A multivariate energy storage capacity planning method considering power and electricity balance constraints

Abstract

A multivariate energy storage capacity planning method that takes into account power and electricity balance constraints, the method analyzes and evaluates the risk of long-term supply and demand imbalance in the power system by constructing an analysis model and creating an evaluation strategy, and obtains data on the risk of long-term supply and demand imbalance in the power system; based on the data on the risk of long-term supply and demand imbalance in the power system, an optimization planning model that takes into account the risk of long-term supply and demand imbalance is constructed; by setting a solver, the optimization planning model that takes into account the risk of long-term supply and demand imbalance is solved, and an optimization planning scheme that takes into account the risk of long-term supply and demand imbalance is obtained. The present invention not only takes into account power and electricity balance, load characteristics, and charge and discharge characteristics of the energy storage system, but also specifically studies energy storage capacity planning under extreme weather conditions to ensure the stable operation of the power system and the continuous supply of power.

Description

Technical Field

[0001] The present invention relates to the technical field of energy internet, and in particular to a multi-element energy storage capacity planning method taking into account power and electricity balance constraints. Background Art

[0002] With the global energy transition and the urgent need to achieve carbon peak and carbon neutrality, the large-scale grid integration of clean energy sources such as wind and solar has become a major development trend in the energy sector. These renewable energy sources, with their clean and renewable characteristics, theoretically help reduce greenhouse gas emissions and promote sustainable energy development. However, due to their inherent volatility and uncertainty, the large-scale integration of renewable energy poses new challenges to the stability and reliability of power systems. In particular, under extreme weather conditions, such as extreme heat with no wind or extreme cold with insufficient sunshine, the output of renewable energy can be significantly reduced. This not only affects the stability of power supply but also poses a serious threat to the power balance of the power system.

[0003] Traditional power system planning methods primarily focus on short-term power balance, while insufficiently considering the risks of long-term supply-demand imbalances. With the increasing proportion of installed renewable energy capacity, the demand for power system planning for power and electricity balance is growing. While existing literature has considered source-grid-load-storage collaborative planning models for efficient renewable energy consumption, power system optimization planning models for long-term energy storage, and the impact of electrochemical energy storage on system power and electricity balance, these studies still have limitations and shortcomings.

[0004] First, existing models often overlook the characteristics and optimal combinations of different energy storage technologies, failing to fully leverage the comprehensive benefits of a multi-storage system. For example, electrochemical energy storage, with its rapid response, is well-suited for addressing short-term power fluctuations, while hydroelectric storage, with its large-scale storage capacity, is well-suited for seasonal regulation. The lack of a deep understanding of the characteristics of these different energy storage technologies and their optimal combination limits the flexibility and effectiveness of system planning.

[0005] Second, existing research lacks understanding of the planning and operation strategies for energy storage systems under special scenarios, such as extreme weather. Predicting and responding to extreme weather events is a crucial aspect of power system planning, and new models and methods are needed to evaluate and optimize the performance of energy storage systems under these conditions.

[0006] Finally, existing energy storage capacity planning methods often focus on a single energy storage technology, lacking a systematic approach that comprehensively considers multiple energy storage methods, such as electrochemical energy storage and hydroelectric storage. This limits the comprehensiveness and depth of power system planning, especially in the face of the increasing proportion of renewable energy generation and the increasing frequency of extreme weather events.

[0007] Therefore, how to invent a multi-element energy storage capacity planning method under the constraints of power and electricity balance, which can comprehensively consider the advantages and disadvantages of different energy storage methods such as electrochemical energy storage and hydroelectric energy storage, and achieve the optimal balance between the economy and stability of the power system, has become an urgent problem to be solved. Summary of the Invention

[0008] To this end, this paper provides a multivariate energy storage capacity planning method that takes into account power balance constraints. This method not only considers power balance, load characteristics, and the charge and discharge characteristics of the energy storage system, but also specifically addresses energy storage capacity planning under extreme weather conditions to ensure stable operation of the power system and a continuous supply of power. This method can better adapt to the volatility of renewable energy, improve the reliability and flexibility of the power system, reduce system operating costs, and promote sustainable energy development.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a multi-element energy storage capacity planning method taking into account power and electricity balance constraints, comprising:

[0010] By building an analytical model and creating an assessment strategy, we analyze and assess the risks of long-term supply and demand imbalance in the power system, and obtain data on the risks of long-term supply and demand imbalance in the power system;

[0011] Based on the data on the long-term supply and demand imbalance risk of the power system, an optimization planning model that considers the long-term supply and demand imbalance risk is constructed;

[0012] By setting a solver, the optimization planning model considering the long-term supply and demand imbalance risk is solved to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk.

[0013] As a preferred solution for a multivariate energy storage capacity planning method that takes into account power balance constraints, in the process of analyzing and evaluating the risk of long-term supply and demand imbalance in the power system by building an analysis model and creating an evaluation strategy, the analysis and evaluation steps include:

[0014] Criteria are established for extreme weather events with sustained low output of renewable energy, and the average daily output of renewable energy is determined;

[0015] By constructing node power balance constraints in extreme scenarios with low output of new energy, the node power balance in extreme scenarios with low output of new energy is constrained;

[0016] By building a linear model, the start-up and shutdown operation of conventional thermal power units is analyzed and evaluated;

[0017] Analyze and evaluate the power flow of the power system by constructing power flow models of existing / to-be-built lines;

[0018] By establishing new energy operation constraints, the output operation status of new energy wind power, photovoltaic power and hydropower is constrained;

[0019] By building energy storage operation constraints, the seasonal energy storage of the power system can achieve energy balance on a year-round time scale;

[0020] By calculating the imbalance risk of the power system under extreme weather scenarios, a long-term supply and demand imbalance risk index is obtained; and the safe operation of the power system is ensured by the long-term supply and demand imbalance risk index;

[0021] By constructing a monthly power balance model for the power system on a long time scale, the long-term power balance risk of the power system is assessed;

[0022] Based on the CVaR strategy, the monthly electricity imbalance risk of the power system is quantitatively assessed.

[0023] As a preferred solution for a multivariate energy storage capacity planning method that takes into account power balance constraints, the objective function expression of the optimization planning model that considers the risk of long-term supply and demand imbalance is:

[0024] minC ove =C Inv +(1-w)C SB,Ope +wC LB,Ope

[0025]

[0026] Where minC ove To optimize the total system cost; C Inv To minimize the annual investment cost; C SB,Ope is the typical daily operating cost; C LB,Ope is the monthly electricity imbalance operation and risk cost; w is the weight factor of long-term operation cost in total operation cost, which reflects the proportion of monthly electricity imbalance operation cost considered in the optimization problem; The unit investment costs of the generator set, the transmission line to be built, and the energy storage system are respectively; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are the investment discount rate and investment payback period respectively; and are the unit variable operating cost and unit startup cost of thermal power unit g respectively; is the unit load shedding cost of grid node n; are the unit output, operating capacity and load shedding power of the thermal power unit in the tth period of the kth typical day; c TG,LIBThe unit power generation cost of the additional output of thermal power units to smooth out the monthly power imbalance; is the monthly power generation of thermal power units; is the number of scenario-years involved in the analysis; It is the risk of electricity imbalance caused by monthly electricity fluctuations in several scenarios.

[0027] As a preferred solution for a multi-element energy storage capacity planning method that takes into account power and electricity balance constraints, in the process of solving the optimization planning model that takes into account the risk of long-term supply and demand imbalance, the constraints of the optimization planning model that takes into account the risk of long-term supply and demand imbalance include: typical daily operation constraints, power balance constraints for extreme weather scenarios, and monthly electricity balance constraints that take into account the seasonal characteristics of new energy.

