Distributed photovoltaic maximum absorptive capacity measuring and calculating method considering response characteristics

Abstract

The invention discloses a distributed photovoltaic maximum absorptive capacity measuring and calculating method considering response characteristics, and the method comprises the following steps: building a main network scheduling layer objective function based on the response coefficient of each node, the operation constraint of a thermal power generating unit, the operation constraint of new energy, and the power balance constraint of a main network scheduling layer, and taking the minimum total power generation cost of the main network as an objective; based on a power flow constraint, a photovoltaic output constraint, a node power balance constraint, a line power range constraint, a node voltage constraint, a photovoltaic capacity constraint and an energy storage planning constraint of the power distribution network, constructing a distribution network operation layer objective function by taking the minimum total operation cost of the power distribution network as an objective; the optimal solutions of the main network scheduling layer objective function and the distribution network operation layer objective function are solved based on a diagonalization algorithm, and the distributed photovoltaic maximum absorptive capacity is obtained based on the optimal solutions; according to the method, the acceptance capability of distributed photovoltaic power generation can be improved, and the method plays an important role in reducing carbon emission and optimizing an energy structure.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method for calculating the maximum absorptive capacity of distributed photovoltaic power generation taking into account response characteristics. Background Art

[0002] With the transformation of the global energy structure and the increasingly severe climate change problem, renewable energy, especially distributed photovoltaics, has become a key force in achieving the low-carbon transformation of the power system. With the rapid development of distributed photovoltaics, its intermittency and volatility have challenged the power grid's absorption capacity. Traditional measurement methods usually do not take into account the dynamic response characteristics of nodes and systems, resulting in poor photovoltaic absorption effects. With the advancement of demand response technology, the absorption capacity of photovoltaics can be effectively improved by combining node investment on the distribution network side with the market clearing mechanism on the main grid side. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a method for calculating the maximum absorptive capacity of distributed photovoltaics taking into account the response characteristics, which aims to calculate the maximum absorptive capacity of distributed photovoltaics based on the response characteristics, optimize the utilization effect of distributed photovoltaic resources, and improve the allocation efficiency of power resources.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for calculating the maximum absorbable capacity of distributed photovoltaic power taking into account the response characteristics, comprising the following steps:

[0005] Step S1: Calculate the total cost of main grid power generation based on the clearing power of thermal power units; calculate the response coefficient of each node based on the economic response capability, independent investment capability and load regulation capability of each node; construct the main grid dispatching layer objective function with the goal of minimizing the total cost of main grid power generation based on the response coefficient of each node, the operation constraints of thermal power units, the operation constraints of new energy sources and the power balance constraints of the main grid dispatching layer;

[0006] Step S2: Based on the power flow constraints of the distribution network, the photovoltaic output constraints, the node power balance constraints, the line power range constraints, the node voltage constraints, the photovoltaic capacity constraints, and the energy storage planning constraints, the distribution network operation layer objective function is constructed with the goal of minimizing the total cost of distribution network operation;

[0007] Step S3: Solve the optimal solution of the main grid dispatching layer objective function and the distribution network operation layer objective function based on the diagonalization algorithm, and obtain the maximum absorbency capacity of distributed photovoltaics based on the optimal solution.

[0008] Furthermore, the specific process of calculating the total cost of main grid power generation based on the clearing power of thermal power units is as follows:

[0009] The power generation cost of each thermal power unit g is determined based on the clearing power of each thermal power unit in the power system at different times and carbon emission costs represents the marginal generation cost coefficient of thermal power unit g at time t in a typical day s; represents the clearing power of thermal power unit g at time t in a typical day s; E and They represent the unit carbon emission cost and the marginal carbon emission factor of thermal power unit g at time t in a typical day s respectively; Φ T Indicates a collection of moments;

[0010] The total power generation cost of the main grid is calculated based on the power generation cost and carbon emission cost of each thermal power unit.

[0011] Furthermore, the response coefficient R of each node is calculated based on the economic response capacity, independent investment capacity and load regulation capacity of each node. u , expressed as:

[0012] R u =w1L u +w2E u +w3I u ;

[0013] Where, L u is the load regulation capability of the node; E u I is the node economic response capability; u is the node's autonomous investment capability; w1, w2, and w3 are L u 、E u ,I u The weight of

[0014] Node economic response capacity E u It is expressed as:

[0015]

[0016] Where ΔQ sell The change in the amount of electricity sold for a node; ΔP sell is the change in the electricity price; ΔQ purchase The change in the amount of power purchased for the node; ΔP purchase Changes in the price of electricity purchased;

[0017] Node autonomous investment capability I u It is expressed as:

[0018]

[0019] In the formula, I invest Indicates the actual investment of the node in distributed energy or energy storage; I max It is the maximum investment potential that the node can achieve;

[0020] Node load adjustment capability L u It is expressed as:

[0021]

[0022] Where, L max and L min They represent the maximum load and minimum load of the node respectively.

