A short-process steel plant participates in peak shaving control of a high-proportion renewable energy system

Abstract

The application discloses a kind of short process steel enterprise participates in the peak shaving control method of high proportion renewable energy system.The short process steel enterprise participates in the peak shaving control method of high proportion renewable energy system of the application includes: design fusion short process steel enterprise's renewable energy peak shaving architecture and build short process steel enterprise load response model;Establish the non-cooperative game process based on grid demand release, enterprise response solution and aggregator information interaction;With the peak shaving demand in high proportion renewable energy power system and enterprise production electricity pricing and incentive double signal response mechanism are formulated;Build source and load double-layer economic dispatching model, hierarchical iteration solution and reach source and load benefit balance;The effectiveness of the peak shaving control method provided in the application is analyzed.The application aims to comprehensively consider the game equilibrium of grid peak shaving demand and short process steel production electricity, to improve the renewable energy consumption rate of power grid and thus improve the grid peak shaving effect, with the advantages of scientific and reasonable, strong applicability, good effect.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power system peak regulation control, and in particular to the problem that the source-load interaction mechanism is unclear when a short-process steel enterprise participates in the peak regulation of a power system containing a high proportion of renewable energy, which limits the system peak regulation effect, and proposes a short-process steel enterprise participating in high-proportion renewable energy system peak regulation control method. BACKGROUND

[0002] With the continuous expansion of large-scale renewable energy grid connection and load peak-valley difference, the pressure on power system peak regulation is increasing. The traditional peak regulation mode dominated by the power generation side faces problems such as insufficient regulation flexibility and declining economic efficiency, and it is urgent to improve the system regulation capacity through source-load interaction. Demand side response as a key technology to realize source-load cooperation can optimize power grid operation efficiency through flexible load adjustment. Research shows that industrial load, due to its large scale and high adjustable potential, has become the focus of demand side resource exploration. Among many demand side loads, short-process steel enterprises, which have great demand response potential, have become a key resource for power system peak regulation and renewable energy consumption. The traditional industrial load peak regulation control method faces double contradictions in guiding industrial load peak regulation and renewable energy consumption: a single price signal cannot balance enterprise production electricity and power grid peak regulation demand, and pure incentive compensation is easy to cause "free riding" behavior, leading to imbalance of source-load benefit distribution. Existing technologies have achieved some results in guiding short-process steel enterprises to participate in power grid peak regulation. However, due to imperfect information interaction mechanism in the peak regulation process, the equilibrium and stability of the game equilibrium solution are restricted.

[0003] Therefore, a new technical solution is needed to solve this problem. SUMMARY

[0004] In view of the problems in the prior art, the purpose of the present application is to provide a scientific and reasonable, highly applicable, and extremely effective short-process steel enterprise participating in high-proportion renewable energy system peak regulation control method, which aims to comprehensively consider the game equilibrium of power grid peak regulation demand and short-process steel enterprise production electricity to improve the renewable energy consumption rate of the power grid and thus improve the power grid peak regulation effect.

[0005] The present application provides a short-process steel enterprise participating in high-proportion renewable energy system peak regulation control method, which comprises:

[0006] Constructing a renewable energy system peak regulation architecture integrating short-process steel enterprises and constructing a short-process steel enterprise load response model;

[0007] Taking the power grid dispatching center of the renewable energy peak regulation architecture as the leader and the short-process steel enterprise as the follower in the game to construct a non-cooperative game model;

[0008] formulate the electricity price and incentive dual-signal response mechanism to which the short-process steel enterprises respond in the non-cooperative game model based on the electricity purchasing power and response capacity of the short-process steel enterprise load response model;

[0009] The upper power grid dispatching center takes minimization of system operation cost as an objective function, and the lower short-process steel enterprise takes minimization of comprehensive electricity cost as an objective function, a source-load two-level economic dispatching model is constructed, and an equilibrium solution of the non-cooperative game model under the electricity price and incentive dual-signal response mechanism is solved by hierarchical iteration to achieve source-load benefit balance.

