Microgrid planning method and system considering carbon transaction mechanism and demand response

Abstract

The invention provides a micro-grid planning method and system considering a carbon transaction mechanism and demand response, which considers low-carbon planning elements such as new energy and energy storage, introduces hydrogen energy storage instead of traditional electric energy storage into micro-grid planning, combines the carbon transaction mechanism, integrates source-side carbon transaction cost into a planning decision target, and performs capacity fixing on a power supply. And low-carbon-economic optimal matching of the power grid for solving the long-time scale variable model is realized. And according to a capacity configuration scheme, the load after demand response is calculated by taking the minimum sum of the absolute values of the difference values of the renewable energy power generation power and the load power as a target, and optimal matching on a short time scale variable model is solved. According to the method, the carbon transaction and the demand response are taken into consideration at the same time, and collaborative optimization of the micro-grid system in the aspects of low carbon and economical efficiency is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of microgrid planning, and in particular relates to a microgrid planning method and system taking into account a carbon trading mechanism and demand response. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the high proportion of clean and renewable energy connected to the power grid, microgrid planning needs to comprehensively consider the output characteristics of various distributed power sources. At the same time, microgrid planning also faces the dual challenges of imperfect market mechanisms and insufficient operating experience. In addition, hydrogen energy storage is a new energy storage technology developed based on the mutual conversion of electrical energy and hydrogen energy, which plays an important role in the consumption of new energy and the optimization of system operation.

[0004] At present, the carbon trading mechanism is one of the most effective means to control carbon emissions for the optimization of microgrid configuration towards energy conservation and emission reduction. The carbon trading mechanism constrains carbon emissions through economic means, guides enterprises to develop low-carbon technologies, and brings together capital flows and low-carbon development, which helps to develop towards a green and low-carbon energy structure and promote the achievement of dual carbon goals. Therefore, it is of great significance to introduce the carbon trading mechanism into the research on the optimization of microgrid configuration.

[0005] For microgrids with high penetration of renewable energy generation, it is less economical to rely solely on the power generation side to maintain the power supply balance of the microgrid system. Demand response can make the user's energy use strategy more consistent with the output of renewable energy in timing, which becomes an effective way to deal with the uncertainty of new energy output and promote the consumption of new energy.

[0006] However, in the field of microgrid planning research, most studies only focus on the single impact of the tiered carbon trading mechanism or demand response. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a microgrid planning method and system taking into account carbon trading mechanism and demand response. The present invention takes carbon trading and demand response into consideration at the same time to achieve coordinated optimization of the microgrid system in terms of low carbon and economy.

[0008] According to some embodiments, the present invention adopts the following technical solutions:

[0009] A microgrid planning method considering carbon trading mechanism and demand response includes the following steps:

[0010] Obtain historical load and wind and solar output data in the target area, as well as load forecast and wind and solar forecast data during the planning period;

[0011] Establish and solve the planning model of long-time scale variables. The planning model takes the optimal comprehensive equal-annual value as the economic goal and plans the rated capacity of key equipment in the microgrid.

[0012] The obtained planning results are simulated in time series operation, and the operation simulation results are used as known quantities for demand response calculation to update the load;

[0013] Establish a demand response model for short-time scale variables. The demand response model aims to minimize the sum of the absolute values ​​of the difference between renewable energy generation and load demand at each moment in the scheduling cycle. According to the rated capacity of each planned device, the output of each distributed power source and energy storage device in the system is allocated.

[0014] Establish demand response optimization objectives and solve them;

[0015] Determine whether the solution meets the iterative convergence conditions. If so, determine the final microgrid planning scheme. Otherwise, use the optimized load data as the acquired data and repeat the above steps.

[0016] As an optional implementation method, in the process of obtaining historical load and wind and solar power output data in the target area, as well as load forecast and wind and solar power forecast data during the planning period, annual meteorological data, annual load data and load-related parameters are obtained, and the K-means algorithm is used to cluster wind speed, light and load according to summer, winter, spring and autumn to obtain typical scenarios for planning.

