Multi-village energy system coordinated dispatching method, system, and computer program product

Abstract

The present application relates to a collaborative scheduling method. In view of the fact that current research on rural energy systems is mostly focused on the coupling of internal energy sources, and the impact of seasonal loads on rural planning is not considered, a collaborative scheduling method, system, and computer program product for multi-village energy systems are provided. Energy interaction between multiple villages is achieved through pipeline interconnection and mobile energy storage. The energy consumption differences between different types of villages can be considered, and a two-layer configuration model combining the planning and operation of multi-village energy systems is constructed. The objective function of the upper model is to minimize the annualized total cost of the multi-village energy system, and the objective function of the lower model is to minimize the annual operating cost of the multi-village energy system. The upper model solves the problem of equipment capacity configuration within the multi-village energy system, and the lower model solves the problem of optimal operation of the multi-village energy system. The improved particle swarm algorithm and CPLEX solver are used to solve the upper model and the lower model respectively to obtain an optimized scheduling strategy.

Description

Technical Field

[0001] The present application relates to a collaborative scheduling method, and specifically to a collaborative scheduling method, system, and computer program product for multiple rural energy systems. Background Art

[0002] Due to the unique production and living characteristics of rural areas, the load is highly seasonal, resulting in high operating costs, low energy utilization, low reliability, and poor environmental protection of rural energy systems. Therefore, how to make full use of the abundant clean energy in rural areas, balance the impact of seasonal peak loads on rural energy systems, and meet rural production and living energy needs in a clean manner under the premise of green and high efficiency, and ultimately guide the low-carbon transformation of rural energy use and help rural energy conservation and emission reduction, has become an urgent problem that needs to be solved in the rural energy system. However, current research on rural energy systems is mostly focused on the coupling of internal energy, without considering the impact of seasonal loads on rural planning. Summary of the invention

[0003] In response to the technical problem that current research on rural energy systems mostly focuses on the coupling of internal energy sources and does not consider the impact of seasonal loads on rural planning, this application provides a method, system, and computer program product for coordinated scheduling of multiple rural energy systems.

[0004] In order to achieve the above objectives, this application adopts the following technical solutions:

[0005] In the first aspect, the present application proposes a method for coordinated dispatching of multi-village energy systems, wherein multiple villages are interconnected through pipelines and mobile energy storage to achieve energy interaction between villages; the method includes:

[0006] Obtain data on energy systems in multiple villages; where multiple villages can interact with each other through pipeline interconnection and mobile energy storage;

[0007] A two-layer configuration model combining planning and operation of multi-village energy systems is constructed; the two-layer configuration model includes an upper model and a lower model; the decision variable of the upper model is the number of equipment, and the objective function is to minimize the annualized total cost of the multi-village energy system; the decision variable of the lower model is the hourly output of the equipment, and the objective function is to minimize the annual operating cost of the multi-village energy system;

[0008] The upper model includes:

[0009]

[0010] in, represents the annualized total cost of the multi-village energy system, represents the annual investment cost, represents the annual maintenance cost, represents the annual operating cost, represents the annual rental cost of the mobile energy storage vehicle, express minimize;

[0011] The lower layer model comprises:

[0012]

[0013] in, represents the annual energy purchase cost of multiple rural energy systems from the upper power grid and gas grid, represents the annual transportation cost of mobile energy storage, represents the annual environmental cost of multiple rural energy systems, represents the annual penalty cost of wind and solar curtailment in multiple rural energy systems, represents the annual carbon trading cost of multiple rural energy systems, express minimize;

[0014] Taking the multi-village energy system data as input, an improved particle swarm algorithm is used to solve the upper model, and a CPLEX solver is used to solve the lower model according to the capacity scale transmitted by the upper model to obtain an optimized scheduling strategy; the improved particle swarm algorithm includes dynamically adjusting the inertia weight and learning factor parameters in the particle swarm algorithm;

[0015] The obtained optimal dispatching strategy is used to coordinate the dispatch of multiple rural energy systems.

[0016] Furthermore, the annual investment cost ,include:

[0017]

[0018] in, Indicates A village, represents the total number of villages, It represents the energy supply, conversion and storage equipment inside the village. Indicates The first village The construction factor of each device, Indicates The first village The unit construction cost of each device, Indicates The first village The number of devices installed, Indicates The first village The rated capacity of each device is represents the residual value rate of fixed assets, is the discount rate, Indicates The first village The life cycle of a device represents the construction coefficient of power lines between villages, represents the unit construction cost of power lines between villages, Indicates the construction length of power lines between villages. represents the life cycle of power lines between villages, Indicates the construction coefficient of heat pipes between villages, represents the unit construction cost of heat pipes between villages, Indicates the construction length of the heat pipes between villages. Represents the life cycle of heat pipes between villages;

[0019] Stated annual maintenance costs ,include:

[0020]

[0021] in, Indicates The first village Unit maintenance cost of each device, represents the unit maintenance cost of power lines between villages, represents the unit maintenance cost of heating pipes between villages;

[0022] The annual rental fee of the mobile energy storage vehicle ,include:

[0023]

[0024] in, Indicates the number of days the mobile battery storage vehicle is rented in a year. Indicates the rental price of the mobile battery storage vehicle, Indicates the number of days the mobile thermal storage vehicle is rented in a year, represents the rental price of the mobile thermal storage vehicle, represents the number of mobile battery storage vehicles, Indicates the number of mobile heat storage vehicles.

[0025] Furthermore, the constraints of the upper model include:

[0026]

[0027]

[0028] in, Indicates The first village The maximum number of devices that can be installed, Represents the collection of all devices, Represents photovoltaic power generation equipment, Represents a wind turbine. represents a gas turbine, Indicates gas boiler, Indicates electric boiler. Indicates power-to-gas equipment. represents waste heat recovery boiler, Represents a fixed power storage device. Represents heat storage equipment, Indicates gas storage equipment, Indicates mobile battery storage vehicle. Indicates mobile heat storage vehicle.

[0029] Furthermore, the annual energy purchase cost of the multi-village energy system from the upper power grid and gas grid is ,include:

[0030]

[0031] in, Indicates Typical day, represents the total number of categories on a typical day, Indicates The number of days similar to typical days, Indicates the number of time periods in a day, express The unit price of electricity purchased by the village from the upper power grid at any given moment. express Moment The village in The power purchased from the upper grid on a typical day, express The unit price of gas purchased by the village from the upper-level gas grid at any given moment. express Moment The village in Gas purchase power from the upper gas grid on a typical day;

[0032] The annual transportation cost of the mobile energy storage ,include:

[0033]

[0034] in, represents the charging and discharging cost of the mobile battery storage vehicle, represents the charging and discharging cost of the mobile thermal storage vehicle, represents the deployment cost of mobile electric storage vehicles during the dispatching period, represents the deployment cost of the mobile heat storage vehicle during the dispatch period;

[0035] Annual environmental costs of multiple rural energy systems ,include:

[0036]

[0037] in, Indicates the total type of pollutants, , Indicates Unit treatment cost of pollutants, Indicates the gas turbine Emission coefficient of pollutants, express Moment Gas turbines in villages Typical daily output power, Indicates the gas boiler Emission coefficient of pollutants, express Moment Gas boilers in villages Typical daily output power, Indicates the upper grid Emission coefficient of pollutants, express Moment The upper level power grid in the village is Output power of a typical day;

[0038] The annual penalty cost of wind and solar curtailment for multiple rural energy systems ,include:

[0039]

[0040] in, represents the cost coefficient of abandoned light, express Moment Photovoltaic power generation equipment in villages The forecast output for a typical day, express Moment Photovoltaic power generation equipment in villages The actual output of a typical day, represents the wind curtailment cost coefficient, express Moment Wind turbines in villages The forecast output for a typical day, express Moment Wind turbines in villages Actual output on a typical day;

[0041] Annual carbon trading costs for multiple rural energy systems ,include:

[0042]

[0043] in, represents the number of carbon-free emission sources, represents the total number of carbon-free emission sources, Indicates The price of carbon emission rights of carbon-free sources, Indicates Carbon-free sources can consume quantity, express Moment Carbon-free emission sources in Typical daily output power, represents the number of carbon emission sources, represents the total number of carbon emission sources, Indicates The purchase price of carbon emission rights of carbon emission sources, Indicates Carbon emission sources Emission intensity, Indicates The carbon emission rights allocation of each carbon emission source, express Moment Carbon emission sources are Typical daily output power.

