CN115544726A - Virtualization-oriented power spot market combined clearing optimization method and device - Google Patents
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Abstract
The invention discloses a virtualization-oriented power spot market combined clearing optimization method and device in the technical field of power market transaction, and the method comprises the following steps: s1: uploading submitted operation parameters and bidding information of a market main body in a market transaction declaration process to a power dispatching organization and a transaction center; s2: the electric power dispatching mechanism determines boundary conditions of market clearing according to predicted generated output of new energy, load prediction, network electric parameters and topological structure information of an electric power system; s3: the power dispatching mechanism carries out virtualization processing on the operation parameters of different flexible resources of the source to construct a constraint model; s4: constructing a mathematical model of a power market combined clearing optimization model based on continuous power change; s5: and solving and releasing the bid winning capacity and price of each market main body so as to furthest improve the utilization rate of the high-proportion new energy power system to the flexible resources.
Description
Technical Field
The invention relates to the technical field of electric power market trading, in particular to an electric power spot market clearing optimization method and device for virtualization flexible resource efficient utilization.
Background
With the large-scale grid connection of renewable energy sources, the demand of a power system on power flexibility resources such as frequency modulation, peak shaving, slope climbing and the like is greatly increased, and a power spot market and a power auxiliary service market are important trading platforms for guaranteeing the flexibility of the power system.
The unit combination model adopted by the traditional spot market clearing optimization method adopts a step-type scheduling plan mode, simplifies the climbing process of flexible resources such as units and energy storage when power changes, has obvious difference with the continuous and smooth output condition in actual operation, and particularly causes the problems of overlarge power deviation in the conversion process, missing availability of auxiliary service capacity and the like when the flexibility adjustment process of the system is increasingly frequent, so that the flexibility is not favorable for the full calling and economic allocation of the resources. Meanwhile, flexible resource monomers such as a distributed power supply, an energy storage device and demand side response are small in scale and distributed dispersedly, so that the distributed power supply, the energy storage device and the demand side response are difficult to directly perform market clearing together with a large-scale generator set, integration and integration are required in a virtualization mode, and higher requirements are provided for market organization and clearing.
The current concept of virtualization is mainly focused on virtual pricing in load-side resource-integrated virtual power plants and power wholesale markets, but there is a lack of research on market clearing patterns of virtualized resources from a system-wide perspective.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention, simplifications or omissions may be made in order to avoid obscuring the purpose of the section, the abstract and the title of the invention, and such simplifications or omissions are not intended to limit the scope of the invention.
The invention is provided in view of the problems of the existing virtualization-oriented electric power spot market combined clearing optimization method and device.
Aiming at the problem that the existing electric power market clearing method is insufficient in utilization extent and depth of flexible resources, the invention provides a virtualized flexible resource-oriented electric power spot goods and auxiliary service market clearing method based on a continuous output plan, so that the utilization rate of the flexible resources of an electric power system with high-proportion new energy is improved to the greatest extent. In order to solve the technical problems, the invention provides a virtualization-oriented power spot market combined clearing optimization method, which comprises the following steps of:
s1: uploading submitted operation parameters and bidding information of a market main body in a market transaction declaration process to a power dispatching organization and a transaction center;
s2: the power dispatching mechanism determines boundary conditions of market clearing according to the predicted power generation output, load prediction, network electrical parameters and power system topological structure information of the new energy;
s3: the power dispatching mechanism carries out virtualization processing on the operation parameters of different flexible resources of the source to construct a constraint model;
s4: constructing a mathematical model of a power market combined clearing optimization model based on continuous power change;
s5: and solving and releasing the bid winning capacity and price of each market subject.
Optionally, the market transaction declaration in S1 indicates that the market subject declares the transaction electric quantity and the auxiliary service capacity and price to be bought or sold within the aggregate bidding transaction declaration time window, and declaration information is not disclosed; the market main body in the S1 refers to a power generation enterprise, a power selling company, a wholesale user and an auxiliary service independent provider which meet admission conditions and complete admission registration; the operation parameters in the S1 comprise rated active power of the generator set, minimum stable technical output, active power regulation rate, maximum start-stop times allowed in a day, minimum start-stop time and initial state; and the bidding information in the S1 comprises a starting point and a terminal point of the output section, an output avoiding section and quotations of each section.
