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

Abstract

This invention discloses a method for optimizing power supply and demand balance considering the impact of coal prices. The method includes acquiring data information of the target power system; constructing power generation cost factors and power generation willingness factors for coal-fired power units and obtaining a coal-fired power generation willingness curve; constructing a set of uncertain scenarios for wind power, photovoltaic power, non-adjustable hydropower, and load power; establishing and solving a power supply and demand balance optimization model under the uncertain scenario set, using the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives; and completing the power supply and demand balance optimization of the target power system considering the impact of coal prices based on the solution results. This invention also discloses a system for implementing the aforementioned method for optimizing power supply and demand balance considering the impact of coal prices. This invention not only achieves power supply and demand balance optimization considering the impact of coal prices but also offers higher reliability and better accuracy.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, specifically relating to a method and system for optimizing the balance of power supply and demand considering the impact of coal prices. Background Technology

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

[0003] Currently, new energy power generation systems have developed rapidly, with a large number of these systems being integrated into the power grid and generating electricity. However, in terms of total electricity supply, thermal power generation still accounts for more than 60% of the total. Therefore, thermal power generation remains the most important source of electricity supply for the power system.

[0004] The study of power system supply and demand balance optimization has always been a top priority in the field. With the integration of a large number of renewable energy power systems into the grid, the research on power system supply and demand balance optimization has gradually shifted to the study of power system supply and demand balance optimization under the access of a high proportion of renewable energy power systems. Most existing supply and demand balance optimization schemes focus on the risks brought by uncertain sources of load such as wind, solar, hydro, and electricity, treating coal-fired power as a deterministic power source that meets operational constraints.

[0005] However, in extreme scenarios, such as a dramatic drop in coal prices, the conflict between "planned electricity" and "market coal" could erupt, potentially leading to a significant power supply shortage due to tight coal inventories at coal-fired power plants and a decline in their willingness to generate electricity. This situation would severely impact the safe and stable operation of the power system and the overall supply of electricity. Summary of the Invention

[0006] One of the objectives of this invention is to provide a highly reliable and accurate method for optimizing the power supply and demand balance that takes into account the impact of coal prices.

[0007] A second objective of this invention is to provide a system for implementing the aforementioned method for optimizing the power supply and demand balance considering the impact of coal prices.

[0008] The power supply and demand balance optimization method considering the impact of coal prices provided by this invention includes the following steps:

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

[0010] S2. Based on the data obtained in step S1, construct the coal power unit power generation cost factor and power generation willingness factor index based on coal price and on-grid electricity price, and obtain the coal power generation willingness curve;

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

[0012] S4. Taking the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives, establish a power supply and demand balance optimization model under the set of uncertain scenarios constructed in step S3;

[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 considering the impact of coal prices based on the solution results.

[0014] Step S1, which involves acquiring data information about 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 data on coal-fired power installed capacity, historical monthly coal price data, monthly power generation data, on-grid electricity price data, historical wind power data, historical photovoltaic data, historical non-adjustable small hydropower data, historical load data, coal-fired power parameter data, adjustable hydropower parameter data, energy storage equipment data, and pumped storage parameter data.

[0017] Step S2, based on the data obtained in step S1, constructs coal-fired power unit power generation cost factors and power generation willingness factors based on coal prices and grid-connected electricity prices, and obtains a coal-fired power generation willingness curve. Specifically, this includes the following steps:

[0018] The power generation cost factor λ of coal-fired power units is defined using the following formula. b :

[0019] λ b =c cc / c cb

[0020] In the formula c cb The unit on-grid electricity price; c cc The cost per unit of electricity generation, and c cc =c c / p c c c p is the unit coal price. c This refers to the amount of electricity that can be generated per unit of coal.

[0021] The power generation intention factor λ of coal-fired power units is defined using the following formula. w :

[0022] λ w =Pg / S c

[0023] In the formula P g Monthly power generation of coal-fired power units under the scenario where the coal price is higher than the set price; S c The installed capacity of coal-fired power units;

[0024] A coal-fired power generation intention curve is constructed with the coal-fired power unit power generation cost factor as the horizontal axis and the coal-fired power unit power generation intention factor as the vertical axis to reflect the power generation intention of coal-fired power units.

