Source-network-load-storage combined scheduling system based on multistage complex energy flow

Abstract

The invention relates to the technical field of power dispatching, in particular to a multi-stage complex energy flow-based source, network, load and storage combined dispatching system, which comprises a multi-stage complex energy flow system, a multi-stage complex energy flow system structure and a source, network, load and storage combined dispatching system. The method comprises the following steps: S1, predicting load demands and output conditions of renewable energy sources; s2, combining a prediction result and a scheduling strategy to realize energy flow control and coordination; and S3, realizing dynamic control of the system through real-time monitoring and adjustment. The method achieves remarkable effects in the aspects of energy dispatching, economic benefits, environmental protection, energy collaborative development and the like, important technical support and guarantee are provided for sustainable development of a multi-stage complex energy flow system through popularization and application of source-network-load-storage combined dispatching, an energy structure can be optimized, the reliability and quality of power supply are improved, and the method is suitable for popularization and application of the multi-stage complex energy flow system. The method is of great significance in promoting energy transformation and achieving the sustainable development goal.

Description

Technical Field

[0001] The present invention relates to a source-grid-load-storage combined dispatching system, in particular to a source-grid-load-storage combined dispatching system based on multi-level complex energy flow, belonging to the technical field of power dispatching. Background Art

[0002] As the global climate warms, countries around the world are paying more and more attention to the issue of carbon emissions. The transformation of energy to clean and low-carbon is an urgent requirement for the sustainable development of the national economy and for responding to global climate change.

[0003] In recent years, the newly installed capacity of distributed photovoltaic, natural gas, biomass energy and other power generation forms in my country has increased year by year, gradually becoming the development trend of renewable energy in the future. However, the traditional "source follows load" control method cannot dispatch distributed power sources with random output such as wind power and photovoltaic power, and it is difficult to ensure the safe and economic operation of the distribution network. At present, the wind power abandonment rate, solar power abandonment rate and water abandonment rate of new energy power generation are still at a high level. The comprehensive efficiency of the power system is not high, the coordination of the source, grid, load and storage links is not enough, and the complementary and mutual assistance of various power sources is insufficient. Deep-seated contradictions are becoming increasingly prominent, and they urgently need to be coordinated and optimized.

[0004] With the development of a new generation of smart grid technology, renewable energy and energy storage devices are connected to the grid on a large scale. The power system has shifted from unilateral transmission of power sources to bilateral interaction of "source-load", and further transformed into a complex system with multiple participants of "source-grid-load-storage", which has brought huge challenges to the existing dispatching model. In order to cope with the high volatility and intermittency of renewable energy and the uncertainty on the load side, the dispatching scheme based on coordinated interaction of "source-grid-load-storage" has become a hot topic among scholars in recent years.

[0005] It is necessary to conduct further research and adopt appropriate research methods to solve problems such as how to realize the joint dispatch of "source, grid, load and storage" of multi-level complex energy flow systems and how to improve the efficiency and reliability of the system. The importance of this research lies in that it can provide a theoretical basis and practical guidance for the dispatch and operation optimization of multi-level complex energy flow systems, and promote the application of renewable energy and the development of smart grids.

[0006] Therefore, it is urgent to improve the source-grid-load-storage combined dispatching system with multi-level complex energy flow to solve the above-mentioned problems. Summary of the invention

[0007] The purpose of the present invention is to provide a source-grid-load-storage combined scheduling system based on multi-level complex energy flow. Through the evaluation and analysis of the implementation effect of the source-grid-load-storage combined scheduling, remarkable results have been achieved in energy scheduling, economic benefits, environmental protection and energy coordinated development. The promotion and application of the source-grid-load-storage combined scheduling will provide important technical support and guarantee for the sustainable development of multi-level complex energy flow systems, help optimize the energy structure, improve the reliability and quality of power supply, and are of great significance for promoting energy transformation and achieving sustainable development goals.

