Integrated energy system double-layer scheduling method and system

Abstract

The application provides a kind of integrated energy system double-layer scheduling method and system, integrated demand response mechanism and compressed air energy storage system are introduced into traditional energy system, to plan the optimal operation cost of compressed air energy storage system in period as target to construct upper layer optimization model, to consider the minimum system operation cost of integrated demand response integrated energy system as target to construct lower layer optimization model;The lower layer optimization problem is converted into the constraint condition of upper layer problem, the nonlinear constraint in the slack complementary condition is converted into linear constraint, the problem of upper layer optimization model is solved, and the final scheduling scheme is obtained.The application considers integrated demand response and energy storage collaborative optimization, which can effectively reduce the capacity of energy storage system and significantly improve the economy, low carbon, reliability of integrated energy system operation.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology, and relates to a two-level scheduling method and system for integrated energy systems. Background Technology

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

[0003] Integrated Energy Systems (IES) combine cyber-physical systems, multi-energy supply technologies, and diverse energy storage technologies such as thermal, electrical, and gas storage. They organically integrate thermal, electrical, and gas networks to achieve multi-energy conversion, storage, and consumption. This is of great significance for improving the efficiency of comprehensive energy utilization, promoting the large-scale development of renewable energy, improving the utilization rate of social infrastructure, and ensuring energy supply security.

[0004] However, the randomness of power sources and loads, along with time-of-use pricing, significantly impacts the optimal scheduling of integrated energy systems. Energy storage shifting and demand response load shifting are two effective approaches to addressing this issue. However, current research largely focuses only on energy storage systems, the role of demand response in reducing system operating costs, or the synergistic effect of integrated demand response with individual energy storage systems. Research considering the synergy between distributed energy storage networks and integrated demand response to improve the flexibility of interaction between integrated energy systems and power supply networks, thereby reducing system capacity and investment costs, remains lacking. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a two-layer scheduling method and system for integrated energy systems. This invention considers integrated demand response and energy storage collaborative optimization, which can effectively reduce the capacity of energy storage systems and significantly improve the economy, low carbon emissions, and reliability of integrated energy system operation.

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

[0007] A two-level scheduling method for an integrated energy system includes the following steps:

[0008] The integrated demand response mechanism and compressed air energy storage system are introduced into the traditional energy system. The upper-level optimization model is constructed with the goal of optimizing the operating cost of the compressed air energy storage system during the planning period, and the lower-level optimization model is constructed with the goal of minimizing the operating cost of the integrated energy system that takes into account the integrated demand response.

[0009] The lower-level optimization problem is transformed into constraints for the upper-level problem. The nonlinear constraints in the relaxed complementarity conditions are transformed into linear constraints. The problem of the upper-level optimization model is solved to obtain the final scheduling scheme.

[0010] As an alternative implementation, the integrated energy system includes an energy supply side, an energy conversion side, an energy storage side, and a load side.

[0011] As an alternative implementation, the upper-level optimization model is responsible for solving the problem of optimizing the operating cost of the compressed air energy storage system during the planning period. The decision variables include the charging and discharging power of the compressed air energy storage system and the maximum capacity of the storage tank.

[0012] As a further limitation, a thermodynamic model is established for the compressed air energy storage system, which is divided into compression, expansion, and storage processes. The modeling and analysis are carried out under the following assumptions.

[0013] 1) Air is an ideal gas and its specific heat capacity is constant;

[0014] 2) The gas flow rate is constant during the gas storage and expansion process, and the air temperature is the same as the ambient temperature;

[0015] 3) The energy storage and release of compressed air energy storage systems can occur simultaneously.

[0016] As an alternative implementation, the constraints of the upper-level optimization model include residual energy constraints and energy initialization constraints.

[0017] As an optional implementation, the lower-level optimization model is responsible for solving the integrated energy system optimization operation considering the coordination of energy storage network and integrated demand response. The decision variables are gas turbine power generation and heat production, gas boiler heat production, absorption chiller output cooling power, power purchased from the grid, CAES energy storage power and energy storage flag, CAES discharge power and discharge flag, ice storage air conditioning cold storage power and cold storage flag, ice storage air conditioning cooling power and cooling flag, ice storage air conditioning ice melting cooling power and ice melting cooling flag, thermal storage system heat storage power and thermal storage flag, and thermal storage system heat release power and heat release flag.

