Park multi-energy coupling energy system optimization scheduling method considering demand side response

Abstract

The invention relates to the technical field of power systems, and discloses a park multi-energy coupling energy system optimization scheduling method considering demand side response, comprising the following steps: constructing a multi-energy coupling system architecture model; constructing a multi-energy coupling energy system optimization scheduling model, wherein constraint conditions comprise battery energy storage charging and discharging constraint, charge state constraint and peak clipping constraint; operation constraint of the electric vehicle charging station; carrying out output constraint on the energy conversion equipment model; performing new energy power generation constraint, electric load balance constraint, thermal load balance constraint, cold load balance constraint, gas power rate balance constraint and hydrogen load balance constraint; public network line transmission power constraint; constraining transmission power of an internal distribution network line; constructing a demand side response model; and solving the multi-energy coupling energy system optimization scheduling model to obtain an optimization scheduling scheme. The method has the advantages that efficient, economical and low-carbon operation of an energy system is realized through multi-energy coupling architecture design, demand side response dynamic optimization and an intelligent scheduling model.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, in particular to an optimal scheduling method for a multi-energy coupling energy system in a park considering demand-side response. Background Art

[0002] In the operation and scheduling of a multi-energy coupling energy system in a park, the uncertainty and complexity of demand-side response pose great challenges to the optimal scheduling of the system. At the same time, how to effectively coordinate the coupling relationship of multiple energies, improve the system operation efficiency and reduce the energy consumption cost has also become an urgent problem to be solved. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an optimal scheduling method for a multi-energy coupling energy system in a park considering demand-side response.

[0004] The purpose of the present invention is achieved through the following technical solutions: an optimal scheduling method for a multi-energy coupling energy system in a park considering demand-side response, the method comprising:

[0005] Constructing a multi-energy coupling system architecture model;

[0006] Based on the multi-energy coupling system architecture model, constructing an optimal scheduling model for the multi-energy coupling energy system, the objective function of which is:

[0007]

[0008] In the formula, C represents the present value of the full life cycle operation and maintenance cost of the multi-energy coupling system; C ope,n 、C main,n 、 are respectively the operation, maintenance and carbon trading costs of the multi-energy coupling system in the nth year; C cut is the loss cost of insufficient power supply; F L is the annual net income of the user-side energy storage;

[0009] The constraint conditions of the multi-energy coupling system architecture model include battery energy storage charge and discharge constraints, state of charge constraints and peak shaving constraints;

[0010] The operation constraints of the electric vehicle charging station;

[0011] The output constraints of the energy conversion equipment model;

[0012] New energy power generation constraints, electric load balance constraints, heat load balance constraints, cold load balance constraints, gas power balance constraints, hydrogen load balance constraints;

[0013] The transmission power constraints of the public network line;

[0014] The transmission power constraints of the internal distribution network line;

[0015] Construct a demand-side response model;

[0016] Based on the demand-side response model, solve the optimal scheduling model of the multi-energy coupling energy system to obtain the optimal scheduling plan. Specifically, the operating cost calculation formula of the multi-energy coupling system is as follows:

[0017] C ope,n = C buy,n + C w,cut,n

[0018]

[0019] In the formula, C buy,n is the energy purchase cost for the scheduling year; a t , b t respectively represent the electricity price and gas price at time t of the scheduling year; P e,buy,n,t , P g,buy,n,t are the purchased electricity power and purchased gas power at time t of the scheduling year respectively; T represents the annual operating hours; C w,cut,n is the cost of curtailed wind and curtailed light for the scheduling year; c t is the unit cost of curtailed wind and curtailed light penalty; P w,cut,n,t , P pv,cut,n,t are the costs of curtailed wind and curtailed light for the scheduling year. Specifically, the maintenance cost calculation formula of the multi-energy coupling system is as follows:

[0020]

[0021] In the formula, c main,n is the maintenance cost vector per unit power of various devices; P main,n,t is the output power matrix of the device in the nth year at time t; T represents the annual operating hours.