[0025] Step S3, which involves constructing a set of scenarios for wind power, photovoltaic power, unadjustable hydropower, and load power uncertainty based on the data obtained in step S1, specifically includes the following steps:

[0026] The data information obtained in step S1 is preprocessed;

[0027] Based on the maximum likelihood estimation algorithm, the historical time-series probability distribution parameters of various types of source loads in each area of ​​the target power system 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] Based on the analysis results, the Latin hypercube sampling-Cholesky decomposition method was used to construct a source-load scenario with target autocorrelation.

[0030] Based on the analysis results, the particle swarm optimization algorithm was used to screen out source-load scenarios with target cross-correlation.

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

[0032] Step S4 describes establishing a power supply and demand balance optimization model under the uncertainty scenario set constructed in step S3, with the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives. This model 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] In the formula, f1 is the expected daily power generation cost of the system; sc is the total number of scenarios in the set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios constructed in step S3; c represents the coal-fired power generation capacity at time t in scenario d; ch The unit cost of hydropower generation; c represents the hydroelectric power generation at time t in scenario d; cw Cost per unit of wind power generation; c represents the wind power generation capacity at time t in scenario d; cp Cost per unit of photovoltaic power generation; f1 represents the photovoltaic power generation at time t in scenario d; f2 represents the expected daily power shortage. f3 represents the electrical load at time t in scenario d; f3 represents the expected daily inter-regional power transmission; area represents the total number of areas in step S3. Let be the transmission power between the i-th and j-th regions at time t in scenario d; for uncertain power sources, the corresponding output value is determined by the scenario in the set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios constructed in step S3;

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

[0037] Constraints on coal-fired power plant operation:

[0038]

[0039] In the formula This is the minimum output coefficient for coal-fired power plants; The installed capacity of coal-fired power plants in region a; Let t represent the coal-fired power output of region a in scenario d at time t. K is the maximum output coefficient of coal-fired power plants. PP This is the gradient constraint coefficient; The threshold for the power generation cost factor in scenarios where the coal price is higher than a set price;

[0040] Adjustable hydropower operation constraints:

[0041]

[0042] In the formula This is the minimum output coefficient for adjustable hydropower. The adjustable hydropower installed capacity of region a; Let be the adjustable hydropower output of region a in scenario d at time t; This is the adjustable maximum output coefficient of hydropower.

[0043] Energy storage operation constraints:

[0044]

[0045] In the formula Let be the amount of electrical energy stored in region a at time t in scenario d; This represents the loss coefficient of electrical energy storage; Let be the energy storage charging power of region a in scenario d at time t; The charging efficiency of electrical energy storage; Let be the energy storage and discharge power of region a at time t in scenario d; The discharge efficiency of electrical energy storage; Let the energy storage charging of region a at time t in scenario d be a binary variable. This indicates that the electrical energy storage in region a at time t in scenario d is in a charging state. This indicates that the electrical energy storage in region a at time t in scenario d is in a non-charging state. This is the maximum charging power for electrical energy storage. Let be the binary variable representing the electrical energy storage and discharge of region a at time t in scenario d. This indicates that the electrical energy stored in region a at time t in scenario d is in a discharged state. This indicates that the electrical energy storage in region a at time t in scenario d is in a non-discharge state. This represents the maximum discharge power of the electrical energy storage. Let the minimum energy storage capacity of region a be denoted as . K represents the maximum energy storage capacity of region a. ES This represents the initial output coefficient of electrical energy storage; Let be the initial energy storage capacity of region a in scenario d; Let be the amount of electricity stored in region a at the moment the energy storage in scenario d terminates.

[0046] Pumped storage operation constraints:

[0047]

[0048] In the formula Let be the pumped storage charging power of region a at time t in scenario d; To improve the charging efficiency of pumped hydro storage; Let be the pumped storage discharge power of region a at time t in scenario d; For pumped-storage energy discharge efficiency; Let be the binary variable representing the pumped storage charging of region a at time t in scenario d. This indicates that the pumped hydro storage in region a at time t in scenario d is in a charging state. This indicates that the pumped storage of region a in scenario d at time t is in a non-charging state. This is the maximum charging power of pumped hydro storage. Let be the binary variable representing the pumped storage discharge of region a at time t in scenario d. This indicates that the pumped storage in region a at time t in scenario d is in a discharged state. This indicates that the pumped storage of region a in scenario d at time t is in a non-discharge state.