Optionally, the load prediction in S2 is a behavior of predicting a load demand of a power grid governed by the power dispatching mechanism at a specific future time by integrating natural conditions, economic conditions and social event factors according to the operating characteristics of the power grid; considering uncertainty of load and new energy output, realizing a plurality of typical uncertain scenes through scene generation and aggregation technology, and using the scenes as the calculation input of uncertain optimization; in each typical scene, the prediction theory of the new energy can generate output which is used as the time-sharing maximum power generation output of the corresponding unit, while the traditional output controllable unit keeps compatibility, the maximum power generation output is used as a time-varying parameter, and each time interval is a constant value; the network electrical parameters refer to impedance and admittance of the power transmission line and impedance and admittance of the transformer; the nodes of the topological structure of the power system comprise power generation, transmission and transformation equipment which is connected to a power grid and is regulated and managed by provincial and above power dispatching mechanisms in the provincial and jurisdiction range, and a generator set which transmits power to the provincial by a point-to-network special line power transmission mode outside the provincial.
Optionally, the constraint model includes three types, namely transient, time-varying and accumulation; the constructing of the constraint model of S3 specifically includes:
s301, constructing an instantaneous operation constraint model of the flexible resources participating in the market;
s302, constructing a time-varying operation constraint model of the flexible resources participating in the market;
and S303, constructing a cumulative operation constraint model of the flexible resources participating in the market.
Optionally, the constructing an instantaneous operation constraint model of the flexible resource participating in the market includes:
if the unit is used to substitute all flexible resource types, the unit state model is:
u g,t -u g,t-1 ＝v g,t -w g,t ；
in the formula u g,t The identifier is used for identifying whether the unit g is higher than the minimum output operation in the time period t, if not, 0 is used, and if so, 1 is used; v. of g,t Whether the unit g is in the starting process in the time period t or not is judged, if not, 0 is judged, and if yes, 1 is judged; w is a g,t Judging whether the unit g is in the shutdown process in the time period t, if not, judging that 0 is yes and 1 is yes;
the state constraint model for providing the non-rotating standby in the participation of the unit is as follows:
in the formula
And
respectively represents whether the unit g participates in the provision in the time period tUpward or downward non-rotation for standby, no in 0, yes in 1;
considering the time-varying property of the available output of the intermittent energy sources, the available output constraint model of the unit is as follows:
in the formula
The minimum stable output of the unit g in the time period t is obtained;
the maximum available output of the unit g in the time period t is obtained;
considering the conditions of a vibration area and the like of the hydroelectric generating set, the constraint model of the generating set avoiding the output section is as follows:
in the formula
Setting the lower limit of the unit g in the jth avoidance power band;
setting the upper limit of the unit g in the jth avoided power band;
the capacity technical constraint model of frequency modulation and rotation reserve is as follows:
in the formula
Assisting a plan (MWh) of service k for the unit g at time t; the six auxiliary services corresponding to k are upward frequency modulation (Reg +), downward frequency hopping (Reg-), and upward frequency modulationSpinning reserve (SR +), spinning reserve downward (SR-), non-spinning reserve upward (NR +), non-spinning reserve downward (NR-);
considering the situations of charging and discharging, power generation and utilization and the like, and expressing the electric quantity by positive numbers respectively, the power generation and utilization plan constraint model of the unit is as follows:
in the formula
A discharge plan (MW) for the unit g at the end instant of the time period t;
a charging plan (MW) for the unit g at the end instant of the time period t;
the constraint model for meeting the power load requirement by the unit electric energy output is as follows:
p 'in the formula' g,t A total output plan (MW) of the unit g at the instant of the time period t end; d t Load (MW) at the instant of time t end;
the constraint model for satisfying the system safe operation requirement by the upward frequency modulation of the unit is as follows:
in the formula
A plan (MWh) for frequency modulation service for the unit g in time period t;
a demand (MWh) for frequency up modulation service for time period t;
the constraint model for satisfying the system safe operation requirement by the unit down frequency modulation is as follows:
