Photovoltaic and energy storage capacity assessment method and device taking into account main distribution network power fluctuations

Abstract

The present invention relates to the field of power grids, and specifically discloses a photovoltaic and energy storage capacity assessment method and device that takes into account the fluctuation of the power of the main distribution network. It includes obtaining historical demand data of the distribution network, determining the scale of the distribution network, selecting the distribution network demand curve at different times for calculation based on the scale of the distribution network, obtaining the regulation cost of different capacities, performing simulation fitting according to the regulation cost of the different capacities, determining the cost calculation expression that meets the scale of the distribution network, and constructing a two-layer assessment model and related target constraints based on the cost calculation expression and the public information related to power distribution, wherein the two-layer assessment model includes an upper model and a lower model, obtaining the public power cost parameters, inputting the two-layer assessment model for optimization and solution, and obtaining the optimal capacity assessment scheme to meet the green requirements of the active distribution network.

Description

Technical Field

[0001] The present invention relates to the field of power grids, and in particular to a photovoltaic and energy storage capacity assessment method and device that takes into account the main distribution network power fluctuation. Background Art

[0002] In recent years, the green development of active distribution networks has become a major issue of concern. On the one hand, as the penetration rate of centralized renewable energy reaches a high level, the promotion of distributed renewable energy is likely to further increase the penetration rate of green electricity. At the same time, due to the large-scale promotion of renewable energy, its volatility will not only affect the balance of the distribution network and increase the regulation cost, but also threaten the stable operation of the distribution network. Therefore, in order to maintain the stability of the power grid, energy storage is often used in distribution network planning. Moreover, as a low-cost mobile energy storage facility, promoting the development of electric vehicles can not only reduce the regulation cost, but also help achieve decarbonization in the transportation sector. Therefore, for a distribution network of a certain scale, in order to achieve the green development of the distribution network, it is necessary to analyze and evaluate the capacity of distributed photovoltaics and energy storage.

[0003] At present, the research on the evaluation methods of distributed photovoltaic and energy storage capacity in distribution networks mainly takes the maximization of power generation revenue or the minimization of investment cost as the optimization goal, and takes the physical characteristics of distributed photovoltaic or energy storage as constraints to form a capacity evaluation model. However, most of the existing studies only consider the maximization of private investors' benefits. The resulting distributed photovoltaic or energy storage capacity is only a local optimal amount. These models do not consider the maximization of the overall welfare of the society under the corresponding subsidy strategy. Summary of the invention

[0004] In view of the above problems, an object of the present invention is to provide a photovoltaic and energy storage capacity assessment method that takes into account the fluctuation of the power supply of the main distribution network.

[0005] The second object of the present invention is to provide a photovoltaic and energy storage capacity assessment device that takes into account the fluctuation of the power supply of the main distribution network.

[0006] The first technical solution adopted by the present invention is: S1, obtaining historical demand data of the distribution network and determining the scale of the distribution network;

[0007] S2. Based on the scale of the distribution network, select the distribution network demand curve at different times for calculation to obtain the regulation cost of different capacities;

[0008] S3. Perform simulation fitting according to the regulation costs of different capacities to determine a cost calculation expression that meets the scale of the distribution network;

[0009] S4. Based on the cost calculation expression and the public information related to power distribution, a two-layer evaluation model and related target constraints are constructed, wherein the two-layer evaluation model includes an upper model and a lower model;

[0010] S5. Obtain power cost parameters, input them into the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation solution.

[0011] Optionally, the S4 includes:

[0012] S41. Obtain subsidy optimization targets and requirements for distribution networks;

[0013] S42, based on the cost calculation expression and the subsidy optimization target and demand, target division is performed to obtain a subsidy target function and subsidy constraints, and then the upper model is determined;

[0014] S43. Obtaining the investment objectives and requirements of the distribution network;

[0015] S44. Based on the upper-level model and the investment objectives and needs, target division is performed to obtain investment target functions and investment constraints, and then the lower-level model is determined.

[0016] Optionally, the cost calculation expression is:

[0017]

[0018] In the formula, Δp 2 is the variance and of the net demand curve, C5 is the adjustment cost, α balance is the cost coefficient, f(·) is a fitting function, Q VRE is the installed capacity of distributed photovoltaics, Q EV,sch is the number of dispatchable electric vehicles, Q STR The investment capacity for energy storage.

[0019] Optionally, the subsidy objective function is:

[0020]

[0021] In the formula, R1 is the carbon emission reduction benefit of subsidizing electric vehicles to replace fuel vehicles, R2 is the carbon emission reduction benefit of subsidizing distributed photovoltaics to replace centralized traditional power generation units, C1 is the subsidy for electric vehicles, C2 is the subsidy for charging stations, C3 is the subsidy for distributed photovoltaics, C4 is the subsidy for energy storage, C5 is the adjustment cost, and s EV,sub,SUB is the unit subsidy for electric vehicles, Q EV,sub is the number of fuel vehicles replaced by electric vehicles after subsidies, s EV,cha,SUB is the unit subsidy for the charging station, Q EV,cha is the number of charging stations after subsidies, s VRE,dis,SUB is the unit megawatt subsidy for distributed photovoltaics, Q VRE,dis is the installed capacity of distributed photovoltaic units after subsidies, s STR,dis,SUBis the subsidy per megawatt-hour of energy storage, Q STR,dis is the capacity of energy storage after subsidies.

