A multi-energy type energy supply optimization operation method, system, equipment and medium

Abstract

The present application relates to the technical field of power system optimization operation, and in particular to an optimization operation method, system, equipment and medium for energy supply of multiple energy types, including obtaining system operation data of electric energy, hydrogen energy and thermal energy, constructing a multi-scenario confidence gap decision uncertainty model, analyzing to obtain a confidence uncertainty set, constructing a cost calculation model according to a carbon trading mechanism, analyzing to obtain the impact results of carbon trading, and constructing a hydrogen-electric-heat-carbon coupled park system optimization planning model to obtain basic capacity configuration information; constructing a multi-energy type flexible load model according to the park source-storage-load system structure information and electric-hydrogen-heat multi-energy load information; obtaining park flexible load flexibility evaluation information, constructing evaluation indicators based on the evaluation information, and quantifying the evaluation indicators to obtain a flexible load demand response potential value; constructing an energy supply model based on the flexible load demand response potential value, performing energy scheduling prediction, obtaining energy scheduling plan information, and improving the utilization efficiency of energy supply of multiple energy types.

Description

Technical Field

[0001] The present application relates to the technical field of optimized operation of power systems, and in particular to an optimized operation method, system, equipment and medium for energy supply of multiple energy types. Background Art

[0002] With the transformation of energy structure and the improvement of environmental protection requirements, traditional independently operated energy systems such as electricity, heat, and hydrogen can no longer meet the needs of green and efficient energy use. In particular, the output of renewable energy such as wind and solar energy is intermittent and uncertain, which brings great challenges to the energy configuration and stable operation of the park. In terms of the utilization of renewable energy, renewable energy such as wind and solar energy is intermittent and uncertain, and changes in their output and load demand will have an adverse impact on the energy configuration of the park. Traditional energy planning methods are often unable to effectively deal with this uncertainty, resulting in low energy utilization efficiency and high carbon emissions. With the continuous development of multiple markets such as the electricity market and the carbon market, the energy regulation strategy of the park's energy supply system also needs to be adjusted accordingly.

[0003] Existing energy system configuration and regulation methods are often limited to a single energy type and lack coordination and complementarity among multiple energy sources, resulting in low energy utilization efficiency and difficulty in coping with the uncertainty of renewable energy output and load demand. In addition, traditional energy regulation strategies lack in-depth consideration of market mechanisms, especially the impact of the carbon trading market on energy configuration and regulation, making it difficult for the park energy system to adapt to the complex and changing market environment. The above problems need to be solved. Summary of the invention

[0004] In order to enable the park energy system to adapt to complex environments and improve the utilization efficiency of multi-energy type energy supply, this application provides an optimized operation method, system, equipment and medium for multi-energy type energy supply, adopting the following technical solutions:

[0005] In a first aspect, the present application provides an optimized operation method for energy supply of multiple energy types, comprising:

[0006] Obtain the system operation data of electric energy, hydrogen energy and thermal energy, build a multi-scenario confidence gap decision uncertainty model based on the system operation data, and obtain the confidence uncertainty set based on the multi-scenario confidence gap decision uncertainty model analysis.

[0007] Introduce the carbon trading mechanism, build a cost calculation model based on the carbon trading mechanism, analyze the impact of carbon trading, and build a hydrogen-electric-heat-carbon coupled park system optimization planning model based on the confidence uncertainty set and the carbon trading impact results to obtain basic information on capacity configuration;

[0008] Obtain the park's source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, and build a multi-energy type flexible load model based on the capacity configuration basic information according to the park's source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information;

[0009] Obtain the flexible load flexibility assessment information of the park, construct the assessment index according to the multi-energy type flexible load model and the flexible load flexibility assessment information of the park, and quantify the assessment index to obtain the flexible load demand response potential value;

[0010] An energy supply model is constructed based on the flexible load demand response potential value, and energy scheduling prediction is performed according to the energy supply model to obtain energy scheduling plan information.

[0011] Preferably, it also includes:

[0012] Obtain the park's carbon dioxide information, evaluate the park's carbon dioxide information based on the energy scheduling plan information, and obtain the impact results.

[0013] Preferably, it also includes:

[0014] Constraints are introduced and the cost calculation model is solved according to the constraints to obtain the impact results of carbon trading.

[0015] Preferably, the constraints include wind power and photovoltaic output constraints, unit constraints, electric boiler constraints, absorption refrigerator constraints, electric refrigerator constraints, electric energy storage constraints, and thermal storage tank constraints.

[0016] Preferably, the multi-energy type flexible load model divides the flexible load into shiftable load, interruptible load and curtailable load according to different adjustment methods.

[0017] Preferably, the flexible load demand response potential value is:

[0018]

[0019] In the formula, is the peak usage rate, is the frequency of use, For consistency of use, is the peak energy consumption, is the weight coefficient of peak usage rate, is the weight coefficient of the usage frequency, is the weight coefficient for using consistency, is the weight coefficient of energy consumption during peak period.

[0020] In a second aspect, the present application provides an optimized operation system for energy supply of multiple energy types, including:

[0021] The first acquisition module is used to acquire the system operation data of electric energy, hydrogen energy and thermal energy, build a multi-scenario confidence gap decision uncertainty model based on the system operation data, and obtain the confidence uncertainty set based on the multi-scenario confidence gap decision uncertainty model analysis.

[0022] The cost calculation module is used to introduce the carbon trading mechanism, build a cost calculation model based on the carbon trading mechanism, analyze the impact of carbon trading, and build a hydrogen-electric-heat-carbon coupled park system optimization planning model based on the confidence uncertainty set and the carbon trading impact results to obtain basic information on capacity configuration;

[0023] The second acquisition module is used to obtain the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, and build a multi-energy type flexible load model based on the capacity configuration basic information according to the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information;

[0024] The third acquisition module is used to obtain the flexibility evaluation information of the park flexible load, construct the evaluation index according to the multi-energy type flexible load model and the flexibility evaluation information of the park flexible load, and quantify the evaluation index to obtain the flexible load demand response potential value;

[0025] The energy dispatch prediction module is used to build an energy supply model based on the flexible load demand response potential value, perform energy dispatch prediction according to the energy supply model, and obtain energy dispatch plan information.