[0022] Specifically, the carbon trading cost calculation formula of the multi-energy coupling system is as follows:

[0023] E PIES,n = E e,buy,n + E GB,n + E CHP,n

[0024]

[0025] In the formula, E PIES,n , E e,buy,n , E GB,n , E CHP,n are the actual carbon emissions of the externally purchased electricity, gas boiler, and combined heat and power unit of the multi-energy coupling system in the nth year respectively; P e,buy,n,t is the purchase power from the superior power grid in the nth year at time t; P h,GB,n,t is the heating power of the gas boiler in the nth year at time t; Ph,CHP,n,t , P e,CHP,n,t are the heating and power generation powers of the CHP unit in the t period of the nth year respectively; μ e and μ h are the carbon emission intensities per unit of electricity and per unit of heat respectively; is the electricity-heat conversion coefficient of the CHP unit;

[0026] E' PIES,n = E' e,buy,n + E' GB,n + E' CHP,n

[0027] In the formula, E' PIES,n , E' e,buy,n , E' GB,n , E' CHP,n are the carbon emission allowances of purchased electricity, gas boilers, and CHP units respectively;

[0028] E 0,n = E PIES,n - E' PIES,n

[0029] In the formula, E 0,n is the tradable carbon emission of the park considering carbon allowances;

[0030]

[0031] In the formula, is the carbon trading cost; λ is the carbon trading benchmark price.

[0032] Specifically, the calculation formula for the cost of power supply shortage loss is as follows:

[0033]

[0034] In the formula, C GDP is the GDP loss per degree of electricity in the industry, is the amount of curtailed electricity at each moment.

[0035] Specifically, the calculation formula for the annual net income of user-side energy storage is as follows:

[0036] F L = (f1 + f2 + F EV,n ) × λ DOD × (1 - γ) -n+1

[0037] In the formula, f1 is the daily income from the peak-valley electricity price difference arbitrage of the energy storage; f2 is the annual subsidy income for installing the energy storage; λ DOD is the charge-discharge depth of the energy storage; γ is the annual decay rate of the energy storage capacity; n is the year.

[0038]

[0039] P L,t,d = P load,t,d + P c,t,d + P dc,t,d

[0040] Wherein, P load,t,d represents the net load power; P L,t,d represents the equivalent load of the industrial park after installing the energy storage system; △t represents the output duration, taken as 1 hour; at represents the electricity price at time t; P c,t,d represents the charging power of the energy storage at time t, with charging being positive; P dc,t,d represents the discharging power of the energy storage at time t, with discharging being negative; D1 represents the number of days the energy storage operates in a year in the peak-valley arbitrage scenario;

[0041]

[0042] Wherein, α is the unit electricity subsidy price, with a value of 0.3; D4 represents the number of days the energy storage operates in a year; P dc,t represents the discharging power of the energy storage at time t, with discharging being negative.

[0043]

[0044] Wherein, ζ t represents the user's charging cost; represents the charging power of the electric vehicle cluster, represents the discharging power of the electric vehicle cluster.

[0045] Specifically, the demand-side response model includes a reducible and transferable electric and thermal load model, an alternative electric and thermal load model, and a reducible and transferable cooling load model.

[0046] Specifically, the reducible and transferable electric and thermal load model is as follows:

[0047]

[0048] Wherein, is the original electric load; is the mean value of the original electric load; △δ e,j and δ e,j respectively represent the change in the electricity selling price and the initial electricity selling price in the j-th period; P al,t is the reducible electric load in the i-th period; P sl,t is the transferable electric load in the i-th period; P al,i,0 、P sl,i,0 are the initial electric loads before the reducible and transferable types in the i-th period; e ij is the proportion of the flexible electric load. When i = j, e ii is the self-elasticity coefficient in the i-th period, eij is the mutual elasticity coefficient for time periods i and j, e ij constitutes the elastic reducible and transferable demand matrix E al (t, j), E sl (t, j), where T day is the number of moments in a day.

[0049] Specifically, the alternative electric - heat load model is as follows:

[0050]

[0051] In the formula, P el,t and P hl,t are the electric load and heat load after response; are respectively the electric - heat alternative loads; ε eh is the electric - heat substitution coefficient; are respectively the proportions of the electric - heat alternative loads;

[0052]

[0053] In the formula: P el,t is the electric load after response; is the original electric load; P al,t is the reducible electric load in time period i; P sl,t is time period i; are respectively the transferable electric - heat alternative loads; is the cold - alternative load; ε eh is the electric - heat substitution coefficient; ε ec is the electric - cold substitution coefficient.