[0022] Optionally, the investment objective function is:

[0023]

[0024] Where, T EV is the net benefit of investing in electric vehicles, T cha is the net benefit of investing in charging stations, T VRE is the net income of investing in distributed photovoltaics, T STR is the net benefit of investing in energy storage, C EV For the total cost of investing in electric vehicles, is the total benefit of investing in electric vehicles, C cha is the total cost of investing in a charging station, is the total benefit of investing in charging stations, C VRE is the total cost of investing in distributed photovoltaics, is the total income from investing in distributed photovoltaics, C STR is the total cost of investing in energy storage, C VRE is the total cost of investing in distributed photovoltaics, is the total return on investment in energy storage, r EV,sub is the replacement rate of electric vehicles after investment under different subsidy amounts.

[0025] The second technical solution adopted by the present invention is: a photovoltaic and energy storage capacity assessment device taking into account the fluctuation of the power of the main distribution network, comprising: a scale determination module for obtaining historical demand data of the distribution network and determining the scale of the distribution network;

[0026] A cost calculation module, for selecting distribution network demand curves at different times for calculation based on the distribution network scale, to obtain regulation costs of different capacities;

[0027] A simulation fitting module, used for performing simulation fitting according to the regulation costs of different capacities to determine a cost calculation expression that meets the scale of the distribution network;

[0028] A model building module, used to build a two-layer evaluation model and related target constraints based on the cost calculation expression and the public information related to power distribution, wherein the two-layer evaluation model includes an upper model and a lower model;

[0029] The model optimization module is used to obtain power cost parameters, input the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation solution.

[0030] Optionally, the model building module includes:

[0031] Subsidy target determination module, used to obtain subsidy optimization targets and requirements for distribution networks;

[0032] An upper model determination module, used to perform target division based on the cost calculation expression and the subsidy optimization target and demand, obtain the subsidy target function and subsidy constraint, and then determine the upper model;

[0033] Investment target determination module, used to obtain the investment targets and requirements of the distribution network;

[0034] The lower-level model determination module is used to perform target division based on the upper-level model and the investment targets and requirements, obtain investment target functions and investment constraints, and then determine the lower-level model.

[0035] Optionally, the cost calculation expression is:

[0036]

[0037] In the formula, Δp 2 is the variance and of the net demand curve, C5 is the adjustment cost, α balance is the cost coefficient, f(·) is a fitting function, Q VRE is the installed capacity of distributed photovoltaics, Q EV,sch is the number of dispatchable electric vehicles, Q STR The investment capacity for energy storage.

[0038] Optionally, the subsidy objective function is:

[0039]

[0040] In the formula, R1 is the carbon emission reduction benefit of subsidizing electric vehicles to replace fuel vehicles, R2 is the carbon emission reduction benefit of subsidizing distributed photovoltaics to replace centralized traditional power generation units, C1 is the subsidy for electric vehicles, C2 is the subsidy for charging stations, C3 is the subsidy for distributed photovoltaics, C4 is the subsidy for energy storage, and C5 is the adjustment cost; EV,sub,SUB is the unit subsidy for electric vehicles, Q EV,sub is the number of fuel vehicles replaced by electric vehicles after subsidies, s EV,cha,SUB is the unit subsidy for the charging station, Q EV,cha is the number of charging stations after subsidies, s VRE,dis,SUB is the unit megawatt subsidy for distributed photovoltaics, Q VRE,dis is the installed capacity of distributed photovoltaic units after subsidies, s STR,dis,SUB is the subsidy per megawatt-hour of energy storage, Q STR,dis is the capacity of energy storage after subsidies.

[0041] Optionally, the investment objective function is:

[0042]

[0043] Where, T EV is the net benefit of investing in electric vehicles, T cha is the net benefit of investing in charging stations, T VRE is the net income of investing in distributed photovoltaics, T STR is the net benefit of investing in energy storage, C EV For the total cost of investing in electric vehicles, is the total benefit of investing in electric vehicles, C cha is the total cost of investing in a charging station, is the total benefit of investing in charging stations, C VRE is the total cost of investing in distributed photovoltaics, is the total income from investing in distributed photovoltaics, C STR is the total cost of investing in energy storage, C VRE is the total cost of investing in distributed photovoltaics, is the total return on investment in energy storage, r EV,sub is the replacement rate of electric vehicles after investment under different subsidy amounts.

[0044] Beneficial effects of the above technical solution:

[0045] The present invention is in line with reality. By simulating the demand and power supply in the distribution network, a method for calculating the adjustment cost that takes into account the fluctuation of the power energy in the distribution network is obtained, making the model highly interpretable. Global optimization is achieved by combining the social and environmental benefits such as carbon emission reduction at the upper level with the investment benefits of private investors at the lower level to obtain a subsidy investment model that maximizes overall social welfare. It is generalizable. The structure of the distribution network used in this article is general and can be extended to distribution networks with the same structure. It is generalizable. A method for evaluating the distributed photovoltaic and energy storage capacity of distribution networks with energy fluctuations is used to meet the green requirements of active distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a photovoltaic and energy storage capacity assessment method taking into account fluctuations in power of the main distribution network provided by the present invention;

[0047] Figure 2 A schematic diagram of the structure of a photovoltaic and energy storage capacity assessment device that takes into account fluctuations in power in the main distribution network provided by the present invention;

[0048] Figure 3 This is the simulation fitting result diagram for adjusting cost;

[0049] Figure 4 Schematic diagram of the structure of the two-layer evaluation model. DETAILED DESCRIPTION

[0050] The following detailed description of the embodiments of the present invention is further described in detail in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present invention, but cannot be used to limit the scope of the present invention, that is, the present invention is not limited to the preferred embodiments described, and the scope of the present invention is defined by the claims.

