A distributed energy storage power station planning method and system considering marketization benefits

Abstract

The application discloses a distributed energy storage power station planning method and system considering marketization benefits, and the method constructs a double-layer model for distributed energy storage power station planning, wherein the double-layer model comprises an upper-layer model considering distributed energy storage power station site selection and capacity determination in a long time scale and a lower-layer model of power distribution network dynamic optimal power flow in a short time scale, and the double-layer model is solved to obtain a distributed energy storage power station planning scheme. In the double-layer model constructed in the application, the upper-layer model takes the minimum annual operation cost of the energy storage power station considering energy storage annual price difference arbitrage and frequency modulation benefit as the target, so that the power grid company not only integrates the energy storage into the power distribution network planning, but also provides a decision basis for market subjects to invest and operate the distributed energy storage power station.

Description

Technical Field

[0001] This invention belongs to the field of distributed energy storage optimization planning technology, and in particular relates to a planning method and system for distributed energy storage power stations that considers market benefits. Background Technology

[0002] Distributed energy storage has diverse application scenarios, and can be located on the user side, distributed power source side, and distribution network side. It can be presented in the form of energy storage combined with users (household energy storage), energy storage combined with distributed power sources (such as photovoltaic energy storage systems), independent energy storage systems, or microgrids. Individually configuring energy storage for users or distributed photovoltaic systems faces challenges such as limited application scenarios, high investment and operating costs, and insufficient capacity configuration flexibility. Distributed independent energy storage connected to the public distribution network is an important means to solve problems such as difficulties in absorbing distributed power sources, exceeding distribution network voltage flow limits, and large peak-valley load differences, and represents a crucial scenario for the future development of distributed energy storage.

[0003] Due to their small project capacity, distributed independent energy storage cannot directly participate in the electricity market, and previous planning did not consider distributed energy storage as a market player. With the gradual improvement of the electricity market, lowering the entry barriers for energy storage and allowing distributed resources, including energy storage, to enter the market has become a trend. Current optimization plans for distributed energy storage primarily focus on reducing grid losses and promoting the consumption of distributed renewable energy, without considering the profitability of distributed energy storage after participating in the electricity market, making them unsuitable for future development. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a planning method and system for distributed energy storage power stations that considers market-based benefits.

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

[0006] A planning method for distributed energy storage power stations that considers market-based returns includes:

[0007] A two-layer model for planning distributed energy storage power stations is constructed. The two-layer model includes an upper-layer model that considers the site selection and capacity determination of distributed energy storage power stations over a long time scale and a lower-layer model that considers the dynamic optimal power flow of the distribution network over a short time scale.

[0008] The upper-level model aims to minimize the annual operating cost of the energy storage power station while considering the annual frequency regulation revenue of energy storage. It 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 charge / discharge duration range constraints as constraints.

[0009] The lower layer model takes the minimum daily power purchase cost of the distribution network as the target, and takes the branch current, node voltage, branch power, main network injected power, distributed photovoltaic output, energy storage charge / discharge power, actual energy storage capacity, and reactive power compensation device reactive power output as the decision variables, and takes the energy storage charge / discharge power constraint, energy storage state of charge constraint, frequency modulation state constraint, distributed power output constraint, reactive power compensation device output constraint, power flow constraint, Ohm's law constraint, branch current constraint, node voltage constraint, second-order cone constraint, and main network power purchase power constraint as the constraint conditions;

[0010] Solving the double-layer model obtains a distributed energy storage power station planning scheme.

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

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

[0013] In the formula, C UP is the total annual operation 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 is the annual charging cost of the distributed energy storage power station; I dis is the annual discharging income of the distributed energy storage power station; I fre is the annual frequency modulation 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 the energy storage; r is the discount rate; n is the expected life limit; C e is the unit capacity cost of the distributed energy storage power station; E es is the rated capacity of the distributed energy storage power station, T es is the energy storage time; C p is the unit power cost of the distributed energy storage power station; P esK is the rated power of the distributed energy storage power station; k om k is the annual operation and maintenance cost coefficient of the distributed energy storage power station;

[0018]

[0019]

[0020] wherein k t is the cost corresponding to the unit charge loss electric quantity in the charge and discharge process of the energy storage power station; Δt is the spot settlement time interval; is the charge power and discharge power of the energy storage at t period; η ch , η dis is the charge efficiency and discharge efficiency of the energy storage; is the power reserved by the energy storage for frequency modulation at t period; β is the ratio of the average charge electric quantity of the energy storage for frequency modulation at t period to ; T is the scheduling period, n day is the annual operation days of the energy storage power station; is the spot clearing price at t period.

