Optimization method for collaborative operation of multi-type integrated energy system

Abstract

The invention relates to the technical field of electric power integrated energy optimization, and particularly discloses an optimization method for collaborative operation of a multi-type integrated energy system. Comprising the following steps: S01, dividing the types of the town integrated energy system, and carrying out modeling according to the system types to obtain a plurality of town integrated energy system models; s02, calculating the operation cost according to the plurality of township integrated energy system models and an in-situ double-layer balance mechanism, and taking the minimum operation cost as a target function; s03, constructing a multi-type integrated energy system game optimization model and constraint conditions according to the objective function; and step S04, seeking an optimal solution for the game optimization model and the constraint condition of the multi-type integrated energy system, and obtaining an optimization scheme of collaborative operation of the multi-type integrated energy system. Mutual aid and mutual protection, interconnection and intercommunication, mutual supply and mutual backup of multi-type village and town comprehensive energy systems are realized, and a green, low-carbon, safe and efficient modern rural energy system can be constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power integrated energy optimization, and in particular to an optimization method for coordinated operation of multiple types of integrated energy systems. Background Art

[0002] Energy is the foundation of human survival and development and the driving force for social development. With the in-depth penetration of global energy and the rapid development of information and communication technology, energy utilization is developing in the direction of multi-energy coordination and complementarity. Improving the comprehensive utilization rate of energy, promoting the consumption of renewable energy, and realizing the ladder utilization of energy have attracted widespread attention from the international community. Townships have a vast area and often have abundant reserves of renewable resources such as solar energy, wind energy, hydropower and biomass energy. Therefore, following the orientation of different township positioning, considering heterogeneous energy use scenarios such as township households, industries and agriculture, and developing a multi-type township comprehensive energy system with a high proportion of renewable energy access will help to form a characteristic township clean energy development and utilization model with sustainable economic environment. Township energy is suitable for the development of distributed energy systems to solve the problem of renewable energy consumption. Therefore, establishing an energy stratification and grading local balance mechanism to achieve mutual assistance, mutual protection, interconnection, mutual supply and mutual backup of multi-type township comprehensive energy systems will help to build a green, low-carbon, safe and efficient modern rural energy system. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide an optimization method for the coordinated operation of multiple types of integrated energy systems, comprehensively consider the typical multi-energy complementary modes of different types of township-level integrated energy systems, including centralized energy consumption, family life, agricultural breeding, and industrial industries, introduce integrated energy service providers, and propose a local double-layer balance mechanism for the electricity of township integrated energy systems and a method for the coordinated operation optimization of clusters of multiple types of township integrated energy systems. Realizing mutual assistance, mutual protection, interconnection, mutual supply and mutual backup of multiple types of township integrated energy systems will help build a green, low-carbon, safe and efficient modern rural energy system.

[0006] The technical solution adopted by the present invention is: an optimization method for the coordinated operation of multiple types of integrated energy systems, comprising the following steps:

[0007] Step S01: Divide the types of township integrated energy systems, and model them according to the system types to obtain multiple township integrated energy system models;

[0008] Step S02: Calculating the operating cost based on the multiple township integrated energy system models and the local two-layer balancing mechanism, with minimizing the operating cost as the objective function;

[0009] Step S03: constructing a multi-type integrated energy system game optimization model and constraint conditions according to the objective function;

[0010] Step S04: Seeking the optimal solution for the game optimization model and constraint conditions of the multi-type integrated energy system to obtain an optimization plan for the coordinated operation of the multi-type integrated energy system.

[0011] Preferably, the multi-type integrated energy system game optimization model includes:

[0012]

[0013] Among them, C i,sell,IESS and C i,buy,IESS They are respectively based on the purchase and sale prices of electricity issued by the township integrated energy service providers for multiple types of township integrated energy systems, P i,buy,IESS and P i,sell,IESS The optimal purchasing power and the optimal selling power are determined by optimizing their own game strategies.

