Industrial park micro-grid optimization scheduling method based on electric furnace load active adjustment

Abstract

The invention discloses an industrial park micro-grid optimization scheduling method based on electric furnace load active adjustment. The method comprises the following steps: constructing an economical operation objective function for generating power by using a thermal power generating unit to share blast furnace gas of a steel mill; dynamically adjusting the maximum exchange power between the micro-grid and the public grid by iterating the shrinkage constraint, and calculating a minimum feasible value to reduce basic electricity price expenditure; taking the gear of the electric furnace transformer as an adjusting variable, and constructing a load adjusting constraint model containing a fixed load and an adjustable load; the micro-grid adopts a double-bus topology, power mutual assistance is realized through a back-to-back converter, and a distributed power supply, an energy storage system and a new energy power generation set are configured. The method can efficiently utilize energy and reduce the operation cost; the public network expenditure is reduced by dynamically optimizing power exchange; the system flexibility is improved by flexibly regulating and controlling the load; new energy fluctuation is stabilized, and stability is enhanced; the double-bus structure realizes grid-connected and off-grid switching, improves reliability, and is suitable for iron and steel enterprises to improve the utilization rate of renewable energy sources and reduce operation cost.

Description

Technical Field

[0001] This invention belongs to the field of microgrid optimization scheduling, and relates to an industrial park microgrid optimization scheduling method based on active adjustment of electric furnace load. Background Technology

[0002] With increasing global emphasis on sustainable development and environmental protection, the industrial sector faces significant challenges in reducing energy consumption, minimizing greenhouse gas emissions, and improving energy efficiency. As an energy-intensive industry, the steel industry accounts for a substantial portion of global energy consumption and carbon emissions. Therefore, seeking efficient energy management strategies and technologies is crucial for the sustainable development of the steel industry.

[0003] The steel production process involves multiple stages, including ironmaking, steelmaking, and rolling, each requiring significant amounts of electricity and fuel. Statistics show that the steel industry accounts for approximately 20% of global industrial energy consumption and about 7% of global anthropogenic carbon emissions. Furthermore, energy waste exists in steel production, such as low waste heat recovery rates and high power system losses, leading to inefficient energy utilization. Industrial park microgrids typically incorporate various energy sources, such as thermal power, renewable energy generation, and energy storage systems, as well as diverse loads, such as industrial loads and lighting loads. Given the different characteristics of various energy sources and loads, coordinating their operation to meet load demands and reduce operating costs is a key challenge for optimizing the operation of industrial park microgrids.

[0004] Currently, research on the optimized operation of microgrids in industrial parks mainly focuses on the following aspects: 1. Economic optimization: By optimizing dispatch strategies, the costs of thermal power fuel, electricity purchase, and energy storage operation can be effectively reduced, thereby improving economic efficiency. For example, based on electricity price information and load forecasts, the generation and dispatch strategies of distributed energy resources can be optimized to achieve peak-valley price arbitrage; the operation strategies of energy storage systems can also be optimized based on their charging and discharging characteristics and economics, reducing energy storage operation costs. 2. Introducing back-to-back converters, energy storage systems, and other equipment can improve system flexibility and adapt to load fluctuations and the volatility of renewable energy generation. For example, back-to-back converters can achieve flexible connections between the microgrid and the main grid, improving the independence of the microgrid; energy storage systems can smooth out load fluctuations and the volatility of renewable energy generation, improving the stability of the microgrid. 3. Incentive mechanisms can be used to guide users to actively adjust loads through demand response, improving the system's adaptability and reliability. For example, peak-valley electricity pricing policies can be formulated to guide users to consume electricity during off-peak hours; demand response subsidies can also be provided to encourage users to participate in demand response.

[0005] However, existing technologies rarely consider the coupling characteristics of blast furnace gas power generation and the active regulation of electric furnace loads, making it difficult to fully leverage the advantages of industrial park microgrids. Existing technologies suffer from the following drawbacks: Most existing industrial park microgrid optimization and scheduling methods focus on local optimization or balancing, failing to fully consider the flexible regulation characteristics of the microgrid as a "micro-power source" on the load side. This makes it difficult to achieve active support from the microgrid and effectively reduce power exchange between the microgrid and the main grid, resulting in higher maximum demand. When optimizing resource operation and the response of flexible loads and energy storage in the microgrid, existing methods do not fully consider the active regulation of electric furnace loads, making it difficult to flexibly adjust load demand according to actual conditions, affecting system operating costs and reliability. Existing technologies lack consideration for the coupling characteristics of shared blast furnace gas power generation by thermal power units within the park, failing to fully utilize the blast furnace gas from steel plants to reduce microgrid operating costs, resulting in an incomplete economic operating objective function for the microgrid. When dealing with the maximum power exchange constraint between the microgrid and the public grid, existing methods lack an effective constraint contraction mechanism, making it difficult to accurately control minimum demand and affecting the system scheduling optimization effect. Therefore, in order to improve the economy and flexibility of industrial park operation, a method for optimizing the operation of industrial park microgrids that includes active load regulation and consideration of bus interconnection is needed. Summary of the Invention

