A low-carbon power supply method based on a power distribution network system

Abstract

The application discloses a low-carbon power supply method based on a power distribution network system, in which a power supply center acquires an electricity load and a predicted electricity load of a current period of the power distribution network, generates a total evaluation basis of a power plant according to the electricity load and the predicted electricity load of the current period, determines a load period state of the current period according to the electricity load of the current period of the power distribution network, and determines a power supply mode of the current period in a plurality of game mode power supply modes according to the load period state of the current period. The power supply mode of the appropriate game mode is selected through the load period state of the current period, so that the load fluctuation can be more flexibly coped with, the carbon emission amount of the power supply process is reduced while the power supply stability of the power distribution network is ensured, and the method has stronger practicability and robustness.

Description

Technical Field

[0001] This invention relates to the field of power supply in power distribution network systems, and more particularly to a low-carbon power supply method based on power distribution network systems. Background Technology

[0002] Maintaining a low-carbon power supply is increasingly important, not only because it urgently addresses global climate change, but also because it is a core element in achieving sustainable development goals. With the signing of the Paris Agreement and the establishment of carbon neutrality targets by various countries, the power industry, as one of the major sources of carbon emissions, faces unprecedented pressure to reduce emissions. Traditional thermal power generation relies on fossil fuels such as coal and natural gas, generating large amounts of carbon dioxide (CO2) and other pollutants during the power generation process, exacerbating the greenhouse effect and causing serious air pollution problems. At the same time, the rapid development and cost reduction of renewable energy technologies such as photovoltaics and wind power have provided feasible technological paths for low-carbon power supply. Furthermore, increased public environmental awareness, the rise of green finance, and the maturity of carbon trading markets have further promoted the power industry's transformation towards low-carbon development. However, while pursuing low-carbon development, the stability of power supply load remains a core requirement of the power system.

[0003] Unstable power supply loads can lead to grid frequency fluctuations, voltage instability, and even large-scale power outages, severely impacting industrial production and daily life. Therefore, developing new power supply methods and technologies that balance low-carbon development with stable power supply is not only of significant environmental importance but also an inevitable choice to meet policy requirements, market demands, and achieve energy transition.

[0004] Existing technologies rely on distributed control based on real-time carbon emission incremental rates, using a hierarchical architecture and consensus algorithms to minimize carbon emissions during power supply. However, they do not introduce dynamic adjustment strategies or design differentiated control modes to address the differences in load characteristics between peak and off-peak electricity consumption periods.

[0005] For example, a distributed low-carbon control method for isolated AC / DC hybrid microgrid groups is disclosed on the Chinese patent website, with application number 202411555714.9. This patent's technical solution does not design differentiated control modes to address the differences in load characteristics between peak and off-peak electricity consumption periods, resulting in poor ability to cope with load fluctuations. Furthermore, the patent's solution uses "equal carbon emission incremental rate" as the optimization objective, which is essentially a static optimization problem and does not consider the dynamic game relationships among multiple stakeholders. In complex scenarios, such as conflicts of interest among multiple microgrids, a fixed consistency protocol has a high probability of leading to non-global optimum. Summary of the Invention

[0006] The purpose of this invention is to reduce carbon emissions during the power supply process while ensuring a stable power load.

[0007] Another objective of this invention is to select different power supply modes based on the current load conditions, so that the power supplier can respond more flexibly to fluctuations in electricity load and improve its ability to cope with load fluctuations during the power supply process.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] The power supply center obtains the current and predicted power load of the distribution network, and generates the power plant's overall evaluation base based on the current and predicted power load. The power supply center determines the load period status of the current period based on the current power load of the distribution network. Based on the load period status of the current period, the power supply center determines the power supply mode for the current period from several game-theoretic power supply modes.

[0010] Preferably, the power supply method includes: a stable carbon emission power supply method; in the stable carbon emission power supply method, a bidding power volume game is conducted until the total evaluation base of the power plant converges; if the bidding power volume game ends, a carbon emission game is conducted until the total evaluation base of the power plant converges; if the carbon emission game ends, the power plant supplies power to the distribution network.

[0011] Preferably, the power supply method includes: a stable carbon balance power supply method; In the aforementioned stable carbon balance power supply mode, a comprehensive carbon-electricity game is conducted until the overall evaluation base of the power plant converges; if the comprehensive carbon-electricity game ends, the power plant supplies power to the distribution network.

[0012] Preferably, in the bidding for electricity volume game, the constraints on the power plant do not include renewable energy constraints; in the carbon emission game, the constraints on the power plant are the renewable energy constraints.

[0013] Preferably, in the bidding power volume game, the iteration method of the power plant's total evaluation base includes: the weighted sum of the scores of each power plant in this round of the bidding power volume game, with respect to the bidding power volume.

[0014] Preferably, in the carbon emission game, the iteration method for the overall rating base of the power plant is: the weighted sum of the scores of each power plant in this round of the carbon emission power game regarding the carbon factor.

[0015] Preferably, in the comprehensive carbon emission game, the iteration method for the overall evaluation base of the power plant includes: the weighted sum of the scores of each power plant in this round of comprehensive carbon emission game, with respect to the carbon emission factor and the bid electricity volume.

[0016] Preferably, the process of the power plant supplying power to the distribution network includes: the power supply center generating power dispatch information based on the game result, transmitting the power dispatch information to the power plant, and the power plant transmitting power to the distribution network based on the power dispatch information.

[0017] Preferably, the convergence condition of the power plant total evaluation base includes: the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in the current round of the game is less than the convergence threshold.

[0018] Preferably, the method for obtaining the predicted electricity load includes: predicting the electricity load through a four-stage distribution network load prediction architecture to obtain the predicted electricity load.

[0019] The beneficial effects of this invention are that by selecting a suitable power supply mode based on the load status of the current period, it can more flexibly cope with load fluctuations, reduce carbon emissions during the power supply process while ensuring the stability of the power distribution network, and has stronger practicality and robustness. Attached Figure Description

[0020] Figure 1 This is a flowchart of the low-carbon power supply method involved in the present invention.

[0021] Figure 2 This is a flowchart of the carbon-stabilized power supply method involved in the present invention.

[0022] Figure 3 This is a flowchart of the carbon-stabilized and balanced power supply method involved in the present invention.

[0023] Figure 4 This is a schematic diagram of a power distribution network system. Detailed Implementation

[0024] Example 1: This example discloses the low-carbon power supply method involved in the present invention.

[0025] The power supply center obtains the current period's electricity load, predicted electricity load, and target carbon factor. Based on the current period's electricity load, it determines the current period's load status. Based on the current period's load status, it determines the power supply mode under different game modes. Finally, it generates the initial power plant total evaluation base based on the current period's electricity load and predicted electricity load.

[0026] It should be noted that the carbon factor is the amount of carbon emissions generated per kilowatt-hour of electricity.

[0027] For example, in natural gas turbine generator sets, a natural gas generator set with a combustion efficiency of 30% emits 723 grams of carbon per kilowatt-hour and has an electrical carbon factor of 0.723 kg CO2 / kWh; a natural gas generator set with a combustion efficiency of 50% emits 434 grams of carbon per kilowatt-hour and has an electrical carbon factor of 0.434 kg CO2 / kWh.

[0028] The power supply center predicts the electricity load based on historical data, thereby obtaining the predicted electricity load.

[0029] The electricity load forecasting method is as follows.

[0030] By constructing a four-stage architecture that includes a data preprocessing layer, a feature mining layer, a model training layer, and a dynamic optimization layer, accurate prediction of short-term load in the distribution network can be achieved.

[0031] First, in the data preprocessing layer, the improved local weighted regression interpolation method (LOESS-LSTM) is used to process missing data, combined with wavelet packet decomposition technology with adaptive threshold to eliminate noise interference, and the user load curves are subjected to fine cluster analysis by K-means algorithm to form several typical electricity consumption pattern libraries.

[0032] At the feature engineering level, a multi-dimensional feature matrix is ​​constructed, including: time-series features such as sliding window statistics, Fourier transform periodic components, and empirical mode decomposition intrinsic mode functions; spatial topology such as, but not limited to, node voltage sensitivity matrix and feeder transmission capacity constraint factor; environmental features such as cubic spline interpolation surface of temperature-humidity-precipitation and spatiotemporal distribution field of light intensity; and economic behavior features such as heat map of group activities based on mobile phone signaling and moving average sequence of industrial and commercial production index.

