CN115986743A - Power cross-region configuration optimization method, device and equipment based on implicit decision method - Google Patents
Power cross-region configuration optimization method, device and equipment based on implicit decision method Download PDFInfo
- Publication number
- CN115986743A CN115986743A CN202211663611.5A CN202211663611A CN115986743A CN 115986743 A CN115986743 A CN 115986743A CN 202211663611 A CN202211663611 A CN 202211663611A CN 115986743 A CN115986743 A CN 115986743A
- Authority
- CN
- China
- Prior art keywords
- scene
- uncertainty
- region
- representing
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power cross-region configuration optimization method, a device and equipment based on an implicit decision method, wherein the method is used for establishing a new energy and load uncertainty model of each region; constructing an uncertainty key scene set and an unexpected constraint condition by using an implicit decision method according to the new energy and load uncertainty model of each region; and establishing a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition so as to determine the cross-region power system optimization configuration. The method can realize the combined coordination and optimization of the flexible resources, new energy and direct current channels of the cross-regional power system, fully excavate the cross-regional flexibility potential, reduce the overall economic cost of the system, and ensure the non-predictability and the whole scene feasibility.
Description
Technical Field
The invention belongs to the field of intelligent new energy power grids, and particularly relates to a power cross-region configuration optimization method, device and equipment based on an implicit decision method.
Background
The reverse distribution characteristic of new energy resources and power demand determines that a cross-regional power system considering a long-distance and large-capacity direct-current channel is a main mode for meeting new energy consumption in western and northern regions. With the continuous increase of the grid-connected scale of a high-proportion new energy base, the problems of difficult system peak regulation, low channel utilization efficiency and the like are brought to a trans-regional power system due to the intermittent and random characteristics of the power of the new energy. In this context, abundant flexible regulation resources become a necessary condition for supporting new energy consumption through the cross-regional power system. The flexible adjustment capability of the direct current channel is fully utilized, multiple flexible resources such as energy storage, demand side management and flexible thermal power are configured, multiple support functions such as smooth new energy output, power transmission service flexibility improvement and channel utilization efficiency improvement can be provided for the system, and a series of problems caused by high-proportion new energy consumption across provinces and regions can be effectively relieved.
According to the organization mode of the current cross-regional power system dispatching in China, a superior dispatching mechanism manually makes a power transmission plan of each time period of a direct current channel according to the predicted power generation and the demand of each region, and sends the power transmission plan to a subordinate dispatching mechanism as a tie line plan; and the lower-level scheduling mechanism takes the tie line plan as a marginal condition and optimizes and makes the unit plan in the region. However, the hierarchical scheduling organization mode does not achieve coordination and optimization of the transmitting and receiving end system and the direct current transmission, and cannot fully exert the benefit of cross-region system coordination and optimization. In addition, the new energy and load prediction uncertainty is not considered in the organization mode, the whole scene feasibility cannot be ensured in a dispatching plan, the problem of wind and light abandonment possibly exists in the actual operation, and the cross-regional consumption effect of the new energy is greatly reduced. Therefore, it is necessary to establish an effective and specific cross-regional power system optimization model, to implement joint coordination optimization of the cross-regional power system, and to alleviate the influence of uncertainty on the cross-regional power system.
The above information disclosed in this background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is well known to those of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an electric power cross-region configuration optimization method, device and equipment based on an implicit decision method.
The invention aims to realize the purpose through the following technical scheme, and the power trans-regional configuration optimization method based on the implicit decision method comprises the following steps:
step S100, establishing a new energy and load uncertainty model of each region, wherein the new energy comprises a photovoltaic unit and a wind turbine unit;
step S200, based on the new energy and load uncertainty models in various regions, constructing an uncertainty key scene set and unexpected constraint conditions by using an implicit decision method, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint conditions comprise thermal power unexpected constraints, energy storage unexpected constraints and direct current channel unexpected constraints;
and step S300, establishing a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition so as to determine the optimal configuration of the cross-region power system.
In the method, the new energy and load uncertainty model is as follows:
wherein the content of the first and second substances,
cartesian product representing a set of uncertainties at all times, <' > H >>
Representing an indeterminate set at time T, T representing time, T representing the total number of time periods, (.) T Represents a transpose of a vector>
Representing a set of times, a representing a region, ->
Set of representation areas, P t a,pv 、P t a,w 、P t a,l Respectively representing the uncertain power of the photovoltaic unit, the wind turbine unit and the load of the area a at the moment t, pv, w and l respectively representing the photovoltaic unit, the wind turbine unit and the load, and/or>
And respectively representing the lower uncertainty boundary and the upper uncertainty boundary of the uncertainty v of the a area at the time t, wherein v represents the type of an uncertainty source.
In the method, the prediction scene is as follows:
wherein, the BS represents a set of predicted scenes,
a predictor matrix representing the uncertainty, based on the prediction value matrix and the prediction value matrix>
A predicted value, N, representing uncertainty v of a region at time t a Indicates the total number of regions, N v Representing the number of uncertainty source categories;
the extreme climbing scene is as follows:
ERS＝{OS,ES}，
wherein ERS represents an extreme climbing scene set, OS represents a scene set in which an uncertainty value reaches the maximum in an odd period and reaches the minimum in an even period, ES represents a scene set in which an uncertainty value reaches the minimum in an odd period and reaches the maximum in an even period,
a matrix of values representing uncertainty in the OS context, <' >>
Denotes the a regionThe OS scene value of the uncertainty v of the field at time t is taken and is based on>
A matrix of values representing uncertainty in an ES scenario, <' >>
Representing the ES scene value of uncertainty v of a region at a time t;
the vertex scene is:
wherein, SVS represents a set of vertex scenes,
a value matrix representing an nth vertex scene, based on a predetermined criterion>
Representing the SVS scene value of uncertainty v of a area at time t, N SVS Represents the number of vertex scenes, and Σ () represents a summation function.