[0021] I fre = K st × D × p fre × n day

[0022]

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

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

[0025] 0.5≤P es ≤3

[0026] 1≤T es ≤4

[0027] 1≤n es ≤N node

[0028] wherein P es is the rated power of the distributed energy storage power station; T es is the energy storage time length; n es is the node position number of the energy storage installation, and N node is the maximum node number of the distribution line.

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

[0030] minC DN = C grid + C pv

[0031] wherein C DN is the daily purchase cost of the distribution network; C grid is the purchase cost of the distribution network from the main network; C pv is the purchase cost of the distribution network from photovoltaic.

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

[0033]

[0034] wherein P max is the upper limit of the energy storage charging and discharging power; α es,t , β es,t are 0-1 variables of the charging and discharging state of the energy storage at time t;

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

[0036]

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

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

[0039]

[0040] wherein, is a 0-1 variable; is the upper and lower limits of the state of charge when the energy storage participates in frequency regulation; is the upper limit of the power reported by the energy storage for participating in frequency regulation;

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

[0042]

[0043] where B PV is the set of photovoltaic access nodes; is the active power output of the photovoltaic connected to node j at time period t; is the predicted active power output of the photovoltaic connected to node j at time period t;

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

[0045]

[0046] where B SVC is the set of reactive power compensation device SVC access nodes; is the reactive power output of the reactive power compensation device SVC connected to node j at time period t; are the upper and lower limits of the reactive power compensation amount of the reactive power 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; I ij,t is the square of the current amplitude flowing through branch ij at time t; r ij , x ij are the resistance and reactance of line ij; P jk,t , Q jk,t are the active power and reactive power at the head of branch jk at time t; P j,t , Q j,t are the active power and reactive power injected at nodes i and j at time t; 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 the energy storage at node j at time period t; is the load power of node j at time period t;

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

[0052]

[0053] where U j,t and U i,trespectively, the square of voltage amplitude of node i and node j; set L represents all branch set in the network;

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

[0055]

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

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

[0058]

[0059] In the formula, U min , U max are the lower limit and upper limit of node voltage square, U j,t is the square of node j voltage amplitude at t time;

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

[0061]

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

[0063]

[0064]

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

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

[0067] The particle swarm algorithm is used to solve the upper-layer model, and the second-order cone programming method is used to solve the lower-layer model.

[0068] Further, the specific method of solving the upper-layer model by using the particle swarm algorithm includes:

[0069] A group of particles with population size n and decision variables m are randomly initialized, wherein the i-th particle is represented by x i , v iRespectively represent its position vector and velocity vector, particle in solution space according to its own and group information together to determine the speed and direction of its movement, through the iteration of particle position, velocity vector update to search for the optimal solution of decision variables;

[0070] Particle velocity and position update formula as follows:

[0071]

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

[0073] In the formula, i is the particle number, k is the iteration number; The d-dimensional velocity and position of the ith particle in the kth iteration; c1, c2 are acceleration factors, which affect the flight speed of the particle to the individual optimal and global optimal particle; ω is the inertia weight coefficient, r1, r2 are random numbers between 0 and 1; The d-dimensional vector of the individual optimal position and the d-dimensional vector of the global optimal position in the kth iteration, respectively; ω max , ω min , ω k - Respectively, the maximum value, the minimum value and the inertia weight of the kth iteration; kma x , k- Respectively, the maximum iteration number and the kth iteration number.

