Demand response scheduling method and device based on high-proportion renewable energy and stored energy

Abstract

The invention discloses a demand response scheduling method and device based on high-proportion renewable energy and stored energy, and relates to the technical field of energy optimization scheduling. The method comprises the following steps: step 1, acquiring historical target power load data of a target area, daily output optimization influence factors of each generator set, daily charging / discharging optimization influence factors of energy storage equipment and historical meteorological data; 2, based on the data obtained in the step 1, on the basis of the total power generation cost of the internalized power system in the target area, constructing a target function optimization model, and obtaining an optimal solution of a target function; 3, outputting a source network load storage interactive scheduling scheme based on the optimal solution; the target function is solved by taking the lowest total power generation cost of the power system in the target area as the target, the optimal unit daily output and the optimal energy storage can be obtained, and a more accurate and efficient daily power utilization configuration scheme is provided for power consumers, so that the self-sufficiency and self-consumption rate of wind and light is improved, and the power utilization cost of the power consumers is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization scheduling, and more specifically to a demand response scheduling method and device based on a high proportion of renewable energy and energy storage. Background Art

[0002] In recent years, with the increasing pressure of global climate change and the gradual depletion of fossil energy, the integration and development of renewable energy and energy storage technology has become an important direction in the energy field. Renewable energy such as photovoltaics and wind power has been rapidly promoted due to its clean and sustainable characteristics, but its power generation is intermittent and volatile, making it difficult to achieve efficient coordination with traditional power systems. Especially after large-scale access, the problem of abandoned solar and wind power due to insufficient absorption capacity has become increasingly prominent. As a key supporting solution for renewable energy, the deep integration of energy storage technology with renewable energy not only helps to optimize the configuration of the power generation system and reduce dependence on traditional thermal power, but also can significantly reduce power supply costs and carbon emissions through coordinated scheduling, providing technical support for building a low-carbon and efficient modern energy system.

[0003] The integration of energy storage and renewable energy generation technologies still faces a number of technical challenges, including how to accurately characterize the seasonal characteristics of renewable energy generators and energy storage systems while reducing data dimensions when incorporating demand response; how to improve optimization calculation efficiency, especially in scenarios where energy storage systems are combined with load demand response behaviors, to simulate the combined effects of unit power generation on user power supply and energy storage coordinated scheduling; and how to efficiently solve the optimal configuration of generator output, energy storage charging and discharging strategies, and power user electricity consumption in the optimization model.

[0004] Therefore, there is an urgent need to provide a demand response scheduling method and device based on a high proportion of renewable energy and energy storage, which can coordinate new energy power generation, energy storage systems and load demand to solve the above problems and provide an efficient solution for the optimal configuration of the energy system. Summary of the invention

[0005] In view of this, the present invention provides a demand response scheduling method and device based on a high proportion of renewable energy and energy storage, which can coordinate new energy power generation, energy storage system and load demand.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A demand response dispatch method based on a high proportion of renewable energy and energy storage, comprising:

[0008] Step 1: Obtain historical target power load data of the target area, daily power optimization influencing factors of each generator set, daily charging / discharging optimization influencing factors of energy storage equipment, and historical meteorological data;

[0009] Step 2: Based on the data obtained in step 1 and the total power generation cost of the localized power system in the target area, an optimization model of the objective function is constructed to obtain an optimal solution of the optimization model of the objective function;

[0010] Step 3: Based on the optimal solution of the objective function, output the optimal source-grid-load-storage interactive scheduling plan.

[0011] Preferably, the factors affecting the daily power generation optimization of the generator sets and the daily charge / discharge optimization of the energy storage devices in step 1 include: the daily power load of each generator set, the daily charge and discharge status of each energy storage device and the dynamic changes in meteorology; the historical target power load data include the annual historical power load data and the daily historical power load data in the target area; the historical meteorological data include: the historical target daily demand load, the historical real-time wind speed, the historical real-time solar radiation and the historical real-time water flow.

[0012] Preferably, the daily power load data of each power generating set includes: composite data of historical thermal power generating sets, hydropower generating sets, wind power generating sets and photovoltaic generating sets in the target area.

