Optimal configuration method and system for energy storage of photovoltaic power distribution network

Abstract

The invention discloses an optimal configuration method and system for energy storage of a photovoltaic power distribution network, and the method comprises the steps: defining the node carbon intensity, and building an energy storage carbon emission flow model considering the time sequence characteristics based on the carbon emission intensity; calculating the accumulated carbon emission of the energy storage system in a specific time period according to the charging and discharging conditions of the energy storage system; based on the energy storage carbon emission flow model, an objective function of a power distribution network energy storage optimization configuration model is established, and the total cost is reduced to the maximum degree to achieve the emission reduction target of an energy storage system; determining constraint conditions of the power distribution network energy storage optimization configuration model; the energy storage optimization configuration model is equivalently converted into a MINCQCP model, the MINCQCP model is solved based on an improved near-end distance algorithm, and a local optimal solution is obtained. According to the method, carbon emission constraints are considered, optimization is carried out by taking the lowest operation cost or investment cost as a target, and the solving efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization technology, and in particular to an optimized configuration method and system for photovoltaic power distribution network energy storage. Background Technology

[0002] The main means of carbon emission reduction in the power system industry include optimizing system operation, deep integration of renewable energy, multi-energy comprehensive utilization, and comprehensive system transformation. Regarding operation: existing carbon emission reduction or low-carbon methods are not suitable for power systems with a high proportion of renewable energy integration, and also rely on the implementation of new mechanisms related to carbon pricing or carbon trading, which is not conducive to the implementation of carbon reduction measures.

[0003] System planning and configuration can fundamentally accelerate the low-carbon transformation of the power system. Although existing technologies have studied the impact of carbon emission constraints on multi-energy system planning, different carbon emission targets may lead to significant changes in the optimal resource combination, and based on this, carbon pricing mechanisms have been introduced to formulate new low-carbon planning schemes. However, existing technologies focus on the planning and configuration of distributed power sources, energy storage, and other resources in the distribution network, mainly optimizing with the goal of minimizing operating or investment costs. They do not consider the carbon emission constraints of energy storage devices, cannot guide the low-carbon transformation of the distribution network, and the planning models are highly nonlinear, resulting in low solution efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized configuration method and system for photovoltaic power distribution network energy storage, taking into account carbon emission constraints, optimizing with the goal of minimizing operating costs or investment costs, and improving solution efficiency.

[0005] To achieve the above objectives, the present invention provides an optimized configuration method for photovoltaic distribution network energy storage, comprising:

[0006] Define node carbon intensity, and based on carbon emission intensity, establish an energy storage carbon emission flow model that considers time-series characteristics;

[0007] Calculate the cumulative carbon emissions of the energy storage system over a specific time period based on its charging and discharging status.

[0008] Based on the energy storage carbon emission flow model, an objective function is established for the distribution network energy storage optimization configuration model to minimize the total cost in order to achieve the emission reduction target of the energy storage system.

[0009] The constraints of the energy storage optimization configuration model for the distribution network are determined; the energy storage optimization configuration model is equivalently converted into the MINCQCP model, and the MINCQCP model is solved based on the improved near-end distance algorithm to obtain the local optimum.

[0010] Furthermore, the node carbon intensity is the weighted average carbon emissions of all power flowing into the node;

[0011] Among them, the distribution of carbon emission flow in the power distribution network and the carbon intensity of related nodes change over time.

[0012] Furthermore, based on the charging and discharging status of the energy storage system, the cumulative carbon emissions of the energy storage system over a specific time period are calculated, including:

[0013] When the energy storage system is charging, the energy storage system acts as a load, and the carbon intensity of the connected bus is the nodal carbon intensity of the energy storage system. Therefore, the carbon emissions of the energy storage system are:

[0014]

[0015] In the formula, This represents the carbon intensity of the energy storage system at time t+1 of the access node i. The energy storage carbon intensity of the energy storage system at access node i at time t; Let represent the node carbon intensity of the energy storage system connected to node i at time t, defined as the weighted average carbon emissions of all power flowing into the node; Let Δt represent the discharge power of the energy storage system at access node i at time t, and let Δt represent the duration between time t and time t+1, i.e. the duration of adjacent times. ES represents the charging power of the energy storage system at access node i at time t; ES represents the energy storage system, and t represents time t.

[0016] When the energy storage system discharges, the node carbon intensity of the energy storage system is the average of the current total carbon emissions and the maximum possible power generation. Therefore, the carbon emissions of the energy storage system are quantified as the product of the energy flow and the node carbon intensity of the energy storage system.

[0017] The node carbon intensity during discharge of the energy storage system is:

[0018]

[0019] In the formula, η d Indicates the discharge efficiency of the energy storage system. This represents the energy stored by the energy storage system at access node i at time t.

