Power distribution network switching and energy storage coordinated optimization method considering insufficient flexibility risk

Abstract

The invention discloses a power distribution network switch and energy storage coordinated optimization method considering an insufficient flexibility risk, and the method comprises the steps: firstly, analyzing a flexible adjustment mechanism of an intelligent soft switch and multi-type shared energy storage flexible resources, and constructing an insufficient flexibility risk index based on a conditional value-at-risk; secondly, flexibility, safety and economic indexes are comprehensively considered, and a novel power distribution network intelligent soft switching and multi-type shared energy storage coordinated optimization model is established. Finally, the proposed method is tested in an improved node system, and the result shows that the proposed novel power distribution network intelligent soft switching and multi-type shared energy storage coordinated optimization method considering the risk of insufficient flexibility can fully deal with the fluctuation of new energy output and the randomness of load demand. The active loss of the novel power distribution network is effectively reduced, and the operation flexibility, safety and economy of the novel power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the field of power optimization, and in particular to a method for coordinated optimization of switches and energy storage in a distribution network taking into account the risk of insufficient flexibility. Background Art

[0002] After a high proportion of new energy is connected to the new distribution network, the operating characteristics of the distribution network are uncertain, resulting in some complex problems such as voltage exceeding the limit of the distribution network node, overload of branch capacity, and three-phase imbalance. This makes the safe, stable, flexible and economical operation of the distribution system face new challenges. Traditional regulation methods can no longer meet the needs of refined power flow control of new distribution systems with high penetration of new energy.

[0003] Smart soft switch (soft open point, SOP) is a new type of intelligent power electronic device that replaces the traditional contact switch. It can quickly respond to power transmission, thereby effectively coping with operational problems caused by the volatility of new energy and the randomness of load. The prior art takes into account the volatility of distributed new energy output and the randomness of load, and establishes a multi-objective random information gap decision-making intelligent soft switch optimization planning model to quantify the improvement of system operation flexibility and economy by intelligent soft switches. The prior art takes into account the volatility of distributed new energy output and the randomness of load, and proposes an optimal parameter setting method for intelligent soft switches in unbalanced distribution networks to improve harmonic distortion, voltage imbalance and active power loss. The prior art proposes a two-stage robust optimization scheduling model for intelligent soft switches to coordinate multiple adjustment methods to deal with the problem of voltage exceeding the limit at distribution network nodes.

[0004] The application of shared energy storage systems in new distribution networks can effectively cope with the operational challenges brought about by the uncertainty of "source" and "load". However, the access of shared energy storage at fixed locations in the distribution network limits the regulation capacity between feeders, making it difficult to cope with the problems of excess renewable energy output and insufficient load power supply in other feeders of the distribution network. Intelligent soft switches have strong flexibility and controllability, and can compensate for the problem of insufficient spatial regulation capacity. At present, there has been extensive research on the optimization of energy storage and intelligent soft switches in distribution networks. In the prior art, interval optimization is considered, and a two-stage optimization model of intelligent soft switches and energy storage in distribution networks is established to cope with the uncertainty of distributed renewable energy output and the randomness of loads, and to improve the economic efficiency of system operation. In the prior art, a robust optimization scheduling model of intelligent soft switches integrated with energy storage is proposed to alleviate the problem of power flow uncertainty constraints and economic loss costs, and to improve the flexibility of system operation under extreme conditions. Reference

[19] takes into account the uncertainty of renewable energy and considers demand response to establish a three-terminal intelligent soft switch power optimization strategy for integrated energy storage in active distribution networks to solve problems such as economy, safety, and renewable energy consumption.

[0005] The above research has been conducted on the operation and control of intelligent soft switches and energy storage systems. The randomness of new energy output and load has been considered in the optimization model, but the impact of its flexibility resources on the improvement of distribution network operation flexibility has not been paid attention to. The existing technology considers the coordinated planning-operation method of the new distribution network hybrid energy storage intelligent soft switch with flexibility to improve the safety and flexible adjustment capability of the distribution system operation. The effectiveness of the model was verified by simulation, but the model failed to consider the effect of the coordinated optimization of shared energy storage, new heat storage type energy storage and SOP on the improvement of the flexibility of the new distribution network operation. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0008] A method for coordinated optimization of switches and energy storage in a distribution network taking into account the risk of insufficient flexibility comprises the following steps:

[0009] S1. Based on the topological structure of the distribution network, the distribution network flexibility index is constructed with the upper power grid, intelligent soft switch, shared energy storage and thermal storage type shared energy storage, and the distribution network flexibility risk deficiency index based on risk value is calculated;

[0010] S2. Based on the calculated distribution network flexibility risk deficiency index, establish a coordination model of intelligent soft switch, shared energy storage and thermal storage type shared energy storage that takes into account the risk of insufficient flexibility;

[0011] S3, constructing objective functions and constraints to optimize the coordination model established in S2;

[0012] S4. Solve the optimized coordination model to obtain a coordinated optimization model of distribution network intelligent soft switches and multi-type shared energy storage.

