Method, system, and apparatus for clearing an expandable period power spot day-ahead market

Abstract

The application discloses an extensible time period power spot day-ahead market clearing method, system and device, obtains D+1 day and D+2 day partial time data, constructs a day-ahead spot market calculation data set, and generates a time sequence with non-uniform time intervals; a spot market clearing constraint condition and an optimization target function suitable for the non-uniform time sequence are constructed, an optimal solution of the target function is calculated, and a D+1 day full-time unit commitment plan and a D+2 day extensible time period unit commitment plan are obtained. The extensible time period power spot day-ahead market clearing method, system and device have the characteristics of high flexibility and strong practicability.

Description

Technical Field

[0001] This invention relates to the field of power automation, and specifically to a method, system, and apparatus for clearing the day-ahead electricity spot market. Background Technology

[0002] The current electricity market can be mainly divided into the day-ahead spot market and the real-time spot market. The day-ahead spot market requires a computerized unit combination plan. The goal of unit combination planning is to reduce the operating costs of the power grid system. That is, under certain constraints, to minimize the sum of the system's operating costs and start-up costs. The objective function should include some basic parameters of the units in the power grid system, such as the number of generating units, generator power, unit status, generation costs for a certain period, start-up costs, etc. Depending on the actual situation, different constraints are commonly used. Commonly used constraints include: unit power constraints, start-up and shutdown time constraints, and system average power constraints. In the actual process of establishing the mathematical model of unit combination, the objective function and constraints can be modified according to specific circumstances to make the mathematical model more consistent with reality.

[0003] Meanwhile, the bidding space for generating units is limited and fluctuates over time. Generally, system load is closely related to people's production and daily life. Based on people's electricity consumption habits, the electricity market typically experiences two peak periods in the morning and evening, and two low periods at midday and night. However, the existing day-ahead spot market only calculates the generating unit combination plan for the next day. This results in a large number of generating units being shut down after the evening peak to conserve resources. For conventional coal-fired power units, it takes more than a day to restart after a shutdown. This leads to a situation where large-scale unit shutdowns after the evening peak cannot immediately meet the electricity demand during the peak load of the following day.

[0004] Therefore, a new technical solution is needed to solve the above problems. Summary of the Invention

[0005] To address the problems arising from existing technologies, this invention provides a highly flexible and practical scalable day-ahead market clearing method, system, and apparatus for electricity spot prices.

[0006] To achieve the above objectives, the scalable day-ahead electricity spot market clearing method of the present invention can adopt the following technical solution:

[0007] A method for clearing the day-ahead spot market for electricity with scalable timeframes, comprising the following steps:

[0008] 1) Obtain the calculated market data for the spot market with a clearing date of D, wherein the calculated data includes the calculated data for D+1 day and the calculated data for D+2 day; the time interval of the calculated data for D+2 day is uneven;

[0009] 2) Construct the calculation dataset of the spot day-ahead market based on the calculation data, generate a time series with multiple time points of uneven intervals, and convert the time difference between two adjacent time points into the time granularity of the spot day-ahead market;

[0010] 3) Construct a model for the day-ahead spot market adapted to the given time series, including constraints and an objective function;

[0011] 4) Solve the objective function based on the constraints to obtain the unit combination plan corresponding to the calculation dataset.

[0012] The constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints. The network security constraints include unit power output increase / decrease constraints, ramping constraints, and power flow constraints at critical sections.

[0013] Furthermore, the calculated data for day D+1 has a uniform time interval; the calculated data for day D+2 consists of data from certain moments with non-uniform time intervals, including data from peak and off-peak load times and data from peak and off-peak renewable energy output times.

[0014] Furthermore, in step 2), the conversion process is as follows:

[0015]

[0016] In the formula, Let F be the time difference between time t and time t-1, F be the granularity of the day-ahead spot market calculation, T be the end time of day D+1, M be the end time of day D+2, 1~T be the normal clearing period, and T+1~M be the extended clearing period. This is a time series calculated by granularity.