[0017] As an optional implementation, the comprehensive annual value includes the annual investment cost of the unit, the annual maintenance cost of the unit, the system carbon trading cost, the hydrogen energy storage operation cost and the system annual operation cost;

[0018] The key equipment includes wind turbines, photovoltaic arrays, diesel generators, electrolyzers, hydrogen storage tanks and fuel cells in the microgrid.

[0019] As an optional implementation method, the constraints of the planning model for long-term variables include unit construction capacity constraints, system power balance constraints, new energy output constraints, hydrogen energy storage system constraints, upper and lower limit constraints on power purchase and sales, and wind and solar power abandonment constraints.

[0020] As an optional implementation method, the process of establishing a demand response optimization target includes adopting an incentive-based demand response to adjust the power consumption time of the time-shiftable load so that the load curve within the scheduling cycle is as consistent as possible with the wind and solar output curve in terms of timing.

[0021] As an optional implementation, the demand response optimization target is:

[0022]

[0023] Among them, P pv,t , P wt,t are the output power of photovoltaic power and wind power at time t, respectively, and Δt is the time difference; and are the load power before and after the demand response at time t; ΔP load,t It is the load transfer amount at time t. Its value greater than 0 indicates load transfer in, and its value less than 0 indicates load transfer out.

[0024] As an optional implementation manner, the constraints of the demand response optimization objective include load transfer period constraints, load transfer amount constraints and consumption cost constraints.

[0025] As an optional implementation method, the process of determining whether the solution result satisfies the iterative convergence condition includes determining the iterative solution result of the two-layer optimization configuration model composed of the planning model and the demand response model. When the comprehensive equal-annual value of the microgrid is the smallest, that is, the results of the two iterations remain unchanged or the change is less than the set value, it is considered that the iterative convergence condition is met.

[0026] As a further implementation method, the double-layer optimization configuration model is solved by introducing an improved particle swarm algorithm with inertia weight. The improved particle swarm algorithm with inertia weight is to add the influence weight of the global operator on the currently analyzed particles during the operation of the algorithm to balance the effects of the global particles and the current particles, and adopt a bacterial foraging algorithm to improve the particle update speed and position, and set the maximum time value and the minimum value of the inertia weight in the iterative update.

[0027] A microgrid planning system considering carbon trading mechanism and demand response, comprising:

[0028] A data acquisition module is configured to acquire historical load and wind and solar output data in a target area, as well as load forecast and wind and solar forecast data within a planning period;

[0029] The long-term planning module is configured to establish and solve a planning model for long-term variables. The planning model plans the rated capacity of key equipment in the microgrid with the optimal comprehensive equal-annual value as the economic goal;

[0030] An operation simulation module is configured to perform a time-series operation simulation on the obtained planning results, and use the operation simulation results as known quantities for demand response calculation to perform load updates;

[0031] The short-term planning module is configured to establish a demand response model for short-term variables. The demand response model aims to minimize the sum of the absolute values ​​of the difference between the renewable energy generation and the load demand at each moment in the scheduling cycle. According to the rated capacity of each planned device, the output of each distributed power source and energy storage device in the system is allocated, and the demand response optimization target is established and solved.

[0032] The judgment module is configured to judge whether the solution meets the iterative convergence condition. If so, the final microgrid planning scheme is determined; otherwise, the optimized load data is used as the acquired data and returned to the data acquisition module.

[0033] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The present invention comprehensively considers the economy and low-carbon nature of microgrids, and proposes a two-layer optimization configuration method for microgrids that considers step-by-step carbon trading and incentive-based demand response. The upper optimization model takes the minimization of the annual comprehensive cost of the microgrid as the optimization goal, and the lower optimization model takes the minimization of the difference between the microgrid's renewable energy power generation and load demand as the optimization goal.

[0036] (2) The present invention adopts a method combining an improved particle swarm algorithm and mixed integer linear programming to solve the two-level optimization configuration model. The introduction of a demand response mechanism will promote the consumption of renewable energy, reduce the configuration capacity of hydrogen energy storage, and obtain better economic benefits. At the same time, it will also reduce the power generation of diesel generators and the amount of electricity purchased from the upper power grid, thereby reducing the carbon emissions of the system and improving the low-carbon benefits of the system, thereby achieving the optimal compromise between the economy and low-carbon nature of the microgrid.

[0037] (3) Taking into account the disadvantage of the basic particle swarm optimization algorithm that is prone to local optimization, the present invention improves the performance of the algorithm by improving the inertia weight and particle speed of the particle swarm algorithm. At the same time, in order to improve the performance of the algorithm in the microgrid optimization configuration, the particle speed transformation of the particle swarm algorithm is improved on the basis of improving the particle swarm parameters. That is, the process of particle speed change during the particle swarm optimization process is improved, and the bacterial foraging algorithm is used to improve the speed and position of the improved particle swarm algorithm, thereby improving the convergence speed and accuracy of the algorithm.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] Figure 1 The present invention is a flow chart of a microgrid planning method taking into account a carbon trading mechanism and demand response according to an embodiment. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] In the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0045] Embodiment 1

[0046] A microgrid planning method considering carbon trading mechanism and demand response, such as Figure 1 As shown, the following steps are included:

[0047] Step 1: Data collection. Collect historical load and wind and solar output data within the planning area, as well as load forecast and wind and solar forecast data within the planning period. Read annual meteorological data, annual load data and related parameters, and use the K-means algorithm to cluster wind speed, light and load according to summer, winter, spring and autumn to obtain typical scenarios for system planning.

[0048] Step 2: Establish a planning model for solving long-time scale variables. Taking the annual cost optimization of the unit, annual maintenance cost of the unit, and annual operation cost of the system as the economic goal, the rated capacity of wind turbines, photovoltaic arrays, diesel generators, electrolyzers, hydrogen storage tanks, and fuel cells in the microgrid are planned.

[0049] The comprehensive equivalent annual cost of the microgrid is calculated by formula (1):

[0050]

[0051] Among them, D is the comprehensive cost of equal annual value; D inv is the investment cost of equal annual value; D main is the annual maintenance cost; D om is the annual operating cost; is the system carbon trading cost; D H The operating costs of hydrogen energy storage;

[0052] The annual investment cost of the computer group is calculated by formula (2):

[0053]

[0054] Among them, Q k,N is the planned capacity of device k; Q et,N , Q H,N , Q hfc,N are the planned capacities of electrolyzer, hydrogen storage tank and hydrogen fuel cell, respectively. k is the capital recovery coefficient; γ is the discount rate; y k is the operating life of equipment k; is the unit capacity investment cost of equipment k They are the unit capacity investment costs of electrolyzer, hydrogen storage tank and hydrogen fuel cell respectively.

[0055] The annual maintenance cost of the computer group is calculated by formula (3):

[0056]

[0057] in, is the annual maintenance cost per unit capacity of equipment k; They are the annual maintenance costs per unit capacity of electrolyzer, hydrogen storage tank and hydrogen fuel cell respectively.

[0058] The annual operating cost is calculated by formula (4):

[0059] D om =D fuel +D grid +D waste (4)

[0060] Among them, D fuel is the annual fuel cost, considering only the fuel consumption of diesel generators; D grid is the electricity purchase and sales cost between the microgrid and the upper grid; D waste The penalty cost for annual wind and solar power abandonment.