In the method, the thermal power unexpected constraint is as follows:
where at represents the time interval between two adjacent time periods, i represents a key scene,
representing a set of key scenes, beta a Representing the minimum coefficient of thermal power output, C, of the region a a,g Thermal power installed capacity, P, of the area a t a,g,min 、P t a,g,max A minimum safety margin and a maximum safety margin, respectively, which represent the thermal power output of the region a>
Represents the output, R, of the thermal power of the area a in the scene of time t i a,g Representing the maximum ramp rate of the thermal power of the area a;
the energy storage unintended constraints are:
wherein alpha is a Indicating the maximum depth of discharge of the stored energy, C, of region a a,sto The energy storage installed capacity of the area a is shown,
respectively represents the minimum safety boundary and the maximum safety boundary of the energy storage capacity of the area a at the moment t, and>
represents the electric quantity, eta, of the stored energy of the area a in the scene of time t i a,d 、η a,c Respectively representing the maximum discharge rate coefficient and the maximum charge rate coefficient, mu, of the stored energy of the area a a,d 、μ a,c Respectively representing the discharging efficiency and the charging efficiency of the energy storage of the area a;
the DC channel unintended constraint is:
wherein the content of the first and second substances,
respectively representing the minimum transmission power and the maximum transmission power of the k-th direct current channel,
respectively represents the minimum safety boundary and the maximum safety boundary of the transmitted power of the kth direct current channel,
represents the transmission power of the kth direct current channel in the scene of t time i, v k,t A 0-1 variable which indicates whether the delivery power of the kth DC channel is adjusted at time t or not, and/or whether a regulation of the delivery power of the kth DC channel is effected at time t or not>
Representing the maximum ramp rate of the kth dc path.
In the method, the power cross-region optimization model is as follows,
an objective function:
where ρ is i Representing the probability of occurrence of the ith key scene, F sto (·)、F pv (·)、F w (. H) represents the investment cost function of energy storage, photovoltaic and wind power, respectively, F g Representing the thermal power operating cost function, F ls Representing a demand-side response cost function, C a ,sto 、C a,pv 、C a,w Respectively representing the installed capacities of the energy storage, the photovoltaic and the wind power of the area a,
represents the contribution of the thermal power of the area a in the situation at time t i>
The reduced power of the controllable load of the area a in a scene of time t i is shown;
the constraint conditions include: the method comprises the steps of power supply and demand balance constraint of each region in a key scene, operation constraint of thermal power generating units of each region in the key scene, energy storage operation constraint of each region in the key scene, response operation constraint of each region demand side in the key scene, direct current channel operation constraint and unexpected constraint in the key scene.
In the method, the electric power cross-region optimization model can be optimized to obtain the direct current channel transaction electric quantity.
An apparatus for carrying out the method comprises,
the system comprises a first modeling unit, a second modeling unit and a third modeling unit, wherein the first modeling unit is configured to establish a new energy and load uncertainty model of each region, and the new energy comprises a photovoltaic unit and a wind turbine unit;
the second modeling unit is connected with the first modeling unit so as to construct an uncertainty key scene set and unexpected constraint conditions by using an implicit decision method based on the new energy and load uncertainty models of all regions, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint conditions comprise a thermal power unexpected constraint, an energy storage unexpected constraint and a direct current channel unexpected constraint;
and the third modeling unit is connected with the second modeling unit to establish a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition.
In the device, the first modeling unit, the second modeling unit and the third modeling unit comprise a central processing unit.
A storage device storing a plurality of instructions adapted to be loaded and executed by a processor, the instructions comprising:
establishing a new energy and load uncertainty model of each region, wherein the new energy comprises a photovoltaic unit and a wind turbine unit;
based on a new energy and load uncertainty model of each region, constructing an uncertainty key scene set and an unexpected constraint condition by using an implicit decision method, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint condition comprises a thermal power unexpected constraint, an energy storage unexpected constraint and a direct current channel unexpected constraint;
and establishing a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition so as to determine the cross-region power system optimization configuration.
A computer device, comprising:
a memory for storing computer instructions;
a processor for executing computer instructions to implement the method.
The invention utilizes an implicit decision method to construct a key scene set and unexpected constraints, ensures the unexpected performance and the whole scene feasibility of an optimization result, avoids the problem of serious wind and light abandonment possibly existing in actual operation, and effectively relieves the influence of uncertainty on a cross-regional power system. The method can realize the combined coordination and optimization of the flexibility resources, the new energy and the direct current channel of the cross-regional power system, determine the optimal configuration of the flexibility resources and the new energy, optimize a direct current channel tie line dispatching plan, fully excavate the flexibility potential of the cross-regional power system, and improve the cross-regional new energy consumption capacity. The optimization of the direct current channel transaction electric quantity can be realized, and compared with the existing transaction plan, the overall economic cost of the system can be obviously reduced. The method has wide application prospect in a cross-regional power system optimization model, and comprises the following steps: optimizing a cross-region system new energy consumption boundary, optimizing the cross-region system new energy uncertainty consumption, planning the cross-region system investment, optimizing cross-region system tie line transaction electric quantity, and comprehensively configuring, operating and optimizing cross-region system flexible resources.