[0028] As an optimal solution for a multivariate energy storage capacity planning method that takes into account power and electricity balance constraints, in the process of solving the optimization planning model that takes into account the risk of long-term supply and demand imbalance and obtaining the optimization planning scheme that takes into account the risk of long-term supply and demand imbalance, the annual average load shedding expectation is calculated through hourly operation simulations of a large number of scenario years; the reliability of the optimization planning scheme obtained under the set conditions is evaluated based on the annual average load shedding expectation, thereby realizing quantitative analysis of the optimization planning scheme.

[0029] The present invention further provides a multi-element energy storage capacity planning method taking into account power and electricity balance constraints, based on the above multi-element energy storage capacity planning method taking into account power and electricity balance constraints, comprising:

[0030] The power system long-term supply and demand imbalance risk analysis and assessment module is used to analyze and assess the risk of long-term supply and demand imbalance in the power system by building an analysis model and creating an assessment strategy, and to obtain data on the risk of long-term supply and demand imbalance in the power system;

[0031] An optimization planning model construction module that considers the risk of long-term supply and demand imbalance is used to construct an optimization planning model that considers the risk of long-term supply and demand imbalance based on the data of the risk of long-term supply and demand imbalance in the power system;

[0032] The optimization planning model solving module considering the long-term supply and demand imbalance risk is used to solve the optimization planning model considering the long-term supply and demand imbalance risk by setting a solver to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk.

[0033] As a multivariate energy storage capacity planning method that takes into account power balance constraints, the analysis and evaluation submodules in the power system long-term supply and demand imbalance risk analysis and evaluation module include:

[0034] The submodule for judging the continuous low output state of new energy is used to judge the extreme weather events of continuous low output of new energy and determine the daily average output state of new energy;

[0035] The node power balance constraint construction submodule is used to constrain the node power balance in the extreme scenario of low output of new energy by constructing the node power balance constraint in the extreme scenario of low output of new energy;

[0036] The linearization model construction and analysis submodule is used to analyze and evaluate the start-up and shutdown operation of conventional thermal power units by constructing a linearization model;

[0037] The power flow model construction and analysis submodule is used to analyze and evaluate the power flow of the power system by constructing the power flow model of the built / to-be-built lines;

[0038] The new energy operation constraint construction submodule is used to constrain the output operation status of new energy wind power, photovoltaic power and hydropower by constructing new energy operation constraints;

[0039] The energy storage operation constraint construction submodule is used to achieve energy balance of the seasonal energy storage of the power system on a year-round time scale by constructing energy storage operation constraints;

[0040] A long-term supply-demand imbalance risk index calculation submodule is used to calculate the imbalance risk of the power system under extreme weather scenarios to obtain a long-term supply-demand imbalance risk index; and to ensure the safe operation of the power system through the long-term supply-demand imbalance risk index;

[0041] The power system monthly electricity balance model construction submodule is used to evaluate the long-term power balance risk of the power system by constructing the power system monthly electricity balance model under long-term scale;

[0042] The power system monthly electricity imbalance risk quantitative assessment submodule is used to quantitatively assess the power system monthly electricity imbalance risk based on the CVaR strategy.

[0043] As a multivariate energy storage capacity planning method that takes into account power balance constraints, in the optimization planning model construction module that considers the risk of long-term supply and demand imbalance, the objective function expression of the optimization planning model that considers the risk of long-term supply and demand imbalance is:

[0044] minC ove =C Inv +(1-w)C SB,Ope +wC LB,Ope

[0045]

[0046]

[0047] Where minC ove To optimize the total system cost; C Inv To minimize the annual investment cost; C SB,Ope is the typical daily operating cost; C LB,Ope is the monthly electricity imbalance operation and risk cost; w is the weight factor of long-term operation cost in total operation cost, which reflects the proportion of monthly electricity imbalance operation cost considered in the optimization problem; The unit investment costs of the generator set, the transmission line to be built, and the energy storage system are respectively; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are the investment discount rate and investment payback period respectively; and are the unit variable operating cost and unit startup cost of thermal power unit g respectively; is the unit load shedding cost of grid node n; are the unit output, operating capacity and load shedding power of the thermal power unit in the tth period of the kth typical day; c TG,LIB The unit power generation cost of the additional output of thermal power units to smooth out the monthly power imbalance; is the monthly power generation of thermal power units; is the number of scenario-years involved in the analysis; It is the risk of electricity imbalance caused by monthly electricity fluctuations in several scenarios.

[0048] As a multivariate energy storage capacity planning method that takes into account power and electricity balance constraints, in the optimization planning model solving module that takes into account the long-term supply and demand imbalance risk, in the process of solving the optimization planning model that takes into account the long-term supply and demand imbalance risk, the constraints of the optimization planning model that takes into account the long-term supply and demand imbalance risk include: typical daily operation constraints, power balance constraints for extreme weather scenarios, and monthly electricity balance constraints that take into account the seasonal characteristics of new energy.

[0049] As a multivariate energy storage capacity planning method that takes into account power and electricity balance constraints, in the optimization planning model solving module that considers the risk of long-term supply and demand imbalance, in the process of solving the optimization planning model that considers the risk of long-term supply and demand imbalance and obtaining the optimization planning scheme that considers the risk of long-term supply and demand imbalance, the annual average load shedding expectation is calculated through hourly operation simulations of a large number of scenario years; the reliability of the optimization planning scheme obtained under the set conditions is evaluated based on the annual average load shedding expectation, thereby realizing quantitative analysis of the optimization planning scheme.

[0050] The present invention has the following advantages: by constructing an analytical model and creating an assessment strategy, the risk of long-term supply and demand imbalance in the power system is analyzed and assessed, and data on the risk of long-term supply and demand imbalance in the power system is obtained; based on the data on the risk of long-term supply and demand imbalance in the power system, an optimization planning model that considers the risk of long-term supply and demand imbalance is constructed; and by setting a solver, the optimization planning model that considers the risk of long-term supply and demand imbalance is solved to obtain an optimization planning scheme that considers the risk of long-term supply and demand imbalance. The present invention achieves an optimal balance between the economy and stability of the power system by comprehensively considering the advantages and disadvantages of different energy storage methods, such as electrochemical energy storage and hydroelectric energy storage. This method not only considers power and energy balance, load characteristics, and the charge and discharge characteristics of the energy storage system, but also specifically studies energy storage capacity planning under extreme weather conditions to ensure stable operation of the power system and continuous power supply. This method can better adapt to the volatility of renewable energy, improve the reliability and flexibility of the power system, reduce system operating costs, and promote sustainable energy development. The present invention proposes a power and energy balance planning method that integrates multiple energy storage technologies (such as electrochemical energy storage and hydroelectric energy storage). This method takes into account the characteristics of different energy storage technologies, such as the rapid response characteristics of electrochemical energy storage and the large-scale energy storage capacity of hydroelectric energy storage, and achieves the best balance between the economy and stability of the power system through optimized combination strategies. Secondly, special attention is paid to the problem of power and electricity balance in the long term. By analyzing the seasonal fluctuation characteristics of renewable energy power generation and the stability of the power system under extreme weather conditions, corresponding risk assessment models and energy storage capacity planning methods are proposed. In response to extreme weather events, such as extremely hot and windless or extremely cold and lightless, this patent proposes an identification method and corresponding energy storage system response strategy to ensure the stable operation of the power system and the continuous supply of electricity. The optimization model in the present invention not only takes into account the power and electricity balance, load characteristics, and charging and discharging characteristics of the energy storage system, but also specifically studies the energy storage capacity planning under extreme weather conditions, which is its unique technical contribution. The present invention is original and practical in solving the challenges faced by power system planning methods in the context of high penetration of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0052] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0053] Figure 1 This is a flow chart of a multi-element energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

