage access location and solve the problem of difficulty in selecting the energy storage access location. On the other hand, by adding flexibility demand matching coefficient constraints to the configuration model, the system maintains a high degree of flexibility during the scheduling cycle, and the scheduling plan is more reasonable.

[0111] like Fig.11As shown, the present invention also provides a double-layer energy storage optimization configuration device taking into account the flexibility demand matching coefficient, including: A construction module is used to establish an energy storage site selection model based on a comprehensive evaluation index of node importance according to the flexibility demand matching coefficient index; A site selection module is used to add a flexibility demand matching coefficient constraint within the constraint conditions based on the energy storage site selection model to obtain a two-layer energy storage model, wherein the two-layer energy storage model includes an upper-layer energy storage capacity optimization model with energy storage investment and construction costs and system overall voltage deviation as targets and a lower-layer energy storage operation strategy optimization model with the minimum total operation cost as the target; The scheduling module is used to solve the energy storage double-layer model to obtain multiple solutions, and use them as scheduling solutions when the value of the joint optimal objective function is the minimum.

[0112] like Fig.12 As shown, the proposed energy storage dual-layer optimization configuration device taking into account the flexibility demand matching coefficient can adopt various types of computing devices such as desktop computers, notebooks, PDAs and cloud servers. This device is composed of multiple components such as processors and memories, but is not limited thereto and may include other components or different combinations. The device examples provided here are for illustrative purposes only and are not restrictive.

[0113] The processor can be various types of devices, such as CPU, DSP, ASIC, FPGA, etc., and can also include programmable logic devices, discrete gates, transistor logic devices and discrete hardware components, etc. The memory is used to store computer programs and modules. The processor runs or executes the computer program modules stored in the memory and calls the data stored in the memory, thereby solving the problem of insufficient flexibility of the new power system with a high proportion of new energy grid connection.

[0114] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0115] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the two-layer energy storage optimization configuration method taking into account the flexibility demand matching coefficient.

[0116] If the energy storage dual-layer optimization configuration method taking into account the flexibility demand matching coefficient is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device.

[0117] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned energy storage dual-layer optimization configuration method taking into account the flexibility demand matching coefficient, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned energy storage dual-layer optimization configuration method taking into account the flexibility demand matching coefficient can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0118] Computer-readable storage media may refer to any entity or device that can carry computer program code, including recording media, USB flash drives, mobile hard drives, disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), software distribution media, etc.

[0119] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0120] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A two-layer optimization configuration method for energy storage taking into account the flexibility demand matching coefficient, characterized in that: include: According to the flexibility demand matching coefficient index, an energy storage site selection model based on the comprehensive evaluation index of node importance is established; Based on the energy storage site selection model, the flexibility demand matching coefficient constraint is added to the constraint conditions to obtain the energy storage double-layer model, where the energy storage double-layer model includes an upper-layer energy storage capacity optimization model with energy storage investment and construction costs and system overall voltage deviation as targets and a lower-layer energy storage operation strategy optimization model with the minimum total operation cost as the target; The multiple solutions obtained by solving the energy storage double-layer model are used as scheduling solutions when the value of the joint optimal objective function is minimized.

2. The method according to claim 1, characterized in that The flexibility demand matching coefficient indicator specifically includes flexibility supply resources, flexibility demand resources and flexibility demand matching coefficient.

3. The method according to claim 2, characterized in that The mathematical model of flexible supply resources is as follows: （1） （2） （3） （4） （5） （6） Where: and are the upward and downward flexibility supply of thermal power units at time t, respectively; For thermal power units; For thermal power generation units; and They are the upward climbing and downward sliding speeds of the thermal power unit respectively; for a given time interval; and are the upper and lower limits of thermal power unit power respectively; is the maximum charging power of the energy storage; is the charging power of the energy storage at time t; is the rated charging power of the energy storage; is the power of the thermal power unit at time t; and They are t Upward and downward flexibility of energy storage at all times; and are the energy storage charging and energy storage discharging efficiencies respectively; is the unit time interval; is the energy storage charge state; is the minimum value of energy storage capacity; is the maximum value of the energy storage discharge power; It is energy storage t Discharge power at the moment; is the rated discharge power of the energy storage; node Energy storage unit; It is electrical energy storage.

4. The method according to claim 3, characterized in that The expression of flexibility demand resource is: （7） In the formula, for t The flexibility requirements of the time system; for t Load power at all times; for t Fan power at all times; for t Photovoltaic power at all times.

