Distributed Energy System Capacity Optimization Method Considering Comfortable Energy Supply of Two-Stage Heat Pump

Abstract

The present invention discloses a distributed energy system capacity optimization method considering the comfortable energy supply of a combined heat pump. The method adopted by the present invention includes: obtaining the historical load data of the distributed energy system, establishing an investment model and an operation model of the flexibility resources of the distributed energy system; establishing an indoor temperature control load and a combined heat pump model, and through a thermal comfort evaluation index, achieving real-time adjustment of the cooling and heating output of the heat pump according to the outdoor temperature; constructing a distributed energy system flexibility resource optimization configuration model; converting the established distributed energy system flexibility resource optimization configuration model into a mixed integer linear optimization configuration model, the optimization result of which is the optimal installation capacity of each device in the distributed energy system, and outputting the configuration cost. The present invention takes economic optimization as the goal, and ensures the user's comfort on the basis of achieving accurate consumption of new energy and improving load flexibility.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system capacity optimization, and relates to a distributed energy system capacity optimization method considering comfortable energy supply of a dual-generation heat pump in an integrated energy mode. Background Art

[0002] Distributed energy systems are characterized by high energy density, high load utilization hours, and diverse energy supply and consumption. However, due to the lack of unified energy analysis, it is difficult to optimize the supply and demand matching relationship. This leads to problems such as energy waste and low economic efficiency, which significantly affect the operational efficiency and economic and environmental benefits of distributed energy systems. Currently, regional integrated energy systems that couple and utilize multiple energy systems have become one of the development and transformation directions of the energy industry. Distributed energy systems based on multi-energy complementarity, as the primary implementation method of regional integrated energy systems, can effectively enhance the economic and environmental benefits of industrial parks.

[0003] Research on distributed energy system optimization strategies addresses two key aspects of demand response: first, flexible load regulation. Distributed energy systems can be viewed as virtual energy storage systems, integrating their flexible regulation capabilities into distributed energy system optimization and scheduling. Alternatively, a multi-load collaborative interaction model within distributed energy systems can be constructed based on time-of-use energy pricing strategies. Second, the ability to complement and replace multiple energy sources. Distributed energy systems offer ample top-level space, allowing for ample solar radiation, effectively increasing renewable energy penetration and the economic benefits of industrial parks. Incorporating cascaded energy utilization into system operation can further tap into the potential for user-side response. In distributed energy systems, such as those in commercial centers, residential areas, and office buildings, energy output is directly connected to users, and this output is closely linked to user comfort. Ignoring the regulation of energy output during distributed energy system operation not only compromises user experience but also increases operating costs. Summary of the Invention

[0004] In response to the technical problems of high operating costs and poor comfort in existing distributed energy systems, the present invention provides a new feasible method for optimizing the configuration of distributed energy system equipment. This method takes into account flexible resources such as heat pumps (HP), photovoltaics (PV), combined heat and power (CHP), absorption chillers (AC), gas boilers (GB), electrical energy storage (EES) and thermal energy storage (TES). It not only makes full use of renewable energy, but also realizes flexible operation of equipment and multi-energy complementarity. During the operation stage, the output of the heat pump is constrained by the comfort evaluation index, and the most comfortable indoor temperature is adjusted in real time according to the outdoor temperature. With the goal of optimizing economic efficiency, the user's comfort is guaranteed on the basis of realizing the precise consumption of new energy and improving load flexibility.

[0005] To achieve the above-mentioned object, the present invention adopts the following technical solution: a distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps, which includes:

[0006] Step 1: Obtain historical load data for the distributed energy system, predict the rigid electric and thermal base load, outdoor temperature, and solar radiation intensity curve profiles, and use a clustering algorithm to obtain the load conditions for typical summer and winter days, as well as the typical day's outdoor temperature and solar radiation intensity curves. Establish an investment model and operation model for the distributed energy system's flexible resources, reflecting the relationship between input power and output power.

[0007] Step 2: Establish indoor temperature control load and dual heat pump models, and use thermal comfort evaluation indicators to adjust the heat pump's cooling and heating output in real time according to outdoor temperature;

[0008] Step 3: Build a distributed energy system flexibility resource optimization model. The model takes minimizing the total cost as the objective function, and the constraints meet the actual operation safety requirements. The total cost is the sum of the flexibility resource investment cost, the system gas purchase cost, the system electricity purchase cost, and the penalty cost for curtailed solar power.