[0013] Preferably, the energy storage device daily charge / discharge optimization influencing factors include: rated capacity data of each energy storage device, charge / discharge efficiency, self-discharge rate and daily charge / discharge optimization influencing factors.

[0014] Preferably, the optimal source-grid-load-storage interactive scheduling plan includes: configuring the output of each generator set, configuring the daily output load of each generator set and the daily charging / discharging state of each energy storage device and the daily electricity load of power users, and configuring the capacity of each generator set and the capacity of each energy storage device.

[0015] Preferably, the objective function optimization model includes: investment and construction cost and operation and maintenance cost, and the formula is as follows:

[0016]

[0017] Where, IC is the investment and construction cost; OP is the operation and maintenance cost; T is the year, T = 1, 2, .... 40;

[0018] N is the different technologies in the power system, and N = i, e, where i represents different power generation technologies, including photovoltaic, wind power, hydropower and thermal power generation technologies, i = PV, WD, Hydro, Thermal; e is different energy storage devices, including pumped storage devices and battery energy storage devices, that is, e = PS, BS; IC T,N For investment costs; is the operating cost; r is the discount rate; L N For the service life.

[0019] Preferably, the investment and construction cost is related to the life cycle of each power generation equipment and the annual newly installed capacity, and the newly installed capacity includes the new units required due to the increase in electricity demand and the unit capacity required to compensate for the loss in the previous year.

[0020] Preferably, the operation and maintenance cost includes the sum of the operation and maintenance costs of each power generation technology.

[0021] Preferably, constructing the optimization model of the objective function further includes: constructing constraint conditions for solving the objective function, wherein the constraint conditions include upper and lower limit constraints on the output of each generator set, power balance constraints, upper and lower limit constraints on energy storage charge / discharge, and demand response intervention constraints;

[0022] The upper and lower limit constraints on the output of each generator set include: coal power generation constraints, photovoltaic power generation constraints, wind power generation constraints and hydropower generation constraints.

[0023] Preferably, the present invention also provides a demand response scheduling method and device based on a high proportion of renewable energy and energy storage, comprising:

[0024] An acquisition module is used to obtain historical target power load data of the target area, daily power optimization influencing factors of each generator set, daily charge / discharge optimization influencing factors of energy storage equipment, and historical meteorological data;

[0025] A construction module is provided to construct an objective function optimization model based on the historical target power load data of the target area, the daily power generation optimization influencing factors of each generator set, the daily charging / discharging optimization influencing factors of the energy storage device and the historical meteorological data, and the total power generation cost of the domesticated power system in the target area, and obtain the optimal solution of the objective function optimization model;

[0026] The configuration module outputs an optimal source-grid-load-storage interactive scheduling plan based on the optimal solution of the objective function.

[0027] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a demand response scheduling method and device based on a high proportion of renewable energy and energy storage; by accurately characterizing the historical target power load data and the charging / discharging status of the energy storage equipment, while taking into account the demand response behavior of power users, an objective function and constraint conditions for achieving the daily power generation of each unit and the daily charging / discharging optimization configuration of each energy storage are constructed; by solving the objective function with the goal of minimizing the total power generation cost of the power system in the target area, the optimal unit daily power generation and optimal energy storage can be obtained, providing power users with a more accurate and efficient daily electricity configuration plan, thereby improving the self-sufficiency and self-consumption rate of wind and solar power, and helping to reduce the electricity cost of power users. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0029] Figure 1 A schematic flow chart of a demand response scheduling method based on a high proportion of renewable energy and energy storage provided by the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. 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.

[0031] The embodiment of the present invention discloses a demand response scheduling method based on a high proportion of renewable energy and energy storage, such as Figure 1 As shown, including:

[0032] Step 1: Obtain historical target power load data of the target area, daily power optimization influencing factors of each generator set, daily charging / discharging optimization influencing factors of energy storage equipment, and historical meteorological data;

[0033] Step 2: Based on the data obtained in step 1 and the total power generation cost of the localized power system in the target area, an optimization model of the objective function is constructed to obtain an optimal solution of the optimization model of the objective function;

[0034] Step 3: Based on the optimal solution of the objective function, output the optimal source-grid-load-storage interactive scheduling plan.

