Spot goods electric energy market clearing method

Abstract

The invention discloses a spot goods electric energy market clearing method. According to the method, aiming at the problems of insufficient system gradeability and excessive reserve capacity reservation caused by wind power output uncertainty, upward and downward gradeability of a thermal power generating unit in each operation state is accurately described by establishing different gradeability rate constraint models of the thermal power generating unit in an output state and a starting / stopping process; meanwhile, a flexible gradeability demand model of the system is constructed according to the predicted load, the wind power and the uncertainty of the predicted load and the wind power, the power generation cost, the standby cost, the climbing cost and the start-stop cost of the thermal power generating unit and the power generation cost and the wind curtailment penalty cost of the wind power plant are comprehensively considered in a target function, and optimal scheduling of spot market clearing is achieved. A simulation result shows that the method can effectively reduce the standby cost and the total operation cost and improve the wind power integration consumption level on the premise of guaranteeing the safe supply and demand balance of the system, and has remarkable economic benefits and popularization and application values.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical engineering, and particularly to a method for clearing the spot electric energy market. Background Art

[0002] In recent years, with the increasing global emphasis on renewable energy and low-carbon economy, wind energy, as a green, low-carbon and renewable energy, has been playing an increasingly important role in the power system. Although the large-scale grid connection of wind power helps with energy transformation, it also brings problems such as unstable, intermittent and random wind power output, directly leading to severe fluctuations in the system net load, and further posing higher requirements for the safe and stable operation and flexibility of the power system.

[0003] Traditional spot electric energy market clearing models mainly schedule conventional units such as thermal power units under stable output states. These models usually adopt a unified ramp rate constraint to ensure the supply-demand balance of the system in each time period. However, with the continuous increase in the proportion of wind power access, the units in the power system not only need to meet the ramp requirements under the output state, but also must cope with the change in the ramp ability during the startup or shutdown process of the units. In fact, the unit only has the upward ramp ability during the startup process, and only has the downward ramp ability during the shutdown process, and its contribution to the system's flexible ramp ability is significantly different from that under the traditional output state. Ignoring this actual situation in the traditional model often leads to excessive reserve capacity reservation, further driving up the system operation cost.

[0004] To address the challenges brought by the high proportion of wind power access, some countries and regions have begun to introduce flexible ramp products, by pre-reserving a certain system ramp ability to cope with future possible load changes. However, the existing flexible ramp ancillary service products and their clearing models mainly focus on the ramp rate constraint of the units under the stable output state, and fail to fully consider the one-way limitation of the ramp ability of the units during the state transition process (i.e., during the startup or shutdown process) and its negative impact on the overall flexibility of the system. In addition, due to certain errors in wind power output prediction, wind curtailment often occurs. Traditional scheduling models often need to reserve a high reserve capacity to cope with this uncertainty, further increasing the system operation cost and reducing the wind power consumption rate at the same time.

[0005] At present, although there are already literatures conducting research from the perspectives of thermal power reserve capacity optimization, flexible ramping ancillary service design, and wind power curtailment penalty mechanism respectively, these solutions are independent of each other and it is difficult to overall consider the uncertainty problems caused by the actual ramping ability of units, system load fluctuations, and wind power prediction errors. Therefore, there is an urgent need to design a spot electricity energy market clearing model that comprehensively considers the ramping rate differences of units in different operating states, the flexible ramping ability required by the system due to net load fluctuations, and the phenomenon of wind power curtailment caused by wind power prediction errors. This model can not only more accurately describe the actual regulation ability of units, but also encourage full grid connection of wind power by introducing a wind power curtailment penalty mechanism, thereby reducing the reserve capacity reservation, reducing the total system operating cost, and increasing the wind power grid connection and consumption rate.

