A power distribution network source-network-load-storage multi-objective collaborative planning method

Abstract

The application discloses a power distribution network source network load storage multi-objective collaborative planning method, which comprises the following steps: S1, acquiring power distribution network source network load storage planning data containing adjustable load; S2, determining a target function, electrical constraint conditions and decision variables, and constructing a power distribution network source network load storage multi-objective collaborative planning model considering adjustable load; S3, taking the minimum annual comprehensive cost of the power distribution network and the maximum installation capacity of distributed new energy as the target, and performing multi-objective optimization configuration on the power distribution network source network load storage by using an epsilon-constraint method; and S4, acquiring an optimal scheme set of the power distribution network source network load storage multi-objective collaborative planning. The power distribution network source network load storage multi-objective collaborative planning model is constructed by fully considering the adjustable load, research on the power distribution network source network load storage multi-objective collaborative planning method considering the adjustable load is realized, and the flexible reliability of the power distribution network operation is improved.

Description

Technical Field

[0001] This invention belongs to the field of distribution network planning technology, specifically relating to a multi-objective collaborative planning method for distribution network sources, grids, loads and storage. Background Technology

[0002] Scientific and rational distribution network planning is a fundamental measure to ensure a reasonable power grid structure and is related to the stable development of the social economy within the power supply area. Traditional single-objective distribution networks, which aim to minimize investment costs or network losses, can no longer meet the needs of coordinated planning of distribution network sources, grids, loads, and storage. Against this backdrop, multi-objective distribution network planning has attracted widespread attention from researchers both domestically and internationally because it can unify and coordinate multiple objectives of varying importance and even conflicting goals, such as the economic and security requirements of the distribution network.

[0003] Traditional distribution network planning methods do not fully consider adjustable load resources during the distribution network planning process, and cannot optimize the allocation and rational utilization of various resources on the power supply and demand sides. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a multi-objective collaborative planning method for power distribution network sources, grids, loads and storage, so as to optimize various resources on the power supply side and demand side while considering adjustable loads.

[0005] To address the aforementioned technical problems, this invention provides a multi-objective collaborative planning method for power distribution networks (source, grid, load, and storage), comprising:

[0006] Step S1: Obtain distribution network source-grid-load-storage planning data including adjustable loads;

[0007] Step S2: Determine the objective function, electrical constraints, and decision variables, and construct a multi-objective collaborative planning model for distribution network sources, grids, loads, and storage that considers adjustable loads;

[0008] Step S3: With the goal of minimizing the annual comprehensive cost of the distribution network and maximizing the installed capacity of distributed new energy sources, the ε-constraint method is used to perform multi-objective optimization configuration of the distribution network source, grid, load and storage.

[0009] Step S4: Obtain the optimal solution set for multi-objective collaborative planning of power generation, grid, load and storage in the distribution network.

[0010] Furthermore, in step S2, the objective function is as follows:

[0011]

[0012] Among them, f1 and f2 are the annual comprehensive cost and the installed capacity of distributed new energy, respectively; These include: line investment costs, wind power investment costs, photovoltaic investment costs, energy storage investment costs, substation investment costs, substation power purchase costs, and equipment operation and maintenance costs; P i PVG P i WTG These represent the installed capacity of photovoltaic and wind power, respectively; M and L represent the total number of photovoltaic and wind power connections to the distribution network, respectively.

[0013] Furthermore, the specific calculation formulas for the line investment cost, wind power investment cost, photovoltaic investment cost, energy storage investment cost, substation investment cost, substation power purchase cost, and equipment operation and maintenance cost in the objective function are as follows:

[0014] Line investment costs:

[0015]

[0016] Among them, y line For the service life of the line, Ω line For the collection of lines to be built, l ij Let (i,j) be the length of the line. These are the decision variables for the construction of line (i,j), and are 0-1 variables;

[0017] Wind power investment costs:

[0018]

[0019] Among them, y WTG For the service life of wind power, Ω WTG c is the set of wind turbine nodes to be installed. WTG For wind power investment costs;

