Optimized scheduling method for wind-solar-water tile integrated unit

Abstract

The invention discloses a wind-light-water-tile integrated unit optimization scheduling method which specifically comprises the following steps: step 1, constructing a wind-light-water-tile integrated topological structure of a city based on a wind power generation system, a photovoltaic power generation system, a hydroelectric power generation system and a gas power generation system of the city; 2, aiming at different types of gas power generation resources, establishing a distributed gas power generation resource adjustment model; the distributed gas power generation resource adjusting model comprises an adjustable gas power generation model and a direct power generation gas model; 3, on the basis of the distributed gas power generation resource adjustment model established in the step 2, an optimization model is established with the purpose of minimizing the total economic cost; and 4, solving the optimization model to obtain optimal configuration and scheduling strategies of various power generation systems. According to the invention, efficient scheduling of the energy system can be realized, reliability, economy and environmental benefits of energy supply are guaranteed, and powerful support is provided for sustainable development of clean energy.

Description

Technical Field

[0001] The present invention belongs to the field of energy dispatching, and in particular relates to an optimization dispatching method for a wind-solar-water-tile integrated unit. Background Art

[0002] With the acceleration of global energy transformation, the proportion of clean energy and renewable energy continues to increase, and green energy such as wind energy, solar energy and hydropower has gradually become the main force of electricity supply. However, these renewable energy sources are intermittent and volatile, which poses certain challenges to the stability and security of the power grid. In order to improve the stability and regulation capacity of the energy system, gas power generation, as a flexible and regulated traditional energy, has gradually been incorporated into the multi-energy system, and coordinated with renewable energy such as wind, light and water, becoming an important supplement to the energy system. By optimizing the combination of different energy sources, the fluctuation of the power grid can be effectively reduced and the stability of power supply can be guaranteed. However, in a complex multi-energy environment, how to achieve efficient coordination and scheduling between gas power generation and other renewable energy sources has become a key issue that needs to be solved urgently.

[0003] Traditional wind, solar, water and gas integrated scheduling methods usually optimize each energy source separately, and fail to effectively achieve coordinated scheduling between different energy systems. In this decentralized scheduling, wind energy, solar energy and hydropower operate independently, and the scheduling strategy is based on historical data and static models, ignoring the dynamic interaction between energy sources. This method is slow to respond to the volatility and intermittency of renewable energy, and it is difficult to flexibly adjust according to real-time changes, resulting in the system being unable to give full play to the advantages of each energy source when facing load fluctuations or changes in resource supply. For example, when wind energy is insufficient or solar energy is unstable, the system cannot use gas power generation to supplement it in time, resulting in waste of energy resources and low system operation efficiency. In addition, the lack of coordinated scheduling also makes the system scheduling inflexible, and it is impossible to achieve the optimal energy configuration when multiple energy sources are running in parallel, resulting in a decrease in scheduling efficiency and potential safety hazards in energy supply.

[0004] Traditional methods often fail to fully tap the regulation capacity of gas power generation in the scheduling of gas power generation. As a flexible regulating energy source, gas power generation can provide balancing support when load changes and renewable energy fluctuates. However, traditional scheduling models usually ignore the key role of gas power generation in the system and fail to adjust the output power of gas power generation in real time according to system load changes and fluctuations in renewable energy output. In addition, traditional methods do not fully consider the storage and regulation capacity of gas resources, resulting in the failure to maximize the regulation role of gas power generation. Since the supply of gas resources is limited by multiple factors such as coal mining and geological conditions, traditional methods cannot effectively deal with the volatility and supply uncertainty of gas resources, resulting in gas generators being unable to provide sufficient regulation capacity in time when resources are tight or demand fluctuates, thereby affecting the economy and reliability of the system. Therefore, the existing methods have major deficiencies in resource scheduling and mining of gas power generation, making it difficult to achieve the optimized operation of the energy system. Summary of the invention

[0005] Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the present invention provides an optimization scheduling method for a wind-solar-water-tile integrated unit;

[0006] Technical solution: The present invention discloses a method for optimizing the dispatching of a wind-solar-water-tile integrated unit, which specifically includes the following steps:

[0007] Step 1: Based on the city’s wind power generation system, photovoltaic power generation system, hydropower generation system and gas power generation system, build the city’s wind, photovoltaic, hydropower and gas integrated topological structure;

[0008] Step 2: Establish a distributed gas power generation resource regulation model for different types of gas power generation resources; the distributed gas power generation resource regulation model includes an adjustable gas power generation model and a direct power generation gas model;

[0009] Step 3: Taking the distributed gas power generation resource regulation model established in step 2 as a constraint condition, an optimization model is constructed with the goal of minimizing the total economic cost;

[0010] Step 4: Solve the optimization model to obtain the optimal configuration and scheduling strategy for various power generation systems.

[0011] Furthermore, step 1 is specifically as follows:

[0012] The nodes in the wind-solar-water-gas integrated topology are set, including power generation nodes, load nodes and conversion nodes. The power generation nodes include wind farms, photovoltaic power stations, hydropower stations and gas power plants; the load nodes include residential areas, industrial areas and commercial areas; the conversion nodes include substations and key hubs in the distribution network; and the nodes are connected through branches;

[0013] Collect branch information, including topological structure, transmission capacity, and impedance parameters of distribution lines; determine the connection relationship between nodes based on branch information;

[0014] Wind power generation systems, photovoltaic power generation systems, hydropower generation systems and gas power generation systems are matched with corresponding power generation nodes, and the power demand of load nodes is matched with the transmission path in the distribution system.

[0015] Furthermore, the expression of the adjustable gas power generation model in step 2 is as follows:

[0016] S t =S t-1 +(S in,t -S out,t )Δt

[0017] b in,t S in,min ≤S in,t ≤ b in,t S in,max

[0018] b out,t S out,min ≤S out,t ≤ b out,t S out,max

[0019] b in,t +b out,t ≤1

[0020] S min ≤S t ≤S max

[0021] P G1,t =η G1 ρ G1,t C G V G1,t

[0022] 0≤V G1,t ≤V G1,max

[0023] P G1,min ≤P G1,t ≤P G1,max

[0024] -P G1,down ≤P G1,t -P G1,t-1 ≤P G1,up

[0025] Among them, S t , S t-1 are the gas storage volume in the gas tank at time t and t-1, Sin,t , S out,t are the injection and release amounts of gas in the gas storage tank at time t; Δt is the time step; b in,t The variable of 0 or 1 indicates the state of gas injection in the gas tank at time t, 0 indicates relationship, 1 indicates opening, b out,t The variable of 0 or 1 indicates the state of gas release from the gas storage tank at time t; S in,min YesS in,max are the minimum and maximum values ​​of gas injected into the gas storage tank per unit time, S out,min and S out,max are the minimum and maximum values ​​of gas released by the gas storage tank per unit time, S min , S max are the minimum and maximum values ​​of gas stored in the gas storage tank, G1 represents the gas generator set of the gas storage tank, and P G1,t , P G1,t-1 are the power generation of the gas generator set with gas storage tank at time t and t-1, η G1 is the power generation efficiency of the gas generator set with gas storage tank, ρ G1,t is the gas volume fraction injected into the gas generator set containing the gas storage tank at time t, C G is the lower calorific value of gas, V G1,t is the volume of gas injected into the gas generator set containing the gas storage tank at time t, V G1,max is the maximum volume of gas injected into the gas generator set containing the gas storage tank, P G1,min , P G1,max are the minimum and maximum power generation of the gas generator set with gas storage tank, P G1,down , P G1,up They are the downslope rate and the climbing rate of the gas generator set with gas storage tank respectively;

[0026] The expression of direct power generation gas model is:

[0027] P G2,t =η G2 ρ G2,t C G V G2,t

[0028] P G2,min ≤P G2,t ≤P G2,max

[0029] -P G2,down ≤P G2,t -P G2,t-1 ≤P G2,up

[0030] Among them, G2 represents the gas generator set for direct power generation, PG2,t is the power generation of the gas generator set directly generating electricity at time t, P G2,t-1 is the power generation of the gas generator set directly generating electricity at time t-1; ρ G2,t C is the gas volume fraction injected into the gas generator set for direct power generation at time t; G is the lower calorific value of gas, V G2,t is the volume of gas injected into the gas generator set for direct power generation at time t, P G2,min , P G2,max are the minimum and maximum power generation of gas generator sets for direct power generation; P G2,down , P G2,up They are respectively the downslope rate and climbing rate of the gas generator set for direct power generation.

