Distributed photovoltaic and energy storage combined planning method for power distribution network

Abstract

The invention discloses a power distribution network distributed photovoltaic and energy storage combined planning method, which comprises the following steps: constructing power distribution network annual average construction cost, power distribution network annual average operation cost, power distribution network annual carbon emission reduction income, network loss, voltage deviation and voltage stability index objective functions according to the structure and operation condition of a power distribution network; forming constraint conditions of power balance, distributed photovoltaic access capacity, distributed energy storage access capacity, node voltage and line current according to the structure of the power distribution network, photovoltaic generator sets accessed to the power distribution network and the quantity and condition of energy storage; normalizing the six objective functions by adopting a fuzzy theory, and converting the six objective functions into a single objective optimization problem; and solving the access node and the installation capacity of the distributed photovoltaic of the power distribution network and the access node and the installation capacity of the energy storage device, which meet the constraint conditions, by adopting an eddy current search algorithm. According to the method disclosed by the invention, the annual average operation cost of the power grid can be reduced, the annual network loss of the power distribution network is lower, and the planning of the power distribution network is more reasonable.

Description

Technical Field

[0001] The present invention relates to a method for distribution network planning, and particularly to a method for joint planning of distributed photovoltaic and energy storage in a distribution network. Background Art

[0002] With the large-scale access of distributed photovoltaic and energy storage, higher requirements are put forward for distribution network planning, and the distribution network planning problem has attracted increasing attention.

[0003] At present, although certain achievements have been made in the research on distributed photovoltaic and energy storage planning in distribution networks, there are more achievements in independently planning distributed photovoltaic and energy storage respectively. In terms of engineering practice, the access of distributed photovoltaic power stations is generally determined by grid enterprises according to the scale of photovoltaic investment and construction and the capacity of adjacent transformers, and the access scheme does not involve the optimal analysis of the benefits of distributed photovoltaic power station investors and the optimal configuration of energy storage. When the installed capacity of photovoltaic reaches a certain scale, this unordered access scheme further exacerbates the grid operation risk, reduces the grid operation efficiency, and affects the overall benefits of distributed photovoltaic power stations.

[0004] Therefore, it is necessary to study the method for joint planning of distributed photovoltaic and energy storage in a distribution network. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for joint planning of distributed photovoltaic and energy storage in a distribution network, so as to achieve the purpose of making the distribution network planning more reasonable.

[0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] A method for joint planning of distributed photovoltaic and energy storage in a distribution network includes the following steps:

[0008] Step 1, according to the structure and operation conditions of the distribution network, construct the annual average construction cost objective function, annual average operation cost objective function, annual carbon emission reduction benefit objective function, network loss objective function, voltage deviation objective function, and voltage stability index objective function of the distribution network;

[0009] Step 2, according to the distribution network structure and the number and conditions of photovoltaic generating units and energy storage connected to the distribution network, form the power balance constraint condition, distributed photovoltaic access capacity constraint condition, distributed energy storage access capacity constraint condition, node voltage constraint condition, and line current constraint condition;

[0010] Step 3, use the fuzzy theory to normalize the six objective functions and transform them into a single-objective optimization problem;

[0011] Step 4: Use the eddy current search algorithm to solve for the access nodes and installation capacities of distributed PV in the distribution network that meet the constraints, as well as the access nodes and installation capacities of energy storage devices.

[0012] In the above solution, in Step 1, analyze the distribution network structure, and construct the annual average construction cost objective function of the distribution network based on the unit investment costs of distributed PV and distributed energy storage, and the installation capacities of distributed PV and energy storage at each node.

[0013] The calculation method of the annual average construction cost F1 of the distribution network is as follows:

[0014]

[0015] In the formula, N is the number of nodes in the distribution network, q is the discount rate, Y pv and Y c are the planning periods of distributed PV and energy storage respectively; c1 is the unit investment cost of distributed PV, and c2 is the unit investment cost of distributed energy storage; S pv,i and S c,i are the installation capacities of distributed PV and energy storage at the i-th node respectively.

[0016] In the above solution, in Step 1, calculate the annual operation and maintenance cost of the system based on the unit operation and maintenance costs of distributed PV and energy storage, the output power of distributed PV, and the charge and discharge power of energy storage, and calculate the annual power purchase cost based on the average power purchase price of the superior power grid and the active power injected into the superior power grid at each node. Construct the annual average operation cost objective function of the distribution network from the annual operation and maintenance cost and the power purchase cost of the system.