[0014] Preferably, the optimal solution is a unique definite solution that simultaneously satisfies the multi-type integrated energy system game optimization model and constraint conditions.

[0015] Preferably, the multi-type integrated energy system game optimization model also includes:

[0016]

[0017] Among them, C i,buy,IESS and C i,sell,IESS are the optimal electricity purchase price and electricity sales price respectively, P i,buy,IESS and P i,sell,IESS The optimal purchasing power and the optimal selling power are determined by optimizing their own game strategies.

[0018] Preferably, the constraints include:

[0019] Power balance constraints:

[0020]

[0021] Among them, P i,b,use is the usage of the bth new energy source in the ith IES; P i,buy The i-th IES purchases electric power from other IES; Pi,dis is the discharge power of the battery; P i,buy,grid The i-th IES purchases power from the county power grid; P i,load The i-th IES electrical load, P i,cha is the battery discharge power; P i,sell is the electricity sales power of the i-th IES.

[0022] Preferably, the constraint conditions also include:

[0023] Output constraints of each power supply:

[0024] 0≤P i,b (t)≤P i,b,max

[0025] Among them, P i,b,max is the maximum output power of the bth power source of the i-th IES;

[0026] Exchange constraints between towns:

[0027]

[0028] Among them, P buy / sell,max is the maximum exchange power between IES;

[0029] Constraints on integrated energy service providers in townships:

[0030]

[0031] Among them, P IESS,max It is the maximum interaction power between IES and the township integrated energy service provider;

[0032] County power grid constraints:

[0033] 0≤P i,buy,grid (t)≤P grid,max

[0034] Among them, P grid,max is the maximum interactive power between IES and the county power grid.

[0035] Preferably, the objective function includes an upper layer optimization objective function and a lower layer optimization objective function:

[0036] The upper optimization objective function is used to minimize the operating cost of the township integrated energy service provider;

[0037] The lower optimization objective function is used to minimize the total operating cost of multi-type township integrated energy systems.

[0038] Preferably, the upper layer optimizes the objective function C IESS The minimum value of is:

[0039]

[0040] C1=C i,sell,IESS (t)P i,sell,IESS (t)

[0041] C2=C i,buy,IESS (t)P i,buy,IESS(t)

[0042] Among them, C1 is the total revenue from electricity sales of multi-type township integrated energy systems to township integrated energy service providers; C2 is the total cost of electricity purchase from township integrated energy service providers by multi-type township integrated energy systems; P i,sell,IESS Selling power to township integrated energy service providers for the i-th integrated energy system; C i,buy,IESS P is the price of electricity purchased by the i-th integrated energy system from the township integrated energy service provider; i,buy,IESS Purchase electricity for the i-th integrated energy system from the township integrated energy service provider.

[0043] Preferably, the lower layer optimizes the objective function C IES The minimum value of is:

[0044]

[0045] C3=C i,buy,grid (t)P i,buy,grid (t)

[0046] C4=μ ESS P i,buy (t)

[0047] C5=C price,gas (t)·p i,gas (t)

[0048] Among them, C3 is the cost of purchasing electricity from the county power grid; C4 is the internal power exchange cost, which is mainly the network fee generated by the power exchange between various integrated energy systems; C5 is the gas purchase cost of the system; C i,buy,grid is the price of electricity purchased from the county power grid; P i,buy,grid is the power purchased from the county power grid; μ ESS P is the unit network fee; i,buy Purchase power from other systems for the i-th system; C price,gas is the gas price, p i,gas is the gas power consumption of the comprehensive energy system at moment t.

[0049] Preferably, the multiple township comprehensive energy system models include a centralized energy consumption type township comprehensive energy system model, a household life energy consumption type township comprehensive energy system model, an agricultural breeding energy consumption type township comprehensive energy system model and an industrial energy consumption type township comprehensive energy system model.

