A day-ahead scheduling method for integrated electric and thermal systems considering multiple thermal inertias

Abstract

The present invention relates to a scheduling method, and more particularly to a day-ahead scheduling method for an electric-heat integrated system that considers multiple thermal inertias. System data is first collected, including heat network pipeline data information, node temperature, mass flow rate of pipeline water, indoor temperature, outdoor temperature, heat load, electric load, and wind power generation data. The collected system data is then input into an electric-heat integrated scheduling objective function that considers multiple thermal inertias, and combined with constraints, the optimal scheduling parameters are output, including the electric power and thermal power output by the CHP unit. Therefore, the present invention has the following advantages: 1. It combines the thermal inertia of thermal buildings with the elasticity of the thermal load, effectively freeing the thermal load from restrictions, converting the thermal load from the initial curve into an interval that can fluctuate up and down, thereby maximizing the flexibility of electric-heat scheduling. 2. It combines different thermal inertias, removes the restriction of the CHP unit on determining electricity based on heat, and maximizes the flexibility of CHP unit scheduling.

Description

Technical Field

[0001] The present invention relates to a scheduling method, and in particular to a day-ahead scheduling method for an electric-thermal integrated system considering multiple thermal inertias. Background Art

[0002] Currently, there is little research on the thermal inertia of electric heating systems, and the only research focuses on the modeling of single thermal inertia. This patent combines the thermal inertia of buildings with the thermal load elasticity, and optimizes the heat dissipation of the radiator through the temperature constraint brought by the thermal load elasticity, thereby removing the limitation of strictly tracking the prediction curve of the thermal load demand, increasing the flexible adjustment capability of the load, and improving the flexibility of scheduling. Summary of the Invention

[0003] The above technical problems of the present invention are mainly solved by the following technical solutions:

[0004] A day-ahead scheduling method for an electric and thermal integrated system considering multiple thermal inertias is characterized by:

[0005] Collect system data, including heating network pipeline data information, node temperature, pipeline water mass flow rate, indoor temperature, outdoor temperature, heat load, electrical load, and wind power generation data;

[0006] The collected system data is input into the electric and thermal integrated scheduling objective function considering multiple thermal inertias, and combined with the constraints, the optimal scheduling parameters are output, including the electric power and thermal power output of the CHP unit;

[0007] The objective function is a comprehensive objective function that takes into account economic dispatch and minimization of wind power consumption objectives, and the constraints include energy supply network security, power balance, new energy output, cogeneration units, unit ramping, and grid constraints.

[0008] In the above-mentioned day-ahead dispatch method for a combined electric and thermal system considering multiple thermal inertias, the economic dispatch objective is to minimize the system operating fuel cost, namely:

[0009]

[0010]

[0011]

[0012] Among them, α i , β i , γ i ,θ i , δ i ,ζ i is the operating cost coefficient of thermal power unit i, is the electrical output of cogeneration unit i at time t, in MW; is the heat output of cogeneration unit i at time t, in MW; the above formula is the same for extraction condensing units and pure condensing units; C W,i , is the unit power operation and maintenance cost of renewable energy power generation; represents the cost of the i-th cogeneration unit at time t, represents the power generation cost of the i-th wind turbine at time t.

[0013] In the above-mentioned day-ahead dispatch method for a combined electric and thermal system considering multiple thermal inertias, the wind power consumption target is to convert the wind power abandonment minimization target into the wind power abandonment penalty minimization target by combining the wind power abandonment penalty coefficient, that is:

[0014]

[0015] in, is the penalty factor for the i-th wind turbine generator set to abandon wind power at time t, and They represent the predicted power output of the i-th wind turbine at time t and the actual power output of the grid-connected wind turbine.

[0016] In the above-mentioned day-ahead scheduling method for the electric and thermal integrated system considering multiple thermal inertias, the objective function is the comprehensive objective

[0017] minF=ω1F1+ω2F2

[0018] Among them, ω1 and ω2 represent the weight coefficients of the objective function respectively.

