Distributed energy storage power station planning method and system considering marketization benefits

Abstract

The invention discloses a distributed energy storage power station planning method and system considering marketization benefits, and the method comprises the steps: constructing a double-layer model for the planning of a distributed energy storage power station; the double-layer model comprises an upper-layer model for locating and sizing the distributed energy storage power station under a long time scale and a lower-layer model for dynamic optimal power flow of the power distribution network under a short time scale, and then solving the double-layer model to obtain a distributed energy storage power station planning scheme. In the double-layer model constructed by the invention, the upper-layer model aims at minimizing the annual operation cost of the energy storage power station considering the annual price difference arbitrage of energy storage and the frequency modulation income, so that a power grid company is guided to integrate energy storage into the planning of a power distribution network, and a decision basis is provided for a market subject to invest and operate the distributed energy storage power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy storage optimization planning, and in particular relates to a distributed energy storage power station planning method and system taking market benefits into consideration. Background Art

[0002] Distributed energy storage has various application scenarios. It can be located on the user side, distributed power supply side and distribution network side, and can be presented in the form of energy storage combined with users (household storage), energy storage combined with distributed power supply (such as photovoltaic storage system), independent energy storage system or microgrid. Users or distributed photovoltaics configured with energy storage alone have problems such as single application scenario, high investment and operation costs, and insufficient capacity configuration flexibility. Distributed independent energy storage connected to the public distribution network is an important means to solve the problems of distributed power supply absorption difficulties, distribution network voltage flow exceeding the limit, and large load peak-to-valley difference. It is an important scenario for the future development of distributed energy storage.

[0003] Due to the small project capacity, it is difficult for distributed independent energy storage to directly participate in the electricity market. Previous planning did not consider distributed energy storage as a market player to participate in the electricity market. With the gradual improvement of the construction of the electricity market, it has become a trend to lower the entry threshold for energy storage to participate in the electricity market and allow distributed resources including energy storage to enter the electricity market. The current optimization planning of distributed energy storage is mainly aimed at reducing network losses and promoting the consumption of distributed new energy. It does not consider the benefits of distributed energy storage after participating in the electricity market, and it is difficult to adapt to future development. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a distributed energy storage power station planning method and system taking market benefits into consideration.

[0005] The technical solution of the present invention is as follows:

[0006] A distributed energy storage power station planning method considering market benefits includes:

[0007] Constructing a two-layer model for distributed energy storage power station planning, the two-layer model includes an upper model for site selection and capacity determination of distributed energy storage power stations considering a long time scale and a lower model for dynamic optimal power flow of distribution networks considering a short time scale;

[0008] The upper model aims to minimize the annual operating cost of the energy storage power station taking into account the annual frequency regulation benefits of energy storage, and uses the rated energy storage power, energy storage duration and access location selection of the energy storage power station as decision variables, and energy storage power range constraints, node space constraints, and charging and discharging duration range constraints as constraints;

[0009] The lower model aims to minimize the daily electricity purchase cost of the distribution network, and uses the branch current, node voltage, branch power, main network injection power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive power output of the reactive compensation device of the distribution network as decision variables, and uses energy storage charging / discharging power constraints, energy storage charge state constraints, frequency regulation state constraints, distributed power output constraints, reactive power compensation device output constraints, power flow constraints, Ohm's law constraints, branch current constraints, node voltage constraints, second-order cone constraints, and main network power purchase constraints as constraints;

[0010] The two-layer model is solved to obtain a distributed energy storage power station planning solution.

[0011] Furthermore, the objective function of the upper model is:

[0012] minC UP =C inv +C om +C loss +C ch -I dis -I fre

[0013] In the formula, C UP is the total annual operating cost of the distributed energy storage power station; C inv is the annual value of the initial investment cost of the distributed energy storage power station; C om is the annual operation and maintenance cost of the distributed energy storage power station; C loss is the cost corresponding to the annual charge and discharge loss of the distributed energy storage power station, C ch I is the annual charging cost of distributed energy storage power station; dis is the annual discharge income of the distributed energy storage power station; I fre The annual frequency regulation income of the distributed energy storage power station;

[0014]

[0015] E es =P es ×T es

[0016]

[0017] In the formula, is the initial investment cost of energy storage; r is the discount rate; n is the expected life span; C e is the unit capacity cost of distributed energy storage power station; E es is the rated capacity of the distributed energy storage power station, T es is the energy storage duration; C p is the unit power cost of distributed energy storage power station; P esis the rated power of the distributed energy storage power station; k om is the annual operation and maintenance cost coefficient of the distributed energy storage power station;

[0018]

[0019]

[0020] In the formula, k t The cost of unit charging loss during the charging and discharging process of the energy storage power station; Δt is the spot settlement time interval; is the charging power and discharging power of the energy storage in period t; η ch , η dis It is the energy storage charging efficiency and discharging efficiency; is the power reserved for energy storage to participate in frequency regulation during period t; β is the average charging power of energy storage frequency regulation during period t and ratio; T is the scheduling period, n day is the annual operating days of the energy storage power station; is the spot clearing electricity price during period t.

