[0053] The embodiments of the present invention provide a microgrid optimization dispatching method, system, and equipment, which are used to solve the technical problem that the energy storage cycle life loss is not considered in the prior art research on the microgrid including the energy storage system.
[0054] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following The described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.
[0055] The purpose of the embodiments of the present invention is to provide a micro-network operator with a micro-network optimization scheduling method. Optimal dispatch of microgrid refers to the energy management and economic dispatch method of microgrid that minimizes the total operating cost under the premise of satisfying the constraints of the system. The microgrid includes uncontrollable power generation resources such as wind power and photovoltaics, dispatchable units, and battery energy storage equipment. The invention establishes the day-ahead planning and dispatching strategy optimization model of the micro-grid by modeling the operating parameters of different power supply equipment with the goal of minimizing the total power generation cost, so as to obtain the power generation plan decomposition curve of each power supply.
[0056] For the modeling process of energy storage cycle loss, the difficulty lies in the need to identify each cycle by selecting local extreme points after confirming the state of charge change curve, which is suitable for post-mortem evaluation of life loss. However, in the optimal dispatching decision-making model of the microgrid, the decision variables of the model will affect the operation strategy of energy storage (the charging and discharging power of the energy storage), thereby changing the state-of-charge change curve and local extreme points, which is feasible for any set In order to calculate the cycle life loss, it is necessary to search for the extreme point of the state of charge and identify each half cycle to calculate the cycle life loss.
[0057] At present, scholars at home and abroad have done some related researches on the optimal dispatching of microgrids including energy storage systems. There are literatures on the optimal configuration of microgrids with energy storage systems under different strategies, and the microgrid optimization schemes for each strategy are given. Some documents analyze the basic output characteristics of distributed power generation and energy storage devices based on different operating modes, and formulate operating plans according to the principle of economic optimization, but the above documents do not involve the loss of energy storage. Frequent charging and discharging make the energy storage life relatively short, so the cost of energy storage needs to be taken into account in the economic operation of the microgrid. Some documents describe the cost of energy storage loss with a constant loss factor in microgrid scheduling, without considering the impact of the state of charge (SOC) on the loss cost; some documents consider the impact of the depth of energy storage charge and discharge on the number of charge and discharge cycles, through accumulation Energy storage charging and discharging loss is used to determine the cost of energy storage loss in a scheduling cycle, but the loss caused by the same depth of charging and discharging under different SOCs is not the same, showing highly nonlinear characteristics. Because the operating characteristics of energy storage will have a huge impact on the economic operation of microgrids, a more accurate and practical energy storage loss model must be adopted. However, in general, in the existing microgrid optimization scheduling research, energy storage Accurate modeling methods for cycle life loss are rare.
[0058] Regarding the highly non-linear characteristics of the energy storage cycle loss function, how to deal with the coupling relationship among the decision variables, the extreme points of the state of charge change curve, and the cycle life loss is the focus of the embodiments of the present invention. The embodiment of the present invention first obtains the initial energy storage charging and discharging power curve and the state of charge curve through the microgrid economic dispatch model that does not consider the cycle life of the electrical energy storage, and then linearizes the approximation to the function containing the decision variable near the initial value , Which greatly simplifies the calculation of energy storage cycle loss and reduces the nonlinearity of the model, so that the model can be solved by an efficient commercial linear programming solver such as Cplex, and the loss of model accuracy is small.
[0059] See figure 1 , figure 1 This is a method flowchart of a micro-grid optimized scheduling method provided by an embodiment of the present invention.
[0060] Such as figure 1 As shown, a microgrid optimization scheduling method provided by the present invention includes the following steps:
[0061] Step S1: Obtain power grid parameters in the microgrid;
[0062] Step S2: According to the grid parameters and the system constraints of the microgrid, establish a microgrid economic dispatch model MO that does not consider the cycle life of the electric energy storage;
[0063] Step S3: Solve the microgrid economic dispatch model MO that does not consider the cycle life of the electric energy storage, and obtain the expected energy storage charge and discharge power curve;
[0064] Step S4: Establish a linearized energy storage cycle loss model according to the expected energy storage charge and discharge power curve;
[0065] Step S5: Substituting the linearized energy storage cycle loss model into the microgrid economic dispatch model MO that does not consider the cycle life of electric energy storage to obtain a microgrid economic dispatch model M1 that considers the energy storage cycle loss cost;
[0066] Step S6: Solve the microgrid economic dispatch model M1 considering the energy storage cycle loss cost, and obtain the microgrid optimal scheduling decision considering the energy storage cycle life.