[0035] Specifically, the factors affecting the daily power generation optimization of the generator sets and the daily charging / discharging optimization of the energy storage equipment in step 1 include: the daily power load of each generator set, the daily charging and discharging status of each energy storage device and the dynamic changes in the weather; the historical target power load data includes the annual historical power load data and the daily historical power load data in the target area; the historical meteorological data includes: the historical target daily demand load, the historical real-time wind speed, the historical real-time solar radiation and the historical real-time water flow.

[0036] Specifically, the daily power load data of each power generating unit includes: composite data of historical thermal power generating units, hydropower generating units, wind power generating units and photovoltaic generating units in the target area.

[0037] Specifically, the energy storage device daily charge / discharge optimization influencing factors include: rated capacity data of each energy storage device, charge / discharge efficiency, self-discharge rate and daily charge / discharge optimization influencing factors.

[0038] In a specific embodiment, the historical data of the target area are divided into spring, summer, autumn and winter, and the output load of each unit in each season per hour on a typical day in each season, the charge / discharge amount of each energy storage device in each season per hour on a typical day in each season, and the power load data of each target in each season per hour on a typical day in each season are divided according to the season.

[0039] Specifically, the target area's historical annual daily hourly daily power load, energy storage data and power user power load data are divided into power demand data matrices of daily power load, daily charge / discharge and power user power load data according to spring, summer, autumn and winter respectively. The matrix rows represent each day of each season, and the matrix columns represent 1 hour of each day.

[0040] Specifically, after dividing the target area's historical annual daily hourly power load, energy storage data and power user power load data into a power demand data matrix of unit daily power load, daily charge / discharge and power user power load data according to spring, summer, autumn and winter, the 90-day × 24-hour × 1-hour matrix of each season can be imported into the model. The model result is that each category includes hourly power demand data or unit output data or energy storage device charge / discharge data for 24 hours a day.

[0041] Specifically, the measured data of solar radiation, wind speed and water flow output every hour throughout the day, the installed capacity of each power generation technology, and the electricity consumption of power users every hour throughout the day are used to impose dynamic upper and lower limits based on meteorological monitoring data, the design load capacity of the unit / equipment, and the discharge depth of the energy storage equipment.

[0042] In a specific embodiment, an embodiment of the present invention provides a demand response scheduling method based on a high proportion of renewable energy and energy storage. It uses an optimization model method to accurately characterize the daily power load of units in different seasons and the typical characteristics of power consumption of power users, while reducing data dimensions and improving calculation efficiency.

[0043] In a specific embodiment, the factors affecting the optimization of the daily output of each generator set include:

[0044] Technical parameters of wind and solar power generating sets, economic parameters of wind and solar power generating sets, technical parameters of battery energy storage and pumped storage, economic parameters of battery energy storage and pumped storage, and meteorological parameters in the target area.

[0045] In a specific embodiment, the unit economic parameter may be a parameter related to the unit cost, such as the unit unit capacity investment cost or operation and maintenance cost.

[0046] In a specific embodiment, the economic parameter of the energy storage device may be a parameter related to the cost of the energy storage device, such as the capital expenditure of the energy storage device capacity.

[0047] In a specific embodiment, the factors affecting the optimization of the unit output, namely the technical parameters of the generator set, the economic parameters of the generator set and the meteorological parameters, are obtained; the factors affecting the optimization of the charging / discharging of the energy storage device, namely the technical parameters of the energy storage device and the economic parameters of the energy storage device, are obtained; the operating parameters of the power user load in the target area are obtained; a target can be constructed based on the above parameters, with the goal of minimizing the annual total power generation cost of the power system in the target area, to obtain the optimal solution for the unit output and the charging / discharging of the energy storage.

[0048] In a specific embodiment, the optimal source-grid-load-storage interactive scheduling scheme includes:

[0049] Configure the output of each unit, configure the daily output load of each unit and the daily charging / discharging status of each energy storage device and the daily power load of power users, configure the capacity of each generator unit and the capacity of each energy storage device, etc.

