Power supply and demand balance optimization method and system considering coal price influence

Abstract

The invention discloses a power supply and demand balance optimization method considering coal price influence. The method comprises the following steps: acquiring data information of a target power system; constructing coal power unit power generation cost factor and power generation willingness factor indexes and obtaining a coal power generation willingness curve; constructing a wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scene set; and establishing a power supply and demand balance optimization model under the uncertainty scene set by taking the daily power generation cost expectation, the daily power shortage expectation and the daily interval transmission power expectation of the system as optimization targets, solving the model, and completing the power supply and demand balance optimization of the target power system under the consideration of the coal price influence according to the solving result. The invention also discloses a system for realizing the power supply and demand balance optimization method considering the coal price influence. According to the method, the power supply and demand balance optimization considering the coal price influence can be realized, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to a method and system for optimizing power supply and demand balance taking into account the influence of coal prices. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] At present, the new energy power generation system has been rapidly developed, and a large number of new energy power generation systems have begun to be integrated into the power system and generate electricity. However, in terms of the total amount of electricity supply, the amount of electricity supplied by the thermal power generation system still accounts for more than 60%. Therefore, thermal power generation is still the most important source of electricity supply for the power system.

[0004] The optimization research of supply and demand balance of power system has always been the top priority of power system. With the large number of new energy power generation systems connected to the grid, the optimization research of supply and demand balance of power system has gradually transformed into the optimization research of supply and demand balance of power system under the access of high proportion of new energy power generation systems. Most of the existing optimization schemes of supply and demand balance focus on the risks brought by uncertain sources such as wind, light, water and load, and regard coal-fired power as a deterministic power source under the conditions of operation constraints.

[0005] However, in extreme situations, such as extreme drops in coal prices, the contradiction between “planned electricity and market coal” may erupt, and it is very likely that coal power companies will have tight coal inventories and a decline in their willingness to generate electricity, leading to a large gap in electricity supply. This will also greatly affect the safe and stable operation of the power system and the supply of electricity. Summary of the invention

[0006] One of the purposes of the present invention is to provide a method for optimizing power supply and demand balance taking into account the influence of coal prices with high reliability and good accuracy.

[0007] A second object of the present invention is to provide a system for realizing the method for optimizing the balance between electricity supply and demand taking into account the influence of coal prices.

[0008] The power supply and demand balance optimization method considering the influence of coal price provided by the present invention comprises the following steps:

[0009] S1. Obtain data information of the target power system;

[0010] S2. According to the data information obtained in step S1, based on the coal price and the on-grid electricity price, construct the power generation cost factor and power generation willingness factor indicators of the coal-fired power unit, and obtain the coal-fired power generation willingness curve;

[0011] S3. Based on the data information obtained in step S1, a set of wind power, photovoltaic power, non-adjustable hydropower and load power uncertainty scenarios is constructed;

[0012] S4. Taking the expected daily power generation cost of the system, the expected daily power shortage and the expected daily inter-zone transmission power as optimization targets, under the uncertainty scenario set constructed in step S3, establish an optimization model for power supply and demand balance;

[0013] S5. Solve the power supply and demand balance optimization model constructed in step S4, and complete the power supply and demand balance optimization of the target power system under the influence of coal prices according to the solution results.

[0014] The step S1 of acquiring the data information of the target power system specifically includes the following steps:

[0015] Obtain historical data information of the target power system;

[0016] The historical data information includes coal-fired power installed capacity data information, historical monthly coal price data information, monthly power generation data information, on-grid electricity price data information, wind power historical data information, photovoltaic historical data information, non-adjustable small hydropower historical data information, load historical data information, coal-fired power parameter data information, adjustable hydropower parameter data information, electric energy storage equipment data information and pumped storage parameter data information.

[0017] According to the data information obtained in step S1, step S2 constructs the power generation cost factor and power generation willingness factor index of the coal-fired power unit based on the coal price and the on-grid electricity price, and obtains the coal-fired power generation willingness curve, which specifically includes the following steps:

[0018] The following formula is used to define the power generation cost factor λ of coal-fired power plants: b :

[0019] λ b =c cc / c cb

[0020] Where c cb is the unit on-grid electricity price; c cc is the unit power generation cost, and c cc =c c / p c , c c is the unit coal price, p c is the amount of electricity that can be generated per unit of coal;

[0021] The following formula is used to define the power generation willingness factor λ of coal-fired power units: w :

[0022] λ w =Pg / S c

[0023] Where P g is the monthly power generation of coal-fired power units when the coal price is higher than the set price; S c is the installed capacity of coal-fired power units;

[0024] With the coal-fired power generation cost factor as the horizontal axis and the coal-fired power generation willingness factor as the vertical axis, a coal-fired power generation willingness curve is constructed to reflect the power generation willingness of the coal-fired power units.