[0020] The carbon emissions during the discharge of the energy storage system are:

[0021]

[0022]

[0023] In the formula, NC represents the carbon emissions of the energy storage system at node i when it discharges at time t. i,tThis represents the nodal carbon intensity of the energy storage system connected to node i at time t. Let represent the discharge power of the j-th energy storage system at time t, where j represents the number of energy storage systems.

[0024] Furthermore, the objective function is: minC DSO =C Cap +C ope ,

[0025] In the formula, minC DSO C represents the minimum total cost including a photovoltaic power distribution network. Cap C represents the total investment cost including the photovoltaic power distribution network. ope This represents the operating cost of a distribution network including photovoltaic power.

[0026] The operating cost is calculated based on a typical day scenario method, and the total investment cost including the photovoltaic distribution network and the operating cost are respectively:

[0027]

[0028] In the formula, γ k c represents the cost recovery factor. IC,k This represents the investment cost of asset k. D represents the installed capacity of asset k on node i, where ES, WT, and PV represent energy storage system, wind turbine, and distributed photovoltaic, respectively. w This represents a typical day (w), which is the equivalent number of days in a year. This represents the operating cost of the photovoltaic distribution network on the w-th typical day, where w represents the number of the typical day, i.e., the number of the typical scenario; Ψ represents the total number of typical days. p represents the transmission and distribution price of the photovoltaic distribution network at time t. 01,t,w Δt represents the power transmitted by the photovoltaic distribution network at time t on the w-th typical day, where Δt represents the duration of adjacent times. This represents the marginal electricity price of the gas turbine connected to node i; This represents the power of the gas turbine at node i at time t; This represents the marginal electricity price of the energy storage system connected to node i; This represents the power of the energy storage system at node i at time t on a typical day w.

[0029] Furthermore, the constraints include investment constraints for renewable energy and energy storage systems, operational constraints for energy storage systems and photovoltaics, and network constraints for distribution networks containing photovoltaics.

[0030] The investment constraints include: the new investment capacity that node i can connect to the energy storage system and distributed photovoltaic, and the total investment capacity limit of the entire system of a single power source; wherein, the single power source is either an energy storage system or distributed photovoltaic.

[0031] The operational constraints include: operational constraints of the energy storage system and operational constraints of distributed photovoltaic systems;

[0032] The operational constraints of the energy storage system include: the charging and discharging power of the energy storage system is within the rated power range, and simultaneous charging and discharging are not allowed; the energy stored in the energy storage system needs to meet the time-series power constraints, and the initial energy level and the final energy level of the energy storage system must remain equal throughout the day to ensure that the energy storage system can operate continuously.

[0033] The operational constraints of the distributed photovoltaic system include: the power output of the distributed photovoltaic system cannot exceed the installed capacity of the distributed photovoltaic system.

[0034] The network constraints include: based on this, a linearized power flow model is adopted to construct active power balance constraints, reactive power balance constraints, long-line voltage drop constraints, voltage amplitude constraints, and carbon emission constraints for bus nodes.

[0035] Furthermore, the investment constraints are as follows:

[0036]

[0037] In the formula, This represents the installed capacity of asset k on node i. This represents the upper limit of the installed capacity of asset k on node i, where ES and PV represent energy storage system and photovoltaic, respectively;

[0038] The operating constraints of the energy storage system are:

[0039]

[0040]

[0041] In the formula, This represents the charging power of the energy storage system at time t on the w-th typical day; This represents the upper limit of the charging power of the energy storage system connected to node i. This represents the discharge power of the energy storage system at time t on the w-th typical day; η represents the energy of the energy storage system at time t on a typical day w; ES,C Indicates the charging efficiency of the energy storage system; η ES,D This indicates the discharge efficiency of the energy storage system; Let represent the power of the energy storage system at the start of the charging cycle of the intervention node i, where t = 1 indicates the start of the charging cycle; This represents the power of the energy storage system at the end of the charging cycle of access node i, where t = |T| + 1 indicates the end of the charging cycle; w represents the number of a typical day, i.e., the number of a typical scenario.

[0042] The carbon emission constraints are:

[0043]

[0044] In the formula, π w NC represents the probability of a typical day. GT This indicates the nodal carbon intensity of the gas turbine connection node; NC represents the power generation of the gas turbine at node i at time t on the w-th typical day. WT Indicates the nodal carbon intensity of the wind turbine connection node; NC represents the power of the wind turbine at node i at time t on the w-th typical day; PV Indicates the nodal carbon intensity of the photovoltaic access node; p represents the photovoltaic power of access node i at time t on the w-th typical day; 01,t,w This represents the power of a regular node at time t on the w-th typical day. Indicates the upper limit of carbon emissions. This represents the carbon emission intensity of a conventional node at time t.