[0049] Inter-regional power transfer constraints:

[0050]

[0051] In the formula This represents the maximum allowable transmission power between region i and region j.

[0052] Step S5 involves solving 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 expressions in the objective function are transformed into a single objective expression using a fuzzy membership function, and a solver is used to solve the transformed single objective expression.

[0054] This invention also provides a system for implementing the power supply and demand balance optimization method considering the impact of coal prices, 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, indicator construction module, scenario construction module, model construction module, and balance optimization module are connected in series; 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 coal-fired power unit power generation cost factor and power generation willingness factor indicators based on the received data information and the acquired data information, based on coal prices and on-grid electricity prices, 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 uncertain scenarios for wind power, photovoltaic power, non-adjustable hydropower, and load power based on the received and acquired 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 set of uncertain scenarios, with the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives, based on the received data information, 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 based on the received data information, and complete the power supply and demand balance optimization of the target power system considering the impact of coal prices based on the solution results.

[0055] The present invention provides a method and system for optimizing power supply and demand balance that takes into account the impact of coal prices. It considers both source and load uncertainties and the impact of coal prices on power supply security risks, and constructs and solves a corresponding power supply and demand balance optimization model. Therefore, the present invention can not only achieve power supply and demand balance optimization that takes into account the impact of coal prices, but also has higher reliability and better accuracy. Attached Figure Description

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

[0057] Figure 2This is a schematic diagram of the coal-fired power generation intention 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 intention curve in an embodiment of the method of the present invention.

[0059] Figure 4 This is a schematic diagram illustrating the changing trends of the optimization target under different coal price scenarios in embodiments of the present invention.

[0060] Figure 5 This is a schematic diagram of the system power gap on the day of maximum load under different coal prices, as shown in the embodiments of the method of the present invention.

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

[0062] Figure 7 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0063] like Figure 1 The diagram shown is a flowchart of the method of the present invention: This method for optimizing the power supply and demand balance considering the impact of coal prices, disclosed in the present invention, includes the following steps:

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

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

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

[0067] S2. Based on the data obtained in step S1, construct the coal-fired power generation cost factor and power generation willingness factor indicators based on coal price and on-grid electricity price, and obtain the coal-fired power generation willingness curve; specifically including the following steps:

[0068] This step is mainly used to quantitatively analyze 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 the power supply and demand balance through the upper limit of power generation.

[0069] The power generation cost factor λ of coal-fired power units is defined using the following formula. b Used to reflect the revenue from coal-fired power generation:

[0070] λ b=c cc / c cb

[0071] In the formula c cb The unit on-grid electricity price; c cc The cost per unit of electricity generation, and c cc =c c / p c c c p is the unit coal price. c This refers to the amount of electricity that can be generated per unit of coal.

[0072] The power generation intention factor λ of coal-fired power units is defined using the following formula. w Used to reflect the willingness to generate coal-fired power:

[0073] λ w =P g / S c

[0074] In the formula P g Monthly power generation of coal-fired power units under the scenario where the coal price is higher than the set price; S c The installed capacity of coal-fired power units;

[0075] A coal-fired power generation willingness curve is constructed using the coal-fired power unit power generation cost factor as the x-axis and the coal-fired power unit power generation willingness factor as the y-axis. This curve reflects the power generation willingness of coal-fired power units. Figure 2 As shown;

[0076] In low coal price scenarios, coal-fired power output is mainly affected by factors such as load, and its upper limit is mainly constrained by equipment operation. When the coal price reaches a certain threshold, the revenue from 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 correlation between historical data of coal-fired power unit power generation cost factors and power generation willingness factors can be analyzed by Spearman coefficient analysis, and then high coal price historical scenarios can be selected to determine the threshold of high coal price scenarios.

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

[0078] The data information obtained in step S1 is preprocessed;

[0079] Based on the maximum likelihood estimation algorithm, the historical time-series probability distribution parameters of various types of source loads in each area of ​​the target power system 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] Based on the analysis results, the Latin hypercube sampling-Cholesky decomposition method was used to construct a source-load scenario with target autocorrelation.

[0082] Based on the analysis results, the particle swarm optimization algorithm was used to screen out source-load scenarios with target cross-correlation.

[0083] Based on the obtained source-load scenarios, construct a set of scenarios for wind power, photovoltaic power, unadjustable hydropower, and load power uncertainty.

