Multi-element energy storage capacity planning method considering electric power and electric quantity balance constraint

Abstract

A multivariate energy storage capacity planning method considering electric power and electric quantity balance constraint comprises the following steps: analyzing and evaluating the risk of long-period supply and demand imbalance of an electric power system by constructing an analysis model and creating an evaluation strategy, and obtaining data of the risk of long-period supply and demand imbalance of the electric power system; according to the data of the long-period supply and demand imbalance risk of the power system, constructing an optimization planning model considering the long-period supply and demand imbalance risk; and through setting a solver, solving the optimization planning model considering the long-period supply and demand imbalance risk, and obtaining an optimization planning scheme considering the long-period supply and demand imbalance risk. According to the method, electric power and electric quantity balance, load characteristics and charging and discharging characteristics of the energy storage system are considered, and energy storage capacity planning under extreme weather conditions is researched, so that stable operation of an electric power system and continuous supply of electric power and electric quantity are ensured.

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 transformation of the global energy structure and the urgent need to achieve the goals of "carbon peak and carbon neutrality", the large-scale grid connection of clean energy such as wind power and solar energy has become the main development trend in the energy field. These renewable energy sources are clean and renewable, which theoretically help reduce greenhouse gas emissions and promote sustainable energy development. However, due to their inherent volatility and uncertainty, large-scale grid-connected renewable energy has posed new challenges to the stability and reliability of the power system. Especially under extreme weather conditions, such as no wind due to extremely hot weather or insufficient sunshine due to extremely cold weather, the output of renewable energy may be greatly reduced, which 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 mainly focus on short-term power balance, but do not take into account the risk of supply and demand imbalance in the long term. With the continuous increase in the proportion of new energy installed capacity, the power system has an increasing demand for power and electricity balance planning. In the existing literature, although some studies have considered the source-grid-load-storage coordinated planning model for efficient consumption of new energy, the power system optimization planning model for long-term energy storage, and the impact of electrochemical energy storage on the system power and electricity balance level, these studies still have some limitations and shortcomings.

[0004] First, existing models often ignore the characteristics and optimal combinations of different energy storage technologies, and fail to fully utilize the comprehensive benefits of multi-energy storage systems. For example, electrochemical energy storage technology is suitable for coping with short-term power fluctuations with its rapid response characteristics, while hydroelectric energy storage is suitable for seasonal regulation with its large-scale energy storage capacity. The lack of in-depth understanding and optimal combination of the characteristics of these different energy storage technologies limits the flexibility and effectiveness of system planning.

[0005] Secondly, existing research lacks research on the planning and operation strategies of energy storage systems in special scenarios such as extreme weather. The prediction and response to extreme weather events is an important aspect in power system planning, and new models and methods need to be developed to evaluate and optimize the performance of energy storage systems in these situations.

[0006] Finally, existing energy storage capacity planning methods mostly focus on a single energy storage technology, lacking a systematic approach that comprehensively considers multiple energy storage methods such as electrochemical energy storage and water 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 method for multi-energy storage capacity planning under the constraint of power and electricity balance, which can comprehensively consider the advantages and disadvantages of different energy storage methods such as electrochemical energy storage and water storage energy storage, and achieve the best 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, the present invention provides a multivariate energy storage capacity planning method that takes into account power and electricity balance constraints. This method not only takes into account power and electricity balance, load characteristics, and the charging and discharging characteristics of the energy storage system, but also specifically studies the energy storage capacity planning under extreme weather conditions to ensure the stable operation of the power system and the continuous supply of power and electricity. Through this method, it is possible to better adapt to the volatility of new energy, improve the reliability and flexibility of the power system, reduce the operating costs of the system, and promote the sustainable development of energy.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a multivariate energy storage capacity planning method taking into account power and electricity balance constraints, comprising:

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

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

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

[0013] As a preferred solution of a multivariate energy storage capacity planning method taking into account power and electricity 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] To judge extreme weather events with continuous low output of new energy and determine the daily average output of new energy;

[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 the power flow model of the built / to-be-built lines;

[0018] By building 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-demand imbalance risk index is obtained; and the safe operation of the power system is ensured by the long-term supply-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 evaluated;

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

[0023] As a preferred solution of a multivariate energy storage capacity planning method taking into account power and electricity balance constraints, the objective function expression of the optimization planning model considering 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 unbalanced 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 unbalanced operation cost considered in the optimization problem; They are the unit investment costs of the generator set, the transmission line to be built, and the energy storage system; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are 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, startup 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 balance 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 scheme for a multivariate energy storage capacity planning method taking 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 in extreme weather scenarios, and monthly electricity balance constraints that take into account the seasonal characteristics of new energy.

