Optimal dispatching method and system for coupled microgrids considering coordinated electric and thermal load demands

Abstract

The present disclosure provides a coupled microgrid optimization scheduling method and system considering the coordination of electric and thermal load demands, and relates to the technical field of microgrid optimization scheduling, including: constructing a hydrogen-containing microgrid operation model and considering the peak-valley complementary characteristics of the original electric load and thermal load in the system in time distribution, constructing an electric and thermal load demand coordinated response model, characterizing the electric load with a time-of-use electricity price demand response method, considering that the thermal load has the heat transfer inertia and the fuzziness of the user's temperature perception, and making flexible adjustments based on different electricity prices; based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, constructing an objective function with the goal of minimizing the sum of the total operating cost, the cost of renewable energy abandonment of wind and solar power, and the demand response compensation cost, and establishing an optimal scheduling model for an electric and hydrogen coupled microgrid based on constraint conditions, using mixed integer linear programming to solve the hydrogen-electric coupled microgrid optimization scheduling model, and applying the obtained optimal operation scheduling strategy of the equipment to the microgrid for execution.

Description

Technical Field

[0001] The present disclosure relates to the technical field of microgrid optimization scheduling, and in particular to a coupled microgrid optimization scheduling method and system considering the coordination of electric and thermal load demands. Background Art

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

[0003] At present, fossil energy represented by coal and oil will be gradually replaced by renewable energy represented by wind power. Hydrogen energy, as an important green, clean and renewable energy, is considered to have broad development prospects due to its advantages of high energy, zero carbon emissions and high energy conversion efficiency. With the continuous advancement of energy technology, hydrogen-containing microgrids, as a new type of energy network that can realize electricity-hydrogen conversion, have shown great application potential in multi-energy conversion and storage, resource optimization and allocation, etc. This power grid can not only make full use of distributed renewable energy and reduce dependence on traditional fossil energy, but also solve the problem of unstable output of renewable energy such as wind power and photovoltaics through efficient storage and utilization of hydrogen, and effectively solve the supply problem of different types of energy such as electricity and heat.

[0004] However, since the relevant research on hydrogen-electricity coupled microgrids is still in its preliminary stage, the actual application scheduling process faces problems such as complex equipment operation and changing user needs. In order to solve the above problems, the existing methods introduce optimization algorithms and combine big data analysis and machine learning technology to deeply mine and predict user electricity consumption behaviors, and realize real-time monitoring and dynamic adjustment of the microgrid operation status. Based on the optimization algorithms such as evolutionary particle swarm optimization algorithm, improved genetic algorithm, and gray wolf algorithm, the optimization configuration and technical and economic analysis problems of hydrogen-electricity coupled microgrids are solved; based on big data analysis technology, the power production, conversion, and storage data of distributed power sources in microgrids are monitored and analyzed to realize intelligent energy scheduling and management and help complete fault diagnosis and maintenance; based on machine learning technology, load demand, weather information, user behavior, and market electricity prices are predicted to help plan energy production and distribution.

[0005] However, most of the research on the optimized operation of microgrids focuses on improving the efficiency of renewable energy and controlling the operation and maintenance costs of microgrids and the cost of pollution control, without considering the behavior of users adjusting their electricity consumption time and demand to participate in the optimization of microgrid operation. In addition, the current optimization scheduling strategies for hydrogen-electricity coupled microgrids often only consider the scheduling and planning of hydrogen-electricity conversion equipment units, and have not yet considered renewable energy generators such as wind turbines and photovoltaic generators, which has limitations in decision-making. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a coupled microgrid optimization scheduling method and system considering the coordination of electric and thermal load demands, considering the relevant characteristics of the electric, thermal, gas and hydrogen multi-energy conversion of new energy output and the peak-valley complementary characteristics of the original electric load and thermal load in the system in the time distribution, firstly, the electric load demand response method is used to study the time-of-use electricity price demand response method, and then the thermal load is flexibly adjusted considering the heat transfer inertia and the fuzziness of user temperature perception, and a hydrogen-containing microgrid optimization scheduling model is established, and the operating output level of each device in the system is comprehensively optimized to ensure that the microgrid operates in an efficient and environmentally friendly state.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] The coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands includes:

[0009] Based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units, a hydrogen-containing microgrid operation model is constructed;

[0010] Considering the peak-valley complementary characteristics of the original electric load and thermal load in the system in time distribution, a coordinated response model of electric and thermal load demand is constructed, and the electric load is characterized by the time-of-use electricity price demand response method. Considering the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, the thermal load is flexibly adjusted based on different electricity prices.

