A virtual power plant multi-resource coordination optimization scheduling method

Abstract

The application discloses a kind of virtual power plant multi-resource coordination optimization scheduling method, comprising: with the minimum virtual power plant operation cost as target, build day-ahead optimal economic dispatch model;With the minimum adjustment of the deviation amount of photovoltaic forecast and on day-ahead scheduling plan as target, build virtual power plant intra-day correction scheduling model;According to day-ahead optimal economic dispatch model and virtual power plant intra-day correction scheduling model, multi-resource coordination optimization scheduling is carried out to virtual power plant.The application proposes a kind of virtual power plant multi-resource coordination optimization scheduling method, considering the complementary characteristics of energy storage, distributed photovoltaic, adjustable load and other resources, realize the flexible control of a large number of distributed resources, realize resource integration and distribution, realize the coordinated operation management of distributed interactive resources, improve the safe, economic and efficient operation ability of virtual power plant.

Description

Technical Field

[0001] This invention mainly relates to the field of multi-resource coordination technology in virtual power plants, and in particular to a method for multi-resource coordination and optimization scheduling of virtual power plants. Background Technology

[0002] The significantly increased pressure on power balance within the distribution network poses a severe challenge to its load regulation capabilities. The large-scale integration of distributed resources presents a significant challenge to the distribution network, making the optimized management and control of these massive distributed resources a pressing issue. Virtual power plants aggregate massive user-side resources such as distributed power sources, flexible loads, and energy storage through information networks, providing ancillary services to the power grid. They overcome geographical and topological constraints, flexibly adapting to market transaction demands, thereby enhancing grid balance regulation capabilities, promoting the absorption of distributed power sources, improving power quality, and exhibiting significant low-carbon characteristics. Achieving large-scale, flexible online interaction and coordinated control of resources, promoting the activation of massive resources, full interaction between power sources, grids, loads, and storage, and improving both grid security and efficiency have become urgent needs for the clean and low-carbon transformation of the energy and power system. Summary of the Invention

[0003] The purpose of this application is to provide a multi-resource coordinated optimization scheduling method for virtual power plants, so as to achieve flexible control of a large number of distributed resources, realize resource integration and allocation, realize coordinated operation and management of distributed interactive resources, and improve the safe, economic and efficient operation capabilities of virtual power plants.

[0004] This application provides a method for multi-resource coordinated optimization scheduling of virtual power plants, including: constructing a day-ahead optimal economic scheduling model with the goal of minimizing the operating cost of the virtual power plant; constructing a day-ahead corrected scheduling model for the virtual power plant with the goal of minimizing the deviation of photovoltaic forecasts and the adjustment in the day-ahead scheduling plan; and performing multi-resource coordinated optimization scheduling of the virtual power plant based on the day-ahead optimal economic scheduling model and the day-ahead corrected scheduling model.

[0005] In this application, in order to promote the active consumption of a high proportion of distributed resources and ensure the safe and stable operation of the distribution network, a two-stage coordinated optimization scheduling model for virtual power plants is established, which comprehensively considers multiple resources such as energy storage, distributed photovoltaics, and adjustable loads, under the background of massive distributed resources participating in demand response within the virtual power plant. The first stage is the day-ahead optimal economic scheduling model for the virtual power plant, and the second stage is the intraday corrected scheduling model for the virtual power plant, taking into account both the economy and accuracy of scheduling.

[0006] In one implementation, the day-ahead optimal economic dispatch model includes an equation for the operating cost of a virtual power plant, where the operating cost of the virtual power plant is:

[0007]

[0008] Obtain the operating cost C of the virtual power plant p Where T is the set of time periods, Δt is the operating unit duration, p1 is the electricity purchase price, and P l (t) represents the user's expected electricity load in time period t, P pvzy (t) represents the amount of photovoltaic power generated by a user with a photovoltaic power generation device during time period t, P. pvuse (t) represents the amount of photovoltaic power generated by other users in time period t, P c (t) represents the power of user participation in demand response changes during time period t, δ dis (t) represents the discharge state of the stored energy, δ dis (t) = 1 indicates that P is in the discharge state. dis (t) represents the discharge power of the stored energy in time period t, p b p is the compensation price for user participation in demand response. bat The unit power dispatch cost for energy storage, δ ch (t) represents the charging state of the stored energy, δ ch P(t) = 1 indicates that the charging state is reached. ch (t) represents the charging power of the energy storage in time period t.

