Hybrid energy sharing optimal dispatching method for electric-hydrogen coupling smart distribution network area

Abstract

This invention relates to a hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area, comprising the following steps: Step 1, establishing a hybrid energy sharing system for the electric-hydrogen coupled smart distribution network area; Step 2, based on the hybrid energy sharing system established in Step 1, establishing a revenue model for the intelligent building aggregator (AOP); Step 3, based on the hybrid energy sharing system established in Step 1, establishing an intelligent building group model; Step 4, using a distributed solution algorithm to solve the revenue model of the intelligent building aggregator (AOP) and the intelligent building group model established in Steps 2 and 3, thereby completing the distributed optimization of hybrid energy sharing in the electric-hydrogen coupled smart distribution network area. This invention can maximize profits, reduce carbon emissions, and protect the privacy and security of each intelligent building.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network technology and relates to a hybrid energy sharing optimization scheduling method, particularly a hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart power distribution network area. Background Technology

[0002] Energy and climate issues have become a global focus. Hydrogen energy, as a clean and flexible solution, offers an important pathway to addressing many pressing global challenges. Smart distribution network areas, through advanced monitoring and control technologies, can achieve efficient energy allocation and utilization, further improving system reliability and stability. Smart distribution network areas can integrate regional energy sources such as electricity, natural gas, and heat, enabling coordinated planning and complementary operation.

[0003] With the gradual development of electricity-to-hydrogen (P2H) and hydrogen-blending technologies, an electricity-hydrogen coupled smart distribution network area is gradually taking shape. However, the coordinated and optimized operation of such systems, especially in the case of multiple smart buildings, still faces significant challenges.

[0004] From a modeling perspective, in the field of power-to-gas (P2G) conversion, much research has focused on power-to-methane (P2M), while research on power-to-hydrogen (P2H) conversion is relatively limited. Research on the integration of P2H with natural gas and hydrogen, as well as hydrogen-blended micro-combined heat and power (CHP) systems, represents a key challenge in reducing costs and carbon emissions. Optimizing the sharing of electricity and hydrogen among multiple smart buildings within a coupled smart distribution network remains an important issue for achieving cost reduction and improving energy efficiency.

[0005] Optimization methods can be categorized into centralized and distributed optimization. In centralized optimization, data on load, generation, and grid conditions must be sent to a central controller, potentially posing a risk of privacy breaches. Furthermore, the complex interests and information barriers inherent in interactions between different entities present significant challenges to the operation, management, and energy sharing of smart distribution network areas. Improper management can also lead to privacy breaches in smart buildings.

[0006] Therefore, to solve the above problems, this invention proposes a hybrid energy sharing optimization scheduling method for electric-hydrogen coupled smart distribution network areas.

[0007] A search revealed no publicly available literature of the same or similar prior art as this invention. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and propose a hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area, which can maximize profits, reduce carbon emissions, and protect the privacy and security of each smart building.

[0009] The present invention solves its practical problem by adopting the following technical solution:

[0010] A hybrid energy-sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area includes the following steps:

[0011] Step 1: Establish a hybrid energy sharing system for the electric-hydrogen coupled smart distribution network area;

[0012] Step 2: Based on the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area established in Step 1, establish the revenue model of the smart building aggregator (AOP).

[0013] Step 3: Based on the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area established in Step 1, establish a smart building cluster model;

[0014] Step 4: Use a distributed solution algorithm to solve the revenue model of the intelligent building aggregator (AOP) and the intelligent building group model established in Steps 2 and 3, and then complete the distributed optimization of hybrid energy sharing in the electric-hydrogen coupled intelligent distribution network area.