[0028] As a preferred scheme for a multivariate energy storage capacity planning method taking 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 through the annual average load shedding expectation, so as to achieve quantitative analysis of the optimization planning scheme.

[0029] The present invention also 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 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;

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

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

[0034] The submodule 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;

[0035] A 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 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 obtain a long-term supply-demand imbalance risk index by calculating the imbalance risk of the power system under extreme weather scenarios; 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 taking into account power and electricity balance constraints, in the optimization planning model construction module 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:

[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 unbalanced 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 unbalanced operation cost considered in the optimization problem; They are the unit investment costs of the generator set, the transmission line to be built, and the energy storage system; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are 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, startup 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 balance 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 risk of long-term supply and demand imbalance, 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 in 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 taking 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 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, so as to realize the quantitative analysis of the optimization planning scheme.

[0050] The present invention has the following advantages: 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 the data of the risk of long-term supply and demand imbalance in the power system is obtained; according to the data of the risk of long-term supply and demand imbalance in the power system, an optimization planning model considering the risk of long-term supply and demand imbalance is constructed; by setting a solver, the optimization planning model considering the risk of long-term supply and demand imbalance is solved to obtain an optimization planning scheme considering the risk of long-term supply and demand imbalance. The present invention achieves the best balance of the power system in terms of economy and stability by comprehensively considering the advantages and disadvantages of different energy storage methods such as electrochemical energy storage and water storage energy storage. The method not only considers the power and electricity balance, load characteristics and charging and discharging characteristics of the energy storage system, but also studies the energy storage capacity planning under extreme weather conditions to ensure the stable operation of the power system and the continuous supply of power and electricity. Through this method, the volatility of new energy can be better adapted, the reliability and flexibility of the power system can be improved, the operating cost of the system can be reduced, and the sustainable development of energy can be promoted. The present invention proposes a power and electricity balance planning method that comprehensively applies multiple energy storage technologies (such as electrochemical energy storage and water storage 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 water storage, and achieves the best balance between the economy and stability of the power system through optimizing the combination strategy. Secondly, special attention is paid to the problem of power and electricity balance in the long period. By analyzing the seasonal fluctuation characteristics of renewable energy power generation and the stability of the power system under extreme weather conditions, a corresponding risk assessment model and energy storage capacity planning method are proposed. For extreme weather events, such as extremely hot and windless or extremely cold and lightless, this patent proposes an identification method and a 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 under the background of high penetration of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0052] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

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

[0054] Figure 2 A schematic diagram of the risk of long-term imbalance between supply and demand of 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 A schematic diagram of an extreme weather scenario of continuous low output of new 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;

[0056] Figure 4 A statistical schematic 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 multivariate 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 new 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 risk value 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 A schematic diagram of a monthly electricity imbalance risk calculation process in a multivariate energy storage capacity planning method taking into account power and electricity balance constraints provided in Example 1 of the present invention;

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

[0061] Fig. 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] Fig.10 A schematic diagram of a modified IEEE RTS-79 system topology in a possible embodiment provided in Embodiment 1 of the present invention;

[0063] Fig.11 A schematic diagram of comparative analysis results of a calculation example based on a modified IEEE RTS-79 system in a possible embodiment provided in Embodiment 1 of the present invention;

[0064] Fig.12 A schematic diagram of energy balance results of an IEEE RTS-79 system at different time scales in a possible embodiment provided in Embodiment 1 of the present invention;

[0065] Fig.13 A schematic diagram of monthly electricity imbalance results in an IEEE RTS-79 scenario in a possible embodiment provided in Embodiment 1 of the present invention;

