Multi-element energy storage optimal configuration and economic evaluation method and system, electronic equipment and medium

Abstract

The invention provides a multi-element energy storage optimal configuration and economic evaluation method and system, electronic equipment and a medium, and belongs to the technical field of electric power systems, and the method comprises the following steps: S1, obtaining new energy installation status and future development trend information in an administrative region; s2, constructing a multi-element energy storage optimal configuration model; s3, solving the multi-element energy storage optimal configuration model to obtain an optimal multi-element energy storage configuration scheme; and S4, performing economic evaluation by combining long-time and short-time energy storage adjusting equipment of pumped storage and electrochemical energy storage. According to the multi-element energy storage optimal configuration and economic evaluation method and system, the electronic equipment and the medium, reasonable planning and investment of the two types of energy storage facilities are beneficial for solving the problem of new energy excess, dependence on a thermal power generating unit can be remarkably reduced, an annual net load curve is smoothed, and the economic performance of the thermal power generating unit is improved. Therefore, the stability and economy of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a multi-element energy storage optimization configuration and economic evaluation method, system, electronic equipment and medium. Background Art

[0002] With the transformation of the global energy structure and the enhancement of environmental awareness, renewable energy power generation, such as wind power and solar power, has become an increasingly important component of the power system. However, due to the obvious intermittent and random nature of renewable energy power generation, the supply and demand balance of the power system faces unprecedented challenges. On the one hand, the access of a large number of renewable energy sources to the power grid may lead to excess electricity in local periods, resulting in a waste of resources; on the other hand, when the output of renewable energy is insufficient, it may cause power shortages and affect the stable operation of the power system. Therefore, how to effectively absorb renewable energy and maintain the stability of power supply has become an urgent problem to be solved.

[0003] As one of the key means to solve the above problems, energy storage technology has received extensive attention and development in recent years. The energy storage system can store excess electricity during the peak period of renewable energy generation, and release the stored energy when demand peaks or renewable energy output is insufficient, thereby achieving the time-space transfer of electricity and smoothing the load curve. At present, there are a variety of energy storage technologies on the market, including pumped storage, electrochemical energy storage (such as lithium-ion batteries), flywheel energy storage, compressed air energy storage, etc., each of which has different technical characteristics and application scenarios.

[0004] Although the development of energy storage technology has provided strong support for the stable operation of the power system, there are still some problems in practical application. First, the selection and configuration of different types of energy storage technologies lack systematic methodological guidance, which makes it difficult to find the optimal energy storage solution in actual projects. Secondly, the construction and operation costs of energy storage systems are relatively high. How to maximize economic benefits while ensuring the safe and reliable operation of the power system has also become an important issue. In addition, the design of the energy storage system also needs to consider factors such as its compatibility with the existing power network and its impact on the environment and society. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-energy storage optimization configuration and economic evaluation method, system, electronic equipment and medium. Reasonable planning and investment in these two types of energy storage facilities will not only help solve the problem of excess new energy, but also significantly reduce dependence on thermal power units and smooth the net load curve throughout the year, thereby improving the stability and economy of the power system.

[0006] To achieve the above object, the present invention provides a multi-element energy storage optimization configuration and economic evaluation method, comprising the following steps:

[0007] Step S1, obtaining the current status and future development trend information of new energy installed capacity in the administrative area;

[0008] Step S2, constructing a multivariate energy storage optimization configuration model;

[0009] Step S3, solving the multi-element energy storage optimization configuration model to obtain the optimal multi-element energy storage configuration solution;

[0010] Step S4: economically evaluate the two types of long-term and short-term energy storage and regulation equipment, namely, pumped storage and electrochemical energy storage.

[0011] Preferably, in step S1, the current status of new energy installed capacity includes: the installed capacity and location of existing wind power, photovoltaic, and pumped storage resources in the region; future development trend information includes: the increase in wind power and photovoltaic installed capacity in the next three years, as well as the growth in electricity demand in the region and the market absorption capacity of new energy.

[0012] Preferably, in step S2, the objective function of the multivariate energy storage optimization configuration model is as follows:

[0013] Objective=C op +C plan (1);

[0014] C op =C inj +C ch1 +C ch2 (2);

[0015]

[0016] C plan =C1+C2×κ(6);

[0017] C1=c p ×P max +c e ×E max (7);

[0018] C2=c m ×P max (8);

[0019]