[0009] Step 4: Convert the established distributed energy system flexibility resource optimization configuration model into a mixed integer linear optimization configuration model, and solve it using Matlab, Yalmip toolbox and Gurobi solver. The optimization result is the optimal installation capacity of each device in the distributed energy system, and the configuration cost is output.

[0010] Furthermore, in step 3, the objective function expression of the distributed energy system flexibility resource optimization configuration model is:

[0011] minC=C inv +C fuel +C elec +C pv

[0012] Where C is the total cost, C inv The investment cost of flexibility resources; C fuel is the gas purchase cost of the system; C elec is the system electricity purchase cost; C pv The penalty cost for abandoning light.

[0013] Furthermore, the calculation formula for the investment cost of flexibility resources is:

[0014]

[0015] dev∈{PV,CHP,AC,GB,HP,EES,TES}

[0016] Where dev is the device type, PV, CHP, AC, GB, HP, EES, and TES represent photovoltaic, combined heat and power unit, absorption chiller, gas boiler, heat pump, electric energy storage, and thermal energy storage, respectively. Devices are divided into continuous devices and discrete devices. Continuous devices include PV, EES, and TES. Discrete devices include CHP, AC, GB, and HP, and discrete devices are configured according to the capacity corresponding to the specific model. dev Indicates the configuration capacity of each device; w dev is the dev investment cost per unit quantity; Y dev The whole life cycle of the dev type equipment; o is the discount rate;

[0017] The calculation formula for the system gas purchase cost is as follows:

[0018] The operation of the cogeneration unit requires gas purchase from the upstream gas network to ensure the normal energy supply of the system;

[0019]

[0020] Where: S is the typical day type; is the natural gas price at time t during period s on a typical day; and are the natural gas consumption of the cogeneration unit and gas boiler during the s period of a typical day; θ s is the typical day length; T N is the total duration of the scheduling period;

[0021] The calculation formula for the system electricity purchase cost is as follows:

[0022] When the electricity generated by the distributed energy system is insufficient to supply regional consumption, it is necessary to purchase electricity from the grid. The cost is:

[0023]

[0024] Where, Under the typical day s is the electricity price at time t; The power purchased by the system from the grid during period t on a typical day s;

[0025] The calculation formula for the abandoned light penalty cost is as follows:

[0026]

[0027] Where, ξ PV The penalty cost per unit power for abandoned light; are the abandoned photovoltaic power in period t during a typical day s.

[0028] Furthermore, in step three, the constraints include flexibility resource installation area constraints, indoor temperature constraints, temperature control load constraints, heat pump operation constraints, comfort operation constraints, distributed energy system operation constraints, power balance constraints and equipment operation constraints.

[0029] Furthermore, the flexibility resource installation area is constrained as follows:

[0030] n dev,min ≤n dev ≤n dev,max

[0031] Where n dev,min and n dev,max The minimum and maximum capacity that can be installed for distributed energy system dev equipment; n dev Indicates the configuration capacity of each device.

[0032] Furthermore, the indoor temperature constraint is as follows:

[0033]

[0034] Where, is the indoor temperature during period t in energy supply season s, is the outdoor temperature during period t in energy supply season s; R and C are the equivalent thermal resistance and equivalent heat capacity of the distributed energy system respectively; H s,t is the temperature control load demand during period t in energy supply season s; Indicates the indoor temperature during period t-1 in energy supply season s.

[0035] The temperature control load constraints are as follows:

[0036]

[0037]

[0038] Where, and are the control quantities of heat regulation load and cold regulation load respectively; Indicates the room temperature heat load demand; Indicates room temperature cooling load demand;

[0039] The heat pump operation constraints are as follows:

[0040]

[0041]

[0042]

[0043]

[0044] Where: and are the electric power consumed by the heat pump at time t in scenario s, the total thermal power generated, the thermal power used to meet the rigid thermal load, the thermal power used for user room temperature adjustment, and the cooling power used for room temperature adjustment; and is the control quantity of the heat pump's heating output and cooling output, set as a binary variable; and are the energy conversion efficiencies of the heat pump for heating and cooling, respectively.