[0066] Fig.14 A schematic diagram of reliability indicators of an IEEE RTS-79 system under different wind-solar penetration rates in a possible embodiment provided in Embodiment 1 of the present invention;

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

[0068] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 analysis model and creating an evaluation 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 of long-term supply and demand imbalance risk of the power system, an optimization planning model considering the long-term supply and demand imbalance risk is constructed;

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

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

[0075] Specifically, Figure 1 As shown, the present invention divides the long-term supply and demand imbalance risk of the new power system into two aspects: on the one hand, there is the long-term supply and demand imbalance risk caused by extreme weather scenarios with continuous low output of new energy. Scenarios such as extremely hot and windless, extremely cold and lightless bring great challenges to the power and electricity balance of the system; on the other hand, there is the long-term monthly electricity imbalance risk caused by the seasonal fluctuation characteristics of new energy power generation. Based on this, this section proposes analysis models and evaluation methods for the specific manifestations of the long-term supply and demand imbalance risk.

[0076] The analysis and evaluation steps include:

[0077] S11. Criteria for extreme weather events with continuous low output of new energy sources, and determine the daily average output status of new energy sources;

[0078] Specifically, the continued low output of new 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] In the formula, is the average daily output of renewable energy; max is the rated output level; δ is the duration of continuous low output; d is the time, which is used to calculate the duration of renewable energy;

[0081] Average daily output of renewable energy Below rated output level ρ max When the daily average output of new energy is 10% lower than the rated output and the duration exceeds 2 days (i.e. 48h), it is identified as a continuous low output scenario. It should be noted that there is currently a lack of 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 new energy may be different.

[0082] Based on formula (1), the present invention extracts extreme weather scenarios with continuous low output of new energy based on statistical analysis of the new energy output data of a certain power grid in China. Figure 4 As shown in the figure, 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 the low wind power output scenario lasts the longest in summer, reaching 10 days, while the low photovoltaic output scenario lasts the longest, 6 days, in winter. By comparison, it can be found that the frequency and duration of extreme weather scenarios in different seasons are different, and there are obvious seasonal distribution characteristics.

[0083] S12, constraining 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;

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

[0085]

[0086] In the formula, 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 charging and discharging 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 start / end 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 variation process of 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 of the built / to-be-built lines;

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

[0100]

[0101] In the formula, They refer to the built capacity and expanded 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 plan of the transmission line is only reflected in the investment expansion of the transmission capacity.

[0102] S15. By constructing 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] In the formula, Hourly output of wind power / photovoltaic power; The amount of wind power / photovoltaic power wasted; Represents the hourly wind and solar fluctuation curve in extreme scenarios; This indicates the installed capacity of wind and solar power.

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

[0107]

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

[0109] S16. By constructing energy storage operation constraints, the seasonal energy storage of 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] In the formula, Represents the storage capacity of energy storage s at time h.

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

[0114]

[0115]

[0116] In the formula, 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] In the formula, h start Indicates the starting time of each extreme scenario day; 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 by using the long-term supply-demand imbalance risk index;

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

[0123]

[0124] By summing up all the load shedding in extreme scenarios, the long-term supply and demand imbalance risk index in extreme weather scenarios is obtained. In order to ensure the safety of power system operation, the imbalance risk index of the system in the target extreme weather scenario is set to 0, that is, through resource optimization configuration and flexible equipment operation, the system does not have load shedding in extreme weather scenarios, so as to ensure the safe operation of the system.

[0125] like Figure 5 As shown in the figure, the normalized monthly average output of wind power and photovoltaic power is calculated. Figure 5 (a) 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 the photovoltaic output reaches the highest in summer (July) and the lowest in winter (January). It can be seen that wind and solar power generation has obvious seasonal fluctuation characteristics.

[0126] S18. 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;

[0127] Specifically, power system operation simulation usually only considers hourly power balance within a typical day, and lacks corresponding models and evaluation methods for the power balance of the system and its risk assessment on a long time scale. The present invention 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 on a long time scale for the massive scenario year. Each scenario year y corresponds to a different monthly power generation curve of new energy. and monthly load demand D n,y,m , the imbalanced electricity in each scenario is obtained by simulating and calculating the electricity balance of a large number of scenarios. The generation of the massive scenario year series is based on historical observation data, and the corresponding distribution law of the data is statistically calculated. Then, based on the probability distribution of the wind and solar sequence, 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 in the long time scale. Equation (16) is the annual energy balance constraint of the whole system, which ensures that the energy supply of the system remains balanced in the whole year.