[0017] The calculation formula of the annual average operation cost F2 of the distribution network is as follows:

[0018] F2 = D(C ope + C buy )

[0019] In the formula, D is the number of typical days within the planning period, C ope represents the annual operation and maintenance cost of the system, and the calculation formula is as follows:

[0020]

[0021] In the formula, T is the number of time periods within the planning day; c opv and c oc are the unit operation and maintenance costs of distributed PV and energy storage respectively; P pv,i,t 、P c,i,t are the output power of distributed PV and the charge and discharge power of energy storage at the i-th node in the t-th period respectively;

[0022] C buy represents the annual power purchase cost, and the calculation formula is as follows:

[0023]

[0024] Wherein, c price is the average electricity price for purchasing electricity from the superior power grid, and P s,i,t is the active power injected into the superior power grid at the i-th node during the t-th period.

[0025] In the above solution, in step 1, according to the real-time price of the carbon emission rights trading market, the CO2 emission coefficient of the coal-fired unit for producing unit electric energy, and the CO2 emission reduction coefficient of the energy storage for consuming unit electric energy, a target function for the annual carbon emission reduction benefit of the distribution network is constructed;

[0026] The calculation formula for the annual carbon emission reduction benefit F3 of the distribution network is as follows:

[0027]

[0028] Wherein, D is the number of typical days within the planned year, and p T is the real-time price of the carbon emission rights trading market; ε1 is the CO2 emission coefficient of the coal-fired unit for producing unit electric energy; ε2 is the CO2 emission reduction coefficient of the energy storage for consuming unit electric energy, and P pv,i,t , P c,i,t are respectively the distributed photovoltaic output power and the energy storage charge and discharge power at the i-th node during the t-th period.

[0029] In the above solution, in step 1, according to the conductance between nodes and the operating voltage conditions of each node, a target function for network loss is constructed;

[0030] The calculation formula for network loss F4 is as follows:

[0031]

[0032] Wherein, D is the number of typical days within the planned year, and G ij is the conductance between node i and node j; U i,t and U j,t are respectively the voltages of node i and node j at time t; θ ij is the phase difference between nodes i and j, N is the number of distribution network nodes, and T is the number of time periods within the planned day.

[0033] In the above solution, in step 1, according to the operating voltage and rated voltage conditions of each node, a target function for voltage deviation is constructed; the calculation formula for voltage deviation F5 is as follows:

[0034]

[0035] Wherein, U i,t is the voltage at the i-th node at time t; U Nis the rated voltage, N is the number of nodes in the distribution network, and T is the number of time periods within the planning day.

[0036] In the above solution, in step 1, according to the voltage stability indexes of each branch, a system voltage stability index objective function is constructed; the calculation formula for the system voltage stability index F6 is as follows:

[0037]

[0038] L VSI = max{L1, L2, …, L b}

[0039] In the formula, {L1, L2, …, L b} is the set of voltage stability indexes of all branches in the distribution network; b is the total number of branches in the distribution network;

[0040] For any branch b ij the voltage stability index L ij The calculation formula is:

[0041]

[0042] In the formula, i and j are the head and end nodes of the branch respectively; P j , Q j are the active and reactive loads flowing through node j respectively; X ij , R ij are the reactance and resistance of branch b ij respectively; U i is the voltage amplitude of node i.

[0043] In the above solution, in step 2, the power balance constraint conditions are as follows:

[0044]

[0045] In the formula, P s,i,t , Q s,i,t are the active and reactive powers injected by the superior power grid of the i-th node respectively; P pv,i,t , Q pv,i,t are the active and reactive powers injected by the photovoltaic at the i-th node; P load,i,t , Q load,i,t respectively represent the active and reactive loads of the i-th node at time t; P loss,ij,t , Q loss,ij,t are the network losses between lines i and j; P c,i,t , Q c,i,t are the charge and discharge powers of the energy storage at the i-th node;

[0046] The constraint conditions for the access capacity of distributed photovoltaics are as follows:

[0047]

[0048] In the formula, S pv,i is the distributed photovoltaic capacity connected to node i, is the maximum allowable installation capacity at node i;

[0049] The constraint conditions for the access capacity of distributed energy storage are as follows:

[0050]

[0051] In the formula, S c,i is the distributed energy storage capacity connected to node i, is the maximum allowable installation capacity of distributed energy storage at node i;

[0052] The node voltage constraint conditions are as follows:

[0053] U min ≤U i ≤U max

[0054] In the formula, U i is the voltage of node i, U min and U max are the minimum voltage limit and the maximum voltage limit of the bus;

[0055] The line current constraint conditions are as follows:

[0056] I ij ≤I max

[0057] In the formula, I ij is the current between nodes i and j, I max is the maximum value of the line current.