[0074] A distributed energy storage station planning system considering marketization benefits, comprising a model construction module and a model solving module;

[0075] The model construction module is configured to construct a double-layer model for distributed energy storage power station planning, the double-layer model comprising an upper-layer model considering distributed energy storage power station siting and sizing in a long time scale and a lower-layer model of dynamic optimal power flow of a distribution network in a short time scale; the upper-layer model takes the minimum annual operation cost of the energy storage power station considering the annual arbitrage and frequency modulation benefits of the energy storage as a target, and takes the rated energy storage power, energy storage time length and access location selection of the energy storage power station as decision variables, and takes the energy storage power range constraint, node space constraint and charging / discharging time length range constraint as constraint conditions; the lower-layer model takes the minimum daily power purchase cost of the distribution network as a target, and takes 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 compensation device reactive power output of the distribution network as decision variables, and takes the energy storage charging / discharging power constraint, energy storage state of charge constraint, frequency modulation state constraint, distributed power output constraint, reactive power compensation device output constraint, power flow constraint, Ohm's law constraint, branch current constraint, node voltage constraint, second-order cone constraint and main grid power purchase power constraint as constraint conditions.

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

[0077] An electronic device comprises a memory and a processor, the memory stores a computer program, and the processor is configured to invoke and run the computer program stored in the memory to execute the method according to any one of the above.

[0078] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method according to any one of the above.

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

[0080] The present application proposes a distributed energy storage power station planning method and system considering market benefits, the method constructs a double-layer model for distributed energy storage power station planning, the double-layer model comprises an upper-layer model considering distributed energy storage power station siting and sizing in a long time scale and a lower-layer model of dynamic optimal power flow of a distribution network in a short time scale, and the double-layer model is solved to obtain a distributed energy storage power station planning scheme. In the double-layer model constructed in the present application, the upper-layer model takes the minimum annual operation cost of the energy storage power station considering the annual arbitrage and frequency modulation benefits of the energy storage as a target, so that in addition to guiding the power grid company to integrate the energy storage into the distribution network planning, the method also provides a decision basis for market subjects to invest and operate the distributed energy storage power station.

[0081] The objective function of the upper model of the method considers the cost corresponding to the annual charge and discharge loss electric quantity of the distributed energy storage power station, and by comparing the cost with the loss reduction benefit brought by the energy storage smooth load curve, suggestions can be provided for improving the allocation mechanism of the energy storage loss electric quantity cost. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 A flowchart of the distributed energy storage power station planning method considering marketization benefits in the embodiment;

[0083] Figure 2 A branch power flow model example diagram of the distribution network in the embodiment;

[0084] Figure 3 A modified IEEE 33-node system schematic diagram in the application embodiment;

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

[0086] Figure 5 A curve diagram of the annual operation cost of the distributed energy storage power station changing with the iteration number in the application embodiment. DETAILED DESCRIPTION

[0087] The application will be further illustrated below in conjunction with the drawings and specific embodiments, and it should be understood that these embodiments are only used to illustrate the application and not to limit the scope of the application, and after reading the application, various modifications of the application by those skilled in the art fall within the scope defined by the appended claims.

[0088] Embodiment one:

[0089] A distributed energy storage power station planning method considering marketization benefits of the application, as shown in the figure, comprises: Figure 1

[0090] S1, a bi-level model for distributed energy storage power station planning is constructed, the bi-level model comprises an upper model considering distributed energy storage power station site selection and capacity selection in a long time scale and a lower model of dynamic optimal power flow of a distribution network in a short time scale;

[0091] The upper model takes the minimum annual operation cost of the energy storage power station considering the annual frequency modulation benefit of the energy storage as the target, and takes the rated energy storage power of the energy storage power station, the energy storage time and the access location selection as the decision variable, and takes the energy storage power range constraint, the node space constraint and the charge and discharge time range constraint as the constraint condition; wherein 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 nodes 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 time range constraint is determined by the net load of the distribution line.​

[0092] The lower layer model takes the minimum daily power purchase cost of the distribution network as the target, and takes the branch current, node voltage, branch power, main network injected power (i.e. purchased power), distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, reactive power compensation device reactive power output of the distribution network as the decision variable, and takes the energy storage charging / discharging power constraint, energy storage state of charge constraint, frequency modulation state constraint, distributed power output constraint, reactive power compensation device output constraint, power flow constraint, Ohm's law constraint, branch current constraint, node voltage constraint, second-order cone constraint and main network power purchase power constraint as the constraint condition;

[0093] S2, solving the double-layer model to obtain a distributed energy storage power station planning scheme.