[0050] Beneficial effects of the above technical solution:

[0051] Compared with the prior art, the optimization method for the coordinated operation of multiple types of integrated energy systems provided by the present invention, through the established township integrated energy system cluster coordinated operation model, makes it possible to reduce the operating cost of the integrated energy system compared with the independent operation of a single township integrated energy system, and maximize the local clean energy consumption; the introduction of township integrated energy service providers, on the one hand, optimizes the output of each integrated energy system through flexible price guidance, and on the other hand, reduces the dependence on the county power grid, which can not only reduce the operating cost of township integrated energy service providers, but also reduce the cost of rural multiple integrated energy systems to a certain extent, and significantly improve the operating economy of both parties; the study describes the two-level on-site double-layer balancing mechanism of the township integrated energy system as a master-slave game model, which can reasonably solve the problem of power interaction between township integrated energy service providers and multiple township integrated energy systems, and the final electricity price optimization results and power interaction results verify the rationality of the master-slave game optimization model. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of an optimization method for coordinated operation of multi-type integrated energy systems provided by an embodiment of the present invention;

[0053] Figure 2 A biogas cogeneration flow chart provided for one embodiment of the present invention;

[0054] Figure 3 This is a result diagram of the predicted power of electric cooling and heating loads, photovoltaic power and wind power of the township integrated energy system in the first scenario predicted by the optimization method provided by the present invention;

[0055] Figure 4 A result diagram of the predicted power of electric cooling and heating loads, photovoltaic power and wind power of the township integrated energy system in the second scenario predicted by the optimization method provided by the present invention;

[0056] Figure 5 This is a result diagram of the predicted power of the third scenario township integrated energy system electric cooling and heating load, photovoltaic and wind power predicted by the optimization method provided by the present invention;

[0057] Figure 6 This is a graph of predicted power results of the electric cooling and heating loads, photovoltaic and wind power of the fourth scenario township integrated energy system predicted using the optimization method provided by the present invention. DETAILED DESCRIPTION

[0058] The following is a further detailed description of the implementation methods of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0059] The terms "first", "second", etc. (if any) in the specification and claims are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0060] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0061] Embodiment 1

[0062] An embodiment of the present invention provides an optimization method for coordinated operation of multiple types of integrated energy systems, which specifically includes the following steps:

[0063] Mathematical modeling of multi-type township integrated energy systems

[0064] 1.1. Model of integrated energy system for concentrated energy-using townships

[0065] For the rural centralized energy consumption integrated energy system, the unit composition is shown in Table 1. It is mainly composed of energy supply units and energy storage units. The energy supply units directly meet the user's electric heating and cooling load needs, and the energy storage units use their flexible charging and discharging functions to meet the user's electric heating load.

[0066] Table 1 Rural centralized energy consumption units

[0067]

[0068] Specifically, the main raw materials for biomass power generation include industrial, forestry, agricultural waste and domestic garbage. The power generation methods include gasification and direct combustion, including agricultural, forestry and industrial waste gasification and direct combustion power generation, garbage incineration power generation and landfill gasification power generation, biogas power generation, etc. The power generation characteristics of direct combustion and gasification are unified as shown in formula (1)-(2):

[0069] P BE (t) = η BE m BE (t) (1)

[0070] P BE,min P BE (t)P BE,min (2)

[0071] Where: P BE is the power of biomass energy generation; η BE is the power generation efficiency of biomass combustion or gasification; m BE The mass of biomass raw materials for combustion or gasification; P BE,min is the minimum value of biomass power generation; P BE,max It is the maximum value of biomass power generation.