[0010] As a preferred embodiment, the short-process steel enterprise load response model comprises:

[0011]

[0012] In the formula, t represents time, P steel,t is the electricity purchasing power of the short-process steel enterprise, DR,t is the response capacity of the short-process steel enterprise, SF,t and △P SF,t are respectively the total power and response capacity value of J electric arc furnaces as reducible load in the short-process steel enterprise, RF,t and △P RF,t are respectively the total power and response capacity value of I rolling mills as transferable load in the short-process steel enterprise, others,t and △P others,t are respectively the total power and response capacity value of L other devices other than rolling mill load and electric arc furnace load.

[0013] As a preferred embodiment, formulating the electricity price and incentive dual-signal response mechanism to which the short-process steel enterprises respond in the non-cooperative game model comprises:

[0014] According to the climbing ability of the unit, the current peak regulation state of the renewable energy system is evaluated;

[0015] When in normal peak regulation state, the electricity price information π t optimizes the electricity purchasing power of the short-process steel enterprise;

[0016] When the system is in an emergency peak regulation state, the incentive compensation c re,t guides the short-process steel enterprise load dispatch to obtain the response capacity.

[0017] As a preferred embodiment, the electricity price information π t is a real-time electricity price generated based on the node marginal cost theory, and the calculation process comprises:

[0018] Solving the unit commitment model of the power grid dispatching center to determine the operation state of each period of the conventional unit;

[0019] According to the objective function F of the power grid dispatching center and the constraint condition, a Lagrange function is constructed and the partial derivative of the steel enterprise electricity demand is obtained;

[0020] The KKT (Kuhn-Tucker) condition is used to obtain the partial derivative result of the day-ahead real-time electricity price π (π t = [t = 1, 2, …, T]).

[0021] As a preferred embodiment, the calculation formula of the electricity price information π is:

[0022]

[0023] In the formula, P steel is the electricity purchasing power of the short-process steel enterprise; P RES is the renewable energy unit output; P G,t is the thermal power unit output; P base is the background load prediction value; A i , B i , and C j are the coefficient matrices corresponding to P G,i , U i , and P RES,j ; η and σ are the Lagrange multiplier vectors corresponding to the equality constraint and the inequality constraint; and U*i is a T-dimensional vector composed of the operation state of the conventional unit i at each time period determined by the unit commitment model.

[0024] As a preferred embodiment, the incentive compensation c re,t is generated by using a four-element cost-benefit function U and is expressed as:

[0025]

[0026] In the formula, ω and C comp are economic coefficients, P need,t and P ave are the peak regulation demand and the historical average peak regulation demand at the time period t, C re,min and C re,max are the upper limit and the lower limit of the incentive compensation, and β is an exponential coefficient.

[0027] As a preferred embodiment, the upper-layer power grid dispatching center takes the minimization of the system operation cost as an objective function and is expressed as:

[0028]

[0029] In the formula, F is the total system operation cost, I is the number of thermal power units, is the output of the thermal power unit i at the time period t, a i , b i , and c iis the coal consumption characteristic coefficient of thermal power unit i; S i is the start-stop cost of thermal power unit i; is the operation state variable of thermal power unit i at time period t, represents the start-up state, represents the shut-down state. J is the amount of renewable energy, P RES,f,j is the predicted output value of renewable energy j at time period t; P RES,j is the scheduled output of renewable energy j at time period t; C pre,j is the degree of electricity cost of renewable energy j, which is obtained by converting the investment and operation and maintenance cost of renewable energy j into the power generation capacity in the whole life cycle.

[0030] As a preferred embodiment, the lower short-process steel enterprise is represented as a target function of minimizing the comprehensive electricity cost as follows:

[0031]

[0032] In the formula, C is the comprehensive electricity cost of the short-process steel enterprise, N is the number of industrial users of the short-process steel enterprise, C RE is the incentive income, C EP is the electricity purchase cost.

[0033] As a preferred embodiment, the equilibrium solution of the non-cooperative game model under the electricity price and incentive double-signal response mechanism is solved by hierarchical iteration, and the source and load benefit equilibrium is reached, which includes:

[0034] The decision of the grid dispatching center is formalized as: where the electricity purchase power P steel,t and the response capacity P DR,t are the optimal responses of the short-process steel enterprise to the electricity price information π t and the incentive compensation c re,t ;

[0035] The decision of the short-process steel enterprise is formalized as: where the electricity price information π t and the incentive compensation c re,t are the optimal responses of the grid dispatching center to the electricity purchase power P steel,t and the response capacity P DR,t .

