Evaluation method of electric magnesium furnace response to power peak load regulation based on multi-agent simulation

Abstract

The present invention provides a method for evaluating the ability of a fused magnesium furnace to respond to power peak-shaving based on multi-agent simulation, including determining an evaluation index of the ability of a power grid agent to respond to power peak-shaving and establishing a power consumption decision model for the ability of a fused magnesium furnace agent to respond to power peak-shaving, taking the minimum power fluctuation between the planned dispatching output of the fused magnesium furnace and the real-time power generation of renewable energy as the control target, and establishing an evaluation index expression; using the planned dispatching output of the fused magnesium furnace obtained by the power grid agent control as a reference value for the fused magnesium agent to respond to peak-shaving, comparing the actual output of the fused magnesium furnace with the planned dispatching output of the fused magnesium furnace, and establishing the objective function of the decision model with the minimum difference between the two. Power peak-shaving by this method can effectively promote the consumption of renewable energy, improve the load curve, and improve the flexibility and reliability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system and automation thereof, and in particular to a method for evaluating the power peak-shaving capability of a molten magnesium furnace in response to multi-agent simulation. Background Art

[0002] At present, the world's energy industry is transforming from oil and gas to new energy. Against the background of world energy, my country's energy transformation is steadily advancing towards the goal of clean energy and low-carbon energy. New energy, mainly wind power and solar energy, is developing rapidly in my country. By 2030, the proportion of non-fossil energy in my country's primary energy will reach 20%, and the proportion of non-fossil energy power generation in total power generation will strive to reach 50%; by 2050, the proportion of non-fossil energy in my country will exceed half, and by then, my country's energy structure will undergo fundamental changes.

[0003] With the further growth of renewable energy installed capacity in the future, the random volatility of renewable energy generation will put more and more severe pressure on the safe operation of the power grid. With the continuous advancement of smart grid construction and the continuous fermentation of power system reform, from the perspective of demand side, appropriately adjusting the load to adapt to the uncertainty of power generation output is a new idea to promote wind power consumption under the background of smart grid. At present, the demonstration application of demand side response is limited to small areas such as parks, communities, and families. It is in a small-scale and unreliable situation and cannot solve the problems of peak and frequency regulation of power system and renewable energy consumption. High-energy-consuming industrial loads have the advantages of large installed capacity and convenient centralized control. They can complete technical transformation at a low cost in a short time to realize the mining of high-energy-consuming load regulation characteristics in the power system. By combining with the regulation capacity of power generation side regulation resources such as thermal power units, it can significantly improve the power system in the renewable energy volatility and eliminate the peak-to-valley difference between day and night. Therefore, the present invention will deeply explore the regulation characteristics of high-energy-consuming fused magnesium loads, combine the multi-agent method, realize large-scale high-energy-consuming fused magnesium load power regulation, and improve the power system's ability to absorb renewable energy and the ability to smooth disturbances. Summary of the invention

[0004] In order to solve the technical problems raised by the background technology, the present invention provides a method for evaluating the power peak-shaving capability of an electric molten magnesium furnace based on multi-agent simulation. Power peak-shaving by this method can effectively promote the consumption of renewable energy, improve the load curve, and enhance the flexibility and reliability of the power system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for evaluating the power peak load regulation capability of a fused magnesium furnace based on multi-agent simulation includes the following steps:

[0007] 1) Determine the evaluation index of the power grid intelligent body’s ability to respond to power peak load regulation

[0008] In order to effectively mobilize the fused magnesium furnace load with huge demand response potential to participate in power peak regulation, the power grid intelligent body hopes to maximize the time series fit between the fused magnesium furnace load curve and the renewable energy output under the premise of ensuring the stability of the power system and the normal production of the fused magnesium furnace load. That is, the power fluctuation between the planned dispatch output of the fused magnesium furnace and the real-time power generation of renewable energy is minimized as the control target. The expression of the established evaluation index F1 is as follows:

[0009]

[0010] Where T is the number of time periods; N is the number of electric magnesium furnaces in the main melting condition; P is the planned dispatching output of the i-th fused magnesium furnace after responding to power peak regulation in period t; t,R is the power generated by renewable energy in period t;

[0011] 2) Establish a power consumption decision model for the intelligent agent of the fused magnesium furnace to respond to the power peak load regulation capability

[0012] Combined with the smelting mechanism of fused magnesium furnace, a power consumption decision model of fused magnesium furnace intelligent agent responding to power peak load regulation capability is established. The model consists of objective function and constraint conditions.