[0025] Step S3, based on the data information obtained in step S1, constructs a set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios, specifically including the following steps:

[0026] Preprocessing the data information obtained in step S1;

[0027] Based on the maximum likelihood estimation algorithm, the historical time series probability distribution parameters of the target power system and various types of sources and loads in each area are calculated;

[0028] Based on the Spearman correlation coefficient method, the autocorrelation and cross-correlation of various types of source loads in each area are analyzed;

[0029] According to the analysis results, Latin hypercube sampling-Cholesky decomposition method is used to construct source-load scenarios with target autocorrelation;

[0030] According to the analysis results, the particle swarm optimization algorithm is used to screen the source-load scenarios with target mutual correlation;

[0031] Based on the obtained source-load scenarios, a set of wind power, photovoltaic power, non-adjustable hydropower and load power uncertainty scenarios are constructed.

[0032] In step S4, taking the expected daily power generation cost, the expected daily power shortage and the expected daily inter-zone transmission power as the optimization targets, an optimization model for power supply and demand balance is established under the uncertainty scenario set constructed in step S3, which specifically includes the following steps:

[0033] The following formula is used as the objective function of the power supply and demand balance optimization model:

[0034]

[0035] Where f1 is the expected daily power generation cost of the system; sc is the total number of scenarios in the wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenario set constructed in step S3; is the coal-fired power generation at time t in scenario d; c ch is the unit electricity generation cost of hydropower; is the hydroelectric power generation power at time t in scenario d; c cw is the unit power generation cost of wind power; is the wind power generation power at time t in scenario d; c cp is the unit electricity generation cost of photovoltaic power; is the photovoltaic power generation power at time t in scenario d; f2 is the expected daily power shortage; is the electric load at time t in scenario d; f3 is the expected daily transmission power; area is the total number of areas in step S3; is the transmission power between the ith area and the jth area at time t in scenario d; for uncertain power sources, the corresponding output value is determined by the scenarios in the wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenario set constructed in step S3;

[0036] The following formula is used as the constraint condition:

[0037] Coal power operation constraints:

[0038]

[0039] In the formula is the minimum output coefficient of coal-fired power; is the coal-fired power installed capacity in region a; is the coal power output of region a at time t in scenario d; is the maximum output coefficient of coal-fired power; K PP is the climbing rate constraint coefficient; The threshold value of the power generation cost factor corresponding to the scenario where the coal price is higher than the set price;

[0040] Adjustable hydropower operation constraints:

[0041]

[0042] In the formula It is the minimum output coefficient of adjustable hydropower; is the adjustable hydropower installed capacity of region a; is the adjustable hydropower output of area a at time t in scenario d; It is the maximum output coefficient of adjustable hydropower;

[0043] Energy storage operation constraints:

[0044]

[0045] In the formula is the electrical energy storage capacity of area a at time t in scenario d; is the loss coefficient of electric energy storage; is the energy storage charging power of area a at time t in scenario d; Charging efficiency for electrical energy storage; is the energy storage discharge power of area a at time t in scenario d; is the discharge efficiency of the electric energy storage; is the binary variable of the energy storage charge in area a at time t in scenario d, Indicates that the energy storage in area a at time t in scene d is in a charging state, Indicates that the electric energy storage in area a at time t in scene d is in a non-charging state; The maximum charging power for electric energy storage; is the binary variable of the energy storage discharge in area a at time t in scenario d, Indicates that the energy storage in area a at time t in scene d is in a discharged state, Indicates that the electrical energy storage in area a at time t in scene d is in a non-discharge state; is the maximum discharge power of the electric energy storage; is the minimum storage capacity of the electric energy storage in area a; is the maximum storage capacity of the electric energy storage in area a; K ES is the initial output coefficient of the electric energy storage; is the storage capacity of the electric energy storage in area a at the initial moment in scenario d; is the storage capacity of area a at the end of the energy storage in scenario d;