[0072] Biogas cogeneration is a process of fermenting livestock manure in rural areas as raw material for heating and power supply. After the biogas is processed by a booster to reach certain parameters, it enters the gas internal combustion engine and burns to drive the generator set to operate and supply power. The waste heat generated in the cogeneration process is collected by a waste heat recovery device for heating. The biogas residue and biogas liquid produced can be used as agricultural fertilizer, saving a certain amount of fertilizer costs. Figure 2 The characteristics of biogas cogeneration are shown in formula (3):

[0073] E BP (t) = P BP (t)+H BP (t) = λV biogas C b η b (3)

[0074]

[0075] Where: E BP The electrical and thermal power produced by biogas; P BP is the biogas power generation power; H BP is the heating power of biogas; λ is the percentage of biogas volume to the volume of biogas tank; V Biogas is the volume of the biogas digester; C b is the calorific value of biogas; η b is the energy utilization rate of biogas cogeneration; ω is the power ratio of biogas electricity and heating.

[0076] Air source heat pump heating is a clean heating method, and its actual operation in cold regions has a good prospect. It utilizes energy that cannot be directly utilized, converts heat energy from low level to high level, thereby achieving the purpose of energy saving. The characteristics of air source heat pump heating are shown in formula (5).

[0077] H i,HP (t) = P i,HP (t) COP HP (5)

[0078] Where: H i,HP is the heat supply of the air source heat pump, P i,HP is the power consumption of the air source heat pump, COP HP is the energy efficiency coefficient of the air source heat pump.

[0079] 1.2. Model of integrated energy system for households using energy in rural areas

[0080] For the rural household life energy consumption township model, the multi-energy complementary system components under this model are shown in Table 2. They are mainly composed of energy supply units, energy conversion units, and energy storage units. The energy supply unit directly meets the user's electric heating and cooling load needs, the energy conversion unit meets the heat load needs through electric energy conversion, and the energy storage unit uses its flexible charging and discharging functions to meet the user's electric heating load.

[0081] Table 2 Rural household energy consumption composition

[0082]

[0083]

[0084] Solar thermal collectors use collectors to absorb solar energy and provide heat energy for rural users, which is mainly determined by the intensity of solar radiation and conversion efficiency. The heat energy generated by solar thermal collectors is shown in formula (6):

[0085] H SH (t) = η SH S SH θ t (6)

[0086] Where: H SH The heat energy generated by the solar thermal collector; η SH is the solar collector conversion efficiency; S SH is the heat collection area; θ t is the solar radiation intensity at time t.

[0087] The firewood-saving stove directly burns coal and straw in the firewood-saving stove. Compared with the old firewood stove, the design of the stove chamber, the relative distance between the pot wall and the stove chamber, the height of the hanging fire, the flue and ventilation are optimized, and insulation measures and waste heat utilization devices are added. Compared with the traditional earth stove, it saves time, efficiency, fuel, and is convenient and hygienic to use. The thermal power generated by the firewood-saving stove is shown in formula (7):

[0088] H FS (t) = η FS q cls m cls (t) (7)

[0089] Where: HFS is the thermal power generated by the firewood-saving stove; η FS To save fuel stove energy conversion efficiency; cls is the calorific value of coal (straw); m cls is the mass of coal (straw) used, and t is the time.

[0090] Electric vehicle batteries can be used to store electrical energy and have flexible charging and discharging characteristics. This characteristic determines that there is a lot of room for electric vehicles to be used. The batteries of electric vehicles can participate in the operation and dispatch of rural multi-energy complementary systems. When charging, the batteries convert electrical energy into chemical energy; when discharging, the chemical energy is converted into electrical energy. The introduction of the state of charge (SOC) indicates the percentage of the remaining power of the battery of an electric vehicle to its total capacity.

[0091]

[0092]

[0093] Where: SOC EV is the state of charge of the electric vehicle; Q EV is the battery capacity of the electric vehicle; C EV is the battery capacity; EV,chr , μ EV,dis and μ EV,drive are 0-1 variables, representing the charging, discharging and driving states of electric vehicles respectively; η chr and η dis are charging and discharging efficiency respectively; P EV,chr and P EV,dis are the charging and discharging power of electric vehicles respectively; P EV,drive It is the driving power load of electric vehicle.