[0006] In view of the above background and deficiencies in the prior art, the present invention proposes a spot electricity energy market clearing method. By finely modeling the different ramping abilities of units during the output state and start-up / shutdown process, and combining system load and wind power prediction uncertainty factors, an optimal scheduling model that comprehensively considers various cost expenses is constructed, which has significant economic benefits and broad prospects for popularization and application. Summary of the Invention

[0007] Aiming at the problems existing in the prior art, the present invention provides a spot electricity energy market clearing method. For flexible ramping services and spot electricity energy markets, an optimal scheduling model that comprehensively considers various cost expenses is constructed to achieve the purpose of reducing reserve capacity reservation, reducing the total system operating cost, and increasing the wind power grid connection and consumption rate.

[0008] To achieve the above object, the present invention adopts the following technical solutions: A spot electricity energy market clearing method, comprising the following steps:

[0009] (1) Establish an upward and downward ramping rate constraint model for thermal power units during the output state and start-up / shutdown process, where when the thermal power unit is in the output state, it satisfies the conventional upward / downward ramping rate constraint, and when the thermal power unit is in the start-up or shutdown process, it only satisfies the start-up or shutdown ramping rate constraint;

[0010] (2) According to the actual output and ramping ability of thermal power units in each operating state, establish a mathematical expression reflecting the flexible ramping ability of thermal power units;

[0011] (3) Based on the predicted load, wind power, and their prediction errors, establish a system upward / downward flexible ramping ability demand model, where the demand model includes a net load change part and an uncertainty part caused by wind power prediction deviation;

[0012] (4) Construct the objective function, which comprehensively considers the power generation cost, reserve cost, ramping cost, start-stop cost, no-load cost of thermal power units, as well as the power generation cost and curtailment penalty cost of wind farms, so as to minimize the total operating cost of the system;

[0013] (5) Solve the non-linear mixed integer programming model of the spot electricity energy market clearing model constructed in steps (1) to (4), so as to obtain the optimal clearing and dispatching scheme of the spot electricity energy market.

[0014] Furthermore, in step (1), the ramping rate constraint model adopts the conventional upward / downward ramping rate constraint when the thermal power unit is in the output state, only adopts the start-up ramping rate constraint during the start-up process of the thermal power unit, and only adopts the shutdown ramping rate constraint during the shutdown process of the thermal power unit, to obtain the flexible upward and downward ramping constraints of a single thermal power unit, and its mathematical expression is:

[0015]

[0016] where i and t are the thermal power unit number and time period respectively, P i,t is the output of thermal power unit i at time period t, ΔP i U and ΔP i D are the upward ramping rate and downward ramping rate of thermal power unit i per time period respectively, ΔP i SU and ΔP i SD are the start-up and shutdown ramping rates of thermal power unit i per time period respectively, P i min is the minimum operating output of thermal power unit i.

[0017] Furthermore, in step (2), the mathematical expression reflecting the flexible ramping ability of the thermal power unit is determined by the current output, minimum output, conventional ramping rate and start-up / shutdown ramping rate of the thermal power unit, and considers the negative impact on system flexibility caused by the unidirectional ramping ability limitation during the state transition of the thermal power unit:

[0018]

[0019] where, is the negative impact of the withdrawal of thermal power unit i at time period t + 1 on the upward ramping ability, is the negative impact of the start-up of thermal power unit i at time period t + 1 on the downward ramping ability, u i,t is whether the output of thermal power unit i reaches the minimum operating output at time period t, N is the number of thermal power units, are the upward and downward ramping rates of thermal power unit i at time t, respectively, and are the upward and downward flexible ramping capacity requirements of the system at time t, respectively.

[0020] Furthermore, in step (3), the system's upward / downward flexible ramping capacity requirement model is determined by predicting the load, wind power, and their prediction errors. Among them, the system demand is respectively equal to the sum of the predicted net load change in the corresponding time period and the uncertainty caused by the wind power prediction error. The mathematical expression of the uncertainty is as follows:

[0021]

[0022] where P t L and are the predicted values of the system load at times t and t + 1, respectively. P t WF and are the predicted values of the wind power output at times t and t + 1, respectively. α t+1 is the predicted deviation value of the wind power output, taking β w as the maximum value of the wind power output prediction error.