[0018] As an alternative implementation method, the system operating costs of an integrated energy system include operation and maintenance costs, natural gas purchase costs and grid interaction costs, carbon trading costs, and demand response costs.

[0019] As an optional implementation, the constraints of the lower-level optimization model include electrical balance constraints, thermal balance constraints, cold balance constraints, integrated demand response modeling constraints, upper and lower limits of integrated energy system equipment output constraints, and constraints on exchanging electrical energy with the external power grid.

[0020] As an alternative implementation method, during the solution process, the Lagrangian function of the lower-level model is constructed, and the Lagrangian function is processed by KKT conditions to transform the lower-level optimization problem into the constraints of the upper-level problem. Then, the Big M method is used to transform the nonlinear constraints in the relaxed complementary conditions into linear constraints, and then the bi-level optimization problem is solved.

[0021] A two-tiered dispatch system for integrated energy systems, comprising:

[0022] The model building module is configured to introduce the integrated demand response mechanism and compressed air energy storage system into the traditional energy system, build an upper-level optimization model with the goal of optimizing the operating cost of the compressed air energy storage system during the planning period, and build a lower-level optimization model with the goal of minimizing the operating cost of the integrated energy system that takes into account the integrated demand response.

[0023] The model solving module is configured to transform the constraints of the lower-level optimization problem into the constraints of the upper-level problem, transform the nonlinear constraints in the relaxed complementarity conditions into linear constraints, solve the problem of the upper-level optimization model, and obtain the final scheduling scheme.

[0024] A computer-readable storage medium storing a plurality of instructions adapted for loading by a processor of a terminal device and executing steps in the method.

[0025] A terminal device includes a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer-readable storage medium being configured to store a plurality of instructions adapted to be loaded by the processor and executed in accordance with the steps of the method described therein.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] This invention constructs a distributed energy storage network that includes an adiabatic compressed air energy storage system, an ice storage air conditioner, and a heat storage device. Compared with single energy storage, it can improve the flexibility and peak-shaving capability of the integrated energy system, thereby reducing system operating costs and controlling carbon emissions.

[0028] This invention addresses the optimization of energy storage capacity configuration in integrated energy systems that combine distributed energy storage networks with integrated demand response. This synergy effectively improves the flexibility of interaction between the integrated energy system and the energy supply network, while reducing system capacity, operating costs, and carbon emissions. It offers valuable insights for addressing the high-carbon energy and industrial structure challenges of the future energy sector and accelerating the achievement of dual-carbon goals. Attached Figure Description

[0029] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0030] Figure 1 This is a structural diagram of the integrated energy system containing a distributed energy storage network proposed in this invention;

[0031] Figure 2 This is a comparison chart of load curves before and after the overall demand response of this invention;

[0032] Figure 3(a) is the power balance diagram of Scheme 1 in the example analysis of the present invention;

[0033] Figure 3(b) is the heat load power balance diagram of Scheme 1 in the example analysis of the present invention;

[0034] Figure 4(a) is the power balance diagram of Scheme 2 in the example analysis of the present invention;

[0035] Figure 4(b) is the heat load power balance diagram of Scheme 2 in the example analysis of the present invention;

[0036] Figure 5(a) is the power balance diagram of Scheme 3 in the example analysis of the present invention;

[0037] Figure 5(b) is the heat load power balance diagram of Scheme 3 in the example analysis of the present invention;

[0038] Figure 6 This is the CAES operation status diagram of Scheme 1 in the example analysis of this invention;

[0039] Figure 7 This is the CAES operation status diagram of Scheme 2 in the example analysis of this invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0043] This example discloses a two-layer scheduling method for integrated energy systems that takes into account the coordination between distributed energy storage networks and integrated demand response.

[0044] Firstly, consider the overall energy system structure, such as... Figure 1As shown, the integrated energy system mainly consists of four parts: the energy supply side, the energy conversion side, the energy storage side, and the load side. The energy supply side includes renewable energy sources such as photovoltaic and wind power, as well as the power grid and natural gas network. The energy conversion side includes gas turbines, gas-fired boilers, and absorption chillers. The energy storage side includes compressed air energy storage, thermal storage devices, and ice storage air conditioning. Electrical load is met by photovoltaic, wind power, electricity purchased from the upstream grid, gas turbines, and compressed air energy storage; thermal load is met by the heat release and absorption of energy from gas turbines, gas-fired boilers, thermal storage devices, and compressed air energy storage; and cooling load is met by absorption chillers and ice storage air conditioning.