The above description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, to the extent that those skilled in the art can implement the technical solutions according to the description, and to make the above and other objects, features, and advantages of the present invention more obvious and understandable, the following description is given by way of example of the embodiments of the present invention.
Drawings
Various other advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can also be derived from them without inventive effort. Also, like parts are designated by like reference numerals throughout the drawings.
In the drawings:
fig. 1 is a flowchart of an electric power cross-region configuration optimization method based on an implicit decision method provided by the present disclosure;
FIG. 2 is a schematic diagram of a trans-regional power system in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating an optimal solution for trading power with respect to a DC channel according to an embodiment of the present disclosure;
fig. 4 is a total cost of the transmitting and receiving terminals calculated by the power cross-regional configuration optimization method in different dc channel operation modes in the embodiment of the present disclosure for 10 years;
fig. 5 is a timing logic diagram for a rescheduling decision process based on implicit decisions in an embodiment of the present disclosure.
The invention is further explained below with reference to the figures and examples.
Detailed Description
Specific embodiments of the present invention will be described in more detail below with reference to fig. 1 to 5. While specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It should be noted that certain terms are used throughout the description and claims to refer to particular components. As one skilled in the art will appreciate, various names may be used to refer to a component. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. The description which follows is a preferred embodiment of the invention, but is made for the purpose of illustrating the general principles of the invention and not for the purpose of limiting the scope of the invention. The scope of the present invention is defined by the appended claims.
For the purpose of facilitating understanding of the embodiments of the present invention, the following description will be made by taking specific embodiments as examples with reference to the accompanying drawings, and the drawings are not to be construed as limiting the embodiments of the present invention.
The power trans-regional configuration optimization method based on the implicit decision method comprises the following steps,
s100, establishing a new energy and load uncertainty model of each region, wherein the new energy comprises a photovoltaic unit and a wind turbine.
In this step, assuming that the load and the new energy output are independent at each time phase, that is, the coupling characteristic between uncertainty periods is not considered, the new energy and load uncertainty model is expressed as follows:
wherein the content of the first and second substances,
cartesian product representing a set of uncertainties at all times, <' > H >>
Representing an indeterminate set at time T, T representing time, T representing the total number of time periods, (.) T Represents a transpose of a vector>
Representing a set of times, a representing a region, ->
Representing a collection of areas and +>
s, r respectively represent a transmitting end region and a receiving end region, P t a,pv 、P t a,w 、P t a,l Respectively representing the uncertain power of the photovoltaic unit, the wind turbine unit and the load of the area a at the moment t, pv, w and l respectively representing the photovoltaic unit, the wind turbine unit and the load, and/or>
And respectively representing the lower uncertainty boundary and the upper uncertainty boundary of the uncertainty v of the a area at the time t, wherein v represents the type of an uncertainty source. Formula (1) represents full timeThe definition of the set of segment uncertainties, i.e., the set of full-time uncertainties, is equal to the cartesian product of the set of uncertainties at each time instant. Equation (2) represents the definition of the uncertainty set at each time instant. The boundary of the load uncertainty set may be directly obtained according to an extreme value of the load history data or a boundary of the prediction confidence interval, and the present embodiment uses the boundary of the prediction confidence interval with 95% confidence as the boundary of the load uncertainty set, which may be expressed as:
wherein the content of the first and second substances,P t a,l 、
confidence interval boundaries are predicted for loads with 95% confidence. Unlike loading, the new energy uncertainty set boundary will change as its installed capacity changes. Thus, the boundary of the new energy uncertainty set may be represented as:
wherein the content of the first and second substances,
and the new energy prediction confidence interval boundary with 95% confidence is represented under the new energy v unit installed capacity. C a,v Indicating the installed capacity of the new energy v of the area a.
And S200, constructing an uncertainty key scene set and an unexpected constraint condition by using an implicit decision method according to the new energy and load uncertainty model of each region in the step S100.
In this step, the set of uncertain key scenarios includes the following three scenarios of new energy and load:
first, a prediction scenario can be expressed as:
wherein, the BS represents a set of predicted scenes,
a predictor matrix representing the uncertainty, based on the prediction value matrix and the prediction value matrix>
The predicted value of uncertainty v in a area at time t, N a Indicates the total number of regions, N v Representing the number of uncertainty source categories. The load prediction scenario may be obtained according to the load history data, and this embodiment uses the history data expected value as the prediction scenario, which may be expressed as:
wherein, P t a,l Represents the history data of the load of the area a at time t, and E (-) represents the expectation function. Unlike the load, the new energy forecast will change as its installed capacity changes. Thus, the new energy prediction value may be expressed as:
wherein the content of the first and second substances,
and (3) historical data of the new energy v at the time t under the unit installed capacity of the area a is shown.
Secondly, the extreme climbing scene can be expressed as:
ERS＝{OS,ES}(8)
wherein ERS represents an extreme climbing scene set, OS represents a scene set in which an uncertainty value reaches the maximum in an odd period and reaches the minimum in an even period, ES represents a scene set in which an uncertainty value reaches the minimum in an odd period and reaches the maximum in an even period,
a matrix of values representing uncertainty in an OS context,/>>
An OS scene value representing the uncertainty v of the a-region at time t, ->
A matrix of values representing uncertainty in an ES scenario, <' >>
And representing the ES scene value of uncertainty v of the a area at the time t.
