Wind and light energy storage capacity optimal configuration method based on main target method

Abstract

The invention discloses a wind and light energy storage capacity optimal configuration method based on a main target method, which belongs to the field of electric power, and comprises the following steps: S1, according to the full life cycle of energy storage, calculating the total annual cost of energy storage, and constructing an energy storage life attenuation model; s2, establishing an energy storage capacity planning model according to the total energy storage annual cost and the energy storage life attenuation model; and S3, converting and solving the energy storage capacity planning model by using a main target method to obtain the optimal energy storage capacity and power. According to the wind and light energy storage capacity optimal configuration method based on the main target method, the main target method is used for model conversion to obtain the mixed integer nonlinear programming model, so that on one hand, the problem that the linear weighting method and the intelligent algorithm are used for performing normalization processing on multiple targets can be avoided; and on the other hand, the model can be directly solved by using software, and the calculation process is simplified.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for optimizing the configuration of wind-solar energy storage capacity based on a main objective method. Background Art

[0002] Since wind power and photovoltaic power generation have the characteristics of randomness, volatility and intermittency, they are prone to serious wind and solar power abandonment. Therefore, energy storage devices are needed to smooth out the fluctuations of wind power and photovoltaic power generation.

[0003] In the wind, solar and storage system, the energy storage device is mainly responsible for stabilizing the output of renewable energy, including smoothing output power, providing backup power, and providing charge and discharge control. In order to achieve optimal control of the wind, solar and storage system, the energy storage device must first be optimized and configured, and the optimal energy storage capacity configuration plan must be determined based on its operating characteristics and influencing factors in the system.

[0004] At present, there are three common methods for optimizing energy storage capacity:

[0005] 1. The capacity optimization configuration method based on the linear weighted algorithm is a commonly used decision-making method. The basic operating principle can be divided into the following five steps:

[0006] 1) Determine the decision factors, that is, it is necessary to determine the various factors that affect the capacity configuration decision, such as demand, cost, risk, etc. The above factors can be selected according to the actual situation and assigned corresponding weights;

[0007] 2) Set weights, assign a weight to each decision factor to indicate its importance in the decision. The weights can be determined based on expert judgment, experience, or mathematical models. Usually, the sum of the weights is 1;

[0008] 3) Normalization: For each decision factor, it is necessary to normalize it and convert its value into a range between 0 and 1. This can be achieved through linear transformation or standardization;

[0009] 4) Weighted summation: multiply the normalized decision factors by the corresponding weights, and then add them together to get a weighted summation result. This result reflects the comprehensive impact of each decision factor;

[0010] 5) Decision results: Based on the weighted summation results, a comprehensive evaluation value can be obtained to indicate the pros and cons of different capacity configuration schemes. Based on this evaluation value, a decision can be made to select the optimal capacity configuration scheme.

[0011] This method has the following disadvantages: subjectivity, no consideration of interactions, lack of flexibility, inability to handle uncertainty, and lack of optimization capabilities.

[0012] 2. The capacity optimization configuration method based on particle swarm algorithm has the following working principle and main process:

[0013] First, a random solution is set to start iteration, and then the quality of the solution is evaluated by fitness, leaving the optimal solution value, repeating the process repeatedly for continuous optimization, and finally obtaining the optimal solution.

[0014] This method has the following drawbacks: dependence on the initial solution, difficulty in parameter selection, dependence on the problem, slow convergence, and difficulty in handling constraints.

[0015] 3. Capacity optimization configuration method based on dynamic programming method, which transforms the energy storage capacity configuration problem into an optimization problem, and solves the optimal energy storage capacity configuration scheme through dynamic programming algorithm. This method takes into account the energy demand in different time periods and the charging and discharging characteristics of the energy storage system, and is suitable for scenarios that need to consider time changes. Its basic operating principle can be divided into the following 5 steps:

[0016] 1) Determine the state: Divide the energy storage capacity optimization configuration problem into different time periods and define the state of each time period. The state may include the load demand in the current time period, the state of the energy storage system (such as energy storage capacity, charging state, etc.);

[0017] 2) Define the state transfer equation: Based on the characteristics and constraints of the energy storage system, define the state transfer equation. This equation describes how to transfer from the state of one time period to the state of the next time period, as well as the energy charging and discharging during the transfer process;

[0018] 3) Determine the objective function: Define the objective function based on the optimization goal. The objective function can be to minimize the cost of the energy storage system, maximize the energy utilization of the system, etc.

[0019] 4) Recursive solution to the optimal solution: Using the recursive solution method of dynamic programming, starting from the last time period, gradually deduce the optimal energy storage capacity configuration plan. By comparing different state transition plans, select the state transition path that can make the objective function optimal;

[0020] 5) Backtrack to get the optimal solution: Based on the results of the recursive solution, backtrack to get the optimal energy storage capacity configuration plan. You can also backtrack step by step to get the optimal solution by recording the optimal state and transfer path of each time period.

[0021] This method has the following drawbacks: high computational complexity, limited by the problem size, difficult to handle continuous state space, complex handling of constraints, and dependence on the characteristics of the problem. Summary of the invention

[0022] To solve the above problems, the present invention provides a method for optimizing the configuration of wind and solar energy storage capacity based on the main objective method. The main objective method is used to perform model conversion to obtain a mixed integer nonlinear programming model. On the one hand, it can avoid the problem that the linear weighted method and the intelligent algorithm conceal the output variation characteristics of wind and solar loads in different seasons when normalizing multiple objectives. On the other hand, the model can be directly solved by software, which simplifies the calculation process.