[0046] Pumped storage operation constraints:

[0047]

[0048] In the formula is the pumped storage charging power in area a at time t in scenario d; charging efficiency for pumped storage; is the pumped storage discharge power in area a at time t in scenario d; The discharge efficiency of pumped storage; is the pumped storage charging binary variable of area a at time t in scenario d, Indicates that the pumped storage in area a at time t in scenario d is in a charging state, It indicates that the pumped storage in area a at time t in scenario d is in a non-charging state; The maximum charging power for pumped storage; is the binary variable of pumped storage discharge in area a at time t in scenario d, Indicates that the pumped storage in area a at time t in scenario d is in a discharging state, It indicates that the pumped storage in area a at time t in scenario d is in a non-discharging state;

[0049] Power transfer constraints between regions:

[0050]

[0051] In the formula is the maximum allowed transmission power between the i-th area and the j-th area.

[0052] Step S5 solves the power supply and demand balance optimization model constructed in step S4, specifically including the following steps:

[0053] For the power supply and demand balance optimization model constructed in step S4, the three objective formulas in the objective function are converted into a single objective formula using a fuzzy membership function, and the converted single objective formula is solved using a solver.

[0054] The present invention also provides a system for realizing the power supply and demand balance optimization method considering the influence of coal price, comprising a data acquisition module, an indicator construction module, a scenario construction module, a model construction module and a balance optimization module; the data acquisition module, the indicator construction module, the scenario construction module, the model construction module and the balance optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system, and upload the data information to the indicator construction module; the indicator construction module is used to construct the power generation cost factor and the power generation willingness factor index of the coal-fired power unit according to the received data information, according to the acquired data information, based on the coal price and the on-grid electricity price, and obtain the coal-fired power generation willingness curve, and upload the data information to the scenario construction module; The scenario construction module is used to construct a set of uncertainty scenarios for wind power, photovoltaic power, non-adjustable hydropower and load power according to the received data information, and upload the data information to the model construction module; the model construction module is used to establish an electricity supply and demand balance optimization model under the constructed uncertainty scenario set based on the received data information, with the system's daily power generation cost expectation, daily power shortage expectation and daily interval transmission power expectation as optimization objectives, and upload the data information to the balance optimization module; the balance optimization module is used to solve the constructed electricity supply and demand balance optimization model according to the received data information, and complete the electricity supply and demand balance optimization of the target power system under the influence of coal prices according to the solution results.

[0055] The method and system for optimizing the power supply and demand balance considering the influence of coal prices provided by the present invention simultaneously consider the influence of source-load uncertainty and coal prices on the risk of power supply security, and construct and solve the corresponding power supply and demand balance optimization model. Therefore, the present invention can not only realize the optimization of the power supply and demand balance considering the influence of coal prices, but also has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0057] Figure 2Schematic diagram of the coal-fired power generation willingness curve of the method of the present invention.

[0058] Figure 3 This is a schematic diagram of the calculation results of the coal-fired power generation willingness curve of an embodiment of the method of the present invention.

[0059] Figure 4 This is a schematic diagram of the optimization target change trend under different coal price scenarios of the method embodiment of the present invention.

[0060] Figure 5 Schematic diagram of system power shortage on maximum load day under different coal prices in an embodiment of the method of the present invention.

[0061] Figure 6 The figure is a schematic diagram of the power flow distribution at the maximum load moment under different coal prices in an embodiment of the method of the present invention.

[0062] Figure 7 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0063] like Figure 1 The method flow diagram of the method of the present invention is shown as follows: The power supply and demand balance optimization method considering the influence of coal price disclosed in the present invention comprises the following steps:

[0064] S1. Acquire data information of the target power system; specifically includes the following steps:

[0065] Obtain historical data information of the target power system;

[0066] The historical data information includes coal-fired power installed capacity data information, historical monthly coal price data information, monthly power generation data information, grid-connected power price data information, wind power historical data information, photovoltaic historical data information, non-adjustable small hydropower historical data information, load historical data information, coal-fired power parameter data information, adjustable hydropower parameter data information, electric energy storage equipment data information and pumped storage parameter data information, etc.;

[0067] S2. According to the data information obtained in step S1, based on the coal price and the on-grid electricity price, construct the power generation cost factor and power generation willingness factor index of the coal-fired power unit, and obtain the coal-fired power generation willingness curve; specifically, it includes the following steps:

[0068] This step is mainly used to achieve quantitative analysis of the impact of coal prices on the willingness to generate coal-fired power. The willingness is essentially an upper limit, and the willingness to generate coal-fired power can be reflected in the optimization of power supply and demand balance through the power generation upper limit.