[0006] Existing technologies for optimizing the scheduling of industrial park microgrids fail to adequately consider the coupling characteristics of active load regulation by electric furnaces and blast furnace gas power generation, resulting in high operating costs and poor reliability of the microgrids. Therefore, the purpose of this invention is to provide an optimized scheduling method for industrial park microgrids based on active load regulation by electric furnaces.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An optimized scheduling method for industrial park microgrids based on active adjustment of electric furnace load includes the following steps:

[0009] S1. Constructing the economic operation objective function of the industrial park microgrid: The thermal power units in the industrial park generate electricity by sharing the blast furnace gas of the steel plant as fuel, and based on this, establish the economic operation objective function of the microgrid to reduce the operating cost of the microgrid;

[0010] S2: Maximum exchange power constraint and iterative optimization between microgrid and public grid: By iteratively shrinking constraints, the maximum exchange power between microgrid and public grid is dynamically adjusted, and the minimum feasible value of the maximum exchange power between microgrid and public grid that satisfies system power balance is calculated to minimize the basic electricity price expenditure of public grid.

[0011] S3. Construct an adjustable load model and adjustment constraints for the electric furnace: Using the electric furnace transformer tap as the adjustment variable, the total load is set as a fixed load that cannot be adjusted and an adjustable load that can be dynamically adjusted by the electric furnace control. Construct a load adjustment constraint model to reduce the operating cost of the industrial park microgrid.

[0012] Furthermore, the industrial park microgrid has a dual-bus topology; the two buses are connected by back-to-back converters; each bus is connected to the public power grid through a transformer to purchase electricity from or sell electricity to the public grid, or to supply electricity independently; each bus is configured with the same distributed energy and load units, and the two buses are bus 1 and bus 2;

[0013] The distributed energy and load units specifically include: distributed generation (DG), energy storage system (ESS), new energy power generation units and AC loads; the distributed generation is a thermal power unit.

[0014] Furthermore, S1 aims to minimize the total cost within the maximum scheduling period T. Based on the fuel cost of thermal power, the electricity purchase cost, and the energy storage operation cost, the economic operation objective function in S1 is constructed and expressed as the following formula:

[0015]

[0016] P DG1 (t)+P DG2 (t)=P bg (t)d bg

[0017] Among them, a DG1 A cost parameter for the DG configured for bus 1; a DG2 Cost parameter 1 for the DG configured for bus 2; b DG1 Another cost parameter for the DG configured for bus 1; b DG2 Another cost parameter for the DG configured for the network bus; P UG1 (t) represents the actual interaction power between microgrid bus 1 and the main grid during time period t (positive values ​​indicate power purchase from the main grid, negative values ​​indicate power sale to the main grid); P UG2 (t) represents the actual interaction power between microgrid bus 2 and the main grid during time period t (positive values ​​indicate power purchase from the main grid, negative values ​​indicate power sale to the main grid); P DG1 (t) represents the actual power generation of DG1 configured on microgrid bus 1 during time period t; P DG2 (t) represents the actual power generation of DG2 configured on microgrid bus 2 during time period t; P ESS1(t) represents the actual power of ESS1 configured on microgrid bus 1 during time period t (positive value indicates discharge, supplying power to the microgrid; negative value indicates charging, absorbing electrical energy from the microgrid); P ESS2 (t) represents the actual power of ESS2 configured on microgrid bus 2 during time period t (positive value indicates discharge, supplying power to the microgrid; negative value indicates charging, absorbing energy from the microgrid); λ(t) is the purchase price of electricity from the main grid, which includes the basic price and the fluctuating price. The basic price is calculated based on the maximum demand, and the fluctuating price is determined based on the real-time price curve; c ESS1 The power generation cost of ESS1 configured for microgrid bus 1; c ESS2 The power generation cost of ESS2 configured for microgrid bus 2; minF OP Let be the overall objective function.

[0018] Furthermore, in step S1, the power generation of the thermal power unit is constrained by the blast furnace gas volume, as expressed by the following formula:

[0019] P DG1 (t)+P DG2 (t)=P bg (t)d bg

[0020] Among them, P bg (t) represents the blast furnace gas production rate during time period t; d bg This indicates the conversion relationship between the power generated by thermal power plants through the consumption of blast furnace gas and the amount of blast furnace gas produced.

[0021] The maximum demand constraint that the maximum exchange power between the microgrid and the public power grid in S2 satisfies is expressed by the following formula:

[0022]

[0023] Among them, P UG1,max and P UG2,max This represents the maximum power demand of bus 1, bus 2, and the public power grid.

[0024] Furthermore, S2 also includes the following steps:

[0025] Initialize the maximum power demand limits for bus 1 and bus 2 based on the historical maximum power demand.

