Price prediction system and method for peak regulation market in which gas unit participates

Abstract

The invention relates to a peak regulation market price prediction system and method in which a gas unit participates. The method is technically characterized in that a data input module inputs related parameters and historical peak regulation prices of a power system; the production analog simulation module calculates the electricity abandoning rate of the gas unit and the output curve of the coal power unit and the gas unit; the data processing module compares the simulation result with the requirement of the power system on the new energy power abandonment rate, and uses a deviation feedback correction module to correct the minimum output parameter of the unit, or transmits the output curve of the coal power unit and the output curve of the gas unit to a fitting calculation module; the data output module outputs the full-day time sequence output of the coal generator set and the gas generator set which meet the index requirements; and the fitting calculation module calculates the peak regulation price of each time period. According to the method, the actual operation mode of the power system is fully considered, the peak regulation market characteristics are combined, a peak regulation price prediction function is provided for gas turbine power generation enterprises, and the participation decision of the peak regulation market can be effectively assisted.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation, and in particular to a peak load regulation market price forecasting system and method involving gas turbines. Background Art

[0002] The output of renewable energy power generation has uncertain characteristics such as intermittency, randomness, and volatility. As the installed capacity increases, the balance and safety issues these characteristics bring to the power system are gradually becoming more prominent.

[0003] As gas-fired and coal-fired power units continuously reduce their output in order to make room for wind and solar power generation, their unit coal consumption will continue to increase. Therefore, gas-fired and coal-fired power units need to obtain compensation in the peak-shaving market to fully reflect the basic supporting role of the units in the power system and the value of their system regulation functions, which will be conducive to the effective market positioning and transformation and upgrading of the thermal power industry under the rapid development of new energy.

[0004] Peak shaving is a type of power auxiliary service, and peak shaving services are currently carried out in a market-based competitive manner. The main providers of peak shaving are gas-fired units and coal-fired units. In the peak shaving market, gas-fired units and coal-fired units declare peak shaving prices every day. After the peak shaving market is cleared, the generators that win the bid for peak shaving services provide peak shaving services during operation, and the peak shaving service compensation is calculated based on the peak shaving price and the amount of electricity provided by the peak shaving service. Before declaring in the peak shaving market, the participating entities need to predict the peak shaving price to determine the peak shaving income, and then make auxiliary decisions to participate in the peak shaving market. How to accurately predict the peak shaving market price in which gas-fired units and coal-fired units participate is an urgent problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and to propose a peak-shaving market price prediction system and method involving gas-fired units, which combines the peak-shaving market price prediction model with the production simulation model, and can accurately predict the peak-shaving market price involving gas-fired units and coal-fired units.

[0006] The present invention solves the technical problem by adopting the following technical solutions:

[0007] A peak-shaving market price prediction system involving gas units, comprising a login authentication module, a data input module, a production simulation module, a data processing module, a deviation feedback correction module, a fitting calculation module and a data output module;

[0008] The login authentication module is used for user identity authentication, and after identity authentication, the user enters the peak-shaving market price prediction system;

[0009] The data input module is connected to the login authentication module and is used to input the power system related parameters and historical peak-shaving prices required by the simulation calculation module and the data processing module;

[0010] The production simulation module is connected to the data input module, and is used to obtain the power system related parameters transmitted by the data input module, establish a power spot market production simulation model, run the D-day 96-period operation mode that meets the actual transmission safety constraints of the system, solve and obtain the production simulation results, and transmit the simulation results to the data processing module. The simulation results are the power abandonment rate of the gas unit and the output curves of the coal-fired unit and the gas unit;

[0011] The data processing module is connected to the production simulation module and the deviation feedback correction module, and compares the simulation results obtained by the production simulation result module with the relevant parameter indicators of the power system to determine whether the new energy power abandonment rate is within the allowable range; if the new energy power abandonment rate result does not meet the indicator requirements, the minimum output parameters of the unit are corrected and the parameters are returned to the production simulation module; if it meets the indicator requirements, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module;

[0012] The deviation feedback correction module is connected to the data processing module. When the result of the new energy power abandonment rate in the data processing module does not meet the planning requirements, the minimum output parameters of the unit are corrected according to the target new energy power abandonment rate, and the parameters are returned to the production simulation module for re-simulation until the new energy power abandonment rate meets the system requirements;

[0013] The fitting calculation module is connected to the data processing module, and calculates the peak-shaving price for each period according to the output curves of the coal-fired power units and gas-fired power units transmitted by the data processing module and the peak-shaving demand of the system in each period;

[0014] The data output module is connected to the fitting calculation module, and outputs the peak-shaving prices of the coal-fired power units and the gas-fired power units in each time period transmitted by the fitting calculation module to form a report.