[0015] Moreover, the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area in step 1 includes: smart building aggregator (AOP) and smart building cluster;

[0016] The intelligent building aggregator (AOP) includes a PEM electrolyzer and a hydrogen storage tank. One end of the AOP is connected to the power grid, and the other end is connected to the hydrogen market. It can convert electricity into hydrogen, providing the necessary electricity and hydrogen for the photovoltaic intelligent building cluster. The AOP is also connected to the intelligent building cluster, performing local optimization through their respective energy management systems (EMS) and exchanging energy and price information. The photovoltaic intelligent building cluster includes multiple intelligent buildings, each equipped with a corresponding photovoltaic system and a micro-hydrogen-doped combined heat and power (CHP) unit for local power generation and thermal energy production.

[0017] If a smart building generates excess electricity, it will be charged at price p. mb They sell surplus electricity to smart building aggregators (AOP); conversely, if there is a power shortage, they sell it at a price p. ms The smart building purchases electricity from the smart building aggregator AOP at a price p. hs Purchase the required hydrogen energy from smart building aggregator AOP.

[0018] Furthermore, the revenue model for the intelligent building aggregator (AOP) in step 2 is as follows:

[0019]

[0020] In the formula: For the benefits of AOP; and For the electricity sales price and purchase price of the large power grid; NL nt Let n be the net load power of the intelligent building. This refers to the input power of the electrolytic cell; and The electricity sales price and purchase price for AOP; The internal hydrogen sales price of AOP; SH nt The amount of hydrogen purchased for smart building n; C td ω represents carbon cost; ω represents carbon emissions per unit of electricity generated; and γ represents the carbon allowance per unit of electricity generated.

[0021] The model expression for the PEM electrolyzer is as follows:

[0022]

[0023] In the formula: η represents the hydrogen production rate of the electrolyzer; PEM The efficiency of hydrogen production by water electrolysis.

[0024] The model expression for the hydrogen storage tank is as follows:

[0025]

[0026] In the formula: This refers to the capacity of the hydrogen storage tank; and η H2,out The hydrogen storage and release efficiency of the hydrogen storage tank; and The hydrogen storage and release capacity of the hydrogen storage tank; and These represent the minimum and maximum capacity of the hydrogen storage tank. and This represents the maximum value of hydrogen storage and release power.

[0027] Furthermore, the intelligent building cluster model in step 3 is as follows:

[0028] Defining the cost of each smart building as follows:

[0029]

[0030] In the formula: p CH4 For natural gas prices; Natural gas consumed for hydrogen-doped CHP; Hydrogen gas consumed for hydrogen-doped CHP; x is the utility parameter of intelligent building n; nt The electricity consumption of smart building n; δ nCost of thermal discomfort in smart buildings; The optimized heat load for intelligent building n; Let n be the initial heat load of the intelligent building.

[0031] Among these measures, in order to tap the carbon reduction potential of the system, hydrogen will be injected into the natural gas pipeline to directly use the hydrogen-mixed natural gas.

[0032] Among them, the micro-combined heat and power system uses HCNG as fuel for local power generation and heating, and its model can be expressed as:

[0033]

[0034] 0≤ξ≤20%

[0035]

[0036] In the formula: ξ t The ratio of hydrogen to natural gas in HCNG; and Power for hydrogen and natural gas; L H2 and L CH4 It has the low calorific value of hydrogen and natural gas; The electrical and thermal efficiency of hydrogen-doped CHP; The electrical and thermal power of hydrogen-doped CHP.

[0037] The coupling relationship between electricity and heat is as follows:

[0038]

[0039] In the formula: κ heat The heating coefficient; η loss This is the heat loss coefficient.

[0040] Among them, in the time period [α] n ,β n During operation, the adjustable load constraint is defined as follows:

[0041] SL min ≤SL nt ≤SL max

[0042] In the formula: SL nt For the movable load of intelligent building n; SL min ,SL max These are the minimum and maximum values ​​of the transferable load.

[0043] The net load power of intelligent building n is defined as follows:

[0044] NL nt =FL nt+SL nt -pv nt

[0045] Where: NL nt FL is the net load power of intelligent building n. nt The fixed load power of intelligent building n; pv nt Let n be the photovoltaic power of the smart building.