[0084] S4. Taking the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives, an optimization model for power supply and demand balance is established under the set of uncertain scenarios constructed in step S3; specifically, it includes the following steps:

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

[0086]

[0087] In the formula, f1 is the expected daily power generation cost of the system; sc is the total number of scenarios in the set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios constructed in step S3; c represents the coal-fired power generation capacity at time t in scenario d; ch The unit cost of hydropower generation; c represents the hydroelectric power generation at time t in scenario d; cw Cost per unit of wind power generation; c represents the wind power generation capacity at time t in scenario d; cp Cost per unit of photovoltaic power generation; f1 represents the photovoltaic power generation at time t in scenario d; f2 represents the expected daily power shortage. f3 represents the electrical load at time t in scenario d; f3 represents the expected daily inter-regional power transmission; area represents the total number of areas in step S3. Let be the transmission power between the i-th and j-th regions at time t in scenario d; for uncertain power sources, the corresponding output value is determined by the scenario in the set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios constructed in step S3;

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

[0089] Constraints on coal-fired power plant operation:

[0090]

[0091] In the formula This is the minimum output coefficient for coal-fired power plants; The installed capacity of coal-fired power plants in region a; Let t represent the coal-fired power output of region a in scenario d at time t. K is the maximum output coefficient of coal-fired power plants. PP This is the gradient constraint coefficient; The threshold for the power generation cost factor in scenarios where the coal price is higher than a set price;

[0092] Adjustable hydropower operation constraints:

[0093]

[0094] In the formula This is the minimum output coefficient for adjustable hydropower. The adjustable hydropower installed capacity of region a; Let be the adjustable hydropower output of region a in scenario d at time t; This is the adjustable maximum output coefficient of hydropower.

[0095] Energy storage operation constraints:

[0096]

[0097] In the formula Let be the amount of electrical energy stored in region a at time t in scenario d; This represents the loss coefficient of electrical energy storage; Let be the energy storage charging power of region a in scenario d at time t; The charging efficiency of electrical energy storage; Let be the energy storage and discharge power of region a at time t in scenario d; The discharge efficiency of electrical energy storage; Let the energy storage charging of region a at time t in scenario d be a binary variable. This indicates that the electrical energy storage in region a at time t in scenario d is in a charging state. This indicates that the electrical energy storage in region a at time t in scenario d is in a non-charging state. This is the maximum charging power for electrical energy storage. Let be the binary variable representing the electrical energy storage and discharge of region a at time t in scenario d. This indicates that the electrical energy stored in region a at time t in scenario d is in a discharged state. This indicates that the electrical energy storage in region a at time t in scenario d is in a non-discharge state. This represents the maximum discharge power of the electrical energy storage. Let the minimum energy storage capacity of region a be denoted as . K represents the maximum energy storage capacity of region a. ES This represents the initial output coefficient of electrical energy storage; Let be the initial energy storage capacity of region a in scenario d; Let be the amount of electricity stored in region a at the moment the energy storage in scenario d terminates.

[0098] Pumped storage operation constraints:

[0099]

[0100] In the formula Let be the pumped storage charging power of region a at time t in scenario d; To improve the charging efficiency of pumped hydro storage; Let be the pumped storage discharge power of region a at time t in scenario d; For pumped-storage energy discharge efficiency; Let be the binary variable representing the pumped storage charging of region a at time t in scenario d. This indicates that the pumped hydro storage in region a at time t in scenario d is in a charging state. This indicates that the pumped storage of region a in scenario d at time t is in a non-charging state. This is the maximum charging power of pumped hydro storage. Let be the binary variable representing the pumped storage discharge of region a at time t in scenario d. This indicates that the pumped storage in region a at time t in scenario d is in a discharged state. This indicates that the pumped storage of region a in scenario d at time t is in a non-discharge state.

[0101] Inter-regional power transfer constraints:

[0102]

[0103] In the formula This represents the maximum allowable transmission power between region i and region j.

[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 considering the impact of coal prices based on the solution results; specifically including the following steps:

[0105] For the power supply and demand balance optimization model constructed in step S4, the three objective expressions in the objective function are transformed into a single objective expression using a fuzzy membership function. The single objective expression is then solved using a solver, and the power supply and demand balance optimization of the target power system under the influence of coal prices is completed based on the solution results.