5. The method according to claim 4, characterized in that Flexibility demand matching coefficient The specific calculation process is as follows: （8） （9） （10） （11） （12） Where: for t The upward flexibility requirements of the system at all times, for t Downward flexibility requirements of the moment system; is the flexibility requirement of the system at time t+1; and Divided into t The flexibility of the moment system feeds up and down; is the scheduling period; , They represent the total flexibility supply of flexibility resources in the system at time t and the total flexibility demand in the system respectively.

6. The method according to claim 1, characterized in that Establishing an energy storage site selection model based on comprehensive evaluation indicators of node importance includes pre-selecting a site for energy storage, establishing a comprehensive evaluation index of node importance based on the impact factor of node power change on power flow, node betweenness index, and proximity centrality index, and selecting the node with the highest comprehensive evaluation index of node importance as the location for energy storage access.

7. The method according to claim 6, characterized in that The expression of the influence factor of node power change on power flow is: （13） （14） In the formula, is the power transfer distribution factor; is the line-node admittance matrix; Set the reference item of the node admittance matrix to 0; k For the k lines; L is the total number of lines; Is a node The impact factor of power change on power flow.

8. The method according to claim 7, characterized in that The expression of node betweenness index is: （15） Where: For Node Betweenness of; For Node To Node All shortest paths; For Node To Node When passing through the node The number of shortest paths; is the number of nodes in the network topology.

9. The method according to claim 8, characterized in that The expression of the proximity centrality index is: （16） （17） （18） Where: For Node proximity centrality; For Node The number of neighboring power nodes other than itself; is the number of nodes in the network topology; For Node The sum of the distances to all neighboring power nodes; For Node Its neighboring power nodes The normalized impedance of the transmission line between .

10. The method according to claim 9, characterized in that First , and After normalization, we get: （19） （20） （21） Where: , and They are the normalized indices of the influence factor of node power change on power flow, node betweenness and proximity centrality; , and are the minimum values ​​of the node power change impact factor on power flow, node betweenness and proximity centrality, respectively; , and They are the maximum values ​​of the node power change on the power flow impact factor, node betweenness and proximity centrality respectively; Using the weighted summation method, the comprehensive evaluation index value of the node location based on the node importance index is obtained The expression is: （22） Where: , and Represents the weight coefficient.

11. The method according to claim 1, characterized in that The energy storage investment and construction cost in the upper-level fixed capacity optimization model of energy storage and the overall node voltage deviation of the system are expressed as: （23） Energy storage investment and construction costs The calculation formula is as follows: （24） Overall voltage deviation The calculation formula is as follows: （25） Where: is the objective function of the upper storage capacity optimization model; weight coefficient ; Weight coefficient ; and are the unit power cost and unit capacity cost of the configured energy storage, respectively; and are the power and capacity of energy storage respectively; The expected number of days of use of the configured energy storage; for i Node in t Voltage at the moment; is the voltage rating; A collection of system nodes.

12. The method according to claim 11, characterized in that The capacity and power constraints of the energy storage are configured as follows: （26） （27） The node voltage constraints are as follows: （28） The installation constraints of the candidate nodes are as follows: （29） Where: , , , They are the upper and lower limits of the output power and capacity allowed by the configured energy storage respectively; , are the upper and lower limits of the node voltage respectively; is the pre-selected node; is a binary variable indicating whether energy storage is installed; is the number of energy storage connected.

13. The method according to claim 1, characterized in that The objective function of the energy storage lower-layer operation strategy optimization model is: （30） In the formula, is the total cost of system operation; , , They are respectively the system operation cost, line network loss cost and flexibility deficiency penalty cost obtained by running the simulation layer.

14. The method according to claim 13, characterized in that The operating costs of thermal power units and energy storage are as follows: （31） （32） （33） Where: The operating cost of thermal power units; The operating cost of energy storage; For thermal power units g exist t The power at the moment; a, b, c are the coefficients of the operating cost function of the thermal power unit; is the operating cost coefficient of energy storage; , Energy storage t Moment and t- 1 moment capacity.

15. The method according to claim 13, characterized in that The expression is as follows: （34） In the formula, is the unit power loss cost; For the j The branch road t Active power loss at the moment.

16. The method according to claim 13, characterized in that Including penalties for curtailing wind and solar power and load shedding penalties , whose expression is: （35） （36） （37） Where: is the interruptible load power; for t Network loss at any time; is the unit time interval; , are wind power abandonment and solar power abandonment respectively; , , are the penalty coefficients respectively.

17. The method according to claim 13, characterized in that The constraints of the objective function include wind and solar power output constraints, thermal power unit constraints, energy storage unit operation constraints, DC power flow constraints, flexibility demand coefficient constraints and power balance constraints.