[0046] The heat load constraint for intelligent building n is as follows:

[0047] TL min ≤TL nt ≤TL max

[0048] Where: TL nt The heat load power of intelligent building n; TL min ,TL max Let n be the minimum and maximum values ​​of the heat load power of the intelligent building.

[0049] Furthermore, the specific method for step 4 is as follows:

[0050] A two-layer distributed optimization method is adopted. In the optimization process, the upper-layer optimization is performed by AOP, which generates electricity and hydrogen prices and notifies each photovoltaic smart building; in the lower-layer optimization, the smart buildings determine the optimal adjustable load, the power generation and heating of the micro-cogeneration system, and send the adjustable load to AOP.

[0051] The specific steps include:

[0052] (1) AOP sets electricity and hydrogen prices;

[0053] (2) AOP sends electricity and hydrogen prices to the smart building cluster;

[0054] (3) Each smart building executes a local optimization algorithm to determine the optimal adjustable load and the power generation and heating of the micro cogeneration system;

[0055] Each smart building executes a local optimization algorithm based on the smart building cluster model from step 3;

[0056] (4) Each smart building will send the adjustable load to AOP;

[0057] (5) Based on information from the intelligent building, AOP calculates the objective function value;

[0058] Calculate the objective function value based on the revenue model of the Intelligent Building Aggregator (AOP) in step 2;

[0059] (6) Perform mutation and crossover operations to generate offspring populations, and AOP recalculates the objective function value;

[0060] (7) By comparing the objective function values ​​of steps (5) and (6), the electricity and hydrogen prices corresponding to the optimal objective function value are selected, and the optimization process is carried out according to the user's needs until the optimization process converges. Finally, the hybrid energy sharing distributed optimization of the electric-hydrogen coupled smart distribution network area is completed.

[0061] Advantages and beneficial effects of the present invention:

[0062] 1. This invention proposes a hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area. During the iteration process, the smart building only shares its adjustable load information with the AOP, thereby protecting the privacy information of the smart building.

[0063] 2. The AOP of this invention can flexibly adjust the prices of electricity and hydrogen, hydrogen storage and water electrolysis strategies, and photovoltaic smart buildings can flexibly adjust their energy consumption behavior. Considering the micro-hydrogen cogeneration system and flexible electric and heat loads, it enhances the operational flexibility of the smart distribution network area.

[0064] 3. This invention utilizes the iterative interaction between AOP and intelligent building clusters to flexibly determine dynamic electricity and hydrogen prices, thereby maximizing profits and reducing system carbon emissions.

[0065] 4. Based on the hydrogen energy multi-utilization strategy proposed in this invention, which includes electro-hydrogen conversion (P2H) and hydrogen doping technologies, excess photovoltaic water electrolysis can be used to produce hydrogen, and the hydrogen can be stored for use in subsequent periods, thus promoting the consumption of renewable energy.

[0066] 5. The iterative optimization of the AOP and smart building in this invention continuously refines and converges to the optimal electricity and hydrogen prices. This invention designs a dynamically adjusted electricity and hydrogen pricing mechanism, enabling prices to be dynamically adjusted based on real-time demand and supply conditions until the optimization process converges. During these iterations, the smart building only shares its adjustable load information with the AOP, ensuring its privacy is protected. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of a hybrid energy sharing system in an electric-hydrogen coupled smart distribution network area according to the present invention;

[0068] Figure 2 This is a schematic diagram of the flexible electricity pricing of AOP based on electricity-hydrogen coupling sharing according to the present invention;

[0069] Figure 3 This is a schematic diagram of the internal hydrogen valence of the AOP of the present invention. Detailed Implementation

[0070] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings:

[0071] A hybrid energy-sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area includes the following steps:

[0072] Step 1: Establish a hybrid energy sharing system for the electric-hydrogen coupled smart distribution network area;

[0073] like Figure 1 As shown, the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area in step 1 includes: smart building aggregator (AOP) and smart building cluster;

[0074] The intelligent building aggregator (AOP) includes a PEM electrolyzer and a hydrogen storage tank. One end of the AOP is connected to the power grid, and the other end is connected to the hydrogen market. It can convert electricity into hydrogen, providing the necessary electricity and hydrogen for the photovoltaic intelligent building cluster. The AOP is also connected to the intelligent building cluster, performing local optimization through their respective energy management systems (EMS) and exchanging energy and price information. The photovoltaic intelligent building cluster includes multiple intelligent buildings, each equipped with a corresponding photovoltaic system and a micro-hydrogen-doped combined heat and power (CHP) unit for local power generation and thermal energy production.