[0054] Figure 2 A schematic diagram of the risk of long-term supply and demand imbalance in a power system in a multi-element energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

[0055] Figure 3 This is a schematic diagram of an extreme weather scenario with continuous low output of new energy in a multi-element energy storage capacity planning method taking into account power balance constraints provided in Example 1 of the present invention;

[0056] Figure 4 This is a statistical diagram of the annual average occurrence frequency of scenarios of continuous low output of new energy sources in different seasons and for different durations in a multi-element energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

[0057] Figure 5 A schematic diagram of seasonal fluctuation characteristics of renewable energy in a multi-element energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

[0058] Figure 6 A schematic diagram of conditional value at risk in a multivariate energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

[0059] Figure 7 This is a schematic diagram of a monthly electricity imbalance risk calculation process in a multivariate energy storage capacity planning method taking into account power balance constraints provided in Example 1 of the present invention;

[0060] Figure 8 This is a schematic diagram of a power system planning model framework that considers the risk of long-term supply and demand imbalance in a multi-element energy storage capacity planning method that takes into account power and electricity balance constraints provided in Example 1 of the present invention;

[0061] Figure 9 A schematic diagram of a process for solving an optimization problem in a multivariate energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

[0062] Figure 10 A schematic diagram of a modified IEEE RTS-79 system topology in a possible embodiment provided in Example 1 of the present invention;

[0063] Figure 11 This is a schematic diagram of comparative analysis results based on a modified IEEE RTS-79 system in a possible embodiment provided in Example 1 of the present invention;

[0064] Figure 12 This is a schematic diagram of energy balance results of the IEEE RTS-79 system at different time scales in a possible embodiment provided in Example 1 of the present invention;

[0065] Figure 13 This is a schematic diagram of monthly electricity imbalance results in an IEEE RTS-79 scenario in a possible embodiment provided in Example 1 of the present invention;

[0066] Figure 14 This is a schematic diagram of reliability indicators of the IEEE RTS-79 system under different wind and solar penetration rates in a possible embodiment provided in Example 1 of the present invention;

[0067] Figure 15 Schematic diagram of the architecture of a multi-element energy storage capacity planning method taking into account power balance constraints provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0068] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0069] Example 1

[0070] See also Figure 1 Embodiment 1 of the present invention provides a multi-element energy storage capacity planning method taking into account power and electricity balance constraints, comprising the following steps:

[0071] S1. Analyze and evaluate the risk of long-term imbalance between supply and demand in the power system by building an analytical model and creating an assessment strategy, and obtain data on the risk of long-term imbalance between supply and demand in the power system;

[0072] S2. Based on the data on the long-term supply and demand imbalance risk of the power system, an optimization planning model that considers the long-term supply and demand imbalance risk is constructed;

[0073] S3. By setting a solver, the optimization planning model considering the long-term supply and demand imbalance risk is solved to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk.

[0074] In this embodiment, in step S1, by constructing an analysis model and creating an evaluation strategy, the risk of long-term supply and demand imbalance in the power system is analyzed and evaluated, and data on the risk of long-term supply and demand imbalance in the power system is obtained;

[0075] Specifically, such as Figure 1 As shown, this paper categorizes the long-term supply-demand imbalance risk of new power systems into two aspects: First, there's the risk of long-term supply-demand imbalances caused by extreme weather scenarios involving sustained low renewable energy output. Scenarios like extreme heat with no wind or extreme cold with no sunlight pose significant challenges to system power and electricity balance. Second, there's the risk of long-term monthly electricity imbalances caused by seasonal fluctuations in renewable energy generation. Based on this, this section proposes analytical models and assessment methods for specific manifestations of long-term supply-demand imbalance risk.

[0076] The analysis and evaluation steps include:

[0077] S11. Criteria for extreme weather events with sustained low output of renewable energy sources to determine the daily average output of renewable energy sources;

[0078] Specifically, the continued low output of renewable energy is an important reason for the power imbalance in the new power system. Figure 3 As shown in Figure 1, the power curve fluctuations of the extreme scenario of continuous low output of new energy and the general output scenario are shown. The criterion for extreme weather events with continuous low output of new energy is shown in formula (1):

[0079]

[0080] Where, is the average daily output of renewable energy; ρ max is the rated output level; δ is the duration of continuous low output; d is the time used to calculate the duration of renewable energy;

[0081] Average daily output of new energy Below rated output level ρ max When the average daily output of renewable energy is 10% lower than the rated output and the duration exceeds 2 days (i.e. 48 hours), it is considered to be a continuous low output period. It should be noted that there is currently no consensus on the selection criteria in the research. In different studies, the values ​​of the continuous low output period δ and the low output rate α of renewable energy may be different.

[0082] Based on formula (1), the present invention extracts extreme weather scenarios with continuous low output of renewable energy based on statistical analysis of renewable energy output data of a certain power grid in China. Figure 4 As shown in Figure 2, the annual average frequency of extreme weather scenarios with low wind power / photovoltaic output in different seasons and durations is shown. Figure 4 It can be seen that low wind power output scenarios lasted the longest in the summer, reaching 10 days, while low photovoltaic output scenarios lasted the longest, at 6 days, in the winter. This comparison reveals that the frequency and duration of extreme weather scenarios vary across seasons, showing distinct seasonal distribution patterns.

[0083] S12. Constraining the node power balance in the extreme scenario of low output of new energy by constructing a node power balance constraint in the extreme scenario of low output of new energy;

[0084] Specifically, the expression of the node power balance constraint in the extreme scenario of low output of new energy is:

[0085]

[0086] Where, the superscript θ refers to the set of different types of generators θ:{H,S,F,G}, including thermal power H, hydropower S, wind power F and photovoltaic G; the energy storage set K:{A,B} includes short-term energy storage A and seasonal energy storage is the output of generator set g at hour h in the extreme scenario; are the charge and discharge power of energy storage s in h hours respectively; is the transmission power of line l; is the load power of node n; The amount of load shedding that occurs in extreme weather scenarios; is the generator set / energy storage set connected to node n; Represents the set of transmission lines with node n as the starting / ending node.

[0087] S13. Analyze and evaluate the start-up and shutdown operation of conventional thermal power units by constructing a linearized model;

[0088] Specifically, the expression of the linearized model is:

[0089]

[0090] Formula (3) is the upper and lower limit constraints of thermal power output; where, is the online startup capacity of thermal power unit tg; is the output power of thermal power unit tg; is the minimum output rate of thermal power unit tg; is the installed capacity of thermal power unit tg.

[0091]

[0092] Formula (4) is the thermal power ramp constraint; where, It is the up / down ramp rate of thermal power unit tg.

[0093]

[0094] Formula (5) models the change process of the online startup capacity of thermal power units in adjacent time periods; where, Indicates the on / off capacity of the thermal power unit in the h period.

[0095]

[0096]

[0097] Formulas (6) and (7) represent the minimum on / off time constraints of thermal power units; where, Indicates the minimum on / off time of the thermal power unit tg.

[0098] S14. Analyze and evaluate the power flow of the power system by constructing a power flow model for existing / to-be-built lines;

[0099] The expression of the power flow model of the constructed / to-be-constructed lines is:

[0100]

[0101] Where, They refer to the built capacity and expansion capacity of line l respectively; the network flow model assumes that the power flow of the transmission line can be freely dispatched within the capacity limit, and the expansion planning of the transmission line is only reflected in the investment expansion of the transmission capacity.