[0013] 2-1) Planned dispatch output of fused magnesium furnace obtained by grid intelligent control As a reference value for the peak load response of the fused magnesium intelligent body, the actual output of the fused magnesium furnace and the planned output of the fused magnesium furnace are compared. By comparison, the objective function of the decision model is established with the minimum difference between the two. The expression is as follows:

[0014]

[0015] Where T is the number of time periods; N is the number of electric magnesium furnaces in the main melting condition; P t,i is the actual output of the i-th electric magnesium furnace after responding to power peak regulation in period t;

[0016] 2-2) Establish constraints for the decision-making model.

[0017] The constraints of the decision model include the following:

[0018] 1) Constraints on the power range of electric fused magnesium furnace:

[0019]

[0020] in They are respectively the maximum and minimum values ​​of the electric power used by the electric fused magnesium furnace.

[0021] The constraints of the decision model also include the following:

[0022] 2) Electric magnesium furnace climbing rate constraints:

[0023]

[0024] in is the downward climbing rate limit of the electric magnesium furnace i, is the ramp rate limit of the fused magnesium furnace i, P t-1,i is the actual output of the i-th electric magnesium furnace in period t-1.

[0025] 3) Constraints on the production of fused magnesia:

[0026] The output of fused magnesia is mainly determined by the actual output of the magnesium furnace in period t, P t,i Therefore, the actual output fluctuation of the fused magnesium furnace will inevitably affect the output of fused magnesia. According to production conditions and market sales, the output of fused magnesia must meet the upper and lower limit constraints:

[0027]

[0028] in is the lower limit of the output of fused magnesium furnace i during period t, is the upper limit of the output of the fused magnesium furnace i during period t, y i,t is the actual output of the i-th electric fused magnesium furnace in period t.

[0029] 4) Constraints on energy consumption indicators of fused magnesium furnaces:

[0030]

[0031] in is the lower limit of energy consumption of electric magnesium furnace i, is the upper limit of energy consumption of electric fused magnesium furnace i;

[0032] The energy consumption of electric magnesium furnace is Q i,t It is expressed as:

[0033]

[0034] Where Δt is a scheduling period; P t,i is the actual power of the i-th electric magnesium furnace in period t; y i,t is the actual output of the i-th electric fused magnesium furnace in period t.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1) The present invention uses a multi-agent modeling method to reflect macroscopic phenomena by simulating microscopic individual behaviors, providing a more effective modeling framework for studying power systems, which is scientific, reasonable and highly applicable;

[0037] 2) From the demand side perspective, the fused magnesium industrial load has the advantages of large capacity, stable load, and easy management compared with commercial and residential demand response resources, and has more obvious advantages in improving the power system's ability to smooth disturbances and absorb renewable energy;

[0038] 3) The electric fused magnesium furnace of the present invention responds to power peak regulation with good economic and environmental benefits, reduces grid investment and fossil energy consumption, reduces system costs, optimizes resource allocation, and has a favorable impact on the public environment and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is the overall block diagram of the multi-agent system for industrial users;

[0040] Figure 2 It is a flow chart of the interaction mechanism of multiple agents;

[0041] Figure 3 1. It is a working condition curve diagram of the smelting process of the fused magnesium furnace according to an embodiment of the present invention;

[0042] Figure 4 is a baseline load diagram of a fused magnesium furnace according to an embodiment of the present invention;

[0043] Figure 5 It is a curve diagram of the output of renewable energy and fused magnesium load power after optimization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The specific implementation modes provided by the present invention are described in detail below with reference to the accompanying drawings.

[0045] Reference Figure 1 and Figure 2 The present invention provides a method for evaluating the power peak load regulation capability of a fused magnesium furnace based on multi-agent simulation, including:

[0046] 1) Determine the evaluation index of the power grid intelligent body’s ability to respond to power peak load regulation

[0047] In order to effectively mobilize the fused magnesium furnace load with huge demand response potential to participate in power peak regulation, the power grid intelligent body hopes to maximize the time series fit between the fused magnesium furnace load curve and the renewable energy output under the premise of ensuring the stability of the power system and the normal production of the fused magnesium furnace load. That is, the power fluctuation between the planned dispatch output of the fused magnesium furnace and the real-time power generation of renewable energy is minimized as the control target. The expression of the established evaluation index F1 is as follows:

[0048]

[0049] Where T is the number of time periods; N is the number of electric magnesium furnaces in the main melting condition; P is the planned dispatching output of the i-th fused magnesium furnace after responding to power peak regulation in period t; t,R is the power generated by renewable energy in period t;

[0050] 2) Electricity consumption decision model of fused magnesium furnace intelligent agent responding to power peak load regulation capability

[0051] Combined with the smelting mechanism of fused magnesium furnace, a power consumption decision model of fused magnesium furnace intelligent agent responding to power peak load regulation capability is established. The model consists of objective function and constraint conditions.

