Shared energy storage configuration method and device for micro-energy network and storage medium

Abstract

The invention discloses a micro-energy network shared energy storage configuration method and device and a storage medium. The method comprises the following steps: acquiring basic data of a micro-energy network and a shared energy storage system; constructing a double-layer optimization model based on the basic data, wherein the double-layer optimization model comprises an inner-layer optimization model and an outer-layer optimization model; according to the inner layer optimization model, the inner layer optimization problem is determined to minimize the annual operation cost under the given shared energy storage configuration; according to the outer-layer optimization model, determining that an outer-layer optimization problem is maximization of the consumption rate of photovoltaic power generation; and solving the double-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme. According to the embodiment of the invention, the double-layer optimization model is constructed to optimize the operation of the micro energy grid and the shared energy storage system, the photovoltaic consumption rate can be effectively improved, the total cost can be reduced, a better energy storage configuration optimization scheme can be solved, and the energy storage optimization effect realized based on the energy storage configuration optimization scheme can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration, and in particular to a method, device and storage medium for configuring shared energy storage in a micro energy network. Background Art

[0002] As the world's attention to renewable energy continues to increase, and my country has proposed the goal of achieving "carbon peak" and "carbon neutrality", the development and utilization of new energy has become particularly critical. In the field of new energy, due to the many advantages of solar energy, the research on photovoltaic technology is gradually becoming a very important field. Photovoltaic systems are mainly divided into two forms: centralized and distributed. Distributed photovoltaic power generation refers to power generation facilities built around users. Its characteristics are that users can generate and use it by themselves, and can feed excess electricity into the power grid, while achieving supply and demand balance in the distribution network. This power generation method is based on the principle of first considering nearby use, pursuing cleanliness and efficiency, and selecting specific methods according to specific circumstances, so that the consumption of distributed photovoltaics is as large as possible, thereby increasing the use of new energy and reducing dependence on traditional fossil fuels. At present, the consumption of distributed photovoltaics is usually maximized by optimizing energy storage configuration. With the country's strong support and promotion of clean energy, the installed capacity of distributed photovoltaics is growing rapidly, and the optimization of energy storage configuration for distributed photovoltaics and shared energy storage systems is becoming more and more important.

[0003] Existing shared energy storage configuration methods for micro-energy grids usually do not fully consider the factors affecting energy storage configuration optimization, resulting in poor energy storage configuration effects. Summary of the invention

[0004] The present invention provides a micro energy network shared energy storage configuration method, device and storage medium to solve the technical problem that the existing micro energy network shared energy storage configuration method usually does not fully consider the influencing factors of energy storage configuration optimization, resulting in poor energy storage configuration effect.

[0005] The present invention provides a method for configuring a micro energy network shared energy storage, comprising:

[0006] Obtaining basic data of the micro energy grid and the shared energy storage system, wherein the basic data includes photovoltaic power generation, load demand and energy storage system capacity;

[0007] A two-layer optimization model is constructed based on the basic data, and the two-layer optimization model includes an inner optimization model and an outer optimization model; wherein the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy grid, and the first constraint condition of the inner optimization model includes the power supply system constraint of the micro energy grid and the cooling and heating constraint of the micro energy grid; the second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy grid, and the second constraint condition of the outer optimization model includes the energy rate constraint, the charging and discharging constraint, and the state of charge constraint of the energy storage battery;

[0008] According to the inner optimization model, the inner optimization problem is determined to be minimizing the annual operating cost under a given shared energy storage configuration; according to the outer optimization model, the outer optimization problem is determined to be maximizing the absorption rate of photovoltaic power generation;

[0009] The double-layer optimization model is solved based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution.

[0010] Furthermore, the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy grid, including:

[0011] Based on the revenue obtained from the micro energy grid selling electricity to the shared energy storage system, the cost of the micro energy grid purchasing electricity from the shared energy storage system and the service fees paid by the micro energy grid, the first objective function is constructed with the goal of minimizing the annual operating cost of the micro energy grid.

[0012] Furthermore, the second objective function of the outer optimization model constructed by minimizing the overall cost of the shared energy storage system and the micro energy grid includes:

[0013] According to the investment cost of the shared energy storage system, the cost of the micro energy grid purchasing electricity from the outside and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy grid.