[0094] Embodiment two:

[0095] The embodiment in this embodiment is further designed on the basis of embodiment one, and the objective function of the upper layer model in this embodiment is:

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

[0097] In the formula, C UP is the total annual operation 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 charging and discharging loss of the distributed energy storage power station, including the transmission and distribution cost and the network loss cost, C ch is the annual charging cost of the distributed energy storage power station; I dis is the annual discharging income of the distributed energy storage power station; I fre is the annual frequency modulation income of the distributed energy storage power station;

[0098]

[0099]

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

[0101]

[0102]

[0103] In the formula, k t This represents the cost per unit of charging loss during the charging and discharging process of an energy storage power station; Δt is the spot settlement time interval. η represents the charging and discharging power of energy storage during time period t; ch η dis The charging efficiency and discharging efficiency of energy storage during time period t; β represents the power reserved for energy storage to participate in frequency regulation during time period t; β represents the average charging capacity of energy storage for frequency regulation during time period t. The ratio; T is the scheduling period, taken as 24, n day This refers to the number of days the energy storage power station operates per year. The spot clearing electricity price is for period t.

[0104]

[0105]

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

[0107] Example 3:

[0108] This embodiment, based on embodiment two, further designs the energy storage power range constraint, charge / discharge duration range constraint, and node space constraint as follows:

[0109] 0.5≤P es ≤4

[0110] 1≤T es ≤4

[0111] 1≤n es ≤N node

[0112] In the formula, P es The rated power of the distributed energy storage power station ranges 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 For the node position number of energy storage installation, N node For the maximum number of nodes of the distribution line.

[0113] Example four:

[0114] This embodiment is further designed on the basis of example three, and in this example, the objective function of the lower layer model is:

[0115] min C DN =C grid +C pv

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

[0117] Example five:

[0118] This embodiment is further designed on the basis of example four, and in this example, the expression of the energy storage charge and discharge power constraint is:

[0119]

[0120] In the formula, P max is the upper limit of the energy storage charge and discharge power; α es,t , β es,t are 0-1 variables of the energy storage charge and discharge state at t period; is the discharge power of the energy storage at t period; is the charge power of the energy storage at t period; is the power reserved for frequency modulation of the energy storage in t period;

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

[0122]

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

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

[0125]

[0126] In the formula, is a 0-1 variable; are the upper and lower limits of the SOC of the energy storage when participating in frequency modulation; is the upper limit of the power reported by the energy storage for participating in frequency modulation;

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

[0128] For simplicity, only one type of distributed photovoltaic is considered for the distributed power, the distributed photovoltaic is allowed to abandon electricity, and the reactive power output is not considered, and the active power output constraint is as follows:

[0129]

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

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

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

[0133]

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

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

[0136]

[0137]

[0138] In the formula, P ij,t , Q ij,t are the active power and reactive power flowing through the branch ij at time t; I ij,tis the square of the current amplitude flowing through branch ij at time t; r ij is the resistance and reactance of branch ij; P ij is the active power and reactive power of the first end of branch jk at time t; P jk,t is the active power and reactive power of the first end of branch jk at time t; P jk,t is the active power and reactive power of the first end of branch jk at time t; P j,t is the active power and reactive power of the first end of branch jk at time t; P j,t is the active power and reactive power injected into node i and j at time t; w j is the set of end nodes of branch with j as the first end node, and set B represents the set of all nodes in the network; is the discharging power of node j at time t; is the load power of node j at time t;

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

[0140]

[0141] In the formula, U j,t and U i,t are the square of the voltage amplitude of node i and node j, respectively; and 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 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] In the formula, U min , U max are the lower limit and upper limit of the node voltage, respectively, and U j,t is the square of the voltage amplitude of 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, are the active power and reactive power purchased from the main grid, respectively; respectively represent the minimum value and the maximum value of the active power purchased from the main grid; respectively represent the minimum value and the maximum value of the reactive power purchased from the main grid.

