Micro-grid optimization scheduling method and device, electronic equipment and storage medium

Abstract

The invention discloses a micro-grid optimization scheduling method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining an energy transaction parameter, a user electricity purchase parameter, a user gas load and a user thermal load of a micro-grid operator; constructing a user game model according to the time-sharing coupling electricity carbon price and the user electricity purchasing parameter at each moment; solving the user game model, and generating the actual electricity purchase quantity of the user at each moment; constructing a micro-grid operator game model according to the actual electricity purchase quantity of the user at each moment, the user gas load, the user heat load and the energy transaction parameters of the micro-grid operator; solving the micro-grid operator game model, and generating the electricity purchase quantity of the micro-grid operator at each moment, the gas purchase quantity of the micro-grid operator at each moment, the energy power of the micro-grid at each moment and the energy flow of the micro-grid at each moment; and scheduling the micro-grid according to the solving result of the micro-grid operator game model. According to the invention, the accuracy of micro-grid dispatching can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a microgrid optimization dispatching method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of distributed energy, microgrids have become an important part of modern power systems. Its main goal is to maximize energy efficiency and reduce operating costs by rationally allocating resources such as power generation, energy storage, and loads, while ensuring system stability and security. Microgrids can be flexibly dispatched according to local needs and resource conditions, which not only improves energy self-sufficiency, but also effectively reduces dependence on the main power grid, thereby enhancing the resilience of the overall power system.

[0003] During the operation of microgrids, there is a complex game relationship between users and operators. Operators usually guide users to adjust their electricity consumption behavior by formulating time-of-use electricity prices to optimize the overall operation efficiency of microgrids. However, the response behavior of users to price signals has great uncertainty. This response will not only directly affect the load distribution of users, that is, the actual amount of electricity purchased, but will also further react on the operator's electricity and gas purchase decisions. However, existing microgrid scheduling optimization technologies usually regard user loads as static or simply predictable input parameters, and fail to fully consider the dynamic interactive relationship between users and operators. This simplified processing makes it difficult to fully reflect the actual complexity in the operation of microgrids, thereby reducing the accuracy of microgrid scheduling optimization results. Summary of the invention

[0004] The embodiments of the present invention provide a microgrid optimization scheduling method, device, electronic device and storage medium. The accuracy of microgrid scheduling can be improved by implementing the present invention.

[0005] An embodiment of the present invention provides a microgrid optimization scheduling method, including:

[0006] Obtain the energy trading parameters of the microgrid operator, the user's electricity purchasing parameters, the user's gas load, and the user's heat load; the energy trading parameters of the microgrid operator include the time-of-use electricity-carbon coupling price at each moment;

[0007] According to the time-sharing coupled electricity-carbon price and the user's electricity purchasing parameters at each moment, with the goal of minimizing the user's operating cost, a user game model and a first constraint condition are constructed;

[0008] Under the first constraint, the user game model is solved to generate the actual amount of electricity purchased by the user at each moment;

[0009] According to the actual power purchase amount, gas load, heat load and energy trading parameters of microgrid operators at each moment, the game model of microgrid operators and the second constraint condition are constructed with the goal of maximizing the total revenue of microgrid operators.

[0010] Under the second constraint, the game model of the microgrid operator is solved to generate the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment;

[0011] The microgrid is dispatched according to the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment.

[0012] Furthermore, the time-sharing electricity-carbon coupling price at each moment is determined by the following method:

[0013] Obtain the time-of-use carbon price parameters and time-of-use electricity price parameters of the microgrid operator at each moment;

[0014] According to the time-of-use carbon price parameters and the time-of-use electricity price parameters at each moment, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated and generated.

[0015] Furthermore, the user game model includes:

[0016]

[0017] Among them, F user Running costs for users; P is the electricity purchase cost of the user at time t; user，t The actual amount of electricity purchased by the user at any given moment; is the time-sharing electricity-carbon coupling price at time t; T is the scheduling time.

[0018] Furthermore, the first constraint condition includes: power balance constraint and user energy purchase constraint;

[0019] The power balance constraints include:

[0020]

[0021] Among them, ΔP user，t is the change in the amount of electricity purchased by the user at time t; P is the planned power purchase of the user at time t; user，t is the actual amount of electricity purchased by the user at time t; T is the dispatch time;

[0022] The user energy purchase constraints include:

[0023]

[0024] Among them, P user，t is the actual amount of electricity purchased by the user at time t; is the minimum amount of electricity purchased by the user at time t; is the maximum amount of electricity purchased by the user at time t.

