Energy storage system benefit evaluation method considering new energy output uncertainty

Abstract

The invention discloses an energy storage system benefit evaluation method considering new energy output uncertainty, and the method comprises the steps: describing a low-carbon performance index through employing CO2 emission, SO2 emission and a system coal consumption index, and describing an economic performance index through employing system global operation cost; and the reliability index is described by adopting the expected insufficient power supply quantity, the expected power failure duration and the system power reserve rate. The method comprises the following steps: under the condition of constructing low-carbon-economic-reliability operation indexes of an energy storage system, determining an operation objective function of the energy storage system by adopting the minimum global operation cost of the system, formulating constraint conditions including operation of multiple types of units and systems, and determining an influence mechanism of the energy storage system; and on the basis of the established model, configuring an energy storage system on a power grid side by adopting a power planning decision and evaluation system, and establishing and solving a low-carbon-economy-reliability benefit evaluation model of the energy storage system by taking the capacity of the energy storage system as an analysis variable. And reference is provided for consumption of renewable energy sources and construction of a novel power system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system evaluation, and in particular to a method for evaluating the benefits of an energy storage system taking into account the uncertainty of new energy output. Background Art

[0002] In the field of new energy resource quantification technology, international research has extensively explored the regional wind and solar power generation potential and built new energy resource data sets for countries around the world. However, with the rapid growth of large-scale wind power, photovoltaic grid connection, and distributed renewable energy on the load side, the real-time balance of random energy supply and demand will reshape the structural form of the energy system, and the optimization control, operation scheduling, and resource allocation of the entire energy production-transmission-consumption link will face fundamental changes. At present, the natural uncertainty of new energy is an important issue that cannot be ignored in the operation planning of the new energy system.

[0003] The present invention uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators. In the case of constructing low-carbon-economic-reliability operating indicators for energy storage systems, the minimum system global operating cost is used to determine the operating objective function of the energy storage system, and constraints including multiple types of units and system operations are formulated to determine the impact mechanism of the energy storage system; based on the above-mentioned model, an electric power planning decision-making and evaluation system is used to configure an energy storage system on the grid side, and the capacity of the energy storage system is used as the analysis variable to establish and solve a low-carbon-economic-reliability benefit evaluation model for the energy storage system. This provides a reference for absorbing renewable energy and building a new power system. Summary of the invention

[0004] The present invention proposes a method for evaluating the benefits of an energy storage system taking into account the uncertainty of the output of new energy sources, which can at least solve one of the technical problems in the background technology.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for evaluating the benefits of an energy storage system considering the uncertainty of the output of new energy sources, the specific steps are as follows:

[0007] S100. Under the premise of new energy grid connection, considering the uncertainty of new energy output, the energy storage system uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators;

[0008] S200, based on S100, using the minimum system global operation cost to determine the energy storage system operation objective function, and by analyzing the constraint conditions, determining the energy storage system impact mechanism;

[0009] S300, based on the models built by S100 and S200, adopts the power planning decision-making and evaluation system, configures the energy storage system on the grid side, takes the capacity of the energy storage system as the analysis variable, and establishes and solves the low-carbon-economic-reliability benefit evaluation model of the energy storage system.

[0010] Furthermore, the description method of the low carbon performance index, economic performance index and reliability index in S100 of the present invention is as follows:

[0011] S101, use CO2 emissions, SO2 emissions and coal consumption of the coal consumption system to describe the low carbon performance indicators;

[0012] S102, using the system global operation cost to describe the economic performance index, comprehensively considering the system power generation economy, load shedding cost and the scheduling decision of cutting off renewable energy;

[0013] S103, describing the reliability index using expected power shortage, expected power outage duration and system power reserve rate;

[0014] Furthermore, the specific calculation process of the low-carbon performance index described in S101 of the present invention is as follows:

[0015] The carbon emission-power generation external characteristic model of coal-fired power units is fitted in the form of a quadratic function:

[0016]