[0023] To achieve the above object, the present invention provides a method for optimizing the configuration of wind-solar energy storage capacity based on the main target method, comprising the following steps:

[0024] S1. Calculate the annualized total cost of energy storage based on the entire life cycle of energy storage and build an energy storage life decay model;

[0025] S2. Taking the maximum total benefit of energy storage and the maximum comprehensive utilization rate of wind and solar power as the objective function, and taking the operation constraints of the generator set, the constraints of the energy storage system, and the constraints of the power system as the constraints, an energy storage capacity planning model is established according to the annualized total cost of energy storage and the energy storage life attenuation model;

[0026] S3. Convert the energy storage capacity planning model: Set the maximum comprehensive utilization rate of wind and solar power as the main target, introduce the target scaling factor, convert the maximum total energy storage benefit into a constraint condition, and use the main target method to convert and solve the energy storage capacity planning model to obtain the optimal energy storage capacity and power.

[0027] Preferably, the calculation steps of the annualized total cost of energy storage described in step S1 are as follows:

[0028] The first step is to determine the composition of energy storage costs:

[0029] The energy storage cost is mainly composed of the initial investment cost of the energy storage system, the subsequent maintenance cost, the charging expenditure cost, the energy storage loss cost, the energy storage system discharge income, and the compensation income from participating in peak load regulation;

[0030] The second step is to consider the time value of money and convert the equivalent life of energy storage into annualized investment cost. According to microeconomic theory, the equal series capital recovery coefficient is determined to obtain the annualized total cost of energy storage.

[0031] Preferably, the calculation steps of the annualized total cost of energy storage described in step S1 are as follows:

[0032] The first step is to take the energy storage cost as the objective function and establish the following expression:

[0033] C E =C E,inves +C S,ch +C S,loss -C S,dis -C a(1)

[0034] In the formula, C E is the energy storage cost; C E,inves Cost planning for energy storage systems; C S,ch Cost of charging; C S,loss is the energy storage loss cost; C s,dis is the discharge income of the energy storage system; C a Compensation for participating in peak load regulation;

[0035] in,

[0036]

[0037] Where, T life is the expected life of the energy storage system; C E,pre Initial investment for energy storage system; C E,lat is the subsequent maintenance cost; η is the power capacity conversion coefficient; C σ is the unit capacity investment cost; C τ is the unit capacity maintenance cost; P E Plan the maximum power for the energy storage system; E E Plan the maximum capacity for the energy storage system;

[0038] The second step is to consider the time value of funds, convert the equivalent life of energy storage into annualized investment costs, and determine the equal series capital recovery coefficient based on microeconomic theory:

[0039]

[0040] Where: η0 is the equal amount series capital recovery coefficient; r is the discount rate; n ess is the energy storage equivalent life;

[0041] The annual total cost of energy storage is C ess for:

[0042] C ess =η0C E (4).

[0043] Preferably, the factors considered in establishing the energy storage life attenuation model described in step S1 include ambient temperature, charge and discharge rate, discharge depth and number of charge and discharge cycles;

[0044] It specifically includes the following steps:

[0045] The first step is to calculate the equivalent number of energy storage cycles per day using typical days in four seasons:

[0046]

[0047] Where n eqis the equivalent number of cycles per day of energy storage; is the actual discharge amount of energy storage in time period t in season i; i = 1, 2, 3, 4 represent the four seasons of spring, summer, autumn and winter respectively; t = 1, 2, ..., 24 represent the 24 time periods of a day respectively;

[0048] The second step is to calculate the equivalent life of the energy storage system based on the rated number of cycles of the energy storage life cycle:

[0049]

[0050] Where n ess is the equivalent life of the converted energy storage system; n0 is the rated number of cycles of the energy storage life cycle, that is, the maximum number of cycles when the discharge depth is 100%.

[0051] Preferably, the objective function for maximizing the total energy storage benefit described in step S2 is as follows:

[0052]

[0053]

[0054] In the formula, S ess is the total energy storage benefit; S new The revenue from the connection of new renewable energy to the grid; w The on-grid electricity price for wind power; is the additional wind power generation in season i and period t; p pv The photovoltaic grid-connected electricity price; is the additional photovoltaic power generation in time period t of season i;

[0055] The objective function for maximizing the comprehensive utilization rate of wind and solar power in step S2 is as follows:

[0056]

[0057] In the formula, R is the comprehensive utilization rate of wind and solar power; is the wind power output in time period t in season i; is the photovoltaic output in time period t of season i; is the upper limit of wind power output in time period t of season i; is the upper limit of photovoltaic output in period t of season i.

[0058] Preferably, the generator set operation constraints described in step S2 include gas turbine operation constraints and renewable energy operation constraints, wherein the gas turbine operation constraints impose the following constraints on its output upper and lower limits, start and stop time, and ramp rate:

[0059]

[0060] In the formula, Qgas-min It is the lower limit of gas turbine output; is the gas turbine output in time period t during season i; Q gas-max The upper limit of gas turbine output; is the continuous operation time of the gas turbine in period t of season i; M on is the minimum continuous operation time of the gas turbine; u i,t u is a 0-1 variable for the start and stop of the gas turbine in time period t of season i, where 0 and 1 represent the shutdown and running states respectively; i,t+1 is the gas turbine start-stop 0-1 variable in season i period t+1, 0 and 1 represent shutdown and running status respectively; is the continuous downtime of the gas turbine in period t of season i; M off The minimum continuous downtime of the gas turbine; is the gas turbine ramp rate at time t+1 in season i; is the ramp rate of the gas turbine in period t of season i; Q th-v is the upper limit of the gas turbine ramp rate;

[0061] The renewable energy operation constraint expression is as follows;

[0062]

[0063] In the formula, are the lower limits of wind power and photovoltaic processing in time period t of season i, respectively; are the upper limits of wind power and photovoltaic power processing in time period t of season i, respectively.