Third, the vertex scenario, can be represented as:
wherein, SVS represents a set of vertex scenes,
to representThe value matrix of the nth vertex scene>
SVS scene value, N, representing uncertainty v of a area at time t SVS Represents the number of vertex scenes, and Σ () represents a summing function. And at this point, the construction of the uncertain key scene set is completed.
Then, based on the key scene set, an unexpected constraint condition is constructed, which specifically includes the following three constraints:
first, thermal power unexpected constraint:
where at represents the time interval between two adjacent time periods, i represents a key scene,
represents a set of key scenes, t represents time, and->
Represents a set of times, β a Representing the minimum coefficient of thermal power output, C, of the region a a,g Thermal power installed capacity, P, of the area a t a,g,min 、P t a,g,max Represents a minimum safety margin and a maximum safety margin, respectively, of the thermal power output of the region a>
Represents the contribution, R, of the thermal power of the area a in the scenario of time t i a,g Representing the thermal power maximum ramp rate for region a.
Second, energy storage unintended constraints:
wherein alpha is a Indicating the maximum depth of discharge of the stored energy, C, of region a a,sto The energy storage installed capacity of the area a is shown,
respectively represents the minimum safety boundary and the maximum safety boundary of the energy storage capacity of the area a at the moment t,
represents the electric quantity, eta, of the stored energy of the area a in the scene of time t i a,d 、η a,c Respectively representing the maximum discharge rate coefficient and the maximum charge rate coefficient, mu, of the stored energy of the area a a,d 、μ a,c Respectively representing the discharging efficiency and the charging efficiency of the energy stored in the area a.
Thirdly, the direct current channel is not restrained in an expected way:
wherein the content of the first and second substances,P dc 、
respectively representing the minimum and maximum transmission power, P, of the DC channel t dc,min 、P t dc,max Represents a minimum safety margin and a maximum safety margin, respectively, of the DC channel transmission power>
Represents the transmission power, v, of the DC channel in the scenario of time t t Variable 0-1, R, indicating whether the DC path transmission power is adjusted at time t dc Representing the maximum ramp rate of the dc link.
In summary, [ P ] t a,g,min ,P t a,g,max ]，
And [ P t dc,min ,P t dc,max ]And (3) respectively representing implicit decisions of thermal power channels, energy storage channels and direct current channels, wherein the implicit decisions are the key for ensuring the non-predictability of the solution and the whole-scene feasibility.
S300, establishing a power cross-region optimization model according to the uncertainty key scene set and the non-expectation constraint condition in the step S200 to determine the cross-region power system optimization configuration.
In the step, the power cross-regional optimization model aims to minimize investment and operation cost on the premise of considering uncertainty of new energy and load, jointly optimize a cross-regional power system, fully excavate cross-regional flexibility potential, search an optimal configuration mode of flexible resources and new energy, and ensure non-predictability and full-scene feasibility of a solution.
The electric power cross-region optimization model aims to minimize investment cost of energy storage and wind-solar units and minimize fuel cost and demand side response cost of traditional thermal power generation, and a specific objective function is expressed as follows:
wherein ρ i Representing the probability of occurrence of the ith key scene, F sto (·)、F pv (·)、F w (. Cndot.) represents the investment cost function of energy storage, photovoltaic and wind power, respectively, F g Representing the thermal power operating cost function, F ls Representing a demand-side response cost function, C a ,sto 、C a,pv 、C a,w Respectively representing the installed capacities of the energy storage, the photovoltaic and the wind power of the area a,
represents the contribution of the thermal power of the region a in the situation at time t i>
The reduced power for the controllable load representing area a in the scenario of time t i.
Modeling is respectively carried out on power supply and demand balance constraint of each region in a key scene of a cross-region power system, operation constraint of thermal power generating units of each region in the key scene, energy storage operation constraint of each region in the key scene, response operation constraint of each region on demand side in the key scene, and direct current channel operation constraint in the key scene:
1. power supply and demand balance constraint of each region under key scene
Wherein the content of the first and second substances,
respectively representing the sending end and the receiving endTerminal traditional thermal power unit power device>
Respectively represents the energy storage and discharge rates of a transmitting end and a receiving end, and>
respectively represents the energy storage charging rate of the sending end and the receiving end, and>
represents the DC channel delivering power->
Respectively representing the photovoltaic and the wind power of the delivery end>
Respectively representing the large-scale normal load and flexible load power of the receiving end. Equation (23) represents the transmission-side region power supply and demand balance constraint, and equation (24) represents the reception-side region power supply and demand balance constraint.
2. Thermal power generating unit operation constraint of each region under key scene
Where Δ t represents the time interval between two adjacent time periods, β a Minimum coefficient of thermal power output, C, representing area a a,g Indicating the thermal power installed capacity of region a,
represents the output, R, of the thermal power of the area a in the scene of time t i a,g Representing the thermal maximum ramp rate for region a. And (25) representing the output limit constraint of the thermal power generating unit in each region. Formula (26) representsAnd (5) climbing restraint of the regional thermal power generating units.