[0020] Photovoltaic investment costs:

[0021]

[0022] Among them, y PVG For the service life of photovoltaics, Ω PVG c is the set of photovoltaic nodes to be installed. PVG The cost per unit capacity of photovoltaic power generation;

[0023] Energy storage investment costs:

[0024]

[0025] Among them, y ESS For the lifespan of energy storage, Ω ESS c is a set of energy storage nodes to be installed. ESS P represents the investment cost per unit capacity of energy storage. iESS It refers to the installed capacity of energy storage;

[0026] Substation investment costs:

[0027]

[0028] Where b is the discount rate, y sub For the service life of the substation, Ω sub For the substation to be constructed, c sub The unit capacity investment cost of the substation. The construction capacity of substation i;

[0029] Electricity purchase cost for substations:

[0030]

[0031] Among them, f t sub Let P be the unit electricity purchase cost of the substation at time t. sub,s,t The amount of electricity purchased by the substation from the upper-level power grid at time t in quarter s;

[0032] Equipment operation and maintenance costs:

[0033]

[0034] Among them, D s Λ represents the number of days in a quarter (s). WTG This refers to the set of nodes for wind power integration. The cost of operation and maintenance per unit of wind power output. Let Δt be the active power of wind power at node i at time t in quarter s, and Δt be the time period; Λ PVG This is the set of nodes for photovoltaic grid connection. The cost of operation and maintenance per unit of photovoltaic power. Λ represents the photovoltaic active power at node i at time t in quarter s; ESS A set of nodes for energy storage access. The cost of operation and maintenance of the energy storage unit's electricity. denoted as the energy storage charging and discharging power at node i at time t in quarter s.

[0035] Furthermore, the electrical constraints specifically include:

[0036] Substation capacity constraints:

[0037]

[0038] in, Let the newly built capacity of the i-th substation be , For the expandable capacity of the i-th substation, The decision variables for substation construction are represented by 0-1 variables;

[0039] Network topology constraints:

[0040]

[0041] Among them, f di f represents the virtual load of node i. ij,t Let be the virtual flow passing through branch ij at time t. Let N be the decision variable for the construction of branch road ij, where l represents the type of the line to be constructed for branch road ij, and N is the variable for the construction of branch road ij. b N is the number of nodes. n The number of branches;

[0042] Single-node distributed power supply capacity constraints:

[0043]

[0044] Among them, P i PVG P i WTG , P represents the installed power of photovoltaic and wind power and the installed capacity of energy storage at node i, respectively. i PVG ,max P i WTG,max , These represent the maximum installed power of photovoltaic and wind power, and the maximum installed capacity of energy storage at node i, respectively.

[0045] Node voltage and branch current constraints:

[0046]

[0047] Among them, V i max V i min These represent the upper and lower limits of the voltage at node i, respectively; I ij,t Let be the current flowing through branch ij at time t. The maximum allowable electrical value for branch ij;

[0048] Node power balance constraints:

[0049]

[0050] Among them, P is,t Q is,t These represent the injected active and reactive power at node i at time t, respectively; V i,t V j,t Let G be the voltages at nodes i and j at time t;ij B ij Let δ be the conductance and susceptance of branch ij. ij,t Let be the voltage phase angle difference of branch ij at time t; C(i) is the set of nodes connected to node i;

[0051] Distributed renewable energy output power constraints:

[0052]

[0053] Among them, P WTG,i,t P PVG,i,t Q WTG,i,t Q PVG,i,t Let i be the active and reactive power output of the new energy source at node i at time t; Let i be the upper limit of active and reactive power output of the new energy source at node i at time t; Let i be the lower limit of the active and reactive power output of the new energy source at node i at time t;

[0054] Energy storage charge / discharge status and power constraints:

[0055]

[0056] in, These represent the charging and discharging states of the energy stored at node i at time t, and are 0-1 variables; These represent the maximum charging and discharging power of the energy stored at node i, respectively. This represents the charging and discharging power of the energy stored at node i at time t; Let be the remaining energy storage capacity at node i at time t; and η represents the upper and lower limits of the energy storage capacity at node i; ch η dis These represent the charging and discharging efficiencies of energy storage, respectively.