[0011] Based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, the objective function is constructed with the goal of minimizing the sum of total operating cost, renewable energy curtailment cost and demand response compensation cost, and the optimal scheduling model of the electric and hydrogen coupled microgrid is established according to the constraints. The mixed integer linear programming is used to solve the hydrogen-electricity coupled microgrid optimization scheduling model, and the obtained optimal operation scheduling strategy of the equipment is applied to the microgrid execution.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions:

[0013] The coupled microgrid optimal dispatching system considering the coordinated electric and thermal load demands includes:

[0014] Model building module, which is used to build a hydrogen-containing microgrid operation model based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units; considering the peak-valley complementary characteristics of the original electric load and heat load in the system in time distribution, to build a coordinated response model of electric and thermal load demand;

[0015] The demand collaborative response optimization module is used to characterize the electric load using the time-of-use electricity price demand response method, taking into account the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, and making flexible adjustments based on different electricity prices;

[0016] The operation and scheduling module is used to construct an objective function based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, with the goal of minimizing the sum of total operating cost, renewable energy wind and solar power abandonment cost, and demand response compensation cost, and establish the optimal scheduling model of the electric and hydrogen coupled microgrid according to the constraints. Mixed integer linear programming is used to solve the hydrogen-electricity coupled microgrid optimization scheduling model, and the obtained optimal operation and scheduling strategy of the equipment is applied to the microgrid execution.

[0017] According to some embodiments, the present disclosure adopts the following technical solutions:

[0018] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands is implemented.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The disclosed coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demand first fully considers the relevant characteristics of the electric, thermal, gas and hydrogen multi-energy conversion of new energy output, and constructs a hydrogen-containing microgrid operation model based on the operating characteristics and interaction effects of new energy generator sets, electric and hydrogen coupling units, heat generating units and energy storage units. Under the premise of ensuring the stable operation of the microgrid, the model promotes the comprehensive utilization of electric, thermal, gas and hydrogen energy, and further improves the overall operation efficiency of the energy system.

[0023] The present invention discloses a coupled microgrid optimization scheduling method that considers the coordination of electric and thermal load demands. It considers the peak-valley complementary characteristics of the original electric load and thermal load in the system in terms of time distribution, and proposes a coordinated response to electric and thermal load demands to improve the transfer potential between electric and thermal loads. First, the electric load demand response is studied using the time-of-use electricity price demand response method. Then, considering that the thermal load has the inertia of heat transfer and the fuzziness of user temperature perception, flexible adjustments are made based on different electricity prices. Through the coordinated response of electric and thermal loads, the energy within the system can be used more efficiently while reducing operating costs, thereby improving the stability of system operation.

[0024] The present invention discloses a coupled microgrid optimization scheduling method that considers the coordination of electric and thermal load demands, and establishes a hydrogen-containing microgrid optimization scheduling model with the minimum sum of the total system operating cost, the cost of renewable energy wind and solar power abandonment, and the demand response compensation cost as the objective function. The model is based on the established hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, and comprehensively optimizes the operating output level of each device in the system to ensure that the microgrid operates in an efficient and environmentally friendly state. Finally, the model is piecewise linearized and converted into a mixed integer linear programming model, and the commercial solver CPLEX is used to solve it, and the optimal operation strategy is obtained and sent to the corresponding equipment for execution in a timely manner, which reduces the microgrid operation cost and improves the operation efficiency and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0026] Figure 1 This is a framework diagram of a coordinated response strategy for electric and thermal load demands according to an embodiment of the present disclosure;

[0027] Figure 2 This is a framework diagram for solving the operation of a hydrogen-containing microgrid according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0029] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used in the present disclosure have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0030] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. 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.

[0031] Example 1

[0032] In one embodiment of the present disclosure, a coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands is provided, comprising the following steps:

[0033] Step 1: Based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units, a hydrogen-containing microgrid operation model is constructed;

[0034] Step 2: Considering the peak-valley complementary characteristics of the original electric load and thermal load in the system in time distribution, a coordinated response model of electric and thermal load demand is constructed, and the electric load is characterized by the time-of-use electricity price demand response method. Considering the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, the thermal load is flexibly adjusted based on different electricity prices;

[0035] Step 3: Based on the hydrogen microgrid operation model and the electric and thermal load demand coordinated response model, the objective function is constructed with the goal of minimizing the sum of total operating cost, renewable energy wind and solar power abandonment cost and demand response compensation cost, and the optimal scheduling model of the electric and hydrogen coupled microgrid is established according to the constraints. The mixed integer linear programming is used to solve the hydrogen-electricity coupled microgrid optimization scheduling model, and the obtained optimal operation scheduling strategy of the equipment is applied to the microgrid execution.