[0052] 2-1) Planned dispatch output of fused magnesium furnace obtained by grid intelligent control As a reference value for the peak load response of the fused magnesium intelligent body, the actual output of the fused magnesium furnace and the planned output of the fused magnesium furnace are compared. By comparison, the objective function of the decision model is established with the minimum difference between the two. The expression is as follows:

[0053]

[0054] Where T is the number of time periods; N is the number of electric magnesium furnaces in the main melting condition; P t,i is the actual output of the i-th electric magnesium furnace after responding to power peak regulation in period t;

[0055] 2-2) Establish constraints for the decision-making model.

[0056] The constraints of the decision model include the following:

[0057] 1) Constraints on the power range of electric fused magnesium furnace:

[0058]

[0059] in They are respectively the maximum and minimum values ​​of the electric power used by the electric fused magnesium furnace.

[0060] The constraints of the decision model also include the following:

[0061] 2) Electric magnesium furnace climbing rate constraints:

[0062]

[0063] in is the downward climbing rate limit of the electric magnesium furnace i, is the ramp rate limit of the fused magnesium furnace i, P t-1,i is the actual output of the i-th electric magnesium furnace in period t-1.

[0064] 3) Constraints on the production of fused magnesia:

[0065] The output of fused magnesia is mainly determined by the actual output of the magnesium furnace in period t, P t,i Therefore, the actual output fluctuation of the fused magnesium furnace will inevitably affect the output of fused magnesia. According to production conditions and market sales, the output of fused magnesia must meet the upper and lower limit constraints:

[0066]

[0067] in is the lower limit of the output of fused magnesium furnace i during period t, is the upper limit of the output of the fused magnesium furnace i during period t, y i,t is the actual output of the i-th electric fused magnesium furnace in period t.

[0068] 4) Constraints on energy consumption indicators of fused magnesium furnaces:

[0069]

[0070] in is the lower limit of energy consumption of electric magnesium furnace i, is the upper limit of energy consumption of electric fused magnesium furnace i;

[0071] The energy consumption of electric magnesium furnace is Q i,t It is expressed as:

[0072]

[0073] Where Δt is a scheduling period; P t,i is the actual power of the i-th electric magnesium furnace in period t; i,t is the actual output of the i-th electric fused magnesium furnace in period t.

[0074] The following embodiment verifies the effectiveness of a method for evaluating the response of an electric molten magnesium furnace to power peak load regulation based on multi-agent simulation: the system of the embodiment takes an electric molten magnesia enterprise as an example. The plant has 14 electric molten magnesium furnaces with an upper limit of 20500KW of peak load. The smelting cycle of each batch is 10 hours, and one smelting cycle is one scheduling cycle.

[0075] The fused magnesium furnace is a buried electric arc furnace. The entire smelting process is frequently switched between charging, main melting and exhaust conditions. The smelting process condition curve of the fused magnesium furnace is as follows: Figure 3 As shown in the figure, the working conditions s(t) are represented by 1, 2, and 3 respectively, which represent exhaust, charging, and main melting working conditions. The furnaces in different working conditions are operated in parallel, and the baseline load condition is referred to Figure 4. Since the main melting condition accounts for 80% of the smelting process and the power of the fused magnesium furnace is relatively stable under this condition, the electric furnace in the main melting condition is preferred for power adjustment. After the power grid intelligent agent sends the peak load demand and demand response signal to the fused magnesium furnace intelligent agent, the fused magnesium furnace intelligent agent optimizes and adjusts the load power after response decision. The optimized fused magnesium furnace operation curve and renewable energy output reference Figure 5 .

[0076] Depend on Figure 5 It can be seen that the fused magnesium load can track the output curve of renewable energy well and has good peak-shaving ability. Among them, in the sampling period [0,2100], the adjustment amplitude of the fused magnesium load is the largest, and the effect of tracking renewable energy is the best. The fused magnesium furnace response power peak-shaving ability evaluation method based on multi-agent simulation proposed in the present invention effectively promotes the consumption of renewable energy and improves the load curve under the premise of meeting the normal production of fused magnesium enterprises. Therefore, the scheme is reliable and effective.

[0077] In summary, after the electric magnesium furnace intelligent body participates in automatic demand response, it can improve the power system's ability to absorb renewable energy and smooth disturbances, alleviate grid pressure, improve power supply reliability, reduce grid investment and fossil energy consumption, and have a positive impact on the public environment and resource utilization.