in the formula
A schedule (MWh) for frequency modulation service for the unit g downwards at time period t;
demand for downward frequency modulation service (MWh) for time period t;
the constraint model for the unit to perform upward frequency modulation and rotate for later use to meet the safe operation requirement of the system is as follows:
in the formula
Rotating a plan of backup service (MWh) for the unit g in time period t upwards;
plan for non-rotating standby service (MWh) for unit g in time period t;
a demand for up-standby service (MWh) for time period t;
the constraint model for downward frequency modulation and standby meeting the safe operation requirement of the system of the unit is as follows:
in the formula
A plan (MWh) to rotate the standby service downwards for the unit g at time t;
plan for non-rotating standby service (MWh) down for crew g at time t;
demand for down standby service (MWh) for time period t;
the electric energy input scalar quantity constraint model of the unit is as follows:
0≤e g,t ≤E g,t ；
in the formula e g,t An electric energy plan (MWh) for the unit g at time t; e g,t The bid amount (MWh) of the unit g in the electric energy market in the time period t;
the unit is used for carrying out the following steps of:
in the formula
The bid amount (MWh) of auxiliary service k for unit g at time period t;
the constraint model of the total output of the unit considering power generation and auxiliary service is as follows:
the charge capacity constraint model considering that the unit has the energy storage function is as follows:
in the formula
The maximum charge capacity (MWh) of the unit g.
Optionally, the constructing a time-varying operation constraint model of the flexible resource participating in the market includes:
the unit output constraint model is as follows:
p 'in the formula' g,t A total output plan (MW) of the unit g at the moment at the end of the time period t; p is a radical of g,t An output plan (MW) that the unit g is higher than the minimum output at the end of the time period t instantly;
the unit electric energy plan constraint model considering the time point climbing process is as follows:
in the formula
The minimum running output (MW) of the unit g;
the unit climbing constraint model is as follows:
-RD g ≤p g,t -p g,t-1 ≤RU g ；
in the formula RD g The average climbing capacity (MW/min) of the unit g within one hour; RU (RU) g The average downhill climbing capacity (MW/min) of the unit g within one hour.
Optionally, the building a model of cumulative operating constraints of flexible resources participating in the market includes:
the minimum start-stop time length constraint of the unit is as follows:
in the formula TU g Starting the unit g for the duration (hours); TD g The duration (hours) of the shutdown process of the unit g is set;
the state of charge constraint model considering energy storage is:
in the formula SOC g,t The state of charge (MWh) of the unit g at the end instant of the time period t; SOC g,0 Is the initial state of charge (MWh) of the unit g; eta g The charge-discharge efficiency (%) of the unit g.
Optionally, in the step S4,
the method is characterized in that the minimum total cost for purchasing electric energy and auxiliary service is taken as a target, an auxiliary service plan, an electric energy plan and a unit state are taken as decision variables, an objective function comprises 5 parts of auxiliary service cost, startup cost, shutdown cost, no-load cost and power generation cost, and a mathematical model is as follows:
in the formula
Starting cost (yuan/MWh) for a unit g in a time period t;
shutdown cost (yuan/MWh) for the unit g at time t;
the unit g is quoted for auxiliary service (yuan/MWh) in the time period t;
quote the unit g at the time t no-load cost (yuan/MWh);
the unit g is quoted for the electric energy cost (yuan/MWh) in the time period t;
assist the planning (MWh) of service k for a crew g for a period t.
Another object of the present invention is to provide a virtualization-oriented power spot market joint clearing optimization apparatus, including a power scheduling mechanism, where the power scheduling mechanism includes:
the receiving unit is used for receiving the operation parameters and bidding information submitted by the market main body in the market transaction reporting process;
the boundary condition determining unit is used for determining the boundary condition of market clearing according to the predicted power generation output, load prediction, network electrical parameters and topological structure information of the power system of the new energy;
the constraint model building unit is used for performing virtualization processing on the operation parameters of different flexible resources of the source to build a constraint model;
the mathematical model construction unit is used for constructing a mathematical model of the electric power market combined clearing optimization model based on continuous power change;
and the solving and publishing unit is used for solving and publishing the bid winning capacity and the price of each market subject.