[0033] The model architecture adopts a hybrid neural network structure. The bottom layer uses 3D-CNN to extract the correlation patterns of spatial-temporal-environment features. The middle layer constructs a bidirectional gated recurrent unit (Bi-GRU) to capture long-term and short-term temporal dependencies. The top layer introduces an attention mechanism to dynamically weight key features. At the same time, a physical constraint module is connected in parallel, embedding the node power balance equation and the line thermal stability limit as regularization terms.

[0034] To improve the model's dynamic adaptability, the training method is divided into a two-stage training mechanism, including an offline stage and an online stage.

[0035] In the offline phase, transfer learning techniques are employed to pre-train the base model using historical data from the source domain.

[0036] The Deep Deterministic Policy Gradient (DDPG) algorithm is deployed in the online phase. It updates model parameters in real time by interacting with the digital twin system of the distribution network and establishes a prediction error feedback compensation mechanism. When a special event such as a typhoon warning or a sudden epidemic is detected, the Bayesian probabilistic graphical model is automatically triggered to reconstruct the load scenario.

[0037] The trained model is used to predict electricity load.

[0038] During a certain period, the power supply center obtains the power load and target carbon factor of the distribution network system. Based on the size of the power load, it determines the power supply mode of the power supply center to the distribution network system, and determines the power plant's total evaluation base based on the predicted power load and the power load during that period.

[0039] The initial overall assessment baseline for power plants during this period is: Among them, Q i Let Q be the electricity load predicted by the power supply center for the i-th time period. t q represents the electricity load predicted by the power supply center for this time period. t This represents the electricity load for that time period. d is the power plant's overall assessment baseline adjustment parameter. T is the number of time intervals.

[0040] The power supply methods include: a stable carbon reduction power supply method and a stable carbon balance power supply method.

[0041] The stable and carbon-reducing power supply method reduces carbon emissions during high-load periods while ensuring stable power supply.

[0042] The carbon-stabilizing and balanced power supply mode ensures stable power supply and reduces carbon emissions during non-high-load periods.

[0043] The method for determining the load period status is as follows.

[0044] The power supply center determines the load status of the current time period based on the size of the power load of the distribution network system at the current time period. Based on the load status of the current time period, it determines the power supply mode among several different power supply modes in a game-theoretic framework.

[0045] If the electricity load is greater than the peak electricity load threshold, the current period is considered a high load period; if the electricity load is less than the peak electricity load threshold, the current period is considered a low load period.

[0046] The power supply center determines the power supply method for different game modes based on the power load of the distribution network.

[0047] If the current load period is a high load period, the power supply center will use a stable and carbon-reducing power supply mode; if the current load period is a low load period, the power supply center will use a stable and balanced power supply mode.

[0048] The current period is a high-load period, requiring priority to ensure power supply stability before considering carbon emissions during the power supply process. Therefore, a stable and carbon-reducing power supply approach needs to be implemented to ensure stable power supply during peak electricity demand while minimizing carbon emissions during the power supply process.

[0049] The current period is a low-load period, and the requirements for power supply stability are lower compared to high-load periods. Therefore, a stable and balanced power supply mode needs to be implemented, ensuring that the power plant meets both the requirements for power supply stability and low carbon emissions during the off-peak electricity consumption period, based on the relative importance of power supply stability and low carbon emissions.

[0050] In the stable and carbon-reducing power supply mode, each power plant competes for electricity volume. After the electricity volume competition ends, a carbon emission competition is held. After the carbon emission competition ends, the power supply center generates power dispatch information based on the competition results and transmits it to the generator units of each power plant. The generator units of each power plant then transmit the power to the distribution network based on the power dispatch information.

[0051] In the carbon-stabilized and balanced power supply mode, each power plant engages in a comprehensive carbon-electricity game. After the comprehensive carbon-electricity game ends, the power supply center generates power dispatch information based on the game results and transmits it to the generator units of each power plant. The generator units of each power plant then transmit power to the distribution network based on the power dispatch information.

[0052] Example 2 discloses the stable carbon reduction power supply method described in Example 1.

[0053] The power supply method involved in this embodiment is as follows: Power plants compete in bidding for electricity volume until the overall baseline of power plant evaluation in the bidding process stabilizes. After the bidding process concludes, the bidding volume of each power plant is obtained, and carbon emission competition is conducted based on the bidding volume until the overall baseline of power plant evaluation in the carbon emission competition stabilizes. After the competition concludes, the power supply center performs power dispatch based on the competition results.

[0054] The specific process of the power supply method involved in this embodiment is as follows.

[0055] Each power plant uses its initial total power plant rating as its score. Each power plant generates its bidding volume based on its own score and constraints, and then engages in a bidding game.

[0056] In the bidding for electricity volume, each power plant's score is related to the amount of electricity it bids. If the ratio of a power plant's bid volume to the total bid volume of all power plants is greater than the power plant's maximum power generation ratio, the power plant's score will decrease; if the ratio of a power plant's bid volume to the total bid volume of all power plants is less than the power plant's maximum power generation ratio, the power plant's score will increase.

[0057] The scoring expression for the i-th power plant during the t-th time interval is: in, Let i be the score of the i-th power plant in the n-th round of bidding for electricity in time period t. This represents the base value for the total evaluation of power plants in the nth round of bidding for electricity volume during time period t. Let i be the electricity volume bid by the i-th power plant in the n-th round of bidding for electricity volume in time period t. Let m be the maximum power generation ratio of the i-th power plant in time period t, m be the number of power plants participating in the game, and β be the penalty coefficient for bidding on electricity volume.

[0058] The maximum power generation ratio of the i-th power plant during the t-th time period is: in, for, Let be the predicted power generation value of the renewable energy generating unit of the i-th power plant during the time period t. Let be the energy storage capacity of the i-th power plant during the t-th time period. Let be the maximum interruption load of the i-th power plant at the k-th interruption level during the t-th time period.

[0059] After each power plant engages in a round of bidding for electricity volume, the power supply center generates a score for each power plant. Based on the scores of each power plant and the bidding volume in this round of bidding, the power supply center generates an iterative overall evaluation base for the power plants.

[0060] The iterative steps for the overall power plant rating base are: the weighted sum of the scores of each power plant in this round of bidding for electricity volume, in terms of carbon emissions.

[0061] The numerical expression for the iterative power plant overall evaluation base is: Let represent the bidding volume of the i-th power plant in the nth round of bidding during the t-th time period. Let be the score of the i-th power plant after the nth round of bidding for electricity during the t-th time period.

[0062] If the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of bidding is less than the convergence threshold, the bidding for electricity ends, and the power plant total evaluation base in this round of bidding is used as the initial power plant total evaluation base for the carbon emission game. If the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of bidding is greater than the convergence threshold, each power plant adjusts its bidding electricity based on the score after this round of bidding. After each power plant adjusts its bidding electricity, the next round of bidding for electricity is conducted based on the adjusted bidding electricity of each power plant and the iterative power plant total evaluation base, until the power plant total evaluation base converges.

[0063] The goal of each power plant in the bidding process is to optimize its overall coefficient of bidding volume, while taking into account the power generation of each power plant and ensuring the stability of power supply.

[0064] During the bidding process for electricity volume, if a power plant's bid exceeds its generating capacity, it may lead to a decrease in the overall bidding coefficient, causing the power plant to adjust its bid and reduce its total bid volume. To a certain extent, this will ensure that the bid volumes of each power plant are within their respective generating capacity, thus guaranteeing the stability of the power supply process.

[0065] In the carbon emission game, the total evaluation base of the power plants in the last round of bidding is used as the total evaluation base of the power plants in the carbon emission game. Each power plant generates the power generation ratio of renewable energy generator units and the power generation ratio of gas turbine generator units according to the constraints, the generated bidding power volume, and the scores of each power plant. Based on the bidding power volume, the power generation ratio of renewable energy generator units and the power generation ratio of gas turbine generator units, the carbon factor corresponding to the bidding power volume is generated, thereby obtaining the carbon emissions of each power plant.

[0066] In carbon emission game theory, each power plant's score is related to its corresponding carbon factor. If the carbon factor corresponding to the bid volume of a power plant is greater than the geometric mean of the target carbon factor and the average carbon factor of all power plants, the power plant's score will decrease. If the carbon factor corresponding to the bid volume of a power plant is less than the geometric mean of the target carbon factor and the average carbon factor of all power plants, the virtual power plant score corresponding to that power plant will decrease.

[0067] In the carbon emissions game, the score of the i-th power plant is: in, Let be the score of the i-th power plant in the nth round of the carbon emission game within time period t. This represents the base score for power plants during the carbon emission game process within time period t. Let C be the carbon factor corresponding to the bidding electricity volume of the i-th power plant in the nth round of carbon emission game within time period t. min The target electrocarbon factor is α, where α is the penalty coefficient. Let be the average value of the electricity carbon factor dataset for each power plant in the nth round of the carbon emission game during the t-th time period.