[0020] Wherein, Objective represents the objective function; i=1,2,...,24 represents the operation time, and 24 represents 24 hours; the combination of multiple energy storage involves two different types of energy storage, which are represented by energy storage 1 and 2 respectively; C op represents the operating cost; C plan represents the energy storage planning cost; C inj Represents the cost of purchasing electricity from the higher-level power grid in the administrative region; Cch represents the cost of multi-energy storage charging and discharging loss; C ch1 , C ch2 Respectively represent the charging cost of energy storage 1 and 2; P inj (i) represents the power injected into the administrative area by the upper power grid at the i-th moment; c ele (i) represents the price of electricity purchased by the administrative region from the higher-level power grid at the i-th moment; t i Indicates the time granularity; P ch1 (i) P ch2 (i) represents the charging power of energy storage 1 and 2 at the i-th moment; P dis1 (i) P dis2 (i) represents the discharge power of energy storage 1 and 2 at the i-th moment; c d1 、c d2 Respectively represent the unit charge and discharge loss cost of energy storage 1 and 2; C1 represents the initial investment cost of energy storage; C2 represents the operation and maintenance cost of energy storage; c e represents the unit capacity energy storage construction cost; c m Represents the unit power energy storage operation cost; P max Indicates the rated power of energy storage; E max represents the rated capacity of energy storage; κ represents the annual value coefficient; c p represents the construction cost of energy storage per unit power; r represents the discount rate; and n represents the number of years the energy storage is in operation.

[0021] Preferably, in step S2, the constraints of the multi-element energy storage optimization configuration model include: power balance constraints of administrative regions, power limit constraints of each output entity, and charging and discharging characteristic constraints of multi-element energy storage equipment;

[0022] The power limit constraints of the output entity include the upper grid injection power constraint, the total charge and discharge power constraint of the energy storage device, and the charge and discharge power constraint of the energy storage device;

[0023] The charging and discharging characteristic constraints of the multi-element energy storage device include the energy ratio constraint between the rated capacity and the rated power of the energy storage, the continuity constraint of the energy storage state of charge, the energy storage state of charge constraint, the energy storage state of charge initialization constraint, and the response speed constraint of the energy storage device.

[0024] Preferably, the administrative area power balance constraint is as follows:

[0025]

[0026] The time granularity of the power balance constraint in the administrative area is 15 minutes, and there are 96 timing points in 24 hours a day, that is, j = 1, 2, ..., 96; P w (j), P v (j) respectively represent the output of wind turbines and photovoltaic units in the administrative area at the jth moment; Pchk (j), P disk (j) respectively represent the charging and discharging power of the k-th energy storage device at the j-th moment; P D (j) represents the power load of the administrative area at the jth moment; Represents a collection of energy storage.

[0027] Preferably, the upper grid injection power constraint is as follows:

[0028] 0≤P inj ≤P injm (11);

[0029] The total charge and discharge capacity constraints of energy storage equipment are as follows

[0030]

[0031] The charging / discharging power constraints of energy storage equipment are as follows:

[0032] 0≤P disk (j)≤B disk (j)P maxk (13);

[0033] 0≤P chk (j)≤B chk (j)P maxk (14);

[0034] Among them, P inj Indicates the injected power of the upper power grid; E maxk represents the rated capacity of the kth energy storage device; P injm Indicates the maximum power injected by the upper power grid into the administrative area; P maxk represents the rated charge and discharge power of the kth energy storage device; B disk (j), B chk (j) represent 0-1 variables that limit the energy storage charging and discharging states, satisfying the following constraints:

[0035] B disk (j)+B chk (j)≤1(15).

[0036] Preferably, the energy ratio constraint between the rated capacity and the rated power of the energy storage is as follows:

[0037] E maxk =βP maxk (16);

[0038] The continuity constraints of energy storage charge state are as follows:

[0039]

[0040] The energy storage state of charge constraints are as follows:

[0041] SOC mink ≤SOC k (j)≤SOC maxk (18);

[0042] The energy storage state of charge initialization constraints are as follows:

[0043] SOC k (1) = SOC k (96)(19);

[0044] The response speed constraints of energy storage devices are as follows:

[0045] P disk (j) = P disk (j+1)=…=P disk (j+n) (20);

[0046] Among them, β is the energy multiplier between the rated capacity and rated power of energy storage; SOC k (j) represents the state of charge of the k-th energy storage device at the jth moment; SOC k (j+1) represents the charge state of the k-th energy storage device at the j+1th moment; η k represents the charge / discharge efficiency of the kth energy storage device; SOC maxk , SOC mink They represent the maximum and minimum charge states of the kth energy storage device respectively; n represents the duration of the response.

[0047] The present invention also provides a multi-element energy storage optimization configuration and economic evaluation system, comprising:

[0048] The data collection module is used to obtain the current status and future development trend information of new energy installed capacity in the administrative area. The current status of new energy installed capacity includes the installed capacity and location of wind power, photovoltaic power and pumped storage resources in the region. The future development trend information includes the increase in wind power and photovoltaic installed capacity in the next three years, the growth of electricity demand in the region and the market absorption capacity of new energy.

