An operation method and system of an integrated energy system

Abstract

The application provides a kind of operation method and system of integrated energy system, comprising: obtaining the predicted curve of each load of the region to be planned in optimization period and the conversion parameter of each energy conversion technology, and the meteorological information corresponding to various renewable energies of typical day;The predicted curve of each load, the conversion parameter and the meteorological information are brought into the supply-demand matching model constructed in advance to calculate, to obtain the priority of different energy conversion technologies at each time in optimization period and the maximum output provided by each energy conversion technology;The supply-demand matching model is based on the conversion cost of each energy conversion technology on the energy supply side and the demand regulation cost when participating in demand response on the energy consumption side, and is constructed with the lowest total technical cost of integrated energy system as the optimization goal.The energy supply side and the energy consumption side of the integrated energy system are optimized coordinately, and the energy supply and consumption cost in the planned region is reduced.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy systems, and in particular to an operation method and system of an integrated energy system. Background Art

[0002] Integrated energy systems for industrial parks have the potential to increase renewable energy consumption and reduce energy supply and consumption costs. By modeling the integrated energy system for industrial parks and using various optimization algorithms to solve the objective function, the energy configuration, capacity, and strategy for the optimal scenario within the industrial park can be obtained. Brandoni C. et al. proposed an optimal configuration for a hybrid distributed energy system suitable for residential buildings in specific regions based on factors such as energy prices, energy demand, and regional characteristics. GeorgiosChalkiadakis et al. believe that the creation of a virtual power plant can be used as a means to achieve effective integration of distributed energy. Applying game theory, they proposed a pricing mechanism as an alternative to on-grid electricity prices to promote the planning, configuration, and operational optimization of distributed energy. Alvarado DC et al. combined energy prices with energy demand, considered life cycle costs and carbon emissions, and constructed a simulation model for the configuration and operational optimization of industrial park integrated energy systems.

[0003] For the supply-demand matching problem of integrated energy systems in industrial parks that do not consider demand adjustability, the key issues to address are: 1) what energy resources and energy conversion equipment should be used; 2) what installed capacity of the energy conversion equipment should be; and 3) what operating strategies should each device adopt to meet energy demand. Chen X et al. studied the centralized scheduling problem on the supply side of an integrated energy system incorporating heat storage tanks. They established a linear programming model for meeting heat and electricity demand, comprehensively considering combined heat and power (CHP) systems, wind power generation, electric boilers, and heat storage tanks. The results showed that both electric boilers and heat storage tanks can improve the energy supply flexibility of CHP. Based on the economic performance of the integrated energy system, Y Ruan et al. established a linear integrated energy system model that incorporates ice storage equipment, CHP, and electric refrigeration equipment. The results showed that natural gas price is the most important factor affecting the economic performance of the integrated energy system, followed by public electricity market prices and generator prices. Li Guo et al. established a two-stage optimization planning and design model for cooling, heating, and electricity microgrid systems, minimizing the total net present value and carbon dioxide emissions over the entire life cycle as the optimization objectives. However, these models are generally static models. In actual operation, due to the partial load characteristics of the equipment, differences in energy time-of-use prices and changes in renewable energy output, adjusting the output of each energy conversion equipment at different times will lead to changes in the production costs of terminal energy products such as cooling, heating, electricity and steam.

[0004] For demand-adjustable integrated energy systems, there are three primary methods for shifting demand-side user load: energy storage systems (ESS), electric vehicles (EVs), and demand response (DR). Energy storage can be used to shift cooling, heating, and electricity demands of end users (such as public buildings, residences, and factories). The application of energy storage systems on the user side can effectively increase the penetration of renewable energy in integrated campus energy systems, islanded energy systems, or distributed microgrids. To reduce peak energy demand, Oudalov et al. established a capacity determination method and an operation strategy optimization method for battery energy storage systems, obtaining the economically optimal battery capacity and operation strategy. Cooling and heat storage devices can be used to adjust end-user cooling and heating demands, as well as to adjust end-user electricity demand by reducing their cooling and heating power consumption during peak load periods. Electric vehicles are also commonly used to regulate energy demand on the demand side. In addition to energy storage devices, demand response uses dynamic electricity pricing mechanisms to encourage or penalize consumer electricity consumption. However, the implementation of various demand-side regulation measures or technologies carries a certain cost.

[0005] Simply regulating the output of integrated energy equipment or regulating with demand response measures has the problem of incomplete consideration. Therefore, the inventors propose to consider the regulation of supply-side and demand-side resources in a coordinated manner from the perspective of the economic cost of causing changes in energy supply and the economic cost of causing demand shifts, and explore the economically optimal energy supply and consumption regulation strategy. Summary of the Invention

[0006] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides an operation method of an integrated energy system, comprising:

[0007] Obtain the forecast curves of each load and the conversion parameters of each energy conversion technology in the planned area within the optimization period, as well as the meteorological information corresponding to various renewable energy sources on a typical day;

[0008] The forecast curves of the loads, the conversion parameters, and the meteorological information are fed into a pre-built supply-demand matching model for calculation to obtain the priority of different energy conversion technologies at each moment in the optimization period and the maximum output provided by each energy conversion technology;

[0009] The supply and demand matching model is constructed based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy consumption side participates in demand response, with the minimum total technical cost of the integrated energy system as the optimization goal.

