Market clearing model construction method considering source load uncertainty

Abstract

The invention relates to a market clearing model construction method considering source load uncertainty. The method comprises the following steps: S1, determining market boundary conditions considering source load operation uncertainty; s2, proposing a system multi-time scale unit commitment optimization scheduling model considering the wind and light uncertainty; s3, constructing a market clearing model considering the source load uncertainty; through the steps, market means can be reasonably exerted, the utilization efficiency of various kinds of energy can be improved, the flexibility and safety of a power grid are improved, and meanwhile benign interaction between the energy and users is achieved; the method has the advantages of reasonably exerting market means, improving utilization efficiency, improving flexibility and safety and realizing benign interaction.

Description

Technical Field

[0001] The present invention belongs to the technical field of power market, and in particular relates to a method for constructing a market clearing model taking into account source and load uncertainty. Background Art

[0002] Building a new power system with new energy as the main body is the core task of the energy and power industry. In recent years, with the continuous increase in the scale and proportion of new energy installed capacity, new elements and new business forms on the load side such as energy storage, electric vehicles, load aggregators, and virtual power plants have emerged vigorously. The volatility and intermittent characteristics of new energy and the uncertainty of load have put forward new requirements for the development of the power market. The power market needs to have sufficient flexibility and adaptability, analyze the source and load characteristics of the power grid and its fluctuation characteristics, and build a market optimization clearing model that considers the uncertainty of source and load. Reasonable use of market means can improve the utilization efficiency of various types of energy, improve the flexibility and safety of the power grid, and realize the benign interaction between energy and users. It has great value for the future development of the power system. The power system is a large-scale nonlinear complex system. The optimization and clearing of power market transactions involves the coordinated optimization of the start and stop plans and output plans of the units after considering many complex factors. It is a high-dimensional, non-convex, The linear optimization, discreteness and non-linearity problem is a difficult point in the research of power system optimization. The optimization clearing models and algorithms of the power market have been widely studied and applied at home and abroad, mainly focusing on the following two categories: one is the safety constrained unit commitment (SCUC), that is, optimizing the unit power generation plan according to the system load forecast in each period during the research period, including the unit start-up and shutdown mode and the power generation output; the second is the safety constrained unit commitment (SCUC), that is, optimizing the unit power generation plan according to the system load forecast, including the unit start-up and shutdown mode and the power generation output in each period, meeting the system load demand and the unit operation constraints, and meeting the power flow constraints of the power grid operation; the research on clearing models that fully consider the source and load uncertainty under the large-scale access of new energy needs to be further strengthened; therefore, it is very necessary to provide a market clearing model construction method that considers source and load uncertainty, which can reasonably give play to market means, improve utilization efficiency, improve flexibility and safety, and achieve benign interaction. Summary of the invention

[0003] 1. Technical issues

[0004] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0005] 1. The optimization and clearing of power market transactions involves the coordinated optimization of the start-stop plan and output plan of the units after considering many complex factors, which is a difficult point in the research of power system optimization;

[0006] 2. Domestic and foreign research focuses on two categories: one is the safety-constrained unit combination; the other is the safety-constrained unit combination; research on clearing models that fully consider source-load uncertainties under large-scale access to new energy needs to be further strengthened.

[0007] (II) Technical solution

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for constructing a market clearing model that takes into account source-load uncertainty and reasonably utilizes market means, improves utilization efficiency, increases flexibility and safety, and achieves benign interaction.

[0009] The object of the present invention is achieved by: a method for constructing a market clearing model taking into account source-load uncertainty, the method comprising the following steps:

[0010] Step 1: Determine the market boundary conditions considering the uncertainty of source and load operation;

[0011] Step 2: Propose a system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power;

[0012] Step 3: Construct a market clearing model considering source-load uncertainty.

[0013] Furthermore, the market boundary conditions in step 1 include: system reserve retention without considering wind and solar energy and new energy, and system reserve retention considering wind and solar energy and new energy.