[0013] Furthermore, the specific method of constructing the distribution network flexibility index by using the upper power grid, intelligent soft switch, shared energy storage and thermal storage type shared energy storage is:

[0014] The upper grid provides upward / downward flexibility adjustment capability through the power on the interconnection line connected to the distribution network. Shared energy storage and thermal storage-type shared energy storage provide upward / downward flexibility adjustment capability through charging / discharging. Its flexibility adjustment capability is expressed as:

[0015]

[0016] In the formula, They provide the shared energy storage with the ability to flexibly adjust upward and downward for node i in period t, respectively. are the energy storage capacity and its lower limit of the energy storage device respectively; P ess,i 、E ess,i are the rated power and rated capacity of the energy storage device at node i respectively; is the charging and discharging power of the energy storage device at node i in time period t. They are respectively the upward and downward flexible adjustment capabilities provided by the upper main network in time period t. ΔP g,max is the upper limit of the climbing power of the upper main grid; P g,max It is the upper limit of power transmitted from the upper main grid to the distribution grid. The active power injected into the distribution network by the upper main grid during time period t. Shared thermal energy storage provides the ability to flexibly adjust upward and downward at node j in time period t. are the energy storage capacity of the heat storage device and its lower limit; P cses,j 、E cses,j are the rated power and rated capacity of the heat storage device at node j respectively; is the heat storage power of the heat storage device at node j in time period t;

[0017] Intelligent soft switch improves the flexibility of distribution network by regulating active and reactive power. The specific power control model is expressed as:

[0018]

[0019] Where P SOP,i , P SOP,j are the transmitted active powers at the smart soft switch nodes i and j respectively; are the power losses of smart soft switch nodes i and j respectively; Q SOP,i , Q SOP,j are the transmitted reactive power of the intelligent soft switch nodes i and j respectively; A SOP is the SOP power loss coefficient; Q SOP,i , Q SOP,j are the transmitted reactive power of the intelligent soft switch nodes i and j respectively; S SOP,ij is the rated capacity of the SOP connected between nodes i and j; ξ represents the absolute value of the sine of the power factor angle.

[0020] Furthermore, the specific method of calculating the risk deficiency index of distribution network flexibility based on risk value in S1 is:

[0021]

[0022]

[0023] In the formula, c up and c downI represents the cost coefficient of insufficient flexibility for upward and downward adjustment, respectively. SD Indicates the difference between the flexibility adjustment capacity and flexibility demand in each period; and They represent the sum of the differences between flexibility supply and flexibility demand within the scheduling period; u t The state variable representing the change in net load power during period t, a value of 1 indicates an increase in net load, and vice versa, a value of 0; It represents the change of net load power in time period t; T is the total number of time periods in the dispatching cycle; and They represent the total upward and downward flexibility adjustment capabilities of the distribution network in period t respectively; S ess and S eses They are respectively installation node sets for shared energy storage system and shared heat storage system.

[0024] Furthermore, the objective function in S3 is expressed as:

[0025] min F Total =F OM +F CVaR

[0026]

[0027] In the formula, F OM Represents the total system operating cost, including the flexibility resource scheduling cost C FRSC , network loss cost C NLC 、Shared Energy Storage Benefits I SES 、Shared thermal energy storage benefits I CSES ; Indicates the cost of purchasing electricity from the upper-level main grid; Indicates operating loss; c g Indicates the price of electricity purchased from the superior; c SOP represents the SOP unit capacity operating cost coefficient; c ess represents the SES unit power operating cost coefficient; c cses represents the CSES unit power operating cost coefficient; c loss represents the unit power loss cost coefficient; and They represent the discharge power and charging power of the interaction between SES and the distribution network respectively; and They represent the electricity purchase and sales prices of the distribution network to SES and CSES respectively; and They represent the discharge and charging power of the interaction power between CSES and distribution network respectively; Indicates the heating power supplied to the heat load; κ t Represents the heating electricity price coefficient.

[0028] Furthermore, the constraints in S3 include: power flow constraints, system operation constraints, electric and thermal power balance constraints, thermal storage type shared energy storage operation constraints, shared energy storage operation constraints, upper main grid injected active power constraints and new energy output constraints, among which:

[0029] The power flow constraint is expressed as:

[0030]

[0031] Where P ji,t and Q ji,t Respectively represent the branch active power and reactive power; Ω B represents the branch set; P i,t and Q i,t Respectively represent the active power and reactive power injected by the node; r ji 、x ji and I ji,t They represent branch resistance, branch reactance and branch current respectively; U i,t Represents the node voltage; P L,i,t and Q L,i,t Respectively represent the active power and reactive power of the node load; P DG,i,t represents the active power injected into the node by renewable energy;

[0032] The system operation constraints are expressed as:

[0033]

[0034] Where U max and U min Respectively represent the maximum and minimum values ​​allowed for the system voltage; Indicates the maximum capacity allowed by the branch;

