A Method for Constructing a Market Clearing Model Considering Source Load Uncertainty

Abstract

This invention relates to a method for constructing a market clearing model considering source-load uncertainty, comprising the following steps: S1, determining market boundary conditions considering source-load operational uncertainty; S2, proposing a system multi-timescale unit combination optimization scheduling model considering wind and solar uncertainties; S3, constructing a market clearing model considering source-load uncertainty. Through these steps, this invention can rationally utilize market mechanisms, improve the utilization efficiency of various energy sources, enhance grid flexibility and security, and simultaneously achieve positive interaction between energy and users. This invention has the advantages of rationally utilizing market mechanisms, improving utilization efficiency, enhancing flexibility and security, and achieving positive interaction.

Description

Technical Field

[0001] This invention belongs to the field of electricity market technology, specifically relating to a method for constructing a market clearing model that considers the uncertainty of source and load. Background Technology

[0002] Building a new power system with new energy sources as the mainstay is a core task of the energy and power industry. In recent years, with the continuous increase in the installed capacity and proportion of new energy, new load-side elements and new business models such as energy storage, electric vehicles, load aggregators, and virtual power plants have emerged vigorously. The volatility and intermittency of new energy, as well as the uncertainty of load, have placed new demands on the development of the power market. The power market needs sufficient flexibility and adaptability. Analyzing the characteristics and volatility of power grid sources and loads, constructing a market optimization and clearing model that considers source and load uncertainty, and rationally leveraging market mechanisms can improve the utilization efficiency of various energy sources, enhance the flexibility and security of the power grid, and simultaneously achieve a positive interaction between energy and users. This has significant value for the future development of the power system. The power system is a large-scale, nonlinear, and complex system. The optimization and clearing of power market transactions involves the coordinated optimization of unit start-up and shutdown plans and output plans after considering numerous complex factors. It is a high-dimensional, non-convex... Linear optimization of discrete, non-linear problems is a challenging aspect of power system optimization research. Extensive research and application of power market optimization clearing models and algorithms have been conducted both domestically and internationally, primarily focusing on two categories: First, Security-Constrained Unit Combination (SCUC), which optimizes unit generation plans based on system load forecasts for each time period within the research cycle, including unit start-up and shutdown methods and generation output; second, Security-Constrained Unit Combination (SCUC), which optimizes unit generation plans based on system load forecasts, including unit start-up and shutdown methods and generation output for each time period, satisfying system load demand and unit operation constraints, as well as power flow constraints of the grid. Further research is needed on clearing models that fully consider source-load uncertainties under large-scale renewable energy integration. Therefore, it is essential to provide a method for constructing market clearing models that rationally leverage market mechanisms, improve utilization efficiency, enhance flexibility and security, and achieve positive interaction while considering source-load uncertainties. Summary of the Invention

[0003] (I) Technical Issues

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

[0005] 1. Optimization and clearing of electricity market transactions involves coordinating and optimizing unit start-up and shutdown plans and output plans after considering numerous complex factors, which is a difficult point in power system optimization research;

[0006] 2. Domestic and international research focuses on two categories: one is the combination of units with safety constraints; the other is the combination of units with safety constraints. Further research is needed on clearing models that fully consider the uncertainty of source and load under the large-scale integration of new energy sources.

[0007] (II) Technical Solution

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for constructing a market clearing model that takes into account the uncertainty of source load, so as to make reasonable use of market means, improve utilization efficiency, enhance flexibility and security, and achieve positive interaction.

[0009] The objective of this invention is achieved as follows: a method for constructing a market clearing model that considers source load uncertainty, the method comprising the following steps:

[0010] Step 1: Determine market boundary conditions that take into account the uncertainties of source-load operation;

[0011] Step 2: Propose a multi-timescale unit combination optimization scheduling model that considers the uncertainties of wind and solar power.

[0012] Step 3: Construct a market clearing model that takes into account the uncertainty of source load.