[0051] In the description of the present invention, it should be noted that, unless otherwise specified, “plurality” means two or more than two; the terms “first”, “second”, etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; for ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0052] Embodiment 1

[0053] An embodiment of the present invention provides a photovoltaic and energy storage capacity assessment method taking into account the fluctuation of power in the main distribution network, such as Figure 1 As shown,

[0054] S1. Obtain historical demand data of the distribution network and determine the scale of the distribution network;

[0055] The demand for the distribution network and historical data such as the output of related photovoltaic power generation are obtained based on the RTS-GMLC dataset to determine the scale of the distribution network.

[0056] S2. Based on the scale of the distribution network, select the distribution network demand curve at different times for calculation to obtain the regulation cost of different capacities;

[0057] According to the scale of the distribution network, the regulation cost between the main distribution network of distributed photovoltaic, energy storage and electric vehicles with different capacities is obtained.

[0058] S3. Perform simulation fitting according to the regulation costs of different capacities to determine a cost calculation expression that meets the scale of the distribution network;

[0059] Under the above-mentioned distribution network scale, by selecting multiple distribution network demand curves in different time periods, the variance and of the net demand curve of the distribution network under different capacities of distributed photovoltaics, energy storage and different numbers of electric vehicles are calculated, and then the regulation cost that takes into account the fluctuation of the main distribution network power is obtained. After averaging, the average regulation cost of the distribution network is obtained, and then the quadratic function is fitted to obtain the capacity calculation expression of the regulation cost for distributed photovoltaics and energy storage. The simulation fitting results are shown in Figure 2. Figure 3 shown.

[0060] The cost calculation expression is:

[0061]

[0062] In the formula, Δp 2is the variance and of the net demand curve, C5 is the adjustment cost, α balance is the cost coefficient, f(·) is a fitting function, Q VRE is the installed capacity of distributed photovoltaics, Q EV,sch is the number of dispatchable electric vehicles, Q STR The investment capacity for energy storage.

[0063] Among them, the net demand curve of the distribution network under distributed photovoltaics of different capacities can be obtained based on the historical data in the data set, while the net demand curve of the distribution network under different capacities of energy storage and different numbers of electric vehicles is obtained based on the operation optimization model. The operation optimization model takes the variance and minimization of the net demand curve as the objective function, and the constraints include physical constraints, capacity constraints, and maximum and minimum power constraints. The net demand curve under different capacities of energy storage and different numbers of electric vehicles is obtained through optimization.

[0064] Through the regulation cost trend chart under multiple net demand curves with different capacities of distributed photovoltaics, energy storage and different numbers of electric vehicles; taking the average of the regulation costs of multiple curves to obtain the regulation cost containing electricity volatility; and then fitting according to the quadratic function, the calculation expression of the regulation cost of distributed photovoltaics and energy storage with different capacities is obtained.

[0065] The regulation costs of distributed photovoltaic, energy storage and electric vehicles with different capacities are fitted, and the relevant parameters of the regulation cost equation that takes into account the fluctuation of power energy in the main distribution network are obtained.

[0066] like Figure 4 As shown, S4, based on the cost calculation expression and the public information related to power distribution, a two-layer evaluation model and related target constraints are constructed, wherein the two-layer evaluation model includes an upper model and a lower model;

[0067] On the basis of the above steps, according to the subsidy targets of the upper-level relevant departments and satisfying the relevant constraints, an upper-level model for the evaluation of distributed photovoltaic and energy storage capacity in the distribution network taking into account the fluctuations of the main and distribution network electricity energy is constructed; at the same time, private investors, under different subsidy levels, under the corresponding investment targets and satisfying the relevant constraints, construct a lower-level model for the evaluation of distributed photovoltaic and energy storage capacity in the distribution network taking into account the fluctuations of the main and distribution network electricity energy; on the basis of the above, a distribution network distributed photovoltaic and energy storage capacity evaluation model taking into account the fluctuations of the main and distribution network electricity energy is constructed.

[0068] S41. Obtain subsidy optimization targets and requirements for distribution networks.

[0069] Obtain the content of distribution network development planning documents and determine subsidy optimization targets and needs in the distribution network.

[0070] In order to achieve the "dual carbon" goal, it is necessary to vigorously develop renewable energy, but the current centralized new energy installed capacity is close to 30%. In order to further increase the proportion of new energy in power generation and reduce the high construction cost of centralized units, investment subsidies for distributed new energy represented by distributed photovoltaics have become an important focus; at the same time, due to the high volatility of distributed photovoltaic output, connecting large-scale distributed photovoltaics to the distribution network will not only threaten the balance and stability of the power grid, but also increase the regulation cost between the main grid. Therefore, in order to increase the installed capacity of distributed new energy units, while maintaining the stability of the system and reducing the regulation cost, it is necessary to evaluate the capacity of energy storage; and with the vigorous promotion of electric vehicles, electric vehicles can be used as a low-cost mobile energy storage facility. By investing in and subsidizing a certain number of electric vehicles to replace current fuel vehicles, the high cost of energy storage construction can be reduced.