[0045] Furthermore, the energy storage optimization configuration model is equivalently converted into the MINCQCP model, including:

[0046] The bilinear terms of the energy storage optimization configuration model are rewritten by introducing auxiliary variables z1 and z2 into the general bilinear term z = xy, resulting in the equivalent model of the energy storage optimization configuration model:

[0047]

[0048] Based on the equivalent model, the energy storage optimization configuration model is rewritten as a MINCQCP problem, resulting in the following MINCQCP model:

[0049] min c T n+d T h

[0050] stAn+Bh+Cu≤b; u∈S1; u∈S2,

[0051] In the formula, n represents the investment decision variable, c represents the investment cost vector; h represents the operation decision variable, d represents the operating cost vector; An+Bh+Cu≤b simultaneously covers both investment cost and operating cost constraints, where A, B, and C are the corresponding coefficient matrices; u represents the vector of variables in the equivalent model, including x, y, z1, and z2; S1 represents the closed set {(x,y,z1)|(x+y)2-4z1=0}, and S2 represents the closed set {(x,y,z2)|(xy)2-4z2=0}.

[0052] Furthermore, based on the improved near-end distance algorithm, the MINCQCP model is solved to obtain local optima, including:

[0053] Based on the fundamental idea of ​​the Lagrange penalty method, the MINCQCP model is rewritten using a penalty term as follows:

[0054]

[0055] In the formula, n represents the investment decision variable, c represents the investment cost vector; h represents the operational decision variable, and d represents the operating cost vector; dist(u,S a ) represents u and closed set S a The distance between them, ρ represents the vector penalty factor; where, if ρ is large enough, the MINCQCP model is equivalent to the MINCQCP model rewritten with the penalty term;

[0056] Based on the flattening method, the dist(u,S) in the rewritten MINCQCP model of the penalty term a ) via proxy function To approximate the expression; among which, Indicates that in the closed S a Projection on; when When the approximation error is close to 0.

[0057] Based on the same inventive concept, the present invention also provides an optimized configuration system incorporating photovoltaic power distribution network energy storage, the optimized configuration system comprising:

[0058] The model building unit is used to define the node carbon intensity and, based on the carbon emission intensity, to build an energy storage carbon emission flow model that considers time-series characteristics.

[0059] The calculation unit is used to calculate the cumulative carbon emissions of the energy storage system over a specific time period based on the charging and discharging status of the energy storage system.

[0060] The objective function establishment unit is used to establish the objective function of the distribution network energy storage optimization configuration model based on the energy storage carbon emission flow model, so as to minimize the total cost and achieve the emission reduction target of the energy storage system.

[0061] The solution unit is used to determine the constraints of the energy storage optimization configuration model of the distribution network; it converts the energy storage optimization configuration model into an equivalent MINCQCP model, and solves the MINCQCP model based on the improved near-end distance algorithm to obtain the local optimum.

[0062] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, including: a memory and a processor; the processor is used to read and execute a computer program stored in the memory to realize the aforementioned optimized configuration method for photovoltaic power distribution network energy storage.

[0063] Based on the same inventive concept, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned optimized configuration method for photovoltaic power distribution network energy storage.

[0064] The technical effects and advantages of this invention are as follows: It proposes a carbon emission flow model for energy storage systems based on carbon emission intensity, which can calculate the carbon emission flow distribution in chronological order and ensure accurate quantification of the carbon emissions of a single energy storage device; based on the characteristic that the nonlinearity of the carbon emission flow model of energy storage systems consists of bilinear terms, the bilinear terms are rewritten and transformed into a mixed integer nonconvex quadratic constrained programming (MINCQCP) problem. Then, by combining the ideas of penalty method and principal minimization method, the penalty-based near-end distance algorithm is improved to solve it, resulting in higher solution efficiency and better local solutions.

[0065] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart illustrating an optimized configuration method for photovoltaic power distribution network energy storage according to an embodiment of the present invention;

[0068] Figure 2 This is a schematic diagram of an optimized configuration system incorporating photovoltaic power distribution network energy storage according to an embodiment of the present invention;

[0069] Figure 3This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention;

[0070] Figure 4 This is a schematic diagram of an improved IEEE 33-node distribution network in an embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram of the energy storage power curve and the carbon intensity at the installation point in an embodiment of the present invention. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] To address the shortcomings of existing technologies, this invention discloses an optimized configuration method incorporating photovoltaic power distribution network energy storage, such as... Figure 1 As shown, it includes the following steps:

[0074] Step S1: Define node carbon intensity, and based on carbon emission intensity, establish an energy storage carbon emission flow model that considers time-series characteristics; specifically including:

[0075] The node carbon intensity is defined as the weighted average carbon emission of all power flowing into the node. Then, based on the carbon emission intensity, an energy storage carbon emission flow model considering time-series characteristics is established.

[0076] The distribution of carbon emission flows depends on the power injection of various resources in the distribution network. As the load fluctuates over time, various regulating resources will change their operating states to reach a new equilibrium, thereby causing changes in injected power and corresponding changes in carbon emission flow injection. Therefore, the distribution of carbon emission flows and the carbon intensity of related nodes in the distribution network will also change over time. The carbon emissions of energy storage systems are closely related to past charging and discharging behavior. When energy storage transfers load through charging or discharging, the carbon flow distribution will also change and be coupled in a time sequence.