[0019] In the above-mentioned day-ahead dispatching method for a combined electric and thermal system considering multiple thermal inertias, the energy supply network security constraint is based on the following formula:

[0020] T r,min ≤T r,t ≤T r,max

[0021] T s,min ≤T s,t ≤T s,max

[0022] T out,min ≤T out,t ≤T out,max

[0023] V i,min ≤V i ≤V i,max ;T r,min Indicates the minimum return water temperature, T r,t Indicates the return water temperature at time t, T r,maxIndicates the maximum value of the return water temperature, T s,min Indicates the minimum value of the water supply temperature, T s,t Indicates the water supply temperature at time t, T s,max Indicates the maximum value of the water supply temperature, T out,min Indicates the minimum value of the outlet temperature of the node to which it belongs, T out,t The outlet temperature at time t, T out,max Indicates the maximum value of the outlet temperature of the node; V i,min Indicates the minimum value of the voltage amplitude of the i-th node, V i Represents the voltage amplitude of the i-th node, V i,max Indicates the maximum voltage amplitude of the i-th node.

[0024] In the above-mentioned day-ahead dispatching method for a combined electric and thermal system considering multiple thermal inertias, the power balance constraint is based on the following formula:

[0025]

[0026]

[0027]

[0028] and They represent the thermal load power, active load and reactive load of node i, in MW; P i t and denote the thermal power, active power and reactive power injected into node i respectively; represents the grid-connected power output of the i-th wind turbine at time t, and They represent the thermal power, active power and reactive power output by the cogeneration unit injected into node i respectively.

[0029] In the above-mentioned day-ahead dispatching method for a combined electric and thermal system considering multiple thermal inertias, the renewable energy output constraint is based on the following formula:

[0030]

[0031]

[0032] Among them, cosθ is the power factor of the new energy; δ represents the power factor angle, P represents the reactive power output of the i-th wind turbine group at time t. W,max Indicates the maximum value of the dispatched grid-connected power output of the wind turbine.

[0033] In the above-mentioned day-ahead dispatching method for a combined heat and power system considering multiple thermal inertias, the constraints of the combined heat and power units are based on the following formula:

[0034] P CHPmin ≤P CHP ≤P CHPmax

[0035] H CHPmin ≤H CHP ≤H CHPmax ;P CHPmin Indicates the minimum power output of the CHP unit, P CHPmax Indicates the maximum power output of the CHP unit, H CHPmin Indicates the minimum thermal output of the CHP unit, H CHPmax Indicates the maximum thermal output of the CHP unit.

[0036] In the above-mentioned day-ahead dispatching method for a comprehensive electric and thermal system considering multiple thermal inertias, the unit ramp constraint is based on the following formula:

[0037]

[0038] Among them, r d is the downward climbing rate, r u is the upward climbing rate, ΔT is the unit scheduling time; represents the power output of the i-th CHP unit at time t, Represents the power output of the i-th CHP unit at time t-1.

[0039] In the above-mentioned day-ahead dispatching method for a combined electric and thermal system considering multiple thermal inertias, the grid constraint is based on the following formula:

[0040]

[0041] P i t 、 are the active power and reactive power injected by node i in period t respectively;

[0042] G ij 、B ij are the real and imaginary parts of the node admittance matrix respectively;

[0043] V i t 、 are the node voltage amplitude and voltage phase difference respectively; represents the voltage at node j at time t.

[0044] Therefore, this invention has the following advantages: 1. It combines the thermal inertia of thermal buildings with the elasticity of heat loads, effectively removing heat load constraints and transforming the heat load from an initial curve into a range that allows for fluctuations, thereby maximizing the flexibility of power and heat scheduling. 2. It combines different thermal inertias, removing the limitation of CHP units being restricted by heat-based power generation, thereby maximizing the flexibility of CHP unit scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Attachment Figure 1 It is the structural diagram of the thermal system;

[0046] Attachment Figure 2 It is a schematic diagram of the PMV indicator;

[0047] Attachment Figure 3 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0049] Example:

[0050] The present invention considers multiple thermal inertias in the following two aspects:

[0051] Thermal inertia caused by heat transfer delay in thermal system pipelines and thermal inertia caused by heat storage in the building group in the heat load.

[0052] By treating heating networks and buildings as heat storage devices, the heat storage capacity of these networks and buildings can be leveraged to adjust the heating system's operation within the user's thermal comfort range, making the system's heat supply flexible and adjustable over time. This can effectively reduce the intensity of the electric-thermal coupling in the combined electric-heat energy supply system and improve its coordination. Thermal energy in the IES (Integrated Energy System) exhibits inertia. On the one hand, due to long transmission pipelines, there is a thermal lag of several minutes to several hours between the heat source and the heat load. On the other hand, the heat load can operate within the comfort range. Even if the heat source stops supplying heat, the heat load can maintain a comfortable temperature for a long time due to thermal inertia.