[0015] Furthermore, the power system related parameters include power generation side information, grid side information, load side information, and benchmark minimum outputs of coal-fired power units and gas-fired power units.

[0016] Furthermore, the power generation side information includes the installed capacity, ramp rate, ramp rate, no-load cost, parameters of the power generation cost characteristic function, emission rate of the unit, domain carbon emission price, and the installed capacity, bus and time sequence power forecast of the gas unit;

[0017] The grid side information includes transmission topology and transmission safety constraints;

[0018] The load side information includes the load geographical location and time sequence power.

[0019] A prediction method for a peak-shaving market price prediction system in which a gas unit participates comprises the following steps:

[0020] Step 1: The data input module inputs the power system related parameters and historical peak-shaving prices required by the simulation calculation module and the data processing module;

[0021] Step 2: The production simulation module receives relevant parameters of the power system, determines the objective function of minimizing the total power generation cost of the system, and performs production simulation calculations for 96 periods of the power system on D day to obtain the power abandonment rate of the gas unit and the output curves of the coal-fired power unit and the gas unit;

[0022] Step 3: The data processing module compares the simulation results with the requirements of the power system for the new energy power abandonment rate. If the results do not meet the system's new energy power abandonment rate requirements, the deviation feedback correction module is used to correct the minimum output parameters of the unit, and the parameters are sent back to the production simulation module; if they meet the index requirements, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module;

[0023] Step 4: The data output module outputs the full-day time sequence output of the coal-fired power units and gas-fired power units that meet the index requirements;

[0024] Step 5: The fitting calculation module calculates the peak-shaving price for each period according to the sum of the peak-shaving demands of the coal-fired power units and the gas-fired power units in each period and the relationship function between the peak-shaving price and the peak-shaving demand.

[0025] Furthermore, the specific implementation method of step 2 includes the following steps:

[0026] Step 2.1: Determine the objective function that minimizes the total power generation cost of the system. The objective function is as follows:

[0027]

[0028] In the formula, T is the number of full-day periods, NG is the number of gas-fired units in the system; NC is the number of coal-fired units in the system; NRE is the number of renewable energy units in the system; p i,t 、p j,t and p h,t are the active power of coal-fired unit i, gas-fired unit j and renewable energy unit h in time period t; C i (p i,t ), C j (p j,t ) and C h,t (p h,t ) is the variable cost of coal-fired power unit i, gas-fired power unit j and renewable energy unit h. The cost model of conventional coal-fired power units and gas-fired power units can include carbon emission costs;

[0029] C i [(p i,t )]=a i ×p i，t 2 +b i ×p i，t +c i +[p i，t ×R EMi ×f r ]

[0030] C j [(p j，t )]=a j ×p j，t 2 +b j ×p j，t +c j +[p j，t ×R EMj ×T r ]

[0031] Where: a i , b i 、c i is the parameter of the power generation cost characteristic function of coal-fired power unit i, where c i is the no-load cost of coal-fired power units; R EMi is the emission rate of coal-fired power units, in ton / MWh; T r is the regional carbon emission price, in yuan / ton. k , b j 、c j is the parameter of the power generation cost characteristic function of gas unit j, where c j is the no-load cost of the gas unit; R EMj is the emission rate of the gas unit, in ton / MWh;

[0032] Step 2.2, determine the system safety constraints including system load balance constraints, unit output upper and lower limit constraints, unit load ramp rate constraints, renewable energy generation constraints and power grid transmission safety constraints, and establish a production simulation module to perform D-day 96-period simulation.