[0064] Preferably, the energy storage system constraints described in step S2 include energy storage operation constraints, energy storage power constraints and continuous discharge time constraints;

[0065] Among them, the energy storage operation constraint expression is as follows:

[0066]

[0067] In the formula, is the state of charge of the energy storage system at time t+1 in season i; is the state of charge of the energy storage system at time period t in season i; is the actual energy storage charge at time t+1 in season i; E soc-max E is the upper limit of the state of charge of the energy storage system; soc-min The lower limit of the charge state of the energy storage system; is the state of charge at the beginning of the scheduling cycle of season i; is the state of charge at the end of the scheduling cycle in season i; c is the charging efficiency of the energy storage system; d is the discharge efficiency of the energy storage system;

[0068] The energy storage power constraint expression is as follows:

[0069]

[0070] In the formula, is the actual charging capacity of energy storage in time period t of season i; P ess is the rated power of the energy storage system;

[0071] The continuous discharge time constraint expression is as follows:

[0072]

[0073] Where H is the expected continuous discharge time of energy storage; E ess is the energy storage capacity; H max is the maximum continuous discharge time of energy storage; H min is the minimum duration of energy storage.

[0074] Preferably, the power system constraints described in step S2 include power balance constraints, reserve capacity constraints and renewable energy penetration constraints;

[0075] Among them, the power balance constraint and the reserve capacity constraint are expressed as:

[0076]

[0077]

[0078] In the formula, G i,t is the demand in time period t of season i; α is the reserve capacity factor of gas turbine;

[0079] The renewable energy penetration constraint expression is as follows:

[0080]

[0081] Where: β is the maximum penetration rate of renewable energy.

[0082] Preferably, step S3 specifically includes the following steps:

[0083] S31. Use the main objective method to transform the energy storage capacity planning model to obtain the following mixed integer nonlinear programming model:

[0084]

[0085] Where: The optimal value is solved with the maximum total benefit of energy storage as the single objective; is the target scaling factor, in A value of 0 means that as long as the energy storage system does not suffer losses, it is acceptable. 1 means that the maximum benefit can be achieved;

[0086] S32, solving mixed integer nonlinear programming models;

[0087] It specifically includes the following steps:

[0088] S321. Based on the constructed energy storage life attenuation model, set the initial conditions:

[0089] Set the predicted wind and solar output and load demand, constraints, and expected continuous discharge time of energy storage;

[0090] S322. In the energy storage optimization configuration model, solving the problem with maximizing the total energy storage benefit as a single objective;

[0091] S323, determine whether the solution of S322 is the global optimal solution, if not, return to S322, if yes, execute S324;

[0092] S324, converting the global optimal solution obtained in S323 into a constraint condition as the optimal value, and solving it with the maximum comprehensive utilization rate of wind and solar as the single objective;

[0093] S325, determine whether the solution of S324 is the global optimal solution, if not, return to S324, if yes, execute S326;

[0094] S327. Use the global optimal solution obtained in S325 as the optimal energy storage capacity and power output.

[0095] Preferably, in step S32, Lingo software is used to solve the mixed integer nonlinear programming model.

[0096] The present invention has the following beneficial effects:

[0097] 1. The mixed integer nonlinear programming model is obtained by using the main objective method to transform the model. On the one hand, it can avoid the problem that the linear weighted method and intelligent algorithm conceal the output variation characteristics of wind and solar loads in different seasons when normalizing multiple objectives. On the other hand, the model can be solved directly by software, which simplifies the calculation process.

[0098] 2. The energy storage capacity optimization configuration method considering the seasonal fluctuation of wind and solar power output can be used to study the changes in the comprehensive utilization rate of wind and solar power under different profit conditions;

[0099] 3. Through the cost analysis of the entire life cycle of energy storage, a model is established with energy storage cost as the objective function, and the planning cost of the energy storage system, charging expenditure cost, energy storage loss cost, energy storage system discharge income and peak load compensation income are taken into account. At the same time, the time value of money is taken into account, and the annualized investment cost is converted based on the equivalent life of the energy storage. Then, the microeconomic theory is applied to calculate the equal series capital recovery coefficient, which makes the calculation more practical.

[0100] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 A logic block diagram of a method for optimizing wind and solar energy storage capacity configuration based on a main objective method of the present invention;

[0102] Figure 2 The wind power output prediction curve diagram of typical days in four seasons of the embodiment of the present invention;

[0103] Figure 3 The photovoltaic output prediction curve diagram of typical days in four seasons of the embodiment of the present invention;

[0104] Figure 4 The load forecast diagrams for typical days in four seasons of the embodiment of the present invention are as follows;

[0105] Figure 5 The energy storage charge state diagram of scenario 1 of an embodiment of the present invention;

[0106] Figure 6 The energy storage charge state diagram of scenario 2 of an embodiment of the present invention;

[0107] Figure 7 This is a diagram of the energy storage charge state of scenario three of an embodiment of the present invention. DETAILED DESCRIPTION

[0108] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0109] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or devices.

[0110] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0111] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0112] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" 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 an indirect connection 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 the present invention can be understood according to specific circumstances.