[0057] Adjustable load constraints:

[0058]

[0059] in, It is the power before and after the adjustable load participates in demand response. These represent the minimum and maximum demand response electricity prices, respectively.

[0060] Furthermore, in step S3, the specific steps for multi-objective optimization configuration of the distribution network's source-grid-load-storage using the ε-constraint method include:

[0061] Step S301: Input a multi-objective collaborative planning model for power generation, grid, load and storage of the distribution network, taking into account adjustable loads;

[0062] Step S302: Optimize with f1 as the single objective to obtain a set of solutions.

[0063] Step S303: Optimize with f2 as the single objective to obtain a set of solutions.

[0064] Step S304, with and A rectangular region is formed by the diagonal vertices;

[0065] Step S305 will be Divide the interval into n equal segments to form a vector.

[0066] Step S306: Perform n-1 single-objective optimizations with f1 as the optimization objective, and add the following constraints: Find the solution set of the objective function [f1] (0) f1 (1) f1 (2) ,...,f1 (n) ];

[0067] Step S307, [f1] (0) f1 (1) f1 (2) ,...,f1 (n) ] and [f2 (0) f2 (1) f2 (2) ,...,f2 (n) This constitutes the parato front of this optimization problem.

[0068] Furthermore, the method also includes converting network losses and various electrical constraints into second-order cone constraints.

[0069] Furthermore, the transformation of network losses and various electrical constraints into second-order cone constraints specifically involves: using a power flow relaxation method, and based on the network structure constraints, performing equivalent substitutions on the quadratic terms in the network losses and various electrical constraints. The substitution process is as follows:

[0070]

[0071] C ij,t =V i,t V j,t cos(δ ij,t );

[0072] D ij,t =V i,t V j,t sin(δ ij,t );

[0073] Among them, u i,t C ij,t D ij,t All of these introduce intermediate variables and have no actual physical meaning.

[0074] Based on the above equations, we can obtain:

[0075]

[0076] Relaxing it yields the following formula:

[0077]

[0078] Transforming this equation further, we obtain the second-order cone form:

[0079]

[0080] After second-order cone transformation and relaxation, the following function and equation can be obtained:

[0081] The nodal power balance constraint formula is transformed into:

[0082]

[0083] The node voltage constraint formula is transformed into:

[0084] (V i min ) 2 ≤u i,t ≤(V i max ) 2 ;

[0085] Energy storage constraints are transformed into:

[0086]

[0087] Implementing this invention has the following beneficial effects: This invention aims to minimize the annual comprehensive cost of the distribution network and maximize the installed capacity of distributed new energy sources. It uses the objective function of the multi-objective collaborative planning model of distribution network source-grid-load-storage method to optimize the configuration, thereby improving the flexibility and reliability of distribution network operation. Attached Figure Description

[0088] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0089] Figure 1This is a flowchart illustrating a multi-objective collaborative planning method for power distribution network sources, grid, load, and storage according to an embodiment of the present invention.

[0090] Figure 2 This is a schematic diagram of the ε-constraint method for planning and configuring in an embodiment of the present invention.

[0091] Figure 3 This is a schematic diagram of the Portuguese 54-node system to be planned in an embodiment of the present invention.

[0092] Figure 4 This is a schematic diagram of typical daily data for wind power, photovoltaic, electric vehicle charging stations, and loads in an embodiment of the present invention.

[0093] Figure 5 These are schematic diagrams of the Parato front under different scenarios in embodiments of the present invention.

[0094] Figure 6 This is a schematic diagram of the planning result of Scheme 1 in the embodiment of the present invention.

[0095] Figure 7 This is a schematic diagram of the planning result of Scheme 2 in the embodiment of the present invention. Detailed Implementation

[0096] The following description of the embodiments is taken with reference to the accompanying drawings, which illustrate specific embodiments in which the invention can be implemented.