[0017] Furthermore, the objective function is to minimize the cost of electricity purchase. The objective function is:

[0018]

[0019] In the formula, This indicates the total number of generating units; SUM indicates the number of D+1 and D+2 time periods considered. Indicates the unit exist Efforts during a specific time period; For the unit During the period Operating costs, It is a multi-segment linear function related to the output ranges declared by the generating unit and the corresponding energy prices; For the unit During the period The startup cost is ρ, which is the network flow constraint relaxation penalty factor used for market clearing optimization. For the positive power flow relaxation variable of section s, For the reverse power flow relaxation variable of section s, This represents the total number of cross-sections.

[0020] Furthermore, the unit operating time constraint is as follows:

[0021]

[0022]

[0023] In the formula, For the unit exist Start / stop status during a given time period; Minimum continuous operating time of the unit This refers to the minimum continuous downtime of the unit. For the unit exist The time period refers to the time during which the machine has been continuously powered on. For the unit exist The period refers to the duration of continuous downtime.

[0024] Furthermore, for each time period The system equilibrium constraint condition is:

[0025]

[0026] in, Indicates the unit exist Efforts during the period This indicates the planned power of tie line j during time period t, with input being positive and output being negative; For the total number of connecting lines, for The system load for the time period, 1 to M is a continuous sequence with a tolerance of 1.

[0027] Furthermore, the unit's output is within its maximum and minimum technical output range, and the unit's output range constraint is as follows:

[0028]

[0029] In the formula, The lower limit of unit output, This is the upper limit of the unit's output; when the unit is shut down, When the unit is started, .

[0030] Furthermore, the network security constraints are specifically as follows:

[0031] There are two types of climbing constraints. The first type of climbing constraint is:

[0032]

[0033] ;

[0034] The second type of climbing constraint is:

[0035]

[0036]

[0037] In the formula, For the unit Maximum uphill speed, For the unit Maximum downhill climbing rate Variables for identifying the start-up time of the unit, Variables used to identify the time of unit shutdown;

[0038] The unit's lifting output constraint changes with the unit's operating state, which includes operating state, start-up state, and shutdown state. When the unit is in the start-up state, the unit's lifting output range is... Decision; When the unit is in a shutdown state, the range of the unit's output increase / decrease is determined by... Decision; when the unit is in operation, the range of the unit's lifting output is determined by... , Decide;

[0039]

[0040]

[0041] In the formula, For the unit's ramp rate, For the landslide rate of the unit, Let t be the time difference between time t and time t-1;

[0042] The power flow constraints at the critical section are as follows:

[0043]

[0044] In the formula, cross-section Minimum limit of current transmission cross-section The maximum limit of current transmission For the unit The node is located on the cross section The generator output power transfer distribution factor For nodes cross section The generator output power transfer distribution factor This is the predicted value of the bus load k at time t.

[0045] To achieve the above objectives, the present invention provides a scalable day-ahead electricity spot market clearing system, which can adopt the following technical solution:

[0046] A scalable day-ahead electricity spot market clearing system, the system comprising:

[0047] The calculation data acquisition module is used to acquire the calculation data of the spot market before the clearing date is D. The calculation data includes the calculation data of day D+1 and the calculation data of day D+2, and the time interval of the calculation data of day D+2 is uneven.

[0048] The time series generation module is used to construct the calculation dataset of the spot day-ahead market based on the calculation data, generate a time series with multiple time points of uneven intervals, and convert the time difference between two adjacent time points into the time granularity of the spot day-ahead market.

[0049] The model building module is used to build a model of the spot day-ahead market adapted to the time series, and the model includes constraints and an objective function; wherein, the constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints, and the network security constraints include unit output increase and decrease constraints, ramping constraints, and power flow constraints at critical sections;

[0050] The module for solving the unit combination plan solves the objective function based on the constraints and obtains the unit combination plan corresponding to the calculation dataset.

[0051] To achieve the above objectives, the present invention provides a day-ahead market clearing device for scalable electricity spot prices, which can adopt the following technical solution:

[0052] A scalable-period day-ahead electricity spot market clearing device, the device implementing the steps of the method.

[0053] The present invention has the following beneficial effects:

[0054] 1. By converting time series with uneven time intervals, the data requirements for day-ahead spot market clearing calculations are met, the time period is expanded, and flexibility is high.