In summary, the present invention includes at least one of the following advantages:
1. in the power market environment, the starting, stopping and climbing processes of the flexible resources are embodied by a continuous output curve which is closer to the actual running condition, and the utilization depth of the flexible resources can be greatly improved.
2. By converting the virtualized flexible resources, the operation characteristics of the flexible resources are abstractly aggregated, the operation constraints are classified into three types of unified treatment, namely instantaneous treatment, time-varying treatment and accumulative treatment, and the utilization range of the flexible resources can be expanded to the maximum extent.
3. The interaction relation between the power supply and the flexible resources in the power change process is fully considered, the possibility of actual execution and landing application is provided, the optimized scheduling plan is more operable, and the technical power deviation is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention is described in further detail below with reference to FIG. 1.
Example one
The invention discloses a virtualization-oriented combined clearing optimization method for a power spot market, which comprises the following steps of:
s1, submitting operation parameters and bidding information to a power dispatching mechanism and a trading center by a market main body in a market trading reporting process.
In the S1, the market transaction declaration refers to that a market main body declares the transaction electric quantity and the auxiliary service capacity and price to be bought or sold in a set bidding transaction declaration time window, and declaration information is not disclosed; the market main body in the S1 refers to a power generation enterprise, a power selling company, a wholesale user, an auxiliary service independent provider and the like which accord with the admission condition and complete admission registration; the operation parameters in the S1 include but are not limited to rated active power of a generator set, minimum stable technical output, active power regulation rate, maximum start-stop times allowed in a day, minimum start-stop time, initial state and the like; the bidding information in the S1 includes but is not limited to a starting point and an ending point of an output section, an avoidance output section and quotations in each section.
In this application, the operation parameters and the bidding information are shown in the following tables 1 to 4:
TABLE 1 Unit parameters
TABLE 2 Power load and auxiliary service demand forecast
TABLE 3 New energy prediction output
| Time point | New energy prediction power generation capacity |
| 1 | 20MW |
| 2 | 10MW |
| 3 | 30MW |
TABLE 4 Unit individual market quotes
S2: the electric power dispatching mechanism determines the boundary conditions of market clearing according to the information of the predicted power generation output, load prediction, network electric parameters, the topological structure of the electric power system and the like of uncontrollable new energy sources such as wind, light, runoff hydropower and the like.
Wherein: the load prediction is a behavior of predicting the load demand of the power grid governed by the power dispatching mechanism at a specific future moment by integrating factors such as natural conditions, economic conditions, social events and the like according to the operating characteristics of the power grid. Considering uncertainty of load and new energy output, a plurality of typical uncertain scenes can be realized through a scene generation and aggregation technology and used as the calculation input of uncertain optimization; in each typical scene, the prediction theory of the new energy can generate output which is used as the time-sharing maximum power generation output of the corresponding unit, while the traditional output controllable unit keeps compatibility, the maximum power generation output is used as a time-varying parameter, and a constant value is taken at each time interval. The network electrical parameters refer to the impedance and admittance of the transmission line, the impedance and admittance of the transformer, and the like. The nodes of the topological structure of the power system comprise power generation, transmission and transformation equipment which is connected with a power grid and is regulated and managed by provincial and above power dispatching mechanisms in the provincial and jurisdiction range, and a generator set which transmits power to the provincial and provincial by using a point-to-grid special line power transmission mode outside the provincial and above the power dispatching mechanisms. In the application example, the predicted generated output and the predicted load of the new energy are shown in tables 2 and 3, and the power grid constraint is not considered for the moment.
S3: the power dispatching mechanism carries out virtualization processing on the operation parameters of different flexible resources of the source to construct a constraint model;
in the virtualization, all flexible resources are regarded as a 'full-parameter unit' with the same parameter entries, and the parameters of the redundancy configuration reflect differences according to actual conditions when aiming at specific resources, such as
Shown in table 5.