[0023] Furthermore, in step (4), the objective function is:

[0024]

[0025] where K is the number of system clearing time periods, N is the number of thermal power units, M is the number of wind power plants, C i,t is the power generation cost of thermal power unit i at time t, and are the start-up and shutdown costs of thermal power unit i at time t, respectively, is the no-load cost of thermal power unit i at time t, v i,t is whether thermal power unit i is in the start-up upward ramping stage at time t, w i,t is whether thermal power unit i is in the shutdown downward ramping stage at time t, and are the positive / negative reserve costs of the system at time t, respectively. P t pr and P t nr are the positive / negative reserve demands of the system at time t, respectively, is the upper limit of the ramping capacity price at time t, is the day-ahead market power generation bid of wind farm m at time t, λ m is the curtailment penalty of wind farm m, and are the predicted output and the day-ahead market planned output of wind farm m at time period t, and are the actual upward and downward ramping requirements of thermal power unit i at time period t, respectively.

[0026] Furthermore, in step (4), an improved flexible ramping product is introduced into the model to reduce the reserve capacity reserved by thermal power units for the uncertainty of the net load. The mathematical expressions for its positive / negative demands are:

[0027]

[0028] where β a is the generation reserve coefficient of the wind farm (taking the maximum value of the current-stage wind power prediction error), and β c is the generation reserve coefficient of the thermal power unit.

[0029] Furthermore, the non-linear mixed integer programming model is solved using a high-performance solver; the high-performance solver is CPLEX or GUROBI.

[0030] Furthermore, steps (1) to (5) constitute the entire process of the spot market clearing dispatch. Among them, the wind power prediction deviation described in step (3) is the difference between the wind power prediction value and the actual output, and is reflected by the curtailment penalty cost in the objective function to improve the wind power grid connection and consumption rate.

[0031] Furthermore, this method is applicable to the power system dispatch under different wind power prediction errors and wind power penetration rates, and is used to reduce the system reserve capacity and operating costs, while improving the full consumption level of wind power.

[0032] The beneficial effects of the present invention are as follows:

[0033] 1. Improve system flexibility: By finely modeling the ramping capabilities of units during the output state and the startup / shutdown process, ensure that the system can flexibly respond to load fluctuations and the uncertainty of wind power output, thereby ensuring the safe and stable operation of the system.

[0034] 2. Reduce reserve capacity reservation and operating costs: Accurately describe the actual regulation capabilities of units, reduce the waste of resources caused by excessive reserve capacity reservation, and at the same time comprehensively consider various cost expenses to effectively reduce the total operating cost of the system.

[0035] 3. Improve the wind power grid connection and consumption capacity: Introduce a curtailment penalty mechanism to encourage full grid connection of wind power, and achieve a high wind power consumption rate at a low curtailment penalty cost, effectively promoting the full utilization of new energy. Description of the Drawings

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] Figure 1 It is a flowchart of the spot electric energy market clearing method of the present invention;

[0038] Figure 2 It is a schematic diagram of the flexible ramping of the unit. Specific embodiments

[0039] The following will describe the present invention in detail with reference to the drawings. Without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0040] A spot electric energy market clearing method of the present invention is characterized by including the following steps:

[0041] (1) Establish an upward and downward ramping rate constraint model for thermal power units in the output state and during the startup / shutdown process, where when the thermal power unit is in the output state, it satisfies the conventional upward / downward ramping rate constraint, and when the thermal power unit is in the startup or shutdown process, it only satisfies the startup or shutdown ramping rate constraint;

[0042] (2) Establish a mathematical expression reflecting the flexible ramping ability of thermal power units according to the actual output and ramping ability of thermal power units in each operating state;