[0106] The method of the present invention will be further described below with reference to an embodiment:

[0107] An optimization method for power supply and demand balance that considers the impact of coal prices on the willingness to generate electricity from coal-fired power plants is applied to a certain province. Historical data on coal prices, monthly power generation, on-grid electricity prices, and installed capacity of 13 coal-fired power plants in the province are analyzed. A willingness factor is calculated using monthly power generation and installed capacity. A cost factor is calculated using unit power generation cost and on-grid electricity price. Analysis of the Spearman coefficient shows a strong negative correlation between the historical data of the willingness factor and the cost factor from September to December 2021, consistent with the actual situation at that time when coal prices were too high and the willingness of coal-fired power plants to generate electricity was reduced. Historical data of 10 coal-fired power plants with Spearman coefficients below -0.8 are selected to construct a coal-fired power generation willingness curve. The power function is used to construct the coal-fired power generation willingness curve as shown below. Figure 3 As shown, the cost factor of coal-fired power generation gradually increases, while the willingness to generate electricity gradually decreases. Correlation analysis based on Spearman correlation coefficients shows that the cost factor threshold for a high coal price scenario is 0.61, at which point the coal price is approximately 1300 yuan / ton. Historical data from January to August 2021 generally did not reach this threshold, while the values ​​from September to December were generally higher than it. Under the high coal price scenario, coal-fired power units generally report the highest on-grid electricity price to maximize profits, i.e., a 20% increase on the benchmark electricity price. In this case, the power generation cost factor mainly depends on the unit coal price.

[0108] The method presented in this paper was applied to this province to analyze power shortages under different coal prices. Table 1 shows the three optimization objectives: expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission volume, under the scenario of coal prices ranging from 1200 to 1900 yuan / ton. The changing trends of the three objectives are as follows: Figure 3 As shown.

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

[0110]

[0111] Depend on Figure 4 It can be seen that as the unit coal price gradually increases, the expected daily power shortage gradually increases, while the expected daily power generation cost gradually decreases. At a coal price of 1200 yuan / ton, the threshold of the high coal price scenario is not reached; at this point, there is no expected power shortage, coal-fired power output is relatively high, and the system's daily power generation cost is relatively high. When the coal price reaches 1300 yuan / ton, the threshold of the high coal price scenario is reached, the willingness of coal-fired power units to generate electricity decreases, and the system experiences a power shortage. As the coal price continues to increase, the upper limit of coal-fired power output decreases, the power shortage gradually increases, and the power generation cost decreases as coal-fired power output decreases. Before the coal price reaches the threshold, the output of coal-fired power units in each zone is relatively high, and the inter-regional power transmission is relatively low. However, under the high coal price scenario, the output of coal-fired power units is relatively low. To minimize the power shortage and power deficiency, inter-regional 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 inter-regional power transmission increases after reaching the high coal price scenario threshold.

[0112] Figure 5 This diagram illustrates the system's power shortage under typical maximum load scenarios when coal prices are 1200 and 1800 yuan / ton. At 1200 yuan / ton, coal-fired power output follows a similar trend to load changes, with higher output during the midday and evening peak hours and lower output at night. Electric energy storage and pumped hydro storage charge at night and discharge at their peak during the midday and evening peak hours. When the coal price reaches 1800 yuan / ton, the willingness to generate electricity from coal-fired power plants decreases, leading to a decline in coal-fired power unit output. The system experiences a power shortage for most of the daytime. At night, due to reduced power output, the charging amount of electric energy storage and pumped hydro storage decreases, resulting in a reduction in peak capacity. Considering factors such as self-loss of electricity, electric energy storage and pumped hydro storage discharge between 2 PM and 3 PM, at which time there is no power shortage. The remaining electricity from pumped hydro storage is released during the evening peak hour, alleviating the pressure on electricity supply during that period. Figure 6 The system power flow distribution at maximum load is shown under different coal prices. When the coal price is 1200 yuan / ton, there is no power shortage in any region, and two regions achieve self-balancing. When the coal price reaches 1800 yuan / ton, power shortages occur in all four regions, with region 2 experiencing a larger power shortage due to its higher load. Regions 4 and 5, in addition to achieving self-balancing, also transmit power to regions 2 and 6 respectively to reduce the power shortages in each region.