[0023] Furthermore, the operation constraints of thermal power units are expressed as:

[0024]

[0025] In the formula, and are the minimum and maximum outputs of thermal power unit g at time t in a typical day s; P g,t,s is the output of thermal power unit g at time t in a typical day s; is the output of thermal power unit g at time t in the typical day s in the quotation period q; is the maximum restricted output of thermal power unit g at time t in typical day s in quotation period q;

[0026] Step S2.3: The new energy operation constraints include wind power operation constraints and centralized photovoltaic operation constraints; the wind power operation constraints are expressed as:

[0027]

[0028] Where P gw,t,s is the winning bid output of wind turbine gw at time t in a typical day s; is the abandoned wind power of wind turbines gw in a typical day s; is the output of wind turbine gw at time t in a typical day s;

[0029] The operation constraints of centralized photovoltaic are expressed as:

[0030]

[0031] Where P gpv,t,s represents the winning bid output of the centralized photovoltaic unit gpv at time t in a typical day s; It represents the abandoned power of the centralized photovoltaic system GPV in a typical day; is the output of the centralized photovoltaic unit gpv at time t in a typical day s;

[0032] The power balance constraint of the main network scheduling layer is expressed as:

[0033]

[0034] Where P i,t,srepresents the clearing power of node i at time t in a typical day s; P g,t,s represents the clearing power of thermal power unit g at time t in a typical day s; P gw,t,s represents the output of wind turbine gw at time t in a typical day s; P gpv,t,s represents the output of the centralized photovoltaic unit gpv at time t in a typical day s; Represents the rigid demand in the power system that is not affected by market prices; D t,s is the total net load curve of each market member at time t in a typical day s; and They represent the remaining load demand and excess distributed PV output of node i at time t within a typical day s, respectively; represents the wholesale market clearing electricity price of node i at time t in a typical day s; Φ G , Φ W , Φ P Respectively represent the thermal power generation unit set, wind power generation unit set and centralized photovoltaic unit set; Φ I , Φ L , Φ RC They respectively represent the set of all nodes in the power system, the set of nodes in the power system that are not affected by market prices, and the set of nodes in the power system that are affected by market prices.

[0035] Furthermore, the main network scheduling layer objective function is expressed as:

[0036]

[0037] In the formula, It represents the clearing power of thermal power unit g at time t affected by the response coefficient.

[0038] Furthermore, the power flow constraint of the distribution network is expressed as:

[0039]

[0040] In the formula, m and n are the beginning and end points of the line respectively; ch(n) represents the set of child nodes of n; P mn,t and Q mn,t are respectively the active and reactive power transmitted on the line at time t; P n,t and Q n,t are the active and reactive power injected by n at time t; r mn and x mn are the resistance and reactance of the line respectively; V m,t and V n,t is the voltage between m and n at time t; M is a constant; α mn is the line switch status; r is the total number of nodes in the distribution network; r s is the number of substations; Pnk,t represents the active power transmitted from n to its child node k at time t; k represents any node in ch(n); Q nk,t represents the reactive power transmitted from n to its own child node k at time t;

[0041] The photovoltaic output constraint is expressed as:

[0042]

[0043] In the formula, Δ i,t,s represents the actual power generation of distributed photovoltaic connected to node i at time t in a typical day s; α i is the capacity factor of the distributed photovoltaic connected to node i; is the power generation efficiency of distributed photovoltaic connected to node i at time t in a typical day s; represents the distributed photovoltaic access capacity of node i;

[0044] The node power balance constraint is expressed as:

[0045]

[0046] In the formula, represents the initial load demand of node i; and are the charging and discharging power of the energy storage device at node i at time t in a typical day s; plt i represents the power delivered by the line to node i;

[0047] The photovoltaic capacity constraint is expressed as:

[0048]

[0049] In the formula, is the maximum value of distributed photovoltaic accessibility at node i;

[0050] Energy storage planning constraints include energy storage power balance constraints, maximum charge and discharge constraints, maximum storage capacity constraints, and energy storage equipment access constraints;

[0051] The energy storage power balance constraint is expressed as:

[0052]

[0053] In the formula, and are the current storage capacity of the energy storage device at node i at time t within a typical day s and the charging and discharging efficiency of the energy storage device at node i; represents the current storage capacity of the energy storage device at node i at time t-1 in a typical day s; and They represent the charging and discharging power of the energy storage device at node i at time t-1 in a typical day s;

[0054] The maximum charge and discharge constraint is expressed as:

[0055]

[0056] In the formula, and They represent the charging and discharging power of the energy storage device at node i at time t in a typical day s; and They represent the upper limits of the charging and discharging power of the energy storage device at node i at time t in a typical day s; and Respectively represent the charge / discharge status of the energy storage device;

[0057] The maximum storage capacity constraint is expressed as:

[0058]

[0059] In the formula, represents the energy storage device access capacity of node i;

[0060] The energy storage device access constraint is expressed as:

[0061]

[0062] In the formula, f inv Indicates the energy storage device access mark;

[0063] The node voltage constraint is expressed as:

[0064] V n,min ≤V n,t ≤V n,max ;

[0065] Where V n,t is the voltage value at time t; V n,max 、V n,min are the upper and lower voltage limits of n respectively;

[0066] The line power range constraint is expressed as:

[0067]

[0068] In the formula, plt m is the power delivered to m by the line; Pl m is the maximum transmission power of the branch where m is located; plt is the inflow power of all nodes; pflt is the active power flowing out of all lines; is the node-line association matrix; pflt m,n is the active power from m to n on the line; θm,n is the phase angle difference between the voltages m and n; x m is the branch reactance of m.