[0113] like Figure 1 As shown, a method for optimizing the configuration of wind-solar energy storage capacity based on the main objective method includes the following steps:

[0114] S1. Calculate the annualized total cost of energy storage based on the entire life cycle of energy storage and build an energy storage life decay model;

[0115] The calculation steps of the annualized total cost of energy storage described in step S1 are as follows:

[0116] The first step is to determine the composition of energy storage costs:

[0117] The energy storage cost is mainly composed of the initial investment cost of the energy storage system (the investment cost is mainly related to the energy storage capacity and power, and the operation and maintenance cost can be estimated approximately according to a certain proportion of the investment cost), the subsequent maintenance cost, the charging expenditure cost, the energy storage loss cost, the energy storage system discharge income, and the compensation income from participating in peak load regulation;

[0118] The second step is to consider the time value of money and convert the equivalent life of energy storage into annualized investment cost. According to microeconomic theory, the equal series capital recovery coefficient is determined to obtain the annualized total cost of energy storage.

[0119] More specifically, the calculation steps of the annualized total cost of energy storage described in step S1 are as follows:

[0120] The first step is to take the energy storage cost as the objective function and establish the following expression:

[0121] C E =C E,inves +C s,ch +C s,loss -C s,dis -C a (1)

[0122] In the formula, C E is the energy storage cost; C E,inves Cost planning for energy storage systems; C S,ch Cost of charging; C S,loss is the energy storage loss cost; C s,dis is the discharge income of the energy storage system; C a Compensation for participating in peak load regulation;

[0123] in,

[0124]

[0125] Where, T life is the expected life of the energy storage system; C E,pre Initial investment for energy storage system; C E,lat is the subsequent maintenance cost; η is the power capacity conversion coefficient; C σ is the unit capacity investment cost; C τ is the unit capacity maintenance cost; P E Plan the maximum power for the energy storage system; E E Plan the maximum capacity for the energy storage system;

[0126] The second step is to consider the time value of funds, convert the equivalent life of energy storage into annualized investment costs, and determine the equal series capital recovery coefficient based on microeconomic theory:

[0127]

[0128] Where: η0 is the equal amount series capital recovery coefficient; r is the discount rate; n ess is the energy storage equivalent life;

[0129] The annual total cost of energy storage is C ess for:

[0130] Cess =η0C E (4).

[0131] Since the life of the energy storage system is mainly affected by factors such as ambient temperature, charge and discharge rate, discharge depth and number of charge and discharge cycles. Taking lithium batteries as an example, the number of charge and discharge cycles is usually used to measure the energy storage life. When the maximum number of cycles is reached, it can be considered that the battery loss has reached the limit. At the same time, since the energy storage system does not always work at the maximum discharge depth, the number of cycles should be converted according to the actual charge and discharge behavior. Therefore, the factors considered in establishing the energy storage life attenuation model described in step S1 include ambient temperature, charge and discharge rate, discharge depth and number of charge and discharge cycles;

[0132] It specifically includes the following steps:

[0133] The first step is to calculate the equivalent number of energy storage cycles per day using typical days in four seasons:

[0134]

[0135] Where n eq is the equivalent number of cycles per day of energy storage; is the actual discharge amount of energy storage in time period t in season i; i = 1, 2, 3, 4 represent the four seasons of spring, summer, autumn and winter respectively; t = 1, 2, ..., 24 represent the 24 time periods of a day respectively;

[0136] The second step is to calculate the equivalent life of the energy storage system based on the rated number of cycles of the energy storage life cycle:

[0137]

[0138] Where n ess is the equivalent life of the converted energy storage system; n0 is the rated number of cycles of the energy storage life cycle, that is, the maximum number of cycles when the discharge depth is 100%.

[0139] S2. Taking the maximum total benefit of energy storage and the maximum comprehensive utilization rate of wind and solar power as the objective function, and taking the operation constraints of the generator set, the constraints of the energy storage system, and the constraints of the power system as the constraints, an energy storage capacity planning model is established according to the annualized total cost of energy storage and the energy storage life attenuation model;

[0140] When the grid’s capacity to absorb renewable energy is insufficient, the energy storage system can store electricity and release it when the renewable energy absorption capacity is sufficient, thereby alleviating the problem of wind and solar power abandonment. Therefore, the increased wind power and photovoltaic grid-connected income after installing the energy storage system can be used as the income of energy storage.

[0141] The objective function for maximizing the total energy storage benefit described in step S2 is as follows:

[0142]

[0143]

[0144] In the formula, S ess is the total energy storage benefit; S new The revenue from the connection of new renewable energy to the grid; w The on-grid electricity price for wind power; is the additional wind power generation in season i and period t; p pv The photovoltaic grid-connected electricity price; is the additional photovoltaic power generation in time period t of season i;

[0145] In order to further enhance the role of energy storage systems in promoting the consumption of renewable energy, it is necessary to optimize the comprehensive utilization rate of wind and solar power. Most existing studies only select typical days in a single season for optimization, while this embodiment optimizes typical days in four seasons at the same time. Simply summing or averaging the daily utilization rates does not make much sense, nor can it better demonstrate the renewable energy output and load characteristics in different seasons. Therefore, minimizing the comprehensive utilization rate of wind and solar power in four seasons is the optimization goal, so that the energy storage scale can achieve a higher level of wind power and photovoltaic utilization throughout the year.

[0146] The objective function for maximizing the comprehensive utilization rate of wind and solar power in step S2 is as follows:

[0147]

[0148] In the formula, R is the comprehensive utilization rate of wind and solar power; is the wind power output in time period t in season i; is the photovoltaic output in time period t of season i; is the upper limit of wind power output in time period t of season i; is the upper limit of photovoltaic output in period t of season i.

