Comprehensive energy system operation optimization method and system considering comprehensive demand response

Abstract

The present invention provides an integrated energy system operation optimization method and system taking into account comprehensive demand response, respectively constructing power models of various energy supply equipment and energy storage equipment included in the integrated energy system of an industrial park to form an integrated energy system model of the industrial park; considering the self-elasticity coefficient and cross-elasticity coefficient of the corresponding loads, respectively constructing electricity and gas load demand response models, considering the dynamic characteristics of temperature and user comfort, constructing a cooling load demand response model to form an industrial park comprehensive demand response model; combining the integrated energy system model and the comprehensive demand response model, taking the minimization of the operation cost of the industrial park integrated energy system including carbon quota trading as the optimization goal, configuring constraints, and forming an operation optimization model; adopting an adaptive fruit fly optimization algorithm with microhabitat technology to solve the operation optimization model; the present invention can improve the economy and environmental protection of the entire park comprehensive energy system.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation optimization of integrated energy systems, and specifically relates to an operation optimization method and system for an integrated energy system taking integrated demand response into account. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In order to realize the construction of an integrated energy system, many regions have proposed the "Internet +" smart energy action plan, taking the power system as the core, building a multi-energy interconnected network, and utilizing the mutual conversion capabilities between different energy sources to achieve energy synergy and complementarity. Under the architecture of the integrated energy network, the coupling of different forms of energy in the production, transmission and consumption links is becoming stronger and stronger. For example, the cold, heat and electricity produced by trigeneration equipment are all coupled. Therefore, when consuming at the terminal, users can also achieve the same effect by choosing different types of energy. The characteristics of mutual coupling and mutual substitution between energy sources provide the demand side with the ability to change energy use methods between different energy flows, and also provide a research background for comprehensive demand response. Comprehensive demand response provides an important entry point for realizing the two-way interaction between supply and demand in the integrated energy network and promoting the transformation of energy consumers into prosumers.

[0004] In existing studies, comprehensive demand response considers the coupling relationship between electricity and heat more, and less the demand response of cooling load. In the process of system operation, usually only economic goals are considered, and environmental protection goals are ignored. In the context of dual carbon goals, the carbon emissions of the park are an important control content. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes an integrated energy system operation optimization method and system taking into account comprehensive demand response. The present invention considers the coupling effect between multiple energy sources, price elasticity coefficient, temperature dynamic characteristics and user comfort, constructs a comprehensive demand response model, and utilizes multiple energy supply equipment to tap the demand response potential. More flexible loads can be integrated to reduce the energy supply pressure during system peak periods, and improve the economy and environmental protection of the entire park's integrated energy system.

[0006] According to some embodiments, the present invention adopts the following technical solutions:

[0007] A method for optimizing the operation of an integrated energy system taking into account integrated demand response comprises the following steps:

[0008] Construct power models of various energy supply equipment and energy storage equipment included in the comprehensive energy system of the industrial park to form a comprehensive energy system model of the industrial park;

[0009] Considering the self-elasticity coefficient and cross-elasticity coefficient of the corresponding load, the demand response models of electricity and gas loads are constructed respectively. Considering the dynamic characteristics of temperature and user comfort, the demand response model of cooling load is constructed to form a comprehensive demand response model of the industrial park.

[0010] Combining the comprehensive energy system model and the comprehensive demand response model, taking the minimization of the operation cost of the comprehensive energy system of the industrial park including carbon quota trading as the optimization goal, configuring the constraint conditions, and forming an operation optimization model;

[0011] The adaptive fruit fly optimization algorithm with microhabitat technology is used to solve the operation optimization model.

[0012] As an optional embodiment, the energy supply equipment includes several of a gas turbine, a waste heat boiler, a gas boiler, an electric boiler, a ground source heat pump, an electric refrigerator and an absorption refrigerator.

[0013] As a further limitation, the gas turbine power model is:

[0014] P gt (t) = V gt (t)×L NG ×η gt

[0015] Where: P CCHP,e (t) is the output power of the gas turbine during the period t; V gt (t) is the natural gas consumption of the gas turbine in period t; L NG is the lower calorific value of natural gas, 9.77kWh / m 3 ; η gt The power generation efficiency of the gas turbine.

[0016] The waste heat boiler power model is

[0017]

[0018] Where: H HRB (t) is the output thermal power of the waste heat boiler in period t; η hrb is the heat exchange efficiency of the waste heat boiler unit.

[0019] The gas boiler power model is:

[0020] H gb (t) = V gb (t)×η gb

[0021] Where: H gb (t) is the output thermal power of the gas boiler during period t; V gb (t) is the natural gas consumption of the gas boiler in period t; ηgb It is the electric heat conversion efficiency of gas boiler.