[0077] Step S2: Calculate the cumulative carbon emissions of the energy storage system over a specific time period based on its charging and discharging status; specifically including:

[0078] When the energy storage system is charging, the energy storage system acts as a load, and the carbon intensity of the connected bus is the nodal carbon intensity of the energy storage system. Therefore, the carbon emissions of the energy storage system are:

[0079]

[0080] In the formula, This represents the carbon intensity of the energy storage system at time t+1 of the access node i. This represents the carbon intensity of the energy storage system at time t of the access node i. Let represent the nodal carbon intensity of the energy storage system connected to node i at time t, defined as the weighted average carbon emissions of all power flowing into the node; t represents time t. Let Δt represent the discharge power of the energy storage system at access node i at time t, and let Δt represent the duration between time t and time t+1, i.e. the duration of adjacent times. Let represent the charging power of the energy storage system at access node i at time t, and ES represent the energy storage system.

[0081] When the energy storage system discharges, the node carbon intensity of the energy storage system is the average of the current total carbon emissions and the maximum possible power generation. Therefore, the carbon emissions of the energy storage system are quantified as the product of the energy flow and the node carbon intensity of the energy storage system.

[0082] The node carbon intensity during discharge of the energy storage system is:

[0083]

[0084] In the formula, η represents the nodal carbon intensity of energy storage node i at time t, defined as the weighted average carbon emissions of all power flowing into the node; d Indicates the discharge efficiency of the energy storage system. Let represent the energy stored in the energy storage system of access node i at time t.

[0085] The carbon emissions during the discharge of the energy storage system are:

[0086]

[0087] In the formula, NC represents the carbon emissions of the energy storage system at node i when it discharges at time t. i,t This represents the nodal carbon intensity of the energy storage system connected to node i at time t. Δt represents the discharge power of the j-th energy storage system at time t, where j represents the number of energy storage systems; Δt represents the duration between time t and time t+1, i.e., the duration of adjacent times.

[0088] Step S3: Based on the energy storage carbon emission flow model, establish the objective function of the distribution network energy storage optimization configuration model. On the basis of realizing carbon emission interaction between the distribution network and the energy storage system, minimize the total cost to achieve the emission reduction target of the energy storage system.

[0089] The objective function is: minCDSO =C Cap +C ope ,

[0090] In the formula, minC DSO C represents the minimum total cost including a photovoltaic power distribution network. Cap C represents the total investment cost including the photovoltaic power distribution network. ope This indicates the operating cost of a distribution network including photovoltaic power.

[0091] The operating costs were calculated based on a typical day scenario method. The total investment cost and operating cost of the PDN are as follows:

[0092]

[0093] In the formula, γ k c represents the cost recovery factor. IC,k This represents the investment cost of asset k. D represents the installed capacity of asset k on node i, in MW; ES, WT, and PV represent energy storage system, wind turbine, and distributed photovoltaic, respectively. w Typical day w represents the equivalent number of days in a year; This represents the operating cost of the photovoltaic distribution network on the w-th typical day, where w represents the typical day number, i.e., the typical scenario number; Ψ represents the total number of typical days (typical scenarios). p represents the transmission and distribution price of the photovoltaic distribution network at time t. 01,t,w Δt represents the power transmitted by the photovoltaic distribution network at time t on the w-th typical day, where Δt represents the duration of adjacent times. This represents the marginal electricity price of the gas turbine connected to node i. This represents the power of the gas turbine at node i at time t. This represents the marginal electricity price of the energy storage system connected to node i. This represents the power of the energy storage system at node i at time t on a typical day w.

[0094] It is important to note that operating costs are calculated based on a typical day scenario method, where each typical day is mathematically equivalent to a probability of π. w =D w / 365 is a representative scenario; the annual cost can be calculated by multiplying the daily operating cost of scenario w (i.e., typical day w) by the number of days it represents (i.e., 365π). w =D w The results of all scenarios (typical day) are summed together to obtain the result.

[0095] Step S4: Determine the constraints of the distribution network energy storage optimization configuration model; convert the energy storage optimization configuration model into an equivalent MINCQCP model, solve the MINCQCP model based on the improved near-end distance algorithm, and obtain the local optimum solution; specifically including:

[0096] Step S401: Determine the constraints of the distribution network energy storage optimization configuration model, including:

[0097] The constraints include investment constraints for renewable energy and energy storage systems, operational constraints for energy storage systems and photovoltaics, and network constraints for distribution networks that include photovoltaics.

[0098] The investment constraints for renewable energy and energy storage systems include two parts: first, the new investment capacity that bus i can connect to energy storage systems and distributed photovoltaics; and second, the total investment capacity limit for the entire system of a single power source (energy storage system or distributed photovoltaics).