[0010] Preferably, the construction of the supply and demand matching model includes:

[0011] Determine the conversion cost of each energy conversion technology based on the initial investment, operating costs, and maintenance costs of each energy conversion technology on the energy supply side;

[0012] Determine the demand control cost when participating in demand response based on the analysis of changes in cooling demand, heating demand and electricity demand on the energy consumption side;

[0013] Taking the minimum total technical cost of the integrated energy system as the optimization goal, the objective function of the supply and demand matching model is constructed based on the conversion costs of the energy conversion technologies and the demand regulation costs when participating in demand response;

[0014] Based on the balance principle between the energy supply side and the energy consumption side in the matching process, constraint conditions are constructed for the objective function of the supply and demand matching model.

[0015] Preferably, the objective function of the supply-demand matching model is as follows:

[0016] g(τ)=min(SCost τ,total +DCost τ,total )

[0017] Where: g(τ) represents the minimum technical cost of matching supply and demand at time τ; SCost τ,total represents the supply technology cost at time τ; DCost τ,total represents the demand control cost at time τ.

[0018] Preferably, the supply technology cost SCost at the time τ τ,total , calculated as follows:

[0019]

[0020] Where: b j represents the sum of the basic electricity cost coefficient and the fixed maintenance cost coefficient of energy conversion technology j; represents the initial investment coefficient of energy conversion technology j in equal annual value; Q t,j represents the output of energy conversion technology j at time t; c j represents the sum of the variable maintenance cost coefficient and the operating cost coefficient of energy conversion technology j; Q τ,j represents the output of energy conversion technology j at time τ.

[0021] Preferably, the energy conversion technology includes:

[0022] Gas generator sets, lithium bromide absorption units, electric refrigeration units, heat pumps, boilers, energy storage equipment, power grids, photovoltaic power generation, solar thermal power generation, wind power generation and geothermal power generation.

[0023] Preferably, the demand control cost DCost at the time τ is τ,total , calculated as follows:

[0024] DCost τ,total=DCost τ,12 +DCost τ,13

[0025] Where: DCost τ,12 represents the technical cost of cold and heat storage at time τ; DCost τ,13 represents the technical cost of electricity demand response at time τ.

[0026] Preferably, the technical cost of cold and heat storage at the time τ is DCost τ,12 , calculated as follows:

[0027]

[0028] Where: a 12 represents the basic cost coefficient of operation of cold and heat storage device; α 12 represents the fixed maintenance cost of the cold and heat storage device; Indicates the variable maintenance cost coefficient related to cold and heat storage technology; ΔD t,12 represents the change in demand caused by distributed energy storage at time t; β 12 Represents the cost coefficient corresponding to the cold storage loss; ε 12 Indicates the cost coefficient corresponding to the heat storage loss; ΔD τ,12 Represents the demand change caused by distributed energy storage at time τ.

[0029] Preferably, the technical cost of power demand response at the time τ is DCost τ,13 , calculated as follows:

[0030] Where: α 13 represents the fixed cost of grid demand response; Indicates the variable cost coefficient related to the scale of technical demand response; ΔD t,13 represents the energy load demand at time t; represents the initial energy price at time t; is the initial energy load demand at time t; D τ,13 represents the energy load demand at time τ; l represents the first regression parameter; represents the energy price at time t; h represents the second regression parameter.

[0031] Preferably, the constraint condition is as shown below:

[0032]

[0033] Where ΔQ τ,total Indicates the total change in supply during the matching process; ΔD τ,totalIndicates the total change in demand during the matching process; represents the initial demand value at time τ; represents the initial supply value at time τ; Q τ,j represents the output of energy conversion technology j at time τ; Q m,j Indicates that energy conversion technology j is limited by the output limit of natural resources; Q τ,j represents the energy demand of energy conversion technology j at time τ; D m,j Indicates that the energy conversion technology j is limited by the output upper limit of environmental conditions.

[0034] Based on the same inventive concept, the present invention also provides an operation system of an integrated energy system, comprising:

[0035] The acquisition module is used to obtain the forecast curves of each load and the conversion parameters of each energy conversion technology in the planned area within the optimization period, as well as the meteorological information corresponding to various renewable energy sources on a typical day;

[0036] A matching module is used to bring the forecast curve of each load, the conversion parameter and the meteorological information into a pre-built supply and demand matching model for calculation to obtain the priority of adopting different energy conversion technologies at each time point in the optimization period and the maximum output provided by each energy conversion technology;

[0037] The supply and demand matching model is constructed based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy consumption side participates in demand response, with the minimum total technical cost of the integrated energy system as the optimization goal.

[0038] Preferably, the construction of the supply and demand matching model is specifically used to:

[0039] Determine the conversion cost of each energy conversion technology based on the initial investment, operating costs, and maintenance costs of each energy conversion technology on the energy supply side;

[0040] Determine the demand control cost when participating in demand response based on the analysis of changes in cooling demand, heating demand and electricity demand on the energy consumption side;

[0041] Taking the minimum total technical cost of the integrated energy system as the optimization goal, the objective function of the supply and demand matching model is constructed based on the conversion costs of the energy conversion technologies and the demand regulation costs when participating in demand response;