[0025] Furthermore, the microgrid operator game model includes:

[0026]

[0027] C es,buy,t =e es,buy,t P es,buy,t

[0028] C gs,buy,t =e gs,buy,t G buy,t

[0029]

[0030] Where S is the total revenue of the microgrid operator; C eu，sell，t is the electricity sales revenue of the microgrid operator at time t; is the time-sharing electricity-carbon coupling price at time t; user，t is the actual amount of electricity purchased by the user at time t; C es，buy，t is the electricity purchase cost of the microgrid operator at time t; e es，buy，t is the electricity purchase price of the microgrid operator at time t; P es，buy，t is the amount of electricity purchased by the microgrid operator at time t; C gs，buy，t is the gas purchase cost of the microgrid operator at time t; e gs，buy，t is the gas purchase price of the microgrid operator at time t; G buy，t is the gas purchase volume of the microgrid operator at time t; is the carbon trading cost of the microgrid operator at time t; c ct is the unit carbon emission trading price; δ p is the carbon emission factor of electricity purchase; δ g is the unit natural gas carbon emission factor; T is the scheduling time.

[0031] Furthermore, the second constraint condition includes: energy purchase constraint and power balance constraint of the microgrid operator;

[0032] The energy purchase constraints of the microgrid operator include:

[0033]

[0034] Among them, P es,buy,t is the amount of electricity purchased by the microgrid operator at time t; is the maximum amount of electricity purchased by the microgrid operator at time t; G buy，t is the gas purchase volume of the microgrid operator at time t; is the maximum gas purchase volume of the microgrid operator at time t;

[0035] The power balance constraint includes:

[0036] P WT，t +P CHP，t +P ES，dis,t +P e,buy,t +P HFC,e，t =P user，t +P ES,chr,t

[0037] H CHP (t)+H GB (t)+H TS，dis (t) = H TS，chr (t)+H load (t)

[0038] G buy,t +G TS,dis,t +G MR，t =G load,t +G TS，chr,t +G CHP,t +G GB,t

[0039]

[0040] Among them, P WT，t is the wind turbine output power of the microgrid at time t; P CHP，t P is the power output of the cogeneration unit in the microgrid at time t; ES，dis，t P is the discharge power of the power storage device in the microgrid at time t; es,buy,t is the amount of electricity purchased by the microgrid operator at time t; P HFC,e，t P is the power generated by the hydrogen fuel cell in the microgrid at time t; user，t is the actual amount of electricity purchased by the user at time t; P ES,chr,t H is the charge amount of the power storage device in the microgrid at time t; CHP (t) is the thermal output power of the cogeneration unit; H GB (t) is the heat generated by the gas boiler; H TS，dis (t) is the heat released by the heat storage device; H TS，chr (t) is the heat storage capacity of the heat storage device; H load (t) is the heat load; G buy，t is the gas purchase volume of the microgrid operator at time t; G TS，dis，t G is the gas discharge volume of the gas storage tank at time t in the microgrid; MR，t G is the amount of CH4 produced by methanogenesis of power-to-gas technology in the microgrid at time t;load,t G is the gas load of the user at time t; TS，chr,t G is the gas storage capacity of the gas storage equipment in the microgrid at time t; CHP,t is the gas consumption of the cogeneration unit in the microgrid at time t; G GB,t is the natural gas consumption of the gas boiler unit in the microgrid at time t; is the amount of hydrogen produced by the electrolyzer; P HS，dis (t) is the hydrogen storage capacity of the hydrogen storage equipment; is the hydrogen consumption of methanation reaction; is the hydrogen consumption of hydrogen fuel cell; P HS，chr (t) is the amount of hydrogen released by the hydrogen storage equipment.

[0041] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0042] An embodiment of the present invention provides a microgrid optimization scheduling device, including: a data acquisition module, a user game optimization module, a microgrid operator game optimization module and a microgrid scheduling module;

[0043] The data acquisition module is used to obtain the energy transaction parameters of the microgrid operator, the user's electricity purchase parameters, the user's gas load and the user's heat load; wherein the energy transaction parameters of the microgrid operator include the time-of-use electricity-carbon coupling price at each moment;

[0044] The user game optimization module is used to construct a user game model and a first constraint condition according to the time-sharing coupled electricity-carbon price and the user's electricity purchase parameters at each moment, with the goal of minimizing the user's operating cost; under the first constraint condition, the user game model is solved to generate the actual amount of electricity purchased by the user at each moment;

[0045] The microgrid operator game optimization module is used to construct a microgrid operator game model and a second constraint condition based on the actual power purchase amount, gas load, heat load and energy trading parameters of the microgrid operator at each moment, with the goal of maximizing the total revenue of the microgrid operator;

[0046] The microgrid dispatching module is used to dispatch the microgrid according to the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment.

