Oil and gas district microgrid group light storage heat capacity configuration and operation optimization method and system

Abstract

The application discloses an oil and gas area micro-grid group light storage heat capacity configuration and operation optimization method and system, comprising the following steps: constructing an oil and gas plant area micro-grid group system architecture; constructing a double-layer planning operation collaborative optimization model, including an upper-layer capacity planning model and a lower-layer collaborative operation model; the upper-layer capacity planning model takes the minimum annual comprehensive cost of the system as the target; the lower-layer collaborative operation model optimizes centralized photovoltaic power distribution, centralized electric energy storage charging and discharging power, well site micro-grid node feeder power purchase, heat supply point electric heating power, heat storage charging and discharging power and abandoned light power for each typical day scene on the basis of the given upper-layer capacity configuration; key constraint conditions of the lower-layer collaborative operation model are constructed; a hierarchical iteration method is adopted to solve the optimization model, and the optimal operation strategy and operation cost under each typical day scene are obtained; and the application matches the capacity configuration scheme and the operation scheduling strategy, and improves the overall economy and low-carbon operation level of the system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization and operation technology, and more specifically, to a method and system for optimizing the configuration and operation of photovoltaic, thermal, and power capacity of microgrid clusters in oil and gas fields. Background Technology

[0002] Oil and gas production areas typically include multiple decentralized well sites, pumping units and other electrical loads, as well as water extraction points for heating. With increasing demands for cost reduction, energy conservation, and low-carbon operation at oil and gas production sites, utilizing developable land within the production area to construct centralized photovoltaic power stations, and achieving multi-node coordinated energy supply through energy storage devices and thermoelectric coupling equipment, has become an important way to improve the local consumption of renewable energy and reduce the cost of purchased electricity.

[0003] Existing power supply methods for oil and gas stations are mostly based on independent configurations of single well sites or single nodes, which can easily lead to problems such as redundant equipment capacity, insufficient distribution of photovoltaic power output, inadequate utilization of peak and off-peak electricity prices, and insufficient ability to regulate heating load timing. For well site microgrid nodes, multiple pumping units experience bidirectional power fluctuations during start-up, shutdown, and stroke cycles. If the local absorption of regenerative power on the DC bus within the well site is not considered, it will amplify the instantaneous load fluctuations on the external power supply side. For water hauling and heating points, hot washing operations have requirements for hot water temperature and heating timing. If only instantaneous electric heating is used for heating, it can easily lead to high operating costs during peak electricity price periods.

[0004] Furthermore, there is a strong coupling relationship between centralized photovoltaic power, centralized energy storage, external power grid, and hotspot heating and thermal energy storage. Static configuration of equipment capacity alone, or localized scheduling only during operation, is insufficient to simultaneously consider investment costs, operating costs, carbon emission costs, renewable energy integration levels, and load differences across various scenarios. Therefore, a collaborative optimization method is needed to simultaneously describe the relationship between upper-level capacity configuration and lower-level operational scheduling, in order to improve the overall economic efficiency and low-carbon operation level of microgrid systems in oil and gas plant areas. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing the configuration and operation of photovoltaic, thermal, and electrical capacity of microgrid groups in oil and gas fields, in order to solve the problems of redundant equipment configuration, insufficient photovoltaic absorption, uncoordinated energy storage scheduling, lack of time-series adjustment of heating load, and disconnect between planning and operation in existing multi-wellfield microgrid systems in oil and gas plants.

[0006] This invention constructs a microgrid cluster architecture comprising centralized photovoltaic power, centralized energy storage, multiple well site microgrid nodes, power supply nodes, and combined heat and power (CHP) storage and regulation devices. It employs a two-layer optimization model combining upper-layer capacity planning and lower-layer collaborative operation to achieve unified optimization of equipment capacity configuration, photovoltaic allocation, energy storage charging and discharging, electricity purchase strategies, and coordinated power and heat operation. Specifically, the technical solution provided by this invention is as follows:

[0007] The method for configuring and optimizing the photovoltaic, thermal, and power capacity of microgrid clusters in oil and gas fields includes the following steps:

[0008] A microgrid system architecture for oil and gas plant areas is constructed. This system includes a centralized energy supply center, multiple well site microgrid nodes, hotspot nodes, and an external power grid. The centralized energy supply center includes a centralized photovoltaic power station and an energy storage device. Well site microgrid nodes are used to handle the electrical load demands of production equipment. Hotspot nodes are equipped with combined heat and power (CHP) and energy storage systems, which include electric heating modules and thermal energy storage modules to achieve coordinated heating through electric heating and thermal energy storage. This system converts electrical energy into heat energy and performs time-series regulation of the heat load. The external power grid provides supplementary power when the output of the centralized photovoltaic and centralized energy storage systems is insufficient.

[0009] A two-layer planning and operation collaborative optimization model is constructed, comprising an upper-layer capacity planning model and a lower-layer collaborative operation model. The upper-layer capacity planning model aims to minimize the annualized comprehensive cost of the system and determines the centralized photovoltaic installed capacity, the rated power and rated capacity of centralized electric energy storage, and the capacity of the electric heating module and the thermal energy storage module in the combined heat and power storage and regulation device. Based on the given upper-layer capacity configuration, the lower-layer collaborative operation model optimizes the centralized photovoltaic power allocation, centralized electric energy storage charging and discharging power, well site microgrid node feeder power purchase power, hot spot electric heating power, thermal energy storage charging and discharging power, and curtailed photovoltaic power for each typical daily scenario.

