Double-layer collaborative optimization planning method and system

Abstract

The invention relates to the technical field of power systems, in particular to a double-layer collaborative optimization planning method and system, and the method comprises the steps: obtaining related technical data and parameters which comprise but are not limited to different unit types and costs; constructing an energy equipment model, an upper-layer planning model and a lower-layer operation model; and obtaining a target planning scheme through alternate iteration of the upper-layer planning model and the lower-layer operation model. The method has the beneficial effects that the multi-time scale demand is introduced into the lower-layer operation model through the double-layer collaborative optimization model, so that the planning scheme can adaptively adjust the energy storage and power transmission collaborative demand under different time scales, and the dynamic response capability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a double-layer collaborative optimization planning method and system. Background Art

[0002] With the proposal of the "dual carbon" goal, the rapid development of new energy has prompted more and more renewable energy to be connected to the transmission grid, and the power system is facing many challenges brought by a high proportion of new energy. The power supply characteristics of renewable energy are uncertain, volatile and intermittent, which make the balance between power supply and demand more complicated, especially during the peak hours of wind power and photovoltaic power generation, wind and light abandonment often occur, which brings significant uncertainty risks to the economic and safe operation of the power system. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a two-layer collaborative optimization planning method and system, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a two-layer collaborative optimization planning method, including obtaining relevant technical data and parameters, the technical data and parameters including but not limited to different unit types and costs;

[0008] Construct energy equipment model, upper-level planning model and lower-level operation model;

[0009] The target planning solution is obtained by alternating the upper-level planning model and the lower-level operation model.

[0010] As a preferred solution of the dual-layer collaborative optimization planning method of the present invention, wherein: the upper-layer planning model and the lower-layer operation model are alternately iterated, including:

[0011] The upper-level planning model uses the snake optimization algorithm to solve the multi-objective optimization problem and passes the planning solution to the lower-level operation model;

[0012] The lower-level operation model uses a mixed integer linear programming algorithm to calculate the optimal operation plan of the system, determine the conventional unit operation and energy storage charging and discharging strategies, and return them to the upper-level planning model.

[0013] As a preferred solution of the two-layer collaborative optimization planning method of the present invention, wherein: constructing the upper-layer planning model includes:

[0014] Define energy storage installed capacity and transmission line construction plan as decision variables;

[0015] A multi-objective optimization model is established with the goal of minimizing investment cost and system operating cost and maximizing system flexibility; and a first objective constraint is added.

[0016] As a preferred solution of the two-layer collaborative optimization planning method of the present invention, wherein: constructing the lower layer operation model includes:

[0017] Define the output of conventional units, energy storage charging and discharging power, and network loss power as decision variables;

[0018] An operation model is established with the goal of maximizing comprehensive operation benefits; and a second objective constraint is added.

[0019] As a preferred solution of the two-layer collaborative optimization planning method of the present invention, wherein: constructing the energy equipment model includes:

[0020] Establish photovoltaic generator model, wind turbine model, gas turbine model and energy storage unit model.

[0021] As a preferred solution of the two-layer collaborative optimization planning method of the present invention, the first objective constraint includes energy storage installed capacity constraint, new line constraint and grid-side flexibility margin constraint.

[0022] As a preferred solution of the double-layer collaborative optimization planning method of the present invention, the second objective constraint includes node and branch flow balance constraints, energy storage operation constraints, output constraints of each unit and rotating reserve constraints.

[0023] In a second aspect, the present invention provides a two-layer collaborative optimization planning system, including: an acquisition module, for acquiring relevant technical data and parameters, the technical data and parameters including but not limited to different unit types and costs;

[0024] Construct models for constructing energy equipment models, upper-level planning models, and lower-level operation models;

[0025] The determination module is used to obtain the target planning solution through alternating iteration of the upper-level planning model and the lower-level operation model.

[0026] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0028] Compared with the prior art, the present invention has the following beneficial effects: through a two-layer collaborative optimization model, multi-time scale demands are introduced into the lower-layer operation model, so that the planning scheme can adaptively adjust the energy storage and transmission coordination demands at different time scales, thereby improving the dynamic response capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0030] Figure 1 Flow chart of the two-layer collaborative optimization planning method.

[0031] Figure 2 A schematic diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example 1

[0036] Reference Figure 1, which is the first embodiment of the present invention, and provides a two-layer collaborative optimization planning method, which includes:

[0037] S1. Obtain relevant technical data and parameters, including but not limited to different unit types and costs;

[0038] S2. Construct energy equipment model, upper-level planning model and lower-level operation model.

[0039] Furthermore, building the energy device model includes,

[0040] Establish photovoltaic generator model, wind turbine model, gas turbine model and energy storage unit model.