[0069] Furthermore, the total cost of distribution network operation includes the cost of energy storage charging and discharging The cost of photovoltaic investment The cost of purchasing electricity from the main network dispatch layer Line network loss cost and the benefits of photovoltaic power generation

[0070] It is expressed as:

[0071]

[0072] In the formula, C ES represents the unit initial access cost of energy storage equipment; y ES Indicates the access cost payback period of the energy storage equipment; represents the average annual unit operation and maintenance cost of energy storage equipment; represents the access capacity of energy storage equipment at node i; r represents the interest rate;

[0073] It is expressed as:

[0074]

[0075] In the formula, C PV represents the unit initial access cost of distributed photovoltaics; y PV It represents the access cost payback period of distributed photovoltaics; Represents the average annual unit operation and maintenance cost of distributed photovoltaics; represents the distributed photovoltaic access capacity of node i;

[0076] It is expressed as:

[0077]

[0078] In the formula, β d They represent the wholesale market clearing price and transmission and distribution price of node i at the main grid dispatching layer respectively; represents the residual load demand of node i at time t in a typical day s;

[0079] It is expressed as:

[0080]

[0081] In the formula, represents the excess distributed photovoltaic output of node i at time t in a typical day s; represents the recovery electricity price of node i in the main network scheduling layer;

[0082] It is expressed as:

[0083]

[0084] In the formula, β loss is the unit network loss cost of the line; P ij,t and Q ij,t are the active and reactive power transmitted on the line at time t, r ij is the line resistance.

[0085] Furthermore, the objective function of the distribution network operation layer is expressed as:

[0086]

[0087] In the formula, i represents the node number; Represents the cost of charging and discharging energy storage; represents the cost of PV investment; Represents the cost of purchasing electricity from the main grid dispatching layer; represents the line network loss cost; Represents the revenue of photovoltaic power generation, that is, the revenue of the node selling electricity to the distribution network.

[0088] Furthermore, the specific process of step S3 is as follows:

[0089] The optimal solution of the main grid dispatching layer objective function and the distribution network operation layer objective function is solved based on the diagonalization algorithm mode∈{PV,ES} represents the combination of photovoltaic and energy storage, PV, ES represent photovoltaic and energy storage respectively; Represents the investment capacity of node i in mode in the x′th iteration, including the following steps:

[0090] Initialize the number of iterations x′=0, and initialize the access decision of all nodes, which is expressed as:

[0091] represents the investment capacity of node i in mode in the first iteration. At this time, the rigid demand curve of the node is the net load curve, that is, in, represents the initial net load curve;

[0092] Before the iteration begins, the main grid dispatching layer objective function is used to obtain the initial wholesale market clearing price of the main grid dispatching layer and the initial recovery price of excess electricity based on the initial net load curve;

[0093] After the iteration starts, in each round of iteration:

[0094] Based on the objective function of the distribution network operation layer, the optimal solution is sought for each node in turn. That is, when solving each node, keep the access strategy and net load curve of other nodes unchanged, and find the optimal solution of this round of iteration for the target node alone;

[0095] Based on the distribution network operation layer objective function, the optimal solution of each node is obtained in turn to determine the net load curve and total net load curve of each node after this round of iterative update;

[0096] Based on the iteratively updated net load curve and total net load curve of each node, the main grid dispatching layer objective function is solved to obtain the intermediate variables of the wholesale market clearing electricity price and the recovery price of excess electricity in this round of iteration, which are used as the known data for the next round of distribution network operation layer objective function iteration to solve the distributed photovoltaic access capacity;

[0097] When x′>1, determine whether this round of iteration meets the iterative convergence condition: If it is satisfied, the iteration ends, otherwise the number of iterations is updated, that is, x′=x′+1, and the next round of iteration begins; where ξ is the set convergence criterion, ξ=0.01.

[0098] Compared with the existing technology, the present invention has the following beneficial effects:

[0099] (1) The present invention improves the dispatching efficiency of the power system, reduces energy waste, and ensures the stable operation of the power grid through the coordinated optimization dispatching of the main grid and the distribution grid; by introducing the node response coefficient, each node can make reasonable distributed photovoltaic and energy storage investment decisions based on its own power demand response capability, load regulation capability and economic incentives, thereby improving the utilization efficiency of distributed energy; at the same time, the present invention reduces the impact of reverse flow on the power grid by adjusting the photovoltaic power generation and energy storage discharge strategy, thereby ensuring the stability of the power grid; in addition, through flexible dispatching, the present invention enhances the adaptability of the power grid and its ability to cope with peak loads; finally, through overall optimization, the present invention enhances the safety and reliability of the power system and ensures the stability of the power grid under various operating conditions.