[0049] Model building module, used to build a multivariate energy storage optimization configuration model and establish corresponding objective functions and constraints;

[0050] The calculation and analysis module is used to solve the multi-element energy storage optimization configuration model and obtain the optimal multi-element energy storage configuration plan;

[0051] Economic evaluation module, used to compare and study the economic benefits of various multi-energy storage configuration schemes during the operation cycle;

[0052] The result output module is used to display the results of the calculation and analysis, including the optimal energy storage configuration plan and its economic benefit evaluation.

[0053] The present invention also provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned multi-element energy storage optimization configuration and economic evaluation method when executing the computer program.

[0054] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned multi-element energy storage optimization configuration and economic evaluation method are implemented.

[0055] Therefore, the present invention adopts the above-mentioned multi-element energy storage optimization configuration and economic evaluation method, system, electronic device and medium, and the beneficial technical effects are as follows:

[0056] (1) Optimizing the allocation of power system resources: By establishing a multi-element energy storage optimization allocation model and combining the current status of new energy installed capacity in the administrative region and future development forecasts, this method can help decision makers find the best energy storage configuration plan to achieve effective allocation of power resources. This optimal configuration not only takes into account the minimization of operating costs, but also takes into account economy and safety, thereby improving the operating efficiency of the entire power system.

[0057] (2) Improving the ability to absorb new energy: By utilizing different types of energy storage technologies such as electrochemical energy storage and pumped storage, this method can flexibly adjust electricity supply and demand on different time scales. Especially when the output of new energy is unstable, it can effectively alleviate the problem of power surplus and shortage, promote the full absorption of new energy, and reduce the occurrence of wind and solar power abandonment.

[0058] (3) Reduce overall operating costs: By carefully analyzing the comprehensive cost of energy storage equipment (including initial investment cost and operation and maintenance costs throughout the life cycle) and comparing it with the on-grid electricity price and operating costs of thermal power units, this method can identify the most economically efficient energy storage technology combination. Experiments have shown that reasonable planning of energy storage facilities can significantly reduce the total operating cost of the entire region and improve the economic efficiency of the power system.

[0059] (4) Enhance the flexibility and stability of the power system: Electrochemical energy storage is mainly responsible for daily peak-shaving and valley-filling tasks, while pumped storage focuses on power dispatching on a longer time scale. The combination of the two can effectively smooth the net load curve of the power system, reduce dependence on thermal power units, and make the power supply curve of thermal power more stable. This not only improves the flexibility of the power system, but also enhances its emergency response capabilities in the face of emergencies.

[0060] (5) Delaying the need to expand the grid: By deploying energy storage solutions suitable for short-term and long-term regulation, this method can effectively postpone the need to expand the capacity of thermal power units and grid infrastructure, reducing the cost of power system construction and transformation, while reducing the impact on the environment.

[0061] (6) Promote the development of energy storage technology: The economic evaluation method provided by the present invention provides a clear direction for the research and development and commercialization of energy storage technology, which helps to promote the healthy development of the energy storage industry. By quantitatively analyzing the economic benefits of various energy storage technologies, it can help related companies better understand market demand and accelerate the pace of technological innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is the annual output characteristics of the two types of energy storage power stations in the whole region;

[0063] Figure 2 It is the annual output characteristics of wind and solar power stations in the whole region;

[0064] Figure 3 It is a full-area, year-round load reduction feature;

[0065] Figure 4 It is the output characteristics of thermal power units in the whole area;

[0066] Figure 5 This is a comparison chart of the full-year net load characteristics of the entire region before and after optimization. DETAILED DESCRIPTION

[0067] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0068] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0069] Embodiment 1

[0070] The present invention provides a multi-element energy storage optimization configuration and economic evaluation method, comprising the following steps:

[0071] Step S1: Obtain information on the current status and future development trends of new energy installed capacity within the administrative area.

[0072] The current status of new energy installed capacity includes: the installed capacity and location of existing wind power, photovoltaic and pumped storage resources in the region; future development trend information includes: the increase in wind power and photovoltaic installed capacity in the next three years, as well as the growth in electricity demand in the region and the market absorption capacity of new energy.

[0073] Step S2: construct a multivariate energy storage optimization configuration model.