[0009] In one implementation, the day-ahead optimal economic dispatch model also includes day-ahead optimal economic dispatch constraints. These constraints include adjustable load constraints, power balance constraints, and energy storage constraints. The energy storage constraints include charging state, discharging state, and output constraints. The adjustable load constraint |P c (t)+δ dis (t)×P dis (t)-P goal (t)|≤0.1·P goal (t), P goal (t) represents the power reduction required by the system in time period t. The power of the adjustable load called up in each time period should be less than the load P that can be called up in that time period. c (t)≤P c,max (t), P c,max (t) represents the maximum response potential of the adjustable load in time period t;

[0010] In power balance constraints,

[0011] The electricity supplied by photovoltaic power to other users should be less than the actual load required by those users, and the sum of the electricity supplied by photovoltaic power to other users and the charging power should be less than the electricity supplied to the grid by photovoltaic power.

[0012] P pvuse (t)≥0, P pvuse (t)≤P l (t)-P pvzy (t), Ppvuse (t)≤P pv (t)-P ch (t)-P pvzy (t);

[0013] When the energy storage is in the discharge state, the energy storage discharge power should be less than or equal to the actual load required by other users: P dis (t)≤P l (t)-P pvuse (t)-P pvzy (t)+P goal (t);

[0014] Energy storage constraints are used to extend the lifespan of energy storage, avoid deep charging and discharging, and limit the state of charge of energy storage; in energy storage constraints,

[0015] State of charge E(t) = E(t-1) + δ ch (t)×ΔT×P ch (t)×η ch E c SOC min ≤E(t)≤E c SOC max ,

[0016] Discharge state: E(t) = E(t-1) - δ dis (t)×ΔT×P dis (t) / η dis E c SOC min ≤E(t)≤E c SOC max ,

[0017] Output constraint: 0 ≤ P ch (t)≤P ch,max , 0≤P dis (t)≤P dis,max ,

[0018] Where E(t) is the total energy of the energy storage device during time period t; η ch and η dis These represent the charging power and discharging power of the energy storage device, respectively; E c Energy storage device capacity; SOC min and SOC max These are the minimum and maximum state-of-charge values ​​for the energy storage device, P. ch,max and P dis,max These represent the maximum power for discharging and charging the energy storage device, respectively.

[0019] In one implementation, a virtual power plant intraday correction scheduling model is constructed with the objective of minimizing the deviation in photovoltaic forecasting and adjusting the day-ahead dispatch plan. This model employs a dual-objective optimization approach, comprising a first objective and a second objective.

[0020] The primary objective is to minimize the system's imbalance ΔP(t), with the regulation target being the energy storage charging and discharging power P. dis (t), P ch (t) and adjustable load regulation power P c ΔP(t) represents the energy storage charge / discharge state, which is consistent with the day-ahead scheduling results. ΔP(t) is related to the energy storage's operating state.

[0021] Energy storage charging state: ΔP(t)=ΔP ch (t)-ΔP c (t)-ΔP pv (t)t=1,2,...,T,

[0022] Wherein, ΔP ch (t) represents the adjustment amount of the energy storage charging power; a positive value indicates an increase in charging power, ΔP c (t) represents the adjustment amount of the adjustable load, where a positive value indicates that the load being called up has increased, ΔP pv (t) represents the imbalance between the actual output power and the predicted power of the photovoltaic system. A positive value indicates that the actual output power is greater than the predicted value.

[0023] Energy storage discharge state: ΔP(t)=-ΔP c (t)-ΔP dis (t)-ΔP pv (t)t=1,2,...,T;

[0024] Wherein, ΔP dis (t) represents the adjustment amount of the energy storage discharge power, ΔP dis A negative (t) indicates a decrease in discharge power.

[0025] In one implementation, the bi-objective optimization also includes a second objective.

[0026] The second objective is to minimize the deviation between the adjusted output and the previous day's output. The objective function is coupled with a penalty factor, and the objective function changes to: F=minα1×F1+α2×F2, where α1 is the penalty factor for the imbalance and α2 is the penalty factor for the adjustment.