[0043] (3) Based on the predicted load, wind power and their prediction errors, establish a system upward / downward flexible ramping ability demand model, where the demand model includes a net load change part and an uncertainty part caused by wind power prediction deviation;

[0044] (4) Construct an objective function, which comprehensively considers the generation cost, reserve cost, ramping cost, startup and shutdown cost, no-load cost of thermal power units, as well as the generation cost and curtailment penalty cost of wind farms to minimize the total system operating cost;

[0045] (5) Solve the nonlinear mixed-integer programming model of the spot electric energy market clearing model constructed in steps (1) to (4) to obtain the optimal clearing dispatch plan for the spot electric energy market.

[0046] Such as Figure 1 It is a flowchart of a spot electric energy market clearing method provided by an embodiment of the present invention, and the clearing method includes the following steps:

[0047] (1) Establish the upward and downward ramping rate constraint models for thermal power units in the output state and during start-up / shutdown processes. When the thermal power unit is in the output state, it satisfies the conventional upward / downward ramping rate constraints. When the thermal power unit is in the start-up or shutdown process, it only satisfies the start-up or shutdown ramping rate constraints. The ramping rate constraint model adopts the conventional upward / downward ramping rate constraints when the thermal power unit is in the output state, only adopts the start-up ramping rate constraint during the start-up process of the thermal power unit, and only adopts the shutdown ramping rate constraint during the shutdown process of the thermal power unit, obtaining the flexible upward and downward ramping constraints for a single thermal power unit, and its mathematical expression is:

[0048]

[0049] where \(i\) and \(t\) are the thermal power unit number and time period respectively, \(P\) i,t is the output of thermal power unit \(i\) at time period \(t\), \(\Delta P\) i U and \(\Delta P\) i D are the upward ramping rate and downward ramping rate of thermal power unit \(i\) every 15 minutes respectively, \(\Delta P\) i SU and \(\Delta P\) i SD are the start-up and shutdown ramping rates of thermal power unit \(i\) every 15 minutes respectively, \(P\) i min is the minimum operating output of thermal power unit \(i\).

[0050] (2) According to the actual output and ramping capacity of thermal power units in each operating state, establish a mathematical expression reflecting the flexible ramping capacity of thermal power units. This expression can accurately describe the positive and negative impacts of the state conversion of thermal power units on the system's upward or downward ramping capacity, and its mathematical expression is:

[0051]

[0052] where, is the negative impact of the withdrawal of thermal power unit \(i\) on the upward ramping capacity at time period \(t + 1\), is the negative impact of the start-up of thermal power unit \(i\) on the downward ramping capacity at time period \(t + 1\), \(u\) i,t is whether the output of thermal power unit \(i\) reaches the minimum operating output at time period \(t\), \(N\) is the number of thermal power units, are the upward ramping rate and downward ramping rate of thermal power unit \(i\) at time period \(t\) respectively, and are the upward and downward flexible ramping capacity requirements of the system at time period \(t\) respectively.

[0053] (3) Based on the predicted load, wind power, and their prediction errors, establish a demand model for the system's upward / downward flexible ramping capacity. See Figure 2 , the demand model for the system's upward / downward flexible ramping capacity is determined by the changes in the predicted load and wind power and their prediction errors. Among them, the system demand is respectively equal to the sum of the predicted net load change in the corresponding time period and the uncertainty caused by the wind power prediction error. The upward and downward flexible ramping capacity demands of the system at time t include two parts: variability and uncertainty. The mathematical expression of uncertainty is as follows:

[0054]

[0055] where P t L and are respectively the predicted values of the system load at times t and t + 1, P t WF and are respectively the predicted values of the wind power output at times t and t + 1, α t+1 is the predicted deviation value of the wind power output, taking β w as the maximum value of the wind power output prediction error.