[0021] I fre =K st ×D×p fre ×n day

[0022]

[0023] In the formula, I fre is the annual frequency regulation benefit of energy storage, K st is the daily average frequency regulation performance coefficient of energy storage, D is the daily average frequency regulation mileage, and p fre is the frequency modulation clearing price; m is the ratio of the average frequency modulation mileage within Δt time to the reserved frequency modulation power.

[0024] Furthermore, the energy storage power range constraint, charge and discharge time range constraint and node space constraint are respectively:

[0025] 0.5≤P es ≤3

[0026] 1≤T es ≤4

[0027] 1≤n es ≤N node

[0028] Where P es is the rated power of the distributed energy storage power station; T es is the energy storage duration; n es N is the node location number of the energy storage installation. node The maximum number of nodes in the distribution line.

[0029] Furthermore, the objective function of the lower model is:

[0030] minC DN =C grid +C pv

[0031] In the formula, C DN is the daily electricity purchase cost of the distribution network; C grid The cost of purchasing electricity from the main grid for the distribution network; C pv The cost of purchasing electricity from photovoltaic power generation for the distribution network.

[0032] Furthermore, the expression of the energy storage charging and discharging power constraint is:

[0033]

[0034] Where P max is the upper limit of energy storage charging and discharging power; α es,t , β es,t is a 0-1 variable indicating the charge and discharge state of the energy storage during period t;

[0035] The expression of the energy storage charge state constraint is:

[0036]

[0037] In the formula, SOC es,max , SOC es,min SOC is the upper and lower limits of the energy storage state of charge; es,t SOC is the state of charge of the energy storage at the end of period t; es,t-1 SOC is the state of charge of the energy storage at the end of the t-1 period; es,0 , SOC es,T E is the charge state at the beginning and end of the energy storage operation day; es is the rated capacity of the energy storage power station; η ch , η dis They are the charging efficiency and discharging efficiency of the energy storage power station respectively.

[0038] The expression of the frequency modulation state constraint is:

[0039]

[0040] In the formula, is a 0-1 variable; The upper and lower limits of SOC when energy storage participates in frequency regulation; Report the power upper limit for energy storage to participate in frequency regulation;

[0041] The expression of the distributed generation output constraint is:

[0042]

[0043] In the formula, B PV is the collection of photovoltaic access nodes; is the active power output of the PV connected to node j in period t; is the predicted output of the photovoltaic connected to node j in period t;

[0044] The expression of the output constraint of the reactive power compensation device is:

[0045]

[0046] In the formula, B SVC It is a collection of SVC access nodes of reactive power compensation device; is the reactive power output of the reactive power compensation device SVC connected to node j in period t; are the upper and lower limits of the reactive compensation amount of the reactive compensation device SVC connected to node j respectively;

[0047] The expression of the power flow constraint is:

[0048]

[0049]

[0050] Where P ij,t , Q ij,t are the active power and reactive power flowing through branch ij at time t respectively; ij,t is the square of the current amplitude flowing through branch ij at time t; r ij 、x ij is the resistance and reactance of line ij; P jk,t , Q jk,t are respectively the active power and reactive power at the head end of branch jk at time t; P j,t , Q j,t are the active power and reactive power injected into nodes i and j at time t respectively; w j is the set of branch end nodes with j as the head node, and set B represents the set of all nodes in the network; is the discharge power of node j during energy storage period t; is the load power of node j in period t;

[0051] The expression for the Ohm's law constraint is:

[0052]

[0053] Where U j,t and U i,tare the squares of the voltage amplitudes at nodes i and j respectively; the set L represents the set of all branches in the network;

[0054] The expression of the branch current constraint is:

[0055]

[0056] In the formula, I max,ij It represents the maximum value of the square of the current amplitude of branch ij;

[0057] The expression of the node voltage constraint is:

[0058]

[0059] Where U min , U max is the lower and upper limits of the square of the node voltage, U j,t is the square of the voltage amplitude at node j at time t;

[0060] The expression of the second-order cone constraint is:

[0061]

[0062] The expression of the main grid purchased power constraint is:

[0063]

[0064]

[0065] In the formula, They are respectively the active power and reactive power purchased from the main grid; They are the minimum and maximum active power purchased from the main grid respectively; They respectively represent the minimum and maximum reactive power purchased from the main grid.

[0066] Furthermore, the specific steps of solving the double-layer model to obtain a distributed energy storage power station planning solution include:

[0067] The upper model is solved by using a particle swarm algorithm and the lower model is solved by using a second-order cone programming method.