[0027] The constraints for the second objective include adjustable load dispatch constraints and energy storage constraints. Energy storage constraints include energy storage capacity constraints and output constraints. The adjustable load dispatch constraints are... The energy storage capacity constraint is E(t) = E(t-1) + δ ch (t)×ΔT×(P ch (t)+ΔP ch (t))×η ch -δ dis (t)×ΔT×(P dis (t)+ΔP dis (t)) / η dis E c SOC min ≤E(t)≤E c SOC max The output constraint is 0 ≤ P ch (t)+ΔP ch (t)≤P ch,max 0≤P dis (t)+ΔP dis (t)≤P dis,max .

[0028] The beneficial effects of this invention are as follows:

[0029] This invention proposes a multi-resource coordinated optimization scheduling method for virtual power plants, taking into account the complementary characteristics of resources such as energy storage, distributed photovoltaics, and adjustable loads. It enables flexible control of a large number of distributed resources, resource integration and allocation, coordinated operation and management of distributed interactive resources, and improves the safe, economical and efficient operation capabilities of virtual power plants. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the virtual power plant multi-resource coordination and optimization scheduling method according to an embodiment of this application. Detailed Implementation

[0031] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0032] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0033] like Figure 1 As shown, the following embodiments of this application provide a method for multi-resource coordination and optimization scheduling of virtual power plants, including: 110. Constructing a day-ahead optimal economic scheduling model with the goal of minimizing the operating cost of the virtual power plant.

[0034] In one embodiment, the day-ahead optimal economic dispatch model includes an equation for the operating cost of a virtual power plant, where the operating cost of the virtual power plant is:

[0035]

[0036] Obtain the operating cost C of the virtual power plant p Where T is the time period set, p1 is the electricity purchase price, and P l (t) represents the user's expected electricity load in time period t, P pvzy (t) represents the amount of photovoltaic power generated by a user with a photovoltaic power generation device during time period t, P. pvuse (t) represents the amount of photovoltaic power generated by other users in time period t, P c (t) represents the power of user participation in demand response changes during time period t, δ dis (t) represents the discharge state of the stored energy, δ dis (t) = 1 indicates that P is in the discharge state. dis (t) represents the discharge power of the stored energy in time period t, p b p is the compensation price for user participation in demand response. bat The unit power dispatch cost for energy storage, δ ch (t) represents the charging state of the stored energy, δ ch P(t) = 1 indicates that the charging state is reached. ch (t) represents the charging power of the energy storage in time period t.

[0037] In one embodiment, the intraday photovoltaic (PV) forecast is more accurate, requiring adjustments to the day-ahead dispatch plan. Efforts should be made to absorb as much variation in the PV forecast as possible. If the PV forecast is too large to be absorbed, it must be directly curtailed; if the forecast is too small to be absorbed, it must be purchased from the hourly electricity market, where the price is higher. The day-ahead optimal economic dispatch model also includes day-ahead optimal economic dispatch constraints, including adjustable load constraints, power balance constraints, and energy storage constraints. Energy storage constraints include charging state, discharging state, and output constraints. The adjustable load constraint |P c (t)+δ dis (t)×P dis (t)-P goal (t)|≤0.1·P goal (t), P goal (t) represents the power reduction required by the system in time period t. The power of the adjustable load called up in each time period should be less than the load P that can be called up in that time period.c (t)≤P c,max (t), P c,max (t) represents the maximum response potential of the adjustable load in time period t;

[0038] In power balance constraints, the electricity supplied by photovoltaic (PV) systems to other users should be less than the actual load required by those users, and the sum of the electricity supplied by PV systems to other users and the charging power should be less than the electricity supplied by PV systems to the grid: P pvuse (t)≥0, P pvuse (t)≤Pl(t)-P pvzy (t), P pvuse (t)≤P pv (t)-P ch (t)-P pvzy (t); When the energy storage is in the discharge state, the energy storage discharge power should be less than or equal to the actual load required by other users: P dis (t)≤P l (t)-P pvuse (t)-P pvzy (t)+P goal (t);

[0039] Energy storage constraints are used to extend the lifespan of energy storage, avoid deep charging and discharging, and limit the state of charge of energy storage; in energy storage constraints,

[0040] State of charge E(t) = E(t-1) + δ ch (t)×ΔT×P ch (t)×η ch E c SOC min ≤E(t)≤E c SOC max ,

[0041] Discharge state: E(t) = E(t-1) - δ dis (t)×ΔT×P dis (t) / η dis E c SOC min ≤E(t)≤E c SOC max ,

[0042] Output constraint: 0 ≤ P ch (t)≤P ch,max , 0≤P dis (t)≤P dis,max ,

[0043] Where E(t) is the total energy of the energy storage device during time period t; η ch and η disThese represent the charging power and discharging power of the energy storage device, respectively; E c Energy storage device capacity; SOC min and SOC max These are the minimum and maximum state-of-charge values ​​for the energy storage device, P. ch,max and P dis,max These represent the maximum power for discharging and charging the energy storage device, respectively.