[0036] The grid strategy space S G is defined, the short-process steel enterprise strategy space S S is defined, and the mapping is constructed, where, since the target function F and C are continuous and convex on the compact convex set, the optimal response corresponds to a closed mapping and has a non-empty convex value, satisfies the Kakutani fixed point theorem condition, and there is a unique strategy (P steel , t, P DR,t, π t , c re,t ) so that the strategies of both parties are optimal responses to each other, that is, a Stackelberg equilibrium is reached to determine the equilibrium solution of the non-cooperative game model.

[0037] As a preferred embodiment, the control method further comprises:

[0038] calculating the system equivalent load according to the renewable energy output prediction value and the load prediction value;

[0039] according to the system equivalent load and the current peak shaving state of the renewable energy system;

[0040] P d is the equivalent load demand of the renewable energy system, P G is the thermal power unit output; R d,t / R u,t is the maximum / minimum ramping constraint of the thermal power unit, and the high-proportion renewable energy system includes the following operating states:

[0041] 1) Q1:

[0042] When the system equivalent load demand is greater than the up-regulation peak capacity, the up-ramping capability of the thermal power unit is satisfied, the system is in a normal peak shaving state, and the short-process steel enterprise meets the peak shaving demand by optimizing the power purchase:

[0043]

[0044] 2) Q2:

[0045] When the system equivalent load demand is greater than the down-regulation peak capacity, the down-ramping capability of the thermal power unit is satisfied, the system is in a normal peak shaving state, and the short-process steel enterprise meets the peak shaving demand by optimizing the power purchase:

[0046]

[0047] 3) Q3:

[0048] When the system equivalent load demand is greater than the up-regulation peak capacity, the up-ramping capability of the thermal power unit is not satisfied, the system is in an "peak clipping" emergency peak shaving state, and the short-process steel enterprise supplements the emergency peak shaving demand of the system by reducing the electric arc furnace load on the basis of ensuring the continuity of production:

[0049]

[0050] 4) Q4:

[0051] When the equivalent load demand of the system is greater than the down-regulation peak capacity, and the climbing ability of the thermal power unit does not meet the requirement, the system is in the "filling valley" emergency peak regulation state, and the short-process steel enterprise supplements the emergency peak regulation demand of the system by transferring the rolling line load on the basis of ensuring the continuity of production:

[0052]

[0053] 5) Q5:

[0054] When the equivalent load demand is greater than the up / down regulation peak capacity, and the climbing ability of the thermal power unit does not meet the requirement, the system is in the "peak clipping and valley filling" emergency peak regulation state, and the short-process steel enterprise supplements the emergency peak regulation demand of the system by reducing the electric arc furnace load and transferring the rolling line load on the basis of ensuring the continuity of production:

[0055]

[0056] 6) Q6:

[0057] When the equivalent load demand is between the adjustable peak capacity range and the climbing ability of the thermal power unit meets the requirement, the peak regulation demand of the system can only rely on the regulation of the thermal power unit, and the short-process steel enterprise can normally produce according to the order plan.

[0058] Compared with the prior art, the present application has the following beneficial effects:

[0059] The present application considers the game equilibrium of the power grid peak regulation demand and the production electricity of the short-process steel enterprise to improve the renewable energy consumption rate of the power grid and improve the peak regulation effect of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0061] Figure 1 The present application is designed to fuse the high-proportion renewable energy peak regulation system architecture of the short-process steel enterprise;

[0062] Figure 2 The present application is designed to fuse the high-proportion renewable energy peak regulation system architecture of the short-process steel enterprise;

[0063] Figure 3 The present application is designed to fuse the high-proportion renewable energy peak regulation system architecture of the short-process steel enterprise;

[0064] Figure 4A photovoltaic scene consumption comparison chart before and after the application of the peak regulation control method proposed in the application example of the present application;

[0065] Figure 5 A wind power scene consumption comparison chart before and after the application of the peak regulation control method proposed in the application example of the present application;

[0066] Figure 6 The peak regulation effect effectiveness verification of the peak regulation control method proposed in the application example of the present application under a long time scale.