[0099] The investment constraint formula for renewable energy and energy storage systems is:

[0100]

[0101] In the formula, This represents the installed capacity of asset k on node i. This represents the upper limit of the installed capacity of asset k on node i, where ES and PV represent energy storage system and photovoltaic, respectively;

[0102] The operational constraints of energy storage systems and photovoltaics include: operational constraints of energy storage systems and operational constraints of distributed photovoltaics. Specifically, the operational constraints of energy storage systems include:

[0103] (1) The charging and discharging power of the energy storage system is within the rated power range and it cannot charge and discharge simultaneously;

[0104] (2) The energy stored in the energy storage system needs to meet the time-series power constraint. The initial energy and final energy level of the energy storage system should remain equal throughout the day to ensure that the energy storage system can operate continuously.

[0105] The operating constraints of the two energy storage systems are as follows:

[0106]

[0107]

[0108] In the formula, This represents the charging power of the energy storage system at node i at time t on a typical day w. This represents the upper limit of the charging power of the energy storage system connected to node i. This represents the discharge power of the energy storage system at the t-th time on the w-th typical day, where w represents the number of the typical day, i.e., the number of the typical scenario. This represents the energy of the energy storage system at node i at time t+1 on the w-th typical day. η represents the energy of the energy storage system at time t on a typical day w. ES,C η represents the charging efficiency of an energy storage system. ES,D The discharge efficiency of the energy storage system is represented by Δt, where Δt represents the duration between adjacent moments. Let represent the power of the energy storage system at the start of the charging cycle of the intervention node i, where t = 1 indicates the start of the charging cycle; Let represent the power of the energy storage system at the end of the charging cycle of access node i, where t = |T| + 1 indicates the end of the charging cycle.

[0109] The operational constraints of distributed photovoltaic (PV) systems include: the power output of distributed PV systems cannot exceed the installed capacity of distributed PV systems.

[0110] The network constraints of the distribution network include: the distribution network is mainly based on a radial topology, and a linearized power flow model is adopted to construct active power balance constraints, reactive power balance constraints, long line voltage drop constraints, voltage amplitude constraints, and carbon emission constraints (i.e., the maximum carbon emission allowance limit of the distribution network).

[0111] Carbon emission constraints are:

[0112]

[0113] In the formula, π w π represents the probability of a typical day. w =D w / 365;NC GT Indicates the nodal carbon intensity at the gas turbine connection node. NC represents the power generation of the gas turbine at node i at time t on the w-th typical day. WT This indicates the nodal carbon intensity of the wind turbine connection node. NC represents the power of the wind turbine at node i at time t on the w-th typical day. PV Indicates the nodal carbon intensity of the photovoltaic access node. This represents the photovoltaic power of access node i at time t on a typical day w. p represents the carbon emission intensity of a conventional node at time t. 01,t,w This represents the power of a conventional node at time t on the w-th typical day. This indicates the upper limit for carbon emissions.

[0114] Step S402: Convert the energy storage optimization configuration model into an equivalent MINCQCP model, including:

[0115] Energy storage optimization configuration models are typical nonlinear problems, but unlike other nonlinear programming problems, their nonlinear terms are bilinear. Therefore, the bilinear terms of the energy storage optimization configuration model are rewritten by introducing auxiliary variables z1 and z2 into the general bilinear term z = xy, resulting in the equivalent model:

[0116]

[0117] Based on the equivalent model, the energy storage optimization configuration model is rewritten as a MINCQCP problem, resulting in the following MINCQCP model:

[0118] min c T n+d T h

[0119] stAn+Bh+Cu≤b; u∈S1; u∈S2,

[0120] In the formula, n represents the investment decision variable, c represents the investment cost vector; h represents the operation decision variable, d represents the operating cost vector; An+Bh+Cu≤b simultaneously covers both investment cost and operating cost constraints, where A, B, and C are the corresponding coefficient matrices; u represents the vector of variables in the equivalent model, including x, y, z1, and z2; S1 represents the closed set {(x,y,z1)|(x+y)2-4z1=0}, and S2 represents the closed set {(x,y,z2)|(xy)2-4z2=0}.

[0121] Step S403: Solve the MINCQCP model based on the improved proximal distance algorithm to obtain local optima, including:

[0122] Based on the fundamental idea of ​​the Lagrange penalty method, the MINCQCP model is rewritten using a penalty term as follows:

[0123]

[0124] In the formula, n represents the investment decision variable, c represents the investment cost vector; h represents the operational decision variable, and d represents the operating cost vector; dist(u,S a ) represents u and closed set S a The distance between them, ρ represents the vector penalty factor; where, if ρ is large enough, the MINCQCP model is equivalent to the MINCQCP model rewritten with the penalty term;

[0125] Based on the Majorization-Minimization method, the dist(u,S) in the rewritten MINCQCP model of the penalty term... a ) via proxy function To approximate the expression; among which, Indicates that in the closed S a The projection on. When When the approximation error is close to 0.