[0069] The following formula is used to define the power generation cost factor λ of coal-fired power plants: b , used to reflect the income from coal-fired power generation:

[0070] λ b=c cc / c cb

[0071] Where c cb is the unit on-grid electricity price; c cc is the unit power generation cost, and c cc =c c / p c , c c is the unit coal price, p c is the amount of electricity that can be generated per unit of coal;

[0072] The following formula is used to define the power generation willingness factor λ of coal-fired power units: w , used to reflect the willingness of coal-fired power generation:

[0073] λ w =P g / S c

[0074] Where P g is the monthly power generation of coal-fired power units when the coal price is higher than the set price; S c is the installed capacity of coal-fired power units;

[0075] With the coal-fired power generation cost factor as the horizontal axis and the coal-fired power generation willingness factor as the vertical axis, a coal-fired power generation willingness curve is constructed to reflect the power generation willingness of the coal-fired power generation unit, such as Figure 2 As shown;

[0076] In the low coal price scenario, coal-fired power output is mainly affected by factors such as load, and the upper limit is mainly constrained by equipment operation. When the coal price reaches a certain threshold, the revenue of coal-fired power generation decreases, and coal-fired power output is constrained not only by equipment operation but also by the willingness to generate electricity. The Spearman coefficient can be used to analyze the correlation between the historical data of the power generation cost factor and the power generation willingness factor of coal-fired power units, and then the high coal price historical scenario can be selected to determine the high coal price scenario threshold.

[0077] S3. Based on the data information obtained in step S1, a set of wind power, photovoltaic power, non-adjustable hydropower and load power uncertainty scenarios is constructed; specifically, the following steps are included:

[0078] Preprocessing the data information obtained in step S1;

[0079] Based on the maximum likelihood estimation algorithm, the historical time series probability distribution parameters of the target power system and various types of sources and loads in each area are calculated;

[0080] Based on the Spearman correlation coefficient method, the autocorrelation and cross-correlation of various types of source loads in each area are analyzed;

[0081] According to the analysis results, Latin hypercube sampling-Cholesky decomposition method is used to construct source-load scenarios with target autocorrelation;

[0082] According to the analysis results, the particle swarm optimization algorithm is used to screen the source-load scenarios with target mutual correlation;

[0083] Based on the obtained source-load scenarios, a set of wind power, photovoltaic power, non-adjustable hydropower and load power uncertainty scenarios is constructed;

[0084] S4. Taking the expected daily power generation cost, the expected daily power shortage and the expected daily inter-zone transmission power as the optimization targets, under the uncertainty scenario set constructed in step S3, an optimization model for power supply and demand balance is established; specifically, the following steps are included:

[0085] The following formula is used as the objective function of the power supply and demand balance optimization model:

[0086]

[0087] Where f1 is the expected daily power generation cost of the system; sc is the total number of scenarios in the wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenario set constructed in step S3; is the coal-fired power generation at time t in scenario d; c ch is the unit electricity generation cost of hydropower; is the hydroelectric power generation power at time t in scenario d; c cw is the unit power generation cost of wind power; is the wind power generation power at time t in scenario d; c cp is the unit electricity generation cost of photovoltaic power; is the photovoltaic power generation power at time t in scenario d; f2 is the expected daily power shortage; is the electric load at time t in scenario d; f3 is the expected daily transmission power; area is the total number of areas in step S3; is the transmission power between the ith area and the jth area at time t in scenario d; for uncertain power sources, the corresponding output value is determined by the scenarios in the wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenario set constructed in step S3;

[0088] The following formula is used as the constraint condition:

[0089] Coal power operation constraints:

[0090]

[0091] In the formula is the minimum output coefficient of coal-fired power; is the coal-fired power installed capacity in region a; is the coal power output of region a at time t in scenario d; is the maximum output coefficient of coal-fired power; K PP is the climbing rate constraint coefficient; The threshold value of the power generation cost factor corresponding to the scenario where the coal price is higher than the set price;