[0074] The objective function of the multivariate energy storage optimization configuration model is as follows:

[0075] Objective=C op +C plan (1);

[0076] C op =C inj +C ch1 +C ch2 (2);

[0077]

[0078] C plan =C1+C2×κ(6);

[0079] C1=c p ×P max +c e ×E max (7);

[0080] C2=c m ×P max (8);

[0081]

[0082] Among them, Objective represents the objective function; i=1,2,...,24 represents the time of operation, and 24 represents 24 hours; the combination of multiple energy storage involves two different types of energy storage, represented by energy storage 1 and 2 respectively; for example, 1 is a combination of power-type energy storage and 2 is a combination of backup-type energy storage, which can smooth the output of renewable energy, regulate the system frequency, provide reactive power support, etc. 1 is a combination of capacity-type energy storage and 2 is a combination of backup-type energy storage, which can improve the transient stability of the system, track the power generation plan, and alleviate equipment congestion, etc. 1 is an energy-type energy storage and 2 is a combination of power-type energy storage, which can operate microgrids, improve power quality, participate in grid auxiliary services, and backup power scenarios. C op represents the operating cost; C plan represents the energy storage planning cost; C inj Represents the cost of purchasing electricity from the higher-level power grid in the administrative region; C ch represents the cost of multi-energy storage charging and discharging loss; C ch1 , C ch2 Respectively represent the charging cost of energy storage 1 and 2; P inj (i) represents the power injected into the administrative area by the upper power grid at the i-th moment; c ele (i) represents the price of electricity purchased by the administrative region from the higher-level power grid at the i-th moment; t i Indicates the time granularity; P ch1 (i) P ch2 (i) represents the charging power of energy storage 1 and 2 at the i-th moment; P dis1 (i) Pdis2 (i) represents the discharge power of energy storage 1 and 2 at the i-th moment; c d1 、c d2 Respectively represent the unit charge and discharge loss cost of energy storage 1 and 2; C1 represents the initial investment cost of energy storage; C2 represents the operation and maintenance cost of energy storage; c e represents the unit capacity energy storage construction cost; c m Represents the unit power energy storage operation cost; P max Indicates the rated power of energy storage; E max represents the rated capacity of energy storage; κ represents the annual value coefficient; c p represents the construction cost of energy storage per unit power; r represents the discount rate; and n represents the number of years the energy storage is in operation.

[0083] The constraints of the multi-element energy storage optimization configuration model include: power balance constraints in administrative regions, power limit constraints of each output entity, and charging and discharging characteristics constraints of multi-element energy storage equipment;

[0084] The power limit constraints of the output entity include the upper grid injection power constraint, the total charge and discharge power constraint of the energy storage device, and the charge and discharge power constraint of the energy storage device;

[0085] The charging and discharging characteristic constraints of the multi-element energy storage device include the energy ratio constraint between the rated capacity and the rated power of the energy storage, the continuity constraint of the energy storage state of charge, the energy storage state of charge constraint, the energy storage state of charge initialization constraint, and the response speed constraint of the energy storage device.

[0086] The power balance constraints of the administrative area are as follows:

[0087]

[0088] The time granularity of the power balance constraint in the administrative area is 15 minutes, and there are 96 timing points in 24 hours a day, that is, j = 1, 2, ..., 96; P w (j), P v (j) respectively represent the output of wind turbines and photovoltaic units in the administrative area at the jth moment; P chk (j), P disk (j) respectively represent the charging and discharging power of the k-th energy storage device at the j-th moment; P D (j) represents the power load of the administrative area at the jth moment; Represents a storage collection.

[0089] The upper grid injection power constraints are as follows:

[0090] 0≤P inj ≤P injm (11);

[0091] The total charge and discharge capacity constraints of energy storage equipment are as follows

[0092]

[0093] The charging / discharging power constraints of energy storage equipment are as follows:

[0094] 0≤P disk (j)≤B disk (j)P maxk (13);

[0095] 0≤P chk (j)≤B chk (j)P maxk (14);

[0096] Among them, P inj Indicates the injected power of the upper power grid; E maxk represents the rated capacity of the kth energy storage device; P injm Indicates the maximum power injected by the upper power grid into the administrative area; P maxk represents the rated charge and discharge power of the kth energy storage device; B disk (j), B chk (j) represent 0-1 variables that limit the energy storage charging and discharging states, satisfying the following constraints:

[0097] B disk (j)+B chk (j)≤1(15);

[0098] The energy ratio constraint between the rated capacity and rated power of energy storage is as follows:

[0099] E maxk =βP maxk (16);

[0100] The continuity constraints of energy storage charge state are as follows:

[0101]

[0102] The energy storage state of charge constraints are as follows:

[0103] SOC mink ≤SOC k (j)≤SOC maxk (18);

[0104] The energy storage state of charge initialization constraints are as follows:

[0105] SOC k (1) = SOC k (96)(19);

[0106] The response speed constraints of energy storage devices are as follows:

[0107] P disk (j) = P disk (j+1)=…=P disk (j+n) (20);

[0108] Among them, β is the energy multiplier between the rated capacity and rated power of energy storage; SOC k (j) represents the state of charge of the k-th energy storage device at the jth moment; SOC k (j+1) represents the charge state of the k-th energy storage device at the j+1th moment; η k represents the charge / discharge efficiency of the kth energy storage device; SOC maxk , SOC mink They represent the maximum and minimum charge states of the kth energy storage device respectively; n represents the duration of the response.