[0044] Furthermore, the constraints of the lower model include power balance constraints, pipeline interconnection constraints, equipment constraints, mobile energy storage vehicle time and space path constraints, geographic resource constraints and construction constraints;

[0045] The power balance constraint includes:

[0046]

[0047] in, express Moment The village in Typical daily electrical load, express Moment The village in Typical day heat load, express Moment The village in Typical day gas load, express Moment The village in The power purchased from the upper grid on a typical day, express Moment The village in The gas power purchased from the upper grid on a typical day, express Moment The village in The output power of a typical solar photovoltaic power generation device in Japan, express Moment The village in Typical daily wind turbine output power, express Moment The village in The electrical power of a typical Japanese gas turbine, express Moment The village in Gas power of a typical Japanese and Chinese gas turbine, express Moment The village in The electrical power of a typical daily power-to-gas device, express Moment The village in Gas power of a typical daily power-to-gas device, express Moment The village in Thermal power of typical Japanese and Chinese gas boilers, express Moment The village in Gas power of typical Japanese and Chinese gas boilers, express Moment The village in The power of a typical Japanese and Chinese electric boiler, express Moment The village in Thermal power of typical Japanese and Chinese electric boilers, express Moment The village in Thermal power of a typical waste heat recovery boiler during a day, express Moment The village in Typical daily power of fixed storage equipment, express Moment The village in The power of a typical daytime heat storage device, express Moment The village in The power of a typical daily gas storage device, express Moment The village in The power of a typical daily mobile battery storage vehicle, express Moment The village in The power of a typical mobile thermal storage vehicle during a typical day, express Moment The village in Typical daytime electricity exchange with other villages through pipelines, express Moment The village in Thermal interaction with other villages through pipelines during typical days;

[0048] The pipeline interconnection constraints include:

[0049]

[0050] in, express Moment Village to The first The transmission power of energy, express Moment Village to The first The energy transfer coefficient, express Moment Village to The first The transmission power of energy, express Moment Village to The first The energy transfer coefficient, Indicates Village to The first The upper limit of the transmission power of the energy Indicates Village to The first The upper limit of the transmission power of the energy;

[0051] The equipment constraints include constraints on equipment operating power and equipment operating capacity;

[0052] The space-time path constraints of the mobile energy storage vehicle include:

[0053]

[0054] in, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Indicates that the end A collection of routes for villages, Indicates that from A collection of routes starting from villages, Indicates that mobile energy storage vehicle 1 is Village to The location marker variable of a village, Indicates that the mobile energy storage vehicle starts from the Village to The location marker variable of a village, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Indicates the number of time periods in a day, take ;

[0055] The geographical resource constraints include the construction number constraints of equipment in the multi-village energy system area, the construction length constraints of power lines and the construction length constraints of thermal pipelines;

[0056] The construction constraints include usage constraints of equipment, power lines and thermal pipelines within the multi-village energy system area within a preset year.

[0057] Furthermore, the dynamic adjustment of the inertia weight and learning factor parameters in the particle swarm algorithm includes:

[0058]

[0059] in, Indicates The particle in The inertia weight in the iteration, represents the initial value of inertia weight, represents the final value of the inertia weight, Indicates The particle in The population evolution rate in iterations, Indicates The particle in The individual experience learning factor in the iteration, represents the maximum value of the individual experience learning factor, Indicates The particle in The group information learning factor in the iteration, Represents the minimum value of the group information learning factor.

[0060] In the second aspect, the present application proposes a multi-village energy system coordinated dispatching system, comprising:

[0061] An acquisition module, used to acquire data of multiple rural energy systems;

[0062] A modeling module is used to construct a two-layer configuration model combining multi-village energy system planning and operation; the two-layer configuration model includes an upper model and a lower model; the decision variable of the upper model is the number of equipment, and the objective function is to minimize the annualized total cost of the multi-village energy system; the decision variable of the lower model is the hourly output of the equipment, and the objective function is to minimize the annual operating cost of the multi-village energy system;

[0063] The upper model includes:

[0064]

[0065] in, represents the annualized total cost of the multi-village energy system, represents the annual investment cost, represents the annual maintenance cost, represents the annual operating cost, represents the annual rental cost of the mobile energy storage vehicle, express minimize;

[0066] The lower layer model comprises:

[0067]

[0068] in, represents the annual energy purchase cost of multiple rural energy systems from the upper power grid and gas grid, represents the annual transportation cost of mobile energy storage, represents the annual environmental cost of multiple rural energy systems, represents the annual penalty cost of wind and solar curtailment in multiple rural energy systems, represents the annual carbon trading cost of multiple rural energy systems, express minimize;

[0069] A solution module, which is used to use the data of multiple rural energy systems as input, adopt an improved particle swarm algorithm to solve the upper model, and adopt a CPLEX solver to solve the lower model according to the capacity scale transmitted by the upper model to obtain an optimized scheduling strategy; the improved particle swarm algorithm includes dynamically adjusting the inertia weight and learning factor parameters in the particle swarm algorithm;

[0070] The dispatching module is used to coordinate the dispatching of multiple rural energy systems using the obtained optimized dispatching strategy.

[0071] In a third aspect, the present application proposes a computer program product, which includes instructions, and when the instructions are executed by a processor, the above-mentioned multi-village energy system coordinated scheduling method is implemented.

[0072] Compared with the prior art, this application has the following beneficial effects:

[0073] The present application proposes a method for coordinated dispatching of multi-village energy systems. The energy interaction between villages is realized through pipeline interconnection and mobile energy storage between multiple villages. The energy consumption differences between different types of villages can be considered, and a two-layer configuration model combining planning and operation of multi-village energy systems is constructed. The objective function of the upper model is to minimize the annualized total cost of the multi-village energy system, and the objective function of the lower model is to minimize the annual operation cost of the multi-village energy system. The upper model solves the problem of equipment capacity configuration in the multi-village energy system, and the lower model solves the problem of optimal operation of the multi-village energy system. The improved particle swarm algorithm and CPLEX solver are used to solve the upper model and the lower model respectively, and the optimized dispatching strategy for coordinated dispatching is obtained. The method for coordinated dispatching of multi-village energy systems in the present application has been verified, and the capacity of equipment in the multi-village energy system can be reasonably planned under the influence of seasonal loads, thereby realizing the economic operation and clean and low-carbon operation of the multi-village energy system. It can solve the problem of optimal configuration of equipment capacity in the multi-village energy system, alleviate the impact of seasonal peak loads on the system, and improve energy utilization, thereby realizing the clean, low-carbon, economical and reliable operation of the entire multi-village energy system.

[0074] The present application also proposes a multi-village energy system collaborative dispatching system and a computer program product, which have all the advantages of the above-mentioned multi-village energy system collaborative dispatching method. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0076] Figure 1 This is a schematic diagram of the first process of the method for coordinated dispatching of multiple rural energy systems in this application;

[0077] Figure 2 This is a schematic diagram of the structure of a multi-village energy system in an embodiment of the present application;

[0078] Figure 3 A schematic diagram of a multi-village energy system coordinated dispatching system for this application. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0080] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0081] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0082] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0083] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0084] In the description of the embodiments of the present application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0085] Although the current research on multi-village energy systems has made some progress, especially in the coupling and coordination of energy equipment, distributed energy utilization framework, construction of virtual energy storage models and comprehensive utilization of renewable energy, these studies have laid a solid foundation for improving the efficiency and reliability of multi-village energy systems. However, a significant problem is that most of these studies focus on the optimization and adjustment of multi-village energy systems, ignoring a very critical factor in rural energy demand: seasonal load changes.

[0086] The seasonal load characteristics of rural areas are very obvious. The busy farming season and heating season are the peak periods of energy demand. During these two periods, the load of each rural energy system will increase sharply, causing huge pressure on energy supply. If we only rely on internal energy coupling and optimization, it is difficult to effectively cope with the impact of such seasonal loads. Therefore, it is necessary to consider planning rural energy systems from a more macro and systematic perspective.