[0036] As an embodiment, the specific implementation process of a coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands disclosed in the present invention is as follows:

[0037] Step 1: Based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units, a hydrogen-containing microgrid operation model is constructed;

[0038] Specifically, the microgrid that integrates four energy forms of electricity, heat, gas and hydrogen meets internal energy needs by dispatching multiple energy sources and functional equipment. The microgrid architecture designed in this disclosure contains distributed new energy units, cogeneration units, electrolyzer models, methane reactor models, hydrogen fuel cell models and energy storage models;

[0039] The hydrogen-containing microgrid operation model includes a distributed new energy generator set model, an electric-hydrogen coupling unit model, a heat generating unit model and an energy storage model. The distributed new energy generator set model includes a wind generator set and a photovoltaic generator set. The electric-hydrogen coupling unit model includes an electrolyzer model, a methane reactor model and a hydrogen fuel cell model. The heat generating unit model includes a cogeneration unit and a gas boiler model. The energy storage model is a unified modeling of four energy devices: electricity, heat, gas and hydrogen. It includes the following:

[0040] Step 1.1 Modeling of distributed renewable energy units

[0041] Step 1.1.1 Photovoltaic generator set

[0042] Ignoring the effect of temperature, the power generated by the photovoltaic generator set can be calculated as:

[0043] (1)

[0044] in is the power generation capacity of the photovoltaic unit, is the efficiency of the photovoltaic system, is the rated photovoltaic power, is the total irradiance of the sun actually incident on the photovoltaic array, It is the total radiation of the sun incident on the photovoltaic array under standard test conditions.

[0045] Step 1.1.2 Wind turbine

[0046] The relationship between the power generation of a wind turbine and the wind speed is shown below:

[0047] (2)

[0048] in is the power generation capacity of the wind turbine, is the wind speed, is the rated power of the wind turbine, is the rated wind speed, and are the cut-in wind speed and the cut-off wind speed respectively.

[0049] Step 1.2 Modeling of the electric-hydrogen coupling unit

[0050] Step 1.2.1 Electrolyzer model

[0051] Traditional hydrogen production mainly relies on fossil fuels. The application scope of water electrolysis hydrogen production coupling technology is gradually expanding. Among the various hydrogen production technology routes, proton exchange membrane hydrogen production technology has occupied the mainstream. This method uses a proton exchange membrane to separate the hydrogen ions and electrons in water molecules to produce pure hydrogen. The hydrogen energy power generated by the electrolyzer unit is as follows:

[0052] (3)

[0053] in, is the electrical power input to the electrolyzer model, is the hydrogen power output by the electrolyzer model, is the energy conversion efficiency of the electrolyzer model.

[0054] Step 1.2.2 Methane reactor model

[0055] The methane reactor burns hydrogen energy and outputs natural gas to supply the cogeneration unit and gas boiler model. The natural gas power output by the methane reactor is as follows:

[0056] (4)

[0057] in, is the hydrogen power input to the methane reactor model, is the natural gas power output of the methane reactor model, is the energy conversion efficiency of the methane reactor.

[0058] Step 1.2.3 Hydrogen fuel cell model

[0059] Hydrogen fuel cells can achieve the coupling between hydrogen energy, thermal energy and electrical energy. The specific output thermal energy power and electrical energy power are as follows:

[0060] (5)

[0061] in, is the hydrogen power input to the hydrogen fuel cell, , are the electrical power and thermal power output of the hydrogen fuel cell, , The efficiency of hydrogen fuel cells converting into electricity and heat energy respectively.

[0062] Step 1.3 Modeling of heat generating units

[0063] Step 1.3.1 Combined Heat and Power Unit

[0064] The combined heat and power unit generates electricity by burning natural gas, and supplies the waste heat generated in the power generation process to the heat load. The combined heat and power unit with adjustable heat-to-electricity ratio can adjust the electricity and heat output according to the real-time electricity and heat energy demand, further optimizing the operation efficiency. Its working model is as follows:

[0065] (6)

[0066] in, is the natural gas power input to the cogeneration unit, , are the electricity and heat energy output by the cogeneration unit, , They are the efficiency of the cogeneration unit in converting energy into electricity and heat, respectively.

[0067] Step 1.3.2 Gas boiler model

[0068] The gas boiler model burns natural gas to supply heat energy, and the heat energy power is as follows:

[0069] (7)

[0070] in, for The gas energy power input to the gas boiler during the period, for The thermal power output of the gas boiler during the period, It is the energy conversion efficiency of gas boiler.

[0071] Step 1.4 Energy storage model

[0072] The energy storage equipment of four energy sources, electricity, heat, gas and hydrogen, is uniformly modeled, and the formula is as follows:

[0073] (8)

[0074] in, , They are Time period The charging and discharging power of the energy storage device, , Both The binary variables for the time period are Parameters of charge and discharge status of energy storage devices; for Time period The output power of the energy storage device, , They are Time period The charging and discharging efficiency of the energy storage device, yes Time period The capacity of an energy storage device.