[0042] Based on the balance principle between the energy supply side and the energy consumption side in the matching process, constraint conditions are constructed for the objective function of the supply and demand matching model.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The technical solution provided by the present invention obtains the forecast curves of each load in the planned area within the optimization period and the conversion parameters of each energy conversion technology, as well as the meteorological information corresponding to various renewable energy sources on a typical day; the forecast curves of each load, the conversion parameters and the meteorological information are brought into a pre-constructed supply and demand matching model for calculation, so as to obtain the priority of adopting different energy conversion technologies at each moment within the optimization period and the maximum output provided by each energy conversion technology; the supply and demand matching model is based on the conversion cost of each energy conversion technology on the energy supply side and the demand regulation cost when the energy consumption side participates in demand response, and is constructed with the minimum total technical cost of the integrated energy system as the optimization goal. By collaboratively optimizing the energy supply side and the energy consumption side of the integrated energy system, the supply and consumption costs in the planning area are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of an operation method of a comprehensive energy system of the present invention;

[0046] Figure 2 This is a schematic diagram of the supply and demand matching principle of the integrated energy system of the present invention;

[0047] Figure 3 are the cooling, heating, and electricity loads of a typical week in the embodiment of the present invention;

[0048] Figure 4 is the time-of-use electricity price in Shanghai in the embodiment of the present invention;

[0049] Figure 5 The typical daily wind speed and solar radiation in Shanghai in the embodiment of the present invention;

[0050] Figure 6 The matching curve of the typical day supply and cooling demand of the park integrated energy system in the embodiment of the present invention;

[0051] Figure 7 The matching curve of the typical day supply and heat demand of the park integrated energy system in the embodiment of the present invention;

[0052] Figure 8 The matching curve of the typical daily supply and demand of the park integrated energy system in the embodiment of the present invention;

[0053] Figure 9 The order in which the four types of ECT appear and the increment of technical cost at time τ=10 in the cold network control of the embodiment of the present invention;

[0054] Figure 10 The order in which the four ECTs appear and the increment of technical costs at time τ=9 in the heat network control of the embodiment of the present invention;

[0055] Figure 11The order in which the four ECTs appear and the increment of their technical costs at time τ=14 in the power grid control according to an embodiment of the present invention are shown. DETAILED DESCRIPTION

[0056] In order to better understand the present invention, the present invention is further described below with reference to the accompanying drawings and examples.

[0057] Example 1: Figure 1 As shown, the present invention provides an operation method of an integrated energy system, comprising:

[0058] S1 obtains the forecast curves of each load in the planned area within the optimization period and the conversion parameters of each energy conversion technology, as well as the meteorological information corresponding to various renewable energy sources on a typical day;

[0059] S2 brings the forecast curve of each load, the conversion parameter and the meteorological information into a pre-built supply and demand matching model for calculation to obtain the priority of adopting different energy conversion technologies at each moment in the optimization period and the maximum output provided by each energy conversion technology;

[0060] The supply and demand matching model is constructed based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy consumption side participates in demand response, with the minimum total technical cost of the integrated energy system as the optimization goal.

[0061] This embodiment reduces the energy supply and consumption costs in the planning area by collaboratively optimizing the energy supply side and the energy consumption side of the integrated energy system. At the same time, this embodiment does not simply superimpose the economic cost of the energy supply side and the economic cost of the energy consumption side demand transfer, but collaboratively considers the regulation of supply-side and demand-side resources, provides the priority of using different energy conversion technologies at each moment in the optimization cycle and the maximum output provided by each energy conversion technology, and generates a feasible strategy that shortens the investment payback period and reduces carbon emissions.

[0062] In this embodiment, the construction of the supply-demand matching model includes:

[0063] Determine the conversion cost of each energy conversion technology based on the initial investment, operating costs, and maintenance costs of each energy conversion technology on the energy supply side;

[0064] Determine the demand control cost when participating in demand response based on the analysis of changes in cooling demand, heating demand and electricity demand on the energy consumption side;

[0065] Taking the minimum total technical cost of the integrated energy system as the optimization goal, the objective function of the supply and demand matching model is constructed based on the conversion costs of the energy conversion technologies and the demand regulation costs when participating in demand response;

[0066] Based on the balance principle between the energy supply side and the energy consumption side in the matching process, constraint conditions are constructed for the objective function of the supply and demand matching model.

[0067] In this embodiment, the park energy system is used as an example of the area to be planned. Various energy supply technologies on the energy supply side are considered to determine the total technical cost of the energy supply side. The steps of this process include:

[0068] The energy system in the park draws on a variety of sources, including natural gas, coal, oil, the main grid, solar energy, wind energy, geothermal energy, water, air, and sewage sources. Various energy conversion technologies (ECTs) are used to generate electricity, heat, and cooling for users within the park. The ECTs selected in this embodiment include, but are not limited to, gas-fired generator sets (ICS), lithium bromide absorption units (ARUs), electric refrigeration units, heat pumps, boilers, energy storage devices (for both cold and heat storage), the main grid, photovoltaic power generation (PV), solar thermal (PT), wind power, and geothermal energy. This embodiment uses different values ​​of j to represent the ECT of the energy conversion technology. Table 1 lists the ECTs corresponding to different values ​​of j.

[0069] Current energy storage methods primarily include battery storage, pumped hydro storage, supercapacitor storage, compressed air storage, and superconducting energy storage. However, due to high investment costs, low efficiency, and poor economic benefits, these methods are rarely used in typical integrated energy systems for industrial parks. This example only considers cold and heat storage in integrated energy systems for industrial parks.