[0022] The electric boiler power model is:

[0023] H eb (t) = P eb (t)×η eb

[0024] Where: H eb (t) is the output thermal power of the electric boiler during period t; P eb (t) is the natural gas consumption of the electric boiler in period t; η eb It is the electric heat conversion efficiency of the electric boiler.

[0025] The ground source heat pump power model is:

[0026] C gsh (t) = P gsh (t)×COP gsh

[0027] Where: C gsh (t) is the output cooling power of the ground source heat pump during period t; P gsh (t) is the power consumption of the ground source heat pump in period t; COP gsh It is the electricity-to-cooling conversion efficiency of the ground source heat pump.

[0028] The power model of the electric refrigerator is:

[0029] C ec (t) = P ec (t)×COP ec

[0030] Where: C ec (t) is the output cooling power of the electric refrigerator during period t; P ec (t) is the power consumption of the electric refrigerator in period t; COP ec It is the electricity-to-cooling conversion efficiency of the electric refrigerator.

[0031] The absorption refrigerator power model is:

[0032] C ac (t) = H ac (t)×COP ac

[0033] Where: C ac (t) is the output cooling power of the absorption refrigerator during period t; H ac (t) is the heat consumption of the absorption refrigerator in period t; COP ac is the heat conversion efficiency of the absorption chiller.

[0034] As an optional implementation, the energy storage device power model is:

[0035]

[0036] Where: S i (t) and S i (t-1) are the energy storage states of the i-th energy storage device in period t and period t-1 respectively; σ i is the self-loss rate of the electric energy storage device; and are the charging and discharging power of the i-th energy storage device at time t respectively; and are the charging and discharging efficiencies of the i-th energy storage device respectively; i=1, 2, 3 represent electricity storage, heat storage, and cold storage devices respectively.

[0037] As an optional implementation, the electric load demand response model is:

[0038]

[0039] Where: P0(t) is the initial electrical load in period t; P S The total initial power load; T is the total calculation time, which is 24h; P a is the initial average power consumption; α is the real-time power price floating factor; C EX0 (t) is the initial electricity price in period t; C EX (t) is the real-time electricity price during period t; C EXmin , C EXmax They are the maximum and minimum electricity prices respectively.

[0040]

[0041] Where: E p (i,i),E p (i, j) are the self-elastic coefficient and cross-elastic coefficient of the electric load respectively; P oi , C EX0i are the electricity load and initial electricity price in period i respectively; ΔP i is the change in electric load during period i; ΔC EXi , ΔC EXj are the changes in electricity prices in period i and period j respectively.

[0042]

[0043] Where: P de (t) is the electric load after participating in the electric load demand response in the t period; when t = t', E p(t, t') is the elastic coefficient; when t≠t', E p (t, t') is the cross elastic coefficient; C EX (t'), C EX0 (t') are the real-time electricity price and initial electricity price during period t' respectively.

[0044] The cooling load demand response model is:

[0045]

[0046] Where: Q C (t) is the cooling load demand of the park during period t; T w (t) is the outdoor temperature during period t; T n (t), T n (t-1) are the indoor temperatures at time t and time t-1, respectively. R and C are the equivalent thermal resistance and equivalent heat capacity of the indoor cooling building in the park, respectively. Δt is the time interval, which is 1h here.

[0047]

[0048] Where: M and W are the human metabolic rate and human thermal power respectively; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the indoor pressure; t r and t cl are the average radiation temperature and clothing outer surface temperature, λ PMV is the PMV index value.

[0049] The gas load demand response model is:

[0050]

[0051] Where: G0(t) is the initial gas load in period t; G S The total initial gas load; T is the total calculation time; G a is the initial gas consumption average; α is the real-time gas price floating factor; C gas0 (t) is the initial gas price in period t; C gas (t) is the real-time gas price in period t; C gasmin , C gasmax They are the maximum and minimum gas prices respectively.

[0052]

[0053] Where: E g (i,i),E g(i, j) are the air load autoelastic coefficient and cross elastic coefficient respectively; G oi , C gas0i are the gas load and initial gas price in period i respectively; ΔG i is the change in gas load during period i; ΔC gasi , ΔC gasj are the changes in gas prices in period i and period j respectively.

[0054]

[0055] Where: G de (t) is the electric load after participating in the electric load demand response in the t period; when t = t', E g (t, t') is the elastic coefficient; when t≠t', E g (t, t') is the cross elastic coefficient; C gas (t'), C gas 0(t') are the real-time electricity price and initial electricity price during period t' respectively.