[0068] Furthermore, the specific method of using the particle swarm algorithm to solve the upper model includes:

[0069] First, a group of particles with a population size of n and a decision variable of m are randomly initialized, where the i-th particle is x i 、v iRepresent their position vector and velocity vector respectively. In the solution space, particles jointly determine the speed and direction of their movement based on their own and group information. The optimal solution of the decision variable is searched by iteratively updating the particle position and velocity vector.

[0070] The particle velocity and position update formula is as follows:

[0071]

[0072] ω k =(ω max -ω min )×(k max -k) / k max

[0073] In the formula, i is the particle number and k is the number of iterations; is the velocity and position of the dth dimension of the i-th particle at the k-th iteration; c1 and c2 are acceleration factors that affect the flight speed of the particle toward the individual optimal and overall optimal particles; ω is the inertia weight coefficient, and r1 and r2 are random numbers ranging from 0 to 1; are the d-th dimension vector of the individual optimal position in the k-th iteration and the d-th dimension vector of the global optimal position; ω max ,ω min ,ω k —respectively the maximum value, minimum value and inertia weight of the kth iteration; kma x , k—are the maximum number of iterations and the kth iteration number respectively.

[0074] A distributed energy storage power station planning system considering market benefits includes a model building module and a model solving module;

[0075] The model building module is used to build a two-layer model for distributed energy storage power station planning, and the two-layer model includes an upper model for site selection and capacity determination of distributed energy storage power stations under long time scales and a lower model for dynamic optimal power flow of distribution networks under short time scales; the upper model aims to minimize the annual operating cost of the energy storage power station considering the annual frequency regulation benefits of energy storage, and uses the rated energy storage power, energy storage duration and access location selection of the energy storage power station as decision variables, and uses energy storage power range constraints, node space constraints, and charging and discharging duration range constraints as constraints; The lower model aims to minimize the daily electricity purchase cost of the distribution network, and uses the branch current, node voltage, branch power, main grid injection power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive power output of the reactive compensation device as decision variables, and uses energy storage charging / discharging power constraints, energy storage charge state constraints, frequency regulation state constraints, distributed power output constraints, reactive power compensation device output constraints, power flow constraints, Ohm's law constraints, branch current constraints, node voltage constraints, second-order cone constraints, and main grid power purchase power constraints as constraints.

[0076] The model solving module is used to solve the double-layer model to obtain a distributed energy storage power station planning solution.

[0077] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0078] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention proposes a distributed energy storage power station planning method and system that considers market benefits. The method constructs a two-layer model for distributed energy storage power station planning. The two-layer model includes an upper model that considers the site selection and capacity determination of distributed energy storage power stations under long time scales and a lower model of the dynamic optimal flow of the distribution network under short time scales. The two-layer model is then solved to obtain a distributed energy storage power station planning scheme. In the two-layer model constructed by the present invention, the upper model aims to minimize the annual operating cost of the energy storage power station considering the annual price difference arbitrage of energy storage and the frequency regulation benefits. Therefore, in addition to guiding power grid companies to integrate energy storage into distribution network planning, it also provides a decision-making basis for market entities to invest in and operate distributed energy storage power stations.

[0081] The objective function of the upper model of the method of the present invention takes into account the cost corresponding to the annual charging and discharging loss of distributed energy storage power stations. By comparing this cost with the loss reduction benefit brought by the smoothed net load curve of energy storage, suggestions can be provided for improving the allocation mechanism of the energy storage loss cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a flow chart of a distributed energy storage power station planning method considering market benefits in an embodiment;

[0083] Figure 2 This is an example diagram of a power flow model of a distribution network branch in the embodiment;

[0084] Figure 3 It is a schematic diagram of a modified IEEE 33-node system in an application embodiment;

[0085] Figure 4 It is a typical daily load value, photovoltaic output and time-of-use electricity price diagram in the application example;

[0086] Figure 5 It is a curve chart showing the change of annual operating cost of the distributed energy storage power station with the number of iterations in the application embodiment. DETAILED DESCRIPTION

[0087] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0088] Embodiment 1:

[0089] A distributed energy storage power station planning method considering market benefits of the present invention is as follows: Figure 1 As shown, including:

[0090] S1. Constructing a two-layer model for distributed energy storage power station planning, the two-layer model includes an upper model for site selection and capacity determination of distributed energy storage power stations under long time scales and a lower model for dynamic optimal power flow of distribution networks under short time scales;

[0091] The upper model aims to minimize the annual operating cost of the energy storage power station taking into account the annual frequency regulation benefits of energy storage, and uses the rated energy storage power, energy storage duration and access location selection of the energy storage power station as decision variables, and uses energy storage power range constraints, node space constraints, and charging and discharging duration range constraints as constraint conditions; among which, the upper limit of the energy storage power range constraint does not exceed the rated capacity of the distribution line, the node space constraint is generally composed of each node of the distribution line, the lower limit of the constraint is determined by the charge and discharge rate of the energy storage battery, and the upper limit of the charge and discharge duration range constraint is determined by the net load of the distribution line.