[0014] Furthermore, solving the double-layer optimization model based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution includes:

[0015] The inner optimization problem is converted into a third constraint condition of the outer optimization problem through a preset KKT condition, wherein the KKT condition includes a gradient condition, a feasibility condition and a complementary relaxation condition;

[0016] Based on the penalty number method and the third constraint condition, the outer optimization problem is solved to obtain an energy storage configuration optimization solution for the double-layer optimization model.

[0017] Furthermore, the solution of the outer optimization problem based on the penalty number method and the third constraint condition to the energy storage configuration optimization scheme of the double-layer optimization model includes:

[0018] defining a penalty number, and converting data in the two-level optimization model that does not meet the first constraint condition, the second constraint condition, and the third constraint condition into a penalty term of the second objective function based on the penalty number;

[0019] The second objective function is updated according to the penalty term, and each updated second objective function is iteratively solved, and when the two-layer optimization model converges, an optimal energy storage configuration optimization solution is output.

[0020] The present invention also provides a micro energy network shared energy storage configuration device, comprising:

[0021] A basic data acquisition module is used to acquire basic data of the micro energy grid and the shared energy storage system, wherein the basic data includes photovoltaic power generation, load demand and energy storage system capacity;

[0022] A two-layer optimization model construction module is used to construct a two-layer optimization model based on the basic data, and the two-layer optimization model includes an inner optimization model and an outer optimization model; wherein the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy network, and the first constraint condition of the inner optimization model includes the power supply system constraint of the micro energy network and the cooling and heating constraint of the micro energy network; the second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy network, and the second constraint condition of the outer optimization model includes the energy rate constraint, the charging and discharging constraint and the energy storage battery state of charge constraint;

[0023] An optimization problem determination module is used to determine, according to the inner optimization model, that the inner optimization problem is to minimize the annual operating cost under a given shared energy storage configuration; and to determine, according to the outer optimization model, that the outer optimization problem is to maximize the absorption rate of photovoltaic power generation;

[0024] The double-layer optimization model solving module is used to solve the double-layer optimization model based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution.

[0025] Furthermore, the two-layer optimization model building module is also used to:

[0026] Based on the revenue obtained from the micro energy grid selling electricity to the shared energy storage system, the cost of the micro energy grid purchasing electricity from the shared energy storage system and the service fees paid by the micro energy grid, the first objective function is constructed with the goal of minimizing the annual operating cost of the micro energy grid.

[0027] Furthermore, the two-layer optimization model building module is also used for:

[0028] According to the investment cost of the shared energy storage system, the cost of the micro energy grid purchasing electricity from the outside and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy grid.

[0029] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the micro energy network shared energy storage configuration method as described above is implemented.

[0030] The present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the micro-energy network shared energy storage configuration method as described above.

[0031] The embodiment of the present invention optimizes the operation of the micro-energy grid and the shared energy storage system by constructing a two-layer optimization model. The inner optimization model is used to focus on the economic operation optimization of the micro-energy grid, with the goal of minimizing the annual operation cost of the micro-energy grid. The outer optimization model is used to solve the configuration of shared energy storage and the economic absorption of photovoltaic power, which can effectively improve the absorption rate of photovoltaic power and reduce the overall cost, so as to solve a better energy storage configuration optimization scheme, and further effectively improve the energy storage optimization effect achieved based on the energy storage configuration optimization scheme.

[0032] Furthermore, the embodiment of the present invention can obtain the final shared energy storage configuration optimization plan and the micro-energy network economic dispatch plan through the joint solution of KKT conditions and the penalty function method, and can transform the double-layer planning problem into a single-layer planning problem through the construction of KKT conditions, which can effectively simplify the complexity of the solution, and by introducing the penalty function method, it can ensure that all constraints are met through an external penalty mechanism, which can effectively improve the solution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of a method for configuring a micro energy network shared energy storage provided by an embodiment of the present invention;

[0034] Figure 2 It is a structural schematic diagram of a micro energy network shared energy storage configuration device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0037] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0038] See also Figure 1 The present invention provides a method for configuring a micro energy network shared energy storage, comprising:

[0039] S1. Obtain basic data of the micro energy grid and shared energy storage system, including photovoltaic power generation, load demand and energy storage system capacity;

[0040] In the embodiment of the present invention, the micro energy grid includes distributed photovoltaics, and its internal equipment configuration includes gas boilers, waste heat boilers, gas turbines, heat exchangers, electric refrigerators, absorption refrigerators, etc. The micro energy grids are each connected to a shared energy storage system, but there is no direct power exchange.