[0153] Embodiment six:

[0154] The embodiment is further designed on the basis of embodiment five, and in the embodiment, the double-layer model is solved, and the specific steps of obtaining the distributed energy storage power station planning scheme include:

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

[0156] Embodiment seven:

[0157] The embodiment is further designed on the basis of embodiment six, and in the embodiment, the specific method of solving the upper-layer model by using a particle swarm algorithm includes:

[0158] The upper-layer model is solved by using a particle swarm algorithm. The particle swarm algorithm is a random optimization algorithm, and the solving process is as follows: a group of particles with a population size of n and decision variables of m are randomly initialized, wherein the i-th particle is represented by x i 、v i respectively represent the position vector and the velocity vector thereof, the particle determines the speed and direction of its movement in the solution space according to its own and group information, and the optimal solution of the decision variable is searched by updating the position and velocity vectors of the particle through iteration;

[0159] The particle velocity and position updating 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 iteration number; is the d-dimensional velocity and position of the i-th particle in the k-th iteration; c1 and c2 are acceleration factors, which affect the flight speed of the particle to the individual optimal and global optimal particles; ω is an inertia weight coefficient, and r1 and r2 are random numbers between 0 and 1; respectively represent the d-dimensional vector of the individual optimal position in the k-th iteration and the d-dimensional vector of the global optimal position; ω max 、ω min 、ω k respectively represent the maximum value, the minimum value and the inertia weight in the k-th iteration; k maxk are the maximum iteration number and the kth iteration number respectively.

[0163] The convergence of the PSO algorithm is improved by introducing a linearly decreasing inertia weight ω in the velocity update equation. When the inertia weight ω is large, the global search ability of the algorithm is better. When the inertia weight ω is small, the local search ability of the algorithm is better.

[0164] Embodiment eight

[0165] The embodiment is further designed on the basis of embodiment seven, and in this example, a second order cone programming method is specifically described by taking an example. The distribution network branch power flow model used in the example is as follows Figure 2 As shown in the figure, the constraint condition of the distribution network branch ij at time t is:

[0166]

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

[0168] Let The first two formulas of the power flow constraint become linear constraints, and the third formula is a nonlinear constraint. The third formula is processed by using the second order cone relaxation (SOCR) technology:

[0169]

[0170] Under the conditions that the objective function is a strict increasing function and the node load has no upper bound, the relaxation is strict. Through equivalent transformation, the above formula can be written in the 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 by using mature commercial software.​

[0173] Embodiment Nine:

[0174] The application discloses a distributed energy storage power station planning system considering marketization benefits.

[0175] The model construction module is used for constructing a double-layer model for distributed energy storage power station planning, wherein the double-layer model comprises an upper-layer model considering distributed energy storage power station site selection and capacity selection in a long time scale and a lower-layer model of dynamic optimal power flow of a distribution network in a short time scale; the upper-layer model takes the minimum annual operation cost of the energy storage power station considering energy storage annual price difference arbitrage and frequency modulation benefits as a target, and takes the rated energy storage power, energy storage time length and access position selection of the energy storage power station as decision variables, and takes energy storage power range constraints, node space constraints and charging and discharging time length range constraints as constraint conditions; the lower-layer model takes the minimum daily power purchase cost of the distribution network as a target, and takes branch current, node voltage, branch power, main network injected power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value and reactive power compensation device reactive power output of the distribution network as decision variables, and takes energy storage charging and disarging power constraints, energy storage state of charge constraints, frequency modulation 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 power constraints as constraint conditions.

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

[0177] Embodiment Ten:

[0178] An electronic device includes a memory and a processor, 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 one of the above embodiments.

[0179] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of any one of the above embodiments.

[0180] Application Embodiment:

[0181] In this example, the method is implemented on the modified IEEE 33-node distribution system to verify the effectiveness of the method. In the modified IEEE 33-node distribution system, the nodes are numbered as shown in the table. Figure 3 The 33rd node is the upper power supply point of the distribution line, and it is assumed that a distributed photovoltaic power station with a rated capacity of 1 MW is connected to the 16th node, and a reactive power compensation device SVC with an output range of -0.2-1 Mvar is connected to the 31st node.

