General modeling and comprehensive evaluation method for representing economic characteristics of multi-element energy storage technology

Abstract

The invention discloses a universal modeling and comprehensive evaluation method for representing economic characteristics of a multi-element energy storage technology, relates to the technical field of economic modeling of a new energy and electric power system, and aims to solve the problem that different energy storage technology economic models are split and are difficult to uniformly compare and optimize in the existing research. According to the method, electrochemical energy storage, pumped storage, compressed air energy storage and gravity energy storage are taken as representatives, a unified framework of an energy storage technical characteristic model and an economic characteristic model is established by summarizing and extracting common technical characteristics and economic indexes, and transverse comparability among different energy storage types is realized by constructing and calculating leveling cost-per-kilowatt-hour cost indexes. On the basis of a universal model, a multi-scene fitness comprehensive evaluation index is further provided, the problems that an existing energy storage model is split and the evaluation dimension is single are solved, technical and economic characterization and multi-scene comprehensive optimization of the multi-element energy storage technology under a unified framework are achieved, and a scientific basis is provided for power system planning and investment decision making.

Description

Technical Field

[0001] This invention relates to the field of economic modeling technology for new energy and power systems, and in particular to a generalized modeling and comprehensive evaluation method for characterizing the economic features of multiple energy storage technologies. Background Technology

[0002] With the large-scale integration of renewable energy sources such as wind and solar power into the power grid, energy storage plays a crucial role in peak shaving, frequency regulation, emergency backup, and energy balance. Different types of energy storage (such as electrochemical storage, pumped hydro storage, compressed air storage, and gravity storage) vary significantly in terms of construction costs, lifespan, efficiency, and operating modes. This has led to most existing studies employing economic models tailored to a single energy storage type, making unified comparisons and optimization decisions difficult. This fragmented modeling approach hinders the scientific allocation of diverse energy storage resources in power grid planning and investment.

[0003] Therefore, there is an urgent need for a generalized modeling method that can be compatible with different energy storage technologies and comprehensively characterize their economic features. To this end, we have designed a generalized modeling and comprehensive evaluation method to characterize the economic features of multiple energy storage technologies and address the above issues. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, which mostly employ economic models for single energy storage types, making it difficult to conduct unified comparisons and optimization decisions. This hinders the scientific allocation of diverse energy storage resources in power grid planning and investment. The invention proposes a generalized modeling and comprehensive evaluation method that characterizes the economic features of diverse energy storage technologies. This method is compatible with different energy storage technologies, comprehensively characterizes their economic features, and overcomes the shortcomings of traditional methods, such as fragmented models and lack of comparability.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A generalized modeling and comprehensive evaluation method for characterizing the economic features of multiple energy storage technologies includes the following:

[0007] Construct a characteristic model of energy storage technology, define the charging power, discharging power and upper and lower limit constraints of energy storage, set charging and discharging state variables, and establish dynamic constraint relationships of energy storage capacity;

[0008] An economic characteristic model for energy storage is constructed, and the economic performance is characterized by the whole life cycle cost, which consists of the initial investment cost, operation and maintenance cost, electricity purchase cost, financial expenses, correction cost and power station residual value.

[0009] Based on the levelized cost of electricity (LCOE) calculated using the full life-cycle cost, a multi-dimensional energy storage adaptability comprehensive evaluation index applicable to multiple scenarios is proposed to quantitatively assess the comprehensive adaptability of energy storage technology to specific application scenarios.

[0010] More preferably, the expression for constructing the energy storage technology characteristic model is as follows:

[0011]

[0012] In the formula, P d For energy storage discharge power, and These represent the upper and lower limits of the energy storage discharge power, respectively; P c For energy storage charging power, and These represent the upper and lower limits of the energy storage charging power; S c and S d These represent the charging and discharging states of energy storage, respectively; η c and η dis These represent the charging and discharging efficiencies of the energy storage, respectively; Δt is the duration of charging or discharging the energy storage, and E... t Let E be the stored energy at time t. t-1 Let E be the stored energy at time t-1. s and E min These represent the upper and lower limits of the energy storage capacity.