[0109] Step S3: Solve the multi-element energy storage optimization configuration model to obtain the optimal multi-element energy storage configuration solution.

[0110] Step S4: economically evaluate the two types of long-term and short-term energy storage and regulation equipment, namely, pumped storage and electrochemical energy storage.

[0111] Equipment selection: Based on the multi-energy storage optimization configuration model, compare and study the economic benefits of various multi-energy storage configuration schemes during the operation cycle.

[0112] In this model, two types of long- and short-term energy storage and regulation equipment are mainly considered, namely pumped storage and electrochemical energy storage.

[0113] Pumped storage power stations are water-based batteries with the advantages of rapid start and stop, flexible and reliable operation, and rapid response to load changes. It can give full play to the functions of peak load regulation, frequency regulation, phase regulation and emergency standby according to the specific conditions of the power system in which it is located, and make full use of the water volume of the reservoir and the pumping / power generation output to meet the needs of safe and economical operation of the power system. In the power generation process, pumped storage is equivalent to a conventional hydropower station, and the pumping process is equivalent to a large power user. Of course, pumped storage power stations also have efficiency losses. The comprehensive efficiency of pumped storage (the ratio of power generation to pumped power) is generally about 75%, and pumped storage power stations with superior conditions can reach more than 80%. Even so, it is cost-effective, because its rapid and flexible peak load regulation function avoids the high coal consumption operation and equipment loss of thermal power units, and ensures the consumption of new energy.

[0114] Electrochemical energy storage technology mainly refers to the application of chemical battery energy storage systems, which realize charging and discharging through oxidation reactions between the positive and negative electrodes of chemical batteries, and ultimately realize the conversion and storage between chemical energy and electrical energy. Chemical energy storage systems can achieve rapid power throughput processing and are also one of the energy storage technologies with relatively mature technical levels. The specific comparison of various types of chemical energy storage is shown in Table 1 below.

[0115] Table 1 Performance comparison of different chemical energy storage technologies

[0116]

[0117] When choosing energy storage technology, the application scenario of energy storage should be considered first. Different application scenarios have different emphases on energy storage requirements, so it is necessary to comprehensively consider the purpose of demand and technology type. Ensuring the safety and reliability of the energy storage system is the top priority. In addition, it is necessary to consider the various characteristics of energy storage technology, including but not limited to initial investment, maintenance cost, energy conversion efficiency, self-discharge rate, cycle life, technical complexity, response speed, and adaptability to site and environment. Especially for chemical energy storage, ambient temperature is an important factor. For example, under extreme low temperature conditions (such as -40°C), cadmium nickel (Cd / Ni) batteries and supercapacitors exhibit the best performance; under high temperature conditions (such as 60°C), zinc nickel (Zn / Ni) batteries, metal hydride nickel (MH / Ni) batteries and supercapacitors are more suitable.

[0118] In addition, economic benefits are an indispensable consideration when selecting energy storage technology. The economic benefits of energy storage are mainly reflected in the following aspects: unit investment and construction cost, operation and maintenance cost, cycle life, energy conversion efficiency and self-discharge rate. Unit cost can be further subdivided into capacity cost and power cost. Through the comprehensive comparison of these indicators, various energy storage technologies can be comprehensively evaluated. The data obtained from the evaluation of major chemical energy storage is shown in Table 2 below.

[0119] Table 2 Economic benefits of different chemical energy storage technologies

[0120]

[0121] By comprehensively comparing the performance and economic benefits of energy storage technologies, we can select the energy storage technology that best meets specific needs. Through a comprehensive analysis of these indicators, we can ensure that the selected energy storage technology is not only economically competitive, but also meets the performance requirements of specific application scenarios, thereby achieving the optimal energy storage configuration solution.

[0122] The present invention will be further described below through specific examples.

[0123] 1. First, analyze the two types of equipment, pumped storage and electrochemical energy storage, from a cost comparison perspective.

[0124] 1. The cost of a pumped-storage power station is mainly composed of construction engineering costs, electromechanical equipment engineering and installation costs, construction auxiliary fees, environmental protection fees, construction land acquisition and resettlement compensation fees, etc. According to the "China Renewable Energy Engineering Cost Management Report 2023" issued by the China Hydropower Engineering Institute and the Pumped Storage Industry Branch of the China Hydropower Engineering Society, the installation cost of a pumped-storage power station is approximately between 0.58 and 0.70 yuan / Wh. For cost estimation, we take the middle value of 0.65 yuan / Wh, which means that the installation cost per megawatt-hour (MWh) is approximately 650,000 yuan.