[0050] Furthermore, after obtaining the output of each unit and the charge state of each energy storage device corresponding to the lowest annual total electricity cost for electricity users, that is, the optimal output of each unit and the charge / discharge amount of each energy storage device, the optimal output of each unit and the charge / discharge configuration of the energy storage device can be determined. In this scheme, the output of wind power generation and photovoltaic power generation units is prioritized, which can improve the self-sufficiency and self-consumption rate of wind and solar power, and reduce the electricity cost of electricity users.

[0051] More specifically, the embodiment of the present invention constructs an objective function and constraints for achieving the daily power generation of each unit and the optimal configuration of the daily charge / discharge state of each energy storage device based on the accurate characterization of historical target power load data and power users' power consumption, while taking into account the charge and discharge configuration of different energy storage devices. By solving the objective function with the goal of minimizing the total power generation cost of the power system in the target area, the optimal daily power generation of the unit and the optimal energy storage charge / discharge state can be obtained, providing power users with a more accurate and efficient daily production power configuration plan, and improving the self-sufficiency and self-consumption rate of wind and solar power.

[0052] In a specific embodiment, an embodiment of the present invention provides a demand response scheduling method based on a high proportion of renewable energy and energy storage. By obtaining the influencing factors of unit output and energy storage device charging / discharging optimization, namely, unit technical parameters, unit economic parameters and meteorological parameters, obtaining the influencing factors of energy storage device charging / discharging optimization, namely, energy storage device technical parameters, energy storage device economic parameters, and obtaining the operating parameters of the power user load in the target area, an optimization model of the objective function can be constructed based on the above parameters, thereby providing power users with a more accurate and efficient electricity configuration plan, improving wind and solar self-sufficiency and local consumption capabilities, avoiding wind and solar curtailment and large-scale grid access, and reducing the electricity costs of power users.

[0053] Establishing the objective function based on the technical parameters of the generator set, the economic parameters of the generator set, the technical parameters of the energy storage device, the economic parameters of the energy storage device, the meteorological parameters and the operating parameters of the flexible loads of the power users in the target area;

[0054] Based on one or more of the unit output load per hour on typical days in each season of the target area, the charge / discharge amount of the energy storage equipment per hour on typical days in each season and the historical target demand load data per hour on typical days in each season, the technical parameters of each generator set, the economic parameters of each generator set, the technical parameters of the energy storage equipment, the economic parameters of the energy storage equipment, the meteorological parameters and the power load parameters of the power users in the target area.

[0055] In a specific embodiment, the generator set technical parameters, generator set economic parameters, energy storage device technical parameters, energy storage device economic parameters, meteorological parameters and operating parameters of the power user's flexible load in the target area are used to establish an objective function optimization model;

[0056] Specifically, the objective function optimization model includes: investment and construction cost and operation and maintenance cost, and the formula is as follows:

[0057]

[0058] Where, IC is the investment and construction cost; OP is the operation and maintenance cost; T is the year, T = 1, 2, .... 40;

[0059] N is the different technologies in the power system, and N = i, e, where i represents different power generation technologies, including photovoltaic, wind power, hydropower and thermal power generation technologies, i = PV, WD, Hydro, Thermal; e is different energy storage devices, including pumped storage devices and battery energy storage devices, that is, e = PS, BS; IC T,N For investment costs; is the operating cost; r is the discount rate; L N For the service life.

[0060] Specifically, the investment and construction costs are related to the life cycle of each power generation technology and the annual newly installed capacity. The newly installed capacity includes the new units required due to the increase in electricity demand and the unit capacity required to compensate for the losses in the previous year.

[0061] Preferably, the operation and maintenance cost includes the sum of the operation and maintenance costs of each power generation technology.

[0062] In the embodiments of the present invention, taking into account the long-term plan for developing green electricity, it is assumed that no new thermal power units will be added in the future, and only the operating cost is considered when simulating and calculating the cost of thermal power generation; it is worth noting that pumped storage technology is part of a hydropower station, so this study does not calculate the investment and construction cost of pumped storage technology, but only sets the operating cost during charging and discharging.

[0063] The investment and construction cost of the power system is related to the life cycle of each power generation technology and the annual newly installed capacity. The annual newly installed capacity includes the new units required due to the growth of power demand and the units required to compensate for the losses in the previous year, which can be expressed by formula (2):

[0064]

[0065] The investment cost per unit capacity of the unit.