[0129]

[0130] In the formula, for wind power, photovoltaic and hydropower capacity; is the monthly power generation curve of wind power, photovoltaic and hydropower generating 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] In the formula, In order to cope with the risk of monthly power imbalance, thermal power units need to generate more power than originally planned. 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 / transportation of new energy g and transmission line l in the mth month of typical scenario year y, are the monthly charge / discharge capacity of seasonal energy storage, D n,y,m is the monthly load demand power at node n.

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

[0135]

[0136] In the formula, 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] In the formula, It indicates the monthly abandoned electricity amount 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),

[0141]

[0142] In the formula, 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] In the formula, represents the storage energy of seasonal energy storage in the mth month of scenario year y; Represents the charging / discharging efficiency of seasonal energy storage.

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

[0147]

[0148]

[0149] In the formula, and Respectively 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. Based on the CVaR strategy, the monthly power imbalance risk of the power system is quantitatively evaluated.

[0154] Specifically, Figure 6 As shown, the present invention quantitatively evaluates the monthly power imbalance risk of the power system based on the CVaR method. Through the quantitative evaluation of the scenario-year-month unbalanced power risk, the power supply and demand sufficiency of the power system can be intuitively evaluated from a long-term scale, and the optimization model can also be embedded to effectively control the monthly power imbalance risk of the system from the perspective of investment planning.

[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 the 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 Through the CVaR risk analysis method, the tail risk of variable distribution can be effectively considered, and the quantitative modeling and evaluation of the seasonal fluctuation characteristics of power system electricity can be achieved.

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

[0160] Specifically, 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. The traditional power planning model only considers the annualized investment cost and the typical day operation cost, and only considers the operation constraints based on typical days in terms of constraints. On the basis of 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 operation cost in the total operation cost, reflecting the proportion of the monthly electricity imbalance operation cost considered in the optimization problem; in terms of operation constraints, the long-term supply and demand imbalance risk constraint constructed in step one is added.

[0161] The optimization model constructed by the present invention considers minimizing the total cost of the power system, including the annualized investment cost, the typical day operation cost, and the monthly power imbalance risk cost. In order to ensure the safe operation of the power system of the system, the present invention sets no load shedding in extreme weather scenarios and no load shedding penalty cost. Only the safe operation constraints of the system in extreme weather scenarios are considered in the operation constraints.

[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 unbalanced 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 unbalanced operation cost considered in the optimization problem; They are the unit investment costs of the generator set, the transmission line to be built, and the energy storage system; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are 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, startup 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 balance 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 risk of long-term supply and demand imbalance, so as to obtain an optimization planning scheme considering the risk of long-term supply and demand imbalance.

[0169] The constructed optimization problem is a typical linear programming problem, which can be directly solved and calculated by commercial solvers such as Gurobi. In the process of solving the optimization planning model considering the risk of long-term supply and demand imbalance, the constraints of the optimization planning model considering the risk of long-term supply and demand imbalance include: typical daily operation constraints, power balance constraints in extreme weather scenarios, and monthly power balance constraints considering the seasonal characteristics of new energy.

[0170] Specifically,

[0171] 1) Typical day operation constraints

[0172] The traditional optimization planning model only considers the typical day operation constraints, including the power balance constraints of power network nodes, the combination of thermal power units, operation reserve, long-term and short-term energy storage operation, and new energy operation constraints. The typical day operation constraints can be divided into three parts: intra-day operation constraints, daytime operation constraints, and planned reserve.