3. Energy storage operation constraint of each region under key scene
Wherein alpha is a Indicating the maximum depth of discharge of the stored energy, C, of region a a,sto The energy storage installed capacity of the area a is shown,
represents the electric quantity, eta, of the stored energy of the area a in the scene of time t i a,d 、η a,c Respectively representing the maximum discharge rate coefficient and the maximum charge rate coefficient, mu, of the stored energy of the area a a,d 、μ a,c Respectively representing the discharging efficiency and the charging efficiency of the energy stored in the area a. Equation (27) represents the update constraint of the energy storage capacity of each area. Equation (28) represents the energy storage capacity limit constraint for each region. Equation (29) represents the energy storage charge rate constraints for each region. Equation (30) represents the region energy storage discharge rate constraint. Equation (31) represents the constraint that the energy storage capacity of each region is equal to the beginning and ending time periods in the scheduling cycle.
4. Response operation constraint of each region demand side under key scene
Wherein, the first and the second end of the pipe are connected with each other,
representing the compliant load power, beta, of the receiving end r Representing the percentage of the maximum compliant load at the receiving end,
and the load predicted value of the receiving end is shown. The above equation represents the demand-side response limit constraints for each zone.
5. Direct current channel operation constraint under key scene
Wherein the content of the first and second substances,P dc 、
respectively represents the minimum and maximum transmission power of the DC channel->
Represents the transmission power, v, of the DC channel in the scenario of time t t Variable 0-1, R, indicating whether the DC path transmission power is adjusted at time t dc Represents the maximum ramp rate, T, of the DC path M The minimum stabilization time of the direct current channel after adjustment is shown, X shows the maximum adjustment times of the direct current channel, and Q shows the transmission electric quantity of the direct current channel. Equation (33) represents the dc path transmission power rate of change limited constraint. Equation (34) represents the dc channel transmit power limit constraint. Equation (35) represents the minimum settling time constraint after adjustment of the transmission power of the dc channel. Equation (36) represents the dc channel transmission power adjustment times limit constraint. Equation (37) represents the dc channel transaction power constraint.
It should be noted that in the critical scenario, the dc channel operation constraint is not limited to
v t Besides the decision variable, the direct current channel transaction electric quantity Q is also the decision variable. As shown in fig. 3, the amount of power transmitted through the dc channel is influenced by the cost factors of the respective regions. Ideally, the intersection point of fig. 3 is the optimal point of the transmission power.
Up to this point, the establishment of the power cross-region optimization model can be summarized as follows:
the power cross-region optimization model is a Mixed Integer Programming (MIP) optimization problem, and can be directly solved through a commercial solver, so that the configuration results of the flexible resources and the new energy of the cross-region power system are obtained.
In order to better understand the technical solution of the present disclosure, the following experiment is performed based on the two-region dc channel interconnected power grid shown in fig. 2. The experimental test environment is a computer: intel (R) Xeon (R) Gold-5118 server, master frequency 2.30GHZ, memory 256GB, programming language: matlab 2019b, solver: gurobi 9.1.2.
According to historical data of wind and light loads of a certain transmitting end province and a certain receiving end province in China, 24-hour daily typical curves in 4 seasons are extracted for simulation test, 96 time periods are totally provided, and time resolution is 1 hour. The load peak of the receiving area is set as 107GW according to the actual data of 2020. The investment costs of the energy storage, photovoltaic and wind power units are set to $385/kWh, $534/kW and $877/kW respectively. The planning time scale considered in this embodiment is 10 years, so the investment cost of each unit is firstly converted to the annual average investment cost according to the equivalent annuity method, and then the average value is taken to obtain the daily average investment cost. The fuel cost and flex load compensation cost are set to $0.045/kWh and $0.1/kW, respectively. In the objective function, the weight of the predicted scene is set to 0.6, and the weights of the rest key scenes are all 0.04. Technical characteristic parameters of a traditional thermal power generating unit, an energy storage, a flexible load and a direct current channel of a cross-regional power system are shown in table 1.
TABLE 1 technical characteristic parameters of the embodiment of the transregional power system
In order to verify the effectiveness of the power cross-region configuration optimization method, the following three direct current channel operation modes are contrastively analyzed:
mode 1: the dc channel transmission power is set according to current engineering practice.
Mode 2: the dc channel delivers power optimized but without rescheduling action.
Mode 3: the DC channel transmission power is optimized and has re-scheduling action.
In order to ensure the fairness of comparison, the direct current channel transmission electric quantity contract under the three modes is obtained by current engineering practice. Under three direct current channel operation modes, the total planned cost of 10 years calculated by the power cross-regional configuration optimization method is shown in fig. 4. Obviously, the total cost calculated in mode 3 is the lowest, $ 3344.5 billion. This is because the dc link mode of operation 3 gives the system cross-regional flexibility. When uncertainty is achieved, the dc channel transmit power may be adjusted, i.e., rescheduling action is taken. Furthermore, the energy storage, thermal power and flexible load of the receiving end area can take rescheduling action to absorb the change of the transmission power of the direct current channel. Therefore, the system exerts the advantage of cross-regional flexibility to eliminate uncertainty, and improves the system efficiency, thereby reducing the overall cost. The total cost calculated in mode 2 is $ 3352.8 billion, which is an increase of $ 8.3 billion over mode 1. This is because the dc channel of mode 2 does not have the ability to reschedule, resulting in a loss of cross-region flexibility of the system. However, since mode 2 employs an optimized dc channel to deliver power, the total cost of the computation is lower than mode 1. These results show that the power cross-region configuration optimization method can reduce the overall cost of the system by exploiting the cross-region flexibility and optimizing the way of transmitting power through the direct current channel.