[0087] The collaborative optimization configuration method of multi-village energy systems for rural seasonal peak loads is proposed based on such considerations. This method not only focuses on the optimization within the rural energy system, but also pays more attention to the coordination and cooperation between different rural energy systems. Through reasonable planning and scheduling, we can make full use of the clean energy resources of each village, balance the impact of seasonal peak loads, and ensure the stable and efficient operation of the rural energy system.

[0088] Based on the above situation, the present application proposes a method, system, and computer program product for collaborative scheduling of multiple rural energy systems, which are used to determine the economic rationality of equipment capacity during rural energy system planning. The present application is described in detail below in conjunction with embodiments and drawings.

[0089] like Figure 1 As shown, it is a first flow chart of the method for coordinated dispatching of multi-village energy systems of the present application, in which energy interaction among multiple villages is achieved through pipeline interconnection and mobile energy storage.

[0090] Rural energy consumption is closely related to people's daily life and work. For example, during the busy farming season in summer, rural electricity load will surge, and in winter, due to heating demand, rural heat load will also increase significantly. Therefore, the annual load curve in rural areas has significant seasonal characteristics. At the same time, because the busy farming seasons in each village are slightly different, the seasonal characteristics between different types of villages will also vary from time to time. According to the characteristics of rural production and life, this application divides villages into planting and irrigation villages, agricultural product processing villages, and leisure and tourism villages, and on this basis, a specific analysis of the seasonal loads of the three types of villages is conducted.

[0091] The production work in irrigation-planting villages is mainly based on the sowing, growth, irrigation and harvesting of crops, and production activities are closely linked to seasonal climate. June to August in summer is the busy farming season for such villages, and irrigation equipment needs to operate continuously to meet the high water demand of crops. At this time, the electricity load reaches its peak. In winter, because most crops are dormant, the electricity load drops to the lowest point of the year, and there is almost no irrigation demand. Only greenhouse planting maintains basic electricity use. The heat load curve of this type of village also shows obvious seasonal changes. The heat load is moderate in spring and autumn, mainly used for greenhouse insulation. The heat load is the lowest in summer, because the heating demand is almost zero in hot weather, and there is only a demand for greenhouse cooling. In winter, the heat load reaches the highest point of the year. In cold weather, the demand for greenhouse insulation and heating for residents' lives increases significantly, and a large amount of heat energy is required to maintain a suitable temperature for crops and living environment.

[0092] Agricultural product processing villages refer to villages dominated by activities such as processing, storage and transportation of agricultural products. The demand for electricity and heat energy in such villages is relatively stable, but it varies in different seasons. The period from July to September is the busy farming season for such villages. The concentrated harvesting and processing of a large number of agricultural products makes the demand for electricity reach the highest level of the year; the electricity load decreases from the end of autumn to winter (October to January), but it still remains at a high level because the operation of processing activities and storage equipment requires a stable supply of electricity. Due to the high temperature in summer, the demand for heat energy in such villages is reduced, while the ambient temperature is low in winter. In order to maintain the normal operation of processing workshops and storage equipment, a large amount of heat energy is required. At the same time, the heating demand for residents' lives also increases significantly, so the heat load also shows the characteristics of low in summer and high in winter.

[0093] Leisure and tourism villages are villages that use rural tourism resources as carriers and a variety of tourism activities rich in rural characteristics as their main content. From mid-June to mid-August, the number of tourists during the holidays in leisure and tourism villages increases significantly, and the demand for electricity for air conditioning, lighting, catering services, etc. increases significantly, which is the peak period of rural electricity consumption. Then, with climate change and the end of the holidays, the rural electricity load gradually decreases until winter, when the electricity load drops to the lowest level of the year. The heat load changes in leisure and tourism villages are similar to those of the first two types of villages, with the lowest heat load in summer and the peak in winter.

[0094] In summary, the overall annual electricity load of the three types of villages is characterized by peak in summer and trough in winter, and the heat load is low in summer and high in winter. However, due to the different production and living habits of the three types of villages, the time periods of peak electricity consumption are slightly different. June to August is the busy farming season for planting and irrigation villages, July to September is the busy farming season for agricultural product processing villages, and mid-June to mid-August is the busy season for leisure and entertainment villages. Therefore, based on the above, this application proposes a multi-village energy system collaborative scheduling strategy based on the seasonal differences of villages, which improves the economy and equipment utilization of each village by realizing energy interaction between villages.

[0095] At present, considering each village as a separate micro-energy system according to the load demand of different types of villages, in order to reduce rural carbon emissions and improve environmental quality, a high proportion of renewable energy is used, such as wind power (WT) and photovoltaic (PV) energy supply, and at the same time, electric boiler (EB), electricity to gas (P2G), gas turbine (GT), gas boiler (GB), waste heat recovery boiler (WHRB), stationary electric energy storage (SEES), stationary heat energy storage (SHES), stationary gas energy storage (SGES) and other energy supply, conversion and storage equipment are configured. In addition, considering the characteristics of rural electricity load surging during the busy farming season in summer and heat load surging during the heating season in winter, mobile electrical energy storage (MEES) equipment and mobile heat energy storage (MHES) equipment can be configured to avoid the problem of low utilization of fixed energy storage due to changes in load characteristics. In addition, the configuration of mobile energy storage equipment can effectively promote energy interaction between villages and improve the utilization efficiency of equipment within each village. In practical applications, mobile electricity storage vehicles and mobile heat storage vehicles can be used.

[0096] As a new type of energy storage equipment, mobile battery storage vehicles have the characteristics of flexibility, intelligent peak-shaving, green environmental protection, and uninterrupted power supply. They can be used in scenarios such as non-stop power supply operations, power supply for important occasions, and emergency rescue of electric vehicles. Mobile battery storage vehicles are mainly composed of energy storage systems, vehicle systems, and auxiliary control systems. They use standard truck boxes as carriers and motor vehicles as transportation tools. The energy storage system of mobile battery storage vehicles is mostly electrochemical batteries. The charging and discharging model of a mobile battery storage vehicle is as follows:

[0097]

[0098] in, Indicates the leakage proportional coefficient of the electrochemical battery per hour, , They represent the charging power and discharging efficiency of electrochemical batteries, respectively. represents the time interval between two adjacent scheduling times, is the total scheduling period, for Mobile battery storage vehicle The electrical energy stored in the electrochemical battery, , Respectively Mobile battery storage vehicle The charging power and discharging power of electrochemical batteries, , They represent the maximum value of the charging power and the maximum value of the discharging power of the electrochemical battery, respectively. , are 0-1 variables representing the charge state and discharge state of the electrochemical battery, respectively. Mobile battery storage vehicle Installed capacity of electrochemical batteries.

[0099] Mobile heat storage vehicles break the traditional pipeline transportation model and are a new type of waste heat utilization and heating model. They are mainly composed of heat storage components, control components, heat release / heat storage pipelines, and vehicles. With high-performance heat storage materials and heat storage components as the core, they can recover and store waste heat and waste heat from high-energy-consuming units in thermal power plants, and transport them to heat users by car to provide domestic hot water and heating. The mathematical model of a mobile thermal storage vehicle is as follows:

[0100]

[0101] in, represents the self-loss coefficient of the heat storage device, , They represent the charging efficiency and releasing efficiency of thermal energy storage, express Mobile heat storage vehicle Energy state level of medium thermal energy storage, , Respectively Mobile heat storage vehicle The charging and discharging power of medium heat energy storage, , They represent the maximum value of the thermal energy storage charging power and the maximum value of the thermal energy storage releasing power, , are 0-1 variables representing the charging state of thermal energy storage and the releasing state of thermal energy storage, respectively. Mobile heat storage vehicle Installed capacity of medium thermal energy storage.

[0102] Based on the analysis of the rural load demand characteristics and the determination of the single village structure, in order to promote energy interaction between villages, solve the problems caused by seasonal peak loads to the operation of multi-village energy systems, and improve the economy of the entire multi-village energy system, this application proposes a multi-village energy system interconnection model. Through the energy interaction of electric heating pipeline interconnection and mobile energy storage, each village in the multi-village energy system area is planned as a whole, thereby improving the utilization rate of energy conversion and storage equipment and reducing the wind and solar power abandonment in the multi-village energy system.