[0041] S2. Construct a two-layer optimization model based on basic data, the two-layer optimization model includes an inner optimization model and an outer optimization model; wherein, the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy grid, and the first constraint of the inner optimization model includes the power supply system constraint of the micro energy grid and the cooling and heating constraint of the micro energy grid; the second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy grid, and the second constraint of the outer optimization model includes the energy rate constraint, the charge and discharge constraint, and the state of charge constraint of the energy storage battery;

[0042] S3. According to the inner optimization model, the inner optimization problem is determined to be minimizing the annual operating cost under a given shared energy storage configuration; according to the outer optimization model, the outer optimization problem is determined to be maximizing the absorption rate of photovoltaic power generation;

[0043] In an embodiment of the present invention, the two-layer planning problem can be divided into an outer problem and an inner problem, wherein the outer problem is the main problem and the inner problem is the subordinate problem. In an embodiment of the present invention, the configuration of the shared energy storage system and the scheduling of the micro energy network in the power system can be optimized through two-layer planning. Among them, the outer optimization problem can be: optimize the configuration of the shared energy storage system to maximize the absorption rate of photovoltaic power generation and ensure the overall economy of the shared energy storage system; the inner problem can be: under a given shared energy storage configuration, optimize the economic operation of the micro energy network and minimize the annual operating cost.

[0044] S4. Solve the double-layer optimization model based on the inner optimization problem and the outer optimization problem to obtain the energy storage configuration optimization solution.

[0045] The embodiment of the present invention optimizes the operation of the micro-energy grid and the shared energy storage system by constructing a two-layer optimization model. The inner optimization model is used to focus on the economic operation optimization of the micro-energy grid, with the goal of minimizing the annual operation cost of the micro-energy grid. The outer optimization model is used to solve the configuration of shared energy storage and the economic absorption of photovoltaic power, which can effectively improve the absorption rate of photovoltaic power and reduce the overall cost, so as to solve a better energy storage configuration optimization scheme, and further effectively improve the energy storage optimization effect achieved based on the energy storage configuration optimization scheme.

[0046] In one embodiment, step S2, constructing a first objective function of the inner optimization model to minimize the annual operating cost of the micro energy grid, includes:

[0047] Based on the revenue obtained from the micro-energy grid selling electricity to the shared energy storage system, the cost of the micro-energy grid purchasing electricity from the shared energy storage system and the service fee paid by the micro-energy grid, the first objective function is constructed with the goal of minimizing the annual operating cost of the micro-energy grid.

[0048] In the embodiment of the present invention, the first objective function of the inner optimization model can be constructed with the goal of minimizing the annual operating cost of the micro energy grid:

[0049] minC MG =C grid +C flue -C ess,s +C ess,b +C serve (1)

[0050] Among them, C ess,s represents the revenue obtained by the micro energy grid from selling electricity to the shared energy storage system; C ess,bThe cost of the micro energy grid purchasing electricity from the shared energy storage system; C serve Represents the service fee paid by the micro energy grid.

[0051] Furthermore, the revenue obtained by the micro energy grid from selling electricity to the shared energy storage system is expressed as follows:

[0052]

[0053] in, is the revenue of the Nth micro energy grid selling electricity to the shared energy storage system on the Mth typical day; δ s A price matrix for selling power units to the shared energy storage system for each dispatch period; It represents the power matrix of the Nth micro energy grid selling electricity to the energy storage power station in each scheduling period on the Mth typical day.

[0054] The cost of the micro energy grid purchasing electricity from the shared energy storage system is expressed as follows:

[0055]

[0056] in, represents the cost of the Nth micro-energy grid purchasing electricity from the shared energy storage system on the Mth typical day; δ b It is an electricity price matrix that lists the price of purchasing unit electricity from the energy storage power station in each dispatch period; It is a power matrix that records the electricity purchased by the Nth micro-energy grid from the energy storage power station in each dispatch period of the Mth typical day.

[0057] The expression of the service fee paid by the micro energy network is as follows:

[0058]

[0059] in, represents the service fee paid by the Nth microgrid system to the shared energy storage system on the Mth typical day; δ s It is a service fee matrix used to calculate the unit power service fee paid by the microgrid to the energy storage power station.

[0060] In one embodiment, the micro energy grid power supply system constraint in the first constraint condition of the inner optimization model is a constraint condition for maintaining a balance between power supply and demand within the micro energy grid, which is expressed as follows:

[0061]

[0062] in, represents the output power matrix of the photovoltaic system of the Nth micro energy grid in different scheduling periods of the Mth typical day; The matrices represent the power consumption and electric load power of the electric refrigerator of the micro energy grid in each scheduling period on the same typical day.