[0031] Furthermore, the method further includes establishing priority constraints and a coordinated scheduling strategy for the optimization model in step 3 based on the distributed gas power generation resource regulation model in step 2, wherein the priority constraints are to give priority to using gas generator sets that directly generate electricity to meet load demand and adjustable power generation gas as a backup:

[0032] P G2,t =min(P load,t -P wind,t -P solar,t -P hydro,t +P loss,t ,P G2,max )

[0033] P load,t is the actual power load demand of the system at time t, P wind,t , P solar,t , P hydro,t are the power generation of the wind power generation system, photovoltaic power generation system, and hydropower generation system at time t, respectively, loss,t is the system loss at time t, P G2,max is the maximum power generation of the gas generator set for direct power generation. When the output power of the gas generator set for direct power generation reaches the maximum value, P G2,t =P G2,max , any remaining load will be supplied by the adjustable power generation gas P G1,t To supplement, the specific expression is as follows:

[0034] P G1,t =max(P load,t -P wind,t -P solar,t -P hydro,t +P loss,t ,P G1,min )

[0035] The adjustable power generation gas is a gas generator set containing a gas storage tank;

[0036] The coordinated dispatching strategy includes: during the peak load period, the gas generator sets that directly generate electricity give priority to responding to the load demand, and the gas generator sets with gas storage tanks store gas when the load decreases.

[0037] Furthermore, the expression of the optimization model in step 3 is:

[0038]

[0039] Where T represents the total time, C1, C2, C3, C4, C5, and C6 represent the gas power generation cost, wind power generation cost, photovoltaic power generation cost, hydropower generation cost, system loss cost, and electricity purchase cost respectively; the expressions of C1, C2, C3, C4, C5, and C6 are:

[0040] C1=a1P G1,t +b1+C storage,t +a2P G2,t +b2

[0041] C2=C wind,om P wind,t +C wind,c (P wind,a,t -P wind,t )

[0042] C3=C solar,om P solar,t +C solar,c (P solar,a,t -P solar,t )

[0043] C4=C hydro,om P hydro,t +C hydro,u Q hydro,t

[0044] C5=C energy E loss,t

[0045] C6=p ele,t P buy,t

[0046] Among them, a1 and b1 are the power generation cost and fixed operating cost of the adjustable gas power generation model respectively; a2 and b2 are the power generation cost and fixed operating cost of the direct power generation gas model respectively; C storage,t is the cost of releasing gas from the gas storage tank at time t, C storage,t =C in S in,t +C out S out,t , C in , Cout are the unit costs of injecting and releasing gas into the gas storage tank; C wind,om is the unit operation and maintenance cost of wind power generation, P wind,t , P solar,t , P hydro,t are the power generation of wind power generation system, photovoltaic power generation system and hydropower generation system at time t; C wind,c is the wind curtailment cost of the wind power generation system, P wind,a,t is the available wind power generation at time t, C solar,om is the unit operation and maintenance cost of photovoltaic power generation, C solar,c is the cost of abandoned light in the photovoltaic power generation system, P solar,a,t is the available photovoltaic power generation at time t; C hydro,om is the unit operation and maintenance cost of hydropower generation, C hydro,u is the unit cost of water resources use, Q hydro,t is the water flow used for power generation at time t, C energy is the unit electricity cost, E loss,t is the system energy loss at time t, Ω bc is the set of nodes in the topological structure, P i'j',t is the active power between nodes i' and j' at time t, r i'j' is the resistance between nodes i' and j'.

[0047] Furthermore, the constraints of the optimization model in step 3 also include: volatility constraints of wind power generation and photovoltaic power generation, grid load demand constraints, distribution network flow constraints, equipment operation constraints, generator set output constraints, generator set operation status constraints, safe scheduling constraints of gas resources, gas pressure balance constraints, gas supply reliability constraints, gas supply chain optimization constraints and power balance constraints.

[0048] Furthermore, the expression of the volatility constraint of wind power generation and photovoltaic power generation is:

[0049]

[0050] Where ΔP wind,t It represents the deviation between the actual wind power generation and the predicted power generation at time t. represents the expected value, ΔP solar,t It represents the deviation between the actual solar power generation and the predicted power generation at time t;

[0051] The grid load demand constraint is:

[0052] P load,t =P load,p,t +ΔP load,t

[0053] Among them, P load,t , P load,p,t are the actual load demand and load forecast of the system at time t; ΔP load,t is the electric load prediction error at time t;

[0054] The power flow constraint of the distribution network is:

[0055]

[0056] U min ≤U i,t ≤U max

[0057] Q i,min ≤Q i,t ≤Q i,max

[0058] Where: i, j and h are all nodes in the topological structure of the urban power distribution system, and node i is the upstream node of node j, and node j is the upstream node of node h; Ω bc is a node set; t represents the time period; P ij,t , Q ij,t are respectively the active power and reactive power from node i to node j, r ij is the resistance between node i and node j; α i,t , β ij,t , α j,t are slack variables, I ij,t is the current from node i to node j at time t, U i,t , U j,t are the voltage amplitudes of nodes i and j at time t respectively; P j,t , Q j,t are the injected active power and reactive power of node j at time t respectively; ij is the reactance between node i and node j; P jh,t , Q jh,t are respectively the active power and reactive power from node j to node h; U min , U max are the lower and upper limits of the node voltage amplitude, Q i,min , Q i,max are the minimum and maximum capacities of the reactive power compensation device at node i in the urban distribution network, Q i,t is the reactive compensation amount at the node i of the urban distribution network at time t;

[0059] The operation constraints of the equipment include the generator set output constraint and the generator set operation state constraint. The expression of the generator set operation state constraint is as follows:

[0060] Δu G1,t ∈{-1,0,1}

[0061] Δu G2,t ∈{-1,0,1}

[0062] Among them, Δu G1,t , Δu G2,t They are the changes in the operating status of the gas generator set with a gas storage tank and the state change of the gas generator set for direct power generation, 1 means starting at time t, -1 means stopping at time t, and 0 means no state change;

[0063] The gas pressure balance constraint is:

[0064]

[0065] P gas,min ≤P gas,t ≤P gas,max

[0066] Among them, P gas,t , P gas,t-1 are the gas tank pressures at time t and t-1 respectively; C p is the system gas pressure volume coefficient; S in,t , S out,t are the injection and release amounts of gas in the gas storage tank at time t; Δt is the time step; V gas is the volume of gas tank; P gas,min , P gas,max They are the minimum and maximum pressures of the gas storage tank respectively;