According to the assumption, the constraint mathematical model for constructing the virtualization-oriented power spot market combined clearing optimization model comprises three types of instantaneous, time-varying and accumulation:
s301, constructing an instantaneous operation constraint model of the flexible resources participating in the market;
if the unit is used to substitute all flexible resource types, the unit state model is:
u g,t -u g,t-1 ＝v g,t -w g,t ；
in the formula u g,t The identifier is used for identifying whether the unit g is higher than the minimum output operation in the time period t, if not, 0 is used, and if so, 1 is used; v. of g,t Whether the unit g is in the starting process in the time period t is judged, if not, 0 is judged, and if yes, 1 is judged; w is a g,t Whether the unit g is in the stopping process in the time period t or not is judged, if not, 0 is judged, and if yes, 1 is judged;
the state constraint model for providing the non-rotating standby in the participation of the unit is as follows:
in the formula
And
respectively indicating whether the unit g participates in providing upward or downward non-rotation standby in the time period t, wherein 0 is negative, and 1 is positive;
considering the time-varying property of the available output of the intermittent energy, the constraint model of the available output of the unit is as follows:
in the formula
The minimum stable output of the unit g in the time period t is obtained;
the maximum available output of the unit g in the time period t is obtained;
considering the conditions of a vibration area and the like of the hydroelectric generating set, the constraint model of the generating set avoiding the output section is as follows:
in the formula
Setting the lower limit of the unit g in the jth avoidance power band;
setting the upper limit of the unit g in the jth avoidance power band;
the capacity technical constraint model of frequency modulation and rotation reserve is as follows:
in the formula
Assisting the plan (MWh) of service k for the unit g for a period t; the six auxiliary services corresponding to k are up frequency modulation (Reg +), down frequency hopping (Reg-), up spinning reserve (SR +), down spinning reserve (SR-), up non spinning reserve (NR +), down non spinning reserve (NR-);
considering the situations of charging and discharging, power generation and utilization and the like, and expressing the electric quantity by positive numbers respectively, the power generation and utilization plan constraint model of the unit is as follows:
in the formula
A discharge plan (MW) for the unit g at the end instant of the time period t;
a charging plan (MW) for the unit g at the end instant of the time period t;
the constraint model that the electric energy output of the unit meets the power load requirement is as follows:
p 'in the formula' g,t A total output plan (MW) of the unit g at the moment at the end of the time period t; d t Load (MW) at the instant of the end of time period t;
the constraint model for satisfying the system safe operation requirement by the upward frequency modulation of the unit is as follows:
in the formula
A schedule (MWh) for tuning up the frequency service for the unit g at time t;
demand for frequency up modulation service (MWh) for time period t;
the constraint model for satisfying the safe operation requirement of the system by the downward frequency modulation of the unit is as follows:
in the formula
A schedule (MWh) for frequency modulation service for the unit g downwards at time period t;
demand for downward frequency modulation service (MWh) for time period t;
the constraint model for the unit to perform upward frequency modulation and rotate for later use to meet the safe operation requirement of the system is as follows:
in the formula
A plan (MWh) to rotate the standby service upwards for the crew g for a period t;
plan for non-rotating standby service (MWh) for unit g in time period t;
a demand for up-standby service (MWh) for time period t;
the constraint model for downward frequency modulation and standby meeting the system safe operation requirement of the unit is as follows:
in the formula
Rotating a plan (MWh) of standby service downwards for the unit g for a time period t;
for unit g downwards at time tPlan for non-rotational backup service (MWh);
demand for down standby service (MWh) for time period t;
the electric energy throwing amount constraint model of the unit is as follows:
0≤e g,t ≤E g,t ；
in the formula e g,t An electric energy plan (MWh) for the unit g at time t; e g,t The unit g is the bid amount (MWh) of the electric energy market in the time period t;
the unit is used for carrying out the following steps of:
in the formula
The bid amount (MWh) of auxiliary service k for unit g at time period t;
the constraint model considering the total output of the generating set and the auxiliary service is as follows:
the charge capacity constraint model considering that the unit has the energy storage function is as follows:
in the formula
The maximum charge capacity (MWh) of the unit g.