[0100] (2) The present invention uses a diagonalization algorithm to solve the constructed main and auxiliary grid objective functions, accurately calculates the maximum absorptive capacity of distributed photovoltaics at each node, optimizes the utilization of distributed photovoltaic resources, and improves the allocation efficiency of power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0102] like Figure 1 As shown, the present invention provides a technical solution: a method for calculating the maximum absorbable capacity of distributed photovoltaics taking into account the response characteristics, comprising the following steps:

[0103] Step S1: Calculate the total cost of main grid power generation based on the clearing power of thermal power units; calculate the response coefficient of each node based on the economic response capability, independent investment capability and load regulation capability of each node; construct the main grid dispatching layer objective function based on the response coefficient of each node, the operation constraints of thermal power units, the operation constraints of new energy, and the power balance constraints of the main grid dispatching layer with the goal of minimizing the total cost of main grid power generation.

[0104] The specific process of calculating the total cost of main grid power generation based on the clearing power of thermal power units is as follows:

[0105] The power generation cost of each thermal power unit g is determined based on the clearing power of each thermal power unit in the power system at different times and carbon emission costs represents the marginal generation cost coefficient of thermal power unit g at time t in a typical day s; represents the clearing power of thermal power unit g at time t in a typical day s; E and They represent the unit carbon emission cost and the marginal carbon emission factor of thermal power unit g at time t in a typical day s respectively; Φ T Indicates a time collection.

[0106] The total power generation cost of the main grid is calculated based on the power generation cost and carbon emission cost of each thermal power unit.

[0107] The response coefficient R of each node is calculated based on the economic response capacity, independent investment capacity and load regulation capacity of each node. u , expressed as:

[0108] R u =w1L u +w2E u +w3I u (1);

[0109] Where, L u is the load regulation capability of the node, that is, the ability of the node to adjust the power load in different time periods; E u I is the node economic responsiveness, i.e. the sensitivity of the node to electricity price fluctuations or rewards; u is the node's autonomous investment capability, that is, whether the node invests in distributed energy or energy storage equipment and uses these devices to respond to grid demand; w1, w2, and w3 are L u 、E u ,I u The weight of L u 、Eu ,I u The degree of influence on the overall response coefficient.

[0110] Node economic response capacity E u It is expressed as:

[0111]

[0112] Where ΔQ sell The change in the amount of electricity sold for a node; ΔP sell is the change in the electricity price; ΔQ purchase The change in the amount of power purchased for the node; ΔP purchase Changes in the price of electricity purchased.

[0113] Node autonomous investment capability I u It is expressed as:

[0114]

[0115] In the formula, I invest Indicates the actual investment of the node in distributed energy or energy storage; I max It is the maximum investment potential that a node can achieve.

[0116] Node load adjustment capability L u It is expressed as:

[0117]

[0118] Where, L max and L min They represent the maximum load and minimum load of the node respectively, reflecting the adjustment range of the node.

[0119] Among them, the operation constraints of thermal power units are expressed as:

[0120]

[0121] In the formula, and are the minimum and maximum outputs of thermal power unit g at time t in a typical day s; P g,t,s is the output of thermal power unit g at time t in a typical day s; is the output of thermal power unit g at time t in the typical day s in the quotation period q; It is the maximum restricted output of thermal power unit g at time t in typical day s in quotation period q.

[0122] Among them, the new energy operation constraints include wind power operation constraints and centralized photovoltaic operation constraints; wind power operation constraints are expressed as:

[0123]

[0124] Where P gw,t,s is the winning bid output of wind turbine gw at time t in a typical day s; is the abandoned wind power of wind turbines gw in a typical day s; is the output of wind turbine gw at time t in a typical day s.

[0125] The operation constraints of centralized photovoltaic are expressed as:

[0126]

[0127] Where P gpv,t,s represents the winning bid output of the centralized photovoltaic unit gpv at time t in a typical day s; It represents the abandoned power of the centralized photovoltaic system GPV in a typical day; is the output of the centralized photovoltaic unit gpv at time t in a typical day s.

[0128] Among them, the power balance constraint of the main network scheduling layer is expressed as:

[0129]

[0130] Where P i,t,s represents the clearing power of node i at time t in a typical day s; P g,t,s represents the clearing power of thermal power unit g at time t in a typical day s; P gw,t,s represents the output of wind turbine gw at time t in a typical day s; P gpv,t,s represents the output of the centralized photovoltaic unit gpv at time t in a typical day s; Represents the rigid demand in the power system that is not affected by market prices; D t,s is the total net load curve of each market member at time t in a typical day s; and They represent the remaining load demand and excess distributed PV output of node i at time t within a typical day s, respectively; represents the wholesale market clearing electricity price of node i at time t in a typical day s; Φ G , Φ W , Φ P Respectively represent the thermal power generation unit set, wind power generation unit set and centralized photovoltaic unit set; Φ I , Φ L , Φ RC They respectively represent the set of all nodes in the power system, the set of nodes in the power system that are not affected by market prices, and the set of nodes in the power system that are affected by market prices.