[0008] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0009] A source-grid-load-storage combined dispatching system based on multi-level complex energy flow, comprising a multi-level complex energy flow system, a multi-level complex energy flow system structure and a source-grid-load-storage combined dispatching system, wherein the multi-level complex energy flow system comprises energy flow elements, energy storage equipment elements, and load demand elements at multiple levels;

[0010] The multi-level complex energy flow system structure includes energy input link, conversion link, transmission link and output link;

[0011] The dispatching method of the source-grid-load-storage combined dispatching system is:

[0012] S1. Forecasting load demand and renewable energy output: collecting historical load data and renewable energy output data, and using time series models for analysis and forecasting;

[0013] By analyzing and modeling historical data, we can obtain the forecast results of load demand and renewable energy output in the future time period;

[0014] S2. Combine the prediction results with the dispatch strategy to achieve control and coordination of energy flow. After obtaining the prediction results of load demand and renewable energy output in the future time period, formulate a reasonable energy dispatch plan;

[0015] S3. Realize dynamic control of the system through real-time monitoring and adjustment, and timely feedback of real-time data and make adjustments based on the predicted results.

[0016] Preferably, the multi-level complex energy flow system structure includes an energy source, an energy grid, an energy load and an energy storage, and the energy source, the energy grid, the energy load and the energy storage are interconnected;

[0017] The energy sources include renewable energy and traditional energy;

[0018] The energy grid includes transmission lines and substations;

[0019] The energy load includes power load and heat exchange load;

[0020] The energy storage includes batteries and heat storage devices.

[0021] Preferably, the source-grid-load-storage combined dispatching system includes a mixed integer linear dispatching model, specifically:

[0022] First, the energy source, the energy grid, the energy load and the energy storage are used as input variables of the mixed integer linear scheduling model, and corresponding constraints and objective functions are defined;

[0023] Secondly, by setting the decision variables as continuous and discrete variables, defining appropriate objective functions and constraints, and solving the mixed integer linear programming problem, the optimal energy scheduling scheme is obtained;

[0024] Finally, numerical experiments and comparative analysis are used to verify the effectiveness and feasibility of the scheduling model based on mixed integer linear programming.

[0025] Preferably, the mixed integer linear scheduling model includes determining evaluation indicators, determining indicator weights, determining evaluation levels, selecting evaluation methods and evaluation result methods.

[0026] Preferably, the determined evaluation indicators include economy, reliability, energy utilization, technology and environmental protection;

[0027] The determination of index weights includes subjective weighting method, objective weighting method and combined weighting method;

[0028] The determined evaluation levels include excellent, good, medium and poor.

[0029] Preferably, the output end for determining the evaluation index and determining the index weight is connected to the selected evaluation method, and the evaluation result method is set at the output end of the selected evaluation method and outputs the result.

[0030] Preferably, the selected evaluation method includes fuzzy comprehensive evaluation method, grey system evaluation method, data envelopment analysis method, rank sum ratio comprehensive evaluation method and Topsisi evaluation method;

[0031] The evaluation result method includes evaluating the result, refining the index, changing the weight and improving the accuracy.

[0032] Preferably, a time series model is established and modeled for the mixed integer linear scheduling model, specifically:

[0033] A quantity y(t) that changes with variable t at t1 <t2<···t n < ··· observations y(t1), y(t2), ···y(tn ),..., becomes a time series;

[0034] Among them, in the medium- and long-term load forecasting, the sampling interval of t is usually in years;

[0035] A trend forecasting method that uses several past observations of the same variable arranged in chronological order and the number of times the chronological variable appears as a weight to calculate the weighted arithmetic mean of the observations and use this number as a forecast for the future value of the variable.

[0036] The weighted moving average method catalog function is as follows:

[0037]

[0038] Among them: W1, W2, W3…Wm are the weights corresponding to m points.

[0039] Preferably, the mixed integer linear dispatch model power system dispatch problem uses the following formula:

[0040] min[sum(c1*pg+c2*psh+c3*pl)];

[0041] Among them, pg represents the output of each generator set, psh represents the current of each transmission line, pl represents the demand for electricity at each load node, and c1, c2, and c3 are the coefficients of each cost respectively.