[0026] Calculate the actual interactive power between bus 1 and bus 2, and determine whether it exceeds the maximum demand power limit;

[0027] If the maximum power demand limit is exceeded, the maximum power demand limit is iteratively adjusted according to a preset step size to obtain a new minimum value of the maximum power demand.

[0028] Furthermore, S2 also includes the following steps:

[0029] The power balance equation is set up and expressed as the following formula:

[0030]

[0031] Where, η B2B P represents the conversion efficiency of back-to-back converters in a microgrid. B2B1 (t), P B2B2 (t) represents the transmission power from bus 1 to bus 2 and from bus 2 to bus 1; P RES1 (t), P RES2 (t) represents the power output of the new energy source for busbar 1 and busbar 2, P L1 (t), P L2 (t) represents the load on busbar 1 and busbar 2.

[0032] Furthermore, S3 also includes the following steps:

[0033] Peak electricity pricing period optimization: Actively reduce load by lowering the tap level of electric furnace transformers and slow down electricity production by extending the cycle according to production scheduling.

[0034] Unit energy consumption optimization: By adopting process measures such as increasing the proportion of molten iron and adjusting the scrap steel ratio in the converter, the unit energy consumption is reduced, which indirectly reduces the electricity demand.

[0035] Furthermore, step S3 also includes the following steps:

[0036] Using the electric furnace's speed setting as an adjustment variable, and adding constraints to the model, it can be expressed as the following formula:

[0037] P L (t)=P Ls (t)+P Lad (t)

[0038] The load in the microgrid consists of fixed loads and adjustable loads from electric furnaces, where P L (t), P Ls (t), P Lad (t) represents the total microgrid load, fixed load, and adjustable load, respectively.

[0039] Furthermore, step S3 also includes the following steps:

[0040] The following constraint model is represented by the following formula:

[0041]

[0042] Among them, the power demand of adjustable loads is determined by different gear levels, b i It is a 0-1 variable, representing the state variable of the i-th gear of the electric furnace, P. iThis represents the power requirement of the electric furnace when it is in the i-th gear.

[0043] Furthermore, step S3 also includes the following steps:

[0044] The following constraint model is represented by the following formula:

[0045]

[0046] Among them, the electric furnace load has a fixed daily electricity demand, C Lad This is the daily electricity requirement for an electric furnace. The beneficial effects of this invention are:

[0047] 1. By constructing an economic operation objective function for the industrial park microgrid, fully considering the shared blast furnace gas power generation of thermal power units, and utilizing the blast furnace gas of the steel plant to reduce the operating cost of the microgrid, the efficient utilization of multiple energy sources is achieved, and energy utilization efficiency is improved.

[0048] 2. An iterative optimization method was used to calculate the maximum power exchange constraint between the microgrid and the public power grid. By dynamically adjusting the upper limit of power exchange, the basic electricity price expenditure of the public grid was effectively reduced, and the economic efficiency of the system was improved.

[0049] 3. Based on the active adjustment characteristics of the electric furnace load, the tap position of the electric furnace transformer is used as an adjustment variable to establish constraints. By optimizing the scheduling strategy, flexible load control is achieved, which improves the system's adjustment flexibility.

[0050] 4. By using back-to-back converters to achieve power mutual assistance between the two buses, combined with the charging and discharging regulation of the energy storage system, the fluctuations in the output of new energy sources are effectively mitigated, and the stability of the system is improved.

[0051] 5. The system adopts a dual-bus topology and is connected to the public power grid through a transformer, enabling flexible switching between "grid-connected operation" and "off-grid operation", thus enhancing the system's resilience and reliability.

[0052] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0054] Figure 1 This is a structural diagram of an industrial park microgrid system including new energy, thermal power, and energy storage equipment in an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating the iteration of the minimum maximum required power in an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the load curves of dual busbars, wind power, and photovoltaic power on a typical day in an industrial park, as described in an embodiment of the present invention.

[0057] Figure 4 This is a schematic diagram of the equipment scheduling results on a typical day in an industrial park, as described in an embodiment of the present invention. Detailed Implementation

[0058] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0059] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0060] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the accompanying drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0061] Please see Figure 1 The diagram below shows the structure of an industrial park microgrid system including new energy, thermal power, and energy storage equipment in an embodiment of the present invention. The structure and operating logic of the industrial park microgrid with back-to-back converters are summarized below:

[0062] System overall architecture:

[0063] This industrial park microgrid adopts a dual-bus topology, comprising two independent buses, Bus 1 and Bus 2. Each bus is connected to the external public grid via a dedicated transformer, forming a bidirectional interactive channel between the microgrid and the public grid. Electrical isolation and bidirectional power regulation are achieved between the two buses via back-to-back converters, forming a flexible and controllable closed-loop system. A back-to-back converter (B2B) is a power electronic device that directly connects two converters, typically a rectifier and an inverter, via a DC link but without energy storage components. Its core function is to achieve energy conversion and bidirectional flow control between different AC systems, such as grids with different frequencies, voltage levels, or phases. It can flexibly adjust active and reactive power and is commonly used in scenarios such as renewable energy grid connection, grid interconnection, and motor speed regulation. In the industrial park microgrid, it isolates the electrical connection between the two buses and improves the system's stability in response to load fluctuations and changes in renewable energy output through precise power exchange control.