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

[0014] Furthermore, the system reserve consideration for wind and solar renewable energy specifically involves: firstly, calculating the reliable prediction accuracy of wind and solar renewable energy: the root mean square error (RMSE) of the predicted renewable energy output is: In the formula, P ft Predict the power at time t; P tt P represents the theoretical power at time t. Nt Let be the total operating capacity at time t; T be the total number of power generation periods at renewable energy power plants; based on the prediction error of renewable energy, the prediction accuracy of renewable energy is: A pi =1-RMSE(2), where i represents the i-th sample within the statistical period; A p It is used to indicate the reliability of the prediction results.

[0015] Furthermore, the reliable prediction accuracy of new energy refers to the lowest prediction accuracy of new energy under a certain probability C%, which can be used as the reliable predicted output of new energy under the corresponding probability for reserve; 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 using the following formula: N C％ =max{floor[n(1-C%)],1}(3), where NC％ The integers are 1 ≤ N C％ ≤n; floor indicates rounding down.

[0016] Furthermore, the system multi-timescale unit combination optimization scheduling model considering the uncertainties of wind and solar power in step 2 mainly involves optimizing the unit combination scheduling at different time scales, formulating optimized unit combination plans for different time scales, and obtaining information on units that must be started or stopped on the operating day; including: the 24-hour plan for the day before and the real-time rolling 15-minute plan.

[0017] Furthermore, the day-ahead 24-hour scheduling employs a safety-constrained unit combination algorithm to arrange the 24-hour unit combination output, resulting in the day-ahead unit combination 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 constraints, spinning reserve constraints, unit output constraints, minimum start-up and shutdown time constraints, and ramp-up constraints.

[0018] Furthermore, step 3 involves constructing a market clearing model that considers source-load uncertainty, including: determining the objective function and boundary conditions for clearing; the boundary conditions include system load balance constraints, line limit power and cross-sectional limit power constraints, and system positive and negative reserve capacity constraints.

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

[0020] Furthermore, the system's positive and negative backup capacity constraints are as follows: Where, α i,t This indicates the start / stop status of unit i during time period t; This represents the maximum output of unit i during time period t. Let be the minimum output of unit i during time period t; The system's positive reserve capacity requirement for time period t; The system's negative backup capacity requirement for time period t.

[0021] Furthermore, the clearing objective function is specifically derived from the following: Based on generation cost, start-up cost, shutdown cost, ramp-up cost, reduction cost, reserve cost, and load shedding cost, the objective function aims to achieve "the lowest system generation cost," as shown in the following formula: In the formula, T represents the total number of time periods within the optimization period; C(P) t Let P be the output power of each type of generator unit during time period t.t Operating costs at that time; subscripts c, f, h, p, w respectively represent thermal power that cannot be started or stopped within the day, thermal power that can be started or stopped within the day, hydropower, pumped storage, and new energy; C w To eliminate the costs of new energy sources; Switching to new energy power during time period t; V represents the load shedding power during time period t; d For load shedding losses at each node; C f C c The unit start-up and shutdown costs are θ, η, and γ, which are weighting coefficients.

[0022] (III) Beneficial Effects

[0023] 1. This invention determines market boundary conditions considering the uncertainty of source load operation, proposes a system multi-timescale unit combination optimization scheduling model considering the uncertainty of wind and solar power, and finally constructs a market clearing model considering the uncertainty of source load.

[0024] 2. This invention can make reasonable use of market mechanisms, improve the utilization efficiency of various energy sources, enhance the flexibility and security of the power grid, and achieve a positive interaction between energy and users. Attached Figure Description

[0025] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the embodiments and / or accompanying drawings.

[0027] Example 1

[0028] like Figure 1 As shown, a method for constructing a market clearing model that considers source load uncertainty is presented. The method includes the following steps:

[0029] Step 1: Determine market boundary conditions that take into account the uncertainties of source-load operation;

[0030] In this invention, the determination of market boundary conditions considering the uncertainty of source load operation in step 1 mainly includes two aspects:

[0031] ①System backup reserves not considered for wind and solar new energy sources

[0032] Power system reserves include three types: load reserve capacity, contingency reserve capacity, and maintenance reserve capacity. The arrangement of start-up methods before dispatching operations primarily considers the retention of load reserve capacity and contingency reserve capacity. Load reserve capacity refers to the spinning reserve capacity connected to the busbar and immediately capable of carrying load, used to balance instantaneous load fluctuations and load forecast errors; it is generally 2% to 5% of the maximum load. Contingency reserve capacity refers to the reserve capacity available for use within a specified time (e.g., within 10 minutes). This reserve is generally undertaken by units capable of rapid start-up and shutdown (such as hydropower). Since thermal power units still account for the largest proportion of units in most regional power grids in China, and their start-up and shutdown processes are relatively long, in dispatching and production practice, the start-up methods of thermal power units need to be arranged before dispatching operations according to the load reserve capacity.