[0149] The generator set operation constraints described in step S2 include gas turbine operation constraints and renewable energy operation constraints. The gas turbine cannot change its output size and start / stop status at will during operation. Therefore, the gas turbine operation constraints are required to constrain its output upper and lower limits, start / stop time, and ramp rate as follows:

[0150]

[0151] In the formula, Q gas-min It is the lower limit of gas turbine output; is the gas turbine output in time period t during season i; Q gas-max It is the upper limit of gas turbine output; is the continuous operation time of the gas turbine in period t of season i; Mon is the minimum continuous operation time of the gas turbine; u i,t u is a 0-1 variable for the start and stop of the gas turbine in time period t of season i, where 0 and 1 represent the shutdown and running states respectively; i,t+1 is the gas turbine start-stop 0-1 variable in season i period t+1, 0 and 1 represent shutdown and running status respectively; is the continuous downtime of the gas turbine in period t of season i; M off The minimum continuous downtime of the gas turbine; is the gas turbine ramp rate at time t+1 in season i; is the ramp rate of the gas turbine in period t of season i; Q th-v is the upper limit of the gas turbine ramp rate;

[0152] Affected by the natural environment, wind power and photovoltaic power have upper and lower limits in actual operation, so the operation constraint expression of renewable energy is as follows;

[0153]

[0154] In the formula, are the lower limits of wind power and photovoltaic processing in time period t of season i, respectively; are the upper limits of wind power and photovoltaic power processing in time period t of season i, respectively.

[0155] The energy storage system constraints described in step S2 include energy storage operation constraints, energy storage power constraints and continuous discharge time constraints;

[0156] Among them, the state of charge is usually used to represent the operation of the energy storage system. In order to ensure the stable operation of the energy storage system, the state of charge is limited to a certain range, and the state of charge at the beginning and end of a scheduling cycle should be equal to ensure the regulation ability of the energy storage system in the next cycle. Therefore, the energy storage operation constraint expression is as follows:

[0157]

[0158] In the formula, is the state of charge of the energy storage system at time t+1 in season i; is the state of charge of the energy storage system at time period t in season i; is the actual energy storage charge at time t+1 in season i; E soc-max E is the upper limit of the state of charge of the energy storage system; soc-min The lower limit of the charge state of the energy storage system; is the state of charge at the beginning of the scheduling cycle of season i; is the state of charge at the end of the scheduling cycle in season i; c is the charging efficiency of the energy storage system; dis the discharge efficiency of the energy storage system;

[0159] The energy storage system cannot be in the state of charging and discharging at the same time, and the maximum charging and discharging power cannot exceed the rated power during planning. Therefore, the energy storage power constraint expression is as follows:

[0160]

[0161] In the formula, is the actual charging capacity of energy storage in time period t of season i; P ess is the rated power of the energy storage system;

[0162] When configuring energy storage capacity, if the capacity is too small, the regulatory effect on the dispatching system will be limited, and if the capacity is too large, the investment and operation and maintenance costs will be significantly increased. The continuous discharge time of energy storage refers to the time that the energy storage continuously works at the rated power, which is directly related to the capacity and power of the energy storage. Therefore, the expected continuous discharge time is constrained to further control the energy storage cost. The continuous discharge time constraint expression is as follows:

[0163]

[0164] Where H is the expected continuous discharge time of energy storage; E ess is the energy storage capacity; H max H is the maximum continuous discharge time of energy storage; min is the minimum duration of energy storage.

[0165] The power system constraints described in step S2 include power balance constraints, reserve capacity constraints and renewable energy penetration constraints;

[0166] Among them, the power balance constraint and the reserve capacity constraint are expressed as:

[0167]

[0168]

[0169] In the formula, G i,t is the demand in time period t of season i; α is the reserve capacity factor of gas turbine;

[0170] The power volatility and randomness of renewable energy will bring shocks to the power grid. Under the premise of ensuring continuous and stable power supply, the maximum grid-connected proportion of renewable energy should be constrained. Therefore, the constraint expression of renewable energy penetration is as follows:

[0171]

[0172] Where: β is the maximum penetration rate of renewable energy.

[0173] S3. Convert the energy storage capacity planning model: Set the maximum comprehensive utilization rate of wind and solar power as the main target, introduce the target scaling factor, convert the maximum total energy storage benefit into a constraint condition, and use the main target method to convert and solve the energy storage capacity planning model to obtain the optimal energy storage capacity and power.

[0174] Step S3 specifically includes the following steps:

[0175] S31. Use the main objective method to transform the energy storage capacity planning model to obtain the following mixed integer nonlinear programming model:

[0176]

[0177] Where: The optimal value is solved with the maximum total benefit of energy storage as the single objective; is the target scaling factor, in A value of 0 means that as long as the energy storage system does not suffer losses, it is acceptable. 1 means that the maximum benefit can be achieved;

[0178] S32. Solve the mixed integer nonlinear programming model.

[0179] In step S32, the mixed integer nonlinear programming model is solved using Lingo software;

[0180] It specifically includes the following steps:

[0181] S321. Based on the constructed energy storage life attenuation model, set the initial conditions:

[0182] Set the predicted wind and solar output and load demand, constraints, and expected continuous discharge time of energy storage;

[0183] S322. In the energy storage optimization configuration model, solving the problem with maximizing the total energy storage benefit as a single objective;

[0184] S323, determine whether the solution of S322 is the global optimal solution, if not, return to S322, if yes, execute S324;

[0185] S324, converting the global optimal solution obtained in S323 into a constraint condition as the optimal value, and solving it with the maximum comprehensive utilization rate of wind and solar as the single objective;

[0186] S325, determine whether the solution of S324 is the global optimal solution, if not, return to S324, if yes, execute S326;

[0187] S327. Use the global optimal solution obtained in S325 as the optimal energy storage capacity and power output.