[0092] Adjustable hydropower operation constraints:

[0093]

[0094] In the formula It is the minimum output coefficient of adjustable hydropower; is the adjustable hydropower installed capacity of region a; is the adjustable hydropower output of area a at time t in scenario d; It is the maximum output coefficient of adjustable hydropower;

[0095] Energy storage operation constraints:

[0096]

[0097] In the formula is the electrical energy storage capacity of area a at time t in scenario d; is the loss coefficient of electric energy storage; is the energy storage charging power of area a at time t in scenario d; Charging efficiency for electrical energy storage; is the energy storage discharge power of area a at time t in scenario d; is the discharge efficiency of the electric energy storage; is the binary variable of the energy storage charge in area a at time t in scenario d, Indicates that the energy storage in area a at time t in scene d is in a charging state, Indicates that the electric energy storage in area a at time t in scene d is in a non-charging state; The maximum charging power for electric energy storage; is the binary variable of the energy storage discharge in area a at time t in scenario d, Indicates that the energy storage in area a at time t in scene d is in a discharged state, Indicates that the electrical energy storage in area a at time t in scene d is in a non-discharge state; is the maximum discharge power of the electric energy storage; is the minimum storage capacity of the electric energy storage in area a; is the maximum storage capacity of the electric energy storage in area a; K ES is the initial output coefficient of the electric energy storage; is the storage capacity of the electric energy storage in area a at the initial moment in scenario d; is the storage capacity of area a at the end of the energy storage in scenario d;

[0098] Pumped storage operation constraints:

[0099]

[0100] In the formula is the pumped storage charging power in area a at time t in scenario d; charging efficiency for pumped storage; is the pumped storage discharge power in area a at time t in scenario d; The discharge efficiency of pumped storage; is the pumped storage charging binary variable of area a at time t in scenario d, Indicates that the pumped storage in area a at time t in scenario d is in a charging state, It indicates that the pumped storage in area a at time t in scenario d is in a non-charging state; The maximum charging power for pumped storage; is the binary variable of pumped storage discharge in area a at time t in scenario d, Indicates that the pumped storage in area a at time t in scenario d is in a discharging state, It indicates that the pumped storage in area a at time t in scenario d is in a non-discharging state;

[0101] Power transfer constraints between regions:

[0102]

[0103] In the formula is the maximum allowed transmission power between the i-th area and the j-th area;

[0104] S5. Solve the power supply and demand balance optimization model constructed in step S4, and complete the power supply and demand balance optimization of the target power system under the influence of coal prices according to the solution results; specifically include the following steps:

[0105] For the electricity supply and demand balance optimization model constructed in step S4, the fuzzy membership function is used to convert the three objective formulas in the objective function into a single objective formula, and the solver is used to solve the converted single objective formula. According to the solution results, the electricity supply and demand balance optimization of the target power system under the influence of coal prices is completed.

[0106] The method of the present invention is further described below in conjunction with an embodiment:

[0107] The electricity supply and demand balance optimization method considering the impact of coal prices on the willingness to generate coal-fired power is applied to a certain province. Historical data such as coal prices, monthly power generation, grid-connected electricity prices, and installed capacity of 13 coal-fired power plants in the province are selected for analysis. The willingness factor is calculated by monthly power generation and installed capacity. The cost factor is calculated by unit power generation cost and grid-connected electricity price. Analysis of the Spearman coefficient shows that there is a strong negative correlation between the historical data of willingness factors and cost factors from September to December 2021, which is consistent with the actual situation at that time when coal prices were too high and coal-fired power plants were less willing to generate electricity. The historical data of 10 coal-fired power plants with a Spearman coefficient lower than -0.8 are selected to construct a coal-fired power generation willingness curve. The power function is selected to construct the coal-fired power generation willingness curve as shown in Figure 3 As shown. As the cost factor of coal-fired power generation gradually increases, the willingness to generate electricity factor gradually decreases. From the correlation analysis based on the Spearman correlation coefficient, it can be seen that the cost factor threshold corresponding to the high coal price scenario is 0.61, when the coal price is about 1,300 yuan / ton. The historical data from January to August 2021 basically did not reach the threshold, while the values ​​from September to December were basically higher than the threshold. Under the high coal price scenario, the on-grid electricity price of coal-fired power units is basically reported at the highest price to seek maximum profit, that is, a 20% increase on the basis of the benchmark electricity price. At this time, the power generation cost factor mainly depends on the unit coal price.