[0131] Among them, the main network scheduling layer objective function is expressed as:

[0132]

[0133] In the formula, It represents the clearing power of thermal power unit g at time t affected by the response coefficient.

[0134] Step S2: Based on the power flow constraints, photovoltaic output constraints, node power balance constraints, line power range constraints, node voltage constraints, photovoltaic capacity constraints, energy storage planning constraints, etc. of the distribution network, the distribution network operation layer objective function is constructed with the goal of minimizing the total cost of distribution network operation.

[0135] Among them, the power flow constraint of the distribution network is expressed as:

[0136]

[0137] In the formula, m and n are the beginning and end points of the line respectively; ch(n) represents the set of child nodes of n; P mn,t and Q mn,t are respectively the active and reactive power transmitted on the line at time t; P n,t and Q n,t are the active and reactive power injected by n at time t; r mn and x mn are the resistance and reactance of the line respectively; V m,t and V n,t is the voltage between m and n at time t; M is a constant; α mn is the line switch status; r is the total number of nodes in the distribution network; r s is the number of substations; P nk,t represents the active power transmitted from n to its child node k at time t; k represents any node in ch(n); Q nk,t Represents the reactive power transmitted from n to its own child node k at time t.

[0138] Among them, the photovoltaic output constraint is expressed as:

[0139]

[0140] In the formula, Δ i,t,s represents the actual power generation of distributed photovoltaic connected to node i at time t in a typical day s; α i is the capacity factor of the distributed photovoltaic connected to node i; is the power generation efficiency of distributed photovoltaic connected to node i at time t in a typical day s; Represents the distributed photovoltaic access capacity of node i.

[0141] Among them, the node power balance constraint is expressed as:

[0142]

[0143] In the formula, represents the initial load demand of node i; and are the charging and discharging power of the energy storage device at node i at time t in a typical day s; plt i It represents the power delivered to node i by the line.

[0144] Among them, the photovoltaic capacity constraint is expressed as:

[0145]

[0146] In the formula, is the maximum value of distributed photovoltaic accessibility at node i.

[0147] Among them, energy storage planning constraints include energy storage power balance constraints, maximum charge and discharge constraints, maximum storage capacity constraints, and energy storage equipment access constraints;

[0148] The energy storage power balance constraint is expressed as:

[0149]

[0150] In the formula, and are the current storage capacity of the energy storage device at node i at time t within a typical day s and the charging and discharging efficiency of the energy storage device at node i; represents the current storage capacity of the energy storage device at node i at time t-1 in a typical day s; and They respectively represent the charging and discharging power of the energy storage device at node i at time t-1 within a typical day s.

[0151] The maximum charge and discharge constraint is expressed as:

[0152]

[0153] In the formula, and They represent the charging and discharging power of the energy storage device at node i at time t in a typical day s; and They represent the upper limits of the charging and discharging power of the energy storage device at node i at time t in a typical day s; and They represent the charge / discharge status of the energy storage device, which are 0-1 variables; , it means charging at time t; , indicating discharge at time t.

[0154] The maximum storage capacity constraint is expressed as:

[0155]

[0156] In the formula, Represents the access capacity of the energy storage device at node i.

[0157] Among them, the energy storage device access constraint is expressed as:

[0158]

[0159] In the formula, f inv Indicates the energy storage device access flag, f inv When it is 1, it means the energy storage device is connected. inv When it is 0, it means no energy storage device is connected.

[0160] Among them, the node voltage constraint is expressed as:

[0161] V n,min ≤V n,t ≤V n,max (18);

[0162] Where V n,t is the voltage value at time t; V n,max 、V n,min are the upper and lower voltage limits of n respectively.

[0163] Among them, the line power range constraint is expressed as:

[0164]

[0165] In the formula, plt m is the power delivered to m by the line; Pl m is the maximum transmission power of the branch where m is located; plt is the inflow power of all nodes; pflt is the active power flowing out of all lines; is the node-line association matrix; pflt m,n is the active power from m to n on the line; θ m,n is the phase angle difference between the voltages m and n; x m is the branch reactance of m.

[0166] Among them, the total cost of distribution network operation includes the cost of energy storage charging and discharging The cost of photovoltaic investment The cost of purchasing electricity from the main network dispatch layer Line network loss cost and the benefits of photovoltaic power generation

[0167] It is expressed as:

[0168]

[0169] In the formula, C ES represents the unit initial access cost of energy storage equipment; y ES Indicates the access cost payback period of the energy storage equipment; represents the average annual unit operation and maintenance cost of energy storage equipment; represents the access capacity of the energy storage device at node i; r′ represents the interest rate.

[0170] It is expressed as:

[0171]

[0172] In the formula, C PV represents the unit initial access cost of distributed photovoltaics; y PV It represents the access cost payback period of distributed photovoltaics; Represents the average annual unit operation and maintenance cost of distributed photovoltaics; Represents the distributed photovoltaic access capacity of node i.

[0173] It is expressed as:

[0174]

[0175] In the formula, β d They represent the wholesale market clearing price and transmission and distribution price of node i at the main grid dispatching layer respectively; represents the residual load demand of node i at time t in a typical day s.