[0066] The installed capacity / energy storage capacity lost in the Tth year.

[0067] Installed capacity / storage capacity of each technology in the initial year.

[0068] The installed capacity / equipment capacity that needs to be replenished when its service life expires.

[0069] The installed capacity / equipment capacity required to meet electricity demand when the service life expires within the new use cycle.

[0070] Installed capacity / equipment capacity lost in year T-1.

[0071] The additional installed capacity required for each power generation technology due to losses each year is shown in formula (3):

[0072]

[0073] δ i : The loss rate of technology N;

[0074] The installed capacity / equipment capacity lost in the Tth year.

[0075] The total annual operation and maintenance cost of the power system is equal to the sum of the operation and maintenance costs of each power generation technology in that year, as shown in formula (4):

[0076]

[0077] in, and They represent the unit operating cost and unit maintenance and management cost of each power generation technology N in year T respectively; It represents the power generation / charge and discharge capacity of technology N in year T.

[0078] Preferably, constructing the optimization model of the objective function further includes: constructing constraint conditions for solving the objective function, wherein the constraint conditions include upper and lower limit constraints on the output of each generator set, power balance constraints, upper and lower limit constraints on energy storage charge / discharge, and demand response intervention constraints;

[0079] The upper and lower limit constraints on the output of each generator set include: coal power generation constraints, photovoltaic power generation constraints, wind power generation constraints and hydropower generation constraints.

[0080] In a specific embodiment, the coal power generation constraint is that the power generation of the coal power plant in each time period must be less than or equal to the installed capacity multiplied by its power generation hours, as shown in formulas (5) and (6):

[0081]

[0082] Annual power generation and installed capacity of thermal power plants in year T;

[0083] The power generation of the coal-fired power plant in the t period on the dth day in season j;

[0084] h j,d,t : The number of operating hours of the coal-fired power plant in the t period on the dth day in season j;

[0085] j: season, all four seasons of the year were considered in this study;

[0086] d: day, d = [1, 2, ..., 90], where d = 1 means the first day of each season, each period is 90 days, and there are 4 periods in a year;

[0087] t: time, t = [1,2,…,24], indicating 24 hours in a day.

[0088] In a specific embodiment, the photovoltaic power generation is set and constrained by referring to the photovoltaic power station design specification (GB 50797-2012). The annual photovoltaic power generation is shown in equations (7) and (8):

[0089]

[0090] The required PV installed capacity in year T;

[0091] It represents the solar radiation of the photovoltaic unit during the period t on the dth day in season j;

[0092] e PV Indicates the irradiance under standard conditions (constant = 1kWh / m2);

[0093] represents the power generation of photovoltaic power generation in the t period on the dth day in season j;

[0094] θ PV Represents the overall system efficiency of photovoltaics.

[0095] The maximum value of photovoltaic installed capacity cannot exceed the maximum exploitable capacity of local photovoltaic power generation, as shown in formula (9):

[0096]

[0097] The maximum exploitable capacity of photovoltaics.

[0098] In a specific embodiment, wind power generation constraint: the annual power generation of the wind turbine depends on the wind speed in each period, and the wind speed varies according to different seasons and different weather, as shown in formulas (10) and (11):

[0099]

[0100] The installed capacity of wind power in year T;

[0101] Wind power generation in period t on day d in season j;

[0102] v j,d,t : The real-time wind speed of the wind farm during the t period on the dth day in season j;

[0103] The annual power generation of the wind turbine in year T;

[0104] v cut-in : Cut-in wind speed;

[0105] v cut-out : Cut-out wind speed;

[0106] v rated : Rated wind speed.

[0107] The maximum installed capacity of wind power cannot exceed the maximum exploitable capacity of local wind power, as shown in formula (12):

[0108]

[0109] The maximum exploitable capacity of wind power.