[0014] Furthermore, the system reserve reservation considering wind and solar energy is specifically as follows: firstly, the reliable prediction accuracy of wind and solar energy is calculated: the root mean square error RMSE of the predicted output of new energy is: Where P ft is the predicted power at time t; P tt is the theoretical power at time t; P Nt is the total startup capacity at time t; T is the total number of power generation periods of new energy stations; based on the prediction error of new energy, the prediction accuracy of new energy can be obtained as: A pi =1-RMSE(2), where i represents the i-th sample in the statistical period; A p Used to indicate the credibility of the prediction results.

[0015] Furthermore, the reliable prediction accuracy of new energy refers to the minimum prediction accuracy of new energy under a certain probability C%, which can be used as a reliable prediction output of new energy under the corresponding probability and included in the backup; for the historical new energy prediction power accuracy sequence A p , the quantile corresponding to the credible prediction accuracy is set as NC%, which can be calculated as follows: N C％ =max{floor[n(1-C%)],1}(3), where NC％ is an integer, satisfying 1≤N C％ ≤n; floor means round down.

[0016] Furthermore, the system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power in step 2 mainly performs unit combination optimization scheduling on different time scales, formulates optimized unit combination plans on different time scales, and obtains the information of units that must be started or shut down on the operating day; including: 24h plan on the day before and real-time rolling 15min plan.

[0017] Furthermore, in the 24h day-ahead scheduling, a safety-constrained unit combination algorithm is used to arrange the 24h unit combination output to obtain a unit combination day-ahead plan; the objective function of the unit combination is to minimize the total cost of power generation; the constraints considered in the unit combination include: system power balance constraints, spinning reserve constraints, unit output constraints, minimum start / stop time constraints, and ramp constraints.

[0018] Furthermore, in step 3, a market clearing model considering source-load uncertainty is constructed, including: determining the objective function and boundary conditions of clearing; the boundary conditions include system load balance constraints, line limit power and section limit power constraints, and system positive and negative reserve capacity constraints.

[0019] Furthermore, the system load balancing constraint is: Among them, P i,t represents the output of unit i in period t; T j,t represents the planned power of tie line j in time period t; NT is the total number of tie lines; D t is the system load in time period t; N represents the total number of units.

[0020] Furthermore, the positive and negative spare capacity constraints of the system are: Among them, α i,t Indicates the start and stop status of unit i in time period t; is the maximum output of unit i in period t; is the minimum output of unit i in period t; is the system positive reserve capacity requirement for time period t; is the system negative reserve capacity requirement for period t.

[0021] Furthermore, the objective function of the clearing is specifically: according to the power generation cost, startup cost, shutdown cost, ramp cost, reduction cost, standby cost and load shedding cost, the objective function with the goal of "minimizing the system power generation cost" is obtained, and the formula is as follows: Where, T is the total number of time periods in the optimization cycle; C(P t ) is the output power of each type of unit in time period t, Pt The subscripts c, f, h, p, and w represent the thermal power that cannot be started or stopped within the day, the thermal power that can be started or stopped within the day, hydropower, pumped storage, and new energy, respectively; C w To cut down on new energy costs; Cut the power of new energy for period t; is the load shedding power during period t; V d is the load shedding loss of each node; C f , C c is the start-up and shutdown cost of the unit; θ, η, γ are weighting coefficients.

[0022] (III) Beneficial effects

[0023] 1. The present invention determines the market boundary conditions that take into account the uncertainty of source and load operation, and proposes a system multi-time scale unit combination optimization scheduling model that takes into account the uncertainty of wind and solar power, and finally constructs a market clearing model that takes into account the uncertainty of source and load;

[0024] 2. The present invention can reasonably utilize market means, improve the utilization efficiency of various energy sources, enhance the flexibility and security of the power grid, and realize a positive interaction between energy and users. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0026] The present invention is further described below in conjunction with embodiments and / or drawings.