[0055] 2. The SCUC day-ahead spot market optimization model, constructed based on the set of constraints and the objective function, takes into account the peak and off-peak system load data of day D+2. It saves energy while meeting the immediate power demand after a large number of shutdowns in the spot market during the evening peak, and is highly practical. Attached Figure Description

[0056] Figure 1 This is a flowchart of the day-ahead market clearing method for electricity spot prices according to the present invention. Detailed Implementation

[0057] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0058] Please see Figure 1 As shown, this invention discloses a method for clearing the day-ahead electricity spot market over a scalable time period. The method includes the following steps:

[0059] 1) Obtain the calculated market data for the spot market with a clearing date of D, wherein the calculated data includes the calculated data for D+1 day and the calculated data for D+2 day; the time interval of the calculated data for D+2 day is uneven;

[0060] 2) Construct the calculation dataset of the spot day-ahead market based on the calculation data, generate a time series with multiple time points of uneven intervals, and convert the time difference between two adjacent time points into the time granularity of the spot day-ahead market;

[0061] The data for day D+1 is calculated at a uniform time interval; the data for day D+2 is calculated at certain times with a non-uniform time interval, including data from peak and off-peak load periods, and data from peak and off-peak renewable energy output periods. The conversion process is as follows:

[0062]

[0063] In the formula, Let F be the time difference between time t and time t-1, F be the granularity of the day-ahead spot market calculation, T be the end time of day D+1, M be the end time of day D+2, 1~T be the normal clearing period, and T+1~M be the extended clearing period. This is a time series calculated by granularity.

[0064] 3) Construct a day-ahead spot market model adapted to the time series. The model includes constraints and an objective function. The constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints. The network security constraints include unit output increase / decrease constraints, ramp-up constraints, and power flow constraints at critical sections.

[0065] The objective function is to minimize the cost of electricity purchase.

[0066]

[0067] In the formula, This indicates the total number of generating units; SUM indicates the number of D+1 and D+2 time periods considered. Indicates the unit exist Efforts during a specific time period; For the unit During the period Operating costs, It is a multi-segment linear function related to the output ranges declared by the generating unit and the corresponding energy prices; For the unit During the period The startup cost is ρ, which is the network flow constraint relaxation penalty factor used for market clearing optimization. For the positive power flow relaxation variable of section s, For the reverse power flow relaxation variable of section s, This represents the total number of cross-sections.

[0068] The unit operating time constraints are as follows:

[0069]

[0070]

[0071] In the formula, For the unit exist Start / stop status during a given time period; Minimum continuous operating time of the unit This refers to the minimum continuous downtime of the unit. For the unit exist The time period refers to the time during which the machine has been continuously powered on. For the unit exist The period refers to the duration of continuous downtime.

[0072] in, , State variables can be used To indicate:

[0073]

[0074]

[0075] For each time period The system equilibrium constraint condition is:

[0076]

[0077] in, Indicates the unit exist Efforts during the period This indicates the planned power of tie line j during time period t, with input being positive and output being negative; For the total number of connecting lines, for The system load for the time period, 1 to M is a continuous sequence with a tolerance of 1.

[0078] The unit's output is within its maximum and minimum technical output range, and the constraints on the unit's output range are as follows:

[0079]

[0080] In the formula, The lower limit of unit output, This is the upper limit of the unit's output; when the unit is shut down, When the unit is started, If the unit is shut down, This constraint condition can limit the unit's output to 0; when the unit is started, This constraint is a conventional upper and lower limit constraint for output.

[0081] The specific network security constraints are as follows:

[0082] There are two types of climbing constraints. The first type of climbing constraint is:

[0083]

[0084] ;

[0085] The second type of climbing constraint is a simplification of the first type of climbing constraint, specifically as follows:

[0086]

[0087]

[0088] In the formula, For the unit Maximum uphill speed, For the unit Maximum downhill climbing rate Variables for identifying the start-up time of the unit, Variables used to identify the time of unit shutdown;

[0089] The unit's lifting output constraint changes with the unit's operating state, which includes operating state, start-up state, and shutdown state. When the unit is in the start-up state, the unit's lifting output range is... Decision; When the unit is in a shutdown state, the range of the unit's output increase / decrease is determined by... Decision; when the unit is in operation, the range of the unit's lifting output is determined by... , Decide;

[0090]

[0091]

[0092] In the formula, For the unit's ramp rate, For the landslide rate of the unit, Let t be the time difference between time t and time t-1;

[0093] The power flow constraints at the critical section are as follows:

[0094]

[0095] In the formula, cross-section Minimum limit of current transmission cross-section The maximum limit of current transmission For the unit The node is located on the cross section The generator output power transfer distribution factor For nodes cross section The generator output power transfer distribution factor This is the predicted value of the bus load k at time t.