[0067] The gas supply reliability constraint is:

[0068]

[0069] Among them, Q em is the emergency gas reserve, R gas Redundancy rate for gas supply;

[0070] The gas supply chain optimization constraints are:

[0071]

[0072] Among them, I represents coal mine I, D I,t is the amount of gas obtained from coal mine I at time t, V G2,t is the volume of gas injected into the gas generator set for direct power generation at time t; x I,t is the location variable of coal mine I at time t, N re is the redundant number of coal mines, C tris the unit transportation cost of gas, C bud budgeting for gas transportation costs;

[0073] The power balance constraint is:

[0074] P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t ≥P load,p,t +P loss,t +ΔP load,min,t

[0075] P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t ≤P load,p,t +P loss,t +ΔP load,max,t

[0076] Among them, P G1,t is the power generation of the gas generator set with gas storage tank at time t, P G2,t is the power generation of the gas generator set directly generating electricity at time t, P wind,t , P solar,t , P hydro,t are the power generation of the wind power generation system, photovoltaic power generation system, and hydropower generation system at time t, respectively, loss,t is the system loss at time t, ΔP load,min,t , ΔP load,max,t are the minimum and maximum values ​​of load forecast error, respectively.

[0077] Furthermore, it includes a processor and a memory, the memory stores execution instructions of the processor, and the processor is configured to execute the execution instructions to implement the wind-solar-water-tile integrated unit optimization scheduling method as described in any one of claims 1-7.

[0078] Furthermore, the program is executed to implement the optimization scheduling method of the wind-solar-water-tile integrated unit described in any one of claims 1-7.

[0079] Beneficial effects: This paper proposes an optimization scheduling method and system for wind, solar, hydropower and gas integrated units. This method establishes a more flexible scheduling model by comprehensively considering the dynamic characteristics of wind, solar, hydropower and gas power generation, and can respond to supply and demand fluctuations of different energy sources in real time. In the scheduling of gas power generation, special consideration is given to the storage and regulation capacity of gas resources, so that it can provide necessary flexible support when renewable energy fluctuates. At the same time, the coordinated scheduling strategy between wind, solar, hydropower and gas units is optimized to improve the overall operating efficiency and responsiveness of the system. Through this method, efficient scheduling of the energy system can be achieved, the reliability, economy and environmental benefits of energy supply can be guaranteed, and strong support can be provided for the sustainable development of clean energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The present invention is a flow chart of the method.

[0081] Figure 2 This is the structural topology diagram of the wind-solar-water-tile integrated unit system of the present invention.

[0082] Figure 3 The photovoltaic, wind and hydroelectric power sources of the present invention are predicted to produce power diagrams in the past day.

[0083] Figure 4 This is a diagram showing the optimization scheduling results of the wind-solar-water-tile integrated unit of the present invention. DETAILED DESCRIPTION

[0084] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0085] like Figure 1 , 2 shows an optimization scheduling method and system for a wind-solar-water-tile integrated unit provided by the present invention, comprising the following steps:

[0086] Step 1: Based on the selected urban power distribution system, build a wind, solar, water and tile integrated system topology. Input basic data such as system nodes, branch information, wind power generation forecast value, photovoltaic power generation forecast value, and hydropower generation forecast value.

[0087] Step 2: Based on the system topology provided in step 1, a distributed gas power generation resource regulation model is established for different types of gas power generation resources, including an adjustable gas power generation model, a direct power generation gas model, and a collaborative optimization model.

[0088] Step 3: Based on the system topology provided in step 1 and the gas power generation model provided in step 2, a dispatch optimization model for wind, solar, hydropower and gas integration is constructed. The model aims to minimize the total economic cost and comprehensively considers the regulation capacity of gas resources, the volatility of wind, solar and hydropower generation, and the load demand of the power grid. The optimization model needs to consider the power flow constraints of the distribution network, the operation constraints of the equipment, the safe dispatch constraints of gas resources, and the power balance constraints.

[0089] Step 4: Use the solver to solve the wind, solar, water and gas integrated optimization scheduling model provided in step 3. The solution process will dynamically schedule the output of gas power generation according to the real-time grid load and the fluctuation of renewable energy, and determine the optimal configuration and scheduling strategy of various types of wind, solar, water and gas resources. Output the optimized gas power generation resource configuration capacity and scheduling results.

[0090] (1) Based on the selected urban power distribution system, construct a wind, solar, hydropower and water-power integrated system topology. Input basic data such as system nodes, branch information, wind power generation forecast value, photovoltaic power generation forecast value, and hydropower generation forecast value.

[0091] For this embodiment, building a wind, photovoltaic, hydropower and gas integrated system topology is the basis and prerequisite for realizing multi-energy coordinated optimization scheduling. This step aims to integrate wind, photovoltaic, hydropower and gas power generation resources through a systematic approach to build an efficient, stable and flexible integrated energy system. Specifically, the implementation process of step 1 includes the following key links:

[0092] 1.1 Select the city power distribution system

[0093] First, the power distribution system of the target city needs to be selected as the research object. The selected city should have the distribution characteristics of various renewable energy resources, such as coastal cities or mountainous areas with rich wind energy resources, areas with sufficient sunshine and abundant photovoltaic resources, and areas near waters with relatively rich hydropower resources. Through a detailed understanding of the urban power distribution system, it can be ensured that the constructed topological model has a realistic foundation and practical application value. The urban power distribution system selected in this embodiment contains four types of power generation resources: wind, solar, water and tile.

[0094] 1.2 Collect and organize basic data

[0095] The key to building a system topology lies in the comprehensiveness and accuracy of data. The following basic data needs to be collected:

[0096] System node information: including generator nodes, load nodes, substation nodes, etc. These nodes represent the key locations of energy input, output and consumption, and are the basic units for building topological structures.

[0097] Branch information: covers the topology, transmission capacity, impedance parameters, etc. of the distribution line. Branch information determines the connection mode and energy transmission path between nodes, which directly affects the stability and scheduling efficiency of the system.

[0098] Wind power generation forecast: Based on historical wind speed data and weather forecasts, statistical models or machine learning algorithms are used to predict wind power generation within a certain period of time in the future. These forecasts help the system plan the use of wind energy resources in advance and reduce the uncertainty caused by wind fluctuations.

[0099] Photovoltaic power generation forecast value: By analyzing solar radiation data and combining the performance parameters of photovoltaic modules, the power generation of photovoltaic power generation systems in different time periods is predicted. Photovoltaic power generation forecast is of great significance for optimizing scheduling and ensuring stable power supply.

[0100] Hydropower generation forecast: Based on hydrological data and the operating characteristics of hydropower generators, the hydropower generation in the future is predicted. Hydropower generation is often used to balance the volatility of other renewable energy sources due to its strong adjustability.

[0101] 1.3 Building a system topology model

[0102] After collecting and organizing the basic data, the next step is to build a system topology model. This process includes the following steps:

[0103] Node definition and classification: Based on the system node information, nodes are divided into power generation nodes, load nodes and conversion nodes. Power generation nodes include wind farms, photovoltaic power stations, hydropower stations and gas power plants; load nodes cover residential areas, industrial areas and commercial areas; conversion nodes mainly refer to substations and key hubs in the distribution network.

[0104] Branch connection and parameter setting: Based on the branch information, determine the connection relationship between nodes and set the corresponding line parameters, such as line impedance, capacity, etc. These parameters determine the transmission efficiency and transmission capacity of electric energy in the system and are the key to ensuring the safe operation of the system.