[0067] Figure 7 、 Figure 8 、 Figure 9 The sensitivity verification of the peak regulation control method proposed in the application example of the present application in terms of model robustness and practicality promotion. DETAILED DESCRIPTION

[0068] The application will be further described below with reference to the drawings. The following examples are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0069] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0070] The present embodiment is a short-process steel plant participating in high-proportion renewable energy system peak regulation control method, step 1 designs a high-proportion renewable energy system architecture integrating short-process steel plant production load as shown in Figure 1 The architecture diagram is composed of a primary system diagram and a secondary system diagram. The upper area is the primary system diagram, and the grid-side energy is composed of wind power, photovoltaic and thermal power units to ensure power supply load demand. The short-process steel plant, as a typical industrial load, participates in the stable operation of the renewable energy system in the form of demand response load. The lower area is the secondary system diagram, which adopts a double-layer optimization framework of a grid dispatching center-industrial aggregator. The grid dispatching center evaluates the peak regulation state based on renewable energy, background load and thermal power unit output, and generates electricity price and incentive compensation. The industrial aggregator aggregates the short-process steel plant load into a response cluster through a load classification model, and feeds back the power purchase power and response capacity. At the same time, the classification model of each production device of the steel plant is introduced to depict the interaction relationship between tasks and materials of each process of the steel plant, and the "electricity flow-material flow-information flow" transmission mode is improved, breaking through the production safety and peak regulation potential bottleneck under the traditional rigid dispatching mode.

[0071] Step 1, the short-process steel enterprise load response model is constructed.

[0072]

[0073] In the formula, K is the total number of electric arc furnaces, P SF,t is the total power of J electric arc furnaces in the short-process steel enterprise as a reducible load, P RF,t is the total power of I rolling mills in the short-process steel enterprise as a transferable load, P others,t is the total power of L other devices in addition to the rolling mill load and the electric arc furnace load, δ others is the random fluctuation power size when other types of loads are running, which can be considered as an arbitrary value fluctuating in the interval (-5%, +5%).

[0074]

[0075] In the formula, P steel,t is the power purchased by the short-process steel enterprise, P DR,t is the response capacity of the short-process steel enterprise

[0076] Step 2, a non-cooperative game process based on the demand release of the power grid, the response solution of the enterprise, and the information interaction of the aggregator is established.

[0077] In order to describe the interaction between the power grid dispatching center and the short-process steel enterprise, a non-cooperative game model is constructed with the power grid dispatching center as the leader in the game and the short-process steel enterprise as the follower. The game model takes into account the order and dynamics, and the specific game process is as follows. In order to simplify the formula, the following expressions are used for each variable at time t: P d is the equivalent load demand of the renewable energy system; P nf,t is the peak shaving demand of the power grid dispatching center; P RES,t is the output of the renewable energy unit; P G,t is the output of the thermal power unit; R d,t / R u,t is the maximum / minimal ramping constraint of the thermal power unit; π t , C re,t is the electricity price information and incentive compensation; C RE is the incentive income, C EP is the electricity purchase cost; P base is the background load prediction value.

[0078] 1) The power grid dispatching center calculates the system equivalent load according to the renewable energy output prediction value and the load prediction value, and the peak shaving evaluation center evaluates the current peak shaving state in combination with the unit ramping capability. When the system is in a normal peak shaving state, the electricity price information π t is optimized, and when the system is in an emergency peak shaving state, the incentive compensation c re,tThe guiding short-process steel enterprise load dispatching obtains the response capacity, and formulates the next-day unit generation plan under the conditions of meeting the power balance constraint, operation safety constraint and unit operation constraint, with the minimum system operation cost as the target.

[0079] 2) Each short-process steel enterprise in the typical industrial area adjusts the next-day power purchase P according to the received price-incentive double signal, with the minimum comprehensive power consumption cost as the target steel ,t and the response capacity P DR,t Obtains the additional peak shaving benefit, and the multi-agent collaborative aggregation is performed by the industrial aggregator, and the adjusted response result is resubmitted to the grid dispatching center.