[0075] If a smart building generates excess electricity, it will be charged at price p. mb They sell surplus electricity to smart building aggregators (AOP); conversely, if there is a power shortage, they sell it at a price p. ms The smart building purchases electricity from the smart building aggregator AOP at a price p. hs Purchase the required hydrogen energy from smart building aggregator AOP.

[0076] In this embodiment, due to the limited capacity of a single smart building, an Intelligent Building Aggregator (AOP) is introduced to facilitate hybrid energy sharing within the smart distribution network area. On the AOP side, the AOP is equipped with a PEM electrolyzer and hydrogen storage tank, capable of converting electricity into hydrogen. The AOP provides the necessary electricity and hydrogen to the photovoltaic smart building cluster. When there is a power shortage or surplus, the AOP can trade with the grid. The AOP is also connected to the hydrogen market to supplement any hydrogen shortages.

[0077] On the user side, each smart building is equipped with a photovoltaic system. In addition, to mitigate power losses during long-distance transmission and heat losses in the district heating network, the smart buildings are equipped with micro-hydrogen-doped CHP units for local power generation and heat production. If the smart buildings generate excess electricity, they share it at a price p. mb They sell surplus electricity to AOP. Conversely, if there is a power shortage, they sell it at a price p. msPurchase electricity from AOP. Smart buildings use p... hs Purchase the required hydrogen energy from AOP.

[0078] Step 2: Based on the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area established in Step 1, establish the revenue model of the smart building aggregator (AOP).

[0079] The revenue model for the intelligent building aggregator (AOP) in step 2 is as follows:

[0080]

[0081] In the formula: For the benefits of AOP; and For the electricity sales price and purchase price of the large power grid; NL nt Let n be the net load power of the intelligent building. This refers to the input power of the electrolytic cell; and The electricity sales price and purchase price for AOP; The internal hydrogen sales price of AOP; SH nt The amount of hydrogen purchased for smart building n; C td ω represents carbon cost; ω represents carbon emissions per unit of electricity generated; and γ represents the carbon allowance per unit of electricity generated.

[0082] The model expression for the PEM electrolyzer is as follows:

[0083]

[0084] In the formula: η represents the hydrogen production rate of the electrolyzer; PEM The efficiency of hydrogen production by water electrolysis.

[0085] The model expression for the hydrogen storage tank is as follows:

[0086]

[0087] In the formula: This refers to the capacity of the hydrogen storage tank; and The hydrogen storage and release efficiency of the hydrogen storage tank; and The hydrogen storage and release capacity of the hydrogen storage tank; and These represent the minimum and maximum capacity of the hydrogen storage tank. and This represents the maximum value of hydrogen storage and release power.

[0088] In this embodiment, the AOP facilitates internal energy sharing among photovoltaic smart buildings and provides them with hydrogen energy. Simultaneously, the AOP is connected to the power grid, incurring grid transaction costs and corresponding carbon costs. Considering the benefits of sharing electricity and hydrogen with smart buildings, grid transaction costs, carbon costs, and water electrolysis costs, the AOP's revenue model is established as follows:

[0089]

[0090] In the formula: For the benefits of AOP; and For the electricity sales price and purchase price of the large power grid; NL nt Let n be the net load power of the intelligent building. This refers to the input power of the electrolytic cell; and The electricity sales price and purchase price for AOP; The internal hydrogen sales price of AOP; SH nt The amount of hydrogen purchased for smart building n; C td ω represents carbon cost; ω represents carbon emissions per unit of electricity generated; and γ represents the carbon allowance per unit of electricity generated.