[0113] like Figure 7 The diagram shows the functional modules of the system of this invention: The system disclosed in this invention for implementing the power supply and demand balance optimization method considering the impact of coal prices includes a data acquisition module, an indicator construction module, a scenario construction module, a model construction module, and a balance optimization module; these modules are connected in series. The data acquisition module acquires data information of the target power system and uploads it to the indicator construction module. The indicator construction module, based on the received and acquired data information, constructs coal-fired power unit generation cost factors and generation willingness factors based on coal prices and grid-connected electricity prices, obtains a coal-fired power generation willingness curve, and uploads the data information to the grid-connected electricity price module. The system comprises three modules: a scenario construction module and a model construction module. The scenario construction module constructs a set of uncertain scenarios for wind power, solar power, non-adjustable hydropower, and load power based on received and acquired data, and uploads this data to the model construction module. The model construction module, based on received data and with the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives, establishes a power supply and demand balance optimization model under the constructed uncertain scenario set, and uploads this data to the balance optimization module. The balance optimization module solves the constructed power supply and demand balance optimization model based on received data, and completes the power supply and demand balance optimization of the target power system considering the impact of coal prices based on the solution results.

Claims

1. A method for optimizing the power supply and demand balance considering the impact of coal prices, comprising the following steps: S1. Obtain data information about the target power system; S2. Based on the data obtained in step S1, construct the coal-fired power generation cost factor and power generation willingness factor indicators based on coal price and on-grid electricity price, and obtain the coal-fired power generation willingness curve; specifically including the following steps: The power generation cost factor of coal-fired power units is defined using the following formula. : In the formula The unit on-grid electricity price; For the unit cost of electricity generation, and , Price per unit of coal This refers to the amount of electricity that can be generated per unit of coal. The power generation intention factor of coal-fired power units is defined using the following formula. : In the formula This refers to the monthly power generation of coal-fired power units under the scenario where the coal price is higher than the set price. The installed capacity of coal-fired power units; A coal-fired power generation intention curve is constructed with the coal-fired power unit power generation cost factor as the horizontal axis and the coal-fired power unit power generation intention factor as the vertical axis to reflect the power generation intention of coal-fired power units. S3. Based on the data obtained in step S1, construct a set of scenarios for wind power, photovoltaic power, non-adjustable hydropower, and load power uncertainty. S4. Taking the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives, establish a power supply and demand balance optimization model under the uncertainty scenario set constructed in step S3; specifically including the following steps: The following formula is used as the objective function of the power supply and demand balance optimization model: In the formula This represents the expected daily power generation cost of the system. The total number of scenarios in the set of wind power, photovoltaic, non-adjustable hydropower, and load power uncertainty scenarios constructed in step S3; Let t be the coal-fired power generation capacity in scenario d at time t; The unit cost of hydropower generation; Let t be the hydropower generation capacity at time t in scenario d; Cost per unit of wind power generation; Let t be the wind power generation capacity in scenario d at time t; Cost per unit of photovoltaic power generation; Let t be the photovoltaic power generation at time t in scenario d; Expected daily power shortage; Let t be the electrical load in scenario d at time t; Expected daily inter-regional power transmission; This represents the total number of regions in step S3; Let be the transmission power between the i-th and j-th regions at time t in scenario d; for uncertain power sources, the corresponding output value is determined by the scenario in the set of wind power, photovoltaic, non-adjustable hydropower and load power uncertainty scenarios constructed in step S3; 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 considering the impact of coal prices based on the solution results.

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

3. The method for optimizing power supply and demand balance considering the impact of coal prices according to claim 2, characterized in that... Step S3, which involves constructing a set of scenarios for wind power, photovoltaic power, unadjustable hydropower, and load power uncertainty based on the data obtained in step S1, specifically includes the following steps: The data information obtained in step S1 is preprocessed; Based on the maximum likelihood estimation algorithm, the historical time-series probability distribution parameters of various types of source loads in each area of ​​the target power system 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. Based on the analysis results, the Latin hypercube sampling-Cholesky decomposition method was used to construct a source-load scenario with target autocorrelation. Based on the analysis results, the particle swarm optimization algorithm was used to screen out source-load scenarios with target cross-correlation. Based on the obtained source-load scenarios, a set of scenarios for wind power, photovoltaic power, non-adjustable hydropower, and load power uncertainty is constructed.