Table 2 shows the photovoltaic, wind power and energy storage installed capacities calculated by the power cross-region configuration optimization method in three dc channel operation modes. In mode 1, the installed energy capacity is 21.2GWh (14.3 GWh +6.9GWh = 21.2gwh), the installed new energy capacity is 6.3GW, and the installed new energy types are all photovoltaic units. In mode 2, the installed energy storage capacity is 9.1GWh (3.0 GWh +6.1gwh = 9.1gwh), and the installed new energy capacity is 4.6GW (4.5gw +0.1gw =4.6 GW). Compared with the mode 1, the energy storage installed capacity of the mode 2 is reduced by 12.1Gwh, because the problem of channel blockage is relieved by the optimized direct current channel transmission power in the mode 2, the energy storage requirement is greatly reduced, and the overall cost is obviously reduced by combining with the graph shown in FIG. 4. However, the large reduction of the installed capacity of the stored energy also affects the installed capacity of the new energy, so that the installed capacity of the new energy is reduced by 1.7GW. In mode 3, the installed energy storage capacity is 11GWh (2.5gwh +8.5gwh = 11gwh), and the installed new energy capacity is 5.6GW (3.0gw +2.6gw =5.6 GW). Compared with mode 2, the mode 3 energy storage installed capacity is increased by 1.9GWh, and the new energy installed capacity is increased by 1GW. The overall cost of mode 3 is reduced as shown in connection with fig. 4. This shows that the cross-regional flexibility of mode 3 can increase the installed capacity of new energy while ensuring economic benefits.
TABLE 2 wind and solar energy storage device capacity of trans-regional power system under three DC channel operation modes
Another point of the present disclosure, namely, optimizing the impact of the dc channel power delivery contract on the trans-regional system, is discussed below. Therefore, different direct current channel power transmission contracts are set for comparative analysis. Under the two contract formulation modes of the electric quantity transmitted by the direct current channel, the 10-year cost result calculated by the power trans-regional configuration optimization method is shown in table 3. As can be seen from the table, under current engineering practice, the total cost of the grid at the transmitting end for 10 years is $ 3344.5 billion. And the total cost of the transmitting-receiving end power grid of the method is 3324.4 hundred million dollars in 10 years. Thus, the method of the present disclosure can save $ 20.1 (3344.5-3324.4 = 20.1) billion of the total cost of the receiving end for 10 years, compared to current engineering practice. These results indicate that the dc channel power delivery optimization of the method of the present disclosure can significantly reduce the overall cost of the cross-regional system.
TABLE 3 calculation results of 10-year cost under different DC channel transmission power contracts
Finally, in order to verify the feasibility of the power cross-regional configuration optimization method, 500 groups of wind and light load scenes are used for verifying the result. The installed capacity of the wind and light machine set is obtained by the power cross-regional configuration optimization method, and the wind and light load scene is obtained by sampling in the wind and light load prediction confidence interval based on a Monte Carlo sampling method. The result shows that in 500 groups of wind-light load scenes, no phenomenon that wind and light are abandoned or large-scale conventional loads are cut off occurs in any scene, and the whole scene feasibility of the power cross-regional configuration optimization method is verified. It should be noted that, in practical engineering application, an implicit decision obtained by the power cross-region configuration optimization method may be used as a marginal condition, an Economic Dispatch problem (ED) is solved according to an implemented uncertainty value, and a rescheduling action of the cross-region system is obtained in a rolling optimization manner, as shown in fig. 5. In this way, knowledge of non-predictability is guaranteed. Therefore, the power cross-regional configuration optimization method can simultaneously guarantee the non-predictability of the solution and the whole-scene feasibility.
In another embodiment, the present disclosure further provides an implicit decision method-based power cross-region configuration optimization apparatus, including:
the system comprises a first modeling unit, a second modeling unit and a third modeling unit, wherein the first modeling unit is used for establishing a new energy and load uncertainty model of each region, and the new energy comprises a photovoltaic unit and a wind turbine unit;
the second modeling unit is used for constructing an uncertainty key scene set and unexpected constraint conditions by using an implicit decision method according to the new energy and load uncertainty models of the regions built by the first modeling unit, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint conditions comprise a thermal power unexpected constraint, an energy storage unexpected constraint and a direct current channel unexpected constraint;
and the third modeling unit is used for establishing a power cross-region optimization model according to the uncertainty key scene set and the non-expectation constraint conditions established by the second modeling unit so as to determine the optimal configuration of the cross-region power system.
In another embodiment, the present disclosure also provides a storage device having stored therein a plurality of instructions adapted to be loaded and executed by a processor to:
establishing a new energy and load uncertainty model of each region, wherein the new energy comprises a photovoltaic unit and a wind turbine unit;
according to the new energy and load uncertainty models of each region, an implicit decision method is used for constructing an uncertainty key scene set and unexpected constraint conditions, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint conditions comprise a thermal power unexpected constraint, an energy storage unexpected constraint and a direct current channel unexpected constraint;
and according to the uncertain key scene set and the unexpected constraint condition, constructing the key scene set and the unexpected constraint condition based on an implicit decision method, and establishing a power cross-region optimization model to determine the cross-region power system optimization configuration.