[0091] PEM electrolyzers are widely used to convert electricity into hydrogen. The energy conversion efficiency of an electrolyzer is approximately 75% to 85%, and its model expression can be defined as:

[0092]

[0093] In the formula: η represents the hydrogen production rate of the electrolyzer; PEM The efficiency of hydrogen production by water electrolysis.

[0094] In this invention, the hydrogen storage tank provides a stable hydrogen source for HCNG production. Its model can be defined as:

[0095]

[0096] In the formula: This refers to the capacity of the hydrogen storage tank; and The hydrogen storage and release efficiency of the hydrogen storage tank; and The hydrogen storage and release capacity of the hydrogen storage tank; and These represent the minimum and maximum capacity of the hydrogen storage tank. and This represents the maximum value of hydrogen storage and release power.

[0097] Step 3: Based on the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area established in Step 1, establish a smart building cluster model;

[0098] The intelligent building cluster model in step 3 is as follows:

[0099] Defining the cost of each smart building as follows:

[0100]

[0101] In the formula: p CH4 For natural gas prices; Natural gas consumed for hydrogen-doped CHP; Hydrogen gas consumed for hydrogen-doped CHP; x is the utility parameter of intelligent building n; nt The electricity consumption of smart building n; δ n Cost of thermal discomfort in smart buildings; The optimized heat load for intelligent building n; Let n be the initial heat load of the intelligent building.

[0102] Among these measures, in order to tap the carbon reduction potential of the system, hydrogen will be injected into the natural gas pipeline to directly use the hydrogen-mixed natural gas.

[0103] Among them, the micro-combined heat and power system uses HCNG as fuel for local power generation and heating, and its model can be expressed as:

[0104]

[0105] 0≤ξ≤20%

[0106]

[0107] In the formula: ξ t The ratio of hydrogen to natural gas in HCNG; and Power for hydrogen and natural gas; L H2 and L CH4 It has the low calorific value of hydrogen and natural gas; The electrical and thermal efficiency of hydrogen-doped CHP; The electrical and thermal power of hydrogen-doped CHP.

[0108] The coupling relationship between electricity and heat is as follows:

[0109]

[0110] In the formula: κ heat The heating coefficient; η loss This is the heat loss coefficient.

[0111] Among them, in the time period [α] n ,β n During operation, the adjustable load constraint is defined as follows:

[0112] SL min ≤SL nt ≤SL max

[0113] In the formula: SL nt For the movable load of intelligent building n; SL min ,SL max These are the minimum and maximum values ​​of the transferable load.

[0114] The net load power of intelligent building n is defined as follows:

[0115] NL nt =FL nt +SL nt -pv nt

[0116] Where: NL nt FL is the net load power of intelligent building n. nt The fixed load power of intelligent building n; pv nt Let n be the photovoltaic power of the smart building.

[0117] The heat load constraint for intelligent building n is as follows:

[0118] TL min ≤TL nt ≤TL max

[0119] Where: TL nt The heat load power of intelligent building n; TL min ,TL max Let n be the minimum and maximum values ​​of the heat load power of the intelligent building.

[0120] In this embodiment, models for each smart building are established, taking into account the operating costs of the micro-hydrogen-coated cogeneration system, the shared electricity and hydrogen costs with the AOP, and the flexible demand response to combined electricity and heat loads. These models aim to optimize the integration and efficiency of energy resources, ensuring cost-effectiveness and responding to diverse energy demands. The cost of each smart building... It can be defined as follows:

[0121]

[0122] In the formula: p CH4 For natural gas prices; Natural gas consumed for hydrogen-doped CHP; Hydrogen gas consumed for hydrogen-doped CHP; x is the utility parameter of intelligent building n; nt The electricity consumption of smart building n; δ n Cost of thermal discomfort in smart buildings; The optimized heat load for intelligent building n; Let n be the initial heat load of the intelligent building.