[0041] It should be noted that the photovoltaic generator model is:

[0042]

[0043] In the formula, σ f is the loss coefficient of photovoltaic power output power, generally taken as 0.95; P pv,cap is the installed capacity of the photovoltaic power generation unit; H t is the light intensity on the photovoltaic surface at time t; H 0 It is the light intensity on the photovoltaic surface under standard test conditions, generally 800kw / m 2 ; is the temperature influence coefficient of photovoltaic power output; T c,t is the photovoltaic surface temperature at time t; T c0 The photovoltaic surface temperature under standard test conditions is generally 25°

[0044] The wind turbine model is:

[0045]

[0046] Where P wind,t is the power generation of the wind turbine at time t; v t is the wind speed around the wind turbine at time t; v in is the cut-in wind speed; v co is the safe wind speed; v out is the cut-out wind speed; P r is the rated output power of the wind turbine.

[0047] The gas turbine model is:

[0048] P GT,t =η GT λ gas F GT,t

[0049] Where PGT,t is the power generation of the gas turbine at time t; η GT is the thermal efficiency of the gas turbine; gas is the lower calorific value of natural gas; F GT,t Enter the heating value of the fuel for the gas turbine at time t.

[0050] The energy storage unit model is:

[0051] P ess,t =P ess,cha,t -P ess,dis,t

[0052]

[0053] Where P ess,t is the output power of the energy storage at time t; P ess,cha,t is the charging power of energy storage at time t; P ess,dis,t is the discharge power of energy storage at time t; α ess,cha is the charging efficiency of energy storage; α ess,dis is the discharge efficiency of energy storage; SOC ess,t is the capacity of energy storage at time t; E ess is the installed capacity of energy storage; SOC t-1 is the capacity of the energy storage at time t-1; Δt is the energy storage charging / discharging period.

[0054] Furthermore, the construction of the upper-level planning model includes:

[0055] Define energy storage installed capacity and transmission line construction plan as decision variables;

[0056] It should be noted that the definition of ψ={C ess ,x line,ab} is the decision variable of the model, E ess is the installed capacity of energy storage, x line,ab For the construction of transmission lines.

[0057] A multi-objective optimization model is established with the goal of minimizing investment cost and system operating cost and maximizing system flexibility; and a first objective constraint is added.

[0058] It should be noted that the objective function is:

[0059] f 1 =f ess +f line +C year

[0060]

[0061]

[0062]

[0063] In the formula, f ess is the annual investment and operating cost of energy storage; f line is the annual investment cost of the transmission line; C year is the annual operating cost of the system; c ess,inv is the unit investment cost of energy storage; c line,ab is the unit investment cost of the new line (a, b); m is the discount rate of energy storage; n is the discount rate of transmission line; y is the planning years of energy storage and transmission line; E ess is the planned capacity of energy storage; x line,ab It is a 0-1 integer variable, which determines whether to create a new line (a, b). When the value is 1, it means to create a new line (a, b). When the value is 0, it means not to create a new line (a, b). line,ab is the length of the new line (a, b); n line,ab is the number of new lines (a, b); Ω l is the line set to be selected; n s is the number of days in a typical scenario; C g,ope,s , C ess,ope,s are the operating costs of conventional units and energy storage in scenario s respectively; C pv,s , C wind,s are the cost of abandoned solar power and abandoned wind power of photovoltaic power generation units and wind turbine units in scenario s respectively.

[0064] f 2 =1-I Flex

[0065] I Flex =min{I flex (t 1 ),I flex (t 2 ),...,I flex (t n )}

[0066]

[0067]

[0068]

[0069] In the formula, I Flex It is the grid flexibility index in the whole operation cycle; I flex (t) is the weighted sum of the line load rates of the grid flexibility weight coefficients at time t; μ i is the flexibility weight coefficient; L i (t) is the load rate of line i at time t; is the average load rate of line i during the operation cycle; P i (t) is the transmission power of line i at time t; P Lmax is the maximum transmission power of line i; T is the operation period; Ω is the line set.

[0070] At the planning level, economy and flexibility are the planning objectives. Since the calculated values ​​of the economy and flexibility are not of the same order of magnitude, they need to be normalized to obtain the objective function F 1 :

[0071] min F 1 =γ 1 f 1 +γ 2 f 2

[0072] In the formula, γ 1 and γ 2 are the weight coefficients of the economic target and the flexibility target respectively.

[0073] Furthermore, the first target constraint includes energy storage installed capacity constraint, new line constraint and grid-side flexibility margin constraint.

[0074] It should be noted that the energy storage installed capacity constraints are:

[0075] E ess,min ≤E ess ≤E ess,max

[0076] In the formula, E ess,max and E ess,min They are the upper and lower limits of energy storage installed capacity respectively.