[0068] After each power plant engages in a round of carbon emission negotiation, it adjusts the proportion of renewable energy generation based on the outcome of the negotiation, and generates a corresponding carbon factor for each power plant based on the bid volume and the adjusted proportion of renewable energy generation.

[0069] After each power plant engages in a round of carbon emission game, the power supply center generates a score for each power plant. Based on the scores of each power plant after this round of carbon emission game and the carbon factor of each power plant in this round of carbon emission game, the power supply center generates an iterative overall evaluation base for the power plants.

[0070] The iterative steps for the overall power plant rating base are: the weighted sum of the scores of each power plant in this round of carbon emission game regarding the carbon factor of electricity.

[0071] The iterative expression for the power plant overall evaluation cardinality is: Let be the carbon factor of the electricity bid by the i-th power plant in the nth round of carbon emission game during the t-th time period. Let represent the score of the i-th power plant after the nth round of carbon emission game within the t-th time period.

[0072] If the absolute difference between the iterative power plant overall assessment baseline and the power plant overall assessment baseline of this round of carbon emission game is less than the convergence threshold, the carbon emission game ends. If the absolute difference between the iterative power plant overall assessment baseline and the power plant overall assessment baseline of this round of carbon emission game is greater than the convergence threshold, each power plant adjusts its carbon emission factor based on its own score after this round of carbon emission game. After each power plant adjusts its carbon emission factor, the next round of carbon emission game is conducted based on the adjusted carbon emission factor of each power plant and the iterative power plant overall assessment baseline, until the power plant overall assessment baseline converges.

[0073] In the process of carbon emission competition, each power plant aims to achieve the optimal carbon emission score while ensuring power supply stability and reducing the carbon factor of electricity during the power supply process.

[0074] In the carbon emission game, if a power plant has a high carbon factor, it may lead to a decrease in the overall carbon emission coefficient, causing the power plant to adjust its carbon factor and reduce it. Under the premise of ensuring power supply stability, the carbon factor of each power plant tends to decrease, thereby reducing carbon emissions during the power supply process.

[0075] Based on the outcome of the game, the power supply center obtains the bidding power volume of each power plant and the power generation ratio of renewable energy generator units, and then performs power dispatching on the renewable energy generator units and non-renewable energy generator units of each power plant. The generator units of each power plant transmit power to the distribution network system according to the power dispatching information.

[0076] The technical solution of this embodiment can ensure stable power supply while reducing carbon emissions from power plants during the power supply process, especially during periods of high electricity load.

[0077] Example 3 discloses the carbon-stabilized and balanced power supply method described in Example 1.

[0078] The power supply method involved in this embodiment is as follows: each power plant conducts several rounds of comprehensive power carbon game until the overall evaluation base of the power plant tends to stabilize, and the power supply center performs power dispatching based on the game results.

[0079] The specific process of the power supply method involved in this embodiment is as follows.

[0080] Each power plant uses its initial total power plant rating as its initial score. Each power plant generates its bid volume and the power generation ratio of its renewable energy generators based on its own score and constraints. Each power plant also generates its own carbon factor based on its bid volume and the power generation ratio of its renewable energy generators.

[0081] In the comprehensive carbon emission game, the scores of each power plant are related to the bid volume and carbon emissions. Specifically, if a power plant's carbon emission factor is greater than the geometric mean of the target carbon emission factor and the average carbon emission factor of all power plants, and the ratio of the bid volume to the total bid volume of all power plants is greater than the power plant's maximum power generation ratio, then the power plant's score will be reduced.

[0082] If a power plant's carbon factor is less than the geometric mean of the target carbon factor and the average carbon factor of all power plants, and the ratio of the bid volume to the total bid volume of all power plants is less than the power plant's maximum power generation ratio, then the power plant's score will be increased.

[0083] If a power plant's carbon factor is greater than the geometric mean of the target carbon factor and the average carbon factor of all power plants, and the ratio of the bid volume to the total bid volume of all power plants is less than the power plant's maximum power generation ratio, then the power plant's score will decrease if the score due to the decrease in carbon factor is greater than the score due to the increase in bid volume, and the power plant's score will increase if the score due to the decrease in carbon factor is less than the score due to the increase in bid volume.

[0084] If a power plant's carbon factor is less than the geometric mean of the target carbon factor and the average carbon factor of all power plants, and the ratio of the bid volume to the total bid volume of all power plants is greater than the power plant's maximum power generation ratio, then the power plant's score will increase if the score due to the increase in carbon factor is greater than the score due to the decrease in bid volume; otherwise, the power plant's score will decrease if the score due to the increase in carbon factor is less than the score due to the decrease in bid volume.

[0085] The score for the i-th power plant is: in, This serves as the base for the overall evaluation of power plants in the nth round of the comprehensive electricity-carbon game. Let C be the carbon factor of the i-th power plant during time period t. min For the target electrocarbon factor, The bidding volume for each power plant. This represents the maximum power generation ratio of each power plant. Let be the average value of the carbon emission factor dataset for each power plant in the nth round of the carbon emission game within the t-th time period. α and β are penalty coefficients.

[0086] Since α>β, the scores of each power plant are most affected by the bidding volume of each power plant in the comprehensive electricity carbon game. During the comprehensive electricity carbon game, there will be a bias to adjust the bidding volume of power plants within the power generation capacity of the power plants, so as to improve the power supply stability of the power plants.

[0087] When α < β, each power plant is most affected by its own carbon factor in the comprehensive carbon game. During the comprehensive carbon game, there will be a tendency to reduce the carbon factor of the power plant and improve its low-carbon performance.

[0088] α = β. In the comprehensive carbon emission game, each power plant is affected by its own carbon emission factor and the amount of electricity it bids for in the same way. In the comprehensive carbon emission game, power plants simultaneously and equally enhance their power supply stability / low carbon emission.

[0089] It should also be noted that α+β=1, and 0<α<1, 0<β<1.

[0090] The target power supply stability of the distribution network is obtained during the current time period. The power supply center determines the penalty coefficient α and penalty coefficient β based on the target power supply stability.

[0091] If the target power supply stability of the distribution network is high, the power supply stability requirements of the power plant are also high, then the penalty coefficient α is greater than the penalty coefficient β. If the target power supply stability of the distribution network is low, the power supply stability requirements of the power plant are also low. Under the premise of meeting the power supply stability requirements, carbon emissions during the power supply process should be reduced as much as possible, and the penalty coefficient α is less than the penalty coefficient β. If the target power supply stability of the distribution network reaches a certain value, making the power supply stability and low carbon emissions of the power plant equally important, then the adjustment parameter α and the adjustment parameter β are the same.

[0092] The expression for adjusting parameter α is: The expression for adjusting parameter β is:

[0093] Among them, S t Let δ represent the target power supply stability of the distribution network during time period t. δ is the adjustment parameter.

[0094] As described in Example 2 The same applies, so I won't go into details here.

[0095] After each power plant engages in a comprehensive game of electricity and carbon emissions, a score is generated for each power plant.

[0096] Based on the scores of each power plant after this round of comprehensive carbon emission competition, the adjusted carbon emission factor and the adjusted bidding volume in this round of comprehensive carbon emission competition, the power supply center generates an iterative overall evaluation base for power plants.

[0097] The iteration method for the overall evaluation base of power plants is: the weighted sum of the scores of each power plant in this round of comprehensive carbon emission competition, which is related to the carbon emission factor and the bid volume.

[0098] The expression for the iterative power plant overall evaluation base is: Let be the carbon factor of the i-th power plant in the nth round of the comprehensive carbon game during the t-th time period. Let represent the bidding volume of the i-th power plant in the nth round of the comprehensive electricity-carbon game during the t-th time period. Let represent the score of the i-th power plant in the nth round of the comprehensive electricity-carbon game during the t-th time period.

[0099] After one round of comprehensive carbon emission negotiation, if the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of comprehensive carbon emission negotiation is less than the convergence threshold, the comprehensive carbon emission negotiation ends. If the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of comprehensive carbon emission negotiation is greater than the convergence threshold, each power plant adjusts its bidding volume and carbon emission factor based on its own score after this round of comprehensive carbon emission negotiation. After each power plant adjusts its bidding volume and carbon emission factor, the next round of comprehensive carbon emission negotiation is conducted based on the adjusted bidding volume, the adjusted carbon emission factor, and the iterative power plant total evaluation base, until the power plant total evaluation base converges.