[0102] S15. By establishing new energy operation constraints, the output operation status of new energy wind power, photovoltaic power and hydropower is constrained;

[0103] Specifically, formula (9) is the upper and lower limit constraints of wind power and photovoltaic power output,

[0104]

[0105] Where, Hourly output of wind power / photovoltaic power; Wind power / photovoltaic power curtailment; Represents the hourly wind and solar fluctuation curve in extreme scenarios; This represents the installed capacity of wind and solar power.

[0106] Formula (10) represents the hourly output of hydropower,

[0107]

[0108] Where, To provide hourly hydropower output in extreme weather scenarios, Represents installed hydropower capacity.

[0109] S16. By establishing energy storage operation constraints, seasonal energy storage in the power system can achieve energy balance on a year-round time scale.

[0110] Specifically, formula (11) represents the change process of the stored energy of energy storage s in adjacent time periods,

[0111]

[0112] Where, Represents the storage capacity of energy storage s at time h.

[0113] Equations (12) and (13) represent the upper and lower limits of the charging and discharging power and storage capacity of energy storage s, respectively.

[0114]

[0115]

[0116] Where, is the power capacity of energy storage s; is the continuous discharge hours of energy storage s.

[0117] Formula (14) represents the energy balance constraint of short-term energy storage,

[0118]

[0119] Where h start Indicates the starting time of each day in the extreme scenario; T is the number of hours in the day.

[0120] Taking into account that seasonal energy storage needs to achieve energy balance on a year-round time scale, the storage capacity of seasonal energy storage in extreme weather scenarios does not need to meet the intraday balance constraints. Therefore, the storage capacity of seasonal energy storage considered in the present invention is a variable to be optimized in any period of time in extreme weather scenarios.

[0121] S17. Obtain a long-term supply-demand imbalance risk index by calculating the imbalance risk of the power system under extreme weather scenarios; and ensure safe operation of the power system using the long-term supply-demand imbalance risk index;

[0122] Specifically, the calculation formula for the long-term supply and demand imbalance risk indicator is:

[0123]

[0124] By summing up all load shedding within extreme scenarios, we obtain a long-term supply-demand imbalance risk index for extreme weather scenarios. To ensure the safety of power system operation, this paper sets the imbalance risk index for the system to 0 within the target extreme weather scenario. This means that through optimized resource allocation and flexible equipment operation, the system avoids load shedding in extreme weather scenarios, ensuring safe operation.

[0125] like Figure 5 As shown in Figure 2, the normalized monthly average output of wind power and photovoltaic power is calculated. Figure 5 (a) Shows that the monthly average wind power output reaches two peaks in spring (March) and autumn (October), and reaches the lowest in summer (August); Figure 5 (b) shows that photovoltaic power generation reaches its highest point in summer (July) and its lowest point in winter (January). This shows that wind and solar power generation has obvious seasonal fluctuations.

[0126] S18. Evaluate the long-term power balance risk of the power system by constructing a monthly power balance model for the power system over a long time scale;

[0127] Specifically, power system operation simulations usually only consider hourly power balance within a typical day, and lack corresponding models and evaluation methods for the system's power balance and risk assessment over long time scales. This paper introduces a massive scenario year y to model the long-term uncertainty of the power system, and constructs a monthly power balance model for the power system over long time scales for massive scenario years. Each scenario year y corresponds to a different monthly power generation curve for new energy sources. and monthly load demand D n,y,m By simulating and calculating the annual power balance of a large number of scenarios, we can obtain the annual power imbalance of each scenario. The generation of the massive scenario-year series is based on historical observation data, and the corresponding distribution patterns of the data are statistically analyzed. Then, based on the probability distribution of the wind and solar sequences, the Monte Carlo method is used to simulate and generate massive time series data with the same distribution characteristics. The specific model is constructed as follows:

[0128] Equations (16)-(20) construct the monthly power balance constraints of the power system over a long time scale. Equation (16) is the annual energy balance constraint of the entire system, ensuring that the system's energy supply remains balanced over the entire year.

[0129]

[0130] Where, for wind power, photovoltaic and hydropower capacity; is the monthly power generation curve of wind power, photovoltaic and hydropower generation unit i in the mth month of typical scenario year y; It represents the planned monthly power generation of thermal power unit tg in the mth month of year y in the typical scenario. is the variable to be optimized; D n,y,m is a known curve.

[0131] Formula (17) is the monthly power balance constraint of node n,

[0132]

[0133] Where, In order to cope with the risk of monthly power imbalance, thermal power units tg need to increase their original planned power generation The additional power generation based on the above formula reflects the imbalance degree of monthly power of the system under the premise of ensuring the annual power balance of the system in formula (16); are the power generation and transportation of renewable energy g and transmission line l in the mth month of year y in the typical scenario, are the monthly charge / discharge capacity of seasonal energy storage, D n,y,m is the monthly load demand electricity at node n.

[0134] Formula (18) adds upper and lower limit constraints to the annual utilization hours of thermal power unit tg,

[0135]

[0136] Where, Indicates the upper / lower limit of the utilization hours of the thermal power unit tg.

[0137] Formula (19) is the power generation constraint of wind power, photovoltaic power and hydropower,

[0138]

[0139] Where, It represents the monthly power abandonment of the new energy generator set g.

[0140] Formula (20) is the power generation capacity penetration constraint of new energy (including hydropower, wind power and photovoltaic power),

[0141]

[0142] Where β is the new energy power penetration rate of the whole system.

[0143] Equations (21)-(24) establish the operation constraints of seasonal energy storage on a long time scale. Equation (21) models the energy transfer process of seasonal energy storage in adjacent months.

[0144]

[0145] Where, represents the stored energy of seasonal energy storage in the mth month of scenario year y; Indicates the charging / discharging efficiency of seasonal energy storage.

[0146] Formulas (22) and (23) set the upper and lower limits of the monthly charge / discharge capacity and monthly storage capacity of seasonal energy storage, respectively.

[0147]

[0148]

[0149] Where, and Represent the installed capacity and continuous discharge hours of seasonal energy storage; T and D m Represents the hour of the day and the day of the month respectively.

[0150] Formula (24) represents the annual electricity balance constraint of seasonal energy storage,

[0151]

[0152] Where M is the number of months in a year.

[0153] S19. Quantitatively assess the monthly power imbalance risk of the power system based on the CVaR strategy.

[0154] Specifically, such as Figure 6 As shown, this invention uses the CVaR method to quantitatively assess the monthly imbalance risk of power systems. By quantitatively assessing the risk of imbalanced electricity in each scenario, the supply and demand of power systems can be intuitively evaluated over a long period of time. It can also be embedded in an optimization model to effectively control the monthly imbalance risk of the system from an investment planning perspective.

[0155] The construction of the monthly power imbalance risk constraint set of the power system is shown in formula (25):

[0156]

[0157] The present invention defines the monthly power imbalance risk of the system as the power imbalance risk caused by monthly power fluctuations in several scenarios within a year. The confidence level of the monthly electricity imbalance risk event is set as κ, and the unbalanced electricity in each scenario year y is Additional power generation from thermal power units Adding up, φ y The probability of scenario corresponding to scenario year y, v y Represents the auxiliary variable corresponding to scenario year y.

[0158] The specific process of calculating conditional risk value based on monthly unbalanced electricity is as follows: Figure 7 As shown in the figure, the CVaR risk analysis method can effectively consider the tail risk of variable distribution and realize the quantitative modeling and evaluation of the seasonal fluctuation characteristics of power system electricity.