[0026] Preferably, it also includes:

[0027] The fourth acquisition module acquires the carbon dioxide information of the park, evaluates the carbon dioxide information of the park according to the energy scheduling plan information, and obtains the impact result.

[0028] In a third aspect, the present application provides an optimized operation device for energy supply of multiple energy types, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the optimized operation method for energy supply of multiple energy types as described above.

[0029] In a fourth aspect, the present application provides an optimized operating medium for energy supply of multiple energy types, wherein the computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the optimized operating method for energy supply of multiple energy types as described above when running.

[0030] In summary, compared with the prior art, the technical solution provided by this application has at least the following beneficial effects:

[0031] This application constructs a multi-scenario confidence gap decision uncertainty model by acquiring system operation data of multiple energy sources, obtains a confidence uncertainty set through model analysis, introduces a carbon trading mechanism to construct a cost calculation model, thereby analyzing the impact of carbon trading results, and then processes to obtain a hydrogen-electric-heat-carbon coupled park system optimization planning model. The basic capacity configuration information obtained by the model analysis provides a system capacity configuration basis for subsequent steps, clarifies the correlation between the park's source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, constructs a multi-energy type flexible load model, evaluates the park's flexible load flexibility assessment information, constructs evaluation indicators, quantifies the park's electricity, hydrogen, and heat load demand response potential, and then constructs an energy supply model based on the flexible load demand response potential value, performs energy scheduling prediction based on the energy supply model, obtains energy scheduling plan information execution, enables the park's energy system to adapt to complex environments, and improves the utilization efficiency of multi-energy types of energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of an optimized operation method for multi-energy type energy supply described in an embodiment of the present application.

[0033] Figure 2 It is a schematic diagram of the solution process of the adaptive harmonic aliasing multi-objective composite differential evolution algorithm described in an embodiment of the present application.

[0034] Figure 3 It is a schematic diagram of the comprehensive energy structure of the park described in the embodiment of the present application.

[0035] Figure 4 This is a schematic diagram of the MPC rolling optimization described in the embodiment of the present application.

[0036] Figure 5 This is a schematic diagram of the dynamic scrolling optimization adjustment described in an embodiment of the present application.

[0037] Figure 6 It is a schematic diagram of the park framework in which the carbon capture technology is added as described in the embodiment of the present application.

[0038] Figure 7 It is a module schematic diagram of an optimized operation system for multi-energy type power supply described in an embodiment of the present application.

[0039] Description of reference numerals:

[0040] 1. First acquisition module; 2. Cost calculation module; 3. Second acquisition module; 4. Third acquisition module; 5. Energy scheduling prediction module. DETAILED DESCRIPTION

[0041] The following combination Figure 1-Figure 7 The present application is described in further detail. The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to be limiting.

[0042] Reference Figure 1 The present application relates to an optimized operation method for multi-energy type energy supply, which specifically includes:

[0043] Step S1: Obtain system operation data of electric energy, hydrogen energy and thermal energy, build a multi-scenario confidence gap decision uncertainty model based on the system operation data, and obtain a confidence uncertainty set based on the multi-scenario confidence gap decision uncertainty model analysis.

[0044] Step S2: Introduce the carbon trading mechanism, build a cost calculation model based on the carbon trading mechanism, analyze and obtain the carbon trading impact results, and build a hydrogen-electric-heat-carbon coupled park system optimization planning model based on the confidence uncertainty set and the carbon trading impact results to obtain basic capacity configuration information;

[0045] Step S3: Obtain the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, and build a multi-energy type flexible load model based on the capacity configuration basic information according to the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information;

[0046] Step S4: obtaining the flexibility evaluation information of the park flexible load, constructing the evaluation index according to the multi-energy type flexible load model and the flexibility evaluation information of the park flexible load, and quantifying the evaluation index to obtain the flexible load demand response potential value;

[0047] Step S5: construct an energy supply model based on the flexible load demand response potential value, perform energy scheduling prediction according to the energy supply model, and obtain energy scheduling plan information.

[0048] Specifically, the present application obtains system operation data of multiple energy sources to construct a multi-scenario confidence gap decision uncertainty model. The types of multiple energy sources include but are not limited to electricity, hydrogen and thermal energy. Through model analysis, a confidence uncertainty set is obtained, and a carbon trading mechanism is introduced to construct a cost calculation model, thereby analyzing the impact of carbon trading results, and then processing to obtain a hydrogen-electric-heat-carbon coupled park system optimization planning model. The basic capacity configuration information obtained from the model analysis provides a basis for system capacity configuration for subsequent steps, clarifies the correlation between the park's source-storage-load system structure information and the electricity-hydrogen-heat multi-energy load information, constructs a multi-energy type flexible load model, evaluates the park's flexible load flexibility assessment information, constructs an evaluation index, conducts a quantitative analysis of the park's electricity, hydrogen and thermal load demand response potential, and then constructs an energy supply model based on the flexible load demand response potential value. Energy scheduling prediction is performed according to the energy supply model, and energy scheduling plan information is obtained and executed, so that the park's energy system can adapt to complex environments and improve the utilization efficiency of multi-energy types of energy supply.

[0049] The uncertainty of renewable energy output and load demand will have an adverse impact on the configuration effect of the park. As one of the implementation methods, a multi-scenario confidence gap decision uncertainty model is established to target the intermittent and uncertain nature of wind and light, and a multi-scenario cluster analysis is performed to refine the construction of a confidence uncertainty set. The carbon trading mechanism is introduced, and a cost calculation model based on step-by-step carbon trading is constructed to analyze the impact of different carbon trading mechanisms and carbon trading parameters on system planning schemes. Considering the coupling of multiple energy flows of electricity, hydrogen and heat, and the coordinated complementarity of various energy sources, a park confidence gap robust planning scheme under the carbon trading market is proposed.