18. The method according to claim 17, characterized in that The wind power output constraint is the probability density of the wind turbine output in the region Probability density of photovoltaic output They are approximately subject to the two-parameter Weibull distribution and Beta distribution, as shown below: （38） （39） The discrete sequence is obtained by discretizing the continuous variable of wind turbine power, which is recorded as , a probabilistic sequence The length is: （40） Using the known output power probability density function of the wind turbine , through piecewise integration, we can get Specific values ​​of discrete points: （41） During the dispatching process, the output of wind turbines and photovoltaics is less than the expected value calculated, which can be expressed as follows: （42） （43） In the formula, , are the actual wind turbine and PV power respectively; C , K represent the shape parameter and scale parameter of the Weibull distribution respectively; , is the parameter of the Beta distribution; is the actual wind speed; is the cut-in wind speed; is the rated installed capacity of the wind turbine; ; is the known rated wind speed; is the maximum photovoltaic power; is the gamma function; Represents the maximum value of the fan power in the time period; represents the step size of discretization; is the integer of the maximum power value divided by the previous step length; d is the differential; , for t The wind power and photovoltaic power absorbed by the system at all times.

19. The method according to claim 17, characterized in that The constraints of thermal power units include output constraints, ramp power constraints, and start-stop time constraints. The expressions of output constraints and ramp power constraints are as follows: （44） （45） The expression of start and stop time constraint is as follows: （46） （47） Where: , They are the upper and lower limits of thermal power generation units’ power respectively; is the power of the thermal power unit at time t; , They are the downward climbing rate and upward climbing rate of the thermal power unit respectively; is the unit time interval; For thermal power units t The start and stop status at all times; and Respectively represent the thermal power units in t period The time of operation and downtime; and Respectively represent the operation and shutdown time of thermal power units.

20. The method of claim 17, wherein: The expression of the energy storage unit operation constraint table is as follows: （48） （49） （50） （51） （52） In the formula, , Energy storage t Moment and t- 1 moment capacity; is the capacity of energy storage; For energy storage t The charging and discharging power at the moment; , , are the 24-hour and 0-hour capacities of energy storage respectively; , Energy storage t A binary variable indicating the charging and discharging status at all times; , are the charging and discharging efficiencies of energy storage, respectively.

21. The method of claim 17, wherein: The DC power flow constraint is expressed as follows: （53） （54） Where: for t Timeline the power passed; For line The maximum power allowed to pass; for t Time Node b The voltage phase angle at and are the minimum and maximum values ​​of the voltage phase angle respectively.

22. The method of claim 17, wherein: The flexibility demand coefficient constraint is expressed as follows: （55） Where: is the flexibility demand matching coefficient; is the matching factor for the specified flexibility requirements.

23. The method of claim 17, wherein: The power balance constraint is expressed as follows: （56） Where: for t The load in the system at any moment; For thermal power units; For thermal power generation units; For energy storage t The charging and discharging power at the moment; is the power of the thermal power unit at time t; for t The wind power and photovoltaic power absorbed by the system at all times.

24. The method of claim 1, wherein: The joint optimization objective function is recorded as : （57） In the formula, , are the weight coefficients of the upper and lower layers respectively; The objective function of the upper storage capacity optimization model; Optimize the model objective function for the lower-level operation strategy of energy storage.

25. A double-layer energy storage optimization configuration device taking into account the flexibility demand matching coefficient, characterized in that: include: A construction module is used to establish an energy storage site selection model based on a comprehensive evaluation index of node importance according to the flexibility demand matching coefficient index; A site selection module is used to add a flexibility demand matching coefficient constraint within the constraint conditions based on the energy storage site selection model to obtain a two-layer energy storage model, wherein the two-layer energy storage model includes an upper-layer energy storage capacity optimization model with energy storage investment and construction costs and system overall voltage deviation as targets and a lower-layer energy storage operation strategy optimization model with the minimum total operation cost as the target; The scheduling module is used to solve the energy storage double-layer model to obtain multiple solutions, and use them as scheduling solutions when the value of the joint optimal objective function is the minimum.

26. The device according to claim 25, characterized in that The flexibility demand matching coefficient index specifically includes flexibility supply resources, flexibility demand resources and flexibility demand matching coefficient. .

