Power distribution network multi-region wind-solar-storage collaborative planning method and device, electronic equipment and storage medium

Abstract

The invention discloses a power distribution network multi-region wind-solar-storage collaborative planning method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining historical data of each sub-region of a power distribution network to be optimized, hardware parameters of fire-solar-storage equipment and operation and maintenance cost; generating a plurality of typical scenes according to the historical data; according to the operation and maintenance cost and the hardware parameters, constructing a wide-area planning upper-layer model and model related constraints; calculating uncertainty factors of each typical scene according to historical data and hardware parameters of the typical scenes; according to the operation and maintenance cost, the hardware parameters and the uncertainty factors, constructing a plurality of sub-region planning lower-layer models and model related constraints; according to historical data of a typical scene, carrying out collaborative iteration solution on the upper layer model and the lower layer model to generate an optimal solution of the model; and according to the optimal solution of the model, power distribution network wind-solar-storage collaborative planning is carried out. By implementing the invention, the configuration scheme of renewable energy sources and stored energy can be optimized, and the overall operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a multi-region wind-solar-storage collaborative planning method, device, electronic equipment and storage medium. Background Art

[0002] With the depletion of fossil fuels and the increasing environmental pollution, promoting a low-carbon economy and sustainable energy system has become a common goal of all countries worldwide. In this context, the development of renewable energy such as wind and solar energy and the realization of efficient configuration through energy storage systems have become key links in optimizing the power system. However, due to the differences in resource endowments and load demands in various regions, resource optimization in a single region cannot fully tap the potential for cross-regional power resources and load complementarity. Therefore, the development of multi-regional wind, solar and storage collaborative planning is of great significance to improving the utilization efficiency of renewable energy and achieving coordinated dispatch of power resources between regions.

[0003] In the existing technology, although some studies have explored the coordinated planning of wind, solar and storage systems, most of them are still limited to the optimization within a single region, ignoring the comprehensiveness of cross-regional resource allocation. Due to the uneven distribution of resources between regions, differences in climate conditions and fluctuations in load demand, wind and solar energy resources in various places are highly uncertain and volatile. When dealing with these problems, the existing technology lacks in-depth analysis and utilization of resource complementarity between different regions, and pays too much attention to local optimization within a single region, while lacking systematic and holistic considerations. This not only limits the optimal utilization of renewable energy resources, but may also lead to a waste of investment resources and reduced scheduling efficiency. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for wind, solar and energy storage collaborative planning in multiple regions of a distribution network. By implementing the present invention, it is possible to achieve in-depth analysis and utilization of resource complementarity between different regions in wind, solar and energy storage collaborative planning, and to use it for wind, solar and energy storage collaborative planning in multiple regions, thereby optimizing the configuration of renewable energy and energy storage, improving resource utilization efficiency and reducing overall operating costs.

[0005] An embodiment of the present invention provides a method for coordinated planning of wind, solar and storage in multiple regions of a distribution network, including: obtaining historical data of each sub-region of the distribution network to be optimized, hardware parameters of the thermal, wind, solar and storage equipment in each sub-region, and operation and maintenance costs of each sub-region; wherein the historical data includes historical load data, historical output data of renewable energy units, and historical output data of thermal power units.

[0006] Determine a number of scenarios based on the historical data; classify the several scenarios into a number of different categories through a clustering algorithm, and set the cluster center of each category as a typical scene to generate a number of typical scenarios; wherein a scene contains the historical data of all sub-areas within the same preset time period.

[0007] According to the operation and maintenance costs and hardware parameters, with the goal of minimizing the overall operation cost of the distribution network, a wide-area planning upper-level model and related constraints of the wide-area planning upper-level model are constructed.

[0008] The uncertainty factors of each typical scenario are calculated based on the historical data of the typical scenario and the hardware parameters; wherein the uncertainty factors include future load peak demand, future output boundary of thermal power plants, volatility of future renewable energy output and volatility of future load demand.

[0009] According to the operation and maintenance costs, hardware parameters and uncertainty factors, with the goal of minimizing the sub-region operation costs, several sub-region planning lower-level models and related constraints of the sub-region planning lower-level models are constructed.

[0010] Based on the historical data of typical scenarios, the wide-area planning upper-level model and the sub-regional planning lower-level model are collaboratively iterated to generate the optimal solution of the model.

[0011] Based on the optimal solution of the model, coordinated planning of wind, solar and storage in the distribution network is carried out.

[0012] Furthermore, the wide area planning upper layer model is specifically:

[0013]

[0014] Where n represents the nth sub-region; s represents the sth typical scene; F WR The objective function of the upper model for wide-area planning; is the wind turbine installed capacity in sub-area n of the wide-area planning problem; is the installed PV capacity of sub-region n in the wide-area planning problem; is the energy storage capacity of sub-region n in the wide-area planning problem; is the state of power exchange between the entire wide area and the neighboring areas at time t in scenario s; Ω n is the sub-region set; Ω r A complete set of power plants including wind power plants and solar power plants and energy storage devices; B n，r is the investment cost per unit capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem; is the installed capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem; θ is the discount rate; Y ris the service life of the rth type of power supply to be planned; cur the cost of penalties for renewable energy cuts; is the amount of renewable energy reduction in sub-region n in the wide-area planning problem; σ is the equivalence coefficient between annualized investment cost and operating cost; Ω s is a typical scene set; Ω T A collection of time periods within a typical planning scenario; is the probability of the sth typical scenario occurring; n The transmission cost of injecting electricity into sub-region n; is the power injection amount of sub-area n in the wide-area planning problem under the typical scenario s and time t; bug the price at which electricity is purchased from neighboring areas; is the amount of electricity purchased by the entire wide area from neighboring areas at time t in a typical scenario; sell the price at which electricity is sold to neighboring areas; is the amount of electricity sold from the entire wide area to neighboring areas at time t in a typical scenario; v n，r ,υ n，s，t and is the Lagrange multiplier; is the installed capacity of the rth type of power source in sub-region n in the sub-region planning problem; ω n，r ,ω n，s，t and is the penalty parameter; is the power injection amount of sub-area n in the sub-area planning problem under the typical scenario s and time t; is the amount of renewable energy curtailment in subregion n in the subregion planning problem.

[0015] Furthermore, the constraints related to the upper model of the wide area planning include installed capacity constraints, power balance constraints, sub-region injection power constraints, inter-wide area exchange power constraints and first renewable energy curtailment constraints:

[0016] The installed capacity constraint is:

[0017]

[0018] The power balance constraint is:

[0019] The sub-area injection power constraint is:

[0020] The wide area exchange power constraint is:

[0021]

[0022] The first renewable energy curtailment capacity constraint is:

[0023]

[0024] in, is the maximum installed capacity limit of wind turbines in sub-area n; is the maximum installed capacity limit of PV in sub-region n; is the maximum installed capacity limit of energy storage in sub-region n; The maximum installed capacity limit of wind turbines in a wide area; The maximum installed capacity limit for photovoltaic power generation in a wide area; The maximum installed capacity limit for energy storage in a wide area; is the maximum configuration ratio of energy storage; is the minimum configuration ratio of energy storage; The maximum value of the power injected into the nth region; The minimum value of the injected power for the nth region; is the maximum value of the wide area exchange power; is the minimum value of wide area exchange power; μ cur is the maximum amount of renewable energy reduction; The existing wind turbine installed capacity for sub-area n; is the existing PV installed capacity in sub-region n.