[0050] Specifically, the existing robust optimization and stochastic programming methods are both insufficient. The robustness setting in robust optimization research is often conservative, and a preset uncertainty set is usually used to characterize multiple uncertainties, which makes it difficult to reflect the actual polymorphism of various random factors. For example, the random distribution characteristics of wind and solar power output have seasonal differences and asymmetry, which makes the robust evaluation too rough and difficult to reflect the true robustness; while stochastic planning simplifies the uncertainty set represented by the interval domain into several typical scenarios through scenario reduction for multi-scenario deterministic optimization, which is easy to lose interval traversal and difficult to ensure the robustness of planning. In order to overcome the drawbacks of robust optimization being too conservative and stochastic programming being insufficiently robust, Distributedly Robust Optimization (DRO) is adopted. This method is mainly divided into DRO based on moment information and DRO based on distance, and understands the optimization results of uncertain parameters under the worst probability distribution. Compared with robust optimization, DRO takes into account the probability distribution information of uncertain variables and does not require a large amount of resources to deal with extreme scenarios with extremely low probability of occurrence. Compared with stochastic programming, DRO does not require an accurate probability distribution function, and its results are more robust in a statistical sense.

[0051] First, we propose the Confidence Gap Decision Theory (CGDT), which is as follows:

[0052]

[0053] In the formula, is the confidence robustness; is the confidence level of the uncertain variable; represents probability; represents the target significance level; represents the confidence uncertainty interval, and The upper and lower range.

[0054] First, the multi-dimensional scenes are reduced by the K-mean-+ clustering algorithm, and the massive scenes of large spatiotemporal scales are subdivided into N clusters. Then, the spatiotemporal correlation and random characteristics of each component in each scene are analyzed, which can significantly reduce the complexity and diversity of the probability distribution model of uncertain variables. The weight coefficient of each scene set is calculated by the following formula :

[0055]

[0056] In the formula, For scene set The number of scenes included, is the total number of scenes.

[0057] Gaussian Mixture Model, GMM, as a linear combination of multiple Gaussian distributions, can well approximate the real probability distribution and is not limited to a single distribution. Suppose a certain item of GMM is , and its weight coefficient is , Gaussian mixture model Written as:

[0058]

[0059] Assume the confidence level of the uncertain variable is , the embodiment of the present application adopts GMM to fit the probability density function of multi-energy prediction error, and its corresponding confidence uncertainty interval is described as follows:

[0060]

[0061]

[0062] In the formula, and They are The expected and actual values ​​of the prediction errors of each energy source at each moment; and The intervals The upper and lower limits of .

[0063] In order to achieve high-quality energy utilization in the integrated energy system, the exergy efficiency is used to accurately evaluate the high-quality utilization of energy and achieve efficient utilization of electricity, heat, cold and other energies. Therefore, the following model is established:

[0064]

[0065]

[0066] Where: Exergy efficiency; and are the energy quality coefficients of heat energy and cold energy respectively. The energy quality coefficient of electric energy is 1; , , They are ambient temperature, heat load temperature and cold load temperature respectively; , , System Power values ​​of electrical load, heating load and cooling load in each period; and They are The actual energy stored after the loss of the time period electrical energy storage and thermal storage tank; and They are The actual energy released after the loss of the time period electric energy storage and thermal storage tank; for The system power purchase during the time period; and They are Wind power and photovoltaic output during the period.

[0067] As one of the implementation methods, constraints are introduced, and the cost calculation model is solved according to the constraints to obtain the carbon trading impact results. The constraints include wind power and photovoltaic output constraints, unit constraints, electric boiler constraints, absorption chiller constraints, electric chiller constraints, electric energy storage constraints, and thermal storage tank constraints.

[0068] Specifically, in this integrated energy system, it is also necessary to meet its operating economy as the main goal, so the objective function is established as follows:

[0069]

[0070] In the formula, The cost of purchasing electricity from the grid; Costs of operating and maintaining the equipment; Penalty costs for curtailing wind and solar power; The cost of carbon trading.

[0071]

[0072] In the formula, is the total running time; For this system The electricity price during the period; and They are the numbers of each component in the system and the total number of system devices; and They are Time period equipment Operation and maintenance costs and output value; and They are The unit penalty cost coefficient for wind and solar power abandonment during the period and the amount of wind and solar power abandoned.

[0073] Calculate carbon emission quota:

[0074]

[0075] In the formula, for carbon emission quotas; The carbon emission quota per unit of electricity purchased; For the system Power purchased from the external power grid during the period; It is the settlement period for carbon trading fees.

[0076]

[0077] In the formula, For carbon trading costs; The carbon trading price approved by the regulatory authorities; is the system carbon emission price growth coefficient; is the total carbon emissions of the system; The carbon emission excess range; Carbon emission quotas allocated to regulatory authorities.

[0078] Power balance constraints:

[0079]

[0080]

[0081]

[0082] In the formula, and Respectively Discharge power of time period electric energy storage and discharge power of GT units; , and Respectively The charging power of the electric energy storage, the power consumption of the electric refrigerator and the power consumption of the electric boiler during the period; , and Respectively Heat release power of GT units, heat release power of electric boilers and heat consumption power of absorption chillers during the period; and Respectively The charging and discharging power of the heat storage tank during the time period; and Respectively Refrigeration capacity of absorption chillers and electric chillers during the period.

[0083] Wind power and photovoltaic output constraints:

[0084]

[0085]

[0086] In the formula, and Represent the upper limits of wind power and photovoltaic output respectively.

[0087] GT unit constraints:

[0088]

[0089] In the formula, , , Indicates the upper and lower limits of the GT unit's power generation capacity and Generating power of GT units during the period; , and Respectively represent the upper and lower limits of the GT unit's heating power and The heating power of GT unit during the period; and It is the upper and lower limits of the GT unit output.

[0090] Electric boiler constraints:

[0091]

[0092] In the formula, , , They are the upper and lower limits of electric boiler power consumption and Power consumption of electric boilers during the time period; and They are respectively the upper and lower limits of the climbing capacity of the electric boiler.

[0093] Absorption Chiller Constraints:

[0094]

[0095] In the formula, and They are the upper and lower limits of heat consumption of absorption chillers respectively; and It is the upper and lower limits of the absorption chiller output.

[0096] Electric Refrigerator Constraints:

[0097]

[0098] In the formula, and They are the upper and lower limits of heat consumption of the electric refrigerator respectively; and It is the upper and lower limits of the climbing capacity of the electric refrigerator output.