[0054] Specifically, the reducible and transferable cold - load model is as follows:

[0055]

[0056] In the formula, is the original cold load; is the cold - load reducible coefficient; is the reducible cold - load; are respectively the electric - cold alternative loads; ε ec is the electric - cold substitution coefficient; are respectively the proportions of the electric - cold alternative loads; is the original electric load.

[0057] The present invention has the following advantages:

[0058] The present invention realizes the complementary and collaborative utilization of energy through the production, storage, and conversion equipment that couples multiple energy sources such as electricity, heat, cold, gas, and hydrogen; dynamic supply-demand matching: combining the demand-side response mechanism, adjusting the energy distribution strategy in real time, reducing energy waste, and significantly improving the comprehensive energy efficiency.

[0059] The present invention aims to minimize the operation and maintenance costs, curtailment penalties for wind and solar power, and carbon trading costs. Through strategies such as peak-valley electricity price arbitrage and energy storage subsidy benefits, the total cost of the park's energy system is significantly reduced. Introduction of the carbon trading mechanism: By quantifying the carbon emissions of externally purchased electricity, gas equipment, etc. and comparing them with carbon quotas, it encourages the preferential use of renewable energy, effectively reducing system carbon emissions and supporting the low-carbon transformation.

[0060] The present invention suppresses the fluctuations of renewable energy and ensures the system's supply-demand balance through the dynamic adjustment of adjustable electricity / heat / cold loads and the charge-discharge strategies of multiple types of energy storage devices. Multi-level constraint guarantee: The internal constraint model ensures that the scheduling plan obtains the optimal solution between physical feasibility and economy. Brief Description of the Drawings

[0061] Figure 1 It is a schematic flow diagram of the optimization scheduling method of the present invention;

[0062] Figure 2 It is a schematic diagram of the multi-energy coupling system architecture model of the present invention. Detailed Embodiments

[0063] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0064] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0065] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0066] The present invention will be further described below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following. As Figures 1 to 2As shown in the figure, a method for optimizing the scheduling of a multi-energy coupling energy system in a park considering demand-side response, the method includes: constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture integrates various energy production and consumption units to achieve efficient energy management and optimized scheduling. The system includes renewable energy power generation equipment such as wind power and photovoltaic (PV), as well as electric vehicle charging stations and demand-side response (DSR) units for electrical load. In terms of cooling load, the system adopts technologies such as brushless chillers, water-based cold storage, and refrigerator cooling. The heating load part meets the demand through equipment such as combined heat and power (CHP), combined cooling, heating and power (CCHP), gas turbines, and absorption chillers. In addition, the system also includes a two-stage power-to-gas (P2G) technology, which converts electrical energy into hydrogen and methane through an electrolyzer and a methanation reactor, and stores them using hydrogen storage and gas storage. Hydrogen fuel cells and thermal storage equipment further enhance the flexibility and stability of the system. The entire system realizes the coupling and coordination of various energy forms such as electricity, cooling, heating, and gas through the power grid, gas grid, and demand-side response mechanism, improving energy utilization efficiency and the reliability of system operation. Based on the multi-energy coupling system architecture model, a multi-energy coupling energy system optimization scheduling model is constructed with the lowest energy consumption cost as the objective function:

[0067]

[0068] In the formula, C represents the present value of the total life cycle operation and maintenance cost of the multi-energy coupling system; C ope,n 、C main,n 、 are the operation, maintenance, and carbon trading costs of the multi-energy coupling system in the nth year respectively; C cut is the cost of power supply shortage loss; F L is the annual net income of the user-side energy storage.

[0069] The calculation formula for the operation cost of the multi-energy coupling system is as follows:

[0070] C ope,n = C buy,n + C w,cut,n

[0071]

[0072] In the formula, C buy,n is the energy purchase cost for the dispatching year; a t , b t respectively represent the electricity price and gas price at time t of the dispatching year; P e,buy,n,t , P g,buy,n,t are respectively the electricity purchase power and gas purchase power at time t of the dispatching year; T represents the annual operating hours; C w,cut,n is the curtailment cost of wind and light for the dispatching year; c t is the unit curtailment penalty cost; P w,cut,n,t , P pv,cut,n,t are the curtailment costs of wind and light for the dispatching year; C ope,n is the operating cost for the dispatching year, which consists of the energy purchase cost and the curtailment costs of wind and light. The energy purchase cost includes the electricity purchase cost and the gas purchase cost.