[0025] Furthermore, the uncertainty factors of typical scenarios are calculated by the following methods, including:

[0026] The highest load value in the historical load data of each sub-region in the typical scenario is taken as the peak demand corresponding to each sub-region;

[0027] For each sub-region, the quotient of each historical load data and the corresponding peak demand is calculated to generate the corresponding future load demand volatility;

[0028] Calculate the quotient of each renewable energy unit's historical output data and the corresponding unit's installed power generation capacity to generate the corresponding future renewable energy output volatility; where the unit's installed power generation capacity includes wind turbine unit installed power generation capacity and photovoltaic unit installed power generation capacity;

[0029] According to the historical data of typical scenarios, based on the robust optimization model, an uncertainty robust model is built to characterize the future load peak demand and thermal power output boundary; the uncertainty robust model is specifically:

[0030]

[0031] Among them, U D is the robust set of load peak demands; U gminis the robust set of minimum output boundaries of thermal power units; U gmax is the robust set of maximum output boundaries of thermal power units; is the set of real numbers; is the peak load of sub-area n under typical scenario s; is the upper limit of the output of thermal power units in sub-region n under typical scenario s; is the lower bound of the output of thermal power units in sub-region n under typical scenario s; is the load peak prediction value of sub-area n under typical scenario s; is the deviation between the actual value and the predicted value of the load peak of sub-area n under typical scenario s; is the uncertainty budget of the peak load of sub-area n under typical scenario s; is the predicted value of the upper limit of thermal power unit output in sub-region n under typical scenario s; is the deviation between the actual value and the predicted value of the upper limit of thermal power unit output in sub-region n under typical scenario s; is the uncertainty budget of the upper bound of the output of n thermal power units in the sub-region; is the predicted value of the lower limit of thermal power unit output in sub-region n under typical scenario s; is the deviation between the actual value and the predicted value of the lower limit of thermal power unit output in sub-region n under typical scenario s; is the uncertainty budget of the lower bound of the output of n thermal power units in the sub-region;

[0032] According to the historical data of the typical scenario and the hardware parameters, the uncertainty robust model is solved to generate the output boundary of the future thermal power plant and the future load peak demand.

[0033] Furthermore, the sub-region planning lower-level model is specifically:

[0034]

[0035] in, The wind turbine installed capacity of sub-region n for the sub-region planning problem; is the PV installed capacity of sub-region n in the sub-region planning problem; is the energy storage installed capacity of sub-region n in the sub-region planning problem; is the charging state of the energy storage in sub-region n at time t in a typical scenario s; is the discharge state of the energy storage in sub-region n at time t in a typical scenario s; is the energy storage discharge power of sub-area n in the typical scenario time s; is the energy storage charging power of sub-area n at time t in a typical scenario s; is the output of thermal power units in sub-region n at time t in typical scenario s; is the output of wind turbines in sub-area n at time s in a typical scenario; is the output of the PV unit in sub-area n at time t in the typical scenario s; c is the state of charge of energy storage in sub-area n at a typical scenario s and time t; bt Depreciation expense for energy storage;

[0036] is the cost of thermal power unit; a n , b n and c n is the generator cost coefficient.

[0037] Furthermore, the sub-regional planning lower-level model-related constraints include power balance constraints, thermal power output constraints, energy storage output constraints, new energy output constraints, and second new energy curtailment constraints:

[0038] The power balance constraint is:

[0039]

[0040] The firepower output constraints are:

[0041]

[0042] The energy storage output constraint is:

[0043]

[0044] The new energy output constraints are:

[0045]

[0046] The second renewable energy curtailment capacity constraint is:

[0047]

[0048]

[0049] in, is the load change factor of sub-area n at time t under typical scenario s; is the ramp rate limit of the thermal power unit; is the ramp down rate limit of the thermal power unit; is the charging efficiency of energy storage in sub-region n; is the discharge efficiency of energy storage in sub-region n; is the maximum proportion of the energy storage charge state; is the minimum proportion of energy storage charge state; is the maximum charging power of the energy storage in sub-area n; is the maximum discharge power of energy storage in sub-area n; is the unit capacity wind turbine output of sub-area n at time t under typical scenario s; is the unit capacity PV output of sub-area n at time t under typical scenario s.

[0050] Furthermore, the above-mentioned method of collaboratively iterating and solving the wide-area planning upper model and the sub-regional planning lower model based on the historical data of typical scenarios to generate the optimal solution of the model includes:

[0051] The solver is used to solve the upper model of the wide area planning and generate the optimal solution of the coupling parameters of the upper model;

[0052] The row-column generation algorithm is used to solve the lower-level model of sub-regional planning and generate the optimal solution of the coupling parameters of the lower-level model;

[0053] According to the optimal solution of coupling parameters of the upper model and the optimal solution of coupling parameters of the lower model, the model convergence is judged;

[0054] If the model converges, the optimal solution of the coupling parameters of the upper model and the optimal solution of the coupling parameters of the lower model are set as the optimal solution of the model; and the optimal solution of the model is output;

[0055] If the model does not converge, the upper model of wide-area planning and the lower model of sub-regional planning are collaboratively iterated and solved based on the target cascade method until the model converges and the optimal solution is output.

[0056] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0057] An embodiment of the present invention provides a distribution network multi-region wind, solar and storage collaborative planning device, including: a data acquisition module, a typical scenario generation module, an upper model construction module, an uncertainty factor calculation module, a lower model construction module, a model solving module and a distribution network wind, solar and storage collaborative planning module.

[0058] The data acquisition module is used to obtain the historical data of each sub-area of ​​the distribution network to be optimized, the hardware parameters of the thermal, wind, solar and storage equipment in each sub-area, and the operation and maintenance cost of each sub-area; wherein the historical data includes historical load data, historical output data of renewable energy units, and historical output data of thermal power units.

[0059] The typical scene generation module is used to determine a number of scenes based on the historical data; classify the several scenes into several different categories through a clustering algorithm, and set the cluster center of each category as a typical scene to generate several typical scenes; wherein one scene contains the historical data of all sub-areas within the same preset time period.

[0060] The upper model construction module is used to construct a wide-area planning upper model and related constraints of the wide-area planning upper model based on the operation and maintenance costs and hardware parameters with the goal of minimizing the overall operation cost of the distribution network.

[0061] The uncertainty factor calculation module is used to calculate the uncertainty factors of each typical scenario based on the historical data of the typical scenario and the hardware parameters; wherein the uncertainty factors include future load peak demand, future thermal power plant output boundary, future volatility of renewable energy output and future volatility of load demand.

[0062] The lower-level model construction module is used to construct several sub-region planning lower-level models and related constraints of the sub-region planning lower-level models based on the operation and maintenance costs, hardware parameters and uncertainty factors, with the goal of minimizing the sub-region operating costs.

[0063] The model solving module is used to perform collaborative iterative solving of the wide-area planning upper-level model and the sub-regional planning lower-level model based on historical data of typical scenarios to generate an optimal solution for the model.

[0064] The distribution network wind, solar, and storage collaborative planning module is used to perform distribution network wind, solar, and storage collaborative planning based on the optimal solution of the model.

[0065] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0066] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for coordinated planning of multi-region wind, solar, and storage in a distribution network as described in any one of the above method embodiments can be implemented.