[0173] Among them, 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 variable definition in the extreme scenario LR. The difference is that all variables here are defined in the typical scenario day, not the operating variables in the extreme scenario LR. Formulas (30)-(35) respectively represent node power balance, load shedding upper and lower limits, 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 respectively represent the spinning reserve rate at node n to cope with the uncertainty of load and new energy (wind power, photovoltaic). According to the reserve requirements of China's power grid operation, the reserve rate is set to 5% in the present invention; It represents the maximum climbing rate of the thermal power unit g in 10 minutes, and the value 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 constraint set for a typical day mainly considers the energy storage operation constraints, among which short-term energy storage is set to consider the daily operation balance, and seasonal energy storage is set to consider the annual operation balance. The constraint set models the storage power change process between adjacent time periods within a short-term energy storage day, the storage power change process between adjacent time periods within a seasonal energy storage day / day, the upper and lower limit constraints of energy storage charging and discharging power / storage power, and the short-term / seasonal energy storage power 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 the operation constraints of 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. The annual average load shedding expectation (expected energy not supplied, EENS) is calculated through hourly operation simulations of a large number of scenario years to evaluate the reliability of each planning scheme.

[0191] like Fig. 9 As shown in the figure, the solution and evaluation process of the optimization model constructed in this paper is demonstrated. By setting the objective function, adding constraints, and performing reliability evaluation on the calculation results obtained by the optimization solution, 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 5 groups of examples for comparative analysis: Example I adopts the classic planning method and only considers the operation simulation within a typical day; Example II adds the power system operation constraints in extreme scenarios on the basis of Example I; Example III considers the system monthly power imbalance risk on the basis of Example I; Example IV considers both the extreme scenario operation simulation constraints and the monthly power imbalance risk on the basis of Example I; Example V considers seasonal energy storage on the basis of Example IV to cope with the long-term supply and demand imbalance risk of the system. For the selection of extreme weather scenarios, based on the extreme weather scenario criterion proposed in formula (1), the union of the continuous low output intervals of each wind power / photovoltaic unit is selected as the extreme scenario boundary and optimized. Each factor is expressed in the form of and in the table √, 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: Fig.10 As shown. In order to balance the impact of long-term and short-term time scale energy balance on the 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 taken as 95% and 99% respectively to compare the planning results of the system under different confidence levels. The key technical and economic parameters of seasonal hydrogen storage in this embodiment are set as 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 Fig.11 As shown in the figure, the comparative analysis results of the examples based on the modified IEEE RTS-79 system are shown. In the examples, various types of generators (including thermal power, hydropower, wind power and photovoltaic power) are considered. Electrochemical energy storage, seasonal hydrogen storage and transmission lines provide the system with flexibility at different time scales, and together with thermal power units, they constitute the main source of system flexibility. From the comparison between Example I and Example II, it can be seen that the introduction of extreme scenario constraints puts higher requirements on the adequacy of power supply. The installed capacity of electrochemical energy storage has increased by 362MW to improve the adequacy and flexibility of the system's power supply. Through the reliability verification of the planning scheme, it can be seen that the percentage of the annual average load shedding expectation (expected energy notserved, EENS) in the total annual load of the system has dropped from 0.9879% in Example I to 0.3890% in Example II, which further proves that the planning scheme considering the continuous low-output extreme weather scenario can effectively improve the reliability of system power supply. In terms of the amount of abandoned renewable energy, the abandoned renewable energy rate in Case I reached 13.2%, which was due to the high penetration rate of wind and solar renewable energy in the system (over 40%); in contrast, the abandoned renewable energy rate in Case II dropped to 11.5%.

[0200] Based on Example I, Example III considers the monthly power imbalance risk under different confidence levels κ. Since the seasonal power fluctuations of new energy (including wind power, photovoltaic power and hydropower) are the main cause of the system's monthly power imbalance, the installed capacity of new energy in Example III is reduced compared with Example I, and the corresponding thermal power installed capacity is increased to cope with the long-term supply and demand imbalance risk. In Example III, as the confidence level increases, the transmission line capacity increases from 9907MW (κ=95%) to 11990MW (κ=99%) to improve the system's long-term power regulation capability; since electrochemical energy storage cannot provide the system with long-term flexibility, its installed capacity decreases from 395MW (κ=95%) to 226MW (κ=99%). In terms of economy and reliability, the system electricity cost increased from 0.421 yuan / kW·h (κ=95%) to 0.422 yuan / kW·h (κ=99%), while the percentage of EENS in the total annual system 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 load of the system is also reduced 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 annual average monthly unbalanced electricity volume decreased from 15910MW·h / year (Example IV, κ=95%) to 2740MW·h / year (Example V, κ=95%), and the proportion of EENS in the total annual load of the system was also reduced to 0.5035% (κ=95%) and 0.0961% (κ=99%) respectively. In terms of economy, the cost per kilowatt-hour of the whole system was reduced to 0.424 yuan / (kW·h) compared with Example IV. The results of the example show that considering seasonal energy storage can effectively improve the reliability of the power supply of the system, and also improve the economy of the planning scheme to a certain extent.