[0188] Example

[0189] In this embodiment, a microgrid configured with 800kW wind turbines, 500kW photovoltaic units and 1000kW gas turbine units is used as an example for simulation analysis. According to the meteorological division method, January, April, July and October are selected as typical months of winter, spring, summer and autumn.

[0190] Using the historical data of wind speed, temperature and sunshine intensity at different times of the day in a typical month in a certain area in 2019, the following predictions are obtained: Figure 2 and Figure 3 The output curves of wind power and photovoltaic power on typical days in four seasons are shown in Figure 1. The on-grid electricity prices of wind power and photovoltaic power are 0.54 yuan / kW.h and 0.6 yuan / kW.h respectively, and the following are obtained: Figure 4 The load forecast curves for typical days in the four seasons are shown.

[0191] Energy storage achieves peak load shaving and valley filling of electricity load and efficient utilization of renewable energy through energy time shifting, so a longer discharge time is required. Therefore, in this embodiment, lithium batteries are used to configure the energy storage system on the power generation side, and H is set to 3 to 6 hours; c p is the power price, 1000 yuan / kW; c e is the capacity price, which is 1600 yuan / (kW·h); r is 4.9%; n0 is 1500; ζ c , d Both are 90%; is 0.5; E soc-max , E soc-min They are 0.9 and 0.1 respectively. Due to the regulation of energy storage, α is reduced to 10% and β is increased to 80%.

[0192] The following three scenarios are proposed to analyze the configuration scale of energy storage and the operation effect of the power system:

[0193] Scenario 1: Energy storage configuration is carried out with the single goal of maximizing the total benefit of the energy storage system;

[0194] Scenario 2: Based on Scenario 1, the main target method is used to configure energy storage with the maximum comprehensive utilization rate of wind and solar power as the goal;

[0195] Scenario 3: Select a single typical day, take spring as an example to configure energy storage, and analyze the impact of the energy storage scale on the operation of the power system in other seasons.

[0196] Table 1 Energy storage configuration and power system operation effects under three scenarios

[0197]

[0198]

[0199] At present, the cost of energy storage is relatively high. When optimizing the configuration with the maximum total benefit of energy storage as the single goal, the capacity and power of energy storage can be reduced as much as possible while ensuring the safe operation of the power system, and the discharge of energy storage can be reduced, the life of energy storage can be extended, and the cost of energy storage can be reduced. After installing energy storage, the minimum comprehensive utilization rate of wind and solar power on typical days in four seasons of renewable energy increased from 93.01% to 95.34%, and the comprehensive utilization rate of wind and solar power in four seasons increased by an average of 2.05%. At the same time, in order to cope with the impact of high penetration of renewable energy on the power system, the scale of energy storage can be greatly increased, and the original abandoned electricity can be stored when the renewable energy generation is large, and the energy time shifting function of the energy storage system can be better played by extending the continuous discharge time. When sacrificing part of the energy storage benefits and pursuing a higher utilization rate of renewable energy, the minimum comprehensive utilization rate of wind and solar power is further increased to 96.55%, and the comprehensive utilization rate of wind and solar power in four seasons is increased by an average of 3.05%.

[0200] However, the large-scale consumption of renewable energy means that energy storage can only maintain the stability of the power system through frequent charging and discharging, which has a huge impact on the life of energy storage. However, the renewable energy grid-connected benefits brought by the new power generation of wind power and photovoltaic power can offset part of the cost of energy storage construction and operation and maintenance, and the impact on the total benefits of energy storage is not significant. This is even more so in scenario three. When only typical days in spring are selected for energy storage configuration, due to the low comprehensive utilization rate of wind and solar power in spring, a larger energy storage capacity and power are required to achieve efficient consumption of renewable energy, resulting in a situation where the energy storage scale is excessive in other seasons. Although this situation increases the minimum comprehensive utilization rate of wind and solar power to 98.58%, the average comprehensive utilization rate of wind and solar power increases by 4.67%, and obtains a higher new renewable energy grid-connected benefits, under the dual effects of increased energy storage configuration scale and reduced equivalent life, the annualized total cost of energy storage has increased by nearly 2 times compared with scenario one, resulting in negative benefits.

[0201] like Figure 5-Figure 7 As shown, in Scenario 1, in pursuit of maximum profit, the energy storage system tends to operate at a higher state of charge level, with a small discharge depth, which is more friendly to the energy storage life. In Scenario 2, energy storage presents a completely different operation mode and has a significant seasonal effect. In summer and winter when wind power and photovoltaic power generation are high, energy storage is mainly charged, while in spring and autumn when wind power and photovoltaic power generation are low, energy storage is mainly discharged, while increasing the discharge depth and improving the overall utilization rate of renewable energy. In Scenario 3, the operation of energy storage in spring can better match the output characteristics of wind power and photovoltaic power, but the excess energy storage capacity cannot be fully utilized even in summer and winter when wind power and photovoltaic power generation are high.

[0202] The utilization rate of renewable energy in each season under the three scenarios is shown in Table 2. It can be seen that with the increase in energy storage scale, the utilization rates of wind power and photovoltaic power have increased to a certain extent, and scenario three has achieved 100% photovoltaic absorption. In addition to the scale of energy storage, the actual output of wind power and photovoltaic power and load demand are also two important factors affecting the utilization rate of renewable energy. In terms of seasons, the load demand in summer and winter is significantly higher than that in spring and autumn. Wind power output is mainly concentrated in winter, so the utilization rate of wind power is the highest in winter under the three scenarios. Spring also has good wind resources, but the load is low, and the absorption effect of wind power is not ideal. The high output and high load demand of photovoltaic power in summer make the utilization rate of photovoltaic power the highest in summer. Affected by temperature and radiation intensity, photovoltaic power generation resources are the worst in winter, but due to the high load demand in winter, photovoltaic power still has a good absorption effect. In addition, due to the small scale of photovoltaic installed capacity, its overall utilization rate is higher than that of wind power.