[0159] In this embodiment, in step S2, an optimization planning model considering the long-term supply and demand imbalance risk is constructed based on the data of the long-term supply and demand imbalance risk of the power system;

[0160] Specifically, such as Figure 8 As shown, the present invention constructs a power optimization planning model framework that considers the risk of long-term supply and demand imbalance. Traditional power planning models only consider annualized investment costs and typical daily operating costs, and in terms of constraints, only consider operational constraints based on typical days. However, based on traditional optimization planning, the present invention also considers the monthly electricity imbalance risk cost in terms of cost, where w represents the weight factor of the monthly electricity imbalance operating cost in the total operating cost, reflecting the proportion of the monthly electricity imbalance operating cost considered in the optimization problem; in terms of operational constraints, the long-term supply and demand imbalance risk constraint constructed in step 1 is added.

[0161] The optimization model constructed in this paper minimizes the total cost of the power system, including annualized investment costs, typical daily operating costs, and monthly power imbalance risk costs. To ensure safe power system operation, this paper assumes no load shedding and no load shedding penalty costs in extreme weather scenarios. Only operational constraints are considered for safe operation of the system in extreme weather scenarios.

[0162] The objective function expression of the optimization planning model considering the long-term supply and demand imbalance risk is:

[0163] minC ove =C Inv +(1-w)C SB,Ope +wC LB,Ope (26)

[0164]

[0165]

[0166]

[0167] Where minC ove To optimize the total system cost; C Inv To minimize the annual investment cost; C SB,Ope is the typical daily operating cost; C LB,Ope is the monthly electricity imbalance operation and risk cost; w is the weight factor of long-term operation cost in total operation cost, which reflects the proportion of monthly electricity imbalance operation cost considered in the optimization problem; The unit investment costs of the generator set, the transmission line to be built, and the energy storage system are respectively; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are the investment discount rate and investment payback period respectively; and are the unit variable operating cost and unit startup cost of thermal power unit g respectively; is the unit load shedding cost of grid node n; are the unit output, operating capacity and load shedding power of the thermal power unit in the tth period of the kth typical day; c TG,LIB The unit power generation cost of the additional output of thermal power units to smooth out the monthly power imbalance; is the monthly power generation of thermal power units; is the number of scenario-years involved in the analysis; It is the risk of electricity imbalance caused by monthly electricity fluctuations in several scenarios.

[0168] In this embodiment, in step S3, a solver is set to solve the optimization planning model considering the long-term supply and demand imbalance risk, so as to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk.

[0169] The constructed optimization problem is a typical linear programming problem that can be directly solved using commercial solvers such as Gurobi. The constraints of the optimization planning model that considers the risk of long-term supply and demand imbalances include: typical daily operation constraints, power balance constraints for extreme weather scenarios, and monthly power balance constraints that consider the seasonal characteristics of renewable energy.

[0170] Specifically,

[0171] 1) Typical day operation constraints

[0172] Traditional optimization planning models only consider typical day operation constraints, including power balance constraints of power network nodes within a typical day, thermal power unit combination, operation reserve, long-term and short-term energy storage operation, and new energy operation constraints. Typical day operation constraints can be divided into three parts: intraday operation constraints, daytime operation constraints, and planned reserve.

[0173] The specific construction of the constraint set for typical day operation is shown in equations (30)-(35):

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] The specific meaning of each variable in the formula is similar to the definition of variables in the extreme scenario LR. The difference is that all variables here are defined within the typical scenario day, rather than the operating variables in the extreme scenario LR. Formulas (30)-(35) respectively represent node power balance, upper and lower limits of load shedding, wind and solar curtailment, thermal power unit combination constraint set, spinning reserve constraints at each node of the power grid, and new energy penetration constraints. Among them, in the spinning reserve constraint set (34), represents the spinning reserve of thermal power unit g; and They represent the spinning reserve rate at node n to cope with the uncertainty of load and renewable energy (wind power, photovoltaic). According to the reserve requirements of China's power grid, the reserve rate is set to 5% in the present invention; It represents the maximum ramp rate of the thermal power unit g in 10 minutes, which is 20% in the present invention.

[0181] The specific construction of the typical daytime operation simulation constraint set is shown in formula (36):

[0182]

[0183] The typical daytime constraint set primarily considers energy storage operational constraints. Short-term energy storage is designed to account for daily operational balance, while seasonal energy storage is designed to account for annual operational balance. The constraint set models the storage capacity variations between adjacent time periods within a day for short-term energy storage, between adjacent time periods within a day and within a day for seasonal energy storage, upper and lower limits on energy storage charge and discharge power and stored capacity, and short-term and seasonal energy storage capacity balance constraints.

[0184] The specific construction of the planned power backup constraint set is shown in formula (37):

[0185]

[0186] 2) Power balance constraints in extreme weather scenarios

[0187] The planning model embeds operational constraints that take into account extreme weather scenarios with continuous low output of renewable energy, as shown in Equations (2)-(14). By adding safe operation constraints under extreme scenarios, the imbalance risk of power supply under extreme scenarios can be effectively controlled in the planning problem.

[0188] 3) Monthly electricity balance constraints considering seasonal characteristics of new energy

[0189] The monthly electricity imbalance risk of the power system is modeled by the monthly electricity balance operation constraint, as shown in Equations (16)-(25). The monthly electricity imbalance risk control of the power system is achieved by embedding the monthly electricity imbalance risk assessment model.

[0190] In this embodiment, the reliability of the optimized planning schemes obtained under different condition settings is evaluated. Through hourly operation simulations of a large number of scenario years, the annual average load shedding expectation (EENS) is calculated to evaluate the reliability of each planning scheme.

[0191] like Figure 9 The figure shows the solution and evaluation process of the optimization model constructed in this paper. By setting the objective function, adding constraints, and performing reliability evaluation on the calculation results obtained by the optimization solution, a quantitative analysis of different optimization planning schemes can be achieved.

[0192] In a possible embodiment, a modified IEEE RTS-79 system optimization planning example is provided as follows:

[0193] As shown in Table 1, this embodiment sets up five sets of examples for comparative analysis: Example I uses a classical planning method and only considers the operation simulation within a typical day; Example II builds on Example I by adding power system operation constraints within an extreme scenario; Example III builds on Example I by considering the system's monthly power imbalance risk; Example IV builds on Example I by simultaneously considering the extreme scenario operation simulation constraints and the monthly power imbalance risk; and Example V builds on Example IV by considering seasonal energy storage to address the system's long-term supply and demand imbalance risk. For the selection of extreme weather scenarios, based on the extreme weather scenario criterion proposed in Equation (1), the union of the continuous low output intervals of each wind power / photovoltaic unit is selected as the extreme scenario boundary and optimized for analysis. Each factor is represented in the table as a sum (√) and a (°), indicating that the optimization problem considers the factor, and χ5 indicates that the factor is not considered.

[0194]

[0195] Table 1 Example settings

[0196] The revised IEEE RTS-79 system includes 24 network nodes, 38 transmission lines, 11 thermal power units, 14 wind turbines and 19 photovoltaic units. The system topology is as follows: Figure 10 To balance the impact of long- and short-term energy balances on planning results, the weight factor w in Examples III, IV, and V is set to 0.5. When considering the risk of monthly power imbalance, the confidence level κ is set to 95% and 99%, respectively, to compare the planning results of the system at different confidence levels. The key technical and economic parameters for seasonal hydrogen storage in this example are shown in Table 2.