[0073] Considering that the maintenance cost consists of the maintenance costs of wind power, photovoltaic power, energy conversion, and various energy storage devices, and is determined by the actual output power of each device during operation. The vector composed of the output powers of each device during the t period of the dispatching year is P main,n,t , and the calculation formula for the maintenance cost of the multi-energy coupling system is as follows:

[0074]

[0075] In the formula, c main,n is the maintenance cost vector per unit power of each device; P main,n,t is the output power matrix of the device during the t period of the nth year; T represents the annual operating hours; if the j-type device represents an energy storage device, P main,n,t (1,j) represents the sum of the charging and discharging powers of the energy storage device.

[0076] The carbon emissions of the multi-energy coupling system mainly come from three carbon emission sources: externally purchased electricity, gas turbines, and gas boilers. Considering that all the externally purchased electricity from the superior power grid comes from thermal power units, the calculation formula for the carbon trading cost of the multi-energy coupling system is as follows:

[0077] E PIES,n = E e,buy,n + E GB,n + E CHP,n

[0078]

[0079] In the formula, EPIES,n , E e,buy,n , E GB,n , E CHP,n are the actual carbon emissions of the purchased electricity, gas boiler, and CHP unit of the multi - energy coupling system in the nth year respectively; P e,buy,n,t is the purchase power from the superior power grid at time t in the nth year; P h,GB,n,t is the heating power of the gas boiler at time t in the nth year; P h,CHP,n,t , P e,CHP,n,t are the heating and power generation powers of the CHP unit at time t in the nth year respectively; μ e and μ h are the carbon emission intensities per unit of electricity and per unit of heat respectively; is the electricity - heat conversion coefficient of the CHP unit; The carbon quota is calculated as follows:

[0080] E' PIES,n = E' e,buy,n + E' GB,n + E' CHP,n

[0081] In the formula, E' PIES,n , E' e,buy,n , E' GB,n , E' CHP,n are the carbon emission quotas of the purchased electricity, gas boiler, and CHP unit respectively;

[0082] E 0,n = E PIES,n - E' PIES,n

[0083] In the formula, E 0,n is the traded carbon emissions of the park considering the carbon quota;

[0084] Calculate the carbon trading cost in a fixed - carbon trading mode:

[0085]

[0086] In the formula, is the carbon trading cost; λ is the carbon trading benchmark price.

[0087] Insufficient power supply will cause direct economic losses to enterprises. Since the gross production values of different industries are different, the losses caused by power supply are also different. Next, a unified model is established for the power - supply shortage loss cost of each industry. Using the GDP per kilowatt - hour as the method to describe the loss, the formula for calculating the power - supply shortage loss cost is as follows:

[0088]

[0089] In the formula, C GDP is the GDP loss per kilowatt - hour of the industry, is the amount of electricity discarded at each moment.

[0090] Furthermore, the calculation formula for the annual net income of user-side energy storage is as follows:

[0091] F L =(f1 + f2 + F EV,n )×λ DOD ×(1 - γ) -n+1

[0092] In the formula, f1 is the daily income from arbitrage of the peak-valley electricity price difference of energy storage; f2 is the annual subsidy income for installing energy storage; λ DOD is the depth of charge and discharge of energy storage; γ is the annual attenuation rate of energy storage capacity; n is the year.

[0093] When the valley-time electricity price is at a low level, the energy storage power station stores electric energy, and then releases electric energy when the peak-time electricity price is at a high level, so the income obtained by using the peak-valley electricity price difference can be expressed as:

[0094]

[0095] P L,t,d =P load,t,d +P c,t,d +P dc,t,d

[0096] In the formula, P load,t,d represents the net load power; P L,t,d represents the equivalent load of the industrial park after installing the energy storage system; △t represents the output duration, taken as 1 hour; at represents the electricity price at time t; P c,t,d represents the charging power of the energy storage at time t, with charging being positive; P dc,t,d represents the discharging power of the energy storage at time t, with discharging being negative; D1 represents the number of days the energy storage operates in a year in the peak-valley arbitrage scenario;

[0097] For the energy storage subsidy income, for demonstration projects that have obtained recognition, passed verification, and completed acceptance, starting from the second month after commissioning, based on the actually verified discharged electricity volume, the investment entity will be given ex-post support of no more than 0.3 yuan per kilowatt-hour. Then the corresponding annual income is calculated as follows:

[0098]

[0099] In the formula, α is the subsidy price per unit of electricity, with a value of 0.3; D4 represents the number of days the energy storage operates in a year; P dc,t represents the discharging power of the energy storage at time t, with discharging being negative.