[0056] As an optional implementation, the optimization goal is:

[0057]

[0058] Where: P x (t) is the purchase amount of the system’s i-th energy in period t; C x (t) is the price of the i-th energy in period t; P i (t) is the output power of the ith device in period t; μ i is the operation and maintenance cost per unit output of the i-th equipment: grid is the carbon emission coefficient of power purchase units from the power grid; gas is the unit carbon emission coefficient of gas purchased from the gas grid; δ is the fixed carbon emission quota of the park; It is the carbon quota trading price of the industrial park.

[0059] As an optional implementation, the constraints include power balance constraints, main grid purchase upper limit constraints, equipment operation constraints, energy storage constraints and indoor environment thermal comfort constraints.

[0060] As an optional implementation, the specific process of solving the problem by using the adaptive fruit fly optimization algorithm with the addition of the niche technology includes:

[0061] Step 101: Initialize the fruit fly population size, maximum iteration number, flavor concentration variance threshold, chaotic traversal times, and fruit fly foraging distance;

[0062] Step 102: using a chaotic operator to reset the position of the fruit fly population and divide the microhabitats;

[0063] Step 103: Estimate the distance between the individual position of the fruit fly and the origin, and then calculate the flavor concentration judgment value;

[0064] Step 104: Substitute the flavor concentration judgment value Si into the flavor concentration judgment function to obtain the flavor concentration at the position of the fruit fly individual;

[0065] Step 105: Screening out the fruit fly individuals with the best flavor concentration in the fruit fly population;

[0066] Step 106: retain the optimal flavor concentration value and the corresponding coordinate value, and the fruit fly group flies to the location to forage;

[0067] Step 107: Calculate the average flavor concentration and flavor concentration variance of the fruit fly population respectively;

[0068] Step 108: If the flavor concentration variance is less than the threshold and the number of chaotic traversal is greater than 0, the individual position of the fruit fly is converted into a new individual position of the fruit fly in the search space through the Logistic mapping chaos technology, and the number of chaotic traversal is reduced by one. Otherwise, execute step 101;

[0069] Step 109: Calculate the distance between the new position and the origin, and then calculate the flavor concentration determination value;

[0070] Step 110: Substitute the flavor concentration determination value into the flavor concentration determination function to obtain the flavor concentration of the new position of the fruit fly individual;

[0071] Step 111: if the flavor concentration is less than the optimal value at this time, redefine this value as the optimal flavor concentration value, determine the optimal position of the fruit fly population in each microhabitat, record the position of the fruit fly at this time, and return to step 108 for iterative update, otherwise, directly return to step 108;

[0072] Step 112: Repeat steps 102-111 to perform iterative optimization using the niche technology until the current number of iterations is equal to the maximum number of iterations or the accuracy target requirement is reached;

[0073] Step 113: Output the system's scheduling optimization results and the output status of each device.

[0074] A comprehensive energy system operation optimization system taking into account comprehensive demand response, comprising:

[0075] The integrated energy system model building module is configured to respectively build power models of various energy supply devices and energy storage devices included in the integrated energy system of the industrial park to form an integrated energy system model of the industrial park;

[0076] The comprehensive demand response model building module is configured to consider the self-elasticity coefficient and cross-elasticity coefficient of the corresponding load, respectively build the electricity and gas load demand response models, consider the temperature dynamic characteristics and user comfort, build the cooling load demand response model, and constitute the comprehensive demand response model of the industrial park;

[0077] An operation optimization model establishment module is configured to combine the comprehensive energy system model and the comprehensive demand response model, take the minimum operation cost of the industrial park comprehensive energy system including carbon quota trading as the optimization goal, configure constraint conditions, and form an operation optimization model;

[0078] The iterative solution module is configured to solve the running optimization model by using an adaptive fruit fly optimization algorithm with microhabitat technology.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention takes into account the coupling effect between multiple energy sources, price elasticity coefficient, temperature dynamic characteristics and user comfort, constructs a comprehensive demand response model, establishes an economic target that considers carbon quota trading, and uses an adaptive chaotic fruit fly optimization algorithm to solve the model. It can reduce the operating costs of the integrated energy system, meet the dual carbon goals, reduce the carbon emissions of the park system, and alleviate the tense situation of energy supply during peak periods.

[0081] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings in the specification, 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.