[0056] (4) Construct an objective function. The objective function comprehensively considers the power generation cost, reserve cost, ramping cost, start-stop cost, no-load cost of thermal power units, as well as the power generation cost and curtailment penalty cost of wind farms to minimize the total operating cost of the system. Its mathematical expression is:

[0057]

[0058] where, in this embodiment, the interval is 15 minutes (taking 15 minutes as the interval is not restrictive. Those skilled in the art should understand that other time intervals can also be used). K is the number of system clearing time periods, M is the number of wind farms, C i,t is the power generation cost of thermal power unit i at time t, and are respectively the start-up and shutdown costs of thermal power unit i at time t, is the no-load cost of thermal power unit i at time t, v i,t is whether thermal power unit i is in the start-up upward ramping stage at time t, w i,t is whether thermal power unit i is in the shutdown downward ramping stage at time t, and are respectively the positive / negative reserve costs of the system at time t, P t pr and are respectively the positive / negative reserve demands of the system at time t, is the upper limit of the ramping capacity price for period t, is the day-ahead market power generation bid of wind farm m in period t, λ m is the curtailment penalty of wind farm m, and are the predicted output and the day-ahead market planned output of wind farm m in period t, respectively, and are the actual upward and downward ramping requirements of thermal power unit i in period t, respectively.

[0059] (5) Introduce an improved flexible ramping ancillary product according to the constructed spot electric energy market clearing model above, reduce the reserve capacity reserved by thermal power units for the uncertainty of the net load, and the mathematical expressions for its positive / negative demand are:

[0060]

[0061] where β a is the power generation reserve coefficient of the wind farm (taking the maximum value of the current-stage wind power prediction error), β c is the power generation reserve coefficient of the thermal power unit.

[0062] (6) Use a high-performance solver to solve the constructed non-linear mixed integer programming model (i.e., the spot electric energy market clearing model), so as to obtain the optimal clearing dispatch plan of the spot electric energy market. The non-linear mixed integer programming model can be solved by high-performance solvers such as CPLEX and GUROBI to obtain the optimal output dispatch plan of each thermal power unit in each period.

[0063] The present invention proposes a method for clearing the spot electric energy market. By finely modeling the different ramping capabilities of thermal power units in the output state and the start-up / shutdown process, and combining the system load and the uncertainty factors of wind power prediction, an optimal dispatch model considering various cost expenses is constructed, which has significant economic benefits and broad application prospects for promotion.

[0064] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0065] The above embodiments are only used to illustrate the design idea and characteristics of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made according to the principles and design ideas disclosed by the present invention shall be within the protection scope of the present invention.

Claims

1. A spot electricity energy market clearing method, characterized in that: The following steps are involved: (1) Establishing the upward and downward ramp rate constraint model of the thermal power unit in the output state and the startup / shutdown process, wherein when the thermal power unit is in the output state, the conventional upward / downward ramp rate constraint is satisfied, and when the thermal power unit is in the startup or shutdown process, only the startup or shutdown ramp rate constraint is satisfied; (2) Based on the actual output and gradeability of the thermal power unit under various operating conditions, a mathematical expression reflecting the flexible gradeability of the thermal power unit is established; (3) Based on the predicted load and wind power and their prediction errors, a system upward / downward flexible ramping capability demand model is established, wherein the demand model includes a net load change part and an uncertainty part caused by wind power prediction deviation; (4) constructing an objective function, which comprehensively considers the power generation cost, standby cost, ramp cost, start-stop cost, no-load cost of the thermal power unit, and the power generation cost and wind curtailment penalty cost of the wind farm to minimize the total operating cost of the system; (5) Solving the nonlinear mixed integer programming model of the spot electricity market clearing model constructed from steps (1) to (4) to obtain the optimal clearing scheduling plan for the spot electricity market.