[0067] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0068] An embodiment of the present invention provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, it can implement the distribution network multi-region wind, solar and storage collaborative planning method described in any one of the above method embodiments.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] Embodiments of the present invention provide a method, device, electronic device and storage medium for the coordinated planning of wind, solar and storage in multiple regions of a distribution network. The method performs a clustering operation on the historical data of each sub-region of the distribution network, determines the typical daily data of the distribution network required for modeling, and constructs a wide-area planning upper-level model with the hardware parameters of the thermal, wind, solar and storage equipment in each sub-region and the operation and maintenance costs of each sub-region. Multiple uncertainty factors of the distribution network are calculated based on the typical daily data of the distribution network, and several sub-region planning lower-level models are constructed based on the multiple uncertainty factors, the hardware parameters of the thermal, wind, solar and storage equipment in each sub-region and the operation and maintenance costs of each sub-region. Based on the historical data of typical scenarios, the wide-area planning upper-level model and the sub-region planning lower-level model are collaboratively iterated and solved to generate the optimal solution of the model; the optimal solution of the model is used for the coordinated planning of wind, solar and storage in the distribution network.

[0071] The present invention realizes the coordinated planning of wind, solar and storage in multiple regions of the distribution network through collaborative iterative solutions using the upper-layer wide-area planning upper-layer model and the lower-layer sub-regional planning lower-layer models. The wide-area planning upper-layer model formulates the initial investment strategy and the operation boundary plan for a typical day from a macro perspective. Subsequently, these decision results are passed to the sub-regional planning lower-layer model for further optimization. This solves the problem of lack of systematic and holistic considerations in the prior art when planning wind, solar and storage in the distribution network. The present invention comprehensively considers the synergistic effects of multi-regional power systems, optimizes the configuration scheme of renewable energy and energy storage, improves resource utilization efficiency, and reduces overall operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of a method for coordinated planning of wind, solar and storage in multiple regions of a distribution network provided by an embodiment of the present invention.

[0073] Figure 2 It is a structural schematic diagram of a multi-regional wind, solar and storage multi-layer coordinated planning framework provided by an embodiment of the present invention.

[0074] Figure 3 It is a schematic diagram of a solution process based on a target cascade method and a row and column generation algorithm provided in one embodiment of the present invention.

[0075] Figure 4 It is a schematic diagram of installed capacity of renewable energy and renewable energy system provided by one embodiment of the present invention.

[0076] Figure 5 It is a schematic diagram of the reduction of renewable energy power and reduction rate provided by an embodiment of the present invention.

[0077] Figure 6 (a) is a schematic diagram of renewable energy and ESS installed capacity in sub-area 1 under different uncertainty budgets provided by an embodiment of the present invention; Figure 6(b) is a schematic diagram of renewable energy and ESS installed capacity in sub-area 2 under different uncertainty budgets provided by an embodiment of the present invention; Figure 6 (c) is a schematic diagram of renewable energy and ESS installed capacity in sub-area 3 under different uncertainty budgets provided by an embodiment of the present invention.

[0078] Figure 7 It is a schematic diagram of the renewable energy power reduction and reduction rate of the entire system under different uncertainty budgets provided by an embodiment of the present invention.

[0079] Figure 8 (a) is a schematic diagram of power exchange between a typical scenario 1 and an external power grid under different uncertainty budgets provided by an embodiment of the present invention; Figure 8 (b) is a schematic diagram of power exchange between a typical scenario 2 and an external power grid under different uncertainty budgets provided by an embodiment of the present invention; Figure 8 (c) is a schematic diagram of power exchange with an external power grid under typical scenario 3 with different uncertainty budgets provided by an embodiment of the present invention; Figure 8 (d) is a schematic diagram of power exchange between a typical scenario 4 and an external power grid under different uncertainty budgets provided by an embodiment of the present invention.

[0080] Fig. 9 It is a structural schematic diagram of a distribution network multi-region wind, solar and storage collaborative planning device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] like Figure 1 As shown, an embodiment of the present invention provides a method for coordinated planning of wind, solar and storage in multiple regions of a distribution network, comprising at least the following steps:

[0083] Step S1: Obtain historical data of each sub-area of ​​the distribution network to be optimized, hardware parameters of the thermal, wind, solar and storage equipment in each sub-area, and operation and maintenance costs of each sub-area.

[0084] Specifically, the historical data includes historical load data, historical output data of renewable energy units, and historical output data of thermal power units; the parameters corresponding to the historical data of each sub-area of ​​the distribution network to be optimized in the present invention include:

[0085] The hardware parameters of the thermal, wind, solar and storage equipment in each sub-area of ​​the distribution network to be optimized correspond to the parameters in the present invention, including: the maximum installed capacity limit of the wind turbine in sub-area n Maximum installed capacity limit for PV in sub-region n Maximum installed capacity limit of energy storage in sub-region n Maximum installed capacity of wind turbines in a wide area Maximum installed capacity limit for photovoltaic power generation in a wide area Maximum installed capacity limit for energy storage in a wide area Maximum configuration ratio of energy storage Minimum configuration ratio of energy storage The maximum value of the injected power in the nth region Minimum value of the injected power in the nth region Maximum value of wide area exchange power Minimum value of wide area exchange power Maximum reduction of renewable energy μ cur ; Existing wind turbine installed capacity in sub-area n Existing PV installed capacity in sub-region n The ramp rate limit of thermal power units The ramp rate limit of thermal power units Charging efficiency of energy storage in sub-region n Discharge efficiency of energy storage in sub-region n Maximum ratio of energy storage state of charge Minimum ratio of energy storage state of charge Maximum charging power of energy storage in sub-area n Maximum discharge power of energy storage in sub-area n

[0086] The parameters corresponding to the operation and maintenance cost of each sub-area of ​​the distribution network to be optimized in the present invention include: the investment cost B per unit capacity of the rth type of power source to be planned in the sub-area n in the wide-area planning problem n，r ; Discount rate θ; Service life Y of the rth type of power source to be planned r ; Penalty cost of renewable energy curtailment λ cur ; The equivalent coefficient σ between the annualized investment cost and the operating cost can be 365 in the present invention; The transmission cost λ of the injected power in sub-region n n ; The price of electricity purchased from neighboring areas λ buy ; The price of electricity sold to neighboring areas λ sell ; Depreciation cost of energy storage c bt ; Cost of thermal power units Generator cost coefficient a n , b n and c n.

[0087] Step S2: determine a number of scenes according to the historical data; classify the several scenes into a number of different categories through a clustering algorithm, and set the cluster center of each category as a typical scene to generate a number of typical scenes.

[0088] What needs to be explained here is that a scene contains the historical data of all sub-areas in the same preset time period. The preset time period can be one day, that is, a scene contains the historical data of all sub-areas in the same day, and the sampling frequency can be adjusted by yourself; for example, if the sampling frequency is 1 hour, then a scene contains the historical data of all sub-areas in the 1st hour, 2nd hour, 3rd hour...24th hour in the same day;

[0089] In a preferred embodiment, the K-means clustering technology is used to cluster the scenes into a number of clusters, different clusters are recorded as different categories, and the cluster center of each category is set as a typical scene to generate a number of typical scenes, wherein each category includes a number of scenes.

[0090] Step S3: Based on the operation and maintenance costs and hardware parameters, with the goal of minimizing the overall operation cost of the distribution network, construct a wide-area planning upper-level model and related constraints of the wide-area planning upper-level model.