[0045] Then, each part of the system is modeled according to the system's two-layer optimization scheduling framework.

[0046] 1. Planning model for upper-level compressed air energy storage system

[0047] The upper-level model is responsible for solving the problem of optimizing the operating cost of the compressed air energy storage system during the planning period. The decision variables include the charging and discharging power of the compressed air energy storage system and the maximum capacity of the storage tank.

[0048] Compressed air energy storage system operation model:

[0049] A-CAES is a large-scale energy storage system composed of multiple components and stages, including compression, heat exchange, gas storage, throttling, and expansion. A-CAES systems offer advantages such as large energy storage capacity, long service life, and environmental friendliness, and related research has received considerable attention in recent years. This paper presents an A-CAES system structure employing a three-stage compression, three-stage expansion, and interstage heat exchange operating mode. This structure ensures high energy storage efficiency while maintaining adiabatic conditions throughout the overall compression / expansion process.

[0050] This paper establishes a thermodynamic model for a compressed air energy storage system, dividing it into compression, expansion, and storage processes, and analyzes them separately, while adhering to the following assumptions:

[0051] 1) Air is an ideal gas and its specific heat capacity is constant;

[0052] 2) The gas flow rate is constant during the gas storage and expansion process, and the air temperature is the same as the ambient temperature;

[0053] 3) The energy storage and release of compressed air energy storage systems can occur simultaneously.

[0054] (1) Modeling of the compression process

[0055] During the charging process of the A-CAES system, the compressor works to compress air, and its energy storage capacity... air mass flow The following relationship exists:

[0056]

[0057] In the formula, T com,in,i , λ com,i , γ, η com,ist These represent the inlet air temperature of the i-th stage compressor, the compression ratio of the i-th stage compressor (i.e., the ratio of the outlet air pressure to the inlet air pressure of the i-th stage compressor), the specific heat capacity index of air (numerically, the ratio of the specific heat capacity at constant pressure to the specific heat capacity at constant volume of air), and the adiabatic index of the compressor, respectively.

[0058] During the charging process of the A-CAES system, the compressors at each stage compress air, generating a large amount of heat of compression. In this study, the heat of compression is collected by heat exchangers between each stage and stored through a heat storage device.

[0059] In the energy storage process, the heat power obtained by each stage of heat exchanger can be expressed by the following equation:

[0060]

[0061] In the formula, c a T represents the specific heat capacity of air. com,out,i and T com,in,i+1 η represents the outlet air temperature of the i-th stage compressor and the inlet air temperature of the (i+1)-th stage compressor, respectively. ex For heat exchanger efficiency.

[0062] outlet temperature T of each stage of compressor com,out,i and inlet temperature T com,in,i The following relationship exists between them:

[0063]

[0064] This study assumes that during the compression process, the inlet air temperature of the three-stage compressor is the same, numerically equal to the room temperature T0, that is:

[0065] T com,in,1 =T com,in,2 =T com,in,3 =T0 (4)

[0066] According to equations (1), (2), (3) and (4), the relationship between the heat power obtained by the heat exchanger in the A-CAES system energy storage process and the charging power can be obtained:

[0067]

[0068] (2) Modeling of the expansion process

[0069] Similarly, in the A-CAES discharge process, its discharge power Mass flow rate of air passing through the expander The following relationships also exist between them:

[0070]

[0071] In the formula, T tur,in,i , τ tur,i , γ, η tur,ist represents the inlet air temperature of the i-th stage expander, the expansion ratio of the i-th stage expander, the specific heat capacity index of air, and the adiabatic index of the expander, respectively.

[0072] In order to improve efficiency during the energy release and discharge process of the A-CAES system, the high-pressure air needs to be preheated by the interstage heat exchanger before entering the expander. The heat required for preheating of each stage heat exchanger is provided by the heat storage device.

[0073] The thermal power required by the heat storage device for each stage of the heat exchanger during energy release can be expressed by the following formula:

[0074]

[0075] In the formula, c a T represents the specific heat capacity of air. tur,out,i and T tur,in,i+1 η represents the outlet air temperature of the i-th stage expander and the inlet air temperature of the (i+1)-th stage expander, respectively. ex For heat exchanger efficiency.