[0125] 2. The installation cost of the electrochemical energy storage system can be divided into several key components:

[0126] The battery pack (cell) accounts for about 67% of the total cost, with a cost of about 0.62 yuan / Wh;

[0127] The energy storage inverter (PCS) accounts for about 10% of the total cost, with a cost of about 0.28 yuan / Wh;

[0128] The battery management system (BMS) accounts for about 9% of the total cost, with a cost of about 0.15 yuan / Wh;

[0129] The energy management system (EMS) accounts for about 2% of the total cost, with a cost of about 0.18 yuan / Wh;

[0130] Other electrical equipment, such as boosters, cabinet cables, etc., account for about 3% of the total cost, with a cost of about 0.1 yuan / Wh.

[0131] Adding up these costs, the installation cost of the electrochemical energy storage system is approximately 1.33 yuan / Wh, equivalent to 1.33 million yuan per megawatt-hour.

[0132] 2. Combining the comprehensive comparison of the operating costs, charging and discharging efficiency, and maximum operating time of pumped storage and electrochemical energy storage equipment, the on-grid electricity price of thermal power units is about 0.3 yuan / kWh, that is, the power generation cost is 30,000 yuan per megawatt-hour; while the cost per kilowatt-hour of a pumped-storage power station is about 0.213 yuan / kWh, which translates into an operation and maintenance cost of about 20,000 yuan per megawatt-hour. The cost per kilowatt-hour of electrochemical energy storage is set at 30,000 yuan / MWh, and its charging and discharging efficiency is 95%. The maximum discharge time of electrochemical energy storage is set at 2 hours, while that of a pumped-storage power station can reach 7 hours. In addition, the water-to-electricity conversion coefficient of a pumped-storage power station is 561.75m 3 / MWh, 748.5m when charging 3 / MWh, and the power balance ratio is 50%. As for thermal power units, their up and down ramp capabilities are limited to 80% of their maximum power generation.

[0133] 3. According to calculations, when the optimization goal is to minimize the sum of the total investment cost of electrochemical energy storage and pumped storage power stations and the overall operating cost, the results of the long-term and short-term energy storage planning configuration in the region are shown in Table 3.

[0134] Table 3. Operation costs of long-term and short-term energy storage planning for the entire region

[0135]

[0136] Combined with the safe, stable and economic power supply needs of the entire region within ten years, a total of 1270.21MW of pumped storage power stations and a total of 1375.81MW of electrochemical energy storage power stations will be added on the original basis. In order to further analyze the specific planning schemes for long-term and short-term energy storage, the planning and layout results of the two types of energy storage at each node are summarized in Table 4. It can be seen that the planning and expansion plans for pumped storage power stations have been formed at both pumped storage nodes, and for electrochemical energy storage, although the existing 7 electrochemical energy storage nodes can add energy storage capacity, after considering the topological constraints of the entire network and the safety and economy of operation, the planning and expansion plans for electrochemical energy storage have only been formed at nodes 1 and 19.

[0137] Table 4 Results of the overall long- and short-term energy storage planning and layout

[0138] Node location Energy storage type Planned power (MW) Planned capacity (MWh) 4 Pumped Storage 649.47 1498.66 8 Pumped Storage 620.74 4345.18 1 Electrochemical Energy Storage 697.51 1395.02 19 Electrochemical Energy Storage 678.29 1356.59

[0139] 4. Comprehensive Analysis

[0140] By analyzing the deployment of various power generation units and flexible adjustment resources in the whole region, and drawing charts of electrochemical energy storage power stations, pumped storage power stations, wind and photovoltaic power generation units, load reduction measures in the whole region, and the output characteristics of thermal power units throughout the year (such as Figures 1 to 4 ), it can be observed that, with the joint action of electrochemical energy storage and pumped storage, the whole region has achieved 100% local consumption of new energy electricity. Specifically, electrochemical energy storage is mainly responsible for daily peak-shaving tasks, while pumped storage focuses on monthly power dispatching.

[0141] The changes in the global net load characteristics before and after optimization were further analyzed. Figure 5 The comparison of the net load characteristics of the whole year before and after optimization is shown in Figure 1. The results show that by rationally planning and investing in two types of energy storage facilities, not only the problem of excess new energy is solved, but also, after combining the flexibility adjustment function of energy storage facilities and appropriate load reduction measures, the dependence of the whole region on thermal power units throughout the year is significantly reduced, and the power supply curve of thermal power becomes smoother. This shows that by deploying energy storage solutions suitable for short-term and long-term regulation, the net load curve of the whole year can be effectively smoothed, and the need to expand the capacity of thermal power units and power grids can be postponed.