[0197] Object Value Unit investment cost of electricity-to-hydrogen / hydrogen-to-electricity (yuan / kW) 3500 Unit investment cost of hydrogen storage tank / (yuan / kg) 200 Energy conversion efficiency of electricity to hydrogen / hydrogen to electricity / % 60 / 55

[0198] Table 2 Economic and technical parameters of seasonal hydrogen storage

[0199] like Figure 11 The figure shows comparative analysis results for a modified IEEE RTS-79 system. This example considers various types of generators (including thermal, hydro, wind, and photovoltaic). Electrochemical energy storage, seasonal hydrogen storage, and transmission lines provide flexibility at different timescales, and together with the thermal generators, they form the primary source of system flexibility. A comparison of Example I and Example II shows that the introduction of extreme scenario constraints places higher demands on power supply adequacy. The electrochemical energy storage capacity was increased by 362 MW to improve the system's power supply adequacy and flexibility. Reliability verification of the planning scheme shows that the annual average expected energy not served (EENS) as a percentage of the system's total annual load decreases from 0.9879% in Example I to 0.3890% in Example II, further demonstrating that planning schemes that consider sustained low-output extreme weather scenarios can effectively improve system power supply reliability. In terms of renewable energy curtailment, the renewable energy curtailment rate in Case I reached 13.2%, due to the high penetration rate of wind and solar power in the system (over 40%). In contrast, the renewable energy curtailment rate in Case II dropped to 11.5%.

[0200] Case III builds on Case I and considers the risk of monthly power imbalance under different confidence levels κ. Since seasonal fluctuations in renewable energy (including wind, photovoltaic, and hydropower) are the primary cause of the system's monthly power imbalance, the installed capacity of renewable energy in Case III is reduced compared to that in Case I, while thermal power capacity is increased to mitigate the risk of long-term supply-demand imbalance. In Case III, as the confidence level increases, the transmission line capacity is increased from 9,907 MW (κ = 95%) to 11,990 MW (κ = 99%) to enhance the system's long-term power regulation capability. Because electrochemical energy storage cannot provide long-term system flexibility, its installed capacity is reduced from 395 MW (κ = 95%) to 226 MW (κ = 99%). In terms of economy and reliability, the system's cost per kilowatt-hour increased from 0.421 yuan / kW·h (κ=95%) to 0.422 yuan / kW·h (κ=99%), while the percentage of EENS in the system's total annual load decreased from 0.6657% to 0.4881%.

[0201] Case IV comprehensively considers the extreme scenario constraints and the risk of monthly power imbalance. Its planning scheme further increases the installed capacity of thermal power, and the system's cost per kilowatt-hour also increases to 0.424 yuan.

[0202] / (kW·h)(κ=95%) and 0.425 yuan / (kW·h)(κ=99%); and the proportion of EENS in the total annual system load also decreased to 0.5035% and 0.3028% respectively. Example V considers the role of seasonal hydrogen storage in stabilizing the risk of monthly power imbalance based on Example IV. By introducing seasonal hydrogen storage of 460MW(κ

[0203] =95%), the average monthly unbalanced power consumption decreased from 15,910 MW·h / year (Case IV, κ = 95%) to 2,740 MW·h / year (Case V, κ = 95%), and the proportion of EENS in the total annual system load also decreased to 0.5035% (κ = 95%) and 0.0961% (κ = 99%), respectively. In terms of economic efficiency, the total system cost per kilowatt-hour (kWh) was reduced to 0.424 yuan per kW·h compared to Case IV. These results demonstrate that incorporating seasonal energy storage not only effectively improves system power reliability but also enhances the economic efficiency of the planning scheme.

[0204] like Figure 12 As shown in Figure 2, taking Example V as an example, the energy balance results of the system in different scenarios are shown at the confidence level κ = 95%: Figure 12 (a) shows the hourly power balance results of the system on a typical day. The comparison between the original load and the load after adjustment (load energy storage charging and energy storage discharging) in the figure reflects the regulatory effect of energy storage (electrochemical energy storage, seasonal hydrogen storage) on the system load. Figure 12 (b) shows the hourly power balance results in the extreme scenario: compared with Example I, the wind and solar power output in the extreme scenario is significantly reduced. The wind power output is continuously zero from the 10th to the 35th period, and the wind and solar power output in other periods is also relatively small. To effectively address the problem of intermittent wind and solar power output, the output of thermal power and hydropower in the extreme scenario is correspondingly increased. Figure 12 (c) shows the monthly electricity balance results of the scenario year. The photovoltaic power generation curve reflects the seasonal characteristics of high in summer and low in winter; wind power resources are most abundant in spring (March-May); and hydropower has the largest power generation during the flood season in summer (July-August).

[0205] like Figure 13The figure shows the monthly power imbalance of the system in Example V. Each point in the figure describes the power balance of each month in all scenarios. The vertical axis is the normalized monthly renewable energy power generation, and the horizontal axis is the normalized monthly system load power. When the scatter point falls on the diagonal, it means that the system's renewable energy power generation and load power are balanced, and the system does not have a monthly power imbalance problem in that month; when the color point falls below the diagonal, it means that the system's monthly load demand power is greater than the renewable energy monthly power generation, and the system has a monthly power imbalance problem in that month; when the scatter point falls above the diagonal, it means that the renewable energy monthly power generation is greater than the system's monthly load demand power, and the system has a renewable energy power curtailment problem in that month. Among them, the scatter points corresponding to different months have different colors and shapes. The same month in different scenario years is represented by scatter points of the same color and shape.

[0206] like Figure 13 In the example V shown in Figure 2, the monthly power imbalance of the system is shown at different confidence levels. Figure 13 It can be seen that in the photovoltaic-dominated power system of Example IV, a large amount of renewable energy power was abandoned in July and August, while load and power imbalance occurred more frequently in November and December. This is because there are a large number of photovoltaic installations in the system, and the photovoltaic units reach their peak output in the summer; while the load reaches the second peak demand in winter (November-December). Figure 13 From the comparison between (a) and (b), we can see that as the confidence level increases from 95% to 99%, the number of scattered points deviating from the diagonal in the figure decreases significantly, further proving that the monthly unbalanced electricity can be effectively smoothed out by increasing the confidence level.

[0207] like Figure 14 As shown in Figure 1, using Cases IV and V as examples, the reliability indicators change as the system's renewable energy penetration rate increases from 10% to 90% at a confidence level of κ = 95%. As can be seen from the figure, as renewable energy penetration gradually increases, system reliability gradually decreases. In Case IV, the ratio of EENS to annual total load increases from 0.1985% (10% wind and solar penetration rate) to 0.9001% (90% wind and solar penetration rate). When the wind and solar penetration rate exceeds 30%, the introduction of seasonal energy storage can effectively improve system reliability compared to a power system without seasonal energy storage.

[0208] Through the comparison of Example IV, it can be seen that the optimization planning method proposed in this embodiment can provide a more reliable planning scheme. At the same time, seasonal energy storage can also effectively control the system's electricity cost, further proving the effectiveness of the planning method.

