A demand response scheduling method and device based on high proportion of renewable energy and energy storage

Abstract

The application discloses a demand response scheduling method and device based on high-proportion renewable energy and energy storage, relates to the technical field of energy optimization scheduling, and comprises the following steps: step 1, historical target power load data of a target area, daily output optimization influencing factors of each generator unit, daily charge / discharge optimization influencing factors of energy storage equipment and historical meteorological data are acquired; step 2, based on the data acquired in step 1, a target function optimization model is constructed based on the total power generation cost of a power system in the target area, and an optimal solution of the target function is acquired; and step 3, based on the optimal solution, a source-network-load-storage interactive scheduling scheme is output; by taking the total power generation cost of the power system in the target area as the lowest target to solve the target function, the optimal daily output of the unit and the optimal energy storage can be obtained, a more accurate and efficient daily power consumption configuration scheme is provided for power users, the wind and light self-sufficiency and self-consumption rate is improved, and the power consumption cost of the power users is reduced.

Description

Technical Field

[0001] This invention relates to the field of energy optimization and dispatching technology, and more specifically to a demand response dispatching method and apparatus based on a high proportion of renewable energy and energy storage. Background Technology

[0002] In recent years, with increasing global pressure from climate change and the gradual depletion of fossil fuels, the integrated development of renewable energy and energy storage technologies has become an important direction in the energy sector. Renewable energy sources such as photovoltaics and wind power have been rapidly promoted due to their clean and sustainable characteristics. However, their power generation is intermittent and fluctuating, making it difficult to achieve efficient coordination with traditional power systems. Especially after large-scale grid integration, the problem of curtailment of solar and wind power due to insufficient absorption capacity has become increasingly prominent. Energy storage technology, as a key supporting solution for renewable energy, can not only help optimize the configuration of power generation systems and reduce reliance on traditional thermal power through deep integration with renewable energy sources, but also significantly reduce power supply costs and carbon emissions through coordinated dispatch, 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 several technical challenges, including how to accurately characterize the seasonality of renewable energy generators and energy storage systems while reducing data dimensionality during demand response; how to improve optimization calculation efficiency, especially in scenarios where energy storage systems are combined with load demand response behavior, to simulate the comprehensive effect of generator power generation on user power supply and energy storage coordinated scheduling; and how to efficiently solve for the optimal configuration of generator output, energy storage charging and discharging strategies, and electricity consumption by power users 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 energy systems. Summary of the Invention

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

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A demand response scheduling method based on a high proportion of renewable energy and energy storage includes:

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

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

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

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

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

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

[0014] Preferably, 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 status 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 costs and operation and maintenance costs, as shown in the following formula:

[0016]

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

[0018] N represents 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 represents different energy storage devices, including pumped hydro storage devices and battery energy storage devices, i.e., e = PS, BS; IC T,N For investment costs; R is the operating cost; r is the discount rate; L N For service life.

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

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

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

[0022] The upper and lower limits of the output of each generator set include: coal-fired 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 apparatus based on a high proportion of renewable energy and energy storage, comprising:

[0024] The acquisition module is used to acquire historical target electricity load data for the target area, daily power output optimization factors for each generator set, daily charging / discharging optimization factors for energy storage equipment, and historical meteorological data.

[0025] The module constructs an objective function optimization model based on the historical target electricity load data of the target area, the daily power output optimization factors of each generator set, the daily charging / discharging optimization factors of energy storage equipment, and historical meteorological data, with the total power generation cost of the localized power system in the target area as the basis, and obtains the optimal solution of the objective function optimization model.

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

[0027] As can be seen from the above technical solution, 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 depicting historical target electricity load data and the charging / discharging status of energy storage devices, and considering the demand response behavior of electricity users, an objective function and constraints are constructed to achieve the optimal configuration of daily power output of each generating unit and daily charging / discharging of each energy storage unit. 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 output of the generating units and the optimal energy storage can be obtained, providing electricity users with a more accurate and efficient daily electricity configuration scheme, thereby improving the self-sufficiency and self-consumption rate of wind and solar power, and helping to reduce the electricity cost of electricity users. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating a demand response scheduling method based on a high proportion of renewable energy and energy storage, provided by the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] This invention discloses a demand response scheduling method based on a high proportion of renewable energy and energy storage, such as... Figure 1 As shown, it includes:

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

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

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

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

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

[0037] Specifically, the factors affecting the daily charging / discharging optimization of the energy storage devices include: the rated capacity data of each energy storage device, charging and discharging efficiency, self-discharge rate, and other factors affecting the daily charging and discharging optimization.