[0110] In a specific embodiment, hydropower generation constraint: the present invention adopts a hybrid pumped storage power station with both pumped storage and runoff power generation functions, which has the advantages of scale, economy and maintenance of grid stability compared with conventional pumped storage power stations; the calculation method of the power generation of the hydropower unit in each period is shown in formulas (13) and (14):

[0111]

[0112] The output load of the hydropower station in the period t on the dth day in season j;

[0113] K: comprehensive output coefficient, which is the comprehensive benefit coefficient to measure the overall efficiency of the hydroelectric generator set, usually a constant of 9.81;

[0114] Water flow at time t on day d in season j;

[0115] H1: Head, which refers to the height difference of water flowing from the upper reservoir to the turbine;

[0116] η: power generation efficiency of the turbine;

[0117] N T : The number of hydropower stations in year T.

[0118] The total capacity of the hydropower station throughout the year needs to meet the peak power generation of hydropower in that year, as shown in formula (15):

[0119]

[0120] The total hydraulic capacity of the hydropower station in year T.

[0121] In the power system, hydropower stations are greatly affected by the geographical environment and are difficult to expand. Therefore, this study constrains the capacity of hydropower stations according to the maximum exploitable capacity of local water resources when designing the capacity of hydropower stations, as shown in formula (16):

[0122]

[0123] The maximum exploitable capacity of hydropower technology.

[0124] Furthermore, in another specific embodiment, in order to ensure the safe and stable operation of the hydropower station at the rated frequency, the present invention also sets a reserve capacity; the real-time load of the hydropower station cannot exceed the maximum load of the hydropower, nor can it be lower than the load reserve capacity of the hydropower, as shown in formula (17):

[0125]

[0126] Load reserve capacity of hydropower;

[0127] The maximum load of the hydropower station power supply system.

[0128] In a specific embodiment, the upper and lower limits of energy storage charge / discharge are constrained: the power system needs to ensure the source-load balance of power, that is, the power generation of each power generation technology and the load stored by the energy storage need to meet the power demand of the end user, as shown in equations (18) and (19):

[0129]

[0130] The difference between supply and demand of electricity load in period t on day d in season j;

[0131] ω j,d,t : The load transfer-out or transfer-in status in the t period on the dth day in season j;

[0132] The electricity load in period t on day d in season j;

[0133] D j,d,t : Electricity demand in period t on day d in season j;

[0134] The power generation of generation technology i in period t on day d in season j.

[0135] In another specific embodiment, in order to solve the problem of accommodating photovoltaic and wind power and shorten the supply-demand gap of power load as much as possible, the present invention introduces two energy storage technologies, battery energy storage technology and pumped storage technology, into the optimization model; the charge / discharge amount of the two technologies in each period of the day does not exceed the supply-demand gap of the period, as shown in formula (20):

[0136]

[0137] The charge / discharge of energy storage technology e during period t on day d in season j.

[0138] Specifically, pumped storage technology is the most mature, economical and largest energy storage technology. Its working principle is to convert the water energy stored in the upper reservoir into electrical energy and transmit it to the power consumption side when photovoltaic and wind power generation is insufficient and cannot meet the power demand; when photovoltaic and wind power generation is abundant and exceeds the power demand, the water in the lower reservoir is pumped to the upper reservoir by electrical energy, and the surplus energy of wind and solar power is stored in the form of water energy; considering that the site selection and construction of pumped storage power stations are relatively limited and heavily dependent on geographical conditions; if the wind power station / photovoltaic power station is too far away from the pumped storage power station, the transmission process of excess electricity will not only produce a large amount of energy loss, but also the transmission technology is difficult and the transmission cost is high, and its charging-discharging cycle efficiency is relatively weaker than battery energy storage; in addition, the construction period of pumped storage power stations is usually 6-8 years, which is much longer than the construction period of wind power stations / photovoltaic power stations. Therefore, this study gives priority to the grid connection of wind power stations and photovoltaic power stations with battery energy storage technology, and when battery energy storage cannot meet the abandoned electricity / power demand during this period, pumped storage is used to supplement it.

[0139] In addition, considering that the operational reliability of a high-proportion renewable energy system is limited by the intraday fluctuations and seasonal changes in renewable energy power output and power demand, in order to clarify the role of energy storage systems in high-proportion renewable energy systems, this study considers short-term and long-term energy storage modes to ensure that the energy storage system can effectively play its role in the long-term and large-scale development process; among them, the short-term energy storage mode is designed to meet the needs of daily energy storage; the long-term energy storage mode includes seasonal energy storage, which is used to adjust the energy balance migration in the power system, and transfer the energy that cannot be consumed in the current season to the energy-deficient season, thereby reducing energy supply pressure and reducing energy waste. When considering the long-term energy storage mode, if the electricity in the battery energy storage system and the pumped storage system cannot be fully consumed in the current year, the remaining electricity will be transferred to the next year.