[0203] Table 2 Renewable energy utilization rate in each season under 3 scenarios

[0204]

[0205]

[0206] Among the four seasons, the lowest wind and solar comprehensive utilization rate on typical days occurs in spring. This is because the output volatility of wind power is the largest in spring, and the load demand is low in spring, which brings certain difficulties to the consumption of wind power. On the other hand, due to the large scale of wind power, its impact on the comprehensive utilization rate of wind and solar power is greater than that of photovoltaic power generation.

[0207] However, low utilization provides space for energy storage to play a role. The installation of energy storage systems has the most obvious effect on improving the comprehensive utilization rate of wind and solar power in spring. Compared with the scenario without energy storage, scenario one increased by 2.33%, scenario two increased by 3.54%, and scenario three increased by 5.57%.

[0208] From the perspective of power source type, energy storage configuration has a better effect on improving the consumption of photovoltaic power generation than wind power. This is because the photovoltaic grid-connected electricity price is higher, and the priority consumption of photovoltaic power can increase the total energy storage benefits. Therefore, in terms of improving the comprehensive utilization rate of wind and solar power, the installation of energy storage systems has a more obvious effect on photovoltaic power generation in spring.

[0209] In short, spring is the season with the lowest wind-solar comprehensive utilization rate on a typical day. The installation of energy storage system can significantly improve the utilization rate, especially for photovoltaic power generation in spring. This proves the present invention.

[0210] Therefore, the present invention adopts the above-mentioned wind-solar energy storage capacity optimization configuration method based on the main objective method, and uses the main objective method to perform model conversion to obtain a mixed integer nonlinear programming model. On the one hand, it can avoid the problem that the linear weighted method and the intelligent algorithm conceal the output variation characteristics of the wind and solar loads in different seasons when normalizing multiple objectives. On the other hand, the model can be directly solved by software, which simplifies the calculation process.

[0211] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution 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 they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing the configuration of wind and solar energy storage capacity based on the main objective method, characterized by: The following steps are involved: S1. Calculate the annualized total cost of energy storage based on the entire life cycle of energy storage and build an energy storage life decay model; S2. Taking the maximum total benefit of energy storage and the maximum comprehensive utilization rate of wind and solar power as the objective function, and taking the operation constraints of the generator set, the constraints of the energy storage system, and the constraints of the power system as the constraints, an energy storage capacity planning model is established according to the annualized total cost of energy storage and the energy storage life attenuation model; S3. Convert the energy storage capacity planning model: Set the maximum comprehensive utilization rate of wind and solar power as the main target, introduce the target scaling factor, convert the maximum total energy storage benefit into a constraint condition, and use the main target method to convert and solve the energy storage capacity planning model to obtain the optimal energy storage capacity and power.

2. The method for optimizing the wind and solar energy storage capacity based on the main objective method according to claim 1 is characterized by: The calculation steps of the annualized total cost of energy storage described in step S1 are as follows: The first step is to determine the composition of energy storage costs: The energy storage cost is mainly composed of the initial investment cost of the energy storage system, the subsequent maintenance cost, the charging expenditure cost, the energy storage loss cost, the energy storage system discharge income, and the compensation income from participating in peak load regulation; The second step is to consider the time value of money and convert the equivalent life of energy storage into annualized investment cost. According to microeconomic theory, the equal series capital recovery coefficient is determined to obtain the annualized total cost of energy storage.

3. The method for optimizing the wind and solar energy storage capacity based on the main objective method according to claim 2 is characterized by: The calculation steps of the annualized total cost of energy storage described in step S1 are as follows: The first step is to take the energy storage cost as the objective function and establish the following expression: C E =C E,inves +C S,ch +C S,loss -C s,dis -C a (1) In the formula, C E is the energy storage cost; C E,inves Cost planning for energy storage systems; C S,ch Cost of charging; C S,loss is the energy storage loss cost; C s,dis is the discharge income of the energy storage system; C a Compensation for participating in peak load regulation; in, Where, T life is the expected life of the energy storage system; C E,pre Initial investment for energy storage system; C E,lat is the subsequent maintenance cost; η is the power capacity conversion coefficient; C σ is the unit capacity investment cost; C τ is the unit capacity maintenance cost; P E Plan the maximum power for the energy storage system; E E Plan the maximum capacity for the energy storage system; The second step is to consider the time value of funds, convert the equivalent life of energy storage into annualized investment costs, and determine the equal series capital recovery coefficient based on microeconomic theory: Where: η0 is the equal amount series capital recovery coefficient; r is the discount rate; n ess is the energy storage equivalent life; The annual total cost of energy storage is C ess for: Cx ss =η0C E (4)。 4. The method for optimizing the wind-solar energy storage capacity based on the main objective method according to claim 3 is characterized by: The factors considered in establishing the energy storage life attenuation model described in step S1 include ambient temperature, charge and discharge rate, discharge depth and number of charge and discharge cycles; It specifically includes the following steps: The first step is to calculate the equivalent number of energy storage cycles per day using typical days in four seasons: Where n eq is the equivalent number of cycles per day of energy storage; is the actual discharge amount of energy storage in time period t in season i; i = 1, 2, 3, 4 represent the four seasons of spring, summer, autumn and winter respectively; t = 1, 2, ..., 24 represent the 24 time periods of a day respectively; The second step is to calculate the equivalent life of the energy storage system based on the rated number of cycles of the energy storage life cycle: Where n ess is the equivalent life of the converted energy storage system; n0 is the rated number of cycles of the energy storage life cycle, that is, the maximum number of cycles when the discharge depth is 100%.