[0083] Figure 1 A structural diagram of a comprehensive energy system according to at least one embodiment of the present invention;

[0084] Figure 2 A flowchart of the operation optimization steps of at least one embodiment of the present invention;

[0085] Figure 3 The present invention is a flowchart of a solution algorithm according to at least one embodiment of the present invention. DETAILED DESCRIPTION

[0086] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0087] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0088] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0089] A low-carbon operation optimization method for an integrated energy system of an industrial park taking into account integrated demand response comprises the following steps:

[0090] S1: Construct an integrated energy system model for an industrial park that includes gas turbines, waste heat boilers, gas boilers, electric boilers, ground source heat pumps, electric refrigerators, absorption refrigerators, and energy storage equipment;

[0091] S2: Establish a comprehensive demand response model that includes electricity, cooling and gas load demand response;

[0092] S3: Establish an economic target model for the integrated energy system of industrial parks including carbon quota trading;

[0093] S4: Establish a constraint model for the integrated energy system of industrial parks;

[0094] S5: Solving the operational optimization model using the adaptive fruit fly optimization algorithm with microhabitat technology

[0095] Specifically, the equipment in step S1 includes: a gas turbine, a waste heat boiler, a gas boiler, an electric boiler, a ground source heat pump, an electric refrigerator, an absorption refrigerator and an energy storage device.

[0096] The gas turbine power model is:

[0097] P gt (t) = V gt (t)×L NG ×η gt

[0098] Where: P CCHP,e (t) is the output power of the gas turbine during the period t; V gt (t) is the natural gas consumption of the gas turbine in period t; L NG is the lower calorific value of natural gas, 9.77kWh / m 3 ; η gtThe power generation efficiency of the gas turbine.

[0099] The waste heat boiler power model is

[0100]

[0101] Where: H HRB (t) is the output thermal power of the waste heat boiler in period t; η hrb is the heat exchange efficiency of the waste heat boiler unit.

[0102] The gas boiler power model is:

[0103] H gb (t) = V gb (t)×η gb

[0104] Where: H gb (t) is the output thermal power of the gas boiler during period t; V gb (t) is the natural gas consumption of the gas boiler in period t; η gb It is the electric heat conversion efficiency of gas boiler.

[0105] The electric boiler power model is:

[0106] H eb (t) = P eb (t)×η eb

[0107] Where: H eb (t) is the output thermal power of the electric boiler during period t; P eb (t) is the natural gas consumption of the electric boiler in period t; η eb It is the electric heat conversion efficiency of the electric boiler.

[0108] The ground source heat pump power model is:

[0109] C gsh (t) = P gsh (t)×COP gsh

[0110] Where: C gsh (t) is the output cooling power of the ground source heat pump during period t; P gsh (t) is the power consumption of the ground source heat pump in period t; COP gsh It is the electricity-to-cooling conversion efficiency of the ground source heat pump.

[0111] The power model of the electric refrigerator is:

[0112] C ec (t) = P ec (t)×COP ec

[0113] Where: C ec (t) is the output cooling power of the electric refrigerator during period t; P ec (t) is the power consumption of the electric refrigerator in period t; COP ec It is the electricity-to-cooling conversion efficiency of the electric refrigerator.

[0114] The absorption refrigerator power model is:

[0115] C ac (t) = H ac (t)×COP ac

[0116] Where: C ac (t) is the output cooling power of the absorption refrigerator during period t; H ac (t) is the heat consumption of the absorption refrigerator in period t; COP ac is the heat conversion efficiency of the absorption chiller.

[0117] The energy storage device power model is:

[0118]

[0119] Where: S i (t) and S i (t-1) are the energy storage states of the i-th energy storage device in period t and period t-1 respectively; σ i is the self-loss rate of the electric energy storage device; and are the charging and discharging power of the i-th energy storage device at time t respectively; and are the charging and discharging efficiencies of the i-th energy storage device respectively; i=1, 2, 3 represent electricity storage, heat storage, and cold storage devices respectively.

[0120] Specifically, in step S1, the power generation efficiency of the gas turbine satisfies the power-efficiency characteristic curve.

[0121] η gt (t) = aP gt (t)+bPgt(t) 2 +cP gt (t) 3 +dP gt (t) 4

[0122] Where: a, b, c, d are the variable operating condition operating characteristic parameters of the gas turbine.

[0123] Specifically, in step S1, the heat exchange efficiency of the waste heat boiler satisfies the variable operating condition characteristic curve.

[0124]

[0125] Where: S is the heat exchange area of ​​the waste heat boiler; A is a constant related to the boiler structure; G and T are the exhaust flow rate and average exhaust temperature of the gas turbine, respectively; m, n, k, and l are constants related to the tube cluster form and the thermal properties of the flue gas.

[0126] Specifically, in step S1, the refrigeration efficiency of the electric refrigerator satisfies the variable operating condition characteristic curve, and a cubic polynomial fitting is adopted.

[0127]

[0128] Where: ν ac,n is the nth-order fitting coefficient of the electric refrigerator.

[0129] Specifically, the comprehensive demand response model in step S2 includes electricity, cooling and gas load demand response models.