[0126] Based on the same inventive concept, this invention also provides an optimized configuration system incorporating photovoltaic power distribution network energy storage, such as... Figure 2 As shown, the optimized configuration system includes:

[0127] The model building unit is used to define the node carbon intensity and, based on the carbon emission intensity, to build an energy storage carbon emission flow model that considers time-series characteristics.

[0128] The calculation unit is used to calculate the cumulative carbon emissions of the energy storage system over a specific time period based on the charging and discharging status of the energy storage system.

[0129] The objective function establishment unit is used to establish the objective function of the distribution network energy storage optimization configuration model based on the energy storage carbon emission flow model, so as to minimize the total cost and achieve the emission reduction target of the energy storage system.

[0130] The solution unit is used to determine the constraints of the energy storage optimization configuration model of the distribution network; it converts the energy storage optimization configuration model into an equivalent MINCQCP model, and solves the MINCQCP model based on the improved near-end distance algorithm to obtain the local optimum.

[0131] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0132] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, the structure of which is as follows: Figure 3 As shown, it includes: a memory and a processor, wherein the processor is used to read and execute the computer program stored in the memory to implement the aforementioned optimized configuration method for photovoltaic power distribution network energy storage.

[0133] Based on the same inventive concept, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned optimized configuration method for photovoltaic power distribution network energy storage.

[0134] Example: The present invention is verified by simulation using an improved IEEE 33-node distribution network.

[0135] like Figure 1 As shown, the distribution network currently has two existing photovoltaic (PV) units and two turbine generators. The planned installation locations for the turbine generators, distributed PV systems, and energy storage systems are highlighted, and these locations are determined based on a comprehensive consideration of geographical conditions, node capacity availability, and line congestion. Three typical days (out of a total of 12 days per year) are selected for calculations in each season to address uncertainties in various scenarios.

[0136] Three case studies were set up to compare and verify the effectiveness of the results of this invention.

[0137] Case 1: Allowing the installation of all power sources (i.e., energy storage systems, turbine generators, distributed photovoltaics) and disregarding carbon emission limits for the distribution network (baseline case);

[0138] Case 2: Only allow the installation of energy storage systems, and increase the carbon emission reduction target of the distribution network by 20% based on the carbon emissions calculated in Case 0;

[0139] Case 3: All power sources are permitted to be installed, with the same carbon emission restrictions as in Case 2.

[0140] The results for the three scenarios are shown in Table 1:

[0141] Table 1. Planning and Operation Results

[0142]

[0143]

[0144] The specific analysis of Table 1 is as follows:

[0145] Case 1: Ignoring carbon emission limits for the distribution network, and considering that direct power supply from the upstream grid is more economical, no power source is installed, resulting in 25,547 tons of carbon emissions annually. This demonstrates that without carbon reduction awareness, the energy consumption patterns of the distribution network cannot naturally lead to a low-carbon transition.

[0146] Case 2: With stricter carbon emission restrictions, installing energy storage units in the system enhances its load regulation capabilities, thereby avoiding the use of high carbon intensity energy sources and reducing carbon emissions. To achieve the 20% carbon emission reduction target, a total investment of 7.615MW of energy storage units is required. Figure 4 Power curves of energy storage units and carbon intensity at installation points.

[0147] like Figure 5As shown, the energy storage unit stores energy during periods of low node carbon intensity (e.g., hours 1-8 and 19-24) and releases energy during periods of high node carbon intensity (e.g., hours 9-18) to reduce carbon emissions. It is important to note that, based on the proposed energy storage emission model, its carbon intensity in discharge mode is determined by its cumulative carbon emissions. Therefore, energy storage can effectively reduce node carbon intensity by releasing energy during periods of low carbon intensity (e.g., hours 9, 11, 14, and 17). However, the high investment cost of energy storage makes the total cost of Case 2 higher than that of Case 3.

[0148] Case 3: A total of 5.76MW of turbines and 2.62MW of distributed photovoltaic (PV) power were installed in the distribution network to achieve a 20% carbon emission reduction. This increased the penetration rate of turbine generators and distributed PV, realizing clean energy substitution to reduce carbon emissions. Compared with Case 2, although the investment cost increased by RMB 702,000, the operating cost of Case 3 was significantly reduced by RMB 1,342,000 because the installed turbine generators and distributed PV reduced energy trading with the upstream grid. Thus, the total cost was 11.9% lower than that of Case 2.

[0149] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimized configuration method for photovoltaic power distribution network energy storage, characterized in that, include: Define node carbon intensity, and based on carbon emission intensity, establish an energy storage carbon emission flow model that considers time-series characteristics; Calculate the cumulative carbon emissions of the energy storage system over a specific time period based on its charging and discharging status. Based on the energy storage carbon emission flow model, an objective function is established for the distribution network energy storage optimization configuration model to minimize the total cost in order to achieve the emission reduction target of the energy storage system. The constraints of the energy storage optimization configuration model for the distribution network are determined; the energy storage optimization configuration model is equivalently converted into the MINCQCP model, and the MINCQCP model is solved based on the improved near-end distance algorithm to obtain the local optimum.