[0010] Key constraints for constructing the lower-level collaborative operation model;

[0011] A hierarchical iterative method is used to solve the two-level planning operation collaborative optimization model to obtain the optimal operation strategy and operation cost under each typical daily scenario.

[0012] Furthermore, the method for solving the bi-level programming operation collaborative optimization model is as follows:

[0013] The upper layer uses the centralized photovoltaic installed capacity, the rated power and capacity of centralized electric energy storage, the rated power of electric heating modules, the rated charging and discharging power of thermal energy storage modules, and the rated thermal storage capacity of thermal energy storage modules as capacity decision variables to generate candidate capacity configuration schemes; the lower layer uses the candidate capacity configurations as known parameters to construct a mixed integer linear programming model to solve the optimal operation strategy and operation cost under each typical daily scenario.

[0014] Furthermore, the dual-layer planning operation collaborative optimization model uses typical daily scenarios to represent the annual operating conditions, and converts the typical daily operating cost into the annual operating cost based on the number of representative days or scenario weights of each typical day; multiple pumping units inside the well site achieve local consumption of regenerative power through the DC bus, and the optimization model uses the equivalent net electrical load of the well site after regenerative power mutual assistance and smoothing; the centralized photovoltaic power station prioritizes supplying power to the well site microgrid nodes and hotspot nodes, and surplus photovoltaic power is used for charging electric energy storage; the power sources of the hotspot nodes include the output of centralized photovoltaic inverters and the purchase of power from the external power grid, and the electric heating module in the hotspot joint storage and regulation equipment converts electrical energy into thermal energy, and the thermal energy storage module is used to realize the charging, releasing and storage status management.

[0015] Furthermore, the objective function of the upper-level capacity planning model aims to minimize the annualized comprehensive cost of the system, and its expression is:

[0016] ;

[0017] In the formula, C represents the annualized comprehensive cost of the system; C inv Indicates the annualized cost of equipment investment; C op,s p represents the operating cost of a typical daily scenario s; s The weights represent typical daily scenarios s;

[0018] The objective function of the lower-level collaborative operation model aims to minimize the comprehensive operating cost on a typical day, and its expression is as follows:

[0019] ;

[0020] In the formula, The cost of purchasing electricity for the system; This includes equipment operation and maintenance costs, including depreciation and maintenance expenses of centralized photovoltaic equipment, centralized energy storage, and combined heat and power (CHP) equipment during operation; The carbon emission cost associated with purchasing electricity for the system; The costs of curtailing solar power and insufficient utilization of renewable energy are used to constrain the system's level of renewable energy consumption.

[0021] Furthermore, the key constraints of the lower-level collaborative operation model include centralized photovoltaic output constraints, photovoltaic power distribution balance constraints, centralized energy storage charging and discharging constraints, centralized energy storage zoning support constraints, well site microgrid node power balance constraints, power supply and heat source power balance constraints, power supply and heat source heat source power balance constraints, thermal energy storage operation constraints, and typical daily and thermal load constraints.

[0022] Furthermore, when using a hierarchical iterative method to solve the bi-level planning operation collaborative optimization model, in each iteration, the upper level first generates candidate capacity schemes. Then, the capacity parameters are passed to the lower-level collaborative operation model; the lower level solves for the optimal operating cost under each typical daily scenario. Next, calculate the fitness function value of the candidate capacity scheme; then, the upper layer updates the candidate capacity configuration scheme based on the fitness evaluation results and enters the next iteration;

[0023] When the maximum number of iterations is reached, the change in the optimal objective function is less than a set threshold for several consecutive generations, or the change in the optimal capacity scheme of adjacent generations is less than a set threshold, the iteration stops, and the final output is the capacity configuration scheme with the lowest annualized comprehensive cost of the system and its corresponding typical daily operation strategy.

[0024] Furthermore, the fitness function value is calculated as follows:

[0025] ;

[0026] In the formula, For capacity scheme The fitness function; This represents the annualized cost of equipment investment corresponding to this capacity plan; In capacity scheme Typical daily scene The optimal operating cost; Typical daytime scene The weights; This represents the number of typical daily scenarios.

[0027] The system for configuring and optimizing the capacity of microgrids for photovoltaic, thermal, and power storage in oil and gas fields includes:

[0028] The microgrid system architecture construction module for oil and gas plant areas is used to construct the microgrid system architecture for oil and gas plant areas. The microgrid system includes a centralized power supply center, multiple well site microgrid nodes, hotspot nodes, and an external power grid. The centralized power supply center includes a centralized photovoltaic power station and an energy storage device. The well site microgrid nodes are used to handle the electrical load demand of production equipment. The hotspot nodes are equipped with a combined heat and power (CHP) system, which includes an electric heating module and a thermal energy storage module to achieve coordinated heating through electric heating and thermal energy storage, converting electrical energy into heat energy and regulating the heat load in a timely manner. The external power grid provides supplementary power when the output of the centralized photovoltaic and centralized energy storage systems is insufficient.