[0099] Electric energy storage constraints:

[0100]

[0101] In the formula, for Time period electrical energy storage capacity; and The upper and lower limits of the electric energy storage capacity; and They are The charging and discharging state of the energy storage in the time period is a 0-1 variable; and The upper and lower limits of the charging power for the electric energy storage; and The upper and lower limits of charging and discharging of energy storage.

[0102] Thermal storage tank constraints:

[0103]

[0104] In the formula, for Capacity of heat storage tank during the period; and The upper and lower limits of the heat storage tank capacity; and They are The charging and discharging state of the heat storage tank during the period, which is a 0-1 variable; and The upper and lower limits of the heat storage tank charging power; and It is the upper and lower limits of the heat storage tank's heat release power.

[0105] Reference Figure 2 , and then an adaptive harmonic aliasing multi-objective composite differential evolution algorithm is used to solve it.

[0106] As one of the implementation methods, the multi-energy type flexible load model divides the flexible load into shiftable load, interruptible load and curtailable load according to different adjustment methods.

[0107] Specifically, refer to Figure 3The source-storage-load system structure of the park refers to the energy supply and storage facilities in the park, including the generation, storage and distribution of energy. The electric, hydrogen and heat multi-energy load refers to the energy demand in the park, involving the consumption of multiple energy forms such as electricity, hydrogen and heat, and there is a close correlation between the two. Due to the demand for electricity, hydrogen and heat multi-energy loads, the source-storage-load system of the park is connected to the upper power grid, and is equipped with photovoltaic power stations PV, wind power stations WT, electrolyzers ELE, hydrogen fuel cells and heat pumps and other energy supply facilities to match the energy supply of the park with the energy load. At the same time, due to the energy balance requirements of electricity, hydrogen and heat multi-energy loads, the source-storage-load system of the park is equipped with batteries, hydrogen storage tanks, heat storage tanks and other devices to meet the balance and stable supply of energy loads. The source-storage-load system of the park also takes into account the interconnection and comprehensive utilization of energy, and sets up hydrogen fuel cell cogeneration equipment CHP such as hydrogen fuel cells and heat recovery devices to promote the comprehensive utilization of energy and improve energy utilization efficiency and economy. However, the energy load in the park also has certain peaks and valleys. The embodiment of the present application can achieve a certain peak-shaving and valley-filling effect by dispatching the flexible load in the electric, hydrogen and heat load demand. The energy load is composed of important loads and flexible loads. Under the premise of ensuring the normal operation of important loads, the system can improve the economic efficiency of system operation by adjusting the flexible load on the demand side and optimizing the load curve. According to different adjustment methods, flexible loads are divided into shiftable loads, interruptible loads, and reducible loads.

[0108] The interruptible load proposed in the embodiment of the present application refers to a load that temporarily interrupts or reduces the demand for energy supply under specific circumstances. Usually, this load is related to certain non-critical activities or equipment. When the energy supply is tight or the price is high, these loads are interrupted or reduced through agreements or contracts to ensure the stability and reliability of energy supply. Including but not limited to some process equipment in industrial production that can be interrupted or reduced according to the needs of the energy system to ensure the priority and balance of energy supply.

[0109] The shiftable load proposed in the embodiment of the present application refers to the energy demand that can be shifted from the peak period to the off-peak period without changing the total energy demand. This load balances energy supply and demand by adjusting the usage time, reduces load peaks, and optimizes energy utilization. This includes but is not limited to adjusting the usage time of certain electrical equipment from peak periods to off-peak periods, such as during the day and off-peak periods such as at night, to balance energy supply and demand and reduce the pressure on the energy system.

[0110] The load that can be cut as proposed in the embodiment of the present application refers to the energy demand of the load that can be cut by agreement or contract when the energy supply is tight or the demand exceeds the supply. This load is usually related to certain non-critical activities or equipment and is cut when necessary to ensure the stability and reliability of energy supply. This includes but is not limited to lighting systems or air conditioning systems in commercial buildings that can be cut according to the needs of the energy system to cope with energy supply shortages or peak demand situations.

[0111]

[0112] In the formula, for Energy load in The total flexible load at time, It can be electricity, hydrogen, or thermal energy. , , They are Energy load in Loads that can be interrupted, reduced and shifted at any time.

[0113] Interruptible load can be realized by simply interrupting the load during the peak period and opening the load during the peak and valley period. Its model is:

[0114]

[0115] In the formula , , , For judging Energy load in A binary variable indicating whether the load is interrupted at the moment, The total amount of interrupted load allowed for energy load, the total cost of interrupted load, Energy load in Cost factor for interruptible energy load per unit time.

[0116] The shiftable load can be realized by working shifts, and its model is:

[0117]

[0118] In the formula , for Energy load in The moment the translatable load is transferred to Always remove the front and back loads, it is important to note that Before the transfer, the load is 0. After the transfer, the load is 0. , , , For judging Energy load in A binary variable indicating whether the load is shifted at the moment, The total amount of shifting load allowed for the energy load, the total cost of shifting load, Energy load in Cost factor for shiftable energy load per unit time.

[0119] The load that can be reduced can be partially reduced while meeting the needs of users, so as to meet the overall needs of the system and relieve the pressure of energy supply. The model of the load that can be reduced is:

[0120]

[0121] In the formula for Energy load in The proportion of load reduced at any time, for The amount of energy load being reduced. , , , For judging Energy load in A binary variable indicating whether the load is reduced at the moment, The total amount of load reduction allowed for energy load, the total cost of load reduction, Energy load in Cost factor for reducing energy load per unit of time.

[0122] As one implementation method, the evaluation indicators include peak usage rate, usage frequency, usage consistency and peak energy consumption.

[0123] Specifically, in order to effectively evaluate the flexibility and adjustability of flexible loads, it is necessary to establish a flexible load potential evaluation index system. This application embodiment proposes four indicators, namely, peak utilization rate, Frequency of use , Use consistency , Peak energy consumption .

[0124] Considering the peak usage time of all working hours of energy-consuming equipment, it is believed that the greater the proportion of the equipment's usage time during the peak period, the greater the potential contribution of the equipment to the peak demand response. As an evaluation indicator, its calculation formula is:

[0125]

[0126] In the formula, For energy facilities Peak usage duration, is the total usage time in a cycle, For energy facilities The power value when used, Energy-using facilities It can be determined as the minimum power value during peak usage.