[0042] The present invention has at least the following beneficial effects:

[0043] 1. From the perspective of economic benefits, the combined dispatch of sources, grids, loads and storage has also brought about significant benefits. By optimizing the allocation and flexible dispatch of resources at each energy node, it can reduce energy production costs and supply risks and improve overall economic benefits. At the same time, combined dispatch can also effectively reduce the peak and valley differences in energy supply and demand, further optimize the operation of the energy market, and improve the stability and sustainability of power supply.

[0044] 2. In terms of environmental protection, the combined dispatch of sources, grids, loads and storage also plays a positive role. Through reasonable dispatch and management of energy flow, energy waste and unnecessary environmental pollution can be reduced. Combined dispatch can better utilize renewable energy and clean energy, reduce dependence on traditional energy, and achieve sustainable development of energy.

[0045] 3. After the implementation of combined dispatching of sources, grids, loads and storage, it can also promote the development of multi-energy complementarity and energy interconnection. Through coordinated dispatching and flexible allocation among different energy sources, it can promote the development of multi-energy complementarity and improve the overall energy utilization efficiency. At the same time, combined dispatching can also strengthen the interconnection between energy sources, realize the sharing and optimal allocation of energy resources, and further enhance the overall benefits of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 is a flow chart of the method of the present invention;

[0048] Figure 2 is a system structure diagram of the present invention;

[0049] Figure 3 This is a model framework diagram of the present invention.

[0050] In the figure, 1. Multi-level complex energy flow system; 101. Level energy flow element; 102. Energy storage equipment element; 103. Load demand element; 2. Multi-level complex energy flow system structure; 201. Energy input link; 202. Conversion link; 203. Transmission link; 204. Output link; 205. Energy source; 206. Energy network; 207. Energy load; 208. Energy storage; 3. Source-grid-load-storage combined scheduling system; 301. Mixed integer linear scheduling model. DETAILED DESCRIPTION

[0051] The following will describe the implementation methods of the present application in detail with the help of accompanying drawings and examples, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0052] like Figure 1-Figure 3 As shown, the source-grid-load-storage combined dispatching system based on multi-level complex energy flow provided in this embodiment includes a multi-level complex energy flow system 1, a multi-level complex energy flow system structure 2, and a source-grid-load-storage combined dispatching system 3. The multi-level complex energy flow system 1 includes multiple levels of energy flow elements 101, energy storage equipment elements 102, and load demand elements 103. The multi-level complex energy flow system includes multiple levels of energy flow, energy storage equipment, load demand and other elements, and there are complex interactions between these elements. This structural feature is systematically analyzed and described to better perform system dispatching and management.

[0053] The multi-level complex energy flow system structure 2 includes energy input link 201, conversion link 202, transmission link 203 and output link 204. For example, in the power system, energy input can be different types of generators, and energy output can be power supply from the power grid to different loads. At the same time, the installation and management of energy storage equipment need to be considered to achieve energy balance and flexible scheduling. Such structural analysis can help us gain a deep understanding of the characteristics of each subsystem and its impact on the overall system.

[0054] The dispatching method of the source-grid-load-storage combined dispatching system 3 is:

[0055] S1. Forecasting load demand and renewable energy output: collecting historical load data and renewable energy output data, and using time series models for analysis and forecasting;

[0056] By analyzing and modeling historical data, we can obtain the forecast results of load demand and renewable energy output in the future time period;

[0057] S2. Combine the prediction results with the dispatching strategy to realize the control and coordination of energy flow. After obtaining the prediction results of load demand and renewable energy output in the future time period, formulate a reasonable energy dispatching plan, such as the charging and discharging characteristics of the energy storage device, the power supply capacity of the power grid, etc., so as to formulate a reasonable energy dispatching plan. By optimizing the dispatching of energy, the utilization efficiency of energy can be maximized, and the impact of energy waste and imbalance between supply and demand on the system can be avoided;

[0058] S3. Realize dynamic control of the system through real-time monitoring and adjustment. Real-time monitoring and adjustment, timely feedback of real-time data and adjustment based on forecast results. In actual operation, the load demand and output of renewable energy in the energy system are often uncertain and variable, so real-time monitoring and adjustment are necessary. The scheduling method based on time series can achieve more accurate and flexible energy flow control goals by feedback of real-time data and adjustment based on forecast results.