[0063] The micro energy grid can convert electric energy with the shared energy storage system, but charging and discharging are not allowed in any scheduling period. The restriction conditions of energy exchange are:

[0064]

[0065] Among them, P ess,max Indicates the maximum allowed exchange power between the micro energy grid and the shared energy storage system; They represent the amount of electricity sold to and purchased from the shared energy storage system by the Nth micro energy grid during the t scheduling period on the Mth typical day; are indicator variables, respectively indicating whether the Nth micro energy grid is charged and discharged in the scheduling period t.

[0066] The amount of electricity generated by electrical equipment within the microgrid and the amount of electricity purchased by the microgrid from the main grid must comply with specific restrictions, which can be expressed as:

[0067]

[0068] Among them, P GT,min P GT,max Respectively represent the lower and upper limits of the power generation of the gas turbine; P EC,min P EC,max Respectively represent the lower and upper limits of the power consumption of the electric refrigerator; P grid,max The maximum power that the micro energy grid can purchase from the main grid; are the output power of the gas turbine and the power consumption of the electric refrigerator of the Nth micro-energy grid during the t-scheduling period of the Mth typical day, It is the power that micro energy grid No. N purchases from the main grid during the scheduling period t on the Mth typical day.

[0069] In the embodiment of the present invention, the cooling and heating constraints of the micro energy grid are to maintain the power balance of cooling energy and heating energy, and at the same time, the balance request of waste heat must be met. The corresponding restriction conditions are expressed as follows:

[0070]

[0071] in, The matrices representing the heat power of the heat exchanger and the cooling power of the absorption chiller of the Nth micro energy grid on the Mth typical day respectively; They represent the matrix of heat load and cooling power of the same micro energy grid in the scheduling period t respectively; η HX , ηAC , η WH , η EC Respectively represent the efficiency of the heat exchanger, the energy efficiency ratio of the absorption chiller, the efficiency of the waste heat boiler and the energy efficiency ratio of the chiller; γ GT is the heat-to-electricity ratio of the gas turbine.

[0072] The output of the cooling and heating system equipment within the micro-energy network must also meet certain restrictions. The constraints are:

[0073]

[0074] Among them, P HX,min and P HX,max are the maximum and minimum values ​​of the heat exchanger power respectively; Q AC,min Q AC,max are the maximum and minimum power of the absorption chiller respectively; Q GB,min Q GB,max They are the upper and lower limits of gas boiler power respectively; They respectively represent the heat exchanger power, absorption chiller power and gas boiler power of the Nth microgrid during the Mth typical day t scheduling period.

[0075] In the embodiment of the present invention, the first constraint condition also includes a microgrid new energy consumption constraint, which is expressed as:

[0076]

[0077] in, It represents the power generation power of the photovoltaic system of the Nth micro energy grid in the t scheduling period of the Mth typical day; It represents the maximum available photovoltaic resources of the same micro-energy grid in the t-th scheduling period on the Mth typical day; α is the annual comprehensive absorption rate of new energy in the micro-energy grid; t0 refers to the number of scheduling periods per typical day.

[0078] The embodiment of the present invention can effectively reduce the operating expenses of the micro-energy network by constructing an inner optimization model with the goal of minimizing the annual operating cost of the micro-energy network; and the inner optimization model takes into account the cost of purchasing electricity from the shared energy storage system, the income from selling electricity to it, and the service fees paid, so that the micro-energy network can achieve cost optimization in electricity trading.

[0079] Furthermore, the inner optimization model of the embodiment of the present invention can effectively optimize the energy management strategy of the micro-energy grid by considering the constraints of power supply, cooling and heating, thereby effectively improving the energy utilization efficiency, and enhancing the system's responsiveness to demand fluctuations, helping to balance supply and demand and reduce energy waste, thereby effectively improving the effect of energy storage configuration optimization.

[0080] In one embodiment, step S2, constructing a second objective function of the outer optimization model by minimizing the overall cost of the shared energy storage system and the micro energy grid, includes:

[0081] According to the investment cost of the shared energy storage system, the cost of the micro energy grid purchasing electricity from the outside and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy grid.