[0033] Furthermore, the system safety constraints are as follows:

[0034] (1) System load balancing constraints:

[0035]

[0036] In the above formula, D k is the bus load power, NLD is the number of load buses;

[0037] (2) Upper and lower limits of unit output:

[0038] pi,min≤pi≤pi,max

[0039] p j，min ≤p j ≤p j，max

[0040] In the above formula, p i,min , p i,max are the upper and lower limits of the output power of coal-fired power unit i, p j,min , p j，max are the upper and lower limits of the output power of gas generator set j respectively;

[0041] (3) Unit load ramp rate constraints:

[0042] -Δp i ≤p i,t -p i,t-1 ≤Δp i

[0043] -△p j ≤p j，t -p j，t-1 ≤Δp j

[0044] In the above formula, Δp i is the maximum value of the load that can be added or reduced by coal-fired power unit i in each period, Δp j is the maximum value of the load that can be added or subtracted by gas generator set j in each period;

[0045] (4) Constraints on renewable energy generation:

[0046] 0≤p h ≤P h

[0047] In the above formula, P h is the theoretical output of renewable energy unit h;

[0048] (5) Grid transmission safety constraints:

[0049]

[0050] In the above formula, GSF l-i , GSF l-j , GSF l-h and GSF l-k are the power generation transfer factors of coal-fired unit i, gas-fired unit j, renewable energy unit h and load k to the transmission equipment, F l，min and F l，max They are the power flow power and upper and lower limits of the transmission equipment respectively.

[0051] Furthermore, the step 3 uses the following formula to calculate the system new energy abandonment rate:

[0052]

[0053] Among them, p h，t It is the variable optimized and solved in step 2; if it is lower than the renewable energy power abandonment rate requirement, there is no peak-shaving demand, and the system's full-day peak-shaving price is 0; if it is higher than the renewable energy power abandonment rate standard, the minimum output of the coal-fired power units and gas-fired power units in the system is reduced by a step size of k%, and the production simulation module is executed until the system's renewable energy power abandonment rate is lower than the renewable energy power abandonment rate standard.

[0054] Furthermore, the specific implementation method of step 5 includes the following steps:

[0055] Step 5.1: Determine the peak load demand of the system in each period:

[0056] Calculate the total peak-shaving demand of the system in each period of the day. First, calculate the peak-shaving power of each gas-fired unit and coal-fired unit, that is, the difference between the actual minimum output benchmark and the output obtained in step 4, and sum up the peak-shaving power of each gas-fired unit and coal-fired unit to obtain the total peak-shaving demand of the system. The formula is as follows:

[0057]

[0058] In the above formula, P i，MIN is the benchmark minimum output of coal-fired power unit i, P j，MIN is the benchmark minimum output of gas generator set j.

[0059] Step 5.2: Construct the relationship function between peak-shaving price and peak-shaving demand:

[0060] The historical time-series peak-shaving prices of each unit in each period and the time-series peak-shaving demand information of the corresponding period are obtained, and the relationship function between the peak-shaving price and the peak-shaving demand is constructed by polynomial fitting; the binomial fitting is used, and the fitting function is:

[0061]

[0062] In the above formula is the peak-shaving price of the entire system during period t. t ,y t 、z t are the corresponding coefficients of the fitted binomial;

[0063] Step 5.3: According to the sum of the peak-shaving demands in each time period and their relationship function, substitute the peak-shaving demand in each time period into the relationship function of the corresponding time period to calculate the peak-shaving price in each time period.

[0064] The advantages and positive effects of the present invention are:

[0065] The present invention is reasonably designed, and it uses power system production simulation technology to accurately predict peak-shaving demands in 96 time periods. In the prediction process, based on the peak-shaving market price prediction model involving gas-fired units and coal-fired units, according to historical peak-shaving price information and peak-shaving demand, polynomial fitting is used to construct a peak-shaving price and peak-shaving demand relationship function, thereby realizing the peak-shaving market price prediction function; and according to the predicted peak-shaving demand and the peak-shaving price and peak-shaving demand relationship function, a time-series peak-shaving price prediction is performed. The present invention fully considers the actual operation mode of the power system, and combined with the characteristics of the peak-shaving market, provides a peak-shaving price prediction function for gas turbine power generation enterprises, which can effectively assist in decision-making in participating in the peak-shaving market. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A connection diagram of a peak-shaving market price prediction system in which a gas generator set of the present invention participates;

[0067] Figure 2 It is a flow chart of the peak load market price prediction method involving gas generating units and coal generating units of the present invention. DETAILED DESCRIPTION

[0068] The present invention is further described in detail below with reference to the accompanying drawings.