[0033] ②Consider the system backup for wind and solar new energy sources

[0034] First, calculate the reliable prediction accuracy of wind and solar renewable energy: the root mean square error (RMSE) of the predicted renewable energy output is: In the formula, P ft Predict the power at time t; P tt P represents the theoretical power at time t (the sum of actual power and power-limited power); Nt Let T be the total operating capacity at time t; T is the total number of power generation periods for the renewable energy power plants.

[0035] Based on the prediction error of new energy sources, the prediction accuracy of new energy sources can be obtained as: A pi =1-RMSE(2), where i represents the i-th sample within the statistical period; A p Used to represent the confidence level of the prediction result, it is an ascending sorted sequence, A. p ={A p1 A p2 ,...,A pn}(A p1 <A p2 <...<A pn ), where n is the total number of samples within 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%, and can be used as the reliable predicted output of new energy under the corresponding probability for reserve. Taking C% as 95% and the reliable prediction accuracy as 30% as an example, it means that the prediction accuracy of new energy is above 30% for 95% of historical periods. 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 using the following formula: N C％ =max{floor[n(1-C%)],1}(3), where N C％ The integers are 1 ≤ N C％≤n; floor indicates rounding down.

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

[0038] Considering the future environment of high-proportion renewable energy consumption, it is necessary to break down inter-provincial barriers to maximize the complementary nature of China's resources. This invention adopts a unified reserve method across the entire network to provide a safety guarantee for incorporating the day-ahead forecast of renewable energy into the start-up mode of thermal power units according to a certain proportion. Based on the characteristics and complementarity of renewable energy resources, renewable energy is incorporated into the start-up mode of units according to the reliability and accuracy of the forecast, thereby optimizing the start-up of conventional thermal power and achieving the goals of rationally optimizing the system's real-time reserve, promoting renewable energy consumption, and improving the overall operating efficiency of the power grid.

[0039] Step 2: Propose a multi-timescale unit combination optimization scheduling model that considers the uncertainties of wind and solar power.

[0040] In this invention, the system multi-timescale unit combination optimization scheduling model considering wind and solar uncertainties in step 2 mainly involves optimizing unit combination scheduling at different time scales, formulating optimized unit combination plans for different time scales, and obtaining information on units that must be started or stopped on operating days; specifically:

[0041] ① 24-hour plan

[0042] In the 24-hour dispatching, given the predicted wind and solar power output for the next 24 hours and the wind and solar error reserve at this scale, and considering load demand, a safety-constrained unit combination algorithm is used to arrange the 24-hour unit combination output, resulting in the day-ahead unit combination plan. The objective function of the unit combination is to minimize the total generation cost.

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

[0044] ② Real-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 used to adjust and supplement the upper-level scheduling deviation, and the upper-level plan is corrected in real time to ensure the stable operation of the system. In the real-time rolling plan, the AGC unit is required to be scheduled in real time to meet the system load demand together with the conventional unit.

[0046] Step 3: Construct a market clearing model that takes into account the uncertainty of source load.

[0047] This invention provides a method for constructing a market clearing model that considers source-load uncertainty. In its application, this invention constructs a multi-timescale, multi-objective collaborative market clearing model that considers source-load uncertainty, and determines market boundary conditions considering source-load operational uncertainty. It proposes a system multi-timescale unit combination optimization scheduling model that considers wind and solar uncertainty. The volatility brought about by the large-scale integration of distributed renewable energy into the power system, as well as the emergence of new load-side elements such as electric vehicles, load aggregators, and virtual power plants, increases the difficulty of real-time power supply and demand balance and places higher demands on market clearing. This invention, by considering source-load uncertainty, proposes a method for constructing a market clearing model that can reasonably leverage market mechanisms, improve the utilization efficiency of various energy sources, enhance grid flexibility and security, and simultaneously achieve positive interaction between energy and users. This invention has the advantages of reasonably leveraging market mechanisms, improving utilization efficiency, enhancing flexibility and security, and achieving positive interaction.