[0013] More preferably, the dynamic constraint relationship for establishing energy storage capacity satisfies the following equation:

[0014]

[0015] In the formula, E t+1 Let P be the energy stored at time t+1. c,t Let P be the charging power at time t. d,t Let t be the discharge power at time t.

[0016] A further preferred embodiment of the energy storage economic characteristic model, characterized by its economic efficiency based on life-cycle cost, is expressed as follows:

[0017] LCC = C inv +C op +C elec +C fin +C deg -C res

[0018] In the formula, LCC represents the total life cycle cost, and C inv For the initial investment cost, C op For operation and maintenance costs, C elec For electricity purchase costs, C fin For financial expenses, C deg To correct costs, C res This represents the residual value of the power plant.

[0019] A further preferred embodiment of the calculation expression for the levelized cost of electricity (LCOE) is as follows:

[0020]

[0021] In the formula, LCOE represents the levelized cost of electricity, and E gen,n Let η represent the energy storage capacity in year n, where N represents the total number of years of energy storage and power generation. c and η dis These represent the charging and discharging efficiencies of energy storage, respectively.

[0022] More preferably, the calculation expression for the comprehensive evaluation index of multi-element energy storage adaptability is as follows:

[0023] CSAI=ω T ×T score +ω E ×E score +ω G ×G score

[0024] Among them, CSAI is the comprehensive fitness index, T score E is the score for technological adaptability. score To score economic feasibility, G score To score for green and environmentally friendly features, ω T ω E and ω G These are the weight coefficients for each dimension, and they satisfy ω T +ω E +ω G =1.

[0025] Further preferably, the technology adaptability score T score The calculation expression is:

[0026]

[0027] In the formula, T response Let τ be the response time of the energy storage system, ACC be the time constant required by the scenario, and T be the regulation accuracy of the energy storage system. duration T represents the rated power duration of the energy storage system. required N represents the required duration for the scenario; CycleLife represents the cycle life of the energy storage system. required The total number of iterations is required by the scenario, and ω1, ω2, ω3, and ω4 are the weight coefficients within the technical dimension, and ω1+ω2+ω3+ω4=1;

[0028] The economic feasibility score E score The calculation expression is:

[0029]

[0030] In the formula, LCOE represents the levelized cost of electricity (LCOE), PBP is the payback period, and Y... required The baseline payback period is the requirement for the scenario; VD represents value diversity, which is the ratio of the number of market service types that the energy storage technology can participate in to the total number of market service types; u1, u2, u3 are the weighting coefficients in the economic dimension, and u1+u2+u3=1;

[0031] The green environmental score G score The calculation expression is:

[0032] G score =v1(η round-trip ) k +v2(-CF)+v3MS

[0033] In the formula, η round-trip denoted as the average round-trip efficiency over the entire life cycle of the energy storage system, where k is an amplification factor greater than 1; CF represents the carbon footprint over the entire life cycle of the energy storage system; MS is the material sustainability score, where v1, v2, and v3 are weighting coefficients within the green dimension, and v1+v2+v3=1.

[0034] More preferably, the specific application scenarios include power grid frequency regulation, peak shaving and valley filling, renewable energy smoothing, backup power supply and power supply to remote areas.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a generalized model of the economic characteristics of multiple energy storage technologies, this invention achieves quantitative and horizontal comparison of the economics of different energy storage types within a unified framework. Furthermore, by introducing a comprehensive evaluation index for multi-scenario adaptability, it overcomes the limitations of single-scenarios economic comparisons and the shortcomings of traditional methods such as fragmented models and lack of comparability. It expands the evaluation dimensions to include technical performance and environmental friendliness, and enables dynamic adjustment of evaluation standards according to application scenarios. This method not only comprehensively reflects the investment, operation and maintenance, charging, financial, and correction costs throughout the entire lifecycle of energy storage, but also scientifically guides the selection of optimal energy storage technologies for different application scenarios through a comprehensive evaluation index for multi-scenarios energy storage adaptability. This provides a more comprehensive and accurate scientific basis for power system planning, operation optimization, and investment decisions, thereby improving the rationality and economy of energy storage configuration. Attached Figure Description

[0036] Figure 1 This is a flowchart of a generalized modeling and comprehensive evaluation method for characterizing the economic features of multi-element energy storage technologies in this invention.