[0075] Step 2: Considering the peak-valley complementary characteristics of the original electric load and thermal load in the system in time distribution, a coordinated response model of electric and thermal load demand is constructed, and the electric load is characterized by the time-of-use electricity price demand response method. Considering the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, the thermal load is flexibly adjusted based on different electricity prices;

[0076] Step 2.1 Modeling of electric load demand response model

[0077] This disclosure uses the time-of-use electricity price demand response method as a representative to conduct research on electric load demand response. Since different types of loads have different sensitivities to the same electricity price signal, the electric load is divided into reducible load, transferable load and alternative load. The electric load adopts a contract management model, and the energy supplier signs a contract with the user. The user informs the energy supplier of the power of various types of flexible loads in each time period in advance, and negotiates the compensation price and allowed interactive time period for various types of loads; the energy supplier flexibly adjusts the user's demand for flexible load energy in different time periods according to its own supply capacity, and compensates the user according to the size of the user's interactive power, as follows:

[0078] Step 2.1.1 Load reduction possible

[0079] During the peak price period, the user can reduce the use of some unusable loads to reduce the user's own energy costs. The demand response characteristics are described by the price-demand elasticity matrix, which is the The time load on The time price elasticity coefficient. The formula is as follows:

[0080] (9)

[0081] In the formula, is the autoelastic coefficient, is the cross elastic coefficient. Is the user in Initial load demand at time Is the user in The load change at each moment, and Indicated in Moment and The initial value of electricity price at time and Respectively Moment and The change in electricity price at the moment. After demand response adjustment, The load shedding changes at each moment are as follows:

[0082] (10)

[0083] In the formula, yes The initial load reduction at time, yes The load demand that can be reduced at any time, is the curtailable load demand matrix coefficient of the demand matrix, exist The electricity price at the moment.

[0084] According to the signed contract, the user will receive corresponding economic compensation, the formula is as follows:

[0085] (11)

[0086] In the formula, It is the compensation price per unit of load that can be reduced.

[0087] Step 2.1.2 Transferable Load

[0088] On the basis of keeping the total load power unchanged, the electricity price is used as a signal to shift on the time scale to achieve flexible adjustment of working hours. This paper adopts the price demand elasticity matrix to describe the demand response characteristics. After the demand response processing The change in load that can be transferred at any moment is as follows:

[0089] (12)

[0090] In the formula, is The initial transferable load at time, is the transferable load price elasticity coefficient of the demand matrix.

[0091] According to the signed contract, the user will receive corresponding economic compensation, the formula is as follows:

[0092] (13)

[0093] In the formula, is the compensation price per unit of transferable load.

[0094] Step 2.1.3 Alternative Loads

[0095] The definition of replaceable load is that for a certain type of heat load that can be directly supplied by heat or electricity, the heat load is supplied by electricity when the electricity price is low; and the heat load is directly supplied by heat when the electricity price is high. The formula is as follows:

[0096] (14)

[0097] (15)

[0098] In the formula, is the amount of replaceable electricity load, is the amount of heat load that needs to be replaced, is the electric heat substitution coefficient, is the unit calorific value of electrical energy, It is the unit calorific value of thermal energy. is the energy utilization rate of electrical energy, It is the energy utilization rate of thermal energy. The symbol indicates that the electrical load in the replaceable load is decreasing and the thermal load is increasing.

[0099] According to the signed contract, users will receive corresponding economic compensation:

[0100] (16)

[0101] In the formula, is the compensation price per unit of replaceable load.

[0102] all in all, Power load at the moment It consists of the following three parts:

[0103] (17)

[0104] In the formula, and Before and after demand response The power load at the moment.

[0105] Step 2.2 Heat load demand response model building

[0106] Thermal load has two characteristics: the inertia of heat transfer and the fuzziness of user temperature perception, which together constitute the flexibility of thermal load. Due to the thermal inertia of the thermal system, changes in the amount of heat supplied will not immediately change the user's perception. However, due to the fuzziness and hysteresis of the user's perception of temperature, the temperature can be controlled within a comfortable range to further improve the economy of the integrated energy system.

[0107] Step 2.2.1 Thermal load inertia characteristics;

[0108] Considering the heat transfer inertia of heat load and the fuzziness of user temperature perception, flexible adjustments are made based on different electricity prices, including: according to the inertia of heat transfer of heat load, the thermodynamic characteristics of heating buildings and heating networks are analyzed to obtain the dynamic relationship between the heat output of cogeneration and the room temperature of the building. The autoregressive moving average model is used to describe the temperature dynamics of the heating system, that is, the flow and pressure of the heating network remain unchanged, and only the temperature is adjusted. The formula is as follows:

[0109] (18)

[0110] (19)

[0111] In the formula, , , , , and It is a physical parameter of the thermal inertia of the heating system and can be obtained by parameter identification of the measurement data. It's delayed unit of time. is the order of the autoregressive moving average time series model, which indicates the delay degree of the heat transfer process and reflects the thermal inertia of the heating system. , , The time The return water temperature, supply water temperature and ambient temperature, , , The time The indoor temperature of the building, the water supply temperature and the ambient temperature.

[0112] The relationship between indoor temperature and heat load power can be expressed as:

[0113] (20)

[0114] (twenty one)

[0115] In the formula, is the natural cardinality, is the indoor heat capacity of the building, is the equivalent thermal resistance of the building , It's time The heat load power, is the minimum scheduling period. When the indoor temperature of a building fluctuates within a certain range, the space heat load is also a range.