4. The method for optimizing power supply and demand balance considering the impact of coal prices according to claim 3, characterized in that... Step S4, which uses the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives, establishes a power supply and demand balance optimization model under the set of uncertain scenarios constructed in step S3. Specifically, it also includes the following steps: The following formula is used as the constraint condition for the power supply and demand balance optimization model: Constraints on coal-fired power plant operation: In the formula This is the minimum output coefficient for coal-fired power plants; The installed capacity of coal-fired power plants in region a; Let t represent the coal-fired power output of region a in scenario d at time t. This is the maximum output coefficient of coal-fired power plants; This is the slope rate constraint coefficient; The threshold for the power generation cost factor in scenarios where the coal price is higher than a set price; Adjustable hydropower operation constraints: In the formula This is the minimum output coefficient for adjustable hydropower. The adjustable hydropower installed capacity of region a; Let be the adjustable hydropower output of region a in scenario d at time t; This is the adjustable maximum output coefficient of hydropower. Energy storage operation constraints: In the formula Let be the amount of electrical energy stored in region a at time t in scenario d; This represents the loss coefficient of electrical energy storage; Let be the energy storage charging power of region a in scenario d at time t; The charging efficiency of electrical energy storage; Let be the energy storage and discharge power of region a at time t in scenario d; The discharge efficiency of electrical energy storage; Let the energy storage charging of region a at time t in scenario d be a binary variable. This indicates that the electrical energy storage in region a at time t in scenario d is in a charging state. This indicates that the electrical energy storage in region a at time t in scenario d is in a non-charging state. This is the maximum charging power for electrical energy storage. Let be the binary variable representing the electrical energy storage and discharge of region a at time t in scenario d. This indicates that the electrical energy stored in region a at time t in scenario d is in a discharged state. This indicates that the electrical energy storage in region a at time t in scenario d is in a non-discharge state. This represents the maximum discharge power of the electrical energy storage. Let the minimum energy storage capacity of region a be denoted as . Let be the maximum energy storage capacity of region a; This represents the initial output coefficient of electrical energy storage; Let be the initial energy storage capacity of region a in scenario d; Let be the amount of electricity stored in region a at the moment the energy storage in scenario d terminates. Pumped storage operation constraints: In the formula Let be the pumped storage charging power of region a at time t in scenario d; To improve the charging efficiency of pumped hydro storage; Let be the pumped storage discharge power of region a at time t in scenario d; For pumped-storage energy discharge efficiency; Let be the binary variable representing the pumped storage charging of region a at time t in scenario d. This indicates that the pumped hydro storage in region a at time t in scenario d is in a charging state. This indicates that the pumped storage of region a in scenario d at time t is in a non-charging state. This is the maximum charging power of pumped hydro storage. Let be the binary variable representing the pumped storage discharge of region a at time t in scenario d. This indicates that the pumped storage in region a at time t in scenario d is in a discharged state. This indicates that the pumped storage of region a in scenario d at time t is in a non-discharge state. Inter-regional power transfer constraints: In the formula This represents the maximum allowable transmission power between region i and region j.

5. The power supply and demand balance optimization method considering the impact of coal prices according to claim 4, characterized in that... Step S5 involves solving 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 expressions in the objective function are transformed into a single objective expression using a fuzzy membership function, and a solver is used to solve the transformed single objective expression.

6. A system for implementing the power supply and demand balance optimization method considering the impact of coal prices as described in any one of claims 1 to 5, 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, indicator construction module, scenario construction module, model construction module, and balance optimization module are connected in series; 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 power generation cost factors and power generation willingness factors for coal-fired power units based on received and acquired data, and on the basis of coal prices and grid-connected electricity prices, and obtain the coal-fired power generation willingness curve, and upload the data to the scenario construction module; the scenario construction module is used to construct a set of scenarios for wind power, photovoltaic, non-adjustable hydropower and load power uncertainty based on received and acquired data, and upload the data to the model construction module; the model construction module is used to establish a power supply and demand balance optimization model under the constructed set of uncertainty scenarios, with the expected daily power generation cost, expected daily power shortage, and expected daily inter-regional power transmission as optimization objectives based on received data, and upload the data to the balance optimization module; The balance optimization module is used to solve the constructed power supply and demand balance optimization model based on the received data information, and to complete the power supply and demand balance optimization of the target power system considering the impact of coal prices based on the solution results.

Images