[0100] The annual income of the electric vehicle charging pile. The calculation method for the annual income of the electric vehicle charging pile is as follows:

[0101]

[0102] Where ζ t represents the user's charging cost; represents the charging power of the electric vehicle cluster, and represents the discharging power of the electric vehicle cluster.

[0103] The constraint conditions of the multi - energy coupling system architecture model include the charge - discharge constraints of battery energy storage, state - of - charge constraints, and peak - shaving constraints; the operation constraints of the electric vehicle charging station;

[0104] The charge - discharge constraints of the energy storage battery are shown in the following formula:

[0105]

[0106] Where i ∈ Ω, and Ω is the set of electrical energy storage, thermal energy storage, gas energy storage, hydrogen energy storage, water - cooled energy storage, and ice - cooled energy storage; W i,t is the capacity of the i - th energy storage device at time t; are the charging and discharging powers of the i - th energy storage device at time t, respectively; are the maximum charging and discharging powers of the i - th energy storage device per charge, respectively; β s is the ratio of energy storage power to capacity; are the charging and discharging state parameters of the i - th energy storage device at time t, respectively, and are binary variables. When the energy storage device is in the charging state, and when the energy storage device is in the discharging state; represent the charging and discharging efficiencies of the energy storage device, respectively. Since the functions of ice - storage cooling and water - storage cooling are basically similar, when both appear, spatial constraints are considered to conduct collaborative planning for the two cooling modes. The spatial constraints are as follows:

[0107] 0 <= μ IS S IS + μ WS S WS <= Sq c,max

[0108] Where μ IS , μ WS are the floor areas per unit power of ice - storage cooling and water - storage cooling; S IS , S WS are the total power capacities of ice - storage cooling and water - storage cooling; Sq c,max is the maximum space limit of the energy storage cooling device.

[0109] State - of - charge constraint:

[0110] W i min ≤ W i,t ≤ W i max

[0111] Where: W i min and W i max respectively represent the upper and lower limits of the capacity of the i-th energy storage device;

[0112] Considering the typical charging and discharging capabilities of an electric vehicle cluster in the model of an electric vehicle charging pile, so as to improve the efficiency of planning operations, the charging and discharging capacity model of an electric vehicle (EV) cluster can be obtained by accumulating the charging and discharging capacity models of individual electric vehicles. The operating constraints of an electric vehicle charging station are:

[0113]

[0114] Where, are respectively the charging and discharging powers of the electric vehicle cluster; is the limit of the discharging capacity ratio; is the limit of the charging capacity ratio; W EV is the capacity of the electric vehicle charging station.

[0115] The cooling and heating equipment of the integrated energy system in the industrial park needs to have power limits, meeting the load without exceeding the maximum power of the equipment. The output limits of each system meet the requirements of the following formula. According to the relationship between the energy input and output among the structures of the integrated energy system in the park, the input power, output power, and conversion efficiency of each energy conversion device are respectively expressed as P int,k (t), P out,k (t), η k , and the output constraint of the energy conversion device model is:

[0116]

[0117] Where, i ∈ {EL, MR, HFC, GB, CHP, ER, AC}; P i max represents the upper limit value of the output power of each energy conversion device at time t; P int,i (t), P out,i (t) are respectively the input and output powers of the i-th energy conversion device; η i is the conversion efficiency of the i-th energy conversion device.

[0118] New energy power generation constraint, electric load balance constraint, heat load balance constraint, cold load balance constraint, gas power balance constraint, hydrogen load balance constraint;

[0119] New energy power generation constraint:

[0120]

[0121] Where, Pw,t , P pv,t respectively represent the power outputs of wind power and photovoltaic power in the t period; respectively represent the upper limit values of the predicted output powers of wind power and photovoltaic power.