[0076] The outlet air temperature T of each stage expander tur,out,i and inlet air temperature T tur,in,i The numerical relationship can be expressed by the following formula:

[0077]

[0078] This study assumes the high-pressure air temperature inside the storage tank is room temperature T0. During the expansion process, the inlet air temperature of the three-stage expander is the same, requiring each stage of heat exchanger to preheat the high-pressure air to a specific temperature. The equation is:

[0079] T tur,in,1 =T tur,in,2 =T tur,in,3 (9)

[0080] From the above equations (6), (7), (8) and (9), the relationship between the heat power required for the heat exchanger to preheat the high-pressure air and the discharge power in the expansion and energy release process of the A-CAES system can be easily obtained:

[0081]

[0082] (2) Modeling of gas storage tank

[0083] The A-CAES system cannot store and release energy simultaneously. At time t, the energy state of the energy storage system can be represented by the gas pressure in the storage tank.

[0084]

[0085] In the formula, P stor,0 R represents the initial gas pressure of the gas storage tank. g T0 represents the gas constant and the air temperature at room temperature, respectively. It represents the mass flow rate of air when the i-th stage compressor / expander is working.

[0086] Substituting equations (1) and (6) into (11) and rearranging, we can obtain the relationship between the gas pressure of the gas storage tank and the charging / discharging power:

[0087]

[0088] Upper-level model objective function:

[0089] The upper-level optimization objective is to minimize the daily operating cost of the compressed air energy storage system, namely:

[0090]

[0091] In the formula, T represents 24 hours in a typical midday; λ caes When the utilization hours of the compressed air energy storage system reach 2000h, the cost per kilowatt-hour is approximately RMB 0.44 / (kW·h).

[0092] Upper-level model constraints:

[0093] Compressed air energy storage systems must meet the following power constraints to operate:

[0094]

[0095]

[0096]

[0097]

[0098] In the formula, u E The variables are 0-1; Equation (16) represents the residual energy constraint, and the gas storage pressure of the gas storage tank at time t is... It must be kept within a suitable pressure range; Equation (17) is the energy initialization constraint.

[0099] 2. The underlying layer considers an integrated energy system optimization operation model that coordinates energy storage networks with integrated demand response.

[0100] The lower-level model is responsible for solving the optimization operation of the integrated energy system that considers the coordination of energy storage network and integrated demand response. The decision variables are: gas turbine power generation and heat production, gas boiler heat production, absorption chiller output cooling power, power purchased from the grid, CAES energy storage power and energy storage flag, CAES discharge power and discharge flag, ice storage air conditioning cold storage power and cold storage flag, ice storage air conditioning cooling power and cooling flag, ice storage air conditioning ice melting cooling power and ice melting cooling flag, thermal storage system thermal storage power and thermal storage flag, and thermal storage system heat release power and heat release flag.

[0101] Lower-level model objective function:

[0102] This article takes F OM Operation and maintenance costs, F G,E The cost of purchasing natural gas and the cost of interacting with the power grid, F C Carbon trading costs and F IDR An optimal scheduling model is established with the objective of minimizing the sum of demand response costs, which can be specifically expressed as:

[0103] F min =F OM +F G,E +F C +F IDR (18)

[0104] 1) Operation and maintenance costs

[0105]

[0106] C i The operation and maintenance cost coefficient of the i-th device, P i t Let be the operating power of the i-th device in the system at time t;

[0107] 2) Cost of purchasing natural gas and cost of interacting with the power grid

[0108]

[0109] In the formula, These represent the electricity price purchased from the grid at time t, the electricity price sold to the grid, and the unit price of natural gas, respectively; η GH,CCHP and η GH For the power generation efficiency of gas turbine units and the energy conversion efficiency of gas boilers;

[0110] 3)F C Carbon trading costs

[0111] This paper uses a free allocation method to determine the initial carbon emission allowance. The system mainly uses purchased electricity, CCHP, and GB as carbon emission sources. The initial carbon emission allowance model is as follows:

[0112]

[0113]

[0114]

[0115] In the formula, C p C grid C CCHP C GB These are the initial carbon emission allowances for integrated energy systems, power grid generators, CCHP, and GB, respectively; γ e γ h The carbon emission allowances for each unit of electrical power and each unit of thermal power generated are respectively taken as 0.728 t / (MWh) and 0.102 t / GJ; γ e,h The electrothermal power conversion parameter is taken as 6 MJ / (kWh); T is the scheduling period.

