A method for low-carbon dispatching based on carbon price signal and electricity spinning reserve-demand response coordination

Abstract

This invention discloses a low-carbon scheduling method based on carbon price signals for ammonia-electricity conversion and demand response, comprising: constructing a dynamic coupling model of ammonia-electricity conversion, ammonia storage, and ammonia-coal co-firing; a comprehensive demand response model considering thermal comfort and user response rate; and a tiered carbon trading cost model; establishing a comprehensive benefit objective function including carbon reduction benefits, cost savings, and comfort penalties; and using particle swarm optimization to solve for the optimal carbon price range threshold that maximizes comprehensive benefits, including a low carbon price upper limit and a high carbon price lower limit; executing corresponding source-load coordinated scheduling strategies according to the range in which the real-time carbon price falls; monitoring system operating indicators in real time, and executing multi-dimensional deviation correction strategies when indicators deviate from the set range; and outputting real-time operating parameters of each device and system economic and environmental indicators to form a closed-loop scheduling. The technical solution of this invention achieves precise matching between carbon price and source-load resources.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system dispatching technology, specifically to a low-carbon dispatching method based on carbon price signals for electricity-to-ammonia conversion and demand response. Background Technology

[0002] With the advancement of the "dual carbon" goals, integrated energy systems have become the core carrier for integrating renewable energy, fossil fuels, and new energy storage. Ammonia energy, due to its high hydrogen storage density and zero-carbon combustion characteristics, is widely used in integrated energy systems for carbon reduction; however, existing technologies suffer from the following key drawbacks: (1) Lack of coordination between ammonia energy and demand response: Existing ammonia energy utilization research focuses on the energy flow characteristics of a single device (such as the efficiency optimization of ammonia production by water electrolysis), and has not established a dynamic coupling mechanism between electricity-to-ammonia conversion and comprehensive demand response. When wind and solar power output fluctuates, it is impossible to link load regulation with ammonia energy production, resulting in low renewable energy consumption rate (wind and solar curtailment rates often exceed 15%). (2) Extensive utilization of waste heat from power-to-ammonia (P2A): The high-temperature waste heat generated during the power-to-ammonia (P2A) process (such as the P2A reactor outlet temperature reaching 400-500K) is mostly directly discharged without coordination with the heat load demand, resulting in waste of heat energy. At the same time, it is necessary to start an additional gas boiler (GB) to supply the heat load, increasing carbon emissions and operating costs. (3) Disconnection between carbon trading and scheduling strategies: The existing scheduling scheme does not combine the tiered carbon trading mechanism to dynamically adjust the ratio of ammonia and coal co-firing. When carbon prices fluctuate, it is impossible to balance carbon costs and fossil energy consumption costs, resulting in a total system cost increase of more than 8% or insufficient carbon emission reduction effect (carbon emission reduction rate <10%). (4) Lack of a suitable dynamic incentive model: The existing schemes do not effectively incentivize multiple stakeholders to participate in integrated energy dispatch, resulting in low user participation in IDR (Integrated Demand Response), and the existing schemes do not consider multiple stakeholders; (5) Adaptation challenges brought about by the increase in wind and solar penetration: my country’s wind and solar penetration increases by 5%-8% annually, and the regional comprehensive energy curtailment rate may exceed 20% by 2025. The existing dispatch schemes can no longer meet the “demand for carbon reduction and economic balance under the dual carbon targets”.

[0003] Therefore, there is an urgent need for a low-carbon scheduling method that can achieve deep synergy between electricity-to-ammonia conversion, demand response, and carbon trading, and to establish a multi-stakeholder dynamic incentive model to improve IDR user participation and fill the gaps in existing technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a low-carbon scheduling method based on carbon price signals for electro-ammonia conversion and demand response, so as to solve the technical problems existing in the prior art.

[0005] To achieve the above objectives, this invention provides a low-carbon scheduling method based on carbon price signals for electro-ammonia conversion and demand response, comprising: Construct a dynamic coupling model of electricity-ammonia conversion-ammonia energy storage-ammonia-coal co-combustion, a comprehensive demand response model considering thermal comfort and user response rate, and a tiered carbon trading cost model; A comprehensive benefit objective function is established, and based on the particle swarm optimization algorithm, the optimal carbon price range threshold that maximizes the comprehensive benefit is solved, including the upper limit of low carbon price and the lower limit of high carbon price. Based on the range in which the real-time carbon price falls, the corresponding source-load coordinated scheduling strategy is executed; The system monitors operational metrics in real time, and if they deviate from the set range, it executes a multi-dimensional deviation correction strategy. Output real-time operating parameters of each device and system economic and environmental indicators to form a closed-loop scheduling.

[0006] In some embodiments, the dynamic coupling model of electro-ammonia conversion-ammonia energy storage-ammonia-coal co-firing includes: Power constraints for electro-ammonia conversion: in, For P2A overall efficiency, For hydrogen production power by water electrolysis, This refers to the nitrogen production power of air separation. Dynamic constraints on ammonia storage: in, It is in a charged state of ammonia energy storage. , These represent the ammonia charging and discharging efficiency, This refers to the discharge power of ammonia energy storage. Coupling constraints for ammonia-coal co-firing: in, This refers to the coal consumption of coal-fired power units. For ammonia consumption power, , These are the lower heating values ​​of ammonia and coal, respectively. For coal-fired power unit efficiency.

[0007] In some embodiments, the integrated response model that considers thermal comfort and user response rate includes: Electrical load regulation constraints: Transferable electrical load ≤0.6× , Interruptible electrical loads ≤0.15× ;in, Total electrical load; Heat load regulation constraints: in, Let t be the actual heat load at time t. Predict the heat load at time t. Let t be the outdoor temperature. For the user's indoor temperature, To set the temperature, Thermal comfort correction factor This is the outdoor temperature compensation coefficient.

[0008] In some embodiments, the integrated response model that considers thermal comfort and user response rate further includes: User response rate: This reflects the level of engagement users have historically had with responding to overall demand. Where n is the number of IDR calls in the past three months. This represents the response validity coefficient for the i-th call. This represents the actual interruptible load of the user during the i-th call. This represents the theoretical maximum interruptible load for the user during the i-th call; IDR compensation price calculation: in, The price for interruptible load compensation at time t. Based on the compensation price, For users' historical response rates, For response rate rating coefficients, User type coefficient Let be the carbon price at time t. This is the carbon price impact coefficient. Low response excitation coefficient; Load regulation calculation: in, For commercial users, the available electrical load can be transferred at time t. The thermal comfort impact factor, The influence rate is the influence coefficient. This is the maximum load that can be transferred for business users.

[0009] In some embodiments, the tiered carbon trading cost model includes: Tiered carbon trading cost model: in, This indicates the carbon emission share of the system participating in the carbon trading market. This indicates the cost of tiered carbon trading; This represents the benchmark price for carbon trading; d represents the length of the carbon emission range. This indicates the rate of price increase.

[0010] In some embodiments, the comprehensive benefit objective function includes: in, , , These are the carbon reduction benefit weighting coefficient, economic cost weighting coefficient, and thermal comfort penalty weighting coefficient, respectively. Among them, the carbon reduction benefit sub-function is: in, For the system's carbon quota, Actual carbon emissions The actual carbon price; Economic cost subfunction: in, The baseline cost without optimization. The actual cost after optimization; Thermal comfort penalty subfunction: in, The total number of scheduling cycles. Let t be the indoor temperature of the user at time t.

[0011] In some embodiments, the constraints of the comprehensive benefit objective function include: P2A ramp rate constraint, ammonia storage SOC constraint, ammonia doping rate constraint, indoor temperature constraint, IDR response rate constraint, electrical power balance constraint, and thermal power balance constraint.

[0012] In some embodiments, the system operation indicators monitored in real time include wind and solar curtailment rate, carbon emissions, and safety boundaries, wherein the safety boundaries include P2A ramp rate, indoor temperature, and energy storage SOC.

[0013] In some embodiments, the multi-dimensional deviation correction strategy includes: Safety boundary correction: When the ammonia tank pressure is >1.2MPa, reduce the P2A power, increase the ammonia doping rate, and control the pressure below 1.0MPa; Thermal comfort correction: When the temperature is below 16℃, first reduce the non-core heat load transfer rate; if the temperature does not rise within 15 minutes, start the gas-fired boiler for supplemental heating. Indoor temperature for users; Environmental indicator adjustments: When the curtailment rate of wind and solar power exceeds the standard, increase P2A power or increase IDR transfer amount; when carbon emissions exceed the standard, increase ammonia doping rate or expand the scale of IDR interruptibility; when both the curtailment rate and carbon emissions exceed the standard, prioritize increasing ammonia doping rate, and then increase P2A power. Economic indicator revision: When the total cost increase is greater than 5% under high carbon prices, reduce P2A conventional electricity consumption to within 5%, while increasing the IDR interruptible load scale by 5%.

[0014] In some embodiments, the system further includes: outputting real-time operating parameters of each device and system economic and environmental indicators to form a closed-loop scheduling.

[0015] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: This invention provides a low-carbon scheduling method based on carbon price signals for ammonia-electricity conversion to demand response (IDR) coordination. Addressing the shortcomings of existing technologies, such as the lack of coordination between ammonia energy and demand response, inefficient utilization of waste heat from ammonia production, disconnect between carbon trading and scheduling strategies, and low user participation in IDR, this invention constructs a dynamic coupling model of "P2A-ammonia storage-ammonia-coal co-firing," an IDR dynamic model considering thermal comfort and user response rate, and a tiered carbon trading cost model. The optimal threshold for the carbon price determination interval is solved using the particle swarm optimization (PSO) algorithm, implementing source-load coordination strategies of economic priority, carbon reduction-economic balance, and carbon reduction priority, respectively. Simultaneously, a multi-entity dynamic incentive model and a multi-dimensional deviation correction mechanism are established to achieve a 35%-40% reduction in system carbon emissions while keeping the total cost increase within 5%. This invention is adaptable to scenarios with 20%-80% wind and solar penetration rates and different climate zones, requires no reconstruction of existing multi-energy networks, has a short transformation cycle, and strong universality. It can effectively improve the renewable energy absorption rate and user participation, providing technical support for the low-carbon optimized operation of integrated energy systems.

[0016] This invention constructs a dynamic coupling model of "P2A - ammonia storage - ammonia-coal co-firing" to quantify the constraints of the entire ammonia energy flow chain, achieving real-time closed-loop regulation of ammonia production, storage, and consumption, and solving the dynamic matching problem between ammonia energy and wind and solar power output. It proposes a carbon price hierarchical triggering mechanism based on the PSO algorithm, optimizing the optimal threshold of the carbon price determination interval to achieve precise dynamic adaptation of "carbon price - source-load resources," filling the technological gap in the deep integration of carbon trading and scheduling strategies. It establishes an IDR model considering thermal comfort and user response rate to improve load regulation accuracy and avoid the conflict between user experience and carbon reduction goals in existing technologies. It proposes an IDR incentive mechanism driven by both thermal comfort and user response rate, breaking through the limitations of traditional single-price incentives, improving load regulation accuracy by 30% and increasing user participation rate by more than 40% compared to existing solutions. Finally, it designs a multi-dimensional deviation correction strategy and energy balance constraint system to ensure the safety, feasibility, and robustness of the scheduling scheme, adapting to different climate zones and wind and solar penetration scenarios. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a low-carbon scheduling method based on carbon price signals for electro-ammonia conversion and demand response, according to an embodiment of the present invention. Figure 2 A schematic diagram of the dynamic coupling process of electricity-to-ammonia conversion-ammonia energy storage-ammonia-coal co-firing; Figure 3 A schematic diagram of the IDR process for thermal comfort and user responsiveness; Figure 4 This is a schematic diagram of the carbon price range threshold optimization solution process based on the PSO algorithm of this invention. Detailed Implementation

[0018] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a further detailed explanation of the proposed low-carbon scheduling method based on carbon price signals for electro-ammonia conversion and demand response. The advantages and features of the present invention will become clearer from the following description. It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions, used only to facilitate and clearly illustrate the embodiments of the present invention. Please refer to the accompanying drawings to make the objectives, features, and advantages of the present invention more apparent and understandable. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are only for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to the size, without affecting the effects and objectives achieved by the present invention, should still fall within the scope of the technical content disclosed in the present invention.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0020] The core of this invention lies in constructing a "tiered collaborative rule triggered by carbon price stratification." This involves dynamically matching source-side (electricity-to-ammonia conversion, ammonia-coal co-firing, energy storage) and load-side (integrated demand response) resources through carbon price signals, and combining this with particle swarm optimization (PSO) algorithm to achieve the optimal solution for the carbon price determination interval threshold. Please refer to [link to relevant documentation]. Figure 1 The technical solution of the present invention specifically includes the following steps: Step S1: Construct mathematical models of the core system equipment, including: 1. Dynamic coupling model of electro-ammonia conversion (P2A) - ammonia energy storage - ammonia-coal co-firing (1) Power constraints for electro-ammonia conversion: In the formula, where The overall efficiency of P2A is 67%-72%. For hydrogen production power by water electrolysis, The nitrogen production power is determined by air separation, and : =3:1 (based on) (Reaction stoichiometry), when wind and solar power output fluctuations exceed 20%. : Dynamically adjusted within the range of 2.8:1 to 3.2:1; (2) Dynamic constraints of ammonia energy storage: In the formula, ammonia energy storage in state of charge (20% ≤ ≤80%) , The ammonia charging and discharging efficiencies are 90% and 88%, respectively. This refers to the ammonia energy storage discharge power (i.e., the ammonia consumption power of the coal-fired unit). (3) Coupling constraints of ammonia-coal co-combustion: In the formula, This refers to the coal consumption of coal-fired power units. ammonia consumption power ( = ), , The lower heating values ​​are 18.6 MJ / kg for ammonia and 29.3 MJ / kg for coal. For coal-fired power units, the efficiency is 42%-45%, and the ammonia doping rate K(t) = / ( + (10%≤K(t)≤30%).

[0021] Please see Figure 2 , Figure 2 This is a dynamic coupling flow chart of an embodiment of the present invention, which includes an electro-ammonia-ammonia energy storage-ammonia-coal co-combustion. The process involves driving an electrolytic water (EL) unit to produce hydrogen and an air separation unit (ASU) to produce nitrogen. The hydrogen and nitrogen then enter the electro-ammonia (P2A) unit to synthesize ammonia. The synthesized ammonia is injected into the ammonia energy storage system for storage. The energy storage system releases ammonia to the coal-fired power unit according to scheduling needs, achieving co-combustion of ammonia and coal. Part of the heat energy generated by combustion is used to generate electricity for the steam turbine generator unit, and the other part is directly supplied to the heat load. Simultaneously, the real-time state of charge (SOC) of the ammonia energy storage is fed back to the front end for dynamic adjustment of the synthesized power of the P2A unit.

[0022] 2. Dynamic IDR model considering thermal comfort-incentive price (1) Electrical load regulation constraint: transferable electrical load ≤ 0.6× ( (Total electrical load), interruptible electrical load ≤ 0.15× ; (2) Heat load regulation constraints (related to thermal comfort): In the formula, Let t be the actual heat load (kW) at time t. The predicted heat load (kW) at time t. Let t be the outdoor temperature. For the user's indoor temperature (16℃≤ ≤24℃), Set the temperature (default 20℃). Thermal comfort correction factor (0.05 for residential, 0.04 for commercial, 0.02 for industrial; the larger the temperature deviation, the greater the load correction). The outdoor temperature compensation coefficient is 0.02, when Start when the temperature is below 10 degrees Celsius to avoid a sudden drop in indoor temperature at low temperatures. When the temperature is below -10℃, the thermal comfort limit range is widened to 15-25℃. The coefficient has been increased to 0.03 to avoid user discomfort caused by adjusting the heat load at low temperatures.

[0023] (3) Dynamic incentive price model (incentive adjustment layer): ① User response rate reflects the degree of user engagement with IDR in the past: In the formula, n represents the number of IDR calls in the past three months. This represents the response validity coefficient for the i-th call (1 for a successful response, 0.5 for a partial response, and 0 for no response). This represents the actual interruptible load (KW) of the user in the i-th call. The theoretical maximum interruptible load (KW) of the user in the i-th call is determined based on the user's load characteristics. The value ranges from [0.6, 1.0]. Values ​​below 0.6 are considered low-response users and require stronger incentives; values ​​above 0.9 are considered "high-response users" and incentives can be appropriately reduced.

[0024] ②Calculation of IDR compensation price: = In the formula, The interruptible load compensation price at time t is (RMB / kW·h). The basic compensation price (RMB / kW·h). For users' historical response rates, Response rate rating coefficient User type coefficients (residential 1.2, commercial 1.0, industrial 0.8), Let t be the carbon price (yuan / ton). The carbon price impact coefficient is 0.01. The low response excitation coefficient is 0.005 (the lower the response rate, the greater the compensation increase).

[0025] (4) Calculation of load adjustment (related to thermal comfort and response rate): In the formula, Let t be the transferable electrical load (kW) for commercial users. Thermal comfort impact factor ( The value is 1 in the core interval, 0.5 in the extreme interval, and 0 outside the extreme interval. The influence rate influence coefficient ( ≥0.9 is taken as 1.0, 0.7≤ <0.9, take 0.8. (<0.7, take 0.6). Let t be the actual electrical load of the commercial user.

[0026] 3. Tiered carbon trading cost model Under tiered emissions trading, Emissions are divided into different ranges, when When the carbon emission share of the system participating in the carbon trading market is negative, it means that the system's carbon emissions are lower than the standard amount. In this case, the remaining share can be sold at a prescribed price in the corresponding trading center to obtain a certain subsidy. The carbon trading cost can be expressed as the following piecewise function: In the formula, This indicates the cost of tiered carbon trading; This represents the benchmark price for carbon trading; d represents the length of the carbon emission range. Price increase rate. Benchmark price. The default price is 50 yuan / ton, the carbon emission range length d is 500 tons by default, and the price increase rate is... The default value is 0.2, which is compatible with the mainstream parameters of my country's current carbon trading market. Appropriate carbon trading benchmark pricing and reasonable range length settings can promote a synergistic effect between the system's economic performance and carbon emissions.

[0027] Step S2: Use the Particle Swarm Optimization (PSO) algorithm to find the optimal solution for the carbon price determination interval threshold and match it with the cooperative strategy. Please refer to [link to relevant documentation]. Figure 4 ; The core objective is to introduce the PSO algorithm to find the optimal solution for the carbon price range threshold based on a fixed range of high, low, and medium carbon prices (assuming the upper limit of the low carbon price is...). The lower limit of high carbon price is This system aims to maximize its overall benefits and is primarily suited for cold northern regions, high wind and solar penetration scenarios, and industrial parks with strict equipment constraints.

[0028] 1. Objective function construction (maximizing overall benefits) Using "comprehensive system benefits" as the objective function, integrating carbon reduction gains, cost savings, and comfort penalties, the formula is: (in the formula) These are the parameters to be optimized in the PSO algorithm. In each iteration, the fitness F of the objective function is calculated, and the weights are fine-tuned with a perturbation step size of 0.05. When the change in fitness F is less than 0.1%, the final optimal weight coefficients are output. (1) Carbon reduction benefit sub-function: In the formula, For system carbon quotas (unit: tons), Actual carbon emissions (tons / day, emissions vary depending on the range). Actual carbon price (RMB / ton) (2) Economic cost subfunction: In the formula, The baseline cost without optimization is 1.2 million yuan / day. The optimized actual cost (ten thousand yuan / day, including coal cost) P2A power consumption cost IDR compensation costs ) (3) Thermal comfort penalty function: In the formula, This represents the total number of scheduling cycles (288 five-minute cycles are taken for daily scheduling). Let t be the indoor temperature of the user. When the temperature is ≤2°C, the penalty is 0; otherwise, the penalty is accumulated based on the degree of deviation. The penalty for indoor temperatures deviating from the core range (18-22°C) is calculated to avoid excessively sacrificing user experience. 2. Constructing constraints (to ensure the solution is feasible) (1) P2A gradeability constraint: in, The rated power of the P2A equipment is 0.2, and the ramp-up ratio is 0.2. (2) SOC constraint of ammonia energy storage: In the formula, (3) Ammonia doping rate constraint: K(t) = / ( + ()( = ) In the formula, (10%≤K(t)≤30%) (4) Indoor temperature constraints: 16℃≤ ≤24℃, relaxed to 16℃ in northern regions during winter. ≤25℃ (5) IDR response rate constraint: ≥0.6 (6) Power balance: in, Let t be the output power of the wind turbine. Let be the output electrical power of the photoelectric generator at time t. Let t be the power consumption of device P2A. Let be the electrical load power of the system at time t. Let t be the electrical power consumed by the air separation ammonia production equipment. Let t be the charging power of the energy storage device. Let t be the discharge power of the energy storage device at time t; (7) Thermal power balance: in, Let be the waste heat power generated by device P2A at time t. Let t be the heating power of the coal-fired unit. Let t be the heating capacity of the gas-fired boiler (GB). Let be the system's heat load power at time t. Let t be the charging power of the thermal energy storage device. Let t be the discharge power of the thermal energy storage device at time t.

[0029] 3. The design of the PSO algorithm solution process is as follows: (1) Initialize the particle swarm and parameters 50 groups of particles are randomly generated. , Each group of particles must satisfy 30≤ ≤60, 120≤ ≤180 and < Initialize the velocity of each particle ( , The speed range is [ , Import basic data: wind and solar power output forecasts, load data, and equipment parameters (such as P2A rated power of 80MW and ammonia storage of 500MWh).

[0030] Speed ​​update formula: In the formula: k is the current iteration number, , To increase the randomness of the search by using random numbers in the range [0,1], if... > ,but Conversely, take - , Similarly.

[0031] Dynamic weighted calculation model: in, =0.7 (low carbon price range, global search weight, prioritize expanding the solution space to find the low cost threshold). =0.5 (medium carbon price range, balancing search weights, taking into account both carbon reduction and the economy). =0.3 (High carbon price space, local search weight, focusing on fine-tuning the optimal threshold for carbon reduction). =250 yuan / ton (carbon price ceiling, adapted to future carbon market price fluctuations). The price is the real-time carbon price (RMB / ton). / This is the optimal threshold for the current iteration.

[0032] Position update formula: + + In the formula: if <30, then =30, if >60 then take 60. Similarly ( ). > Then the emphasis is on adjustment = (Ensure that the intervals do not overlap).

[0033] (2) Calculate the fitness (objective function value) of each particle. An intelligent fitness function is introduced to dynamically adjust the weights of carbon reduction, economy, and comfort objectives: (3) Dividing carbon price zones: according to , The real-time carbon price λ is divided into three intervals, and corresponding source-load coordination strategies are matched accordingly. (4) Calculate the objective function value: Based on the strategy execution result, calculate quickly. , , Substituting into the objective function, we get (5) Constraint determination: If a particle violates the constraint, then... Imposing penalties ensures the feasibility of a solution. (6) Updating individual optimality and global optimality: Individual optimality (pbest) update is performed on each particle, if the current fitness is... Historical best fitness = Record the corresponding threshold ( , The global optimal (gbest) update is achieved across all particles. Choose the largest one. As gbest, record the corresponding threshold ( , ), that is, the current optimal carbon price range threshold. 4. Source-load matching mechanism (1) Low carbon price range: economic priority strategy Load-side regulation: Transfer over 60% of the available electrical load to low-price periods (00:00-05:00, price 0.358 yuan / kW·h), widening the comfort range for heat load to 16-24℃, and adjusting IDR compensation accordingly. The implementation of this measure raises the maximum allowable load transfer limit for industrial users to 0.7. Commercial users maintained at 0.6 Differentiated adaptation to load characteristics; Source-side adaptation: P2A power is controlled at 30%-50% of rated capacity, only absorbing surplus power from wind and solar power; ammonia doping rate K(t)≤10%, P2A waste heat is given priority to supply immediate heat load and is not stored.

[0034] (2) Medium carbon price range: carbon reduction-economic balance strategy Formula for calculating the absorption space: In the formula: , For interruptible / transferable power of heat load, The heat-to-electric conversion coefficient is used, with 80% allocated to P2A and 20% stored in electrical energy storage. Dynamic adjustment: P2A power is updated every 15 minutes to match wind and solar output; for every 20 yuan / ton increase in carbon price, the ammonia doping rate increases by 5% (maximum 25%); when P2A waste heat is ≥70% of heat demand, the heat load transfer rate is increased to 50%; when P2A waste heat supply is less than 30% of heat demand, the gas turbine (CHP) is started to supplement heat, and the CHP and P2A waste heat supply is prioritized over the gas boiler (GB).

[0035] (3) High carbon price range: carbon reduction priority strategy Source-side dominance: P2A power is increased to 80%-100% of rated capacity; if insufficient, it can consume up to 10% of conventional power; ammonia doping rate K(t) = 30%, and excess ammonia is stored in ammonia energy storage. (t)≥70%); P2A waste heat is fully stored in thermal energy storage and released to replace GB during peak heat periods (06:00-08:00, 18:00-20:00); Load-side coordination: The IDR compensation price is 30%-50% higher than the medium carbon price, and the interruptible load scale is expanded to 15% of the total load. During off-peak hours (00:00-05:00), 10%-15% of non-core heat loads (such as heating in public buildings) are interrupted. The upper limit of conventional electricity consumption shall not exceed 10% of the P2A rated capacity, and conventional electricity during off-peak hours shall be given priority to avoid increasing peak-hour electricity purchase costs.

[0036] Step S3, Real-time Monitoring and Deviation Correction, specifically includes: 1. Monitoring indicators: Curtailment rates (low carbon price ≤ 8%, medium carbon price 8%-10%, high carbon price ≤ 5%), carbon emissions (low carbon price ≤ 4500 tons, medium carbon price ≤ 3900 tons, high carbon price ≤ 3500 tons), safety margin (P2A ramp rate ≤ 20% / h), ≥16℃, energy storage SOC 20%-80%); 2. Adjustment strategy: (1) Safety boundary correction (highest priority): When the ammonia tank pressure is >1.2MPa, immediately reduce the P2A power by 30% / h and simultaneously increase the ammonia doping rate by 5% / h. Within 10 minutes, control the pressure below 1.0MPa; when the P2A ramp rate is >20% / h, the power reduction shall not exceed 15% in a single instance.

[0037] (2) Thermal comfort correction: When the temperature is below 16℃, first reduce the non-core heat load transfer rate by 20%. If the temperature does not rise within 15 minutes, start GB supplementary heating (supplementary heating power = heat load gap × 1.2).

[0038] (3) Environmental protection indicator correction: When the wind curtailment rate exceeds the standard, increase the P2A power by 10% or increase the IDR transfer amount by 15%; when carbon emissions exceed the standard, increase the ammonia doping rate by 5% or expand the IDR interruptible scale by 5%; when both the wind curtailment rate and carbon emissions exceed the standard, prioritize increasing the ammonia doping rate by 5%, and then increase the P2A power by 10%. (4) Economic indicator correction: When the total cost increase is greater than 5% under high carbon price, reduce P2A conventional electricity consumption to within 5% and increase IDR interruptible load scale by 5%.

[0039] Step S4: Output the scheduling scheme, which specifically includes: Based on the above execution results, the real-time operating parameters of each device (P2A power, ammonia doping rate, IDR adjustment) and the system's economic and environmental indicators (total cost, carbon emissions) are output to form a closed-loop scheduling; if the scheduling cycle has not ended, the output parameters are fed back to the parameter acquisition stage for scheduling in the next time period.

[0040] Please see Figure 3 , Figure 3 This invention demonstrates the dynamic optimization process of Integrated Demand Response (IDR). First, it comprehensively collects user basic data, real-time system operation data, and historical response data. Based on this data, the system performs parallel assessments of user indoor thermal comfort impact and user response rate prediction. Then, a decision-making node verifies whether the thermal comfort index and user response rate simultaneously meet preset conditions. If not, a feedback optimization loop is initiated, dynamically adjusting the IDR compensation excitation parameters and correcting the prediction model. Subsequently, a new assessment and prediction are performed, forming an internal iterative optimization process. If the assessment meets the criteria, a specific IDR load adjustment instruction is generated, and the system enters the strategy execution and real-time monitoring phase. Key data during execution is continuously collected and fed back to the system. Finally, verified and effective strategy parameters are archived and stored to update the historical database, thus forming a closed-loop optimization mechanism covering the entire process of "data input - assessment and prediction - decision-making - strategy generation - execution monitoring - feedback archiving." This process is the core of realizing the "dynamic IDR model considering thermal comfort and user response rate" and the "multi-dimensional deviation correction mechanism," ensuring that load adjustment effectively protects user comfort and energy experience while improving system flexibility.

[0041] The low-carbon dispatching method provided by this invention has been applied and verified in a comprehensive energy demonstration zone in northern China. The specific composition and key parameters of this demonstration system are as follows: wind power installed capacity is 150MW, with an annual utilization of 2200 hours; photovoltaic installed capacity is 100MW, with an annual utilization of 1600 hours. The system's conventional power sources include a 300MW coal-fired unit with a power generation efficiency of 43%; a 100MW gas-fired combined heat and power unit with an efficiency of 85%; and a 50MW gas-fired boiler with an efficiency of 90%. The rated power of the electro-ammonia conversion equipment is 80MW, with a comprehensive efficiency of 70%; the supporting ammonia energy storage system has a capacity of 500MWh, with a permissible state-of-charge (SOC) range of 20% to 80%. The system's annual electricity load is approximately 1.2 billion kWh, with a peak-to-valley ratio of 3.2:1; the annual heat load is approximately 800 million kWh, with a peak winter heat load reaching 40MW. The market parameters are set as follows: the time-of-use electricity price is RMB 0.358 / kW·h during the period from 00:00 to 05:00, and RMB 1.031 / kW·h during the periods from 09:00 to 13:00 and from 17:00 to 22:00; the natural gas price is RMB 2.37-3.52 / cubic meter; the carbon price fluctuates within the range of RMB 50-200 / ton; and the basic compensation price for demand response is RMB 2 / kW·h.

[0042] The implementation process and effects at different carbon price levels are as follows: In the low carbon price scenario (λ=45 yuan / ton), the optimal carbon price range thresholds calculated by the particle swarm optimization algorithm are λ1=45 yuan / ton and λ2=155 yuan / ton. During implementation, 40MW of electrical load was transferred to off-peak hours through demand response, and 25MW of thermal load was set as transferable load. The power of the power-to-ammonia conversion equipment was maintained at 32MW (approximately 40% of its rated capacity) and used only to absorb the surplus power of wind and solar power generation. In this scenario, the wind and solar curtailment rate was 7.2%. The implementation results are: the total system operating cost was reduced by 9.5%, carbon emissions were reduced by 7.8%, and the load curve fluctuations were effectively smoothed, reducing the peak-to-valley difference of the system.

[0043] Under the medium carbon price scenario (λ=100 yuan / ton), the optimized thresholds are λ1=48 yuan / ton and λ2=142 yuan / ton. During implementation, the calculated wind and solar power consumption space ΔP_space is 58MW, of which 50MW is allocated to the electricity-to-ammonia conversion equipment (its power is correspondingly increased to 50MW), and 8MW is stored in the electric energy storage system. The ammonia doping rate in ammonia-coal co-firing is increased to 20%, resulting in a 12% reduction in coal consumption for coal-fired units. The implementation results are: a 13.2% reduction in total system cost and a 20.5% reduction in carbon emissions.

[0044] In a high carbon price scenario (λ = 200 yuan / ton), the optimized thresholds are λ1 = 52 yuan / ton and λ2 = 138 yuan / ton. During implementation, the power of the power-to-ammonia conversion equipment was maintained at 72MW (approximately 90% of its rated capacity). To compensate for the shortfall in wind and solar power generation, an additional 6MW of conventional grid power was consumed. The demand response compensation price was increased to 3.6 yuan / kW·h, and the scale of interruptible load regulation was expanded to 45MW. Simultaneously, 30MW of waste heat was released from thermal energy storage to replace the gas boiler. The results showed that the total system cost increased slightly by 3.8%, but carbon emissions were significantly reduced by 38.2%.

[0045] It should be noted that the apparatus and methods disclosed in the embodiments herein can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments herein. In this regard, each block in a flowchart or block diagram may represent a module, program, or part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system to perform the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0046] In addition, the functional modules in the various embodiments of this article can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0047] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A low-carbon scheduling method based on carbon price signals for electricity-to-ammonia conversion and demand response, characterized in that, include: Construct a dynamic coupling model of electricity-ammonia conversion-ammonia energy storage-ammonia-coal co-combustion, a comprehensive demand response model considering thermal comfort and user response rate, and a tiered carbon trading cost model; A comprehensive benefit objective function is established, and based on the particle swarm optimization algorithm, the optimal carbon price range threshold that maximizes the comprehensive benefit is solved, including the upper limit of low carbon price and the lower limit of high carbon price. Based on the range in which the real-time carbon price falls, the corresponding source-load coordinated scheduling strategy is executed: when the carbon price is ≤ the upper limit of the low carbon price, the economic priority strategy is executed; when the upper limit of the low carbon price is < the carbon price is ≤ the lower limit of the high carbon price, the carbon reduction-economic balance strategy is executed; when the carbon price is > the lower limit of the high carbon price, the carbon reduction priority strategy is executed. The system monitors operational metrics in real time, and if they deviate from the set range, it executes a multi-dimensional deviation correction strategy.

2. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, The dynamic coupling model of electro-ammonia conversion-ammonia energy storage-ammonia-coal co-firing includes: Power constraints for electro-ammonia conversion: in, For P2A overall efficiency, For hydrogen production power by water electrolysis, This refers to the nitrogen production power of air separation. Dynamic constraints on ammonia storage: in, It is in a charged state of ammonia energy storage. , These represent the ammonia charging and discharging efficiency, This refers to the discharge power of ammonia energy storage. Coupling constraints for ammonia-coal co-firing: in, This refers to the coal consumption of coal-fired power units. For ammonia consumption power, , These are the lower heating values ​​of ammonia and coal, respectively. For coal-fired power unit efficiency.

3. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, The comprehensive response model, which considers thermal comfort and user response rate, includes: Electrical load regulation constraints: Transferable electrical load ≤0.6× , Interruptible electrical loads ≤0.15× ;in, Total electrical load; Heat load regulation constraints: in, Let t be the actual heat load at time t. Predict the heat load at time t. Let t be the outdoor temperature. For the user's indoor temperature, To set the temperature, Thermal comfort correction factor This is the outdoor temperature compensation coefficient.

4. The collaborative low-carbon scheduling method as described in claim 3, characterized in that, The comprehensive response model, which considers thermal comfort and user response rate, also includes: User response rate: This reflects the level of engagement users have historically had with responding to overall demand. Where n is the number of IDR calls in the past three months. This represents the response validity coefficient for the i-th call. This represents the actual interruptible load of the user during the i-th call. This represents the theoretical maximum interruptible load for the user during the i-th call; IDR compensation price calculation: in, The price for interruptible load compensation at time t. Based on the compensation price, For users' historical response rates, For response rate rating coefficients, User type coefficient Let be the carbon price at time t. This is the carbon price impact coefficient. Low response excitation coefficient; Load regulation calculation: in, For commercial users, the available electrical load can be transferred at time t. The thermal comfort impact factor, The influence rate is the influence coefficient. This is the maximum load that can be transferred for business users.

5. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, The tiered carbon trading cost model includes: in, This indicates the carbon emission share of the system participating in the carbon trading market. This indicates the cost of tiered carbon trading; This represents the benchmark price for carbon trading; d represents the length of the carbon emission range. This indicates the rate of price increase.

6. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, The comprehensive benefit objective function includes: in, , , These are the carbon reduction benefit weighting coefficient, economic cost weighting coefficient, and thermal comfort penalty weighting coefficient, respectively. Among them, the carbon reduction benefit sub-function is: in, For the system's carbon quota, Actual carbon emissions The actual carbon price; Economic cost subfunction: in, The baseline cost without optimization. The actual cost after optimization; Thermal comfort penalty subfunction: in, The total number of scheduling cycles. Let t be the indoor temperature of the user at time t.

7. The collaborative low-carbon scheduling method as described in claim 6, characterized in that, The constraints of the comprehensive benefit objective function include: P2A ramp rate constraint, ammonia storage SOC constraint, ammonia doping rate constraint, indoor temperature constraint, IDR response rate constraint, electrical power balance constraint, and thermal power balance constraint.

8. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, The real-time monitored system operation indicators include wind and solar curtailment rate, carbon emissions, and safety boundaries, which include P2A ramp rate, indoor temperature, and energy storage SOC.

9. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, The multi-dimensional deviation correction strategy includes: Safety boundary correction: When the ammonia tank pressure is >1.2MPa, reduce the P2A power, increase the ammonia doping rate, and control the pressure below 1.0MPa; Thermal comfort correction: When the temperature is below 16℃, first reduce the non-core heat load transfer rate; if the temperature does not rise within 15 minutes, start the gas-fired boiler for supplemental heating. Indoor temperature for users; Environmental indicator adjustments: When the curtailment rate of wind and solar power exceeds the standard, increase P2A power or increase IDR transfer amount; when carbon emissions exceed the standard, increase ammonia doping rate or expand the scale of IDR interruptibility; when both the curtailment rate and carbon emissions exceed the standard, prioritize increasing ammonia doping rate, and then increase P2A power. Economic indicator revision: When the total cost increase is greater than 5% under high carbon prices, reduce P2A conventional electricity consumption to within 5%, while increasing the IDR interruptible load scale by 5%.

10. The collaborative low-carbon scheduling method as described in claim 1, characterized in that, Also includes: Output real-time operating parameters of each device and system economic and environmental indicators to form a closed-loop scheduling.

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