[0122] Based on historical weather data and historical load data of energy consumption, and on a predetermined time scale, the balance between load demand and supply capacity at the corresponding time scale can be obtained. The electrical load balance equation is shown as follows. This constraint is an important balance condition in the integrated energy system of the industrial park and is related to the final configuration of energy storage. Electrical load balance constraint:

[0123]

[0124] In the formula, P w,t , P pv,t are the output powers of wind and light; P e,buy,t is the power purchase; P e,HFC,t , P e,CHP,t , P DE,t , P e,EL,t , P e,ER,t are the electrical powers of the hydrogen fuel cell, gas turbine, diesel generator, electrolyzer, and electric chiller at time t respectively; P el,t is the power of the controllable resources of the electrical load in the t period; and respectively represent the discharging and charging powers of the electric vehicle; and respectively represent the discharging and charging powers of the electrical energy storage; is the limit of power purchase; β cut is the limit ratio of the curtailed power.

[0125] According to the historical heating demand, combined with the operation strategy of the thermal system, determine the output of each device, and finally obtain the electrical energy that meets the heating load, and substitute it into the electrical load balance equation. The heating load balance constraint is:

[0126]

[0127] In the formula, P h,CHP,t , P h,GB,t , P h,HFC,t , P h,AC,t are the thermal powers of the gas turbine, gas boiler, hydrogen fuel cell, and absorption chiller at time t respectively; P hl,t represents the heating load power in the t period; and respectively represent the heat release and heat charging powers of the thermal energy storage at time t.

[0128] According to the equipment conditions of the cold busbar, considering the output of all devices and substituting them into the electrical load balance equation, the cold load balance constraint:

[0129]

[0130] Wherein, P c,ER,t and P c,AC,t are respectively the cooling powers of the electric chiller and the absorption chiller at time t; respectively represent the charging and discharging powers of water thermal energy storage at time t; respectively represent the charging and discharging powers of ice thermal energy storage at time t; P cl,t represents the cooling load power in the t-th time period; W i min is the minimum capacity limit of energy storage.

[0131] According to the equipment conditions of the gas busbar, considering the output of all equipment and substituting it into the electric load balance equation, the gas power balance constraint:

[0132]

[0133] Wherein, P g,buy,t is the gas purchase power at time t; P g,MR,t and P g,GB,t and P g,CHP,t represent the output or required gas powers of the methane reactor, gas boiler, and gas turbine at time t; P gl,t is the gas consumption load at time t; and respectively represent the gas charging and discharging powers at time t; is the gas purchase power limit.

[0134] Hydrogen load balance constraint:

[0135]

[0136] Wherein, are respectively the hydrogen powers required by the electrolyzer, methane reactor, and hydrogen fuel cell at time t; and respectively represent the hydrogen discharging and charging powers of hydrogen energy storage at time t.

[0137] Public network line transmission power constraint:

[0138]

[0139] Wherein, P e,grid and P g,grid are the upper limits of the line transmission powers at each moment; P g,buy,t is the gas purchase power at time t, and P e,buy,t is the electricity purchase power.

[0140] Internal distribution network line transmission power constraint:

[0141]

[0142] Wherein, P e,pies,t , P g,pies,t , P c,pies,t , P h,pies,t , P h2,pies,t are the input powers of the electricity, gas, cold, heat, and hydrogen buses at each moment; is the upper limit of the transmission power of each line.

[0143] Construct a demand-side response model; the demand-side response model includes a cuttable and shiftable electric and heat load model, an alternative electric and heat load model, and a cuttable and shiftable cold load model.

[0144] The cuttable and shiftable electric and heat loads are mainly the load responses under price-based incentives, which are characterized by the price-demand elasticity matrix. The cuttable and shiftable electric and heat load model is as follows:

[0145]

[0146] Wherein, is the original electric load; is the mean value of the original electric load curve; △δ e,j and δ e,j respectively represent the change in the selling electricity price and the initial selling electricity price at time period j; P al,t is the cuttable electric load at time period i; P sl,t is the shiftable electric load at time period i; P al,i,0 , P sl,i,0 are the initial electric loads before cuttable and shiftable at time period i; e ij is the proportion of the elastic electric load. When i = j, e ii is the self-elasticity coefficient at time period i, e ij is the cross-elasticity coefficient between time period i and time period j, and e ij constitutes the elastic cuttable and shiftable demand matrix E al (t, j), E sl (t, j), where T day is the number of moments in a day, and the elastic matrix is considered with each day as a separate interval.