[0045] Furthermore, the comfort operation constraints are as follows:

[0046]

[0047] Where M is the metabolic rate of a person. When the person does not perform strenuous exercise in the distributed energy system, M can be a fixed value. Thermal resistance of clothing worn by the human body in different scenarios; Indicates the indoor temperature during period t in energy supply season s;

[0048] According to ISO7730 standard, the recommended PMV index range is:

[0049]

[0050] Furthermore, the distributed energy system operation constraints are as follows:

[0051] Assuming that the operating efficiency of CHP remains unchanged within the operating range, its input-output function relationship is shown as follows:

[0052]

[0053]

[0054] Where: and are the output electric power, natural gas consumption rate and output thermal power of CHP at time t in energy supply season s; η e ,η h are the power generation efficiency and heat recovery efficiency of CHP respectively; λ gas is the calorific value of natural gas.

[0055] Gas boilers use natural gas as input energy and output heat energy to users. The input-output function relationship is:

[0056]

[0057] Where, is the output thermal power and gas consumption rate of the gas boiler at time t in the energy supply season s, η GB For gas boiler working efficiency;

[0058] Active power output of photovoltaic and light intensity G C and the outdoor temperature during period t in the s energy supply season The relationship between them can be approximately expressed as:

[0059]

[0060] Where, P STC , G STC and T STC are the rated output power, rated light intensity and standard operating temperature of the photovoltaic power generation system under standard rated conditions, and k is the correction factor. During operation, the photovoltaic system must also meet the following requirements:

[0061]

[0062] Where, is the actual output power of photovoltaic power in the integrated energy system, is the photovoltaic abandoned power;

[0063] Energy storage equipment can decouple energy production and consumption in time, including thermal energy storage and electrical energy storage; the charging and discharging power of energy storage equipment is related to the energy storage capacity and must not be charged and discharged at the same time. In order to ensure the continuity of scheduling during the operation phase, the daily end time T of energy storage equipment scheduling is given. s,TWith the initial state E s,1 The energy storage device operates as follows:

[0064]

[0065] E min ≤E s,t ≤E max

[0066] E s,1 =E s,T

[0067]

[0068]

[0069]

[0070] Where: E s,t+1 is the energy stored in the energy storage device at time t+1 under scenario s; σ is the energy storage self-attenuation coefficient; and is the charging and discharging power of energy storage; η char and η relea is the charging and discharging efficiency of energy storage; Δt represents the time interval; E s,t represents the energy stored in the energy storage device at time t in scenario s; E min and E max are the minimum and maximum storage energy requirements of energy storage equipment respectively; and are the 0-1 state variables of energy storage equipment charging and discharging at time t in energy supply season s, Indicates charging, It means releasing energy; and are the charging and discharging rates of the energy storage device respectively.

[0071] Furthermore, the power balance constraint is as follows:

[0072]

[0073]

[0074]

[0075]

[0076] Where, are the rigid electric load and heat load at time t in energy supply season s; The power purchased by the system from the grid during period t on a typical day s; Indicates the energy storage discharge power at time t in energy supply season s; Indicates the energy storage charging power at time t in energy supply season s; It represents the thermal energy storage discharge power at time t in energy supply season s; It represents the thermal energy storage charging power at time t in energy supply season s.

[0077] Furthermore, the equipment operation constraints are as follows:

[0078] Equipment output The upper and lower limits cannot be exceeded:

[0079]

[0080] Where, and are the minimum output ratio and maximum output ratio of the device respectively; W s dev is the rated output of the device.

[0081] The beneficial effects of the present invention are as follows:

[0082] The present invention's analysis of the urban distributed energy system fully considers the real-time adjustment of the heat pump output to achieve optimal indoor comfort, and then takes minimizing the total cost as the optimization goal. By adjusting the capacity configuration of each device in the urban distributed energy system when the operating cost is at the minimum, it can ensure the low-cost operation of the urban distributed energy system and effectively ensure the thermal comfort on the user side. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0084] Figure 1 A schematic diagram of the topological structure of a distributed energy system in a specific embodiment of the present invention;

[0085] Figure 2 This is a typical day rigid electric and heating load diagram in summer according to a specific embodiment of the present invention;