[0092] The lower model aims to minimize the daily electricity purchase cost of the distribution network, and uses the branch current, node voltage, branch power, main network injection power (i.e., purchased power) of the distribution network, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive power output of the reactive compensation device as decision variables, and uses energy storage charging / discharging power constraints, energy storage charge state constraints, frequency regulation state constraints, distributed power output constraints, reactive power compensation device output constraints, power flow constraints, Ohm's law constraints, branch current constraints, node voltage constraints, second-order cone constraints, and main network purchased power constraints as constraints;

[0093] S2. Solve the double-layer model to obtain a distributed energy storage power station planning solution.

[0094] Embodiment 2:

[0095] This embodiment is further designed on the basis of the first embodiment in that the objective function of the upper layer model in this embodiment is:

[0096] minC UP =C inv +C om +C loss +C ch -I dis -I fre

[0097] In the formula, C UP is the total annual operating cost of the distributed energy storage power station; C inv is the annual value of the initial investment cost of the distributed energy storage power station; C om is the annual operation and maintenance cost of the distributed energy storage power station; C loss is the cost corresponding to the annual charge and discharge loss of distributed energy storage power station, including transmission and distribution cost and network loss cost, C ch I is the annual charging cost of distributed energy storage power station; dis is the annual discharge income of the distributed energy storage power station; I fre The annual frequency regulation income of the distributed energy storage power station;

[0098]

[0099]

[0100] In the formula, is the initial investment cost of energy storage; r is the discount rate; n is the expected life span; C e is the unit capacity cost of distributed energy storage power station; E es is the rated capacity of the distributed energy storage power station, T es is the energy storage duration; C p is the unit power cost of distributed energy storage power station; Pes is the rated power of the distributed energy storage power station; k om is the annual operation and maintenance cost coefficient of the distributed energy storage power station;

[0101]

[0102]

[0103] In the formula, k t The cost of unit charging loss during the charging and discharging process of the energy storage power station; Δt is the spot settlement time interval; is the charging power and discharging power of the energy storage in period t; η ch , η dis is the charging efficiency and discharging efficiency of energy storage in period t; is the power reserved for energy storage to participate in frequency regulation during period t; β is the average charging power of energy storage frequency regulation during period t and ratio; T is the scheduling period, which is 24, n day is the annual operating days of the energy storage power station; is the spot clearing electricity price during period t.

[0104]

[0105]

[0106] In the formula, I fre is the annual frequency regulation benefit of energy storage, K st is the daily average frequency regulation performance coefficient of energy storage, D is the daily average frequency regulation mileage, and p fre is the frequency modulation clearing price; m is the ratio of the average frequency modulation mileage within Δt time to the reserved frequency modulation power.

[0107] Embodiment three:

[0108] This embodiment is further designed on the basis of the second embodiment in that the energy storage power range constraint, the charge and discharge time range constraint and the node space constraint are respectively:

[0109] 0.5≤P es ≤4

[0110] 1≤T es ≤4

[0111] 1≤n es ≤N node

[0112] Where P es is the rated power of the distributed energy storage power station, ranging from 0.5MW to 4MW; T esFor the energy storage duration, the common lithium iron phosphate battery energy storage discharge rate is generally less than 1, so the shortest duration is considered to be 1 hour; n es N is the node location number of the energy storage installation. node The maximum number of nodes in the distribution line.

[0113] Embodiment 4:

[0114] This embodiment is further designed on the basis of the third embodiment in that the objective function of the lower layer model in this embodiment is:

[0115] minC DN =C grid +C pv

[0116] In the formula, C DN is the daily electricity purchase cost of the distribution network; C grid The cost of purchasing electricity from the main grid for the distribution network; C pv The cost of purchasing electricity from photovoltaic power generation for the distribution network.

[0117] Embodiment five:

[0118] This embodiment is further designed on the basis of the fourth embodiment in that the expression of the energy storage charging and discharging power constraint in this embodiment is:

[0119]

[0120] Where P max is the upper limit of energy storage charging and discharging power; α es,t , β es,t is a 0-1 variable indicating the charge and discharge state of the energy storage during period t; is the discharge power of the energy storage in period t; is the charging power of the energy storage in period t; The power reserved for energy storage to participate in frequency regulation during period t;

[0121] The expression of the energy storage charge state constraint is:

[0122]

[0123] In the formula, SOC es,max , SOC es,min SOC is the upper and lower limits of the energy storage state of charge; es,t SOC is the state of charge of the energy storage at the end of period t; es,t-1 SOC is the state of charge of the energy storage at the end of the t-1 period; es,0 , SOC es,T E is the charge state at the beginning and end of the energy storage operation day; es is the rated capacity of the energy storage power station; η ch , ηdis They are the charging efficiency and discharging efficiency of the energy storage power station respectively.