[0147] For a certain type of heat load that can be directly supplied by heat energy or electric energy, it can consume electric energy during low electricity price periods and directly consume heat energy during high electricity price periods to meet its own needs, thereby realizing the mutual substitution of electric energy and heat energy. The alternative electric and heat load model is as follows:

[0148]

[0149] Wherein, P el,t , P hl,t are the electric load and heat load after response; They are respectively electro-thermal replaceable loads; ε eh Electro-thermal replacement coefficient They are respectively the proportions of electro-thermal replaceable loads

[0150]

[0151] In the formula: P el,t is the electrical load after response is the original electrical load; P al,t is the reducible electrical load in the i-th period; P sl,t is the i-th period They are respectively electro-thermal replaceable loads and transferable electrical loads is the cold replaceable load; ε eh Electro-thermal replacement coefficient; ε ec Electric-cooling replacement coefficient

[0152] The modeling method of the proportion limit for the reducible and transferable cold load model. The reducible and transferable cold load model is as follows:

[0153]

[0154] In the formula, is the original cold load is the cold load reduction coefficient is the cold load reducible load They are respectively electric-cooling replaceable loads; ε ec Electric-cooling replacement coefficient They are respectively the proportions of electric-cooling replaceable loads is the original electrical load

[0155] Based on the demand-side response model, the optimal scheduling model of the multi-energy coupling energy system is solved to obtain the optimal scheduling plan

[0156] As described above, it is only the preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes, without departing from the scope of the technical solution of the present invention. Therefore, any changes, modifications, equivalent changes, and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention all fall within the protection scope of this technical solution

Claims

1. An optimization scheduling method for a multi-energy coupling energy system in a park considering demand-side response, characterized in that The method includes: Constructing a multi - energy coupling system architecture model; Based on the multi - energy coupling system architecture model, constructing an optimal scheduling model for the multi - energy coupling energy system, and its objective function is: In the formula, C represents the present value of the full - life - cycle operation and maintenance cost of the multi - energy coupling system; C ope,n , C main,n , are respectively the operation, maintenance, and carbon trading costs of the multi - energy coupling system in the nth year; C cut is the cost of power supply shortage loss; F L is the annual net income of the user - side energy storage; The constraint conditions of the multi - energy coupling system architecture model include battery energy storage charge - discharge constraints, state - of - charge constraints, and peak - shaving constraints; The operation constraints of the electric vehicle charging station; The output constraints of the energy conversion device model; New energy power generation constraints, electric load balance constraints, heat load balance constraints, cold load balance constraints, gas power balance constraints, and hydrogen load balance constraints; The transmission power constraints of the public network line; The transmission power constraints of the internal distribution network line; Constructing a demand - side response model; Based on the demand - side response model, solving the optimal scheduling model for the multi - energy coupling energy system to obtain an optimal scheduling plan.

2. The optimized scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 1, characterized in that: The calculation formula for the operation cost of the multi - energy coupling system is as follows: C ope,n = C buy,n + C w,cut,n Where, C buy,n is the annual energy purchase cost of the dispatching year; a t , b t respectively represent the electricity price and gas price at time t of the dispatching year; P e,buy,n,t , P g,buy,n,t are the electricity purchase power and gas purchase power at time t of the dispatching year respectively; T represents the annual operating hours; C w,cut,n is the curtailment cost of wind and light of the dispatching year; c t is the unit curtailment penalty cost of wind and light; P w,cut,n,t , P pv,cut,n,t are the curtailment costs of wind and light of the dispatching year.

3. The optimal scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 1, characterized in that: The calculation formula for the maintenance cost of the multi - energy coupling system is as follows: where c main,n is the maintenance cost vector of the unit power of various devices; P main,n,t is the output power matrix of the device in the t-th period of the n-th year; T represents the annual operating hours.