[0064] Core equipment functions:

[0065] The back-to-back converter is the core power electronic device connecting bus 1 and bus 2. It achieves precise control of active and reactive power between the two buses through a DC link, without relying on external energy storage components. It is used to isolate voltage and frequency fluctuations between the two buses, dynamically adjust the direction and magnitude of power exchange, improve the microgrid's response to load fluctuations and new energy output fluctuations, and optimize the interaction strategy with the public grid.

[0066] With the connection to the public power grid, both busbars are connected to the public power grid through transformers, enabling switching between two modes: "grid-connected operation" (purchasing electricity from or selling electricity to the public grid) and "off-grid operation" (independent power supply), thus enhancing system resilience.

[0067] Composition and characteristics of a single busbar:

[0068] Each busbar (busbar 1, busbar 2) is equipped with the same distributed energy and load units, specifically including:

[0069] Thermal power units (blast furnace gas power generation), used as baseload power sources for microgrids, operate with two units coupled together. The total power generation is fixed at each dispatch time (unadjustable) and cannot be shut down (it needs to continuously absorb blast furnace gas). The power generation of a single unit can be adjusted within a small range, but the total output of the two units is constrained by the gas production, resulting in low adjustment flexibility.

[0070] Energy storage systems are used to smooth out fluctuations in new energy sources and realize peak-valley electricity price arbitrage. They reduce system operating costs through optimized charging and discharging strategies and have a faster response speed than thermal power.

[0071] New energy power generation units, including wind power, photovoltaic and other fluctuating power sources, are affected by weather conditions. They need to be coordinated and controlled through energy storage or back-to-back converters to avoid impacting the bus voltage / frequency.

[0072] AC loads cover industrial park production loads (such as electric furnaces) and auxiliary loads. Among them, electric furnace loads have the potential for active adjustment (flexible load control can be achieved through transformer tap adjustment) and can participate in system peak shaving.

[0073] The system's power balancing mechanism:

[0074] Within a single busbar: Thermal power base load + renewable energy output + energy storage charging and discharging + public grid interaction power = load demand.

[0075] Between buses: Power mutual assistance is achieved through back-to-back converters. When a power surplus / shortage occurs on a bus, power can be quickly transferred / absorbed to another bus, reducing dependence on the public grid.

[0076] Key constraints for system operation:

[0077] Coupling constraints between thermal power and coal: The total power generation of the two units is fixed, and priority should be given to ensuring the consumption of coal gas.

[0078] Demand control constraints: Based on the historical maximum demand power, the upper limit of power exchange between back-to-back converters is iteratively limited to avoid exceeding the demand charge of the public power grid.

[0079] Load regulation potential: Taking the electric furnace as the core regulation object, the load curve can be flexibly changed by adjusting the transformer tap, and economic optimization can be achieved in conjunction with energy storage and electricity pricing mechanisms.

[0080] The effectiveness of the proposed method can be verified by applying it to the power grid of a steel enterprise and optimizing the equipment capacity configuration.

[0081] In this implementation, the method is applied to the power grid of a steel enterprise that includes wind power, photovoltaics, electric energy storage, electrolyzers, hydrogen energy storage, and electric and hydrogen loads.

[0082] The method specifically includes the following steps:

[0083] S1. Construct the objective function for the economic operation of the industrial park microgrid:

[0084] Considering that thermal power units within the industrial park share blast furnace gas for power generation, and utilizing the blast furnace gas from the steel plant to reduce the operating costs of the microgrid, an objective function for the economic operation of the industrial park microgrid is established.

[0085] Background for establishing the objective function:

[0086] Based on the above-mentioned dual-bus industrial park microgrid structure with back-to-back converters (see...), Figure 1 Each busbar is equipped with distributed generation (DG), such as thermal power units (blast furnace gas power generation), energy storage systems (ESS), renewable energy power generation units (wind power / solar power), and AC loads. The thermal power units primarily use blast furnace gas, a byproduct of steel mills, as fuel (which can supplement primary energy). They possess coupling characteristics of "non-stop operation, fixed total power generation of two units in real time, and limited adjustment range of a single unit." This step aims to minimize the total operating cost of the microgrid through optimized scheduling. The core pathways include utilizing blast furnace gas to reduce fuel consumption and optimizing power purchase and energy storage strategies.

