Energy scheduling method for power grid peak shaving

Abstract

The present application relates to the technical field of power grid, disclose a kind of energy scheduling method of power grid peak shaving, comprising the following steps: S1, establish gas network hydrogen doping model, the relationship between hydrogen doping ratio and hydrogen doping cost is calculated by gas network hydrogen doping model;S2, establish risk assessment model, calculate the risk loss caused by source and load volatility;S3, establish peak shaving model, calculate peak shaving cost;S4, establish multi-zone scheduling model, with the minimum total cost as the goal, under the constraint condition, the model is solved using alternating multiplier algorithm, the upper limit value of gas network hydrogen doping is optimized.The risk assessment model of the present application quantifies the risk brought by source and load uncertainty to the system, can provide reference for decision makers, improve the accuracy of scheduling strategy, the peak shaving model involves the equipment in hydrogen energy comprehensive utilization link in the peak shaving of power system, suppresses load fluctuation, realizes peak clipping and valley filling, changes the proportion of peak shaving cost by adjusting economic coefficient, realizes different peak shaving effect.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an energy dispatching method for power grid peak shaving. Background Technology

[0002] The transformation of the energy structure has led to increased randomness and volatility in the power system. The anti-peak-shaving characteristics of green power generation methods such as wind and solar power have exacerbated the peak-shaving pressure on the power system. Traditional thermal power peak-shaving methods suffer from drawbacks such as long response times and limited output in the context of low-carbon transformation. Hydrogen power generation, such as electrolyzers, hydrogen fuel cells, and hydrogen-blended gas turbines, possesses advantages in rapid response and flexible adjustment capabilities, making it an important means of achieving grid peak-shaving.

[0003] However, the hydrogen produced by electrolyzing water using wind power is subject to significant supply uncertainty due to the volatility and randomness of wind power output, which in turn affects the hydrogen energy allocation plan in grid peak shaving. On the other hand, in the process of using gas pipelines to achieve hydrogen blending into the gas network, it is necessary to ensure that the proportion of hydrogen blending into the gas network does not exceed the prescribed safety threshold. This leads to the dual challenges of output uncertainty and gas network operation risks for hydrogen blending into the gas network.

[0004] Existing research primarily focuses on minimizing system peak-shaving costs, lacking both an understanding of the peak-shaving characteristics of hydrogen energy and a quantification and assessment of uncertainty risks. It also neglects the impact of wind power volatility on gas grid hydrogen blending strategies and operational risks. Furthermore, current research indicates room for improvement in hydrogen energy utilization, lacking the ability to achieve hydrogen energy interaction in multi-regional collaborative operations. This leads to both unclear risk attitudes among decision-makers and low wind power absorption and hydrogen energy utilization rates within the system. Summary of the Invention

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] An energy dispatching method for power grid peak shaving includes the following steps: S1, establishing a gas grid hydrogen blending model, and calculating the relationship between the hydrogen blending ratio and the hydrogen blending cost through the gas grid hydrogen blending model;

[0007] S2. Establish a risk assessment model to calculate the risk loss caused by source load volatility;

[0008] S3. Establish a peak-shaving model and calculate peak-shaving costs;

[0009] S4. Establish a multi-zone scheduling model with the goal of minimizing total cost. Under constraints, use the alternating multiplier algorithm to solve the model and optimize the upper limit of hydrogen doping in the gas network.

[0010] Further, step S1 includes the following sub-steps:

[0011] S101. Obtain data on the volume of purchased hydrogen, the volume of hydrogen injected into the electrolyzer, the volume of methane output from the methane reactor, and the volume of gas purchased from gas source k.

[0012] S102. Calculate the hydrogen blending ratio based on the data of purchased hydrogen volume, hydrogen volume injected into the electrolyzer, methane volume output from the methane reactor, and purchased gas volume from gas source k. The hydrogen blending ratio satisfies the hydrogen energy balance constraint and the upper limit constraint of the gas network hydrogen blending ratio:

[0013]

[0014] In the formula: The volume of purchased hydrogen at node m during time period t; The volume of hydrogen injected from node m into the electrolyzer during time period t; Let be the volume of methane output from methane reactor j during time period t; This is the upper limit for the hydrogen doping ratio in the gas network; Let K be the volume of gas purchased from gas source k. The hydrogen doping ratio during time period t;

[0015] S103. Calculate the cost of hydrogen blending based on the hydrogen blending ratio. Hydrogen blending in the gas network enables the transportation and use of hydrogen energy through pipelines. As the hydrogen blending ratio in the gas network increases, the pipeline operation and maintenance costs will also increase.

[0016] ;

[0017] In the formula: The hydrogen doping ratio is represented by h and f, which are coefficients of the pipeline operation and maintenance cost function; C is the hydrogen doping ratio. yw To measure the maintenance cost per unit length for transporting a unit volume of mixed gas; and These represent the maintenance costs per unit length for transporting a unit volume of natural gas and pure hydrogen, respectively.

[0018] Furthermore, step S2 uses CVaR to calculate the risk loss caused by source load volatility:

[0019]

[0020]

[0021]

[0022] In the formula: The loss function is the distribution function whose value is no greater than the boundary value h; θ is the confidence level; the calculation is simplified by using a transformation function:

[0023]

[0024]

[0025] Discretize and solve:

[0026]

[0027]

[0028] In the formula: p s h represents the probability of typical scenario s occurring; s The values ​​of random variables in scenario s;

[0029] Wind power output and load forecasts follow a normal distribution, and the forecast deviation is the predicted value minus the actual value.

[0030]

[0031] In the formula: Let t be the total prediction error of the system at time t. Let t be the wind power prediction deviation. Let the load forecast deviation at time t be , then the risk loss cost of the system is:

[0032]

[0033] In the formula, C loss,t Let g1 be the risk loss cost of the system at time t; g2 be the load shedding penalty coefficient; g3 be the wind curtailment penalty coefficient.

[0034] The risk loss caused by source load volatility is:

[0035] .

[0036] Furthermore, in step S3, the difference between the electrical load, the power consumed by the electrolyzer, the power generated by the hydrogen-mixed gas turbine, the power generated by the hydrogen fuel cell, and the wind power output is the net load, and peak shaving is performed on the net load:

[0037]

[0038] In the formula For the number of electrical loads, Let g be the electrical load power at node g during time period t. The number of hydrogen-mixed gas turbines, Let t be the electrical power of the hydrogen-mixed gas turbine a during time period t. For the number of hydrogen fuel cells, Let t be the electrical power of hydrogen fuel cell k during time period t. The number of electrolytic cells, Let t be the electrical power of electrolytic cell i during time period t. For the number of wind farms, The actual output power of the wind farm d during time period t;

[0039] The average net load is:

[0040] .

[0041] Furthermore, in step S4, the total cost includes operating costs, risk costs, and peak-shaving costs. The operating costs include the sum of the unit's operating costs, start-up and shutdown costs, wind turbine operating costs, carbon sequestration costs, solvent loss costs, daily depreciation costs of the carbon capture power plant, gas purchase costs, water feedstock costs, the cost of purchased carbon dioxide feedstock for MR (Metal-Oxygen Fusion) and purchased hydrogen costs, as well as pipeline hydrogen blending costs and carbon trading costs.

[0042]

[0043] In the formula: C total The total cost of a multi-zone hydrogen hybrid integrated energy system; C 1,a The operating cost for region A; C 2,a Risk costs for a single region; C 3,a Let $a$ be the peak-shaving cost for region $a$. N area For the number of regions in the integrated energy system;

[0044] in C 1,a :

[0045]

[0046] In the formula: C SU 、C OP These are the start-up and shutdown costs and operating costs of thermal power units in region a, respectively. C R Solvent loss cost for the carbon capture unit in region a; C Z The depreciation cost of the carbon capture power plant in region a; g cs Cost per unit of carbon sequestration; C YW The maintenance cost of pipelines in a single area; C tra The carbon trading cost for region a; P forecast d ( t ) represents the predicted power output of wind farm d during time period t; Pact d ( t () represents the actual power output of wind farm d during time period t; l wind This represents the operation and maintenance cost coefficient for wind farms. N source The quantity of gas source; l ource,k Let k be the gas price. V source,k ( t () represents the volume of gas purchased from gas source k; l H2O The price of purified water; m H2O This represents the total mass of purified water consumed by the electrolyzer. l H2 For the price of hydrogen; j total w( t Carbon capture unit w Total CO2 captured; The price of purchased CO2; The mass of CO2 purchased from external sources for methane reactor j during time period t; The power of purchased hydrogen injected into node m during time period t; q hHHv is the volumetric calorific value of hydrogen.

[0047] C 2,a :

[0048]

[0049] The peak-shaving target is converted into peak-shaving cost through the peak-shaving economic coefficient. C 3,a

[0050]

[0051] In the formula: e1 is the economic conversion factor for the peak-shaving target. Further, the constraints include transmitted power constraints, tie-line power change rate constraints, tie-line power peak-valley difference constraints, minimum number of power start-stop cycles constraints, tie-line power adjustment direction constraints for adjacent time periods, and tie-line power step-wise constraints.

[0052] Furthermore, the alternating multiplier algorithm includes the following sub-steps:

[0053] S401. Initialize the number of iterations and the algorithm multiplier. k =1, l 0 g , P 0 g (0) P(~)0 g (0);

[0054]

[0055] S402. Solve the mixed-integer second-order cone programming problem for the region participating in energy interaction through the tie line, while updating the region's interaction variables and Lagrange multipliers:` and And pass it on to the adjacent area:

[0056]

[0057] S403. After solving all regions, determine whether the current iteration satisfies the convergence condition:

[0058]

[0059] If the convergence condition is met, the solution is complete; otherwise, update the Lagrange multipliers and continue with the (k+1)th iteration until the convergence condition is met.

[0060] .

[0061] The beneficial effects of this invention are:

[0062] 1. Establish a risk assessment model to quantify the risks brought to the system by the uncertainty of source load. This model can provide a reference for decision-makers and improve the accuracy of scheduling strategies.

[0063] 2. Establish a peak-shaving model to involve equipment in the comprehensive utilization of hydrogen energy in the power system's peak shaving, smooth load fluctuations, and achieve peak shaving and valley filling. By adjusting the economic coefficient, the proportion of peak-shaving costs can be changed to achieve different peak-shaving effects.

[0064] 3. When electrolyzers, gas turbines, and hydrogen fuel cells participate in peak shaving, their synergistic effect can effectively smooth fluctuations in net load, achieving a significant "peak shaving and valley filling" effect. Decision-makers can coordinate the relationship between economic targets and peak shaving targets based on economic coefficients and select appropriate economic coefficients according to the system's operating conditions. For the same region, the inflection point of the upper limit of hydrogen blending in the gas network will also be different for different economic coefficients. It is necessary to determine the optimal upper limit of hydrogen blending in the gas network based on the proportion of peak shaving costs in the total cost to obtain the corresponding dynamic hydrogen blending strategy. Attached Figure Description

[0065] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of the invention.

[0066] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart of the present invention;

[0068] Figure 2 A graph showing electricity load and wind power forecast data;

[0069] Figure 3 This is a graph showing gas load data;

[0070] Figure 4 This is a graph showing the impact of risk loss on costs in region 1.

[0071] Figure 5 This is a graph showing the impact of risk loss on costs in region 2.

[0072] Figure 6 A graph showing the impact of risk loss on costs in region 3;

[0073] Figure 7 A comparison chart of flexibility resource contributions before and after participating in the ancillary services market in Region 1;

[0074] Figure 8 The graph shows the impact of an economic coefficient of 0.1 on system costs in region 1.

[0075] Figure 9 The graph shows the impact of an economic coefficient of 0.4 on system costs in region 1.

[0076] Figure 10 The graph shows the impact of the economic coefficient of 0.7 on the system cost in region 1.

[0077] Figure 11 The diagram shows the dynamic hydrogen doping strategies for different economic coefficients in Region 1.

[0078] Figure 12 The diagram shows the dynamic hydrogen doping strategies for different economic coefficients in Region 2.

[0079] Figure 13 This is a diagram showing the dynamic hydrogen doping strategy for different economic coefficients in Region 3. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0081] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0082] An energy dispatching method for power grid peak shaving includes the following steps:

[0083] S1. Establish a gas network hydrogen doping model and calculate the relationship between the hydrogen doping ratio and the hydrogen doping cost using the gas network hydrogen doping model.

[0084] The construction cost of pure hydrogen pipelines is high. This invention utilizes existing pipelines to achieve large-scale, low-cost transportation and use of hydrogen energy. It calculates the relationship between the hydrogen blending ratio and the cost of hydrogen blending by establishing a gas network hydrogen blending model. Specifically, it includes the following sub-steps:

[0085] S101. Obtain data on the volume of purchased hydrogen, the volume of hydrogen injected into the electrolyzer, the volume of methane output from the methane reactor, and the volume of gas purchased from gas source k.

[0086] S102. Calculate the hydrogen blending ratio based on the data of purchased hydrogen volume, hydrogen volume injected into the electrolyzer, methane volume output from the methane reactor, and purchased gas volume from gas source k. The hydrogen blending ratio satisfies the hydrogen energy balance constraint and the upper limit constraint of the gas network hydrogen blending ratio:

[0087]

[0088] In the formula: The volume of purchased hydrogen at node m during time period t; The volume of hydrogen injected from node m into the electrolyzer during time period t; Let be the volume of methane output from methane reactor j during time period t; This is the upper limit for the hydrogen doping ratio in the gas network; Let K be the volume of gas purchased from gas source k. The hydrogen doping ratio during time period t;

[0089] S103. Calculate the cost of hydrogen blending based on the hydrogen blending ratio. Hydrogen blending in the gas network enables the transportation and use of hydrogen energy through pipelines. As the hydrogen blending ratio in the gas network increases, the pipeline operation and maintenance costs will also increase.

[0090]

[0091] In the formula: The hydrogen doping ratio is represented by h and f, which are coefficients of the pipeline operation and maintenance cost function; C is the hydrogen doping ratio. yw To measure the maintenance cost per unit length for transporting a unit volume of mixed gas; and These represent the maintenance costs per unit length for transporting a unit volume of natural gas and pure hydrogen, respectively.

[0092] S2. Establish a risk assessment model to calculate the risk loss caused by source load volatility;

[0093] The volatility of wind power output and the randomness of load side bring uncertain risks to grid peak shaving, making it difficult to meet users' energy demands. When wind power is in surplus, some wind power is wasted, resulting in curtailment losses for the system; conversely, when wind power is in short supply, some users' energy demands cannot be met, resulting in load curtailment losses for the system. Furthermore, the amount of green hydrogen produced by electrolyzers is closely related to the amount of wind power curtailed. Electrolyzers generally use electricity generated from curtailed wind power for green hydrogen production, but the uncertainty of wind power source and load also affects hydrogen production in electrolyzers, thus affecting the formulation of hydrogen blending strategies. This invention uses CVaR to quantitatively analyze the risk losses brought to the integrated energy system by source and load volatility. CVaR measures the expected value of losses exceeding VaR at a given VaR level, i.e., the average loss in the worst-case scenario. Compared to VaR, CVaR measures tail risk, providing decision-makers with a more comprehensive risk metric. The specific calculation is as follows:

[0094]

[0095]

[0096]

[0097] In the formula: Let θ be the distribution function whose loss function is no greater than the boundary value h; θ is the confidence level.

[0098] The CVaR value is difficult to calculate directly from the above formula, but the calculation can be simplified by using a transformation function:

[0099]

[0100]

[0101] However, the probability density function is difficult to solve. Therefore, we can further discretize the above equation using a set of typical scenarios to solve it:

[0102]

[0103]

[0104] In the formula: p s h represents the probability of typical scenario s occurring; s The values ​​of random variables in scenario s;

[0105] Wind power output and load forecasts follow a normal distribution, and the forecast deviation is the predicted value minus the actual value.

[0106]

[0107] In the formula: Let t be the total prediction error of the system at time t. Let t be the wind power prediction deviation. Let the load forecast deviation at time t be , then the risk loss cost of the system is:

[0108]

[0109] In the formula, C loss,t Let g1 be the risk loss cost of the system at time t; g2 be the load shedding penalty coefficient; g3 be the wind curtailment penalty coefficient.

[0110] The risk loss caused by source load volatility is:

[0111]

[0112] S3. Establish a peak-shaving model and calculate peak-shaving costs;

[0113] In this invention, the difference between electrical load, the power consumed by the electrolyzer, and the power generated by the mixed-hydrogen gas turbine, the power generated by the hydrogen fuel cell, and the wind power output is defined as the net load, which is then used for peak shaving. The electrolyzer consumes surplus wind power to produce green hydrogen, converting electrical energy into hydrogen energy and improving the grid load level during off-peak hours. The mixed-hydrogen gas turbine responds quickly, converting mixed-hydrogen natural gas into electrical energy, effectively alleviating the pressure on conventional units during peak hours. The hydrogen fuel cell has the same function as the mixed-hydrogen gas turbine, converting hydrogen energy into electrical energy during peak load periods, achieving peak shaving on one hand and reducing carbon dioxide emissions on the other. The electrolyzer, the mixed-hydrogen gas turbine, and the hydrogen fuel cell work synergistically to effectively smooth load fluctuations and reduce the peak-to-valley load difference. The net load can be expressed as:

[0114]

[0115] In the formula

[0116] For the number of electrical loads,

[0117] Let g be the electrical load power at node g during time period t.

[0118] The number of hydrogen-mixed gas turbines,

[0119] Let t be the electrical power of the hydrogen-mixed gas turbine a during time period t.

[0120] For the number of hydrogen fuel cells,

[0121] Let t be the electrical power of hydrogen fuel cell k during time period t.

[0122] The number of electrolytic cells,

[0123] Let t be the electrical power of electrolytic cell i during time period t.

[0124] For the number of wind farms,

[0125] The actual output power of the wind farm d during time period t;

[0126] The average net load is:

[0127]

[0128] S4. Establish a multi-zone scheduling model with the goal of minimizing total cost. Under constraints, use the alternating multiplier algorithm to solve the model and optimize the upper limit of hydrogen doping in the gas network.

[0129] The constraints include transmission power constraints, tie-line power change rate constraints, tie-line power peak-valley difference constraints, minimum power start-stop frequency constraints, tie-line power adjustment direction constraints for adjacent time periods, and tie-line power step-wise constraints, among which the transmission power constraints are:

[0130]

[0131] In the formula: T The scheduling period is P; g The total transaction volume of the g-th connection line; P linluo,g ( t () represents the power transmitted by the g-th tie line at time t;

[0132] Tie line power change rate constraint:

[0133] The rate of change of electrical power traded on each interconnection line should be between the upper and lower limits of the allowable range.

[0134]

[0135] In the formula: P max linluo, g , P min linluo, g These are the maximum and minimum power values ​​for the tie line, respectively.

[0136] Peak-valley difference constraints for tie-line power:

[0137]

[0138] In the formula: T is the scheduling period; P g The total transaction volume of the g-th connection line; Pm g ( t ) represents the state variable of the g-th tie line at time t; b represents the peak-to-valley power difference rate of the tie line.

[0139] Minimum number of start-stop cycles for power consumption:

[0140]

[0141] In the formula: m g ( t ) represents the state variable of the g-th tie line at time t; b represents the peak-to-valley power difference rate of the tie line. c g (t) and n g (t) represents the start / stop state variable of the g-th tie line at time t; y g This is a limit on the number of times the g-th connection line can be opened and closed;

[0142] Directional adjustment constraints for adjacent power periods of tie lines:

[0143] To avoid frequent equipment adjustments, the equipment should meet the following constraints.

[0144]

[0145] In the formula: b + g Let be the 0-1 state variables of the g-th connecting line at time t, representing the uphill and landslide conditions. b + g =1 indicates climbing a slope, when b + g =0 indicates no climbing; when b - g =1 indicates a landslide, when b - g=0 indicates no landslide;

[0146] Tie line power stepping constraint:

[0147]

[0148] The total cost includes operating costs, risk costs, and peak-shaving costs. Operating costs include the sum of the operating costs of the generating units, start-up and shutdown costs, wind turbine operating costs, carbon sequestration costs, solvent loss costs, daily depreciation costs of the carbon capture power plant, gas purchase costs, water feedstock costs, costs of purchased carbon dioxide feedstock for MR (Metallurgical Recycling) and purchased hydrogen, as well as pipeline hydrogen blending costs and carbon trading costs.

[0149]

[0150] In the formula: C total The total cost of a multi-zone hydrogen hybrid integrated energy system; C 1,a The operating cost for region A; C 2,a Risk costs for a single region; C 3,a Let $a$ be the peak-shaving cost for region $a$. N area For the number of regions in the integrated energy system;

[0151] in C 1,a :

[0152]

[0153] In the formula: C SU 、C OP These are the start-up and shutdown costs and operating costs of thermal power units in region a, respectively. C R Solvent loss cost for the carbon capture unit in region a; C Z The depreciation cost of the carbon capture power plant in region a; g cs Cost per unit of carbon sequestration; C YW The maintenance cost of pipelines in a single area; C tra The carbon trading cost for region a; P forecast d ( t ) represents the predicted power output of wind farm d during time period t; P act d (t () represents the actual power output of wind farm d during time period t; l wind This represents the operation and maintenance cost coefficient for wind farms. N source The quantity of gas source; l ource,k Let k be the gas price. V source,k ( t () represents the volume of gas purchased from gas source k; l H2O The price of purified water; m H2O This represents the total mass of purified water consumed by the electrolyzer. l H2 For the price of hydrogen; j total w( t Carbon capture unit w Total CO2 captured; The price of purchased CO2; The mass of CO2 purchased from external sources for methane reactor j during time period t; The power of purchased hydrogen injected into node m during time period t; q hHHv is the volumetric calorific value of hydrogen.

[0154] C 2,a :

[0155]

[0156] The peak-shaving target is converted into peak-shaving cost through the peak-shaving economic coefficient. C 3,a

[0157]

[0158] In the formula: e1 is the economic conversion factor for the peak-shaving target.

[0159] Regions a, b, and c interact with each other via tie lines. The low-carbon economic dispatch of a single region operating independently can be linearized into a MISOCP problem, allowing for the solution of low-carbon economic dispatch plans for each region. The low-carbon economic dispatch problem involving multiple regions requires distributed coordination optimization across multiple regions using the ADMM algorithm, which offers good convergence performance while ensuring the privacy of each region.

[0160] A dynamic hydrogen doping strategy was formulated by using the controlled variable method to study the impact of each factor on the system cost and to develop the dynamic hydrogen doping strategy. The specific steps are as follows:

[0161] Step 1: Keep other variables constant, change the risk loss in fixed steps, and compare the impact of risk loss on the trend of system cost changes.

[0162] Step 2: By comparing the peak-shaving effect of hydrogen energy with and without introducing peak-shaving costs, the effectiveness of the hydrogen energy peak-shaving process is verified.

[0163] Step 3: Keep other variables constant, change the economic coefficient with a fixed step size, compare the impact of the economic coefficient on the trend of system cost change, and obtain the optimal upper limit of hydrogen doping under different economic coefficients.

[0164] Step 4: Based on the optimal hydrogen doping upper limit obtained in Step 3, formulate corresponding dynamic hydrogen doping strategies for different regions and different economic coefficients.

[0165] Specifically, the electricity load, wind power data, and gas load data for the three regions are as follows: Figure 2 and Figure 3 As shown,

[0166] Risk loss analysis, the impact of risk loss (i.e., risk coefficient) on operating costs and total risk costs in the three regions, as follows: Figure 4 , Figure 5 and Figure 6 As shown, to mitigate risk losses, decision-makers will increase the output of conventional units with high controllability during the scheduling process to reduce potential future system risks, significantly lowering risk costs. However, the growth rate of risk costs gradually decreases with increasing risk losses, eventually reaching zero. Therefore, decision-makers need to comprehensively consider both the system's economics and risks when formulating scheduling plans.

[0167] Peak shaving analysis, taking Region 1 as an example, shows the comparison of the output of flexible resources in Region 1 before and after the system participates in peak shaving, as shown in the figure below. Figure 7 As shown, during the off-peak period from 00:00 to 08:00, the output of the electrolyzers in Scenario 4 is significantly higher than that in Scenario 3. The overall output of the electrolyzers in Scenario 4 is 730 MW higher than that in Scenario 3. After the electrolyzers participate in peak shaving, they increase the net load during the off-peak period, thereby increasing the absorption of wind power, weakening the "anti-peak shaving" characteristics of wind power, and playing a significant "valley filling" role.

[0168] In Scenario 3, the gas turbine's output is mainly concentrated between 13:00 and 14:00, and its output is relatively low. Compared to Scenario 3, in Scenario 4, the gas turbine's output period increases, mainly concentrated between 13:00 and 18:00, and its output is significantly higher than in Scenario 3. The hydrogen fuel cell's output is low in Scenario 3, mainly concentrated between 05:00 and 20:00, while in Scenario 4, its output period is mainly concentrated between 05:00 and 23:00, a significant increase in the output period, with the total output approximately doubling. The gas turbine converts natural gas into electricity, and the hydrogen fuel cell converts hydrogen into electricity; their synergistic effect reduces the net load during peak load periods, thus achieving a "peak shaving" effect. The synergistic effect of the electrolyzer, gas turbine, and hydrogen fuel cell smooths out fluctuations in net load, enabling the system to achieve a significant "peak shaving and valley filling" effect.

[0169] Economic coefficient analysis: For region 1, with economic coefficients set to 0, 0.1, 0.4, and 0.7 respectively, the changes in operating costs for region 1 under different upper limits of hydrogen doping ratios at given economic coefficients are as follows: Figure 8 , Figure 9 and Figure 10 As shown. By Figure 8 , Figure 9 It can be seen that when the peak-shaving economic coefficient is greater than zero and is a constant value, the system's operating cost gradually decreases and then gradually stabilizes as the upper limit of the hydrogen blending ratio increases. Injecting green hydrogen produced from surplus wind power into the gas grid can replace natural gas to provide energy for the gas load, reducing the amount of gas purchased by the gas grid and thus reducing the total cost of the electricity-gas-hydrogen system. When the peak-shaving economic coefficient is 0, the corresponding upper limit inflection point for hydrogen blending in the gas grid is 11%, which is the optimal value. When the peak-shaving economic coefficient is 0.1, the corresponding upper limit inflection point is 13%, which is also the optimal value. When the peak-shaving economic coefficient is 0.4, the corresponding upper limit inflection point is 15%, which is also the optimal value. When the peak-shaving economic coefficient is 0.7, the corresponding upper limit inflection point is 18%, which is also the optimal value. The same applies to regions 2 and 3.

[0170] For Region 1, when the peak-shaving economic coefficients are 0, 0.1, 0.4, and 0.7, the upper limit inflection points of hydrogen doping in the gas network of the system are 11%, 13%, 15%, and 18%, respectively. The corresponding dynamic hydrogen doping strategies are as follows: Figure 11 As shown.

[0171] For Region 2, when the peak-shaving economic coefficients are 0, 0.1, 0.4, and 0.7, the upper limit inflection points of hydrogen doping in the gas network of the system are 13%, 18%, 20%, and 21%, respectively. The corresponding dynamic hydrogen doping strategies are as follows: Figure 12 As shown.

[0172] For region 3, when the peak-shaving economic coefficients are 0, 0.1, 0.4, and 0.7, the upper limit inflection points of hydrogen doping in the gas network of the system are 14%, 17%, 19%, and 22%, respectively. The corresponding dynamic hydrogen doping strategies are as follows: Figure 13 As shown.

[0173] (1) Unless otherwise defined, the same reference numerals in the embodiments and drawings of this disclosure have the same meaning.

[0174] (2) The accompanying drawings of the embodiments of this disclosure only involve the structures involved in the embodiments of this disclosure. Other structures can be referred to the general design.

[0175] (3) For clarity, components or areas are enlarged in the drawings used to describe embodiments of the present disclosure. It will be understood that when an element is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be an intermediate element.

[0176] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. An energy dispatching method for power grid peak shaving, characterized in that: Includes the following steps: S1. Establish a gas network hydrogen doping model and calculate the relationship between the hydrogen doping ratio and the hydrogen doping cost using the gas network hydrogen doping model. S2. Establish a risk assessment model to calculate the risk loss caused by source load volatility; S3. Establish a peak-shaving model and calculate peak-shaving costs; S4. Establish a multi-zone scheduling model with the goal of minimizing total cost. Under constraints, use the alternating multiplier algorithm to solve the model and optimize the upper limit of hydrogen doping in the gas network. Step S1 includes the following sub-steps: S101. Obtain data on the volume of purchased hydrogen, the volume of hydrogen injected into the electrolyzer, the volume of methane output from the methane reactor, and the volume of gas purchased from gas source k. S102. Calculate the hydrogen blending ratio based on the data of purchased hydrogen volume, hydrogen volume injected into the electrolyzer, methane volume output from the methane reactor, and purchased gas volume from gas source k. The hydrogen blending ratio satisfies the hydrogen energy balance constraint and the upper limit constraint of the gas network hydrogen blending ratio: ； In the formula: The volume of purchased hydrogen at node m during time period t; The volume of hydrogen injected from node m into the electrolyzer during time period t; Let be the volume of methane output from methane reactor j during time period t; This is the upper limit for the hydrogen doping ratio in the gas network; Let K be the volume of gas purchased from gas source k. The hydrogen doping ratio during time period t; S103. Calculate the cost of hydrogen blending based on the hydrogen blending ratio. Hydrogen blending in the gas network enables the transportation and use of hydrogen energy through pipelines. As the hydrogen blending ratio in the gas network increases, the pipeline operation and maintenance costs will also increase. ； In the formula: The hydrogen doping ratio is represented by h and f, which are coefficients of the pipeline operation and maintenance cost function; C is the hydrogen doping ratio. yw To measure the maintenance cost per unit length for transporting a unit volume of mixed gas; and These represent the maintenance costs per unit length for transporting a unit volume of natural gas and pure hydrogen, respectively.

2. The energy dispatching method for power grid peak shaving according to claim 1, characterized in that: Step S2 uses CVaR to calculate the risk loss caused by source load volatility: ； ； ； In the formula: Let θ be the distribution function whose loss function is no greater than the boundary value h; θ is the confidence level. Simplify the calculation by transforming the function: ； ； Discretize and solve: ； ； In the formula: p s h represents the probability of typical scenario s occurring; s The values ​​of random variables in scenario s; Wind power output and load forecasts follow a normal distribution, and the forecast deviation is the predicted value minus the actual value. ； In the formula: Let t be the total prediction error of the system at time t. Let t be the wind power prediction deviation. Let the load forecast deviation at time t be , then the risk loss cost of the system is: ； In the formula, C loss,t Let g1 be the risk loss cost of the system at time t; g2 be the load shedding penalty coefficient; g3 be the wind curtailment penalty coefficient. The risk loss caused by source load volatility is: 。 3. The energy dispatching method for power grid peak shaving according to claim 2, characterized in that: In step S3, the difference between the electrical load, the electrical power consumed by the electrolyzer, the power generated by the hydrogen-mixed gas turbine, the power generated by the hydrogen fuel cell, and the wind power output is the net load, and peak shaving is performed on the net load. ； In the formula For the number of electrical loads, Let g be the electrical load power at node g during time period t. The number of hydrogen-mixed gas turbines, Let t be the electrical power of the hydrogen-mixed gas turbine a during time period t. For the number of hydrogen fuel cells, Let t be the electrical power of hydrogen fuel cell k during time period t. The number of electrolytic cells, Let be the electrical power of electrolytic cell i during time period t. For the number of wind farms, The actual output power of the wind farm d during time period t; The average net load is: 。 4. The energy dispatching method for power grid peak shaving according to claim 3, characterized in that: In step S4, the total cost includes operating costs, risk costs, and peak-shaving costs. Operating costs include the sum of the operating costs of the generating units, start-up and shutdown costs, wind turbine operating costs, carbon sequestration costs, solvent loss costs, daily depreciation costs of the carbon capture power plant, gas purchase costs, water feedstock costs, costs of purchased carbon dioxide feedstock for MR (Metal-Oxygen Harvester) and purchased hydrogen, as well as pipeline hydrogen blending costs and carbon trading costs. ； In the formula: C total The total cost of a multi-zone hydrogen hybrid integrated energy system; C 1,a The operating cost for region A; C 2,a Risk costs for a single region; C 3,a Let $a$ be the peak-shaving cost for region $a$. N area For the number of regions in the integrated energy system; in C 1,a : ； In the formula: C SU 、C OP These are the start-up and shutdown costs and operating costs of thermal power units in region a, respectively. C R Solvent loss cost for the carbon capture unit in region a; C Z The depreciation cost of the carbon capture power plant in region a; g cs Cost per unit of carbon sequestration; C YW The maintenance cost of pipelines in a single area; C tra The carbon trading cost for region a; P forecast d ( t ) represents the predicted power output of wind farm d during time period t; P act d ( t () represents the actual power output of wind farm d during time period t; l wind This represents the operation and maintenance cost coefficient for wind farms. N source The quantity of gas source; l ource,k Let k be the gas price. V source,k ( t () represents the volume of gas purchased from gas source k; l H2O The price of purified water; m H2O This represents the total mass of purified water consumed by the electrolyzer. l H2 For the price of hydrogen; j total w( t Carbon capture unit w Total CO2 captured; The price of purchased CO2; The mass of CO2 purchased from external sources for methane reactor j during time period t; The power of purchased hydrogen injected into node m during time period t; q hHHv is the volumetric calorific value of hydrogen. C 2,a ： ； The peak-shaving target is converted into peak-shaving cost through the peak-shaving economic coefficient. C 3,a ； In the formula: e1 is the economic conversion factor for the peak-shaving target.

5. The energy dispatching method for power grid peak shaving according to claim 4, characterized in that: The constraints include transmitted power constraints, tie line power change rate constraints, tie line power peak-valley difference constraints, minimum power start-stop times constraints, tie line power adjustment direction constraints for adjacent time periods, and tie line power step constraints.

6. The energy dispatching method for power grid peak shaving according to claim 5, characterized in that: The alternating multiplier algorithm includes the following sub-steps: S401. Initialize the number of iterations and the algorithm multiplier. k =1, l 0 g , P 0 g (0) P(~) 0 g (0); ； S402. Solve the mixed-integer second-order cone programming problem for the region participating in energy interaction through the tie line, while updating the region's interaction variables and Lagrange multipliers:` and And pass it on to the adjacent area: ； S403. After solving all regions, determine whether the current iteration satisfies the convergence condition: ； If the convergence condition is met, the solution is complete; otherwise, update the Lagrange multipliers and continue with the (k+1)th iteration until the convergence condition is met. 。

Images