[0037] Figure 2 This is a modeling framework diagram of the generalized energy storage model in this embodiment of the invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the specific embodiments of this invention will be described in detail below with reference to the accompanying drawings. These embodiments are merely preferred examples of this invention, used to aid in understanding the inventive concept, and do not constitute a limitation on the scope of protection.

[0039] This embodiment proposes a generalized modeling and comprehensive evaluation method to characterize the economic features of multi-element energy storage technologies, such as... Figure 1 and Figure 2 As shown, the method includes the following:

[0040] Construct a characteristic model of energy storage technology, define the charging power, discharging power and upper and lower limit constraints of energy storage, set charging and discharging state variables, and establish dynamic constraint relationships of energy storage capacity;

[0041] The expression for constructing the characteristic model of energy storage technology is as follows:

[0042]

[0043] In the formula, P d For energy storage discharge power, and These represent the upper and lower limits of the energy storage discharge power, respectively; P c For energy storage charging power, and These represent the upper and lower limits of the energy storage charging power; S c and S d Let and represent the charging and discharging states of energy storage, respectively. No energy storage system can operate in both charging and discharging states simultaneously; therefore, the sum of the charging and discharging states must be less than 1. η c and η dis These represent the charging and discharging efficiencies of the energy storage, respectively; Δt is the duration of charging or discharging the energy storage, and E... t Let E be the stored energy at time t. t-1 Let E be the stored energy at time t-1. s and E min These represent the upper and lower limits of the energy storage capacity.

[0044] The dynamic constraint relationship for energy storage capacity is established as follows:

[0045]

[0046] In the formula, E t+1 Let P be the energy stored at time t+1. c,t Let P be the charging power at time t. d,t Let t be the discharge power at time t.

[0047] An economic characteristic model for energy storage is constructed, and the economic performance is characterized by the whole life cycle cost, which consists of the initial investment cost, operation and maintenance cost, electricity purchase cost, financial expenses, correction costs and the residual value of the power station.

[0048] The economic characteristic model of energy storage is constructed, and the expression for economic characterization using the whole life cycle cost is as follows:

[0049] LCC = C inv +C op +C elec +C fin +C deg -C res

[0050] In the formula, LCC represents the total life cycle cost, and C inv For the initial investment cost, C op For operation and maintenance costs, C elec For electricity purchase costs, C fin For financial expenses, C deg To correct costs, C res This represents the residual value of the power plant.

[0051] Based on the full life cycle cost calculation, the levelized cost of electricity (LCOE) is used to make a horizontal comparison of the economics of different energy storage types. The generalized economic indicators of energy storage are output, including rated power, energy storage capacity, charge and discharge efficiency, life cycle, and LCOE. Under a unified framework, the key indicators of different energy storage types are obtained, and horizontal comparison and optimized configuration are achieved based on the LCOE.

[0052] The formula for calculating the levelized cost of electricity (LCOE) is as follows:

[0053]

[0054] In the formula, LCOE represents the levelized cost of electricity, and E gen,n The energy storage capacity in year n can be determined by E. t The formula for calculating energy storage capacity is obtained, where N represents the total number of years of energy storage power generation, and η... c and η dis These represent the charging and discharging efficiencies of energy storage, respectively.