[0047] Furthermore, in the microgrid optimization dispatching device, the data acquisition module determines the time-sharing electricity-carbon coupling price at each moment in the following manner:

[0048] Obtain the time-of-use carbon price parameters and the time-of-use electricity price parameters of the microgrid operator at each time;

[0049] According to the time-of-use carbon price parameters and the time-of-use electricity price parameters at each moment, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated and generated.

[0050] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0051] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the microgrid optimization scheduling method described in any one of the above method embodiments can be implemented.

[0052] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0053] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the microgrid optimization scheduling method described in any one of the above method embodiments can be implemented.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The embodiment of the present invention provides a microgrid optimization scheduling method, device, electronic device and storage medium. The method obtains the energy trading parameters of the microgrid operator, the user's electricity purchasing parameters, the user's gas load and the user's heat load. The energy trading parameters of the microgrid operator include the time-sharing electricity-carbon coupling price at each moment; with the goal of minimizing the user's operating cost, a user game model is established and the user's actual electricity purchase amount is generated; with the goal of maximizing the total revenue of the microgrid operator, a microgrid operator game model is constructed, and the microgrid operator's electricity purchase amount, gas purchase amount, energy power and energy flow are obtained after solving, and the microgrid is dynamically scheduled according to the optimization result. Through the optimization goal of minimizing the user's operating cost, the user's electricity purchase amount at each moment is dynamically adjusted, so that the user can make a reasonable response to the time-sharing electricity-carbon price signal while meeting his own needs, which effectively avoids the static assumption of the user's electricity purchasing behavior and improves the flexibility of the model. By implementing the present invention, the dynamic interactive relationship between the user and the operator can be fully considered, and the user response and the operator's electricity and gas purchase decision can be effectively coordinated, thereby improving the accuracy of microgrid scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of a microgrid optimization scheduling method provided by one embodiment of the present invention.

[0057] Figure 2 It is a schematic diagram of energy price optimization for a microgrid operator provided by an embodiment of the present invention.

[0058] Figure 3It is a schematic diagram of optimizing the dispatching of electric power balance in a microgrid provided by an embodiment of the present invention.

[0059] Figure 4 It is a schematic diagram of optimizing the scheduling of thermal power balance in a microgrid provided by an embodiment of the present invention.

[0060] Figure 5 It is a structural schematic diagram of a microgrid optimization scheduling device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, an embodiment of the present invention provides a microgrid optimization scheduling method, which at least includes the following steps:

[0063] Step S1, obtaining the energy transaction parameters of the microgrid operator, the user's electricity purchase parameters, the user's gas load and the user's heat load.

[0064] Specifically, the energy trading parameters of microgrid operators include the time-of-use electricity-carbon coupling price at each moment;

[0065] In a preferred embodiment, the time-sharing electricity-carbon coupling price at each moment is determined by:

[0066] Obtain the time-of-use carbon price parameters and time-of-use electricity price parameters of the microgrid operator at each moment;

[0067] According to the time-of-use carbon price parameters and the time-of-use electricity price parameters at each moment, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated and generated.

[0068] Optionally, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated by the following formula:

[0069]

[0070] in, is the time-sharing electricity carbon price at time t; C，p is the peak-time carbon price; is the carbon price identifier variable during peak hours; α C，f T is the incremental coefficient of carbon price during peak period relative to carbon price during normal period; C，f is the carbon price duration during the peak period; T C，g The duration of the carbon price during the valley period; is the carbon price identifier variable during valley period; P，p The electricity price during normal hours; is the peak period electricity price identification variable; α P，f T is the incremental coefficient of the electricity price during peak hours relative to the normal hours; P，f T is the duration of peak electricity price; P，g The duration of off-peak electricity price; It is the variable identifying the electricity price during off-peak hours.

[0071] Step S2: construct a user game model and a first constraint condition according to the time-sharing coupled electricity-carbon price and the user's electricity purchasing parameters at each moment with the goal of minimizing the user's operating cost.

[0072] In a preferred embodiment, the user game model includes:

[0073]

[0074] Among them, F user Running costs for users; P is the electricity purchase cost of the user at time t; user，t The actual amount of electricity purchased by the user at any given moment; is the time-sharing electricity-carbon coupling price at time t; T is the scheduling time.

[0075] In a preferred embodiment, the first constraint condition includes: power balance constraint and user energy purchase constraint;

[0076] The power balance constraints include:

[0077]

[0078] Where ΔP user，t is the change in the amount of electricity purchased by the user at time t; P is the planned power purchase of the user at time t; user，t is the actual amount of electricity purchased by the user at time t; t is the dispatch time;

[0079] The user energy purchase constraints include:

[0080]

[0081] Among them, P user，t is the actual amount of electricity purchased by the user at time t; is the minimum amount of electricity purchased by the user at time t; is the maximum amount of electricity purchased by the user at time t.

[0082] Step S3: Under the first constraint condition, solve the user game model to generate the actual power purchase amount of the user at each moment.