[0204] like Fig.12 As shown, taking Example V as an example, the energy balance results of the system in different scenarios are shown at the confidence level κ = 95%: Fig.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. Fig.12 (b) shows the hourly power balance results in extreme scenarios: compared with Example I, the wind and solar power output in extreme scenarios is greatly 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; in order to effectively deal with the problem of intermittent wind and solar power output, the output of thermal power and hydropower in extreme scenarios is increased accordingly. Fig.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 Fig.13As shown in the figure, the monthly power imbalance of the system in Example V is displayed. Each point in the figure describes the power balance of each month in all scenarios in the year. The ordinate is the normalized monthly power generation of new energy, and the abscissa is the normalized monthly load power of the system. When the scattered points fall on the diagonal, it means that the system's new 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 monthly power generation of new energy, and the system has a monthly power imbalance problem in that month; when the scattered points fall above the diagonal, it means that the monthly power generation of new energy is greater than the monthly load demand power of the system, and the system has a new energy power abandonment problem in that month. Among them, the colors and shapes of the scattered points corresponding to different months are different, and the same months in different scenario years are represented by scattered points of the same color and shape.

[0206] like Fig.13 In the example V shown in Figure 1, the monthly power imbalance of the system at different confidence levels is shown. Fig.13 It can be seen that in the photovoltaic-dominated power system in 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 summer; while the load reaches the second demand peak in winter (November-December). Fig.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 line in the figure decreases significantly, further proving that the monthly unbalanced electricity can be effectively smoothed out by increasing the confidence level.

[0207] like Fig.14 As shown, taking Case IV and V as examples, under the condition of confidence level κ=95%, the changes in reliability indicators as the system's renewable energy penetration rate increases from 10% to 90% are studied. As can be seen from the figure, as the renewable energy penetration rate gradually increases, the system reliability gradually decreases. The ratio of EENS / annual total load in Case IV increases from 0.1985% wind-solar penetration rate (10% wind-solar penetration rate) to 0.9001% wind-solar penetration rate (90% wind-solar penetration rate). When the system's wind-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 planning scheme with higher reliability. 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 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; according to the data on the risk of long-term supply and demand imbalance in the power system, an optimization planning model considering the risk of long-term supply and demand imbalance is constructed; by setting a solver, the optimization planning model considering the risk of long-term supply and demand imbalance is solved to obtain an optimization planning scheme considering the risk of long-term supply and demand imbalance. The present invention achieves the best balance of the power system in terms of economy and stability by comprehensively considering the advantages and disadvantages of different energy storage methods such as electrochemical energy storage and water storage energy storage. The method not only considers the power and electricity balance, load characteristics, and charging and discharging characteristics of the energy storage system, but also studies the energy storage capacity planning under extreme weather conditions to ensure the stable operation of the power system and the continuous supply of power and electricity. Through this method, the volatility of new energy can be better adapted, the reliability and flexibility of the power system can be improved, the operating cost of the system can be reduced, and the sustainable development of energy can be promoted. The present invention proposes a power and electricity balance planning method that comprehensively applies multiple energy storage technologies (such as electrochemical energy storage and water storage 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 water storage, and achieves the best balance between the economy and stability of the power system through optimizing the combination strategy. Secondly, special attention is paid to the problem of power and electricity balance in the long period. By analyzing the seasonal fluctuation characteristics of renewable energy power generation and the stability of the power system under extreme weather conditions, a corresponding risk assessment model and energy storage capacity planning method are proposed. For extreme weather events, such as extremely hot and windless or extremely cold and lightless, this patent proposes an identification method and a 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 under the background of high penetration of new energy.