S302, constructing a time-varying operation constraint model of the flexible resources participating in the market;
the unit output constraint model is as follows:
p 'in the formula' g,t A total output plan (MW) of the unit g at the moment at the end of the time period t; p is a radical of g,t An output plan (MW) that the unit g is higher than the minimum output at the end of the time period t instantly;
the unit electric energy plan constraint model considering the time point climbing process is as follows:
in the formula
A minimum operating output (MW) for the unit g;
the unit climbing constraint model is as follows:
-RD g ≤p g,t -p g,t-1 ≤RU g ；
in the formula RD g The average climbing capacity (MW/min) of the unit g within one hour; RU (RU) g The average downward climbing capacity (MW/min) of the unit g within one hour.
S303, constructing a cumulative operation constraint model of the flexible resources participating in the market;
the minimum start-stop time length constraint of the unit is as follows:
in the formula TU g Starting for unit gMachine process duration (hours); TD g The duration (hours) of the shutdown process of the unit g is set;
the state of charge constraint model considering energy storage is:
in the formula SOC g,t The state of charge (MWh) of the unit g at the end instant of the time period t; SOC (system on chip) g,0 Is the initial state of charge (MWh) of the unit g; eta g The charge-discharge efficiency (%) of the unit g.
S4: constructing a mathematical model of a power market combined clearing optimization model based on continuous power change;
the method is characterized in that the minimum total cost for purchasing electric energy and auxiliary service is taken as a target, an auxiliary service plan, an electric energy plan and a unit state are taken as decision variables, an objective function comprises 5 parts of auxiliary service cost, startup cost, shutdown cost, no-load cost and power generation cost, and a mathematical model is as follows:
in the formula
Starting cost (yuan/MWh) for a unit g in a time period t;
shutdown cost (yuan/MWh) for unit g at time t;
the unit g is quoted for auxiliary service (yuan/MWh) in the time period t;
quote the unit g at the time t no-load cost (yuan/MWh);
the unit g is quoted for the electric energy cost (yuan/MWh) in the time period t;
assist the planning (MWh) of service k for a crew g for a period t.
S5: and solving and releasing the bid winning capacity and price of each market subject. As shown in tables 6-7, the results of winning the bid-winning status are shown in Table 6
Clear result-time-sharing price
Example two
This embodiment has still provided a to virtualized electric power spot market unites clearing optimization device, including power scheduling mechanism, power scheduling mechanism includes:
the receiving unit is used for receiving the operation parameters and bidding information submitted by the market main body in the market transaction reporting process;
the boundary condition determining unit is used for determining the boundary condition of market clearing according to the predicted power generation output, load prediction, network electrical parameters and topological structure information of the electric power system of the new energy;
the constraint model building unit is used for performing virtualization processing on the operation parameters of different flexible resources of the source to build a constraint model;
the mathematical model construction unit is used for constructing a mathematical model of the electric power market combined clearing optimization model based on continuous power change;
and the solving and publishing unit is used for solving and publishing the bid winning capacity and the price of each market subject.
The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, so: equivalent changes made according to the structure, shape and principle of the invention shall be covered by the protection scope of the invention.
Claims (9)
1. A virtualization-oriented power spot market combined clearing optimization method is characterized by comprising the following steps: the method comprises the following steps:
s1: uploading submitted operation parameters and bidding information of a market main body in a market transaction declaration process to a power dispatching organization and a transaction center;
s2: the power dispatching mechanism determines boundary conditions of market clearing according to the predicted power generation output, load prediction, network electrical parameters and power system topological structure information of the new energy;
s3: the power dispatching mechanism carries out virtualization processing on the operation parameters of different flexible resources of the source to construct a constraint model;
s4: constructing a mathematical model of a power market combined clearing optimization model based on continuous power change;
s5: and solving and releasing the bid winning capacity and price of each market subject.
2. The virtualization-oriented power spot market joint shipment optimization method of claim 1, wherein: the operation parameters in the S1 comprise rated active power of the generator set, minimum stable technical output, active power regulation rate, maximum start-stop times allowed in a day, minimum start-stop time and initial state; and the bidding information in the S1 comprises a starting point and a terminal point of the output section, an output avoiding section and quotations of each section.