[0082] In an embodiment of the present invention, the total cost of the shared energy storage system and the micro energy grid is mainly composed of three parts, namely: the investment cost of the shared energy storage power station, the cost of the micro energy grid purchasing electricity from the external power grid, and the cost of the micro energy grid purchasing fuel.

[0083] In the embodiment of the present invention, the objective function of the outer optimization model is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy network, that is, the second objective function, and its expression is as follows:

[0084] minC=C inv +C grid +C flue (11)

[0085] Among them, C inv represents the annual value of the investment cost of the shared energy storage system; C grid represents the cost of the micro energy grid purchasing electricity from the grid each year; C flue The embodiment of the present invention can determine the economic operation mode of the shared energy storage power station and the micro energy grid system by optimizing these cost parameters to maximize cost-effectiveness.

[0086] The investment cost of the shared energy storage system includes the one-time investment cost in the initial construction phase, which is amortized on an annual basis, and the fixed maintenance expenses that need to be paid each year. When evaluating the cost of a shared energy storage system, the value of funds that change over time can be included, that is, the time value of funds can be calculated. On this basis, the annualized investment cost of the shared energy storage system can be calculated by incorporating the time value factor to more accurately reflect the present value of the investment that increases or decreases over time. The equivalent annual value expression of the investment cost is:

[0087]

[0088] Among them, r is the annual interest rate of funds; γ is the life cycle of the device; δ P is the unit power investment cost; E is the unit capacity investment cost; M is the maintenance cost per unit power; Pess and Eess are the rated charging and discharging power and rated capacity of the shared energy storage power station respectively.

[0089] The cost of purchasing electricity from the external power grid:

[0090]

[0091] in, represents the cost of N micro-energy grid purchasing electricity from the power grid on M typical days; δ0 represents the unit electricity price matrix of the power grid in each dispatching period; is the power consumption matrix of micro-energy grid No. N in each scheduling period of the Mth typical day; m and n are the number of typical days and micro-energy grids respectively.

[0092] The cost of purchasing fuel from Micro Energy Network:

[0093]

[0094] in, is the cost of purchasing fuel for the Nth micro-energy grid on the Mth typical day; c0 is the natural gas cost matrix per unit volume; is the power matrix of gas turbines and gas boilers of the Nth micro energy grid in each dispatching period of the Mth typical day; η GT , η GB are the efficiency of gas turbine and gas boiler respectively; Q0 ​​is the calorific value of gas.

[0095] The energy rate constraint in the second constraint of the outer optimization model means that in the energy storage device, the storage capacity of the battery must maintain a certain energy rate relationship with its nominal power. The expression is as follows:

[0096] E ess =βP ess (15)

[0097] Among them, β represents the energy rate of the energy storage battery.

[0098] Regarding the charging and discharging constraints, whether the power station is charging or discharging in the same scheduling cycle depends on the total energy demand at the bus after all micro-energy grid user power stations complete energy exchange. In addition, the shared energy storage system does not allow simultaneous charging and discharging in any scheduling cycle, and its constraints can be expressed as:

[0099]

[0100] in, The power sold to and purchased from the shared energy storage system by microgrid No. N during the scheduling period t on the Mth typical day, respectively; They represent the charging and discharging power of the shared energy storage system in period t respectively; are the indicator variables for charging and discharging of the shared energy storage system during period t on the Mth typical day.

[0101] Energy storage battery state of charge constraints:

[0102]

[0103] in, represents the state of charge of the energy storage battery in the t scheduling period on the Mth typical day; η abs , η relea represent the charging and discharging efficiency of the shared energy storage system respectively; are the charging and discharging powers of the energy storage power station in the t scheduling period on the Mth typical day; k min , k max They are the upper and lower limits of the power station’s state of charge respectively.

[0104] The embodiment of the present invention takes into account the investment cost of the shared energy storage system, the cost of the micro energy network purchasing electricity from the outside, and the cost of purchasing fuel, so that the double-layer optimization model can effectively reduce the overall cost of the shared energy storage system and the micro energy network, and the constraints of the outer optimization model include energy rate constraints, charge and discharge constraints, and energy storage battery state of charge constraints. These constraints ensure that the energy storage system operates within a safe and effective range, avoid overcharging and discharging, extend the service life of the energy storage system, and keep the battery state of charge within a reasonable range, which can improve the optimization effect of the energy storage configuration.