[0070] Table 1 ECT corresponding to different j

[0071]

[0072] The technical cost level (SCost τ,total ) is used to describe the initial investment, operating costs and maintenance cost levels of all supply-side ECTs of the park integrated energy system at time τ. Generally speaking, the investment in supply-side ECT equipment generally occurs in the construction phase of the park integrated energy system project, while the operating costs usually occur in the operation phase of the project. However, in this embodiment, the equal annual value method (UAVM) is used to convert the initial investment of the supply-side ECT of the park integrated energy system into the equal annual value initial investment over the entire life cycle of the project; on this basis, the annual equivalent operating hours (AEOH) are used to convert the equal annual value initial investment into the initial investment at each operating moment; then the hourly operating costs and maintenance costs are added to the hourly initial investment to obtain the technical cost level of supply (SCost τ,total ).

[0073] As shown in formula (1), SCost τ,totalrepresents the total technical cost of all supply-side ECTs, as shown in formula (2), SCost τ,j This mainly includes the initial investment, operation, and maintenance costs of the jth ECT on the supply side. The initial investment in an ECT depends on the configured capacity of each ECT. The maximum configured capacity is limited by local natural resources and the surrounding environment, so the configured capacity of each ECT has an upper limit.

[0074] SCost τ,total =∑ j SCost τ,j (1)

[0075] SCost τ,j =C in,j +C o,j +C m,j (2)

[0076] Where C in,j represents the initial investment of energy conversion technology j at time τ, which can be calculated by UAVM and AEOH, as shown in formula (3); C o,j represents the operating cost of energy conversion technology j at time τ, which is composed of the basic electricity cost of the transformer and the electricity cost (gas cost, etc.), as shown in formula (4); C m,j It represents the maintenance cost of energy conversion technology j at time τ, which consists of fixed maintenance cost and variable maintenance cost, as shown in formula (5).

[0077]

[0078] C o,j =ε j Q τ,j +C trans,j (4)

[0079]

[0080] Where PR j represents the price of unit configuration capacity of technology j, RMB / kW; RC j represents the configuration capacity of technology j, as shown in formula (6); r is the discount rate; n j is the useful life of technology j, in years; T j represents the AEOH of technology j, as shown in formula (7); ε j represents the operating cost coefficient of technology j, RMB / kWh; Q τ,j represents the output of technology j at time τ, kW; α j represents the fixed maintenance cost coefficient of technology j, RMB / kWh; β j represents the variable maintenance cost coefficient of technology j, RMB / kWh; C trans,jrepresents the basic electricity cost of technology j, as shown in formula (8).

[0081] RC j =max(Q t,j ) (6)

[0082]

[0083]

[0084] Where Q t,j represents the output of technology j at time t, t = 1, 2, ..., 8760; a j represents the basic electricity cost coefficient of technology j, RMB / kVA, and its form is shown in formulas (9, 10):

[0085]

[0086] a j =Tr p,j *m (10)

[0087] Where Tr p,j represents the basic electricity cost of the transformer capacity of technology j, in yuan / (kVA·month); m is the number of months of use, and in this embodiment, m=12.

[0088] Substituting equations (2)-(8) into equation (1), we obtain:

[0089]

[0090] In the formula, t and τ represent different physical meanings. t = 1, 2, ..., 8760 represents every hour of the year, and τ represents the moment when the supply technology cost level is calculated in formula (11); represents the initial investment coefficient of energy conversion technology j in equal annual value, Yuan / kW; each coefficient a j , α j , β j , ε j The values ​​of are shown in Table 2.

[0091] It should be noted that the capacity configuration method of energy storage equipment is different from that of other ECTs. The capacity of energy storage is the cumulative value of stored energy or released energy during the storage and release cycle. The unit of construction cost is yuan / m 3 , while the capacity of other ECTs is the maximum output of the whole year, and the unit of cost is yuan / kW.

[0092] Table 2 Key parameter values ​​of supply technology cost level

[0093]

[0094] This embodiment assumes that the cycle of energy storage and release is 24 hours, that is, energy is stored at night and released during the day. Then the usage capacity of the energy storage equipment on the first day is as shown in formula (12):

[0095]

[0096] The configuration capacity of the energy storage equipment should be the maximum daily utilization of the energy storage throughout the year, as shown in formula (13):

[0097]

[0098] According to equations (12) and (13), the initial investment of energy storage equipment can be derived as shown in equation (14).

[0099]

[0100] Where Q i,6 represents the output of the energy storage device at hour i on day l, with positive values ​​indicating energy release and negative values ​​indicating energy storage; η6 represents the energy storage efficiency of the energy storage device; k represents the conversion factor between one kilowatt-hour and joule, J / kWh; PR6 represents the unit capacity price of the energy storage device, RMB / m 3 ; Δt is the energy storage temperature difference, ℃; C p is the specific heat capacity of the energy storage medium, usually water, J / (kg·℃); ρ w is the density of the energy storage medium, usually water, kg / m 3 .

[0101] Substituting formula (14) into formula (3) yields:

[0102]

[0103] Where n6 is the service life of the energy storage equipment, in years.

[0104] In order to unify Equation (15) and other ECT calculation formulas into the form of Equation (11), two assumptions are made in this embodiment: 1) Energy storage equipment only appears in the park integrated energy system with time-of-use electricity prices and off-peak electricity prices below 0.4 yuan / kWh. Energy storage equipment stores energy during the off-peak electricity price period and releases energy during other periods; 2) Within a storage cycle, the stored energy is equal to the sum of the released energy and the storage loss, that is, energy cannot be stored across storage cycles.

[0105] Under the above two assumptions, equation (15) is transformed into equation (16).

[0106]

[0107] That is, the initial investment coefficient of the equivalent annual value of energy storage equipment As shown in formula (17).