[0108] The proposed method is applied to the province to analyze the power shortage situation under different coal prices. Table 1 shows the three optimization goals of daily power generation cost expectation, daily power shortage expectation, and daily inter-area transmission power expectation under the scenario of coal price ranging from 1200 to 1900 yuan / ton. The changing trends of the three goals are as follows Figure 3 shown.

[0109] Table 1 Schematic diagram of optimization results of power supply and demand balance under different coal price scenarios

[0110]

[0111] Depend on Figure 4 It can be seen that as the unit coal price gradually increases, the daily power shortage expectation gradually increases, and the daily power generation cost expectation gradually decreases. The coal price of 1200 yuan / ton does not reach the high coal price scenario threshold. At this time, there is no expectation of power shortage, the coal-fired power output is large, and the daily power generation cost of the system is high. When the coal price is 1300 yuan / ton, it reaches the high coal price scenario threshold, the coal-fired power unit's willingness to generate electricity decreases, and there is a power gap in the system. With the continuous increase in coal prices, the upper limit of coal-fired power output decreases, the power shortage gradually increases, and the power generation cost also decreases with the decrease in coal-fired power output. Before the coal price reaches the threshold, the output of coal-fired power units in each zone is large, and the interval transmission power is small. In the high coal price scenario, the output of coal-fired power units is small. In order to minimize the power gap and power shortage, the interval resource allocation and complementary coordination are used to ensure the safe and stable operation of the system as much as possible. Therefore, compared with before reaching the threshold, the interval transmission power increases after reaching the high coal price scenario threshold.

[0112] Figure 5 The power gap of the system under the typical scenario of maximum load when the coal price is 1200 and 1800 yuan / ton is shown. When the coal price is 1200 yuan / ton, the coal-fired power output and load change trends are similar, with higher output during the lunch and evening peaks and lower output at night. Electric energy storage and pumped storage are charged at night and discharged at peak times during the lunch and evening peaks. When the coal price reaches 1800 yuan / ton, the willingness of coal-fired power generation decreases and the output of coal-fired power units decreases. The system has a power gap most of the time during the day. At night, due to the reduction in power output, the charging amount of electric energy storage and pumped storage is reduced, and the peak capacity is also reduced. Taking into account factors such as power self-loss, electric energy storage and pumped storage are discharged at 14-15, and there is no power gap at this time. The remaining power of pumped storage is released during the evening peak to alleviate the pressure of evening peak power consumption. Figure 6 The distribution of system flow at the maximum load under different coal prices is shown. When the coal price is 1,200 yuan / ton, there is no power gap in each area, and the two areas achieve self-balancing. When the coal price reaches 1,800 yuan / ton, power gaps appear in the four major areas, and area 2 has a larger power gap due to its heavy load. In addition to being able to meet self-balancing, areas 4 and 5 also transmit power to areas 2 and 6 respectively to reduce the power gap in each area.

[0113] like Figure 7 The functional module diagram of the system of the present invention is shown as follows: the system disclosed in the present invention for realizing the power supply and demand balance optimization method considering the influence of coal price comprises a data acquisition module, an indicator construction module, a scenario construction module, a model construction module and a balance optimization module; the data acquisition module, the indicator construction module, the scenario construction module, the model construction module and the balance optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system, and upload the data information to the indicator construction module; the indicator construction module is used to construct the power generation cost factor and the power generation willingness factor index of the coal-fired power unit according to the received data information, the acquired data information, based on the coal price and the on-grid electricity price, and obtain the coal-fired power generation willingness curve, and upload the data information to the index construction module; The scenario construction module is used to construct a set of uncertainty scenarios for wind power, photovoltaic power, non-adjustable hydropower and load power according to the received data information, and upload the data information to the model construction module; the model construction module is used to establish an electricity supply and demand balance optimization model under the constructed uncertainty scenario set based on the received data information, with the system daily power generation cost expectation, daily power shortage expectation and daily interval transmission power expectation as the optimization objectives, and upload the data information to the balance optimization module; the balance optimization module is used to solve the constructed electricity supply and demand balance optimization model according to the received data information, and complete the electricity supply and demand balance optimization of the target power system under the influence of coal prices according to the solution results.