[0091] The upper model aims to reasonably configure the wind power, photovoltaic and energy storage capacity in different sub-regions, and formulate the operation boundary plan under various typical scenarios. The main goal of the model is to minimize the overall cost associated with these configurations and operations.

[0092] In a preferred embodiment, the wide area planning upper layer model is specifically:

[0093]

[0094] Where n represents the nth sub-region; s represents the sth typical scene; F WR The objective function of the upper model for wide-area planning; is the wind turbine installed capacity in sub-area n of the wide-area planning problem; is the installed PV capacity of sub-region n in the wide-area planning problem; is the energy storage capacity of sub-region n in the wide-area planning problem; is the state of power exchange between the entire wide area and the neighboring areas at time t in scenario s; Ω n is the sub-region set; Ω r A complete set of power plants including wind power plants and solar power plants and energy storage devices; B n，ris the investment cost per unit capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem; is the installed capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem; θ is the discount rate; Y r is the service life of the rth type of power supply to be planned; cur the cost of penalties for renewable energy cuts; is the amount of renewable energy reduction in sub-region n in the wide-area planning problem; σ is the equivalence coefficient between annualized investment cost and operating cost; Ω s is a typical scene set; Ω T A collection of time periods within a typical planning scenario; is the probability of the sth typical scenario occurring; n The transmission cost of injecting electricity into sub-region n; is the power injection amount of sub-area n in the wide-area planning problem under the typical scenario s and time t; buy the price at which electricity is purchased from neighboring areas; is the amount of electricity purchased by the entire wide area from neighboring areas at time t in a typical scenario; sell the price at which electricity is sold to neighboring areas; is the amount of electricity sold from the entire wide area to neighboring areas at time t in a typical scenario; v n，r 、v n，s，t and is the Lagrange multiplier; is the installed capacity of the rth type of power source in sub-region n in the sub-region planning problem; ω n，r ,ω n，s，t and is the penalty parameter; is the power injection amount of sub-area n in the sub-area planning problem under the typical scenario s and time t; is the amount of renewable energy curtailment in subregion n in the subregion planning problem.

[0095] It should be noted that in the wide-area planning upper model, Ω r is a set of complete power plants and energy storage devices including wind power plants and solar power plants, that is, r here represents the set of wind, solar and energy storage (w, pv, ESS); in solving the upper model of wide-area planning, the following parameters are unknown quantities to be solved:

[0096] Installed capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem Wind turbine installed capacity in sub-area n of the wide-area planning problem Photovoltaic installed capacity in sub-region n of the wide-area planning problem Energy storage capacity in sub-region n of the wide-area planning problem The state of power exchange between the entire wide area and the neighboring areas at time t in scenario s Renewable energy curtailment in sub-region n in the wide-area planning problem Power injection amount for sub-area n in a typical scenario s at time t in the wide-area planning problem The amount of electricity purchased by the entire wide area from neighboring areas at time t in a typical scenario The amount of electricity sold from the entire wide area to neighboring areas at time t in a typical scenario

[0097] Specifically, the probability of the sth typical scenario occurring is Calculated by the following formula:

[0098]

[0099] Among them, M s is the total number of scenes contained in the category described in the typical scene in s; Z is the number of all scenes.

[0100] In an optional embodiment, the wide area planning upper model related constraints include installed capacity constraints, power balance constraints, sub-area injection power constraints, inter-wide area exchange power constraints and first new energy curtailment power constraints:

[0101] The installed capacity constraint is:

[0102]

[0103] The power balance constraint is:

[0104]

[0105] The sub-area injection power constraint is:

[0106]

[0107] The wide area exchange power constraint is:

[0108]

[0109] The first renewable energy curtailment capacity constraint is:

[0110]

[0111] in, is the maximum installed capacity limit of wind turbines in sub-area n; is the maximum installed capacity limit of PV in sub-region n; is the maximum installed capacity limit of energy storage in sub-region n; The maximum installed capacity limit of wind turbines in a wide area; The maximum installed capacity limit for photovoltaic power generation in a wide area; The maximum installed capacity limit for energy storage in a wide area; is the maximum configuration ratio of energy storage; is the minimum configuration ratio of energy storage; The maximum value of the power injected into the nth region; The minimum value of the injected power for the nth region; is the maximum value of the wide area exchange power; is the minimum value of wide area exchange power; μ cur is the maximum amount of renewable energy reduction; The existing wind turbine installed capacity for sub-area n; is the existing PV installed capacity in sub-region n.

[0112] It should be noted that, considering the influence of local natural resources, existing grid infrastructure and development policies, formula (2) limits the total installed capacity of wind turbines in the wide area; formula (3) stipulates the installed capacity of wind turbines in each sub-region; formula (4) limits the installed capacity of photovoltaic power stations in the wide area; and formula (5) stipulates the installed capacity limit of photovoltaic power stations in the sub-region; formula (6) limits the installed capacity of energy storage systems in the wide area; formula (7) stipulates the installed capacity of energy storage in the sub-region; formula (8) limits the configuration ratio between energy storage and renewable energy capacity. Formula (9) solves the power balance problem in a large area and ensures that power injection and output are balanced in a wide area. Formula (10) imposes a limit on the amount of electricity that can be injected in each sub-region. Formulas (11) and (12) limit the power exchange with external regions and regulate the energy flow between the system and neighboring regions. This restriction ensures that the interaction with external sources is effective and balanced. Formula (13) ensures that the renewable energy curtailment rate of the entire region remains within a feasible and acceptable range.

[0113] Step S4: Calculate the uncertainty factor of each typical scenario based on the historical data of the typical scenario and the hardware parameters.

[0114] Preferably, the uncertain factors include future load peak demand, future output boundary of thermal power plants, volatility of future renewable energy output, and volatility of future load demand.

[0115] In a preferred embodiment, the uncertainty factor of a typical scenario is calculated in the following manner, including:

[0116] The highest load value in the historical load data of each sub-region in the typical scenario is taken as the peak demand corresponding to each sub-region;

[0117] For each sub-region, the quotient of each historical load data and the corresponding peak demand is calculated to generate the corresponding future load demand volatility;

[0118] Calculate the quotient of each renewable energy unit's historical output data and the corresponding unit's installed power generation capacity to generate the corresponding future renewable energy output volatility; where the unit's installed power generation capacity includes wind turbine unit installed power generation capacity and photovoltaic unit installed power generation capacity;

[0119] According to the historical data of typical scenarios, based on the robust optimization model, an uncertainty robust model is built to characterize the future load peak demand and thermal power output boundary; the uncertainty robust model is specifically:

[0120]

[0121] Among them, U D is the robust set of load peak demands; U g min is the robust set of minimum output boundaries of thermal power units; U g max is the robust set of maximum output boundaries of thermal power units; is the set of real numbers; is the peak load of sub-area n under typical scenario s; is the upper limit of the output of thermal power units in sub-region n under typical scenario s; is the lower bound of the output of thermal power units in sub-region n under typical scenario s; is the load peak prediction value of sub-area n under typical scenario s; is the deviation between the actual value and the predicted value of the load peak of sub-area n under typical scenario s; is the uncertainty budget of the peak load of sub-area n under typical scenario s; is the predicted value of the upper limit of thermal power unit output in sub-region n under typical scenario s; is the deviation between the actual value and the predicted value of the upper limit of thermal power unit output in sub-region n under typical scenario s; is the uncertainty budget of the upper bound of the output of n thermal power units in the sub-region; is the predicted value of the lower limit of thermal power unit output in sub-region n under typical scenario s; is the deviation between the actual value and the predicted value of the lower limit of the thermal power unit output in sub-region n under the typical scenario s; is the uncertainty budget of the lower bound of the output of n thermal power units in the sub-region;

[0122] According to the historical data of the typical scenario and the hardware parameters, the uncertainty robust model is solved to generate the output boundary of the future thermal power plant and the future load peak demand.