[0069] The design idea of ​​the present invention is:

[0070] First, determine the indicator that can fully reflect the grid's new energy consumption target, namely, the new energy power abandonment rate: establish coal-fired power units, gas-fired power units and gas-fired power units models, establish a constraint model that can reflect the system's power transmission safety constraints, and simulate 96 trading periods on D day to evaluate the power system's new energy consumption capacity; obtain simulation results that meet the system's power transmission safety constraints, quantify the power abandonment rate indicator, and compare it with relevant parameter indicators of the power system. If there is a deviation, reduce the minimum output of coal-fired power units and gas-fired power units in the system according to a certain step size, and re-simulate 96 trading periods on D day until the system power abandonment rate is lower than the power abandonment rate standard.

[0071] Then, the deep regulation power of each gas-fired unit and coal-fired unit in each period of the day is calculated, and the sum is added to form the deep regulation power of the system in each period; according to the historical peak-shaving price and peak-shaving demand information, polynomial fitting is used to construct the relationship function between peak-shaving price and peak-shaving demand, and finally, the peak-shaving price for each period is calculated according to the sum of the peak-shaving demands in each period and their relationship function.

[0072] The innovative points of the present invention include: (1) Proposing a model that comprehensively considers historical peak-shaving prices and peak-shaving demand information to construct a model of the relationship function between peak-shaving prices and peak-shaving demand; (2) The constructed model supports setting the benchmark minimum output of each unit, and the simulation process is more in line with the actual operation of the power system, ensuring that the simulation is as close to the actual demand as possible; (3) The peak-shaving market price prediction model is combined with the power system production simulation model, combined with historical peak-shaving price information, to realize the price prediction of the peak-shaving market.

[0073] Based on the above design concept, the present invention proposes a peak-shaving market price prediction system involving gas units, such as Figure 1 As shown, it includes a login authentication module, a data input module, a production simulation module, a data processing module, a deviation feedback correction module, a fitting calculation module and a data output module. The following is a detailed description of each module in the system:

[0074] The login authentication module is used for user identity authentication, and after identity authentication, enters the peak-shaving market price prediction system.

[0075] The data input module is connected to the login authentication module and is used to input the power system related parameters and historical peak-shaving prices required by the simulation calculation module and the data processing module. The power system related parameters include power generation side information, grid side information, load side information, and the benchmark minimum output of coal-fired power units and gas-fired units.

[0076] Information on the power generation side includes the installed capacity, ramp rate, ramp rate, no-load cost, parameters of the power generation cost characteristic function, emission rate of the unit, domain carbon emission price, etc. of coal-fired power units, and the installed capacity, bus and time-series power forecast of gas-fired units.

[0077] Grid-side information includes transmission topology, transmission safety constraints, etc.

[0078] Load side information includes load geographical location, time sequence power, etc.

[0079] The production simulation module is connected to the data input module, and is used to obtain the relevant parameters of the power system transmitted by the data input module, solve the production simulation results, and transmit the simulation results to the data processing module. The specific functions of the production simulation module are: to establish a production simulation model of the power spot market, to run the D-day 96-period operation mode that meets the actual transmission safety constraints of the system, and to solve the power abandonment rate of the gas unit and the output curves of the coal-fired unit and the gas unit.

[0080] The data processing module is connected to the production simulation module and the deviation feedback correction module, and is used to compare the simulation results with the relevant parameter indicators of the power system to determine whether the new energy power abandonment rate is within the allowable range; if it meets the index requirements, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module. The specific function of the data processing module is: according to the production simulation results (the power abandonment rate of the gas unit and the output curves of the coal-fired power unit and the gas-fired power unit), the simulation results are compared with the relevant parameter index requirements of the power system. If the new energy power abandonment rate result does not meet the index requirements, the minimum output parameters of the unit are corrected and the parameters are returned to the production simulation module; if it meets the index requirements, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module.