[0097] Please refer to Figure 1 As shown in the figure, this embodiment of the invention provides a multi-objective collaborative planning method for power distribution networks, including:

[0098] Step S1: Obtain distribution network source-grid-load-storage planning data including adjustable loads;

[0099] Step S2: Determine the objective function, constraints, and decision variables, and construct a multi-objective collaborative planning model for distribution network sources, grids, loads, and storage that considers adjustable loads;

[0100] Step S3: With the goal of minimizing the annual comprehensive cost of the distribution network and maximizing the installed capacity of distributed new energy sources, the ε-constraint method is used to perform multi-objective optimization configuration of the distribution network source, grid, load and storage.

[0101] Step S4: Obtain the optimal solution set for multi-objective collaborative planning of power generation, grid, load and storage in the distribution network.

[0102] Specifically, with the objectives of minimizing the annual comprehensive cost of the distribution network and maximizing the installed capacity of distributed renewable energy, the objective function is as follows:

[0103]

[0104] Among them, f1 and f2 are the annual comprehensive cost and the installed capacity of distributed new energy, respectively; These include: line investment costs, wind power investment costs, photovoltaic investment costs, energy storage investment costs, substation investment costs, substation power purchase costs, and equipment operation and maintenance costs; P i PVG P i WTG These represent the installed capacity of photovoltaic and wind power, respectively; M and L represent the total number of photovoltaic and wind power connections to the distribution network, respectively.

[0105] The specific calculation formulas for line investment costs, wind power investment costs, photovoltaic investment costs, energy storage investment costs, substation investment costs, substation power purchase costs, and equipment operation and maintenance costs in the objective function are as follows.

[0106] Line investment costs:

[0107]

[0108] Among them, y line For the service life of the line, Ω line For the collection of lines to be built, l ij Let (i,j) be the length of the line. These are the decision variables for the construction of line (i,j), and are 0-1 variables;

[0109] Wind power investment costs:

[0110]

[0111] Among them, y WTG For the service life of wind power, Ω WTG c is the set of wind turbine nodes to be installed. WTG For wind power investment costs;

[0112] Photovoltaic investment costs:

[0113]

[0114] Among them, y PVG For the service life of photovoltaics, Ω PVG c is the set of photovoltaic nodes to be installed. PVG The cost per unit capacity of photovoltaic power generation;

[0115] Energy storage investment costs:

[0116]

[0117] Among them, y ESS For the lifespan of energy storage, Ω ESS c is a set of energy storage nodes to be installed. ESS P represents the investment cost per unit capacity of energy storage.i ESS It refers to the installed capacity of energy storage;

[0118] Substation investment costs:

[0119]

[0120] Where b is the discount rate, y sub For the service life of the substation, Ω sub For the substation to be constructed, c sub The unit capacity investment cost of the substation. The construction capacity of substation i;

[0121] Electricity purchase cost for substations:

[0122]

[0123] Among them, f t sub Let P be the unit electricity purchase cost of the substation at time t. sub,s,t The amount of electricity purchased by the substation from the upper-level power grid at time t in quarter s;

[0124] Equipment operation and maintenance costs

[0125]

[0126] Among them, D s Λ represents the number of days in a quarter (s). WTG This refers to the set of nodes for wind power integration. The cost of operation and maintenance per unit of wind power output. Let Δt be the active power of wind power at node i at time t in quarter s, and Δt be the time period; Λ PVG This is the set of nodes for photovoltaic grid connection. The cost of operation and maintenance per unit of photovoltaic power. Λ represents the photovoltaic active power at node i at time t in quarter s; ESS A set of nodes for energy storage access. The cost of operation and maintenance of the energy storage unit's electricity. denoted as the energy storage charging and discharging power at node i at time t in quarter s.