[0080] 3) The grid dispatching center reevaluates the current peak shaving state according to the received load power, generates new price information and incentive compensation, and sends them to the short-process steel enterprise again until the optimal peak shaving state is reached after multiple rounds of information interaction, at which time (P steel ,t, P DR,t ,π t ,c re,t ) are the equilibrium solution in the non-cooperative game process.

[0081] Step 3, considering the grid peak shaving demand in the high-proportion renewable energy power system and the enterprise production power, a price and incentive double signal response mechanism is formulated.

[0082] The price in the method is a real-time price generated based on the node marginal cost theory, which is used to guide the optimal dispatching of short-process steel enterprise production power under normal peak shaving by backstepping the marginal influence of industrial user power consumption on system cost through Lagrange multiplier.

[0083] The calculation process is as follows: first, the unit commitment model of the grid dispatching center is solved to determine the operation state of each period of the conventional unit, second, the Lagrange function is constructed according to the objective function F and the constraint conditions of the grid dispatching center, and the partial derivative of the steel enterprise power demand is taken, and finally the KKT condition is used to obtain the day-ahead real-time price π(π t =[t=1,2,…,T]) while solving the output of each period of the unit.

[0084]

[0085] In the formula, A i , B i , C j are inequality constraints such as system safety constraints and unit operation constraints, and P G,i , U i , P RES,jcorresponding coefficient matrix; η, σ are Lagrange multiplier vectors corresponding to equality constraints and inequality constraints; U*i is a T-dimensional vector composed of the operating state of conventional unit i at each time period determined by the unit commitment model; P steel,t , P base,t is a T-dimensional column vector composed of the purchase power submitted by the short-process steel enterprise to the grid dispatching center and the background load prediction value at each time period.

[0086] Incentive price C re,t is used for emergency peak shaving demand, generated by a four-element cost-benefit function U, including the cost of purchasing peak shaving resources, the control cost of purchased resources during regulation, the income obtained from the superior dispatching control center due to peak shaving, which is closely related to the current peak shaving amount and the closeness of peak shaving demand. The greater the peak shaving amount and the more urgent the peak shaving demand, the more income is obtained.

[0087]

[0088] In the formula: ω, C comp is the economic coefficient, P need,t , P ave is the peak shaving demand at time period t and the historical average peak shaving demand, C re,min , C re,max is the upper limit and lower limit of the incentive compensation.

[0089] The various operating states of the high-proportion renewable energy system are shown in FIG. Figure 3 , and the specific analysis is as follows:

[0090] 1) Q1:

[0091] When the system equivalent load demand is greater than the upper peak shaving capacity, the climbing ability of the thermal power unit is satisfied, the system is in normal peak shaving state, and the short-process steel enterprise meets the peak shaving demand by optimizing the purchase power:

[0092]

[0093] 2) Q2:

[0094] When the system equivalent load demand is greater than the lower peak shaving capacity, the climbing ability of the thermal power unit is satisfied, the system is in normal peak shaving state, and the short-process steel enterprise meets the peak shaving demand by optimizing the purchase power:

[0095]

[0096] 3) Q3:

[0097] When the system equivalent load demand is greater than the up-regulation peak capacity, and the up-regulation climbing ability of the thermal power unit does not meet the requirement, the system is in an "peak clipping" emergency peak regulation state. The short process steel enterprise supplements the system emergency peak regulation demand by reducing the electric arc furnace load on the basis of ensuring production continuity.

[0098]

[0099] 4) Q4:

[0100] When the system equivalent load demand is greater than the down-regulation peak capacity, and the down-regulation climbing ability of the thermal power unit does not meet the requirement, the system is in a "valley filling" emergency peak regulation state. The short process steel enterprise supplements the system emergency peak regulation demand by shifting the rolling line load on the basis of ensuring production continuity.

[0101]

[0102] 5) Q5:

[0103] When the equivalent load demand is greater than the up / down regulation peak capacity, and the up / down regulation climbing ability of the thermal power unit does not meet the requirement, the system is in a "peak clipping and valley filling" emergency peak regulation state. The short process steel enterprise supplements the system emergency peak regulation demand by reducing the electric arc furnace load and shifting the rolling line load on the basis of ensuring production continuity.

[0104]

[0105] 6) Q6:

[0106] When the equivalent load demand is within the range of the adjustable peak capacity and the climbing ability of the thermal power unit meets the requirement, the system peak regulation demand can be adjusted only by the thermal power unit. The short process steel enterprise can produce normally according to the order plan.