In another embodiment, the present disclosure also provides a computer device comprising:
a memory for storing computer instructions;
and the processor is used for executing computer instructions to realize the power cross-region configuration optimization method based on the implicit decision method.
Although embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the specific embodiments and applications described above, which are illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto without departing from the scope of the invention as defined by the appended claims.
Claims (10)
1. An implicit decision method-based power trans-regional configuration optimization method comprises the following steps:
step S100, establishing a new energy and load uncertainty model of each region, wherein the new energy comprises a photovoltaic unit and a wind turbine unit;
step S200, based on the new energy and load uncertainty models in all regions, constructing an uncertainty key scene set and an unexpected constraint condition by using an implicit decision method, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint condition comprises a thermal power unexpected constraint, an energy storage unexpected constraint and a direct current channel unexpected constraint;
and step S300, establishing a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition so as to determine the optimal configuration of the cross-region power system.
2. The method of claim 1, wherein preferably the new energy and load uncertainty model is:
wherein the content of the first and second substances,
cartesian product representing the set of uncertainties at all times, <' >>
Representing an indeterminate set at time T, T representing time, T representing the total number of time periods, (.) T Represents a transpose of a vector>
Representing a set of times, a representing a region, ->
Set of representation areas, P t a,pv 、P t a,w 、P t a,l Respectively represents the uncertain power of the photovoltaic unit, the wind turbine unit and the load of the area a at the moment t, pv, w and l respectively represent the photovoltaic unit, the wind turbine unit and the load, and>
and respectively representing the lower uncertainty boundary and the upper uncertainty boundary of the uncertainty v of the a area at the time t, wherein v represents the type of an uncertainty source.
3. The method of claim 2, wherein the predicted scenario is:
wherein, the BS represents a set of predicted scenes,
a predictor matrix representing an uncertainty>
A predicted value, N, representing uncertainty v of a region at time t a Denotes the total number of regions, N v Representing the number of uncertainty source categories;
the extreme climbing scene is as follows:
ERS＝{OS,ES}，
wherein ERS represents an extreme climbing scene set, OS represents a scene set in which an uncertainty value reaches the maximum in an odd period and reaches the minimum in an even period, ES represents a scene set in which an uncertainty value reaches the minimum in an odd period and reaches the maximum in an even period,
a matrix of values representing uncertainty in the OS context, <' >>
An OS scene value representing the uncertainty v of the a-region at time t, ->
A matrix of values representing uncertainty in an ES scenario, <' >>
Representing the ES scene value of uncertainty v of a region at a time t; />
The vertex scene is:
wherein, SVS represents a set of vertex scenes,
a matrix of values representing the nth vertex scene, based on the value of the reference value>
SVS scene value, N, representing uncertainty v of a area at time t SVS Represents the number of vertex scenes, and Σ () represents a summation function.
4. The method of claim 1, wherein the thermal power non-anticipation constraint is:
where at represents the time interval between two adjacent time periods, i represents a key scene,
representing a set of key scenes, beta a Representing the minimum coefficient of thermal power output, C, of the region a a,g Thermal power installed capacity, P, of the area a t a,g,min 、P t a,g,max Represents a minimum safety margin and a maximum safety margin, respectively, of the thermal power output of the region a>
Represents the contribution, R, of the thermal power of the area a in the scenario of time t i a,g Representing the maximum ramp rate of the thermal power of the area a;
the energy storage unintended constraints are:
wherein alpha is a Indicating the maximum depth of discharge of the stored energy, C, of the region a a,sto The energy storage installed capacity of the area a is shown,
respectively represents the minimum safety boundary and the maximum safety boundary of the energy storage capacity of the area a at the moment t, and>
electric quantity, eta, of stored energy of the area a in the scene of time t i a,d 、η a,c Respectively representing the maximum discharge rate coefficient and the maximum charge rate coefficient, mu, of the stored energy of the area a a,d 、μ a,c Respectively representing the discharging efficiency and the charging efficiency of the energy storage of the area a;
the DC channel unintended constraint is:
wherein the content of the first and second substances,
represents the minimum and maximum transmission power, respectively, of the kth DC channel>
Represents a minimum safety margin and a maximum safety margin, respectively, of the delivery power of the kth DC channel>
Represents the transmission power of the kth direct current channel in the scene of t time i, v k,t A 0-1 variable which indicates whether the delivery power of the kth DC channel is adjusted at time t or not, and/or whether a regulation of the delivery power of the kth DC channel is effected at time t or not>
Representing the maximum ramp rate of the kth dc path.
5. The method of claim 1, wherein the power cross-region optimization model is,
an objective function:
where ρ is i Representing the probability of occurrence of the ith key scene, F sto (·)、F pv (·)、F w (. H) represents the investment cost function of energy storage, photovoltaic and wind power, respectively, F g Representing the thermal power operating cost function, F ls Representing a demand-side response cost function, C a,sto 、C a ,pv 、C a,w Respectively representing the installed capacities of the energy storage, the photovoltaic and the wind power of the area a,
represents the contribution of the thermal power of the area a in the situation at time t i>
Representing the reduction power of the controllable load of the area a in a scene of t time i;
the constraint conditions include: the method comprises the steps of power supply and demand balance constraint of each region in a key scene, operation constraint of thermal power generating units of each region in the key scene, energy storage operation constraint of each region in the key scene, response operation constraint of each region demand side in the key scene, direct current channel operation constraint and unexpected constraint in the key scene.