[0094] 1.3. Model of comprehensive energy system for agricultural and breeding energy-consuming townships

[0095] For the rural agricultural breeding industry energy consumption township model, the system components under this model are shown in Table 3. They are mainly composed of energy supply units and energy storage units. The energy supply unit directly meets the user's electric heating and cooling load needs, the energy conversion unit meets the heat load needs through electric energy conversion, and the energy storage unit uses its flexible charging and discharging functions to meet the user's electric heating load.

[0096] Table 3 Energy Consumption Composition Units of Rural Agricultural Breeding Industry

[0097]

[0098] A gas turbine consumes natural gas and outputs heat energy. The conversion characteristics of gas to heat are shown in the following formula (10) as the thermal efficiency of a gas turbine:

[0099] λ G,H (t) = 1 - λ G,G (t)-λ G,loss (t) (10)

[0100] Where: G,H is the thermal efficiency of the gas turbine; G,G is the power generation efficiency of the gas turbine; G,loss is the heat loss rate of the gas turbine.

[0101] The output thermal power of the gas turbine is shown in equations (11)-(12):

[0102] H G (t) = P G (t)·λ G,H (t) / λ G,G (t) (11)

[0103] H G,min H G (t)H G,max (12)

[0104] Where: H G Represents the thermal power output of the gas turbine; G,H is the thermal efficiency of the gas turbine; G,G is the power generation efficiency of the gas turbine; P G is the power generation of gas turbine; H G,min With H G,max They represent the minimum and maximum values ​​of the thermal power output of the gas turbine respectively.

[0105] 1.4. Model of comprehensive energy system for industrial energy-using townships

[0106] Based on the multi-energy complementarity + rural industrial industry design, the multi-energy complementarity system components under this model are shown in Table 4. They are mainly composed of energy supply units, energy conversion units, and energy storage units. The energy supply unit directly meets the user's electric heating and cooling load needs, the energy conversion unit meets the heat load needs through electric energy conversion, and the energy storage unit uses its flexible charging and discharging functions to meet the user's electric heating load.

[0107] Table 4 Composition of rural industrial energy units

[0108]

[0109] Collaborative operation optimization method

[0110] 2.1 Optimization Objectives

[0111] Based on the on-site two-layer balance mechanism, in order to maximize the on-site consumption of renewable energy resources and achieve economic benefits, the upper optimization goal of the constructed two-layer optimization model is to minimize the operating costs of township integrated energy service providers, and the lower optimization goal is to minimize the total operating costs of multiple types of township integrated energy systems for mutual supply and backup.

[0112] (1) Upper-level optimization goals

[0113] The upper optimization target is the total operating cost C of the township integrated energy service provider IESS Minimize, as shown in formula (13):

[0114]

[0115] C1=C i,sell,IESS (t)P i,sell,IESS (t) (14)

[0116] C2=C i,buy,IESS (t)P i,buy,IESS (t) (15)

[0117] Where: C1 is the total revenue from electricity sales from multi-type township integrated energy systems to township integrated energy service providers; C2 is the total cost of electricity purchase from township integrated energy service providers by multi-type township integrated energy systems; P i,sell,IESS Selling power to township integrated energy service providers for the i-th integrated energy system; C i,buy,IESS P is the price of electricity purchased by the i-th integrated energy system from the township integrated energy service provider; i,buy,IESS Purchase power from township integrated energy service providers for the i-th integrated energy system

[0118] (2) Lower-level optimization goals

[0119] The lower optimization goal is to minimize the total operating cost of multi-type township integrated energy systems. The operating cost mainly includes the power transportation cost between systems, the cost of purchasing electricity from township power grids and township integrated energy service providers, and the cost of purchasing fuel, as shown in formula (16).