[0053] IES thermal inertia is defined as: when the heat source heat supply changes instantaneously, due to the time lag in the heat pipes and the inertia of the heat load, the heat load temperature changes relatively slowly, maintaining a comfortable temperature for a certain period of time. The following content explains these two points.

[0054] The following explains them separately.

[0055] 1. Thermal inertia of thermal system pipelines.

[0056] If thermal systems are considered solely for thermal balance without considering their specific network structure, the heating network and buildings are often treated as static heat load nodes, without considering their dynamic thermal characteristics. However, by leveraging the existing thermal transmission delay characteristics of the heating network and the heat storage capacity of buildings, it is possible to achieve equivalent shifting and peak-shaving of actual heat loads without additional investment, improving the peak-shaving capacity of the integrated power-heat energy system, thereby facilitating the absorption of wind power and enhancing flexibility.

[0057] like Figure 1 It can be seen that the structure of the thermal system is divided into heat source, pipeline network, heat exchange station and heat load.

[0058] Similar to power systems, thermal systems are divided into a transmission system (primary pipeline network) and a distribution system (secondary pipeline network). The primary pipeline network connects the heat source with the heat exchange station, transferring the heat generated by the heat source through a heat carrier (primarily hot water or steam, currently used in my country). The secondary pipeline network connects the heat exchange station with heat users, distributing the heat energy transmitted to the heat exchange station to the heat users through the heat carrier. The cooled heat carrier flows back through the return pipe, forming a closed loop. The secondary pipeline network is typically short, and energy consumption is negligible, so only the primary pipeline network is modeled.

[0059] Existing thermal systems primarily utilize quality regulation, quantity regulation, or a combination of both. Quality regulation maintains constant water flow rate and pressure, with temperature being the only controllable quantity. Quantity regulation, on the other hand, regulates the heat supply to meet user heating needs by adjusting the mass flow rate and pressure of the circulating water in the heating network. This primarily applies to the user side of secondary heating networks. This invention primarily addresses primary heating networks and therefore utilizes quality regulation.

[0060] Because the dynamic process of heat energy transfer has a large time scale, the amount of hot water entering and leaving the pipeline during the same period may not be equal. The heat network can act as a buffer and delay response, thus externally displaying virtual energy storage charging and discharging characteristics similar to those of an energy storage system. The temperature of circulating water in the primary heat network increases after passing through the first heat exchange station, and the increase is related to the heat output of the heat source. The temperature of circulating water decreases after passing through the heat exchange station, and the decrease is related to the size of the user's heat load. During a scheduling period, if the heat output of the heat source is greater than (less than) the user's heat demand, the heat network virtual energy storage system plays an energy storage (or energy release) role, which is reflected by the return water temperature increasing (or decreasing) compared to the temperature of the previous period.

[0061] For the thermal inertia of the heating network, the temperature of the heating network pipeline is used as a constraint, and the storage and release of heat in the heating network can be optimized before the period when the power grid needs to increase electricity consumption.

[0062] There are certain similarities in the structures of thermal systems and power systems. Therefore, each heat source, heat exchange station and pipeline connection point can be regarded as a node, each pipeline as a branch, and the flow direction of water in the pipeline is defined as the direction of the branch. The thermal system can be modeled by referring to the power system method.

[0063] It takes a certain amount of time to transmit from the inlet to the outlet, so the temperature of the heat exchange station will have a certain delay relative to the heat source. This parameter is calculated as follows:

[0064]

[0065]

[0066] Among them, τ t,s and τ t,r Represents the transmission delay time of the water supply pipeline and the return pipeline respectively, N s and N r Represents the collection of water supply pipes and return pipes, l i represents the length of the i-th pipeline, and Respectively represent the water flow velocity of the i-th water supply / return pipe at time t, and They represent the flow rate of the i-th water supply / return pipe at time t, ρ is the water density, d i is the diameter of the i-th pipe.