[0209] In summary, the present invention analyzes and assesses the risk of long-term supply and demand imbalance in the power system by constructing an analytical model and creating an assessment strategy, obtaining data on the risk of long-term supply and demand imbalance in the power system; constructs an optimization planning model that considers the risk of long-term supply and demand imbalance based on the data; and solves the optimization planning model that considers the risk of long-term supply and demand imbalance by setting a solver to obtain an optimization planning scheme that considers the risk of long-term supply and demand imbalance. The present invention achieves an optimal balance between the economy and stability of the power system by comprehensively considering the advantages and disadvantages of different energy storage methods, such as electrochemical energy storage and hydroelectric energy storage. This method not only considers power and energy balance, load characteristics, and the charge and discharge characteristics of the energy storage system, but also specifically studies energy storage capacity planning under extreme weather conditions to ensure stable operation of the power system and continuous power supply. This method can better adapt to the volatility of renewable energy, improve the reliability and flexibility of the power system, reduce the system's operating costs, and promote sustainable energy development. The present invention proposes a power and energy balance planning method that integrates multiple energy storage technologies (such as electrochemical energy storage and hydroelectric energy storage). This method takes into account the characteristics of different energy storage technologies, such as the rapid response characteristics of electrochemical energy storage and the large-scale energy storage capacity of hydroelectric energy storage, and achieves the best balance between the economy and stability of the power system through optimized combination strategies. Secondly, special attention is paid to the problem of power and electricity balance in the long term. By analyzing the seasonal fluctuation characteristics of renewable energy power generation and the stability of the power system under extreme weather conditions, corresponding risk assessment models and energy storage capacity planning methods are proposed. In response to extreme weather events, such as extremely hot and windless or extremely cold and lightless, this patent proposes an identification method and corresponding energy storage system response strategy to ensure the stable operation of the power system and the continuous supply of electricity. The optimization model in the present invention not only takes into account the power and electricity balance, load characteristics, and charging and discharging characteristics of the energy storage system, but also specifically studies the energy storage capacity planning under extreme weather conditions, which is its unique technical contribution. The present invention is original and practical in solving the challenges faced by power system planning methods in the context of high penetration of new energy.

[0210] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0211] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0212] Example 2

[0213] See also Figure 15 Embodiment 2 of the present invention further provides a multi-element energy storage capacity planning device taking into account power and electricity balance constraints, comprising:

[0214] The power system long-term supply and demand imbalance risk analysis and assessment module 001 is used to analyze and assess the risk of long-term supply and demand imbalance in the power system by building an analysis model and creating an assessment strategy, and to obtain data on the risk of long-term supply and demand imbalance in the power system;

[0215] An optimization planning model construction module 002 considering the risk of long-term supply and demand imbalance is used to construct an optimization planning model considering the risk of long-term supply and demand imbalance based on the data of the risk of long-term supply and demand imbalance in the power system;

[0216] The optimization planning model solving module 003 considering the long-term supply and demand imbalance risk is used to solve the optimization planning model considering the long-term supply and demand imbalance risk by setting a solver to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk.

[0217] In this embodiment, in the power system long-term supply and demand imbalance risk analysis and assessment module 001, the analysis and assessment submodules include:

[0218] The submodule 011 for judging the state of continuous low output of new energy is used to judge the extreme weather events of continuous low output of new energy and determine the daily average output state of new energy;

[0219] The node power balance constraint construction submodule 012 is used to constrain the node power balance in the extreme scenario of low output of new energy by constructing the node power balance constraint in the extreme scenario of low output of new energy;

[0220] The linearization model construction and analysis submodule 013 is used to analyze and evaluate the start-up and shutdown operation of conventional thermal power units by constructing a linearization model;

[0221] The power flow model construction and analysis submodule 014 is used to analyze and evaluate the power flow of the power system by constructing the power flow model of the built / to-be-built lines;

[0222] The new energy operation constraint construction submodule 015 is used to constrain the output operation status of new energy wind power, photovoltaic power and hydropower by constructing new energy operation constraints;

[0223] The energy storage operation constraint construction submodule 016 is used to achieve energy balance of seasonal energy storage of the power system on a yearly time scale by constructing energy storage operation constraints;

[0224] The long-term supply and demand imbalance risk index calculation submodule 017 is used to calculate the imbalance risk of the power system under extreme weather scenarios to obtain a long-term supply and demand imbalance risk index; and ensure the safe operation of the power system through the long-term supply and demand imbalance risk index;

[0225] The power system monthly electricity balance model construction submodule 018 is used to evaluate the long-term power balance risk of the power system by constructing the power system monthly electricity balance model under long-term scale;

[0226] The power system monthly electricity imbalance risk quantitative assessment submodule 019 is used to quantitatively assess the power system monthly electricity imbalance risk based on the CVaR strategy.

[0227] In this embodiment, in the optimization planning model construction module 002 considering the risk of long-term supply and demand imbalance, the objective function expression of the optimization planning model considering the risk of long-term supply and demand imbalance is:

[0228] minC ove =C Inv +(1-w)C SB,Ope +wC LB,Ope

[0229]

[0230] Where minC ove To optimize the total system cost; C Inv To minimize the annual investment cost; C SB,Ope is the typical daily operating cost; C LB,Ope is the monthly electricity imbalance operation and risk cost; w is the weight factor of long-term operation cost in total operation cost, which reflects the proportion of monthly electricity imbalance operation cost considered in the optimization problem; The unit investment costs of the generator set, the transmission line to be built, and the energy storage system are respectively; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are the investment discount rate and investment payback period respectively; and are the unit variable operating cost and unit startup cost of thermal power unit g respectively; is the unit load shedding cost of grid node n; are the unit output, operating capacity and load shedding power of the thermal power unit in the tth period of the kth typical day; c TG,LIB The unit power generation cost of the additional output of thermal power units to smooth out the monthly power imbalance; is the monthly power generation of thermal power units; is the number of scenario-years involved in the analysis; It is the risk of electricity imbalance caused by monthly electricity fluctuations in several scenarios.

[0231] In this embodiment, in the optimization planning model solving module 003 that considers the risk of long-term supply and demand imbalance, in the process of solving the optimization planning model that considers the risk of long-term supply and demand imbalance, the constraints of the optimization planning model that considers the risk of long-term supply and demand imbalance include: typical daily operation constraints, power balance constraints for extreme weather scenarios, and monthly power balance constraints considering the seasonal characteristics of new energy.

[0232] In this embodiment, in the optimization planning model solving module 003 that considers the risk of long-term supply and demand imbalance, in the process of solving the optimization planning model that considers the risk of long-term supply and demand imbalance and obtaining the optimization planning scheme that considers the risk of long-term supply and demand imbalance, the annual average load shedding expectation is calculated through hourly operation simulation of a large number of scenario years; the reliability of the optimization planning scheme obtained under the set conditions is evaluated through the annual average load shedding expectation, thereby realizing quantitative analysis of the optimization planning scheme.

[0233] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and no further details will be given here.

[0234] Example 3

[0235] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a multi-element energy storage capacity planning method taking into account power and electricity balance constraints is stored. The program code includes instructions for executing embodiment 1 or any possible implementation thereof, a multi-element energy storage capacity planning method taking into account power and electricity balance constraints.

[0236] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0237] Example 4

[0238] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0239] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a multi-element energy storage capacity planning method that takes into account power balance constraints in embodiment 1 or any possible implementation thereof.

[0240] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0241] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0242] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0243] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints, characterized in that: include: By building an analytical model and creating an assessment strategy, we analyze and assess the risks of long-term supply and demand imbalance in the power system, and obtain data on the risks of long-term supply and demand imbalance in the power system; Based on the data on the long-term supply and demand imbalance risk of the power system, an optimization planning model that considers the long-term supply and demand imbalance risk is constructed; By setting a solver, the optimization planning model considering the long-term supply and demand imbalance risk is solved to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk; In the process of analyzing and evaluating the risk of long-term imbalance between supply and demand in the power system by building an analysis model and creating an evaluation strategy, the analysis and evaluation steps include: Criteria are established for extreme weather events with sustained low output of renewable energy, and the average daily output of renewable energy is determined; By constructing node power balance constraints in extreme scenarios with low output of new energy, the node power balance in extreme scenarios with low output of new energy is constrained; By building a linear model, the start-up and shutdown operation of conventional thermal power units is analyzed and evaluated; Analyze and evaluate the power flow of the power system by constructing power flow models of existing / to-be-built lines; By establishing new energy operation constraints, the output operation status of new energy wind power, photovoltaic power and hydropower is constrained; By building energy storage operation constraints, the seasonal energy storage of the power system can achieve energy balance on a year-round time scale; By calculating the imbalance risk of the power system under extreme weather scenarios, a long-term supply and demand imbalance risk index is obtained; and the safe operation of the power system is ensured by the long-term supply and demand imbalance risk index; By constructing a monthly power balance model for the power system on a long time scale, the long-term power balance risk of the power system is assessed; Based on the CVaR strategy, the monthly electricity imbalance risk of the power system is quantitatively assessed.