[0123] In the specific implementation, the highest load value in the historical load data of each sub-region in the typical scenario is used as the peak demand corresponding to each sub-region;

[0124] For each sub-region, the quotient of each historical load data and the corresponding peak demand is calculated to generate the corresponding future load demand volatility; the future load demand volatility is set as the load change factor.

[0125] The quotient of the historical output data of each renewable energy unit and the corresponding installed power generation capacity of the unit is calculated to generate the corresponding volatility of the future renewable energy output; wherein the installed power generation capacity of the unit includes the installed power generation capacity of the wind unit and the installed power generation capacity of the photovoltaic unit; the volatility of the future renewable energy output can be divided into the volatility of the output of the wind power unit and the volatility of the output of the photovoltaic unit according to the type of the renewable energy unit, which are set as the unit capacity output of the wind turbine and the unit capacity output of the photovoltaic unit, respectively.

[0126] In solving the uncertainty robust model, the following parameters are unknown quantities to be solved:

[0127] Peak load of sub-area n under typical scenario s The upper limit of the output of thermal power units in sub-region n under typical scenario s The lower limit of the output of thermal power units in sub-region n under typical scenario s

[0128] The above three variables represent the future real values ​​of the load or generator output boundary.

[0129] Load peak prediction value of parameter sub-area n under typical scenario s The predicted upper limit of thermal power unit output in sub-region n under typical scenarios And the lower bound prediction value of thermal power unit output in sub-region n under typical scenario s It is a predicted value of the load or generator output boundary, which can be obtained by various prediction methods based on the historical data of the typical scenario and the hardware parameters.

[0130] Deviation between the actual and predicted load peak values ​​of parameter sub-area n in typical scenario s Deviation between the actual value and the predicted value of the upper limit of thermal power unit output in sub-region n under typical scenario s And the deviation between the actual value and the predicted value of the lower limit of the thermal power unit output in sub-area n under typical scenario s is the maximum forecast deviation, obtained based on historical experience.

[0131] By solving the uncertainty robust model, the output boundary of the future thermal power plant and the future load peak demand can be generated. Among them, the load peak of sub-area n under the typical scenario s is obtained. Used to characterize future peak load demand; the upper limit of the output of thermal power units in sub-area n under typical scenario s The lower bound of the output of thermal power units in sub-region n under typical scenario s Used to characterize the output boundaries of future thermal power plants.

[0132] Step S5: Based on the operation and maintenance cost, hardware parameters and uncertainty factors, with the goal of minimizing the sub-region operation cost, construct several sub-region planning lower-level models and related constraints of the sub-region planning lower-level models.

[0133] The present invention simplifies each sub-region planning model into a single-node problem. This simplification is achieved by merging all internal buses in each local region into a single node. In this model, various power sources are aggregated and connected to this central node, and load demands are merged into a unified load representation of the node. This aggregation reduces the complexity of the planning process while providing a comprehensive view of the local power system. The sub-region planning problem is modeled using a stochastic adaptive robust optimization method.

[0134] In a preferred embodiment, the sub-region planning lower layer model is specifically:

[0135]

[0136] in, The wind turbine installed capacity of sub-region n for the sub-region planning problem; is the PV installed capacity of sub-region n in the sub-region planning problem; is the energy storage installed capacity of sub-region n in the sub-region planning problem; is the charging state of the energy storage in sub-region n at time t in a typical scenario s; is the discharge state of the energy storage in sub-region n at time t in a typical scenario s; is the energy storage discharge power of sub-area n in the typical scenario time s; is the energy storage charging power of sub-area n at time t in a typical scenario s; is the output of thermal power units in sub-region n at time t in typical scenario s; is the output of wind turbines in sub-area n at time s in a typical scenario; is the output of the PV unit in sub-area n at time t in the typical scenario s; c is the state of charge of energy storage in sub-area n at a typical scenario s and time t; bt Depreciation expense for energy storage;

[0137] is the cost of thermal power unit; a n , b n and c n is the generator cost coefficient.

[0138] Specifically, in solving the lower-level model of sub-region planning, the following parameters are unknown quantities to be solved:

[0139] Sub-region planning problem Wind turbine installed capacity in sub-region n Photovoltaic installed capacity of sub-region n in the sub-region planning problem Energy storage installed capacity of sub-region n in the sub-region planning problem Renewable energy curtailment in subregion n in the subregion planning problem The power injection amount of sub-area n in the sub-area planning problem under typical scenario s and time t The charging state of the energy storage in sub-area n at time t in a typical scenario s Discharge state of energy storage in sub-area n at time t in a typical scenario s The installed capacity of the rth type of power source in sub-region n in the sub-region planning problem Output of thermal power units in sub-region n at time s in typical scenario Output of wind turbines in sub-region n at time t in typical scenario s Output of PV units in sub-region n at typical scenario s and time t Peak load of sub-area n under typical scenario s The upper limit of the output of thermal power units in sub-region n under typical scenario s The lower limit of the output of thermal power units in sub-region n under typical scenario s The state of charge of energy storage in sub-area n at a typical scenario s and time t Energy storage discharge power of sub-area n at typical scenario s time t Energy storage charging power of sub-area n at typical scenario time t Output of thermal power units in sub-region n at typical scenario s time t

[0140] In an optional embodiment, the sub-regional planning lower-level model-related constraints include power balance constraints, thermal power output constraints, energy storage output constraints, new energy output constraints, and second new energy curtailment constraints:

[0141] The power balance constraint is:

[0142]

[0143] The firepower output constraints are:

[0144]

[0145] The energy storage output constraint is:

[0146]

[0147] The new energy output constraints are:

[0148]

[0149] The second renewable energy curtailment capacity constraint is:

[0150]

[0151] in, is the load change factor of sub-area n at time t under typical scenario s; is the ramp rate limit of the thermal power unit; is the ramp down rate limit of the thermal power unit; is the charging efficiency of energy storage in the sub-area; is the discharge efficiency of energy storage in sub-region n; is the maximum proportion of the energy storage charge state; is the minimum proportion of energy storage charge state; is the maximum charging power of the energy storage in sub-area n; is the maximum discharge power of energy storage in sub-area n; is the unit capacity wind turbine output of sub-area n at time t under typical scenario s; is the unit capacity PV output of sub-area n at time t under typical scenario s.

[0152] It should be explained here that formula (18) defines constraints related to power balance to ensure that power supply matches demand and maintain system stability. Formula (19) limits the output range of thermal power plants to ensure that power generation equipment operates within the specified range; formula (20) describes the ramp constraints of thermal power plants to ensure that power generation equipment is adjusted at an allowed rate. Formula (21) establishes the relationship between the state of charge and power of the energy storage system; formula (22) stipulates the restrictions on the state of charge; formula (23) establishes the restrictions on the charging power of energy storage; (24) establishes the restrictions on the discharge power of energy storage; formula (25) indicates that energy storage charging and discharging cannot be carried out at the same time. Formula (26) is the wind turbine output constraint; formula (27) is the output constraint of the photovoltaic power station. Formula (28) stipulates the amount of renewable energy reduction in region n. Formula (29) stipulates that the renewable energy reduction rate in region n is kept within an acceptable range.