[0055] Based on the above, a multi-dimensional energy storage adaptability comprehensive evaluation index applicable to multiple scenarios is further proposed to quantitatively evaluate the comprehensive adaptability of energy storage technology to specific application scenarios, including grid frequency regulation, peak shaving and valley filling, renewable energy smoothing, backup power supply and power supply in remote areas.

[0056] The formula for calculating the comprehensive evaluation index of multi-element energy storage adaptability is as follows:

[0057] CSAI=ωT ×T score +ω E ×E score +ω G ×G score

[0058] Among them, CSAI is the comprehensive fitness index, T score E is the score for technological adaptability. score To score economic feasibility, G score To score for green and environmentally friendly features, ω T ,ω E ,ω G These are the weight coefficients for each dimension, and they satisfy ω T +ω E +ω G =1, configured according to the technical, economic and environmental requirements of the target application scenario.

[0059] Among them, the technology adaptability score T score The calculation expression is:

[0060]

[0061] In the formula, T response τ is the response time of the energy storage system, ACC is the time constant required by the scenario, and T is the regulation accuracy of the energy storage system, which is a value between 0 and 1. duration T represents the rated power duration of the energy storage system. required N represents the required duration for the scenario; CycleLife represents the cycle life of the energy storage system. required The total number of iterations is required for the scenario. ω1, ω2, ω3, and ω4 are the weight coefficients within the technical dimension, and ω1 + ω2 + ω3 + ω4 = 1. Generally, ω1 = ω2 = ω3 = ω4 = 0.25 is taken.

[0062] Economic feasibility score E score The calculation expression is:

[0063]

[0064] In the formula, LCOE is the levelized cost of electricity, PBP is the payback period, and Y is the investment recovery period. required The baseline payback period is the requirement for the scenario; VD represents the value diversity, which is the ratio of the number of market service types that the energy storage technology can participate in to the total number of market service types; u1, u2, and u3 are the weighting coefficients in the economic dimension, and u1 + u2 + u3 = 1; generally, u1 = u2 = u3 = 1 / 3 is taken.

[0065] Green environmental protection score: G score The calculation expression is:

[0066] G score =v1(η round-trip ) k +v2(-CF)+v3MS

[0067] In the formula, η round-trip , where k is the average round-trip efficiency over the entire life cycle of the energy storage system, and k is an amplification factor greater than 1; CF is the carbon footprint over the entire life cycle of the energy storage system; MS is the material sustainability score, a standardized score (between 0 and 1) obtained by expert scoring or life cycle database based on material scarcity, recyclability, and environmental impact; v1, v2, and v3 are weighting coefficients within the green dimension, and v1 + v2 + v3 = 1; generally, v1 = v2 = v3 = 1 / 3 is approximated.

[0068] Based on the above theoretical foundation, this embodiment further introduces the application of a generalized modeling method for characterizing the economic characteristics of multi-energy storage technologies in different energy storage scenarios.

[0069] (1) Electrochemical energy storage:

[0070] Electrochemical energy storage is a technology that stores and releases electrical energy through reversible chemical reactions, primarily relying on battery systems. During charging, electrical energy induces a chemical reaction in active materials, converting electrical energy into chemical energy for storage. During discharging, the chemical energy is converted back into electrical energy and supplied to an external load. Common electrochemical energy storage technologies include lithium-ion batteries, sodium-sulfur batteries, and lead-acid batteries, which are widely used in power peak shaving, microgrids, and renewable energy integration, offering advantages such as high energy density, fast response speed, and high efficiency.