[0083] It should be noted that the decision variable is P user，t , that is, the actual amount of electricity purchased by the user at time t.

[0084] Step S4: Based on the actual power purchase amount, gas load, heat load and energy trading parameters of the microgrid operator at each moment, a microgrid operator game model and a second constraint condition are constructed with the goal of maximizing the total revenue of the microgrid operator.

[0085] In a preferred embodiment, the microgrid operator game model includes:

[0086]

[0087] C es,buy,t =e es,buy,t P es,buy,t

[0088] C gs,buy,t =e gs,buy,t G buy,t

[0089]

[0090] Where S is the total revenue of the microgrid operator; C eu，sell，t is the electricity sales revenue of the microgrid operator at time t; is the time-sharing electricity-carbon coupling price at time t; user,t is the actual amount of electricity purchased by the user at time t; C es,buy,t is the electricity purchase cost of the microgrid operator at time t; e es,buy,t is the electricity purchase price of the microgrid operator at time t; P es，buy,t is the amount of electricity purchased by the microgrid operator at time t; C gs,buy,t is the gas purchase cost of the microgrid operator at time t; e gs,buy,t is the gas purchase price of the microgrid operator at time t; G buy,t is the gas purchase volume of the microgrid operator at time t; is the carbon trading cost of the microgrid operator at time t; c ct is the unit carbon emission trading price; δ p is the carbon emission factor of electricity purchase; δ g is the unit natural gas carbon emission factor; T is the scheduling time.

[0091] In a preferred embodiment, the second constraint condition includes: energy purchase constraint and power balance constraint of the microgrid operator;

[0092] The energy purchase constraints of the microgrid operator include:

[0093]

[0094] Among them, P es,buy,t is the amount of electricity purchased by the microgrid operator at time t; is the maximum amount of electricity purchased by the microgrid operator at time t; G buy,t is the gas purchase volume of the microgrid operator at time t; is the maximum gas purchase volume of the microgrid operator at time t;

[0095] The power balance constraint includes:

[0096] P WT，t +P CHP，t +P ES，dis,t +P e,buy,t +P HFC,e，t =P user,t +P ES,chr,t

[0097] H CHP (t)+H GB (t)+H TS，dis (t) = H TS，chr (t)+H load (t)

[0098] G buy,t +G TS,dis,t +G MR,t =G load,t +G TS,chr,t +G CHP,t +G GB,t

[0099]

[0100] Among them, P WT，t is the wind turbine output power of the microgrid at time t; P CHP，t P is the power output of the cogeneration unit in the microgrid at time t; ES，dis，t P is the discharge power of the power storage device in the microgrid at time t; es,buy,t is the amount of electricity purchased by the microgrid operator at time t; P HFC,e，t P is the power generated by the hydrogen fuel cell in the microgrid at time t; user，t is the actual amount of electricity purchased by the user at time t; P ES,chr,t H is the charge amount of the power storage device in the microgrid at time t; CHP (t) is the thermal output power of the cogeneration unit; H GB (t) is the heat generated by the gas boiler; H TS，dis (t) is the heat released by the heat storage device; H TS，ch r(t) is the heat storage capacity of the heat storage equipment; H load (t) is the heat load; G buy,t is the gas purchase volume of the microgrid operator at time t; G TS,dis,tG is the gas discharge volume of the gas storage tank at time t in the microgrid; MR,t G is the amount of CH4 produced by methanogenesis of power-to-gas technology in the microgrid at time t; load,t G is the gas load of the user at time t; TS,chr,t G is the gas storage capacity of the gas storage equipment in the microgrid at time t; CHP,t is the gas consumption of the cogeneration unit in the microgrid at time t; G GB,t is the natural gas consumption of the gas boiler unit in the microgrid at time t; is the amount of hydrogen produced by the electrolyzer; P HS，dis (t) is the hydrogen storage capacity of the hydrogen storage equipment; is the hydrogen consumption of methanation reaction; is the hydrogen consumption of hydrogen fuel cell; P HS，chr (t) is the amount of hydrogen released by the hydrogen storage equipment.

[0101] Step S5, under the second constraint condition, solve the microgrid operator game model to generate the microgrid operator's power purchase amount at each moment, the microgrid operator's gas purchase amount at each moment, the microgrid's energy power at each moment, and the microgrid's energy flow at each moment.