[0105] In one embodiment, step S4, solving a double-layer optimization model based on an inner optimization problem and an outer optimization problem to obtain an energy storage configuration optimization solution, includes:

[0106] S41, converting the inner optimization problem into the third constraint condition of the outer optimization problem through a preset KKT condition, wherein the KKT condition includes a gradient condition, a feasibility condition and a complementary relaxation condition;

[0107] In the embodiment of the present invention, the gradient condition is that the partial derivative of the Lagrangian function with respect to the variable is equal to zero; the feasibility condition is that the constraint condition must be satisfied; and the complementary relaxation condition is a condition for processing inequality constraints.

[0108] The embodiment of the present invention can transform the original inner optimization problem into a set of equality constraints and inequality constraints through the preset KKT conditions (Kuhn-Tucker conditions).

[0109] In an embodiment of the present invention, a Lagrangian function L(x,λ) of the inner optimization problem can be defined, where x is a decision variable and λ is a Lagrangian multiplier. Based on the Lagrangian function and the constraints of the inner optimization problem, the KKT conditions of the inner optimization problem are constructed.

[0110] According to the constraints of the inner problem, the KKT conditions of the inner problem are constructed.

[0111] S42. Based on the penalty number method and the third constraint condition, the outer optimization problem is solved to obtain the energy storage configuration optimization solution of the double-layer optimization model.

[0112] The embodiment of the present invention utilizes KKT conditions to convert the inner optimization problem into the constraint conditions of the outer optimization problem, which can ensure that the optimal conditions of the inner problem are met when solving the outer optimization problem, effectively coordinate the operating cost of the micro-energy network and the configuration of the energy storage system, so as to maximize the cost-effectiveness. In addition, the embodiment of the present invention uses the penalty number method to add penalty items that do not meet the constraints to the objective function, further ensuring that the energy storage system can achieve the optimal operating state, thereby effectively improving the optimization effect of the energy storage configuration.

[0113] In one embodiment, step S42, based on the penalty number method and the third constraint condition, solving the outer optimization problem to obtain the energy storage configuration optimization solution of the double-layer optimization model, includes:

[0114] S421, defining a penalty number, and converting data that does not meet the first constraint condition, the second constraint condition, and the third constraint condition in the two-layer optimization model into a penalty term of the second objective function based on the penalty number;

[0115] In the embodiment of the present invention, the role of introducing the penalty term is that if a constraint condition in a two-layer optimization model does not satisfy a constraint condition, a larger penalty value is introduced to force the optimization problem to tend to satisfy the constraint in the next iteration.

[0116] Among them, the penalty function method can be solved by the following formula:

[0117]

[0118] Among them, f(x) is the objective function, g i (x) is the constraint condition, μ is the penalty parameter, which gradually increases with iterations to force the solution to satisfy the constraint condition.

[0119] S422. Update the second objective function according to the penalty term, and iteratively solve the updated second objective function each time, and output the optimal energy storage configuration optimization solution when the two-layer optimization model converges.

[0120] In the embodiment of the present invention, after the penalty function is introduced, solving the transformed single-layer problem can be performed in an iterative manner, including:

[0121] Initially, a small penalty parameter μ is selected;

[0122] Solve the optimization problem and obtain a temporary solution;

[0123] Gradually increase the penalty parameter μ and repeatedly solve the objective function until the optimal solution that satisfies the constraints is obtained.

[0124] In an embodiment of the present invention, during the iteration process, the parameters of the penalty function are adjusted at each step so that the optimized solution gradually satisfies all constraints. After several iterations, the optimal solution of the two-level programming problem can be gradually approached through the KKT condition and the penalty function method, and finally converges to the global optimal solution that satisfies all constraints.

[0125] The embodiment of the present invention can obtain the final shared energy storage configuration optimization plan and micro-energy network economic dispatch plan through the joint solution of KKT conditions and penalty function method, and can transform the double-layer planning problem into a single-layer planning problem through the construction of KKT conditions, which can effectively simplify the complexity of the solution, and by introducing the penalty number method for solution, it can ensure that all constraints are met through an external penalty mechanism, which can effectively improve the solution accuracy. The optimized solution not only meets the technical constraints of the system (such as voltage, line flow, etc.), but also maximizes the absorption rate of photovoltaic power generation and reduces the total operating cost.