3. The virtualization-oriented power spot market joint clearing optimization method according to claim 1, wherein: the load prediction in the S2 is a behavior of predicting the load demand of the power grid governed by the power dispatching mechanism at a future specific moment by integrating natural conditions, economic conditions and social event factors according to the running characteristics of the power grid; the network electrical parameters refer to impedance and admittance of the power transmission line and impedance and admittance of the transformer; the nodes of the power system topological structure comprise power generation, transmission and transformation equipment accessed to a power grid.
4. The virtualization-oriented power spot market joint shipment optimization method of claim 1, wherein: the constraint model comprises three types of instantaneous, time-varying and accumulation; the constructing of the constraint model of S3 specifically includes:
s301, constructing an instantaneous operation constraint model of the flexible resources participating in the market;
s302, constructing a time-varying operation constraint model of the flexible resources participating in the market;
and S303, constructing a cumulative operation constraint model of the flexible resources participating in the market.
5. The virtualization-oriented power spot market joint shipment optimization method of claim 4, wherein: the constructing of the instantaneous operation constraint model of the flexible resources participating in the market comprises the following steps:
if the unit is used to substitute all flexible resource types, the unit state model is as follows:
u g,t -u g,t-1 ＝v g,t -w g,t ；
in the formula u g,t The identifier of whether the unit g is higher than the minimum output operation in the time period t is set, if not, 0 is set to be negative, and if not, 1 is set to be positive; v. of g,t Whether the unit g is in the starting process in the time period t is judged, if not, 0 is judged, and if yes, 1 is judged; w is a g,t Judging whether the unit g is in the shutdown process in the time period t, if not, judging that 0 is yes and 1 is yes;
the state constraint model for providing the non-rotating standby in the participation of the unit is as follows:
in the formula
And
respectively indicating whether the unit g participates in providing upward or downward non-rotation standby in the time period t, wherein 0 is no, and 1 is yes;
considering the time-varying property of the available output of the intermittent energy, the constraint model of the available output of the unit is as follows:
in the formula
The minimum stable output of the unit g in the time period t is obtained;
the maximum available output of the unit g in the time period t is obtained;
considering the conditions of a vibration area and the like of the hydroelectric generating set, the constraint model of the generating set avoiding the output section is as follows:
in the formula
For the unit g under the jth avoidance power bandLimiting;
setting the upper limit of the unit g in the jth avoided power band;
the capacity technical constraint model of frequency modulation and rotation reserve is as follows:
in the formula
Assisting the plan (MWh) of service k for the unit g for a period t; the six auxiliary services corresponding to k are up frequency modulation (Reg +), down frequency hopping (Reg-), up spinning reserve (SR +), down spinning reserve (SR-), up non spinning reserve (NR +), down non spinning reserve (NR-);
considering the situations of charging and discharging, power generation and utilization and the like, and expressing the electric quantity by positive numbers respectively, the power generation and utilization plan constraint model of the unit is as follows:
in the formula
A discharge plan (MW) for the unit g at the end instant of the time period t; p is a radical of - g,t A charging plan (MW) for the unit g at the end instant of the time period t;
the constraint model that the electric energy output of the unit meets the power load requirement is as follows:
p 'in the formula' g,t A total output plan (MW) of the unit g at the moment at the end of the time period t; d t The load at the end instant of time period t: (MW)；
The constraint model for satisfying the safe operation requirement of the system by upward frequency modulation of the unit is as follows:
in the formula
A schedule (MWh) for tuning up the frequency service for the unit g at time t;
a demand (MWh) for frequency up modulation service for time period t;
the constraint model for satisfying the safe operation requirement of the system by the downward frequency modulation of the unit is as follows:
in the formula
A schedule (MWh) for frequency modulation service for the unit g downwards at time period t;
demand for downward frequency modulation service (MWh) for time period t;
the constraint model for the unit to perform upward frequency modulation and rotate for later use to meet the safe operation requirement of the system is as follows:
in the formula
Meter for stand-by service of unit g rotating upwards in time period tScribing (MWh);
plan for non-rotating standby service (MWh) for crew g up time period t;
a demand (MWh) for an upward standby service for a period t;
the constraint model for downward frequency modulation and standby meeting the system safe operation requirement of the unit is as follows:
in the formula
Rotating a plan (MWh) of standby service downwards for the unit g for a time period t;
plan for non-rotating standby service (MWh) down for crew g at time t;
demand for down standby service (MWh) for time period t;
the electric energy input scalar quantity constraint model of the unit is as follows:
0≤e g,t ≤E g,t ；
in the formula e g,t An electric energy plan (MWh) for the unit g at time t; e g,t The bid amount (MWh) of the unit g in the electric energy market in the time period t;
the unit is used for carrying out the following steps of:
in the formula
The bid amount (MWh) of auxiliary service k for unit g at time period t;
the constraint model of the total output of the unit considering power generation and auxiliary service is as follows:
the charge capacity constraint model considering that the unit has the energy storage function is as follows:
in the formula
The maximum charge capacity (MWh) of the unit g.