[0067] As a preferred embodiment, the grid parameters in step S1 include: photovoltaic power generation operating parameters, wind power and other distributed power generation operating parameters, schedulable generating units operating parameters, energy storage operating parameters, microgrid internal load prediction values, and electricity prices in the spot market .
[0068] Photovoltaic and wind power are uncontrollable power generation resources. In principle, they must be fully consumed. When the maximum consumption capacity of the microgrid is exceeded, part of the output can be cut off. Let the maximum output of photovoltaic equipment be The predicted output of photovoltaics is t=1,2,...,T, the benchmark grid price of photovoltaic is The penalty cost of photovoltaic "abandonment" is
[0069] Let the maximum output of wind power equipment be The forecast output of wind power is t=1,2,...,T, the benchmark electricity price of wind power is The penalty cost of wind power “abandonment” is
[0070] The microgrid load is an uncontrollable load, and the predicted value of the microgrid load is t=1,2,...,T.
[0071] As a preferred embodiment, the specific steps for obtaining the operating parameters of the dispatchable unit are as follows:
[0072] Let N in the microgrid G One dispatchable generating set adopts a unified equivalent modeling method, and the generation cost is assumed to be in the form of a quadratic function. The formula is as follows:
[0073]
[0074] Where a n , B n , C n Are the power generation cost coefficients of the nth unit, n=1, 2,...,N G , Is the generating power of the nth unit, Meet the following constraints,
[0075]
[0076]
[0077] among them Is the rated power of the nth unit, versus Is the maximum upward and maximum downward climb rate respectively, and Δt is the time interval.
[0078] As a preferred embodiment, the specific steps for obtaining energy storage operating parameters are as follows:
[0079] Suppose the initial state of charge level of energy storage is The state of charge level at time t is t=1,2,...,T, the total capacity of energy storage is E BS , The maximum charge and discharge power is The charge-discharge conversion efficiency is η ch And η dis When the energy storage is charged, it is equivalent to the load, the real-time power is negative, and the energy stored by the energy storage increases; when the energy storage is discharged, it is equivalent to the power generation equipment, and the real-time power is positive, and the energy stored by the energy storage decreases. The state of charge change equation of energy storage is:
[0080]
[0081] among them, versus Are the charging power and the discharging power at time t, and satisfy the following constraints:
[0082]
[0083]
[0084] The state of charge satisfies the following constraints:
[0085]
[0086] among them, versus These are the maximum and minimum state-of-charge values.
[0087] As a preferred embodiment, suppose the spot market electricity price is t=1,2,...,T; when the net load of the microgrid exceeds 0, the electricity purchase fee is calculated based on the spot market clearing price.
[0088] As a preferred embodiment, in step S2, the system constraint condition of the microgrid is the load balance constraint condition, the microgrid maintains its own load balance through the power exchange with the main network contact point (PCC), and the input of the microgrid to the main network is set Power and output power are versus
[0089] Then the microgrid must meet the load balance conditions:
[0090]
[0091] among them, Is wind power generation power, For photovoltaic power generation, Is the energy storage discharge power, Charging power for energy storage, versus Cannot exceed the maximum exchange power of the contact point And must not be 0 at the same time, so the following constraints are met:
[0092]
[0093]
[0094] Where α t For 0-1 variables.
[0095] By combining the above conditions, the microgrid economic dispatch model MO that does not consider the cycle life of electric energy storage is obtained as follows:
[0096]
[0097]
[0098]
[0099]
[0100]
[0101]
[0102]
[0103] As a preferred embodiment, in step S3, a set of expected energy storage charge and discharge power curves are obtained through the microgrid economic dispatch model MO that does not consider the cycle life of the electric energy storage;
[0104] The expected energy storage charge and discharge power curve is t=1,2,...,T; the state of charge curve is among them, versus They are the optimal decision of MO charging power and the optimal decision of discharging power without considering the cycle life of electric energy storage.