[0077] New line constraints:

[0078]

[0079] In the formula, is the upper limit of the number of new lines (a, b).

[0080] Grid-side flexibility margin constraints:

[0081] I Flex ≥δ flex

[0082] In the formula, δ flex is the lower limit of flexibility margin.

[0083] Furthermore, the construction of the lower-level operation model includes:

[0084] Define the output of conventional units, energy storage charging and discharging power, and network loss power as decision variables;

[0085] It should be noted that the definition of ψ={P g,s,t ,P ess,s,t ,P loss,s,ab,t} is the decision variable of the model, P g,s,t is the power generation of the conventional unit at time t in scenario s, P ess,s,t is the charging and discharging power of the energy storage at time t in scenario s, P loss,s,ab,t is the line network loss power of line (a, b) at time t in scenario s.

[0086] An operation model is established with the goal of maximizing comprehensive operation benefits; and a second objective constraint is added.

[0087] It should be noted that the objective function is:

[0088] max F 2 =λ g R g +λ pv R pv +λ wind R wind -λ g C g,ope,s -λ pv C pv,s -λ wind C wind,s -λ ess C ess,ope,s -λ loss C loss,s

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] In the formula, λ g , pv , wind , ess ,loss are dynamic weight factors for conventional units, photovoltaic generators, wind turbines, energy storage and line losses, which are related to load or market conditions; R g , R pv , R wind are the grid-connected benefits of conventional units, photovoltaic generators, and wind turbines in scenario s; C loss,s is the network loss cost in scenario s; p g 、p pv 、p wind are the on-grid electricity prices of conventional units, photovoltaic generators, and wind turbines respectively; k pv , k wind are the reward coefficients for the output of photovoltaic generators and wind turbines respectively; P g,s,t is the power generation of the conventional unit at time t in scenario s; P ess,s,t is the charging and discharging power of the energy storage at time t in scenario s; P loss,s,ab,t is the line network loss power of line (a, b) at time t in scenario s; P dis,s,t , P cha,s,t are the discharge power and charging power of the energy storage at time t in scenario s; b s,t is the profit coefficient of energy storage; p var is the coefficient based on electricity price fluctuation; a i 、b i 、c i are the quadratic coefficient, linear coefficient and constant term of the conventional unit cost respectively; c ess,ope is the unit operating cost of energy storage; P pv,s,t are respectively the predicted power generation and actual power generation of the photovoltaic generator set at time t in scenario s; P wind,s,t are the predicted power generation and actual power generation of the wind turbine at time t in scenario s; α and β are the unit cost of abandoned light and wind for the photovoltaic generator set and the wind turbine set, respectively.

[0099] Furthermore, the second objective constraints include node and branch flow balance constraints, energy storage operation constraints, output constraints of each unit and rotating reserve constraints, wherein the output constraints of each unit include output constraints of conventional units, photovoltaic generator units and wind turbine units.

[0100] It should be noted that the node and branch power flow balance constraints are:

[0101] P g,s,t +P pv,t +P wind,t +P ess,t =P load,s,t +AP ab,t,s

[0102] P ab,s,t =n line,ab b ab θ ab,s,t

[0103]

[0104] θ min ≤θ a,s,t ≤θ max

[0105] Where P load,s,t is the system load at time t in scenario s; A is the cost-saving association matrix; P ab,t,s is the transmission power of line (a, b) at time t in scenario s; b ab is the susceptance of line (a, b); θ ab,s,t is the phase angle difference between nodes a and b at time t in scene s; is the upper limit of line transmission power; θ a,s,t is the phase angle of node a at time t in scene s; θ max and θ min are the upper and lower limits of the node phase angle respectively.

[0106] Energy storage operation constraints:

[0107] P ess,t =P cha,s,t -P dis,s,t

[0108]

[0109]

[0110]

[0111] In the formula, x cha,s,t and x dis,s,t P is the charging and discharging 0-1 variable of the energy storage at time t in scenario s; cha,max and P dis,max are the upper limits of energy storage charging and discharging; SOC s,t SOC is the charge state of the energy storage at the end of time t in scenario s; s,0 The initial charge state of the energy storage in scenario s; SOC max and SOC min are the upper and lower limits of the energy storage charge state, respectively.

[0112] Output constraints for conventional units, photovoltaic generators, and wind turbines:

[0113]

[0114]

[0115]

[0116]

[0117] In the formula, and They are respectively the upper and lower limits of the output of conventional units; and are respectively the up and down climbing rates of conventional units; P g,s,t-1 The power generation of the conventional unit at time t-1 in scenario s; and They are the output limits of photovoltaic generators and wind turbines respectively.