[0027] Example 1

[0028] like Figure 1 As shown, a method for constructing a market clearing model considering source-load uncertainty comprises the following steps:

[0029] Step 1: Determine the market boundary conditions considering the uncertainty of source and load operation;

[0030] In the present invention, the market boundary conditions for determining the uncertainty of source-load operation in step 1 mainly include two aspects:

[0031] ① System reserve without considering wind and solar energy

[0032] The power system reserve includes three types: load reserve capacity, accident reserve capacity, and maintenance reserve capacity. The retention of load reserve capacity and accident reserve capacity is mainly considered in the arrangement of the startup mode before the dispatching operation day. Load reserve capacity refers to the rotating reserve capacity connected to the bus and can immediately carry load, which is used to balance the instantaneous load fluctuation and the load prediction error, generally 2% to 5% of the maximum load; accident reserve capacity refers to the reserve capacity that can be called upon within a specified time (for example, within 10 minutes). This part of the reserve is generally borne by units that can be started and stopped quickly (such as hydropower). Since thermal power units still account for the largest proportion of units in most regional power grids in China, their start-up and shutdown processes are relatively long. In the dispatching production practice, it is necessary to arrange the thermal power startup mode according to the load reserve capacity in the day before.

[0033] ② Consider the system reserve of wind and solar energy

[0034] First, calculate the accuracy of reliable prediction of wind and solar energy: the root mean square error RMSE of predicted output of new energy is: Where P ft is the predicted power at time t; P tt is the theoretical power at time t (the sum of actual power and power-limited power); P Nt is the total startup capacity at time t; T is the total number of power generation periods of the new energy station.

[0035] Based on the prediction error of new energy, the prediction accuracy of new energy is: A pi =1-RMSE(2), where i represents the i-th sample in the statistical period; A p It is used to indicate the credibility of the prediction results. It is an ascending sort sequence. p ={A p1 ,A p2 ,...,A pn}(A p1 <A p2 <...<A pn ), n is the total number of samples in the statistical period.

[0036] The reliable prediction accuracy of new energy refers to the minimum prediction accuracy of new energy under a certain probability C%, which can be used as the reliable prediction output of new energy under the corresponding probability and included in the reserve; taking C% as 95% and the reliable prediction accuracy of 30% as an example, it means that the prediction accuracy of new energy is above 30% in 95% of the time periods in history; for the historical new energy prediction power accuracy sequence A p , the quantile corresponding to the credible prediction accuracy is set as NC%, which can be calculated as follows: N C％ =max{floor[n(1-C%)],1}(3), where N C％ is an integer, satisfying 1≤N C％≤n; floor means round down.

[0037] Secondly, the system backup determination method based on the reliable prediction accuracy of wind and solar energy

[0038] Taking into account the future high-proportion new energy consumption environment, it is inevitable to break down the barriers between provinces to maximize the use of the complementary characteristics of China's resources. The present invention adopts a unified reserve method for the entire network to provide safety guarantees for the new energy day-ahead forecast value to be included in the startup method of thermal power units according to a certain proportion. On the basis of considering the new energy resource characteristics and complementary characteristics, new energy is included in the unit startup method according to the predicted credible accuracy, thereby optimizing conventional thermal power startup and achieving the goals of reasonably optimizing system real-time backup, promoting new energy consumption, and improving the overall operation efficiency of the power grid.

[0039] Step 2: Propose a system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power;

[0040] In the present invention, the system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power in step 2 mainly performs unit combination optimization scheduling on different time scales, formulates optimized unit combination plans on different time scales, and obtains the information of the units that must be started or stopped on the operating day; specifically:

[0041] ① 24h plan of the day before

[0042] In the 24h day-ahead scheduling, the 24h wind and solar power forecast output and the wind and solar power error reserve at this scale are known. Combined with the load demand, the safety constraint unit combination algorithm is used to arrange the 24h unit combination output to obtain the unit combination day-ahead plan. The objective function of the unit combination is to minimize the total cost of power generation.

[0043] The constraints considered in unit combination mainly include the unit combination constraints in the conventional electricity energy market, including system power balance constraints, spinning reserve constraints, unit output constraints, minimum start and stop time constraints, and ramp constraints.

[0044] ②Real-time rolling 15-minute plan

[0045] In the real-time rolling 15-minute plan, based on the real-time predicted wind and solar power output, the AGC unit is controlled to supplement the previous level scheduling deviation, and the previous plan is corrected in real time to ensure stable operation of the system; in the real-time rolling plan, the AGC unit is required to be dispatched in real time to meet the system load demand together with the conventional units.

[0046] Step 3: Construct a market clearing model considering source-load uncertainty.