[0067] The key idea of the nodal method is to track a section of water flowing from the inlet of a pipe until it reaches the outlet at a specific time. Based on the historical temperature series of other nodes in the pipe, the inlet temperature of each section can be calculated. By then factoring in pipe temperature conduction losses, the outlet temperature of that section can be calculated. In other words, time delay is considered first, and losses are then factored in.

[0068] The thermal delay time defined above can also be called the thermal inertia time constant. For the i-th pipeline, it can be expressed as:

[0069]

[0070] There will be heat loss from the pipe inlet to the outlet, and the temperature will drop. The calculation of the temperature difference in this section is based on the Sukhov temperature drop formula, which is:

[0071]

[0072]

[0073] Where ΔT t is the temperature loss at time t, is the pipe inlet temperature at time t, Te is the external temperature of the pipeline. To simplify the model, this temperature is generally considered to be room temperature. loss is the temperature loss coefficient, and λ is the heat transfer efficiency per unit length of the pipeline.

[0074] So the pipe outlet temperature at time t can be obtained as:

[0075]

[0076] That is

[0077]

[0078] 2. Thermal inertia of buildings.

[0079] During off-peak hours for renewable resources like wind power, the thermal and electrical output of CHP units can be appropriately increased compared to the traditional "heat-to-power" operating mode. Excess heat above the heat load can be stored in the building envelope and the heat transfer medium in the heating network, thereby increasing the building's indoor temperature. During peak hours for renewable resources like wind power, the thermal and electrical output of CHP units can be further reduced compared to the traditional "heat-to-power" operating mode. The shortfall in heat supply can be partially compensated by the release of heat energy stored in the building and the heating network, resulting in a decrease in the building's indoor temperature. Due to the thermal inertia of buildings and the delay characteristics of the heating network, the indoor temperature of buildings changes relatively slowly. When the heat supply of CHP units varies within a certain time period and range, if properly controlled, the indoor temperature of the building can be kept within an acceptable range without affecting the quality of heating for users. Therefore, utilizing heat storage in buildings and the heating network can improve the peak-shaving capacity of CHP units without requiring additional investment or compromising heating quality.

[0080] The dynamic thermal process of heated buildings is a complex process based on the dynamic heat transfer of the building envelope and influenced by various indoor and outdoor disturbances (such as solar radiation intensity, outdoor air temperature, atmospheric and ground thermal radiation). Because accurate dynamic building thermal models involve numerous randomly fluctuating or difficult-to-determine parameters, this paper establishes a dynamic building thermal model based on the lumped heat capacity method and simplifies the building's heat transfer process. The lumped parameter method assumes that the indoor temperature of each building is an average temperature, which then exchanges heat with the radiator and the outside.

[0081] The equation describing the thermal dynamic process of the heating system can be expressed as:

[0082]

[0083] Where C is the specific heat capacity, Q s Q is the heat supply of the heating network to the building, that is, the heat supply of the radiator,loss is the indoor heat loss. Equivalently equate the heat supply to the total heat load and adjust the radiator to change the indoor temperature.

[0084] Known

[0085] Q s =cm(T s -T r )

[0086] Q loss =sγ(T n -T e )

[0087] Where c is the specific heat capacity of water, m is the water flow rate, T s is the water supply temperature, T r is the return water temperature, T n is the indoor temperature, s is the heating area, and γ is the indoor heat loss coefficient.

[0088] Based on the above analysis, the heating area can be considered a first-order inertia link. This thermal inertia can be exploited to increase heating supply before wind curtailment and appropriately reduce it during curtailment. Due to the thermal inertia of buildings, the temperature does not drop instantly due to the sudden reduction in heating supply, so there is generally a certain temperature delay. Therefore, while ensuring the required indoor temperature in the heating area, wind power consumption can be appropriately increased.

[0089] Heating areas receive energy from the heating network to meet indoor temperature requirements. The previous analysis shows that the relationship between radiator heat dissipation in the heating area and the temperature of the heated area can be considered a large inertial link. By adding radiators as controlled objects in combined heat and power scheduling, by adjusting their heat dissipation, the indoor temperature of the heating area can be maintained within a certain range. This also optimizes the heat load, reduces the heat load intensity during peak wind power periods, and promotes wind power consumption.