[0058] In the above scheme, the specific method of step 3 is as follows:

[0059] Use a linear piecewise function to represent the fuzzy membership function of each objective, and its expression is as follows:

[0060]

[0061] In the formula, s = 1, 2…, 6; μ s is the membership degree of the optimization objective, F s,min , F s,max are the minimum value and the maximum value obtained when optimizing the s-th objective separately; F s is the s-th objective function;

[0062] According to the max-min rule of fuzzy set theory, transform the original multi-objective optimization problem into an extreme value problem of the overall satisfaction degree λ, and the expression of λ is as follows:

[0063] λ = min{μ1, μ2, μ3, μ4, μ5, μ6}

[0064] Where μ1, μ2, μ3, μ4, μ5, and μ6 are the membership degree values of six objective functions respectively.

[0065] In the above solution, the specific method of step 4 includes the following steps:

[0066] (1) Initialization:

[0067] The variables to be solved in the model are represented as {S pv,1 , S pv,2 , …, S pv,i , …, S pv,N , S c,1 , S c,2 , …, S c,i , …, S c,N}, where S pv,i and S c,i are the installation capacities of distributed photovoltaic and energy storage at the i-th node respectively; N is the number of nodes in the distribution network, the initial iteration number l = 0, the dimension of the solution space is d, and the value range of the k-th dimension solution is [e lower,k , e upper,k . Then the center μ0 of the initial search space can be determined:

[0068]

[0069] The search radius r0 of the initial solution:

[0070] r0 = σ0·(1 / z)·β(z, a0)

[0071] Where z = 0.1, a0 = 1, and the calculation formula of σ0 is as follows:

[0072]

[0073] The calculation formula of β(z, a0) is as follows:

[0074]

[0075] (2) Generate candidate solutions:

[0076] Taking μ0 as the center of the search space and r0 as the search radius, randomly generate n candidate solutions that follow a Gaussian distribution. The probability density function of the Gaussian distribution is expressed as:

[0077]

[0078] Where x is a d-dimensional random variable, μ is the mean vector, and Σ is the covariance matrix;

[0079] (3) Calculate the objective function value of the candidate solution:

[0080] Substitute the generated candidate solution into the expression of the overall satisfaction degree λ of the objective function to obtain the objective function value of the candidate solution, that is, the fitness value;

[0081] (4) Select the optimal solution from the candidate solutions and update the current solution:

[0082] Compare the fitness values of the optimal solution in this iteration and the optimal solutions in previous iterations, and update the candidate solution with the smaller fitness value as the new optimal candidate solution μ best and use it as the center μ of the new search space l ;

[0083] (5) Update the search radius:

[0084] At this time, judge whether the termination condition is satisfied, that is, whether the maximum number of iterations is reached. If so, output the optimal solution. If not, update the iteration number l = l + 1 and update the search radius according to the following formula:

[0085] r l = σ0·(1 / z)·β(z,a l )

[0086] where, a l can be expressed as:

[0087]

[0088] In the formula, l is the current iteration number, and maxItr is the maximum iteration number;

[0089] The calculation formula of β(z,a l ) is as follows:

[0090]

[0091] (6) Return to step (2), generate new candidate solutions with the new search space center μ l and the search radius r l Repeat steps (2)-(5) until the maximum iteration number maxItr is reached, terminate the loop, and output the optimal solution.

[0092] Through the above technical solutions, a distributed photovoltaic and energy storage joint planning method for a distribution network provided by the present invention has the following

[0093] beneficial effects:

[0094] 1. The present invention constructs the annual average construction cost objective function of the distribution network, the annual average operation cost objective function of the distribution network, the annual carbon emission reduction benefit objective function of the distribution network, the network loss objective function, the voltage deviation objective function, and the voltage stability index objective function. The above six objective functions comprehensively consider the economy, stability, and environmental protection of the distribution system;

[0095] 2. The present invention constructs the power balance constraint condition, the distributed photovoltaic access capacity constraint condition, the distributed energy storage access capacity constraint condition, the node voltage constraint condition, and the line current constraint condition. The above five constraint conditions fully consider the real-time operation constraints of the distribution network;

[0096] 3. The present invention uses the fuzzy theory to normalize the six objective functions and transform them into a single-objective optimization problem, which improves the model solving efficiency;

[0097] 4. The present invention uses the eddy current search algorithm to solve the installation capacity of the distributed photovoltaic in the distribution network and the installation capacity of the energy storage device that meet the constraint conditions, and can effectively obtain the global optimal solution of the model. Description of the Drawings

[0098] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art.