[0116] Based on formula (20), the heat load power relationship based on indoor air temperature is calculated as:

[0117] (twenty two)

[0118] Step 2.2.2 Fuzziness of user temperature perception

[0119] Since users have a certain degree of ambiguity about heating comfort, when the heating temperature changes within the range of human comfort, it has little impact on users, which improves the adjustment capacity of flexible loads to a certain extent. Therefore, the predicted thermal index: predicted average voting index is used as a standard for evaluating indoor thermal comfort in this disclosure. The relationship between this index and temperature is as follows:

[0120] (twenty three)

[0121] In the formula, is the value of the predicted average voting metric.

[0122] The relationship between the average voting index and temperature can be divided into seven levels, as shown in Table 1 below:

[0123] Table 1 Relationship between predicted average voting index and temperature

[0124]

[0125] As shown in formula (22), there is a time-coupled relationship between the thermal power of the heat load and its indoor temperature. When the indoor temperature is allowed to fluctuate within the comfortable range, the thermal power has a certain elasticity. By setting the range of the prediction evaluation voting index, based on Table 1, the minimum indoor temperature of the heat load can be obtained according to formula (23): and maximum indoor temperature According to formula (22), the minimum heating power of the system heat load during the entire scheduling period can be obtained: and maximum heating power .

[0126] Step 2.3: Construct a coordinated response model for electric and thermal load demand

[0127] Obtain the heat balance relationship without considering the flexibility of heat load. According to the analysis of the coordinated demand response strategy of electric and thermal loads, obtain the coordinated mode of electricity and heat demand response under peak demand, including valley load period, peak load period and flat load period.

[0128] Without considering heat load flexibility, the heat balance formula is as follows:

[0129] (twenty four)

[0130] In the formula, The heat load is The actual power at the moment, Without considering the inertia of the heat load, Standard power at all times. and They are The cogeneration heating power and gas boiler heating power at the moment. and The heat storage device is The heat release power and heat storage power at each moment.

[0131] When considering the flexibility of the heat load, the system first allows the heat load to vary between the minimum heating power and the maximum heating power, and then determines the most appropriate heating power within the heating power range according to the peak demand. The formula is as follows:

[0132] (25)

[0133] Based on the analysis of the coordinated demand response strategy for electric and thermal loads, the coordinated mode of electric and thermal demand response under peak load can be summarized. The specific situation is as follows:

[0134] (1) During the off-peak load period, the heating power should be reasonably optimized between the standard heating power and the minimum heating power. The formula is as follows:

[0135] (26)

[0136] In the formula, The heat load is The actual power at the moment, and The heat load is The standard heating power and minimum heating power at the moment.

[0137] (2) During peak load periods, the heating power should be reasonably optimized between the standard heating power and the maximum heating power. The formula is as follows:

[0138] (27)

[0139] In the formula, The heat load is The actual power at the moment, and The heat load is Standard heating power and maximum heating power at the moment.

[0140] (3) During the flat load period, the heating power can be reasonably optimized between the minimum and maximum heating powers, and connected with the peak and valley heating power.

[0141] (28)

[0142] In the formula, The heat load is The actual power at the moment, and The heat load is The minimum heating power and the maximum heating power at the moment.

[0143] Step 3: Based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, an objective function is constructed with the goal of minimizing the sum of total operation cost, renewable energy abandonment cost, and demand response compensation cost;

[0144] In order to maximize the operating benefits of hydrogen-electricity coupled microgrids and meet the energy needs of electricity, hydrogen, gas and heat, it is necessary to coordinate the operating strategies of various equipment. Based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model established above, this paper proposes an optimization scheduling strategy for hydrogen-electricity coupled microgrids with the minimum total operating cost, renewable energy wind and solar power abandonment cost and demand response compensation cost as the objective function, and establishes a mixed integer linear programming model to achieve multi-energy complementarity within the microgrid.

[0145] Step 3.1 Objective function

[0146] Step 3.1.1 Total cost of microgrid operation

[0147] In the planning and design of microgrids, economy is the most important consideration. Therefore, one of the main goals of optimizing the dispatching strategy is to minimize the total operating cost. The formula is as follows:

[0148] (29)

[0149] in, , They are the main equipment purchase cost and operation and maintenance cost respectively; , , They are the water cost for hydrogen production, the hydrogen purchase cost and the transaction cost with the external power grid.

[0150] (1) Equipment cost

[0151] Acquisition cost Refers to the total initial investment cost of the main equipment of the microgrid company. In order to comprehensively consider the impact of equipment life on the microgrid, the purchase cost is converted into an annualized equivalent investment amount. The formula is as follows:

[0152] (30)

[0153] (31)

[0154] in, It is a collection of photovoltaic generators, electrolyzer models and other equipment. Yes Equipment The acquisition cost, Yes Equipment The number of purchases, is the capital recovery factor, is the base interest rate, is the lifespan of the microgrid.