[0071] The technical characteristics of electrochemical energy storage can be mainly represented by the following formula:

[0072] P C，min ≤P C，t ≤P C,max

[0073] P DC，min ≤P DC，t ≤P DC，max

[0074] S c，t +S d，t ≤1

[0075]

[0076] E min ≤E t ≤E max

[0077] Among them, P C,t and P DC,tP represents the discharge and charge power of the electrochemical energy storage at time t, respectively. C,min and P C,max P represents the minimum and maximum charging power of pumped hydro storage at time t. DC,min and P DC,max These represent the minimum and maximum discharge power of the electrochemical energy storage at time t, respectively; S c,t and S d,t S represents the discharge state and charge state value of the energy storage unit at time t, respectively, and is an integer in the range of 0-1. d,t =1 indicates the discharge state, S c,t =1 indicates a charging state; E t E represents the amount of energy stored at time t. t-1 η represents the stored energy at time t-1; ch and η dis E represents the charging and discharging efficiency of energy storage, respectively, where Δt is the duration of charging and discharging at time t; min and E max These represent the upper and lower limits of the amount of electricity that can be stored in electrochemical energy storage.

[0078] (2) Pumped storage:

[0079] Pumped storage is an energy storage technology that uses water as an energy carrier to store and utilize energy through pumping and releasing processes. During periods of low power system load, excess electrical energy is first converted into mechanical energy, and then this mechanical energy is stored as the potential energy of the water. When the power system load peaks, this gravitational potential energy of the water is converted back into mechanical energy, and finally, the mechanical energy is converted back into electrical energy. This process compensates for peak capacity and power shortages in the power system, meeting the system's peak-shaving needs.

[0080] The power output of a hydroelectric generator is mainly determined by three factors, including power generation efficiency η. h Power generation flow Q h,t , Hydropower Head H h,t The power output P of the hydroelectric generator unit h,t The basic formula is:

[0081] P h,t =9.81η h Q h，t H h,t

[0082] The power generation flow of a run-of-river hydropower station is related to the inflow and maximum power generation. If the inflow is greater than the maximum power generation, power is generated based on the maximum power generation capacity; otherwise, power is generated based on the inflow. The Big M method is usually used to model the power generation flow, as shown in the following formula:

[0083]

[0084] In the formula, M is a large number, f h,t This is a 0-1 variable used to characterize whether the inflow of water to the hydropower station exceeds the maximum power generation flow. This represents the actual inflow rate.

[0085] For adjustable power plants with reservoir capacity, the power generation flow rate must meet the maximum and minimum constraints, as shown in the following formula:

[0086] Q h，min ≤Q h，t ≤Q h，max

[0087] Among them, Q h,min and Q h,max These represent the minimum and maximum values ​​of the power generation flow, respectively.

[0088] Substituting the basic formula for the power generation output of a hydropower unit and the formula for modeling the inflow rate using the Big M method into the formula for the maximum and minimum constraints that the inflow rate must satisfy, we can obtain the upper and lower limits of the power generation characteristics of the hydropower unit, as shown in the following expressions:

[0089] P h,min ≤P h,t ≤P h,max

[0090] In the formula, P h,t Let P be the output of the hydroelectric generator at time t. h,min and P h,max These represent the minimum and maximum output of the hydropower unit.

[0091] In power system operation planning, key parameters such as power constraints, charge / discharge frequency limits, charge / discharge duration, and charge / discharge efficiency of pumped storage units need to be considered. The energy stored in pumped storage exhibits temporal continuity, meaning that the energy in the next time period equals the initial energy in the current time period plus the energy generated during charging or discharging in the current time period.

[0092]

[0093] In the formula, E s,t+1 E represents the amount of electricity stored in pumped hydro storage at time t+1. s,t Let P be the amount of electricity stored in pumped hydro storage at time t. sch,t P is the amount of electricity that pumped hydro storage will generate at time t. sdis,t η is the discharge power of pumped hydro storage at time t. sch and η sdis These represent the charging and discharging efficiencies, respectively, with Δt representing the charging and discharging duration; S c,t and S d,t These represent the discharge and charging states of pumped hydro storage at time t, respectively.

[0094] Power constraints:

[0095] E min ≤E s，t ≤E max

[0096] In the formula, E min and E max These represent the upper and lower limits of the amount of electricity that can be stored in pumped-storage hydroelectric power.