[0029] A dual-layer planning and operation collaborative optimization model construction module is used to construct a dual-layer planning and operation collaborative optimization model, which includes an upper-layer capacity planning model and a lower-layer collaborative operation model. The upper-layer capacity planning model aims to minimize the annualized comprehensive cost of the system and determines the centralized photovoltaic installed capacity, the rated power and rated capacity of centralized electric energy storage, and the capacity of the electric heating module and the thermal energy storage module in the combined heat and power storage and regulation device. Based on the given upper-layer capacity configuration, the lower-layer collaborative operation model optimizes the centralized photovoltaic power allocation, centralized electric energy storage charging and discharging power, well site microgrid node feeder power purchase power, hot spot electric heating power, thermal energy storage charging and discharging power, and curtailed photovoltaic power for each typical daily scenario.

[0030] The constraint construction module is used to construct the key constraints of the lower-level collaborative operation model.

[0031] The module for solving the bi-level planning and operation collaborative optimization model is used to solve the bi-level planning and operation collaborative optimization model using a hierarchical iterative method to obtain the optimal operation strategy and operation cost under various typical daily scenarios.

[0032] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields as described above.

[0033] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields as described above.

[0034] The beneficial technical effects of this invention are as follows:

[0035] This invention achieves coordinated energy supply between microgrid nodes and power supply hotspot nodes in oil and gas plant areas by organically combining centralized photovoltaic, centralized electric energy storage, and combined heat and power (CHP) storage and regulation devices. By constructing a centralized energy supply center using centralized photovoltaic and centralized electric energy storage, the on-site absorption capacity of photovoltaic output within the oil and gas plant area is improved, enhancing the clean power supply level of multi-well-site microgrid nodes. Through zonal support constraints of centralized electric energy storage, each group of centralized electric energy storage discharges only to the well-site microgrid nodes within its corresponding service area, maintaining the advantages of centralized energy storage resource utilization while reducing the complexity of power allocation among multiple nodes and minimizing capacity redundancy caused by independent energy storage configurations. By incorporating electric heating and thermal energy storage at power supply hotspot nodes into a unified optimization model, the time-series transfer of heat load is realized, reducing the pressure of electric heating and system operating costs during periods of high electricity prices. Through two-layer planning and collaborative optimization, simultaneously considering investment costs, operating costs, carbon emission costs, and curtailment penalty costs, the capacity configuration scheme and operation scheduling strategy are matched, improving the overall economic efficiency and low-carbon operation level of the system. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas areas, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0037] This invention provides...

[0038] The diffusion-assisted state estimation method for multi-rate measurement in distribution networks provided by this invention will be further described clearly and completely below with reference to the accompanying drawings:

[0039] Example 1

[0040] This embodiment discloses a method for configuring and optimizing the photovoltaic, thermal, and electrical (PV) capacity of microgrid clusters in oil and gas fields. This method uses centralized photovoltaic power, centralized electric energy storage, well site microgrid nodes, hotspot nodes, and an external power grid to form a microgrid cluster system in the oil and gas plant area. A centralized power supply center coordinates power supply to multiple well site microgrid nodes, and the electric heating and thermal energy storage of the hotspot nodes are incorporated into a unified optimized scheduling, achieving coordinated configuration and efficient operation of electrical, thermal, and energy storage resources. This method employs a two-layer planning and operational optimization scheme. The upper layer determines the system equipment capacity configuration, while the lower layer optimizes the typical daily operation strategy to achieve the goals of minimizing the annualized comprehensive cost of the oil and gas plant area microgrid cluster, improving the local photovoltaic absorption capacity, and enhancing operational economy and low-carbon performance. The specific technical solution is as follows:

[0041] (1) Constructing the microgrid cluster system architecture of oil and gas plant area; the microgrid cluster system of oil and gas plant area includes a centralized energy supply center, multiple well site microgrid nodes, hot spot nodes and external power grid; the centralized energy supply center includes a centralized photovoltaic power station and an electric energy storage device; the well site microgrid nodes are used to undertake the electric load demand of production equipment such as pumping units, and their internal components include pumping unit load, existing photovoltaic equipment in the well site, existing electric energy storage equipment in the well site and DC bus, etc., and realize the local absorption and power smoothing of new energy power generation, existing electric energy storage charging and discharging power and pumping unit reverse power generation in the well site through the DC bus. Existing photovoltaic and energy storage devices within the well site microgrid nodes are not considered decision variables in the upper-level capacity planning of this invention; their impact on external power demand is reflected through the equivalent net power load of the well site. Hotspot nodes are equipped with combined heat and power (CHP) and energy storage systems, including electric heating modules and thermal energy storage modules, to achieve coordinated heating through electric heating and thermal energy storage. These systems convert electrical energy into heat energy and perform time-series regulation of the heat load. The external power grid provides supplementary power when the output of centralized photovoltaic and centralized energy storage systems is insufficient.