[0123] To tap the system's carbon reduction potential, hydrogen is injected into the natural gas pipeline for direct use of hydrogen-mixed natural gas (HCNG). The micro-combined heat and power (CHP) system uses HCNG as fuel for local power generation and heating. Its model can be expressed as:

[0124]

[0125] 0≤ξ≤20%

[0126]

[0127] In the formula: ξ t The ratio of hydrogen to natural gas in HCNG; and Power for hydrogen and natural gas; L H2 and It has the low calorific value of hydrogen and natural gas; The electrical and thermal efficiency of hydrogen-doped CHP; The electrical and thermal power of hydrogen-doped CHP.

[0128] The coupling relationship between electricity and heat is as follows:

[0129]

[0130] In the formula: κ heat The heating coefficient; η loss This is the heat loss coefficient.

[0131] In smart grids, adjustable electrical loads such as electric vehicles, washing machines, and dishwashers are common. Smart buildings can flexibly select the operating times of these loads to minimize costs. During the time period [α] n ,β n During operation, the adjustable load constraint is defined as follows:

[0132] SL min ≤SL nt ≤SL max

[0133] In the formula: SL nt For the movable load of intelligent building n; SL min ,SL maxThese are the minimum and maximum values ​​of the transferable load.

[0134] The net load power of a smart building n can be defined as:

[0135] NL nt =FL nt +SL nt -pv nt

[0136] Where: NL nt FL is the net load power of intelligent building n. nt The fixed load power of intelligent building n; pv nt Let n be the photovoltaic power of the smart building.

[0137] Intelligent buildings can participate in heat demand response, flexibly adjusting their heat load requirements. The heat load constraint for intelligent building n is:

[0138] TL min ≤TL nt ≤TL max

[0139] Where: TL nt The heat load power of intelligent building n; TL min ,TL max Let n be the minimum and maximum values ​​of the heat load power of the intelligent building.

[0140] Step 4: Use a distributed solution algorithm to solve the revenue model of the intelligent building aggregator (AOP) and the intelligent building group model established in Steps 2 and 3, and then complete the distributed optimization of hybrid energy sharing in the electric-hydrogen coupled intelligent distribution network area.

[0141] The specific steps of step 4 include:

[0142] In this embodiment, since the revenue functions of the AOP and the smart building are coupled, the decision variables of each entity will affect the other. Therefore, this invention employs a two-layer distributed optimization method. During the optimization process, the upper-layer optimization is performed by the AOP, which generates electricity and hydrogen prices and notifies each photovoltaic smart building. In the lower-layer optimization, the smart building determines the optimal adjustable load, the power generation and heating of the micro-cogeneration system, and sends the adjustable load to the AOP.

[0143] (1) AOP sets electricity and hydrogen prices;

[0144] (2) AOP sends electricity and hydrogen prices to the smart building cluster;

[0145] (3) Each smart building executes a local optimization algorithm to determine the optimal adjustable load and the power generation and heating of the micro cogeneration system;

[0146] Each smart building executes a local optimization algorithm based on the smart building cluster model from step 3;

[0147] (4) Each smart building will send the adjustable load to AOP;

[0148] (5) Based on information from the intelligent building, AOP calculates the objective function value;

[0149] Calculate the objective function value based on the revenue model of the Intelligent Building Aggregator (AOP) in step 2;

[0150] (6) Perform mutation and crossover operations to generate offspring populations, and AOP recalculates the objective function value;

[0151] (7) By comparing the objective function values ​​of (5) and (6), the electricity and hydrogen prices corresponding to the optimal objective function value are selected, and the optimization process is carried out according to the user's needs until the optimization process converges. Finally, the hybrid energy sharing distributed optimization of the electric-hydrogen coupled smart distribution network area is completed.