[0097] Charge and discharge upper and lower limit constraints:

[0098]

[0099] In the formula, P sch,min and P sch,max P represents the minimum and maximum charging power of pumped hydro storage at time t. sdis,min and P sdis,max These represent the minimum and maximum discharge power of pumped storage at time t, respectively.

[0100] Charging cycles constraint:

[0101]

[0102] Among them, F sch,t This indicates whether the charging state is switched during time period t, where T represents the total number of times the charging state is switched, and N represents the total number of charging cycles.

[0103]

[0104] Similarly, a constraint on the number of discharges can be obtained:

[0105]

[0106] Among them, F sdis,t This indicates whether the discharge state is switched during time period t.

[0107]

[0108] (3) Gravity energy storage:

[0109] Specifically, when the energy storage type is gravity energy storage, since gravity energy storage is a physical energy storage method, its principle is similar to pumped hydro storage. It generally uses water or solid materials as the energy storage medium, and achieves the conversion of electrical energy and gravitational potential energy by raising and lowering the height of the energy storage medium, thereby realizing charging and discharging. Therefore, the characteristics of gravity energy storage in terms of gravitational potential energy must be considered. The energy stored and released by a gravity energy storage system can be expressed as:

[0110] ΔW ch =η ch ·mgΔh

[0111] ΔWdis =η dis ·mgΔh

[0112] In the formula, ΔW ch and ΔW dis η represents the energy stored and released by the gravity energy storage system, respectively. ch and η dis Δh represents the charging and discharging efficiency of gravity energy storage, m is the mass of the object, g is the gravitational acceleration, and Δh is the height the object is lifted or lowered.

[0113] Energy stored in the gravity energy storage system at time t: S s，t It can be represented as:

[0114]

[0115] S min ≤S s，t ≤S max

[0116] S c，t +S d，t ≤1

[0117] Among them, P ges，t It can be expressed as the charging or discharging power of the gravity energy storage system at time t, where Δt is the duration of the gravity energy storage charging or discharging, and S max and S min These represent the maximum and minimum energy storage values ​​of the gravity energy storage system; S c，t and S d，t These represent the discharge and charging states of the energy storage unit at time t, respectively.

[0118] The charging and discharging power P of gravity energy storage ges，t It can be represented as:

[0119]

[0120] P min ≤P ges，t ≤P max

[0121] Among them, P max and P min This represents the maximum and minimum values ​​for gravity-based energy storage charging or discharging.

[0122] (4) Compressed air energy storage:

[0123] Compressed air energy storage (CASS) uses an electrically driven air compressor to compress and store air in underground caverns or high-pressure containers when electricity demand is low. During peak electricity demand periods, the compressed air is released and used to drive turbines for power generation through heating or direct expansion. This process effectively balances grid load and is suitable for large-scale energy storage scenarios. CASS offers advantages such as long lifespan, large capacity, and minimal environmental impact; however, its efficiency is affected by heat loss and pressure loss during compression, requiring integration with thermal storage or other energy recovery technologies to improve overall system efficiency.

[0124] The heat storage capacity H of the heat storage device at time t in the compressed air energy storage device t It can be represented as:

[0125]

[0126] S c,t +S d,t ≤1

[0127] H min ≤H t ≤H max

[0128] In the formula, H store,t and H lease,t These represent the stored power and released power of the thermal energy storage device at time t, respectively, where Δt is the duration of gravity energy storage charging or discharging, and η is the energy stored in the device. store and η lease These represent the storage efficiency and release efficiency of the thermal energy storage device, respectively; S c,t and S d,t These represent the two operating states of the heat storage device in the compressed air energy storage system at time t, and S c,t When S = 1, the thermal storage device is in energy storage state. d,t When H = 1, the thermal storage device is in the energy release state. max and H min These represent the maximum and minimum energy storage values ​​of the thermal storage device in the compressed air energy storage system.