2. A multi-element energy storage capacity planning method taking into account power and electricity balance constraints according to claim 1, characterized in that: The objective function expression of the optimization planning model considering the long-term supply and demand imbalance risk is: minC ove =C Inv +(1-w)C SB,Ope +wC LB,Ope Where minC ove To optimize the total system cost; C Inv To minimize the annual investment cost; C SB,Ope is the typical daily operating cost; C LB,Ope is the monthly electricity imbalance operation and risk cost; w is the weight factor of long-term operation cost in total operation cost, which reflects the proportion of monthly electricity imbalance operation cost considered in the optimization problem; The unit investment costs of the generator set, the transmission line to be built, and the energy storage system are respectively; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are the investment discount rate and investment payback period respectively; and are the unit variable operating cost and unit startup cost of thermal power unit g respectively; is the unit load shedding cost of grid node n; are the unit output, operating capacity and load shedding power of the thermal power unit in the tth period of the kth typical day; c TG,LIB The unit power generation cost of the additional output of thermal power units to smooth out the monthly power imbalance; is the monthly power generation of thermal power units; is the number of scenario-years involved in the analysis; It is the risk of electricity imbalance caused by monthly electricity fluctuations in several scenarios.

3. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 2, characterized in that: In the process of solving the optimization planning model that considers the long-term supply and demand imbalance risk, the constraints of the optimization planning model that considers the long-term supply and demand imbalance risk include: typical daily operation constraints, power balance constraints for extreme weather scenarios, and monthly power balance constraints considering the seasonal characteristics of new energy.

4. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 3, characterized in that: In the process of solving the optimization planning model that considers the long-term supply and demand imbalance risk and obtaining the optimization planning scheme that considers the long-term supply and demand imbalance risk, the annual average load shedding expectation is calculated through hourly operation simulation of a large number of scenario years; the reliability of the optimization planning scheme obtained under the set conditions is evaluated based on the annual average load shedding expectation, thereby realizing quantitative analysis of the optimization planning scheme.

5. A multi-element energy storage capacity planning device taking into account power and electricity balance constraints, adopting a multi-element energy storage capacity planning method taking into account power and electricity balance constraints according to any one of claims 1 to 4, characterized in that: include: The power system long-term supply and demand imbalance risk analysis and assessment module is used to analyze and assess the risk of long-term supply and demand imbalance in the power system by building an analysis model and creating an assessment strategy, and to obtain data on the risk of long-term supply and demand imbalance in the power system; An optimization planning model construction module that considers the risk of long-term supply and demand imbalance is used to construct an optimization planning model that considers the risk of long-term supply and demand imbalance based on the data of the risk of long-term supply and demand imbalance in the power system; The optimization planning model solving module considering the long-term supply and demand imbalance risk is used to solve the optimization planning model considering the long-term supply and demand imbalance risk by setting a solver to obtain an optimization planning scheme considering the long-term supply and demand imbalance risk.

6. The multi-element energy storage capacity planning device taking into account power and electricity balance constraints according to claim 5, characterized in that: In the power system long-term supply and demand imbalance risk analysis and assessment module, the analysis and assessment submodule includes: The submodule for judging the continuous low output state of new energy is used to judge the extreme weather events of continuous low output of new energy and determine the daily average output state of new energy; The node power balance constraint construction submodule is used to constrain the node power balance in the extreme scenario of low output of new energy by constructing the node power balance constraint in the extreme scenario of low output of new energy; The linearization model construction and analysis submodule is used to analyze and evaluate the start-up and shutdown operation of conventional thermal power units by constructing a linearization model; The power flow model construction and analysis submodule is used to analyze and evaluate the power flow of the power system by constructing the power flow model of the built / to-be-built lines; The new energy operation constraint construction submodule is used to constrain the output operation status of new energy wind power, photovoltaic power and hydropower by constructing new energy operation constraints; The energy storage operation constraint construction submodule is used to achieve energy balance of the seasonal energy storage of the power system on a year-round time scale by constructing energy storage operation constraints; A long-term supply-demand imbalance risk index calculation submodule is used to calculate the imbalance risk of the power system under extreme weather scenarios to obtain a long-term supply-demand imbalance risk index; and to ensure the safe operation of the power system through the long-term supply-demand imbalance risk index; The power system monthly electricity balance model construction submodule is used to evaluate the long-term power balance risk of the power system by constructing the power system monthly electricity balance model under long-term scale; The power system monthly electricity imbalance risk quantitative assessment submodule is used to quantitatively assess the power system monthly electricity imbalance risk based on the CVaR strategy.

7. The multi-element energy storage capacity planning device taking into account power and electricity balance constraints according to claim 6, characterized in that: In the optimization planning model construction module considering the long-term supply and demand imbalance risk, the objective function expression of the optimization planning model considering the long-term supply and demand imbalance risk is: minC ove =C Inv +(1-w)C SB,Ope +wC LB,Ope Where minC ove To optimize the total system cost; C Inv To minimize the annual investment cost; C SB,Ope is the typical daily operating cost; C LB,Ope is the monthly electricity imbalance operation and risk cost; w is the weight factor of long-term operation cost in total operation cost, which reflects the proportion of monthly electricity imbalance operation cost considered in the optimization problem; The unit investment costs of the generator set, the transmission line to be built, and the energy storage system are respectively; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are the investment discount rate and investment payback period respectively; and are the unit variable operating cost and unit startup cost of thermal power unit g respectively; is the unit load shedding cost of grid node n; are the unit output, operating capacity and load shedding power of the thermal power unit in the tth period of the kth typical day; c TG,LIB The unit power generation cost of the additional output of thermal power units to smooth out the monthly power imbalance; is the monthly power generation of thermal power units; is the number of scenario-years involved in the analysis; It is the risk of electricity imbalance caused by monthly electricity fluctuations in several scenarios.

8. The multi-element energy storage capacity planning device taking into account power and electricity balance constraints according to claim 7, characterized in that: In the optimization planning model solving module that considers the long-term supply and demand imbalance risk, in the process of solving the optimization planning model that considers the long-term supply and demand imbalance risk, the constraints of the optimization planning model that considers the long-term supply and demand imbalance risk include: typical daily operation constraints, power balance constraints for extreme weather scenarios, and monthly power balance constraints that consider the seasonal characteristics of new energy.

9. The multi-element energy storage capacity planning device taking into account power and electricity balance constraints according to claim 8, characterized in that: In the optimization planning model solving module that considers the risk of long-term supply and demand imbalance, in the process of solving the optimization planning model that considers the risk of long-term supply and demand imbalance and obtaining the optimization planning scheme that considers the risk of long-term supply and demand imbalance, the annual average load shedding expectation is calculated through hourly operation simulation of a large number of scenario years; the reliability of the optimization planning scheme obtained under the set conditions is evaluated based on the annual average load shedding expectation, thereby realizing quantitative analysis of the optimization planning scheme.

Images