[0102] It should be noted that the decision variables are: the power output power P of the cogeneration unit of the microgrid at time t CHP，t ; Discharge power P of the power storage device in the microgrid at time t ES，dis，t ; The amount of electricity purchased by the microgrid operator at time t P es，buy,t ; Microgrid hydrogen fuel cell power generation P at time t HFC,e，t ; The charge amount of the power storage device in the microgrid at time t P ES,chr,t ; The gas purchase volume G of the microgrid operator at time t buy,t ; Microgrid gas tank discharge volume G at time t TS,dis,t ; CH4 amount G produced by power-to-gas methanogenesis in microgrid at time t MR,t Gas storage capacity G of gas storage equipment in microgrid at time t TS,chr,t ; Gas consumption G of the cogeneration unit in the microgrid at time t CHP,t ; Natural gas consumption G of gas boiler unit in microgrid at time t GB,t .

[0103] Specifically, the TUA algorithm is improved by the infinite folding iterative chaotic mapping method, and then the two-layer game model is solved to obtain the optimal strategy of each stakeholder.

[0104] (1) Constructing the Tactical Unit Algorithm (TUA) model

[0105] The TUA algorithm is based on a population search mechanism. Each combatant in the tactical unit is regarded as an independent search agent, and its position can be represented by the following matrix:

[0106]

[0107] Where n represents the population size of the tactical unit (i.e. the number of search agents), and d represents the dimension of the decision variable to be optimized. The initial fitness calculation formula of the population is:

[0108]

[0109] Where: Matrix F X Each row of represents the fitness value of the search individual, where the fitness function is the objective function of the upper leader.

[0110] (a) Searcher Behavior Search Stage

[0111] The searcher is the main target search group in the entire team. By sharing their best search information, they provide search direction for the entire team. Therefore, the searcher has the widest search range, and the calculation formula for its position is:

[0112]

[0113] Where: t represents the current iteration number, is the new position of the i-th searcher in the j-th dimension; T max is the maximum number of iterations; γ is a random number in the range (0,1]; P is a uniformly distributed random number (in the range [-1,1]); M is a matrix of size; R1 represents the search ability of the searcher (in the range (0,1]); TSC is the target search range coefficient (in the range [0.7,1.0]). When R1 < TSC, the searcher will perform a large-scale global search in the current area; when R1 ≥ TSC, the searcher will perform a local search in the current area.

[0114] (b) Executor behavior execution stage

[0115] Executors are the secondary target search group in the team, accounting for the largest proportion of the team. They share information from the searcher and any individual in the group. When the target is found, the executor will immediately perform the task. Its position update formula is:

[0116]

[0117] Where: Optimal location information for searchers; is the position of any individual in the executor group; pt is the action coefficient of the executor, and its calculation formula is:

[0118]

[0119] (c) Evaluator Operational Evaluation Phase

[0120] As the command center of the team, the evaluator is responsible for collecting information from the searchers and executors to determine whether the search task is completed. If the searchers and executors do not find the optimal target, the evaluator will participate in the search as the searcher and executor to complete the task.

[0121] The update of the evaluator's current position is performed by combining the optimal position information transmitted by the searcher and the executor in equal proportions, and the formula is as follows:

[0122]

[0123] Where: Indicates the optimal position of the performer.

[0124] The position update formula of the new generation evaluator is:

[0125]

[0126] Where: β is a random number (range (0,1]); α is a nonlinear adaptive weight factor, and its calculation formula is:

[0127]

[0128] During the optimization process, α is nonlinearly reduced from 1 to 0.6 in the first half of the iteration, and nonlinearly increased from 0.6 to 1 in the second half. This design improves the exploration ability of the algorithm in the later stage and avoids falling into the local optimum by perturbing the position of the evaluator.

[0129] (2) Improved TUA algorithm based on infinite folding iterative chaotic mapping

[0130] The present invention uses infinite folding iterative chaotic mapping to distribute the search agents to the entire space when initializing the TUA algorithm, maintaining the diversity of the initial searchers. The infinite folding iterative chaotic mapping is as follows:

[0131]

[0132] λ∈(0,∞),x n ∈(-1.1)

[0133] Where: λ is the folding factor.

[0134] (3) Model solution

[0135] In this paper, the TUA algorithm is nested with the CPLEX solver to solve the master-slave game two-layer model. The specific solution process is as follows:

[0136] 1) Initialize TUA parameters.

[0137] 2) The upper-level microgrid operator uses the TUA algorithm to decide the pricing strategy: each individual X i =[x u1 ,x u2 ] represents the time-of-use electricity-carbon coupling pricing strategy formulated by the operator, and the two elements within the individual represent two decision variables.

[0138] 3) The lower-level user entity receives the electricity-carbon price transmitted from the upper level, takes minimizing the energy cost as the goal, calls the CPLEX solver to optimize the operation strategy under the current time-sharing electricity-carbon coupling price, and feeds back the purchased electricity amount.

[0139] 4) The microgrid operator accepts the power purchase demand feedback from users, takes profit maximization as the goal, and calls the current fitness function of the CPLEX solver (operator objective function).