[0035] The electrothermal power balance constraint is expressed as:

[0036]

[0037] Where P DG,t Represents new energy output, υ represents the heating power and heating efficiency of the resistance wire respectively; ⊥ is an operator, indicating that at least one of the parameters is 0;

[0038] The operation constraints of thermal storage type shared energy storage are expressed as:

[0039]

[0040] In the formula, μ min , μ max They respectively represent the lower limit and upper limit of the heat load inertia adjustment ratio; represents the heat load of thermal storage type shared energy storage dispatch; They represent the maximum values ​​of charging and heating for thermal storage-type shared energy storage respectively;

[0041] The shared energy storage operation constraints are expressed as:

[0042]

[0043] Where P chmax and P chmin are the maximum charging and discharging powers of SES respectively; δ ch,t and δ dch,t is the state variable, δ ch,t / δ dch,t =1 is charging state, δ ch,t / δ dch,t =0 is the discharge state; η is the charge and discharge efficiency of SES; Δt is the optimization period; E t and E0 is the storage capacity before and after ESS optimization;

[0044] The active power constraint injected by the upper main grid is expressed as:

[0045]

[0046] In the formula, ΔP g,max is the upper limit of the climbing power of the upper main grid, The active power injected into the distribution network by the upper main grid during time period t;

[0047] The new energy output constraint is expressed as:

[0048]

[0049] In the formula, represents the rated power of the new energy source connected to node i, P DG,i,t Represents the new energy output of node i.

[0050] Furthermore, in S4, the optimized coordination model is solved by a second-order cone optimization method, which specifically includes linearization of the power flow model and linearization of the intelligent soft switch model.

[0051] Furthermore, the power flow model is linearized as follows:

[0052]

[0053] Where P ji,t and Q ji,t Respectively represent the branch active power and reactive power; P i,t and Q i,t Respectively represent the active power and reactive power injected by the node; r ji、x ji and I ji,t They represent branch resistance, branch reactance and branch current respectively; U i,t Represents the node voltage; P L,i,t and Q L,i,t Respectively represent the active power and reactive power of the node load; P DG,i,t Represents the active power injected into the node by renewable energy; U max and U min Respectively represent the maximum and minimum values ​​allowed for the system voltage; Indicates the maximum capacity allowed by the branch, l ji,t 、v i,t is an intermediate variable.

[0054] Furthermore, the intelligent soft switch model is linearly expressed as:

[0055]

[0056] Where P SOP,i , P SOP,j are the transmitted active powers at the intelligent soft switch nodes i and j respectively; Q SOP,i , Q SOP,j are the transmitted reactive power of the intelligent soft switch nodes i and j respectively; S SOP,ij is the rated capacity of the intelligent soft switch connected between nodes i and j.

[0057] The present invention has the following beneficial effects:

[0058] 1) Considering multiple flexibility resources such as SOP, SES and CSES can optimize the power flow distribution of the new distribution network, improve the system voltage level, reduce the overall operation cost of the system, and realize the economy, safety and flexibility of the new distribution network.

[0059] 2) Due to the introduction of SOP, SES and CSES, the coordinated optimization model proposed in this paper improves the economic efficiency of SOP operation, the comprehensive benefits of SES and CSES operation, and improves the utilization efficiency of multiple types of flexibility resources.

[0060] 3) Comprehensively considering the supply and demand of distribution network flexibility, a flexibility deficiency index based on CVaR is established, which can accurately reflect the flexibility margin status of the new distribution network, reduce the upward and downward flexibility deficiency risks, promote the consumption of new energy, achieve the balance of supply and demand of the distribution network, and improve operational safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of the coordinated optimization method of distribution network switches and energy storage taking into account the risk of insufficient flexibility of the present invention.

[0062] Figure 2 The structure diagram of the IEEE 33-node system modified according to the embodiment of the present invention.

[0063] Figure 3 Schematic diagram of heat load and market clearing price according to an embodiment of the present invention.

[0064] FIG4( a ) is a schematic diagram of a system node voltage curve in Example 4 of an embodiment of the present invention.

[0065] FIG4( b ) is the original graph of the system node voltage curve of Case 5 of the embodiment of the present invention.

[0066] FIG5( a ) is a SOP optimization curve of the system in Case 2 of the embodiment of the present invention.

[0067] FIG5( b ) is a SOP optimization curve of the system in Case 3 of the embodiment of the present invention.

[0068] FIG5( c ) is a SOP optimization curve of the system in Example 4 of the embodiment of the present invention.

[0069] Figure 6 This is the SES optimization curve of Example 4 of the embodiment of the present invention.