[0017] Where: is the carbon emission of the thermal power unit at time t; is the power of the coal-fired power unit at time t; is the startup state variable of the coal-fired power unit at time t; a, b, c are respectively the quadratic power, primary power coefficient and constant term of the power unit;

[0018] On an annual basis, the CO2 emissions of the energy storage system are:

[0019]

[0020] Where: Q CO2 is the total annual carbon emission of the system; N coal is the number of conventional units such as coal-fired thermal power units; D anu is the number of days for system operation simulation, which is generally 365; T is the system single-day operation simulation parameter, which is generally 24. are the power generation and startup state variables of the i-th conventional unit at time t on day d, which are obtained based on the system's 8760-hour refined optimization operation simulation throughout the year; a i , b i 、c i is the CO2 emission coefficient of the i-th conventional unit;

[0021] Similar to the calculation process of CO2 emission indicators, the system SO2 emissions are:

[0022]

[0023] Where: is the total annual sulfur emission of the system; p i ,q i , k i is the SO2 emission coefficient of the i-th conventional unit;

[0024] Similar to the calculation process of CO2 emission indicators, the system coal consumption is:

[0025]

[0026] Where: F coal is the total annual coal consumption of the system; m i 、n i 、r i is the coal consumption conversion coefficient of the i-th conventional unit.

[0027] Furthermore, the method for describing the economic performance index using the system global operating cost in S102 of the present invention is as follows:

[0028]

[0029] Where: C sys is the total cost of system operation; The output power of each type of unit in time period t is p t The subscripts c, f, h, and w represent the thermal power that cannot be started or stopped within the day, the thermal power that can be started or stopped within the day, the hydropower, and the new energy, respectively; C w To cut down on the cost of new energy; is the abandoned power of renewable energy in period t; is the load shedding power during period t; V d is the load shedding loss of each node; C f , C c is the start-up and shutdown cost of the unit; θ, η, γ are weighting coefficients used to adjust the limit level of system load shedding and wind and solar power abandonment targets, and are usually taken as 1.

[0030] Furthermore, the reliability index method described in S103 of the present invention is as follows:

[0031] The expected energy not supplied (EENS) is calculated as follows:

[0032]

[0033] Where: S is the total number of probability sampling of system reliability; [1] T D ds represents the total amount of load shedding of each node corresponding to the sth sampling; [1] represents a column vector whose elements are all 1;

[0034] The expected power outage duration (loss of load expectation, LOLE) is calculated as follows:

[0035]

[0036] Where: n is the total number of system loads; p k represents the probability of power outage of the kth load; t k is the duration of power outage of the kth load;

[0037] Under given reliability benchmark conditions, renewable energy units are equivalent to 100% reliable units of a certain capacity and included in the calculation of power reserve rate. The calculation method of system power reserve rate is:

[0038]

[0039] Where: R L is the power reserve rate of the whole system; Λ is the set of system units; R L,k is the spare capacity that can be provided by the kth unit; L max is the peak value of system load.

[0040] Furthermore, the method for determining the energy storage system impact mechanism in S200 of the present invention is as follows:

[0041] S201, determining the energy storage system operation objective function using the minimum system global operation cost;

[0042] The objective function is as follows:

[0043]

[0044] S202, determine the impact mechanism of the energy storage system by combining analysis of constraint conditions;

[0045] The system includes energy storage, wind power / photovoltaic new energy units, thermal power units that can be started and stopped / not started and stopped during the day, hydropower units and other units. The operation constraints of the energy storage system include upper and lower limits of charge and discharge power, remaining power limit constraints, and remaining power change constraints. The constraints are:

[0046]

[0047] Where: and are the charging and discharging power of the bth energy storage device at time t; p b,pmmax is the rated capacity of the bth energy storage device; E b,t is the remaining power of the bth energy storage device at time t; η b is the charging and discharging efficiency of the b-th energy storage device; H b is the number of hours of electric energy capacity of the bth energy storage device; T is the number of operation simulation calculation periods; The remaining capacity factor of the bth energy storage device during the initial period of the operation simulation;