[0182] As Figure 4 shown in the figure, 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 electricity purchase price is 0.25 yuan / kWh; at time 9-11, 19-22, the electricity purchase price is 0.75 yuan / kWh; at time 12-18, the electricity purchase price is 0.5 yuan / kWh; and the photovoltaic on-grid electricity price is 0.3 yuan / kWh.

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

[0184] Table 1: Parameter values

[0185]

[0186] The double-layer optimization model is solved by calling the commercial solver cplex in matlabR2022b, and the optimal configuration scheme of energy storage is obtained by running the program: 22 nodes are connected to 3MW / 9MWh battery energy storage. At the same time, participating in the spot and frequency modulation market, the energy storage can obtain positive income, and the energy storage capacity reaches the upper limit. Figure 5 For the annual operating cost of the distributed energy storage power station with the number of iterations, it can be seen that when the number of iterations reaches 17 times, the target converges, and at this time the power station cost is-93.9 thousand yuan / year.

[0187] Comparing the three cases of not considering income, only considering spot income, and considering spot and frequency modulation income, the optimal configuration scheme of energy storage is shown in Table 2.

[0188] Table 2: Comparison of optimization results under different conditions

[0189]

[0190]

[0191] As can be seen from Table 2, when the income is not considered, the energy storage is configured with the minimum capacity of 0.5 MW and the minimum duration of 1 hour, and the annual operating cost is 15.2 thousand yuan; only participating in the spot market, the energy storage duration is configured with the upper limit of 4 hours, and the annual operating cost of the energy storage is-30.1 thousand yuan; participating in the spot and frequency modulation market, the energy storage capacity is configured with the upper limit of 3 MW, and the annual operating cost of the energy storage is-93.9 thousand yuan.