[0042] Centralized photovoltaic power plants prioritize supplying power to well site microgrid nodes and hotspot nodes, with surplus photovoltaic power used for charging of energy storage. The power sources for hotspot nodes include the output of centralized photovoltaic inverters and power purchased from the external power grid. In the hotspot co-storage and co-regulation equipment, the electric heating module converts electrical energy into heat energy, and the thermal energy storage module is used to realize the charging, releasing, and storage status management of heat.

[0043] Centralized energy storage devices are grouped and configured according to the operational characteristics of the microgrid nodes they serve. Each group of centralized energy storage corresponds to the service area formed by one or more microgrid nodes. The energy storage grouping is mainly determined based on the load fluctuation characteristics, photovoltaic consumption demand, external electricity purchase peak-valley differences, and production operation sequence differences of each microgrid node. Since there are differences in the operating conditions of pumping units, equivalent net load curves, and renewable energy consumption capabilities of different microgrid nodes, dividing centralized energy storage into several groups and supporting corresponding microgrid nodes respectively can more accurately match the operational needs of different microgrids during the planning stage, avoid capacity redundancy or insufficient local support caused by unified scheduling of a single centralized energy storage, and improve the economy of energy storage capacity configuration and the flexibility of operation scheduling.

[0044] (2) Construct a two-layer planning and operation collaborative optimization model, which includes an upper-layer capacity planning model and a lower-layer collaborative operation model. The upper-layer capacity planning model aims to minimize the annualized comprehensive cost of the system and determines the installed capacity of centralized photovoltaic power, the rated power and rated capacity of centralized electric energy storage, the capacity of electric heating module and the capacity of thermal energy storage module in the combined heat and power storage and regulation device. Based on the given upper-layer capacity configuration, the lower-layer collaborative operation model optimizes the centralized photovoltaic power allocation, centralized electric energy storage charging and discharging power, well site microgrid node feeder power purchase power, hot spot electric heating power, thermal energy storage charging and discharging power and curtailed photovoltaic power for each typical daily scenario.

[0045] It should be noted that the dual-layer planning operation collaborative optimization model uses typical daily scenarios to represent the annual operating conditions, and converts the typical daily operating cost into the annual operating cost based on the number of representative days or scenario weight of each typical day; multiple pumping units inside the well site realize the local consumption of regenerative power through DC bus, and the optimization model uses the equivalent net electrical load of the well site after regenerative power mutual assistance and smoothing; the centralized photovoltaic power station can distribute power to any well site microgrid node, power supply hotspot node and centralized electric energy storage, and the centralized electric energy storage can discharge to the corresponding well site; the power supply hotspot node realizes the coordinated operation of electric heating and thermal energy storage through the combined heat and power storage and regulation device to meet the needs of water hauling and heating load and heat preservation and maintenance load;

[0046] The objective function of the upper-level capacity planning model aims to minimize the annualized comprehensive cost of the system, and its expression is:

[0047] ;

[0048] In the formula, C represents the annualized comprehensive cost of the system; C inv Indicates the annualized cost of equipment investment; C op,s p represents the operating cost of a typical daily scenario s; s This represents the weight of a typical daily scenario s.

[0049] The annualized cost of equipment investment includes the annualized investment cost of centralized photovoltaic, centralized energy storage, and combined heat and power (CHP) systems, and its expression is as follows:

[0050] ;

[0051] In the formula, C BESS,inv C PV,inv C ETSRD,inv These are the annualized investment costs for energy storage equipment, photovoltaic equipment, and combined heat and power (CHP) systems, respectively; the specific cost breakdown for each type of equipment is as follows:

[0052] ;

[0053] In the formula, The rated charging and discharging power of the centralized energy storage in group g; This represents the rated capacity of the centralized energy storage system in group g. For centralized photovoltaic planning capacity; This refers to the rated power of the electric heating module. The rated charge / discharge power of the thermal energy storage module; The rated thermal storage capacity of the thermal energy storage module; These are the construction costs per unit power of electric energy storage, per unit energy storage capacity of electric energy storage, per unit power of electric heating modules, per unit charge / discharge heat power of thermal energy storage modules, per unit heat storage capacity of thermal energy storage modules, and per unit installed capacity of photovoltaic power. The annualized investment cost is calculated using the Capital Recovery Factor (CRF), and the specific calculation formula is as follows:

[0054] ;

[0055] In the formula, a is the discount rate; n is the equipment lifespan.

[0056] The objective function of the lower-level collaborative operation model aims to minimize the comprehensive operating cost on a typical day, and its expression is as follows:

[0057] ;

[0058] In the formula, The cost of purchasing electricity for the system; This includes equipment operation and maintenance costs, including depreciation and maintenance expenses of centralized photovoltaic equipment, centralized energy storage, and combined heat and power (CHP) equipment during operation; The carbon emission cost associated with purchasing electricity for the system; The costs of curtailment penalties for solar power and insufficient renewable energy utilization are used to constrain the system's renewable energy integration level. By incorporating carbon emission costs and curtailment penalty costs into the objective function, the system's operational economy, low-carbon characteristics, and renewable energy integration level can be simultaneously considered.