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

[0039] Specifically, the daily power load, energy storage data, and electricity user load data of the target area for each day of the historical year are divided into electricity demand data matrices according to the four seasons: daily power load, daily charging / discharging amount, and electricity user load data. The rows of the matrix represent each day of each season, and the columns of the matrix represent one hour of each day.

[0040] Specifically, after dividing the daily power load, energy storage data, and electricity user load data of the target area into power demand data matrices for each of the following seasons (spring, summer, autumn, and winter), the 90-day × 24-hour × 1-hour matrices for each season can be imported into the model. The model results include power demand data, unit output data, or energy storage device charging / discharging data for each category.

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

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

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

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

[0045] In one specific embodiment, the unit's economic parameters can be parameters related to the unit's costs, such as the unit's unit capacity investment cost or operation and maintenance cost.

[0046] In one specific embodiment, the economic parameters of the energy storage device can be parameters related to the cost of the energy storage device, such as the capital expenditure for the capacity of the energy storage device.

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

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

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

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

[0051] More specifically, this embodiment of the invention constructs an objective function and constraints for optimizing the daily power output of each generating unit and the daily charging / discharging state of each energy storage device by accurately depicting historical target power load data and electricity consumption of power users, while considering the charging and discharging 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 output of the generating units and the optimal charging / discharging state of the energy storage can be obtained, providing power users with a more accurate and efficient daily power consumption configuration scheme and improving the self-sufficiency and self-consumption rate of wind and solar power.

[0052] In one specific embodiment, the present invention provides a demand response scheduling method based on a high proportion of renewable energy and energy storage. By acquiring the factors influencing the optimization of generator output and energy storage device charging / discharging (i.e., generator technical parameters, generator economic parameters, and meteorological parameters), and the factors influencing the optimization of energy storage device charging / discharging (i.e., energy storage device technical parameters and energy storage device economic parameters), and acquiring 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. This provides power users with a more accurate and efficient power configuration scheme, improves wind and solar self-sufficiency and local consumption capacity, avoids wind and solar power reduction and large-scale grid connection, and reduces the electricity cost for power users.

[0053] The objective function is established 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, meteorological parameters, and the operating parameters of the flexible load of power users in the target area.

[0054] Based on one or more of the following: the unit output load per hour on typical days of each season in the target area, the energy storage device charging / discharging capacity per hour on typical days of each season, the historical target demand load data per hour on typical days of each season, the technical parameters of each generator set, the economic parameters of each generator set, the technical parameters of the energy storage device, the economic parameters of the energy storage device, meteorological parameters, and the electricity load parameters of power users in the target area.

[0055] In one specific embodiment, an objective function optimization model is established 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 load of power users in the target area.

[0056] Specifically, the objective function optimization model includes: investment and construction costs and operation and maintenance costs, as shown in the following formula:

[0057]

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

[0059] N represents 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 represents different energy storage devices, including pumped hydro storage devices and battery energy storage devices, i.e., e = PS, BS; IC T,N For investment costs; R is the operating cost; r is the discount rate; L N For service life.

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

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

[0062] In this embodiment of the invention, considering 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 costs are considered when simulating and calculating the cost of thermal power generation. It is worth noting that pumped storage technology is part of hydropower stations, so this study does not calculate the investment and construction costs of pumped storage technology, but only sets the operating costs during charging and discharging.

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

[0064]

[0065] Investment cost per unit capacity of the generating unit.

[0066] The loss of installed capacity / energy storage capacity in year T.

[0067] Initial year installed capacity / energy storage capacity for each technology.

[0068] The amount of additional equipment capacity / assembly capacity required when the equipment reaches the end of its service life.