5. The method for optimizing the wind-solar energy storage capacity based on the main objective method according to claim 4 is characterized by: The objective function for maximizing the total energy storage benefit described in step S2 is as follows: In the formula, S ess is the total energy storage benefit; S new The revenue from the connection of new renewable energy to the grid; w The on-grid electricity price for wind power; is the additional wind power generation in season i and period t; p pv The photovoltaic grid-connected electricity price; is the additional photovoltaic power generation in season i and period t; The objective function for maximizing the comprehensive utilization rate of wind and solar power in step S2 is as follows: In the formula, R is the comprehensive utilization rate of wind and solar power; is the wind power output in time period t in season i; is the photovoltaic output in time period t in season o; is the upper limit of wind power output in season o and period t; is the upper limit of photovoltaic output in period t of season o.

6. The method for optimizing the configuration of wind-solar energy storage capacity based on the main objective method according to claim 5 is characterized by: The generator set operation constraints described in step S2 include gas turbine operation constraints and renewable energy operation constraints, wherein the gas turbine operation constraints impose the following constraints on its output upper and lower limits, start and stop time, and ramp rate: In the formula, Q gas-min It is the lower limit of gas turbine output; is the gas turbine output in time period t during season i; Q gas-max The upper limit of gas turbine output; is the continuous operation time of the gas turbine in period t of season i; M on is the minimum continuous operating time of the gas turbine; u i,t u is a 0-1 variable for the start and stop of the gas turbine in time period t of season i, where 0 and 1 represent the shutdown and running states respectively; i,t+1 is the gas turbine start-stop 0-1 variable in season i period t+1, 0 and 1 represent shutdown and running status respectively; is the continuous downtime of the gas turbine in period t of season i; M off The minimum continuous downtime of the gas turbine; is the gas turbine ramp rate at time t+1 in season i; is the ramp rate of the gas turbine in time period t in season i; Q th-v is the upper limit of the gas turbine ramp rate; The renewable energy operation constraint expression is as follows; In the formula, are the lower limits of wind power and photovoltaic processing in time period t of season i, respectively; are the upper limits of wind power and photovoltaic power processing in time period t of season i, respectively.

7. The method for optimizing the wind-solar energy storage capacity based on the main objective method according to claim 6 is characterized by: The energy storage system constraints described in step S2 include energy storage operation constraints, energy storage power constraints and continuous discharge time constraints; Among them, the energy storage operation constraint expression is as follows: In the formula, is the state of charge of the energy storage system at time t+1 in season i; is the state of charge of the energy storage system at time period t in season i; is the actual energy storage charge at time t+1 in season i; E soc-max E is the upper limit of the state of charge of the energy storage system; soc-min The lower limit of the charge state of the energy storage system; is the state of charge at the beginning of the scheduling cycle of season i; is the state of charge at the end of the scheduling cycle in season i; c is the charging efficiency of the energy storage system; d is the discharge efficiency of the energy storage system; The energy storage power constraint expression is as follows: In the formula, is the actual charging capacity of energy storage in time period t of season i; P ess is the rated power of the energy storage system; The continuous discharge time constraint expression is as follows: Where, h is the expected continuous discharge time of energy storage; E ess is the energy storage capacity; H max is the maximum continuous discharge time of energy storage; H min is the minimum duration of energy storage.

8. The method for optimizing the wind-solar energy storage capacity based on the main objective method according to claim 7 is characterized by: The power system constraints described in step S2 include power balance constraints, reserve capacity constraints and renewable energy penetration constraints; Among them, the power balance constraint and the reserve capacity constraint are expressed as: In the formula, G i,t is the demand in time period t of season i; α is the reserve capacity factor of gas turbine; The renewable energy penetration constraint expression is as follows: Where: β is the maximum penetration rate of renewable energy.

9. The method for optimizing the configuration of wind-solar energy storage capacity based on the main objective method according to claim 8 is characterized by: Step S3 specifically includes the following steps: S31. Use the main objective method to transform the energy storage capacity planning model to obtain the following mixed integer nonlinear programming model: Where: The optimal value is solved with the maximum total benefit of energy storage as the single objective; is the target scaling factor, in A value of 0 means that as long as the energy storage system does not suffer losses, it is acceptable. 1 means that the maximum benefit can be achieved; S32. Solve the mixed integer nonlinear programming model.

10. The method for optimizing the configuration of wind-solar energy storage capacity based on the main objective method according to claim 9 is characterized in that: In step S32, the mixed integer nonlinear programming model is solved using Lingo software; It specifically includes the following steps: S321. Based on the constructed energy storage life attenuation model, set the initial conditions: Set the predicted wind and solar output and load demand, constraints, and expected continuous discharge time of energy storage; S322. In the energy storage optimization configuration model, solving the problem with maximizing the total energy storage benefit as a single objective; S323, determine whether the solution of S322 is the global optimal solution, if not, return to S322, if yes, execute S324; S324, converting the global optimal solution obtained in S323 into a constraint condition as the optimal value, and solving it with the maximum comprehensive utilization rate of wind and solar as the single objective; S325, determine whether the solution of S324 is the global optimal solution, if not, return to S324, if yes, execute S326; S327. Use the global optimal solution obtained in S325 as the optimal energy storage capacity and power output.

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