[0048] Renewable energy output prediction variables are introduced into the modeling of wind power / photovoltaic new energy units, and a new energy removal mechanism is introduced into the daily operation simulation model, so that the model can remove part of the renewable energy output when the system cannot provide peak load capacity, the system backup capacity is insufficient, or the new energy transmission is blocked. The constraints are:

[0049]

[0050] Where: Provide new energy for period t; The predicted output value of new energy in period t;

[0051] The operation constraints of thermal power units are as follows, taking into account the upper and lower limits of thermal power unit output, thermal power unit climbing constraints and operation status constraints. The thermal power unit can be started and stopped at most once a day. Large-capacity thermal power units are not allowed to be started and stopped. The constraints are:

[0052]

[0053] In the formula, p cmax 、p cmin They are the upper and lower limits of the output of thermal power units that cannot be started or stopped within the day, and p fmax 、p fmin They are the upper and lower limits of the output of the thermal power units that can be started and stopped within the day; I c is the state variable of the unit that cannot be started or stopped within the day, The state variables of the units that can be started and stopped during the day for a period of t, They are the down-slope and up-slope rates of the unit respectively;

[0054] According to the output range of hydropower units and daily power generation given by the mid- and long-term cross-basin cascade hydropower optimization scheduling results, the output of each period is optimally allocated in the model. Its operation constraints take into account the upper and lower limits of the output of the hydropower units and the daily power generation constraints. The constraints are:

[0055]

[0056] Where: p h·min , p h·max The first row represents the upper and lower limits of the hydropower unit output. hydro is the daily power generation of the hydropower unit, and the second line represents the daily power generation constraint of the hydropower unit. If the optimization result equation does not hold, it means that the hydropower has abandoned water, and the abandoned water is converted into electricity.

[0057] The system operation constraints mainly include power balance constraints, backup constraints and network flow constraints. The power balance constraints are:

[0058]

[0059] Where: [1] T is a column vector whose elements are all 1; is the load shedding amount of the system at time t; D t is the system load at time t.

[0060] The alternative constraints are:

[0061]

[0062] Where: D t is the load of each node at time t, p cmax 、p fmax , p b,pmax They are the output limits of thermal power that cannot be started or stopped within a day, thermal power that can be started or stopped within a day, hydropower, and energy storage; p cmin 、p fmin 、p hmin 、p b,pmin They are the lower limits of thermal power that cannot be started or stopped within a day, thermal power that can be started or stopped within a day, hydropower, and energy storage; and They are the switch states of thermal power that cannot be started or stopped and thermal power that can be started or stopped at time t respectively; is the positive reserve rate required by the system in period t. It should be noted that in the formula, the contribution of renewable energy to the system reserve should be based on its predicted output Even if it is cut off, the cut off part is also included in the spare capacity. is the negative reserve rate required by the system in period t. At this time, the output of renewable energy is not included, that is, the minimum output of renewable energy is equivalently considered to be 0, that is, it can be cut off at any time;

[0063] The network power flow constraint conditions established based on the DC power flow model are:

[0064]

[0065] In the formula, and are the discharge and charging power of the energy storage unit at time t; F l t is the line power flow matrix for period t, f l t and f Lmax is the power flow and capacity upper limit of the lth line at time t; W is the generator transfer distribution factor; A ngc , A ngf , A ngh , A ngw , A ngb They are the node-unit association matrices of different types of units.

[0066] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0067] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0068] It can be seen from the above technical solution that the present invention uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators. In the case of constructing low-carbon-economic-reliability operating indicators for energy storage systems, the minimum system global operating cost is used to determine the operating objective function of the energy storage system, and constraints including multiple types of units and system operations are formulated to determine the impact mechanism of the energy storage system; based on the above-mentioned model, an electric power planning decision-making and evaluation system is used to configure an energy storage system on the grid side, and the capacity of the energy storage system is used as the analysis variable to establish and solve a low-carbon-economic-reliability benefit evaluation model for the energy storage system. This provides a reference for absorbing renewable energy and building a new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0071] The present embodiment provides a method for evaluating the benefits of an energy storage system taking into account the uncertainty of the output of new energy sources, and the method specifically includes the following steps:

[0072] S100. Under the premise of new energy grid connection, considering the uncertainty of new energy output, the energy storage system uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators;

[0073] S200, based on S100, using the minimum system global operation cost to determine the energy storage system operation objective function, and by analyzing the constraint conditions, determining the energy storage system impact mechanism;

[0074] S300, based on the models built by S100 and S200, adopts the power planning decision-making and evaluation system, configures the energy storage system on the grid side, takes the capacity of the energy storage system as the analysis variable, establishes and solves the low-carbon-economic-reliability benefit evaluation model of the energy storage system;

[0075] The following are specific instructions:

[0076] S100. Under the premise of new energy grid connection, considering the uncertainty of new energy output, the energy storage system uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators;

[0077] The description methods of the low-carbon performance index, economic performance index and reliability index are as follows:

[0078] S101, use CO2 emissions, SO2 emissions and coal consumption of the coal consumption system to describe the low carbon performance indicators;

[0079] The direct emissions of CO2 and SO2 greenhouse gases in the power system are mainly generated by power generation units that consume coal. The low-carbon performance indicators are evaluated from three dimensions: CO2 emissions, SO2 emissions, and coal consumption. The carbon emission-power generation external characteristic model of coal-fired power units is fitted in the form of a quadratic function:

[0080]

[0081] Where: is the carbon emission of the thermal power unit at time t; is the power of the coal-fired power unit at time t; is the startup state variable of the coal-fired power unit at time t; a, b, c are the quadratic power, primary power coefficient and constant term of the power unit, respectively.

[0082] On an annual basis, the CO2 emissions of the energy storage system are:

[0083]

[0084] Where: Q CO2 is the total annual carbon emission of the system; N coal is the number of conventional units such as coal-fired thermal power units; D anu is the number of days for system operation simulation, which is generally 365; T is the system single-day operation simulation parameter, which is generally 24. are the power generation and startup state variables of the i-th conventional unit at time t on day d, which are obtained based on the system's 8760-hour refined optimization operation simulation throughout the year; a i , b i 、c i is the CO2 emission coefficient of the i-th conventional unit.

[0085] Similar to the calculation process of CO2 emission indicators, the system SO2 emissions are:

[0086]

[0087] Where: is the total annual sulfur emission of the system; p i ,q i , k i is the SO2 emission coefficient of the i-th conventional unit.

[0088] Similar to the calculation process of CO2 emission indicators, the system coal consumption is:

[0089]

[0090] Where: F coal is the total annual coal consumption of the system; m i 、n i 、r i is the coal consumption conversion coefficient of the i-th conventional unit.

[0091] S102, comprehensively considering the economic efficiency of system power generation, load shedding cost and scheduling decision of cutting off renewable energy, and using the system global operation cost to describe the economic performance index;

[0092]

[0093] Where: C sys is the total cost of system operation; The output power of each type of unit in time period t is p t The subscripts c, f, h, and w represent the thermal power that cannot be started or stopped within the day, the thermal power that can be started or stopped within the day, the hydropower, and the new energy, respectively; C w To cut down on the cost of new energy; is the abandoned power of renewable energy in period t; is the load shedding power during period t; V d is the load shedding loss of each node; C f , C c is the start-up and shutdown cost of the unit; θ, η, γ are weighting coefficients used to adjust the limit level of system load shedding and wind and solar power abandonment targets, and are usually taken as 1.

[0094] S103, describing the reliability index using expected power shortage, expected power outage duration and system power reserve rate;

[0095] The expected energy not supplied (EENS) is calculated as follows:

[0096]

[0097] Where: S is the total number of probability sampling of system reliability; [1] T D ds represents the total amount of load shedding of each node corresponding to the sth sampling; [1] represents a column vector whose elements are all 1.

[0098] The expected power outage duration (loss of load expectation, LOLE) is calculated as follows:

[0099]

[0100] Where: n is the total number of system loads; p k represents the probability of power outage of the kth load; t k is the duration of power outage of the kth load.