[0081] The deviation feedback correction module is connected to the data processing module. When the new energy power abandonment rate result in the data processing module does not meet the planning requirements, the minimum output parameters of the unit are corrected according to the target new energy power abandonment rate, and the parameters are returned to the production simulation module for re-simulation until the new energy power abandonment rate meets the system requirements.

[0082] The fitting calculation module is connected to the data processing module, and the peak-shaving price for each time period is calculated according to the peak-shaving demand of the system in each time period through the output curves of the coal-fired power units and the gas-fired power units transmitted by the data processing module.

[0083] The data output module is connected to the fitting calculation module, and outputs the peak-shaving prices of the coal-fired power units and the gas-fired power units in each time period transmitted by the fitting calculation module to form a report, including the peak-shaving prices of the coal-fired power units and the gas-fired power units in each time period.

[0084] Based on the above-mentioned peak-shaving market price prediction system involving gas-fired units, the present invention also proposes a peak-shaving market price prediction method involving gas-fired units and coal-fired units, such as Figure 2 As shown, the prediction method includes the following steps:

[0085] Step 1: The data input module inputs the power system related parameters and historical peak-shaving prices required by the simulation calculation module and the data processing module.

[0086] In this step, the user inputs data related to the peak-shaving market price forecast through the data input module, including the day-ahead power system forecast, including load forecast, wind power forecast, photovoltaic forecast; the benchmark minimum output of coal-fired power units and gas-fired units, for example, the benchmark minimum output is 50% of the rated power generation capacity of the unit.

[0087] Step 2: The production simulation module receives relevant parameters of the power system, and performs production simulation calculations for 96 periods of the power system on D day to obtain the power abandonment rate of the gas-fired units and the output curves of the coal-fired units and the gas-fired units.

[0088] The specific implementation method of this step includes the following steps:

[0089] Step 2.1: Determine the objective function that minimizes the total power generation cost of the system.

[0090] The optimization target of the objective function determined in this step is mainly based on the total power generation cost of the system. Therefore, its objective function minimizes the output cost of the system units. The objective function of this model is as follows:

[0091]

[0092] In the formula, T is the number of full-day periods, NG is the number of gas-fired units in the system; NC is the number of coal-fired units in the system; NRE is the number of renewable energy units in the system; p i，t 、p j,t and p h，t are the active power of coal-fired unit i, gas-fired unit j and renewable energy unit h in time period t; C i (p i，t ), C j (p j，t ) and C h，t (p h，t ) is the variable cost of coal-fired power unit i, gas-fired power unit j and renewable energy unit h. The carbon emission cost can be included in the cost model of conventional coal-fired power units and gas-fired units.

[0093] C i [(p i，t )]=a i ×p i，t 2 +b i ×p i，t +c i +[p i，t ×R EMi ×T r ]

[0094] Cj[(p j，t )]=a j ×p j，t 2 +b j ×p j，t +c j +[p j，t ×R EMj ×T r ]

[0095] Where: a i , b i 、c i is the parameter of the power generation cost characteristic function of coal-fired power unit i, where c i is the no-load cost of coal-fired power units; R EMiis the emission rate of coal-fired power units, in ton / MWh; T r is the regional carbon emission price, in yuan / ton. j , b j 、c j is the parameter of the power generation cost characteristic function of gas unit j, where c j is the no-load cost of the gas unit; R EMj is the emission rate of the gas unit, in ton / MWh.

[0096] Step 2.2: Determine that the system safety constraints are met and establish a production simulation module to simulate the 96 time periods on D-day.

[0097] The system safety constraints determined in this step are as follows:

[0098] (1) System load balancing constraints:

[0099]

[0100] In the above formula, D k is the bus load, and NLD is the number of load buses.

[0101] (2) Upper and lower limits of unit output:

[0102] pi,min≤pi≤pi,max

[0103] p j，min ≤p j ≤p j，max

[0104] In the above formula, p i,min , p i,max are the upper and lower limits of the output power of coal-fired power unit i, p j,min , p j，max are the upper and lower limits of the output power of gas generator set j respectively.

[0105] (3) Unit load ramp rate constraints:

[0106] -Δp i ≤p i,t -p i,t-1 ≤Δp i

[0107] -△p j ≤p j，t -p j，t-1 ≤Δp j

[0108] In the above formula, Δpi is the maximum value of the load that can be added or removed by coal-fired power unit i in each period, and Δp j It is the maximum value of load that can be added or subtracted by gas generator set j in each period.