[0127] The usage frequency can describe how often a device is used over a period of time. Through the analysis of historical energy consumption data, it can be seen that energy-consuming equipment is generally used at the same time period every day, but it is not necessarily used every day. Therefore, it is necessary to understand the daily usage frequency of energy-consuming equipment. As an evaluation indicator, its calculation formula is:

[0128]

[0129] In the formula, is the total number of days in the observation period, is the number of days used in the observation period. It is the minimum power value that can be judged to be used, excluding disturbance interference.

[0130] Whether the daily usage time period is the same can be used to describe the stability of the equipment and measure its operation certainty. Whether the daily average operation time of residential users' electrical equipment is consistent is an important reference value for measuring the demand response potential of the equipment. If the user's equipment usage behavior is relatively certain and the usage consistency is high, it can be considered that it has a greater demand response potential, so the design usage consistency is As an evaluation indicator, consistency quantification is used, and the calculation method is:

[0131]

[0132] Energy-using facilities The smaller the root mean square error of the deviation between the observed value and the predicted value, the higher the consistency of the residential user's electrical equipment use. At the same time, by observing the actual power equipment consumption data, it can be found that the values ​​of various electrical equipment vary greatly and the standards are different. Standardization is convenient for subsequent processing. That is to The standardized values ​​are normalized by the maximum value.

[0133] Peak energy consumption refers to the energy consumption of energy-consuming facilities during peak energy consumption periods, and is used to measure the ability of energy-consuming equipment to participate in demand response. Response capacity is used to indicate energy consumption during peak time periods, and it is believed that user equipment with high energy consumption during peak periods is more suitable for load transfer or reduction. As an evaluation indicator, its expression is:

[0134]

[0135] In the formula, , is the start time and end time of peak energy consumption, represents the potential for energy consumption during peak hours, The value is equal to the normalized value of the energy consumption potential during the peak time.

[0136] The demand response potential index system includes the above four potential scoring indicators. After analyzing the actual meaning of each indicator, it is found that there is a non-negligible correlation between the indicators. Considering the correlation between the indicators, the independent weight method is introduced to analyze the collinearity between the indicators for demand response potential, assign weights to each indicator, and then perform comprehensive calculations. If there is a high correlation between a certain indicator and other indicators, it means that the information overlap between the indicator and the other indicators is large, and the weight assigned to the indicator will be relatively low. If the correlation between a certain indicator and other indicators is low, it means that the indicator contains a large amount of information, and the weight assigned to the indicator will be higher.

[0137] As one implementation method, the flexible load demand response potential value is:

[0138]

[0139] In the formula, , , , is the weight coefficient of each flexible load index. The weight is calculated by the independent weight method to calculate the negative correlation coefficient of each index. To determine the weight, the calculation formula is:

[0140]

[0141] The larger the value, the more relevant information it has and the smaller the weight is, so taking the inverse of it gives , and then normalize it to get .

[0142]

[0143] Then sum up to get the flexible load demand response potential value of a type of energy-consuming equipment.

[0144]

[0145] The embodiments of the present application study the control variables and adjustment methods for power regulation of the park's electric, hydrogen and thermal multi-energy flexible load group, analyze the park's source-storage-load system structure and the correlation between the electric, hydrogen and thermal multi-energy loads, and study the park's multi-scenario flexible load model that can be leveled, reduced and interrupted with multiple energy types. Combined with the park's flexible load flexibility evaluation method, an indicator set characterizing the adjustable capacity of the flexible load group is constructed to quantitatively analyze the park's electric, hydrogen and thermal load demand response potential.

[0146] As one of the implementation methods, an energy supply model is constructed based on the flexible load demand response potential value, and energy scheduling prediction is performed according to the energy supply model to obtain energy scheduling plan information, specifically to study the energy control strategy of the electric, hydrogen, heat and carbon coupled park taking into account the multi-energy load demand response.

[0147] Specifically, the park-level energy supply system has the characteristics of source-load-storage integration and multi-energy complementarity, and its energy regulation technology faces challenges such as strong uncertainty. Uncertainty poses a certain threat to the safety and stability of the park's overall energy supply. The use of multiple types of energy storage such as electricity, hydrogen, and thermal energy storage and flexible loads to participate in regulation can effectively improve the energy utilization efficiency of the park.

[0148] In the embodiment of the present application, there is sufficient renewable energy such as wind and light in the park. At the same time, the park power system trades electricity with the external power grid through the substation. The hydrogen source is hydrogen produced by water electrolysis, and hydrogen is stored in hydrogen storage tanks. The heat source is hydrogen fuel cells and heat pumps, and heat energy is stored in heat storage tanks. The entire park uses water electrolysis to produce hydrogen, hydrogen fuel power generation and heat generation, and heat pumps to produce heat, so that the power system, hydrogen system, and thermal system are tightly coupled together.

[0149] The electrolytic water tank uses water and electricity to generate green energy hydrogen. The generation methods are divided into low-temperature electrolysis of water and high-temperature electrolysis of water. This project uses low-temperature electrolysis of water. The calculation method for the hydrogen production of low-temperature electrolysis of water is:

[0150]

[0151] In the formula, To generate hydrogen mass, is the Faraday efficiency at this temperature, is the molar mass of hydrogen, is the number of charges transferred in the electrolysis reaction, is the Faraday constant, is the current passing through the electrolytic cell.

[0152] Hydrogen fuel cells generate electricity, heat and water through electrochemical reactions. The electricity and heat they produce affect each other, and there is a "one increases while the other decreases" relationship. Studying their operating mechanism is very important for meeting load requirements and improving the production efficiency of hydrogen fuel cells. The calculation method for heat and electricity generation by hydrogen fuel cells is:

[0153]

[0154] In the formula, Output power for hydrogen fuel cells, is the Faraday constant, is the hydrogen supply rate, is the battery output voltage, usually 1.229V.

[0155] A heat pump is a device that uses thermodynamic principles to transfer heat from a low temperature area to a high temperature area. It achieves heat transfer by circulating a working medium, such as a refrigerant. The working principle of a heat pump is similar to that of an air conditioner, but its main purpose is heating rather than cooling. The heat generated by a heat pump is calculated as follows:

[0156]

[0157] In the formula, is the heat output per unit time of the heat pump, is the heat pump efficiency, The efficiency of powering the heat pump.