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

[0070] The loss of installed capacity / equipment capacity in year T-1.

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

[0072]

[0073] δ i Loss rate of technology N;

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

[0075] The total annual operation and maintenance cost of a 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 These represent the unit operating cost and unit maintenance cost of each power generation technology N in year T, respectively. This represents the power generation / charge / discharge of technology N in year T.

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

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

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

[0081]

[0082] The annual power generation and installed capacity of the thermal power plant in year T;

[0083] The power generation of a coal-fired power plant during time period t on day d in season j;

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

[0085] j: Season; this study considers all four seasons.

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

[0087] t: time, t = [1, 2, ..., 24], representing a day of 24 hours.

[0088] In one specific embodiment, the photovoltaic power generation settings and constraints of this invention are based on the photovoltaic power station design code (GB 50797-2012), and the annual on-grid power generation of photovoltaic is shown in equations (7) and (8):

[0089]

[0090] The required photovoltaic installation capacity in year T;

[0091] This represents the solar irradiance of the photovoltaic unit during time period t on day d in season j.

[0092] e PV This represents the irradiance under standard conditions (constant = 1 kWh / m²).

[0093] This represents the amount of electricity generated by photovoltaic power during time period t on day d in season j.

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

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

[0096]

[0097] The maximum exploitable capacity of photovoltaic power.

[0098] In one specific embodiment, the wind power generation constraint is that the annual power generation of the wind turbine depends on the wind speed at different times, and the wind speed varies with different seasons and weather conditions, as shown in formulas (10) and (11):

[0099]

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

[0101] The amount of electricity generated by wind power during time period t on day d in season j;

[0102] v j,d,t Real-time wind speed at the wind farm during time period t on day d 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 off the wind speed;

[0106] v rated Rated wind speed.

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

[0108]

[0109] The maximum exploitable capacity of wind power.

[0110] In one specific embodiment, the constraints of hydropower generation are as follows: The present invention adopts a hybrid pumped storage power station that combines pumped storage and runoff power generation functions. Compared with conventional pumped storage power stations, it has advantages such as large scale, economy and maintenance of grid stability. The calculation methods for the power generation of the hydropower unit at different times are shown in formulas (13) and (14):

[0111]

[0112] The power output load of the hydropower station during time period t on day d in season j;

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

[0114] Water flow rate during time period t on day d in season j;

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

[0116] η: Power generation efficiency of the water turbine;

[0117] N T Number of hydroelectric power stations in year T.

[0118] The total capacity of the hydropower station throughout the year must meet the peak power generation of the 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 constrained the capacity of hydropower stations based on 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, to ensure the safe and stable operation of the hydropower station at the rated frequency, the present invention also sets up 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 reserve capacity of the hydropower load, as shown in formula (17):

[0125]

[0126] Hydropower load reserve capacity;

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

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

[0129]

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

[0131] ω j,d,t The state of load transfer out or in during time period t on day d in season j;

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

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

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

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

[0136]

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

[0138] Specifically, pumped storage technology is the most mature, economical, and largest-scale energy storage technology. Its working principle is that when photovoltaic and wind power generation is insufficient to meet electricity demand, the hydropower stored in the upper reservoir is converted into electrical energy and transmitted to the electricity-consuming side. When photovoltaic and wind power generation is abundant and exceeds electricity demand, electricity is used to pump water from the lower reservoir to the upper reservoir, storing the surplus energy in the form of hydropower. However, pumped storage power stations face significant limitations in site selection and construction, heavily relying on geographical conditions. If the wind / photovoltaic power station is too far from the pumped storage station, the transmission of excess electricity will not only result in substantial energy loss but also face technical difficulties and high costs, and its charge-discharge cycle efficiency is relatively weaker than battery storage. Furthermore, the construction cycle of pumped storage power stations is typically 6-8 years, far longer than that of wind / photovoltaic power stations. Therefore, this study prioritizes grid connection of battery energy storage technology with wind and photovoltaic power stations, using pumped storage to supplement the power supply when battery energy storage cannot meet the surplus electricity demand during a given period.