[0127] Furthermore, various electrical constraints must be met, including:

[0128] (1) Substation capacity constraints

[0129]

[0130] in, Let the newly built capacity of the i-th substation be , For the expandable capacity of the i-th substation, The decision variables for substation construction are represented by 0-1 variables;

[0131] (2) Network topology constraints

[0132]

[0133] Among them, f di The virtual load of node i can generally be taken as unit 1, f ij,t Let be the virtual flow passing through branch ij at time t. Let N be the decision variable for the construction of branch road ij, which is a 0-1 variable. Let l represent the type of the line to be constructed for branch road ij, and N be the variable for decision-making. b N is the number of nodes. n The number of branches;

[0134] (3) Capacity constraints of single-node distributed power supply installation

[0135]

[0136] Among them, P i PVG P i WTG , P represents the installed power of photovoltaic and wind power and the installed capacity of energy storage at node i, respectively. i PVG ,max P i WTG,max , These represent the maximum installed power of photovoltaic and wind power, and the maximum installed capacity of energy storage at node i, respectively.

[0137] (4) Node voltage and branch current constraints

[0138]

[0139] Among them, V i max V i min These represent the upper and lower limits of the voltage at node i, respectively; I ij,t Let be the current flowing through branch ij at time t. The maximum allowable electrical value for branch ij;

[0140] (5) Node power balance constraints

[0141]

[0142] Among them, P is,t Q is,tThese represent the injected active and reactive power at node i at time t, respectively; V i,t V j,t Let G be the voltages at nodes i and j at time t; ij B ij Let δ be the conductance and susceptance of branch ij. ij,t Let be the voltage phase angle difference of branch ij at time t; C(i) is the set of nodes connected to node i;

[0143] (6) Output power constraint of distributed renewable energy sources

[0144]

[0145] Among them, P WTG,i,t P PVG,i,t Q WTG,i,t Q PVG,i,t Let i be the active and reactive power output of the new energy source at node i at time t; Let i be the upper limit of active and reactive power output of the new energy source at node i at time t; Let i be the lower limit of the active and reactive power output of the new energy source at node i at time t;

[0146] (7) Energy storage charge and discharge states and power constraints

[0147]

[0148] in, These represent the charging and discharging states of the energy stored at node i at time t, and are 0-1 variables; These represent the maximum charging and discharging power of the energy stored at node i, respectively. This represents the charging and discharging power of the energy stored at node i at time t; Let be the remaining energy storage capacity at node i at time t; and η represents the upper and lower limits of the energy storage capacity at node i; ch η dis These represent the charging and discharging efficiencies of energy storage, respectively.

[0149] (8) Adjustable load constraints

[0150]

[0151] in, It is the power before and after the adjustable load participates in demand response. These represent the minimum and maximum demand response electricity prices, respectively.

[0152] Furthermore, such as Figure 2 As shown, the specific steps for multi-objective optimization configuration of power generation, grid, load, and storage in the distribution network using the ε-constraint method include:

[0153] Step S301: Input a multi-objective collaborative planning model for power generation, grid, load and storage of the distribution network, taking into account adjustable loads;

[0154] Step S302: Optimize with f1 as the single objective to obtain a set of solutions.

[0155] Step S303: Optimize with f2 as the single objective to obtain a set of solutions.

[0156] Step S304, with and A rectangular region is formed by the diagonal vertices;

[0157] Step S305 will be Divide the interval into n equal segments to form the vector [f2] (0) f2 (1) f2 (2) ,...,f2 (n) ];

[0158] Step S306: Perform n-1 single-objective optimizations with f1 as the optimization objective, and add the following constraints: Find the solution set of the objective function [f1] (0) f1 (1) f1 (2) ,...,f1 (n) ]

[0159] Step S307, [f1] (0) f1 (1) f1 (2) ,...,f1 (n) ]and This constitutes the parato front of this optimization problem.

[0160] Furthermore, it also includes converting the network losses and various electrical constraints into second-order cone constraints.