[0105] Power generation and load matching: Match wind, photovoltaic, hydropower and gas power generation resources with corresponding power generation nodes to ensure that the power generation of each power generation node is consistent with its corresponding energy resources. At the same time, match the power demand of the load node with the transmission path in the distribution system to ensure that the power supply can meet the actual demand.

[0106] Taking all the above factors into consideration, Figure 1As shown, the urban power distribution system constructed in this embodiment includes 33 nodes and 32 branches, among which node No. 5 is connected to photovoltaic power generation, node No. 21 is connected to wind power generation, node No. 14 is connected to hydropower generation, node No. 4 is connected to an adjustable gas power station with a gas storage tank, and node No. 23 is connected to a direct power generation gas power station.

[0107] (2) Based on the system topology provided in step 1, a distributed gas power generation resource regulation model is established for different types of gas power generation resources, including an adjustable gas power generation model and a direct power generation gas model.

[0108] After completing the wind, solar, water and gas integrated system topology constructed in step 1, the next step is to establish a distributed gas power generation resource regulation model for different types of gas power generation resources. This step will build two types of gas power generation models in detail: adjustable gas power generation model and direct power generation gas model, and accurately describe them through mathematical formulas to achieve effective regulation and optimization of gas power generation resources.

[0109] 2.1 Adjustable gas power generation model

[0110] The adjustable gas power generation model refers to a gas power generation system equipped with a gas storage tank. This system can not only adjust the power generation output according to the grid demand, but also has the ability to store excess gas, thereby providing flexible adjustment support when renewable energy fluctuates. Its mathematical model is as follows, where equations (1) to (5) are gas storage tank constraints, and equations (6) to (9) are gas generator output constraints with gas storage tanks.

[0111] S t =S t-1 +(S in,t -S out,t )Δt (1)

[0112] b in,t S in,min ≤S in,t ≤ b in,t S in,max (2)

[0113] b out,t S out,min ≤S out,t ≤ b out,t S out,max (3)

[0114] b in,t +b out,t ≤1 (4)

[0115] S min ≤S t ≤S max (5)

[0116] P G1,t =η G1 ρ G1,t C G V G1,t (6)

[0117] 0≤V G1,t ≤V G1,max (7)

[0118] P G1,min ≤P G1,t ≤P G1,max (8)

[0119] -P G1,down ≤P G1,t -P G1,t-1 ≤P G1,up (9)

[0120] Among them, S t , S t-1 are the gas storage volume in the gas tank at time t and t-1, S in,t , S out,t are the injection and release amounts of gas in the gas storage tank at time t; Δt is the time step; b in,t The variable of 0 or 1 indicates the state of gas injection in the gas tank at time t, 0 indicates relationship, 1 indicates opening, b out,t The variable of 0 or 1 indicates the state of gas release from the gas storage tank at time t; S in,min YesS in,max are the minimum and maximum values ​​of gas injected into the gas storage tank per unit time, S out,min and S out,max are the minimum and maximum values ​​of gas released by the gas storage tank per unit time, S min , S max are the minimum and maximum values ​​of gas stored in the gas storage tank, G1 represents the gas generator set of the gas storage tank, and P G1,t , P G1,t-1 are the power generation of the gas generator set with gas storage tank at time t and t-1, η G1 is the power generation efficiency of the gas generator set with gas storage tank, ρ G1,t is the gas volume fraction injected into the gas generator set containing the gas storage tank at time t, C G is the lower calorific value of gas, V G1,t is the volume of gas injected into the gas generator set containing the gas storage tank at time t, V G1,max is the maximum volume of gas injected into the gas generator set containing the gas storage tank, P G1,min , P G1,max are the minimum and maximum power generation of the gas generator set with gas storage tank, PG1,down , P G1,up They are respectively the downhill rate and climbing rate of the gas generator set with a gas storage tank.

[0121] 2.2 Direct power generation gas model

[0122] The direct power generation gas model refers to a gas power generation system without a gas storage tank, whose power generation output directly depends on the real-time gas supply, similar to a traditional thermal power generator. The regulation capability of this system is relatively limited, and it is mainly suitable for scenarios with small load fluctuations or working in conjunction with other flexible regulation resources. Due to the lack of storage capacity, the gas power generation output is directly limited by the gas supply and the regulation capability of the generator set. Its mathematical model is as follows:

[0123] P G2,t =η G2 ρ G2,t C G V G2,t (10)

[0124] P G2,min ≤P G2,t ≤P G2,max (11)

[0125] -P G2,down ≤P G2,t -P G2,t-1 ≤P G2,up (12)

[0126] Among them, G2 represents the gas generator set for direct power generation, P G2,t is the power generation of the gas generator set directly generating electricity at time t, P G2,t-1 is the power generation of the gas generator set directly generating electricity at time t-1; ρ G2,t C is the gas volume fraction injected into the gas generator set for direct power generation at time t; G is the lower calorific value of gas, V G2,t is the volume of gas injected into the gas generator set for direct power generation at time t, P G2,min , P G2,max are the minimum and maximum power generation of gas generator sets for direct power generation; P G2,down , P G2,up They are respectively the downslope rate and climbing rate of the gas generator set for direct power generation.

[0127] Due to the lack of storage capacity, the direct power generation gas model must match the power generation output with the gas supply and system load demand in real time. This limits its flexibility in dealing with the volatility of renewable energy, but it can still effectively provide the necessary regulatory support for the system when the gas supply is relatively stable or when it works in conjunction with other energy storage systems.

[0128] 2.3 Cooperative optimization model of adjustable gas power generation and direct gas power generation

[0129] In the wind, solar, water and gas integrated system, the adjustable gas power generation model and the direct power generation gas model need to work together to achieve the overall optimized scheduling of the system. The specific coordinated scheduling strategy is: 1) Direct power generation gas (that is, direct power generation gas generator sets) is used first to meet the load demand to ensure the stable supply of the basic load; 2) When direct power generation gas cannot fully meet the load demand, the adjustable gas power generation model is started as a backup resource to supplement and adjust the load; 3) During the peak load period, direct power generation gas responds to the load demand first, while the adjustable gas power generation stores gas when the load decreases to achieve peak and valley smoothing; through the charging and discharging regulation of the gas storage tank, the adjustable gas power generation stores gas when there is excess renewable energy, reducing the energy waste of the system and improving the economy and efficiency of the overall scheduling.

[0130] In order to achieve the coordinated dispatch of adjustable gas power generation and direct power generation gas, priority constraints and coordinated dispatch strategies need to be introduced in the optimization model. The specific constraints include: giving priority to using direct power generation gas to meet load demand and adjustable gas power generation as a backup, as described below:

[0131] 2.3.1 Prioritize the use of direct power generation gas to meet load demand:

[0132] P G2,t =min(P load,t -P wind,t -P solar,t -P hydro,t +P loss,t ,P G2,max ) (13)

[0133] P load,t is the actual power load demand of the system at time t, P wind,t , P solar,t , P hydro,t are the power generation of the wind power generation system, photovoltaic power generation system, and hydropower generation system at time t, respectively, loss,t is the system loss at time t, P G2,max is the maximum power generation of the gas generator set for direct power generation. When the output power of the gas generator set for direct power generation reaches the maximum value, P G2,t =P G2,max , any remaining load will be supplied by the adjustable power generation gas P G1,t To supplement.