[0086] Figure 3 This is a rigid electric heating load diagram for a typical winter day in a specific embodiment of the present invention;

[0087] Figure 4 Graphs of outdoor temperature on typical days in a specific embodiment of the present invention;

[0088] Figure 5 Graphs showing typical sunlight radiation intensity in a specific embodiment of the present invention;

[0089] Figure 6 This is a flow chart of the distributed energy system capacity optimization method of the present invention. DETAILED DESCRIPTION

[0090] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0091] The present invention is a distributed energy system capacity optimization method considering the energy supply comfort of heat pump air conditioner. The specific implementation flow chart is as follows: Figure 6 As shown in the figure, the topology of distributed energy system is as follows: Figure 1 shown.

[0092] A distributed energy system capacity optimization method considering the comfort energy supply of dual heat pumps includes:

[0093] Step 1: Obtain historical load data for the distributed energy system, predict the rigid electric and heating base load, outdoor temperature, and solar radiation intensity curve profiles, and use a clustering algorithm to obtain the load conditions for typical days in the spring and autumn transition season, summer, and winter, as well as the outdoor temperature and solar radiation intensity curves for typical days. Establish an investment model and operation model for the distributed energy system's flexible resources to reflect the relationship between input power and output power.

[0094] Step 2: Establish indoor temperature control load and dual heat pump models, and use thermal comfort evaluation indicators to adjust the heat pump's cooling and heating output in real time according to outdoor temperature;

[0095] Step 3: Build a distributed energy system flexibility resource optimization model. The model takes minimizing the total cost as the objective function, and the constraints meet the actual operation safety requirements. The total cost is the sum of the flexibility resource investment cost, the system gas purchase cost, the system electricity purchase cost, and the penalty cost for curtailed solar power.

[0096] Step 4: Convert the established flexibility resource optimization configuration model into a mixed integer linear optimization configuration model, and solve it using Matlab, Yalmip toolbox and Gurobi solver. The optimization result is the optimal installation capacity of each device in the distributed energy system, and the configuration cost is output.

[0097] In step 3, the objective function expression of the distributed energy system flexibility resource optimization configuration model is:

[0098] minC=C inv +C fuel +C elec +C pv

[0099] Where C is the total cost, C inv The investment cost of flexibility resources; C fuel is the gas purchase cost of the system; C elec is the system electricity purchase cost; C pv The penalty cost for abandoning light.

[0100] The calculation formula for the investment cost of flexibility resources is:

[0101]

[0102] dev∈{PV,MT,HB,HP,EES,TES}

[0103] Where dev is the device type, and PV, CHP, AC, GB, HP, EES, and TES represent photovoltaics, combined heat and power units, absorption chillers, gas boilers, heat pumps, electrical energy storage, and thermal energy storage, respectively. Devices are divided into continuous devices and discrete devices. Continuous devices include PV, EES, and TES; discrete devices include CHP, AC, GB, and HP, and discrete devices are configured according to the capacity corresponding to the specific model. dev Indicates the configuration capacity of each device; w dev is the dev investment cost per unit quantity; Y dev The whole life cycle of the dev type equipment; o is the discount rate;

[0104] The calculation formula for the system gas purchase cost is as follows:

[0105] The operation of the cogeneration unit requires gas purchase from the upstream gas network to ensure the normal energy supply of the system;

[0106]

[0107] Where: S is the typical day type; is the natural gas price at time t during period s on a typical day; and are the natural gas consumption of the cogeneration unit and gas boiler during the s period of a typical day; θ s is the typical day length; T N is the total duration of the scheduling period;

[0108] The calculation formula for the system electricity purchase cost is as follows:

[0109] When the electricity generated by the distributed energy system is insufficient to supply regional consumption, it is necessary to purchase electricity from the grid. The cost is:

[0110]

[0111] Where, Under the typical day s is the electricity price at time t; The power purchased by the system from the grid during period t on a typical day s;

[0112] The calculation formula for the penalty cost of abandoned light is as follows:

[0113]

[0114] Where, ξ PV The penalty cost per unit power for abandoned light; are the abandoned photovoltaic power in period t during a typical day s.