[0070] Figure 7 This is the CSES optimization curve of Example 4 of the embodiment of the present invention. DETAILED DESCRIPTION

[0071] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0072] A coordinated optimization method of distribution network switches and energy storage considering the risk of insufficient flexibility, such as Figure 1 As shown, the following steps are included:

[0073] S1. Based on the topological structure of the distribution network, the distribution network flexibility index is constructed with the upper power grid, intelligent soft switch, shared energy storage and thermal storage type shared energy storage, and the distribution network flexibility risk deficiency index based on risk value is calculated;

[0074] In this embodiment, the topological structure of the distribution network has the characteristics of many nodes and dispersed flexibility resources. Therefore, it is necessary to characterize the flexibility index of the distribution network from multiple dimensions of time, space and value to ensure the balance of supply and demand of the distribution network under the state of safe and economical operation of the flow.

[0075] The flexibility resources of the distribution network are distributed in the entire topological structure of the source, grid and load sides: the source side provides flexibility through the upper power grid; the grid side can improve the topological flexibility through flexible devices represented by network reconstruction and intelligent soft switch SOP; the load side meets the supply and demand balance through flexible resources such as multiple types of new energy storage devices and controllable loads. Since network reconstruction cannot be frequently operated, it is not considered for the time being. In this embodiment, the flexibility resources represented by the upper power grid, intelligent soft switch SOP, shared energy storage SES and thermal storage type shared energy storage CSES jointly construct the distribution network flexibility index.

[0076] The upper grid provides upward / downward flexibility adjustment capability through the power on the distribution network connection line, and the energy storage system provides upward / downward flexibility adjustment capability through charging / discharging. The specific expression of the flexibility adjustment capability of each flexibility resource is as follows:

[0077]

[0078] In the formula, Shared energy storage provides the ability for node i to flexibly adjust upward and downward in time period t. are the energy storage capacity and its lower limit of the energy storage device respectively. ess,i 、E ess,i are the rated power and rated capacity of the energy storage device at node i respectively. is the charging and discharging power of the energy storage device at node i in time period t. They are respectively the upward and downward flexible adjustment capabilities provided by the upper main network in time period t. ΔP g,max It is the upper limit of the climbing power of the upper main grid. g,max It is the upper limit of the power transmitted from the upper main grid to the distribution grid. t g The active power injected into the distribution network by the upper main grid during time period t. Shared thermal energy storage provides the ability to flexibly adjust upward and downward at node j in time period t. are the energy storage capacity of the heat storage device and its lower limit respectively. cses, j.E cses, j are the rated power and rated capacity of the heat storage device at node j respectively. is the heat storage power of the heat storage device at node j in time period t.

[0079] The grid-side flexibility resources mainly consider SOP, which can flexibly, real-time and safely regulate active and reactive power, effectively solve the problem of system voltage exceeding the limit, and improve the flexibility of the grid structure.

[0080] SOP takes the back-to-back voltage source converter as an example. SOP control principle topology diagram reference [21-22] There are two VSCs in one SOP, one adopts PQ control mode and the other adopts Vdc Q control mode. The PQ-controlled VSC control unit controls the active power output of the SOP and the reactive power injected by the VSC, and the VdcQ-controlled VSC control unit keeps the DC bus voltage constant and injects reactive power. The power control model of the SOP is as follows:

[0081]

[0082] Where P SOP,i , P SOP,j are the transmitted active powers at SOP nodes i and j respectively; are the power losses of SOP nodes i and j respectively; Q SOP,i , Q SOP,j are the transmission reactive power of SOP nodes i and j respectively; A SOP is the SOP power loss coefficient; Q SOP,i , Q SOP,j are the transmitted reactive power of SOP nodes i and j respectively; S SOP,ij is the rated capacity of the SOP connected between nodes i and j; ξ represents the absolute value of the sine of the power factor angle.

[0083] The flexibility demand capacity of the distribution network comes from the random fluctuation of the net load, that is, the difference between the total load of each node in the distribution network and the total output power of renewable energy. If the net load uncertainty power is greater than the system's downward flexible adjustment capacity, the system will be limited by insufficient downward flexible adjustment capacity and will not be able to absorb too much renewable energy output power; when the net load uncertainty power is less than the system's upward flexible adjustment capacity, the system lacks upward flexible adjustment capacity and cannot guarantee reliable power supply to the load.

[0084] Value at Risk (VaR) refers to the unilateral quantile of uncertain power under the confidence level β, and its physical meaning is the threshold of the fluctuation range of wind power and photovoltaic new energy output under this confidence level. CVaR is the conditional mean of wind power and photovoltaic new energy output exceeding this threshold under the same confidence level, which makes up for the insufficiency of the tail distribution of VaR and is more conservative and reliable. The simplified mathematical model is as follows:

[0085]

[0086] In the formula, α represents the VaR value under the confidence level β; G β (x,α) represents the estimated value of CvaR; g(x,y k ) represents the loss function; y k Indicates the y value in scene k.