[0108]

[0109] Equation (17) shows that the initial investment coefficient of the equivalent annual value of energy storage equipment is affected by the energy storage efficiency, the energy storage temperature difference, and the cost level of the unit energy storage equipment. Substituting Equation (17) into Equation (11) yields the technical cost level of the energy supply of the park's integrated energy system at time τ, as shown in Equation (18).

[0110]

[0111] Where b j =a j +α j , c j =β j +ε j Equation (18) describes the technical cost level SCost of energy supply at time τ τ,j and max(Q t,j ), and Q τ,j In the existing park integrated energy system, without loss of generality, Q τ,j The value is much smaller than That is, Q τ,j The changes in The influence of can be almost ignored. Therefore, at time τ, when Q τ,j When it changes, it can be considered Remain unchanged, at this time SCost τ,j Only Q τ,j function.

[0112] This embodiment determines the demand control cost based on the energy consumption of the park. The specific determination process includes:

[0113] According to the quantitative method of energy storage technology in the technical cost level model of energy supply, the technical cost level of distributed energy storage is derived, as shown in formula (19), using DCost τ,12 To quantify the demand changes of micro cooling network and micro heating network.

[0114]

[0115] Where, DCost τ,12 represents the technical cost of cold and heat storage at time τ, in yuan; a 12 represents the basic cost coefficient of operation of cold and heat storage device, RMB / kW; α 12 represents the fixed maintenance cost of the cold and heat storage device, RMB / kW; Indicates the variable maintenance cost coefficient related to technical cold storage heat, RMB / kW; β 12 , ε12 Represent the cost coefficients corresponding to the loss of cold storage and heat storage respectively; t=(1,…,8760); ΔD t,12 =D t,12 -D t,0 Denotes the change in demand caused by distributed energy storage at time t; D t,0 is the initial energy demand at time t; ΔD τ,12 =D τ,12 -D τ,0 Different from ΔD t,12 , represents the demand change caused by distributed energy storage at time τ.

[0116] According to existing research on demand response quantification, there are four commonly used functional relationships between energy demand and energy price: linear equation relationship, potential equation relationship, logarithmic equation relationship, and exponential equation relationship. Among them, the linear demand equation is the simplest and most widely used energy demand and price model. Therefore, this embodiment selects the linear demand equation as the quantitative equation for demand response, which is:

[0117] D t,13 =l+hp t,13 (20)

[0118] According to the price elasticity coefficient, Into (20), and according to the literature, the energy price p is obtained by taking the extreme value of the first-order derivative t,13 The equation is:

[0119]

[0120] Therefore, the operating cost of demand response can be expressed as:

[0121]

[0122] Where, and is the initial energy price and initial energy load demand at time t. Substitute equations (21) and (22) into DCost τ,13 In the equation, we get:

[0123]

[0124] Where, DCost τ,13 represents the technical cost of electricity demand response at time τ, RMB; α 13 represents the fixed cost of grid demand response, RMB / kW; Indicates the variable cost coefficient related to the scale of technical demand response, RMB / kW; ΔD t,13 , ΔD τ,13Denote the change in power demand caused by demand response at time t and τ, kW and kW respectively; and is the initial energy price and initial energy load demand at time t, in yuan / kW and kW; D τ,13 represents the energy load demand at time τ, kW; l and h are the model regression parameters, with values ​​of 63.223MWh and -0.038MWh respectively 2 / yuan; where t = (1,…,8760).

[0125] Therefore, the technical cost level model of demand is:

[0126] DCost τ,total =DCost τ,12 +DCost τ,13 (twenty three)

[0127] Based on the technical cost water model of energy supply and demand established above, a unified optimization analysis of energy supply changes and energy demand changes can be carried out on the same quantitative indicator.

[0128] This implementation is based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy user participates in demand response. With the minimum total technical cost of the integrated energy system as the optimization goal, a supply and demand matching model is constructed to coordinate the optimization of production and supply. Specifically, it includes:

[0129] The energy demand and supply of the park's integrated energy system change hourly, and the regulation cost also changes hourly. According to the aforementioned energy conversion cost SCost τ,total (supply technology cost at time τ) and demand control cost DCost τ,total (demand control cost at time τ), and a supply and demand matching model is established based on the two. The economic optimization of supply and demand matching is achieved through the supply and demand matching model. The simplified principle of the supply and demand matching process of the integrated energy system is as follows: Figure 2 As shown in Figure 2, the supply curve A and the demand curve C are the supply capacity and energy demand before the integrated energy system is matched, respectively. Both can be transformed into the final matching curve B by changing the ECT type, capacity, and output, for example, the process of point A'→B'←C' at time τ.

[0130] The integrated energy system of the park seeks to maximize the overall benefits of the system during the supply and demand matching process, that is, to minimize the sum of the technical costs of energy supply and energy demand. Based on the energy supply and energy demand regulation level model, a supply and demand matching model with simultaneous changes in supply and demand is constructed, as shown in Equations (24-26), where g(τ) represents the minimum technical cost for accurate matching of supply and demand at time τ:

[0131] g(τ)=min(SCostτ,total +DCost τ,total ) (twenty four)

[0132]

[0133] Where: SCost τ,j represents the supply technology cost of energy conversion technology j at time τ; Q τ,j represents the output of energy conversion technology j at time τ; DCost τ,j represents the demand regulation cost of energy conversion technology j at time τ; D τ,j represents the energy demand of energy conversion technology j at time τ; and Defined as the marginal technical cost of energy conversion technology j.