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

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

[0212] Example 2

[0213] See also Fig.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 the power system long-term supply and demand imbalance by constructing an analysis model and creating an assessment strategy, and obtain data on the risk of the power system long-term supply and demand imbalance;

[0215] The 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 according to the data of the risk of long-term supply and demand imbalance of the power system;

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

[0217] In this embodiment, in the power system long-term supply and demand imbalance risk analysis and evaluation module 001, the analysis and evaluation 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-demand imbalance risk index calculation submodule 017 is used to obtain a long-term supply-demand imbalance risk index by calculating the imbalance risk of the power system under extreme weather scenarios; and to ensure the safe operation of the power system through the long-term supply-demand imbalance risk index;

[0225] The power system monthly electricity balance model construction submodule 018 is used to evaluate the long-term electricity 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 unbalanced 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 unbalanced operation cost considered in the optimization problem; They are the unit investment costs of the generator set, the transmission line to be built, and the energy storage system; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are 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, startup 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 balance 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 in extreme weather scenarios, and monthly power balance constraints considering the seasonal characteristics of new energy.

[0232] In the present embodiment, in the optimization planning model solving module 003 considering the risk of long-term supply and demand imbalance, in the process of solving the optimization planning model considering the risk of long-term supply and demand imbalance and obtaining the optimization planning scheme considering 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, so as to realize the quantitative analysis of the optimization planning scheme.

[0233] It should be noted that the information interaction, execution process and other contents 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 will not be repeated 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, and 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] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[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-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 implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be 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 a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0242] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0243] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints, characterized in that: include: By building an analysis model and creating an assessment strategy, we analyze and assess the risk of long-term imbalance between supply and demand in the power system, and obtain data on the risk of long-term imbalance between supply and demand in the power system; Based on the data of long-term supply and demand imbalance risk of the power system, an optimization planning model considering the long-term supply and demand imbalance risk is constructed; By setting a solver, the optimization planning model considering the risk of long-term supply and demand imbalance is solved to obtain an optimization planning scheme considering the risk of long-term supply and demand imbalance.

2. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 1, characterized in that: 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: To judge extreme weather events with continuous low output of new energy and determine the daily average output of new energy; 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 the power flow model of the built / to-be-built lines; By building 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-demand imbalance risk index is obtained; and the safe operation of the power system is ensured by the long-term supply-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 evaluated; Based on the CVaR strategy, the monthly electricity imbalance risk of the power system is quantitatively evaluated.

3. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 2, 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 unbalanced 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 unbalanced operation cost considered in the optimization problem; They are the unit investment costs of the generator set, the transmission line to be built, and the energy storage system; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are 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, startup 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 balance 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.

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, 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 in extreme weather scenarios, and monthly power balance constraints considering the seasonal characteristics of new energy.

5. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 4, characterized in that: 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, so as to realize quantitative analysis of the optimization planning scheme.

6. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints, adopting a multivariate energy storage capacity planning method taking into account power and electricity balance constraints as described in any one of claims 1 to 5, 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 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; The optimization planning model solving module considering the risk of long-term supply and demand imbalance is used to solve the optimization planning model considering the risk of long-term supply and demand imbalance by setting a solver to obtain an optimization planning scheme considering the risk of long-term supply and demand imbalance.

7. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 6, 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 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; A 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 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 obtain a long-term supply-demand imbalance risk index by calculating the imbalance risk of the power system under extreme weather scenarios; 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.

8. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 7, characterized in that: In the optimization planning model construction module 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: 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 unbalanced 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 unbalanced operation cost considered in the optimization problem; They are the unit investment costs of the generator set, the transmission line to be built, and the energy storage system; They are the investment capacity of the generator set, the transmission line to be built, and the energy storage system; and p are 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, startup 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 balance 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.

9. A multivariate energy storage capacity planning method 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, 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 in extreme weather scenarios, and monthly power balance constraints considering the seasonal characteristics of new energy.

10. A multivariate energy storage capacity planning method taking into account power and electricity balance constraints according to claim 9, 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 through the annual average load shedding expectation, so as to realize the quantitative analysis of the optimization planning scheme.

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