[0105] As a preferred embodiment, step S4 includes the following steps:
[0106] Step S401: Solve the number of equivalent cycles of energy storage;
[0107] In the operation scheduling of battery energy storage, it is necessary to consider the impact of depth of discharge (DOD) on the cycle life and efficiency of battery energy storage, and establish a cycle loss model of energy storage. The cycle life depends on the battery's cycling strategy. The more frequently the battery is charged and discharged, the deeper the depth of charge and discharge, the faster the aging, and the shorter the cycle life. For the cycle mode with the same depth of charge and discharge, the cycle life of the battery is T cycle As follows:
[0108]
[0109] among them Is the number of cycles of charge and discharge depth d that make a new battery fail, expressed as Based on the experimental data of the battery manufacturer through the fitting method; Is the number of cycles of charge and discharge depth d per day; N day Represents the operating days of the energy storage equipment in a year.
[0110] The battery cycle life loss (calculated as a percentage of the total cost) is as follows, n d Is the number of cycles of charge and discharge depth d;
[0111]
[0112] Considering the wide applicability of the power function to describe different types of battery life, define N f (d) is the power function of the depth of discharge d, as follows:
[0113]
[0114] Where is k P Constant, usually 0.8-2.1; It is the number of failures in a full cycle (that is, d=100%), obtained by the energy storage equipment manufacturer through product testing.
[0115] Therefore, by measuring the battery charge and discharge process, the cycle life of the battery and the loss cost of each charge and discharge process can be obtained. The equivalent number of full cycles per day is:
[0116]
[0117] Step S402: Establish a cycle loss cost function according to the expected energy storage charge and discharge power curve and the equivalent number of energy storage cycles;
[0118] According to the expected energy storage charge and discharge power curve, the state of charge curve is obtained Such as figure 2 Shown in the state-of-charge curve Between every two adjacent local extreme points, the energy storage battery completes a half cycle, and the charge and discharge depth of each half cycle can be obtained as follows:
[0119]
[0120] among them State of charge curve The value of the k-th local extreme point.
[0121] Therefore, the state of charge curve can be obtained Corresponding cycle loss for:
[0122]
[0123] Among them, C is the set of local extreme points, c BS It is the investment cost of energy storage.
[0124] Step S403: Establish a linearized energy storage cycle loss model based on the cycle loss cost function.
[0125] Assuming that the state of charge curve of energy storage after considering the cycle loss is By right Perform Taylor polynomial expansion to get The nearby one-time form is as follows:
[0126]
[0127] among them, Is a constant.
[0128] Therefore, the linearized energy storage cycle loss model is expressed as
[0129]
[0130] As a preferred embodiment, in step S5, in order to maintain the nuclear power state level curve After optimization, the extreme point position distribution does not change, and it needs to be corrected Set the following constraints:
[0131]
[0132] Immediately When, to ensure On the contrary Time,
[0133] Based on the above, the microgrid economic dispatch model M1 considering the cost of energy storage cycle loss is as follows:
[0134]
[0135]
[0136]
[0137]
[0138]
[0139]
[0140]
[0141]
[0142] The above model is a mixed integer linear programming (MILP) problem. Solving M1 can get the optimal scheduling strategy of the microgrid, including the energy storage and the power generation plan decomposition curve of each power generation unit.
[0143] A microgrid optimized dispatch system, including a grid parameter acquisition module, a microgrid economic dispatch model module that does not consider the cycle life of electric energy storage, a linearized energy storage cycle loss model module, and a microgrid economic dispatch that considers the cost of energy storage cycle loss Model module
[0144] The grid parameter acquisition module is used to acquire grid parameters in the microgrid;
[0145] The microgrid economic dispatch model module that does not consider the cycle life of electric energy storage is used to construct and solve a microgrid economic dispatch model that does not consider the cycle life of electric energy storage;
[0146] The linearized energy storage cycle loss model module is used to construct and solve a linearized energy storage cycle loss model;
[0147] The microgrid economic dispatch model module considering the energy storage cycle loss cost is used to construct and solve the microgrid economic dispatch model considering the energy storage cycle loss cost.
[0148] A microgrid optimized scheduling device, the device including a processor and a memory;
[0149] The memory is used to store program code and transmit the program code to the processor;
[0150] The processor is used for the microgrid optimization scheduling method described above according to the instructions in the program code.
[0151] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.
[0152] In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
[0153] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
[0154] In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
[0155] If the integrated unit 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 technical solution of the present invention essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , Including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes.
[0156] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the embodiments are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