4. The optimized scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 1, characterized in that: The calculation formula for the carbon trading cost of the multi - energy coupling system is as follows: E PIES,n = E e,buy,n + E GB,n + E CHP,n Where, E PIES,n , E e,buy,n , E GB,n , E CHP,n are the actual carbon emissions of the multi - energy coupling system's purchased electricity, gas boiler, and CHP unit in the nth year respectively; P e,buy,n,t is the power purchased from the superior power grid at time t in the nth year; P h,GB,n,t is the heating power of the gas boiler at time t in the nth year; P h,CHP,n,t , P e,CHP,n,t are the heating and power generation powers of the CHP unit at time t in the nth year respectively; μ e and μ h are the carbon emission intensities per unit of electricity and per unit of heat respectively; is the electricity - heat conversion coefficient of the CHP unit; It is PIES,n = It is e,buy,n + It is GB,n + It is CHP,n where E' PIES,n PIES,n , E' e,buy,n e,buy,n , E' GB,n GB,n , E' CHP,n CHP,n are the carbon emission allowances of purchased electricity, gas boilers, and cogeneration units respectively; E 0,n = E PIES,n - E' PIES,n where E 0,n is the traded carbon emission after the park considers carbon quotas; In the formula, is the carbon trading cost; λ is the carbon trading benchmark price.

5. The optimized scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 1, characterized in that: The calculation formula for the loss cost of insufficient power supply is as follows: Where C GDP is the industry's GDP loss per kilowatt-hour, is the curtailed power at each moment.

6. The optimal scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 1, characterized in that: The calculation formula for the annual net income of the user - side energy storage is as follows: F L = (f1 + f2 + F EV,n ) × λ DOD × (1 - γ) -n+1 Where, f1 is the daily income from the peak-valley electricity price arbitrage of energy storage; f2 is the annual subsidy income for installing energy storage; λ DOD is the charge-discharge depth of energy storage; γ is the annual attenuation rate of the energy storage capacity; n is the year. P L,t,d = P load,t,d + P c,t,d + P dc,t,d Wherein, P load,t,d represents the net load power; P L,t,d represents the equivalent load of the industrial park after installing the energy storage system; △t represents the output duration, taken as 1 hour; a t represents the electricity price at time t; P c,t,d represents the charging power of the energy storage at time t, with charging being positive; P dc,t,d represents the discharging power of the energy storage at time t, with discharging being negative; D1 represents the number of days the energy storage operates in a year in the peak-valley arbitrage scenario; Where α is the unit electricity subsidy price with a value of 0.3; D4 represents the number of days the energy storage operates in a year; P dc,t represents the discharge power of the energy storage at time t, and discharge is negative. where ζ t represents the user's charging cost; represents the charging power of the electric vehicle cluster, represents the discharging power of the electric vehicle cluster.

7. The optimized scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 1, characterized in that: The demand - side response model includes a reducible and transferable electric - heat load model, an alternative electric - heat load model, and a reducible and transferable cold - load model.

8. The optimized scheduling method for a multi-energy coupling energy system in a park considering demand-side response according to claim 7, characterized in that: The reducible and transferable electric - heat load model is as follows: In the formula, is the original electrical load; is the original load average value; △δ e,j and δ e,j represent the change in the selling electricity price and the initial selling electricity price in period j respectively; P al,t is the reducible electricity load in period i; P sl,t is the transferable electricity load in period i; P al,i,0 , P sl,i,0 are the initial electricity loads before the reducible and transferable types in period i; e ij is the proportion of the flexible electricity load. When i = j, e ii is the self - elasticity coefficient in period i, and e ij is the cross - elasticity coefficient between period i and period j. e ij constitutes the flexible reducible and transferable demand matrix E al (t, j), E sl (t, j), where T day is the number of moments in a day.

9. The optimized scheduling method for the multi-energy coupled energy system in the park considering demand-side response according to claim 7, characterized in that: The alternative electric - heat load model is as follows: In the formula, are respectively the electrothermal replaceable loads; P el,t , P hl,t are the electrical load and thermal load after response; ε eh is the electrothermal substitution coefficient; are respectively the proportions of electrothermal replaceable loads; Where: P el,t is the electrical load after response; is the original electrical load; P al,t is the reducible electrical load in the i-th period; P sl,t is the i-th period; are the heat-electricity replaceable load and the transferable electrical load respectively; is the cold replaceable load; ε eh is the heat-electricity replacement coefficient; ε ec Coefficient of electric cooling substitution 10. The optimized scheduling method for the multi-energy coupling energy system in the park considering demand-side response according to claim 7, characterized in that: The reducible and transferable cold - load model is as follows: In the formula, is the original cooling load; is the cooling load reduction coefficient; is the cooling load reduction type load; are the electric-cooling replaceable loads respectively; ε ec is the electric-cooling replacement coefficient; are the proportions of the electric-cooling replaceable loads respectively; is the original electric load.

Images