[0101] Under given reliability benchmark conditions, renewable energy units are equivalent to 100% reliable units of a certain capacity and included in the calculation of power reserve rate. The calculation method of system power reserve rate is:

[0102]

[0103] Where: RL is the power reserve rate of the whole system; Λ is the set of system units; R L,k is the spare capacity that can be provided by the kth unit; L max is the peak value of system load.

[0104] S200, based on S100, using the minimum system global operation cost to determine the energy storage system operation objective function, and by analyzing the constraint conditions, determining the energy storage system impact mechanism;

[0105] The method for determining the impact mechanism of the energy storage system is as follows:

[0106] S201, determining the energy storage system operation objective function using the minimum system global operation cost;

[0107] The objective function is as follows:

[0108]

[0109] S202, determine the impact mechanism of the energy storage system by combining analysis of constraint conditions;

[0110] The constraints take into account constraints of multiple types of units and system operation constraints. The system includes energy storage, wind power / photovoltaic new energy units, thermal power units that can be started and stopped / cannot be started and stopped during the day, hydropower units and other units. Among them, the operation constraints of the energy storage system include upper and lower limits of charge and discharge power, remaining power limit constraints, and remaining power change constraints. The constraints are:

[0111]

[0112] Where: and are the charging and discharging power of the bth energy storage device at time t; p b,pmmax is the rated capacity of the bth energy storage device; E b,t is the remaining power of the bth energy storage device at time t; η b is the charging and discharging efficiency of the b-th energy storage device; H b is the number of hours of electric energy capacity of the bth energy storage device; T is the number of operation simulation calculation periods; The remaining capacity factor of the bth energy storage device during the initial period of the simulation.

[0113] The difference between new energy and conventional energy is that its output is random, variable and uncontrollable. In dispatching operation, the output is controlled only according to the day-ahead forecast value to operate within the range of the forecast value or below; at the same time, according to the energy-saving and environmental protection requirements of the power grid operation, new energy is required to be fully connected to the grid if the power grid can accommodate it. Renewable energy output prediction variables are introduced into the modeling of wind power / photovoltaic new energy units, and a new energy removal mechanism is introduced into the daily operation simulation model, so that the model can remove part of the renewable energy output when the system cannot provide peak-shaving capacity, the system backup capacity is insufficient or the new energy transmission is blocked. The constraints are:

[0114]

[0115] Where: Provide new energy for period t; It is the predicted output value of new energy in period t.

[0116] The operation constraints of thermal power units are as follows, taking into account the upper and lower limits of thermal power unit output, thermal power unit ramp constraints, and operation status constraints. The thermal power unit can be started and stopped at most once a day. Large-capacity thermal power units are not allowed to be started and stopped. The constraints are:

[0117]

[0118] In the formula, p cmax 、p cmin They are the upper and lower limits of the output of thermal power units that cannot be started or stopped within the day, and p fmax 、p fmin They are the upper and lower limits of the output of the thermal power units that can be started and stopped within the day; I c is the state variable of the unit that cannot be started or stopped within the day, The state variables of the units that can be started and stopped during the day for a period of t, They are the down-slope and up-slope rates of the unit respectively.

[0119] According to the output range of hydropower units and daily power generation given by the mid- and long-term cross-basin cascade hydropower optimization scheduling results, the output of each period is optimally allocated in the model. Its operation constraints take into account the upper and lower limits of the output of the hydropower units and the daily power generation constraints. The constraints are:

[0120]

[0121] Where: p h·min , p h·max The first row represents the upper and lower limits of the hydropower unit output. hydro is the daily power generation of the hydropower unit, and the second line represents the daily power generation constraint of the hydropower unit. If the optimization result equation does not hold, it means that the hydropower has abandoned water, and the abandoned water is converted into electricity.

[0122] The system operation constraints mainly include power balance constraints, backup constraints and network flow constraints. The power balance constraints are:

[0123]

[0124] Where: [1] T is a column vector whose elements are all 1; is the load shedding amount of the system at time t; D t is the system load at time t.