[0109] (4) Constraints on renewable energy generation:

[0110] 0≤p h ≤P h

[0111] In the above formula, P h is the theoretical output of renewable energy unit h.

[0112] (5) Grid transmission safety constraints:

[0113]

[0114] In the above formula, GSF l-i , GSF l-j , GSF l-h and GSF l-k are the power generation transfer factors of coal-fired unit i, gas-fired unit j, renewable energy unit h and load k to the transmission equipment, F l，min and F l，max are the power flow power and upper and lower limits of the transmission equipment l respectively.

[0115] Step 3: The data processing module compares the simulation results with the power system's requirements for the renewable energy power curtailment rate. If the results do not meet the system's renewable energy power curtailment rate requirements, the deviation feedback correction module is used to correct the minimum output parameters of the unit and return the parameters to the production simulation module. If the index requirements are met, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module.

[0116] In this step, the following formula is used to calculate the system's new energy abandonment rate:

[0117]

[0118] Among them, p h t is the variable optimized and solved in step 2. If it is lower than the requirement of the new energy curtailment rate (for example, 5%), there is no peak-shaving demand, and the system's full-day peak-shaving price is 0. If it is higher than the standard of the new energy curtailment rate, the minimum output of the coal-fired power units and gas-fired power units in the system is reduced by a step size of k% (for example, 1%), and the production simulation module is executed until the system's new energy curtailment rate is lower than the standard of the new energy curtailment rate.

[0119] Step 4: The data output module outputs the full-day time sequence output of the coal-fired power units and gas-fired power units that meet the index requirements.

[0120] This step obtains the optimization solution results in step 2 and outputs them, including the full-day time sequence output of the coal-fired power unit p i，t , the full-day sequential output of the gas unit p j，t .

[0121] Step 5: The fitting calculation module calculates the peak-shaving price for each period according to the sum of the peak-shaving demands of the coal-fired power units and the gas-fired power units in each period and the relationship function between the peak-shaving price and the peak-shaving demand.

[0122] The specific implementation method of this step includes the following steps:

[0123] Step 5.1: Determine the peak load demand of the system in each time period.

[0124] The step 5.1 calculates the total peak-shaving demand of the system in each period of the day. First, the peak-shaving power of each gas-fired unit and coal-fired unit is calculated, that is, the difference between its actual minimum output benchmark and the output obtained in step 4, and the peak-shaving power of each gas-fired unit and coal-fired unit is summed up to obtain the total peak-shaving demand of the system. The formula is as follows:

[0125]

[0126] In the above formula, P i，MIN is the benchmark minimum output of coal-fired power unit i, P j，MIN is the benchmark minimum output of gas generator set j.

[0127] Step 5.2: Construct the relationship function between peak-shaving price and peak-shaving demand.

[0128] The step 5.2 obtains the historical time-series peak-shaving price of each unit in each period and the time-series peak-shaving demand information of the corresponding period, and uses polynomial fitting to construct a function of the relationship between the peak-shaving price and the peak-shaving demand. Using binomial fitting, the function formed by fitting is:

[0129]

[0130] In the above formula is the peak-shaving price of the entire system during period t. t ,y t 、z t are the corresponding coefficients of the fitted binomial.

[0131] Step 5.3: According to the sum of the peak-shaving demands in each time period and their relationship function, substitute the peak-shaving demand in each time period into the relationship function of the corresponding time period to calculate the peak-shaving price in each time period.