[0096] 4) Solve the objective function based on the constraints to obtain the unit combination plan corresponding to the calculation dataset. Select the optimal solution.

[0097] This invention also discloses a scalable time-period electricity spot day-ahead market clearing system, characterized in that the system comprises:

[0098] The calculation data acquisition module is used to acquire the calculation data of the spot market before the clearing date is D. The calculation data includes the calculation data of day D+1 and the calculation data of day D+2, and the time interval of the calculation data of day D+2 is uneven.

[0099] The time series generation module is used to construct the calculation dataset of the spot day-ahead market based on the calculation data, generate a time series with multiple time points of uneven intervals, and convert the time difference between two adjacent time points into the time granularity of the spot day-ahead market.

[0100] The model building module is used to build a model of the spot day-ahead market adapted to the time series, and the model includes constraints and an objective function; wherein, the constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints, and the network security constraints include unit output increase and decrease constraints, ramping constraints, and power flow constraints at critical sections;

[0101] The module for solving the unit combination plan solves the objective function based on the constraints and obtains the unit combination plan corresponding to the calculation dataset.

[0102] The present invention also discloses a day-ahead market clearing device for scalable electricity spot prices, the device implementing the steps of the method described.

[0103] In summary, the day-ahead market clearing method, system, and apparatus for scalable electricity spot prices of the present invention are characterized by high flexibility and strong practicality.

Claims

1. A method for clearing the day-ahead electricity spot market with scalable timeframes, characterized in that, The method includes the following steps: 1) Obtain the calculated data of the spot market before the clearing date D. The calculated data includes the calculated data of day D+1 and the calculated data of day D+2. The time interval of the calculated data of day D+2 is uneven. The time interval of the calculated data of day D+1 is uniform. The calculated data of day D+2 is the target time data with uneven time interval, including the peak and trough time data of load and the peak and trough time data of new energy output. 2) Construct a computational dataset for the day-ahead spot market based on the computational data, generating a time series with multiple time points at uneven intervals, and converting the time difference between two adjacent time points into the time granularity of the day-ahead spot market; the conversion process is as follows: In the formula, Let F be the time difference between time t and time t-1, F be the granularity of the day-ahead spot market calculation, T be the end time of day D+1, M be the end time of day D+2, 1~T be the normal clearing period, and T+1~M be the extended clearing period. This is a time series calculated by granularity. 3) Construct a model for the day-ahead spot market adapted to the given time series, including constraints and an objective function; 4) Solve the objective function based on the constraints to obtain the unit combination plan corresponding to the calculation dataset; The constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints. The network security constraints include unit power output increase / decrease constraints, ramping constraints, and power flow constraints at critical sections. The objective function is to minimize the cost of electricity purchase. In the formula, This indicates the total number of generating units; SUM indicates the number of D+1 and D+2 time periods considered. Indicates the unit exist Efforts during a specific time period; For the unit During the period Operating costs, It is a multi-segment linear function related to the output ranges declared by the generating unit and the corresponding energy prices; For the unit During the period The startup cost is ρ, which is the network flow constraint relaxation penalty factor used for market clearing optimization. For the positive power flow relaxation variable of section s, For the reverse power flow relaxation variable of section s, This represents the total number of cross-sections.

2. The day-ahead market clearing method for scalable electricity spot prices according to claim 1, characterized in that, The unit operating time constraint is as follows: In the formula, For the unit exist Start / stop status during a given time period; Minimum continuous operating time of the unit This refers to the minimum continuous downtime of the unit. For the unit exist The time period refers to the time during which the machine has been continuously powered on. For the unit exist The period refers to the duration of continuous downtime.