[0155] (2) Operation and maintenance costs

[0156] Operation and maintenance costs refer to the annual cost of operating and maintaining the main equipment in the microgrid. The annual operation and maintenance cost of each device is proportional to its purchase cost. The formula is as follows:

[0157] (32)

[0158] in, Yes Equipment acquisition cost.

[0159] (3) Water cost for hydrogen production

[0160] Electrolysis of water to produce hydrogen requires water consumption, and the amount of water consumed is related to the specific amount of hydrogen produced. Included in the total cost, the formula is as follows:

[0161] (33)

[0162] in, The amount of water used for hydrogen production; It is the unit price of water used in the electrolysis process.

[0163] (4) Hydrogen purchase cost

[0164] When the hydrogen produced by electrolysis cannot meet the system demand, it is necessary to purchase hydrogen from outside. The formula is as follows:

[0165] (34)

[0166] in, is the total weight of hydrogen purchased; is the unit cost of purchasing hydrogen.

[0167] (5) Transaction costs with external electrical networks

[0168] When a microgrid is connected to an external grid or gas grid, when there is excess electricity or natural gas, its excess energy can be sold to the external grid or gas grid. Conversely, when the microgrid lacks sufficient energy, it can purchase electricity and natural gas from the external grid or gas grid. The transaction cost with the external electrical network is determined by the cost of purchasing the missing electricity and gas from the external grid and the income from selling excess electricity and natural gas to the external grid. The formula is as follows:

[0169] (35)

[0170] in, is the evaluation time of microgrid operation, is the purchase and sale price, is the electricity or gas bought / sold. When the external electrical network supplies is 0; when sold to the external network, Less than 0.

[0171] Step 3.1.2 Cost of curtailing renewable energy

[0172] When the power generation of the renewable energy unit in the microgrid is If the power demand exceeds the limit, power abandonment will occur, resulting in waste. Therefore, this disclosure fully considers the situation of wind and solar power abandonment of new energy. The calculation formula is as follows:

[0173] (36)

[0174] in, is the cost of curtailing renewable energy in the microgrid, is the total power cutoff of the microgrid, It is the sum of wind and solar power generation.

[0175] Step 3.1.3 Electricity demand response compensation costs

[0176] The calculation of demand response cost is based on providing certain compensation to users for participating in demand response and compensating users by shifting load. The compensation cost is shown in the following formula:

[0177] (37)

[0178] in, is the power demand response compensation cost of the microgrid, , and are the compensation prices for curtailable load, transferable load and replaceable load respectively.

[0179] Step 3.2 Constraints

[0180] To ensure the safe and stable operation of the microgrid, the following constraints need to be met.

[0181] Step 3.2.1 Output constraints of renewable energy generators

[0182] The power generation of wind turbines and photovoltaic generators needs to meet the following constraints:

[0183] (38)

[0184] Step 3.2.2 Operation constraints of electricity-hydrogen coupling units

[0185] (1) Electrolyzer model

[0186] The normal operation of the electrolyzer model needs to meet the following constraints:

[0187] (39)

[0188] in, and are the upper and lower limits of the electrical energy input into the electrolyzer model, respectively; and They are the upper and lower limits of the climbing of the electrolytic cell model respectively.

[0189] (2) Methane reactor model

[0190] The normal operation of the methane reactor model needs to meet the following constraints:

[0191] (40)

[0192] in, and are the upper and lower limits of the electrical energy input into the methane reactor model, respectively; and They are the upper and lower limits of the climbing of the methane reactor model respectively.

[0193] (3) Hydrogen fuel cell model

[0194] The normal operation of the hydrogen fuel cell model needs to meet the following constraints:

[0195] (41)

[0196] in, and They are the upper and lower limits of hydrogen energy input into hydrogen fuel cells, and They are the upper and lower climbing limits of hydrogen fuel cells. and They are the upper and lower limits of the thermal-to-electricity ratio of hydrogen fuel cells.

[0197] Step 3.2.3 Heat generating unit operation constraints

[0198] (1) Combined heat and power unit

[0199] The normal operation of the cogeneration unit model needs to meet the following constraints:

[0200] (42)

[0201] in, and are the upper and lower limits of the natural gas power input to the cogeneration unit, and They are the upper and lower limits of the ramp rate of the cogeneration unit, and They are the upper and lower limits of the thermal-to-electricity ratio of hydrogen fuel cells.

[0202] (2) Gas boiler units

[0203] The normal operation of the gas boiler model needs to meet the following constraints:

[0204] (43)

[0205] in, and They are the upper and lower limits of the natural gas power input to the gas boiler unit. and They are the upper and lower limits of the climbing range of the gas boiler unit.

[0206] Step 3.2.4 Energy storage operation constraints

[0207] Since the models of electricity, heat, gas and other energy storage equipment are similar, a unified modeling is performed for electricity, heat, gas and hydrogen energy storage equipment.

[0208] (44)

[0209] in, It is The maximum power of a single charge and discharge of an energy storage device. and Respectively The upper and lower limits of the capacity of the energy storage device.