[0129] The constraints on the storage and release power of the thermal energy storage device can be expressed as:

[0130] H store,min ≤H store,t ≤H store,max

[0131] H lease,min ≤H slease,t ≤H lease,max

[0132] Among them, H store,min and H store,max H represents the minimum and maximum storage power of the thermal storage device, respectively. lease,min and Hlease,max These represent the minimum and maximum values ​​of the power released by the thermal storage device, respectively.

[0133] Based on the above calculations of the economic characteristics of energy storage, the lowest levelized cost of electricity (LCOE) for pumped storage power stations is approximately RMB 0.28 / kWh, while the LCOE for electrochemical energy storage power stations, represented by lithium iron phosphate batteries, is RMB 0.45 / kWh. Compressed air storage and gravity storage, due to their higher initial investment costs, have significantly higher LCOEs than pumped storage and electrochemical energy storage power stations, at RMB 0.64 / kWh and RMB 0.94 / kWh, respectively. This unified framework yields the key indicator of LCOE for different energy storage types, enabling horizontal comparisons of various energy storage types.

[0134] Taking four typical specific application scenarios as examples, their weight configurations are shown in Table 1 below.

[0135] Table 1 Weight Configuration Table for Four Typical Specific Application Scenarios

[0136]

[0137] Based on the table above, set the weight coefficients for each dimension: ω T =0.6, ω E =0.3,ω G =0.1. Set the weight coefficients within the technical dimensions, focusing on responsiveness: ω1 (response time) = 0.4, ω2 (adjustment accuracy) = 0.4, ω3 (duration) = 0.1, ω4 (cycle life) = 0.1.

[0138] Based on the weighting coefficients set in Table 1, the calculation expressions for the multi-element energy storage adaptability comprehensive evaluation index, as well as the calculation expressions for the technical adaptability score, economic feasibility score, and green environmental protection score, are used to calculate the multi-element energy storage adaptability comprehensive evaluation index table for four different types of energy storage technologies. The calculation results are shown in Table 2.

[0139] Table 2. Comprehensive Evaluation Index Based on Multi-Element Energy Storage Adaptability

[0140] Energy storage technology <![CDATA[T score ]]> <![CDATA[E score ]]> <![CDATA[G score ]]> CSAI Ranking Electrochemical energy storage 0.85 0.70 0.65 0.785 1 Pumped storage 0.45 0.90 0.75 0.615 3 Compressed air energy storage 0.60 0.65 0.55 0.605 4 Gravity energy storage 0.55 0.50 0.80 0.560 2

[0141] The calculation results in Table 2 show that, in frequency regulation scenarios, electrochemical energy storage, due to its superior rapid response capability and regulation accuracy, achieves the highest CSAI score and is the optimal choice. While pumped hydro storage leads in economic efficiency, its low technological adaptability score results in a lower overall ranking. This conclusion is highly consistent with current engineering practices, validating the effectiveness and scientific validity of the CSAI evaluation index.

[0142] Based on a generalized model, this invention further proposes a comprehensive evaluation index for multi-scenario adaptability, used to quantitatively assess the comprehensive adaptability of different energy storage technologies to specific application scenarios. This comprehensive evaluation index integrates quantitative scores from three dimensions: technological adaptability, economic feasibility, and environmental friendliness. Through a scenario-based weighting mechanism, it dynamically reflects the evaluation focus of different scenarios. This invention solves the problems of fragmented existing energy storage models and single evaluation dimensions, realizing the techno-economic characterization and comprehensive optimization of multiple energy storage technologies within a unified framework, providing a scientific basis for power system planning and investment decisions.

[0143] Although the above embodiments describe the various contents in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different contents do not necessarily need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0144] Those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims of this invention, any of the claimed embodiments can be used in any combination.

[0145] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, the word "comprising" does not exclude the presence of elements or contents not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed PC.

[0146] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it should be noted that the parts not covered in this invention are the same as or can be implemented using existing technology. It will be readily understood by those skilled in the art that the scope of protection of this invention is obviously not limited to these specific embodiments. Without departing from the principles of this invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions resulting from these changes or substitutions will all fall within the scope of protection of this invention.