[0048] Example 2

[0049] like Figure 1 As shown, a method for constructing a market clearing model that considers source load uncertainty is presented. The method includes the following steps:

[0050] Step 1: Determine market boundary conditions that take into account the uncertainties of source-load operation;

[0051] Step 2: Propose a multi-timescale unit combination optimization scheduling model that considers the uncertainties of wind and solar power.

[0052] Step 3: Construct a market clearing model that takes into account the uncertainty of source load.

[0053] In this invention, step 3 involves constructing a market clearing model that considers source load uncertainty, including: determining the objective function and boundary conditions for clearing.

[0054] Boundary conditions include system load balance constraints, line limit power and cross-sectional limit power constraints, and system positive and negative reserve capacity constraints.

[0055] ①The system load balance constraint is: Among them, P i,t This represents the output of unit i during time period t; T j,t This 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 The system load is t; N represents the total number of generating units.

[0056] ②The system's positive and negative reserve capacity constraints are: Where, α i,t α represents the start-up and shutdown status of unit i during time period t.i,t =0 indicates that the unit is shut down, α i,t =1 indicates that the unit is started; This represents the maximum output of unit i during time period t. Let be the minimum output of unit i during time period t; The system's positive reserve capacity requirement for time period t; The system's negative backup capacity requirement for time period t.

[0057] The objective function is derived based on the generation cost, start-up cost, shutdown cost, ramp-up cost, reduction cost, reserve cost, and load shedding cost, as shown in the following formula:

[0058] To adapt to different dispatching and operation modes, the objective function can be selected as: lowest system power generation cost, energy-saving power generation dispatch, or three-way dispatch; taking "lowest system power generation cost" as an example, the objective function is expressed as: In the formula, T represents the total number of time periods within the optimization period; C(P) t Let P be the output power of each type of generator unit during time period t. t Operating costs at that time; subscripts c, f, h, p, w respectively represent thermal power that cannot be started or stopped within the day, thermal power that can be started or stopped within the day, hydropower, pumped storage, and new energy; C w To eliminate the costs of new energy sources; Switching to new energy power during time period t; V represents the load shedding power during time period t; d For load shedding losses at each node; C f C c θ represents the unit start-up and shutdown cost; θ, η, and γ are weighting coefficients, which are usually 1, but can be adjusted as needed; the above formula shows that the objective function is to comprehensively consider the system's power generation economy, load shedding costs, and dispatch decisions for cutting off renewable energy.

[0059] This invention provides a method for constructing a market clearing model that considers source-load uncertainty. In its application, this invention constructs a multi-timescale, multi-objective collaborative market clearing model that considers source-load uncertainty, and determines market boundary conditions considering source-load operational uncertainty. It proposes a system multi-timescale unit combination optimization scheduling model that considers wind and solar uncertainty. The volatility brought about by the large-scale integration of distributed renewable energy into the power system, as well as the emergence of new load-side elements such as electric vehicles, load aggregators, and virtual power plants, increases the difficulty of real-time power supply and demand balance and places higher demands on market clearing. This invention, by considering source-load uncertainty, proposes a method for constructing a market clearing model that can reasonably leverage market mechanisms, improve the utilization efficiency of various energy sources, enhance grid flexibility and security, and simultaneously achieve positive interaction between energy and users. This invention has the advantages of reasonably leveraging market mechanisms, improving utilization efficiency, enhancing flexibility and security, and achieving positive interaction.