[0153] Step S6: Based on the historical data of typical scenarios, the wide-area planning upper-level model and the sub-regional planning lower-level model are collaboratively iterated to generate the optimal solution of the model.

[0154] In a preferred embodiment, the method of collaboratively iteratively solving the wide-area planning upper model and the sub-regional planning lower model based on historical data of typical scenarios to generate the optimal solution of the model includes:

[0155] The solver is used to solve the upper model of the wide area planning and generate the optimal solution of the coupling parameters of the upper model;

[0156] The row-column generation algorithm is used to solve the lower-level model of sub-regional planning and generate the optimal solution of the coupling parameters of the lower-level model;

[0157] According to the optimal solution of coupling parameters of the upper model and the optimal solution of coupling parameters of the lower model, the model convergence is judged;

[0158] If the model converges, the optimal solution of the coupling parameters of the upper model and the optimal solution of the coupling parameters of the lower model are set as the optimal solution of the model; and the optimal solution of the model is output;

[0159] If the model does not converge, the upper model of wide-area planning and the lower model of sub-regional planning are collaboratively iterated and solved based on the target cascade method until the model converges and outputs the optimal solution.

[0160] In a limited embodiment, after solving the sub-region planning lower model by using the row-column generation algorithm to generate the optimal solution of the coupling parameters of the lower model, it also includes:

[0161] Update the first optimal solution and the second optimal solution according to the following formula:

[0162] x k+1 =x k +τ(x k+1 -x k ) (30)

[0163] z k+1 =z k +τ(z k+1 -z k ) (31)

[0164] Among them, τ is the multiplier for variable update.

[0165] like Figure 3 As shown, a preferred embodiment of the present invention provides a collaborative iterative solution of the wide-area planning upper model and the sub-regional planning lower model based on the target cascade method and the row and column generation algorithm.

[0166] Step S6.1, input the historical data of each sub-region of the distribution network to be optimized into the wide-area planning upper model and the sub-region planning lower model, and incorporate the uncertainty factors into the sub-region planning lower model.

[0167] Step S6.2, initializing parameters. Set the iteration index k = 0. In the following content of this patent, variables with a superscript k represent the value of the variable at the kth iteration in the target cascade method solution process. Initialize coupling parameters and Initialize Lagrangian and penalty multipliers and Here, the initial values ​​of various parameters can be determined based on experience and the actual situation of the power system.

[0168] Step S6.3: Use the solver to solve the wide-area planning upper model and obtain the first optimal solution x k+1 ; Use the row and column generation algorithm to solve the lower model of sub-region planning and obtain the second optimal solution z k+1 .in, Represents the coupling parameters in the upper model of wide-area planning; Represents the coupling parameters in the lower model of sub-regional planning.

[0169] Step S6.4: Determine whether the model has converged according to the following formula:

[0170]

[0171] Among them, ε1 and ε2 are convergence criteria. They can be flexibly set according to actual conditions. If the model converges, the optimal solution of the coupling parameters of the upper model and the optimal solution of the coupling parameters of the lower model are set as the optimal solution of the model; and the optimal solution of the model is output;

[0172] If the model does not converge, update the Lagrange multipliers of the penalty function linear term and quadratic term in the target cascade method according to the following formula, and go to step S6.3 for iteration:

[0173]

[0174]

[0175] Among them, β is the penalty parameter amplification coefficient; γ is the judgment weight for updating the penalty parameter.

[0176] In a preferred embodiment, the row-column generation algorithm is used to solve the sub-region planning lower model to obtain the second optimal solution z k+1 ,include:

[0177] The row and column generation algorithm is used to solve the sub-region operation optimization problem under uncertain conditions. Therefore, the sub-region planning lower-level model is decomposed into a main problem and a sub-problem, and the two problems are iteratively solved until convergence. Among them, the variables with a superscript * represent the solution of the variable in the main problem.

[0178] Step S6.3.1, decompose the sub-region planning lower-level model into the following main problems and sub-problems, where the sub-problems are:

[0179]

[0180] The main question is:

[0181]

[0182]

[0183] Wherein, m is the number of iterations of the row-column generation algorithm. In the following content of this patent, the variables with the subscript m all represent the value of the variable at the mth iteration in the process of solving the row-column generation algorithm. η is the auxiliary variable of the subproblem.

[0184] Step S6.3.2, set the main problem objective function UB = -∞, and the upper bound of the objective function UB = -∞;

[0185] Step S6.3.3: Select the corresponding constraint conditions according to the number of iterations of the row and column generation algorithm to solve the main problem and calculate the third optimal solution of the main problem. Among them, if m=0, the main problem is solved by complying with the constraints corresponding to equations (35) to (37); if m≠0, the main problem is solved by complying with the constraints corresponding to equations (18) to (29) and (35) to (37).

[0186] Step S6.3.4: Solve the subproblem based on the optimal solution obtained in step S6.3.3, and calculate and generate the fourth optimal solution.

[0187] Step S6.3.5, update the main problem objective function LB and the upper bound UB of the objective function by the following formula:

[0188]

[0189] Step S6.3.6, perform model convergence check according to the following formula:

[0190] UB-LB≤ε3 (43)

[0191] If the model converges, the output

[0192] If the model does not converge, then add optimization variables to the main problem and go to step S6.3.3. The optimization variables are:

[0193] Step S7: Perform wind, solar and storage coordinated planning for the distribution network according to the optimal solution of the model.

[0194] like Figure 2 As shown in the figure, the present invention provides a multi-regional wind, solar and storage multi-layer coordinated planning framework. The upper wide-area model formulates the initial investment strategy and the operation boundary plan for a typical day from a macro perspective. Subsequently, these decision results are passed to the lower sub-regional model for further optimization. The core task of the sub-regional planning model is to optimize the operating cost within each sub-region.

[0195] In a preferred embodiment, the actual parameters of a regional power grid are obtained for verification experiments. The power grid consists of three interconnected subgrids. Four typical scenarios are generated based on the historical source and load data of the region. These scenarios retain the demand factors and renewable energy (RES) variability and correlation information. The probabilities of these four typical scenarios are calculated to be 0.221, 0.286, 0.279 and 0.213 respectively.

[0196] The installed capacity of wind, solar and storage is as follows: Figure 4 The expected value of renewable energy curtailment is defined as the sum of the energy curtailed in each scenario multiplied by the probability of the scenario. Figure 5 The expected value of renewable energy curtailment and the corresponding curtailment ratio are shown. The curtailment ratio in each sub-region is very low, remaining below 0.01%, indicating that distributed collaborative planning reduces the curtailment of renewable energy.

[0197] Each sub-region planning model includes a worst-case uncertainty budget. By adjusting this budget value during the planning process, we can control the conservativeness of the optimal plan. The installed capacity of the planning schemes under different uncertainty budgets is evaluated, such as Figure 6 Specifically, B0, B1, B2, and B3 represent gradually increasing uncertainty budget levels, corresponding to different uncertainty budgets of 0, 1, 2, and 3. As the uncertainty budget increases, the installed capacity of renewable energy and energy storage systems in the three sub-regions increases significantly. Figure 7 The expected curtailment of renewable energy and the associated curtailment rate for the entire system under different uncertainty budgets are shown. As the uncertainty budget increases, the curtailment of renewable energy and the curtailment rate increase significantly. For example, the curtailment of renewable energy increases from less than 1000MW to 15,670MW, while the curtailment rate increases from less than 0.1% to 2.87%. Figure 8The dynamics of energy exchange between the wide-area system and neighboring regions under different uncertainty budgets are shown in detail. As the uncertainty budget increases, the amount of energy exchange increases significantly. This trend is consistent in all four considered scenarios.