[0087] The distribution network inflexibility index can be calculated as follows:

[0088]

[0089] In the formula, c up and c down I represents the upward and downward adjustment of the cost coefficient of insufficient flexibility. SD It reflects the difference between the flexibility regulation capacity and the flexibility demand in each period. The larger the value, the more serious the overall flexibility deficiency of the distribution network is, and the worse the ability to cope with the uncertain changes of net load is. and They represent the sum of the differences between flexibility supply and flexibility demand within the scheduling period; u t The state variable representing the change in net load power during period t. A value of 1 indicates an increase in net load, and a value of 0 otherwise. ΔP t nl represents the net load power change in period t; T is the total number of periods in the dispatching cycle, in this paper, T=24 is taken, and the dispatching period interval is 1h; F t up and F t down They represent the total upward and downward flexibility adjustment capabilities of the distribution network in period t respectively; S ess and S eses They are respectively installation node sets for shared energy storage system and shared heat storage system.

[0090] S2. Based on the calculated distribution network flexibility risk deficiency index, establish a coordination model of intelligent soft switch, shared energy storage and thermal storage type shared energy storage that takes into account the risk of insufficient flexibility;

[0091] The purpose of establishing the flexible shortage index is to study the impact of SOP, SES and CSES source-grid-load flexible resource coordination optimization on the operation of the new distribution network. Therefore, the optimization model objective function takes into account economy, flexibility and safety, and considers system flow constraints, distributed power generation operation constraints, system operation constraints, intelligent soft switch operation constraints, shared energy storage system and thermal storage type shared energy storage system operation constraints.

[0092] S3, constructing objective functions and constraints to optimize the coordination model established in S2;

[0093] Objective function:

[0094] f Total =F OM +F CVaR (11)

[0095]

[0096] In the formula, F OM Represents the total operating cost of the system; mainly includes the flexibility resource scheduling cost C FRSC, network loss cost C NLC 、Shared Energy Storage Benefits I SES 、Shared thermal energy storage benefits I CSES ;P t g Indicates the cost of purchasing electricity from the upper main grid; Indicates operating loss. c g Indicates the price of electricity purchased from the superior; c SOP represents the SOP unit capacity operating cost coefficient; c ess represents the SES unit power operating cost coefficient; c cses represents the CSES unit power operating cost coefficient; c loss represents the unit power loss cost coefficient; and They represent the discharge power and charging power of the interaction between SES and the distribution network respectively; and They represent the electricity purchase and sales prices of the distribution network to SES and CSES respectively; and They represent the discharge and charging power of the interaction power between CSES and distribution network respectively; Indicates the heating power supplied to the heat load; κ t Represents the heating electricity price coefficient.

[0097] 2) Power flow constraints:

[0098]

[0099] Where P ji,t and Q ji,t Respectively represent the branch active power and reactive power; P i,t and Q i,t Respectively represent the active power and reactive power injected by the node; r ji 、x ji and I ji,t They represent branch resistance, branch reactance and branch current respectively; U i,t Represents the node voltage; P L,i,t and Q L,i,t Respectively represent the active power and reactive power of the node load; P DG,i,t Represents the active power injected into the node by renewable energy.

[0100] 3) System operation constraints:

[0101]

[0102] Where U max and U min Respectively represent the maximum and minimum values ​​allowed for the system voltage; Indicates the maximum capacity allowed for a branch.

[0103] 3) Electric and thermal power balance constraints:

[0104]

[0105] In the formula, υ represents the heating power of the CSESSO resistance wire and the heating efficiency 0.3; ⊥ is an operator, indicating that at least one of the parameters is 0.

[0106] 4) CSES operation constraints:

[0107]

[0108] In the formula, μ min , μ max They represent the lower and upper limits of the heat load inertia adjustment ratio, which are set to 0.9 and 1.1 respectively.

[25] ; represents the heat load of CSES scheduling; Respectively represent the maximum values ​​of CSES charging and heating.

[0109] 5) SES operation constraints:

[0110]

[0111] Where P chmax and P chmin are the maximum charging and discharging powers of SES respectively; δ ch,t and δ dch,t is the state variable, δ ch,t / δ dch,t =1 is charging state, δ ch,t / δ dch,t =0 is the discharge state; η is the charge and discharge efficiency of SES; Δt is the optimization period; E t and E0 is the storage capacity before and after ESS optimization.

[0112] 6) Active power constraints injected by the upper main grid:

[0113] ΔP gmax ≥P t g ≥-ΔP gmax (twenty two)

[0114] 7) Constraints on new energy output:

[0115]

[0116] In the formula, Indicates the rated power of the new energy source connected to node i

[0117] S4. Solve the optimized coordination model to obtain a coordinated optimization model of distribution network intelligent soft switches and multi-type shared energy storage.

[0118] Considering that the optimization model contains large-scale nonlinear expressions, the CPLEX commercial software embedded in MATLAB is used to solve the coordinated optimization model of the new distribution network intelligent soft switches and multi-type shared energy storage through the second-order cone optimization method.