2. The optimized configuration method for photovoltaic distribution network energy storage according to claim 1, characterized in that, The node carbon intensity is the weighted average carbon emissions of all power flowing into the node; Among them, the distribution of carbon emission flow in the power distribution network and the carbon intensity of related nodes change over time.

3. The optimized configuration method for photovoltaic distribution network energy storage according to claim 1, characterized in that, Based on the charging and discharging status of the energy storage system, calculate the cumulative carbon emissions of the energy storage system over a specific time period, including: When the energy storage system is charging, the energy storage system acts as a load, and the carbon intensity of the connected bus is the nodal carbon intensity of the energy storage system. Therefore, the carbon emissions of the energy storage system are: In the formula, This represents the carbon intensity of the energy storage system at time t+1 of the access node i. The energy storage carbon intensity of the energy storage system at access node i at time t; Let represent the node carbon intensity of the energy storage system connected to node i at time t, defined as the weighted average carbon emissions of all power flowing into the node; Let Δt represent the discharge power of the energy storage system at access node i at time t, and let Δt represent the duration between time t and time t+1, i.e. the duration of adjacent times. ES represents the charging power of the energy storage system at access node i at time t; ES represents the energy storage system, and t represents time t. When the energy storage system discharges, the node carbon intensity of the energy storage system is the average of the current total carbon emissions and the maximum possible power generation. Therefore, the carbon emissions of the energy storage system are quantified as the product of the energy flow and the node carbon intensity of the energy storage system. The node carbon intensity during discharge of the energy storage system is: In the formula, η d Indicates the discharge efficiency of the energy storage system. This represents the energy stored by the energy storage system at access node i at time t. The carbon emissions during the discharge of the energy storage system are: In the formula, NC represents the carbon emissions of the energy storage system at node i when it discharges at time t. i,t This represents the nodal carbon intensity of the energy storage system connected to node i at time t. Let represent the discharge power of the j-th energy storage system at time t, where j represents the number of energy storage systems.

4. The optimized configuration method for photovoltaic distribution network energy storage according to claim 1, 2, or 3, characterized in that, The objective function is: minC DSO =C Cap +C ope , In the formula, minC DSO C represents the minimum total cost including a photovoltaic power distribution network. Cap C represents the total investment cost including the photovoltaic power distribution network. ope This represents the operating cost of a distribution network including photovoltaic power. The operating cost is calculated based on a typical day scenario method, and the total investment cost including the photovoltaic distribution network and the operating cost are respectively: In the formula, γ k c represents the cost recovery factor. IC,k This represents the investment cost of asset k. D represents the installed capacity of asset k on node i, where ES, WT, and PV represent energy storage system, wind turbine, and distributed photovoltaic, respectively. w This represents a typical day (w), which is the equivalent number of days in a year. λ represents the operating cost of the photovoltaic distribution network on the w-th typical day, where w represents the typical day number, i.e., the typical scenario number; Ψ represents the total number of typical days; λ t Power p represents the transmission and distribution price of the photovoltaic distribution network at time t. 01,t,w Δt represents the power transmitted by the photovoltaic distribution network at time t on the w-th typical day, where Δt represents the duration of adjacent times. This represents the marginal electricity price of the gas turbine connected to node i; This represents the power of the gas turbine at node i at time t; This represents the marginal electricity price of the energy storage system connected to node i; This represents the power of the energy storage system at node i at time t on a typical day w.

5. The optimized configuration method for photovoltaic distribution network energy storage according to claim 1, characterized in that, The constraints include investment constraints for renewable energy and energy storage systems, operational constraints for energy storage systems and photovoltaics, and network constraints for distribution networks containing photovoltaics. The investment constraints include: the new investment capacity that node i can connect to the energy storage system and distributed photovoltaic, and the total investment capacity limit of the entire system of a single power source; wherein, the single power source is either an energy storage system or distributed photovoltaic. The operational constraints include: operational constraints of the energy storage system and operational constraints of distributed photovoltaic systems; The operational constraints of the energy storage system include: the charging and discharging power of the energy storage system is within the rated power range, and simultaneous charging and discharging are not allowed; the energy stored in the energy storage system needs to meet the time-series power constraints, and the initial energy level and the final energy level of the energy storage system must remain equal throughout the day to ensure that the energy storage system can operate continuously. The operational constraints of the distributed photovoltaic system include: the power output of the distributed photovoltaic system cannot exceed the installed capacity of the distributed photovoltaic system. The network constraints include: based on this, a linearized power flow model is adopted to construct active power balance constraints, reactive power balance constraints, long-line voltage drop constraints, voltage amplitude constraints, and carbon emission constraints for bus nodes.