6. The virtualization-oriented power spot market joint shipment optimization method of claim 4, wherein: the constructing of the time-varying operation constraint model of the flexible resource participating in the market comprises the following steps:
the output constraint model of the unit is as follows:
p 'in the formula' g,t A total output plan (MW) of the unit g at the moment at the end of the time period t; p is a radical of formula g,t An output plan (MW) that the unit g is higher than the minimum output at the end of the time period t instantly;
the unit electric energy plan constraint model considering the time point climbing process is as follows:
in the formula
A minimum operating output (MW) for the unit g;
the unit climbing constraint model is as follows:
-RD g ≤p g,t -p g,t-1 ≤RU g ；
in the formula RD g The average climbing capacity (MW/min) of the unit g within one hour; RU g The average downward climbing capacity (MW/min) of the unit g within one hour.
7. The virtualization-oriented power spot market joint clearing optimization method according to claim 4, wherein: the building of the cumulative operation constraint model of the flexible resources participating in the market comprises the following steps:
the minimum start-stop time length constraint of the unit is as follows:
in the formula TU g Starting the unit g for the duration (hours); TD g The duration (hours) of the shutdown process of the unit g is set;
the state of charge constraint model considering energy storage is:
SOC in the formula g,t The state of charge (MWh) of the unit g at the end instant of the time period t; SOC (system on chip) g,0 Is the initial state of charge (MWh) of the unit g; eta g The charge-discharge efficiency (%) of the unit g.
8. The virtualization-oriented power spot market joint shipment optimization method of claim 1, wherein: in the above-mentioned step S4, the first step,
the method is characterized in that the minimum total cost for purchasing electric energy and auxiliary service is taken as a target, an auxiliary service plan, an electric energy plan and a unit state are taken as decision variables, an objective function comprises 5 parts of auxiliary service cost, startup cost, shutdown cost, no-load cost and power generation cost, and a mathematical model is as follows:
in the formula
Starting cost (yuan/MWh) for a unit g in a time period t;
shutdown cost (yuan/MWh) for unit g at time t;
b, offering auxiliary service price (yuan/MWh) for the unit g in the time t;
quote the unit g at the time t no-load cost (yuan/MWh);
the unit g is quoted for the electric energy cost (yuan/MWh) in the time period t;
a plan (MWh) to service k is assisted for the unit g for a period t.
9. The virtualization-oriented power spot market joint shipment optimization method according to any one of claims 1 to 8, wherein: still include the electric power spot market of a virtualization-oriented and unite the optimization device that clears out, the device includes power dispatching mechanism, power dispatching mechanism includes:
the receiving unit is used for receiving the operation parameters and bidding information submitted by the market main body in the market transaction declaration process;
the boundary condition determining unit is used for determining the boundary condition of market clearing according to the predicted power generation output, load prediction, network electrical parameters and topological structure information of the power system of the new energy;
the constraint model building unit is used for performing virtualization processing on the operation parameters of different flexible resources of the source to build a constraint model;
the mathematical model construction unit is used for constructing a mathematical model of the electric power market combined clearing optimization model based on continuous power change;
and the solving and publishing unit is used for solving and publishing the bid winning capacity and the price of each market subject.
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| CN117217841B (en) * | 2023-08-25 | 2024-06-11 | 哈尔滨工业大学 | Multi-element market clearing system optimization method considering generalized energy constraint of virtual power plant |
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