[0152] The iterative optimization of AOP and smart buildings continuously refines and converges to the optimal electricity and hydrogen prices. This invention designs a dynamically adjusted electricity and hydrogen pricing mechanism, enabling prices to be dynamically adjusted based on real-time demand and supply conditions until the optimization process converges. During these iterations, the smart building only shares its adjustable load information with AOP, ensuring its privacy is protected.

[0153] The invention will be further illustrated below with specific examples:

[0154] This invention uses a smart power distribution network consisting of six smart buildings as a case study. The simulation was conducted in Matlab over a period of one day with one-hour intervals. Each smart building is equipped with photovoltaic panels of varying capacities. In addition to photovoltaic power generation, the smart buildings also include a micro-combination heat and power (CHP) system to provide both electricity and heat. The AOP (Automatic Power Utility) is equipped with an electrolyzer and a hydrogen storage tank to facilitate energy sharing among the smart buildings.

[0155] Figure 2 This is for a flexible electricity price based on shared AOP (Aspect-Oriented Programming) using an electricity-hydrogen coupling model. For example... Figure 2 As shown, the electricity price sold by the AOP is lower than the price sold by the grid. Therefore, smart buildings prefer to purchase electricity from the AOP to meet their needs, thus avoiding the high costs of purchasing electricity from the grid. Furthermore, Figure 1 The purchase price of electricity by the AOP (Automatic Power Provider) is higher than the grid connection price. Therefore, smart buildings tend to sell excess electricity to the AOP to avoid feeding excess electricity back to the grid at an uneconomical price. For the AOP, it can obtain electricity at a lower price. Purchase electricity from smart buildings with surplus power, and use it in... The price is sold to smart buildings with insufficient power. Due to There are arbitrage opportunities in AOP. Both AOP and smart buildings can benefit from this hybrid shared framework.

[0156] The internal hydrogen valence of AOP, such as Figure 3 As shown, the hydrogen price at AOP is lower than the market hydrogen price. At the same time, it is higher than the equivalent hydrogen price of electricity purchased from the main grid. Therefore, the model proposed in this invention is more advantageous for smart buildings.

[0157] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area, characterized in that: Includes the following steps: Step 1: Establish a hybrid energy sharing system for the electric-hydrogen coupled smart distribution network area; Step 2: Based on the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area established in Step 1, establish the revenue model of the smart building aggregator (AOP). Step 3: Based on the hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area established in Step 1, establish a smart building cluster model; Step 4: Use a distributed solution algorithm to solve the revenue model of the intelligent building aggregator AOP and the intelligent building group model established in Step 2 and Step 3, and then complete the distributed optimization of hybrid energy sharing in the electric-hydrogen coupled intelligent distribution network area. The revenue model for the intelligent building aggregator (AOP) in step 2 is as follows: ; ; In the formula: For the benefits of AOP; and The electricity sales price and purchase price of the large power grid; Let n be the net load power of the intelligent building. This refers to the input power of the electrolytic cell; and The electricity sales price and purchase price for AOP; This refers to AOP's internal hydrogen sales price; The amount of hydrogen purchased for smart building n; For carbon costs; Carbon emissions per unit of electricity generated; Carbon allowance per unit of electricity generated; The model expression for the PEM electrolyzer is as follows: ; In the formula: Hydrogen production from the electrolyzer; The efficiency of hydrogen production through water electrolysis; The model expression for the hydrogen storage tank is as follows: ; ; ; ; In the formula: This refers to the capacity of the hydrogen storage tank; and The hydrogen storage and release efficiency of the hydrogen storage tank; and The hydrogen storage and release capacity of the hydrogen storage tank; and These represent the minimum and maximum capacity of the hydrogen storage tank. and This represents the maximum value of hydrogen storage and release power.