[0115] In step three, the constraints include flexibility resource installation area constraints, indoor temperature constraints, temperature control load constraints, heat pump operation constraints, comfort operation constraints, distributed energy system operation constraints, power balance constraints and equipment operation constraints.

[0116] The flexibility resource installation area constraints are as follows:

[0117] n dev,min ≤n dev ≤n dev,max

[0118] Where n dev,min and n dev,max The minimum and maximum capacity that can be installed for distributed energy system dev equipment; n dev Indicates the configuration capacity of each device.

[0119] The indoor temperature constraints are as follows:

[0120]

[0121] Where, is the indoor temperature during period t in energy supply season s, is the outdoor temperature during period t in energy supply season s; R and C are the equivalent thermal resistance and equivalent heat capacity of the distributed energy system respectively; H s,t is the temperature control load demand during period t in energy supply season s; Indicates the indoor temperature during period t-1 in energy supply season s.

[0122] The temperature control load constraints are as follows:

[0123]

[0124]

[0125] Where, and are the control quantities of heat regulation load and cold regulation load respectively; Indicates the room temperature heat load demand; Indicates room temperature cooling load demand;

[0126] The heat pump operation constraints are as follows:

[0127]

[0128]

[0129]

[0130]

[0131] Where: and are the electric power consumed by the heat pump at time t in scenario s, the total thermal power generated, the thermal power used to meet the rigid thermal load, the thermal power used for user room temperature adjustment, and the cooling power used for room temperature adjustment; and is the control quantity of the heat pump's heating output and cooling output, set as a binary variable; and Used to control heat pumps from running simultaneously; and are the energy conversion efficiencies of the heat pump for heating and cooling, respectively.

[0132] To ensure the indoor temperature is comfortable, the indoor temperature is constrained by the evaluation range of the PMV index. The specific parameters of the PMV index are shown in Table 1, and they meet the following requirements with the indoor temperature:

[0133]

[0134] Where: M is the metabolic rate of a person. When the person does not perform strenuous exercise in the distributed energy system, M can be a fixed value. The thermal resistance of clothing worn by the human body in different scenarios. According to the ISO7730 standard, the recommended PMV index range is:

[0135] Table 1 Indoor temperature constraint parameters

[0136]

[0137] The operation of a distributed energy system requires the purchase of natural gas from a higher-level energy system. If the power generation efficiency of the system's internal equipment is poor, electricity can be purchased from the higher-level energy system. The time-of-use electricity prices are shown in Table 2. In addition to HP, the system also includes CHP, AC, GB, PV, EES, TES, and other equipment. The economic performance indicators of these equipment are shown in Tables 3 and 4. The equipment operation model is as follows:

[0138] CHP uses natural gas as its input energy source and outputs high-temperature, high-pressure steam to drive a steam turbine for power generation. The waste heat from the gas can be recovered to supply fixed heat loads or fed into AC to provide cooling for users. This example assumes that the CHP's operating efficiency remains constant within its operating range. Its input-output function is shown in the equation:

[0139]

[0140]

[0141] Where: and are the output electric power, natural gas consumption rate and output thermal power of CHP at time t in energy supply season s; η e ,η h are the power generation efficiency and heat recovery efficiency of CHP respectively; λ gas is the calorific value of natural gas.

[0142] Gas boilers use natural gas as input energy and output heat energy to users. The input-output function relationship is:

[0143]

[0144] Where: is the output thermal power and gas consumption rate of the gas boiler at time t in the energy supply season s, η GB For gas boiler working efficiency.

[0145] Active power output of photovoltaic and light intensity G C and outdoor temperature The relationship between can be approximately expressed as:

[0146]

[0147] Where: P STC , G STC and T STC They are the rated output power, rated light intensity and standard operating temperature of the photovoltaic power generation system under standard rated conditions, and k is the correction factor. Photovoltaic power generation must also meet the following requirements during operation:

[0148]

[0149] Where: is the actual output power of photovoltaic power in the integrated energy system, is the photovoltaic abandoned power.

[0150] The absorption chiller converts the heat energy generated by the waste heat boiler or gas boiler into the cold energy required by the cooling user. Its input-output function relationship is:

[0151]

[0152] Where: and are the output cooling power and absorbed heat power of the absorption refrigerator, η AC is the working efficiency of the absorption chiller.