[0061] The annual fuel cost is calculated by formula (5):

[0062]

[0063] Among them, Ω D It is a collection of typical days, including three typical days in summer, winter, spring and autumn; is the number of days that a typical day d occupies in a year; c fuel is the unit price of diesel; is the power generated by the diesel generator at time t in a typical day d; a and b are the slope and intercept coefficient of the fuel consumption-power curve respectively;

[0064] The annual electricity purchase and sales cost is calculated by formula (6):

[0065]

[0066] in, are the electricity purchase and sales prices at time t respectively; They are respectively the power purchased and sold by the microgrid at time t within a typical day d;

[0067] The cost of curtailing wind and solar power is calculated by formula (7):

[0068]

[0069] in, They are the penalty fees for the unit amount of wind and solar power abandoned respectively; are the abandoned wind and solar powers at time t within a typical day d, respectively;

[0070] The abandoned wind and solar power is calculated by formula (8):

[0071]

[0072] in, are the power that can be generated by the photovoltaic array and wind turbine at time t in a typical day d; They are the actual output power of the photovoltaic array and wind turbine at time t in a typical day d.

[0073] The carbon trading cost is calculated by formula (9):

[0074]

[0075] in, is the carbon trading price. G Free carbon emission quota for the system; E G is the system carbon emissions; When , the system needs to purchase carbon emission quotas from the outside to meet the system's carbon emission needs; when When carbon emissions are reduced, the system can sell carbon emissions to the outside world to obtain carbon benefits.

[0076] The free carbon emission quota is calculated by formula (10):

[0077]

[0078] Among them, a de is the carbon emission quota per unit of electricity of the diesel generator; buy P is the carbon emission quota per unit of electricity purchased from the upper-level power grid; de,t is the output power of the diesel generator at time t; P buy,t is the power purchased by the microgrid from the upper grid at time t; β buy is the proportion of thermal power in the electricity purchased from the upper-level power grid; T is the settlement period of carbon trading fees; Δt is the time interval.

[0079] The system carbon emissions are calculated by formula (11):

[0080]

[0081] Among them, b de is the carbon emission intensity per unit of electricity of the diesel generator; b buy It is the carbon emission intensity of thermal power per unit of electricity purchased from the upper-level power grid.

[0082] The operating cost of the hydrogen energy storage system is calculated by formula (12):

[0083] D E-H =D et +D H +D hfc (12)

[0084] The operating cost of the electrolytic cell is calculated by formula (13):

[0085]

[0086] The operating cost of the electrolytic cell is calculated by formula (14):

[0087]

[0088] The operating cost of the electrolytic cell is calculated by formula (15):

[0089]

[0090] Among them, c et 、c H 、c hfc are the unit operating cost coefficients of hydrogen production, hydrogen storage and HFC power generation respectively;

[0091] The capacity constraint of the unit construction is guaranteed by formula (16):

[0092]

[0093] Among them, Q pv,N , Q wt,N , Q de,N They are the planned capacities of photovoltaic arrays, wind turbines and diesel generators respectively.

[0094] The system power balance constraint is ensured by equation (17):

[0095]

[0096] in, is the load power at time t in a typical day d; is the output power of the fuel cell at time t in a typical day d; is the input power of the electrolyzer at time t on a typical day d.

[0097] The output constraint of new energy is ensured by formula (18):

[0098]

[0099] The hydrogen energy storage system constraint is ensured by formula (19):

[0100]

[0101] The upper and lower limits of power purchase and sale are guaranteed by formula (20):

[0102]

[0103] in, They are the maximum power limits for the microgrid to purchase electricity from the upper-level grid and sell electricity to the upper-level grid on a typical day.

[0104] The wind and solar power curtailment constraints are ensured by equations (21) and (22):

[0105]

[0106] Among them, a wt-waste 、a pv-waste They are the maximum wind curtailment rate and solar curtailment rate respectively.

[0107] Step 3: Sequential operation simulation. The obtained planning results are simulated sequentially to obtain the operation result data, which are passed to step 4 as known quantities for demand response calculation to update the load in order to re-optimize the calculation.

[0108] Step 4: Establish a demand response model for solving short-time scale variables. With the goal of minimizing the sum of the absolute values ​​of the difference between renewable energy generation and load demand at each moment in the dispatch cycle, the output of each distributed power source and energy storage device in the system is reasonably allocated according to the installed capacity of each device determined in step 2.