2. The method according to claim 1, characterized in that In step (1), the ramp rate constraint model adopts conventional upward / downward ramp rate constraints when the thermal power unit is in the output state, and only adopts the startup ramp rate constraints when the thermal power unit is in the startup process, and only adopts the shutdown ramp rate constraints when the thermal power unit is in the shutdown process, thereby obtaining the upward and downward flexible ramp constraints of a single thermal power unit, and its mathematical expression is: Among them, i and t are the thermal power unit number and time period respectively, P i,t is the output of thermal power unit i in period t, ΔP i U and ΔP i D are the upward climbing rate and downward climbing rate of thermal power unit i in each period, ΔP i SU and ΔP i SD are the startup and shutdown ramp rates of thermal power unit i in each period, P i min is the minimum operating output of thermal power unit i.

3. The method according to claim 1, characterized in that In step (2), the mathematical expression reflecting the flexible ramping capability of the thermal power unit is determined by the current output, minimum output, conventional ramping rate, and startup / shutdown ramping rate of the thermal power unit, and takes into account the negative impact of the thermal power unit on system flexibility due to the limitation of the unidirectional ramping capability during the state transition process: in, is the negative impact of the exit of thermal power unit i at time t+1 on the upward climbing capability, is the negative impact of the start-up of thermal power unit i at time t+1 on the downward climbing capability, u i,t is whether the output of thermal power unit i reaches the minimum operating output in period t, N is the number of thermal power units, are the upward climbing rate and downward climbing rate of thermal power unit i in period t, and They are the upward and downward flexible ramping capacity requirements of the system in period t respectively.

4. The method according to claim 1, characterized in that: In step (3), the system upward / downward flexible ramping capability demand model is determined by predicting the load and wind power and their prediction errors, where the system demand is equal to the sum of the predicted net load change in the corresponding period and the uncertainty caused by the wind power prediction error. The uncertainty mathematical expression is as follows: Among them, P t L and are the predicted values ​​of system load at time periods t and t+1, respectively, t WF and are the predicted values ​​of wind power output at time periods t and t+1, α t+1 is the predicted deviation value of wind power output, β w is the maximum value of wind power output prediction error.

5. The method according to claim 1, characterized in that In step (4), the objective function is: Among them, K is the number of system clearing periods, N is the number of thermal power units, M is the number of wind power plants, C i,t is the power generation cost of thermal power unit i in period t, and are the startup and shutdown costs of thermal power unit i in period t, is the no-load cost of thermal power unit i in period t, v i,t is whether the thermal power unit i is in the startup and upward climbing stage during period t, w i,t Is the thermal power unit i in the shutdown and downward ramping stage during period t? and are the positive / negative reserve costs of the system in period t, P t pr and P t nr are the positive / negative backup requirements of the system in period t, is the upper limit of the ramp capacity price during period t, is the day-ahead market power generation quotation of wind farm m in period t, λ m is the wind curtailment penalty for wind farm m, and are the predicted output of wind farm m in period t and the day-ahead market planned output, and are the actual upward and downward ramping demands of thermal power unit i in period t, respectively.

6. The method according to claim 1, characterized in that In step (4), the model is constructed to introduce an improved flexible ramping assist product to reduce the reserve capacity reserved by thermal power units for net load uncertainty. The mathematical expression of the positive / negative demand is: Among them, β a is the wind farm's power reserve coefficient (the maximum value of the wind power forecast error at the current stage), β c It is the power generation reserve factor of thermal power units.

7. The method according to claim 1, characterized in that The nonlinear mixed integer programming model is solved by using a high-performance solver; the high-performance solver is CPLEX or GUROBI.

8. The method according to claim 1, characterized in that Steps (1) to (5) constitute the entire process of spot market clearing and dispatching, wherein the wind power forecast deviation described in step (3) is the difference between the wind power forecast value and the actual output, and is reflected in the objective function through the wind abandonment penalty fee to improve the wind power grid-connected absorption rate.

9. The method according to claim 1, characterized in that: This method is applicable to power system dispatching under different wind power prediction errors and wind power penetration rates, and is used to reduce system reserve capacity and operating costs while improving the level of full wind power absorption.

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