[0071] The purpose of the subsidy is to increase the installed capacity of distributed photovoltaics in the distribution network and the replacement rate of electric vehicles through subsidies, thereby achieving the effect of carbon emission reduction. Therefore, the upper layer of this subsidy investment model is the goal of the relevant departments, whose main goal is to maximize the benefits of carbon emission reduction. While meeting its goals, it is also necessary to ensure that the electricity demand in the entire system is met, while ensuring the safety and stability of the electricity use of the entire system.

[0072] S42, based on the cost calculation expression and the subsidy optimization target and demand, target division is performed to obtain a subsidy target function and subsidy constraints, and then the upper model is determined;

[0073] The subsidy objective function is:

[0074]

[0075] In the formula, R1 is the carbon emission reduction benefit of subsidizing electric vehicles to replace fuel vehicles, R2 is the carbon emission reduction benefit of subsidizing distributed photovoltaics to replace centralized traditional power generation units, C1 is the subsidy for electric vehicles, C2 is the subsidy for charging stations, C3 is the subsidy for distributed photovoltaics, C4 is the subsidy for energy storage, and C5 is the adjustment cost; EV,sub,SUB is the unit subsidy for electric vehicles, Q EV,sub is the number of fuel vehicles replaced by electric vehicles after subsidies, s EV,cha,SUB is the unit subsidy for the charging station, Q EV,cha is the number of charging stations after subsidies, s VRE,dis,SUB is the unit megawatt subsidy for distributed photovoltaics, Q VRE,dis is the installed capacity of distributed photovoltaic units after subsidies, s STR,dis,SUB is the subsidy per megawatt-hour of energy storage, Q STR,dis is the capacity of energy storage after subsidies.

[0076] In the upper layer of the model, the carbon emission reduction benefit R1 brought by subsidizing electric vehicles to replace fuel vehicles is:

[0077]

[0078] In the formula, is the carbon price, L V,stc is the annual mileage of electric vehicles, e GV and e EV are the carbon emissions per unit of mileage per year for fuel vehicles and electric vehicles, n EV is the operating time of the electric vehicle, and r is the interest rate.

[0079] The carbon reduction benefit R2 brought by subsidizing distributed photovoltaics to replace centralized traditional power generation units in the upper layer of the model is:

[0080]

[0081] In the formula, e pow,con is the average carbon emission per megawatt-hour of electricity from traditional power generation units, h VRE,dis‘ is the annual power generation hours of distributed photovoltaics, ξ VRE,dis is the power generation utilization rate of distributed photovoltaic units, n VRE It is the operation time of distributed photovoltaic.

[0082] Electricity demand balancing constraints in the upper layer of the model:

[0083]

[0084] Where D adn is the average annual power demand in the active distribution network, D EV is the average annual charging capacity of electric vehicles, D vre is the average annual power generation of distributed photovoltaics, D centralized is the average annual power generation of centralized traditional units, h centralized is the annual power generation hours of centralized traditional units, ξ centralized It is the power generation utilization rate of centralized traditional units.

[0085] S43. Obtaining the investment objectives and requirements of the distribution network;

[0086] Obtain industry reports on the energy industry and power companies for analysis to determine investment targets and needs in the distribution network.

[0087] For private investors, the goal is to obtain higher returns by investing in corresponding entities, such as saving power generation costs by investing in distributed photovoltaics to replace centralized photovoltaics. In this model, there are four main types of private investors, namely distributed photovoltaic private investors, electrochemical energy storage private investors, electric vehicle private investors, and charging station private investors. For each type of private investor, the goal is to maximize investment returns. Therefore, the lower layer of this subsidy investment model is the goal of private investors, whose goal is to maximize investment returns, which includes the total returns of all private investors.

[0088] For each type of investment entity, the effect of its investment subsidy is different. For example, by investing in electric vehicles to replace fuel vehicles, it can not only save a certain amount of fuel costs every year, but also obtain certain subsidies from relevant upper-level departments. Therefore, for lower-level private investors, they should meet the charging needs of electric vehicles, capacity balance constraints, and maximum and minimum power constraints.

[0089] S44. Based on the upper-level model and the investment objectives and needs, target division is performed to obtain investment target functions and investment constraints, and then the lower-level model is determined.

[0090] The investment objective function is:

[0091]

[0092] Where, T EV is the net benefit of private investors investing in electric vehicles under different subsidy amounts, T cha is the net benefit of private investors investing in charging stations under different subsidy amounts, T VRE is the net income of private investors investing in distributed photovoltaics under different subsidy amounts, T STR is the net benefit of private investors investing in energy storage under different subsidy amounts, C EV The total cost of investing in electric vehicles for private investors under different subsidy amounts is is the total benefit of private investors investing in electric vehicles under different subsidy amounts, C cha The total cost of investing in charging stations for private investors under different subsidy amounts is is the total income of private investors from investing in charging stations under different subsidy amounts, C VRE The total cost of investing in distributed photovoltaics for private investors under different subsidy amounts is: is the total income of private investors investing in distributed photovoltaics under different subsidy amounts, C STR is the total cost of energy storage investment for private investors under different subsidy amounts, C VREThe total cost of investing in distributed photovoltaics for private investors under different subsidy amounts is: is the total benefit of private investors investing in energy storage under different subsidy amounts, r EV,sub is the replacement rate of electric vehicles after investment under different subsidy amounts.

[0093] The total cost C of investing in electric vehicles for private investors in the lower layer of the model under different subsidy amounts EV and total revenue The calculation formula is:

[0094]

[0095] In the formula, α EV,sub is the unit capital replacement cost of electric vehicles, r EV,sub,ope is the unit operating cost, m GV and m EV Capital cost per kilometer for fuel-powered and electric vehicles respectively.