[0161] In one implementation, the transformation of network losses and various electrical constraints into second-order cone constraints specifically involves using a power flow relaxation method to perform equivalent substitutions on the quadratic terms in the network losses and various electrical constraints based on the network structure constraints. The substitution process is as follows:

[0162]

[0163] C ij,t =V i,t V j,t cos(δ ij,t );

[0164] Dij,t =V i,t V j,t sin(δ ij,t );

[0165] In the formula: u i,t C ij,t D ij,t All of these introduce intermediate variables and have no actual physical meaning.

[0166] Based on the above equations, we can obtain:

[0167]

[0168] Since this formula contains a quadratic term, relaxing it yields the following formula:

[0169]

[0170] Transforming this equation further, we obtain the second-order cone form:

[0171]

[0172] After second-order cone transformation and relaxation, the following function and equation can be obtained:

[0173] The nodal power balance constraint formula is transformed into:

[0174]

[0175] The node voltage constraint formula is transformed into:

[0176] (V i min ) 2 ≤u i,t ≤(V i max ) 2 ;

[0177] Energy storage constraints are transformed into:

[0178]

[0179] The following is a detailed description of an optional embodiment of the present invention.

[0180] As an optional embodiment, the above-described distribution network planning method can be applied to, for example... Figure 3 The Portugal 54-node distribution network system shown was tested, with a total load of 76.3MW. New energy types include wind power (WTG) and photovoltaic (PVG). Information on distributed new energy installations and investment costs are shown in Tables 1-3. Typical daily data for WTG, PVG, electric vehicle loads, and conventional loads are as follows: Figure 4 As shown.

[0181] Table 1 Information on New Energy Sources to be Installed

[0182]

[0183] Table 2 Cost Parameters

[0184]

[0185] Table 3. Substation Information for the Portugal 54-Node System

[0186]

[0187] To analyze the impact of considering and not considering adjustable load on the planning results, two planning scenarios are set up in the example:

[0188] Scenario 1: Multi-objective coordinated planning of power generation, grid, load and storage in distribution network considering adjustable load

[0189] Scenario 2: Multi-objective coordinated planning of distribution network sources, grid, load and storage without considering adjustable loads

[0190] The Parato front under different scenarios is obtained by solving a multi-objective algorithm. Figure 5 As shown. By Figure 5 It can be seen that the system's annual comprehensive cost is lower when considering adjustable load, and the system's distributed renewable energy installation capacity is less. This result demonstrates the effectiveness of considering adjustable load. To further analyze the planning results under scenarios 1 and 2, the optimal solutions for scenarios 1 and 2 are selected for comparison.

[0191] Table 4. Costs for each of the planned years under both Scheme 1 and Scheme 2

[0192]

[0193] Table 4 compares the various costs of Scheme 1 and Scheme 2 within the planned year. As shown in Table 4, the route planning results of Scheme 1 and Scheme 2 are basically the same, therefore the route investment costs are essentially the same. Scheme 2 has a lower total installed capacity of new energy sources and lower investment costs, but the increased electricity purchase volume leads to higher electricity purchase costs. The main expense is electricity purchase costs; therefore, the total cost of Scheme 2 is greater than that of Scheme 1. A comprehensive analysis of various costs shows that considering adjustable load in the planning can reduce the overall annual cost of the system.

[0194] The installation status of distributed new energy sources in Option 1 is as follows: Figure 6As shown in Scheme 1, the installed wind turbine capacity at node 15 is 5.1MW, at node 32 it is 1.2MW, and at node 39 it is 5.5MW, for a total installed wind turbine capacity of 11.8MW. The installed photovoltaic capacity at node 8 is 9.6MW, and at node 43 it is 10.1MW, for a total installed photovoltaic capacity of 19.7MW. The total renewable energy capacity is 31.5MW, the total load is 76.3MW, and the renewable energy penetration rate is 41.3%.