[0090] In order to apply the thermal inertia of buildings to the optimal operation of the power system with the minimum dispatch cycle, the continuous function controlling the thermal inertia of buildings needs to be discretized at the beginning of each dispatch cycle to obtain a differential model of the thermal inertia of buildings.

[0091]

[0092] After finishing, we can get:

[0093]

[0094] Δt is the scheduling time interval, and k1, k2, and k3 are the corresponding coefficients.

[0095] The thermal inertia time of a building can be defined as the time required for the initial indoor temperature to rise to the maximum room temperature allowed by the user, which can be expressed as:

[0096]

[0097] τ building is the thermal inertia time constant of the building, T n,s is the initial indoor temperature of the building, T n,max The maximum room temperature allowed for the household.

[0098] Introducing new heat sources can improve the scheduling flexibility of cogeneration systems and help increase the absorption rate of intermittent renewable energy. However, a large number of flexible heat sources means increased investment. In reality, user heating comfort is fuzzy, so their heat load demand is not a fixed curve, but a range. Heating system temperature parameters, such as the supply and return water temperatures of the heating network, are also coupled quantities over multiple time periods. Leveraging the heat storage characteristics of the heating network pipelines can effectively remove the instantaneous balance constraints between heating output and heat load demand, thereby improving the flexibility of the cogeneration system. Based on this fuzzy thermal perception and the thermal inertia of heated buildings, the heating heat load can be regarded as a flexible "power source" for the power system. By leveraging the elasticity of the heat load and sacrificing some of the user's acceptable thermal comfort, more flexible space can be provided for wind power to be connected to the grid, enhancing the system's absorption capacity during periods of high wind power generation and improving the overall system's operational economics.

[0099] Users' perception of thermal comfort is somewhat ambiguous. Lowering or raising the temperature within a certain range is not easily perceived by users, but this can be exploited to increase load flexibility. A study (NOUREDINE H. Active Smart Distribution Network [M]. Translated by Tao Shun, Xiao Xiangning, and Peng Cheng. Beijing: China Electric Power Press, 2012) points out that temperature loads are a highly flexible "power source" for power systems. Approximately 30% of residential buildings use controllable electric heaters. After deactivating a residential heater for 30 minutes, the theoretical temperature drop in a poorly insulated building is 0.95°C, in a moderately insulated building 0.89°C, and in a well-insulated building 0.83°C. Therefore, the acceptable power outage duration for the average user is approximately one hour, and the temperature change at this time still meets user comfort requirements. User requirements for thermal environment quality are generally characterized by thermal comfort. Thermal comfort is the subjective evaluation and perception of the indoor thermal environment. Temperature, relative humidity, air velocity, mean radiant temperature, metabolic rate, and clothing thermal resistance all contribute to thermal comfort. There are many evaluation indicators for thermal comfort, among which the PMV index is the most commonly used one.

[0100] like Figure 2As shown in Figure 1, the PMV index represents the average value of the hot and cold sensations of most people in the same environment. A 7-level scale corresponds to the seven human sensations. A PMV of 0 corresponds to the optimal thermal comfort state in the indoor thermal environment; PMVs of +1, +2, and +3 represent slightly warm, warm, and hot, respectively; and PMVs of -1, -2, and -3 represent slightly cool, cool, and cold, respectively.[17-18] ISO 7730 recommends a PMV value between ±0.5. China's current "Design Code for Heating, Ventilation, and Air Conditioning" stipulates that the PMV should be between ±1.

[0101] Since this indicator is affected by many factors and the calculation is very complicated, the effects of air velocity and air humidity are often ignored in engineering, and the following is simplified:

[0102]

[0103] M is the human metabolic rate, which can generally be taken as 80W / m2 in residential areas.

[0104] I d Thermal resistance of clothing, which can be taken as 0.11 (m2℃) / W in winter

[0105] t s The average temperature of human skin when it is comfortable can be approximately taken as 33.5℃, t a is the temperature of the air surrounding the human body.

[0106] Since users are more active during the day, their thermal perception is more sensitive than at night, and their comfort requirements are relatively high. However, the comfort requirements at night can be appropriately relaxed. Therefore, in order to be more accurate, the PMV value can be limited by time. When the outdoor temperature T in the next 24 periods is known, e Later, based on the incorporation of other influencing factors, the heat load Q for the next 24 periods can be predicted. s , based on the thermal inertia of the building, the relationship between indoor temperature, outdoor temperature and heat load can be obtained, such as:

[0107]

[0108] That is to say, the indoor temperature T of 24 periods can be obtained n .