[0132] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A price forecasting system for a peak load regulation market involving gas generating units, characterized in that: It includes a login authentication module, a data input module, a production simulation module, a data processing module, a deviation feedback correction module, a fitting calculation module and a data output module; The login authentication module is used for user identity authentication, and after identity authentication, the user enters the peak-shaving market price prediction system; The data input module is connected to the login authentication module and is used to input the power system related parameters and historical peak-shaving prices required by the simulation calculation module and the data processing module; The production simulation module is connected to the data input module, and is used to obtain the power system related parameters transmitted by the data input module, establish a power spot market production simulation model, run the D-day 96-period operation mode that meets the actual transmission safety constraints of the system, solve and obtain the production simulation results, and transmit the simulation results to the data processing module. The simulation results are the power abandonment rate of the gas unit and the output curves of the coal-fired unit and the gas unit; The data processing module is connected to the production simulation module and the deviation feedback correction module, and compares the simulation results obtained by the production simulation result module with the relevant parameter indicators of the power system to determine whether the new energy power abandonment rate is within the allowable range; if the new energy power abandonment rate result does not meet the indicator requirements, the minimum output parameters of the unit are corrected and the parameters are returned to the production simulation module; if it meets the indicator requirements, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module; The deviation feedback correction module is connected to the data processing module. When the result of the new energy power abandonment rate in the data processing module does not meet the planning requirements, the minimum output parameters of the unit are corrected according to the target new energy power abandonment rate, and the parameters are returned to the production simulation module for re-simulation until the new energy power abandonment rate meets the system requirements; The fitting calculation module is connected to the data processing module, and calculates the peak-shaving price for each period according to the output curves of the coal-fired power units and gas-fired power units transmitted by the data processing module and the peak-shaving demand of the system in each period; The data output module is connected to the fitting calculation module, and outputs the peak-shaving prices of the coal-fired power units and the gas-fired power units in each time period transmitted by the fitting calculation module to form a report.

2. The peak load market price forecasting system in which gas generator sets participate according to claim 1 is characterized in that: The power system related parameters include power generation side information, grid side information, load side information, and the benchmark minimum output of coal-fired power units and gas-fired power units.

3. The peak load market price forecasting system in which gas turbines participate according to claim 2 is characterized in that: The power generation side information includes the installed capacity, ramp rate, ramp rate, no-load cost, parameters of the power generation cost characteristic function, emission rate, domain carbon emission price of the coal-fired power unit, and the installed capacity, bus and time-series power forecast of the gas-fired power unit; The grid side information includes transmission topology and transmission safety constraints; The load side information includes the load geographical location and time sequence power.

4. A prediction method for a peak load market price prediction system in which a gas generator set participates according to any one of claims 1 to 3, characterized in that: The following steps are involved: Step 1: The data input module inputs the power system related parameters and historical peak-shaving prices required by the simulation calculation module and the data processing module; Step 2: The production simulation module receives relevant parameters of the power system, determines the objective function of minimizing the total power generation cost of the system, and performs production simulation calculations for 96 periods of the power system on D day to obtain the power abandonment rate of the gas unit and the output curves of the coal-fired power unit and the gas unit; Step 3: The data processing module compares the simulation results with the requirements of the power system for the new energy power abandonment rate. If the results do not meet the system's new energy power abandonment rate requirements, the deviation feedback correction module is used to correct the minimum output parameters of the unit, and the parameters are sent back to the production simulation module; if they meet the index requirements, the output curves of the coal-fired power unit and the gas-fired power unit are transmitted to the fitting calculation module; Step 4: The data output module outputs the full-day time sequence output of the coal-fired power units and gas-fired power units that meet the index requirements; Step 5: The fitting calculation module calculates the peak-shaving price for each period according to the sum of the peak-shaving demands of the coal-fired power units and the gas-fired power units in each period and the relationship function between the peak-shaving price and the peak-shaving demand.

5. The prediction method of the peak load market price prediction system in which the gas generator sets participate according to claim 4 is characterized by: The specific implementation method of step 2 includes the following steps: Step 2.1: Determine the objective function that minimizes the total power generation cost of the system. The objective function is as follows: Where T is the number of full-day periods, NG is the number of gas-fired units in the system; NC is the number of coal-fired units in the system; NRE is the number of renewable energy units in the system; p i,t 、p j,t and p h,t are the active power of coal-fired unit i, gas-fired unit j and renewable energy unit h in time period t; C i (p i,t ), C j (p j,t ) and C h , t (p h,t ) is the variable cost of coal-fired power unit i, gas-fired power unit j and renewable energy unit h. The cost model of conventional coal-fired power units and gas-fired power units can include carbon emission costs; C i [(p i,t )]=a i ×p i,t 2 +b i ×p i,t +c i +[p i,t ×R EM i ×T r ] C j [(p j,t )]=a j ×p j,t 2 +b j ×p j,t +c j +[p j,t ×R EM j ×T r ] Where: a i 、b i 、c i is the parameter of the power generation cost characteristic function of coal-fired power unit i, where c i is the no-load cost of coal-fired power units; R EM i is the emission rate of coal-fired power units, in ton / MWh; T r is the regional carbon emission price, in yuan / ton; a j 、b j 、c j is the parameter of the power generation cost characteristic function of gas unit j, where c j is the no-load cost of the gas unit; R EM j is the emission rate of the gas unit, in ton / MWh; Step 2.2, determine the system safety constraints including system load balance constraints, unit output upper and lower limit constraints, unit load ramp rate constraints, renewable energy generation constraints and power grid transmission safety constraints, and establish a production simulation module to perform D-day 96-period simulation.