[0124] The expression of the frequency modulation state constraint is:

[0125]

[0126] In the formula, is a 0-1 variable; The upper and lower limits of SOC when energy storage participates in frequency regulation; Report the power upper limit for energy storage to participate in frequency regulation;

[0127] The expression of the distributed generation output constraint is:

[0128] For the sake of simplicity, only distributed photovoltaic power generation is considered in this paper. Distributed photovoltaic power generation is allowed to abandon power and reactive power output is not considered. The active power output constraints are as follows:

[0129]

[0130] In the formula, B PV is the collection of photovoltaic access nodes; is the active power output of the PV connected to node j in period t; is the predicted output of the photovoltaic connected to node j in period t;

[0131] The expression of the output constraint of the reactive power compensation device is:

[0132] For simplicity, only static reactive power compensation device (SVC) is considered in the reactive power compensation device, and the reactive power output constraint is as follows:

[0133]

[0134] In the formula, B SVC It is a collection of SVC access nodes of reactive power compensation device; is the reactive power output of the reactive power compensation device SVC connected to node j in period t; are the upper and lower limits of the reactive compensation amount of the reactive compensation device SVC connected to node j respectively;

[0135] The expression of the power flow constraint is:

[0136]

[0137]

[0138] Where P ij,t , Q ij,t are the active power and reactive power flowing through branch ij at time t respectively; ij,tis the square of the current amplitude flowing through branch ij at time t; r ij 、x ij is the resistance and reactance of line ij; P jk,t , Q jk,t are respectively the active power and reactive power at the head end of branch jk at time t; P j,t , Q j,t are the active power and reactive power injected into nodes i and j at time t respectively; w j is the set of branch end nodes with j as the head node, and set B represents the set of all nodes in the network; is the discharge power of node j during energy storage period t; is the load power of node j in period t;

[0139] The expression for the Ohm's law constraint is:

[0140]

[0141] Where U j,t and U i,t are the squares of the voltage amplitudes at nodes i and j respectively; the set L represents the set of all branches in the network;

[0142] The expression of the branch current constraint is:

[0143]

[0144] In the formula, I max,ij It represents the maximum value of the square of the current amplitude of branch ij;

[0145] The expression of the node voltage constraint is:

[0146]

[0147] Where U min , U max is the lower and upper limits of the node voltage, U j,t is the square of the voltage amplitude at node j at time t;

[0148] The expression of the second-order cone constraint is:

[0149]

[0150] The expression of the main grid purchased power constraint is:

[0151]

[0152] In the formula, They are respectively the active power and reactive power purchased from the main grid; They are the minimum and maximum active power purchased from the main grid respectively; They respectively represent the minimum and maximum reactive power purchased from the main grid.

[0153] Embodiment six:

[0154] This embodiment is further designed on the basis of the fifth embodiment in that the specific steps of solving the two-layer model and obtaining the distributed energy storage power station planning scheme in this embodiment include:

[0155] The upper model is solved by using a particle swarm algorithm and the lower model is solved by using a second-order cone programming method.

[0156] Embodiment seven:

[0157] This embodiment is further designed on the basis of the sixth embodiment in that the specific method of using the particle swarm algorithm to solve the upper model in this embodiment includes:

[0158] The upper model is solved by particle swarm algorithm. Particle swarm algorithm is a random optimization algorithm. The solution process is as follows: first, a group of particles with a population size of n and a decision variable of m are randomly initialized, where the i-th particle is x i 、v i Represent their position vector and velocity vector respectively. In the solution space, particles jointly determine the speed and direction of their movement based on their own and group information. The optimal solution of the decision variable is searched by iteratively updating the particle position and velocity vector.

[0159] The particle velocity and position update formula is as follows:

[0160]

[0161] ω k =(ω max -ω min )×(k max -k) / k max

[0162] In the formula, i is the particle number and k is the number of iterations; is the velocity and position of the dth dimension of the i-th particle at the k-th iteration; c1 and c2 are acceleration factors that affect the flight speed of the particle toward the individual optimal and overall optimal particles; ω is the inertia weight coefficient, and r1 and r2 are random numbers ranging from 0 to 1; are the d-th dimension vector of the individual optimal position in the k-th iteration and the d-th dimension vector of the global optimal position; ω max ,ω min ,ω k —respectively the maximum value, minimum value and inertia weight of the kth iteration; k max, k—are the maximum number of iterations and the kth iteration number respectively.

[0163] Improve the convergence of the PSO algorithm by introducing a linearly decreasing inertia weight ω into the velocity update equation. When the inertia weight ω is larger, the global search capability of the algorithm is better, and when the inertia weight ω is smaller, the local search capability of the algorithm is better.