[0192] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A method for planning a distributed energy storage power station considering marketization benefits, characterized in that, The application relates to a double-layer model for distributed energy storage power station planning, which comprises an upper-layer model for distributed energy storage power station site selection and capacity selection in a long time scale and a lower-layer model for dynamic optimal power flow of a distribution network in a short time scale. The upper-layer model takes the minimum annual operation cost of the energy storage power station considering the annual frequency modulation benefit of the energy storage as a target, takes the rated energy storage power, energy storage time length and access position selection of the energy storage power station as decision variables, and takes energy storage power range constraints, node space constraints and charge / discharge time length range constraints as constraint conditions. The lower-layer model takes the minimum daily power purchase cost of the distribution network as a target, takes branch current, node voltage, branch power, main network injected power, distributed photovoltaic output, energy storage charge / discharge power, actual energy storage power value and reactive power compensation device reactive power output of the distribution network as decision variables, and takes energy storage charge / discharge power constraints, energy storage state of charge constraints, frequency modulation 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 power constraints as constraint conditions. The double-layer model is solved to obtain a distributed energy storage power station planning scheme. The target function of the upper-layer model is: The target function of the lower-layer 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 is the annual charging cost of the distributed energy storage power station; I dis is the annual discharge income of the distributed energy storage power station; I fre is the annual frequency modulation income of the distributed energy storage power station; E es = P es x T es In the formula, is the initial investment cost of energy storage; r is the discount rate; n is the expected life in years; C e is the unit capacity cost of distributed energy storage power station; E es is the rated capacity of 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 distributed energy storage power station; k om is the annual operation and maintenance cost coefficient of distributed energy storage power station; In the formula, k t is the cost corresponding to the unit charging loss power in the charging and discharging process of the energy storage power station; Δt is a spot settlement time interval; is the charging power and discharging power of the energy storage in the t period; η ch , η dis is the charging efficiency and discharging efficiency of the energy storage; is the power reserved by the energy storage for frequency modulation in the t period; β is the ratio of the average charging power of the energy storage for frequency modulation in the t period to ; T is a scheduling period, n day is the annual operation days of the energy storage power station; is the spot clearing price in the t period; I fre = K st x D x p fre x n day In the formula, I fre K is the annual frequency modulation benefit of energy storage st D is the daily frequency modulation mileage, and p fre is the frequency modulation clearing price; m is the ratio of the average frequency modulation mileage and the reserved frequency modulation power in the time Δt. The energy storage power range constraints, charge / discharge time length range constraints and node space constraints are respectively: minC DN = C grid + C pv In the formula, C DN is the daily electricity purchase cost of the distribution network; C grid is the electricity purchase cost of the distribution network from the main network; C pv is the electricity purchase cost of the distribution network from photovoltaic. 2.The method of claim 1, wherein, The expression of the energy storage charge / discharge power constraints is: 0.5≤P es ≤3 1≤T es ≤4 1 < n es ≤ N node In the formula, P es is the rated power of the distributed energy storage power station; T es is the energy storage duration; n es is the node position number of the energy storage installation, N node is the maximum number of nodes of the distribution line. 3.The method of claim 2, wherein, The expression of the energy storage state of charge constraints is: In the formula, P max is the upper limit of the energy storage charge and discharge power; α es,t , β es,t is a 0-1 variable of the energy storage charge and discharge state at t period. The expression of the frequency modulation state constraints is: wherein SOC es,max , SOC es,min are upper and lower limits of the state of charge of the energy storage; SOC es,t is the state of charge of the energy storage at the end of the t period; SOC es,t-1 is the state of charge of the energy storage at the end of the t-1 period; SOC es,0 , SOC es,T are the states of charge of the energy storage at the initial and final instants of the day of operation; E es is the rated capacity of the energy storage plant; η ch , η dis are the charging efficiency and discharging efficiency of the energy storage power station, respectively; The expression of the distributed power output constraints is: In the formula, is a 0-1 variable; is the upper and lower limits of SOC when the energy storage participates in frequency modulation; is the upper limit of the power reported by the energy storage to participate in frequency modulation; The expression of the reactive power compensation device output constraints is: where B PV is a set of photovoltaic access points; is the active power output of the photovoltaic connected to node j at time period t; is the predicted power output of the photovoltaic connected to node j at time period t; The expression of the power flow constraints is: where B SVC is the set of SVC access nodes; is the reactive power output of the SVC connected to node j at time period t; are the upper and lower limits of the reactive power compensation of the SVC connected to node j, respectively. The expression of the Ohm's law constraints is: where P ij,t , Q ij,t are active power and reactive power flowing through branch ij at time t; I ij,t is the square of the current amplitude flowing through branch ij at time t; r ij , x ij are resistance and reactance of branch ij; P jk,t , Q jk,t are active power and reactive power at the head of branch jk at time t; P j,t , Q j,t are active power and reactive power injected at nodes i and j at time t; w j is the set of nodes at the end of branches with j as the head node, and set B represents the set of all nodes in the network; is the discharging power of node j at time t; is the load power of node j at time t; The expression of the branch current constraints is: where U j,t and U i,t are the square of the voltage magnitude at node i and node j, respectively; and the set L represents the set of all branches in the network. The expression of the node voltage constraints is: where I max,ij denotes the maximum value of the square of the branch ij current amplitude; The expression of the second-order cone constraints is: where U min , U max are the lower and upper bounds of the square of the node voltage, U j,t is the square of the amplitude of the voltage of node j at time t. The expression of the main network power purchase power constraints is: The specific steps of solving the double-layer model to obtain a distributed energy storage power station planning scheme comprise the following steps: In the formula, respectively, active power and reactive power purchased from the main grid; respectively, minimum and maximum values of the active power purchased from the main grid; respectively, minimum and maximum values of the reactive power purchased from the main grid.