[0059] The scheduling method based on time series is of great significance in the research on the combined scheduling of sources, grids, loads and storage in multi-level complex energy flow systems. By accurately predicting future load demand and the output of renewable energy and formulating effective scheduling strategies, efficient energy utilization and supply-demand balance can be achieved. In addition, the scheduling method based on time series can also dynamically control the energy system through real-time monitoring and adjustment to cope with the challenges brought by uncertainty and variability. Therefore, the scheduling method based on time series provides strong technical support for the combined scheduling of sources, grids, loads and storage.

[0060] Further, such as Figure 2 and Figure 3As shown, the multi-level complex energy flow system structure 2 includes an energy source 205, an energy network 206, an energy load 207 and an energy storage 208. The energy source 205, the energy network 206, the energy load 207 and the energy storage 208 are interconnected. The energy source 205 includes renewable energy and traditional energy. The energy network 206 includes transmission lines and substations. The energy load 207 includes power load and heat exchange load. The energy storage 208 includes batteries and heat storage devices. The four components of the energy source 205, the energy network 206, the energy load 207 and the energy storage 207 are used as input variables of the scheduling model, and the corresponding constraints and objective functions are defined. Among them, the energy source includes renewable energy, traditional energy, etc.; the energy network includes transmission lines, substations, etc.; the energy load includes power load, heat exchange load, etc.; the energy storage includes energy storage equipment such as batteries, heat storage devices, etc. By modeling these four components, we can fully consider the complexity and diversity of the energy system.

[0061] The source-grid-load-storage combined dispatching system 3 includes a mixed integer linear dispatching model 301, specifically:

[0062] First, the energy source 205, energy grid 206, energy load 207 and energy storage 208 are used as input variables of the mixed integer linear scheduling model 301, and the corresponding constraints and objective functions are defined. By modeling these four components, we can fully consider the complexity and diversity of the energy system;

[0063] Secondly, by setting the decision variables as continuous and discrete variables, defining appropriate objective functions and constraints, and solving the mixed integer linear programming problem, we can fully consider the flexibility and feasibility of system scheduling. At the same time, by defining appropriate objective functions and constraints, we can solve the mixed integer linear programming problem to obtain the optimal energy scheduling solution;

[0064] Finally, numerical experiments and comparative analysis are used to verify the effectiveness and feasibility of the scheduling model based on mixed integer linear programming. By comparing with other scheduling methods, we find that the scheduling model based on mixed integer linear programming can achieve better energy utilization efficiency and system performance.

[0065] Furthermore, if Figure 2 As shown, the mixed integer linear scheduling model 301 includes determining evaluation indicators, determining indicator weights, determining evaluation levels, selecting evaluation methods and evaluation result methods, determining evaluation indicators including economy, reliability, energy utilization, technology and environmental protection, determining indicator weights including subjective weighting method, objective weighting method and combined weighting method, and determining evaluation levels including excellent, good, medium and poor;

[0066] The output end for determining the evaluation index and the index weight is connected to the selected evaluation method, and the evaluation result method is set at the output end of the selected evaluation method and outputs the result;

[0067] The selected evaluation methods include fuzzy comprehensive evaluation method, grey system evaluation method, data envelopment analysis method, rank sum ratio comprehensive evaluation method and Topsisi evaluation method;

[0068] The evaluation results method includes evaluating the results, refining the indicators, changing the weights, and improving the accuracy;

[0069] The scheduling model based on mixed integer linear programming has important application value in the combined scheduling of sources, grids, loads and storage in multi-level complex energy flow systems. By accurately modeling and optimizing decisions for the entire system, we can achieve coordinated operation and optimal scheduling among the various components of the energy system, improve energy utilization efficiency, reduce system operating costs, and achieve the goal of sustainable development. In future research, we will further improve the scheduling model and explore new algorithms and technologies to meet the scheduling needs of different complex energy flow systems.