[0099] Figure 1 It is a schematic diagram of a method for jointly planning distributed photovoltaic and energy storage in a distribution network disclosed in an embodiment of the present invention.

[0100] Figure 2 It is a schematic diagram of the process for using the eddy current search algorithm to solve the access nodes and installation capacity of the distributed photovoltaic and the energy storage device. Detailed Embodiments

[0101] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention.

[0102] The present invention provides a method for jointly planning distributed photovoltaic and energy storage in a distribution network, as Figure 1 shown, including the following steps:

[0103] Step 1, according to the structure and operation conditions of the distribution network, construct the annual average construction cost objective function of the distribution network, the annual average operation cost objective function of the distribution network, the annual carbon emission reduction benefit objective function of the distribution network, the network loss objective function, the voltage deviation objective function, and the voltage stability index objective function.

[0104] 1. Annual average construction cost of the distribution network

[0105] Analyze the distribution network structure, and construct the annual construction cost objective function of the distribution network according to the unit investment cost of distributed photovoltaic and distributed energy storage, and the installed capacity of distributed photovoltaic and energy storage at each node. The calculation method of the annual construction cost F1 of the distribution network is as follows:

[0106]

[0107] In the formula, N is the number of nodes in the distribution network, q is the discount rate, Y pv and Y c are the planning periods of distributed photovoltaic and energy storage respectively; c1 is the unit investment cost of distributed photovoltaic, and c2 is the unit investment cost of distributed energy storage; S pv,i and S c,i are the installed capacities of distributed photovoltaic and energy storage at the i-th node respectively.

[0108] 2. Annual operating cost of the distribution network

[0109] Calculate the annual operation and maintenance cost of the system according to the unit operation and maintenance cost of distributed photovoltaic and energy storage, the output power of distributed photovoltaic and the charge and discharge power of energy storage. Calculate the annual power purchase cost according to the average power purchase price of the superior power grid and the active power injected into the superior power grid at each node. Construct the annual operating cost objective function of the distribution network from the annual operation and maintenance cost and the power purchase cost of the system;

[0110] The calculation formula of the annual operating cost F2 of the distribution network is as follows:

[0111] F2 = D(C ope + C buy )

[0112] In the formula, D is the number of typical days in the planning period, C ope represents the annual operation and maintenance cost of the system, and the calculation formula is as follows:

[0113]

[0114] In the formula, T is the number of time periods in the planning day; c opv and c oc are the unit operation and maintenance costs of distributed photovoltaic and energy storage respectively; P pv,i,t , P c,i,t are the output power of distributed photovoltaic and the charge and discharge power of energy storage at the i-th node in the t-th period respectively;

[0115] C buy represents the annual power purchase cost, and the calculation formula is as follows:

[0116]

[0117] In the formula, c price is the average power purchase price from the superior power grid, P s,i,tThe active power injected into the upstream power grid of the i-th node during the t period.

[0118] 3. Annual carbon emission reduction benefits of the distribution network

[0119] According to the real-time price of the carbon emission trading market, the CO2 emission coefficient of the coal-fired unit for producing unit electric energy, and the CO2 emission reduction coefficient of the energy storage for consuming unit electric energy, an objective function for the annual carbon emission reduction benefits of the distribution network is constructed; the calculation formula for the annual carbon emission reduction benefits F3 of the distribution network is as follows:

[0120]

[0121] In the formula, D is the number of typical days within the planning year, p T is the real-time price of the carbon emission trading market; ε1 is the CO2 emission coefficient of the coal-fired unit for producing unit electric energy; ε2 is the CO2 emission reduction coefficient of the energy storage for consuming unit electric energy, P pv,i,t 、P c,i,t are the distributed photovoltaic output power and the energy storage charge and discharge power at the i-th node during the t period, respectively.

[0122] 4. Network loss

[0123] According to the conductance between nodes and the operating voltage conditions of each node, an objective function for network loss is constructed; with the goal of minimizing the system network loss, the calculation formula for network loss F4 is as follows:

[0124]

[0125] In the formula, D is the number of typical days within the planning year, G ij is the conductance between node i and node j; U i,t and U j,t are the voltages of node i and node j at time t, respectively; θ ij is the phase difference between nodes i and j, N is the number of distribution network nodes, and T is the number of time periods within the planning day.

[0126] 5. Voltage deviation

[0127] According to the operating voltage and rated voltage conditions of each node, an objective function for voltage deviation is constructed; the voltage deviation can be expressed with the goal of minimizing the system average voltage deviation, and the calculation formula for voltage deviation F5 is as follows:

[0128]

[0129] In the formula, U i,t is the voltage at the i-th node at time t; U N is the rated voltage, N is the number of distribution network nodes, and T is the number of time periods within the planning day.