[0109] ① Calculate the operating power of the hydrogen energy storage system

[0110] The output power of the electrolytic cell is calculated by formula (23):

[0111]

[0112] in, is the input power of the electrolytic cell in period t; is the output hydrogen power of the electrolyzer in period t; η et is the hydrogen production efficiency of the electrolyzer;

[0113] The upper and lower limits of the input power of the electrolyzer are determined by formula (24):

[0114]

[0115] in, and are the upper and lower limits of the electrolyzer input power respectively;

[0116] The maximum energy storage capacity of the hydrogen storage tank is determined by formula (25):

[0117]

[0118] in, and They are the upper and lower limits of the electrolytic cell climbing power respectively.

[0119] The output power of the fuel cell is determined by formula (26):

[0120]

[0121] in, is the input power of the electrolytic cell in period t; is the output hydrogen power of the electrolyzer in period t; η et is the hydrogen production efficiency of the electrolyzer;

[0122] The upper and lower limits of the fuel cell input power are determined by formula (27):

[0123]

[0124] in, and are the upper and lower limits of fuel cell input power, respectively;

[0125] The power ramp constraint of the fuel cell is determined by equation (28):

[0126]

[0127] in, and They are the upper and lower limits of fuel cell climbing power respectively.

[0128] The hydrogen storage capacity of the hydrogen storage tank is determined by formula (29):

[0129]

[0130] Among them, S H,t is the hydrogen storage capacity of the hydrogen storage tank in the tth period; and are the input and output hydrogen power of the hydrogen storage tank in the tth period respectively; and They are the storage and release efficiency of the hydrogen storage tank respectively.

[0131] The upper and lower limits of the hydrogen storage tank capacity are determined by formula (30):

[0132]

[0133] The charge and discharge constraints are determined by formula (31):

[0134]

[0135] Step 5: Demand response load calculation: By adopting incentive-based demand response, the power consumption time of the time-shiftable load is adjusted to make the load curve within the dispatch cycle as consistent as possible with the wind and solar output curve in terms of timing, so as to promote the consumption of renewable energy.

[0136] The demand response optimization objective is calculated by formula (32):

[0137]

[0138] Among them, P pv,t , P wt,t are the output power of photovoltaic and wind power at time t respectively; and are the load power before and after the demand response at time t; ΔP load,t It is the load transfer amount at time t. Its value greater than 0 indicates load transfer in, and its value less than 0 indicates load transfer out.

[0139] The load transfer period is constrained by equation (33)

[0140] t * ∈T n ,t′∈T n (33)

[0141] Among them, t * is the load transfer-in period; t′ is the load transfer-out period; T nIt is the nth scheduling cycle.

[0142] The load transfer amount is constrained by equation (34)

[0143] The load transfer amount should satisfy the requirement that the total load demand before and after the demand response remains unchanged within a dispatching cycle, and the load transfer amount at each moment should not exceed the maximum load transfer amount:

[0144]

[0145] in, is the maximum load transfer at time t.

[0146] Consumption cost is constrained by equation (35):

[0147]

[0148] Among them, c e is the conventional electricity price; D dr is the demand response cost; c dr is the unit demand response incentive cost.

[0149] The demand response cost is calculated by formula (36):

[0150]

[0151] Step 6: Determine the iterative convergence condition. When the microgrid comprehensive equal-annual value is the smallest, that is, the results of the two iterations before and after remain unchanged, the model converges, and go to step 7. If it does not converge, the optimized load data is transferred to step 1, and the above steps are repeated until the conditions are met.

[0152] Step 7: Output results. When the above convergence conditions are met, the low-carbon-economic collaborative optimization process is completed, the planning and operation result data are output, and a low-carbon-economic microgrid planning scheme is formulated.