[0100] In the comprehensive game of electricity carbon, the scores of each power plant are affected by both the bid volume and the electricity carbon factor. Therefore, in order to optimize their comprehensive scores, each power plant will try to ensure the stability of its power supply and the amount of electricity generated, while also ensuring that the bid volume is within its own power generation capacity. The power generation ratio of each power plant's renewable energy generating units will tend to increase, which will reduce the electricity carbon factor of each power plant.

[0101] Based on the game results, the power supply center obtains the bidding power and renewable energy generation ratio of each power plant, and then performs power dispatching on the renewable energy generator sets and non-renewable energy generator sets of each power plant. The generator sets of each power plant transmit power to the distribution network system according to the power dispatching information.

[0102] The power supply method described in this embodiment can ensure stable power supply during off-peak hours and reduce carbon emissions during the power supply process.

[0103] Example 4 discloses the method by which each power plant adjusts its bidding electricity volume and carbon factor during the process of the stable base and carbon reduction power supply method described in Example 2. (See attached document.) Figure 2 .

[0104] The power supply center generates the overall evaluation base for power plants and obtains the power load for that period. Each power plant generates the bidding volume based on the overall evaluation base.

[0105] The overall evaluation base of the power plant is used as the score for each power plant, and the comprehensive coefficient of the bidding volume of each power plant is related to the score of each power plant.

[0106] Each power plant calculates the optimal comprehensive coefficient for the bidding volume based on the constraints, and generates the bidding volume accordingly.

[0107] After generating their bid volumes, each power plant engages in a bidding game. Following this bidding game, each power plant generates a score. The power supply center then generates an iterative overall power plant rating based on each power plant's bid volume and the scores generated after this round of bidding game.

[0108] Each power plant calculates the optimal bidding volume comprehensive coefficient based on its own score in this round of bidding volume game, thereby generating the bidding volume corresponding to the optimal bidding volume comprehensive coefficient.

[0109] If the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of bidding is less than the convergence threshold, the bidding for electricity ends. If the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of bidding is greater than the convergence threshold, each power plant adjusts its bidding electricity based on its own score after this round of bidding. After each power plant adjusts its bidding electricity, the next round of bidding for electricity is conducted based on the adjusted bidding electricity of each power plant and the iterative power plant total evaluation base, until the power plant total evaluation base converges.

[0110] The process of adjusting the bidding volume is as follows: For each power plant, if the power plant's score increases after this round of bidding volume game, the power plant does not need to solve for the optimal bidding volume comprehensive coefficient, and the adjusted bidding volume is consistent with the bidding volume in this round of bidding volume game; if the power plant's score decreases after this round of bidding volume game, the optimal bidding volume is solved based on the bidding volume of each power plant and the power plant's total evaluation base, and the bidding volume corresponding to the optimal bidding volume comprehensive coefficient is obtained.

[0111] After the bidding for electricity volume concludes, the bidding volume for each power plant is generated, and carbon emission competition is conducted based on the bidding volume for each power plant.

[0112] The total evaluation base of the power plant's electricity volume in the last round of bidding is used as the score for each power plant, and the comprehensive carbon emission coefficient is related to the score of each power plant.

[0113] Each power plant calculates the optimal comprehensive carbon emission coefficient to generate the power generation ratio of renewable energy generator units, and then generates the corresponding electric carbon factor based on the power generation ratio of renewable energy generator units.

[0114] After generating carbon emission factors, each power plant engages in carbon emission game. Following the carbon emission game, a score is generated for each power plant. The power supply center then uses the scores of each power plant after this round of carbon emission game and the total evaluation base of the power plants generated in this round of carbon emission game iterations to determine the overall evaluation base.

[0115] If the absolute difference between the iterative power plant overall assessment baseline and the power plant overall assessment baseline of this round of carbon emission game is less than the convergence threshold, the carbon emission game ends, and the power supply center performs power dispatch according to the game result. The generator units of each power plant transmit power to the distribution network according to the power dispatch information. If the absolute difference between the iterative power plant overall assessment baseline and the power plant overall assessment baseline of this round of carbon emission game is greater than the convergence threshold, then each power plant adjusts its carbon factor according to its own score after this round of carbon emission game.

[0116] The adjustment process for the carbon factor is as follows: For each power plant, if the power plant's score increases after this round of carbon emission game, the power plant will not solve for the optimal comprehensive carbon emission coefficient, and the adjusted bidding volume will be consistent with the bidding volume in this round of carbon emission game; if the power plant's score decreases after this round of carbon emission game, the power plant will solve for the optimal comprehensive carbon emission coefficient, obtain the power generation ratio of renewable energy generator units corresponding to the optimal comprehensive carbon emission coefficient, and generate the carbon factor corresponding to the optimal comprehensive carbon emission coefficient based on the bidding volume and the solved power generation ratio of renewable energy generator units.

[0117] In the bidding for electricity volume, the comprehensive coefficients for the bidding volume of each power plant are as follows.

[0118] The expression for the comprehensive coefficient of bidding electricity is: Bp = Q t,i *P i,t , where P i,t Let Q be the self-rating of the i-th power plant in time period t. t,i Let represent the bidding volume of the i-th power plant during the t-th time period.

[0119] The comprehensive coefficient of bidding electricity volume reflects the power generation of each power plant to a certain extent.

[0120] Equipment depreciation and fixed cost amortization depend on power generation. If a power plant generates a high amount of electricity, the unit cost of generating electricity during operation will be lower.

[0121] Secondly, some equipment in power plants requires frequent adjustments when operating at low loads (such as the need to inject oil to assist combustion when the boiler is operating at low loads), which increases fuel waste and reduces power generation efficiency. Such "efficiency penalties" can be avoided when operating at high loads.

[0122] Therefore, power plants tend to increase their own power generation during the power supply process.

[0123] When a power plant bids for a large amount of electricity, if the bid exceeds its own power generation capacity, the power plant's score will be lowered, which will lead to a decrease in the overall coefficient of the power plant's bid.

[0124] Therefore, solving for the optimal bidding power comprehensive coefficient is actually balancing the power plant's power generation and power supply stability, taking into account the power plant's power generation while improving the power plant's power supply stability.

[0125] It should be noted that the solution to the optimal bidding volume comprehensive coefficient is: the solution to the maximum bidding volume comprehensive coefficient.

[0126] In the bidding for electricity volume, the constraints for each power plant are as follows.

[0127] Gas turbine operating constraints include ramp-up constraints and start-stop constraints for the power generation of the gas turbine generator set.

[0128] Its constraint expression is: binary variables and This represents the state, startup state, and shutdown state of the gas turbine generator set of the i-th power plant. and This sets the maximum downhill and maximum output constraints for the gas turbines of the i-th power plant during adjacent settlement weeks. This constraint ensures that the gas turbine generator set is in a single state of startup or shutdown within each time period, thereby avoiding logical conflicts in operating states.

[0129] Demand response constraints include the upper limit of interruptible power consumption over a continuous time period and the upper limit of interruptible load at a certain level.

[0130] Its constraint expression is: 0≤g i,r,t ≤g i,r,max , Let g be the interrupted load of the i-th power plant during the t-th time period. i,r,max This represents the upper limit of the r-th level interruption load for the i-th power plant. The demand response constraint represents the maximum interruptible load that can be called up within a continuous time period, thus limiting the upper limit of interruptible power generation during that period. The inclusion of this constraint balances the relationship between the overall power generation bidding coefficient of power plants and power supply reliability, increasing the importance of load-side demand in the bidding process and ensuring power supply stability.

[0131] Energy storage constraints include upper and lower limits for energy storage, energy input constraints, and energy output constraints.

[0132] Its constraint expression is:

[0133] The energy storage of the i-th power plant in time period t is:

[0134] Let represent the amount of electricity stored by the energy storage system of the i-th power plant during the t-th time period. Let be the minimum stored capacity of the i-th power plant. Let be the maximum stored capacity of the i-th power plant. Let t be the amount of electricity stored by the energy storage system of the i-th power plant during the time period t. The maximum amount of electricity stored in the energy storage system of the i-th power plant. Let t be the amount of electricity output by the energy storage system of the i-th power plant during time period t. Let t be the amount of electricity output by the energy storage system of the i-th power plant during time period t. Let be the maximum output power of the energy storage system of the i-th power plant.

[0135] Bidding constraints include upper and lower limits on the amount of electricity to be bid and constraints on the electricity load.