[0140] 5) By comparing the fitness function values, the optimal position information transmitted by the searcher and the executor is updated, and the evaluator position is obtained by weighting.

[0141] 6) Repeat steps 3) to 5) until the number of iterations is met.

[0142] Step S6: dispatch the microgrid according to the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment.

[0143] In a preferred embodiment, Figure 2 As shown, Figure 2 It is the result of the energy price optimization of microgrid operators by the present invention. As can be seen from the figure, the peak-valley electricity price shows obvious peak-valley changes. The electricity price is higher during peak hours such as 8:00-10:00 and 16:00-20:00, and the electricity price is lower during valley hours such as 0:00-6:00 and 22:00-24:00. The purpose is to encourage users to use electricity during valley hours through price signals; the peak-valley carbon price reflects the changes in carbon emission costs or market supply and demand; the time-of-use electricity carbon price combines the effects of electricity prices and carbon prices, showing a more complex fluctuation pattern. By setting different peak-valley electricity prices and carbon price increment coefficients, a clear price signal is conveyed to users. During periods of high electricity prices and high carbon emissions, users tend to reduce electricity consumption and turn to periods of low electricity prices and low carbon emissions, thereby optimizing their energy purchasing strategies. After adopting the time-of-use electricity carbon price model, the increase in carbon trading costs will prompt the time-of-use electricity carbon price to tend to reflect the peak-valley characteristics of the carbon emission factor, thereby guiding users to adjust their energy consumption behavior according to the carbon emission factor. In general, the time-of-use electricity-carbon coupling pricing model can effectively guide users to adjust their electricity consumption behavior, reduce electricity demand during peak hours, and reduce carbon emission costs. This model not only improves the operating efficiency and economy of microgrids, but also shows great potential in dealing with the dual constraints of the electricity-carbon market.

[0144] like Figure 3 and Figure 4 As shown, Figure 3 and Figure 4 It is the result of the optimized dispatching of the microgrid of the present invention. It can be seen from the two figures of electric power balance and thermal power balance that the dispatching of the microgrid under the conditions of electric and thermal coupling realizes the dynamic balance between the coordinated operation of multiple devices and the load demand. In the electric power dispatching, the energy storage system effectively realizes the peak shaving and valley filling function through the charging and discharging process, storing electricity when the load is low (such as at night) and releasing electricity when the load is peak (such as daytime and evening); the wind power generation output is stable, making full use of the advantages of renewable energy; fuel cells provide stable power support during critical periods; and during high-load periods, the system is flexibly supplemented by purchasing electricity from the electricity market. In the thermal power dispatching, the heat storage tank also realizes the stabilization of the thermal load demand through alternating operation of heat storage and heat release; the CHP (combined heat and power) device provides the basic heat source with stable output; the gas boiler, as an auxiliary heat source, responds quickly during high-load periods to supplement the heating demand; the fuel cell further improves the overall efficiency of the system through the coordinated output of electricity and heat. In summary, the scheduling scheme for electric power and thermal power, based on the full utilization of energy storage, heat storage and renewable energy, flexibly allocates traditional energy equipment to ensure the reliable satisfaction of electric and thermal load demands, while significantly reducing operating costs, demonstrating the efficiency and practical application value of the optimized scheduling method in the electricity-carbon coupling market.

[0145] The comparison of the optimization results of the algorithm proposed in this invention and other algorithms is shown in the following table:

[0146]

[0147]

[0148] As can be seen from the table, the improved TUA (i.e., this method) has significant advantages. In terms of the number of iterations, the improved TUA only requires 35 times, which is far less than the 48 times of the traditional TUA and the 66 times of the GA, showing higher computational efficiency and better convergence performance. In terms of total cost, the improved TUA is 10215.66 yuan when considering demand response, which is 273.77 yuan and 472.18 yuan lower than the traditional TUA and GA respectively; when the demand response is not considered, the total cost is also the lowest, which is 10601.96 yuan, further verifying its optimization ability. Among the sub-item costs, the carbon trading cost, electricity purchase cost, and gas purchase cost of the improved TUA are significantly lower than those of the other two algorithms, indicating that it has significant advantages in price modeling and multi-energy coordinated scheduling under the electricity-carbon coupling market. In addition, the introduction of demand response significantly reduced the total cost, among which the improved TUA had the largest reduction, showing that the algorithm can more effectively motivate users to flexibly adjust the load and achieve an improvement in the overall benefits of the system. In general, the improved TUA is superior to the traditional method in terms of efficiency and effect, which not only improves the revenue of the microgrid, but also reduces user costs and carbon trading pressure, fully proving its practical application value in the electricity-carbon coupling market. Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0149] like Figure 5 As shown, an embodiment of the present invention provides a microgrid optimization scheduling device, including: a data acquisition module, a user game optimization module, a microgrid operator game optimization module and a microgrid scheduling module;