[0109] After defining the range of PMV, we can get the upper and lower limits of indoor temperature, t amin and t amax It is the upper and lower limits of the average human skin temperature calculated by the PMV index.

[0110] At this time, the deviation rate is defined as:

[0111]

[0112] Where T represents the air temperature of the standard heating environment, that is, the exact temperature without considering the elasticity of the heat load, which is the indoor temperature T of the 24 periods I obtained earlier. n .

[0113] The heat load interval derived from the determined heat load can then be obtained.

[0114]

[0115] 3. Combining the above considerations of multiple thermal inertias, the day-ahead scheduling objective function and related constraints of the electric-thermal integrated system are obtained.

[0116] 1. Objective function.

[0117] 1.1. Economic scheduling objectives.

[0118] For combined power and heat systems, the economic dispatch objective is mainly to minimize the fuel cost of system operation, namely:

[0119] minF1=F CHP +F W

[0120]

[0121]

[0122] Among them, α i , β i , γ i ,θ i , δ i ,ζ i is the operating cost coefficient of thermal power unit i, is the power output of cogeneration unit i at time t, MW; is the heat output of cogeneration unit i at time t, MW. The above formula is the same for extraction condensing units and pure condensing units. W,i , is the unit power operation and maintenance cost of new energy power generation.

[0123] 1.2. Wind power consumption target.

[0124] The wind power abandonment penalty coefficient is usually combined to convert the wind abandonment minimization objective into the wind abandonment penalty minimization objective, that is:

[0125]

[0126] in, is the penalty factor for the i-th wind turbine generator set to abandon wind power at time t, and They represent the predicted power output of the i-th wind turbine at time t and the actual power output of the grid-connected wind turbine.

[0127] 1.3. Comprehensive objectives.

[0128] minF=ω1F1+ω2F2

[0129] Among them, ω1 and ω2 represent the weight coefficients of the objective function respectively.

[0130] 2. Constraints.

[0131] 2.1. Energy supply network security constraints.

[0132] T r,min ≤T r,t ≤T r,max

[0133] T s,min ≤T s,t ≤T s,max

[0134] T out,min ≤T out,t ≤T out,max

[0135] V i,min ≤V i ≤V i,max

[0136] V i,min ≤V i ≤V i,max ;T r,min Indicates the minimum return water temperature, T r,t Indicates the return water temperature at time t, T r,max Indicates the maximum value of the return water temperature, T s,min Indicates the minimum value of the water supply temperature, T s,t Indicates the water supply temperature at time t, T s,max Indicates the maximum value of the water supply temperature, T out,min Indicates the minimum value of the outlet temperature of the node to which it belongs, T out,t The outlet temperature at time t, T out,max Indicates the maximum value of the outlet temperature of the node; V i,min Indicates the minimum value of the voltage amplitude of the i-th node, V i Represents the voltage amplitude of the i-th node, V i,max Indicates the maximum voltage amplitude of the i-th node.

[0137] 2.2. Power balance constraints.

[0138]

[0139]

[0140]

[0141] and They represent the thermal load power, active load and reactive load of node i, in MW; P i t and denote the thermal power, active power and reactive power injected into node i respectively; represents the grid-connected power output of the i-th wind turbine at time t, and They represent the thermal power, active power and reactive power output by the cogeneration unit injected into node i respectively.

[0142] 2.3. Constraints on New Energy Output

[0143]

[0144]

[0145] Among them, cosθ is the power factor of the new energy; δ represents the power factor angle, P represents the reactive power output of the i-th wind turbine group at time t. W,max Indicates the maximum value of the dispatched grid-connected power output of the wind turbine.

[0146] 2.4. Cogeneration unit constraints.

[0147] P CHPmin ≤P CHP ≤P CHPmax

[0148] H CHPmin ≤H CHP ≤H CHPmax

[0149] P CHPmin Indicates the minimum power output of the CHP unit, P CHPmax Indicates the maximum power output of the CHP unit, H CHPmin Indicates the minimum thermal output of the CHP unit, H CHPmax Indicates the maximum thermal output of the CHP unit.