[0130] The electric load demand response model is:

[0131]

[0132] Where: P0(t) is the initial electrical load in period t; P S The total initial power load; T is the total calculation time, which is 24h; P a is the initial average power consumption; α is the real-time power price floating factor; C EX0 (t) is the initial electricity price in period t; C EX (t) is the real-time electricity price during period t; C EXmin , C EXmax They are the maximum and minimum electricity prices respectively.

[0133]

[0134] Where: E p (i,i),E p (i, j) are the self-elastic coefficient and cross-elastic coefficient of the electric load respectively; P oi , C EX0i are the electricity load and initial electricity price in period i respectively; ΔP i is the change in electric load during period i; ΔC EXi , ΔC EXj are the changes in electricity prices in period i and period j respectively.

[0135]

[0136] Where: P de (t) is the electric load after participating in the electric load demand response in the t period; when t = t', Ep (t, t') is the elastic coefficient; when t≠t', E p (t, t') is the cross elastic coefficient; C EX (t'), C EX0 (t') are the real-time electricity price and initial electricity price during period t' respectively.

[0137] The cooling load demand response model is:

[0138]

[0139] Where: Q C (t) is the cooling load demand of the park during period t; T w (t) is the outdoor temperature during period t; T n (t), T n (t-1) are the indoor temperatures at time t and time t-1, respectively. R and C are the equivalent thermal resistance and equivalent heat capacity of the indoor cooling building in the park, respectively. Δt is the time interval, which is 1h here.

[0140]

[0141] Where: M and W are the human metabolic rate and human thermal power respectively; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the indoor pressure; t r and t cl are the average radiation temperature and clothing outer surface temperature, λ PMV is the PMV index value.

[0142] The gas load demand response model is:

[0143]

[0144] Where: G0(t) is the initial gas load in period t; G S The total initial gas load; T is the total calculation time, which is 24h; G a is the initial gas consumption average; α is the real-time gas price floating factor; C gas0 (t) is the initial gas price in period t; C gas (t) is the real-time gas price in period t; C gasmin , C gasmax They are the maximum and minimum gas prices respectively.

[0145]

[0146] Where: E g (i,i),E g(i, j) are the air load autoelastic coefficient and cross elastic coefficient respectively; G oi , C gas0i are the gas load and initial gas price in period i respectively; ΔG i is the change in gas load during period i; ΔC gasi , ΔC gasj are the changes in gas prices in period i and period j respectively.

[0147]

[0148] Where: G de (t) is the electric load after participating in the electric load demand response in the t period; when t = t', E g (t, t') is the elastic coefficient; when t≠t', E g (t, t') is the cross elastic coefficient; C gas (t'), C gas0 (t') are the real-time electricity price and initial electricity price during period t' respectively.

[0149] Specifically, the system economic target established in step S3 includes the benefits or penalties of carbon quota trading.

[0150]

[0151] Where: P x (t) is the purchase amount of the system’s i-th energy in period t; C x (t) is the price of the i-th energy in period t; P i (t) is the output power of the ith device in period t; μ i is the operation and maintenance cost per unit output of the i-th equipment: grid is the carbon emission coefficient of power purchase units from the power grid; gas is the unit carbon emission coefficient of gas purchased from the gas grid; δ is the fixed carbon emission quota of the park; It is the carbon quota trading price of the industrial park.

[0152] Specifically, in the target, when i is a gas turbine:

[0153]

[0154] Specifically, the constraints in step S4 include power balance constraints, main grid purchase upper limit constraints, equipment operation constraints, energy storage constraints, and indoor environment thermal comfort constraints.

[0155] The power balance constraint model is:

[0156]

[0157] The mainnet purchase upper limit constraint model is:

[0158]

[0159] Where: G buymax P is the upper limit of gas purchase in the park; gridmax The upper limit for electricity purchases in the industrial park.

[0160] The equipment operation constraint model is:

[0161]

[0162] Where: P imax is the output upper limit of the i-th type of power generation equipment; H imax is the upper limit of the output of the i-th type of heat-generating equipment; C imax It is the upper limit of the output of the i-th type cooling equipment.

[0163] The energy storage constraint model is:

[0164]

[0165] In the formula, are the upper and lower limits of the SOC of the energy storage device; P i inmax , P i outmax are the maximum charging and discharging power of the energy storage device respectively; X i , Y i is the energy storage charging and discharging state variable; S i (0), S i (24) represents the initial and final energy storage states of the energy storage equipment within the dispatch period, and i = 1, 2, and 3 represent electricity storage, heat storage, and cold storage equipment, respectively.