[0139] Furthermore, considering that the operational reliability of high-proportion renewable energy systems is simultaneously limited by the daily fluctuations and seasonal variations in renewable energy power output and electricity demand, this study considers both short-term and long-term energy storage models to clarify the role of energy storage systems in high-proportion renewable energy systems. This ensures that energy storage systems can effectively play their role during long-term, large-scale development. The short-term energy storage model aims to meet daily energy storage needs, while the long-term energy storage model includes seasonal energy storage to adjust the energy balance shift in the power system, transferring unused energy from the current season to energy-scarce seasons, thereby reducing energy supply pressure and minimizing energy waste. When considering the long-term energy storage model, if the electricity in battery energy storage systems and pumped hydro storage systems is not fully utilized within a given year, the remaining electricity will be transferred to the following year.

[0140] Furthermore, the energy storage system incorporates battery energy storage technology and pumped hydro storage technology, specifically:

[0141] In one specific embodiment, the battery energy storage technology is constrained as follows: battery energy storage includes photovoltaic energy storage and wind power energy storage, and the actual energy storage capacity at all times of the day is not less than the amount of curtailed solar / wind power, as shown in formulas (21)-(22):

[0142]

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

[0144] This indicates the charging / discharging state of the battery during time period t on day d in season j. 1 indicates the charging state of the battery energy storage, -1 indicates the discharging state of the battery energy storage, and 0 indicates that the battery energy storage is not being charged / discharged.

[0145] This represents the amount of solar power wasted / wind power wasted during time period t on day d in season j.

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

[0147]

[0148] This represents the charging / discharging power of the pumped storage hydroelectric power unit during time period t on day d in season j.

[0149] This indicates the charging / discharging status of the pumped storage equipment during time period t on day d in season j. 1 indicates the charging status of the pumped storage equipment, -1 indicates the discharging status of the pumped storage equipment, and 0 indicates that the pumped storage equipment is not charging / discharging.

[0150] The reservoir capacity of the upper reservoir must be kept within the safe storage capacity range at all times; in the discharge state, after the pumped storage technology releases its capacity, the remaining 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 reservoir capacity shall not exceed the maximum 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: Density of local water;

[0155] G: Local gravitational acceleration;

[0156] H2: The height difference between the inflow turbine and the upper water level of the upper reservoir.

[0157] Specifically, the purpose of demand response intervention is to smooth the daily load curve by shifting electricity demand from peak periods to valley periods without changing the total electricity consumption. The shift status of electricity demand in each period can be represented by formulas (26)-(28):

[0158]

[0159] Peak load during time period t on day d in season j;

[0160] The trough load during time period t on day d in season j;

[0161] DSR: Different levels of intervention. If no demand response is implemented, then DSR = 0%.

[0162] The new peak load after adopting a demand response intervention strategy during time period t on day d in season j;

[0163] The new trough load after applying the demand response intervention strategy during time period t on day d in season j.

[0164] Based on the above conditions and constraints, an objective function and constraints are constructed to realize the source-grid-load-storage optimal scheduling method. By solving the objective function with the goal of minimizing the total power generation cost of the power generation system in the target area, the optimal daily power output of the generating units and the optimal daily charging / discharging of energy storage can be obtained. This provides power users with a more accurate and efficient daily power consumption configuration scheme, improves the self-sufficiency and self-consumption rate of wind and solar power, and reduces the power generation cost.