[0176] It is expressed as:

[0177]

[0178] In the formula, represents the excess distributed photovoltaic output of node i at time t in a typical day s; It represents the recovery electricity price of node i in the main grid dispatching layer, which is obtained by clearing the main grid dispatching layer.

[0179] It is expressed as:

[0180]

[0181] In the formula, β loss is the unit network loss cost of the line; P ij,t and Q ij,t are the active and reactive power transmitted on the line at time t, r ij is the line resistance.

[0182] Among them, the objective function of the distribution network operation layer is expressed as:

[0183]

[0184] In the formula, i represents the node number; Represents the cost of charging and discharging energy storage; represents the cost of PV investment; Represents the cost of purchasing electricity from the main grid dispatching layer; represents the line network loss cost; Represents the revenue of photovoltaic power generation, that is, the revenue of the node selling electricity to the distribution network.

[0185] Step S3: Solve the optimal solution of the main grid dispatching layer objective function and the distribution network operation layer objective function based on the diagonalization algorithm, and obtain the maximum absorbency capacity of distributed photovoltaics based on the optimal solution.

[0186] The specific process of step S3 is:

[0187] The optimal solution of the main grid dispatching layer objective function and the distribution network operation layer objective function is solved based on the diagonalization algorithm mode∈{PV,ES} represents the combination of photovoltaic and energy storage, PV, ES represent photovoltaic and energy storage respectively; Represents the investment capacity of node i in mode in the x′th iteration, including the following steps:

[0188] Initialize the number of iterations x′=0, and initialize the access decision of all nodes, which is expressed as:

[0189] represents the investment capacity of node i in mode in the first iteration. At this time, the rigid demand curve of the node is the net load curve, that is, in, represents the initial net load curve;

[0190] Before the iteration begins, the main grid dispatching layer objective function is used to obtain the initial wholesale market clearing price of the main grid dispatching layer and the initial recovery price of excess electricity based on the initial net load curve;

[0191] After the iteration starts, in each round of iteration:

[0192] Based on the objective function of the distribution network operation layer, the optimal solution is sought for each node in turn. That is, when solving each node, keep the access strategy and net load curve of other nodes unchanged, and find the optimal solution of this round of iteration for the target node alone;

[0193] Based on the distribution network operation layer objective function, the optimal solution of each node is obtained in turn to determine the net load curve and total net load curve of each node after this round of iterative update;

[0194] Based on the iteratively updated net load curve and total net load curve of each node, the main grid dispatching layer objective function is solved to obtain the intermediate variables of the wholesale market clearing electricity price and the recovery price of excess electricity in this round of iteration, which are used as the known data for the next round of distribution network operation layer objective function iteration to solve the distributed photovoltaic access capacity;

[0195] When x′>1, determine whether this round of iteration meets the iterative convergence condition:

[0196] If it is satisfied, the iteration ends, otherwise the number of iterations is updated, that is, x′=x′+1, and the next round of iteration begins; where ξ is the set convergence criterion, ξ=0.01.

[0197] It should be noted that, according to practical experience, generally when x′=5, the iterations basically meet the convergence condition, so the iterations may also be terminated based on a preset maximum number of iterations (eg, x′=5).

[0198] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the maximum absorptive capacity of distributed photovoltaic power generation taking into account the response characteristics, characterized in that: The steps include: Step S1: Calculate the total cost of main grid power generation based on the clearing power of thermal power units; calculate the response coefficient of each node based on the economic response capability, independent investment capability and load regulation capability of each node; construct the main grid dispatching layer objective function with the goal of minimizing the total cost of main grid power generation based on the response coefficient of each node, the operation constraints of thermal power units, the operation constraints of new energy sources and the power balance constraints of the main grid dispatching layer; Step S2: Based on the power flow constraints of the distribution network, the photovoltaic output constraints, the node power balance constraints, the line power range constraints, the node voltage constraints, the photovoltaic capacity constraints, and the energy storage planning constraints, the distribution network operation layer objective function is constructed with the goal of minimizing the total cost of distribution network operation; Step S3: Solve the optimal solution of the main grid dispatching layer objective function and the distribution network operation layer objective function based on the diagonalization algorithm, and obtain the maximum absorbency capacity of distributed photovoltaics based on the optimal solution.

2. A method for calculating the maximum amenable capacity of distributed photovoltaic power generation taking into account the response characteristics according to claim 1, characterized in that: The specific process of calculating the total cost of main grid power generation based on the clearing power of thermal power units is as follows: Based on the clearing power of each thermal power unit in the power system at different time periods, the power generation cost of each thermal power unit g is determined separately and carbon emission costs represents the marginal generation cost coefficient of thermal power unit g at time t in a typical day s; represents the clearing power of thermal power unit g at time t in a typical day s; E and They represent the unit carbon emission cost and the marginal carbon emission factor of thermal power unit g at time t in a typical day s respectively; Φ T Indicates a collection of moments; The total power generation cost of the main grid is calculated based on the power generation cost and carbon emission cost of each thermal power unit.