[0150] The data acquisition module is used to obtain the energy transaction parameters of the microgrid operator, the user's electricity purchase parameters, the user's gas load and the user's heat load; wherein the energy transaction parameters of the microgrid operator include the time-of-use electricity-carbon coupling price at each moment;

[0151] The user game optimization module is used to construct a user game model and a first constraint condition according to the time-sharing coupled electricity-carbon price and the user's electricity purchase parameters at each moment, with the goal of minimizing the user's operating cost; under the first constraint condition, the user game model is solved to generate the actual amount of electricity purchased by the user at each moment;

[0152] The microgrid operator game optimization module is used to construct a microgrid operator game model and a second constraint condition based on the actual power purchase amount, gas load, heat load and energy trading parameters of the microgrid operator at each moment, with the goal of maximizing the total revenue of the microgrid operator;

[0153] The microgrid dispatching module is used to dispatch the microgrid according to the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment.

[0154] In a preferred embodiment, the microgrid optimization dispatching device and the data acquisition module determine the time-sharing electricity-carbon coupling price at each moment in the following manner:

[0155] Obtain the time-of-use carbon price parameters and the time-of-use electricity price parameters of the microgrid operator at each time;

[0156] According to the time-of-use carbon price parameters and the time-of-use electricity price parameters at each moment, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated and generated.

[0157] It should be noted that the embodiment of the device described above corresponds to the above-mentioned embodiment of the present invention, and it can implement any one of the microgrid optimization scheduling methods described above in the present invention. In addition, the embodiment of the above-mentioned device is merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the embodiment of the device provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0158] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0159] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the microgrid optimization scheduling method described in any one of the present invention is implemented, or when the processor executes the computer program, the functions of each module in the above-mentioned device embodiments are implemented.

[0160] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0161] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0162] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0163] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0164] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0165] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned microgrid optimization scheduling methods of the present invention.

[0166] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0167] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0168] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A microgrid optimization scheduling method, characterized in that: include: Obtain the energy trading parameters of the microgrid operator, the user's electricity purchasing parameters, the user's gas load, and the user's heat load; the energy trading parameters of the microgrid operator include the time-of-use electricity-carbon coupling price at each moment; According to the time-sharing coupled electricity-carbon price and the user's electricity purchasing parameters at each moment, with the goal of minimizing the user's operating cost, a user game model and a first constraint condition are constructed; Under the first constraint, the user game model is solved to generate the actual amount of electricity purchased by the user at each moment; According to the actual power purchase amount, gas load, heat load and energy trading parameters of microgrid operators at each moment, the game model of microgrid operators and the second constraint condition are constructed with the goal of maximizing the total revenue of microgrid operators. Under the second constraint, the game model of the microgrid operator is solved to generate the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment; The microgrid is dispatched according to the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment.

2. A microgrid optimization scheduling method according to claim 1, characterized in that: The time-sharing electricity-carbon coupling price at each moment is determined by the following method: Obtain the time-of-use carbon price parameters and time-of-use electricity price parameters of the microgrid operator at each moment; According to the time-of-use carbon price parameters and the time-of-use electricity price parameters at each moment, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated and generated.

3. A microgrid optimization scheduling method as claimed in claim 2, characterized in that: The user game model includes: Among them, F user Running costs for users; P is the electricity purchase cost of the user at time t; user，t is the actual amount of electricity purchased by the user at time t; is the time-sharing electricity-carbon coupling price at time t; T is the scheduling time.

4. A microgrid optimization scheduling method as claimed in claim 3, characterized in that: The first constraint condition includes: power balance constraint and user energy purchase constraint; The power balance constraints include: Among them, ΔP user，t is the change in the amount of electricity purchased by the user at time t; P is the planned power purchase of the user at time t; user，t is the actual amount of electricity purchased by the user at time t; T is the dispatch time; The user energy purchase constraints include: Among them, P user，t is the actual amount of electricity purchased by the user at time t; is the minimum amount of electricity purchased by the user at time t; is the maximum amount of electricity purchased by the user at time t.

5. A microgrid optimization scheduling method as claimed in claim 4, characterized in that: The microgrid operator game model includes: C es,buy,t =e es,buy,t P es,buy,t C gs,buy,t =e gs,buy,t G buy,t Where S is the total revenue of the microgrid operator; C eu，sell，t is the electricity sales revenue of the microgrid operator at time t; is the time-sharing electricity-carbon coupling price at time t; user，t is the actual amount of electricity purchased by the user at time t; C es,buy,t is the electricity purchase cost of the microgrid operator at time t; e es,buy,t is the electricity purchase price of the microgrid operator at time t; P es,buy,t is the amount of electricity purchased by the microgrid operator at time t; C gs,buy,t is the gas purchase cost of the microgrid operator at time t; e gs,buy,t is the gas purchase price of the microgrid operator at time t; G buy,t is the gas purchase volume of the microgrid operator at time t; is the carbon trading cost of the microgrid operator at time t; c ct is the unit carbon emission trading price; δ p is the carbon emission factor of electricity purchase; δ g is the unit natural gas carbon emission factor; T is the scheduling time.