[0195] Installation status of distributed new energy sources in Option 2 Figure 7 As shown in the diagram. In Scheme 2, the installed wind turbine capacity at node 15 is 4.5MW, at node 32 it is 1.2MW, and at node 39 it is 4.8MW, for a total installed wind turbine capacity of 10.5MW. The installed photovoltaic capacity at node 8 is 9.7MW, and at node 43 it is 8.3MW, for a total installed photovoltaic capacity of 18MW. The total renewable energy capacity is 28.5MW, the total load is 76.3MW, and the renewable energy penetration rate is 37.4%.

[0196] Table 5 Route Planning Results

[0197]

[0198] Table 6 Results of Substation Expansion Planning

[0199]

[0200] Table 7 Results of New Energy and Energy Storage Planning for Schemes 1 and 2

[0201]

[0202] Table 5 shows the line planning results, Table 6 shows the substation expansion planning results, and Table 7 shows the new energy and energy storage planning results for Scheme 1 and Scheme 2. As shown in Table 7, Scheme 1 installs a larger total amount of distributed new energy than Scheme 2. This is because Scheme 1 considers adjustable loads, transferring some of the load during peak electricity consumption periods to off-peak periods, thereby increasing the installed capacity of distributed new energy through peak shaving and valley filling.

[0203] As can be seen from the above description, compared with the prior art, the beneficial effects of the present invention are as follows: The present invention aims to minimize the annual comprehensive cost of the distribution network and maximize the installed capacity of distributed new energy sources. It uses the objective function of the multi-objective collaborative planning model of distribution network source-grid-load-storage method to optimize the configuration, thereby improving the flexibility and reliability of distribution network operation.

[0204] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A multi-objective collaborative planning method for power distribution network sources, grid, load, and storage, characterized in that, include: Step S1: Obtain distribution network source-grid-load-storage planning data including adjustable loads; Step S2: Determine the objective function, electrical constraints, and decision variables, and construct a multi-objective collaborative planning model for distribution network sources, grids, loads, and storage that considers adjustable loads; Step S3, with the goal of minimizing the annual comprehensive cost of the distribution network and maximizing the installed capacity of distributed renewable energy, utilizes... Constraint methods are used to optimize the allocation of power sources, grids, loads, and storage in the distribution network based on multiple objectives. Step S4: Obtain the optimal solution set for multi-objective collaborative planning of power generation, grid, load and storage in the distribution network; In step S2, the objective function is as follows: ； in, , These are the annual comprehensive cost and the installed capacity of distributed new energy sources, respectively. , , , , , , These include: line investment costs, wind power investment costs, photovoltaic investment costs, energy storage investment costs, substation investment costs, substation power purchase costs, and equipment operation and maintenance costs. , They are nodes The installed capacity of photovoltaic and wind power; , These represent the total number of photovoltaic and wind power connections connected to the distribution network, respectively. In step S3, using The specific steps of using the constraint method to perform multi-objective optimal allocation of power generation, grid, load, and storage in the distribution network include: Step S301: Input a multi-objective collaborative planning model for power generation, grid, load and storage of the distribution network, taking into account adjustable loads; Step S302, with Optimize for a single objective and obtain a set of solutions. ; Step S303, with Optimize for a single objective and obtain a set of solutions. ; Step S304, with and A rectangular region is formed by the diagonal vertices; Step S305, will Divide the interval into n equal segments to form a vector. ; Step S306, with To optimize the objective by performing n-1 iterations of single-objective optimization, the following constraints are added: Find the solution set of the objective function ; Step S307, and This constitutes the parato front of this optimization problem.