[0142] In order to demonstrate the importance of planning long- and short-term energy storage facilities, the operation without new energy storage facilities was calculated, see Table 5. The data shows that if new long- and short-term energy storage facilities are not built, the operating costs of the entire region will rise sharply in the next ten years, exceeding the overall planning costs including energy storage facilities. This is because the lack of sufficient energy storage capacity will lead to larger-scale load reductions, thus affecting the safe and stable operation of the system. Therefore, formulating a reasonable long- and short-term energy storage optimization configuration strategy effectively improves the economic balance between power supply and demand in the entire region, and this method has great economic applicability.

[0143] Table 5 Operation costs of long-term and short-term energy storage planning for the entire region

[0144]

[0145] Embodiment 2

[0146] A multi-element energy storage optimization configuration and economic evaluation system, comprising:

[0147] The data collection module is used to obtain the current status and future development trend information of new energy installed capacity in the administrative area. The current status of new energy installed capacity includes the installed capacity and location of wind power, photovoltaic power and pumped storage resources in the region. The future development trend information includes the increase in wind power and photovoltaic installed capacity in the next three years, the growth of electricity demand in the region and the market absorption capacity of new energy.

[0148] Model building module, used to build a multivariate energy storage optimization configuration model and establish corresponding objective functions and constraints;

[0149] The calculation and analysis module is used to solve the multi-element energy storage optimization configuration model and obtain the optimal multi-element energy storage configuration plan;

[0150] The economic evaluation module is used to compare and study the economic benefits of various diversified energy storage configuration schemes during the operation cycle; various types of energy storage include capacity energy storage, power energy storage, energy energy storage, and backup energy storage.

[0151] The result output module is used to display the results of the calculation and analysis, including the optimal energy storage configuration plan and its economic benefit evaluation.

[0152] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0154] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0155] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0156] Therefore, the present invention adopts the above-mentioned multi-energy storage optimization configuration and economic evaluation method, system, electronic equipment and medium, and rationally plans and invests in these two types of energy storage facilities, which not only helps to solve the problem of excess new energy, but also significantly reduces dependence on thermal power units and smoothes the net load curve throughout the year, thereby improving the stability and economy of the power system.

[0157] 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 multi-element energy storage optimization configuration and economic evaluation method, characterized in that: The following steps are involved: Step S1, obtaining the current status and future development trend information of new energy installed capacity in the administrative area; Step S2, constructing a multivariate energy storage optimization configuration model; Step S3, solving the multi-element energy storage optimization configuration model to obtain the optimal multi-element energy storage configuration solution; Step S4: economically evaluate the two types of long-term and short-term energy storage and regulation equipment, namely, pumped storage and electrochemical energy storage.

2. A multi-element energy storage optimization configuration and economic evaluation method according to claim 1, characterized in that: In step S1, the current status of new energy installed capacity includes: the installed capacity and location of existing wind power, photovoltaic, and pumped storage resources in the region; future development trend information includes: the increase in wind power and photovoltaic installed capacity in the next three years, the growth in electricity demand in the region, and the market absorption capacity of new energy.

3. A multi-element energy storage optimization configuration and economic evaluation method according to claim 2, characterized in that: In step S2, the objective function of the multivariate energy storage optimization configuration model is as follows: Objective=C op +C plan (1); C op =C inj +C ch1 +C ch2 (2); C plan =C1+C2×κ(6); C1=c p ×P max +c e ×E max (7); C2=c m ×P max (8); Wherein, Objective represents the objective function; i=1,2,...,24 represents the operation time, and 24 represents 24 hours; the combination of multiple energy storage involves two different types of energy storage, which are represented by energy storage 1 and 2 respectively; C op Indicates the operating cost; C plan represents the energy storage planning cost; C inj Represents the cost of purchasing electricity from the higher-level power grid in the administrative region; C ch Represents the multi-energy storage charging and discharging loss cost; C ch1 , C ch2 Respectively represent the charging cost of energy storage 1 and 2; P inj (i) represents the power injected into the administrative area by the upper power grid at the i-th moment; c ele (i) represents the price of electricity purchased by the administrative region from the higher-level power grid at the i-th moment; t i Indicates the time granularity; P ch1 (i) P ch2 (i) represents the charging power of energy storage 1 and 2 at the i-th moment; P dis1 (i) P dis2 (i) represents the discharge power of energy storage 1 and 2 at the i-th moment; c d1 、c d2 Respectively represent the unit charge and discharge loss cost of energy storage 1 and 2; C1 represents the initial investment cost of energy storage; C2 represents the operation and maintenance cost of energy storage; c e represents the unit capacity energy storage construction cost; c m Represents the unit power energy storage operation cost; P max Indicates the rated power of energy storage; E max represents the rated capacity of energy storage; κ represents the annual value coefficient; c p represents the construction cost of energy storage per unit power; r represents the discount rate; and n represents the number of years the energy storage is in operation.