[0134] 2.3.2 Adjustable gas power generation as backup:

[0135] P G1,t =max(P load,t -P wind,t -Psolar,t -P hydro,t +P loss,t ,P G1,min ) (14)

[0136] Formula (14) ensures that the adjustable gas power generation starts when the direct power generation gas cannot fully meet the load demand, and is not lower than its minimum output power P G1,min .

[0137] (3) Based on the system topology provided in step 1 and the gas power generation model provided in step 2, a dispatch optimization model for wind, solar, hydropower and gas integration is constructed. The model aims to minimize the total economic cost and comprehensively considers the regulation capacity of gas resources, the volatility of wind, solar and hydropower generation, and the load demand of the power grid. The optimization model needs to consider the power flow constraints of the distribution network, the operation constraints of the equipment, the safe dispatch constraints of gas resources, and the power balance constraints.

[0138] 3.1 Minimizing total economic cost

[0139] The core goal of the optimization model is to minimize the total economic cost of the system. The total economic cost mainly includes the operating costs of various power generation resources, which can be specifically expressed as:

[0140]

[0141] C1=a1P G1,t +b1+C storage,t +a2P G2,t +b2 (16)

[0142] C storage,t =C in S in,t +C out S out,t (17)

[0143] C2=C wind,om P wind,t +C wind,c (P wind,a,t -P wind,t ) (18)

[0144] C3=C solar,om P solar,t +C solar,c (P solar,a,t -P solar,t ) (19)

[0145] C4=C hydro,om P hydro,t +C hydro,u Q hydro,t (20)

[0146] C5=Cenergy E loss,t (twenty one)

[0147]

[0148] C6=p ele,t P buy,t (twenty three)

[0149] Among them, a1 and b1 are the power generation cost and fixed operating cost of the adjustable gas power generation model respectively; a2 and b2 are the power generation cost and fixed operating cost of the direct power generation gas model respectively; C storage,t is the cost of releasing gas from the gas storage tank at time t, C storage,t =C in S in,t +C out S out,t , C in , C out are the unit costs of injecting and releasing gas into the gas storage tank; C wind,om is the unit operation and maintenance cost of wind power generation; C wind,c is the wind curtailment cost of the wind power generation system, P wind,a,t is the available wind power generation at time t, C solar,om is the unit operation and maintenance cost of photovoltaic power generation, C solar,c is the cost of abandoned light in the photovoltaic power generation system, P solar,a,t is the available photovoltaic power generation at time t; C hydro,om is the unit operation and maintenance cost of hydropower generation, C hydro,u is the unit cost of water resources use, Q hydro,t is the water flow used for power generation at time t, C energy is the unit electricity cost, E loss,t is the system energy loss at time t, Ω bc is the set of nodes in the topological structure, P i'j',t is the active power between nodes i' and j' at time t, r i'j' is the resistance between nodes i' and j'.

[0150] 3.2 Comprehensively consider the regulation capacity of gas resources, the volatility of wind, solar and hydropower generation, and the load demand of the power grid

[0151] In order to achieve efficient coordinated operation of multi-energy systems, the optimization model needs to comprehensively consider the following aspects:

[0152] 3.2.1 Regulation capability of gas resources

[0153] It includes the maximum output power, minimum output power, ramp rate and ramp rate of gas power generation, including equations (1)-(12).

[0154] 3.2.2 Volatility of wind, solar and hydropower generation

[0155] Wind and solar energy are highly random and intermittent, and need to be effectively compensated by the regulation capacity of gas power generation. The volatility of wind and solar power generation can be expressed as:

[0156] P wind,t =P wind,p,t +ΔP wind,t (twenty four)

[0157] P solar,t =P solar,p,t +ΔP solar,t (25)

[0158] Where: P wind,p,t , P solar,p,t are the predicted power generation of wind power and photovoltaic power at time t; ΔP wind,t , ΔP solar,t are the deviations between the actual power generation and the predicted power generation. They satisfy:

[0159]

[0160] Where: is the mathematical expectation.

[0161] Formula (26) indicates that at time t, the wind power change ΔP wind,t The expected value of is 0. This means that in a statistical sense, the change in wind power is balanced over a period of time, that is, the increase and decrease in power changes cancel each other out in the long run. Equation (27) indicates that at time t, the change in solar power ΔP solar,t The expected value of is 0. This also means that in a statistical sense, the change in solar power is balanced over a period of time, that is, the increase and decrease in power changes offset each other in the long run.

[0162] 3.2.3 Grid load demand

[0163] Grid load demand is the core factor in the operation of the power system. Its real-time changes directly affect the dispatch of power generation resources and the stability of the power grid. In order to ensure the stability and reliability of power supply, it is necessary to respond to changes in load demand in a timely manner through optimized dispatching strategies. In the wind, solar, hydropower and tile integrated system, accurate prediction and flexible response of grid load demand are crucial to the overall optimization of the system.

[0164] The characteristics of load demand include:

[0165] Time-varying: The load demand of the power grid changes over time, showing obvious daily and seasonal variations. For example, the load demand is different during the day and at night, and there are also significant differences between summer and winter.

[0166] Randomness: Load demand is affected by many random factors, such as weather, social activities and economic development, which makes load demand uncertain.

[0167] Spatial distribution: Load demands may vary in different regions, and the regional load distribution characteristics of the power grid need to be considered.

[0168] In order to effectively consider the real-time load demand in the dispatch optimization model, it is necessary to establish a mathematical model of the load demand and combine it with the optimization algorithm for real-time response.

[0169] P load,t =P load,p,t +ΔP load,t

[0170] Among them, P load,t , P load,p,t are the actual load demand and load forecast of the system at time t; ΔP load,t is the electric load prediction error at time t;

[0171] Load forecasting can be performed using methods such as time series analysis, regression models, or machine learning algorithms based on historical load data and influencing factors. This embodiment uses the autoregressive moving average model (ARIMA) for load forecasting:

[0172]

[0173] Where: φ0 is a constant term; φ i1 and θ j1 is the model parameter; ε j is the white noise error term. p is the order of the autoregressive part of the model; q is the order of the moving average part of the model.

[0174] 3.3 Power flow constraints of distribution network

[0175] The power flow constraints of the distribution network are to ensure the safe transmission of power in the distribution network. This paper uses radial power flow to describe the power transmission relationship between nodes:

[0176]

[0177] U min ≤U i,t ≤U max (28)

[0178] Q i，min ≤Qi,t ≤Q i，max (29)

[0179] Where: i, j and h are all nodes in the topological structure of the urban power distribution system, and node i is the upstream node of node j, and node j is the upstream node of node h; Ω bc is a node set; t represents the time period; P ij,t , Q ij,t are respectively the active power and reactive power from node i to node j, r ij is the resistance between node i and node j; α i,t , β ij,t , α j,t are slack variables, I ij,t is the current from node i to node j at time t, U i,t , U j,t are the voltage amplitudes of nodes i and j at time t respectively; P j,t , Q j,t are the injected active power and reactive power of node j at time t respectively; ij is the reactance between node i and node j; P jh,t , Q jh,t are respectively the active power and reactive power from node j to node h; U min , U max are the lower and upper limits of the node voltage amplitude, Q i,min , Q i,max are the minimum and maximum capacities of the reactive power compensation device at node i in the urban distribution network, Q i,t is the reactive compensation amount at node i of the urban distribution network at time t.

[0180] 3.4 Equipment Operation Constraints

[0181] The operation constraints of equipment are key constraints to ensure that various types of power generation equipment can operate stably within their technical and safety ranges. These constraints include the output limit of the generator set, the operating state limit, and the coordination relationship between equipment.