[0136] Its constraint expression is:

[0137] Q load,t Let be the electrical load during the time period t. Let Q be the predicted power output of the renewable energy generating units of the i-th power plant during time period t. t,i Let represent the bidding volume of the i-th power plant during the t-th time period.

[0138] In the carbon emission game, the comprehensive carbon emission coefficient is as follows.

[0139] The comprehensive carbon emission coefficient is calculated by multiplying the power plant's power supply stability coefficient by its rating. The expression for the comprehensive carbon emission coefficient is: C i,t Let P be the carbon factor of the i-th power plant in time period t. i,t S represents the score of the i-th power plant during time period t. max The power plant's power supply stability is measured when only non-renewable energy generators are producing electricity, with a value ranging from 0 to 1. min This represents the power plant's power supply stability when only renewable energy generators are producing electricity, with a value ranging from 0 to 1. k is an adjustment parameter.

[0140] The carbon emission coefficient reflects the stability of power plant supply to a certain extent. The higher the carbon emission factor of a power plant, the higher the stability of its power supply, which means that the power plant relies more on non-renewable energy generators for power generation.

[0141] Therefore, the higher the power supply stability of a power plant, the higher its carbon emission factor, which leads to a decrease in the power plant's score in the carbon emission game, resulting in a decrease in the power plant's overall carbon emission coefficient, and thus a reduction in the overall carbon emission coefficient.

[0142] Therefore, solving for the optimal comprehensive carbon emission coefficient is actually about balancing low carbon emissions with power supply stability, taking into account both the low carbon emissions of the power plant and the stability of power supply, ensuring the stability of the power plant's power supply under the premise of ensuring stable power supply, and improving the stability of the power plant in the process of low carbon power supply.

[0143] The carbon factor value for each power plant is calculated as: carbon emissions generated by non-renewable energy generators divided by the amount of electricity bid.

[0144] Its expression is: Among them, CE i,avg Let Q be the carbon dioxide emissions per kilowatt-hour generated by the non-renewable energy generator unit of the i-th power plant. The variable type is scalar. i,t Let be the bidding electricity volume of the i-th power plant in time period t, and the variable type is scalar. θ is a column vector representing the power generation ratio of renewable energy generators, with each element representing the power generation ratio of different types of renewable energy generators. v is a unit column vector, with each element having a value of 1.

[0145] In the carbon emissions game, the constraints on each power plant are as follows.

[0146] The renewable energy constraint stipulates that the power generation of renewable energy generators shall not exceed the predicted value of renewable energy generators (the amount of electricity bid multiplied by the power generation ratio of renewable energy generators shall not exceed the predicted power generation value of renewable energy generators).

[0147] Its expression is: Where θ is the column vector of the power generation ratio of renewable energy generator sets. Let be a column vector representing the predicted power generation of renewable energy generator sets in time period t, where each element represents the predicted power generation of different types of renewable energy generator sets.

[0148] This can limit the output of renewable energy generator sets under harsh operating conditions, extend the life of renewable energy generator sets, and reduce maintenance costs.

[0149] The following section provides a more detailed explanation of how various power plants adjust the proportion of bid-bid electricity and renewable energy generation in their stable and carbon-reducing power supply methods.

[0150] Within a certain period, the power supply center generates a total evaluation baseline for power plants based on the electricity load, and uses the total evaluation baseline for power plants as the score for each power plant during that period.

[0151] Each power plant calculates the comprehensive coefficient of the optimal bidding volume based on the score and constraints for that time period using the alternating direction multiplier method, thereby obtaining the bidding volume corresponding to the optimal bidding volume score.

[0152] After each power plant generates its bid volume, a bidding game is conducted based on the bid volume and the power plant's overall evaluation baseline to generate a score for each power plant. The power supply center then generates an iterative overall evaluation baseline for each power plant based on its bid volume in this round of bidding game and its score after the bidding game.

[0153] If the absolute difference between the total power plant rating base in the iteration and the total power plant rating base in the current round of bidding power volume game is less than the convergence threshold, the bidding power volume game ends; if the absolute difference between the total power plant rating base in the iteration and the total power plant rating base in the current round of bidding power volume game is greater than the convergence threshold, each power plant adjusts its bidding power volume according to the score after the current round of bidding power volume game.

[0154] For each power plant, if its own score increases after the bidding for electricity volume, the optimal bidding volume comprehensive coefficient is not calculated, and the adjusted bidding volume is consistent with the bidding volume in this round of bidding volume game; if its own score decreases after the bidding volume game, the optimal bidding volume comprehensive coefficient is calculated, and the bidding volume is adjusted.

[0155] In the process of solving the comprehensive coefficient of the optimal bidding electricity volume, the comprehensive coefficient of the optimal bidding electricity volume is: Bp = Q t,i *P i,t Find the maximum value of BP. At this point, the power plant's score is:

[0156] in, This serves as the baseline for the overall evaluation of power plants in this round of bidding for electricity volume. Q t,i Let β be the bidding electricity quantity to be solved. β is an adjustment parameter.

[0157] The expression for BP is: The optimal bidding quantity comprehensive coefficient (max Bp) is solved under constraints by using the alternating direction multiplier method, thus obtaining the bidding quantity to be solved and the adjusted bidding quantity.

[0158] After the bidding for electricity volume concludes, each power plant generates its corresponding bid volume, and a carbon emission game is then conducted based on the bid volumes of each power plant.

[0159] The total power plant rating base generated from the last round of bidding is used as the total power plant rating base in the carbon emission game, and this total power plant rating base is used as the score for each power plant.

[0160] Each power plant, based on its own score and constraints, solves for the optimal comprehensive carbon emission coefficient using the alternating direction multiplier method, thereby obtaining the proportion of renewable energy generation and the corresponding carbon factor for each power plant.

[0161] Each power plant engages in a carbon emission game based on its carbon emission factor and overall plant rating baseline, generating a score for each plant. The power supply center then uses these scores and the carbon emission factor to generate an iterative overall plant rating baseline.

[0162] If the absolute difference between the iterative power plant overall rating base and the power plant overall rating base in this round of carbon emission game is less than the convergence threshold, then the carbon emission game ends; if the absolute difference between the iterative power plant overall rating base and the initial power plant overall rating base or the power plant overall rating base in the previous round of carbon emission game is greater than the convergence threshold, then each power plant adjusts its carbon emission factor based on the score after this round of carbon emission game.

[0163] For each power plant, if its own score increases after this round of carbon emission game, the optimal carbon emission comprehensive coefficient will not be solved, and the adjusted carbon factor will be consistent with the carbon factor in this round of carbon emission game; if its own score decreases after the carbon emission game, the optimal carbon emission comprehensive coefficient will be solved, the power generation ratio of renewable energy generators corresponding to the optimal carbon emission comprehensive coefficient will be obtained, and the carbon factor will be generated based on the bid power and the solved power generation ratio of renewable energy generators.

[0164] In the process of solving for optimal carbon emissions, the optimal comprehensive carbon emission coefficient is obtained as follows: Find the maximum value of Ce. At this point, the power plant's rating is: in, θ represents the total evaluation base of power plants in this round of bidding for electricity volume, and θ is the column vector of the power generation ratio of renewable energy generating units to be solved.

[0165] Then the expression for Ce is: and The optimal carbon emission comprehensive coefficient is solved under constraints by using the alternating direction multiplier method, which yields the power generation ratio of the renewable energy generator set to be solved, thereby obtaining the adjusted carbon factor.

[0166] If the absolute difference between the iterative power plant total assessment base and the initial power plant total assessment base or the power plant total assessment base of the previous round of carbon emission game is greater than the convergence threshold, then the power plant will participate in the next round of carbon emission game based on the adjusted power carbon factor and the iterative power plant total assessment base; if the absolute difference between the iterative power plant total assessment base and the initial power plant total assessment base or the power plant total assessment base of the previous round of carbon emission game is less than the convergence threshold, then the carbon emission game will end.

[0167] After the carbon emission game ends, the power supply center generates power dispatch information based on the game results and transmits the power dispatch information to the generator units of each power plant. Each power plant then transmits the power to the distribution network based on the power dispatch information.

[0168] It should be noted that the outcome of the game includes: the bidding electricity volume of each power plant, the power generation ratio of renewable energy generators and the power generation ratio of non-renewable energy generators; the bidding electricity volume game involved in this embodiment is the bidding electricity volume game described in Embodiment 2; the carbon emission game involved in this embodiment is the carbon emission game described in Embodiment 2.

[0169] Example 5 discloses how various power plants adjust the ratio of bid-bid electricity to renewable energy generation during the process of achieving a stable and balanced power supply mode. (See attached document.) Figure 3 .