[0047] The present invention is a method for constructing a market clearing model that takes into account source-load uncertainty. During use, the present invention constructs a multi-time-scale, multi-objective collaborative market clearing model that takes into account source-load uncertainty, determines the market boundary conditions that take into account source-load operation uncertainty; proposes a system multi-time-scale unit combination optimization scheduling model that takes into account wind and solar uncertainty; constructs a market clearing model that takes into account source-load uncertainty; the volatility brought about by the access of large-scale distributed renewable energy to the power system and the emergence of new load-side elements such as electric vehicles, load aggregators, and virtual power plants have increased the difficulty of balancing the real-time supply and demand of electric power, and also put forward higher requirements for market clearing. The present invention proposes a method for constructing a market clearing model based on the consideration of source-load uncertainty, which is able to reasonably give play to market means, improve the utilization efficiency of various types of energy, improve the flexibility and safety of the power grid, and at the same time achieve a benign interaction between energy and users; the present invention has the advantages of reasonably giving play to market means, improving utilization efficiency, improving flexibility and safety, and achieving a benign interaction.

[0048] Example 2

[0049] like Figure 1 As shown, a method for constructing a market clearing model considering source-load uncertainty comprises the following steps:

[0050] Step 1: Determine the market boundary conditions considering the uncertainty of source and load operation;

[0051] Step 2: Propose a system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power;

[0052] Step 3: Construct a market clearing model considering source-load uncertainty.

[0053] In the present invention, a market clearing model considering source-load uncertainty is constructed in step 3, including: determining the objective function and boundary conditions of clearing.

[0054] The boundary conditions include system load balance constraints, line limit power and section limit power constraints, system positive and negative reserve capacity constraints, etc.

[0055] ①The system load balance constraint is: Among them, P i,t represents the output of unit i in period t; T j,t represents the planned power of tie line j in time period t (input is positive, output is negative); NT is the total number of tie lines; D t is the system load in time period t; N represents the total number of units.

[0056] ②The system positive and negative reserve capacity constraints are: Among them, α i,t represents the start and stop status of unit i in period t, αi,t =0 means the unit is shut down, α i,t =1 means the unit is on; is the maximum output of unit i in period t; is the minimum output of unit i in period t; is the system positive reserve capacity requirement for time period t; is the system negative reserve capacity requirement for period t.

[0057] The objective function is obtained based on the power generation cost, startup cost, shutdown cost, ramp cost, reduction cost, standby cost and load shedding cost. The formula is as follows:

[0058] In order to adapt to the needs of different dispatching operation modes, the objective function can be selected as follows: minimum system power generation cost, energy-saving power generation dispatching or three public dispatching; taking the selection of "minimum system power generation cost" as an example, the objective function is expressed as: Where, T is the total number of time periods in the optimization cycle; C(P t ) is the output power of each type of unit in time period t, P t The subscripts c, f, h, p, and w represent the thermal power that cannot be started or stopped within the day, the thermal power that can be started or stopped within the day, hydropower, pumped storage, and new energy, respectively; C w To cut down on new energy costs; Cut the power of new energy for period t; is the load shedding power during period t; V d is the load shedding loss of each node; C f , C c is the start-up and shutdown cost of the unit; θ, η, γ are weighting coefficients, which are usually 1 and can be adjusted as needed; the above formula shows that the objective function is a scheduling decision that comprehensively considers the system power generation economy, load shedding cost, and removal of renewable energy.

[0059] The present invention is a method for constructing a market clearing model that takes into account source-load uncertainty. During use, the present invention constructs a multi-time-scale, multi-objective collaborative market clearing model that takes into account source-load uncertainty, determines the market boundary conditions that take into account source-load operation uncertainty; proposes a system multi-time-scale unit combination optimization scheduling model that takes into account wind and solar uncertainty; constructs a market clearing model that takes into account source-load uncertainty; the volatility brought about by the access of large-scale distributed renewable energy to the power system and the emergence of new load-side elements such as electric vehicles, load aggregators, and virtual power plants have increased the difficulty of balancing the real-time supply and demand of electric power, and also put forward higher requirements for market clearing. The present invention proposes a method for constructing a market clearing model based on the consideration of source-load uncertainty, which is able to reasonably give play to market means, improve the utilization efficiency of various types of energy, improve the flexibility and safety of the power grid, and at the same time achieve a benign interaction between energy and users; the present invention has the advantages of reasonably giving play to market means, improving utilization efficiency, improving flexibility and safety, and achieving a benign interaction.