6. The method of claim 1, wherein the power cross-region optimization model is optimized to obtain a DC channel transaction power amount.
7. An apparatus for carrying out the method according to any one of claims 1 to 6, characterized in that it comprises,
the system comprises a first modeling unit, a second modeling unit and a third modeling unit, wherein the first modeling unit is configured to establish a new energy and load uncertainty model of each region, and the new energy comprises a photovoltaic unit and a wind turbine unit;
the second modeling unit is connected with the first modeling unit so as to construct an uncertainty key scene set and unexpected constraint conditions by using an implicit decision method based on the new energy and load uncertainty models of all regions, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint conditions comprise a thermal power unexpected constraint, an energy storage unexpected constraint and a direct current channel unexpected constraint;
and the third modeling unit is connected with the second modeling unit to establish a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition.
8. The apparatus of claim 7, wherein the first, second and third modeling units comprise a central processor.
9. A memory device having stored therein a plurality of instructions adapted to be loaded and executed by a processor, the instructions comprising:
establishing a new energy and load uncertainty model of each region, wherein the new energy comprises a photovoltaic unit and a wind turbine unit;
based on new energy and load uncertainty models in various regions, constructing an uncertainty key scene set and unexpected constraint conditions by using an implicit decision method, wherein the key scene set comprises a prediction scene, an extreme climbing scene and a vertex scene, and the unexpected constraint conditions comprise thermal power unexpected constraint, energy storage unexpected constraint and direct current channel unexpected constraint;
and establishing a power cross-region optimization model according to the uncertainty key scene set and the unexpected constraint condition so as to determine the cross-region power system optimization configuration.
10. A computer device, comprising:
a memory for storing computer instructions;
a processor for executing computer instructions to implement the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211663611.5A CN115986743A (en) | 2022-12-23 | 2022-12-23 | Power cross-region configuration optimization method, device and equipment based on implicit decision method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211663611.5A CN115986743A (en) | 2022-12-23 | 2022-12-23 | Power cross-region configuration optimization method, device and equipment based on implicit decision method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115986743A true CN115986743A (en) | 2023-04-18 |
Family
ID=85964266
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211663611.5A Pending CN115986743A (en) | 2022-12-23 | 2022-12-23 | Power cross-region configuration optimization method, device and equipment based on implicit decision method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115986743A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117519150A (en) * | 2023-11-02 | 2024-02-06 | 浙江大学 | Autonomous implicit indoor scene reconstruction method combined with boundary exploration |
-
2022
- 2022-12-23 CN CN202211663611.5A patent/CN115986743A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117519150A (en) * | 2023-11-02 | 2024-02-06 | 浙江大学 | Autonomous implicit indoor scene reconstruction method combined with boundary exploration |
| CN117519150B (en) * | 2023-11-02 | 2024-06-04 | 浙江大学 | Autonomous implicit indoor scene reconstruction method combined with boundary exploration |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109559035B (en) | Urban distribution network double-layer planning method considering flexibility | |
| CN108599373B (en) | Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system | |
| CN109325608B (en) | Distributed power supply optimal configuration method considering energy storage and considering photovoltaic randomness | |
| CN107301470B (en) | Double-layer optimization method for power distribution network extension planning and optical storage location and volume fixing | |
| Ghofrani et al. | A framework for optimal placement of energy storage units within a power system with high wind penetration | |
| CN108037667B (en) | Base station electric energy optimal scheduling method based on virtual power plant | |
| CN103151797A (en) | Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode | |
| Mohamed et al. | Microgrid online management and balancing using multiobjective optimization | |
| WO2024067521A2 (en) | Online scheduling method and apparatus for wind-storage virtual power plant | |
| CN112800658A (en) | Active power distribution network scheduling method considering source storage load interaction | |
| CN111667136A (en) | Clearing method and device for regional power market and storage medium | |
| CN108512238B (en) | Two-stage optimal scheduling method for smart home based on demand side response | |
| CN112366684A (en) | Island micro-grid system | |
| CN115986743A (en) | Power cross-region configuration optimization method, device and equipment based on implicit decision method | |
| CN116565908A (en) | Flexible traction substation energy management method for photovoltaic and energy storage system access | |
| Chang et al. | Model predictive control based energy collaborative optimization management for energy storage system of virtual power plant | |
| CN112531785B (en) | Photo-thermal-heat-storage active power distribution network main and auxiliary combined scheduling method under multiple time scales | |
| Lamari et al. | Multi‐objective economic/emission optimal energy management system for scheduling micro‐grid integrated virtual power plant | |
| CN112365034B (en) | Electric heating comprehensive energy system scheduling method and system | |
| Baone et al. | Optimal day-ahead scheduling for microgrid participation in frequency regulation markets | |
| Sardi et al. | A comprehensive community energy storage planning strategy based on a cost-benefit analysis | |
| CN115660204A (en) | Power grid congestion relief regulation and control method considering service type virtual power plant | |
| CN115833203A (en) | Power grid peak regulation method based on multi-time interval optimal power flow and energy storage battery | |
| Li et al. | Method of Market Participation for Wind/Photovoltaic/Energy Storage Based on KKT Condition | |
| Segera | Economic Dispatch of PV-Integrated Power System with Optimally Sized Battery Energy Storage System using Particle Swarm Optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |