Multi-energy collaborative scheduling method and system for coal-ammonia co-combustion and wind-solar complementation

Abstract

The invention relates to the technical field of power systems and energy management, in particular to a multi-energy collaborative scheduling method and system for coal-ammonia mixed combustion and wind-solar complementary, and the method comprises the steps: dividing a scheduling period into T time periods, and considering the thermal power, ammonia power output, wind power and photovoltaic output and energy storage system state of a coal-ammonia mixed combustion unit in each time period; constructing a multi-energy coupling scheduling mathematical model; a hybrid algorithm based on a shark olfaction algorithm SSA and a harmony search algorithm HS is adopted to solve the multi-energy coupling scheduling mathematical model, during solving, the SSA algorithm is firstly used to carry out early-stage exploration to obtain a plurality of groups of initial optimal solutions, then the HS algorithm is used to carry out later-stage development, the initial optimal solutions are corrected, and optimal solutions are obtained; and obtaining a scheduling strategy of the T time periods according to the optimal solution. According to the method, balance optimization of economical efficiency and cleanliness of the system is achieved, and compared with the prior art, the method has higher scheduling efficiency, lower carbon emission and higher online rapid decision-making capacity.

Description

Technical Field

[0001] This invention relates to the field of power system and energy management technology, and in particular to a multi-energy coordinated dispatching method and system for coal-ammonia co-firing and wind-solar complementarity. Background Technology

[0002] With the introduction of the "dual carbon" target, traditional coal-fired power systems are facing the dual pressure of reducing carbon emissions and improving regulation flexibility. Although the installed capacity of renewable energy has surpassed that of coal-fired power, the output of renewable energy sources such as wind power and photovoltaics is unstable and difficult to dispatch, so there is still a certain gap between renewable energy generation and coal-fired power generation. In the short term, energy security still needs to be guaranteed by coal-fired power.

[0003] In the long run, coal-fired power is transitioning from being the primary source of electricity to a fundamental and system-regulating power source providing reliable capacity, peak shaving, frequency regulation, and other auxiliary functions. Ammonia, as a carbon-free fuel, can be co-fired with coal in coal-fired power units, reducing carbon intensity. The synergy between clean energy sources and coal-ammonia co-fired units can improve the overall energy efficiency and cleanliness of the system.

[0004] In existing technologies, multi-energy complementary scheduling mainly adopts simple hierarchical or segmented strategies, which lack effective consideration of dynamic uncertainties, and the algorithm solutions mostly rely on commercial solvers, making it difficult to meet the needs of online real-time scheduling.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a multi-energy coordinated scheduling method and system for coal-ammonia co-firing and wind-solar complementarity, thereby effectively solving the problems in the background art.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a multi-energy coordinated scheduling method for coal-ammonia co-firing and wind-solar complementarity, comprising the following steps:

[0008] The scheduling cycle is divided into T time periods. Considering the thermal power, ammonia power output, wind power and photovoltaic power output, and energy storage system status of the coal-ammonia co-fired unit in each time period, a multi-energy coupled scheduling mathematical model is constructed.

[0009] The multi-energy coupled scheduling mathematical model is solved by a hybrid algorithm based on shark olfaction algorithm (SSA) and harmony search algorithm (HS). In the solution process, the SSA algorithm is first used for preliminary exploration to obtain several initial optimal solutions, and then the HS algorithm is used for later development to correct the initial optimal solutions and obtain the optimal solution.

[0010] Based on the optimal solution, a scheduling strategy for T time periods is obtained.

[0011] Furthermore, the construction of the multi-energy coupled scheduling mathematical model includes:

[0012] A target function is constructed with the goal of minimizing the overall network operating cost and carbon emissions.

[0013] The constraints are constructed, including power balance constraints, unit output and co-firing ratio constraints, renewable output constraints, energy storage constraints, and unit dynamic constraints.

[0014] Furthermore, the objective function is:

[0015]

[0016] In the formula, J represents the comprehensive optimization objective, and F... c (P c Let ) be the coal consumption function, representing the unit's coal consumption at a rate of P. c Unit coal consumption per unit of output; F a (P a ) is the ammonia consumption function, representing the unit's ammonia consumption at a rate of P. a ammonia consumption per unit of output; α c Let α be the coal price coefficient. a E is the ammonia valence coefficient. c (P c ) is a function representing the carbon emissions from coal, indicating that the unit emits P c CO2 emissions during power output; E a (P a ) is a function representing the carbon emissions from ammonia, indicating that the unit emits carbon at a rate of P. a CO2 emissions during power output; β is the carbon emission cost pricing coefficient.

[0017] Furthermore, the constraints include:

[0018] Power balance constraints:

[0019]

[0020] In the formula, D(t) represents the load demand, Pc(t) and Pa(t) represent the thermal power and ammonia power output of the coal-ammonia co-fired unit in time t, respectively; Pw(t) and Ps(t) represent the wind power and photovoltaic power output in time t, respectively; and Pch(t) and Pdis(t) represent the charging and discharging power of the energy storage system at time t, respectively.

[0021] Unit output and co-firing ratio constraints:

[0022]

[0023] In the formula, These represent the maximum allowable output of coal and ammonia in a mixed-fuel unit, respectively; r min r max These are the lower and upper limits of the ammonia-mixing ratio, respectively.

[0024] Renewable power output constraints:

[0025]

[0026] In the formula, These are the predicted maximum output of wind power and solar power available in time period t, respectively. These represent the maximum total capacity of the energy storage system, the maximum charging power, and the maximum discharging power, respectively; η ch ,η dis These are the energy storage charging and discharging efficiencies, where 0 < η ≤ 1;

[0027] Unit dynamic constraints:

[0028] |P c (t)-P c (t-1)∣≤Δ up / down ,t≥2;

[0029] If the unit starts during time period t, then:

[0030]

[0031] If the unit is shut down during time period t, then:

[0032]

[0033] In the formula, Δ u p、Δ d own represents the unit's ramp / descent rate limit; T up T down These represent the minimum continuous online / desulfurization shutdown time for the unit, respectively.

[0034] Furthermore, the step of using the SSA algorithm for preliminary exploration during the solution process to obtain several initial optimal solutions includes the following steps:

[0035] The SSA algorithm is used to perform a sniffing-tracking-jump search on the decision vector for each time period, with a step size of s. k By s max linear decay to s min ;

[0036] Every 10 generations, a certain number of solutions are extracted from the current optimal solution and stored in the harmony memory (HM).

[0037] Furthermore, the subsequent use of the HS algorithm for later development to correct the initial optimal solution includes the following steps:

[0038] HM is initialized with the solution set reserved by the early SSA, and the size of the memory is HMS;

[0039] New solutions are generated spontaneously using the harmonic selection rate (HMCR) and pitch adjustment rate (PAR), and then slightly perturbed after generation.

[0040] The new solution is compared with the worst individual in HM; if it is feasible and better, it is replaced.

[0041] For each new solution, a feasibility correction is performed, including: projecting the mixed combustion ratio boundary to a preset range for each new solution; trimming the mutual exclusion of energy storage charging and discharging; and rounding the minimum pulse width of the unit.

[0042] After iterating to the maximum generation number Niter or satisfying the convergence criterion, the optimal scheduling scheme is output.

[0043] Furthermore, in the SSA algorithm, during the sniffing phase, m sniffing points are randomly generated at the current position, the objective function value of each point is evaluated, and the optimal direction is selected; during the tracking phase, the current step size s is used to track the target direction. k Forward; the jump phase occurs during a period of continuous no improvement (n). j After that, a global jump is performed within a set range with a random step size.

[0044] Furthermore, in the HS algorithm, the harmony selection rate (HMCR) decreases linearly from the initial value of 0.9 to 0.6, and the pitch adjustment rate (PAR) increases non-linearly from 0.4 to 0.8; the harmony memory size (HMS) is 20.

[0045] This invention also includes a multi-energy coordinated dispatch system for coal-ammonia co-firing and wind-solar complementarity, using the method described above, the system comprising:

[0046] The modeling unit is used to divide the scheduling cycle into T time periods, and to construct a multi-energy coupled scheduling mathematical model by considering the thermal power, ammonia power output, wind power and photovoltaic power output, and energy storage system status of the coal-ammonia co-fired unit in each time period.

[0047] The solution unit is used to solve the multi-energy coupled scheduling mathematical model using a hybrid algorithm based on the shark olfactory algorithm (SSA) and the harmony search algorithm (HS). In the solution process, the SSA algorithm is first used for preliminary exploration to obtain several initial optimal solutions, and then the HS algorithm is used for later development to correct the initial optimal solutions and obtain the optimal solution.

[0048] The scheduling unit is used to obtain the scheduling strategy for T time periods based on the optimal solution.

[0049] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0050] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0051] The beneficial effects of this invention are as follows: by constructing a multi-energy coupled scheduling mathematical model covering coal-ammonia co-firing and wind-solar-storage energy; by utilizing ammonia co-firing and a high proportion of renewable energy, fuel costs and carbon emissions are effectively reduced; the SSA–HS hybrid algorithm is used for solving and convergence, which combines the advantages of SSA in large-space global exploration with the ability of HS in fine-grained search in quasi-local regions, and can obtain high-quality and feasible multi-energy coordinated scheduling schemes in a short time; it meets the intraday rolling scheduling requirements; it improves the fault tolerance capability for uncertain wind and solar output and load prediction errors; and it achieves a balance optimization of system economy and cleanliness, with higher scheduling efficiency, lower carbon emissions, and stronger online rapid decision-making capabilities compared to existing technologies. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of the method of the present invention;

[0054] Figure 2 This is a schematic diagram of the system structure of the present invention;

[0055] Figure 3 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0057] Example 1:

[0058] like Figure 1 As shown: A multi-energy coordinated scheduling method for coal-ammonia co-firing and wind-solar complementarity includes the following steps:

[0059] The scheduling cycle is divided into T time periods. Considering the thermal power, ammonia power output, wind power and photovoltaic power output, and energy storage system status of the coal-ammonia co-fired unit in each time period, a multi-energy coupled scheduling mathematical model is constructed.

[0060] A hybrid algorithm based on the shark olfactory algorithm (SSA) and the harmony search algorithm (HS) is used to solve the mathematical model of multi-energy coupled scheduling. In the solution process, the SSA algorithm is first used for preliminary exploration to obtain several initial optimal solutions, and then the HS algorithm is used for later development to correct the initial optimal solutions and obtain the optimal solution.

[0061] The scheduling strategy for T time periods is obtained based on the optimal solution.

[0062] By constructing a multi-energy coupled scheduling mathematical model encompassing coal-ammonia co-firing and wind-solar-storage energy; utilizing ammonia co-firing and a high proportion of renewable energy to effectively reduce fuel costs and carbon emissions; and employing the SSA–HS hybrid algorithm for solution and convergence, the hybrid algorithm combines the advantages of SSA in large-space global exploration with the fine-grained search capabilities of HS in quasi-local regions, enabling the acquisition of high-quality and feasible multi-energy coordinated scheduling schemes in a short time; meeting intraday rolling scheduling requirements; improving fault tolerance to uncertain wind and solar output and load forecasting errors; and achieving a balanced optimization of system economy and cleanliness, exhibiting higher scheduling efficiency, lower carbon emissions, and stronger online rapid decision-making capabilities compared to existing technologies.

[0063] In this embodiment, constructing a multi-energy coupled scheduling mathematical model includes:

[0064] A target function is constructed with the goal of minimizing the overall network operating cost and carbon emissions.

[0065] The constraints are constructed, including power balance constraints, unit output and co-firing ratio constraints, renewable output constraints, energy storage constraints, and unit dynamic constraints.

[0066] The objective function is:

[0067]

[0068] In the formula, J represents the comprehensive optimization objective, and F... c (P c Let ) be the coal consumption function, representing the unit's coal consumption at a rate of P. c Unit coal consumption per unit of output; F a (P a ) is the ammonia consumption function, representing the unit's ammonia consumption at a rate of P. a ammonia consumption per unit of output; α c Let α be the coal price coefficient. a E is the ammonia valence coefficient. c (P c ) is a function representing the carbon emissions from coal, indicating that the unit emits P c CO2 emissions during power output; E a (P a ) is a function representing the carbon emissions from ammonia, indicating that the unit emits carbon at a rate of P. aCO2 emissions during power output; β is the carbon emission cost pricing coefficient.

[0069] The constraints include:

[0070] Power balance constraints:

[0071]

[0072] In the formula, D(t) represents the load demand, Pc(t) and Pa(t) represent the thermal power and ammonia power output of the coal-ammonia co-fired unit in time t, respectively; Pw(t) and Ps(t) represent the wind power and photovoltaic power output in time t, respectively; and Pch(t) and Pdis(t) represent the charging and discharging power of the energy storage system at time t, respectively.

[0073] Unit output and co-firing ratio constraints:

[0074]

[0075] In the formula, These represent the maximum allowable output of coal and ammonia in a mixed-fuel unit, respectively; r min r max These are the lower and upper limits of the ammonia-mixing ratio, respectively.

[0076] Renewable power output constraints:

[0077]

[0078] In the formula, These are the predicted maximum output of wind power and solar power available in time period t, respectively. These represent the maximum total capacity of the energy storage system, the maximum charging power, and the maximum discharging power, respectively; η ch ,η dis These are the energy storage charging and discharging efficiencies, where 0 < η ≤ 1;

[0079] Unit dynamic constraints:

[0080] |P c (t)-P c (t-1)∣≤Δ up / down ,t≥2;

[0081] If the unit starts during time period t, then:

[0082]

[0083] If the unit is shut down during time period t, then:

[0084]

[0085] In the formula, Δ up Δdown These are the unit's ramp / downhill rate limits; T up T down These represent the minimum continuous online / desulfurization shutdown time for the unit, respectively.

[0086] The solution process begins with the SSA algorithm for preliminary exploration, yielding several initial optimal solutions. This involves the following steps:

[0087] The SSA algorithm is used to perform a sniffing-tracking-jump search on the decision vector for each time period, with a step size of s. k By s max linear decay to s min ;

[0088] Every 10 generations, a certain number of solutions are extracted from the current optimal solution and stored in the harmony memory (HM).

[0089] In this embodiment, the HS algorithm is used for further development to correct the initial optimal solution, including the following steps:

[0090] HM is initialized with the solution set reserved by the early SSA, and the size of the memory is HMS;

[0091] New solutions are generated spontaneously using the harmonic selection rate (HMCR) and pitch adjustment rate (PAR), and then slightly perturbed after generation.

[0092] The new solution is compared with the worst individual in HM; if it is feasible and better, it is replaced.

[0093] For each new solution, a feasibility correction is performed, including: projecting the mixed combustion ratio boundary to a preset range for each new solution; trimming the mutual exclusion of energy storage charging and discharging; and rounding the minimum pulse width of the unit.

[0094] After iterating to the maximum generation number Niter or satisfying the convergence criterion, the optimal scheduling scheme is output.

[0095] In the SSA algorithm, during the sniffing phase, m sniffing points are randomly generated at the current position, the objective function value of each point is evaluated, and the optimal direction is selected; during the tracking phase, the current step size s is used to determine the direction. k Forward; the jump phase occurs during a period of continuous no improvement (n). j After that, a global jump is performed within a set range with a random step size.

[0096] In the HS algorithm, the harmony selection rate (HMCR) decreases linearly from the initial value of 0.9 to 0.6, and the pitch adjustment rate (PAR) increases non-linearly from 0.4 to 0.8; the harmony memory size (HMS) is set to 20.

[0097] like Figure 2 As shown, this embodiment also includes a multi-energy coordinated dispatch system for coal-ammonia co-firing and wind-solar complementarity, using the method described above. The system includes:

[0098] The modeling unit is used to divide the scheduling cycle into T time periods, and to construct a multi-energy coupled scheduling mathematical model by considering the thermal power, ammonia power output, wind power and photovoltaic power output, and energy storage system status of the coal-ammonia co-fired unit in each time period.

[0099] The solution unit is used to solve the multi-energy coupled scheduling mathematical model using a hybrid algorithm based on the shark olfactory algorithm (SSA) and the harmony search algorithm (HS). In the solution process, the SSA algorithm is first used for preliminary exploration to obtain several initial optimal solutions, and then the HS algorithm is used for later development to correct the initial optimal solutions and obtain the optimal solution.

[0100] The scheduling unit is used to obtain the scheduling strategy for T time periods based on the optimal solution.

[0101] Example 2:

[0102] The following example uses a 24-hour rolling schedule to illustrate the specific implementation process of this embodiment:

[0103] The day is divided into 24 time periods, i.e., T = {1, ..., 24}, with each time period lasting 1 hour;

[0104] Maximum coal output Minimum output is 50MW; maximum ammonia output 100MW; ammonia mixing ratio range [10%, 30%]; ramp rate Δ up =Δ down =50MW / h; Minimum operating / downtime T up =2h,T down =2h;

[0105] The load demand D(t) is given by the load forecasting module, as shown in Table 1:

[0106] Table 1. Forecasted power output and load demand over 24 hours

[0107]

[0108]

[0109] Energy storage parameters: Maximum capacity 100MWh; Maximum charge / discharge power 50MW; Charge / discharge efficiency 0.9; Initial capacity 50MWh;

[0110] Constructing a multi-energy coupled scheduling mathematical model:

[0111] The objective function is:

[0112]

[0113] In the formula, J represents the comprehensive optimization objective, and F...c (P c Let ) be the coal consumption function, representing the unit's coal consumption at a rate of P. c Unit coal consumption per unit of output; F a (P a ) is the ammonia consumption function, representing the unit's ammonia consumption at a rate of P. a ammonia consumption per unit of output; α c Let α be the coal price coefficient. a E is the ammonia valence coefficient. c (P c ) is a function representing the carbon emissions from coal, indicating that the unit emits P c CO2 emissions during power output; E a (P a ) is a function representing the carbon emissions from ammonia, indicating that the unit emits carbon at a rate of P. a CO2 emissions during power output; β is the carbon emission cost pricing coefficient.

[0114] The constraints include:

[0115] Power balance constraints:

[0116]

[0117] In the formula, D(t) represents the load demand, Pc(t) and Pa(t) represent the thermal power and ammonia power output of the coal-ammonia co-fired unit in time t, respectively; Pw(t) and Ps(t) represent the wind power and photovoltaic power output in time t, respectively; and Pch(t) and Pdis(t) represent the charging and discharging power of the energy storage system at time t, respectively.

[0118] Unit output and co-firing ratio constraints:

[0119]

[0120] In the formula, These represent the maximum allowable output of coal and ammonia in a mixed-fuel unit, respectively; r min r max These are the lower and upper limits of the ammonia-mixing ratio, respectively.

[0121] Renewable power output constraints:

[0122]

[0123] In the formula, These are the predicted maximum output of wind power and solar power available in time period t, respectively. These represent the maximum total capacity of the energy storage system, the maximum charging power, and the maximum discharging power, respectively; η ch ,η dis These are the energy storage charging and discharging efficiencies, where 0 < η ≤ 1;

[0124] Unit dynamic constraints:

[0125] |P c (t)-P c (t-1)∣≤Δ up / down ,t≥2;

[0126] If the unit starts during time period t, then:

[0127]

[0128] If the unit is shut down during time period t, then:

[0129]

[0130] In the formula, Δ u p、Δ d own represents the unit's ramp / descent rate limit; T up T down These represent the minimum continuous online / desulfurization shutdown time for the unit, respectively.

[0131] Algorithm solution: SSA-HS hybrid algorithm:

[0132] Overview of Shark Smell Algorithm (SSA);

[0133] SSA is an optimized algorithm that simulates how sharks track the location of prey in the ocean using their sense of smell, and uses a three-stage mechanism of "sniffing-tracking-jumping" for exploration.

[0134] Sense: The shark randomly samples several sniffer points around its current position, evaluates their objective function values, and selects the optimal direction; Trail: It moves along the optimal direction with a certain step size and updates the current position; Leap: When there is no improvement after several iterations, it makes a large random leap to escape the local optimum.

[0135] Harmony Search (HS) Overview;

[0136] HS simulates the "harmonic improvisation" process in music composition, and completes the generation and updating of solutions through operations such as Harmony Memory (HM) and Pitch Adjustment.

[0137] Harmony Memory: Stores several currently optimal solutions;

[0138] Improvisational generation: Selecting components from HM with a certain probability, or randomly generating new components;

[0139] Pitch adjustment: Make small perturbations to the selected components to form a new solution;

[0140] Memory update: Compare the new solution with the worst individual in HM, and replace it if it is better.

[0141] Hybrid strategy design;

[0142] Phase division: The iterative process is divided into an exploration phase (first 40% of generations) and a development phase (last 60% of generations).

[0143] Early exploration (SSA-led):

[0144] The SSA mechanism is used to conduct a broad search in the high-dimensional scheduling space to quickly capture multiple potential good value regions. After every 10 SSA cycles, several optimal solutions are retained in the harmony memory (HM).

[0145] Later-stage development (HS-led):

[0146] The HM of HS is initialized to the reserved solution set provided by the previous SSA;

[0147] The search is refined in the neighborhood of HM by improvisational generation and pitch adjustment of HS;

[0148] After each new solution is generated, the objective function and constraint violation on the scheduling model are evaluated immediately. If the solution is feasible and better, the HM is updated.

[0149] Key parameters and adaptive mechanisms;

[0150] SSA step size k : Decays linearly with algebraic progression, from S max Decrease to S min This is to balance global and local searches;

[0151] HS parameters:

[0152] HM size (HMS): set to 20; Harmony Selection Ratio (HMCR): linearly decrease from 0.9 to 0.6; Pitch Adjustment Ratio (PAR): increase from 0.4 to 0.8, with increased local perturbation intensity in post-production; adaptive switching;

[0153] If there is no improvement in the SSA search for several consecutive generations (e.g., 50 generations), switch to HS early; if the fitness decline exceeds the limit in the HS search, it can revert to the best SSA solution that was previously retained and continue to deploy SSA for local jumps.

[0154] Feasibility revision;

[0155] Candidate solutions generated in the SSA and HS phases may not fully satisfy integer constraints such as the mixing ratio or energy storage dynamics.

[0156] For each candidate solution, respectively:

[0157] 1. Regarding the mixture ratio Pa / (P c +P a Perform boundary projection;

[0158] 2. Perform "mutually exclusive trimming" on the simultaneous charging and discharging quantities of energy storage devices;

[0159] 3. Check the minimum downtime / minimum uptime of the unit, and round up or down according to the minimum pulse width if necessary. Recalculate the objective function and penalty terms for the corrected solution, retaining only truly feasible and high-quality scheduling strategies.

[0160] Detailed solution process:

[0161] 1. Preliminary exploration (SSA phase);

[0162] Parameter settings: Total number of iterations Niter = 1000; SSA search step size linear decay: Smax = 100, Smin = 1; Number of sniffing points m = 30; Continuous no-improvement threshold Nssa_stall = 50.

[0163] Execution process:

[0164] 1. Randomly initialize 30 candidate scheduling solutions (i.e., particle positions);

[0165] 2. Generate sniffing points for each time period, evaluate the objective function, and select the optimal direction;

[0166] 3. Update along the direction with the current step size sk until 10 generations are completed;

[0167] 4. Store the 20 best feasible solutions within 10 generations into HM;

[0168] 5. If no improvement is made for 50 consecutive generations, then exit the SSA stage.

[0169] 2. Later-stage development (HS stage);

[0170] HM initialization: consists of the 20 optimal solutions retained in the SSA phase;

[0171] Parameter settings: HM size HMS = 20; Harmony selection rate HMCR linearly decreases from 0.9 to 0.6; Pitch adjustment rate PAR non-linearly increases from 0.4 to 0.8; Improvisation generation and updates;

[0172] Solution components are selected from HM according to HMCR probability; otherwise, they are generated randomly.

[0173] Apply a small perturbation to the selected component using PAR;

[0174] Calculate the target value of the new solution and make feasibility adjustments;

[0175] If the new solution is better than the worst individual in HM, then replace it;

[0176] Repeat until the total number of iterations reaches 1000.

[0177] Feasibility adjustments are performed for each new solution:

[0178] The mixed-fuel ratio is projected to [10%, 30%];

[0179] Ensure Pch(t)·Pdis(t) = 0; the minimum pulse width of the unit is rounded according to the rules.

[0180] After solving the problem using the above algorithm, the scheduling strategy for 24 time periods is obtained. Some examples of the results are shown in Table 2:

[0181] Table 2: Examples of partial results from the 24-hour scheduling strategy

[0182]

[0183] During peak wind and solar power output (11–14 hours), renewable energy is prioritized for consumption, and energy storage discharges to compensate for the load. During off-peak hours (1–6 hours), low-cost coal-ammonia co-fired units are used for peak shaving, and the energy storage system is charged. The entire process meets constraints such as co-fired ratio, ramp-up rate, and minimum operation / downtime.

[0184] By constructing a multi-energy coupled scheduling mathematical model encompassing coal-ammonia co-firing and wind-solar-storage energy; utilizing ammonia co-firing and a high proportion of renewable energy to effectively reduce fuel costs and carbon emissions; and employing the SSA–HS hybrid algorithm for solution and convergence, the hybrid algorithm combines the advantages of SSA in large-space global exploration with the fine-grained search capabilities of HS in quasi-local regions, enabling the acquisition of high-quality and feasible multi-energy coordinated scheduling schemes in a short time; meeting intraday rolling scheduling requirements; improving fault tolerance to uncertain wind and solar output and load forecasting errors; and achieving a balanced optimization of system economy and cleanliness, exhibiting higher scheduling efficiency, lower carbon emissions, and stronger online rapid decision-making capabilities compared to existing technologies.

[0185] Please see Figure 3 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0186] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0187] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0188] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0189] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0190] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0191] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0192] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0193] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0194] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0195] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-energy coordinated scheduling method for coal-ammonia co-firing and wind-solar complementarity, characterized in that, Includes the following steps: The scheduling cycle is divided into T time periods. Considering the thermal power, ammonia power output, wind power and photovoltaic power output, and energy storage system status of the coal-ammonia co-fired unit in each time period, a multi-energy coupled scheduling mathematical model is constructed. The multi-energy coupled scheduling mathematical model is solved by a hybrid algorithm based on shark olfaction algorithm (SSA) and harmony search algorithm (HS). In the solution process, the SSA algorithm is first used for preliminary exploration to obtain several initial optimal solutions, and then the HS algorithm is used for later development to correct the initial optimal solutions and obtain the optimal solution. Based on the optimal solution, a scheduling strategy for T time periods is obtained.

2. The multi-energy coordinated dispatching method for coal-ammonia co-firing and wind-solar complementarity according to claim 1, characterized in that, The construction of the multi-energy coupled scheduling mathematical model includes: A target function is constructed with the goal of minimizing the overall network operating cost and carbon emissions. The constraints are constructed, including power balance constraints, unit output and co-firing ratio constraints, renewable output constraints, energy storage constraints, and unit dynamic constraints.

3. The multi-energy coordinated dispatching method for coal-ammonia co-firing and wind-solar complementarity according to claim 2, characterized in that, The objective function is: In the formula, J represents the comprehensive optimization objective, and F... c (P c Let ) be the coal consumption function, representing the unit's coal consumption at a rate of P. c Unit coal consumption per unit of output; F a (P a ) is the ammonia consumption function, representing the unit's ammonia consumption at a rate of P. a ammonia consumption per unit of output; α c Let α be the coal price coefficient. a E is the ammonia valence coefficient. c (P c ) is a function representing the carbon emissions from coal, indicating that the unit emits P c CO2 emissions during power output; E a (P a ) is a function representing the carbon emissions from ammonia, indicating that the unit emits carbon at a rate of P. a CO2 emissions during power output; β is the carbon emission cost pricing coefficient.

4. The multi-energy coordinated dispatching method for coal-ammonia co-firing and wind-solar complementarity according to claim 3, characterized in that, The constraints include: Power balance constraints: In the formula, D(t) represents the load demand, Pc(t) and Pa(t) represent the thermal power and ammonia power output of the coal-ammonia co-fired unit in time t, respectively; Pw(t) and Ps(t) represent the wind power and photovoltaic power output in time t, respectively; and Pch(t) and Pdis(t) represent the charging and discharging power of the energy storage system at time t, respectively. Unit output and co-firing ratio constraints: In the formula, P c max P a max These represent the maximum allowable output of coal and ammonia in a mixed-fuel unit, respectively; r min r max These are the lower and upper limits of the ammonia-mixing ratio, respectively. Renewable power output constraints: In the formula, These are the predicted maximum output of wind power and solar power available in time period t, respectively. These represent the maximum total capacity of the energy storage system, the maximum charging power, and the maximum discharging power, respectively; η ch ,η dis These are the energy storage charging and discharging efficiencies, where 0 < η ≤ 1; Unit dynamic constraints: ∣P c (t)-P c (t-1)∣≤Δ up / down ,t≥2; If the unit starts during time period t, then: If the unit is shut down during time period t, then: In the formula, Δ up Δ down These are the unit's ramp / downhill rate limits; T up T down These represent the minimum continuous online / desulfurization shutdown time for the unit, respectively.

5. The multi-energy coordinated dispatching method for coal-ammonia co-firing and wind-solar complementarity according to claim 1, characterized in that, The process of first using the SSA algorithm to explore the solution and obtain several initial optimal solutions includes the following steps: The SSA algorithm is used to perform a sniffing-tracking-jump search on the decision vector for each time period, with a step size of s. k By s max linear decay to s min ; Every 10 generations, a certain number of solutions are extracted from the current optimal solution and stored in the harmony memory (HM).

6. The multi-energy coordinated dispatching method for coal-ammonia co-firing and wind-solar complementarity according to claim 5, characterized in that, The subsequent use of the HS algorithm for later development, and the correction of the initial optimal solution, includes the following steps: HM is initialized with the solution set reserved by the early SSA, and the size of the memory is HMS; New solutions are generated spontaneously using the harmonic selection rate (HMCR) and pitch adjustment rate (PAR), and then slightly perturbed after generation. The new solution is compared with the worst individual in HM; if it is feasible and better, it is replaced. For each new solution, a feasibility correction is performed, including: projecting the mixed combustion ratio boundary to a preset range for each new solution; trimming the mutual exclusion of energy storage charging and discharging; and rounding the minimum pulse width of the unit. After iterating to the maximum generation number Niter or satisfying the convergence criterion, the optimal scheduling scheme is output.

7. The multi-energy coordinated dispatching method for coal-ammonia co-firing and wind-solar complementarity according to claim 6, characterized in that, In the SSA algorithm, during the sniffing phase, m sniffing points are randomly generated at the current position, the objective function value of each point is evaluated, and the optimal direction is selected; during the tracking phase, the current step size s is used to... k Forward; the jump phase occurs during a period of continuous no improvement (n). j After that, a global jump is performed within a set range with a random step size.

8. A multi-energy coordinated dispatching system for coal-ammonia co-firing and wind-solar complementarity, characterized in that, Using the method of any one of claims 1 to 7, the system comprises: The modeling unit is used to divide the scheduling cycle into T time periods, and to construct a multi-energy coupled scheduling mathematical model by considering the thermal power, ammonia power output, wind power and photovoltaic power output, and energy storage system status of the coal-ammonia co-fired unit in each time period. The solution unit is used to solve the multi-energy coupled scheduling mathematical model using a hybrid algorithm based on the shark olfactory algorithm (SSA) and the harmony search algorithm (HS). In the solution process, the SSA algorithm is first used for preliminary exploration to obtain several initial optimal solutions, and then the HS algorithm is used for later development to correct the initial optimal solutions and obtain the optimal solution. The scheduling unit is used to obtain the scheduling strategy for T time periods based on the optimal solution.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-7.

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