US-20260128595-A1 - OPTIMIZATION SCHEDULING METHOD AND SYSTEM FOR COUPLING MICROGRID CONSIDERING ELECTRO-THERMAL LOAD DEMAND COORDINATION

US20260128595A1US 20260128595 A1US20260128595 A1US 20260128595A1US-20260128595-A1

Abstract

An optimization scheduling method for coupling microgrid considering electro-thermal load demand coordination, comprising: building hydrogen-containing microgrid operation model and building electro-thermal load demand coordination response model by considering peak-valley complementary characteristics of original electric load and thermal load in system, characterizing electric load by time-of-use electricity price demand response method, by considering thermal load having heat transfer inertia and fuzziness of user temperature perception, adjusting electro-thermal load demand flexibly based on different electricity prices; based on hydrogen-containing microgrid operation model and electro-thermal load demand coordination response model, building objective function by minimizing sum of total operation cost, wind and light curtailed cost of renewable energy and demand response compensation cost, building electro-hydrogen coupling microgrid optimal scheduling model according to constraint conditions and solving it by mixed integer linear programming, to generate and send instructions based on solved optimal operation scheduling strategy to the microgrid for controlling corresponding unit equipment operation.

Inventors

Tianguang LU
Yuhao Zhang
Yansong MEI
Qian AI
Xing He
Donglei SUN
Ming Yang
Qinzheng WU
Yingdong XU

Assignees

SHANDONG UNIVERSITY

Dates

Publication Date: 20260507
Application Date: 20251105
Priority Date: 20241107

Claims (8)