[0210] Step 3.2.5 Electrical power balance

[0211] (45)

[0212] in, yes Electric load during the period, and are the electric power purchased and sold in each period, yes The power input to the storage during the period.

[0213] Step 3.2.6 Thermal power balance

[0214] (46)

[0215] in, yes Heat load during the period, yes The power input to the thermal storage during the period.

[0216] Step 3.2.7 Natural Gas Balance

[0217] (47)

[0218] in, and are the natural gas purchased and sold in each period, yes Input the power of the gas storage during the period.

[0219] Step 3.2.8 Hydrogen balance

[0220] (48)

[0221] in, and are the hydrogen purchased and sold in each period, yes The power of hydrogen storage input during the period.

[0222] Step 3.2.9 Model linearization

[0223] The constructed hydrogen-containing microgrid economic dispatch model considering the coordinated response of electric and thermal load demand is a mixed integer nonlinear model, so it needs to be processed by piecewise linearization and converted into a mixed integer linear programming problem, and then solved by CPLEX commercial solver. The optimal operation dispatch strategy of the equipment is applied to the microgrid execution.

[0224] Example 2

[0225] In one embodiment of the present disclosure, a coupled microgrid optimization scheduling system considering the coordination of electric and thermal load demands is provided, comprising:

[0226] Model building module, which is used to build a hydrogen-containing microgrid operation model based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units; considering the peak-valley complementary characteristics of the original electric load and heat load in the system in time distribution, to build a coordinated response model of electric and thermal load demand;

[0227] The demand collaborative response optimization module is used to characterize the electric load using the time-of-use electricity price demand response method, taking into account the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, and making flexible adjustments based on different electricity prices;

[0228] The operation and scheduling module is used to construct an objective function based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, with the goal of minimizing the sum of total operating cost, renewable energy wind and solar power abandonment cost, and demand response compensation cost, and establish the optimal scheduling model of the electric and hydrogen coupled microgrid according to the constraints. Mixed integer linear programming is used to solve the hydrogen-electricity coupled microgrid optimization scheduling model, and the obtained optimal operation and scheduling strategy of the equipment is applied to the microgrid execution.

[0229] Example 3

[0230] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands is implemented.

[0231] Example 4

[0232] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands.

[0233] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the 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 generate 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.

[0234] 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.

[0235] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands, characterized in that: include: Based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units, a hydrogen-containing microgrid operation model is constructed; The hydrogen-containing microgrid operation model includes a distributed new energy generator set model, an electric-hydrogen coupling unit model, a heat generating unit model and an energy storage model. The distributed new energy generator set model includes a wind generator set and a photovoltaic generator set. The electric-hydrogen coupling unit model includes an electrolyzer model, a methane reactor model and a hydrogen fuel cell model. The heat generating unit model includes a cogeneration unit and a gas boiler model. The energy storage model is a unified modeling of four energy devices: electricity, heat, gas and hydrogen. Considering the peak-valley complementary characteristics of the original electric load and thermal load in the system in time distribution, a coordinated response model of electric and thermal load demand is constructed, and the electric load is characterized by the time-of-use electricity price demand response method. Considering the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, the thermal load is flexibly adjusted based on different electricity prices. Construct a coordinated response model for electric and thermal load demand, and characterize the electric load with the time-of-use electricity price demand response method, including: conducting a study on the electric load demand response with the time-of-use electricity price demand response method, dividing the electric load into reducible load, transferable load and alternative load, and adopting a contract management model for the electric load. The energy supplier signs a contract with the user, and the user sends the power of various flexible loads in each time period to the energy supplier, and negotiates the compensation price of various loads and the allowed interactive time period; the energy supplier flexibly adjusts the user's demand for flexible load energy in different time periods according to its own supply capacity, and compensates the user according to the size of the user's interactive power; The coordinated response model of electric and thermal load demand includes: Obtain the heat balance relationship without considering the flexibility of heat load. According to the analysis of the coordinated demand response strategy of electric and thermal loads, obtain the coordinated mode of power and heat demand response under peak load, which is divided into valley load period, peak load period and flat load period. Without considering heat load flexibility, the heat balance formula is as follows: In the formula, The heat load is The actual power at the moment, Without considering the inertia of the heat load, Standard power at the moment; and They are The heating power of combined heat and power generation and gas boiler at the moment; and The heat storage device is Heat release power and heat storage power at each moment; When considering the flexibility of the heat load, the system first allows the heat load to vary between the minimum heating power and the maximum heating power, and then determines the heating power within the heating power range according to the peak demand. The formula is as follows: Based on the analysis of the coordinated demand response strategy for electric and thermal loads, the coordinated mode of electric and thermal demand response under peak load is summarized, as shown below: (1) During the off-peak load period, the heating power should be reasonably optimized between the standard heating power and the minimum heating power. The formula is as follows: In the formula, The heat load is The actual power at the moment, and The heat load is Standard heating power and minimum heating power at all times; (2) During peak load periods, the heating power should be reasonably optimized between the standard heating power and the maximum heating power. The formula is as follows: In the formula, The heat load is The actual power at the moment, and The heat load is Standard heating power and maximum heating power at all times; (3) During the flat load period, the heating power is reasonably optimized between the minimum and maximum heating powers, and connected with the peak and valley heating power; In the formula, The heat load is The actual power at the moment, and The heat load is The minimum heating power and the maximum heating power at the moment; Based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, the objective function is constructed with the goal of minimizing the sum of total operating cost, renewable energy curtailment cost and demand response compensation cost, and the optimal scheduling model of the electric-hydrogen coupled microgrid is established according to the constraints. The mixed integer linear programming is used to solve the optimal scheduling model of the electric-hydrogen coupled microgrid, and the obtained optimal operation scheduling strategy of the equipment is applied to the microgrid execution.