[0130] 6. System voltage stability index

[0131] After the access of distributed photovoltaic, the transmission power on the feeder in the original system will decrease, which will in turn cause varying degrees of changes in the voltage of each node in the original power grid structure. According to the voltage stability indexes of each branch, taking the minimum of the distribution network voltage stability indexes as the optimization goal, a target function for the system voltage stability index is constructed. The calculation formula for the system voltage stability index F6 is as follows:

[0132] F6 = L VSI

[0133] L VSI = max{L1, L2, …, L b}

[0134] In the formula, {L1, L2, …, L b} is the set of voltage stability indexes of all branches in the distribution network; b is the total number of branches in the distribution network;

[0135] For any branch b ij the voltage stability index L ij The calculation formula is:

[0136]

[0137] In the formula, i and j are the head and end nodes of the branch respectively; P j , Q j are the active and reactive loads flowing through node j respectively; X ij , R ij are the reactance and resistance of branch b ij respectively; U i is the voltage amplitude of node i.

[0138] Step 2: According to the distribution network structure and the quantity and conditions of the photovoltaic generating units and energy storage connected to the distribution network, form power balance constraint conditions, distributed photovoltaic access capacity constraint conditions, distributed energy storage access capacity constraint conditions, node voltage constraint conditions, and line current constraint conditions.

[0139] 1. The power balance constraint conditions are as follows:

[0140]

[0141] In the formula, P s,i,t , Q s,i,t are the active and reactive powers injected by the superior power grid into the i-th node respectively; P pv,i,t , Q pv,i,t are the active and reactive powers injected by the photovoltaic into the i-th node; P load,i,t , Q load,i,t respectively represent the active and reactive loads of the i-th node at time t; Ploss,ij,t , Q loss,ij,t is the network loss between lines i and j; P c,i,t , Q c,i,t is the charge and discharge power of the energy storage at the i-th node;

[0142] 2. The constraints on the access capacity of distributed photovoltaic are as follows:

[0143]

[0144] In the formula, S pv,i is the access capacity of distributed photovoltaic at node i, is the maximum allowable installation capacity at node i;

[0145] 3. The constraints on the access capacity of distributed energy storage are as follows:

[0146]

[0147] In the formula, S c,i is the access capacity of distributed energy storage at node i, is the maximum allowable installation capacity of distributed energy storage at node i;

[0148] 4. The constraints on the node voltage are as follows:

[0149] U min ≤ U i ≤ U max

[0150] In the formula, U i is the voltage of node i, U min and U max are the minimum voltage limit and the maximum voltage limit of the bus;

[0151] 5. The constraints on the line current are as follows:

[0152] I ij ≤ I max

[0153] In the formula, I ij is the current between nodes i and j, I max is the maximum value of the line current.

[0154] Step 3: Use the fuzzy theory to normalize the six objective functions and transform them into a single-objective optimization problem;

[0155] Since the six optimization objectives of the construction investment cost of the distribution network, the average annual operation cost of the distribution network, the annual carbon emission reduction benefit of the distribution network, network loss, voltage deviation, and voltage stability index have different dimensions and may conflict with each other, in order to coordinate the relationship between different objectives, a linear piecewise function is used to represent the fuzzy membership function of each objective, and its expression is as follows:

[0156]

[0157] In the formula, s = 1, 2…, 6; μ s is the membership degree of the optimization objective, F s,min and F s,max are the minimum value and the maximum value obtained when optimizing the s-th objective alone; F s is the s-th objective function;

[0158] According to the max-min rule of fuzzy set theory, the original multi-objective optimization problem is transformed into an extreme value problem of the overall satisfaction degree λ, and the expression of λ is as follows:

[0159] λ = min{μ1, μ2, μ3, μ4, μ5, μ6}

[0160] In the formula, μ1, μ2, μ3, μ4, μ5, and μ6 are the membership degree values of the six objective functions respectively.

[0161] Step 4: Use the eddy current search algorithm to solve the access nodes and installation capacities of distributed photovoltaics in the distribution network and the access nodes and installation capacities of energy storage devices that meet the constraint conditions.

[0162] The eddy current search algorithm is an optimization algorithm that simulates the eddy current phenomenon. In the initial stage, the algorithm explores with a large radius, and when it enters the local solution area, it starts local development with a small radius.