[0087] Definition of the economic objective function:

[0088] With the maximum time range (scheduling period) The objective is to minimize the total cost within a time step Δt. Taking into account the fuel cost of thermal power, the cost of electricity purchase, and the operating cost of energy storage, the objective function is expressed as follows:

[0089]

[0090] P DG1 (t)+P DG2 (t)=P bg (t)d bg (2)

[0091] Among them, a DG1 A cost parameter for the DG configured for bus 1; a DG2 Cost parameter 1 for the DG configured for bus 2; b DG1 Another cost parameter for the DG configured for bus 1; b DG2 Another cost parameter for the DG configured for the network bus; P UG1 (t) represents the actual interaction power between microgrid bus 1 and the main grid during time period t (positive values ​​indicate power purchase from the main grid, negative values ​​indicate power sale to the main grid); P UG2 (t) represents the actual interaction power between microgrid bus 2 and the main grid during time period t (positive values ​​indicate power purchase from the main grid, negative values ​​indicate power sale to the main grid); P DG1 (t) represents the actual power generation of DG1 configured on microgrid bus 1 during time period t; P DG2 (t) represents the actual power generation of DG2 configured on microgrid bus 2 during time period t; P ESS1 (t) represents the actual power of ESS1 configured on microgrid bus 1 during time period t (positive value indicates discharge, supplying power to the microgrid; negative value indicates charging, absorbing electrical energy from the microgrid); P ESS2(t) represents the actual power of ESS2 configured on microgrid bus 2 during time period t (positive value indicates discharge, supplying power to the microgrid; negative value indicates charging, absorbing energy from the microgrid); λ(t) is the purchase price of electricity from the main grid, which includes the basic price and the fluctuating price. The basic price is calculated based on the maximum demand, and the fluctuating price is determined based on the real-time price curve; c ESS1 The power generation cost of ESS1 configured for microgrid bus 1; c ESS2 The power generation cost of ESS2 configured for microgrid bus 2; minF OP Let be the overall objective function.

[0092] The power generation of the two thermal power units is constrained by the amount of blast furnace gas, as expressed by the following formula:

[0093] P DG1 (t)+P DG2 (t)=P bg (t)d bg (2)

[0094] Among them, P bg (t) represents the blast furnace gas production rate during time period t; d bg This indicates the conversion relationship between the power generated by thermal power plants through the consumption of blast furnace gas and the amount of blast furnace gas produced.

[0095] S2. Maximum power exchange constraints and iterative optimization between microgrids and public power grids:

[0096] For the maximum exchange power constraint between the microgrid and the public grid, the minimum demand that can be achieved each time is calculated iteratively by means of constraint contraction.

[0097] Maximum switching power constraint definition:

[0098] The microgrid exchanges power with the public grid through two buses (bus 1 and bus 2). The exchanged power must meet the maximum demand constraint, as expressed by the following formula:

[0099]

[0100] Among them, P UG1,max and P UG2,max This represents the maximum power demand of bus 1, bus 2, and the public power grid (i.e., the upper limit of the absolute value of power exchange, which directly affects the basic electricity price cost of the public grid).

[0101] Please see Figure 2 This is a flowchart illustrating the iteration of the minimum maximum demand power in this embodiment of the invention; to minimize the basic public grid electricity price expenditure (proportional to the maximum demand), P needs to be dynamically adjusted through iterative shrinkage constraints. UGi,max For each i = 1, 2, find the minimum feasible value that satisfies the system power balance. The specific process is as follows:

[0102] Based on the maximum historical power demand generated during the billing period (denoted as P') UG1,max and P' UG2,max ), for P in future microgrid scheduling UG1,max and P UG2,max To impose a limit, if the historical maximum demand power P' UGi,max If the required power cannot be met, then gradually increase P. UG1,max and P UG2,max Each time ΔP increases UG It can be expressed as the following formula:

[0103]

[0104] Iterative solution to obtain the new maximum power demand The minimum value.

[0105] Through the above closed-loop iteration, excessively high demand limits are avoided, minimizing basic electricity price expenditures while ensuring power balance. During the iteration process, power mutual assistance between the two buses via back-to-back converters needs to be considered simultaneously to reduce dependence on the public grid, thereby indirectly reducing demand. Through this process, the microgrid can dynamically adapt to load characteristics and renewable energy output at different times, achieving refined control of power interaction with the public grid.

[0106] The power balance equation is expressed as follows:

[0107]

[0108] Where, η B2B P represents the conversion efficiency of back-to-back converters in a microgrid. B2B1 (t), P B2B2 (t) represents the transmission power from bus 1 to bus 2 and from bus 2 to bus 1; P RES1 (t), P RES2 (t) represents the power output of the new energy source for busbar 1 and busbar 2, P L1 (t), P L2 (t) represents the load on busbar 1 and busbar 2.

[0109] The energy exchange between DG, ESS, microgrids and the main grid, as well as the power constraints of back-to-back converter transmission, are expressed by the following formula:

[0110]

[0111]

[0112] Among them, P DGi,min and P DGi,maxThe minimum and maximum output ranges of DGi configured for bus i are respectively; R DGi,down and R DGi,up The minimum and maximum climbing speeds of DGi configured for bus i are respectively; P ESSi,dc,max and P ESSi,c,max The maximum discharge and charge rates of ESSi configured for bus i, respectively; η ESSi,c and η ESSi,dc The charging and discharging efficiencies of the ESSi configured for bus i are respectively; P UGi,max This represents the maximum power exchange between bus i and the public power grid. In the above variables: i = 1, 2, corresponding to bus 1 and bus 2 respectively. P B2B,max This refers to the capacity of the back-to-back converter.