[0107] Step 4, a source-load two-layer economic dispatching model is constructed, and a source-load benefit balance is reached by layer-by-layer iteration.

[0108] The upper-layer power grid dispatching center takes the minimization of system operation cost as the target, and the decision variables cover the start-stop state of the thermal power unit, the output plan and the renewable energy dispatching output. The target function can be decomposed into the coal consumption cost of the thermal power unit (including the start-stop cost) and the renewable energy dispatching cost. The constraint conditions include the power balance constraint, the unit operation constraint and the system safety constraint and other safety operation limits.

[0109]

[0110] In the formula: F is the total system operation cost, I is the number of thermal power units, is the output of the thermal power unit i at the time period t, a i , b iWith c i is the coal consumption characteristic coefficient of thermal power unit i; S i is the start-stop cost of thermal power unit i; is the operating state variable of thermal power unit i at time period t, represents the start-up state, represents the shutdown state. J is the amount of renewable energy, P RES,f,j is the predicted output value of renewable energy j at time period t; P RES,j is the scheduled output of renewable energy j at time period t; C pre,j is the degree of electric cost of renewable energy j, which is obtained by converting the investment and operation and maintenance cost of renewable energy j to the power generation capacity in the whole life cycle.

[0111] The upper target function constraint conditions include:

[0112] 1) Power balance constraint

[0113]

[0114] In the formula: P base,t is the predicted value of the background load at time period t.

[0115] 2) Upper and lower power constraints

[0116] For thermal power units:

[0117]

[0118] In the formula: is the lower and upper limit of the output of conventional unit i.

[0119] For renewable energy:

[0120]

[0121] 3) Ramp rate constraint

[0122]

[0123] In the formula: R d,i , R u,i is the upper and lower limit of the ramp rate of conventional unit i.

[0124] 4) Minimum start-stop time constraint

[0125]

[0126] In the formula: is the minimum start-up time and minimum shutdown time of conventional unit i.

[0127] The lower layer short process steel enterprise takes minimization of comprehensive electricity cost as a target, and decision variables are electricity purchase power, EAF load reduction and rolling line load transfer amount.

[0128]

[0129] In the formula, C is the comprehensive electricity cost of the short process steel enterprise, and N is the number of industrial users of the short process steel enterprise.

[0130] Further, regarding model solving and equilibrium proof in step 4, specifically:

[0131] The decision of the power grid dispatching center is formalized as: Wherein the electricity purchase power P steel,t and the response capacity P DR,t are the optimal responses of the short process steel enterprise to the electricity price π t and the incentive c re,t .

[0132] The decision of the short process steel enterprise is formalized as: Wherein the electricity price π t and the incentive c re,t are also the optimal responses of the power grid dispatching center to the electricity purchase power P steel,t and the response capacity P DR,t .

[0133] The solving process of the double-layer economic dispatching model of the application is as shown in Figure 3 The specific process of analyzing the game equilibrium of the model is as follows:

[0134] The strategy space S G of the power grid is defined, the strategy space S S of the short process steel enterprise is defined, and the mapping is constructed, wherein, since the objective function F and C are continuous and convex on a compact convex set, the optimal response pair corresponds to a closed mapping and has a non-empty convex value, satisfies the Kakutani fixed point theorem condition, and it is known that there is a unique strategy (P steel , t, P DR,t , π t , c re,t ) and makes the strategies of both parties optimal responses to each other, that is, a Stackelberg equilibrium is reached.

[0135] Step 5, analyze the effectiveness of the peak shaving control method proposed in the application.

[0136] In terms of promoting the consumption of renewable energy photovoltaic of the power grid, the comparison chart of photovoltaic scene consumption before and after the application of the peak shaving control method is as shown in Figure 4 .