[0134]

[0135] Where ΔQ τ,total and ΔD τ,total are the total changes in supply and demand during the matching process; and They represent the initial demand value and supply value at time τ respectively; Q m,j and D m,j It indicates that the output limit of energy conversion technology j is limited by natural resources or environmental conditions. and Defined as the marginal technical cost of technology j, its physical meaning is: the increase in the energy conversion technology cost level for each additional unit of output of energy conversion technology j. In a park-scale integrated energy system, the marginal technical costs of all energy conversion technologies at time τ show an increasing trend, which means that the technical cost level of each technology increases rapidly for each additional unit of output. Therefore, in the process of matching supply and demand in the park's integrated energy system to maximize overall benefits, in order to achieve this goal, the principle of equal marginal technical costs must be followed when selecting ECT, that is, to satisfy Equation (27).

[0136]

[0137] This embodiment uses a comprehensive energy system project in a business district in Shanghai, China as an example and uses the technical solution provided by the present invention for analysis, specifically including:

[0138] The total construction area of ​​the project is 960,000 m 2 , including office building area of ​​479,000 m 2 , commercial building area 383,000 m 2 and hotel building area of ​​98,000 m 2The hourly loads of cooling, heating and electricity in a typical week are as follows: Figure 3 shown.

[0139] In addition, due to the constraints of local natural resources, the maximum available horizontal area for photovoltaic and solar thermal is 20,000 m 2 and 10,000 m 2 , the largest wind power installed capacity is 1186kW.

[0140] In this case, the functional relationship between COP and part load rate plr was obtained based on the actual operating data of the electric refrigeration unit, as shown in Equation (28), where the rated COP of the electric refrigeration unit is 5.8.

[0141] COP3=-12.46plr 2 +19.13plr-0.879 (28)

[0142] The initial investment cost coefficient, operating cost coefficient and maintenance cost coefficient of each ECT are shown in Table 3. The hourly electricity price of the large power grid is as follows: Figure 4 As shown, the gas cost is 2.73 yuan / m 3 , boiler gas 4.53 yuan / m 3 , the wind speed and solar radiation on a typical day are as follows Figure 5 shown.

[0143] Table 3 Parameters of some ECTs

[0144]

[0145]

[0146] The supply and demand matching optimization model is solved in Matlab. Figure 6-8 This is the final matching curve of the supply and demand of the park's integrated energy system, which describes the matching results of the simultaneous changes in the supply and demand of the micro-cooling network, micro-heating network and microgrid during a typical day.

[0147] from Figure 6 It can be seen that the priority and maximum output of different ECTs at different times in the matching process are different, where De-TSE-c is a distributed cold storage device. When τ = 10, the order of appearance and the increment of technical cost of the four ECTs in the micro-cooling network are as follows: Figure 9As shown, g(τ) is a step function of the sum of the outputs of each ECT. Each step represents the appearance of a different ECT, and the step value is the initial investment level for that ECT. Although lithium bromide absorption chillers have the highest initial investment level, they were the first ECT to be adopted. This is because they utilize waste heat from gas-fired generators, meaning their operating costs are zero and their marginal technical costs are low. The next ECTs to appear are cold storage equipment, distributed cold storage equipment, and electric chillers. Electric chillers account for the highest proportion of the total technical cost, at 53%, while distributed cold storage equipment accounts for the lowest, at 8%. Furthermore, the final result of the optimized matching is that the marginal technical costs of each ECT are equal.

[0148] In the heat network supply and demand matching of the park's integrated energy system, such as Figure 7 As shown, the ECT used to change the energy supply includes lithium bromide absorption units, heat pumps, boilers and solar thermal equipment. Distributed thermal storage (De-TSE-H) is not used to change the energy demand. This is because De-TSE-H uses electric water heaters to convert electrical energy into thermal energy for storage, which has a low energy conversion efficiency, resulting in high operating costs, that is, high marginal technical costs, and is excluded when the ECT of energy supply is optimized uniformly. Therefore, the final matching curve of the micro-heat network is the initial heat demand curve. In addition, the lithium bromide absorption unit and the heat pump alternately supply heat with the fluctuation of electricity prices in the large power grid, and the solar thermal equipment is limited by local solar energy resources and can only supply a small part of the heat. In the micro-heat network, when τ = 9, the order of appearance of the four ECTs and the increment of technical costs are as follows: Figure 10 As shown. Figure 10 It can be seen that the lithium bromide absorption unit and boiler are the first and last two ECTs to be adopted respectively. For boilers, the fewer the annual operating hours, the greater the equivalent initial investment at time τ. This is Figure 10 The reason why the step value of the middle boiler is larger than that of other ECTs.

[0149] In the microgrid supply and demand matching of the park's integrated energy system, such as Figure 8 As shown in Figure 1, the ECTs used to shift the microgrid's energy supply and demand curves toward matching curves include gas-fired generators, the main grid, photovoltaic power generation, wind power generation, and demand response. The main grid and gas-fired generators alternately supply the majority of the electricity. That is, when the main grid's electricity price reaches a certain limit, the gas-fired generators begin supplying power. In the microgrid, when τ = 14, the order in which the four ECTs appear and the incremental technical costs are shown in Figure 1. Figure 11As shown in Figure 2, due to local natural resource constraints, wind and photovoltaic power generation have lower technical costs and output than other ECTs. Demand response was the first ECT to be adopted, accounting for a minimum of 1% of the total technical cost. However, its output is limited by user demand flexibility. At τ = 14, although gas-fired generators appeared last, their economic performance still outperformed the main grid.