[0059] (3) Key constraints for constructing the lower-level collaborative operation model. To ensure that the lower-level collaborative operation results meet the physical operation laws, equipment scheduling boundaries, and electrothermal energy balance of the oil and gas plant microgrid system, based on the given upper-level capacity configuration scheme, and considering typical daily scenarios... and time period The constraints for centralized photovoltaic output, photovoltaic power distribution balance, centralized energy storage charging and discharging, centralized energy storage zoning support, well site microgrid node power balance, power supply and heat source power balance, power supply and heat source heat source power balance, thermal energy storage operation, and typical daily and thermal load constraints are constructed.

[0060] Centralized photovoltaic output constraints are used to limit the actual output power of photovoltaic systems to no more than the product of the installed capacity and the typical daily photovoltaic per-unit output factor. The expression for this constraint is:

[0061] ;

[0062] In the formula, Typical daytime scene Next period The actual output power of centralized photovoltaic power generation; This refers to the centralized photovoltaic installed capacity; Typical daytime scene Next period The photovoltaic per-unit output coefficient.

[0063] The photovoltaic power distribution balance constraint is used to describe the distribution relationship of centralized photovoltaic output power among various well site microgrid nodes, power supply nodes, centralized energy storage, and curtailed photovoltaic power. Its expression is:

[0064]

[0065] In the formula, This represents the actual output power of a centralized photovoltaic system during time period t under a typical daily scenario s. Centralized photovoltaic power distribution to well site microgrid nodes The electrical power; The electrical power distributed from centralized photovoltaic power generation to the power supply nodes; For the first The centralized energy storage system is powered by photovoltaic charging. This refers to the power of discarded light. This refers to the number of nodes in the well site microgrid. This refers to the number of centralized energy storage units.

[0066] The charging power of centralized energy storage consists of both photovoltaic charging power and external grid charging power, and its expression is:

[0067] ;

[0068] In the formula, For the first Total charging power of the centralized energy storage system; For the first The centralized energy storage system is powered by photovoltaic charging. For the first The power of a centralized energy storage system charged by the external power grid.

[0069] The dynamic equilibrium constraint of the state of charge of centralized energy storage is used to describe the energy transfer process across time periods, and its expression is as follows:

[0070] ;

[0071] The upper and lower limits of the state of charge of centralized energy storage are:

[0072] ;

[0073] In the formula, Let g be the discharge power of the centralized energy storage group in the gth group during time period t under a typical daily scenario s; For the first Group of centralized energy storage during time period The state of charge; For the first Rated capacity of centralized energy storage units; and These are charging efficiency and discharging efficiency, respectively. The unit scheduling time interval; and These are the upper and lower limits for the state of charge, respectively.

[0074] Centralized energy storage zoning support constraints are used to limit the allocation of discharge power from each group of centralized energy storage to the well site microgrid nodes within its corresponding service area. Let... Indicates the first Group of centralized energy storage during time period Assigned to well site microgrid nodes The discharge power distribution relationship is as follows:

[0075] ;

[0076] The power balance constraint of the well site microgrid nodes is used to ensure that the equivalent net power load of each well site microgrid node is satisfied by the centralized photovoltaic power distribution, the corresponding centralized energy storage discharge power, and the power purchased from the external power grid. Its expression is:

[0077] ;

[0078] In the formula, For well site microgrid nodes During the period The power purchased from the external power grid; Typical daytime scene Downhole site microgrid node During the period The equivalent net electrical load is formed by the combined effects of the production load of the pumping unit and other components inside the well site, the existing new energy power generation at the well site, the charging and discharging power of the existing electrical storage at the well site, and the local consumption of the reverse power generation of the pumping unit.

[0079] The power balance constraint for the power supply hotspot is used to ensure that the input power of the power supply hotspot electric heating module is met by both centralized photovoltaic power supply and external grid power purchase, and is limited by the rated capacity of the electric heating module. Its capacity constraint is as follows:

[0080] ;

[0081] The power balance relationship between the hot spot and the power supply is as follows:

[0082] ;

[0083] In the formula, Input power to the electric heating module; This refers to the rated power of the electric heating module. Power purchased from the external power grid for hotspot nodes; This refers to the efficiency of the photovoltaic inverter process.

[0084] The dynamic equilibrium constraints of thermal energy storage are:

[0085] ;

[0086] Thermal energy storage capacity constraints are:

[0087] In the formula, , These represent the charging and releasing power of thermal energy storage during time period t under a typical daily scenario s; For thermal energy storage during the period The thermal storage state; This refers to the rated capacity of thermal energy storage. and These are the thermal energy storage charging efficiency and the heat release efficiency, respectively.

[0088] The power balance constraint of the power supply point is used to ensure that the heat generated by the electric heating module, the heat storage charging and discharging power, and the heat load of the power supply point satisfy the energy conservation relationship. Its expression is:

[0089] ;

[0090] In the formula, For electric heating efficiency; Typical daytime scene Next period The heat load of the hot spot.

[0091] Typical daily load constraints are used to characterize the equivalent net electrical load of each well site microgrid node under a typical daily scenario. Typical daily scenario Next period The well site electrical load is obtained by averaging the measured net load for the corresponding time period of historical sample days within this scenario, and its expression is: In the formula, Typical daytime scene The set of sample days included; For the scene The corresponding number of sample days; For sample days Downhole site microgrid node During the period The measured net electrical load power.