[0125] The alternative constraints are:

[0126]

[0127] Where: D t is the load of each node at time t, p cmax 、p fmax , p b,pmax They are the output limits of thermal power that cannot be started or stopped within a day, thermal power that can be started or stopped within a day, hydropower, and energy storage; p cmin 、p fmin 、p hmin 、p b,pmin They are the lower limits of thermal power that cannot be started or stopped within a day, thermal power that can be started or stopped within a day, hydropower, and energy storage; and They are the switch states of thermal power that cannot be started or stopped and thermal power that can be started or stopped at time t respectively; is the positive reserve rate required by the system in period t. It should be noted that in the formula, the contribution of renewable energy to the system reserve should be based on its predicted output Even if it is cut off, the cut off part is also included in the spare capacity. is the negative reserve rate required by the system in period t. At this time, the output of renewable energy is not included, that is, the minimum output of renewable energy is equivalently considered to be 0, that is, it can be cut off at any time.

[0128] The network power flow constraint conditions established based on the DC power flow model are:

[0129]

[0130] In the formula, and are the discharge and charging power of the energy storage unit at time t; F l t is the line power flow matrix for period t, f l t and f Lmax is the power flow and capacity upper limit of the lth line at time t; W is the generator transfer distribution factor; A ngc, A ngf , A ngh , A ngw , A ngb They are the node-unit association matrices of different types of units.

[0131] S300, based on the models built by S100 and S200, adopts the power planning decision-making and evaluation system, configures the energy storage system on the grid side, takes the capacity of the energy storage system as the analysis variable, establishes and solves the low-carbon-economic-reliability benefit evaluation model of the energy storage system;

[0132] Based on the model built by S100 and S200, low-carbon performance indicators, economic performance indicators, and reliability indicators are considered. The minimum system global operating cost is used as the objective function of the energy storage system. Various constraints are considered. The power planning decision and evaluation system is used for production simulation. The energy storage system is configured on the grid side. The energy storage system capacity is equipped with 0%, 5%, 10%, 15%, 20%, 25%, and 30% energy storage devices. The operation of the energy storage system is simulated to obtain the corresponding low-carbon, economic, and reliability evaluation indicators. The power planning decision and evaluation system is used to optimize the energy storage system and obtain an optimized operation plan for the energy storage system that meets the low-carbon, economic, and reliable requirements.

[0133] In summary, the present invention uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators. In the case of constructing low-carbon-economic-reliability operating indicators for energy storage systems, the minimum system global operating cost is used to determine the operating objective function of the energy storage system, and constraints including multiple types of units and system operations are formulated to determine the impact mechanism of the energy storage system; based on the above-mentioned model, an electric power planning decision-making and evaluation system is used to configure an energy storage system on the grid side, and the capacity of the energy storage system is used as the analysis variable to establish and solve a low-carbon-economic-reliability benefit evaluation model for the energy storage system. This provides a reference for absorbing renewable energy and building a new power system.

[0134] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0135] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0136] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any of the energy storage system benefit evaluation methods considering the uncertainty of new energy output in the above embodiments.

[0137] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0138] 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 application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may 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. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. 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 Solid State Disk (SSD)), etc.

[0139] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0140] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0141] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the benefits of an energy storage system considering the uncertainty of new energy output, characterized in that: The specific steps are as follows: S100. Under the premise of new energy grid connection, considering the uncertainty of new energy output, the energy storage system uses CO2 emissions, SO2 emissions and system coal consumption indicators to describe low-carbon performance indicators, uses the system global operating cost to describe economic performance indicators, and uses expected power shortage, expected power outage duration and system power reserve rate to describe reliability indicators; S200, based on S100, using the minimum system global operation cost to determine the energy storage system operation objective function, and by analyzing the constraint conditions, determining the energy storage system impact mechanism; S300, based on the models built by S100 and S200, adopts the power planning decision-making and evaluation system, configures the energy storage system on the grid side, takes the capacity of the energy storage system as the analysis variable, and establishes and solves the low-carbon-economic-reliability benefit evaluation model of the energy storage system.