6. The prediction method of the peak load market price prediction system in which the gas generator sets participate according to claim 5 is characterized by: The system safety constraints are as follows: ⑴ System load balance constraints: In the above formula, D k is the bus load power, NLD is the number of load buses; ⑵ Upper and lower limits of unit output: p i,min ≤p i ≤p i,max p j,min ≤p j ≤p j,max In the above formula, p i,min , p i,mas are the upper and lower limits of the output power of coal-fired power unit i, p j,min , p j,max are the upper and lower limits of the output power of gas generator set j respectively; ⑶ Unit load increase / decrease climbing rate constraints: -Δp i ≤p i,t -p i,t-1 ≤Δp i -Δp j ≤p j,t -p j,t-1 ≤Δp j In the above formula, Δp i is the maximum value of the load that can be added or removed by coal-fired power unit i in each period, Δp j is the maximum value of the load that can be added or subtracted by gas generator set j in each period; (4) Constraints on renewable energy generation: 0≤p h ≤P h In the above formula, P h is the theoretical output of renewable energy unit h; ⑸Grid transmission safety constraints: In the above formula, GSF l-i , GSF l-j , GSF l-h and GSF l-k are the power generation transfer factors of coal-fired unit i, gas-fired unit j, renewable energy unit h and load k to transmission equipment l, respectively. l,min and F l,max are the power flow power and upper and lower limits of the transmission equipment l respectively.

7. The prediction method of the peak load market price prediction system in which the gas generator sets participate according to claim 4 is characterized by: The step 3 uses the following formula to calculate the system's new energy abandonment rate: Among them, p h,t It is the variable optimized and solved in step 2; if it is lower than the renewable energy power abandonment rate requirement, there is no peak-shaving demand, and the system's full-day peak-shaving price is 0; if it is higher than the renewable energy power abandonment rate standard, the minimum output of the coal-fired power units and gas-fired power units in the system is reduced by a step size of k%, and the production simulation module is executed until the system's renewable energy power abandonment rate is lower than the renewable energy power abandonment rate standard.

8. The prediction method of the peak load market price prediction system in which the gas generator sets participate according to claim 4 is characterized by: The specific implementation method of step 5 includes the following steps: Step 5.1: Determine the peak load demand of the system in each period: Calculate the total peak-shaving demand of the system in each period of the day. First, calculate the peak-shaving power of each gas-fired unit and coal-fired unit, that is, the difference between the actual minimum output benchmark and the output obtained in step 4, and sum up the peak-shaving power of each gas-fired unit and coal-fired unit to obtain the total peak-shaving demand of the system. The formula is as follows: In the above formula, P i,MIN is the benchmark minimum output of coal-fired power unit i, P j,MIN is the benchmark minimum output of gas generator set j; Step 5.2: Construct the relationship function between peak-shaving price and peak-shaving demand: The historical time-series peak-shaving prices of each unit in each period and the time-series peak-shaving demand information of the corresponding period are obtained, and the relationship function between the peak-shaving price and the peak-shaving demand is constructed by polynomial fitting; the binomial fitting is used, and the fitting function is: In the above formula is the peak-shaving price of the entire system during period t; x t ,y t 、z t are the corresponding coefficients of the fitted binomial; Step 5.3: According to the sum of the peak-shaving demands in each time period and their relationship function, substitute the peak-shaving demand in each time period into the relationship function of the corresponding time period to calculate the peak-shaving price in each time period.

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