[0119] For the power flow constraint model (13)-(15), by introducing the intermediate variable l ji,t 、v i,t Variable substitution The replaced models are as follows:

[0120]

[0121] For the SOP nonlinear constraints, equations (5) and (7) are transformed into second-order cone constraints and rotation cone constraints respectively as follows:

[0122]

[0123] The above linearization process converts the original model into a mixed integer second-order cone programming model, which is solved using YALMIP and CPLEX.

[0124] In order to verify the effectiveness of the coordinated optimization model of intelligent soft switches and multi-type shared energy storage in distribution networks taking into account the risk of insufficient flexibility, simulation tests were carried out on the improved IEEE 33-bus distribution system. Figure 2 The modified IEEE 33-node distribution system is given. The total active load of the system is 3635kw, and the total reactive load is 2265kvar. The upper and lower limits of the system voltage are set to 0.95pu and 1.05pu respectively. Six DGs are installed in the IEEE 33-node distribution system, and the power factor is set to 1.0. Table 1 gives the installation location and capacity of the DG. Electric load curve, wind power and photovoltaic curve references, Figure 3 Given the heat load curve and the spot clearing price of the power grid, the power grid purchase price (spot market clearing price) is set to 80% of the power selling price. up and c down References. The installation location of SOP is as follows Figure 1 As shown in the figure, the maximum capacity is 500kVA and the loss factor of SOP is set to 0.02. Figure 2 The installation locations of SES and CSES are given. The rated power of SES is 1.2MW and the rated capacity is 9MWh.

[0125] Table 1 Cost coefficient

[0126]

[0127] Two cases are set to conduct a comparative analysis of the coordinated optimization model of intelligent soft switching and multi-type shared energy storage.

[0128] Case 1: The model of a new distribution system with multiple types of shared energy storage is not connected to SOP and is optimized through network reconstruction.

[0129] Case 2: Coordinated optimization of a new distribution system with SOP and non-thermal shared energy storage.

[0130] Case 3: Coordinated optimization of a new distribution system with SOP and thermal energy storage.

[0131] Case 4: Coordinated optimization of a new distribution system with SOP and multi-type shared energy storage.

[0132] The above cases all take into account the risk of insufficient flexibility.

[0133] Case 5: Coordinated optimization of a new distribution system with SOP and multiple types of shared energy storage, without considering the risk of insufficient flexibility.

[0134] Table 2 Comparison of optimization results

[0135]

[0136] It can be concluded from Table 2 that, compared with Case 1, the flexibility dispatching cost of Case 4 increased slightly, but the network loss decreased by 1950 yuan, and the benefits of shared energy storage and shared thermal energy storage increased by 6380 yuan and 8001 yuan respectively. Therefore, SOP is more advantageous than the traditional regulation method of network reconstruction. Compared with Case 2 and Case 3, the flexibility dispatching cost of Case 4 increased by 23380 yuan and 16740 yuan respectively, but the loss decreased by 20.76% and 21.39% respectively. At the same time, the benefits of SES and CSES increased significantly, and the total benefits increased by 49309 yuan and 11932 yuan respectively. The coordinated optimization of multi-type shared energy storage can improve the economic efficiency of system operation better than single-type shared energy storage. Compared with Case 1-Case 3, the risk cost of insufficient flexibility in Case 4 was reduced by 4550 yuan, 81300 yuan, and 55500 yuan respectively. The coordinated operation of SOP and multi-type energy storage and multi-flexibility resources not only improved the comprehensive economic benefits of system operation, but also significantly improved the flexibility and safety of system operation, promoted the consumption of new energy, and enabled the effective complementary utilization of flexible resources. The flexibility dispatch cost and network loss cost of Case 4 were slightly higher than those of Case 5, and the benefits of SES and CSES were also improved. It can be further seen from Figure 4 that Case 4 improved the voltage level of the distribution network. Therefore, Case 4 sacrificed less economy to ensure the flexibility and safety of system operation. Considering the risk index of insufficient flexibility, it is possible to balance economy with flexibility and safety to achieve optimal system operation.

[0137] Figure 5 Figure 6 and Figure 7 The optimization curves of the operation phase of SOP, CSES and SES are shown respectively.

[0138] Figure 5(a), (b), and (c) show the optimization results of SOP in Case 2, Case 3, and Case 4, respectively. It can be seen that in periods 9-10 and 19-20, the capacity of SOP at both ends of node 12 and node 22 in Case 2 and Case 3 is used independently, while SOP in Case 4 transfers the capacity from node 12 to node 22, which optimizes the power flow distribution and reduces network loss. In addition, Case 4 introduces SOP to coordinate operation with SES and CSES, which reduces the transfer capacity compared to Case 2 and Case 3, improves the operation economy of SOP, and improves the utilization efficiency of various flexibility resources.