[0198] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0199] like Fig. 9 As shown, an embodiment of the present invention provides a distribution network multi-region wind, solar and storage collaborative planning device, including: a data acquisition module, a typical scenario generation module, an upper model construction module, an uncertainty factor calculation module, a lower model construction module, a model solving module and a distribution network wind, solar and storage collaborative planning module.

[0200] The data acquisition module is used to obtain the historical data of each sub-area of ​​the distribution network to be optimized, the hardware parameters of the thermal, wind, solar and storage equipment in each sub-area, and the operation and maintenance cost of each sub-area; wherein the historical data includes historical load data, historical output data of renewable energy units, and historical output data of thermal power units.

[0201] The typical scene generation module is used to determine a number of scenes based on the historical data; classify the several scenes into several different categories through a clustering algorithm, and set the cluster center of each category as a typical scene to generate several typical scenes; wherein one scene contains the historical data of all sub-areas within the same preset time period.

[0202] The upper model construction module is used to construct a wide-area planning upper model and related constraints of the wide-area planning upper model based on the operation and maintenance costs and hardware parameters with the goal of minimizing the overall operation cost of the distribution network.

[0203] The uncertainty factor calculation module is used to calculate the uncertainty factors of each typical scenario based on the historical data of the typical scenario and the hardware parameters; wherein the uncertainty factors include future load peak demand, future thermal power plant output boundary, future volatility of renewable energy output and future volatility of load demand.

[0204] The lower-level model construction module is used to construct several sub-region planning lower-level models and related constraints of the sub-region planning lower-level models based on the operation and maintenance costs, hardware parameters and uncertainty factors, with the goal of minimizing the sub-region operating costs.

[0205] The model solving module is used to perform collaborative iterative solving of the wide-area planning upper-level model and the sub-regional planning lower-level model based on historical data of typical scenarios to generate an optimal solution for the model.

[0206] The distribution network wind, solar, and storage collaborative planning module is used to perform distribution network wind, solar, and storage collaborative planning based on the optimal solution of the model.

[0207] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0208] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0209] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the distribution network multi-region wind, solar and storage collaborative planning method described in any one of the present invention is implemented, or, when the processor executes the computer program, the functions of each module in the above-mentioned device embodiments are implemented.

[0210] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0211] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0212] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0213] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0214] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0215] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any of the above-mentioned distribution network multi-region wind, solar and storage collaborative planning methods of the present invention.

[0216] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0217] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0218] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for coordinated planning of wind, solar and storage in multiple regions of a distribution network, characterized in that: include: Obtain the historical data of each sub-area of ​​the distribution network to be optimized, the hardware parameters of the thermal, wind, solar and storage equipment in each sub-area, and the operation and maintenance cost of each sub-area; the historical data includes historical load data, historical output data of renewable energy units, and historical output data of thermal power units; Determine a number of scenarios based on the historical data; classify the several scenarios into a number of different categories using a clustering algorithm, and set the cluster center of each category as a typical scenario to generate a number of typical scenarios; wherein a scenario contains the historical data of all sub-areas within the same preset time period; According to the operation and maintenance costs and hardware parameters, with the goal of minimizing the overall operation cost of the distribution network, constructing a wide area planning upper model and related constraints of the wide area planning upper model; Calculate uncertainty factors of each typical scenario based on the historical data of the typical scenario and the hardware parameters; wherein the uncertainty factors include future load peak demand, future output boundary of thermal power plants, future volatility of renewable energy output, and future volatility of load demand; According to the operation and maintenance costs, hardware parameters and uncertainty factors, with the goal of minimizing the sub-region operation costs, construct several sub-region planning lower-level models and related constraints of the sub-region planning lower-level models; Based on historical data of typical scenarios, the upper model of wide-area planning and the lower model of sub-regional planning are collaboratively iterated to generate the optimal solution of the model; Based on the optimal solution of the model, coordinated planning of wind, solar and storage in the distribution network is carried out.

2. The method for coordinated planning of wind, solar and storage in multiple regions of a distribution network according to claim 1, characterized in that: The wide area planning upper layer model is specifically: Where n represents the nth sub-region; s represents the sth typical scene; F WR The objective function of the upper model for wide-area planning; is the wind turbine installed capacity in sub-area n of the wide-area planning problem; is the installed PV capacity of sub-region n in the wide-area planning problem; is the energy storage capacity of sub-region n in the wide-area planning problem; is the state of power exchange between the entire wide area and the neighboring areas at time t in scenario s; Ω n is the sub-region set; Ω r A complete set of power plants including wind power plants and solar power plants and energy storage devices; B n，r is the investment cost per unit capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem; is the installed capacity of the rth type of power source to be planned in sub-region n in the wide-area planning problem; θ is the discount rate; Y r is the service life of the rth type of power supply to be planned; cur the cost of penalties for renewable energy cuts; is the amount of renewable energy reduction in sub-region n in the wide-area planning problem; σ is the equivalence coefficient between annualized investment cost and operating cost; Ω s is a set of typical scenes; Ω T A collection of time periods within a typical planning scenario; is the probability of the sth typical scenario occurring; n The transmission cost of injecting electricity into sub-region n; is the power injection amount of sub-area n in the wide-area planning problem under the typical scenario s and time t; buy the price at which electricity is purchased from neighboring areas; is the amount of electricity purchased by the entire wide area from neighboring areas at time t in a typical scenario; sell the price at which electricity is sold to neighboring areas; is the amount of electricity sold from the entire wide area to neighboring areas in a typical scenario s at time t; n，r 、v n，s，t and is the Lagrange multiplier; is the installed capacity of the rth type of power source in sub-region n in the sub-region planning problem; ω n，r ,ω n，s，t and is the penalty parameter; is the power injection amount of sub-area n in the sub-area planning problem under the typical scenario s and time t; is the amount of renewable energy curtailment in subregion n in the subregion planning problem.

3. The method for coordinated planning of wind, solar and storage in multiple regions of a distribution network as claimed in claim 2, characterized in that: The constraints related to the wide area planning upper model include installed capacity constraints, power balance constraints, sub-area injection power constraints, inter-wide area exchange power constraints and first renewable energy curtailment power constraints: The installed capacity constraint is: The power balance constraint is: The sub-area injection power constraint is: The wide area exchange power constraint is: The first renewable energy curtailment capacity constraint is: in, For sub-region n Maximum installed capacity limit for medium-sized wind turbines; For sub-region n The maximum installed capacity limit of photovoltaic power generation in China; For sub-region n The maximum installed capacity limit of medium energy storage; The maximum installed capacity limit of wind turbines in a wide area; The maximum installed capacity limit for photovoltaic power generation in a wide area; The maximum installed capacity limit for energy storage in a wide area; is the maximum configuration ratio of energy storage; is the minimum configuration ratio of energy storage; For the n The maximum value of the injected power in each area; For the n The minimum value of the injected power in each area; is the maximum value of the wide area exchange power; is the minimum value of wide area exchange power; μ cur is the maximum amount of renewable energy reduction; The existing wind turbine installed capacity for sub-area n; is the existing PV installed capacity in sub-region n.