Claims

1. A method for constructing a market clearing model considering source-load uncertainty, characterized by: The method comprises the following steps: Step 1: Determine the market boundary conditions considering the uncertainty of source and load operation; Step 2: Propose a system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power; Step 3: Construct a market clearing model considering source-load uncertainty.

2. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 1, characterized in that: The market boundary conditions in step 1 include: system reserve retention without considering wind and solar energy and system reserve retention considering wind and solar energy.

3. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 2, characterized in that: The system reserve reservation considering wind and solar new energy is specifically as follows: First, the reliable prediction accuracy of wind and solar new energy is calculated: the root mean square error RMSE of the predicted output of new energy is: Where P ft is the predicted power at time t; P tt is the theoretical power at time t; P Nt is the total startup capacity at time t; T is the total number of power generation periods of new energy stations; based on the prediction error of new energy, the prediction accuracy of new energy can be obtained as: A pi =1-RMSE(2), where i represents the i-th sample in the statistical period; A p Used to indicate the credibility of the prediction results.

4. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 3, characterized in that: The reliable prediction accuracy of new energy refers to the minimum prediction accuracy of new energy under a certain probability C%, which can be used as a reliable prediction output of new energy under the corresponding probability and included in the backup; for the historical new energy prediction power accuracy sequence A p , the quantile corresponding to the credible prediction accuracy is set as NC%, which can be calculated as follows: N C％ =max{floor[n(1-C%)],1}(3), where N C％ is an integer, satisfying 1≤N C％ ≤n; floor means round down.

5. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 1, characterized in that: The system multi-time scale unit combination optimization scheduling model considering the uncertainty of wind and solar power in step 2 mainly performs unit combination optimization scheduling on different time scales, formulates optimized unit combination plans on different time scales, and obtains the information of units that must be started or stopped on the operating day; including: 24h plan on the day before and real-time rolling 15min plan.

6. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 5, characterized in that: In the 24h day-ahead scheduling, a safety-constrained unit combination algorithm is used to arrange the 24h unit combination output to obtain the unit combination day-ahead plan; the objective function of the unit combination is to minimize the total power generation cost; the constraints considered in the unit combination include: system power balance constraint, spinning reserve constraint, unit output constraint, minimum start / stop time constraint, and ramp constraint.

7. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 1, characterized in that: In step 3, a market clearing model considering source-load uncertainty is constructed, including: determining the objective function and boundary conditions of clearing; the boundary conditions include system load balance constraints, line limit power and section limit power constraints, and system positive and negative reserve capacity constraints.

8. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 7, characterized in that: The system load balancing constraints are: Among them, P i,t represents the output of unit i in period t; T j,t represents the planned power of tie line j in time period t; NT is the total number of tie lines; D t is the system load in time period t; N represents the total number of units.

9. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 7, characterized in that: The system positive and negative spare capacity constraints are: Among them, α i,t Indicates the start and stop status of unit i in time period t; is the maximum output of unit i in period t; is the minimum output of unit i in period t; is the system positive reserve capacity requirement for time period t; is the system negative reserve capacity requirement for period t.

10. A method for constructing a market clearing model taking into account source-load uncertainty as claimed in claim 7, characterized in that: The objective function of the clearing is specifically: according to the power generation cost, startup cost, shutdown cost, ramp cost, reduction cost, standby cost and load shedding cost, the objective function with the goal of "minimizing the system power generation cost" is obtained, and the formula is as follows: Where, T is the total number of time periods in the optimization cycle; C(P t ) is the output power of each type of unit in time period t, P t The subscripts c, f, h, p, and w represent the thermal power that cannot be started or stopped within the day, the thermal power that can be started or stopped within the day, hydropower, pumped storage, and new energy, respectively; C wd To cut down on new energy costs; Cut the power of new energy for period t; is the load shedding power during period t; V d is the load shedding loss of each node; C f , C c is the start-up and shutdown cost of the unit; θ, η, γ are weighting coefficients.

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