Claims

1. A general modeling and comprehensive evaluation method for characterizing economic characteristics of multi-element energy storage technologies, characterized in that, The application relates to a multi-scenario comprehensive evaluation index for a multi-element energy storage adaptability, and a quantitative evaluation method for the comprehensive adaptability of an energy storage technology to a specific application scenario. The expression of the energy storage technology characteristic model is as follows: The energy storage power charging and discharging dynamic constraint relationship is established, and the relationship is as follows: The expression of the energy storage economic characteristic model is as follows:

2. The method of claim 1, wherein the method is characterized by, The expression of the calculation of the flat rate unit power cost is as follows: where P d is the energy storage discharge power, and are upper and lower limits of the energy storage discharge power, respectively; P c is the energy storage charge power, and are upper and lower limits of the energy storage charge power, respectively; S c and S d indicate the charge and discharge state of the energy storage, respectively. η c and η dis are the charging and discharging efficiency of the energy storage, respectively; Δt is the duration of the energy storage charging or discharging, E t is the energy storage power at time t, E t-1 is the energy storage power at time t-1, E s and E min are the upper and lower limits of the energy storage power, respectively.

3. The method of claim 2, wherein the method is characterized by, The expression of the multi-element energy storage adaptability comprehensive evaluation index is as follows: In the formula, E t+1 is the energy storage amount at t+1 time, P c,t is the charging power at t time, P d,t is the discharging power at t time.

4. The method of claim 3, wherein the method is characterized by, The specific application scenarios include grid frequency modulation, peak shaving and valley filling, renewable energy smoothing, standby power supply and remote area power supply. LCC = C inv + C op + C elec + C fin + C deg - C res In the formula, LCC represents the life cycle cost, C inv is the initial investment cost, C op is the operation and maintenance cost, C elec is the electricity purchase cost, C fin is the financial cost, C deg is the correction cost, C res is the power station residual value.

5. The method of claim 4, wherein the method is characterized in that, ​ In the formula, LCOE represents the levelized cost of electricity, E gen,n is the energy storage power, N represents the total number of years of energy storage power generation, η c and η dis are the charging and discharging efficiencies of the energy storage, respectively.

6. The method of claim 1, wherein the method is characterized by, ​ CSAI = ω T x T score + ω E x E score + ω G x G score Wherein, CSAI is a comprehensive fitness index, T score is a technical adaptability score, E score is an economic feasibility score, G score is a green environmental protection score, ω T , ω E and ω G are weight coefficients of each dimension, and satisfy ω T + ω E + ω G = 1.

7. The method of claim 6, wherein the method is characterized in that, The technical adaptability score T score The calculation expression is: In the formula, T response is the response time of the energy storage system, τ is the time constant required by the scene, ACC is the regulation accuracy of the energy storage system, T duration is the rated power duration of the energy storage system, T required is the duration required by the scene; CycleLife is the cycle life of the energy storage system, N required is the total cycle number required by the scene, ω1, ω2, ω3, ω4 are weight coefficients in the technical dimension, and ω1+ω2+ω3+ω4=1. The economic viability score E score The calculation expression is: In the formula, LCOE is the levelized cost of electricity, PBP is the payback period, Y required is the benchmark recovery year required by the scenario; VD is the value diversity, representing the ratio of the number of market services that the energy storage technology can participate in to the total number of market services; u1, u2, and u3 are weight coefficients in the economic dimension, and u1+u2+u3=1; The green environmental protection score G score The calculation expression is: G score = v1(η round-trip ) k + v2(-CF) + v3MS wherein η round-trip is the full life cycle average roundtrip efficiency of the energy storage system, k is a scaling factor greater than 1; CF is the full life cycle carbon footprint of the energy storage system; MS is the material sustainability score, and v1, v2, v3 are weighting coefficients within the green dimension, and v1 + v2 + v3 = 1.

8. The method of claim 1, wherein the method is characterized by, ​

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