[0116] The actual carbon emissions of purchased electricity, CCHP, and GB integrated energy systems are as follows:

[0117]

[0118]

[0119]

[0120] In the formula, C grid,a C CCHP,a C GB,a , respectively, represent the actual carbon emissions of the power grid generator sets, CCHP, and GB;

[0121] δ e The carbon emission coefficient for a coal-fired power plant producing a unit power output is taken as 1.08 t / (MW□h); after converting the electricity output into heat, the equivalent heat supply of a gas turbine and a gas-fired boiler is close, δ h Take 0.065 t / GJ.

[0122] Therefore, carbon trading costs can be expressed as:

[0123] F C =c[(C grid,a -C grid )+(C CCHP,a -C CCHP )+(C GB,a -C GB (27)

[0124] In the formula, c is the carbon trading price, when F CWhen F is negative, it indicates that the actual carbon emissions are less than the carbon emission allowance, and the surplus carbon emission allowance can be sold. C When the value is positive, it means that the actual carbon emissions are greater than the carbon emission allowance, and the excess needs to be purchased according to the carbon trading mechanism.

[0125] 4)F IDR Demand response cost

[0126] This paper discusses heat load and cooling load based on incentive-based demand response. Without affecting user comfort, the load is reduced during peak electricity consumption periods. Simultaneously, users are given incentive subsidies to enhance their participation in the demand response strategy. These incentive subsidies are borne by the energy supply system, and the incentive subsidy model is as follows:

[0127]

[0128] In the formula, F b For the subsidy cost of IES; P h P is the subsidy factor for heat load; c This is the subsidy coefficient for cooling load; For heat loads that can be reduced; Reduceable cooling load;

[0129] Lower-level model constraints:

[0130] 1) Electrical balance constraint

[0131]

[0132] In the formula, This indicates the amount of electricity sold from the power grid. These represent distributed photovoltaic power generation and wind power generation, respectively. This refers to the generating capacity of the CCHP unit; This refers to the total power of the ice storage air conditioning system. Electricity load after demand response based on electricity price;

[0133] 2) Thermal equilibrium constraint

[0134]

[0135] In the formula, The heat output power of the CCHP unit; The heat output of the gas-fired boiler; The heat dissipation power of CAES charging; This refers to the heat absorption power of CAES discharge; These represent the power stored / released by the thermal energy at time t; Input heat power to the absorption chiller; This refers to the heat load after the response to the incentive demand.

[0136] 3) Cold balance constraint

[0137]

[0138] This refers to the total cooling capacity of the ice storage air conditioning system. To output cooling power; The cold load during time period t is the response to the incentive demand.

[0139] 4) Integrated Demand Response Modeling

[0140] Firstly, due to the existence of time-of-use pricing, the electricity load has demand response capability. Secondly, the energy supply side formulates relevant incentive policies, and users have a vague perception of heating and cooling comfort. Changing the indoor temperature within a certain range has little impact on them, so it also has a certain demand response capability and can participate in demand response as a flexible load. This paper divides the load into fixed load and flexible load, and flexible load is divided into load that can be reduced and load that can be transferred.

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] In the formula, Electricity load after demand response based on electricity price; As a fixed electrical load, it does not participate in demand response; As a transferable load, it enables the transfer of electrical load over time within the scheduling cycle; This is based on the heat load after the demand response to the stimulus; For fixed heat load; To reduce heat load; The cold load during time period t is based on the demand response following the incentive. To maintain a fixed cooling load; This can reduce the cooling load; To determine the maximum amount of transferable electrical load that can be called up, this paper sets the adjustable ratio of transferable electrical load to 0-20%. To determine the maximum amount of heat load that can be reduced, this paper sets the adjustable percentage of the heat load reduction to 0-10%. To determine the maximum reduction in cooling load, this paper sets the adjustable ratio of cooling load reduction to 0-10%.

[0148] 5) Modeling of thermal storage system

[0149] Regardless of whether a thermal storage device is in the process of storing or releasing energy, the stored energy is always dissipated over time. This study selects a general model for thermal storage devices:

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] In the formula, η HS,C η HS,D Representing energy storage and energy release efficiency, respectively; η H The energy loss rate per unit time of the thermal storage system; These represent the power stored / released by the thermal energy at time t; Indicates the maximum power for heat storage / release; u H It is a 0-1 variable. These are the minimum and maximum heat storage, respectively.