[0044] 120. With the goal of absorbing the deviation of photovoltaic forecasts and minimizing the adjustment in the day-ahead dispatch plan, construct a virtual power plant intraday correction dispatch model.

[0045] In one embodiment, a virtual power plant intraday correction scheduling model is constructed with the objective of minimizing the deviation in photovoltaic forecasting and the adjustment in the day-ahead scheduling plan. The virtual power plant intraday correction scheduling model adopts a dual-objective optimization, which includes a first objective and a second objective. The first objective is to minimize the system imbalance ΔP(t), and the adjustment object is the energy storage charging and discharging power P. dis (t), P ch (t) and adjustable load regulation power P c ΔP(t) represents the energy storage charge / discharge state, which is consistent with the day-ahead scheduling results. ΔP(t) is related to the energy storage's operating state.

[0046] Energy storage charging state: ΔP(t)=ΔP ch (t)-ΔP c (t)-ΔP pv (t)t=1,2,...,T,

[0047] Where, ΔP ch (t) represents the adjustment amount of the energy storage charging power; a positive value indicates an increase in charging power, ΔP c (t) represents the adjustment amount of the adjustable load, where a positive value indicates that the load being called up has increased, ΔP pv (t) represents the imbalance between the actual output power and the predicted power of the photovoltaic system. A positive value indicates that the actual output power is greater than the predicted value.

[0048] Energy storage discharge state: ΔP(t)=-ΔP c (t)-ΔP dis (t)-ΔP pv (t)t=1,2,...,T

[0049] Where, ΔP dis (t) represents the adjustment amount of the energy storage discharge power; a negative value indicates a decrease in discharge power.

[0050] In one embodiment, the dual-objective optimization further includes a second objective.

[0051] The second objective is to minimize the deviation between the adjusted output and the previous day's output. The objective function is coupled with a penalty factor, and the objective function changes to: F=minα1×F1+α2×F2, where α1 is the penalty factor for the imbalance and α2 is the penalty factor for the adjustment.

[0052] The constraints for the second objective include adjustable load dispatch constraints and energy storage constraints. Energy storage constraints include energy storage capacity constraints and output constraints. The adjustable load dispatch constraints are... The energy storage capacity constraint is E(t) = E(t-1) + δ ch (t)×ΔT×(P ch (t)+ΔP ch (t))×η ch -δ dis (t)×ΔT×(P dis (t)+ΔP dis (t)) / η dis E c SOC min ≤E(t)≤E c SOC max The output constraint is 0 ≤ P ch (t)+ΔP ch (t)≤P ch,max 0≤P dis (t)+ΔP dis (t)≤P dis,max .

[0053] 130. Perform multi-resource coordinated optimization scheduling of the virtual power plant based on the day-ahead optimal economic dispatch model and the intraday modified dispatch model of the virtual power plant.

[0054] In this application, in order to promote the active consumption of a high proportion of distributed resources and ensure the safe and stable operation of the distribution network, a two-stage coordinated optimization scheduling model for virtual power plants is established, which comprehensively considers multiple resources such as energy storage, distributed photovoltaics, and adjustable loads, under the background of massive distributed resources participating in demand response within the virtual power plant. The first stage is the day-ahead optimal economic scheduling model for the virtual power plant, and the second stage is the intraday corrected scheduling model for the virtual power plant, taking into account both the economy and accuracy of scheduling.

[0055] This invention proposes a multi-resource coordinated optimization scheduling method for virtual power plants, taking into account the complementary characteristics of resources such as energy storage, distributed photovoltaics, and adjustable loads. It enables flexible control of a large number of distributed resources, resource integration and allocation, coordinated operation and management of distributed interactive resources, and improves the safe, economical and efficient operation capabilities of virtual power plants.