[0150] To analyze the economic benefits and carbon emission reduction effects of the established simultaneous supply and demand matching model, the initial investment, operating costs, investment payback period (IPP), and carbon emissions (CE) of the optimally matched integrated energy system (optimal IES) and the traditional integrated energy system (traditional IES) were calculated, as shown in Table 4. The optimal IES represents the integrated energy system optimized using the established simultaneous supply and demand matching model; the traditional IES represents the integrated energy system obtained using the traditional configuration method.

[0151] Table 4 Economic performance and carbon emissions of the integrated energy system and traditional energy system in the park with optimized supply and demand matching

[0152]

[0153]

[0154] Table 4 shows that, compared to the traditional IES, while the optimal IES' initial investment increased by 112.53 million yuan, its annual operating costs decreased by 85.68 million yuan. This resulted in a payback period of only 1.94 years for the optimal IES, compared to 4.74 years for the traditional IES. Furthermore, the annual carbon emissions for the optimal IES and the traditional IES were 65,727 tons and 146,282 tons, respectively. This means that planning and designing the integrated energy system for the park using the supply-demand matching model for energy supply and demand, developed in this embodiment, can reduce annual carbon emissions by 80,555 tons.

[0155] This study analyzed the supply-demand matching process for simultaneous changes in supply and demand, using a real-world case study. The results show that when the technical cost of varying park grid demand is minimized, energy supply costs can increase dramatically. Therefore, solely considering the lowest level of energy demand regulation is unreasonable. In practical applications, both energy supply and demand regulation should be considered comprehensively. The integrated park energy system solution, derived from a supply-demand matching model for simultaneous changes in supply and demand, outperforms traditional approaches in both economic efficiency and carbon emissions.

[0156] This embodiment provides technical and economic characteristics of energy conversion technology based on the park's integrated energy system, establishes a technical cost level model for energy supply, and quantifies the dynamic energy supply costs of various energy conversion technologies on the park's supply side; through the analysis of the park's cooling, heating, and electricity demand change technologies, a description model of the technical cost level changes in energy demand is established; based on the quantification of changes in the park's energy supply and energy demand regulation costs, a supply and demand matching model with simultaneous changes in supply and demand is established.

[0157] Example 2: Based on the same inventive concept, an embodiment of the present invention further provides an operation system of an integrated energy system, comprising:

[0158] The acquisition module is used to obtain the forecast curves of each load and the conversion parameters of each energy conversion technology in the planned area within the optimization period, as well as the meteorological information corresponding to various renewable energy sources on a typical day;

[0159] A matching module is used to bring the forecast curve of each load, the conversion parameter and the meteorological information into a pre-built supply and demand matching model for calculation to obtain the priority of adopting different energy conversion technologies at each time point in the optimization period and the maximum output provided by each energy conversion technology;

[0160] The supply and demand matching model is constructed based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy consumption side participates in demand response, with the minimum total technical cost of the integrated energy system as the optimization goal.

[0161] In the embodiment, the construction of the supply and demand matching model is specifically used to:

[0162] Determine the conversion cost of each energy conversion technology based on the initial investment, operating costs, and maintenance costs of each energy conversion technology on the energy supply side;

[0163] Determine the demand control cost when participating in demand response based on the analysis of changes in cooling demand, heating demand and electricity demand on the energy consumption side;

[0164] Taking the minimum total technical cost of the integrated energy system as the optimization goal, the objective function of the supply and demand matching model is constructed based on the conversion costs of the energy conversion technologies and the demand regulation costs when participating in demand response;

[0165] Based on the balance principle between the energy supply side and the energy consumption side in the matching process, constraint conditions are constructed for the objective function of the supply and demand matching model.

[0166] The acquisition module is used to obtain the forecast curves of each load in the planned area within the optimization period and the conversion parameters of each energy conversion technology, as well as the meteorological information corresponding to various renewable energy sources on a typical day; the matching module is called to bring the forecast curves of each load, the conversion parameters and the meteorological information into the pre-built supply and demand matching model for calculation, so as to obtain the priority of adopting different energy conversion technologies at each moment in the optimization period and the maximum output provided by each energy conversion technology; the supply and demand matching model is based on the conversion cost of each energy conversion technology on the energy supply side and the demand regulation cost when the energy consumption side participates in demand response, and is constructed with the minimum total technical cost of the integrated energy system as the optimization goal. Through the operation system, the energy supply side and the energy consumption side of the integrated energy system are collaboratively optimized, thereby reducing the energy supply and consumption costs in the planning area.