[0070] Further, if Figure 1 As shown, a time series model is established and modeled for the mixed integer linear scheduling model 301, specifically:

[0071] A quantity y(t) that changes with variable t at t1 <t2<···t n < ··· observations y(t1), y(t2), ···y(t n ),..., becomes a time series;

[0072] Among them, in the medium- and long-term load forecasting, the sampling interval of t is usually in years;

[0073] The data of power output or load is an ordered set recorded at a certain time interval, which is a time series. The method of using this sequence to analyze the law and characteristics of power output or load changes and predict future loads is called time series method. It is mainly divided into time regression method, moving average method and exponential smoothing method. Considering the accuracy and practicality of the algorithm, we choose weighted moving average method to construct the time series model.

[0074] A trend forecasting method that uses several past observations of the same variable arranged in chronological order and the number of times the chronological variable appears as a weight to calculate the weighted arithmetic mean of the observations and use this number as a forecast for the future value of the variable.

[0075] The weighted moving average method catalog functions are as follows:

[0076]

[0077] Among them: W1, W2, W3…Wm are the weights corresponding to m points.

[0078] In this embodiment, if Figure 1 As shown, the power system dispatch problem of 203 uses the following formula:

[0079] min[sum(c1*pg+c2*psh+c3*pl)];

[0080] Among them, pg represents the output of each generator set, psh represents the current of each transmission line, pl represents the power demand of each load node, and c1, c2, and c3 are the coefficients of each cost respectively;

[0081] From the perspective of economic benefits, the combined dispatch of sources, grids, loads and storage has also brought about significant benefits. By optimizing the allocation of resources and flexible dispatching of various energy nodes, it can reduce energy production costs and supply risks, and improve overall economic benefits. At the same time, combined dispatching can also effectively reduce the peak-valley difference in energy supply and demand, further optimize the operation of the energy market, and improve the stability and sustainability of power supply.

[0082] In terms of environmental protection, the combined dispatch of sources, grids, loads and storage also plays a positive role. By rationally dispatching and managing the flow of energy, energy waste and unnecessary environmental pollution can be reduced. Combined dispatch can better utilize renewable and clean energy, reduce dependence on traditional energy, and achieve sustainable development of energy.

[0083] In addition, after the implementation of combined dispatching of sources, grids, loads and storage, it can also promote the development of multi-energy complementarity and energy interconnection. Through coordinated dispatching and flexible allocation between different energy sources, it can promote the development of multi-energy complementarity and improve the overall energy utilization efficiency. At the same time, combined dispatching can also strengthen the interconnection between energy sources, realize the sharing and optimal allocation of energy resources, and further enhance the overall benefits of the energy system.

[0084] In summary, through the evaluation and analysis of the implementation effect of the combined dispatch of sources, grids, loads and storage, it can be seen that it has achieved remarkable results in energy dispatch, economic benefits, environmental protection and energy coordinated development. The promotion and application of combined dispatch of sources, grids, loads and storage will provide important technical support and guarantee for the sustainable development of multi-level complex energy flow systems, help optimize the energy structure, improve the reliability and quality of power supply, and is of great significance for promoting energy transformation and achieving sustainable development goals.

[0085] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of components as the criteria for distinction. For example, "including" mentioned throughout the specification and claims is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve technical problems within a certain error range and basically achieve technical effects.