[0113] The power limit and ramping constraint of DG are shown in equations (7) and (8), respectively;

[0114] The charging and discharging power constraints of ESS are shown in equations (9)-(13), respectively.

[0115] The power exchange constraints between the microgrid and the main grid are shown in Equation (14), where the microgrid is not allowed to inject power back into the main grid;

[0116] The power transmission constraints of back-to-back converters are shown in Equation (15).

[0117] The charging and discharging of the ESS and the complementary constraints of the power transmission of the back-to-back converter are given in equations (16) and (17), respectively.

[0118] S3. Construction of Adjustable Load Model and Adjustment Constraints for Electric Furnace:

[0119] Taking the electric furnace as the object, and considering the active regulation effect of the electric furnace on the load, the tap position of the electric furnace transformer is used as the regulation variable to establish constraints.

[0120] Based on the aforementioned microgrid structure of the industrial park with dual busbars and back-to-back converters, each busbar is equipped with a thermal power unit (blast furnace gas power generation), an energy storage system, a new energy power generation unit (wind power / photovoltaic), and AC loads. The thermal power units primarily use blast furnace gas, a byproduct of the steel plant, as fuel (which can supplement primary energy). They possess the coupling characteristics of "uninterruptible operation, fixed total power generation of both units in real time, and limited adjustment range of a single unit." Therefore, the electric furnace adjustable load model features "small-range power adjustment" and "constant daily electricity demand." The load is divided into fixed loads and adjustable loads controlled by the electric furnace. The system cost can be optimized by adjusting the electric furnace's operating level. For example, during peak electricity prices, production slows down, such as by controlling the electric furnace transformer's voltage regulator to reduce load, and production scheduling can be coordinated to extend the production cycle; or unit consumption can be reduced, such as by increasing the ratio of molten iron to converter scrap.

[0121] Using the electric furnace's speed setting as an adjustment variable, the following constraints are added:

[0122] P L (t)=P Ls (t)+P Lad (t) (18)

[0123]

[0124] Equation (18) shows that the load in the microgrid consists of a fixed load and an adjustable load from the electric furnace. Wherein, P L P Ls P Lad These are the total microgrid load, fixed load, and adjustable load, respectively.

[0125] Equation (19) shows that the power demand of an adjustable load is determined by different speed settings, b i It is a 0-1 variable, representing the state variable of the i-th gear of the electric furnace, P. i This represents the power requirement of the electric furnace when it is in the i-th gear.

[0126] Equation (20) represents the fixed daily electricity demand of the electric furnace load, C. Lad This is the daily electricity requirement for the electric furnace.

[0127] In this invention, the industrial park microgrid optimization scheduling method based on active adjustment of electric furnace load considers the power exchange between two buses through back-to-back converters, and introduces active adjustment of electric furnace load. The load demand can be adjusted according to the actual situation. Through optimized scheduling and active adjustment, the operating cost of the industrial park microgrid can be effectively reduced and the system reliability and flexibility can be improved.

[0128] In addition, considering that thermal power units in the industrial park share blast furnace gas for power generation, and utilizing the blast furnace gas from the steel plant to reduce the operating costs of the microgrid, an objective function for the economic operation of the industrial park microgrid is established.

[0129] In addition, for the maximum exchange power constraint between the microgrid and the public grid, the minimum demand that can be achieved each time is calculated iteratively by means of constraint contraction.

[0130] In addition, taking the electric furnace as the object, considering the active regulation effect of the electric furnace on the load, the tap position of the electric furnace transformer is used as the regulation variable to establish constraints.

[0131] In addition, the real-time operation control of the industrial park microgrid includes the following steps:

[0132] Real-time monitoring of system operation status, including load changes and changes in new energy output;

[0133] Adjust the power regulation parameters of the back-to-back converters according to the real-time status;

[0134] Adjust the charging and discharging status of the energy storage system according to changes in electricity prices;

[0135] Adjust the bus power distribution according to the operating characteristics of the thermal power unit;

[0136] The overall operating cost of the system is optimized by adjusting the load of the electric furnace.

[0137] Analysis of typical daily dispatch results of microgrids in industrial parks:

[0138] 1. Typical Daily Basic Data Input

[0139] Please see Figure 3 This is a schematic diagram of the load, wind power, and photovoltaic processing curves of a typical day in an industrial park according to a specific embodiment of the present invention. Based on the typical daily operating data of the industrial park, the load, wind power, and photovoltaic output data of the steel enterprises operating on the two busbars on a typical day are as follows:

[0140] Busbar 1: Peak electrical load is approximately 589MW, peak photovoltaic load is approximately 40MW, and peak wind power load is approximately 45MW;

[0141] Busbar 2: Peak electrical load is approximately 289MW, peak photovoltaic load is approximately 67MW, and peak wind power load is approximately 75MW.