[0156] 6) Modeling of ice storage air conditioning

[0157] Ice storage air conditioning includes two modes: ice-making and ice-storage mode, and ice-melting and cooling mode. Its model is as follows, and the specific modeling and constraints are as follows:

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166] In the formula, Total power of ice storage air conditioning; Refrigeration unit power; Power of the ice maker; Total cooling capacity of ice storage air conditioning; These are the energy efficiency ratios of the refrigeration units; This refers to the cooling capacity for ice melting; This represents the maximum cooling capacity for ice melting; For flag position, A value of 1 indicates the ice-making and ice-storage mode. A value of 1 indicates the ice melting and refrigeration mode, meaning that ice melting and refrigeration and ice making and storage cannot be carried out simultaneously. Let β be the capacity of the ice storage tank at time t; ice This is the self-loss coefficient; The energy efficiency ratio of the ice maker; To improve ice melting efficiency; These represent the upper and lower limits of the slope rate for the ice storage tank.

[0167] 7) Upper and lower limits of output of integrated energy system equipment

[0168]

[0169]

[0170]

[0171] In the formula, The maximum power output of the CCHP unit; where, This represents the maximum power output of the gas-fired boiler at time t. These are the energy efficiency ratio and maximum power of the absorption chiller, respectively.

[0172] 8) Constraints related to the integrated energy system needing to exchange electrical energy with the external power grid:

[0173]

[0174]

[0175]

[0176] In the formula, These are the upper and lower limits of the power that can be purchased from the grid; These represent the upper and lower limits of the power purchased from the grid; u G It is a 0-1 variable.

[0177] Thus, a two-layer scheduling model for a comprehensive energy system, considering integrated demand response and energy storage synergistic optimization, has been established. The upper and lower layers have a nested structure; the upper-layer problem depends on the optimal solution of the lower-layer problem, and the optimal solution of the lower-layer problem is also influenced by the decision variables of the upper-layer problem. This paper transforms the lower-layer optimization problem into constraints for the upper-layer problem using KKT conditions, then uses the Big M method to transform the nonlinear constraints in the relaxed complementarity conditions into linear constraints, and finally calls the cplex solver to solve the two-layer optimization problem presented in this paper.

[0178] First, construct the Lagrangian function of the lower-level model:

[0179]

[0180] KKT conditions are used to process the lower-level Lagrangian function, transforming the lower-level model into constraints for the upper-level model:

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[0200]

[0201]

[0202]

[0203]

[0204] The conditions for relaxation complementarity are as follows:

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[0237]

[0238] To verify that considering the synergistic effect of energy storage and integrated demand response can achieve the predetermined goals more efficiently, this paper designs the following three optimization schemes for simulation comparison:

[0239] Scenario 1: Including tiered carbon trading, integrated demand response, and compressed air energy storage devices.

[0240] Scenario 2: Taking into account tiered carbon trading and compressed air energy storage devices, but not comprehensive demand response.

[0241] Scenario 3: Taking into account tiered carbon trading and integrated demand response, but excluding compressed air energy storage devices.

[0242] The optimization scheduling results of different schemes are shown in Table 1:

[0243] Table 1. Optimization and scheduling results of different schemes

[0244]

[0245] 1) Comparative analysis of the three schemes

[0246] As shown in Table 1, compared to Scheme 2, Scheme 1 considers comprehensive demand response. Under the guidance of time-of-use pricing and policy incentives, residents will change their electricity consumption habits within a satisfactory range, with some of the electricity generated during peak hours shifting to off-peak hours, thereby improving the system's economic efficiency. The total system operating cost is reduced by 4.34%, carbon emissions are reduced by 5.7%, and the profit from selling surplus carbon emission credits increases by 4.86%. Compared to Scheme 3, Scheme 1 considers compressed air energy storage devices, resulting in a 0.4% reduction in total system cost, a 3.7% reduction in carbon emissions, an 11.5% increase in profit from selling surplus carbon emission credits, and a 20% reduction in demand response compensation prices. Due to the inclusion of the compressed air energy storage system, the CAES operating cost increases, resulting in a small difference in total operating cost. However, because of the addition of the compressed air energy storage system, the amount of electricity purchased from the grid is reduced, and the output of some carbon emission sources is reduced, thus effectively reducing carbon emissions.