[0092] The typical daily heat load of a heat source consists of the heat load from water heating and the heat load from insulation maintenance, and its expression is:

[0093] ;

[0094] In the formula, Typical daytime scene Next period The heat load of water heating; To maintain the heat load during the water storage process.

[0095] The heat load for water hauling and temperature rise is calculated based on the number of hauls, the volume of water hauled per haul, the density of the injected liquid, its specific heat capacity, and the required temperature rise. Its expression is:

[0096] ;

[0097] In the formula, Typical daytime scene Next period The number of times water was pulled; Density of the injected fluid; This refers to the volume of water pulled in a single operation. Specific heat capacity; The target water supply temperature; This refers to the initial temperature of the incoming water.

[0098] The insulation's ability to maintain heat load is affected by ambient temperature and the characteristics of heat storage and dissipation; its expression is as follows:

[0099] ;

[0100] In the formula, Typical daytime scene Next period The equivalent thermal insulation coefficient; For this typical daytime scenario Ambient temperature.

[0101] Through the above constraints, it is possible to simultaneously describe the centralized photovoltaic output boundary, photovoltaic power distribution relationship, centralized energy storage cross-time period charging and discharging regulation, well site microgrid node power balance, power supply and heat conversion, thermal energy storage time sequence regulation, and typical daily electricity and heat load formation mechanism, thus constituting the feasible region of the lower-level mixed integer linear programming model.

[0102] (4) A hierarchical iterative method is used to solve the two-layer planning and operation collaborative optimization model. To address the coupling relationship between the upper-layer capacity planning and the lower-layer collaborative operation, a hierarchical iterative solution method of "upper-layer heuristic search and lower-layer mathematical programming verification" is adopted. The upper layer uses the centralized photovoltaic installed capacity, the rated power and capacity of centralized electric energy storage, the rated power of electric heating modules, the rated charging and discharging power of thermal energy storage modules, and the rated thermal storage capacity of thermal energy storage modules as capacity decision variables to generate candidate capacity configuration schemes. The lower layer uses the candidate capacity configurations as known parameters to construct a mixed-integer linear programming model to solve for the optimal operation strategy and operating cost under each typical daily scenario.

[0103] The upper-level candidate capacity schemes can be represented as a capacity vector:

[0104] ;

[0105] For any upper-level candidate capacity scheme The lower-level model optimizes centralized photovoltaic power allocation, centralized energy storage charging and discharging power, node power purchase, electric heating power, thermal energy storage charging and discharging power, and curtailed photovoltaic power under various typical daily scenarios, and outputs the optimal operating cost for typical days. The typical daily operating costs at the lower level are converted into annual operating costs according to scenario weights, and together with the annualized cost of equipment investment, they constitute the fitness function of the candidate capacity scheme, the expression of which is:

[0106] ;

[0107] In the formula, For capacity scheme The fitness function; This represents the annualized cost of equipment investment corresponding to this capacity plan; In capacity scheme Typical daily scene The optimal operating cost; Typical daytime scene The weights; This represents the number of typical daily scenarios.

[0108] The upper layer employs the Langevin Equation Evolutionary Algorithm (LEE) to initialize the candidate population within the upper and lower limits of the capacity variable and evaluate the candidate capacity schemes based on the fitness function values. For candidate individuals exceeding the capacity planning boundary, adjustments are made according to the corresponding upper and lower capacity limits to ensure that the search process meets the engineering configuration boundary requirements. The lower layer uses each candidate capacity scheme as known input, combining typical daily electricity load, typical daily heat load, photovoltaic per-unit output, electricity price parameters, carbon emission cost parameters, and curtailment penalty parameters to construct an operation scheduling optimization model, and solves the optimal operation strategy for each typical daily scenario using mathematical programming methods.

[0109] In each iteration, the upper layer first generates candidate capacity schemes. Then, the capacity parameters are passed to the lower-level collaborative operation model; the lower level solves for the optimal operating cost under each typical daily scenario. Next, the fitness function value of the candidate capacity scheme is calculated; then, the upper layer updates the candidate capacity configuration scheme according to the fitness evaluation result and enters the next iteration. If the lower layer model has no feasible solution under a certain candidate capacity scheme, a penalty fitness value is assigned to the candidate scheme to prevent it from entering the subsequent optimization process.

[0110] The iteration stops when the maximum number of iterations is reached, the change in the optimal objective function is less than a set threshold for several consecutive generations, or the change in the optimal capacity scheme of adjacent generations is less than a set threshold. The final output is the capacity configuration scheme with the lowest annualized comprehensive cost and its corresponding typical daily operation strategy. Through the above-mentioned hierarchical iterative process of "capacity search—operation verification—cost feedback—scheme update," the capacity configuration and operation scheduling of centralized photovoltaic, centralized energy storage, and combined heat and power (CHP) devices can be coordinated and optimized, improving the economy, low carbon emissions, and local renewable energy consumption capacity of microgrid systems in oil and gas plant areas.