2. The energy storage system benefit evaluation method considering the uncertainty of new energy output according to claim 1 is characterized in that: The description methods of the low carbon performance index, economic performance index and reliability index in the S100 are as follows: S101, use CO2 emissions, SO2 emissions and coal consumption of the coal consumption system to describe the low carbon performance indicators; S102, using the system global operation cost to describe the economic performance index, comprehensively considering the system power generation economy, load shedding cost and the scheduling decision of cutting off renewable energy; S103. Reliability indicators are described using expected power shortage, expected power outage duration, and system power reserve rate.

3. The energy storage system benefit evaluation method considering the uncertainty of new energy output according to claim 2 is characterized in that: The specific calculation process of the low-carbon performance index described in S101 is as follows: The carbon emission-power generation external characteristic model of coal-fired power units is fitted in the form of a quadratic function: Where: is the carbon emission of the thermal power unit at time t; is the power of the coal-fired power unit at time t; is the startup state variable of the coal-fired power unit at time t; a, b, c are respectively the quadratic power, primary power coefficient and constant term of the power unit; On an annual basis, the CO2 emissions of the energy storage system are: Where: Q CO2 is the total annual carbon emission of the system; N coal is the number of conventional coal-fired power units; D anu is the number of days for system operation simulation, which is generally 365; T is the system single-day operation simulation parameter, which is generally 24. are the power generation and startup state variables of the i-th conventional unit at time t on day d, which are obtained based on the system's 8760-hour refined optimization operation simulation throughout the year; a i 、b i 、c i is the CO2 emission coefficient of the i-th conventional unit; Similar to the calculation process of CO2 emission indicators, the system SO2 emissions are: Where: is the total annual sulfur emission of the system; p i ,q i , k i is the SO2 emission coefficient of the i-th conventional unit; Similar to the calculation process of CO2 emission indicators, the system coal consumption is: Where: F coal is the total annual coal consumption of the system; m i 、n i 、r i is the coal consumption conversion coefficient of the i-th conventional unit.

4. The energy storage system benefit evaluation method considering the uncertainty of new energy output according to claim 2 is characterized in that: The method of using the system global operating cost to describe the economic performance index in S102 is as follows: Where: C sys is the total cost of system operation; The output power of each type of unit in time period t is p t The subscripts c, f, h, and w represent the thermal power that cannot be started or stopped within the day, the thermal power that can be started or stopped within the day, the hydropower, and the new energy, respectively; C w To cut down on new energy costs; is the abandoned power of renewable energy in period t; is the load shedding power during period t; V d is the load shedding loss of each node; C f , C c is the start-up and shutdown cost of the unit; θ, η, γ are weighting coefficients used to adjust the limit level of system load shedding and wind and solar power abandonment targets, and are usually taken as 1.

5. The method for evaluating the benefits of an energy storage system considering the uncertainty of new energy output according to claim 4 is characterized in that: The reliability index method described in S103 is as follows: The expected energy not supplied (EENS) is calculated as follows: Where: S is the total number of probability sampling of system reliability; [1] T D ds represents the total amount of load shedding of each node corresponding to the sth sampling; [1] represents a column vector whose elements are all 1; The expected power outage duration (loss of load expectation, LOLE) is calculated as follows: Where: n is the total number of system loads; p k represents the probability of power outage of the kth load; t k is the duration of power outage of the kth load; Under given reliability benchmark conditions, renewable energy units are equivalent to 100% reliable units of a certain capacity and included in the calculation of power reserve rate. The calculation method of system power reserve rate is: Where: R L is the power reserve rate of the whole system; Λ is the set of system units; R L,k is the spare capacity that can be provided by the kth unit; L max is the peak value of system load.