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

[0166] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0167] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A demand response scheduling method based on a high proportion of renewable energy and energy storage, characterized in that, include: Step 1: Obtain historical target electricity load data for the target area, daily power output optimization factors for each generator set, daily charging / discharging optimization factors for energy storage equipment, and historical meteorological data; Step 2: Based on the data obtained in Step 1, construct an objective function optimization model based on the total power generation cost of the power system in the target area, and obtain the 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 interaction scheduling scheme; The objective function optimization model includes: investment and construction costs and operation and maintenance costs, as shown in the following formula: ； In the formula, IC represents the investment and construction cost; OP represents the operation and maintenance cost; T represents the year, T=1,2,....40; N represents 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 represents different energy storage devices, including pumped storage devices and battery energy storage devices, e = PS, BS; The investment and construction cost of the Nth technology in year T; Let be the operating and maintenance cost of the Nth technology in year T; and r be the discount rate. For service life; The investment and construction costs are related to the life cycle of each power generation technology and the annual new installed capacity. The new installed capacity includes the new units required due to the increase in electricity demand and the unit capacity required to compensate for the losses of the previous year. Operation and maintenance costs include the sum of the operation and maintenance costs of each power generation technology; Assuming no new thermal power units will be added in the future, only the operating costs are considered when simulating and calculating the cost of thermal power generation; pumped storage technology is part of hydropower stations, so the investment and construction costs of pumped storage technology are not calculated, only the operating costs during charging and discharging are set. The investment and construction costs of a power system are related to the life cycle of each power generation technology and the annual increase in installed capacity. The annual increase in installed capacity includes the units added due to the growth in electricity demand and the units that need to be compensated for losses in the previous year, as shown in the following formula: ； In the formula, Investment cost per unit capacity of the generating unit; : The installed capacity / energy storage capacity lost in year T; : The installed capacity / storage capacity lost in year T-1; Initial year installed capacity / energy storage capacity for each technology; : The amount of additional installation capacity / equipment capacity required when the machine reaches the end of its service life; The installed capacity / equipment capacity required to meet power demand when the service life expires within the new service cycle; : New usage cycle The installed capacity / equipment capacity of the units that can meet the electricity demand of the year; : The installed capacity / equipment capacity lost in year T-1; The formulas for the annual increase in installed capacity due to losses for each power generation technology are as follows: ； In the formula, Loss rate of technology N; : The installed capacity / equipment capacity lost in year T; The total annual operation and maintenance cost of a power system is equal to the sum of the operation and maintenance costs of each power generation technology in that year, as shown in the following formula: ； In the formula, and These represent the unit operating cost and unit maintenance cost of each power generation technology N in year T, respectively. This represents the power generation / charge / discharge of technology N in year T.

2. The demand response scheduling method based on a high proportion of renewable energy and energy storage according to claim 1, characterized in that, The factors influencing the daily power output optimization of generator sets and the daily charging / discharging optimization of energy storage devices in step 1 include: the daily power load of each generator set, the daily charging / discharging status of each energy storage device, and dynamic changes in meteorology; the historical target electricity load data includes the annual historical electricity load data and the daily historical electricity load data within the target area; the historical meteorological data includes: historical target daily demand load, historical real-time wind speed, historical real-time solar irradiance, and historical real-time water flow.

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

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

5. The demand response scheduling method based on a 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 status of each energy storage device and the daily electricity load of power users, configuring the capacity of each generator set and the capacity of each energy storage device.

6. The demand response scheduling method based on a high proportion of renewable energy and energy storage according to claim 1, characterized in that, Operation and maintenance costs include the sum of the operation and maintenance costs of each power generation technology.

7. The demand response scheduling method based on a high proportion of renewable energy and energy storage according to claim 1, characterized in that, The optimization model for constructing the objective function also includes: constructing constraints for solving the objective function, including upper and lower limits of output of each generator set, power balance constraints, upper and lower limits of energy storage charging / discharging, and demand response intervention constraints. The upper and lower limits of the output of each generator set include: coal-fired power generation constraints, photovoltaic power generation constraints, wind power generation constraints, and hydropower generation constraints.

8. A demand response dispatching device based on a high proportion of renewable energy and energy storage, applied to the demand response dispatching method based on a high proportion of renewable energy and energy storage as described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire historical target electricity load data for the target area, daily power output optimization factors for each generator set, daily charging / discharging optimization factors for energy storage equipment, and historical meteorological data. The module constructs an objective function optimization model based on the historical target electricity load data of the target area, the daily power output optimization factors of each generator set, the daily charging / discharging optimization factors of energy storage equipment, and historical meteorological data, and takes the total power generation cost of the power system in the target area as the basis, and obtains the optimal solution of the objective function. The configuration module outputs the optimal source-grid-load-storage interaction scheduling scheme based on the optimal solution of the objective function.

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