[0111] Example 2

[0112] This embodiment provides a system for configuring and optimizing the photovoltaic, thermal, and power capacity of microgrid clusters in oil and gas fields, including:

[0113] The microgrid system architecture construction module for oil and gas plant areas is used to construct the microgrid system architecture for oil and gas plant areas. The microgrid system includes a centralized power supply center, multiple well site microgrid nodes, hotspot nodes, and an external power grid. The centralized power supply center includes a centralized photovoltaic power station and an energy storage device. The well site microgrid nodes are used to handle the electrical load demand of production equipment. The hotspot nodes are equipped with a combined heat and power (CHP) system, which includes an electric heating module and a thermal energy storage module to achieve coordinated heating through electric heating and thermal energy storage, converting electrical energy into heat energy and regulating the heat load in a timely manner. The external power grid provides supplementary power when the output of the centralized photovoltaic and centralized energy storage systems is insufficient.

[0114] A dual-layer planning and operation collaborative optimization model construction module is used to construct a dual-layer planning and operation collaborative optimization model, which includes an upper-layer capacity planning model and a lower-layer collaborative operation model. The upper-layer capacity planning model aims to minimize the annualized comprehensive cost of the system and determines the centralized photovoltaic installed capacity, the rated power and rated capacity of centralized electric energy storage, and the capacity of the electric heating module and the thermal energy storage module in the combined heat and power storage and regulation device. Based on the given upper-layer capacity configuration, the lower-layer collaborative operation model optimizes the centralized photovoltaic power allocation, centralized electric energy storage charging and discharging power, well site microgrid node feeder power purchase power, hot spot electric heating power, thermal energy storage charging and discharging power, and curtailed photovoltaic power for each typical daily scenario.

[0115] The constraint construction module is used to construct the key constraints of the lower-level collaborative operation model.

[0116] The module for solving the bi-level planning and operation collaborative optimization model is used to solve the bi-level planning and operation collaborative optimization model using a hierarchical iterative method to obtain the optimal operation strategy and operation cost under various typical daily scenarios.

[0117] Furthermore, the present invention adopts the following technical solution:

[0118] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields according to the present invention.

[0119] Furthermore, the present invention adopts the following technical solution:

[0120] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields as described in this invention.

[0121] From the above description of the embodiments, those skilled in the art will clearly understand that the facilities of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Embodiments of the present invention can be implemented using existing processors, or by dedicated processors used for this or other purposes for suitable systems, or by hardwired systems. Embodiments of the present invention also include non-transitory computer-readable storage media, comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon; such machine-readable media can be any available medium accessible by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures and is accessible by a general-purpose or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hardwired, wireless, or a combination of hardwired and wireless), that connection is also considered a machine-readable medium.

[0122] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for configuring and optimizing the photovoltaic, thermal, and power capacity of microgrid clusters in oil and gas fields, characterized in that: Includes the following steps: A microgrid system architecture for oil and gas plant areas is constructed. This system includes a centralized energy supply center, multiple well site microgrid nodes, hotspot nodes, and an external power grid. The centralized energy supply center includes a centralized photovoltaic power station and an energy storage device. Well site microgrid nodes are used to handle the electrical load demands of production equipment. Hotspot nodes are equipped with combined heat and power (CHP) and energy storage systems, which include electric heating modules and thermal energy storage modules to achieve coordinated heating through electric heating and thermal energy storage. This system converts electrical energy into heat energy and performs time-series regulation of the heat load. The external power grid provides supplementary power when the output of the centralized photovoltaic and centralized energy storage systems is insufficient. A two-layer planning and operation collaborative optimization model is constructed, which includes an upper-layer capacity planning model and a lower-layer collaborative operation model. The upper-layer capacity planning model aims to minimize the annualized comprehensive cost of the system and determines the centralized photovoltaic installed capacity, the rated power and rated capacity of centralized electric energy storage, and the capacity of the electric heating module and the thermal energy storage module in the combined heat and power storage and regulation device. Based on the given upper-layer capacity configuration, the lower-layer collaborative operation model optimizes the centralized photovoltaic power allocation, centralized energy storage charging and discharging power, well site microgrid node feeder power purchase power, hot spot electric heating power, thermal energy storage charging and discharging power, and curtailed photovoltaic power hourly for each typical daily scenario. Key constraints for constructing the lower-level collaborative operation model; A hierarchical iterative method is used to solve the two-level planning operation collaborative optimization model to obtain the optimal operation strategy and operation cost under each typical daily scenario.

2. The method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields according to claim 1, characterized in that: The method for solving the bilevel programming operation collaborative optimization model is as follows: The upper layer uses the centralized photovoltaic installed capacity, the rated power and capacity of centralized electric energy storage, the rated power of electric heating modules, the rated charging and discharging power of thermal energy storage modules, and the rated thermal storage capacity of thermal energy storage modules as capacity decision variables to generate candidate capacity configuration schemes; the lower layer uses the candidate capacity configurations as known parameters to construct a mixed integer linear programming model to solve the optimal operation strategy and operation cost under each typical daily scenario.