[0153] In order to speed up the solution of the model, an improved particle swarm algorithm with inertia weight is introduced. The formulas are as follows: (37)-(38) and the bacterial foraging algorithm is used to improve the speed and position of the improved particle swarm algorithm. The improved particle speed expression is as follows:

[0154] v id =wv id +c 1 rand()·(P id x id ) (37)

[0155]

[0156] Among them, w is the inertia weight, which represents the influence weight of the global operator on the currently analyzed particle during the operation of the algorithm, mainly balancing the effect of the global particle and the current particle; w max and w min They refer to the maximum time value and minimum time value of the inertia weight set in the iterative update of the algorithm; iter max is the value when the number of iterative optimization is the maximum. The typical value of the inertia weight is w max =0.9, w min =0.4.

[0157] v ij =wv ij +c 1 rand()(pbest ij -p ij )+c 2 rand()·(gbest ij -p ij )+c 3 rand()(frw ij -p ij ) (39)

[0158] Where, j is the dimension of the i-th particle; N is the total dimension of particles; frw ij is the j-th dimension coordinate of the random step position of the i-th particle foraging; p ij ,v ij are the j-th coordinates of the position and velocity of the ith particle respectively.

[0159] This embodiment takes into account low-carbon planning elements such as new energy and energy storage, introduces hydrogen energy storage instead of traditional electric energy storage into microgrid planning, combines the carbon trading mechanism, integrates the source-side carbon trading cost into the planning decision-making objectives, and uses an improved particle swarm algorithm to size the power supply, so as to achieve the low-carbon-economic optimization matching of the power grid in solving long-time scale variable models. According to the capacity configuration plan, the load after demand response is calculated with the goal of minimizing the sum of the absolute values ​​of the difference between the power generation power of renewable energy and the load power, and guides users to participate in the DR mechanism to adjust the load curve. It is also possible to simulate the operation of the microgrid according to this load to obtain the optimal operation plan, so as to achieve the low-carbon-economic optimization matching between the source and the load in solving the short-time scale variable model.

[0160] Embodiment 2

[0161] A microgrid planning system considering carbon trading mechanism and demand response, comprising:

[0162] A data acquisition module is configured to acquire historical load and wind and solar output data in a target area, as well as load forecast and wind and solar forecast data within a planning period;

[0163] The long-term planning module is configured to establish and solve a planning model for long-term variables. The planning model plans the rated capacity of key equipment in the microgrid with the optimal comprehensive equal-annual value as the economic goal;

[0164] An operation simulation module is configured to perform a time-series operation simulation on the obtained planning results, and use the operation simulation results as known quantities for demand response calculation to perform load updates;

[0165] The short-term planning module is configured to establish a demand response model for short-term variables. The demand response model aims to minimize the sum of the absolute values ​​of the difference between the renewable energy generation and the load demand at each moment in the scheduling cycle. According to the rated capacity of each planned device, the output of each distributed power source and energy storage device in the system is allocated, and the demand response optimization target is established and solved.

[0166] The judgment module is configured to judge whether the solution meets the iterative convergence condition. If yes, the final microgrid planning scheme is determined. Otherwise, the data acquisition module is called again with the optimized load data as the acquired data.

[0167] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0168] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0169] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present invention without creative labor shall be included in the protection scope of the present invention.

Claims

1. A microgrid planning method taking into account carbon trading mechanism and demand response, characterized in that: The following steps are involved: Obtain historical load and wind and solar output data in the target area, as well as load forecast and wind and solar forecast data during the planning period; Establish and solve the planning model of long-time scale variables. The planning model takes the optimal comprehensive equal-annual value as the economic goal and plans the rated capacity of key equipment in the microgrid. The obtained planning results are simulated in time series operation, and the operation simulation results are used as known quantities for demand response calculation to update the load; Establish a demand response model for short-time scale variables. The demand response model aims to minimize the sum of the absolute values ​​of the difference between renewable energy generation and load demand at each moment in the scheduling cycle. According to the rated capacity of each planned device, the output of each distributed power source and energy storage device in the system is allocated. Establish demand response optimization objectives and solve them; Determine whether the solution meets the iterative convergence conditions. If so, determine the final microgrid planning scheme. Otherwise, use the optimized load data as the acquired data and repeat the above steps.

2. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1, characterized in that: In the process of obtaining the historical load and wind and solar output data in the target area, as well as the load forecast and wind and solar forecast data during the planning period, the annual meteorological data, annual load data and load-related parameters are obtained, and the K-means algorithm is used to cluster the wind speed, light and load according to summer, winter, spring and autumn to obtain typical scenarios for planning.

3. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1, characterized in that: The comprehensive annual value includes the annual investment cost of the unit, the annual maintenance cost of the unit, the system carbon trading cost, the hydrogen energy storage operation cost and the system annual operation cost; The key equipment includes wind turbines, photovoltaic arrays, diesel generators, electrolyzers, hydrogen storage tanks and fuel cells in the microgrid.

4. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1 or 3, characterized in that: The constraints of the planning model for the long-term variables include constraints on unit construction capacity, system power balance constraints, new energy output constraints, hydrogen energy storage system constraints, upper and lower limit constraints on power purchase and sales, and constraints on the amount of wind and solar power abandoned.

5. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1, characterized in that: The process of establishing demand response optimization targets includes adopting incentive-based demand response and adjusting the electricity consumption time of time-shiftable loads so that the load curve within the dispatch cycle is as consistent as possible with the wind and solar output curve in terms of timing.

6. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1, characterized in that: The demand response optimization objective is: Among them, P pv,t , P wt,t are the output power of photovoltaic power and wind power at time t, respectively, and Δt is the time difference; and are the load power before and after the demand response at time t; ΔP load,t It is the load transfer amount at time t. Its value greater than 0 indicates load transfer in, and its value less than 0 indicates load transfer out.

7. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1 or 6, characterized in that: The constraints of the demand response optimization objective include load transfer period constraints, load transfer amount constraints and consumption cost constraints.

8. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 1, characterized in that: The process of judging whether the solution meets the iterative convergence condition includes judging the iterative solution result of the two-layer optimization configuration model composed of the planning model and the demand response model. When the comprehensive annual value of the microgrid is the smallest, that is, the results of the two iterations are unchanged or the change is less than the set value, it is considered that the iterative convergence condition is met.

9. A microgrid planning method taking into account carbon trading mechanism and demand response as claimed in claim 8, characterized in that: The double-layer optimization configuration model is solved by introducing an improved particle swarm algorithm with inertia weight. The improved particle swarm algorithm with inertia weight adds the influence weight of the global operator on the currently analyzed particles during the operation of the algorithm to balance the effects of the global particles and the current particles, and adopts the bacterial foraging algorithm to improve the particle update speed and position, and sets the maximum time value and the minimum value of the inertia weight in the iterative update.

10. A microgrid planning system taking into account carbon trading mechanism and demand response, characterized in that: include: A data acquisition module is configured to acquire historical load and wind and solar output data in a target area, as well as load forecast and wind and solar forecast data within a planning period; The long-term planning module is configured to establish and solve a planning model for long-term variables. The planning model plans the rated capacity of key equipment in the microgrid with the optimal comprehensive equal-annual value as the economic goal; An operation simulation module is configured to perform a time-series operation simulation on the obtained planning results, and use the operation simulation results as known quantities for demand response calculation to perform load updates; The short-term planning module is configured to establish a demand response model for short-term variables. The demand response model aims to minimize the sum of the absolute values ​​of the difference between the renewable energy generation and the load demand at each moment in the scheduling cycle. According to the rated capacity of each planned device, the output of each distributed power source and energy storage device in the system is allocated, and the demand response optimization target is established and solved. The judgment module is configured to judge whether the solution meets the iterative convergence condition. If yes, the final microgrid planning scheme is determined. Otherwise, the data acquisition module is called again with the optimized load data as the acquired data.

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