27. The device according to claim 26, characterized in that The mathematical model of flexible supply resources is as follows: （1） （2） （3） （4） （5） （6） Where: and are the upward and downward flexibility supply of thermal power units at time t, respectively; For thermal power units; For thermal power generation units; and They are the upward climbing and downward sliding speeds of the thermal power unit respectively; for a given time interval; and are the upper and lower limits of thermal power unit power respectively; is the maximum charging power of the energy storage; is the charging power of the energy storage at time t; is the rated charging power of the energy storage; is the power of the thermal power unit at time t; and They are t Upward and downward flexibility of energy storage at all times; and are the energy storage charging and energy storage discharging efficiencies respectively; is the unit time interval; is the energy storage charge state; is the minimum value of energy storage capacity; is the maximum value of the energy storage discharge power; It is energy storage t Discharge power at the moment; is the rated discharge power of the energy storage; node Energy storage unit; It is electrical energy storage.

28. The device according to claim 27, characterized in that The expression of flexibility demand resource is: （7） In the formula, for t The flexibility requirements of the time system; for t Load power at all times; for t Fan power at all times; for t Photovoltaic power at all times.

29. The device according to claim 28, characterized in that Flexibility demand matching coefficient The specific calculation process is as follows: （8） （9） （10） （11） （12） Where: for t The upward flexibility requirements of the system at all times, for t Downward flexibility requirements of the moment system; is the flexibility requirement of the system at time t+1; and Divided into t The flexibility of the moment system feeds up and down; is the scheduling period; , They represent the total flexibility supply of flexibility resources in the system at time t and the total flexibility demand in the system respectively.

30. The apparatus of claim 25, wherein: Establishing an energy storage site selection model based on comprehensive evaluation indicators of node importance includes pre-selecting a site for energy storage, establishing a comprehensive evaluation index of node importance based on the impact factor of node power change on power flow, node betweenness index, and proximity centrality index, and selecting the node with the highest comprehensive evaluation index of node importance as the location for energy storage access.

31. The device according to claim 30, characterized in that The expression of the influence factor of node power change on power flow is: （13） （14） In the formula, is the power transfer distribution factor; is the line-node admittance matrix; Set the reference item of the node admittance matrix to 0; k For the k lines; L is the total number of lines; Is a node The impact factor of power change on power flow.

32. The device according to claim 31, characterized in that The expression of node betweenness index is: （15） Where: For Node Betweenness of; For Node To Node All shortest paths; For Node To Node When passing through the node The number of shortest paths; is the number of nodes in the network topology.

33. The device according to claim 32, characterized in that The expression of the proximity centrality index is: （16） （17） （18） Where: For Node proximity centrality; For Node The number of neighboring power nodes other than itself; is the number of nodes in the network topology; For Node The sum of the distances to all neighboring power nodes; For Node Its neighboring power nodes The normalized impedance of the transmission line between .

34. The device according to claim 33, characterized in that First , and After normalization, we get: （19） （20） （21） Where: , and They are the normalized indices of the influence factor of node power change on power flow, node betweenness and proximity centrality; , and are the minimum values ​​of the node power change impact factor on power flow, node betweenness and proximity centrality, respectively; , and They are the maximum values ​​of the node power change on the power flow impact factor, node betweenness and proximity centrality respectively; Using the weighted summation method, the comprehensive evaluation index value of the node location based on the node importance index is obtained The expression is: （22） Where: is the comprehensive evaluation index value of node location, , and Represents the weight coefficient.

35. The apparatus of claim 25, wherein: The energy storage investment and construction cost in the upper-level fixed capacity optimization model of energy storage and the overall node voltage deviation of the system are expressed as: （23） Energy storage investment and construction costs The calculation formula is as follows: （24） Overall voltage deviation The calculation formula is as follows: （25） Where: is the objective function of the upper storage capacity optimization model; weight coefficient ; Weight coefficient ; and are the unit power cost and unit capacity cost of the configured energy storage, respectively; and are the power and capacity of energy storage respectively; The expected number of days of use of the configured energy storage; for i Node in t Voltage at the moment; is the voltage rating; A collection of system nodes.

36. The device according to claim 35, characterized in that The capacity and power constraints of the energy storage are configured as follows: （26） （27） The node voltage constraints are as follows: （28） The installation constraints of the candidate nodes are as follows: （29） Where: , , , They are the upper and lower limits of the output power and capacity allowed by the configured energy storage respectively; , are the upper and lower limits of the node voltage respectively; is the pre-selected node; is a binary variable indicating whether energy storage is installed; is the number of energy storage connected.

37. The apparatus of claim 25, wherein: The objective function of the energy storage lower-layer operation strategy optimization model is: （30） In the formula, is the total cost of system operation; , , They are respectively the system operation cost, line network loss cost and flexibility deficiency penalty cost obtained by running the simulation layer.