[0153] Energy storage equipment can decouple energy production and consumption in time, mainly including thermal energy storage and electrical energy storage. The charging and discharging power of energy storage equipment is related to the energy storage capacity and must not be charged and discharged at the same time. In order to ensure the continuity of the operation phase scheduling, the daily end time T of the energy storage equipment scheduling is given s,T With the initial state E s,1 The operation of the energy storage device is shown in the following equation.

[0154]

[0155] E min ≤E t ≤E max

[0156] E s,1 =E s,T

[0157]

[0158]

[0159]

[0160] Where: E s,t+1 is the energy stored in the energy storage device at time t+1 under scenario s; σ is the energy storage self-attenuation coefficient; and is the charging and discharging power of energy storage; η char and η relea is the charging and discharging efficiency of energy storage; E min and E max are the minimum and maximum storage energy requirements of energy storage equipment respectively. and are the 0-1 state variables of energy storage equipment charging and discharging at time t in energy supply season s, Indicates charging, It means releasing energy; and are the charging and discharging rates of the energy storage device respectively.

[0161] The power balance constraints are as follows:

[0162]

[0163]

[0164]

[0165]

[0166] Where, are the rigid electric load and heat load at time t in energy supply season s; The power purchased by the system from the grid during period t on a typical day s; Indicates the energy storage discharge power at time t in energy supply season s; Indicates the energy storage charging power at time t in energy supply season s; It represents the thermal energy storage discharge power at time t in energy supply season s; It represents the thermal energy storage charging power at time t in energy supply season s.

[0167] The equipment operation constraints are as follows:

[0168] Equipment output The upper and lower limits cannot be exceeded:

[0169]

[0170] Where, and are the minimum output ratio and maximum output ratio of the device respectively; W s dev is the rated output of the device.

[0171] Table 2 Time-of-use electricity price list

[0172]

[0173] Table 3 Continuous device information table

[0174]

[0175] Table 4 Discrete device information table

[0176]

[0177] Construct a distributed energy system flexibility resource optimization configuration model, build a mixed integer linear optimization configuration model on Matlab, use the Yalmip toolbox and call the Gurobi solver to solve, obtain the optimal configuration result, and output the optimized cost.

[0178] Case Analysis

[0179] Taking a community with 30 residential users as an example, the distributed energy system capacity optimization method proposed in this invention is verified. Assume that the residential users in the community have the same house type and the area is 150m 2 , the equivalent specific heat capacity of the building is 1.2kWh / ℃, and the equivalent thermal resistance is 6.8℃ / kW. These residential users use centralized heating and cooling mode, ignoring the loss during the transmission process, monitoring the indoor temperature and controlling it uniformly. The whole year is divided into two typical seasons, summer and winter. The rigid load curves of each typical day are as follows Figure 2-5 In order to fully analyze and consider the impact of the dual heat pump comfort energy supply on the configuration results, the present invention sets the following five schemes for comparative analysis:

[0180] Option 1: Use a combined heat and power unit, absorption chiller, and heat pump heating mode to achieve combined cooling, heating, and power;

[0181] Option 2: Connect to the energy storage system based on Option 1;

[0182] Option 3: Based on Option 2, a heat pump combined power generation mode is adopted;

[0183] Option 4: Based on Option 3, connect to the photovoltaic system for energy supply.

[0184] The capacity configuration results are shown in Table 5, and the economic calculation results are shown in Table 6. The configuration results are analyzed from different aspects.

[0185] Table 5 Configuration capacity of each solution

[0186]

[0187] Table 6 Economic comparison of various schemes

[0188]

[0189]

[0190] 1) Comparing Schemes 1 and 2, we analyze the impact of energy storage on the optimal configuration of the integrated energy system. The optimized configuration results in Table 2 show that, compared to Scheme 1, Scheme 2 reduces the gas boiler's configuration capacity by 500 kW after considering thermal energy storage. This is because the addition of energy storage enables energy transfer across time periods, reducing the additional gas purchase cost for the gas boiler and indirectly reducing the environmental costs of system operation.

[0191] 2) Comparing Schemes 2 and 3, we analyzed the impact of the combined heat pump heating and cooling system on the capacity configuration of the integrated energy system. Compared to Scheme 2, Scheme 3 does not increase the capacity of the heat pump, while the capacity configuration of the remaining equipment is reduced. This is because the flexibility of the heat pump is fully utilized during the system operation phase, and the independent heating radiation and cooling convection control strategies significantly reduce the total system cost.