[0164] Embodiment eight:

[0165] This embodiment is further designed on the basis of the seventh embodiment. In this embodiment, an example is used to specifically illustrate the second-order cone programming method. The distribution network branch flow model used in this example is as follows: Figure 2 As shown in the figure, the constraints that the distribution network branch ij should meet at time t are:

[0166]

[0167] Where P ij,t , Q ij,t are the active power and reactive power flowing through branch ij at time t respectively; ij,t is the current amplitude flowing through branch ij at time t; r ij 、x ij is the resistance and reactance of line ij; P jk,t , Q jk,t are respectively the active power and reactive power at the head end of branch jk at time t; P j,t , Q j,t are the active power and reactive power injected into nodes i and j at time t respectively; w j is the set of branch end nodes with j as the head node, and set B represents the set of all nodes in the network.

[0168] make The first two formulas of the power flow constraint become linear constraints, and the third formula becomes a nonlinear constraint. The second order cone relaxation (SOCR) technique is used to process the third formula:

[0169]

[0170] When the objective function is satisfied Under the conditions of a strictly increasing function and no upper bound on the node load, the relaxation is strict. After equivalent transformation, the above formula can be written in standard second-order cone form:

[0171]

[0172] In the formula, || ||2 is the 2-norm. Through the above transformation, the power flow model is converted into a second-order cone programming problem, which can be solved using mature commercial software.

[0173] Embodiment nine:

[0174] A distributed energy storage power station planning system considering market benefits of the present invention includes a model building module and a model solving module;

[0175] The model building module is used to build a two-layer model for distributed energy storage power station planning, and the two-layer model includes an upper model for the site selection and capacity determination of distributed energy storage power stations under long time scales and a lower model for the dynamic optimal power flow of distribution networks under short time scales; the upper model aims to minimize the annual operating cost of the energy storage power station considering the annual price difference arbitrage of energy storage and the frequency regulation benefits, and uses the rated energy storage power, energy storage duration and access location selection of the energy storage power station as decision variables, and uses the energy storage power range constraint, node space constraint, and charging and discharging duration range constraint as constraint conditions The lower model aims to minimize the daily electricity purchase cost of the distribution network, and uses the branch current, node voltage, branch power, main grid injection power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive output of the reactive compensation device as decision variables, and uses energy storage charging / discharging power constraints, energy storage charge state constraints, frequency regulation state constraints, distributed power output constraints, reactive compensation device output constraints, power flow constraints, Ohm's law constraints, branch current constraints, node voltage constraints, second-order cone constraints, and main grid power purchase power constraints as constraints.

[0176] The model solving module is used to solve the double-layer model to obtain a distributed energy storage power station planning solution.

[0177] Embodiment ten:

[0178] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method of any of the above embodiments.

[0179] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above embodiments are implemented.

[0180] Application examples:

[0181] This example simulates the implementation of the method of the present invention on a modified IEEE 33-node power distribution system to verify the effectiveness of the method of the present invention. The nodes in the modified IEEE 33-node power distribution system are numbered as follows: Figure 3 As shown, node 33 is the upper power supply point of the distribution line. Assume that the rated capacity of a distributed photovoltaic is 1MW and it is connected to node 16. The output range of a reactive compensation device SVC is -0.2 to 1Mvar and it is connected to node 31.

[0182] like Figure 4 As shown, the total network load is 5084.26+j2547.32 kVA, and the total load is distributed to each node according to the standard IEEE33 node system node power ratio. At time 1-8, 23-24, the purchase price is 0.25 yuan / kWh; at time 9-11, 19-22, the purchase price is 0.75 yuan / kWh; at time 12-18, the purchase price is 0.5 yuan / kWh; the photovoltaic grid-connected electricity price is 0.3 yuan / kWh.

[0183] Now consider selecting one location among nodes 1-33 to configure energy storage, with the rated power of energy storage ranging from 0.5MW to 3MW and the storage duration ranging from 1 to 4 hours. It is assumed that the capacity of energy storage participating in frequency regulation does not exceed 30% of the rated capacity. The relevant parameter values ​​are shown in Table 1.

[0184] Table 1 Parameter values

[0185]

[0186] The two-layer optimization model was solved by calling the commercial solver cplex using matlabR2022b. The optimal energy storage configuration solution was obtained by running the program: 22 nodes were connected to 3MW / 9MWh battery energy storage. At the same time, participating in the spot and frequency regulation markets, energy storage can obtain positive returns, and the energy storage capacity reaches the upper limit allowed. Figure 5 This is the curve of the annual operating cost of the distributed energy storage power station changing with the number of iterations. It can be seen that the target converges when the number of iterations reaches 17, and the cost of the power station is -939,000 yuan / year.

[0187] By comparing the three situations of not considering benefits, only considering spot benefits, and considering both spot and frequency regulation benefits, the optimal energy storage configuration scheme is shown in Table 2.