[0120]

[0121] C3=C i,buy,grid (t)P i,buy,grid (t) (17)

[0122] C4=μ ESS P i,buy (t) (18)

[0123] C5=C price,gas (t)·p i,gas (t) (19)

[0124] Where: C3 is the cost of purchasing electricity from the county power grid; C4 is the internal power exchange cost, which is mainly the network fee generated by the power exchange between various integrated energy systems; C5 is the gas purchase cost of the system; C i,buy,grid is the price of electricity purchased from the county power grid; P i,buy,grid is the power purchased from the county power grid; μ ESS P is the unit network fee; i,buy Purchase power from other systems for the i-th system; C price,gas is the gas price, p i,gas is the gas power consumption of the comprehensive energy system at moment t.

[0125] 3.2 Constraints

[0126] (1) Power balance

[0127] The two-level coordinated operation optimization model of the township integrated energy system needs to meet the power balance constraint

[18] , as shown in formula (20):

[0128]

[0129] Where: P i,b,use is the usage of the bth new energy source in the ith IES; P i,buy The i-th IES purchases electric power from other IES; Pi,dis is the discharge power of the battery; P i,buy,grid The i-th IES purchases power from the county power grid; P i,load The i-th IES electrical load, P i,cha is the battery discharge power; P i,sell is the electricity sales power of the i-th IES.

[0130] (2) Output constraints of each power source

[0131] 0≤P i,b (t)≤P i,b,max (twenty one)

[0132] Where: P i,b,max is the maximum output power of the bth power source of the i-th IES.

[0133] (3) Exchange constraints between towns

[0134]

[0135] Where: P buy / sell,max is the maximum exchange power between IES.

[0136] (4) Constraints on integrated energy service providers in rural areas

[0137]

[0138] Where: P IESS,max It is the maximum interaction power between IES and township integrated energy service providers.

[0139] (5) County power grid constraints

[0140] 0≤P i,buy,grid (t)≤P grid,max (twenty four)

[0141] Where: P grid,max is the maximum interactive power between IES and the county power grid.

[0142] Model Solution

[0143] In the cluster collaborative operation optimization model of multi-type township integrated energy systems, there is a decision-making sequence between township integrated energy service providers and multi-type township integrated energy systems. Under the price guidance of township integrated energy service providers, multi-type township integrated energy systems optimize their own output and determine the external electric energy interaction power. This is in line with the logical thinking of the master-slave game. In the master-slave game model, the leader has the leadership advantage and can take the lead or take a favorable position in the game, and the follower must follow the leader to make a game. Therefore, the township integrated energy service provider is set as the leader, and various types of township rural integrated energy systems are set as followers to establish a one-master-multiple-slave game optimization model.

[0144] Multi-type township integrated energy system is based on the electricity purchase price C released by township integrated energy service providers i,sell,IESS and electricity price C i,buy,IESS , through its own game strategy optimization to determine the optimal power purchase quantity P between the two i,buy,IESS and the optimal electricity sales quantity P i,sell,IESS , as shown in formula (25):

[0145]

[0146] The township integrated energy service provider determines the optimal electricity purchase price C based on the operation of each township integrated energy system and the optimal utility function of its own game strategy. i,sell,IESS and electricity price C i,buy,IESS , as shown in formula (26):

[0147]

[0148] Among them, C i,buy,IESS and C i,sell,IESS are the optimal electricity purchase price and electricity sales price respectively, P i,buy,IESS and P i,sell,IESS The optimal purchasing power and the optimal selling power are determined by optimizing their own game strategies.

[0149] Solving the master-slave game model requires finding an equilibrium solution. Since the strategy spaces of township integrated energy service providers and multi-type township integrated energy systems are both non-empty compact convex sets, and the utility functions of leaders and followers are both quasi-convex functions, it can be seen from equations (25) and (26) that when the leader / follower game strategy has an optimal solution, there is only one optimal solution for the leader and follower.