[0182] Specifically, the equipment operation constraints in this system mainly cover the following aspects:

[0183] 3.4.1 Generator output constraints

[0184] Each generator set (including adjustable gas generation and direct gas generation) has its minimum and maximum output limits to ensure that the equipment operates within a safe and efficient operating range, as shown in equations (1) to (12).

[0185] 3.4.2 Generator operating status constraints

[0186] The generator set needs to follow certain operating state transition rules during the start, stop and operation process to avoid equipment wear and unstable operation caused by frequent start and stop. The corresponding start and stop constraints are as follows:

[0187]

[0188] Among them, Δu G1,t , Δu G2,t They are respectively the change of the operating state of the gas generator set with a gas storage tank and the state change of the gas generator set for direct power generation. 1 means starting at time t, and -1 means stopping at time t.

[0189] 3.5 Constraints on safe dispatch of gas resources

[0190] The safe dispatch constraints of gas resources are designed to ensure the safe use and efficient management of gas in the power generation process, and to prevent safety hazards or resource waste caused by excessive use or insufficient storage. In addition to traditional tank storage constraints and gas supply restrictions, gas pressure balance constraints and gas supply reliability constraints should also be included.

[0191] 3.5.1 Gas pressure balance constraints

[0192] The gas pressure in the gas power generation system needs to be kept within a safe range to ensure the normal operation and safety of the equipment. The pressure balance equation can effectively control the pressure changes of the gas in the system.

[0193]

[0194] P gas,min ≤P gas,t ≤P gas,max (33)

[0195] Where: P gas,t , P gas,t-1 are the gas tank pressures at time t and t-1 respectively; C p is the system gas pressure volume coefficient; S in,t , S out,t are the injection and release amounts of the gas storage tank at time t; Δt is the time step; V gas is the volume of gas tank; P gas,min , P gas,max They are the minimum and maximum pressures of the gas tank respectively.

[0196] The pressure balance equation describes the change of gas pressure over time, which depends on the gas flow rate injected and released from the gas tank. Pressure limitation ensures that the gas pressure in the system is moderately maintained within a safe range to avoid equipment damage or abnormal operation due to excessive or low pressure.

[0197] 3.5.2 Gas supply reliability constraints

[0198] In order to ensure the continuity and reliability of gas supply, it is necessary to set up redundancy and emergency reserves for gas supply to prevent power generation shortages caused by supply interruptions.

[0199]

[0200] Where: Q em is the emergency reserve gas reserve; S out,t is the gas release amount of the gas storage tank at time t; R gas Redundancy rate for gas supply.

[0201] Redundancy constraints ensure that there is sufficient gas reserve in the system to cope with sudden interruptions in gas supply or sudden increases in demand. When gas supply is insufficient, emergency reserves can provide the necessary gas flow to ensure the continuous operation of the power generation system.

[0202] 3.5.3 Gas supply chain optimization constraints

[0203] The gas sources in the application scenario of the present invention are different coal mines. In order to further improve the safety and economy of gas supply, the gas supply chain can be optimized, including the selection of supply routes, supplier redundancy and transportation cost control.

[0204]

[0205] Among them, I represents coal mine I, D I,t is the amount of gas obtained from coal mine I at time t, V G2,t is the volume of gas injected into the gas generator set for direct power generation at time t; x I,t is the location variable of coal mine I at time t, N re is the redundant number of coal mines, C tr is the unit transportation cost of gas, C bud Budget for gas transportation costs.

[0206] Formula (35) ensures the balance of gas supply and ensures that the total amount of gas obtained from all selected suppliers is equal to the gas output demand; Formula (36) sets the redundant number of suppliers to improve the reliability of the supply chain; Formula (37) limits the gas transportation cost to not exceed the predetermined budget and optimizes economic benefits.

[0207] 3.6 Power Balance Constraints

[0208] P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t =P load,t+P loss,t (38)

[0209] In practical applications, due to the existence of load forecasting errors, it is necessary to introduce load balancing constraints and consider the impact of forecasting errors:

[0210] P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t ≥P load,p,t +P loss,t +ΔP load,min,t (39)

[0211] P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t ≤P load,p,t +P loss,t +ΔP load,max,t (40)

[0212] Where P loss,t is the system loss at time t, ΔP load,min,t , ΔP load,max,t are the minimum and maximum values ​​of load forecast error respectively. ΔP load,min,t , ΔP load,max,t According to historical data statistics:

[0213] ΔP load,min,t =-kσ load (41)

[0214] ΔP load,max,t = kσ load (42)

[0215] Where: k is the confidence interval coefficient, for example, k = 2 corresponds to a 95% confidence level; σ load is the standard deviation of load forecast error.

[0216] (4) Use the solver to solve the wind, solar, water and gas integrated optimization scheduling model provided in step 3. The solution process will dynamically schedule the output of gas power generation according to the real-time grid load and the fluctuation of renewable energy, and determine the optimal configuration and scheduling strategy of various types of wind, solar, water and gas resources. Output the optimized gas power generation resource configuration capacity and scheduling results.

[0217] 4.1 Solver selection and configuration

[0218] The solution of the optimization scheduling model needs to rely on an efficient and reliable mathematical optimization solver. According to the characteristics of the model in this embodiment (such as linear, continuous / integer variables), this embodiment selects the Gurobi solver for solution. Gurobi is known for its high performance and ease of use, supports multiple programming interfaces, and is suitable for complex mixed linear integer optimization problems.

[0219] 4.3 Dynamic Adjustment and Real-time Response

[0220] The optimization dispatch model needs to be able to respond to the fluctuations of grid load and renewable energy in real time. The specific implementation steps include:

[0221] Real-time data collection: Through smart meters, sensors and other equipment, load demand and renewable energy power generation data are collected in real time.

[0222] Data preprocessing: Clean, filter and predict the collected data to generate reliable input data.

[0223] Model update and re-optimization: Update model parameters and re-run the optimization solver based on the latest real-time data to generate a new scheduling strategy.

[0224] Execute scheduling strategies: transmit optimization results to generator sets and energy storage systems, and adjust power generation output and energy storage status in real time.

[0225] 4.4 Output optimization results

[0226] The output of the optimization solver includes the optimal configuration and scheduling strategy of various power generation resources, including the gas power generation resource configuration P G1,t , P G2,t ; Gas tank status S t , S in,t , S out,t ; Output of other power generation resources P wind,t , P solar,t , P hydro,t ; System loss P loss,t ; Economic cost C Total and the specific cost distribution of various types of power generation resources.

[0227] The system configuration and parameters involved in this embodiment are shown in Table 1; the time-of-use electricity price is shown in Table 2; Figure 3 Predict the output of photovoltaic, wind turbine and hydropower sources of the same type in the region on the previous day; Figure 4 This is the optimization scheduling result diagram of the wind, solar, water and tile integrated unit.

[0228] Table 1

[0229] equipment Unit maintenance cost (yuan / kW) Fan 0.07 Photovoltaic 0.06 Hydropower 0.08 Adjustable gas 0.045 Direct Gas 0.04 Gas tank <![CDATA[0.008 yuan / m 3 >

[0230] Table 2

[0231]

[0232] The hardware system configuration of this embodiment is: Inter Core i7-11370H processor, 64-bit Windows 11 operating system; the software system configuration is: MATLAB_R2021b software, calling the GUROBI solver (version 9.5.0) through the YALMIP toolkit for example simulation.