[0192] 3) Comparing Schemes 3 and 4, we analyze the impact of renewable energy inclusion on the capacity configuration of the integrated energy system. The optimized configuration results in Table 2 show that while Scheme 4 increases the system's electricity purchase cost compared to Scheme 3, and the system also includes a certain amount of energy storage, which impacts system stability to some extent, the inclusion of the photovoltaic system reduces system operation and maintenance costs and environmental costs, effectively promoting carbon neutrality.

[0193] In summary, the present invention considers the distributed energy system optimization method of heat pump comfortable energy supply, which not only realizes the flexible energy supply of distributed energy on the energy consumption side, but also effectively improves the overall economy of construction and fully considers the participation of users.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps, characterized by: include: Step 1: Obtain historical load data for the distributed energy system, predict the rigid electric and thermal base load, outdoor temperature, and solar radiation intensity curve profiles, and use a clustering algorithm to obtain the load conditions for typical summer and winter days, as well as the typical day's outdoor temperature and solar radiation intensity curves. Establish an investment model and operation model for the distributed energy system's flexible resources, reflecting the relationship between input power and output power. Step 2: Establish indoor temperature control load and dual heat pump models, and use thermal comfort evaluation indicators to adjust the heat pump's cooling and heating output in real time according to outdoor temperature; Step 3: Construct a distributed energy system flexibility resource optimization configuration model. The model takes the minimum total cost as the objective function, and the constraints meet the actual operation safety requirements. The total cost is the flexibility resource investment cost C inv , System gas purchase cost C fuel , System power purchase cost C elec And the penalty cost C for abandoning light pv sum; Step 4: Convert the established distributed energy system flexibility resource optimization configuration model into a mixed integer linear optimization configuration model and solve it using Matlab, Yalmip toolbox, and Gurobi solver. The optimization result is the optimal installation capacity of each device in the distributed energy system, and the configuration cost is output; The calculation formula for active resource investment cost is: dev∈{PV,CHP,AC,GB,HP,EES,TES} Where dev is the device type, PV, CHP, AC, GB, HP, EES, and TES represent photovoltaic, combined heat and power unit, absorption chiller, gas boiler, heat pump, electric energy storage, and thermal energy storage, respectively. Devices are divided into continuous devices and discrete devices. Continuous devices include PV, EES, and TES. Discrete devices include CHP, AC, GB, and HP, and discrete devices are configured according to the capacity corresponding to the specific model. dev Indicates the configuration capacity of each device; w dev is the dev investment cost per unit quantity; Y dev The whole life cycle of the dev type equipment; o is the discount rate; The calculation formula for the system gas purchase cost is as follows: The operation of the cogeneration unit requires gas purchase from the upstream gas network to ensure the normal energy supply of the system; Where: S is the typical day type; is the natural gas price at time t during period s on a typical day; and are the natural gas consumption of the cogeneration unit and gas boiler during the s period of a typical day; θ s is the typical day length; T N is the total duration of the scheduling period; The calculation formula for the system electricity purchase cost is as follows: When the electricity generated by the distributed energy system is insufficient to supply regional consumption, it is necessary to purchase electricity from the grid. The cost is: Where, Under the typical day s is the electricity price at time t; The power purchased by the system from the grid during period t on a typical day s; The calculation formula for the abandoned light penalty cost is as follows: Where, ξ PV The penalty cost per unit power for abandoned light; are the abandoned photovoltaic power in period t during a typical day s; The constraints include flexibility resource installation area constraints, indoor temperature constraints, temperature control load constraints, heat pump operation constraints, comfort operation constraints, distributed energy system operation constraints, power balance constraints and equipment operation constraints.

2. The distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pump according to claim 1 is characterized in that: The flexibility resource installation area constraints are as follows: n dev,min ≤n dev ≤n dev,max Where n dev,min and n dev,max The minimum and maximum capacity that can be installed for distributed energy system dev equipment; n dev Indicates the configuration capacity corresponding to each device.