[0096] The total cost C of investing in charging stations for private investors in the lower layer of the model under different subsidy amounts cha and total revenue The calculation formula is:

[0097]

[0098] In the formula, m EV,cha is the unit construction capital cost of a charging station, Q V,stc is the total number of fuel vehicles and electric vehicles, p EV,sub is the penetration rate of electric vehicles, c EV,cha,ope is the unit operating cost of the charging station, I EV,cha is the annual charging income of the charging station, n cha The operating hours of the charging station.

[0099] The total cost C of private investors investing in distributed photovoltaics under different subsidy amounts in the lower layer of the model VRE and total revenue The calculation formula is:

[0100]

[0101] In the formula, m VRE,dis is the unit construction capital cost of distributed photovoltaics, c VRE,dis,ope is the unit operating cost of distributed photovoltaics, m ELC,GRI and c VRE,dis The unit power generation costs of centralized traditional generators and distributed generators are shown.

[0102] The total cost C of investing in energy storage for private investors in the lower layer of the model under different subsidy amountsSTR and total revenue The calculation formula is:

[0103]

[0104] In the formula, m STR,dis is the unit construction capital cost of energy storage, c STR,dis,ope is the unit operating cost of energy storage, P ELC,disc and P ELC,char The electricity price for energy storage discharge and charging, n STR,dis is the average unit charge and discharge times, I STR,dis is the average unit charge and discharge benefit, n STR The operating time of energy storage.

[0105] Quantity balance constraints in the lower layer of the model:

[0106] Q EV,sub =Q V,stc ×(r EV,sub -α EV,cur × EV,sub );

[0107]

[0108] In the formula, α EV,cur is the penetration rate of electric vehicles before investment under different subsidy amounts.

[0109] Power constraints in the lower layer of the model:

[0110]

[0111] Where P EV,cha is the power of the average charging station, h EV is the average annual charging time for electric vehicles.

[0112] S5. Obtain power cost parameters, input them into the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation solution.

[0113] This model is a two-level optimization model. Through the game between the upper-level relevant departments and the lower-level private investors, an equilibrium solution that maximizes social welfare is obtained. The solution is the optimal capacity of distributed photovoltaics, energy storage, electric vehicles and charging stations under different subsidy levels under the distribution network of this scale. The model can further be used to obtain the optimal capacity investment evaluation plan.

[0114] The overall model for evaluating the distributed photovoltaic and energy storage capacity of the distribution network taking into account the fluctuation of the main distribution network power is:

[0115] Model upper layer:

[0116]

[0117] Model bottom layer:

[0118]

[0119] Q EV,sub =Q V,stc ×(r EV,sub -α EV,cur × EV,sub );

[0120]

[0121] Based on the published parameters such as electricity costs, the constructed model is optimized and solved to output the optimal capacity of distributed photovoltaic, energy storage capacity, electric vehicles and charging stations in the distribution network under different subsidy levels, and further obtain the planning scheme in the distribution network.

[0122] This embodiment is a photovoltaic and energy storage capacity assessment method that takes into account the fluctuation of the main distribution network electricity, including obtaining the historical demand data of the distribution network and determining the scale of the distribution network. Based on the scale of the distribution network, the distribution network demand curves at different times are selected for calculation to obtain the regulation costs of different capacities. According to the regulation costs of the different capacities, simulation fitting is performed to determine the cost calculation expression that meets the scale of the distribution network. Based on the cost calculation expression and the distribution-related public information, a two-layer assessment model and related target constraints are constructed, wherein the two-layer assessment model includes an upper model and a lower model, and the electricity cost parameters are obtained and input into the two-layer assessment model for optimization and solution to obtain the optimal capacity assessment solution. In line with the current green and low-carbon development background, by comprehensively considering the green development of the active distribution network, and taking the maximization of social welfare as the optimization goal, a capacity assessment model is constructed, which can provide accurate guidance on the capacity configuration of the distribution network in the context of green and low-carbon transformation.

[0123] Embodiment 2

[0124] An embodiment of the present invention provides a photovoltaic and energy storage capacity assessment device 200 that takes into account the fluctuation of power in the main distribution network, such as Figure 2 As shown, it includes: a scale determination module 201, which is used to obtain historical demand data of the distribution network and determine the scale of the distribution network;

[0125] The cost calculation module 202 is used to select the distribution network demand curve at different times for calculation based on the distribution network scale to obtain the regulation cost of different capacities;

[0126] A simulation fitting module 203 is used to perform simulation fitting according to the regulation costs of different capacities to determine a cost calculation expression that meets the scale of the distribution network;

[0127] A model building module 204, configured to build a double-layer evaluation model and related target constraints based on the cost calculation expression and the public information related to power distribution, wherein the double-layer evaluation model includes an upper model and a lower model;

[0128] The model optimization module 205 is used to obtain power cost parameters, input them into the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation solution.

[0129] Furthermore, the model building module 204 includes:

[0130] Subsidy target determination module 2041, used to obtain subsidy optimization targets and requirements of the distribution network;

[0131] An upper model determination module 2042 is used to perform target division based on the cost calculation expression and the subsidy optimization target and demand, obtain the subsidy target function and subsidy constraint, and then determine the upper model;

[0132] An investment target determination module 2043 is used to obtain investment targets and requirements of the distribution network;

[0133] The lower-level model determination module 2044 is used to perform target division based on the upper-level model and the investment targets and requirements, obtain investment target functions and investment constraints, and then determine the lower-level model.