[0150] Optimization method test results

[0151] Using the optimization method provided by the present invention, four different types of township integrated energy systems and one township integrated energy service provider were selected for optimization, and the obtained optimization results were studied. The load demand of the four different types of township integrated energy systems and the predicted power of photovoltaic and wind power are shown in Figure 2. Figures 3 to 6 The equipment parameters of each township integrated energy system are shown in Table 5. The electricity purchase price of each township integrated energy system from the county power grid is shown in Table 6. The unit transmission fee cost between each township integrated energy system is 0.1 yuan / kWh, and the fuel purchase price is 3.1 yuan / m 3 .

[0152] Table 5 Equipment parameters for different types of township integrated energy systems

[0153]

[0154]

[0155] Table 6 The electricity price of the township integrated energy system purchased from the county power grid (yuan / kWh)

[0156] Table 6Tariffs for electricity purchased from the county grid for township integrated energy systems (yuan / kWh)

[0157]

[0158] Figure 3 This is a result diagram of the predicted power of electric cooling and heating loads, photovoltaic power and wind power of the township integrated energy system in the first scenario predicted by the optimization method provided by the present invention; Figure 4A result diagram of the predicted power of electric cooling and heating loads, photovoltaic power and wind power of the township integrated energy system in the second scenario predicted by the optimization method provided by the present invention; Figure 5 This is a result diagram of the predicted power of the third scenario township integrated energy system electric cooling and heating load, photovoltaic and wind power predicted by the optimization method provided by the present invention; Figure 6 This is a graph of predicted power results of the electric cooling and heating loads, photovoltaic and wind power of the fourth scenario township integrated energy system predicted using the optimization method provided by the present invention.

[0159] The first scenario (Scenario 1) is the power of the joint operation of four types of township multi-integrated energy systems participating in electricity trading with township integrated energy service providers; the second scenario (Scenario 2) is the power of the non-joint operation of the four types of township integrated energy systems participating in electricity trading with township integrated energy service providers; the third scenario (Scenario 3) is the power of the joint operation of the four types of township integrated energy systems not participating in electricity trading with township integrated energy service providers; the fourth scenario (Scenario 4) is the power of the non-joint operation of the four types of township integrated energy systems not participating in electricity trading with township integrated energy service providers.

[0160] The cost optimization results of township integrated energy service providers and multi-type integrated energy systems under four scenarios are shown in Table 7.

[0161] Table 7 Analysis of cost optimization results under four scenarios (yuan / kWh)

[0162]

[0163] From Table 7, by comparing Scenario 1, Scenario 2 and Scenario 3, the results show that the operating cost of IESS and the total operating cost of IES have both decreased, indicating that participating in the electricity trading of township integrated energy service providers and the formation of joint electricity trading between township multi-integrated energy systems can not only reduce the operating cost of township integrated energy service providers, but also reduce the operating cost of township rural integrated energy systems. By comparing Scenario 1 and Scenario 2, the results show that the operating cost of IESS has decreased while the total operating cost of IES has increased, indicating that the joint state of multi-township integrated energy systems can have a greater advantage in the game with IESS and reduce the total operating cost, while the township integrated energy service provider can obtain lower operating costs when the township multi-integrated energy system is not combined. By comparing Scenario 3 and Scenario 4, the results show that the joint operation optimization of township rural multi-integrated energy systems is conducive to reducing operating costs.

[0164] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. An optimization method for the coordinated operation of multi-type integrated energy systems, characterized in that: The steps include: Step S01: Divide the types of township integrated energy systems, and model them according to the system types to obtain multiple township integrated energy system models; Step S02: Calculating the operating cost based on the multiple township integrated energy system models and the local two-layer balancing mechanism, with minimizing the operating cost as the objective function; Step S03: constructing a multi-type integrated energy system game optimization model and constraint conditions according to the objective function; Step S04: Seeking the optimal solution for the game optimization model and constraint conditions of the multi-type integrated energy system to obtain an optimization plan for the coordinated operation of the multi-type integrated energy system.

2. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 1, characterized in that: The multi-type integrated energy system game optimization model includes: Among them, C i,sell,IESS and C i,buy,IESS They are respectively based on the purchase and sale prices of electricity issued by the township integrated energy service providers for multiple types of township integrated energy systems, P i,buy,IESS and P i,sell,IESS The optimal purchasing power and the optimal selling power are determined by optimizing their own game strategies.

3. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 1, characterized in that: The optimal solution is the only definite solution that satisfies the multi-type integrated energy system game optimization model and constraint conditions at the same time.

4. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 2, characterized in that: The multi-type integrated energy system game optimization model also includes: Among them, C i,buy,IESS and C i,sell,IESS are the optimal electricity purchase price and electricity sales price respectively, P i,buy,IESS and P i,sell,IESS The optimal purchasing power and the optimal selling power are determined by optimizing their own game strategies.

5. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 1, characterized in that: The constraints include: Power balance constraints: Among them, P i,b,use is the usage of the bth new energy source in the ith IES; P i,buy The i-th IES purchases electric power from other IES; Pi,dis is the discharge power of the battery; P i,buy,grid The i-th IES purchases power from the county power grid; P i,load The i-th IES electrical load, P i,cha is the battery discharge power; P i,sell is the electricity sales power of the i-th IES.

6. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 1, characterized in that: The constraints also include: Output constraints of each power supply: 0≤P i,b (t)≤P i,b,max Among them, P i,b,max is the maximum output power of the bth power source of the i-th IES; Exchange constraints between towns: Among them, P buy / sell,max is the maximum exchange power between IES; Constraints on integrated energy service providers in townships: Among them, P IESS,max It is the maximum interaction power between IES and the township integrated energy service provider; County power grid constraints: 0≤P i,buy,grid (t)≤P grid,max Among them, P grid,max is the maximum interactive power between IES and the county power grid.

7. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 1, characterized in that: The objective function includes an upper layer optimization objective function and a lower layer optimization objective function: The upper optimization objective function is used to minimize the operating cost of the township integrated energy service provider; The lower optimization objective function is used to minimize the total operating cost of multi-type township integrated energy systems.

8. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 7, characterized in that: Upper layer optimization objective function C IESS The minimum value of is: C1=C i,sell,IESS (t)P i,sell,IESS (t) C2=C i,buy,IESS (t)P i,buy,IESS (t) Among them, C1 is the total revenue from electricity sales of multi-type township integrated energy systems to township integrated energy service providers; C2 is the total cost of electricity purchase from township integrated energy service providers by multi-type township integrated energy systems; P i,sell,IESS Selling power to township integrated energy service providers for the i-th integrated energy system; C i,buy,IESS P is the price of electricity purchased by the i-th integrated energy system from the township integrated energy service provider; i,buy,IESS Purchase electricity for the i-th integrated energy system from the township integrated energy service provider.

9. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 7, characterized in that: The lower layer optimizes the objective function C IES The minimum value of is: C3=C i,buy,grid (t)P i,buy,grid (t) C4=μ ESS P i,buy (t) C5=C price,gas (t)·p i,gas (t) Among them, C3 is the cost of purchasing electricity from the county power grid; C4 is the internal power exchange cost, which is mainly the network fee generated by the power exchange between various integrated energy systems; C5 is the gas purchase cost of the system; C i,buy,grid is the price of electricity purchased from the county power grid; P i,buy,grid is the power purchased from the county power grid; μ ESS P is the unit network fee; i,buy Purchase power from other systems for the i-th system; C price,gas is the gas price, p i,gas is the gas power consumption of the comprehensive energy system at moment t.

10. The optimization method for coordinated operation of multi-type integrated energy systems according to claim 1, characterized in that: The multiple township comprehensive energy system models include a centralized energy consumption type township comprehensive energy system model, a household life energy consumption type township comprehensive energy system model, an agricultural breeding energy consumption type township comprehensive energy system model and an industrial energy consumption type township comprehensive energy system model.

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