[0103] Therefore, rural For example, the energy supply of the village mainly comes from three aspects: internal supply of the village, supply from other villages and mobile energy storage supply. The energy interaction balance matrix between a village and other villages is:

[0104]

[0105] in, , , Respectively represent The electricity, heating and gas load demand of each village, , , Respectively represent The amount of electricity, heat and gas supplied by the internal facilities of a village. , Respectively indicate that other villages can give The amount of electricity and heat provided to each village , They represent the mobile energy storage that can be used for The amount of electricity and heat provided to villages.

[0106] like Figure 2 Shown is a schematic diagram of a multi-village energy system structure, which includes three villages.

[0107] The coordinated dispatching methods of multiple rural energy systems may include:

[0108] S101, obtaining energy system data of multiple villages.

[0109] The multi-village energy system data can cover the energy consumption patterns of each village, the distribution and potential of renewable energy resources, the capacity and operating conditions of existing energy facilities, etc., which can be selected according to the subsequent two-layer configuration model. In practical applications, the acquired multi-village energy system data may have various problems, and the data can also be pre-processed, such as data cleaning, data conversion and data integration.

[0110] S102, constructing a two-layer configuration model that combines the planning and operation of multi-village energy systems; the two-layer configuration model includes an upper model and a lower model; the decision variable of the upper model is the number of equipment, and the objective function is to minimize the annualized total cost of the multi-village energy system; the decision variable of the lower model is the hourly output of the equipment, and the objective function is to minimize the annual operating cost of the multi-village energy system.

[0111] The upper model includes:

[0112]

[0113] in, represents the annualized total cost of the multi-village energy system, represents the annual investment cost, represents the annual maintenance cost, represents the annual operating cost, represents the annual rental cost of the mobile energy storage vehicle, express minimize;

[0114] The lower layer model comprises:

[0115]

[0116] in, represents the annual energy purchase cost of multiple rural energy systems from the upper power grid and gas grid, represents the annual transportation cost of mobile energy storage, represents the annual environmental cost of multiple rural energy systems, represents the annual penalty cost of wind and solar curtailment in multiple rural energy systems, represents the annual carbon trading cost of multiple rural energy systems, express minimize.

[0117] S103, using the multi-village energy system data as input, using an improved particle swarm algorithm to solve the upper model, and using a CPLEX solver to solve the lower model according to the capacity scale transmitted by the upper model to obtain an optimized scheduling strategy; the improved particle swarm algorithm includes dynamically adjusting the inertia weight and learning factor parameters in the particle swarm algorithm.

[0118] It should be noted that the particle swarm algorithm is an optimization algorithm based on swarm intelligence, which finds the optimal solution by simulating the behavior of biological groups such as bird flocks or fish schools. However, the traditional particle swarm algorithm has problems such as being prone to local optimal solutions and slow convergence. Therefore, this application adopts an improved particle swarm algorithm to dynamically adjust the inertia weight and learning factor parameters in the particle swarm algorithm to improve the performance and solution efficiency of the algorithm. The CPLEX solver is an efficient mathematical optimization solver suitable for solving various linear programming, integer programming, quadratic programming and other problems. When solving the lower-level model, the CPLEX solver can optimize the hourly output of the equipment according to the capacity scale (i.e., the number of equipment) transmitted by the upper-level model to minimize the annual operating cost.

[0119] S104, using the obtained optimal dispatching strategy to coordinate the dispatching of multiple rural energy systems.

[0120] After solving the problem, an optimized scheduling strategy will be obtained, including the optimal configuration and optimal operation mode of each rural energy equipment. The optimized scheduling strategy will be used to coordinate the scheduling of multiple rural energy systems to achieve efficient energy utilization and minimize costs.

[0121] As an embodiment of the coordinated dispatch method of multiple rural energy systems, the two-layer configuration model can be adopted in the following manner:

[0122] In order to fully consider the actual operation strategy in the capacity configuration stage of multi-village energy systems and closely couple capacity planning with operation, this application establishes a two-layer configuration model that combines multi-village energy system planning and operation, including an upper model and a lower model. The upper model is used to solve the problem of collaborative optimization configuration of multi-village energy systems. The decision variable is the number of each device, and the objective function is the annualized total cost of the multi-village energy system. The lower model solves the problem of optimal output of equipment in multi-village energy systems. The decision variable is the hourly output of the equipment, and the objective function is the annual operating cost of the system. Specifically:

[0123] (1) Upper model

[0124] The upper model aims to minimize the annualized total cost of multi-village energy systems, which mainly includes investment cost, maintenance cost, mobile energy storage rental fee and annual operating cost. The calculation formula is as follows:

[0125] .

[0126] The cost of each part is calculated as follows:

[0127] a. Annual investment cost of multiple rural energy systems :

[0128] The annual investment cost of the multi-village energy system mainly includes the investment cost of energy supply, conversion, and storage equipment in each village energy system, as well as the investment cost of pipelines for electricity and heat interconnection between villages. The calculation method is as follows:

[0129]

[0130] in, Indicates A village, represents the total number of villages, It represents the energy supply, conversion and storage equipment inside the village. Indicates The first village The construction factor of each device, Indicates The first village The unit construction cost of each device, Indicates The first village The number of devices installed, Indicates The first village The rated capacity of each device is represents the residual value rate of fixed assets, is the discount rate, Indicates The first village The life cycle of a device represents the construction coefficient of power lines between villages, represents the unit construction cost of power lines between villages, Indicates the construction length of power lines between villages. represents the life cycle of power lines between villages, Indicates the construction coefficient of heat pipes between villages, represents the unit construction cost of heat pipes between villages, Indicates the construction length of the heat pipes between villages. Represents the life cycle of heating pipes between villages.

[0131] b. Annual maintenance costs of multiple rural energy systems

[0132] The annual maintenance cost of the multi-village energy system mainly includes the maintenance cost of the equipment in each village and the maintenance cost of the pipelines between villages. The specific calculation is as follows:

[0133]

[0134] in, Indicates The first village Unit maintenance cost of each device, represents the unit maintenance cost of power lines between villages, Represents the unit maintenance cost of heating pipes between villages.

[0135] c. Annual rental fee for mobile energy storage vehicle

[0136] Since mobile energy storage vehicles are expensive to purchase and are only put into use for a short period of time throughout the year, this application uses leasing as an example to utilize the flexible time and space characteristics of mobile energy storage vehicles to alleviate the power shortage during the busy farming season in rural areas. The annual leasing cost of a mobile energy storage vehicle is calculated as follows:

[0137]

[0138] in, Indicates the number of days the mobile battery storage vehicle is rented in a year. Indicates the rental price of the mobile battery storage vehicle, Indicates the number of days the mobile thermal storage vehicle is rented in a year, represents the rental price of the mobile thermal storage vehicle, represents the number of mobile battery storage vehicles, Indicates the number of mobile heat storage vehicles.

[0139] The constraints of the upper model are:

[0140] The upper-level planning of the multi-village energy system selects the number of devices to be configured as the optimization variable. In order to prevent energy waste caused by over-configuration, and the equipment installation is also limited by the size of the actual installation site, the following constraints are imposed on the equipment:

[0141]

[0142] in, Indicates The first village The maximum number of devices that can be installed, Represents the collection of all devices, Represents photovoltaic power generation equipment, Represents a wind turbine. represents a gas turbine, Indicates gas boiler, Indicates electric boiler. Indicates power-to-gas equipment. represents waste heat recovery boiler, Represents a fixed power storage device. Represents heat storage equipment, Indicates gas storage equipment, Indicates mobile battery storage vehicle. Indicates mobile heat storage vehicle.

[0143] (2) Lower layer model:

[0144] The objective function of the lower model is to minimize the annual operating cost of the multi-village energy system. The annual operating cost mainly includes the energy purchase cost of the multi-village energy system, the mobile energy storage operating cost, the environmental cost, the penalty cost for wind and solar power abandonment, and the carbon trading cost. The specific calculation is as follows:

[0145] .