3. The method for calculating the maximum absorptive capacity of distributed photovoltaic power taking into account the response characteristics according to claim 2, characterized in that: The response coefficient R of each node is calculated based on the economic response capacity, independent investment capacity and load regulation capacity of each node. u , expressed as: R u =w1L u +w2E u +w3I u ; Where, L u is the load regulation capability of the node; E u I is the node economic response capability; u is the node's autonomous investment capability; w1, w2, and w3 are L u 、E u ,I u The weight of Node economic response capacity E u It is expressed as: Where ΔQ sell The change in the amount of electricity sold for a node; ΔP sell is the change in the electricity price; ΔQ purchase The change in the amount of power purchased for the node; ΔP purchase Changes in the price of electricity purchased; Node autonomous investment capability I u It is expressed as: In the formula, I invest Indicates the actual investment of the node in distributed energy or energy storage; I max It is the maximum investment potential that the node can achieve; Node load adjustment capability L u It is expressed as: Where, L max and L min They represent the maximum load and minimum load of the node respectively.

4. The method for calculating the maximum absorptive capacity of distributed photovoltaic power taking into account the response characteristics according to claim 3 is characterized by: The operating constraints of thermal power units are expressed as: In the formula, and are the minimum and maximum outputs of thermal power unit g at time t in a typical day s; P g,t,s is the output of thermal power unit g at time t in a typical day s; is the output of thermal power unit g at time t in the typical day s in the quotation period q; is the maximum restricted output of thermal power unit g at time t in typical day s in quotation period q; Step S2.3: The new energy operation constraints include wind power operation constraints and centralized photovoltaic operation constraints; the wind power operation constraints are expressed as: Where P gw,t,s is the winning bid output of wind turbine gw at time t in a typical day s; is the abandoned wind power of wind turbines gw in a typical day s; is the output of wind turbine gw at time t in a typical day s; The operation constraints of centralized photovoltaic are expressed as: Where P gpv,t,s represents the winning bid output of the centralized photovoltaic unit gpv at time t in a typical day s; It represents the abandoned power of the centralized photovoltaic system GPV in a typical day; is the output of the centralized photovoltaic unit gpv at time t in a typical day s; The power balance constraint of the main network scheduling layer is expressed as: Where P i,t,s represents the clearing power of node i at time t in a typical day s; P g,t,s represents the clearing power of thermal power unit g at time t in a typical day s; P gw,t,s represents the output of wind turbine gw at time t in a typical day s; P gpv,t,s represents the output of the centralized photovoltaic unit gpv at time t in a typical day s; Represents the rigid demand in the power system that is not affected by market prices; D t,s is the total net load curve of each market member at time t in a typical day s; and They represent the remaining load demand and excess distributed PV output of node i at time t within a typical day s, respectively; represents the wholesale market clearing electricity price of node i at time t in a typical day s; Φ G , Φ W , Φ P Respectively represent the thermal power generation unit set, wind power generation unit set and centralized photovoltaic unit set; Φ I , Φ L , Φ RC They respectively represent the set of all nodes in the power system, the set of nodes in the power system that are not affected by market prices, and the set of nodes in the power system that are affected by market prices.

5. The method for calculating the maximum absorptive capacity of distributed photovoltaic power taking into account the response characteristics according to claim 4, characterized in that: The main network scheduling layer objective function is expressed as: In the formula, It represents the clearing power of thermal power unit g at time t affected by the response coefficient.

6. A method for calculating the maximum amenable capacity of distributed photovoltaic power generation taking into account the response characteristics according to claim 5, characterized in that: The power flow constraint of the distribution network is expressed as: In the formula, m and n are the beginning and end points of the line respectively; ch(n) represents the set of child nodes of n; P mn,t and Q mn,t are respectively the active and reactive power transmitted on the line at time t; P n,t and Q n,t are the active and reactive power injected by n at time t; r mn and x mn are the resistance and reactance of the line respectively; V m,t and V n,t is the voltage between m and n at time t; M is a constant; α mn is the line switch status; r is the total number of nodes in the distribution network; r s is the number of substations; P nk,t represents the active power transmitted from n to its child node k at time t; k represents any node in ch(n); Q nk,t represents the reactive power transmitted from n to its own child node k at time t; The photovoltaic output constraint is expressed as: In the formula, Δ i,t,s represents the actual power generation of distributed photovoltaic connected to node i at time t in a typical day s; α i is the capacity factor of the distributed photovoltaic connected to node i; is the power generation efficiency of distributed photovoltaic connected to node i at time t in a typical day s; represents the distributed photovoltaic access capacity of node i; The node power balance constraint is expressed as: In the formula, represents the initial load demand of node i; and are the charging and discharging power of the energy storage device at node i at time t in a typical day s; plt i represents the power delivered by the line to node i; The photovoltaic capacity constraint is expressed as: In the formula, is the maximum value of distributed photovoltaic accessibility at node i; Energy storage planning constraints include energy storage power balance constraints, maximum charge and discharge constraints, maximum storage capacity constraints, and energy storage equipment access constraints; The energy storage power balance constraint is expressed as: In the formula, and are the current storage capacity of the energy storage device at node i at time t in a typical day s and the charging and discharging efficiency of the energy storage device at node i; represents the current storage capacity of the energy storage device at node i at time t-1 in a typical day s; and They represent the charging and discharging power of the energy storage device at node i at time t-1 in a typical day s; The maximum charge and discharge constraint is expressed as: In the formula, and They represent the charging and discharging power of the energy storage device at node i at time t in a typical day s; and They represent the upper limits of the charging and discharging power of the energy storage device at node i at time t in a typical day s; and Respectively represent the charge / discharge status of the energy storage device; The maximum storage capacity constraint is expressed as: In the formula, represents the access capacity of the energy storage device at node i; The energy storage device access constraint is expressed as: In the formula, f inv Indicates the energy storage device access mark; The node voltage constraint is expressed as: In n,min ≤V n,t ≤V n,max ; Where V n,t is the voltage value at time t; V n,max 、V n,min are the upper and lower voltage limits of n respectively; The line power range constraint is expressed as: In the formula, plt m is the power delivered to m by the line; Pl m is the maximum transmission power of the branch where m is located; plt is the inflow power of all nodes; pflt is the active power flowing out of all lines; is the node-line association matrix; pflt m,n is the active power from m to n on the line; θ m,n is the phase angle difference between the voltages m and n; x m is the branch reactance of m.