2. The coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands according to claim 1 is characterized in that: Taking into account the heat transfer inertia of heat load and the fuzziness of user temperature perception, flexible adjustments are made based on different electricity prices, including: analyzing the thermodynamic characteristics of heating buildings and heating networks according to the inertia of heat transfer of heat load, and obtaining the dynamic relationship between the cogeneration heat output and the building room temperature. The autoregressive moving average model is used to describe the temperature dynamics of the heating system, that is, the flow and pressure of the heating network remain unchanged, and only the temperature is adjusted.

3. The coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands according to claim 1 is characterized in that: Considering the heat transfer inertia of heat load and the fuzziness of user temperature perception, flexible adjustments are made based on different electricity prices, including: according to the fuzziness of user temperature perception, the predicted average voting index is used as the standard for evaluating the thermal comfort of the indoor environment. The thermal power of the heat load has a time coupling relationship with the indoor temperature. When the indoor temperature is allowed to fluctuate within the comfortable range, the thermal power has a certain elasticity. By setting the range of the predicted average voting index, the minimum indoor temperature and the maximum indoor temperature of the heat load are obtained; The relationship between the predicted average voting index and temperature is as follows: In the formula, is the value of the predicted average voting indicator; T in,t For time t Indoor temperature of the building at that time; The specific implementation is as follows: Based on the above table, the minimum indoor temperature of the heat load is obtained according to the relationship between the predicted average voting index and temperature. and maximum indoor temperature According to the heat load power relationship of indoor air temperature, the minimum heating power of the system heat load during the entire scheduling period is obtained and maximum heating power , where the heat load power relationship of indoor air temperature is: in, It's time The heat load power, is the minimum scheduling period, is the natural cardinality, is the equivalent thermal resistance of the building, T in,t For time t The indoor temperature of the building.

4. The coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands according to claim 1 is characterized in that: Obtain the heat balance relationship without considering the flexibility of heat load. According to the analysis of the coordinated demand response strategy of electric and thermal loads, obtain the coordinated mode of electricity and heat demand response under peak demand, including valley load period, peak load period and flat load period.

5. The coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands according to claim 1, characterized in that: The total operating cost includes equipment cost, operation and maintenance cost, water cost for hydrogen production, hydrogen purchase cost and transaction cost with external electrical network; the constraints include output constraints of renewable energy power generation units, operation constraints of electric-hydrogen coupling units, operation constraints of heat generating units, operation constraints of energy storage units, electric power balance, thermal power balance, natural gas balance and hydrogen balance.

6. A coupled microgrid optimization dispatching system considering the coordination of electric and thermal load demands, characterized in that: The coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands as described in any one of claims 1 to 5 is implemented, comprising: Model building module, which is used to build a hydrogen-containing microgrid operation model based on the operating characteristics and interaction effects of new energy generating units, electric-hydrogen coupling units, heat generating units and energy storage units; considering the peak-valley complementary characteristics of the original electric load and heat load in the system in time distribution, to build a coordinated response model of electric and thermal load demand; The demand collaborative response optimization module is used to characterize the electric load using the time-of-use electricity price demand response method, taking into account the heat transfer inertia of the thermal load and the fuzziness of the user's temperature perception, and making flexible adjustments based on different electricity prices; The operation and scheduling module is used to construct an objective function based on the hydrogen-containing microgrid operation model and the electric and thermal load demand coordinated response model, with the goal of minimizing the sum of total operating cost, renewable energy wind and solar power abandonment cost, and demand response compensation cost, and establish the optimal scheduling model of the electric and hydrogen coupled microgrid according to the constraints. Mixed integer linear programming is used to solve the hydrogen-electricity coupled microgrid optimization scheduling model, and the obtained optimal operation and scheduling strategy of the equipment is applied to the microgrid execution.

7. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the coupled microgrid optimization scheduling method considering the coordination of electric and thermal load demands as described in any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the coupled microgrid optimization scheduling method considering the coordinated electric and thermal load demands as described in any one of claims 1 to 5.

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