3. The method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields according to claim 1, characterized in that: The dual-layer planning and operation collaborative optimization model uses typical daily scenarios to represent the annual operating conditions, and converts the typical daily operating cost into the annual operating cost based on the number of representative days or scenario weights of each typical day. Multiple pumping units inside the well site achieve local consumption of regenerative power through a DC bus. The optimization model uses the equivalent net electrical load of the well site after regenerative power mutual assistance and smoothing. The centralized photovoltaic power station prioritizes supplying power to the well site microgrid nodes and hotspot nodes, and surplus photovoltaic power is used for charging electric energy storage. The power sources for the hotspot nodes include the output of centralized photovoltaic inverters and power purchased from the external power grid. The electric heating module in the hotspot joint storage and regulation equipment converts electrical energy into thermal energy, and the thermal energy storage module is used to realize the charging, releasing, and storage status management.

4. The method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields according to claim 1, characterized in that: The objective function of the upper-level capacity planning model aims to minimize the annualized comprehensive cost of the system, and its expression is: ； In the formula, C represents the annualized comprehensive cost of the system; C inv Indicates the annualized cost of equipment investment; C op,s p represents the operating cost of a typical daily scenario s; s The weights represent typical daily scenarios s; The objective function of the lower-level collaborative operation model aims to minimize the comprehensive operating cost on a typical day, and its expression is as follows: ； In the formula, The cost of purchasing electricity for the system; This includes equipment operation and maintenance costs, including depreciation and maintenance expenses of centralized photovoltaic equipment, centralized energy storage, and combined heat and power (CHP) equipment during operation; The carbon emission cost associated with purchasing electricity for the system; The costs of curtailing solar power and insufficient utilization of renewable energy are used to constrain the system's level of renewable energy consumption.

5. The method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields according to claim 1, characterized in that: The key constraints of the lower-level collaborative operation model include centralized photovoltaic output constraints, photovoltaic power distribution balance constraints, centralized energy storage charging and discharging constraints, centralized energy storage zoning support constraints, well site microgrid node power balance constraints, power supply and heat source power balance constraints, power supply and heat source heat source power balance constraints, thermal energy storage operation constraints, and typical daily and thermal load constraints.

6. The method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields according to claim 1, characterized in that: When solving the bi-level programming operation collaborative optimization model using a hierarchical iterative method, in each iteration, the upper level first generates candidate capacity schemes. Then, the capacity parameters are passed to the lower-level collaborative operation model; the lower level solves for the optimal operating cost under each typical daily scenario. ; Then calculate the fitness function value of the candidate capacity schemes; The upper layer then updates the candidate capacity configuration scheme based on the fitness evaluation results and enters the next iteration; When the maximum number of iterations is reached, the change in the optimal objective function is less than a set threshold for several consecutive generations, or the change in the optimal capacity scheme of adjacent generations is less than a set threshold, the iteration stops, and the final output is the capacity configuration scheme with the lowest annualized comprehensive cost of the system and its corresponding typical daily operation strategy.

7. The method for configuring and optimizing the photovoltaic, thermal, and power capacity of microgrid clusters in oil and gas fields according to claim 6, characterized in that: The fitness function value is calculated as follows: ； In the formula, For capacity scheme The fitness function; This represents the annualized cost of equipment investment corresponding to this capacity plan; In capacity scheme Typical daily scene The optimal operating cost; Typical daytime scene The weights; This represents the number of typical daily scenarios.

8. A microgrid cluster photovoltaic-storage-thermal-power capacity configuration and operation optimization system for oil and gas fields, characterized in that: include: The microgrid cluster system architecture construction module for oil and gas plant areas is used to build the microgrid cluster system architecture for oil and gas plant areas. The oil and gas plant microgrid system includes a centralized power supply center, multiple well site microgrid nodes, hotspot nodes, and an external power grid. The centralized power supply center includes a centralized photovoltaic power station and an electric energy storage device. The well site microgrid nodes are used to meet the electrical load requirements of production equipment. The hotspot nodes are equipped with a combined heat and power (CHP) system, which includes an electric heating module and a thermal energy storage module to achieve coordinated heating by electric heating and thermal energy storage. This system converts electrical energy into heat energy and performs time-sequential regulation of the heat load. The external power grid is used to provide supplementary power when the output of the centralized photovoltaic and centralized electric energy storage systems is insufficient. A dual-layer planning and operation collaborative optimization model construction module is used to construct a dual-layer planning and operation collaborative optimization model, which includes an upper-layer capacity planning model and a lower-layer collaborative operation model. The upper-layer capacity planning model aims to minimize the annualized comprehensive cost of the system and determines the centralized photovoltaic installed capacity, the rated power and rated capacity of centralized electric energy storage, and the capacity of the electric heating module and the thermal energy storage module in the combined heat and power storage and regulation device. Based on the given upper-layer capacity configuration, the lower-layer collaborative operation model optimizes the centralized photovoltaic power allocation, centralized energy storage charging and discharging power, well site microgrid node feeder power purchase power, hot spot electric heating power, thermal energy storage charging and discharging power, and curtailed photovoltaic power hourly for each typical daily scenario. The constraint construction module is used to construct the key constraints of the lower-level collaborative operation model. The module for solving the bi-level planning and operation collaborative optimization model is used to solve the bi-level planning and operation collaborative optimization model using a hierarchical iterative method to obtain the optimal operation strategy and operation cost under various typical daily scenarios.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas fields as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for configuring and optimizing the photovoltaic, thermal, and electrical capacity of microgrid clusters in oil and gas areas as described in any one of claims 1 to 7.

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