[0146] The cost of each part is calculated as follows:

[0147] a. Annual energy purchase cost of multiple rural energy systems

[0148] The annual energy purchase cost of the multi-village energy system refers to the cost of purchasing electricity and gas from the superior power grid and gas grid for each village throughout the year. The calculation formula is as follows:

[0149]

[0150] in, Indicates Typical day, represents the total number of categories on a typical day, Indicates The number of days similar to typical days, Indicates the number of time periods in a day, express The unit price of electricity purchased by the village from the upper power grid at any given moment. express Moment The village in The power purchased from the upper grid on a typical day, express The unit price of gas purchased by the village from the upper-level gas grid at any given moment. express Moment The village in The gas purchasing power from the upper-level gas grid on a typical day.

[0151] b. Annual transportation cost of mobile energy storage vehicle

[0152] During the dispatch cycle, the transportation cost of mobile energy storage vehicles is mainly related to vehicle size, dispatch route, and charging and discharging plan. The specific cost calculation is as follows:

[0153]

[0154] in, represents the charging and discharging cost of the mobile battery storage vehicle, represents the charging and discharging cost of the mobile thermal storage vehicle, represents the deployment cost of mobile electric storage vehicles during the dispatching period, represents the deployment cost of the mobile heat storage vehicle during the dispatching period, represents the deployment cost within the scheduling cycle, , Mobile electric storage vehicle exist The charging price and discharging price at the moment, , Respectively represent the mobile heat storage vehicle in The charging and releasing prices at the moment, It represents the traffic routes connecting villages in the road network. , , They are respectively expressed as the energy consumption cost per mileage of the mobile electric storage vehicle and the mobile thermal storage vehicle, , Respectively Check whether the mobile electric storage vehicle and mobile heat storage vehicle are on the line at all times The 0-1 variable that runs on , When respectively expressed Mobile electric storage vehicles and mobile heat storage vehicles are always on the line On the road, For Line distance.

[0155] c. Annual environmental costs of multiple rural energy systems

[0156] The annual environmental cost mainly refers to the cost of treating pollutants emitted by gas units as energy supply equipment during operation, as well as the cost of treating pollutants converted from purchasing electricity from the superior power grid. The calculation formula is as follows:

[0157]

[0158] in, Indicates the total type of pollutants, , Indicates Unit treatment cost of pollutants, Indicates the gas turbine Emission coefficient of pollutants, express Moment Gas turbines in villages Typical daily output power, Indicates the gas boiler Emission coefficient of pollutants, express Moment Gas boilers in villages Typical daily output power, Indicates the upper grid Emission coefficient of pollutants, express Moment The upper level power grid in the village is Typical daily output power.

[0159] d. Annual penalty costs for wind and solar curtailment in multiple rural energy systems

[0160]

[0161] in, represents the cost coefficient of abandoned light, express Moment Photovoltaic power generation equipment in villages The forecast output for a typical day, express Moment Photovoltaic power generation equipment in villages The actual output of a typical day, represents the wind curtailment cost coefficient, express Moment Wind turbines in villages The forecast output for a typical day, express Moment Wind turbines in villages The actual output on a typical day.

[0162] e. Annual carbon trading costs for multiple rural energy systems

[0163] Emission sources that have not used up their carbon emission rights can sell their excess carbon quotas in the carbon trading market, while those that do not have enough carbon quotas need to purchase them in the carbon trading market. In the process of planning multiple rural energy systems, rural energy systems in the entire region obtain corresponding benefits by selling carbon emission rights, and the benefits of carbon emission rights are attributed to the entire rural energy system, which reduces energy supply costs and reduces energy costs in rural areas within the region.

[0164] This application defines the annual carbon emission benefit model of multi-village energy systems as:

[0165]

[0166] in, represents the number of carbon-free emission sources, represents the total number of carbon-free emission sources, Indicates The price of carbon emission rights of carbon-free sources, Indicates Carbon-free sources can consume quantity, express Moment Carbon-free emission sources in Typical daily output power, represents the number of carbon emission sources, represents the total number of carbon emission sources, Indicates The purchase price of carbon emission rights of carbon emission sources, Indicates Carbon emission sources Emission intensity, Indicates The carbon emission rights allocation of each carbon emission source, express Moment Carbon emission sources are The output power of a typical day. In the multi-village energy system, the carbon-free emission sources are mainly P2G devices in various villages, and the carbon emission sources are gas units in the villages.

[0167] The constraints of the lower model are:

[0168] a. Power balance constraints

[0169] In a multi-village energy system, it is necessary to balance the supply and demand of various energy sources in each village. Taking a village as an example, the power balance constraint is:

[0170]

[0171] in, express Moment The village in Typical daily electrical load, express Moment The village in Typical day heat load, express Moment The village in Typical day gas load, express Moment The village in The power purchased from the upper grid on a typical day, express Moment The village in The gas power purchased from the upper grid on a typical day, express Moment The village in The output power of a typical solar photovoltaic power generation device in Japan, express Moment The village in Typical daily wind turbine output power, express Moment The village in The electrical power of a typical Japanese gas turbine, express Moment The village in Gas power of a typical Japanese and Chinese gas turbine, express Moment The village in The electrical power of a typical daily power-to-gas device, express Moment The village in Gas power of a typical daily power-to-gas device, express Moment The village in Thermal power of typical Japanese and Chinese gas boilers, express Moment The village in Gas power of typical Japanese and Chinese gas boilers, express Moment The village in The power of a typical Japanese and Chinese electric boiler, express Moment The village in Thermal power of typical Japanese and Chinese electric boilers, express Moment The village in Thermal power of a typical waste heat recovery boiler during a day, express Moment The village in Typical daily power of fixed storage equipment, express Moment The village in The power of a typical daytime heat storage device, express Moment The village in The power of a typical daily gas storage device, express Moment The village in The power of a typical daily mobile battery storage vehicle, express Moment The village in The power of a typical mobile thermal storage vehicle during a typical day, express Moment The village in Typical daytime electricity exchange with other villages through pipelines, express Moment The village in The heat interaction with other villages through pipelines on a typical day. A positive value of the heat interaction indicates that other villages are Village input, negative value means The export from one village to other villages.

[0172] b. Pipeline interconnection constraints

[0173] When electricity and heat are exchanged between villages through interconnected pipelines, the transmission capacity constraints of the lines need to be met, which are:

[0174]

[0175] in, , Represent the upper limits of transmission power of electrical pipelines and thermal pipelines respectively.

[0176] On the basis of ensuring the transmission power constraints of pipelines, for the entire interconnected system, villages cannot transmit the same energy to each other at the same time. The interaction of the same energy is unidirectional. The interconnection between villages also needs to meet the following constraints:

[0177]

[0178] in, express Moment Village to The first The transmission power of energy, express Moment Village to The first The energy transfer coefficient, express Moment Village to The first The transmission power of energy, express Moment Village to The first The energy transfer coefficient, Indicates Village to The first The upper limit of the transmission power of the energy Indicates Village to The first The transmission power limit of this energy.

[0179] c. Equipment constraints

[0180] The power and capacity constraints of energy supply, conversion and storage equipment and mobile energy storage in multi-village energy systems are:

[0181]

[0182] in, , Respectively The operating power and capacity of the equipment at all times, , , , They respectively represent the lower limit of the equipment operating power, the upper limit of the equipment operating power, the lower limit of the equipment operating capacity, and the upper limit of the equipment operating capacity.

[0183] d. Spatiotemporal path constraints of mobile energy storage vehicles

[0184] definition , assuming is a collection of routes, For the rural The set of departure routes, Ends in the countryside The spatial and temporal constraints of the mobile energy storage vehicle are:

[0185]

[0186] in, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Indicates that the end A collection of routes for villages, Indicates that from A collection of routes starting from villages, Indicates that mobile energy storage vehicle 1 is Village to The location marker variable of a village, Indicates that the mobile energy storage vehicle starts from the Village to The location marker variable of a village, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Indicates the number of time periods in a day, take .

[0187] Formula (28) represents the continuity of the trip, that is, the energy storage vehicle can only drive into the adjacent node at the next moment.