[0134] Furthermore, the cost calculation expression is:

[0135]

[0136] In the formula, Δp 2 is the variance and of the net demand curve, C5 is the adjustment cost, α balance is the cost coefficient, f(·) is a fitting function, Q VRE is the installed capacity of distributed photovoltaics, Q EV,sch is the number of dispatchable electric vehicles, Q STR The investment capacity for energy storage.

[0137] Furthermore, the subsidy objective function is:

[0138]

[0139] In the formula, R1 is the carbon emission reduction benefit of subsidizing electric vehicles to replace fuel vehicles, R2 is the carbon emission reduction benefit of subsidizing distributed photovoltaics to replace centralized traditional power generation units, C1 is the subsidy for electric vehicles, C2 is the subsidy for charging stations, C3 is the subsidy for distributed photovoltaics, C4 is the subsidy for energy storage, and C5 is the adjustment cost; EV,sub,SUB is the unit subsidy for electric vehicles, QEV,sub is the number of fuel vehicles replaced by electric vehicles after subsidies, s EV,cha,SUB is the unit subsidy for the charging station, Q EV,cha is the number of charging stations after subsidies, s VRE,dis,SUB is the unit megawatt subsidy for distributed photovoltaics, Q VRE,dis is the installed capacity of distributed photovoltaic units after subsidies, s STR,dis,SUB is the subsidy per megawatt-hour of energy storage, Q STR,dis is the capacity of energy storage after subsidies.

[0140] Furthermore, the investment objective function is:

[0141]

[0142] Where, T EV is the net benefit of investing in electric vehicles, T cha is the net benefit of investing in charging stations, T VRE is the net income of investing in distributed photovoltaics, T STR is the net benefit of investing in energy storage, C EV The total cost of investing in electric vehicles is is the total benefit of investing in electric vehicles, C cha is the total cost of investing in a charging station, is the total benefit of investing in charging stations, C VRE is the total cost of investing in distributed photovoltaics, is the total income from investing in distributed photovoltaics, C STR is the total cost of investing in energy storage, C VRE is the total cost of investing in distributed photovoltaics, is the total return on investment in energy storage, r EV,sub is the replacement rate of electric vehicles after investment under different subsidy amounts.

[0143] This embodiment of the present invention uses a scale determination module to obtain historical demand data of the distribution network and determine the scale of the distribution network; a cost calculation module is used to select the distribution network demand curve at different times for calculation based on the scale of the distribution network to obtain the regulation cost of different capacities; a simulation fitting module is used to perform simulation fitting according to the regulation cost of different capacities to determine the cost calculation expression that meets the scale of the distribution network; a model construction module is used to construct a two-layer evaluation model and related target constraints based on the cost calculation expression and distribution-related public information, wherein the two-layer evaluation model includes an upper model and a lower model; a model optimization module is used to obtain power cost parameters, input the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation plan. A distribution network distributed photovoltaic and energy storage capacity evaluation method that takes into account carbon emission reduction benefits and investment returns, etc., and takes social welfare maximization as the goal, taking into account the fluctuation of the main distribution network power, so as to meet the green requirements of the active distribution network.