[0170] The power supply center obtains the current power load and the predicted power load, and generates an initial power plant total evaluation base based on the current power load and the predicted power load. The initial power plant total evaluation base is used as the score for each power plant, and each power plant generates the bidding volume based on the power plant total evaluation base.

[0171] The comprehensive carbon emission coefficient of each power plant is related to the rating of each power plant, and is calculated as the sum of the power supply stability and power generation values ​​multiplied by the power plant's rating.

[0172] Each power plant calculates its optimal carbon emission coefficient to generate the bidding volume and the power generation ratio of renewable energy generators. The carbon emission factor is then generated based on the power generation ratio of renewable energy generators and the bidding volume.

[0173] Each power plant engages in a comprehensive carbon game based on its carbon factor and the amount of electricity it bids for, generating a score for each plant.

[0174] The power supply center generates an iterative overall evaluation base for each power plant based on the scores of each power plant, the bidding volume of each power plant, and the carbon factor of each power plant.

[0175] If the absolute difference between the iterative power plant overall evaluation base and the power plant overall evaluation base of this round of comprehensive carbon emission control is less than the convergence threshold, the comprehensive carbon emission control game ends, and the power supply center performs power dispatch based on the game result. If the absolute difference between the iterative power plant overall evaluation base and the power plant overall evaluation base of this round of comprehensive carbon emission control is greater than the convergence threshold, each power plant adjusts its bidding volume and carbon emission factor based on its own score after this round of comprehensive carbon emission control. After each power plant adjusts its bidding volume and carbon emission factor, the next round of comprehensive carbon emission control is conducted based on the adjusted bidding volume, the adjusted carbon emission factor, and the iterative power plant overall evaluation base, until the power plant overall evaluation base converges.

[0176] The adjustment process for the bidding volume and carbon factor is as follows: For each power plant, if the power plant's score increases after this round of comprehensive carbon factor game, the optimal comprehensive carbon factor is not calculated, and the adjusted bidding volume and carbon factor are consistent with those in this round of comprehensive carbon factor game; if the power plant's score decreases after this round of comprehensive carbon factor game, the optimal comprehensive carbon factor is calculated, and the bidding volume and carbon factor corresponding to the optimal comprehensive carbon factor are obtained.

[0177] In the comprehensive game of electricity and carbon, the comprehensive coefficient of electricity and carbon is as follows.

[0178] The comprehensive coefficient of electricity carbon emission is the sum of the comprehensive coefficient of bidding electricity volume and the comprehensive coefficient of carbon emission (the comprehensive coefficient of bidding electricity volume and the improved comprehensive coefficient of carbon emission described in Example 4).

[0179] The improved carbon emission comprehensive coefficient is as follows: In the carbon emission comprehensive coefficient described in Example 4, the bidding electricity volume is divided by the maximum power generation, replacing the bidding electricity volume variable in the carbon emission comprehensive coefficient described in Example 4.

[0180] Its expression is:

[0181] Among them, C i,t Let P be the carbon factor of the i-th power plant in time period t. i,t Let Q be the score for the i-th power plant during time period t. t,i Let Q be the bidding volume of the i-th power plant in time period t. t,max The maximum power generation of each power plant in time period t is obtained by the following method: the power supply center obtains the historical maximum power generation of each power plant in time period t, and then obtains the maximum power generation from the dataset of historical maximum power generation of each power plant in time period t.

[0182] The comprehensive carbon emission coefficient reflects, to a certain extent, the power supply stability and power generation of a power plant. In the process of the comprehensive carbon emission game, power plants tend to improve the power supply stability and power generation.

[0183] In the process of comprehensive carbon emission control, if the bidding volume of a power plant exceeds its power generation capacity and the power plant has a high carbon emission factor, the power plant's score will be reduced, which will lead to a decrease in the power plant's comprehensive carbon emission coefficient.

[0184] Therefore, solving the comprehensive coefficient of the optimal bidding power volume is actually balancing the power plant's power generation, low carbon emissions, and power supply stability. While taking into account the power plant's power generation and power supply stability, it is also improving the power plant's low carbon emissions during the power supply process, reducing the power plant's carbon factor, and thus reducing the power plant's carbon emissions during the power supply process.

[0185] It should be noted that the optimal carbon dioxide comprehensive coefficient for each power plant is obtained by finding the maximum value of the carbon dioxide comprehensive coefficient.

[0186] In the comprehensive electricity-carbon game, the constraints for each power plant include: the renewable energy constraints described in Example 4; the gas turbine operation constraints described in Example 4; the demand response constraints described in Example 4; the energy storage constraints described in Example 4; and the bidding electricity constraints described in Example 4.

[0187] The following section provides a more detailed explanation of how various power plants adjust the proportion of bid-bid electricity and renewable energy generation in the context of a stable and balanced power supply model.

[0188] The power supply center generates a total evaluation base for power plants based on the electricity load and the predicted electricity load, and uses the total evaluation base as the score for each power plant.

[0189] Each power plant, based on its own score, uses the alternating direction multiplier method to solve for the optimal comprehensive carbon coefficient under constraints. By solving for the optimal comprehensive carbon coefficient, the optimal comprehensive carbon coefficient of each power plant, the corresponding bidding electricity volume and carbon factor are obtained.

[0190] Each power plant engages in a comprehensive carbon game based on the bid volume and carbon factor. After the comprehensive carbon game is completed, a score is generated for each power plant.

[0191] The power supply center generates an iterative overall evaluation base for each power plant based on the scores of each power plant after this round of competition, the bidding volume of each power plant in this round of competition, and the carbon factor of each power plant in this round of competition.

[0192] If the absolute difference between the iterative power plant overall evaluation base and the power plant overall evaluation base of this round of comprehensive carbon emission control is less than the convergence threshold, the comprehensive carbon emission control game ends, and the power supply center conducts power dispatch based on the game results. Each power plant supplies power to the distribution network based on the power dispatch information. If the absolute difference between the iterative power plant overall evaluation base and the power plant overall evaluation base of this round of comprehensive carbon emission control is greater than the convergence threshold, each power plant adjusts its bidding volume and carbon emission factor based on its own score after this round of comprehensive carbon emission control.

[0193] The adjustment process for bidding electricity volume and carbon factor is as follows.

[0194] For each power plant, if its score increases after the comprehensive carbon game, there is no need to solve for the optimal comprehensive carbon coefficient. The adjusted bidding volume and adjusted carbon factor are consistent with the bidding volume and carbon factor in this round of comprehensive carbon game. If its score decreases after the comprehensive carbon game, the optimal comprehensive carbon coefficient is solved to obtain the bidding volume and carbon factor corresponding to the optimal comprehensive carbon coefficient.

[0195] In the process of solving the optimal comprehensive carbon coefficient, the optimal comprehensive carbon coefficient is solved, and the bidding electricity and the power generation ratio of renewable energy generator sets corresponding to the optimal comprehensive carbon coefficient are obtained, thereby obtaining the bidding electricity and carbon factor corresponding to the optimal comprehensive carbon coefficient.

[0196] That is, to find the maximum value of the comprehensive coefficient of carbon dioxide, the expression for the comprehensive coefficient of carbon dioxide is: At this point, the power plant's rating is:

[0197] Its comprehensive coefficient for carbon dioxide is:

[0198] This represents the baseline for the total carbon emissions and bidding volume of power plants in this round of evaluation. θ is the column vector representing the power generation ratio of the renewable energy generating units to be solved. Q t,i The amount of electricity to be bid is to be determined.

[0199] By using the alternating direction multiplier method, the optimal comprehensive carbon factor is solved under constraints. The bidding volume and the power generation ratio of renewable energy generator sets corresponding to the optimal comprehensive carbon factor are obtained, thereby obtaining the adjusted bidding volume and carbon factor.

[0200] After each power plant adjusts its bid volume and carbon factor, the next round of comprehensive carbon factor competition is conducted based on the adjusted bid volume, adjusted carbon factor, and iterated overall power plant evaluation base.

[0201] It should be noted that the outcome of the game includes: the bidding volume of each power plant, the power generation ratio of renewable energy generators and the power generation ratio of non-renewable energy generators.

[0202] Example 6: This example discloses a power distribution network system, see reference... Figure 4 .

[0203] The power distribution network system involved in this embodiment includes: power plants, power supply centers, and power distribution networks.

[0204] The power plant includes an information acquisition module, an optimal comprehensive coefficient solution module, and generator sets; the power supply center includes a load forecasting and acquisition module, a game theory module, and a power dispatching module; the distribution network includes a load acquisition module and power transmission lines.