3. The day-ahead market clearing method for scalable electricity spot prices according to claim 1, characterized in that, For each time period The system equilibrium constraint condition is: in, Indicates the unit exist Efforts during the period This indicates the planned power of tie line j during time period t, with input being positive and output being negative; For the total number of connecting lines, for The system load for the time period, 1 to M is a continuous sequence with a tolerance of 1.

4. The day-ahead market clearing method for scalable electricity spot prices according to claim 1, characterized in that, The unit's output is within its maximum and minimum technical output range, and the constraints on the unit's output range are as follows: In the formula, The lower limit of unit output, This is the upper limit of the unit's output. This refers to the start-up and shutdown status of unit i during time period t. When the unit is shut down... When the unit is started, .

5. The day-ahead market clearing method for scalable electricity spot prices according to claim 1, characterized in that, The specific network security constraints are as follows: There are two types of climbing constraints. The first type of climbing constraint is: ; The second type of climbing constraint is: In the formula, For the unit Maximum uphill speed, For the unit Maximum downhill climbing rate Variables for identifying the start-up time of the unit, To identify the variable of the unit's shutdown time, The lower limit of unit output, This is the upper limit of the unit's output. This represents the start-up and shutdown status of unit i during time period t; The unit's lifting output constraint changes with the unit's operating state, which includes operating state, start-up state, and shutdown state. When the unit is in the start-up state, the unit's lifting output range is... Decision; When the unit is in a shutdown state, the range of the unit's output increase / decrease is determined by... Decision; when the unit is in operation, the range of the unit's lifting output is determined by... , Decide; In the formula, For the unit's ramp rate, For the landslide rate of the unit, Let t be the time difference between time t and time t-1; The power flow constraints at the critical section are as follows: In the formula, cross-section Minimum limit of current transmission cross-section The maximum limit of current transmission For the unit The node is located on the cross section The generator output power transfer distribution factor For nodes cross section The generator output power transfer distribution factor This is the predicted value of the bus load k at time t.

6. A scalable-period day-ahead electricity spot market clearing system, characterized in that, The system includes: The calculation data acquisition module is used to acquire the calculation data of the spot market before the clearing date, D. The calculation data includes the calculation data of day D+1 and the calculation data of day D+2. The time interval of the calculation data of day D+2 is uneven. The time interval of the calculation data of day D+1 is uniform. The calculation data of day D+2 is the target time data with uneven time interval, including the load peak and valley time data and the new energy output peak and valley time data. The time series generation module is used to construct a computational dataset for the spot market before the current date based on the computational data, generating a time series with multiple time points at uneven intervals, and converting the time difference between two adjacent time points into the time granularity of the spot market before the current date; the conversion process is as follows: In the formula, Let F be the time difference between time t and time t-1, F be the granularity of the day-ahead spot market calculation, T be the end time of day D+1, M be the end time of day D+2, 1~T be the normal clearing period, and T+1~M be the extended clearing period. This is a time series calculated by granularity. The model building module is used to build a model of the spot day-ahead market adapted to the time series, and the model includes constraints and an objective function; wherein, the constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints, and the network security constraints include unit output increase and decrease constraints, ramping constraints, and power flow constraints at critical sections; The module for solving the unit combination plan is used to solve the objective function based on the constraints and obtain the unit combination plan corresponding to the calculation dataset. The constraints include unit operating time constraints, unit output range constraints, system balance constraints, and network security constraints. The network security constraints include unit power output increase / decrease constraints, ramping constraints, and power flow constraints at critical sections. The objective function is to minimize the cost of electricity purchase. In the formula, This indicates the total number of generating units; SUM indicates the number of D+1 and D+2 time periods considered. Indicates the unit exist Efforts during a specific time period; For the unit During the period Operating costs, It is a multi-segment linear function related to the output ranges declared by the generating unit and the corresponding energy prices; For the unit During the period The startup cost is ρ, which is the network flow constraint relaxation penalty factor used for market clearing optimization. For the positive power flow relaxation variable of section s, For the reverse power flow relaxation variable of section s, This represents the total number of cross-sections.

7. A day-ahead market clearing device for scalable electricity spot markets, characterized in that, The apparatus performs the steps of the method according to any one of claims 1 to 5.

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