Claims

1. A method for optimizing power supply and demand balance considering the influence of coal prices, comprising the following steps: S1. Obtain data information of the target power system; S2. According to the data information obtained in step S1, based on the coal price and the on-grid electricity price, construct the power generation cost factor and power generation willingness factor indicators of the coal-fired power unit, and obtain the coal-fired power generation willingness curve; S3. Based on the data information obtained in step S1, a set of wind power, photovoltaic power, non-adjustable hydropower and load power uncertainty scenarios is constructed; S4. Taking the expected daily power generation cost of the system, the expected daily power shortage and the expected daily inter-zone transmission power as optimization targets, under the uncertainty scenario set constructed in step S3, establish an optimization model for power supply and demand balance; S5. Solve the power supply and demand balance optimization model constructed in step S4, and complete the power supply and demand balance optimization of the target power system under the influence of coal prices according to the solution results.

2. The method for optimizing the balance of power supply and demand considering the influence of coal price according to claim 1 is characterized in that The step S1 of acquiring the data information of the target power system specifically includes the following steps: Obtain historical data information of the target power system; The historical data information includes coal-fired power installed capacity data information, historical monthly coal price data information, monthly power generation data information, on-grid electricity price data information, wind power historical data information, photovoltaic historical data information, non-adjustable small hydropower historical data information, load historical data information, coal-fired power parameter data information, adjustable hydropower parameter data information, electric energy storage equipment data information and pumped storage parameter data information.

3. The method for optimizing the balance between power supply and demand considering the influence of coal price according to claim 2 is characterized in that According to the data information obtained in step S1, step S2 constructs the power generation cost factor and power generation willingness factor index of the coal-fired power unit based on the coal price and the on-grid electricity price, and obtains the coal-fired power generation willingness curve, which specifically includes the following steps: The following formula is used to define the power generation cost factor λ of coal-fired power plants: b : l b =c cc / c cb Where c cb is the unit on-grid electricity price; c cc is the unit power generation cost, and c cc =c c / p c , c c is the unit coal price, p c is the amount of electricity that can be generated per unit of coal; The following formula is used to define the power generation willingness factor λ of coal-fired power units: w : l w =P g / S c Where P g is the monthly power generation of coal-fired power units when the coal price is higher than the set price; S c is the installed capacity of coal-fired power units; With the coal-fired power generation cost factor as the horizontal axis and the coal-fired power generation willingness factor as the vertical axis, a coal-fired power generation willingness curve is constructed to reflect the power generation willingness of the coal-fired power units.

4. The method for optimizing the balance of power supply and demand considering the influence of coal price according to claim 3 is characterized in that Step S3, based on the data information obtained in step S1, constructs a set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios, specifically including the following steps: Preprocessing the data information obtained in step S1; Based on the maximum likelihood estimation algorithm, the historical time series probability distribution parameters of the target power system and various types of sources and loads in each area are calculated; Based on the Spearman correlation coefficient method, the autocorrelation and cross-correlation of various types of source loads in each area are analyzed; According to the analysis results, Latin hypercube sampling-Cholesky decomposition method is used to construct source-load scenarios with target autocorrelation; According to the analysis results, the particle swarm optimization algorithm is used to screen the source-load scenarios with target mutual correlation; Based on the obtained source-load scenarios, a set of wind power, photovoltaic power, non-adjustable hydropower and load power uncertainty scenarios are constructed.