[0140] Furthermore, the energy storage system includes battery energy storage technology and pumped storage technology. Specifically:

[0141] In a specific embodiment, the battery energy storage technology constraint is: the battery energy storage includes photovoltaic energy storage and wind power energy storage, and the actual storage capacity at each time period throughout the day is not less than the amount of abandoned photovoltaic / wind power, as shown in formulas (21)-(22):

[0142]

[0143] represents the charge / discharge amount of the battery energy storage during the t period on the dth day in season j;

[0144] Indicates the charge and discharge status of the t period on the dth day in season j, 1 indicates the charging status of the battery energy storage, -1 indicates the discharging status of the battery energy storage, and 0 indicates that the battery energy storage is not charged / discharged;

[0145] It represents the amount of abandoned solar power / wind power in period t on day d in season j.

[0146] Pumped storage technology constraints: Considering the future decline in battery energy storage costs, this study refers to the world's largest hybrid pumped storage project, the Lianghekou Hybrid Pumped Storage Project, and uses pumped storage technology as a supplement to battery energy storage technology to complement the power generation characteristics of photovoltaic power stations and wind power stations. The calculation of pumped storage capacity and charge and discharge status is shown in formulas (23)-(24):

[0147]

[0148] represents the charge / discharge capacity of the pumped storage equipment during period t on day d in season j;

[0149] It represents the charge and discharge status of the pumped storage device in the t period on the dth day in the season j, 1 represents the charge state of the pumped storage device, -1 represents the discharge state of the pumped storage device, and 0 represents that the pumped storage device is not charged / discharged.

[0150] The storage capacity of the upper reservoir must be within the safe storage capacity range at all times; in the discharge state, after the pumped storage technology releases the storage capacity, the remaining storage capacity shall not be less than the minimum safe storage capacity of the upper reservoir; in the charging state, after energy is stored in the pumped storage technology upper reservoir, the storage capacity of the upper reservoir shall not exceed the maximum storage capacity of the upper reservoir, as shown in formula (25):

[0151]

[0152] The maximum storage capacity of the upper reservoir;

[0153] The minimum storage capacity of the upper reservoir;

[0154] ρ: local water density;

[0155] G: local gravitational acceleration;

[0156] H2: The height difference from the water flowing into the turbine to the upper water level of the upper reservoir.

[0157] Specifically, the purpose of demand response intervention is to shift the power demand during peak hours to valley hours by shaving peaks and filling valleys without changing the total power consumption, so as to achieve the purpose of smoothing the daily load curve. The transfer state of power demand in each period can be expressed by formulas (26)-(28):

[0158]

[0159] The peak load in period t on day d in season j;

[0160] The valley load in period t on day d in season j;

[0161] DSR: different intervention levels. If demand response is not implemented, DSR = 0%;

[0162] The new peak load after the demand response intervention strategy is adopted in the t period of the dth day in season j;

[0163] New valley load after the demand response intervention strategy is adopted in period t on day d in season j.

[0164] Based on the above conditions and constraints, the objective function and constraints for realizing the source-grid-load-storage optimization scheduling method are constructed. By solving the objective function with the lowest total power generation cost of the power generation system in the target area as the goal, the optimal daily power generation of the unit and the optimal daily charging / discharging of energy storage can be obtained, providing power users with a more accurate and efficient daily electricity configuration plan, improving the self-sufficiency and self-consumption rate of wind and solar power, and reducing the cost of power generation.

[0165] It can be understood that the demand response scheduling device based on a high proportion of renewable energy and energy storage provided by the present invention corresponds to the demand response scheduling method based on a high proportion of renewable energy and energy storage provided by the above-mentioned embodiments. The relevant technical features of the demand response scheduling device based on a high proportion of renewable energy and energy storage provided by the present invention can refer to the relevant technical features of the demand response scheduling method based on a high proportion of renewable energy and energy storage provided by the above-mentioned embodiments, which will not be repeated here.