[0137] In promoting the consumption amount of the grid renewable energy wind power, the wind power scene consumption comparison chart before and after the application of the control peak shaving method in the application example is as shown in Figure 5

[0138] In the long time scale of the peak shaving effect, the peak shaving effect comparison chart of the peak shaving control method in the application example of the application in a week long time scale is as shown in Figure 6

[0139] In the robustness and practicality of the model, the verification effect of the peak shaving control method in the application example of the application in different short process steel production load parameters is as shown in Figure 7 Figure 8 Figure 9

[0140] Application example

[0141] Based on a certain power structure, 10 thermal power units with a total capacity of 3005MW and a high proportion of renewable energy system with a total capacity of 880MW wind power field and 960MW photovoltaic power station, the effectiveness of the method is analyzed.

[0142] Three short process steel enterprises are selected to participate in the peak shaving of the high proportion of renewable energy system, and the following three scenarios are set.

[0143] Scenario one: using a single time-of-use price signal mechanism to guide short process steel enterprises to participate in peak shaving, double-layer optimization scheduling, the time-of-use price of large industrial users is shown in Table 1.

[0144] Table 1 Time-of-use price of large industrial users

[0145] Period Interval division Tariff [Yuan per (kW.h) -1 ]]]> Peak 08:00-12:0017:00-21:00 0.8650 Flat 12:00-17:0021:00-24:00 0.5843 Valley 00:00-08:00 0.3036

[0146] Scenario two: using a single real-time price signal mechanism, implementing optimization scheduling based on non-cooperative game for short process steel enterprises.

[0147] Scenario three: using the price-incentive double-signal response mechanism proposed in the application, implementing optimization scheduling based on non-cooperative game for short process steel enterprises, that is, the optimization control method proposed in the application.

[0148] By comparing and analyzing the system operation and wind and light abandonment cost in the three scenarios, the influence of the method on the overall operation cost of the system in different peak shaving scenarios is shown in Table 2.

[0149] Table 2 Comparison of system operation and wind and light abandonment cost in three scenarios (cost / ten thousand yuan)

[0150]

[0151] ​​​​​As can be seen from Table 2, compared with the scenario one in which the power grid guides the short-process steel enterprises to transfer load through the time-of-use price mechanism, the real-time price mechanism in scenario two and the double-signal response mechanism in scenario three significantly reduce the system operation cost. Among them, the system operation cost in scenario two is 1237.42 million yuan, which is 3.43% lower than that in scenario one, and the thermal power operation cost is reduced by 52.09 million yuan. This is mainly due to the optimization of unit commitment based on the node marginal cost theory of dynamic electricity price, which effectively reduces the coal consumption cost of thermal power units. At the same time, the wind curtailment rate and the light curtailment rate in scenario two are reduced from 4.47% and 2.34% in scenario one to 3.58% and 1.75% respectively, which improves the renewable energy consumption rate. The system operation cost in scenario three is further reduced to 1222.76 million yuan, which is 1.19% lower than that in scenario two. Although the thermal power operation cost is slightly higher than that in scenario two, through the double-signal response mechanism, the wind and light curtailment rates are greatly reduced to 1.46% and 0.81% respectively. The peak regulation control method proposed in the application inherits the advantages of dynamic electricity price, and at the same time of accurately matching load regulation and renewable energy fluctuation, it adds incentive compensation, improves the peak regulation enthusiasm and potential of short-process steel enterprises, and further alleviates the problem of wind and light curtailment.

[0152] The above describes the specific embodiments of the application in combination with the drawings, but is not a limitation on the protection scope of the application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art without creative labor on the basis of the technical solutions of the application are still within the protection scope of the application.

[0153] The above describes the specific embodiments of the application in combination with the drawings, but is not a limitation on the protection scope of the application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art without creative labor on the basis of the technical solutions of the application are still within the protection scope of the application.