[0205] In the power plant, the information acquisition module obtains the power plant's overall evaluation baseline and game identifier, and transmits the power plant's overall evaluation baseline and game identifier to the optimal comprehensive coefficient solution module. After receiving the power plant's overall evaluation baseline and game identifier, the optimal comprehensive coefficient solution module determines the power plant's game mode based on the game identifier, and calls the bidding electricity solution module, carbon emission solution module, or electricity-carbon comprehensive solution module to generate the bidding electricity and electricity-carbon factor, or the bidding electricity and electricity-carbon factor.

[0206] The bidding electricity calculation module generates the bidding electricity amount by solving for the optimal comprehensive coefficient of the bidding electricity amount.

[0207] The electric carbon factor solution module generates the electric carbon factor by solving for the optimal comprehensive carbon emission coefficient.

[0208] The electricity-carbon comprehensive game module generates the bidding electricity volume and electricity-carbon factor by solving for the optimal electricity-carbon comprehensive coefficient.

[0209] Generating units include renewable energy generating units and non-renewable energy generating units. Generating units receive power dispatch information and transmit power to the distribution network according to the power dispatch information.

[0210] In the power supply center, the functions of the load forecasting and acquisition module include: forecasting electricity load, acquiring electricity load for a certain period of time, and generating the power plant's overall evaluation baseline based on the predicted electricity load value and the real-time electricity load.

[0211] The game theory module functions as follows: acquire electricity load for a specific time period, determine the game mode based on the electricity load, generate a game identifier, and transmit the generated game identifier to the load forecasting and acquisition module. It then adjusts the game mode based on the chosen game mode, such as the bidding electricity game module, carbon emission game module, or a combined electricity and carbon game module.

[0212] The bidding volume game module conducts bidding volume game based on the bidding volume of each power plant and the total evaluation base of the power plant.

[0213] The carbon emission game module conducts carbon emission game based on the carbon factor of each power plant and the overall evaluation base of the power plant.

[0214] The comprehensive carbon game module conducts comprehensive carbon game based on the carbon factors of each power plant, the bidding volume of each power plant, and the total evaluation base of the power plants.

[0215] The power dispatch module obtains the game result from the game module, generates power dispatch information based on the game result, and transmits the power dispatch information to the generator units of the power plant.

[0216] In the power distribution network, the load acquisition module collects the electrical load of the power consumption area and transmits the load information to the load prediction and acquisition module of the power supply center. The generator sets of the power plant transmit electricity to the power transmission line, and the power transmission line transmits the electricity to the power consumption area.

[0217] Example 7 discloses the implementation method of the stable carbon reduction power supply method in the distribution network system described in Example 6. (See attached document.) Figure 4 .

[0218] In the power distribution network, the load acquisition module obtains the power load of the power consumption area and transmits the power load information to the load prediction and acquisition module of the power supply center. The load prediction and acquisition module obtains the power load information and transmits it to the game module. The game module determines the game mode based on the power load information.

[0219] In this embodiment, when the electricity load exceeds the peak electricity load threshold, the game mode determined by the game module is: to conduct bidding for electricity and carbon emission games in sequence.

[0220] After the game module determines the game mode, it generates a game identifier corresponding to the game mode and transmits the game identifier to the load forecasting and acquisition module. After receiving the game identifier and generating the power plant total evaluation base, the load forecasting and acquisition module transmits the game identifier and the initial power plant total evaluation base to the information acquisition module of each power plant.

[0221] After receiving the power plant's overall evaluation base and game identifier, the information acquisition module of each power plant transmits the power plant's overall evaluation base and game identifier to the optimal comprehensive coefficient solution module. The optimal comprehensive coefficient solution module calls the bidding electricity solution module to solve for the optimal bidding electricity comprehensive coefficient based on the game identifier, and obtains the bidding electricity corresponding to the optimal bidding electricity comprehensive coefficient.

[0222] After each power plant generates its bid volume, the optimal comprehensive coefficient solution module transmits the bid volume information to the game theory module at the power supply center. The game theory module then calls the bid volume game theory module to conduct a game of bidding volume. After one round of bidding volume game, a score is generated for each power plant. The game theory module generates an iterative overall power plant rating base based on the bid volume of each power plant and its score.

[0223] If the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of bidding power game is less than the convergence threshold, the game module ends the bidding power game; if the absolute difference between the iterative power plant total evaluation base and the power plant total evaluation base in this round of bidding power game is greater than the convergence threshold, the game module transmits the scores and adjustment flags of each power plant to the optimal comprehensive coefficient solution module of each power plant.

[0224] After receiving the adjustment flag, the optimal comprehensive coefficient solution module of each power plant adjusts the bidding electricity volume according to the score of each power plant. The adjustment process is as follows.

[0225] If the power plant's score decreases after the bidding power volume game, the optimal comprehensive coefficient solution module for that power plant calls the bidding power volume solution module to solve for the optimal bidding power volume comprehensive coefficient based on the bidding power volume information of each power plant in this round of bidding power volume game and the power plant's total evaluation base, and obtains the bidding power volume corresponding to the optimal bidding power volume comprehensive coefficient. If the power plant's score increases after the bidding power volume game, the optimal comprehensive coefficient solution module for that power plant does not call the bidding power volume solution module, does not change the bidding power volume, and the adjusted bidding power volume is the same as the power plant's bidding power volume in this round of game.

[0226] After each power plant adjusts its bidding volume, the adjusted bidding volume is transmitted to the game module of the power supply center through the optimal comprehensive coefficient solution module. The game module conducts the next round of bidding volume game based on the adjusted bidding volume of each power plant, the score of each power plant and the iterative power plant total evaluation base, until the power plant total evaluation base converges.

[0227] After the bidding for electricity volume concludes, the power supply center's game module calls the carbon emission game module to conduct a carbon emission game.

[0228] After each power plant generates its carbon emission factor, the information is transmitted to the power supply center's game theory module via a game theory module. This module then calls the carbon emission game theory module to conduct a carbon emission game based on each power plant's carbon emission factor and its overall assessment baseline. After one round of the carbon emission game, a score is generated for each power plant. The game theory module then generates an iterative overall assessment baseline for each power plant based on its score and its carbon emission factor in this round of the carbon emission game.

[0229] If the absolute difference between the iterative power plant overall assessment baseline and the power plant overall assessment baseline of this round of carbon emission game is less than the convergence threshold, the carbon emission game ends. The game module transmits the game result information to the power dispatch module. The power dispatch module generates power dispatch information for each power plant based on the game result and transmits the power dispatch information to the generator units of each power plant. The generator units of each power plant transmit power to the distribution network based on the power dispatch information, and the distribution network transmits power to the power consumption area through power transmission lines. If the absolute difference between the iterative power plant overall assessment baseline and the power plant overall assessment baseline of this round of carbon emission game is greater than the convergence threshold, the game module transmits the score information of each power plant to the optimal comprehensive coefficient solution module of each power plant. The optimal comprehensive coefficient solution module of each power plant adjusts the carbon factor based on its own score.

[0230] The process of adjusting the electrocarbon factor is as follows.

[0231] If a power plant's score decreases after the carbon emission game, the optimal comprehensive coefficient solution module for that power plant will call the carbon emission solution module to solve for the optimal carbon emission comprehensive coefficient based on the information of each power plant's carbon factor in this round of carbon emission game and the power plant's overall evaluation base, and obtain the carbon factor corresponding to the optimal carbon emission comprehensive coefficient.

[0232] If the power plant's score increases after the carbon emission game, the optimal comprehensive coefficient solution module for that power plant will not call the carbon emission solution module, will not change the carbon factor, and the adjusted carbon factor will be the same as the carbon factor in this round of the game.

[0233] After each power plant adjusts its carbon factor, the adjusted carbon factor is transmitted to the game module of the power supply center through the optimal comprehensive coefficient solution module. The game module of the power supply center then calls the carbon emission game module to conduct the next round of carbon emission game based on the adjusted carbon factor of each power plant and the iterative power plant total evaluation base, until the power plant total evaluation base converges.

[0234] Example 8 discloses the implementation method of the carbon-stabilized and balanced power supply mode in the distribution network system described in Example 6. (See attached document.) Figure 4 .

[0235] In the power distribution network, the load acquisition module obtains the power load of the power consumption area and transmits the power load information to the load prediction and acquisition module of the power supply center. The load prediction and acquisition module obtains the power load information and transmits it to the game module. The game module determines the game mode based on the power load information.