[0163] As Figure 2 shown, the specific method includes the following steps:

[0164] (1) Initialization:

[0165] The variables to be solved in the model are represented as {S pv,1 , S pv,2 , …, S pv,i , …, S pv,N , S c,1 , S c,2 , …, S c,i , …, S c,N}, S pv,i and S c,iare the installed capacities of distributed photovoltaic and energy storage at the i-th node respectively; the result of the installed capacity implies which nodes are connected. For the optimized distribution network, if the installed capacity at a certain node is 0, it means it is not connected, and if there is capacity, it means the node is connected.

[0166] N is the number of nodes in the distribution network, the initial iteration number l = 0, the dimension of the solution space is d-dimensional, and the value range of the k-th dimension solution is [e lower,k , e upper,k , then the initial search space center μ0 can be determined:

[0167]

[0168] The search radius r0 of the initial solution:

[0169] r0 = σ0·(1 / z)·β(z, a0)

[0170] In the formula, z = 0.1, a0 = 1, and the calculation formula of σ0 is as follows:

[0171]

[0172] The calculation formula of β(z, a0) is as follows:

[0173]

[0174] (2) Generate candidate solutions:

[0175] Taking μ0 as the center of the search space and r0 as the search radius, randomly generate n candidate solutions that follow a Gaussian distribution. The probability density function of the Gaussian distribution is expressed as:

[0176]

[0177] In the formula, x is a d-dimensional random variable, μ is the mean vector, and Σ is the covariance matrix;

[0178] (3) Calculate the objective function values of the candidate solutions:

[0179] Substitute the generated candidate solutions into the expression of the overall satisfaction degree λ of the objective function to obtain the objective function values of the candidate solutions, that is, the fitness values;

[0180] (4) Select the optimal solution from the candidate solutions and update the current solution:

[0181] Compare the fitness values of the optimal solution in this iteration and the optimal solutions in previous iterations, and update the candidate solution with the smaller fitness value as the new optimal candidate solution μ best , and use it as the new search space center μ l ;

[0182] (5) Update the search radius:

[0183] At this time, it is judged whether the termination condition is satisfied, that is, the maximum number of iterations is reached. If so, the optimal solution is output. If not, the iteration number l = l + 1 is updated, and the search radius is updated according to the following formula:

[0184] r l = σ0·(1 / z)·β(z,a l )

[0185] where a l can be expressed as:

[0186]

[0187] In the formula, l is the current iteration number, and maxItr is the maximum iteration number;

[0188] The calculation formula of β(z,a l ) is as follows:

[0189]

[0190] (6) Return to step (2), and generate a new candidate solution with the new search space center μ l and search radius r l , and repeat steps (2)-(5) until the maximum iteration number maxItr is reached, terminate the loop, and output the optimal solution.

[0191] In order to verify the performance advantages of the combined planning method of distributed photovoltaic and energy storage in the distribution network, it is compared with the independent planning methods of distributed photovoltaic and energy storage in the distribution network respectively, and the comparison results are shown in Table 1.

[0192] Table 1 Comparison of annual average operating costs and network losses of the distribution network

[0193] Comparison index Joint planning method Independent planning method Average annual operating cost of distribution network (10,000 yuan) 357 429 Annual network loss of distribution network (MW) 296 381

[0194] As can be seen from Table 1, compared with the independent planning method, the combined planning method of distributed photovoltaic and energy storage in the distribution network reduces the annual average operating cost of the distribution network by 16.8% and the annual network loss of the distribution network by 22.3%. Through actual simulation, it is verified that the method proposed by the present invention can make the planning of the distribution network more reasonable and can be applied to the planning of the actual distribution network.

[0195] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A joint planning method for distributed photovoltaic and energy storage in a distribution network, characterized in that, It includes the following steps: Step 1: According to the structure and operation conditions of the distribution network, construct the annual average construction cost objective function of the distribution network, the annual average operation cost objective function of the distribution network, the annual carbon emission reduction benefit objective function of the distribution network, the network loss objective function, the voltage deviation objective function, and the voltage stability index objective function; Step 2: According to the distribution network structure and the quantity and conditions of the photovoltaic generating units and energy storage connected to the distribution network, form the power balance constraint condition, the distributed photovoltaic access capacity constraint condition, the distributed energy storage access capacity constraint condition, the node voltage constraint condition, and the line current constraint condition; Step 3: Use the fuzzy theory to normalize the six objective functions and transform them into a single-objective optimization problem; Step 4: Use the eddy search algorithm to solve the access nodes and installation capacities of distributed photovoltaics in the distribution network and the access nodes and installation capacities of energy storage devices that meet the constraint conditions.