4. A multi-element energy storage optimization configuration and economic evaluation method according to claim 3, characterized in that: In step S2, the constraints of the multi-element energy storage optimization configuration model include: power balance constraints of administrative regions, power limit constraints of each output entity, and charging and discharging characteristic constraints of multi-element energy storage equipment; The power limit constraints of the output entity include the upper grid injection power constraint, the total charge and discharge power constraint of the energy storage device, and the charge and discharge power constraint of the energy storage device; The charging and discharging characteristic constraints of the multi-element energy storage device include the energy ratio constraint between the rated capacity and the rated power of the energy storage, the continuity constraint of the energy storage state of charge, the energy storage state of charge constraint, the energy storage state of charge initialization constraint, and the response speed constraint of the energy storage device.

5. A multi-element energy storage optimization configuration and economic evaluation method according to claim 4, characterized in that: The power balance constraints of the administrative area are as follows: The time granularity of the power balance constraint in the administrative area is 15 minutes, and there are 96 timing points in 24 hours a day, that is, j = 1, 2, ..., 96; P w (j), P v (j) respectively represent the output of wind turbines and photovoltaic units in the administrative area at the jth moment; P chk (j), P disk (j) respectively represent the charging and discharging power of the k-th energy storage device at the j-th moment; P D (j) represents the power load of the administrative area at the jth moment; Represents a collection of energy storage.

6. A multi-element energy storage optimization configuration and economic evaluation method according to claim 5, characterized in that: The upper grid injection power constraints are as follows: 0≤P inj ≤P injm (11); The total charge and discharge capacity constraints of energy storage equipment are as follows The charging / discharging power constraints of energy storage equipment are as follows: 0≤P disk (j)≤B disk (j)P maxk (13); 0≤P chk (j)≤B chk (j)P maxk (14); Among them, P inj Indicates the injected power of the upper power grid; E maxk represents the rated capacity of the kth energy storage device; P injm Indicates the maximum power injected by the upper power grid into the administrative area; P maxk represents the rated charge and discharge power of the kth energy storage device; B disk (j), B chk (j) represent 0-1 variables that limit the energy storage charging and discharging states, satisfying the following constraints: B disk (j)+B chk (j)≤1(15)。 7. A multi-element energy storage optimization configuration and economic evaluation method according to claim 6, characterized in that: The energy ratio constraint between the rated capacity and rated power of energy storage is as follows: AND maxk =βP maxk (16); The continuity constraints of energy storage charge state are as follows: The energy storage state of charge constraints are as follows: SOC mink ≤SOC k (j)≤SOC maxk (18); The energy storage state of charge initialization constraints are as follows: SOCIETY k (1)=SOC k (96)(19); The response speed constraints of energy storage devices are as follows: P disk (j)=P disk (j+1)=…=P disk (j+n) (20); Among them, β is the energy multiplier between the rated capacity and rated power of energy storage; SOC k (j) represents the state of charge of the k-th energy storage device at the jth moment; SOC k (j+1) represents the charge state of the k-th energy storage device at the j+1th moment; η k represents the charge / discharge efficiency of the kth energy storage device; SOC maxk , SOC mink They respectively represent the maximum and minimum charge states of the kth energy storage device; n represents the duration of the response.

8. A multi-element energy storage optimization configuration and economic evaluation system, characterized in that: include: The data collection module is used to obtain the current status and future development trend information of new energy installed capacity in the administrative area. The current status of new energy installed capacity includes the installed capacity and location of wind power, photovoltaic power and pumped storage resources in the region. The future development trend information includes the increase in wind power and photovoltaic installed capacity in the next three years, the growth of electricity demand in the region and the market absorption capacity of new energy. Model building module, used to build a multivariate energy storage optimization configuration model and establish corresponding objective functions and constraints; The calculation and analysis module is used to solve the multi-element energy storage optimization configuration model and obtain the optimal multi-element energy storage configuration plan; Economic evaluation module, used to compare and study the economic benefits of various multi-energy storage configuration schemes during the operation cycle; The result output module is used to display the results of the calculation and analysis, including the optimal energy storage configuration plan and its economic benefit evaluation.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the multi-element energy storage optimization configuration and economic evaluation method described in claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-element energy storage optimization configuration and economic evaluation method described in claims 1-7 are implemented.

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