[0142] 2. Economic efficiency and equipment parameter settings

[0143] Time-of-use electricity price (unit: yuan / kWh):

[0144] Lowest point periods (1-7 AM, 12-2 PM): 0.2454

[0145] Flat period (8:00, 14:00-17:00, 0:00): 0.6458

[0146] Peak hours (9:00-11:00, 18:00-23:00): 1.0333

[0147] Equipment cost and parameters:

[0148] Unit power generation cost of thermal power unit (DG): 0.5 yuan / kWh (same for bus 1 and bus 2);

[0149] Energy Storage System (ESS): Capacity 50kWh, discharge rate 0.6C, charge / discharge efficiency 0.99;

[0150] Back-to-back converter (B2B) efficiency: 0.97.

[0151] 3. Scheduling Strategy and Result Analysis

[0152] Please see Figure 4This is a schematic diagram of the equipment scheduling results on a typical day in an industrial park in a specific embodiment of the present invention; it provides the power scheduling results of each bus, energy storage, grid power purchase and back-to-back converter and other equipment on this typical day.

[0153] 1) Maximum demand control effect

[0154] The maximum power purchased from the public grid by both buses was effectively limited, and the total maximum power demand of bus 1 and bus 2 was reduced to 112.75MW, which verified the effect of the "constrained contraction iterative optimization" method in S2 on reducing demand costs.

[0155] 2) Coordinated regulation of thermal power unit and electric furnace load

[0156] The output of the thermal power unit (DG) can be flexibly adjusted in conjunction with the adjustable load of the electric furnace: by switching the electric furnace transformer (S3 model), the adjustable load power is reduced during peak electricity price periods to reduce the pressure on DG power generation; and the load power is increased during off-peak periods to make full use of low-priced electricity and ensure the real-time power balance of the system.

[0157] 3) Optimized operation of energy storage system

[0158] Energy storage charging and discharging strategies are deeply integrated with electricity prices, load, and renewable energy output:

[0159] Charging times: When electricity prices are low (e.g., 1-7 am), during off-peak hours, and when renewable energy (wind / solar) output is high;

[0160] Discharge timing: During periods of higher electricity prices (such as 9-11 am and 18-23 pm), peak load periods, and when renewable energy output is low, arbitrage between peak and off-peak prices can be realized to improve the economic efficiency of the system.

[0161] 4. Verification of economic efficiency and benefits

[0162] After scheduling optimization, the total daily operating cost is 5.9573 million yuan, and the main benefits are reflected in:

[0163] 1) Full utilization of renewable energy: Wind power and photovoltaic power output should be prioritized to meet load demand and reduce fossil fuel consumption;

[0164] 2) Maximum demand reduction: Through demand constraint iteration and load adjustment, the basic electricity price expenditure of the public grid is significantly reduced;

[0165] 3) Operating cost optimization: By combining time-of-use pricing and energy storage strategies, the overall cost of electricity purchase and DG fuel costs can be minimized.

[0166] Based on the analysis of typical daily dispatch results of microgrids in industrial parks, the model proposed in this invention can effectively coordinate distributed energy sources, adjustable loads, and energy storage systems, achieving efficient utilization of renewable energy and reduced operating costs while ensuring the continuity of steel production.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load, characterized in that, Includes the following steps: S1. Constructing the economic operation objective function of the industrial park microgrid: The thermal power units in the industrial park generate electricity by sharing the blast furnace gas of the steel plant as fuel, and based on this, establish the economic operation objective function of the microgrid to reduce the operating cost of the microgrid; S2: Maximum exchange power constraint and iterative optimization between microgrid and public grid: By iteratively shrinking constraints, the maximum exchange power between microgrid and public grid is dynamically adjusted, and the minimum feasible value of the maximum exchange power between microgrid and public grid that satisfies system power balance is calculated to minimize the basic electricity price expenditure of public grid. S3. Construct an adjustable load model and adjustment constraints for the electric furnace: Using the electric furnace transformer tap as the adjustment variable, the total load is set as a fixed load that cannot be adjusted and an adjustable load that can be dynamically adjusted by the electric furnace control. Construct a load adjustment constraint model to reduce the operating cost of the industrial park microgrid.

2. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load as described in claim 1, characterized in that: The industrial park microgrid has a dual-bus topology; the two buses are connected by back-to-back converters; each bus is connected to the public power grid through a transformer to purchase electricity from or sell electricity to the public grid, or to supply electricity independently; each bus is equipped with the same distributed energy and load units, and the two buses are bus 1 and bus 2; The distributed energy and load units specifically include: distributed generation (DG), energy storage system (ESS), new energy power generation units and AC loads; the distributed generation is a thermal power unit.

3. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load as described in claim 2, characterized in that: S1 aims to minimize the total cost within the maximum scheduling period T. Based on the fuel cost of thermal power, the electricity purchase cost, and the energy storage operation cost, the economic operation objective function in S1 is constructed and expressed as the following formula: P DG1 (t)+P DG2 (t)=P bg (t)d bg Among them, a DG1 A cost parameter for the DG configured for bus 1; a DG2 Cost parameter 1 for the DG configured for bus 2; b DG1 Another cost parameter for the DG configured for bus 1; b DG2 Another cost parameter for the DG configured for the network bus; P UG1 (t) represents the actual interaction power between microgrid bus 1 and the main grid during time period t (positive values ​​indicate power purchase from the main grid, negative values ​​indicate power sale to the main grid); P UG2 (t) represents the actual interaction power between microgrid bus 2 and the main grid during time period t (positive values ​​indicate power purchase from the main grid, negative values ​​indicate power sale to the main grid); P DG1 (t) represents the actual power generation of DG1 configured on microgrid bus 1 during time period t; P DG2 (t) represents the actual power generation of DG2 configured on microgrid bus 2 during time period t; P ESS1 (t) represents the actual power of ESS1 configured on microgrid bus 1 during time period t (positive value indicates discharge, supplying power to the microgrid; negative value indicates charging, absorbing electrical energy from the microgrid); P ESS2 (t) represents the actual power of ESS2 configured on microgrid bus 2 during time period t (positive value indicates discharge, supplying power to the microgrid; negative value indicates charging, absorbing energy from the microgrid); λ(t) is the purchase price of electricity from the main grid, which includes the basic price and the fluctuating price. The basic price is calculated based on the maximum demand, and the fluctuating price is determined based on the real-time price curve; c ESS1 The power generation cost of ESS1 configured for microgrid bus 1; c ESS2 The power generation cost of ESS2 configured for microgrid bus 2; minF OP Let be the overall objective function.

4. The method for optimizing the scheduling of industrial park microgrids based on active adjustment of electric furnace load as described in claim 3, characterized in that: In step S1, the power generation of the thermal power unit is constrained by the blast furnace gas volume, as expressed by the following formula: P DG1 (t)+P DG2 (t)=P bg (t)d bg Among them, P bg (t) represents the blast furnace gas production during time period t; d bg This indicates the conversion relationship between the power generated by thermal power plants through the consumption of blast furnace gas and the amount of blast furnace gas produced. The maximum demand constraint that the maximum exchange power between the microgrid and the public power grid in S2 meets is expressed by the following formula: Among them, P UG1,max and P UG2,max This represents the maximum power demand of bus 1, bus 2, and the public power grid.

5. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load as described in claim 2, characterized in that: S2 further includes the following steps: Initialize the maximum power demand limits for bus 1 and bus 2 based on the historical maximum power demand. Calculate the actual interactive power between bus 1 and bus 2, and determine whether it exceeds the maximum demand power limit; If the maximum power demand limit is exceeded, the maximum power demand limit is iteratively adjusted according to a preset step size to obtain a new minimum value of the maximum power demand.

6. The industrial park microgrid optimization scheduling method based on active adjustment of electric furnace load according to claim 5, characterized in that: S2 further includes the following steps: The power balance equation is set up and expressed as the following formula: Where, η B2B The conversion efficiency of back-to-back converters in a microgrid; P B2B1 (t), P B2B2 (t) represents the transmission power from bus 1 to bus 2 and from bus 2 to bus 1; P RES1 (t), P RES2 (t) represents the power output of the new energy source for busbar 1 and busbar 2, P L1 (t), P L2 (t) represents the load on busbar 1 and busbar 2.

7. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load as described in claim 2, characterized in that: S3 further includes the following steps: Peak electricity pricing period optimization: Actively reduce load by lowering the tap level of electric furnace transformers and slow down electricity production by extending the cycle according to production scheduling. Unit energy consumption optimization: By adopting process measures such as increasing the proportion of molten iron and adjusting the scrap steel ratio in the converter, the unit energy consumption is reduced, which indirectly reduces the electricity demand.

8. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load according to claim 2, characterized in that: S3 also includes the following steps: Using the electric furnace's speed setting as an adjustment variable, and adding constraints to the model, it can be expressed as the following formula: P L (t)=P Ls (t)+P Lad (t) The load in the microgrid consists of fixed loads and adjustable loads from electric furnaces, where P L (t), P Ls (t), P Lad (t) represents the total microgrid load, fixed load, and adjustable load, respectively.

9. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load as described in claim 8, characterized in that: S3 also includes the following steps: The following constraint model is represented by the following formula: Among them, the power demand of adjustable loads is determined by different gear levels, b i It is a 0-1 variable, representing the state variable of the i-th gear of the electric furnace, P. i This represents the power requirement of the electric furnace when it is in the i-th gear.

10. The method for optimized scheduling of industrial park microgrids based on active adjustment of electric furnace load according to claim 9, characterized in that: S3 also includes the following steps: The following constraint model is represented by the following formula: Among them, the electric furnace load has a fixed daily electricity demand, C Lad This is the daily electricity requirement for the electric furnace.

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