1 . An optimization scheduling method for a coupling microgrid considering electro-thermal load demand coordination, comprising: building a hydrogen-containing microgrid operation model based on operation characteristics and interaction of new energy generating units, electro-hydrogen coupling units, heat generating units and energy storage units; wherein the hydrogen-containing microgrid operation model comprise a distributed new energy generating unit model, an electro-hydrogen couple unit model, a heat generating unit model and an energy storage model, wherein the distributed new energy generating unit model comprise a wind power generator unit and a photovoltaic generating unit; the electro-hydrogen coupling unit model comprises the electrolytic cell model, the methane reactor model and the hydrogen fuel cell model; the heat generating unit model comprises the combined heat and power (CHP) unit and a gas-fired boiler model; and, the energy storage model is a unified modeling of four energy devices: electricity, heat, gas, and hydrogen; building an electro-thermal load demand coordination response model by considering peak-valley complementary characteristics of an original electric load and a thermal load in system, characterizing an electric load by a time-of-use electricity price demand response method, by considering a thermal load has heat transfer inertia and fuzziness of user temperature perception, adjusting electro-thermal load demand flexibly based on different electricity prices; wherein building the electro-thermal load demand coordination response model, characterizing the electric load by using the time-of-use electricity price demand response method, comprises: carrying out an electricity load demand response study by using the time-of-use electricity price demand response method, dividing the electricity load into a curtailable load, a transferable load and a substitutional load; using a contract management mode for the electric load, wherein signing contracts between energy suppliers and users, the user informs the energy supplier of various flexible load powers in each time period in advance, and negotiates compensation prices and allowable interaction time periods for various loads; the energy suppliers flexibly adjust the users' demand for flexible load energy in different time periods according to own supply capacity of the energy suppliers, and compensates the users according to a size of a user's participation in an interaction power; the electro-thermal load demand coordination response model comprises: obtaining the heat balance relationship without considering the flexibility of heat load, and based on the analysis of electro-thermal load demand coordination response strategy, obtaining a coordination mode of electrical and thermal power demand response under peaking demand, including a low load period, a peak load period, and an average load period; regardless of the flexibility of the thermal load, the heat balance equation is as follows: P s , t h = P G ⁢ B , t h + P CHP , t h + P ES , t h , d ⁢ i ⁢ s - P ES , t h , c ⁢ h ⁢ a ⁢ r = P s ⁢ t , t h ; wherein, P s , t h is an actual power of the thermal load at the time t, and P s ⁢ t , t h is a standard power at the time t without considering the inertia of the thermal load. P CHP , t h ⁢ and ⁢ P G ⁢ B , t h are heating powers of the CHP and the gas-fired boiler at the time t respectively; P ES , t h , d ⁢ i ⁢ s ⁢ and ⁢ P ES , t h , c ⁢ h ⁢ a ⁢ r are heat release power and heat storage power of the heat storage device at the time t respectively; considering the flexibility of the heat load, the system firstly allows the heat load to vary between minimum heating power and maximum heating power, and then determines heating power within the heating power range according to peak demand; a formula is as follows: P l ⁢ b , t h ≤ P s , t h ≤ P u ⁢ b , t h ; according to the analysis of the electro-thermal load demand coordination response strategy, coordinated mode of power and thermal demand response under peak load can be summarized, and details are as follows: (1) in the low load period, heating power may be optimized between standard heating power and minimum heating power; a formula is as follows: P l ⁢ b , t h ≤ P s , t h ≤ P s ⁢ t , t h ; wherein, P s , t h is an actual power of the thermal load at a time t, P st , t h ⁢ and ⁢ P lb , t h are standard heating power and minimum heating power of the thermal load at the time t, respectively; (2) during the peak load period, heating power may be optimized between standard heating power and maximum heating power; a formula is as follows: P st , t h ≤ P s , t h ≤ P ub , t h ; wherein, P s , t h is an actual power of the thermal load at the time t, P st , t h ⁢ and ⁢ P ub , t h are standard heating power and maximum heating power of the thermal load at the time t; (3) during the average load period, heating power may be optimized between the minimum and maximum heating powers, and connected with the peak and valley heating powers; P lb , t h ≤ P s , t h ≤ P ub , t h ; wherein, P s , t h is an actual power of the heat load at the time t, P lb , t h ⁢ and ⁢ P ub , t h are minimum and maximum heating powers of the heat load at the time t; and, based on the hydrogen-containing microgrid operation model and the electro-thermal load demand coordination response model, building an objective function with an objective of minimizing a sum of a total operation cost, a wind and light curtailed cost of renewable energy and a demand response compensation cost, building an electro-hydrogen coupling microgrid optimal scheduling model according to constraint conditions, then solving the electro-hydrogen coupling microgrid optimal scheduling mode by a mixed integer linear programming (MILP), and applying a solved optimal operation scheduling strategy of equipment to the microgrid for executing.
2 . The optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to claim 1 , wherein considering the thermal load has heat transfer inertia and fuzziness of user temperature perception, flexible adjustment is made based on different electricity prices, comprising: analyzing the thermodynamic characteristics of heating buildings and heating networks according to the inertia of heat transfer of thermal load, obtaining the dynamic relationship between heat output of cogeneration and building room temperature, and describing the temperature dynamics of heating system by using autoregressive moving average model, comprises the flow and pressure of heating networks remain unchanged, and only the temperature is adjusted.
3 . The optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to claim 1 , wherein considering the thermal load has heat transfer inertia and fuzziness of user temperature perception, flexible adjustment is made based on different electricity prices, comprising: according to the fuzziness of user temperature perception, using a predicted average voting index (PAVI) as a criterion for evaluating thermal comfort of indoor environment; there is a time-coupling relationship between thermal power of the thermal load and an indoor temperature, when the indoor temperature is allowed to fluctuate within a comfort range, the thermal power has certain elasticity, and by setting a range of the PAVI, obtaining a minimum indoor temperature and a maximum indoor temperature of the heat load; a relationship between the PAVI and temperature is as follows: λ PMV = { 0.3895 ( T in , t - 26 ) , T in , t ≥ 26 0.4065 ( 26 - T in , t ) , T in , t < 26 wherein, λ PMV is value of the PAVI; T in,t is indoor temperature of building at the time t; implementing as follows: Cold/hot Relatively Slightly Slightly Relatively somatosensory Hot hot hot Comfort cold cold Cold PAVI 3 2 1 0 −1 −2 −3 according to the table above, obtaining minimum indoor temperature T in , t lb and maximum indoor temperature T in , t ub of the heat load by the relationship between the PAVI and the temperature, and obtaining a minimum heating power P lb , t h and a maximum heating power P ub , t h of the thermal load of system in a whole dispatching period according to a formula of thermal load power relationship of indoor air temperature; wherein, the formula of the thermal load power relationship of the indoor air temperature is: P L , t h = 1 R ⁢ ( T in , t + 1 - T in , t ⁢ e - Δ ⁢ t / τ 1 - e - Δ ⁢ t / τ - T out , t ) ; wherein, P L , t h is thermal load power at the time t, Δt is a minimum dispatch period, e is natural base, R is an equivalent thermal resistance of the building, and T in,t is the indoor temperature of the building at the time t.
4 . The optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to claim 1 , wherein obtaining the heat balance relationship without considering the flexibility of heat load, and based on the analysis of electro-thermal load demand coordination response strategy, obtaining the coordination mode of electrical and thermal power demand response under the peaking demand, comprising a low load period, a peak load period, and an average load period.
5 . The optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to claim 1 , wherein the total cost comprises an equipment acquisition cost, an operation-maintenance cost, a cost of hydrogen production water, a hydrogen purchase cost and a transaction cost with external power and gas grids; the constraint conditions comprises a renewable new energy generator unit output constraint, an electro-hydrogen coupling unit operation constraint, a heat generation unit operation constraint, a storage operating constraint, an electrical power balancing, a thermal power balancing, a natural gas balancing, and a hydrogen balancing.
6 . An optimization scheduling system for a coupling microgrid considering electro-thermal load demand coordination, applying to execute the optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to any one of claims 1 to 5 , comprising: a model building module, for building a hydrogen-containing microgrid operation model based on operation characteristics and interaction of new energy generating units, electro-hydrogen coupling units, heat generating units and energy storage units; and, building an electro-thermal load demand coordination response model by considering peak-valley complementary characteristics of an original electric load and a thermal load in system; a demand coordination response optimization module, for characterizing an electric load by a time-of-use price demand response method, by considering a thermal load has heat transfer inertia and fuzziness of user temperature perception, adjusting electro-thermal load demand flexibly based on different electricity prices; and an operation scheduling module, for based on the hydrogen-containing microgrid operation model and the electro-thermal load demand coordination response model, building an objective function with an objective of minimizing a sum of a total operation cost of a microgrid in the future, a wind and light curtailed cost of renewable energy and a demand response compensation cost, building an electro-hydrogen coupling microgrid optimal scheduling model according to constraint conditions, then solving the electro-hydrogen coupling microgrid optimal scheduling model by a mixed integer linear programming (MILP), and applying a solved optimal operation scheduling strategy to the coupling microgrid for executing.
7 . A non-transitory computer-readable storage medium, having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, causing the processor to implement the optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to any one of claims 1 to 5 .
8 . An electronic equipment, comprising a processor, a memory and a computer program, wherein the processor is connecting to the memory, the computer program is stored in the memory, and when the electronic equipment runs, the processor executes the computer program stored in the memory, causing the electronic equipment to execute the optimization scheduling method for the coupling microgrid considering electro-thermal load demand coordination according to any one of claims 1 to 5 .