[0144] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A photovoltaic and energy storage capacity assessment method taking into account the fluctuation of power in the main distribution network, characterized in that: include: S1. Obtain historical demand data of the distribution network and determine the scale of the distribution network; S2. Based on the scale of the distribution network, select the distribution network demand curve at different times for calculation to obtain the regulation cost of different capacities; S3. Perform simulation fitting according to the regulation costs of different capacities to determine a cost calculation expression that meets the scale of the distribution network; S4. Based on the cost calculation expression and the public information related to power distribution, a two-layer evaluation model and related target constraints are constructed, wherein the two-layer evaluation model includes an upper model and a lower model; S5. Obtain power cost parameters, input them into the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation solution; The cost calculation expression is: In the formula, Δp 2 is the variance and of the net demand curve, C5 is the adjustment cost, α balance is the cost coefficient, f(·) is a fitting function, Q VRE is the installed capacity of distributed photovoltaics, Q EV,sch is the number of dispatchable electric vehicles, Q STR Investment capacity for energy storage; The S4 includes: S41. Obtain subsidy optimization targets and requirements for distribution networks; S42: Based on the cost calculation expression and the subsidy optimization target and demand, target division is performed to obtain a subsidy target function and subsidy constraints, and then the upper model is determined; the subsidy target function is: In the formula, R1 is the carbon emission reduction benefit of subsidizing electric vehicles to replace fuel vehicles, R2 is the carbon emission reduction benefit of subsidizing distributed photovoltaics to replace centralized traditional power generation units, C1 is the subsidy for electric vehicles, C2 is the subsidy for charging stations, C3 is the subsidy for distributed photovoltaics, C4 is the subsidy for energy storage, C5 is the adjustment cost, and s EV,sub,SUB is the unit subsidy for electric vehicles, Q EV,sub is the number of fuel vehicles replaced by electric vehicles after subsidies, s EV,cha,SUB is the unit subsidy for the charging station, Q EV,cha is the number of charging stations after subsidies, s VRE,dis,SUB is the unit megawatt subsidy for distributed photovoltaics, Q VRE,dis is the installed capacity of distributed photovoltaic units after subsidies, s STR,dis,SUB is the subsidy per megawatt-hour of energy storage, Q STR,dis is the capacity of energy storage after subsidies; In the upper layer of the model, the carbon emission reduction benefit R1 brought by subsidizing electric vehicles to replace fuel vehicles is: In the formula, is the carbon price, L V,stc is the annual mileage of electric vehicles, e GV and e EV are the carbon emissions per unit of mileage per year for fuel vehicles and electric vehicles, n EV is the operating time of the electric vehicle, r is the interest rate; The carbon reduction benefit R2 brought by subsidizing distributed photovoltaics to replace centralized traditional power generation units in the upper layer of the model is: In the formula, e pow,con is the average carbon emission per megawatt-hour of electricity from traditional power generation units, h VRE,dis‘ is the annual power generation hours of distributed photovoltaics, ξ VRE,dis is the power generation utilization rate of distributed photovoltaic units, n VRE The operating hours of distributed photovoltaics; Electricity demand balancing constraints in the upper layer of the model: D adn +D EV =D vre +D centralized =Q VRE,dis ×h VRE ×ξ VRE,dis +Q centralized ×h centralized ×ξ centralized Where D adn is the average annual power demand in the active distribution network, D EV is the average annual charging capacity of electric vehicles, D vre is the average annual power generation of distributed photovoltaics, D centralized is the average annual power generation of centralized traditional units, h centralized is the annual power generation hours of centralized traditional units, ξ centralized is the power generation utilization rate of centralized traditional units; S43. Obtaining the investment objectives and requirements of the distribution network; S44: Based on the upper model and the investment objectives and requirements, target division is performed to obtain an investment target function and investment constraints, and then the lower model is determined; the investment target function is: Where, T EV is the net benefit of investing in electric vehicles, T cha is the net benefit of investing in charging stations, T VRE is the net income of distributed photovoltaic investment, T STR is the net benefit of investing in energy storage, C EV The total cost of investing in electric vehicles is is the total benefit of investing in electric vehicles, C cha is the total cost of investing in a charging station, is the total benefit of investing in charging stations, C VRE is the total cost of investing in distributed photovoltaics, is the total income from investing in distributed photovoltaics, C STR is the total cost of investing in energy storage, is the total return on investment in energy storage, r EV,sub is the replacement rate of electric vehicles after investment under different subsidy amounts; The total cost of investing in electric vehicles in the lower layer of the model is C EV and total revenue The calculation formula is: In the formula, α EV,sub is the unit capital replacement cost of electric vehicles, r EV,sub,ope is the unit operating cost, m GV and m EV These are the capital costs per kilometer for fuel vehicles and electric vehicles respectively; The total cost C of investing in charging stations in the lower layer of the model cha and total revenue The calculation formula is: In the formula, m EV,cha is the unit construction capital cost of a charging station, Q V,stc is the total number of fuel vehicles and electric vehicles, p EV,sub is the penetration rate of electric vehicles, c EV,cha,ope is the unit operating cost of the charging station, I EV,cha is the annual charging income of the charging station, n cha The operating hours of the charging station; The total cost of investing in distributed photovoltaics in the lower layer of the model is C VRE and total revenue The calculation formula is: In the formula, m VRE,dis is the unit construction capital cost of distributed photovoltaics, c VRE,dis,ope is the unit operating cost of distributed photovoltaics, m ELC,GRI and c VRE,dis They are the unit power generation costs of centralized traditional generators and distributed generators; The total cost C of investing in energy storage for private investors in the lower layer of the model under different subsidy amounts STR and total revenue The calculation formula is: In the formula, m STR,dis is the unit construction capital cost of energy storage, c STR,dis,ope is the unit operating cost of energy storage, P ELC,disc and P ELC,char are the electricity prices for energy storage discharge and charging, n STR,dis is the average unit charge and discharge times, I STR,dis is the average unit charge and discharge benefit, n STR The operating time of the energy storage; Quantity balance constraints in the lower layer of the model: Q EV,sub =Q V,stc ×(r EV,sub -α EV,cur ×r EV,sub ) In the formula, α EV,cur is the penetration rate of electric vehicles before investment under different subsidy amounts; Power constraints in the lower layer of the model: Where P EV,cha is the power of the average charging station, h EV is the average annual charging time for electric vehicles.