2. The hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area according to claim 1, characterized in that: The hybrid energy sharing system of the electric-hydrogen coupled smart distribution network area in step 1 includes: smart building aggregator (AOP) and smart building cluster; The intelligent building aggregator (AOP) includes a PEM electrolyzer and a hydrogen storage tank. One end of the AOP is connected to the power grid, and the other end is connected to the hydrogen market. It can convert electricity into hydrogen, providing the necessary electricity and hydrogen for the photovoltaic intelligent building cluster. The AOP is also connected to the intelligent building cluster, performing local optimization through their respective energy management systems (EMS) and exchanging energy and price information. The photovoltaic intelligent building cluster includes multiple intelligent buildings, each equipped with a corresponding photovoltaic system and a micro-hydrogen-doped combined heat and power (CHP) unit for local power generation and thermal energy production. If smart buildings generate excess electricity, it will be charged at a price. p mb They sell surplus electricity to smart building aggregators (AOP); conversely, if there is a power shortage, they sell it at a price... p ms The smart building purchases electricity from the smart building aggregator AOP, and the smart building pays for it. p hs Purchase the required hydrogen energy from smart building aggregator AOP.

3. The hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area according to claim 1, characterized in that: The intelligent building cluster model in step 3 is as follows: Defining the cost of each smart building as follows: ; In the formula: For natural gas prices; Natural gas consumed for hydrogen-doped CHP; Hydrogen gas consumed for hydrogen-doped CHP; Let n be the utility parameter of the intelligent building. The electricity consumption of smart building n; Cost of thermal discomfort in smart buildings; The optimized heat load for intelligent building n; Let n be the initial heat load of the intelligent building. Among these measures, in order to tap the carbon reduction potential of the system, hydrogen will be injected into the natural gas pipeline to directly use the hydrogen-mixed natural gas. Among them, the micro-combined heat and power system uses HCNG as fuel for local power generation and heating, and its model can be expressed as: ; ; ; ; In the formula: The ratio of hydrogen to natural gas in HCNG; and Power for hydrogen and natural gas; and It has the low calorific value of hydrogen and natural gas; , The electrical and thermal efficiency of hydrogen-doped CHP; , The electrical and thermal power of hydrogen-doped CHP; The coupling relationship between electricity and heat is as follows: ; In the formula: The heating coefficient; This is the heat loss coefficient; Among them, in the time period During internal operation, the adjustable load constraint is defined as follows: ; In the formula: For the movable load of intelligent building n; , These are the minimum and maximum values ​​of the transferable load; The net load power of intelligent building n is defined as follows: ; In the formula: Let n be the net load power of the intelligent building. Let n be the fixed load power of the intelligent building. Let n be the photovoltaic power of the smart building. Among them, smart buildings n The heat load constraint is: ; In the formula: The heat load power of intelligent building n; , Let n be the minimum and maximum values ​​of the heat load power of the intelligent building.

4. The hybrid energy sharing optimization scheduling method for an electric-hydrogen coupled smart distribution network area according to claim 1, characterized in that: The specific method for step 4 is as follows: A two-layer distributed optimization method is adopted. In the optimization process, the upper-layer optimization is performed by AOP, which generates electricity and hydrogen prices and notifies each photovoltaic smart building. In the lower-layer optimization, the smart buildings determine the optimal adjustable load, power generation and heating of the micro-cogeneration system, and send the adjustable load to AOP. The specific steps include: (1) AOP sets the prices of electricity and hydrogen; (2) AOP sends electricity and hydrogen prices to the smart building cluster; (3) Each smart building executes a local optimization algorithm to determine the optimal adjustable load and the power generation and heating of the micro cogeneration system; Each smart building executes a local optimization algorithm based on the smart building cluster model from step 3; (4) Each smart building will send its adjustable load to the AOP; (5) Based on the information from the intelligent building, AOP calculates the objective function value; Calculate the objective function value based on the revenue model of the Intelligent Building Aggregator (AOP) in step 2; (6) Perform mutation and crossover operations to generate offspring populations, and AOP recalculates the objective function value; (7) By comparing the objective function values ​​of steps (5) and (6), select the electricity and hydrogen prices corresponding to the optimal objective function value, and adjust them according to user needs until the optimization process converges. Finally, the hybrid energy sharing distributed optimization of the electric-hydrogen coupled smart distribution network area is completed.

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