[0078] The present invention analyzes the mechanism of the molten magnesium furnace responding to power peak regulation to reduce the load peak, and combines the functions and characteristics of multi-agents to propose a method for evaluating the molten magnesium furnace's ability to respond to power peak regulation based on multi-agent simulation. By analyzing the operating characteristics of the molten magnesium furnace and the characteristics of demand response in the smart grid environment, an interactive coordination mechanism including the molten magnesium furnace agent and the grid agent is designed to guide the response behavior of the molten magnesium industrial users and evaluate the response benefits; evaluation indicators that can characterize the physical characteristics of the response behavior are extracted from the load side and the grid side, respectively, and a corresponding mathematical model is established to evaluate the benefits of the load participating in the automatic demand response to the grid. The method is simple and practical, improves the power system's ability to absorb renewable energy and its ability to smooth disturbances, and alleviates the pressure on the grid. The use of molten magnesium furnaces for power peak regulation has good economic and environmental benefits, reduces grid investment and fossil energy consumption, and has a favorable impact on the public environment and resource utilization.

[0079] The above embodiments are implemented based on the technical solution of the present invention, and detailed implementation methods and specific operation processes are given, but the protection scope of the present invention is not limited to the above embodiments. The methods used in the above embodiments are conventional methods unless otherwise specified.

Claims

1. A method for evaluating the power peak load capacity of a fused magnesium furnace based on multi-agent simulation, characterized in that: These include: 1) Determine the evaluation index of the power grid intelligent body’s ability to respond to power peak load regulation In order to effectively mobilize the fused magnesium furnace load with huge demand response potential to participate in power peak regulation, the power grid intelligent body hopes to maximize the time series fit between the fused magnesium furnace load curve and the renewable energy output under the premise of ensuring the stability of the power system and the normal production of the fused magnesium furnace load. That is, the power fluctuation between the planned dispatch output of the fused magnesium furnace and the real-time power generation of renewable energy is minimized as the control target. The expression of the established evaluation index F1 is as follows: Where T is the number of time periods; N is the number of electric magnesium furnaces in the main melting condition; P is the planned dispatching output of the i-th fused magnesium furnace after responding to power peak regulation in period t; t,R is the power generated by renewable energy during period t; 2) Establish a power consumption decision model for the intelligent agent of the fused magnesium furnace to respond to the power peak load regulation capability Combined with the smelting mechanism of fused magnesium furnace, a power consumption decision model of fused magnesium furnace intelligent agent responding to power peak load regulation capability is established. The model consists of objective function and constraint conditions. 2-1) Planned dispatch output of fused magnesium furnace obtained by grid intelligent control As a reference value for the peak load response of the fused magnesium intelligent body, the actual output of the fused magnesium furnace and the planned output of the fused magnesium furnace are compared. By comparison, the objective function of the decision model is established with the minimum difference between the two. The expression is as follows: Where T is the number of time periods; N is the number of electric magnesium furnaces in the main melting condition; P t,i is the actual output of the i-th electric magnesium furnace after responding to power peak regulation in period t; 2-2) Establish constraints for decision-making models; The constraints of the decision model include the following: Electric power range constraints of electric fused magnesium furnace: in They are the maximum and minimum values ​​of electric power used by the fused magnesium furnace respectively; The constraints of the decision model also include the following: Electric fused magnesium furnace ramp rate constraints: in is the ramp rate limit of the fused magnesium furnace i, R i UP is the ramp rate limit of the fused magnesium furnace i, P t-1,i is the actual output of the i-th fused magnesium furnace in period t-1; The constraints of the decision model also include the following: Constraints on fused magnesia production: The output of fused magnesia is determined by the actual output P of the magnesium furnace in period t. t,i Therefore, the actual output fluctuation of the fused magnesium furnace will inevitably affect the output of fused magnesia. According to production conditions and market sales, the output of fused magnesia must meet the upper and lower limit constraints: in is the lower limit of the output of fused magnesium furnace i during period t, is the upper limit of the output of the fused magnesium furnace i during period t, y i,t is the actual output of the i-th electric fused magnesium furnace in period t; The constraints of the decision model also include the following: Energy consumption index constraints of electric magnesium furnace: in is the lower limit of energy consumption of electric magnesium furnace i, is the upper limit of energy consumption of electric fused magnesium furnace i; The energy consumption of electric magnesium furnace is Q i,t It is expressed as: Where Δt is a scheduling period; P t,i is the actual output of the i-th electric magnesium furnace after responding to power peak regulation in period t; y i,t is the actual output of the i-th electric fused magnesium furnace in period t.

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