4. The method for coordinated planning of wind, solar and storage in multiple regions of a distribution network as claimed in claim 3, characterized in that: Uncertainty factors for typical scenarios are calculated by: The highest load value in the historical load data of each sub-region in the typical scenario is taken as the peak demand corresponding to each sub-region; For each sub-region, the quotient of each historical load data and the corresponding peak demand is calculated to generate the corresponding future load demand volatility; Calculate the quotient of each renewable energy unit's historical output data and the corresponding unit's installed power generation capacity to generate the corresponding future renewable energy output volatility; where the unit's installed power generation capacity includes wind turbine unit installed power generation capacity and photovoltaic unit installed power generation capacity; According to the historical data of typical scenarios, based on the robust optimization model, an uncertainty robust model is built to characterize the future load peak demand and thermal power output boundary; the uncertainty robust model is specifically: Among them, U D is the robust set of load peak demands; U gmin is the robust set of minimum output boundaries of thermal power units; U gmax is the robust set of maximum output boundaries of thermal power units; is the set of real numbers; is the peak load of sub-area n under typical scenario s; is the upper limit of the output of thermal power units in sub-region n under typical scenario s; For sub-region n In a typical scenario s The lower limit of the output of the next thermal power unit; For sub-region n In a typical scenario s The load peak prediction value under For sub-region n In a typical scenario s The deviation between the actual value and the predicted value of the load peak under For sub-region n In a typical scenario s Uncertainty budget for lower load peaks; For sub-region n In a typical scenario s The predicted value of the upper limit of the output of the lower thermal power unit; For sub-region n In a typical scenario s The deviation between the actual value and the predicted value of the upper limit of the thermal power unit output under For sub-region n Uncertainty budget for the upper bound of thermal power unit output; For sub-region n In a typical scenario s The lower limit of the output of the thermal power unit is predicted; For sub-region n In a typical scenario s The deviation between the actual value and the predicted value of the lower limit of the thermal power unit output under For sub-region n Uncertainty budget for the lower bound of thermal power unit output; According to the historical data of the typical scenario and the hardware parameters, the uncertainty robust model is solved to generate the output boundary of the future thermal power plant and the future load peak demand.

5. The method for coordinated planning of wind, solar and storage in multiple regions of a distribution network as claimed in claim 4, characterized in that: The sub-region planning lower-level model is specifically as follows: in, Planning Problem Sub-areas for Sub-areas n Wind turbine installed capacity; Subregions in the subregion planning problem n of photovoltaic installed capacity; Subregions in the subregion planning problem n of energy storage capacity; For sub-region n Energy storage in typical scenarios s time t The charging status; For sub-region n Energy storage in typical scenarios s time t The discharge state; For sub-region n In a typical scenario s Energy storage discharge power over time; For sub-region n In a typical scenario s time t Energy storage charging power; For sub-region n In a typical scenario s time t Output of thermal power units; For sub-region n In a typical scenario s Time wind turbine output; is the output of the PV unit in sub-area n at time t in the typical scenario s; c is the state of charge of energy storage in sub-area n at a typical scenario s and time t; bt Depreciation expense for energy storage; is the cost of thermal power unit; a n , b n and c n is the generator cost coefficient.

6. The method for coordinated planning of wind, solar and storage in multiple regions of a distribution network as claimed in claim 5, characterized in that: The sub-regional planning lower-level model related constraints include power balance constraints, thermal power output constraints, energy storage output constraints, new energy output constraints and second new energy curtailment constraints: The power balance constraint is: The firepower output constraints are: The energy storage output constraint is: The new energy output constraints are: The second renewable energy curtailment capacity constraint is: in, is the load change factor of sub-area n at time t under typical scenario s; is the ramp rate limit of the thermal power unit; is the ramp down rate limit of the thermal power unit; is the charging efficiency of energy storage in sub-region n; For sub-region n Discharge efficiency of medium energy storage; is the maximum proportion of the energy storage charge state; is the minimum proportion of energy storage charge state; For sub-region n Maximum charging power of the medium energy storage; For sub-region n Maximum discharge power of the medium energy storage; For a typical scenario s Lower sub-area n In time t The fan output per unit capacity; For a typical scenario s Lower sub-area n In time t The unit capacity of photovoltaic output.

7. The method for coordinated planning of wind, solar and storage in multiple regions of a distribution network as claimed in claim 6, characterized in that: The above-mentioned method performs collaborative iterative solving of the wide-area planning upper-level model and the sub-regional planning lower-level model based on the historical data of typical scenarios to generate the optimal solution of the model, including: The solver is used to solve the upper model of the wide-area planning and generate the optimal solution of the coupling parameters of the upper model; The row-column generation algorithm is used to solve the lower-level model of sub-regional planning and generate the optimal solution of the coupling parameters of the lower-level model; According to the optimal solution of coupling parameters of the upper model and the optimal solution of coupling parameters of the lower model, the model convergence is judged; If the model converges, the optimal solution of the coupling parameters of the upper model and the optimal solution of the coupling parameters of the lower model are set as the optimal solution of the model; and the optimal solution of the model is output; If the model does not converge, the upper model of wide-area planning and the lower model of sub-regional planning are collaboratively iterated and solved based on the target cascade method until the model converges and outputs the optimal solution.

8. A distribution network multi-region wind, solar and storage collaborative planning device, characterized in that: include: Data acquisition module, typical scenario generation module, upper model construction module, uncertainty factor calculation module, lower model construction module, model solution module and distribution network wind, solar and storage collaborative planning module; The data acquisition module is used to obtain the historical data of each sub-area of ​​the distribution network to be optimized, the hardware parameters of the thermal, wind, solar and storage equipment in each sub-area, and the operation and maintenance cost of each sub-area; wherein the historical data includes historical load data, historical output data of renewable energy units, and historical output data of thermal power units; The typical scene generation module is used to determine a number of scenes according to the historical data; classify the several scenes into a number of different categories through a clustering algorithm, and set the cluster center of each category as a typical scene to generate a number of typical scenes; wherein a scene contains the historical data of all sub-areas in the same preset time period; The upper model construction module is used to construct a wide area planning upper model and related constraints of the wide area planning upper model according to the operation and maintenance costs and hardware parameters with the goal of minimizing the overall operation cost of the distribution network; The uncertainty factor calculation module is used to calculate the uncertainty factors of each typical scenario based on the historical data of the typical scenario and the hardware parameters; wherein the uncertainty factors include future load peak demand, future output boundary of thermal power plants, future volatility of renewable energy output and future volatility of load demand; The lower-level model building module is used to build several sub-region planning lower-level models and related constraints of the sub-region planning lower-level models according to the operation and maintenance costs, hardware parameters and uncertainty factors, with the goal of minimizing the sub-region operation costs; The model solving module is used to perform collaborative iterative solving of the wide-area planning upper model and the sub-regional planning lower model based on historical data of typical scenarios to generate the optimal solution of the model; The distribution network wind, solar, and storage collaborative planning module is used to perform distribution network wind, solar, and storage collaborative planning based on the optimal solution of the model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it can implement the distribution network multi-region wind, solar and storage collaborative planning method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the method for coordinated planning of multi-region wind, solar and storage in distribution networks as described in any one of claims 1 to 7.

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