7. A method for calculating the maximum absorbable capacity of distributed photovoltaic power taking into account the response characteristics according to claim 6, characterized in that: The total cost of distribution network operation includes the cost of energy storage charging and discharging The cost of photovoltaic investment The cost of purchasing electricity from the main network dispatch layer Line network loss cost and the benefits of photovoltaic power generation It is expressed as: In the formula, C ES represents the unit initial access cost of energy storage equipment; y ES Indicates the access cost payback period of the energy storage equipment; represents the average annual unit operation and maintenance cost of energy storage equipment; represents the access capacity of energy storage equipment at node i; r represents the interest rate; It is expressed as: In the formula, C PV represents the unit initial access cost of distributed photovoltaics; y PV It represents the access cost payback period of distributed photovoltaics; Represents the average annual unit operation and maintenance cost of distributed photovoltaics; represents the distributed photovoltaic access capacity of node i; It is expressed as: In the formula, β d They represent the wholesale market clearing price and transmission and distribution price of node i at the main grid dispatching layer respectively; represents the residual load demand of node i at time t in a typical day s; It is expressed as: In the formula, represents the excess distributed photovoltaic output of node i at time t in a typical day s; represents the recovery electricity price of node i in the main network scheduling layer; It is expressed as: In the formula, β loss is the unit network loss cost of the line; P ij,t and Q ij,t are the active and reactive power transmitted on the line at time t, r ij is the line resistance.

8. The method for calculating the maximum absorptive capacity of distributed photovoltaic power taking into account the response characteristics according to claim 7, characterized in that: The objective function of the distribution network operation layer is expressed as: In the formula, i represents the node number; Represents the cost of charging and discharging energy storage; represents the cost of PV investment; Represents the cost of purchasing electricity from the main grid dispatching layer; represents the line network loss cost; Represents the revenue of photovoltaic power generation, that is, the revenue of the node selling electricity to the distribution network.

9. A method for calculating the maximum amenable capacity of distributed photovoltaic power generation taking into account response characteristics according to claim 8, characterized in that: The specific process of step S3 is: The optimal solution of the main grid dispatching layer objective function and the distribution network operation layer objective function is solved based on the diagonalization algorithm mode∈{PV,ES} represents the combination of photovoltaic and energy storage, PV, ES represent photovoltaic and energy storage respectively; Represents the investment capacity of node i in mode in the x′th iteration, including the following steps: Initialize the number of iterations x′=0, and initialize the access decision of all nodes, which is expressed as: represents the investment capacity of node i in mode in the first iteration. At this time, the rigid demand curve of the node is the net load curve, that is, in, represents the initial net load curve; Before the iteration begins, the main grid dispatching layer objective function is used to obtain the initial wholesale market clearing price of the main grid dispatching layer and the initial recovery price of excess electricity based on the initial net load curve; After the iteration starts, in each round of iteration: Based on the objective function of the distribution network operation layer, the optimal solution is sought for each node in turn. That is, when solving each node, keep the access strategy and net load curve of other nodes unchanged, and find the optimal solution of this round of iteration for the target node alone; Based on the distribution network operation layer objective function, the optimal solution of each node is obtained in turn to determine the net load curve and total net load curve of each node after this round of iterative update; Based on the iteratively updated net load curve and total net load curve of each node, the main grid dispatching layer objective function is solved to obtain the intermediate variables of the wholesale market clearing electricity price and the recovery price of excess electricity in this round of iteration, which are used as the known data for the next round of distribution network operation layer objective function iteration to solve the distributed photovoltaic access capacity; When x′>1, determine whether this round of iteration meets the iterative convergence condition: If m∈{PV,ES} is satisfied, the iteration ends, otherwise the number of iterations is updated, that is, x′=x′+1, and the next round of iteration begins; where ξ is the set convergence criterion, ξ=0.01.

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