[0188] e. Geographic resource constraints

[0189] For the coordinated dispatching process of multiple rural energy systems in the region, considering the geographical location, area, resources and other aspects of the entire region, whether it is the installation of pipelines or the configuration of various energy supply, conversion and storage equipment, there will be certain restrictions, namely:

[0190]

[0191] in, , , Respectively represent the devices in the area The number of constructions, as well as the length of power lines and thermal pipes, , , , , , Equipment in each area The minimum number of construction equipment The upper limit of the number of construction, the lower limit of the construction length of power lines, the upper limit of the construction length of power lines, the lower limit of the construction length of thermal power pipelines, and the upper limit of the construction length of thermal power pipelines.

[0192] f. Construction constraints

[0193] Considering the economic feasibility of various equipment and pipeline construction in the coordinated dispatch of multi-village energy systems, it is necessary to After the construction is completed, it is ensured that its operation or use status will remain unchanged in the subsequent period:

[0194]

[0195] in, , , Respectively indicate the device in The usage status of the power lines in The usage status of the thermal pipeline in the year Years of use.

[0196] The following methods can be used to solve the model:

[0197] The two-level optimization configuration model of multi-village energy systems established in this application is a MILP problem. Due to the large number of decision variables, a method combining a particle swarm algorithm with efficient search capabilities and suitable for handling multiple constraints with a CPLEX solver is used to solve it.

[0198] 1) Solution algorithm

[0199] Although the traditional particle swarm algorithm has strong search capabilities, it is easy to fall into the local optimum in the late iteration. Therefore, this application dynamically adjusts the original inertia weight and learning factor parameters in the algorithm to avoid premature maturity or falling into the local optimum in the late iteration. The specific improvement strategies are as follows:

[0200] First, the evolution function is constructed based on the fitness values ​​of particles and populations, so that the optimization range of the objective function solution moves towards the global optimal direction. The evolution rule is:

[0201]

[0202] in, , , are particle evolution degree, population evolution degree and evolution rate respectively, For the The iteration The fitness of a particle, , Respectively represent The worst fitness value and the best fitness value among all particles at the iteration, Indicates The fitness value of the best particle in the population history at the iteration.

[0203] Secondly, by integrating the evolutionary strategy into the above two parameters, the values ​​can be dynamically adjusted during each iteration, so that the particle swarm can search quickly in the global range in the early stage of the iteration, and accurately search to determine the global optimal solution in the later stage. The adjustment formula is:

[0204]

[0205] in, represents the initial value of inertia weight, represents the final value of the inertia weight, represents the maximum value of the individual experience learning factor, represents the minimum value of the group information learning factor, Indicates The particle in The inertia weight in the iteration, Indicates The particle in The population evolution rate in iterations, Indicates The particle in The individual experience learning factor in the iteration, Indicates The particle in The group information learning factor in the iteration.

[0206] The improved particle speed and position information are:

[0207]

[0208] in, , , , Represents particles In the The speed and particles in the iteration In the The speed and particles in the iteration In the The position and particle size of the iteration In the The position in the iteration, , Both are between A random number between Represents particles The best position searched so far is called the individual extreme value. It represents the optimal position searched by the entire particle swarm so far, which is the global extreme value.

[0209] 2) Solution process

[0210] This application adopts a two-layer configuration model. The upper model uses an improved particle swarm algorithm to configure capacity, and the lower model uses MATLAB's CPLEX solver based on the YALMIP platform to optimize operation according to the capacity scale passed down by the upper model.

[0211] The specific solution steps are:

[0212] Step 1: Input the basic data of multiple rural energy systems, which may include load data, wind and solar data, equipment capacity parameters, etc., and set parameters such as the algorithm population size and maximum number of iterations.

[0213] Step 2: Initialize the population, including the population size and the position and velocity of each particle.

[0214] Step 3: Bring the particle information and equipment capacity configuration information into the scheduling layer, set the optimization variables for each typical day’s equipment output, take the lowest annual operating cost as the objective function, and call the CPLEX solver for solution.

[0215] Step 4: The optimization results of the lower model are returned to the upper model to calculate the investment cost and operating cost of the multi-village energy system.

[0216] Step 5: Update the optimal value of individual particles and the optimal value of the group.

[0217] Step 6: Determine whether the maximum number of iterations has been reached. If so, stop searching and directly output the capacity configuration results and run the optimization strategy. If not, proceed to step 7.

[0218] Step 7: Enter the next iteration, update the inertia weight and learning factor parameters, update the new position and velocity information of the particle, and return to step 3.

[0219] The multi-village energy system interconnection model in this application refers to the energy interaction between different types of villages through pipeline interconnection and mobile energy storage. Among them, pipeline interconnection is an energy network that connects villages through power lines and thermal pipelines, which can transmit the surplus energy of a village to villages with energy shortages in the same time period; mobile energy storage is an emerging energy storage technology that breaks the traditional pipeline transportation model and combines energy storage systems with vehicle transportation. It has flexible time and space characteristics and can complete energy interaction between villages across regions and time through transportation networks.

[0220] In order to verify the effectiveness and applicability of the multi-village energy system coordinated dispatching method of this application, this application selects three types of actual villages, brings the basic data and equipment parameters of the three types of actual villages into the two-layer configuration model, and completes the model solution by calling the CPLEX solver on the MATLAB software to obtain the optimal capacity configuration result and the daily operating output of each device. In addition, through the comparison of multiple optimization schemes, pipeline interconnection devices and mobile energy storage devices are added in turn to prove the economy and feasibility of this application.

[0221] (1) Basic data

[0222] This application divides the annual energy consumption into 7 typical days as shown in Table 1. Among them, Village 1 is defined as a planting and irrigation village, Village 2 is an agricultural product processing village, and Village 3 is a leisure and entertainment village:

[0223]

[0224] This application selects energy data from three villages within the jurisdiction of a city in Gansu Province, China, and conducts simulation and comparative analysis on the capacity configuration issues of the three rural energy systems in the region. Village 1 is 5.5 km away from Village 2, Village 1 is 4.2 km away from Village 3, and Village 2 is 6 km away from Village 3. The parameters of the alternative equipment are shown in Table 2, the operating parameters of the mobile energy storage vehicle are shown in Table 3, and the electricity and gas purchase prices are shown in Table 4. The purchase price of carbon emission rights is 0.21 yuan / kg, the sales price of carbon emission rights is 0.85 yuan / kg, the carbon emission allocation is 0.55kg / (kW·h), the residual value rate of fixed assets is 5%, the discount rate is 8%, and the pollutant emission coefficient and governance cost are shown in Table 5:

[0225]

[0226]

[0227]

[0228]

[0229] (2) Comparison plan

[0230] Solution 1: Three villages are configured separately. That is, the interconnection of pipelines and mobile energy storage between villages is not considered, there is no energy transmission between villages, and the traditional planning method is used to carry out separate capacity planning and design for the three villages.

[0231] Solution 2: Consider pipeline interconnection (electricity and heat), and coordinate and optimize the configuration of the three villages. Because the seasonality of rural loads does not completely correspond to the period of maximum renewable energy generation, separate planning will result in low renewable energy consumption rates in villages during a certain period of time. Therefore, this solution connects the three villages through pipeline interconnection, taking advantage of the different load demands of different types of villages, promoting energy interaction between villages, and improving the renewable energy consumption rate of the entire multi-village energy system.

[0232] Solution 3: Considering pipeline interconnection and mobile energy storage (electricity and heat), the three villages are coordinated and optimized. Because pipeline interconnection has limited regulation capacity when the electricity and heat demand in the villages are at peak or trough, and mobile energy storage has flexible time and space characteristics, this solution improves the economy of the entire multi-village energy system and reduces the amount of wind and solar power abandoned by renting mobile electricity and mobile heat storage devices during peak electricity and heat consumption periods.

[0233] (3) Results analysis

[0234] The capacity planning results of the three schemes set in this application are shown in Tables 6 to 8:

[0235]

[0236]

[0237] From the comparison of Tables 6 to 8, we can see that in order to improve the utilization of rural renewable energy and improve the rural environment, Schemes 1, 2, and 3 are all equipped with more photovoltaic and wind power to meet the load demand within the village. At the same time, considering the uncertainty and volatility of renewable energy output, corresponding fixed energy storage equipment is also configured within the village to ensure the safe and stable operation of each village.