[0233] As can be seen from Table 3, the total system cost is 45.2654 million yuan, of which the gas power generation cost is 13.4374 million yuan, the wind power generation cost is 7.6289 million yuan, the photovoltaic power generation cost is 9.4523 million yuan, the hydropower generation cost is 5.8956 million yuan, the system loss cost is 1.9845 million yuan, and the electricity purchase cost is 4.3067 million yuan.

[0234] Table 3

[0235] type Amount (ten thousand yuan) Gas power generation 1343.74 Wind power 762.89 Photovoltaic power generation 945.23 Hydropower 589.56 System loss 198.45 Power purchase cost 430.67 Total Cost 4526.54

[0236] from Figure 4 It can be seen that wind power, photovoltaic power, hydropower and gas power generation jointly support the balance of power load in the power grid. Among them, photovoltaic power generation shows obvious time characteristics. From 11:00 to 15:00, photovoltaic power generation is in full swing. Although this is the peak electricity consumption period, there is still a phenomenon of abandoned light. Direct gas power generation is affected by coal mine production and the power generation is significantly reduced during the period from 11:00 to 16:00. Since the adjustable gas power generation is equipped with a gas storage tank, it is less affected by coal mine maintenance. During the maintenance period, power allocation can still be achieved through the gas in the gas storage tank. In addition, from 18:00 to 21:00, the adjustable gas power generation supplements the power generation by mobilizing the gas in the gas storage tank, which to a certain extent makes up for the shortcomings of wind, solar and hydropower generation and improves the flexibility of the system.

[0237] In summary, the method proposed in the present invention realizes the coordinated optimization scheduling of resources by rationally scheduling gas power generation resources with different characteristics (including adjustable gas and direct power generation gas) and combining the dynamic characteristics of renewable energy sources such as wind, solar, and water. On the basis of ensuring the stability of the power grid, the regulation capacity of gas resources, and the safety of equipment, this method effectively improves the scheduling flexibility and economy of the system, thereby maximizing resource utilization, reducing the overall energy supply cost, and promoting the efficient integration and application of green and low-carbon energy.

[0238] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A method for optimizing the dispatching of wind-solar-water-tile integrated units, characterized in that: The specific steps include: Step 1: Based on the city’s wind power generation system, photovoltaic power generation system, hydropower generation system and gas power generation system, build the city’s wind, photovoltaic, hydropower and gas integrated topological structure; Step 2: Establish a distributed gas power generation resource regulation model for different types of gas power generation resources; the distributed gas power generation resource regulation model includes an adjustable gas power generation model and a direct power generation gas model; Step 3: Taking the distributed gas power generation resource regulation model established in step 2 as a constraint condition, an optimization model is constructed with the goal of minimizing the total economic cost; Step 4: Solve the optimization model to obtain the optimal configuration and scheduling strategy for various power generation systems.

2. The method for optimizing the dispatching of wind-solar-water-tile integrated units according to claim 1 is characterized in that: Step 1 is as follows: The nodes in the wind-solar-water-gas integrated topology are set, including power generation nodes, load nodes and conversion nodes. The power generation nodes include wind farms, photovoltaic power stations, hydropower stations and gas power plants; the load nodes include residential areas, industrial areas and commercial areas; the conversion nodes include substations and key hubs in the distribution network; and the nodes are connected through branches; Collect branch information, including topology, transmission capacity, and impedance parameters of distribution lines; Determine the connection relationship between nodes based on branch information; Wind power generation systems, photovoltaic power generation systems, hydropower generation systems and gas power generation systems are matched with corresponding power generation nodes, and the power demand of load nodes is matched with the transmission path in the distribution system.

3. The method for optimizing the dispatching of wind-solar-water-tile integrated units according to claim 1 is characterized in that: The expression of the adjustable gas power generation model in step 2 is as follows: S t =S t-1 +(S in,t -S out,t )Δt b in,t S in,min ≤S in,t ≤b in,t S in,max b out,t S out,min ≤S out,t ≤b out,t S out,max b in,t +b out,t ≤1 S min ≤S t ≤S max P G1,t =the G1 r G1,t C G V G1,t 0≤V G1,t ≤V G1,max P G1,min ≤P G1,t ≤P G1,max -P G1,down ≤P G1,t -P G1,t-1 ≤P G1,up Among them, S t , S t-1 are the gas storage volume in the gas tank at time t and t-1, S in,t , S out,t are the injection and release amounts of gas in the gas storage tank at time t; Δt is the time step; b in,t The variable of 0 or 1 indicates the state of gas injection in the gas tank at time t, 0 indicates relationship, 1 indicates opening, b out,t The variable of 0 or 1 indicates the state of gas release from the gas storage tank at time t; S in,min YesS in,max are the minimum and maximum values ​​of gas injected into the gas tank per unit time, S out,min and S out,max are the minimum and maximum values ​​of gas released by the gas storage tank per unit time, S min , S max are the minimum and maximum values ​​of gas stored in the gas storage tank, G1 represents the gas generator set of the gas storage tank, and P G1,t , P G1,t-1 are the power generation of the gas generator set with gas storage tank at time t and t-1, η G1 is the power generation efficiency of the gas generator set with gas storage tank, ρ G1,t is the gas volume fraction injected into the gas generator set containing the gas storage tank at time t, C G is the lower calorific value of gas, V G1,t is the volume of gas injected into the gas generator set containing the gas storage tank at time t, V G1,max is the maximum volume of gas injected into the gas generator set containing the gas storage tank, P G1,min , P G1,max are the minimum and maximum power generation of the gas generator set with gas storage tank, P G1,down , P G1,up They are the downslope rate and the climbing rate of the gas generator set with gas storage tank respectively; The expression of direct power generation gas model is: P G2,t =the G2 r G2,t C G V G2,t P G2,min ≤P G2,t ≤P G2,max -P G2,down ≤P G2,t -P G2,t-1 ≤P G2,up Among them, G2 represents the gas generator set for direct power generation, P G2,t is the power generation of the gas generator set directly generating electricity at time t, P G2,t-1 is the power generation of the gas generator set directly generating electricity at time t-1; ρ G2,t C is the gas volume fraction injected into the gas generator set for direct power generation at time t; G is the lower calorific value of gas, V G2,t is the volume of gas injected into the gas generator set for direct power generation at time t, P G2,min , P G2,max are the minimum and maximum power generation of gas generator sets for direct power generation; P G2,down , P G2,up They are respectively the downslope rate and climbing rate of the gas generator set for direct power generation.

4. The method for optimizing the dispatching of wind-solar-water-tile integrated units according to claim 3 is characterized in that: The method further includes establishing a priority constraint and a coordinated scheduling strategy for the optimization model in step 3 based on the distributed gas power generation resource regulation model in step 2, wherein the priority constraint is to give priority to using gas generator sets that directly generate electricity to meet load demand and adjustable power generation gas as a backup: P G2,t =min(P load,t -P wind,t -P solar,t -P hydro,t +P loss,t ,P G2,max ) P load,t is the actual power load demand of the system at time t, P wind,t , P solar,t , P hydro,t are the power generation of the wind power generation system, photovoltaic power generation system, and hydropower generation system at time t, respectively, loss,t is the system loss at time t, P G2,max is the maximum power generation of the gas generator set for direct power generation. When the output power of the gas generator set for direct power generation reaches the maximum value, P G2,t =P G2,max , any remaining load will be supplied by the adjustable power generation gas P G1,t To supplement, the specific expression is as follows: P G1,t =max(P load,t -P wind,t -P solar,t -P hydro,t +P loss,t ,P G1,min ) The adjustable power generation gas is a gas generator set containing a gas storage tank; The coordinated dispatching strategy includes: during the peak load period, the gas generator sets that directly generate electricity give priority to responding to the load demand, and the gas generator sets with gas storage tanks store gas when the load decreases.