[0126] The implementation of the embodiments of the present invention has the following beneficial effects:

[0127] The embodiment of the present invention optimizes the operation of the micro-energy grid and the shared energy storage system by constructing a two-layer optimization model. The inner optimization model focuses on the economic operation optimization of the micro-energy grid, with the goal of minimizing the annual operation cost of the micro-energy grid. The outer optimization model is used to solve the configuration of shared energy storage and the economic absorption of photovoltaic power, which can effectively improve the absorption rate of photovoltaic power and reduce the overall cost, so as to solve a better energy storage configuration optimization plan, and further effectively improve the energy storage optimization effect achieved based on the energy storage configuration optimization plan.

[0128] Furthermore, the embodiment of the present invention can obtain the final shared energy storage configuration optimization plan and the micro-energy network economic dispatch plan through the joint solution of KKT conditions and the penalty function method, and can transform the double-layer planning problem into a single-layer planning problem through the construction of KKT conditions, which can effectively simplify the complexity of the solution, and by introducing the penalty function method, it can ensure that all constraints are met through an external penalty mechanism, which can effectively improve the solution accuracy.

[0129] See also Figure 2 Based on the same inventive concept as the above embodiment, the present invention also provides a micro energy network shared energy storage configuration device, including:

[0130] A basic data acquisition module 10 is used to acquire basic data of the micro energy grid and the shared energy storage system, the basic data including photovoltaic power generation, load demand and energy storage system capacity;

[0131] A two-layer optimization model construction module 20 is used to construct a two-layer optimization model based on basic data, and the two-layer optimization model includes an inner optimization model and an outer optimization model; wherein the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy grid, and the first constraint condition of the inner optimization model includes the micro energy grid power supply system constraint and the micro energy grid cooling and heating constraint; the second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy grid, and the second constraint condition of the outer optimization model includes the energy rate constraint, the charge and discharge constraint, and the energy storage battery state of charge constraint;

[0132] The optimization problem determination module 30 is used to determine the inner optimization problem as minimizing the annual operating cost under a given shared energy storage configuration according to the inner optimization model; and to determine the outer optimization problem as maximizing the absorption rate of photovoltaic power generation according to the outer optimization model;

[0133] The double-layer optimization model solving module 40 is used to solve the double-layer optimization model based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution.

[0134] In one embodiment, the two-layer optimization model building module 20 is further used to:

[0135] Based on the revenue obtained from the micro-energy grid selling electricity to the shared energy storage system, the cost of the micro-energy grid purchasing electricity from the shared energy storage system and the service fee paid by the micro-energy grid, the first objective function is constructed with the goal of minimizing the annual operating cost of the micro-energy grid.

[0136] In one embodiment, the two-layer optimization model building module 20 is further used to:

[0137] According to the investment cost of the shared energy storage system, the cost of the micro energy grid purchasing electricity from the outside and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy grid.

[0138] In one embodiment, the two-layer optimization model solving module 40 is further used to:

[0139] The inner optimization problem is converted into the third constraint condition of the outer optimization problem through the preset KKT condition, wherein the KKT condition includes a gradient condition, a feasibility condition and a complementary relaxation condition;

[0140] Based on the penalty number method and the third constraint, the outer optimization problem is solved and the energy storage configuration optimization scheme of the double-layer optimization model is obtained.

[0141] In one embodiment, based on the penalty number method and the third constraint condition, the outer optimization problem is solved to obtain an energy storage configuration optimization solution of the double-layer optimization model, including:

[0142] A penalty number is defined, and based on the penalty number, data that does not meet the first constraint condition, the second constraint condition, and the third constraint condition in the two-level optimization model is converted into a penalty term of the second objective function;

[0143] The second objective function is updated according to the penalty term, and each updated second objective function is iteratively solved. When the two-layer optimization model converges, the optimal energy storage configuration optimization solution is output.

[0144] Accordingly, an embodiment of the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the micro-energy network shared energy storage configuration method of any one of the above embodiments.

[0145] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment are implemented, such as the optimization problem determination module 30 .

[0146] Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the optimization problem determination module 30 is used to determine the inner optimization problem as minimizing the annual operating cost under a given shared energy storage configuration according to the inner optimization model; and to determine the outer optimization problem as maximizing the absorption rate of photovoltaic power generation according to the outer optimization model.

[0147] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0148] 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. A 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.

[0149] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules 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 terminal, 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 (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0150] Among them, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can 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, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0151] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the micro-energy network shared energy storage configuration method as described in any one of the above embodiments.