2. The method according to claim 1, characterized in that, The specific calculation formulas for the line investment cost, wind power investment cost, photovoltaic investment cost, energy storage investment cost, substation investment cost, substation power purchase cost, and equipment operation and maintenance cost in the objective function are as follows: Line investment costs: ； in, This refers to the service life of the line. This is a collection of lines to be constructed. For the line Length, It is a line The decision variables for construction are 0-1 variables; Wind power investment costs: ； in, The service life of wind power. This is a collection of wind power nodes awaiting installation. The cost per unit power of wind power; Photovoltaic investment costs: ； in, The service life of photovoltaic panels. This is a collection of photovoltaic nodes awaiting installation. Cost per unit power of photovoltaic power; Energy storage investment costs: ； in, For the lifespan of energy storage, This is a collection of energy storage nodes to be installed. The investment cost per unit power of energy storage, It is a node i The installed capacity of energy storage; Substation investment costs: ； in, For the discount rate, This refers to the service life of the substation. This is a substation construction node. The unit capacity investment cost of the substation. For the first The newly built capacity of each substation; Electricity purchase cost for substations: ； in, for The unit electricity purchase cost of the substation at any given time. for Quarter The amount of electricity purchased by the substation from the upper-level power grid at all times; Equipment operation and maintenance costs: in, For quarters The number of days, This refers to the set of nodes for wind power integration. The cost of operation and maintenance per unit power of wind power. for Quarter Time Node The active power of wind power at the location, For a period of time; This is the set of nodes for photovoltaic grid connection. The cost of operation and maintenance per unit power of photovoltaic power. for Quarter Time Node Photovoltaic active power at the location; A set of nodes for energy storage access. The cost of operation and maintenance per unit power of energy storage. , They are respectively Quarter Time Node The energy storage charging and discharging power at the location.

3. The method according to claim 2, characterized in that, The electrical constraints specifically include: Substation capacity constraints: ； in, For the first The newly built capacity of each substation, For the first The substation can be expanded in capacity. The decision variables for substation construction are represented by 0-1 variables; Network topology constraints: ； in, For nodes Virtual load, for Time Branch Virtual traffic flowing through, branch road Decision variables for constructing the railway line Indicates a branch The model of the construction line, For the number of nodes, The number of branches; Single-node distributed power supply capacity constraints: ； in, , , They are nodes The installed power of photovoltaic and wind power, as well as the installed capacity of energy storage, , , They are nodes The maximum installed power of photovoltaic and wind power and the maximum installed capacity of energy storage; Node voltage and branch current constraints: ； in, , Representing nodes respectively Voltage upper and lower limits; for Time flows through the side road The current, branch road Maximum allowable current; Node power balance constraints: ； in, , They are respectively in Time Node The injected active and reactive power; , for Time Node , The voltage; , branch road The conductivity and susceptance, for Time Branch The voltage phase angle difference; For nodes A set of connected nodes; Distributed renewable energy output power constraints: ； in, , , , They are respectively Time Node The active and reactive power outputs of wind and solar power; , , , for Time Node The upper limits of active and reactive power output of wind power and solar power; , , , for Time Node The lower limits of active and reactive power output of wind power and photovoltaic power; Energy storage charge / discharge status and power constraints: ； in, , Representing nodes respectively Energy storage The charging and discharging states are constantly being represented by a 0-1 variable. , Representing nodes respectively The maximum power for charging and discharging energy stored in the device; , Representing nodes respectively Energy storage Constant charging and discharging power; for Time Node The remaining capacity of the energy storage; and They are nodes The upper and lower limits of energy storage capacity; , These are the charging efficiency and discharging efficiency of energy storage, respectively. Adjustable load constraints: in, , These are the power levels before and after the adjustable load participates in demand response. , These represent the minimum and maximum values ​​of the demand response power, respectively.

4. The method according to claim 3, characterized in that, It also includes converting network losses and various electrical constraints into second-order cone constraints.

5. The method according to claim 4, characterized in that, The process of transforming network losses and various electrical constraints into second-order cone constraints involves: using a power flow relaxation method to perform equivalent substitutions on the quadratic terms in the network losses and various electrical constraints based on the network structure constraints. The substitution process is as follows: ； ； ； in, , , All of these introduce intermediate variables and have no actual physical meaning. Based on the above equations, we can obtain: ； Relaxing it yields the following formula: ； Transforming this equation further, we obtain the second-order cone form: ； After second-order cone transformation and relaxation, the following function and equation can be obtained: The nodal power balance constraint formula is transformed into: ； The node voltage constraint formula is transformed into: ； Energy storage constraints are transformed into: 。

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