6. A microgrid optimization scheduling method as claimed in claim 5, characterized in that: The second constraint condition includes: energy purchase constraint and power balance constraint of the microgrid operator; The energy purchase constraints of the microgrid operator include: Among them, P es,buy,t is the amount of electricity purchased by the microgrid operator at time t; G is the maximum amount of electricity purchased by the microgrid operator at time t; buy，t is the gas purchase volume of the microgrid operator at time t; is the maximum gas purchase volume of the microgrid operator at time t; The power balance constraint includes: P WT，t +P CHP，t +P ES，dis,t +P e,buy,t +P HFC,e，t =P user，t +P ES,chr,t H CHP (t)+H GB (t)+H TS，dis (t)=H TS，chr (t)+H load (t) G buy,t +G TS,dis,t +G MR，t =G load,t +G TS,chr,t +G CHP,t +G GB,t Among them, P WT，t is the wind turbine output power of the microgrid at time t; P CHP，t P is the power output of the cogeneration unit in the microgrid at time t; ES，dis，t P is the discharge power of the power storage device in the microgrid at time t; es，buy，t is the amount of electricity purchased by the microgrid operator at time t; P HFC，e，t P is the power generated by the hydrogen fuel cell in the microgrid at time t; user，t is the actual amount of electricity purchased by the user at time t; P ES，chr，t H is the charge amount of the power storage device in the microgrid at time t; CHP (t) is the thermal output power of the cogeneration unit; H GB (t) is the heat generated by the gas boiler; H TS，dis (t) is the heat released by the heat storage device; H TS，chr (t) is the heat storage capacity of the heat storage device; H load (t) is the heat load; G buy,t is the gas purchase volume of the microgrid operator at time t; G TS,dis,t G is the gas discharge volume of the gas storage tank at time t in the microgrid; MR,t G is the amount of CH4 produced by methanogenesis of power-to-gas technology in the microgrid at time t; load,t G is the gas load of the user at time t; TS,chr,t G is the gas storage capacity of the gas storage equipment in the microgrid at time t; CHP,t is the gas consumption of the cogeneration unit in the microgrid at time t; G GB,t is the natural gas consumption of the gas boiler unit in the microgrid at time t; is the amount of hydrogen produced by the electrolyzer; P HS，dis (t) is the hydrogen storage capacity of the hydrogen storage equipment; is the hydrogen consumption of methanation reaction; is the hydrogen consumption of hydrogen fuel cell; P HS，chr (t) is the amount of hydrogen released by the hydrogen storage equipment.

7. A microgrid optimization dispatching device, characterized in that: include: Data acquisition module, user game optimization module, microgrid operator game optimization module and microgrid dispatch module; The data acquisition module is used to obtain the energy transaction parameters of the microgrid operator, the user's electricity purchase parameters, the user's gas load and the user's heat load; wherein the energy transaction parameters of the microgrid operator include the time-of-use electricity-carbon coupling price at each moment; The user game optimization module is used to construct a user game model and a first constraint condition according to the time-sharing coupled electricity-carbon price and the user's electricity purchase parameters at each moment, with the goal of minimizing the user's operating cost; under the first constraint condition, the user game model is solved to generate the actual amount of electricity purchased by the user at each moment; The microgrid operator game optimization module is used to construct a microgrid operator game model and a second constraint condition based on the actual power purchase amount, gas load, heat load and energy trading parameters of the microgrid operator at each moment, with the goal of maximizing the total revenue of the microgrid operator; The microgrid dispatching module is used to dispatch the microgrid according to the amount of electricity purchased by the microgrid operator at each moment, the amount of gas purchased by the microgrid operator at each moment, the energy power of the microgrid at each moment, and the energy flow of the microgrid at each moment.

8. A microgrid optimization dispatching device as claimed in claim 7, characterized in that: The data acquisition module determines the time-sharing electricity-carbon coupling price at each moment in the following manner: Obtain the time-of-use carbon price parameters and the time-of-use electricity price parameters of the microgrid operator at each moment; According to the time-of-use carbon price parameters and the time-of-use electricity price parameters at each moment, the time-of-use electricity-carbon coupling price of the microgrid operator at each moment is calculated and generated.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the microgrid optimization scheduling method described in any one of claims 1 to 6 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the microgrid optimization scheduling method described in any one of claims 1 to 6.

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