Claims

1. A method for short process steel plant participation in peak shaving control of high renewable energy penetration systems, characterized by, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. In the formula: t represents time. P steel,t For the power consumption of short-process steel enterprises, P DR,t To meet the response capacity of short-process steel enterprises P SF,t and △ P SF,t These are the short-process steel enterprises that can reduce their operating rates. J The total power and response capacity of the electric arc furnace. P RF,t The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. △ P RF,t As a transferable load in short-process steel enterprises I The total power and response capacity of the rolling mill. P others,t and △ P others,t These are excluding the load of the rolling mill and the load of the electric arc furnace. The total load power and response capacity of other equipment in the unit; K total number of gears of the electric arc furnace, P SF,t total power of the short-process steel plant, J total power of the electric arc furnace, P RF,t total power of the short-process steel plant, I total power of the rolling mill, P others,t total power of other equipment, total power of other equipment, random fluctuation power size when other types of load are running The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. When the system is in normal peak-shaving state, publish electricity price information Optimize the purchase of electricity power of short-process steel enterprises; Publish incentive compensation when system is in emergency peak shaving state Guide short process steel enterprise load dispatching to obtain response capacity; Electricity price information For real-time electricity prices generated based on the marginal cost theory of nodes, the calculation process includes: The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. According to the grid dispatching center objective function F The Lagrange function is constructed with the constraint condition and the partial derivative of the steel enterprise electricity demand is taken. The KKT (Karush-Kuhn-Tucker) condition is used to obtain the partial derivative result of the electricity price information while solving the unit output of each period ; Tariff information The calculation formula is: wherein: P steel is the power purchased by the short-process steel enterprise; P RES is the output of the renewable energy unit; P G,i is the output of the thermal power unit; P base is the background load prediction value; , , is the coefficient matrix corresponding to , , is the coefficient matrix corresponding to , is the Lagrange multiplier vector corresponding to the equality constraint and the inequality constraint; is the conventional unit output determined by the unit commitment model i is the T dimensional vector composed of the operating states of each period; Incentive compensation Using a four-part cost-benefit function Generation, denoted as: In the formula: , is an economic coefficient, , is a time period t peak shaving demand and historical average peak shaving demand , upper and lower limits of incentive compensation, and β is an index coefficient.

2. The method of claim 1, wherein the short process steel plant participates in the peak shaving control of the high renewable energy system, characterized in that, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. wherein: F is the total running cost of the system, I is the number of thermal power generating units, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. is the thermal power generating unit i in the time period t of the output, a i , b i and c i is the coal consumption characteristic coefficient of the thermal power generating unit i ; S i is the start-stop cost of the thermal power generating unit i ; The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. for a thermal power unit i in a time period t an operating state variable, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. = 1 represents a start-up state, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. = 0 represents a shut-down state; J a quantity of renewable energy, is a predicted output value of the renewable energy j in the time period t; is a scheduled output of the renewable energy j in the time period t; is a degree electricity cost of the renewable energy j, which is obtained by converting an investment proposal and operation and maintenance cost of the renewable energy j to the power generation quantity in the whole life cycle.

3. The method of claim 2, wherein the short process steel plant participates in the peak shaving control of the high renewable energy system, characterized in that, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. In the formula: C is the comprehensive electricity cost of the short-process steel enterprise, N is the number of industrial users of the short-process steel enterprise, C RE is the incentive income, C EP is the electricity purchase cost.

4. The method of claim 3, wherein the short process steel plant participates in the peak shaving control of the high renewable energy system, characterized in that, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The power grid dispatch center decision is formalized as: where the electricity purchase power and the response capacity are the optimal responses of the short-process steel enterprise to the electricity price information and the incentive compensation . The short-process steel enterprise decision formalization is: wherein the electricity price information and the incentive compensation are the optimal responses of the grid dispatch center to the electricity purchase power and the response capacity ; Define the power grid strategy space S G Defining the strategic space for short-process steel enterprises S S Construct a mapping , where, due to the objective function F and C The optimal response is continuous and convex on a compact convex set, and its optimal response corresponds to a closed mapping with nonempty convex values, satisfying the Kakutani fixed-point theorem conditions. There exists a unique pair of policies. This ensures that the strategies of both sides are the optimal responses to each other, thus achieving a Stackelberg equilibrium to determine the equilibrium solution of the non-cooperative game model.

5. The method of claim 1, wherein the short process steel plant participates in the peak shaving control of the high renewable energy system, characterized in that, The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. P d Equivalent load demand for renewable energy system, P G Output of thermal power unit; R d,t / R u,t Maximum / minimum ramp constraint of thermal power unit, high proportion of renewable energy system includes the following operating states: 1）Q1: The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. 2）Q2： The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. 3）Q3： The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. 4）Q4: The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. 5）Q5: The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. 6）Q6: The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. The application relates to a control method for a short-process steel enterprise, and belongs to the field of renewable energy system peak regulation. 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