5. The method for optimizing the dispatching of wind-solar-water-tile integrated units according to claim 3 is characterized in that: The expression of the optimization model in step 3 is: Where T represents the total time, C1, C2, C3, C4, C5, and C6 represent the gas power generation cost, wind power generation cost, photovoltaic power generation cost, hydropower generation cost, system loss cost, and electricity purchase cost respectively; the expressions of C1, C2, C3, C4, C5, and C6 are: <h2 style=";text-align:left;direction:ltr">C1 = a1P<h2 style=";text-align:left;direction:ltr"> G1,t <h2 style=";text-align:left;direction:ltr"> +b1+C<h2 style=";text-align:left;direction:ltr"> storage,t <h2 style=";text-align:left;direction:ltr"> +a2P<h2 style=";text-align:left;direction:ltr"> G2,t <h2 style=";text-align:left;direction:ltr"> +b2 C2=C wind,om P wind,t +C wind,c (P wind,a,t -P wind,t ) C3=C solar,om P solar,t +C solar,c (P solar,a,t -P solar,t ) C4=C hydro,om P hydro,t +C hydro,u Q hydro,t C5=C energy AND loss,t C6=p ele,t P buy,t Among them, a1 and b1 are the power generation cost and fixed operating cost of the adjustable gas power generation model respectively; a2 and b2 are the power generation cost and fixed operating cost of the direct power generation gas model respectively; C storage,t is the cost of releasing gas from the gas storage tank at time t, C storage,t =C in S in,t +C out S out,t , C in , C out are the unit costs of injecting and releasing gas into the gas storage tank; C wind,om is the unit operation and maintenance cost of wind power generation, P wind,t , P solar,t , P hydro,t are the power generation of wind power generation system, photovoltaic power generation system and hydropower generation system at time t; C wind,c is the wind curtailment cost of the wind power generation system, P wind,a,t is the available wind power generation at time t, C solar,om is the unit operation and maintenance cost of photovoltaic power generation, C solar,c is the cost of abandoned light in the photovoltaic power generation system, P solar,a,t is the available photovoltaic power generation at time t; C hydro,om is the unit operation and maintenance cost of hydropower generation, C hydro,u is the unit cost of water resources use, Q hydro,t is the water flow used for power generation at time t, C energy is the unit electricity cost, E loss,t is the system energy loss at time t, Ω bc is the set of nodes in the topological structure, P i'j',t is the active power between nodes i' and j' at time t, r i'j' is the resistance between nodes i' and j'.

6. The method for optimizing the dispatching of wind-solar-water-tile integrated units according to claim 5 is characterized in that: The constraints of the optimization model in step 3 also include: volatility constraints of wind power generation and photovoltaic power generation, grid load demand constraints, distribution network flow constraints, equipment operation constraints, generator set output constraints, generator set operation status constraints, safe scheduling constraints of gas resources, gas pressure balance constraints, gas supply reliability constraints, gas supply chain optimization constraints and power balance constraints.

7. The method for optimizing the dispatching of wind-solar-water-tile integrated units according to claim 1 is characterized in that: The expression of the volatility constraint of wind power generation and photovoltaic power generation is: Among them, ΔP wind,t It represents the deviation between the actual wind power generation and the predicted power generation at time t. represents the expected value, ΔP solar,t It represents the deviation between the actual solar power generation and the predicted power generation at time t; The grid load demand constraint is: P load,t =P load,p,t +ΔP load,t Among them, P load,t , P load,p,t are the actual load demand and load forecast of the system at time t; ΔP load,t is the electric load prediction error at time t; The power flow constraint of the distribution network is: IN min ≤U i,t ≤U max Q i,min ≤Q i,t ≤Q i,max Where: i, j and h are all nodes in the topological structure of the urban power distribution system, and node i is the upstream node of node j, and node j is the upstream node of node h; Ω bc is a node set; t represents the time period; P ij,t , Q ij,t are respectively the active power and reactive power from node i to node j, r ij is the resistance between node i and node j; α i,t , β ij,t , α j,t are slack variables, I ij,t is the current from node i to node j at time t, U i,t , U j,t are the voltage amplitudes of nodes i and j at time t respectively; P j,t , Q j,t are the injected active power and reactive power of node j at time t respectively; ij is the reactance between node i and node j; P jh,t , Q jh,t are respectively the active power and reactive power from node j to node h; U min , U max are the lower and upper limits of the node voltage amplitude, Q i,min , Q i,max are the minimum and maximum capacities of the reactive power compensation device at node i in the urban distribution network, Q i,t is the reactive compensation amount at the node i of the urban distribution network at time t; The operation constraints of the equipment include the generator set output constraint and the generator set operation state constraint. The expression of the generator set operation state constraint is as follows: Δu G1,t ∈{-1,0,1} Δu G2,t ∈{-1,0,1} Among them, Δu G1,t , Δu G2,t They are the changes in the operating status of the gas generator set with a gas storage tank and the state change of the gas generator set for direct power generation, 1 means starting at time t, -1 means stopping at time t, and 0 means no state change; The gas pressure balance constraint is: P gas,min ≤P gas,t ≤P gas,max Among them, P gas,t , P gas,t-1 are the gas tank pressures at time t and t-1 respectively; C p is the system gas pressure volume coefficient; S in,t , S out,t are the injection and release amounts of gas in the gas storage tank at time t; Δt is the time step; V gas is the volume of gas tank; P gas,min , P gas,max They are the minimum and maximum pressures of the gas storage tank respectively; The gas supply reliability constraint is: Among them, Q em is the emergency gas reserve, R gas Redundancy rate for gas supply; The gas supply chain optimization constraints are: Among them, I represents coal mine I, D I,t is the amount of gas obtained from coal mine I at time t, V G2,t is the volume of gas injected into the gas generator set for direct power generation at time t; x I,t is the location variable of coal mine I at time t, N re is the redundant number of coal mines, C tr is the unit transportation cost of gas, C bud budgeting for gas transportation costs; The power balance constraint is: P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t ≥P load,p,t +P loss,t +ΔP load,min,t P G1,t +P G2,t +P wind,t +P solar,t +P hydro,t ≤P load,p,t +P loss,t +ΔP load,max,t Among them, P G1,t is the power generation of the gas generator set with gas storage tank at time t, P G2,t is the power generation of the gas generator set directly generating electricity at time t, P wind,t , P solar,t , P hydro,t are the power generation of the wind power generation system, photovoltaic power generation system, and hydropower generation system at time t, respectively, loss,t is the system loss at time t, ΔP load,min,t , ΔP load,max,t are the minimum and maximum values ​​of load forecast error, respectively.

8. An electronic device / system for optimizing the dispatching method of wind-solar-water-tile integrated unit, characterized in that: It includes a processor and a memory, the memory stores execution instructions of the processor, and the processor is configured to execute the execution instructions to implement the wind-solar-water-tile integrated unit optimization scheduling method as described in any one of claims 1-7.

9. A computer-readable storage medium for storing a program, characterized in that: Execute the program to implement the optimization scheduling method of the wind-solar-water-tile integrated unit described in any one of claims 1-7.

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