3. The distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps according to claim 1 is characterized in that: The indoor temperature constraints are as follows: Where, is the indoor temperature during period t in energy supply season s, is the outdoor temperature during period t in energy supply season s; R and C are the equivalent thermal resistance and equivalent heat capacity of the distributed energy system respectively; H s,t is the temperature control load demand during period t in energy supply season s; It represents the indoor temperature in period t-1 in energy supply season s; The temperature control load constraints are as follows: Where, and are the control quantities of heat regulation load and cold regulation load respectively; Indicates the room temperature heat load demand; Indicates room temperature cooling load demand; The heat pump operation constraints are as follows: Where: and are the electric power consumed by the heat pump at time t in scenario s, the total thermal power generated, the thermal power used to meet the rigid thermal load, the thermal power used for user room temperature adjustment, and the cooling power used for room temperature adjustment; and is the control quantity of the heat pump's heating output and cooling output, set as a binary variable; and are the energy conversion efficiencies of the heat pump for heating and cooling, respectively.

4. The distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps according to claim 1 is characterized in that: The comfort operation constraints are as follows: Where M is the metabolic rate of a person. When the person does not perform strenuous exercise in the distributed energy system, M can be a fixed value. Thermal resistance of clothing worn by the human body in different scenarios; Indicates the indoor temperature during period t in energy supply season s; According to ISO7730 standard, the recommended PMV index range is:

5. The distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps according to claim 3 is characterized in that: The distributed energy system operation constraints are as follows: Assuming that the operating efficiency of the cogeneration unit remains unchanged within the operating range, its input-output function relationship is as follows: Where: and are the output electric power, natural gas consumption rate and output thermal power of CHP at time t in energy supply season s; η e ,η h are the power generation efficiency and heat recovery efficiency of CHP respectively; λ gas is the calorific value of natural gas; Gas boilers use natural gas as input energy and output heat energy to users. The input-output function relationship is: Where, is the output thermal power and gas consumption rate of the gas boiler at time t in the energy supply season s, η GB For gas boiler working efficiency; Active power output of photovoltaic and light intensity G C and the outdoor temperature during period t in the s energy supply season The relationship between them can be approximately expressed as: Where, P STC , G STC and T STC are the rated output power, rated light intensity and standard operating temperature of the photovoltaic power generation system under standard rated conditions, and k is the correction factor. During operation, the photovoltaic system must also meet the following requirements: Where, is the actual output power of photovoltaic power in the integrated energy system, is the photovoltaic abandoned power; Energy storage equipment can decouple energy production and consumption in time, including thermal energy storage and electrical energy storage; the charging and discharging power of energy storage equipment is related to the energy storage capacity and must not be charged and discharged at the same time. In order to ensure the continuity of scheduling during the operation phase, the daily end time T of energy storage equipment scheduling is given. s,T With the initial state E s,1 The energy storage device operates as follows: AND min ≤E t ≤E max AND s,1 =And s,T Where: E s,t+1 is the energy stored in the energy storage device at time t+1 under scenario s; σ is the energy storage self-attenuation coefficient; and is the charging and discharging power of energy storage; η char and η relea is the charging and discharging efficiency of energy storage; Δt represents the time interval; E s,t represents the energy stored in the energy storage device at time t in scenario s; E min and E max are the minimum and maximum storage energy requirements of energy storage equipment respectively; and are the 0-1 state variables of energy storage equipment charging and discharging at time t in energy supply season s, Indicates charging, It means releasing energy; and are the charging and discharging rates of the energy storage device respectively.

6. The distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps according to claim 5 is characterized in that: The power balance constraints are as follows: Where, are the rigid electric load and heat load at time t in energy supply season s; The power purchased by the system from the grid during period t on a typical day s; Indicates the energy storage discharge power at time t in energy supply season s; Indicates the energy storage charging power at time t in energy supply season s; It represents the thermal energy storage discharge power at time t in energy supply season s; It represents the thermal energy storage charging power at time t in energy supply season s.

7. The distributed energy system capacity optimization method considering the comfortable energy supply of dual heat pumps according to claim 1 is characterized in that: The equipment operation constraints are as follows: Equipment output The upper and lower limits cannot be exceeded: Where, and They are the minimum output ratio and maximum output ratio of the device respectively; is the rated output of the device.

Images