[0188] Table 2 Comparison of optimization results under different situations

[0189]

[0190]

[0191] As can be seen from Table 2, when the benefits are not considered, the energy storage is configured according to the minimum capacity of 0.5MW and the minimum duration of 1 hour, and the annual operating cost is 152,000 yuan; when only participating in the spot market, the energy storage duration is configured according to the upper limit of 4 hours, and the annual operating cost of energy storage is -301,000 yuan; after participating in the spot and frequency regulation markets at the same time, the energy storage capacity is configured according to the upper limit of 3MW, and the annual operating cost of energy storage is -939,000 yuan.

[0192] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A distributed energy storage power station planning method considering market benefits, characterized in that: include: Constructing a two-layer model for distributed energy storage power station planning, the two-layer model includes an upper model for site selection and capacity determination of distributed energy storage power stations considering a long time scale and a lower model for dynamic optimal power flow of distribution networks considering a short time scale; The upper model aims to minimize the annual operating cost of the energy storage power station taking into account the annual frequency regulation benefits of energy storage, and uses the rated energy storage power, energy storage duration and access location selection of the energy storage power station as decision variables, and energy storage power range constraints, node space constraints, and charging and discharging duration range constraints as constraints; The lower model aims to minimize the daily electricity purchase cost of the distribution network, and uses the branch current, node voltage, branch power, main network injection power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive power output of the reactive compensation device of the distribution network as decision variables, and uses energy storage charging / discharging power constraints, energy storage charge state constraints, frequency regulation state constraints, distributed power output constraints, reactive power compensation device output constraints, power flow constraints, Ohm's law constraints, branch current constraints, node voltage constraints, second-order cone constraints, and main network power purchase constraints as constraints; The two-layer model is solved to obtain a distributed energy storage power station planning solution.

2. The distributed energy storage power station planning method considering market benefits according to claim 1 is characterized in that: The objective function of the upper model is: minC UP =C inv +C om +C loss +C ch -I dis -I fre In the formula, C UP is the total annual operating cost of the distributed energy storage power station; C inv is the annual value of the initial investment cost of the distributed energy storage power station; C om is the annual operation and maintenance cost of the distributed energy storage power station; C loss is the cost corresponding to the annual charge and discharge loss of the distributed energy storage power station, C ch I is the annual charging cost of distributed energy storage power station; dis is the annual discharge income of the distributed energy storage power station; I fre The annual frequency regulation income of the distributed energy storage power station; AND es =P es ×T es In the formula, is the initial investment cost of energy storage; r is the discount rate; n is the expected life span; C e is the unit capacity cost of distributed energy storage power station; E es is the rated capacity of the distributed energy storage power station, T es is the energy storage duration; C p is the unit power cost of distributed energy storage power station; P es is the rated power of the distributed energy storage power station; k om is the annual operation and maintenance cost coefficient of the distributed energy storage power station; In the formula, k t The cost of unit charging loss during the charging and discharging process of the energy storage power station; Δt is the spot settlement time interval; is the charging power and discharging power of the energy storage in period t; η ch , η dis It is the energy storage charging efficiency and discharging efficiency; is the power reserved for energy storage to participate in frequency regulation during period t; β is the average charging power of energy storage frequency regulation during period t and ratio; T is the scheduling period, n day is the annual operating days of the energy storage power station; is the spot clearing electricity price in period t; I fre =K st ×D×p fre ×n day In the formula, I fre is the annual frequency regulation benefit of energy storage, K st is the daily average frequency regulation performance coefficient of energy storage, D is the daily average frequency regulation mileage, and p fre is the frequency modulation clearing price; m is the ratio of the average frequency modulation mileage within Δt time to the reserved frequency modulation power.

3. The distributed energy storage power station planning method considering market benefits according to claim 2 is characterized in that: The energy storage power range constraint, charge and discharge time range constraint and node space constraint are: 0.5≤P es ≤3 1≤T es ≤4 1≤n es ≤N node Where P es is the rated power of the distributed energy storage power station; T es is the energy storage duration; n es N is the node location number of the energy storage installation. node The maximum number of nodes in the distribution line.

4. The distributed energy storage power station planning method considering market benefits according to claim 3 is characterized in that: The objective function of the lower model is: minC DN =C grid +C pv In the formula, C DN is the daily electricity purchase cost of the distribution network; C grid The cost of purchasing electricity from the main grid for the distribution network; C pv The cost of purchasing electricity from photovoltaic power generation for the distribution network.