6. The energy storage system benefit evaluation method considering the uncertainty of new energy output according to claim 1 is characterized in that: The method for determining the energy storage system impact mechanism in S200 is as follows: S201, determining the energy storage system operation objective function using the minimum system global operation cost; The objective function is as follows: S202, determine the impact mechanism of the energy storage system by combining analysis of constraint conditions; The system includes energy storage, wind power / photovoltaic new energy units, thermal power units that can be started and stopped / not started and stopped during the day, and hydropower units. The operation constraints of the energy storage system include upper and lower limits of charge and discharge power, remaining power limit constraints, and remaining power change constraints. The constraints are: Where: and are the charging and discharging power of the bth energy storage device at time t; p b,pmmax is the rated capacity of the bth energy storage device; E b,t is the remaining power of the bth energy storage device at time t; η b is the charging and discharging efficiency of the b-th energy storage device; H b is the number of hours of electric energy capacity of the bth energy storage device; T is the number of operation simulation calculation periods; The remaining capacity factor of the bth energy storage device during the initial period of the operation simulation; Renewable energy output prediction variables are introduced into the modeling of wind power / photovoltaic new energy units, and a new energy removal mechanism is introduced into the daily operation simulation model, so that the model can remove part of the renewable energy output when the system cannot provide peak load capacity, the system backup capacity is insufficient, or the new energy transmission is blocked. The constraints are: Where: Provide new energy for period t; The predicted output value of new energy in period t; The operation constraints of thermal power units are as follows, taking into account the upper and lower limits of thermal power unit output, thermal power unit climbing constraints and operation status constraints. The thermal power unit can be started and stopped at most once a day. Large-capacity thermal power units are not allowed to be started and stopped. The constraints are: In the formula, p cmax 、p cmin They are the upper and lower limits of the output of thermal power units that cannot be started or stopped within the day, and p fmax 、p fmin They are the upper and lower limits of the output of the thermal power units that can be started and stopped within the day; I c is the state variable of the unit that cannot be started or stopped within the day, The state variables of the units that can be started and stopped during the day for a period of t, They are the down-slope and up-slope rates of the unit respectively; According to the output range of hydropower units and daily power generation given by the mid- and long-term cross-basin cascade hydropower optimization scheduling results, the output of each period is optimally allocated in the model. Its operation constraints take into account the upper and lower limits of the output of the hydropower units and the daily power generation constraints. The constraints are: Where: p h·min , p h·max The first row represents the upper and lower limits of the hydropower unit output. hydro is the daily power generation of the hydropower unit, and the second line represents the daily power generation constraint of the hydropower unit. If the optimization result equation does not hold, it means that the hydropower has abandoned water, and the abandoned water is converted into electricity. The system operation constraints mainly include power balance constraints, backup constraints and network flow constraints. The power balance constraints are: Where: [1] T is a column vector whose elements are all 1; is the load shedding amount of the system at time t; D t is the system load at time t; The alternative constraints are: Where: D t is the load of each node at time t, p cmax 、p fmax , p b,pmax They are the output limits of thermal power that cannot be started or stopped within a day, thermal power that can be started or stopped within a day, hydropower, and energy storage; p cmin 、p fmin 、p hmin 、p b,pmin They are the lower limits of thermal power that cannot be started or stopped within a day, thermal power that can be started or stopped within a day, hydropower, and energy storage; and They are the switch states of thermal power that cannot be started or stopped and thermal power that can be started or stopped at time t respectively; is the positive reserve rate required by the system in period t. It should be noted that in the formula, the contribution of renewable energy to the system reserve should be based on its predicted output Even if it is cut off, the cut off part is also included in the spare capacity. is the negative reserve rate required by the system in period t. At this time, the output of renewable energy is not included, that is, the minimum output of renewable energy is equivalently considered to be 0, that is, it can be cut off at any time; The network power flow constraint conditions based on the DC power flow model are: In the formula, and are the discharge and charging power of the energy storage unit at time t; F l t is the line power flow matrix for period t, f l t and f Lmax is the power flow and capacity upper limit of the lth line at time t; W is the generator transfer distribution factor; A ngc , A ngf , A ngh , A ngw , A ngb They are the node-unit association matrices of different types of units.

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