[0152] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for configuring shared energy storage in a micro energy network, characterized in that: include: Obtaining basic data of the micro energy grid and the shared energy storage system, wherein the basic data includes photovoltaic power generation, load demand and energy storage system capacity; A two-layer optimization model is constructed based on the basic data, and the two-layer optimization model includes an inner optimization model and an outer optimization model; wherein the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy grid, and the first constraint condition of the inner optimization model includes the power supply system constraint of the micro energy grid and the cooling and heating constraint of the micro energy grid; the second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy grid, and the second constraint condition of the outer optimization model includes the energy rate constraint, the charging and discharging constraint, and the state of charge constraint of the energy storage battery; According to the inner optimization model, the inner optimization problem is determined to be minimizing the annual operating cost under a given shared energy storage configuration; according to the outer optimization model, the outer optimization problem is determined to be maximizing the absorption rate of photovoltaic power generation; The double-layer optimization model is solved based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution.

2. The micro energy network shared energy storage configuration method according to claim 1, characterized in that: The first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy grid, including: Based on the revenue obtained from the micro energy grid selling electricity to the shared energy storage system, the cost of the micro energy grid purchasing electricity from the shared energy storage system and the service fees paid by the micro energy grid, the first objective function is constructed with the goal of minimizing the annual operating cost of the micro energy grid.

3. The micro energy network shared energy storage configuration method according to claim 1, characterized in that: The second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy network, including: According to the investment cost of the shared energy storage system, the cost of the micro energy grid purchasing electricity from the outside and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy grid.

4. The micro energy network shared energy storage configuration method according to claim 1, characterized in that: The step of solving the double-layer optimization model based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution includes: The inner optimization problem is converted into a third constraint condition of the outer optimization problem through a preset KKT condition, wherein the KKT condition includes a gradient condition, a feasibility condition and a complementary relaxation condition; Based on the penalty number method and the third constraint condition, the outer optimization problem is solved to obtain an energy storage configuration optimization solution for the double-layer optimization model.

5. The micro energy network shared energy storage configuration method according to claim 4, characterized in that: The method of solving the outer optimization problem based on the penalty number method and the third constraint condition to obtain the energy storage configuration optimization solution of the double-layer optimization model includes: defining a penalty number, and converting data in the two-level optimization model that does not meet the first constraint condition, the second constraint condition, and the third constraint condition into a penalty term of the second objective function based on the penalty number; The second objective function is updated according to the penalty term, and each updated second objective function is iteratively solved, and when the two-layer optimization model converges, an optimal energy storage configuration optimization solution is output.

6. A micro energy network shared energy storage configuration device, characterized in that: include: A basic data acquisition module is used to acquire basic data of the micro energy grid and the shared energy storage system, wherein the basic data includes photovoltaic power generation, load demand and energy storage system capacity; A two-layer optimization model construction module is used to construct a two-layer optimization model based on the basic data, and the two-layer optimization model includes an inner optimization model and an outer optimization model; wherein the first objective function of the inner optimization model is constructed by minimizing the annual operating cost of the micro energy network, and the first constraint condition of the inner optimization model includes the power supply system constraint of the micro energy network and the cooling and heating constraint of the micro energy network; the second objective function of the outer optimization model is constructed by minimizing the overall cost of the shared energy storage system and the micro energy network, and the second constraint condition of the outer optimization model includes the energy rate constraint, the charging and discharging constraint and the energy storage battery state of charge constraint; An optimization problem determination module is used to determine, according to the inner optimization model, that the inner optimization problem is to minimize the annual operating cost under a given shared energy storage configuration; and to determine, according to the outer optimization model, that the outer optimization problem is to maximize the absorption rate of photovoltaic power generation; The double-layer optimization model solving module is used to solve the double-layer optimization model based on the inner optimization problem and the outer optimization problem to obtain an energy storage configuration optimization solution.

7. The micro energy network shared energy storage configuration device according to claim 6, characterized in that: The two-level optimization model building module is also used to: Based on the revenue obtained from the micro energy grid selling electricity to the shared energy storage system, the cost of the micro energy grid purchasing electricity from the shared energy storage system and the service fees paid by the micro energy grid, the first objective function is constructed with the goal of minimizing the annual operating cost of the micro energy grid.

8. The micro energy network shared energy storage configuration device according to claim 6, characterized in that: The two-layer optimization model building module is also used for: According to the investment cost of the shared energy storage system, the cost of the micro energy grid purchasing electricity from the outside and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the micro energy grid.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the micro-energy network shared energy storage configuration method as described in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the micro-energy network shared energy storage configuration method as described in any one of claims 1-5.

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