[0158] Then, the energy supply system model under multiple markets such as the electricity market and the carbon market is established. The objective function is constructed from the economic indicators, mainly from the subsequent operation and maintenance costs of energy storage equipment, the operating costs of flexible load scheduling, the risk costs of flexible load scheduling, and the carbon trading costs. The calculation method is:

[0159]

[0160] In the formula, is the objective function, For subsequent operation and maintenance costs, is the operating cost of flexible load dispatch, is the risk cost of flexible load dispatch, The cost of carbon emissions.

[0161] Operation and maintenance costs of energy storage equipment The calculation formula is as follows:

[0162]

[0163] In the formula, The cost of purchasing electricity from the distribution network, As for network loss, it is worth mentioning that the network loss here does not specifically refer to the power network, but the electric-hydrogen-heat-carbon coupled network.

[0164] Flexible load dispatching operation cost The calculation formula is as follows:

[0165]

[0166] In the formula, the flexible load can be roughly divided into three parts, namely load interruption, load reduction and load translation. , , It is the operating cost of load interruption, load reduction and load shifting.

[0167] Risk cost of flexible load dispatch The calculation formula is as follows:

[0168]

[0169] Risk cost of flexible load dispatch The cost of abandoning photovoltaic power generation and wind power generation under extreme output conditions .

[0170] Carbon emissions costs Only CO2 is considered as the main basis for calculating carbon emissions. The energy account of the park is used to obtain the carbon emission cost through the unit carbon emission coefficient of electricity purchased from the power grid and hydrogen fuel. The calculation method is:

[0171]

[0172] The embodiments of the present application include multiple constraints, including power balance constraints, safety constraints, and energy storage system constraints.

[0173]

[0174] for node The active power supplied by the upper node at the moment, For Node exist The photovoltaic access power at the moment, if the node has no photovoltaic access, then the node The value is 0, for Time Node Total load.

[0175]

[0176] , For Node The maximum and minimum allowed voltages, For transmission lines The maximum current allowed, , is the minimum and maximum value of hydrogen load, , The minimum and maximum pressures that the hydrogen network can withstand. , It is the minimum and maximum value of the heat pump heat generation temperature.

[0177]

[0178] , is an integer with a value of 0 or 1. , For energy storage system At the node The charging power and discharging power at , , , The energy storage system at node The minimum and maximum values ​​of the charging rate and the minimum and maximum values ​​of the discharge power.

[0179]

[0180] The state of charge constraint consists of three inequalities, where For Node In the moment The self-discharge rate at , is the charging and discharging efficiency of the energy storage power station, For Node The total capacity of the energy storage system, For Node exist The state of charge at the time, , For Node The maximum and minimum state of charge, usually the minimum is 10% 30%, maximum value is 80% 100%, here the minimum value is 20% and the maximum value is 80%, is a running cycle, here we take It is 24 hours.

[0181] Reference Figure 4The MPC model predictive control method can effectively handle multivariable systems, consider constraints, adapt to the nonlinear and time-varying characteristics of the system, and can perform online calculations and optimizations in a real-time environment. Its steps include system modeling, prediction, optimization, execution, and updating. This project uses the multi-time scale MPC model predictive control method to perform multi-energy flow coupling optimal scheduling control by scheduling flexible loads to improve the low-carbon economy of the electric hydrogen heat carbon coupling park.

[0182] The objective function of the multi-time scale MPC model is to minimize the system economic objective function within each rolling scheduling cycle, which is consistent with the objective function of the energy supply system model. Its constraints are consistent with the energy supply system model, and its solution is a mixed integer linear programming.

[0183] Reference Figure 5 There are also errors in actual scheduling. Therefore, the embodiment of the present application needs to superimpose a small time scale dynamic adjustment on the large time scale of rolling optimization. The objective function of this small time scale is the operating cost, such as the minimum electricity purchase cost and the unit start-stop penalty cost. It is also a mixed integer linear programming.

[0184] As one implementation method, the carbon dioxide information of the park is obtained, and the carbon dioxide information of the park is evaluated according to the energy scheduling plan information to obtain the impact result, which includes economic impact information and environmental impact information.

[0185] Specifically, refer to Figure 6 The embodiment of the present application explores the low-carbon operation strategy of the electric, hydrogen, heat and carbon coupled park energy supply system, introduces carbon capture equipment, proposes a joint flexible operation model of carbon capture and hydrogen energy utilization, captures and stores the carbon dioxide generated by the park, and evaluates the impact of the low-carbon operation strategy on the environment and economy, so as to reduce carbon emissions in the park and realize economical and environmentally friendly operation of the electric, hydrogen, heat and carbon coupled park energy supply system.

[0186] According to the different positions of carbon capture and storage technology in the operation process of thermal power plants, it can be classified into pre-combustion capture, oxygen-enriched combustion and post-combustion capture. Pre-combustion capture requires pre-treatment of the fuel, and the components containing carbon elements are stripped in advance and do not enter the combustion process. The gasifier is used to react HO2, O2 and fuel to produce synthesis gas, which consists of hydrogen and carbon monoxide. Carbon monoxide is converted into carbon dioxide through the water-gas reaction process, and CO2 is captured at this time to achieve separation. Oxygen-enriched combustion requires the use of purified oxygen produced by the air separation unit. The oxygen in the air is no longer used during the combustion process, but purified O2 reacts with the fuel to produce a combustion reaction, releasing flue gas containing HO2 and carbon dioxide. The CO2 captured after combustion is obtained by separating the flue gas of the thermal power plant. This technology can achieve the separation and enrichment of CO2.

[0187] By simultaneously introducing a solution storage device and a flue gas bypass system into a carbon capture power plant, a comprehensive flexible operation mode can be formed. Under the comprehensive flexible operation mode, the carbon capture power plant achieves the time shift of carbon capture energy consumption by adjusting the liquid storage volume of the solution storage device. During the peak load period of users, the carbon capture power plant fully absorbs carbon dioxide without capturing it. At this time, the carbon capture energy consumption is reduced, and the net output of the system is increased to alleviate the contradiction between carbon capture demand and load demand. During the load trough period, by adjusting the liquid storage volume, the carbon dioxide absorbed during the peak load period is released and captured, which increases the carbon capture energy consumption of the carbon capture power plant, thereby reducing the net output of the system to absorb more wind power output.