Claims

1. A method for constructing a market clearing model considering source load uncertainty, characterized in that: The method includes the following steps: Step 1: Determine market boundary conditions that take into account the uncertainties of source-load operation; Step 2: Propose a multi-timescale unit combination optimization scheduling model that considers the uncertainties of wind and solar power. Step 3: Construct a market clearing model that considers source-load uncertainty; The market boundary conditions in step 1 include: system reserve without considering wind and solar new energy and system reserve with considering wind and solar new energy. The system backup reserve considering wind and solar renewable energy is specifically as follows: First, the reliable prediction accuracy of wind and solar renewable energy is calculated: The root mean square error (RMSE) of the predicted renewable energy output is: In the formula, P ft Predict the power at time t; P tt P represents the theoretical power at time t. Nt Let be the total operating capacity at time t; T be the total number of power generation periods at renewable energy power plants; based on the prediction error of renewable energy, the prediction accuracy of renewable energy is: A pi = 1 - RMSE, where i represents the i-th sample within the statistical period; A p It is used to indicate the reliability of the prediction results.

2. The method for constructing a market clearing model considering source-load uncertainty as described in claim 1, characterized in that: The aforementioned reliable prediction accuracy rate for new energy sources refers to the lowest prediction accuracy of new energy sources under a certain probability C%, which can be used as the reliable predicted output of new energy sources under the corresponding probability for reserve; for the historical new energy prediction power accuracy rate sequence A p The quantile corresponding to the credible prediction accuracy is set to N. C％ Calculate N using the following formula: C％ =max{floor[n(1-C%)],1}, where N C％ The integers are 1 ≤ N C％ ≤n; floor indicates rounding down.

3. The method for constructing a market clearing model considering source-load uncertainty as described in claim 1, characterized in that: The system multi-timescale unit combination optimization scheduling model considering the uncertainties of wind and solar power in step 2 is to optimize the unit combination scheduling at different time scales, formulate optimized unit combination plans at different time scales, and obtain the information of units that must be started and stopped on the operating day; including: the 24-hour plan before the day and the real-time rolling 15-minute plan.

4. The method for constructing a market clearing model considering source-load uncertainty as described in claim 3, characterized in that: The day-ahead scheduling uses a safety-constrained unit combination algorithm to arrange the 24-hour unit combination output, resulting in 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 constraints, spinning reserve constraints, unit output constraints, minimum start-up and shutdown time constraints, and ramp-up constraints.

5. The method for constructing a market clearing model considering source-load uncertainty as described in claim 1, characterized in that: Step 3 involves constructing a market clearing model that considers source-load uncertainty, including: determining the objective function and boundary conditions for clearing; the boundary conditions include system load balance constraints, line limit power and cross-sectional limit power constraints, and system positive and negative reserve capacity constraints.

6. The method for constructing a market clearing model considering source-load uncertainty as described in claim 5, characterized in that: The system load balancing constraint is: Among them, P i,t This represents the output of unit i during time period t; T j,t This represents the planned power of tie line j in time period t; NT is the total number of tie lines; D t The system load is t; N represents the total number of generating units.

7. The method for constructing a market clearing model considering source load uncertainty as described in claim 6, characterized in that: The system's positive and negative backup capacity constraints are as follows: Where, α i,t This indicates the start / stop status of unit i during time period t; This represents the maximum output of unit i during time period t. Let be the minimum output of unit i during time period t; The system's positive reserve capacity requirement for time period t; The system's negative backup capacity requirement for time period t.

8. The method for constructing a market clearing model considering source-load uncertainty as described in claim 5, characterized in that: The clearing objective function is specifically derived from the following: based on generation cost, start-up cost, shutdown cost, ramp-up cost, reduction cost, reserve cost, and load shedding cost, the objective function aims to minimize the system's generation cost. The formula is as follows: In the formula, T represents the total number of time periods within the optimization period; C(P) t Let P be the output power of each type of generator unit during time period t. t Operating costs at that time; subscripts c, f, h, p, w respectively represent thermal power that cannot be started or stopped within the day, thermal power that can be started or stopped within the day, hydropower, pumped storage, and new energy; C wd To eliminate the costs of new energy sources; Switching to new energy power during time period t; V represents the load shedding power during time period t; d For load shedding losses at each node; C f C c The unit start-up and shutdown costs are θ, η, and γ, which are weighting coefficients.

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