[0238] In Plan 2, through the construction of pipeline interconnection between villages, energy exchange can be carried out between different types of villages, so the capacity of the heat storage equipment in Village 2 is slightly reduced, the capacity of the energy conversion equipment inside Village 3 is reduced, and the number of wind turbines in Village 3 is slightly increased under the condition of geographical resource constraints.

[0239] Scheme 3 adds mobile energy storage devices to Scheme 2. When the three villages are in the peak period of electricity and heat consumption, by renting mobile energy storage and taking advantage of its flexible time and space characteristics, together with pipeline interconnection, energy interaction between villages is promoted and the utilization efficiency of equipment within villages is optimized. Therefore, compared with Schemes 1 and 2, the fixed electricity storage capacity in villages in Scheme 3 decreases by 33.33%, and the heat storage capacity decreases by 50% and 40%, respectively. The total planning cost comparison of the three schemes is shown in Table 9:

[0240]

[0241] As can be seen from Table 9, although the capacity of internal facilities in villages changes from Scheme 1 to Scheme 3, the investment cost still rises successively with the construction of pipelines and the leasing of mobile energy storage, and the operating cost of multi-village energy systems also decreases. Compared with Scheme 1 and Scheme 2, Scheme 3 reduces the energy purchase cost by 9.04% and 4.67% respectively, the pollutant treatment cost by 50.17% and 25.87%, the system's new energy consumption rate increases by 7.39% and 3.56%, and the carbon emission benefits increase by 64,900 yuan and 42,800 yuan. Therefore, the annualized total cost of the multi-village energy system of Scheme 3 is reduced by 4.36% and 0.41% compared with Scheme 1 and Scheme 2, respectively. The simulation results prove that the optimization configuration method proposed in this application realizes the complementary and mutual assistance of the energy of the entire multi-village energy system, improves the economy and reliability of the system, and the configuration model is practical and feasible.

[0242] The purpose of this application is to solve the problems of low energy utilization, high pollution, poor economy, and unreasonable equipment capacity planning caused by seasonal loads in multi-village energy systems. By promoting energy interaction between villages, the pressure brought by seasonal loads to multi-village energy systems can be alleviated, the economic benefits of the entire system can be improved, and pollutant emissions can be reduced, and the system's new energy consumption rate can be improved.

[0243] like Figure 3 As shown, it is a schematic diagram of a multi-village energy system coordinated dispatching system of the present application, which may include:

[0244] An acquisition module, used to acquire data of multiple rural energy systems;

[0245] A modeling module is used to construct a two-layer configuration model combining multi-village energy system planning and operation; the two-layer configuration model includes an upper model and a lower model; the decision variable of the upper model is the number of equipment, and the objective function is to minimize the annualized total cost of the multi-village energy system; the decision variable of the lower model is the hourly output of the equipment, and the objective function is to minimize the annual operating cost of the multi-village energy system;

[0246] The upper model includes:

[0247]

[0248] in, represents the annualized total cost of the multi-village energy system, represents the annual investment cost, represents the annual maintenance cost, represents the annual operating cost, represents the annual rental cost of the mobile energy storage vehicle, express minimize;

[0249] The lower layer model comprises:

[0250]

[0251] in, represents the annual energy purchase cost of multiple rural energy systems from the upper power grid and gas grid, represents the annual transportation cost of mobile energy storage, represents the annual environmental cost of multiple rural energy systems, represents the annual penalty cost of wind and solar curtailment in multiple rural energy systems, represents the annual carbon trading cost of multiple rural energy systems, express minimize;

[0252] A solution module, which is used to use the data of multiple rural energy systems as input, adopt an improved particle swarm algorithm to solve the upper model, and adopt a CPLEX solver to solve the lower model according to the capacity scale transmitted by the upper model to obtain an optimized scheduling strategy; the improved particle swarm algorithm includes dynamically adjusting the inertia weight and learning factor parameters in the particle swarm algorithm;

[0253] The dispatching module is used to coordinate the dispatching of multiple rural energy systems using the obtained optimized dispatching strategy.

[0254] It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The module described as a separate component may or may not be physically separated. The component displayed as a module may be a physical unit or multiple physical units, that is, it may be located in one place, or it may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0255] In addition, each module in each embodiment of the present invention may be integrated into a processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0256] The present application also proposes a computer program product, which includes instructions, and when the instructions are executed by a processor, the steps in the above multi-village energy system coordinated scheduling method embodiment are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above system embodiment are implemented.

[0257] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in a memory and executed by the processor to accomplish the present invention.

[0258] The carrier for implementing the above-mentioned computer program product may be a computer device, or the computer program product may be stored in a computer storage medium.

[0259] The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory.

[0260] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0261] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0262] If the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0263] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for coordinated dispatching of multi-village energy systems, characterized in that: Energy interaction between multiple villages is achieved through pipeline interconnection and mobile energy storage; the methods include: Obtain data on energy systems in multiple villages; where multiple villages can interact with each other through pipeline interconnection and mobile energy storage; A two-layer configuration model combining planning and operation of multi-village energy systems is constructed; the two-layer configuration model includes an upper model and a lower model; the decision variable of the upper model is the number of equipment, and the objective function is to minimize the annualized total cost of the multi-village energy system; the decision variable of the lower model is the hourly output of the equipment, and the objective function is to minimize the annual operating cost of the multi-village energy system; The upper model includes: in, represents the annualized total cost of the multi-village energy system, represents the annual investment cost, represents the annual maintenance cost, represents the annual operating cost, represents the annual rental cost of the mobile energy storage vehicle, express minimize; The lower layer model comprises: in, represents the annual energy purchase cost of multiple rural energy systems from the upper power grid and gas grid, represents the annual transportation cost of mobile energy storage, represents the annual environmental cost of multiple rural energy systems, represents the annual penalty cost of wind and solar curtailment in multiple rural energy systems, represents the annual carbon trading cost of multiple rural energy systems, express minimize; Taking multi-village energy system data as input, an improved particle swarm algorithm is used to solve the upper model, and a CPLEX solver is used to solve the lower model according to the capacity scale transmitted by the upper model to obtain an optimized scheduling strategy; the improved particle swarm algorithm includes dynamically adjusting the inertia weight and learning factor parameters in the particle swarm algorithm; the dynamic adjustment of the inertia weight and learning factor parameters in the particle swarm algorithm includes: in, Indicates The particle in The inertia weight in the iteration, represents the initial value of inertia weight, represents the final value of the inertia weight, Indicates The particle in The population evolution rate in iterations, Indicates The particle in The individual experience learning factor in the iteration, represents the maximum value of the individual experience learning factor, Indicates The particle in The group information learning factor in the iteration, Represents the minimum value of the group information learning factor; The obtained optimal dispatching strategy is used to coordinate the dispatch of multiple rural energy systems.

2. The method for coordinated dispatching of multi-village energy systems according to claim 1, characterized in that: Stated annual investment cost ,include: in, Indicates A village, represents the total number of villages, Indicates Energy supply, conversion and storage equipment within villages, Indicates The first village The construction factor of each device, Indicates The first village The unit construction cost of each device, Indicates The first village The number of devices installed, Indicates The first village The rated capacity of each device is represents the residual value rate of fixed assets, is the discount rate, Indicates The first village The life cycle of a device represents the construction coefficient of power lines between villages, represents the unit construction cost of power lines between villages, Indicates the construction length of power lines between villages. represents the life cycle of power lines between villages, Indicates the construction coefficient of heat pipes between villages, represents the unit construction cost of heat pipes between villages, Indicates the construction length of the heat pipes between villages. Represents the life cycle of heat pipes between villages; Stated annual maintenance costs ,include: in, Indicates The first village Unit maintenance cost of each device, represents the unit maintenance cost of power lines between villages, represents the unit maintenance cost of heating pipes between villages; The annual rental fee of the mobile energy storage vehicle ,include: in, Indicates the number of days the mobile battery storage vehicle is rented in a year. Indicates the rental price of mobile battery storage vehicles, Indicates the number of days the mobile thermal storage vehicle is rented in a year, represents the rental price of the mobile thermal storage vehicle, represents the number of mobile battery storage vehicles, Indicates the number of mobile heat storage vehicles.