5. The distributed energy storage power station planning method considering market benefits according to claim 4 is characterized in that: The expression of the energy storage charging and discharging power constraint is: Where P max is the upper limit of energy storage charging and discharging power; α es,t , β es,t is a 0-1 variable indicating the charge and discharge state of the energy storage during period t; The expression of the energy storage charge state constraint is: In the formula, SOC es,max , SOC es,min SOC is the upper and lower limits of the energy storage state of charge; es,t SOC is the state of charge of the energy storage at the end of period t; es,t-1 SOC is the state of charge of the energy storage at the end of the t-1 period; es,0 , SOC ,T E is the state of charge at the beginning and end of the energy storage operation day; es is the rated capacity of the energy storage power station; η ch , η dis They are the charging efficiency and discharging efficiency of the energy storage power station respectively; The expression of the frequency modulation state constraint is: In the formula, is a 0-1 variable; The upper and lower limits of SOC when energy storage participates in frequency regulation; Report the power upper limit for energy storage to participate in frequency regulation; The expression of the distributed generation output constraint is: In the formula, B PV is the collection of photovoltaic access nodes; is the active power output of the PV connected to node j in period t; is the predicted output of the PV connected to node j in period t; The expression of the output constraint of the reactive power compensation device is: In the formula, B SVC It is a collection of SVC access nodes of reactive power compensation device; is the reactive power output of the reactive power compensation device SVC connected to node j in period t; are the upper and lower limits of the reactive compensation amount of the reactive compensation device SVC connected to node j respectively; The expression of the power flow constraint is: Where P ij,t , Q ij,t are the active power and reactive power flowing through branch ij at time t respectively; ij,t is the square of the current amplitude flowing through branch ij at time t; r ij 、x ij is the resistance and reactance of line ij; P jk,t , Q jk,t are respectively the active power and reactive power at the head end of branch jk at time t; P j,t , Q j,t are the active power and reactive power injected into nodes i and j at time t respectively; w j is the set of branch end nodes with j as the head node, and set B represents the set of all nodes in the network; is the discharge power of node j during energy storage period t; is the load power of node j in period t; The expression for the Ohm's law constraint is: Where U j,t and U i,t are the squares of the voltage amplitudes at nodes i and j respectively; the set L represents the set of all branches in the network; The expression of the branch current constraint is: In the formula, I max,ij It represents the maximum value of the square of the current amplitude of branch ij; The expression of the node voltage constraint is: Where U min , U max is the lower and upper limits of the square of the node voltage, U j,t is the square of the voltage amplitude at node j at time t; The expression of the second-order cone constraint is: The expression of the main grid purchased power constraint is: In the formula, They are respectively the active power and reactive power purchased from the main grid; They are the minimum and maximum active power purchased from the main grid respectively; They respectively represent the minimum and maximum reactive power purchased from the main grid.

6. The distributed energy storage power station planning method considering market benefits according to claim 5 is characterized in that: The specific steps of solving the double-layer model to obtain the distributed energy storage power station planning scheme include: The upper model is solved by using a particle swarm algorithm and the lower model is solved by using a second-order cone programming method.

7. The distributed energy storage power station planning method considering market benefits according to claim 6 is characterized in that: The specific method of using the particle swarm algorithm to solve the upper model includes: First, a group of particles with a population size of n and a decision variable of m are randomly initialized, where the i-th particle is x i 、v i Represent their position vector and velocity vector respectively. In the solution space, particles jointly determine the speed and direction of their movement based on their own and group information. The optimal solution of the decision variable is searched by iteratively updating the particle position and velocity vector. The particle velocity and position update formula is as follows: oh k =(ω max -oh min )×(k max -k) / k max In the formula, i is the particle number, k is the number of iterations; is the velocity and position of the dth dimension of the i-th particle at the k-th iteration; c1 and c2 are acceleration factors that affect the flight speed of the particle toward the individual optimal and overall optimal particles; ω is the inertia weight coefficient, and r1 and r2 are random numbers ranging from 0 to 1; are the d-th dimension vector of the individual optimal position in the k-th iteration and the d-th dimension vector of the global optimal position; ω max ,ω min ,ω k —respectively the maximum value, minimum value and inertia weight of the kth iteration; k max , k—are the maximum number of iterations and the kth iteration number respectively.

8. A distributed energy storage power station planning system considering market benefits, characterized in that: It includes a model building module and a model solving module; The model building module is used to build a two-layer model for distributed energy storage power station planning, and the two-layer model includes an upper model for site selection and capacity determination of distributed energy storage power stations under long time scales and a lower model for dynamic optimal power flow of distribution networks under short time scales; the upper model aims to minimize the annual operating cost of the energy storage power station considering the annual frequency regulation benefits of energy storage, and uses the rated energy storage power, energy storage duration and access location selection of the energy storage power station as decision variables, and uses energy storage power range constraints, node space constraints, and charging and discharging duration range constraints as constraints; The lower model aims to minimize the daily electricity purchase cost of the distribution network, and uses the branch current, node voltage, branch power, main grid injection power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive power output of the reactive compensation device as decision variables, and uses energy storage charging / discharging power constraints, energy storage charge state constraints, frequency regulation state constraints, distributed power output constraints, reactive power compensation device output constraints, power flow constraints, Ohm's law constraints, branch current constraints, node voltage constraints, second-order cone constraints, and main grid power purchase power constraints as constraints. The model solving module is used to solve the double-layer model to obtain a distributed energy storage power station planning solution.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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