[0118] Spinning reserve constraints:

[0119]

[0120] In the formula, R t It provides spare capacity for the system.

[0121] S3. By alternately iterating the upper-level planning model and the lower-level operation model, the target planning solution is obtained.

[0122] Furthermore, the upper-level planning model and the lower-level operation model are iterated alternately, including:

[0123] The upper-level planning model uses the snake optimization algorithm to solve the multi-objective optimization problem and passes the planning solution to the lower-level operation model;

[0124] The snake optimization algorithm is an emerging heuristic algorithm with strong global search capability and adaptability when optimizing complex systems. Introducing this algorithm into the upper-level multi-objective optimization not only improves the solution efficiency, but also effectively explores the multi-objective space to ensure the optimal coordinated planning of energy storage and transmission lines.

[0125] The lower-level operation model uses a mixed integer linear programming algorithm to calculate the optimal operation plan of the system, determine the conventional unit operation and energy storage charging and discharging strategies, and return them to the upper-level planning model.

[0126] It should be noted that the combination of the upper-level planning model and the lower-level operation model, as well as the use of the serpentine optimization algorithm and the mixed integer linear programming algorithm, can achieve the optimal planning and operation of energy storage and transmission lines.

[0127] In summary, the beneficial effect of a two-layer collaborative optimization planning method of the present invention is that multi-time scale requirements are introduced into the lower-level operation model through a two-layer collaborative optimization model, so that the planning scheme can adaptively adjust the energy storage and transmission coordination requirements at different time scales, thereby improving the dynamic response capability of the system.

[0128] Example 2

[0129] This embodiment provides a two-layer collaborative optimization planning system, which includes an acquisition module for acquiring relevant technical data and parameters, the technical data and parameters including but not limited to different unit types and costs;

[0130] Construct models for constructing energy equipment models, upper-level planning models, and lower-level operation models;

[0131] The determination module is used to obtain the target planning solution through alternating iteration of the upper-level planning model and the lower-level operation model.

[0132] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0133] Example 3

[0134] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a two-layer collaborative optimization planning system is realized. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0135] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the following: obtaining relevant technical data and parameters, which include but are not limited to different unit types and costs; constructing an energy equipment model, an upper-level planning model and a lower-level operation model; and obtaining a target planning solution through alternating iterations of the upper-level planning model and the lower-level operation model.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A two-layer collaborative optimization planning method, characterized in that: include, Obtain relevant technical data and parameters, including but not limited to different unit types and costs; Construct energy equipment model, upper-level planning model and lower-level operation model; The target planning scheme is obtained by alternately iterating the upper-level planning model and the lower-level operation model.

2. The two-layer collaborative optimization planning method according to claim 1, characterized in that: The alternating iteration of the upper-level planning model and the lower-level operation model includes: The upper-level planning model uses a snake optimization algorithm to solve the multi-objective optimization problem and passes the planning solution to the lower-level operation model; The lower-level operation model uses a mixed integer linear programming algorithm to calculate the optimal operation plan of the system, determine the conventional unit operation and energy storage charging and discharging strategies, and return them to the upper-level planning model.

3. The two-layer collaborative optimization planning method according to claim 2, characterized in that: The constructing of the upper-level planning model includes: Define energy storage installed capacity and transmission line construction plan as decision variables; A multi-objective optimization model is established with the goal of minimizing investment cost and system operating cost and maximizing system flexibility; and a first objective constraint is added.

4. The two-layer collaborative optimization planning method according to claim 3, characterized in that: The construction of the lower layer operation model includes: Define the output of conventional units, energy storage charging and discharging power, and network loss power as decision variables; An operation model is established with the goal of maximizing comprehensive operation benefits; and a second objective constraint is added.

5. The two-layer collaborative optimization planning method according to claim 4, characterized in that: The energy device model building includes: Establish photovoltaic generator model, wind turbine model, gas turbine model and energy storage unit model.

6. The two-layer collaborative optimization planning method according to claim 5, characterized in that: The first target constraint includes energy storage installed capacity constraint, new line constraint and grid-side flexibility margin constraint.

7. The two-layer collaborative optimization planning method according to any one of claims 4 to 6, characterized in that: The second target constraints include node and branch power flow balance constraints, energy storage operation constraints, unit output constraints and spinning reserve constraints.

8. A two-layer collaborative optimization planning system, characterized in that: include: An acquisition module, used to acquire relevant technical data and parameters, including but not limited to different unit types and costs; Construct models for constructing energy equipment models, upper-level planning models, and lower-level operation models; The determination module is used to obtain a target planning solution by alternately iterating the upper-level planning model and the lower-level operation model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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