[0236] In this embodiment, when the electricity load is less than the peak electricity load threshold, the game mode determined by the game module is: to conduct a comprehensive electricity-carbon game.

[0237] After the game module generates the game identifier, it transmits the game identifier information to the load forecasting and acquisition module. After receiving the game identifier information and generating the power plant total evaluation base, the load forecasting and acquisition module uses the power plant total evaluation base as the score for each power plant and transmits the game identifier information and the score information of each power plant to the information acquisition module of each power plant.

[0238] Upon receiving the game identifier and the power plant's overall evaluation base information, the information acquisition module of each power plant calls the comprehensive carbon calculation module based on the game identifier. Based on the power plant's score, it solves the optimal comprehensive carbon coefficient under constraints and obtains the bidding electricity and carbon factor corresponding to the optimal comprehensive carbon coefficient.

[0239] After each power plant generates its bid volume and carbon factor, the bidding volume and carbon factor are transmitted to the game theory module at the power supply center via the optimal comprehensive coefficient solution module. The game theory module then calls the comprehensive carbon theory game module to conduct a comprehensive carbon theory game based on each power plant's bid volume, carbon factor, and overall evaluation base, generating a score for each power plant. Based on the scores of each power plant and its bid volume and carbon factor in the comprehensive carbon theory game, the game theory module generates an iterative overall evaluation base for the power plants.

[0240] If the absolute difference between the iterative power plant overall evaluation baseline and the power plant overall evaluation baseline of this round of comprehensive carbon emission game is less than the convergence threshold, the comprehensive carbon emission game ends. The game module transmits the game result to the power dispatch module. The power dispatch module generates power dispatch information based on the game result and transmits the power dispatch information to the generator units of each power plant. After receiving the power dispatch information, the generator units of each power plant transmit power to the distribution network according to the power dispatch information. The distribution network transmits the power to the power consumption area through power transmission lines. If the absolute difference between the iterative power plant overall evaluation baseline and the power plant overall evaluation baseline of this round of comprehensive carbon emission game is greater than the convergence threshold, the game module transmits the scores of each power plant to the comprehensive optimal score solution module of each power plant. The optimal score solution module of each power plant adjusts the bidding electricity and carbon emission factor according to its own score.

[0241] The process for adjusting the bidding electricity volume and carbon factor is as follows.

[0242] If the power plant's score increases after the comprehensive electricity-carbon game, the optimal comprehensive coefficient solution module for each power plant will not call the comprehensive electricity-carbon solution module, and will not change the bidding electricity and electricity-carbon factor. The adjusted bidding electricity and electricity-carbon factor will be consistent with the bidding electricity and electricity-carbon factor in this round of comprehensive electricity-carbon game.

[0243] If the power plant's score decreases after the comprehensive electricity carbon game, the optimal comprehensive coefficient solution module for each power plant will call the comprehensive electricity carbon solution module to solve for the optimal comprehensive electricity carbon coefficient and obtain the bidding electricity and electricity carbon factor corresponding to the optimal comprehensive electricity carbon coefficient.

[0244] After each power plant adjusts its bidding volume and carbon factor, the adjusted bidding volume and carbon factor are transmitted to the game module of the power supply center through the optimal comprehensive coefficient solution module. The game module then conducts the next round of comprehensive carbon factor game based on the adjusted bidding volume, the adjusted carbon factor, and the iterative power plant total evaluation base, until the power plant total evaluation base converges.

[0245] The alternating multiplier method is further explained below. The specific process of the alternating direction multiplier method involved in Examples 4 and 5 is as follows.

[0246] There exists an optimization problem with the objective function maxF(x) and constraints (vectors) g(x). An augmented Lagrangian function is constructed to transform the objective function into a minimum optimization problem, and its expression is as follows.

[0247] The expression is: Among them, g eq (x) represents the equality constraints in g(x), forming a column vector (g eq,i (x)≤0). g ineq g(x) is a column vector composed of inequality constraints in g(x). σ and γ are Lagrange multipliers. ρ is the penalty coefficient.

[0248] The solution process is as follows: Step 1, solve the problem, x k+1 =argmin x L(x,σ k ,γ k Step 2: Update the Lagrange multipliers, σ k+1 =σ k +ρ*g eq (x), γ k+1 =γ k +ρ*max(0,g ineq (x)). Step 3: Adjust the penalty parameter ρ as needed. For example, ρ can be gradually increased to improve the degree of constraint satisfaction. Step 4: Check the convergence condition. The convergence condition is: ||g eq (x)||<ε eq ,||g ineq (x)||<ε ineq , |F(x k+1 )-F(x k )|<ε, where ε eq With ε ineq ε is the threshold value for the constraint quantity to satisfy; ε is the convergence threshold.

[0249] If the above convergence conditions are met, the solution process ends, and x is... k+1 If the above convergence conditions are not met, return to step one as the optimal solution.

[0250] This invention has illustrated its purpose, technical solutions, and beneficial effects through specific embodiments. However, these embodiments are merely examples to demonstrate the application of the invention and do not constitute a limitation on the scope of protection of this invention. We explicitly state that any reasonable modifications, equivalent substitutions, or technical improvements guided by the spirit and principles of this invention should be included within the scope of protection of this invention. This means that as long as these changes do not deviate from the core idea and basic function of the invention, they should be protected by patent rights. The scope of protection of this invention should be broad, including all directly obvious variations as well as non-obvious innovations that a technical expert can reasonably deduce from the disclosure of this invention. This broad protection aims to promote further research and development based on this invention, while ensuring that its innovativeness and practicality are fully protected by law.

Claims

1. A low-carbon power supply method based on a power distribution network system, characterized in that, The method includes: The power supply center obtains the current and predicted power load of the distribution network and generates the power plant's overall evaluation base based on the current and predicted power load. The power supply center determines the load status of the current period based on the power load of the distribution network at the current time. The power supply center determines the power supply mode for the current time period based on the load status of the current time period and among several power supply modes in a game-theoretic model.

2. The low-carbon power supply method based on a power distribution network system according to claim 1, characterized in that, The power supply methods include: a stable and carbon-reducing power supply method; In the aforementioned stable carbon reduction power supply method, a bidding game for electricity volume is conducted until the total evaluation base of the power plant converges; If the bidding for electricity volume ends, a carbon emission game will be played until the total evaluation base of the power plant converges. If the carbon emissions game ends, the power plant will supply power to the distribution network.

3. The low-carbon power supply method based on a power distribution network system according to claim 1, characterized in that, The power supply method includes: a stable carbon balance power supply method; In the aforementioned stable carbon balance power supply mode, a comprehensive game of electricity carbon is conducted until the overall evaluation base of the power plant converges; If the aforementioned comprehensive electricity-carbon game ends, the power plant will supply power to the distribution network.

4. A low-carbon power supply method based on a power distribution network system according to claim 2, characterized in that, In the bidding for electricity, the constraints on the power plant do not include renewable energy constraints; in the carbon emission game, the constraints on the power plant include renewable energy constraints.

5. A low-carbon power supply method based on a power distribution network system according to claim 2, characterized in that, In the bidding power volume game, the iteration method of the power plant's total evaluation base includes: the weighted sum of the scores of each power plant in this round of the bidding power volume game, with respect to the bidding power volume.

6. A low-carbon power supply method based on a power distribution network system according to claim 2, characterized in that, In the carbon emission game, the iterative steps for the power plant's overall rating base include: the weighted sum of the scores of each power plant in this round of the carbon emission power game, with respect to the carbon factor.

7. A low-carbon power supply method based on a power distribution network system according to claim 3, characterized in that, In the aforementioned comprehensive carbon game, the iterative steps for determining the overall evaluation base of the power plants include: the weighted sum of the scores of each power plant in this round of the comprehensive carbon game, considering the carbon factor and the bidding electricity volume.

8. A low-carbon power supply method based on a power distribution network system according to any one of claims 2 to 7, characterized in that, The process by which the power plant supplies power to the distribution network includes: the power supply center generating power dispatch information based on the game result, transmitting the power dispatch information to the power plant, and the power plant transmitting power to the distribution network based on the power dispatch information.

9. A low-carbon power supply method based on a power distribution network system according to any one of claims 5 to 7, characterized in that, The conditions for the convergence of the power plant total rating base include: the absolute difference between the iterative power plant total rating base and the power plant total rating base in the current round of the game is less than the convergence threshold.

10. A low-carbon power supply method based on a power distribution network system according to any one of claims 1 to 7, characterized in that, The method for obtaining the predicted electricity load includes: predicting the electricity load through a four-stage distribution network load prediction architecture to obtain the predicted electricity load.

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