[0247] In summary, compared to considering integrated demand response and compressed air energy storage systems separately, the synergistic effect of energy storage and load-side demand response can better leverage the regulation capabilities of both the "storage" and "load" sides, achieving a win-win situation of economy and low carbon emissions with half the effort.

[0248] 2) Scheduling results of Scheme 1

[0249] Depend on Figure 2 It is evident that under the demand response system based on time-of-use pricing, users can autonomously change their electricity consumption patterns and structures. Since 11:00 to 17:00 is the peak period for both electricity consumption and prices, users significantly reduce their electricity consumption during this time. Conversely, during the off-peak periods of 1:00 to 6:00 and 23:00 to 24:00, which coincide with peak wind turbine output, users' electricity consumption increases significantly, effectively reducing wind curtailment rates. Due to the incentive of the compensation price, both heat and cooling loads participate in demand response within a range that does not affect comfort, resulting in a 26% and 38% decrease in peak-to-valley ratios, respectively. This demonstrates that the integrated demand response system effectively smooths out peak and off-peak loads and reduces load curves.

[0250] The operation of the CAES can be understood by combining Figures 3(a) and 3(b). Figure 3(a) shows that during periods of low load and low electricity prices (1:00-4:00 and 22:00-24:00), the CAES stores electrical energy. During periods of high load and high electricity prices (11:00-17:00 and 20:00), the CAES releases electrical energy, reducing the amount of electricity purchased from the grid and thus lowering system costs. Figure 3(b) shows that the CAES stores heat through energy storage and release, absorbs heat through energy release, and achieves electrothermal coupling and power complementarity with the gas turbine. This improves the comprehensive electrothermal utilization capacity of the gas turbine, indirectly achieving peak shaving and valley filling, alleviating power supply pressure during peak load periods, and reducing the total operating cost of the system. In this case, the system exhibits better economic efficiency.

[0251] In summary, both demand response and compressed air energy storage can smooth the load curve. Demand response mainly shifts some of the load during peak electricity price periods to off-peak periods, or reduces the load within acceptable limits. Compressed air energy storage systems store energy during peak wind turbine and photovoltaic output periods or during off-peak electricity price periods, and release the energy during peak electricity price periods. This can reduce the output of major carbon emission sources such as gas turbines and gas boilers. The combined effect of both can more effectively improve the system's economics and reduce carbon emissions.

[0252] 3) Option 2

[0253] Scheme 2 considers tiered carbon trading and compressed air energy storage devices, but does not take into account the overall demand response. The load increases significantly in Figures 4(a) and 4(b). Figure 4(a) lacks a demand response based on time-of-use pricing, making it impossible to transfer some of the electricity from peak to off-peak periods. Therefore, self-consumption through power balance is not possible most of the time, requiring the purchase of electricity from the grid to meet load demand. Figure 4(b) lacks a demand response based on incentives for heat load, resulting in a significant increase in load. To meet heat load demand, the output of the gas-fired boiler increases significantly, thereby increasing the system's operating costs and carbon emissions.

[0254] 4) Option 3

[0255] Option 3 considers tiered carbon trading and integrated demand response, but does not take into account compressed air energy storage. In Figure 5(a), the compressed air energy storage system cannot achieve peak-hour energy storage and off-peak-hour discharge; therefore, it cannot achieve power balance for self-consumption during 6:00-11:00, 15:00, and 17:00-19:00, and must purchase electricity from the grid to meet load demand. In Figure 5(b), without the heat release during charging and heat absorption during discharging of the compressed air energy storage system, the output of the gas boiler increases significantly between 1:00 and 6:00. Although considering demand response and thus playing a certain role in peak shaving, this option is not optimal in terms of economy and emission reduction compared to Option 1.

[0256] 5) Analysis of the impact of CAES capacity on system optimization scheduling results

[0257] Compressed air energy storage typically has a lifespan exceeding 30 years. However, constructing compressed air energy storage systems requires substantial investment. Inappropriate storage capacity selection can significantly increase investment costs, making it difficult to generate profits within the safe operating lifespan. While increasing the capacity of compressed air energy storage systems will inevitably reduce the total operating cost and carbon trading cost of the integrated energy system, excessively large storage capacity can lead to increased output from various carbon emission sources to minimize total operating costs. This involves storing energy during periods of low electricity prices and releasing it during peak prices, thus profiting from peak-valley arbitrage and potentially increasing carbon emissions. Therefore, rationally configuring the capacity of energy storage systems will maximize the economic efficiency and low-carbon nature of the integrated energy system.