[0188] For the convenience of comparative analysis, this application selects a separate production system, SPS, as a reference system for the performance optimization of the integrated energy system. SPS is a traditional energy supply method, that is, the electricity, heat, and cooling loads are supplied by the power grid, boiler, and air conditioner respectively, and there is no energy storage device. The integrated energy system consists of power supply, heating, and cooling subsystems. Each subsystem contains a variety of energy supply equipment, such as prime movers, power grids, batteries and other power supply equipment, waste heat recovery devices, supplementary combustion boilers, hot water storage tanks and other heating equipment, as well as electric refrigerators, absorption refrigerators, hot water storage tanks and other cooling equipment.

[0189] This application constructs comprehensive indicators based on system investment cost, IC, and operation and maintenance cost, OM, to optimize the performance and evaluate the economic benefits of the integrated energy system CCIES with integrated carbon dioxide capture technology, as follows:

[0190]

[0191] In the formula, express The total investment cost of components; , , Represents the technology set in the system The power supply technology, cooling or heating technology and energy storage technology subset in the above table; the investment cost of each technology is the unit investment cost of the equipment The number of newly built equipment The sum of the products of is as follows:

[0192]

[0193] In the formula, power supply technology Subsets include prime movers PM and photovoltaic PV; cooling or heating technology subsets Including waste heat recovery device HRD, supplementary combustion boiler ACB, ground source heat pump GSHP, absorption chiller ABS and electric chiller EC; energy storage technology subset It includes a storage battery ES and a hot water storage tank TS.

[0194] Similarly, the investment cost of independent capacity SPS is:

[0195]

[0196] In the formula, It only includes the investment costs of cooling or heating equipment, namely gas boilers GB and electric refrigeration units EC.

[0197] Operation and maintenance costs of integrated energy systems Maintenance cost of the unit , fuel costs , Carbon emission costs and electricity purchase cost The power of the independent power generation system is all provided by the power grid. Maintenance cost of electric refrigeration unit , fuel costs , Carbon emission costs and electricity purchase cost The composition is as follows:

[0198]

[0199]

[0200]

[0201] In the formula, Unit operation and maintenance cost of all technologies in the technology subset Total number of units The sum of the products of ; Fuel unit price , fuel consumption Related; It is worth noting that under the carbon trading model, relevant departments formulate carbon emission quotas. Carbon emissions exceeding the quotas must purchase quotas based on the carbon trading price. Unused quota indicators can also be traded in the carbon market and become income; under the carbon tax model, when the carbon emission cost is defined in the form of carbon tax, By carbon emissions and carbon tax prices When the carbon emission cost is defined in the form of carbon emission quotas, and carbon emissions ,quota and carbon trading prices Cost calculations in both modes use positive values ​​to represent expenditures and negative values ​​to represent revenues.

[0202] In independent capacity systems It is expressed as:

[0203]

[0204] In the formula, and Represented as electric refrigeration machine and gas boiler unit; The amount of electricity purchased from the power grid.

[0205] The carbon emission cost difference between the integrated energy system and the independent production capacity system is defined as carbon emission reduction benefit, CEB, which is used to measure the benefits of the integrated energy system considering carbon dioxide capture technology. The calculation is as follows:

[0206]

[0207] For the emission characteristics of the system, carbon reduction rate and carbon emission intensity can be introduced as environmental indicators for analysis:

[0208]

[0209] In the formula, is the carbon reduction rate; Carbon emission intensity is the amount of carbon dioxide emissions generated per unit of GDP growth, and is used to describe the relationship between economy and carbon emissions.

[0210] Equipment operation constraints:

[0211]

[0212]

[0213] In the formula, , , They are The upper limit of the climbing rate; and They are the corresponding operating states, which are 0-1 variables; For the Energy storage capacity of similar energy storage devices; For the The natural loss rate of energy storage of energy storage devices; and are the upper limits of the corresponding variables respectively; and They are The minimum and maximum values ​​of and Respectively The charging and discharging power of energy storage devices.

[0214] Carbon capture plant constraints:

[0215]

[0216]

[0217]

[0218]

[0219] In the formula, Carbon capture unit carbon emission intensity; Carbon capture unit Total CO2 production; For carbon capture units The flue gas split ratio; Carbon capture unit Total amount of CO2 captured; Carbon capture unit The amount of CO2 to be captured supplied by the solution storage; , , , and Carbon capture unit Total output power, total output power upper limit, net output power, operating energy consumption and fixed energy consumption; For carbon capture units carbon capture efficiency; for maximum carbon capture efficiency; is the working coefficient of the compressor and regeneration tower; It is the energy consumption per unit CO2 captured.

[0220] Further reducing the carbon emissions of the park’s energy supply system will have a positive impact on improving air quality and reducing environmental pollution, benefit human health and the ecological environment, and help address climate change and improve environmental quality.

[0221] Reference Figure 7 , an optimized operation system for multi-energy type energy supply is provided for the embodiment of the present application, the system comprising:

[0222] The first acquisition module 1 is used to acquire the system operation data of electric energy, hydrogen energy and thermal energy, build a multi-scenario confidence gap decision uncertainty model according to the system operation data, and obtain a confidence uncertainty set according to the multi-scenario confidence gap decision uncertainty model analysis.

[0223] Cost calculation module 2 is used to introduce the carbon trading mechanism, build a cost calculation model based on the carbon trading mechanism, analyze the impact of carbon trading, and build a hydrogen-electric-heat-carbon coupled park system optimization planning model based on the confidence uncertainty set and the impact of carbon trading to obtain basic information on capacity configuration;

[0224] The second acquisition module 3 is used to obtain the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, and build a multi-energy type flexible load model based on the capacity configuration basic information according to the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information;

[0225] The third acquisition module 4 is used to obtain the flexibility evaluation information of the park flexible load, construct the evaluation index according to the multi-energy type flexible load model and the flexibility evaluation information of the park flexible load, and quantify the evaluation index to obtain the flexible load demand response potential value;

[0226] The energy dispatch prediction module 5 is used to construct an energy supply model based on the flexible load demand response potential value, perform energy dispatch prediction according to the energy supply model, and obtain energy dispatch plan information.