2. A method for jointly planning a distribution network with distributed photovoltaic and energy storage according to claim 1, characterized in that, In Step 1, analyze the distribution network structure, and construct the annual average construction cost objective function of the distribution network according to the unit investment costs of distributed photovoltaics and distributed energy storage and the installation capacities of distributed photovoltaics and energy storage at each node. The calculation method of the annual average construction cost F1 of the distribution network is as follows: Where N is the number of nodes in the distribution network, q is the discount rate, Y pv and Y c are the planning periods of distributed photovoltaic and energy storage respectively; c1 is the unit investment cost of distributed photovoltaic, and c2 is the unit investment cost of distributed energy storage; S pv,i and S c,i are the installed capacities of distributed photovoltaic and energy storage at the i-th node respectively.

3. A method for jointly planning distributed photovoltaic and energy storage in a distribution network according to claim 1, characterized in that, In Step 1, calculate the annual operation and maintenance cost of the system according to the unit operation and maintenance costs of distributed photovoltaics and energy storage, the output power of distributed photovoltaics, and the charge and discharge power of energy storage. Calculate the annual power purchase cost according to the average power purchase price of the superior power grid and the active power injected into the superior power grid at each node. Construct the annual average operation cost objective function of the distribution network from the annual operation and maintenance cost of the system and the power purchase cost; The calculation formula of the annual average operation cost F2 of the distribution network is as follows: F2 = D(C ope + C buy ) where D is the number of typical days within the planned year, and C ope represents the annual operation and maintenance cost of the system, and the calculation formula is as follows: where T is the number of time periods within the planned day; c opv and c oc are the unit operation and maintenance costs of distributed photovoltaic and energy storage respectively; P pv,i,t and P c,i,t are the output power of distributed photovoltaic and the charge and discharge power of energy storage at the i-th node in the t-th period respectively; C buy Indicates the annual electricity purchase cost, and the calculation formula is as follows: where c price is the average electricity price for purchasing electricity from the superior power grid, and P s,i,t is the active power injected into the i-th node of the superior power grid at time t.

4. A method for jointly planning distributed photovoltaic and energy storage in a distribution network according to claim 1, characterized in that, In Step 1, construct the annual carbon emission reduction benefit objective function of the distribution network according to the real-time price of the carbon emission rights trading market, the CO2 emission coefficient per unit of electric energy produced by coal-fired units, and the CO2 emission reduction coefficient per unit of electric energy consumed by energy storage; The calculation formula of the annual carbon emission reduction benefit F3 of the distribution network is as follows: where D is the number of typical days within the planned year, and p T is the real-time price of the carbon emission trading market; ε1 is the CO2 emission coefficient for the coal-fired unit to produce unit electric energy; ε2 is the CO2 emission reduction coefficient for the energy storage to consume unit electric energy, P pv,i,t 、P c,i,t are respectively the distributed photovoltaic output power and the energy storage charge and discharge power at the i-th node in the t-th period.

5. A method for joint planning of distributed photovoltaic and energy storage in a distribution network according to claim 1, characterized in that, In Step 1, construct the network loss objective function according to the conductance between nodes and the operation voltage conditions of each node; The calculation formula of the network loss F4 is as follows: where D is the number of typical days within the planned year, G ij is the conductance between node i and node j; U i,t and U j,t are the voltages of node i and node j at time t, respectively; θ ij is the phase difference between nodes i and j, N is the number of nodes in the distribution network, and T is the number of time periods within the planning day.

6. The distributed photovoltaic and energy storage joint planning method for a distribution network according to claim 1, characterized in that In Step 1, construct the voltage deviation objective function according to the operation voltage and rated voltage conditions of each node; The calculation formula of the voltage deviation F5 is as follows: where U i,t is the voltage at the i-th node during the t-th period; U N is the rated voltage, N is the number of nodes in the distribution network, and T is the number of time periods within the planning day.

7. A method for jointly planning distributed photovoltaic and energy storage in a distribution network according to claim 1, characterized in that In Step 1, construct the system voltage stability index objective function according to the voltage stability indexes of each branch; The calculation formula of the system voltage stability index F6 is as follows: F6 = L VSI L VSI = max{L1, L2, …, L b} wherein, {L1, L2, …, L b} is the set of all branch voltage stability indexes in the distribution network; b is the total number of branches in the distribution network; The voltage stability index L of any branch b ij is calculated by the following formula: ij The calculation formula is as follows: wherein, i and j are the head and end nodes of the branch respectively; P j and Q j are the active load and reactive load flowing through node j respectively; X ij and R ij are the reactance and resistance of branch b ij respectively; U i is the voltage amplitude of node i.