[0086] It should be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0087] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention concept described herein through the above teachings or the technology or knowledge of the relevant field. Changes and variations made by those skilled in the art do not depart from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A source-grid-load-storage combined dispatching system based on multi-level complex energy flow, comprising a multi-level complex energy flow system (1), a multi-level complex energy flow system structure (2) and a source-grid-load-storage combined dispatching system (3), characterized in that: The multi-level complex energy flow system (1) comprises multiple levels of energy flow elements (101), energy storage device elements (102), and load demand elements (103); The multi-level complex energy flow system structure (2) includes an energy input link (201), a conversion link (202), a transmission link (203) and an output link (204); The dispatching method of the source-grid-load-storage combined dispatching system (3) is: S1. Forecasting load demand and renewable energy output: collecting historical load data and renewable energy output data, and using time series models for analysis and forecasting; By analyzing and modeling historical data, we can obtain the forecast results of load demand and renewable energy output in the future time period; S2. Combine the prediction results with the dispatch strategy to achieve control and coordination of energy flow. After obtaining the prediction results of load demand and renewable energy output in the future time period, formulate a reasonable energy dispatch plan; S3. Realize dynamic control of the system through real-time monitoring and adjustment, and timely feedback of real-time data and make adjustments based on the predicted results.

2. According to claim 1, a source-grid-load-storage combined dispatching system based on multi-level complex energy flow is characterized by: The multi-level complex energy flow system structure (2) comprises an energy source (205), an energy network (206), an energy load (207) and an energy storage (208), wherein the energy source (205), the energy network (206), the energy load (207) and the energy storage (208) are interconnected; The energy source (205) includes renewable energy and traditional energy; The energy grid (206) includes transmission lines and substations; The energy load (207) includes power load and heat exchange load; The energy storage (208) includes batteries and heat storage devices.

3. A source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 2, characterized in that: The source-grid-load-storage combined dispatching system (3) comprises a mixed integer linear dispatching model (301), specifically: First, the energy source (205), the energy grid (206), the energy load (207) and the energy storage (208) are used as input variables of the mixed integer linear scheduling model (301), and corresponding constraints and objective functions are defined; Secondly, by setting the decision variables as continuous and discrete variables, defining appropriate objective functions and constraints, and solving the mixed integer linear programming problem, the optimal energy scheduling scheme is obtained; Finally, numerical experiments and comparative analysis are used to verify the effectiveness and feasibility of the scheduling model based on mixed integer linear programming.

4. The source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 3 is characterized by: The mixed integer linear scheduling model (301) includes determining evaluation indicators, determining indicator weights, determining evaluation levels, selecting evaluation methods and evaluation result methods.

5. The source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 4 is characterized by: The evaluation indicators include economy, reliability, energy utilization, technology and environmental protection; The determination of index weights includes subjective weighting method, objective weighting method and combined weighting method; The determined evaluation levels include excellent, good, medium and poor.

6. The source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 4 is characterized by: The output end for determining the evaluation index and determining the index weight is connected to the selected evaluation method, and the evaluation result method is set at the output end of the selected evaluation method and outputs the result.

7. The source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 4 is characterized by: The selected evaluation methods include fuzzy comprehensive evaluation method, grey system evaluation method, data envelopment analysis method, rank sum ratio comprehensive evaluation method and Topsisi evaluation method; The evaluation result method includes evaluating the result, refining the index, changing the weight and improving the accuracy.

8. The source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 3 is characterized by: A time series model is established and modeled for the mixed integer linear scheduling model (301), specifically: A quantity y(t) that changes with variable t at t1 <t2<···t n < ··· observations y(t1), y(t2), ···y(t n ),..., becomes a time series; Among them, in the medium- and long-term load forecasting, the sampling interval of t is usually in years; A trend forecasting method that uses several past observations of the same variable arranged in chronological order and the number of times the chronological variable appears as a weight to calculate the weighted arithmetic mean of the observations and use this number as a forecast for the future value of the variable. The weighted moving average method catalog functions are as follows: Among them: W1, W2, W3…Wm are the weights corresponding to m points.

9. The source-grid-load-storage combined dispatching system based on multi-level complex energy flow according to claim 8 is characterized by: The mixed integer linear dispatch model (301) for the power system dispatch problem uses the following formula: min[sum(c1*pg+c2*psh+c3*pl)]; Among them, pg represents the output of each generator set, psh represents the current of each transmission line, pl represents the demand for electricity at each load node, and c1, c2, and c3 are the coefficients of each cost respectively.

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