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present invention claims priority from China patent application filed on Nov. 7, 2024 in China National Intellectual Property Administration with application No. 202411578542.7 entitled “Optimization Scheduling Method and System for Coupling Microgrid Considering Electro-thermal Load Demand Coordination,” the entire contents of which are incorporated herein by reference and constitute a part of the present invention for all purposes. TECHNICAL FIELD The present invention relates to the technical field of optimization scheduling of microgrid, in particular to an optimization scheduling method and system for a coupling microgrid considering electro-thermal load demand coordination. BACKGROUND The statements in this section merely provide background information related to the present invention and are not necessarily prior art. At present, fossil energy represented by coal and oil will be gradually replaced by renewable energy represented by wind power. Hydrogen, as an important green and clean renewable energy, is considered to have broad prospects for development because of its advantages of high energy, zero carbon emission and high energy conversion efficiency. With the continuous progress of energy technology, hydrogen-containing microgrid, as a new type of energy network that can realize electro-hydrogen conversion, shows great application potential in multi-energy conversion and storage, resource optimization allocation and so on. The power grid can not only make full use of distributed renewable energy, reduce dependence on traditional fossil energy, but also solve the problem of unstable output of renewable energy such as wind power and photovoltaic through efficient storage and utilization of hydrogen, and effectively solve the problem of supply of different types of energy such as electric energy and thermal energy. However, due to the preliminary study of electro-hydrogen coupling microgrid, it faces the problems of complex equipment operation and changeable user demand in the actual application scheduling process. In order to solve the above problems, the existing methods deeply dig and predict the user's electricity consumption behavior by introducing optimization algorithms and combining big data analysis and machine learning technologies, and realize real-time monitoring and dynamic adjustment of the microgrid operation state. Based on evolutionary particle swarm optimization algorithm, the existing methods improve genetic algorithm, gray wolf algorithm and other optimization algorithms to solve the problem of optimal configuration and technical and economic analysis of the electro-hydrogen coupling microgrid; based on big data analysis technology to monitor and analyze the power production, conversion and storage data of distributed power sources in microgrid, the existing methods 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 price predictions, the existing methods realize to help plan energy production and distribution. However, most of the researches on optimization operation of the microgrid focus on improving renewable energy efficiency and controlling the operation and maintenance costs of microgrid and pollution control costs, without considering the behavior of users to adjust power consumption time and demand to participate in microgrid operation optimization. In addition, at present, the optimal dispatching strategy of electro-hydrogen coupling microgrid often only considers the dispatching planning of electro-hydrogen conversion equipment units, and has not considered renewable energy generators such as wind power generator units and photovoltaic generators, which has limitations in decision-making. SUMMARY To solve the above problems, the present invention provides an optimization scheduling method and system for a coupling microgrid considering electro-thermal load demand coordination, which considers the correlation characteristic of electric-heat-gas-hydrogen multi-energy conversion of new energy output property and the peak-valley complementary characteristic of original electric load and thermal load in the system on time distribution, firstly, electric load demand response research is carried out by using a time-sharing electricity price demand response method, and then flexible adjustment is carried out by considering the fuzziness of heat transfer inertia and user temperature perception of thermal load, An optimal scheduling model for hydrogen-containing microgrid is established to optimize the output level of each device in the system to ensure that the microgrid operates in an efficient and environmentally friendly state. According to some embodiments, the present invention adopts the following technical solutions. An optimization scheduling method for a coupling microgrid co