[0167] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0168] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0169] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0171] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for operating an integrated energy system, characterized in that: include: Obtain the forecast curves of each load and the conversion parameters of each energy conversion technology in the planned area within the optimization period, as well as the meteorological information corresponding to various renewable energy sources on a typical day; The forecast curves of the loads, the conversion parameters, and the meteorological information are fed into a pre-built supply-demand matching model for calculation to obtain the priority of different energy conversion technologies at each moment in the optimization period and the maximum output provided by each energy conversion technology; The supply-demand matching model is constructed based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy consumption side participates in demand response, with the minimum total technical cost of the integrated energy system as the optimization goal; The construction of the supply and demand matching model includes: Determine the conversion cost of each energy conversion technology based on the initial investment, operating costs, and maintenance costs of each energy conversion technology on the energy supply side; Determine the demand control cost when participating in demand response based on the analysis of changes in cooling demand, heating demand and electricity demand on the energy consumption side; Taking the minimum total technical cost of the integrated energy system as the optimization goal, the objective function of the supply and demand matching model is constructed based on the conversion costs of the energy conversion technologies and the demand regulation costs when participating in demand response; Constructing constraint conditions for the objective function of the supply-demand matching model based on the balance principle between the energy supply side and the energy consumption side in the matching process; The objective function of the supply-demand matching model is as follows: g(τ)=min(Scost τ,total +DCost τ,total ) Where: g(τ) represents the minimum technical cost of matching supply and demand at time τ; SCost τ,total represents the supply technology cost at time τ; DCost τ,total represents the demand control cost at time τ; The supply technology cost SCost at the time τ τ,total , calculated as follows: Where: b j represents the sum of the basic electricity cost coefficient and the fixed maintenance cost coefficient of energy conversion technology j; represents the initial investment coefficient of energy conversion technology j in equal annual value; Q t,j represents the output of energy conversion technology j at time t; c j represents the sum of the variable maintenance cost coefficient and the operating cost coefficient of energy conversion technology j; Q τ,j represents the output of energy conversion technology j at time τ.

2. The method according to claim 1, wherein The energy conversion technology includes: Gas generator sets, lithium bromide absorption units, electric refrigeration units, heat pumps, boilers, energy storage equipment, power grids, photovoltaic power generation, solar thermal power generation, wind power generation and geothermal power generation.

3. The method according to claim 1, wherein The demand control cost DCost at the time τ τ,total , calculated as follows: DCost τ,total =DCost τ,12 +DCost τ,13 Where: DCost τ,12 represents the technical cost of cold and heat storage at time τ; DCost τ,13 represents the technical cost of electricity demand response at time τ.

4. The method according to claim 3, wherein The technical cost DCost of cold and heat storage at the time τ τ,12 , calculated as follows: Where: a 12 represents the basic cost coefficient of operation of cold and heat storage device; α 12 represents the fixed maintenance cost of the cold and heat storage device; Indicates the variable maintenance cost coefficient related to cold and heat storage technology; ΔD t,12 represents the change in demand caused by distributed energy storage at time t; β 12 Represents the cost coefficient corresponding to the cold storage loss; ε 12 Indicates the cost coefficient corresponding to the heat storage loss; ΔD τ,12 Represents the demand change caused by distributed energy storage at time τ.

5. The method according to claim 3, wherein The technical cost of electricity demand response DCost at the time τ τ,13 , calculated as follows: Where: α 13 represents the fixed cost of grid demand response; Indicates the variable cost coefficient related to the scale of technical demand response; ΔD t,13 represents the energy load demand at time t; represents the initial energy price at time t; is the initial energy load demand at time t; D τ,13 represents the energy load demand at time τ; l represents the first regression parameter; represents the energy price at time t; h represents the second regression parameter.

6. The method according to claim 1, wherein The constraints are as follows: Where ΔQ τ,total Indicates the total change in supply during the matching process; ΔD τ,total Indicates the total change in demand during the matching process; represents the initial demand value at time τ; represents the initial supply value at time τ; Q τ,j represents the output of energy conversion technology j at time τ; Q m,j Energy conversion technology j is limited by the output limit of natural resources; D τ,j represents the energy demand of energy conversion technology j at time τ; D m,j Indicates that the energy conversion technology j is limited by the output upper limit of environmental conditions.

7. An operating system of an integrated energy system, characterized in that: include: The acquisition module is used to obtain the forecast curves of each load and the conversion parameters of each energy conversion technology in the planned area within the optimization period, as well as the meteorological information corresponding to various renewable energy sources on a typical day; A matching module is used to bring the forecast curve of each load, the conversion parameter and the meteorological information into a pre-built supply and demand matching model for calculation to obtain the priority of adopting different energy conversion technologies at each time point in the optimization period and the maximum output provided by each energy conversion technology; The supply-demand matching model is constructed based on the conversion costs of various energy conversion technologies on the energy supply side and the demand regulation costs when the energy consumption side participates in demand response, with the minimum total technical cost of the integrated energy system as the optimization goal; The construction of the supply and demand matching model is specifically used to: Determine the conversion cost of each energy conversion technology based on the initial investment, operating costs, and maintenance costs of each energy conversion technology on the energy supply side; Determine the demand control cost when participating in demand response based on the analysis of changes in cooling demand, heating demand and electricity demand on the energy consumption side; Taking the minimum total technical cost of the integrated energy system as the optimization goal, the objective function of the supply and demand matching model is constructed based on the conversion costs of the energy conversion technologies and the demand regulation costs when participating in demand response; Constructing constraint conditions for the objective function of the supply-demand matching model based on the balance principle between the energy supply side and the energy consumption side in the matching process; The objective function of the supply-demand matching model is as follows: g(τ)=min(SCost τ,total +DCost τ,total ) Where: g(τ) represents the minimum technical cost of matching supply and demand at time τ; SCost τ,total represents the supply technology cost at time τ; DCost τ,total represents the demand control cost at time τ; The supply technology cost SCost at the time τ τ,total , calculated as follows: Where: b j represents the sum of the basic electricity cost coefficient and the fixed maintenance cost coefficient of energy conversion technology j; represents the initial investment coefficient of energy conversion technology j in equal annual value; Q t,j represents the output of energy conversion technology j at time t; c j represents the sum of the variable maintenance cost coefficient and the operating cost coefficient of energy conversion technology j; Q τ,j represents the output of energy conversion technology j at time τ.

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