3. The method for coordinated dispatching of multi-village energy systems according to claim 2 is characterized in that: The constraints of the upper model include: in, Indicates The first village The maximum number of devices that can be installed, Represents the collection of all devices, Represents photovoltaic power generation equipment, Represents a wind turbine. represents a gas turbine, Indicates gas boiler, Indicates electric boiler. Indicates power-to-gas equipment. represents waste heat recovery boiler, Represents a fixed power storage device. Represents heat storage equipment, Indicates gas storage equipment, Indicates mobile battery storage vehicle. Indicates mobile heat storage vehicle.

4. The method for coordinated dispatching of multi-village energy systems according to claim 3 is characterized in that: The annual energy purchase cost of the multi-village energy system from the upper power grid and gas grid ,include: in, Indicates Typical day, represents the total number of categories on a typical day, Indicates The number of days similar to typical days, Indicates the number of time periods in a day, express The unit price of electricity purchased by the village from the upper power grid at any given moment. express Moment The village in The power purchased from the upper grid on a typical day, express The unit price of gas purchased by the village from the upper-level gas grid at any given moment. express Moment The village in Gas purchase power from the upper gas grid on a typical day; The annual transportation cost of the mobile energy storage ,include: in, represents the charging and discharging cost of the mobile battery storage vehicle, represents the charging and discharging cost of the mobile thermal storage vehicle, represents the deployment cost of mobile electric storage vehicles during the dispatching period, represents the deployment cost of the mobile heat storage vehicle during the dispatch period; Annual environmental costs of multiple rural energy systems ,include: in, Indicates the total type of pollutants, , Indicates Unit treatment cost of pollutants, Indicates the gas turbine Emission coefficient of pollutants, express Moment Gas turbines in villages Typical daily output power, Indicates the gas boiler Emission coefficient of pollutants, express Moment Gas boilers in villages Typical daily output power, Indicates the upper grid Emission coefficient of pollutants, express Moment The upper level power grid in the village is Output power of a typical day; The annual penalty cost of wind and solar curtailment for multiple rural energy systems ,include: in, represents the cost coefficient of abandoned light, express Moment Photovoltaic power generation equipment in villages The forecast output for a typical day, express Moment Photovoltaic power generation equipment in villages The actual output of a typical day, represents the wind curtailment cost coefficient, express Moment Wind turbines in villages The forecast output for a typical day, express Moment Wind turbines in villages Actual output on a typical day; Annual carbon trading costs for multiple rural energy systems ,include: in, represents the number of carbon-free emission sources, represents the total number of carbon-free emission sources, Indicates The price of carbon emission rights for carbon-free sources, Indicates Carbon-free sources can consume quantity, express Moment Carbon-free emission sources in Typical daily output power, represents the number of carbon emission sources, represents the total number of carbon emission sources, Indicates The purchase price of carbon emission rights of carbon emission sources, Indicates Carbon emission sources Emission intensity, Indicates The carbon emission rights allocation of each carbon emission source, express Moment Carbon emission sources are Typical daily output power.

5. The method for coordinated dispatching of multi-village energy systems according to claim 4 is characterized in that: The constraints of the lower model include power balance constraints, pipeline interconnection constraints, equipment constraints, mobile energy storage vehicle time and space path constraints, geographical resource constraints and construction constraints; The power balance constraint includes: in, express Moment The village in Typical daily electrical load, express Moment The village in Typical day heat load, express Moment The village in Typical day gas load, express Moment The village in The power purchased from the upper grid on a typical day, express Moment The village in The gas power purchased from the upper grid on a typical day, express Moment The village in The output power of a typical solar photovoltaic power generation device in Japan, express Moment The village in Typical daily wind turbine output power, express Moment The village in The electrical power of a typical Japanese gas turbine, express Moment The village in Gas power of typical Japanese and Chinese gas turbines, express Moment The village in The electrical power of a typical daily power-to-gas device, express Moment The village in Gas power of a typical daily power-to-gas device, express Moment The village in Thermal power of typical Japanese and Chinese gas boilers, express Moment The village in Gas power of typical Japanese and Chinese gas boilers, express Moment The village in The power of a typical Japanese and Chinese electric boiler, express Moment The village in Thermal power of typical Japanese and Chinese electric boilers, express Moment The village in Thermal power of a typical waste heat recovery boiler during a day, express Moment The village in Typical daily power of fixed storage equipment, express Moment The village in The power of a typical daytime heat storage device, express Moment The village in The power of a typical daily gas storage device, express Moment The village in The power of a typical daily mobile battery storage vehicle, express Moment The village in The power of a typical mobile thermal storage vehicle during a typical day, express Moment The village in Typical daytime electricity exchange with other villages through pipelines, express Moment The village in Thermal interaction with other villages through pipelines during typical days; The pipeline interconnection constraints include: in, express Moment Village to The first The transmission power of energy, express Moment Village to The first The energy transfer coefficient, express Moment Village to The first The transmission power of energy, express Moment Village to The first The energy transfer coefficient, Indicates Village to The first The upper limit of the transmission power of the energy Indicates Village to The first The upper limit of the transmission power of the energy; The equipment constraints include constraints on equipment operating power and equipment operating capacity; The space-time path constraints of the mobile energy storage vehicle include: in, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Indicates that the end A collection of routes for villages, Indicates that from A collection of routes starting from villages, Indicates that mobile energy storage vehicle 1 is Village to The location marker variable of a village, Indicates that the mobile energy storage vehicle starts from the Village to The location marker variable of a village, Represents mobile energy storage vehicle Time from the Village to The location marker variable of a village, Indicates the number of time periods in a day, take ; The geographical resource constraints include the construction number constraints of equipment in the multi-village energy system area, the construction length constraints of power lines and the construction length constraints of thermal pipelines; The construction constraints include usage constraints of equipment, power lines and thermal pipelines within the multi-village energy system area within a preset year.

6. A multi-village energy system coordinated dispatching system, characterized in that: include: An acquisition module, used to acquire data of multiple rural energy systems; A modeling module is used to construct a two-layer configuration model combining multi-village energy system planning and operation; the two-layer configuration model includes an upper model and a lower model; the decision variable of the upper model is the number of equipment, and the objective function is to minimize the annualized total cost of the multi-village energy system; The decision variable of the lower model is the hourly output of the equipment, and the objective function is to minimize the annual operating cost of the multi-village energy system; The upper model includes: in, represents the annualized total cost of the multi-village energy system, represents the annual investment cost, represents the annual maintenance cost, represents the annual operating cost, Indicates the annual rental cost of the mobile energy storage vehicle; The lower layer model comprises: in, represents the annual energy purchase cost of multiple rural energy systems from the upper power grid and gas grid, represents the annual transportation cost of mobile energy storage, represents the annual environmental cost of multiple rural energy systems, represents the annual penalty cost of wind and solar curtailment in multiple rural energy systems, represents the annual carbon trading costs of multiple rural energy systems; A solution module is used for taking multi-village energy system data as input, using an improved particle swarm algorithm to solve the upper model, and using a CPLEX solver to solve the lower model according to the capacity scale transmitted by the upper model to obtain an optimized scheduling strategy; the improved particle swarm algorithm includes dynamically adjusting the inertia weight and learning factor parameters in the particle swarm algorithm; the dynamic adjustment of the inertia weight and learning factor parameters in the particle swarm algorithm includes: in, Indicates The particle in The inertia weight in the iteration, represents the initial value of inertia weight, represents the final value of the inertia weight, Indicates The particle in The population evolution rate in iterations, Indicates The particle in The individual experience learning factor in the iteration, represents the maximum value of the individual experience learning factor, Indicates The particle in The group information learning factor in the iteration, Represents the minimum value of the group information learning factor; The dispatching module is used to coordinate the dispatching of multiple rural energy systems using the obtained optimized dispatching strategy.

7. A computer program product, characterized in that: The computer program product comprises instructions, and when the instructions are executed by a processor, the method for coordinated dispatching of multiple rural energy systems according to any one of claims 1 to 5 is implemented.

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