[0166] The indoor environment thermal comfort constraint model is:

[0167] -1≤λ PMV ≤1

[0168] Specifically, in step S5, an adaptive fruit fly optimization algorithm with microhabitat technology is used to distribute fruit fly individuals in different habitats, thereby increasing diversity, improving the global convergence ability and optimization accuracy of the fruit fly algorithm, and making the solution result of the park integrated energy system model closer to the optimal solution. Step S5 includes:

[0169] Step 501: Initialize the fruit fly population size, maximum number of iterations, flavor concentration variance threshold, number of chaotic traversals, and fruit fly foraging distance.

[0170] Step 502: Use a chaotic operator to reset the position of the fruit fly population and divide the microhabitats.

[0171] Step 503: Estimate the distance between the individual position of the fruit fly and the origin, and then calculate the flavor concentration judgment value;

[0172] Step 504: Substitute the flavor concentration judgment value Si into the flavor concentration judgment function to obtain the flavor concentration at the individual fruit fly position.

[0173] Step 505: Screen out the fruit fly individuals with the best flavor concentration in the fruit fly population.

[0174] Step 506: The optimal flavor concentration value and the corresponding coordinate value are retained, and the fruit fly colony now flies to the location to forage for food.

[0175] Step 507: Calculate the average flavor concentration and flavor concentration variance of the fruit fly population respectively.

[0176] Step 508: If the flavor concentration variance is less than the threshold and the number of chaotic traversal times is greater than 0, the individual position of the fruit fly is converted into a new individual position of the fruit fly in the search space through the Logistic mapping chaos technology, and the number of chaotic traversal times is reduced by one. Otherwise, execute step 1.

[0177] Step 509: Calculate the distance between the new position and the origin, and then calculate the flavor concentration judgment value.

[0178] Step 510: Substitute the flavor concentration determination value into the flavor concentration determination function to obtain the flavor concentration of the new position of the fruit fly individual.

[0179] Step 511: If the flavor concentration is less than the optimal value at this time, redefine this value as the optimal flavor concentration, determine the optimal position of the fruit fly population in each microhabitat, record the position of the fruit flies at this time, and return to step 508 for iterative update, otherwise, directly return to step 508.

[0180] Step 512: Repeat steps S502-S511 to perform iterative optimization using the microhabitat technology until the current number of iterations is equal to the maximum number of iterations or the accuracy target requirement has been reached.

[0181] Step 513: Output the system scheduling optimization results and the output status of each device.

[0182] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0183] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 processes in the flowchart and / or block diagram. 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.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0186] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0187] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of an integrated energy system taking into account integrated demand response, characterized in that: The following steps are involved: Construct power models of various energy supply equipment and energy storage equipment included in the comprehensive energy system of the industrial park to form a comprehensive energy system model of the industrial park; Considering the self-elasticity coefficient and cross-elasticity coefficient of the corresponding load, the demand response models of electricity and gas loads are constructed respectively. Considering the dynamic characteristics of temperature and user comfort, the demand response model of cooling load is constructed to form a comprehensive demand response model of the industrial park. Combining the comprehensive energy system model and the comprehensive demand response model, taking the minimization of the operation cost of the comprehensive energy system of the industrial park including carbon quota trading as the optimization goal, configuring the constraint conditions, and forming an operation optimization model; The energy supply equipment includes several of a gas turbine, a waste heat boiler, a gas boiler, an electric boiler, a ground source heat pump, an electric refrigerator and an absorption refrigerator; The power generation efficiency of the gas turbine satisfies a power-efficiency characteristic curve, which takes into account the variable operating condition operating characteristic parameters of the gas turbine; η gt (t)=aP gt (t)+bPgt(t) 2 +cP gt (t) 3 +dP gt (t) 4 Where: a, b, c, d are the variable operating condition operation characteristic parameters of the gas turbine; The heat exchange efficiency of the waste heat boiler meets the variable operating condition characteristic curve; Where: S is the heat exchange area of ​​the waste heat boiler; A is a constant related to the boiler structure; G and T are the exhaust flow rate and average exhaust temperature of the gas turbine respectively; m, n, k, l are constants related to the tube cluster form and the thermal properties of the flue gas; The refrigeration efficiency of the electric refrigerator meets the variable working condition characteristic curve and is fitted by a cubic polynomial; Where: ac,n is the nth-order fitting coefficient of the electric refrigerator; The adaptive fruit fly optimization algorithm with microhabitat technology is used to solve the operation optimization model.

2. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The gas turbine power model is: P gt (t)=V gt (t)×L NG ×η gt Where: P CCHP,e (t) is the output power of the gas turbine during the period t; V gt (t) is the natural gas consumption of the gas turbine in period t; L NG is the lower calorific value of natural gas, η gt is the power generation efficiency of the gas turbine; Or, the waste heat boiler power model is Where: H HRB (t) is the output thermal power of the waste heat boiler in period t; η hrb is the heat exchange efficiency of the waste heat boiler unit; Or, the gas boiler power model is: H gb (t)=V gb (t)×η gb Where: H gb (t) is the output thermal power of the gas boiler during period t; V gb (t) is the natural gas consumption of the gas boiler in period t; η gb The electric heat conversion efficiency of the gas boiler; Or, the electric boiler power model is: H eb (t)=P eb (t)×η eb Where: H eb (t) is the output thermal power of the electric boiler during period t; P eb (t) is the natural gas consumption of the electric boiler in period t; η eb is the electric-heat conversion efficiency of the electric boiler; Or, the ground source heat pump power model is: C gsh (t)=P gsh (t)×COP gsh Where: C gsh (t) is the output cooling power of the ground source heat pump during period t; P gsh (t) is the power consumption of the ground source heat pump in period t; COP gsh The electricity-to-cooling conversion efficiency of the ground-source heat pump; Or, the power model of the electric refrigerator is: C ec (t)=P ec (t)×COP ec Where: C ec (t) is the output cooling power of the electric refrigerator during period t; P ec (t) is the power consumption of the electric refrigerator in period t; COP ec is the electricity-to-cooling conversion efficiency of the electric refrigerator; Or, the absorption refrigerator power model is: C ac (t)=H ac (t)×COP ac Where: C ac (t) is the output cooling power of the absorption refrigerator during period t; H ac (t) is the heat consumption of the absorption refrigerator in period t; COP ac is the heat conversion efficiency of the absorption chiller.

3. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The energy storage device power model is: Where: S i (t) and S i (t-1) are the energy storage states of the i-th energy storage device in period t and period t-1 respectively; σ i is the self-loss rate of the electric energy storage device; and are the charging and discharging power of the i-th energy storage device at time t respectively; and are the charging and discharging efficiencies of the i-th energy storage device respectively; i=1, 2, 3 represent electricity storage, heat storage, and cold storage devices respectively.

4. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The electric load demand response model is: Where: P0(t) is the initial electrical load in period t; P S The total initial power load; T is the total calculation time; P a is the initial average power consumption; α is the real-time power price floating factor; C EX0 (t) is the initial electricity price in period t; C EX (t) is the real-time electricity price during period t; C EXmin , C EXmax They are the maximum and minimum prices for electricity prices; Where: E p (i,i),E p (i, j) are the self-elastic coefficient and cross-elastic coefficient of the electric load respectively; P oi , C EX0i are the electricity load and initial electricity price in time period i respectively; ΔP i is the change in electric load during period i; ΔC EXi , ΔC EXj are the changes in electricity prices in period i and period j respectively; Where: P de (t) is the electric load after participating in the electric load demand response in the t period; when t = t', E p (t, t') is the elastic coefficient; when t≠t', E p (t, t') is the cross elastic coefficient; C EX (t'), C EX0 (t') are the real-time electricity price and initial electricity price during period t' respectively.

5. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The cooling load demand response model is: Where: Q C (t) is the cooling load demand of the park during period t; T w (t) is the outdoor temperature during period t; T n (t), T n (t-1) are the indoor temperatures at time t and time t-1, respectively. R and C are the equivalent thermal resistance and equivalent heat capacity of the indoor cooling building in the park, respectively. Δt is the time interval. Where: M and W are the human metabolic rate and human thermal power respectively; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the indoor pressure; t r and t cl are the average radiation temperature and clothing outer surface temperature, λ PMV is the PMV index value.

6. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The gas load demand response model is: Where: G0(t) is the initial gas load in period t; G S The total initial gas load; T is the total calculation time; G a is the initial gas consumption average; α is the real-time gas price floating factor; C gas0 (t) is the initial gas price in period t; C gas (t) is the real-time gas price in period t; C gasmin , C gasmax They are the maximum and minimum gas prices respectively; Where: E g (i,i),E g (i, j) are the air load autoelastic coefficient and cross elastic coefficient respectively; G oi , C gas0i are the gas load and initial gas price in period i respectively; ΔG i is the change in gas load during period i; ΔC gasi , ΔC gasj are the changes in gas prices in period i and period j respectively; Where: G de (t) is the electric load after participating in the electric load demand response in the t period; when t = t', E g (t, t') is the elastic coefficient; when t≠t', E g (t, t') is the cross elastic coefficient; C gas (t'), C gas0 (t') are the real-time electricity price and initial electricity price during period t' respectively.

7. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The optimization goal is: Where: P x (t) is the purchase amount of the system’s i-th energy in period t; C x (t) is the price of the i-th energy in period t; P i (t) is the output power of the ith device in period t; μ i is the operation and maintenance cost per unit output of the i-th equipment: grid The carbon emission coefficient of the power purchase unit of the power grid; λ gas is the unit carbon emission coefficient of gas purchased from the gas grid; δ is the fixed carbon emission quota of the park; The transaction price of the park's carbon quota; Alternatively, the constraints include power balance constraints, main grid purchase upper limit constraints, equipment operation constraints, energy storage constraints, and indoor environment thermal comfort constraints.

8. The method for optimizing the operation of an integrated energy system taking into account integrated demand response according to claim 1, characterized in that: The specific process of solving the problem by using the adaptive fruit fly optimization algorithm with microhabitat technology includes: Step 101: Initialize the fruit fly population size, maximum iteration number, flavor concentration variance threshold, chaotic traversal times, and fruit fly foraging distance; Step 102: using a chaotic operator to reset the position of the fruit fly population and divide the microhabitats; Step 103: Estimate the distance between the individual position of the fruit fly and the origin, and then calculate the flavor concentration judgment value; Step 104: Substitute the flavor concentration judgment value Si into the flavor concentration judgment function to obtain the flavor concentration at the position of the fruit fly individual; Step 105: Screening out the fruit fly individuals with the best flavor concentration in the fruit fly population; Step 106: retain the optimal flavor concentration value and the corresponding coordinate value, and the fruit fly group flies to the location to forage; Step 107: Calculate the average flavor concentration and flavor concentration variance of the fruit fly population respectively; Step 108: If the flavor concentration variance is less than the threshold and the number of chaotic traversal is greater than 0, the individual position of the fruit fly is converted into a new individual position of the fruit fly in the search space through the Logistic mapping chaos technology, and the number of chaotic traversal is reduced by one. Otherwise, execute step 101; Step 109: Calculate the distance between the new position and the origin, and then calculate the flavor concentration determination value; Step 110: Substitute the flavor concentration determination value into the flavor concentration determination function to obtain the flavor concentration of the new position of the fruit fly individual; Step 111: if the flavor concentration is less than the optimal value at this time, redefine this value as the optimal flavor concentration value, determine the optimal position of the fruit fly population in each microhabitat, record the position of the fruit fly at this time, and return to step 108 for iterative update, otherwise, directly return to step 108; Step 112: Repeat steps 102-111 to perform iterative optimization using the niche technology until the current number of iterations is equal to the maximum number of iterations or the accuracy target requirement is reached; Step 113: Output the system's scheduling optimization results and the output status of each device.

9. An integrated energy system operation optimization system taking into account integrated demand response, characterized by: include: The integrated energy system model building module is configured to respectively build power models of various energy supply devices and energy storage devices included in the integrated energy system of the industrial park to form an integrated energy system model of the industrial park; The energy supply equipment includes several of a gas turbine, a waste heat boiler, a gas boiler, an electric boiler, a ground source heat pump, an electric refrigerator and an absorption refrigerator; The power generation efficiency of the gas turbine satisfies a power-efficiency characteristic curve, which takes into account the variable operating condition operating characteristic parameters of the gas turbine; η gt (t)=aP gt (t)+bPgt(t) 2 +cP gt (t) 3 +dP gt (t) 4 Where: a, b, c, d are the variable operating condition operation characteristic parameters of the gas turbine; The heat exchange efficiency of the waste heat boiler meets the variable operating condition characteristic curve; Where: S is the heat exchange area of ​​the waste heat boiler; A is a constant related to the boiler structure; G and T are the exhaust flow rate and average exhaust temperature of the gas turbine respectively; m, n, k, l are constants related to the tube cluster form and the thermal properties of the flue gas; The refrigeration efficiency of the electric refrigerator meets the variable working condition characteristic curve and is fitted by a cubic polynomial; Where: ac,n is the nth-order fitting coefficient of the electric refrigerator; The comprehensive demand response model building module is configured to consider the self-elasticity coefficient and cross-elasticity coefficient of the corresponding load, respectively build the electricity and gas load demand response models, consider the temperature dynamic characteristics and user comfort, build the cooling load demand response model, and constitute the comprehensive demand response model of the industrial park; An operation optimization model establishment module is configured to combine the comprehensive energy system model and the comprehensive demand response model, take the minimum operation cost of the industrial park comprehensive energy system including carbon quota trading as the optimization goal, configure constraint conditions, and form an operation optimization model; The iterative solution module is configured to solve the running optimization model by using an adaptive fruit fly optimization algorithm with microhabitat technology.

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