8. A method for jointly planning a distribution network with distributed photovoltaic and energy storage according to claim 1, characterized in that In Step 2, the power balance constraint condition is as follows: Wherein, P s,i,t , Q s,i,t are the active and reactive powers injected by the superior power grid of the i-th node respectively; P pv,i,t , Q pv,i,t are the active and reactive powers injected by the photovoltaic at the i-th node; P load,i,t , Q load,i,t respectively represent the active and reactive loads of the i-th node at time t; P loss,ij,t , Q loss,ij,t are the network losses between lines i and j; P c,i,t , Q c,i,t are the charge and discharge powers of the energy storage at the i-th node; The distributed photovoltaic access capacity constraint condition is as follows: where S pv,i is the distributed PV capacity connected to node i, and [[0000168]] is the maximum allowable installation capacity at node i; The distributed energy storage access capacity constraint condition is as follows: where S c,i is the distributed energy storage capacity connected to node i, and is the maximum installed capacity of distributed energy storage allowed to be connected at node i; The node voltage constraint condition is as follows: U min ≤U i ≤U max Where, U i is the voltage of node i, U min and U max are the minimum voltage limit and the maximum voltage limit of the busbar; The line current constraint condition is as follows: I ij ≤I max Where, I ij is the current between nodes i and j, and I max is the maximum value of the line current.

9. A method for jointly planning a distribution network with distributed photovoltaic and energy storage according to claim 1, characterized in that The specific method of Step 3 is as follows: Use a linear piecewise function to represent the fuzzy membership functions of each objective, and its expression is as follows: where \(s = 1, 2, \cdots, 6\); \(\mu\) s is the membership degree of the optimization objective, \(F\) s,min , \(F\) s,max are the minimum and maximum values obtained when optimizing the \(s\)-th objective separately; \(F\) s is the \(s\)-th objective function; According to the max-min rule of fuzzy set theory, transform the original multi-objective optimization problem into an extreme value problem of the overall satisfaction degree λ, and the expression of λ is as follows: λ = min{μ1, μ2, μ3, μ4, μ5, μ6} In the formula, μ1, μ2, μ3, μ4, μ5, and μ6 are the membership values of the six objective functions respectively.

10. A method for jointly planning distributed photovoltaic and energy storage in a distribution network according to claim 9, characterized in that, The specific method of Step 4 includes the following steps: (1) Initialization: The variables to be solved for the model are represented as {S pv,1 , S pv,2 , …, S pv,i , …, S pv,N , S c,1 , S c,2 , …, S c,i , …, S c,N}, where S pv,i and S c,i are the installed capacities of distributed photovoltaic and energy storage at the i-th node respectively; N is the number of nodes in the distribution network, the initial iteration number l = 0, the dimension of the solution space is d, and the value range of the k-th dimension solution is [e lower,k , e upper,k . Then the center μ0 of the initial search space can be determined as follows: The search radius r0 of the initial solution: r0 = σ0·(1 / z)·β(z, a0) where z = 0.1, a0 = 1, and the calculation formula for σ0 is as follows: The calculation formula for β(z, a0) is as follows: (2) Generate candidate solutions: Taking μ0 as the center of the search space and r0 as the search radius, randomly generate n candidate solutions that follow a Gaussian distribution. The probability density function of the Gaussian distribution is expressed as: where x is a d-dimensional random variable, μ is the mean vector, and Σ is the covariance matrix; (3) Calculate the objective function values of the candidate solutions: Substitute the generated candidate solutions into the expression of the overall satisfaction λ of the objective function to obtain the objective function values of the candidate solutions, that is, the fitness values; (4) Select the optimal solution from the candidate solutions and update the current solution: Compare the fitness values of the optimal solution of this iteration and the optimal solutions of previous iterations, and update the candidate solution with the smaller fitness value as the new optimal candidate solution μ best , and use it as the center μ of the new search space l ; (5) Update the search radius: At this time, determine whether the termination condition is satisfied, that is, whether the maximum number of iterations is reached. If so, output the optimal solution. If not, update the iteration number l = l + 1 and update the search radius according to the following formula: r l = σ0·(1 / z)·β(z,a l ) Among them, a l can be expressed as: where l is the current iteration number and maxItr is the maximum number of iterations; β(z,a l ) The calculation formula is as follows: (6) Return to step (2) with the new search space center μ l and the search radius r l Generate new candidate solutions, and repeat steps (2)-(5) until the maximum number of iterations maxItr is reached, terminate the loop, and output the optimal solution.

Images