[0227] Specifically, it also includes a fourth acquisition module, which acquires the carbon dioxide information of the park, evaluates the carbon dioxide information of the park according to the energy scheduling plan information, and obtains the impact result.

[0228] An embodiment of the present application provides an optimized operation device for energy supply of multiple energy types, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the optimized operation method for energy supply of multiple energy types as described above.

[0229] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned optimization operation method for multi-energy type energy supply when running.

[0230] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and product can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0231] In several embodiments provided in this application, it should be understood that the disclosed methods, systems, devices and program products may be implemented in other ways.

[0232] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0233] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the operation of multi-energy type energy supply, characterized in that: include: Obtain the system operation data of electric energy, hydrogen energy and thermal energy, build a multi-scenario confidence gap decision uncertainty model based on the system operation data, and obtain the confidence uncertainty set based on the multi-scenario confidence gap decision uncertainty model analysis. Introduce the carbon trading mechanism, build a cost calculation model based on the carbon trading mechanism, analyze the impact of carbon trading, and build a hydrogen-electric-heat-carbon coupled park system optimization planning model based on the confidence uncertainty set and the carbon trading impact results to obtain basic information on capacity configuration; Obtain the park's source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, and build a multi-energy type flexible load model based on the capacity configuration basic information according to the park's source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information; Obtain the flexible load flexibility assessment information of the park, construct the assessment index according to the multi-energy type flexible load model and the flexible load flexibility assessment information of the park, and quantify the assessment index to obtain the flexible load demand response potential value; The energy supply model is constructed based on the flexible load demand response potential value, where the energy supply model is: , where is the objective function, For subsequent operation and maintenance costs, is the operating cost of flexible load dispatch, is the risk cost of flexible load dispatch, Carbon emission costs; operation and maintenance costs for , The cost of purchasing electricity from the distribution network, is network loss; flexible load dispatching operation cost for , , , It is the operating cost of load interruption, load reduction and load shifting; Risk cost of flexible load dispatch for , The cost of abandoned photovoltaic and wind power under extreme output conditions is calculated; the energy supply model is subject to power balance constraints, safety constraints and energy storage system constraints; energy scheduling prediction is performed based on the energy supply model, where the energy scheduling prediction processing is MPC model predictive control processing to obtain energy scheduling plan information.

2. The optimization operation method of multi-energy type energy supply according to claim 1 is characterized in that: Also includes: Obtain the park's carbon dioxide information, evaluate the park's carbon dioxide information based on the energy scheduling plan information, and obtain the impact results.

3. The optimization operation method of multi-energy type energy supply according to claim 1 is characterized in that: Also includes: Constraints are introduced and the cost calculation model is solved according to the constraints to obtain the impact results of carbon trading.

4. The optimization operation method of multi-energy type energy supply according to claim 3 is characterized in that: The constraints include wind power and photovoltaic output constraints, unit constraints, electric boiler constraints, absorption refrigerator constraints, electric refrigerator constraints, electric energy storage constraints, and thermal storage tank constraints.

5. The optimization operation method of multi-energy type energy supply according to claim 1 is characterized in that: The multi-energy type flexible load model divides flexible loads into shiftable loads, interruptible loads and curtailable loads according to different adjustment methods.

6. The optimization operation method of multi-energy type energy supply according to claim 1 is characterized in that: The flexible load demand response potential value is: In the formula, is the peak usage rate, is the frequency of use, For consistency of use, is the peak energy consumption, is the weight coefficient of peak usage rate, is the weight coefficient of the usage frequency, is the weight coefficient for using consistency, is the weight coefficient of energy consumption during peak period.

7. An optimized operation system for energy supply of multiple energy types, characterized in that: include: The first acquisition module is used to acquire the system operation data of electric energy, hydrogen energy and thermal energy, build a multi-scenario confidence gap decision uncertainty model based on the system operation data, and obtain the confidence uncertainty set based on the multi-scenario confidence gap decision uncertainty model analysis. The cost calculation module is used to introduce the carbon trading mechanism, build a cost calculation model based on the carbon trading mechanism, analyze the impact of carbon trading, and build a hydrogen-electric-heat-carbon coupled park system optimization planning model based on the confidence uncertainty set and the carbon trading impact results to obtain basic information on capacity configuration; The second acquisition module is used to obtain the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information, and build a multi-energy type flexible load model based on the capacity configuration basic information according to the park source-storage-load system structure information and the electric-hydrogen-heat multi-energy load information; The third acquisition module is used to obtain the flexibility evaluation information of the park flexible load, construct the evaluation index according to the multi-energy type flexible load model and the flexibility evaluation information of the park flexible load, and quantify the evaluation index to obtain the flexible load demand response potential value; The energy dispatch prediction module is used to build an energy supply model based on the flexible load demand response potential value, where the energy supply model is: , where is the objective function, For subsequent operation and maintenance costs, is the operating cost of flexible load dispatch, is the risk cost of flexible load dispatch, Carbon emission costs; operation and maintenance costs for , The cost of purchasing electricity from the distribution network, is network loss; flexible load dispatching operation cost for , , , It is the operating cost of load interruption, load reduction and load shifting; Risk cost of flexible load dispatch for , The cost of abandoned photovoltaic and wind power under extreme output conditions is calculated; the energy supply model is subject to power balance constraints, safety constraints and energy storage system constraints; energy scheduling prediction is performed based on the energy supply model, where the energy scheduling prediction processing is MPC model predictive control processing to obtain energy scheduling plan information.

8. The optimized operation system for multi-energy type energy supply according to claim 7 is characterized in that: Also includes: The fourth acquisition module acquires the carbon dioxide information of the park, evaluates the carbon dioxide information of the park according to the energy scheduling plan information, and obtains the impact result.

9. An optimized operation device powered by multiple energy types, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method for optimizing operation of multi-energy type energy supply according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the method for optimizing operation of multi-energy type energy supply as described in any one of claims 1-6 when running.

Images