2. A photovoltaic and energy storage capacity assessment device that takes into account the fluctuation of power in the main distribution network, characterized in that: include: The scale determination module is used to obtain the historical demand data of the distribution network and determine the scale of the distribution network; A cost calculation module is used to select the distribution network demand curve at different times for calculation based on the scale of the distribution network to obtain the regulation cost of different capacities, including: according to the scale of the distribution network, obtaining the regulation cost between the main distribution network of distributed photovoltaic, energy storage and electric vehicles under different capacities; The simulation fitting module is used to perform simulation fitting according to the regulation costs of the different capacities to determine a cost calculation expression that meets the scale of the distribution network; the cost calculation expression is: In the formula, Δp 2 is the variance and of the net demand curve, C5 is the adjustment cost, α balance is the cost coefficient, f(·) is a fitting function, Q VRE is the installed capacity of distributed photovoltaics, Q EV,sch is the number of dispatchable electric vehicles, Q STR Investment capacity for energy storage; A model building module, used to build a two-layer evaluation model and related target constraints based on the cost calculation expression and the public information related to power distribution, wherein the two-layer evaluation model includes an upper model and a lower model; A model optimization module is used to obtain power cost parameters, input the two-layer evaluation model for optimization and solution, and obtain the optimal capacity evaluation solution; Wherein, the model building module includes: Subsidy target determination module, used to obtain subsidy optimization targets and requirements for distribution networks; The upper model determination module is used to divide the objectives based on the cost calculation expression and the subsidy optimization objectives and requirements, obtain the subsidy objective function and subsidy constraints, and then determine the upper model; the subsidy objective function is: In the formula, R1 is the carbon emission reduction benefit of subsidizing electric vehicles to replace fuel vehicles, R2 is the carbon emission reduction benefit of subsidizing distributed photovoltaics to replace centralized traditional power generation units, C1 is the subsidy for electric vehicles, C2 is the subsidy for charging stations, C3 is the subsidy for distributed photovoltaics, C4 is the subsidy for energy storage, C5 is the adjustment cost, and s EV,sub,SUB is the unit subsidy for electric vehicles, Q EV,sub is the number of fuel vehicles replaced by electric vehicles after subsidies, s EV,cha,SUB is the unit subsidy for the charging station, Q EV,cha is the number of charging stations after subsidies, s VRE,dis,SUB is the unit megawatt subsidy for distributed photovoltaics, Q VRE,dis is the installed capacity of distributed photovoltaic units after subsidies, s STR,dis,SUB is the subsidy per megawatt-hour of energy storage, Q STR,dis is the capacity of energy storage after subsidies; In the upper layer of the model, the carbon emission reduction benefit R1 brought by subsidizing electric vehicles to replace fuel vehicles is: In the formula, is the carbon price, L V,stc is the annual mileage of electric vehicles, e GV and e EV are the carbon emissions per unit of mileage per year for fuel vehicles and electric vehicles, n EV is the operating time of the electric vehicle, r is the interest rate; The carbon reduction benefit R2 brought by subsidizing distributed photovoltaics to replace centralized traditional power generation units in the upper layer of the model is: In the formula, e pow,con is the average carbon emission per megawatt-hour of electricity from traditional power generation units, h VRE,dis‘ is the annual power generation hours of distributed photovoltaics, ξ VRE,dis is the power generation utilization rate of distributed photovoltaic units, n VRE The operating hours of distributed photovoltaics; Electricity demand balancing constraints in the upper layer of the model: D adn +D EV =D vre +D centralized =Q VRE,dis ×h VRE ×ξ VRE,dis +Q centralized ×h centralized ×ξ centralized Where D adn is the average annual power demand in the active distribution network, D EV is the average annual charging capacity of electric vehicles, D vre is the average annual power generation of distributed photovoltaics, D centralized is the average annual power generation of centralized traditional units, h centralized is the annual power generation hours of centralized traditional units, ξ centralized is the power generation utilization rate of centralized traditional units; Investment target determination module, used to obtain the investment targets and requirements of the distribution network; The lower model determination module is used to divide the target based on the upper model and the investment target and demand, obtain the investment target function and investment constraints, and then determine the lower model; the investment target function is: Where, T EV is the net benefit of investing in electric vehicles, T cha is the net benefit of investing in charging stations, T VRE is the net income of distributed photovoltaic investment, T STR is the net benefit of investing in energy storage, C EV The total cost of investing in electric vehicles is is the total benefit of investing in electric vehicles, C cha is the total cost of investing in a charging station, is the total benefit of investing in charging stations, C VRE is the total cost of investing in distributed photovoltaics, is the total income from investing in distributed photovoltaics, C STR is the total cost of investing in energy storage, is the total return on investment in energy storage, r EV,sub is the replacement rate of electric vehicles after investment under different subsidy amounts; The total cost of investing in electric vehicles in the lower layer of the model is C EV and total revenue The calculation formula is: In the formula, α EV,sub is the unit capital replacement cost of electric vehicles, r EV,sub,ope is the unit operating cost, m GV and m EV These are the capital costs per kilometer for fuel vehicles and electric vehicles respectively; The total cost C of investing in charging stations in the lower layer of the model cha and total revenue The calculation formula is: In the formula, m EV,cha is the unit construction capital cost of a charging station, Q V,stc is the total number of fuel vehicles and electric vehicles, p EV,sub is the penetration rate of electric vehicles, c EV,cha,ope is the unit operating cost of the charging station, I EV,cha is the annual charging income of the charging station, n cha The operating hours of the charging station; The total cost of investing in distributed photovoltaics in the lower layer of the model is C VRE and total revenue The calculation formula is: In the formula, m VRE,dis is the unit construction capital cost of distributed photovoltaics, c VRE,dis,ope is the unit operating cost of distributed photovoltaics, m ELC,GRI and c VRE,dis They are the unit power generation costs of centralized traditional generators and distributed generators; The total cost of investing in energy storage in the lower layer of the model is C STR and total revenue The calculation formula is: In the formula, m STR,dis is the unit construction capital cost of energy storage, c STR,dis,ope is the unit operating cost of energy storage, P ELC,disc and P ELC,char are the electricity prices for energy storage discharge and charging, n STR,dis is the average unit charge and discharge times, I STR,dis is the average unit charge and discharge benefit, n STR The operating time of the energy storage; Quantity balance constraints in the lower layer of the model: Q EV,sub =Q V,stc ×(r EV,sub -α EV,cur ×r EV,sub ) In the formula, α EV,cur is the penetration rate of electric vehicles before investment under different subsidy amounts; Power constraints in the lower layer of the model: Where P EV,cha is the power of the average charging station, h EV is the average annual charging time for electric vehicles.

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