6. The optimized configuration method for photovoltaic distribution network energy storage according to claim 5, characterized in that, The investment constraints are as follows: In the formula, This represents the installed capacity of asset k on node i. This represents the upper limit of the installed capacity of asset k on node i, where ES and PV represent energy storage system and photovoltaic, respectively; The operating constraints of the energy storage system are: In the formula, This represents the charging power of the energy storage system at time t on the w-th typical day; This represents the upper limit of the charging power of the energy storage system connected to node i. This represents the discharge power of the energy storage system at time t on the w-th typical day; η represents the energy of the energy storage system at time t on a typical day w; ES,C Indicates the charging efficiency of the energy storage system; η ES,D Indicates the discharge efficiency of the energy storage system; Let represent the power of the energy storage system at the start of the charging cycle of the intervention node i, where t = 1 indicates the start of the charging cycle; This represents the power of the energy storage system at the end of the charging cycle of access node i, where t = |T| + 1 indicates the end of the charging cycle; w represents the number of a typical day, i.e., the number of a typical scenario. The carbon emission constraints are: In the formula, π w NC represents the probability of a typical day. GT This indicates the nodal carbon intensity of the gas turbine connection node; NC represents the power generation of the gas turbine at node i at time t on the w-th typical day. WT Indicates the nodal carbon intensity of the wind turbine connection node; NC represents the power of the wind turbine at node i at time t on the w-th typical day; PV Indicates the nodal carbon intensity of the photovoltaic access node; p represents the photovoltaic power of access node i at time t on the w-th typical day; 01,t,w This represents the power of a regular node at time t on the w-th typical day. Indicates the upper limit of carbon emissions. This represents the carbon emission intensity of a conventional node at time t.

7. The optimized configuration method for photovoltaic distribution network energy storage according to claim 1, characterized in that, The energy storage optimization configuration model is converted into an equivalent MINCQCP model, including: The bilinear terms of the energy storage optimization configuration model are rewritten by introducing auxiliary variables z1 and z2 into the general bilinear term z = xy, resulting in the equivalent model of the energy storage optimization configuration model: Based on the equivalent model, the energy storage optimization configuration model is rewritten as a MINCQCP problem, resulting in the following MINCQCP model: my name T n+d T h stAn+Bh+Cu≤b; u∈S1; u∈S2, In the formula, n represents the investment decision variable, c represents the investment cost vector; h represents the operation decision variable, d represents the operating cost vector; An+Bh+Cu≤b simultaneously covers both investment cost and operating cost constraints, where A, B, and C are the corresponding coefficient matrices; u represents the vector of variables in the equivalent model, including x, y, z1, and z2; S1 represents the closed set {(x,y,z1)|(x+y)2-4z1=0}, and S2 represents the closed set {(x,y,z2)|(xy)2-4z2=0}.

8. The optimized configuration method for photovoltaic distribution network energy storage according to claim 1 or 7, characterized in that, The MINCQCP model is solved based on an improved proximal distance algorithm to obtain local optima, including: Based on the fundamental idea of ​​the Lagrange penalty method, the MINCQCP model is rewritten using a penalty term as follows: In the formula, n represents the investment decision variable, c represents the investment cost vector; h represents the operational decision variable, and d represents the operating cost vector; dist(u,S a ) represents u and closed set S a The distance between them, ρ represents the vector penalty factor; where, if ρ is large enough, the MINCQCP model is equivalent to the MINCQCP model rewritten with the penalty term. Based on the flattening method, the dist(u,S) in the rewritten MINCQCP model of the penalty term a ) via proxy function To approximate the expression; among which, Indicates that in the closed S a Projection on; when When the approximation error is close to 0.

9. An optimized configuration system incorporating photovoltaic power distribution network energy storage, characterized in that, The optimized configuration system includes: The model building unit is used to define the node carbon intensity and, based on the carbon emission intensity, to build an energy storage carbon emission flow model that considers time-series characteristics. The calculation unit is used to calculate the cumulative carbon emissions of the energy storage system over a specific time period based on the charging and discharging status of the energy storage system. The objective function establishment unit is used to establish the objective function of the distribution network energy storage optimization configuration model based on the energy storage carbon emission flow model, so as to minimize the total cost and achieve the emission reduction target of the energy storage system. The solution unit is used to determine the constraints of the energy storage optimization configuration model of the distribution network; it converts the energy storage optimization configuration model into an equivalent MINCQCP model, and solves the MINCQCP model based on the improved near-end distance algorithm to obtain the local optimum.

10. An electronic device, characterized in that, include: Memory, processor; The processor is used to read and execute the computer program stored in the memory to implement the optimized configuration method for photovoltaic power distribution network energy storage as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, implement the optimized configuration method for photovoltaic power distribution network energy storage as described in any one of claims 1-8.

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