[0056] The scope of protection of the virtual power plant multi-resource coordination and optimization scheduling method according to the embodiments of this application is not limited to the execution order of the steps involved in this embodiment. Any solution implemented by adding, subtracting or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0057] In practice, the above modules can be implemented as independent entities or combined in any way to be implemented as the same or several entities. For the specific implementation of the above modules, please refer to the previous method implementation examples, which will not be repeated here.

[0058] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0059] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0060] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0061] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for multi-resource coordinated optimal scheduling of a virtual power plant, characterized in that, Comprise: A day-ahead optimal economic dispatch model is constructed with the minimum virtual power plant operation cost as the target; A virtual power plant intra-day correction dispatch model is constructed with the minimum adjustment of the deviation of the predicted photovoltaic and the day-ahead dispatch plan as the target; Multi-resource coordinated optimal scheduling of the virtual power plant is performed according to the day-ahead optimal economic dispatch model and the virtual power plant intra-day correction dispatch model; The virtual power plant intra-day correction dispatch model adopts double-target optimization, and the double-target optimization includes a first target and a second target, wherein, First target: imbalance of the system Minimum, the adjustment object is the energy storage charging and discharging power , And the adjustable load regulation power The energy storage charging and discharging state is consistent with the day-ahead scheduling result, Related to the working state of the energy storage, ; State of charge of the energy storage: ; wherein, is the adjustment amount of the energy storage charging power, which is positive indicating an increase in the charging power, is the adjustment amount of the adjustable load, which is positive indicating an increase in the called load, is the imbalance amount of the actual output power and the predicted power of the photovoltaic, which is positive indicating that the actual output is greater than the predicted value; Energy storage discharge state: ; wherein, is the amount of adjustment of the energy storage discharge power, is negative if the discharge power is reduced; The double-target optimization further includes a second target, The second target is a target in which a deviation of the adjusted output from the day before is minimized The objective function is coupled with the penalty factor, and the objective function changes to: Wherein, The penalty factor is a penalty factor of the unbalance amount, The penalty factor is a penalty factor of the adjustment amount.

2. The method of claim 1, wherein, The day-ahead optimal economic dispatch model includes a virtual power plant operation cost equation, and the virtual power plant operation cost is: ； Obtain the operating cost of a virtual power plant ,in, For time period sets, For the duration of operation, For electricity purchase price, For the first Expected electricity load of users during the time period For users who own photovoltaic power generation devices The amount of photovoltaic power generated for self-use during a given period. Let t represent the amount of photovoltaic power generated by other users during time period t. For the first The power of user participation in demand response changes over time periods. Indicates the discharge state of the stored energy. =1 indicates that it is in the discharge state. For the first Discharge power of time-limited energy storage The compensation price for user participation in demand response. The unit electricity dispatch cost for energy storage, Indicates the charging status of energy storage. =1 indicates that it is in the charging state. For the first Charging power of time-limited energy storage.

3. The method of claim 2, wherein, The day-ahead optimal economic dispatch model further includes day-ahead optimal economic dispatch constraint conditions, and the day-ahead optimal economic dispatch constraint conditions include adjustable load constraints, power balance constraints and energy storage constraints, and the energy storage constraints include state of charge, state of discharge and output constraints, wherein, The adjustable load constraint: ; For the system to require the t period of the cut power, the power of the adjustable load called by each period should be less than the load that can be called by the period ; maximum response potential for the tth period of time; In the power balance constraint, the power provided by the photovoltaic to other users should be less than the actual load required by other users, and the sum of the power provided by the photovoltaic to other users and the charging power should be less than the on-grid power of the photovoltaic: ， ， ； When the energy storage is in a discharging state, the energy storage discharging power should be less than or equal to the actual load required by other users: ; In the energy storage constraint, the state of charge ; ​ Discharge state: ; ; Output constraints: , ; wherein, is the total energy of the energy storage device t period; and are the charging and discharging power of the energy storage device, respectively; is the energy storage device capacity; and are the minimum and maximum state of charge values of the energy storage device, respectively, and are the maximum power of the energy storage device discharging and charging, respectively.

4. The method of claim 1, wherein, The constraint conditions of the second target include an adjustable load calling constraint and a storage energy constraint, the storage energy constraint includes a storage energy capacity constraint and an output constraint, and the adjustable load calling constraint is ; The energy storage capacity constraint is: ； ； The output constraint is ; .

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