[0166] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0167] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A demand response scheduling method based on high proportion of renewable energy and energy storage, characterized in that: include: Step 1: Obtain historical target power load data of the target area, daily power optimization influencing factors of each generator set, daily charging / discharging optimization influencing factors of energy storage equipment, and historical meteorological data; Step 2: Based on the data obtained in step 1 and the total power generation cost of the target area's domesticated power system, an objective function optimization model is constructed to obtain an optimal solution of the objective function optimization model; Step 3: Based on the optimal solution of the objective function optimization model, output the optimal source-grid-load-storage interactive scheduling plan.

2. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 1, characterized in that: The factors affecting the daily power generation optimization of the generator sets and the daily charge / discharge optimization of the energy storage equipment in step 1 include: the daily power load of each generator set, the daily charge and discharge status of each energy storage device and the dynamic changes of the weather; the historical target power load data includes the annual historical power load data and the daily historical power load data in the target area; the historical meteorological data includes: the historical target daily demand load, the historical real-time wind speed, the historical real-time solar radiation and the historical real-time water flow.

3. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 2, characterized in that: The daily power load of each generator set includes: composite data of historical thermal power sets, hydropower sets, wind power sets and photovoltaic sets in the target area.

4. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 2, characterized in that: The energy storage device daily charge / discharge optimization influencing factors include: rated capacity data of each energy storage device, charge and discharge efficiency, self-discharge rate and daily charge and discharge optimization influencing factors.

5. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 4, characterized in that: The optimal source-grid-load-storage interactive scheduling scheme includes: configuring the output of each generator set, configuring the daily output load of each generator set and the daily charging / discharging state of each energy storage device and the daily electricity load of power users, and configuring the capacity of each generator set and the capacity of each energy storage device.

6. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 1, characterized in that: The objective function optimization model includes: investment and construction cost and operation and maintenance cost, and the formula is as follows: Where, IC is the investment and construction cost; OP is the operation and maintenance cost; T is the year, T = 1, 2, .... 40; N is the different technologies in the power system, and N = i, e, where i represents different power generation technologies, including photovoltaic, wind power, hydropower and thermal power generation technologies, i = PV, WD, Hydro, Thermal; e is different energy storage devices, including pumped storage devices and battery energy storage devices, that is, e = PS, BS; IC T,N For investment costs; is the operating cost; r is the discount rate; L N For the service life.

7. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 6, characterized in that: The investment and construction costs are related to the life cycle of each power generation equipment and the annual newly installed capacity. The newly installed capacity includes the new units required due to the increase in electricity demand and the unit capacity required to compensate for the losses in the previous year.

8. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 6, characterized in that: Operation and maintenance costs include the sum of operation and maintenance costs of various power generation technologies.

9. A demand response scheduling method based on high proportion of renewable energy and energy storage according to claim 1, characterized in that: Constructing the optimization model of the objective function also includes: constructing constraint conditions for solving the objective function, wherein the constraint conditions include upper and lower limit constraints on the output of each generator set, power balance constraints, upper and lower limit constraints on energy storage charge / discharge, and demand response intervention constraints; The upper and lower limit constraints on the output of each generator set include: coal power generation constraints, photovoltaic power generation constraints, wind power generation constraints and hydropower generation constraints.

10. A demand response scheduling device based on a high proportion of renewable energy and energy storage, applied to a demand response scheduling method based on a high proportion of renewable energy and energy storage as claimed in any one of claims 1 to 9, characterized in that: include: An acquisition module is used to obtain historical target power load data of the target area, daily power optimization influencing factors of each generator set, daily charge / discharge optimization influencing factors of energy storage equipment, and historical meteorological data; A construction module is provided to construct an objective function optimization model based on the historical target power load data of the target area, the daily power generation optimization influencing factors of each generator set, the daily charging / discharging optimization influencing factors of the energy storage device and the historical meteorological data, and the total power generation cost of the domesticated power system in the target area to obtain the optimal solution of the objective function; The configuration module outputs an optimal source-grid-load-storage interactive scheduling plan based on the optimal solution of the objective function.

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