[0139] Figure 6 and Figure 7 The optimization results of SES and CSES are given respectively. As can be seen from Figure 5, SES supplies power to the grid in time periods 10 and 20. Since SES is connected to system node 12, SOP transfers capacity from node 12 to node 22 while SES is discharging. Figure 6It can be seen that the wind power curtailment is serious in the off-peak period 24 and period 1-6, and the solar power curtailment is serious in period 12-16. CSES can absorb the curtailed wind and solar power and convert them into thermal energy to provide energy for thermal load and peak load periods of the power grid. CSES purchases electricity from the power grid at 1:00 during the off-peak period and converts it into thermal energy. During the peak period, it converts thermal energy into electrical energy to supply power to the power grid, which improves the comprehensive benefits of CSES. The coordinated optimization of CSES, SES, and SOP has significantly improved the absorption and load support capabilities of new energy, and improved the flexibility and economy of system operation.

[0140] It can be seen from Table 1 that considering the risk index of insufficient flexibility will have a significant impact on the operation of the distribution network, and considering flexibility, economy and safety comprehensively, the coordinated optimization model of multiple types of flexibility resources proposed in this paper is better. In order to further analyze the impact of considering the risk of insufficient flexibility, this embodiment compares the distribution of insufficient flexibility risks of Case 2-Case 3, as shown in Table 3.

[0141] Table 3 Comparison of risk distribution of insufficient flexibility

[0142]

[0143] It can be seen that, compared with case 2, case 3 has lower downward adjustment costs in the low-peak periods 22-24 and periods 1-3, and the overall upward adjustment costs have decreased. This is because after considering the flexibility shortage index based on multiple types of flexibility resources, the upward or downward flexibility margin has been improved to a certain extent. There is enough downward flexibility margin in the valley period to reduce the amount of new energy abandoned, and there is enough upward flexibility margin in the peak load period to ensure power supply reliability. The proposed model improves the overall level of the risk of insufficient flexibility in the new distribution network, and the distribution of insufficient flexibility risks in each period is more uniform, which helps to improve the economy and safety of system operation and achieve the balance of supply and demand in the new distribution network.

[0144] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0147] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0148] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility, characterized in that: The steps include: S1. Based on the topological structure of the distribution network, the distribution network flexibility index is constructed with the upper power grid, intelligent soft switch, shared energy storage and thermal storage type shared energy storage, and the distribution network flexibility risk deficiency index based on risk value is calculated; S2. Based on the calculated distribution network flexibility risk deficiency index, establish a coordination model of intelligent soft switch, shared energy storage and thermal storage type shared energy storage that takes into account the risk of insufficient flexibility; S3, constructing objective functions and constraints to optimize the coordination model established in S2; S4. Solve the optimized coordination model to obtain a coordinated optimization model of distribution network intelligent soft switches and multi-type shared energy storage.

2. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 1, characterized in that: The specific method of constructing the distribution network flexibility index by using the upper power grid, intelligent soft switch, shared energy storage and thermal storage type shared energy storage is: The upper grid provides upward / downward flexibility adjustment capability through the power on the interconnection line connected to the distribution network. Shared energy storage and thermal storage-type shared energy storage provide upward / downward flexibility adjustment capability through charging / discharging. Its flexibility adjustment capability is expressed as: In the formula, They provide the shared energy storage with the ability to flexibly adjust upward and downward for node i in period t, respectively. are the energy storage capacity and its lower limit of the energy storage device respectively; P ess，i 、E ess，i are the rated power and rated capacity of the energy storage device at node i respectively; is the charging and discharging power of the energy storage device at node i in time period t; They are the upward and downward flexible adjustment capabilities provided by the upper main network in time period t; ΔP g，max It is the upper limit of the climbing power of the upper main grid; P g，max The upper limit of power transmitted from the upper main grid to the distribution grid; The active power injected into the distribution network by the upper main grid during time period t; Shared thermal energy storage provides the ability to flexibly adjust upward and downward at node j in time period t. are the energy storage capacity of the heat storage device and its lower limit; P cses，j 、E cses，j are the rated power and rated capacity of the heat storage device at node j respectively; is the heat storage power of the heat storage device at node j in time period t; Intelligent soft switch improves the flexibility of distribution network by regulating active and reactive power. The specific power control model is expressed as: Where P SOP，i , P SOP，j are the transmitted active powers at the smart soft switch nodes i and j respectively; are the power losses of smart soft switch nodes i and j respectively; Q SOP，i , Q SOP，j are the transmitted reactive power of the intelligent soft switch nodes i and j respectively; A SOP is the SOP power loss coefficient; Q SOP，i , Q SOP，j are the transmitted reactive power of the intelligent soft switch nodes i and j respectively; S SOP，ij is the rated capacity of the SOP connected between nodes i and j; ξ represents the absolute value of the sine of the power factor angle.

3. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 1, characterized in that: The specific method of calculating the risk deficiency index of distribution network flexibility based on risk value in S1 is: In the formula, c up and c down They represent the cost coefficients of insufficient flexibility for upward and downward adjustment, I SD Indicates the difference between the flexibility adjustment capacity and flexibility demand in each period; and They represent the sum of the differences between flexibility supply and flexibility demand within the scheduling period; u t A state variable representing the change in net load power during period t. A value of 1 indicates an increase in net load, and a value of 0 otherwise. It represents the change of net load power in time period t; T is the total number of time periods in the dispatching cycle; and They represent the total upward and downward flexibility adjustment capabilities of the distribution network in period t respectively; S ess and S eses They are respectively installation node sets for the shared energy storage system and the shared heat storage system.

4. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 1, characterized in that: The objective function in S3 is expressed as: min F Total =F OM +F CVaR In the formula, F OM Represents the total system operating cost, including the flexibility resource scheduling cost C FRSC , network loss cost C NLC 、Shared Energy Storage Benefits I SES 、Shared thermal energy storage benefits I CSES ; Indicates the cost of purchasing electricity from the upper main grid; Indicates operating loss; c g Indicates the price of electricity purchased from the superior; c SOP It represents the SOP unit capacity operating cost coefficient; c ess represents the SES unit power operating cost coefficient; c cses represents the CSES unit power operating cost coefficient; c loss represents the unit power loss cost coefficient; and They represent the discharge power and charging power of the interaction between SES and the distribution network respectively; and They represent the electricity purchase and sales prices of the distribution network to SES and CSES respectively; and They represent the discharge and charging power of the interaction power between CSES and distribution network respectively; Indicates the heating power supplied to the heat load; κ t Represents the heating electricity price coefficient.

5. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 4, characterized in that: The constraints in S3 include: power flow constraints, system operation constraints, electric and thermal power balance constraints, thermal storage shared energy storage operation constraints, shared energy storage operation constraints, upper main grid injected active power constraints and new energy output constraints, among which: The power flow constraint is expressed as: Where P ji，t and Q ji，t Respectively represent the branch active power and reactive power; Ω B represents the branch set; P i，t and Q i，t Respectively represent the active power and reactive power injected by the node; r ji 、x ji and I ji，t They represent branch resistance, branch reactance and branch current respectively; U i，t Represents the node voltage; P L，i，t and Q L，i，t Respectively represent the active power and reactive power of the node load; P DG，i，t represents the active power injected into the node by renewable energy; The system operation constraints are expressed as: Where U max and U min Respectively represent the maximum and minimum values ​​allowed for the system voltage; Indicates the maximum capacity allowed by the branch; The electrothermal power balance constraint is expressed as: Where P DG，t Represents new energy output, v represents the heating power and heating efficiency of the resistance wire respectively; ⊥ is an operator, indicating that at least one of the parameters is 0; The operation constraints of thermal storage type shared energy storage are expressed as: In the formula, μ min , μ max They respectively represent the lower limit and upper limit of the heat load inertia adjustment ratio; represents the heat load of thermal storage type shared energy storage dispatch; They represent the maximum values ​​of charging and heating for thermal storage-type shared energy storage respectively; The shared energy storage operation constraints are expressed as: Where P chmax and P chmin are the maximum charging and discharging powers of SES respectively; δ ch，t and δ dch,t is the state variable, δ ch，t / δ dch,t =1 is charging state, δ ch，t / δ dch,t =0 is the discharge state; η is the charge and discharge efficiency of SES; Δt is the optimization period; E t and E0 is the storage capacity before and after ESS optimization; The active power constraint injected by the upper main grid is expressed as: In the formula, ΔP g，max is the upper limit of the climbing power of the upper main grid, The active power injected into the distribution network by the upper main grid during time period t; The new energy output constraint is expressed as: In the formula, represents the rated power of the new energy source connected to node i, P DG,i，t Represents the new energy output of node i.

6. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 1, characterized in that: In S4, the optimized coordination model is solved by a second-order cone optimization method, which specifically includes linearization of a power flow model and linearization of an intelligent soft switch model.

7. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 6, characterized in that: The linearization of the power flow model is expressed as: Where P ji，t and Q ji，t Respectively represent the branch active power and reactive power; P i，t and Q i，t Respectively represent the active power and reactive power injected by the node; r ji 、x ji and I ji，t They represent branch resistance, branch reactance and branch current respectively; U i，t Represents the node voltage; P L，i，t and Q L，i，t Respectively represent the active power and reactive power of the node load; P DG，i，t Represents the active power injected into the node by renewable energy; U max and U min Respectively represent the maximum and minimum values ​​allowed for the system voltage; Indicates the maximum capacity allowed by the branch, l ji，t 、v i，t is an intermediate variable.

8. The method for coordinated optimization of distribution network switches and energy storage taking into account the risk of insufficient flexibility according to claim 6, characterized in that: The linearization of the intelligent soft switch model is: Where P SOP，i , P SOP，j are the transmitted active powers at the intelligent soft switch nodes i and j respectively; Q SOP，i , Q SOP，j are the transmitted reactive power of the intelligent soft switch nodes i and j respectively; S SOP，ij is the rated capacity of the intelligent soft switch connected between nodes i and j.

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