4. The method for distributed energy storage station planning considering marketization benefits according to claim 3, characterized in that, The upper-layer model is solved by using a particle swarm algorithm, and the lower-layer model is solved by using a second-order cone programming method. The specific method for solving the upper-layer model by using a particle swarm algorithm comprises the following steps:

5. The method for distributed energy storage station planning considering marketization benefits according to claim 4, characterized in that, The particle velocity and position updating formulae are as follows: First, a group of particles with population size n and decision variables m are randomly initialized, where the i-th particle is represented by x i i The position vector and velocity vector of the particle are represented by x and v, respectively. The particle determines its speed and direction in the solution space according to its own and group information, and searches for the optimal solution of the decision variable by updating the particle position and velocity vector through iteration.​ The application relates to a double-layer model for distributed energy storage power station planning, which comprises an upper-layer model for distributed energy storage power station site selection and capacity selection in a long time scale and a lower-layer model for dynamic optimal power flow of a distribution network in a short time scale. ω k = (ω max - ω min ) x (k max - k) / k max where i is the particle number, k is the iteration number; is the velocity and position of the dth dimension of the ith particle in the kth iteration; c1, c2 are acceleration factors, which affect the flight speed of the particle to the individual optimal and global optimal particles; ω is the inertia weight coefficient, and r1, r2 are random numbers between 0 and 1; are the dth dimension vector of the individual optimal position and the dth dimension vector of the global optimal position in the kth iteration, respectively; ω max , ω min , ω k are the maximum value and the minimum value of the inertia weight, respectively, and the inertia weight of the kth iteration; k max , k are the maximum iteration number and the kth iteration number, respectively.

6. A distributed energy storage station planning system considering marketization benefits, characterized in that, ​ The model construction module is configured to construct a double-layer model for distributed energy storage power station planning, the double-layer model comprising an upper-layer model considering distributed energy storage power station siting and sizing in a long time scale and a lower-layer model of dynamic optimal power flow of a distribution network in a short time scale; the upper-layer model takes the minimum annual operation cost of the energy storage power station considering the annual frequency modulation benefit of the energy storage as an objective, and takes the rated energy storage power, energy storage time length and access location selection of the energy storage power station as decision variables, and takes the energy storage power range constraint, node space constraint and charging / discharging time length range constraint as constraint conditions; the lower-layer model takes the minimum daily power purchase cost of the distribution network as an objective, and takes the branch current, node voltage, branch power, main network injected power, distributed photovoltaic output, energy storage charging / discharging power, actual energy storage power value, and reactive power compensation device reactive power output of the distribution network as decision variables, and takes the energy storage charging / discharging power constraint, energy storage state of charge constraint, frequency modulation state constraint, distributed power output constraint, reactive power compensation device output constraint, power flow constraint, Ohm's law constraint, branch current constraint, node voltage constraint, second-order cone constraint and main network power purchase power constraint as constraint conditions; The model solution module is configured to solve the double-layer model to obtain a distributed energy storage power station planning scheme. The objective function of the upper-layer 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 operation 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 is the annual charging cost of the distributed energy storage power station; I dis is the annual discharging income of the distributed energy storage power station; I fre is the annual frequency modulation income of the distributed energy storage power station; E es = P es x T es In the formula, is the initial investment cost of energy storage; r is the discount rate; n is the expected life in years; C e is the unit capacity cost of distributed energy storage power station; E es is the rated capacity of 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 distributed energy storage power station; k om is the annual operation and maintenance cost coefficient of distributed energy storage power station; In the formula, k t is the cost corresponding to the unit charging loss power in the charging and discharging process of the energy storage power station; Δt is a spot settlement time interval; is the charging power and discharging power of the energy storage in the t period; η ch , η dis is the charging efficiency and discharging efficiency of the energy storage; is the power reserved by the energy storage for frequency modulation in the t period; β is the ratio of the average charging power of the energy storage for frequency modulation in the t period to ; T is a scheduling period, n day is the annual operation days of the energy storage power station; is the spot clearing price in the t period; I fre = K st x D x p fre x n day In the formula, I fre is the annual energy storage frequency adjustment benefit, K st is the daily average energy storage frequency adjustment performance coefficient, D is the daily average frequency adjustment mileage, and p fre is the frequency adjustment clearing price; m is the ratio of the average frequency adjustment mileage and the reserved frequency adjustment power in the time Δt. The objective function of the lower-layer 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 is the electricity purchase cost of the distribution network from the main network; C pv is the electricity purchase cost of the distribution network from photovoltaic.

7. An electronic device, comprising: The electronic device comprises a memory and a processor, 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 of any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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