38. The device according to claim 37, characterized in that The operating costs of thermal power units and energy storage are as follows: （31） （32） （33） Where: The operating cost of thermal power units; The operating cost of energy storage; For thermal power units g exist t The power at the moment; a, b, c are the coefficients of the operating cost function of the thermal power unit; is the operating cost coefficient of energy storage; , Energy storage t Moment and t- 1 moment capacity.

39. The device according to claim 37, characterized in that The expression is as follows: （34） In the formula, is the unit power loss cost; For the j The branch road t Active power loss at the moment.

40. The apparatus of claim 37, wherein: Including penalties for curtailing wind and solar power and load shedding penalties , whose expression is: （35） （36） （37） Where: is the interruptible load power; for t Network loss at any time; is the unit time interval; , are wind power abandonment and solar power abandonment respectively; , , are the penalty coefficients respectively.

41. The apparatus of claim 37, wherein: The constraints of the objective function include wind and solar power output constraints, thermal power unit constraints, energy storage unit operation constraints, DC power flow constraints, flexibility demand coefficient constraints and power balance constraints.

42. The device according to claim 41, characterized in that The wind power output constraint is the probability density of the wind turbine output in the region Probability density of photovoltaic output They are approximately subject to the two-parameter Weibull distribution and Beta distribution, as shown below: （38） （39） The discrete sequence is obtained by discretizing the continuous variable of wind turbine power, which is recorded as , a probabilistic sequence The length is: （40） Using the known output power probability density function of the wind turbine , through piecewise integration, we can get Specific values ​​of discrete points: （41） During the dispatching process, the output of wind turbines and photovoltaics is less than the expected value calculated, which can be expressed as follows: （42） （43） In the formula, , are the actual wind turbine and PV power respectively; C , K represent the shape parameter and scale parameter of the Weibull distribution respectively; , is the parameter of the Beta distribution; is the actual wind speed; is the cut-in wind speed; is the rated installed capacity of the wind turbine; ; is the known rated wind speed; is the maximum photovoltaic power; is the gamma function; Represents the maximum value of the fan power in the time period; represents the step size of discretization; is the integer of the maximum power value divided by the previous step length; d is the differential; , for t The wind power and photovoltaic power absorbed by the system at all times.

43. The device according to claim 41, characterized in that The constraints of thermal power units include output constraints, ramp power constraints, and start-stop time constraints. The expressions of output constraints and ramp power constraints are as follows: （44） （45） The expression of start and stop time constraint is as follows: （46） （47） Where: , They are the upper and lower limits of thermal power generation units’ power respectively; is the power of the thermal power unit at time t; , They are the downward climbing rate and upward climbing rate of the thermal power unit respectively; is the unit time interval; For thermal power units t The start and stop status at all times; and Respectively represent the thermal power units in t The time it is running and downtime at all times; and Respectively represent the operation and shutdown time of thermal power units.

44. The apparatus of claim 41, wherein: The expression of the energy storage unit operation constraint table is as follows: （48） （49） （50） （51） （52） In the formula, , Energy storage t Moment and t- 1 moment capacity; is the capacity of energy storage; For energy storage t The charging and discharging power at the moment; , , are the 24-hour and 0-hour capacities of energy storage respectively; , Energy storage t A binary variable indicating the charging and discharging status at all times; , are the charging and discharging efficiencies of energy storage, respectively.

45. The apparatus of claim 41, wherein: The DC power flow constraint is expressed as follows: （53） （54） Where: for t Timeline the power passed; For line The maximum power allowed to pass; for t Time Node b The voltage phase angle at and are the minimum and maximum values ​​of the voltage phase angle respectively.

46. ​​The apparatus of claim 41, wherein: The flexibility demand coefficient constraint is expressed as follows: （55） Where: is the flexibility demand matching coefficient; is the matching factor for the specified flexibility requirements.

47. The apparatus of claim 41, wherein: The power balance constraint is expressed as follows: （56） Where: for t The load in the system at any moment; For thermal power units; For thermal power generation units; For energy storage t The charging and discharging power at the moment; is the power of the thermal power unit at time t; for t The wind power and photovoltaic power absorbed by the system at all times.

48. The apparatus of claim 25, wherein: The joint optimization objective function is recorded as : （57） In the formula, , are the weight coefficients of the upper and lower layers respectively; The objective function of the upper storage capacity optimization model; Optimize the model objective function for the lower-level operation strategy of energy storage.

49. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 24 when executing the computer program.

50. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 24 are implemented.

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