5. The method for optimizing the balance of power supply and demand considering the influence of coal price according to claim 4 is characterized in that In step S4, taking the expected daily power generation cost, the expected daily power shortage and the expected daily inter-zone transmission power as the optimization targets, an optimization model for power supply and demand balance is established under the uncertainty scenario set constructed in step S3, which specifically includes the following steps: The following formula is used as the objective function of the power supply and demand balance optimization model: Where f1 is the expected daily power generation cost of the system; sc is the total number of scenarios in the wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenario set constructed in step S3; is the coal-fired power generation at time t in scenario d; c ch is the unit electricity generation cost of hydropower; is the hydroelectric power generation power at time t in scenario d; c cw is the unit power generation cost of wind power; is the wind power generation power at time t in scenario d; c cp is the unit electricity generation cost of photovoltaic power; is the photovoltaic power generation power at time t in scenario d; f2 is the expected daily power shortage; is the electric load at time t in scenario d; f3 is the expected daily transmission power; area is the total number of areas in step S3; is the transmission power between the ith area and the jth area at time t in scenario d; for uncertain power sources, the corresponding output value is determined by the scenarios in the wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenario set constructed in step S3; The following formula is used as the constraint condition: Coal power operation constraints: In the formula is the minimum output coefficient of coal-fired power; is the coal-fired power installed capacity in region a; is the coal power output of region a at time t in scenario d; is the maximum output coefficient of coal-fired power; K PP is the climbing rate constraint coefficient; The threshold value of the power generation cost factor corresponding to the scenario where the coal price is higher than the set price; Adjustable hydropower operation constraints: In the formula It is the minimum output coefficient of adjustable hydropower; is the adjustable hydropower installed capacity of region a; is the adjustable hydropower output of area a at time t in scenario d; It is the maximum output coefficient of adjustable hydropower; Energy storage operation constraints: In the formula is the electrical energy storage capacity of area a at time t in scenario d; is the loss coefficient of electric energy storage; is the energy storage charging power of area a at time t in scenario d; Charging efficiency for electrical energy storage; is the energy storage discharge power of area a at time t in scenario d; is the discharge efficiency of the electric energy storage; is the binary variable of the energy storage charge in area a at time t in scenario d, Indicates that the energy storage in area a at time t in scene d is in a charging state, Indicates that the electric energy storage in area a at time t in scene d is in a non-charging state; The maximum charging power for electric energy storage; is the binary variable of the energy storage discharge in area a at time t in scenario d, Indicates that the energy storage in area a at time t in scene d is in a discharged state, Indicates that the electrical energy storage in area a at time t in scene d is in a non-discharge state; is the maximum discharge power of the electric energy storage; is the minimum storage capacity of electric energy storage in area a; is the maximum storage capacity of the electric energy storage in area a; K ES is the initial output coefficient of the electric energy storage; is the storage capacity of the electric energy storage in area a at the initial moment in scenario d; is the storage capacity of area a at the end of the energy storage in scenario d; Pumped storage operation constraints: In the formula is the pumped storage charging power in area a at time t in scenario d; Charging efficiency for pumped storage; is the pumped storage discharge power in area a at time t in scenario d; The discharge efficiency of pumped storage; is the pumped storage charging binary variable of area a at time t in scenario d, Indicates that the pumped storage in area a at time t in scenario d is in a charging state, It indicates that the pumped storage in area a at time t in scenario d is in a non-charging state; The maximum charging power for pumped storage; is the binary variable of pumped storage discharge in area a at time t in scenario d, Indicates that the pumped storage in area a at time t in scenario d is in a discharging state, It indicates that the pumped storage in area a at time t in scenario d is in a non-discharging state; Power transfer constraints between regions: In the formula is the maximum allowed transmission power between the i-th area and the j-th area.

6. The method for optimizing the balance of power supply and demand considering the influence of coal price according to claim 5 is characterized in that Step S5 solves the power supply and demand balance optimization model constructed in step S4, specifically including the following steps: For the power supply and demand balance optimization model constructed in step S4, the three objective formulas in the objective function are converted into a single objective formula using a fuzzy membership function, and the converted single objective formula is solved using a solver.

7. A system for realizing the power supply and demand balance optimization method considering the influence of coal price as claimed in any one of claims 1 to 6, characterized in that It includes a data acquisition module, an indicator construction module, a scenario construction module, a model construction module and a balance optimization module; the data acquisition module, the indicator construction module, the scenario construction module, the model construction module and the balance optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and upload the data information to the indicator construction module; The indicator construction module is used to construct the power generation cost factor and power generation willingness factor indicators of the coal-fired power unit based on the received data information, the acquired data information, the coal price and the on-grid electricity price, and obtain the coal-fired power generation willingness curve, and upload the data information to the scenario construction module; the scenario construction module is used to construct a set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios based on the received data information, and upload the data information to the model construction module; the model construction module is used to establish a power supply and demand balance optimization model under the constructed uncertainty scenario set based on the received data information, with the system daily power generation cost expectation, daily power shortage expectation and daily interval transmission power expectation as optimization targets, and upload the data information to the balance optimization module; The balance optimization module is used to solve the constructed power supply and demand balance optimization model according to the received data information, and complete the power supply and demand balance optimization of the target power system under the influence of coal prices according to the solution results.

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