[0258] Compare Figure 6 and Figure 7 It can be seen that Scheme 1, which considers the synergistic effect of flexible energy storage and integrated demand response, can effectively smooth the load curve, and the optimal CAES capacity is only 1250m³. 3 Option 2, without considering comprehensive demand response, has an optimal CAES capacity of 2050m. 3 When the energy storage system works in conjunction with Integrated Demand Response (ICD), the CAES capacity is reduced by 39%. In summary, considering the synergy between compressed air energy storage (CAES) and ICD, the optimal capacity of CAES can be effectively reduced, thus balancing economic efficiency and low carbon emissions.

[0259] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0260] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0261] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0262] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0263] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0264] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this 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 without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A two-layer scheduling method for an integrated energy system, characterized in that, Includes the following steps: The integrated demand response mechanism and compressed air energy storage system are introduced into the traditional energy system. The upper-level optimization model is constructed with the goal of optimizing the operating cost of the compressed air energy storage system during the planning period, and the lower-level optimization model is constructed with the goal of minimizing the operating cost of the integrated energy system that takes into account the integrated demand response. The lower-level optimization problem is transformed into constraints for the upper-level problem. The nonlinear constraints in the relaxed complementarity conditions are transformed into linear constraints. The problem of the upper-level optimization model is solved to obtain the final scheduling scheme. The upper-level optimization model is responsible for solving the problem of optimizing the operating cost of the compressed air energy storage system during the planning period. The decision variables include the charging and discharging power of the compressed air energy storage system and the maximum capacity of the storage tank. The constraints of the upper-level optimization model include residual energy constraints and energy initialization constraints; The lower-level optimization model is responsible for solving the optimized operation of the integrated energy system that takes into account the coordination of energy storage network and integrated demand response. The system operating costs of an integrated energy system include operation and maintenance costs, natural gas purchase costs and grid interaction costs, carbon trading costs, and demand response costs. During the solution process, the Lagrangian function of the lower-level model is constructed, and the Lagrangian function is processed through KKT conditions to transform the lower-level optimization problem into the constraints of the upper-level problem. Then, the Big M method is used to transform the nonlinear constraints in the relaxation and complementarity conditions into linear constraints, and finally the bi-level optimization problem is solved.

2. The two-layer scheduling method for an integrated energy system as described in claim 1, characterized in that, The integrated energy system includes an energy supply side, an energy conversion side, an energy storage side, and a load side.

3. The two-layer scheduling method for an integrated energy system as described in claim 1, characterized in that, A thermodynamic model is established for the compressed air energy storage system, dividing it into compression, expansion, and storage processes. Modeling and analysis are conducted under the following assumptions. 1) Air is an ideal gas and its specific heat capacity is constant; 2) The gas flow rate is constant during the gas storage and expansion process, and the air temperature is the same as the ambient temperature; 3) The energy storage and release of compressed air energy storage systems can occur simultaneously.

4. The two-layer scheduling method for an integrated energy system as described in claim 1, characterized in that, The constraints of the lower-level optimization model include electrical balance constraints, thermal balance constraints, cold balance constraints, integrated demand response modeling constraints, upper and lower limits of integrated energy system equipment output constraints, and constraints on exchanging electrical energy with the external power grid.

5. A two-layer dispatch system for an integrated energy system, employing the method described in claim 1, characterized in that, include: The model building module is configured to introduce the integrated demand response mechanism and compressed air energy storage system into the traditional energy system, build an upper-level optimization model with the goal of optimizing the operating cost of the compressed air energy storage system during the planning period, and build a lower-level optimization model with the goal of minimizing the operating cost of the integrated energy system that takes into account integrated demand response. The model solving module is configured to transform the constraints of the lower-level optimization problem into the constraints of the upper-level problem, transform the nonlinear constraints in the relaxed complementarity conditions into linear constraints, solve the problem of the upper-level optimization model, and obtain the final scheduling scheme.

6. A computer-readable storage medium, characterized in that, It stores multiple instructions adapted for loading by the processor of a terminal device and executing the steps of the method according to any one of claims 1-4.

7. A terminal device, characterized in that, It includes a processor and a computer-readable storage medium, the processor being used to implement various instructions; the computer-readable storage medium being used to store a plurality of instructions adapted to be loaded by the processor and executed as steps in the method of any one of claims 1-4.

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