[0150] 2.5. Unit climbing constraints.

[0151]

[0152] Among them, r d is the downward climbing rate, ru is the upward climbing rate, ΔT is the unit scheduling time; represents the power output of the i-th CHP unit at time t, Represents the power output of the i-th CHP unit at time t-1.

[0153] 2.6. Grid constraints.

[0154]

[0155] P i t 、 are the active power and reactive power injected by node i in period t respectively;

[0156] G ij 、B ij are the real and imaginary parts of the node admittance matrix respectively;

[0157] V i t 、 are the node voltage amplitude and voltage phase difference respectively; represents the voltage at node j at time t.

[0158] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A day-ahead scheduling method for an electric and thermal integrated system considering multiple thermal inertias, characterized in that: Collect system data, including heating network pipeline data information, node temperature, pipeline water mass flow rate, indoor temperature, outdoor temperature, heat load, electrical load, and wind power generation data; The collected system data is input into the electric and thermal integrated scheduling objective function considering multiple thermal inertias, and combined with the constraints, the optimal scheduling parameters are output, including the electric power and thermal power output of the CHP unit; The objective function is a comprehensive objective function that takes into account economic dispatch and minimization of wind power consumption objectives, and the constraints include energy supply network security, power balance, renewable energy output, cogeneration units, unit ramping, and grid constraints; The economic dispatch goal is to minimize the fuel cost of system operation, that is: Among them, α i , β i , γ i ,θ i , δ i ,ζ i is the operating cost coefficient of thermal power unit i, is the electrical output of cogeneration unit i at time t, in MW; is the heat output of cogeneration unit i at time t, in MW; the above formula is the same for extraction condensing units and pure condensing units; C W,i , is the unit power operation and maintenance cost of renewable energy power generation; represents the cost of the i-th cogeneration unit at time t, represents the power generation cost of the i-th wind turbine at time t; The wind power consumption target is to convert the wind power abandonment minimization target into the wind power abandonment penalty minimization target by combining the wind power abandonment penalty coefficient, that is: in, is the penalty factor for the i-th wind turbine generator set to abandon wind power at time t, and They represent the predicted power output of the i-th wind turbine at time t and the actual power output of the grid-connected wind turbine.

2. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1, characterized in that: The objective function is a comprehensive objective minF=ω1F1+ω2F2 Among them, ω1 and ω2 represent the weight coefficients of the objective function respectively.

3. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1, characterized in that: The energy supply network security constraint is based on the following formula T r,min ≤T r,t ≤T r,max T s,min ≤T s,t ≤T s,max T out,min ≤T out,t ≤T out,max V i,min ≤V i ≤V i,max ;T r,min Indicates the minimum return water temperature, T r,t Indicates the return water temperature at time t, T r,max Indicates the maximum value of the return water temperature, T s,min Indicates the minimum value of the water supply temperature, T s,t Indicates the water supply temperature at time t, T s,max Indicates the maximum value of the water supply temperature, T out,min Indicates the minimum value of the outlet temperature of the node to which it belongs, T out,t The outlet temperature at time t, T out,max Indicates the maximum value of the outlet temperature of the node; V i,min Indicates the minimum value of the voltage amplitude of the i-th node, V i Represents the voltage amplitude of the i-th node, V i,max Indicates the maximum voltage amplitude of the i-th node.

4. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1, characterized in that: The power balance constraint is based on the following formula and They represent the thermal load power, active load and reactive load of node i, in MW; and denote the thermal power, active power and reactive power injected into node i respectively; represents the grid-connected power output of the i-th wind turbine at time t, and They represent the thermal power, active power and reactive power output by the cogeneration unit injected into node i respectively.

5. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1 is characterized in that: The new energy output constraint is based on the following formula Among them, cosθ is the power factor of the new energy; δ represents the power factor angle, P represents the reactive power output of the i-th wind turbine group at time t. W,max Indicates the maximum value of the dispatched grid-connected power output of the wind turbine.

6. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1, characterized in that: The CHP unit constraint is based on the following formula P CHPmin ≤P CHP ≤P CHPmax H CHPmin ≤H CHP ≤H CHPmax ; P CHPmin Indicates the minimum power output of the CHP unit, P CHPmax Indicates the maximum power output of the CHP unit, H CHPmin Indicates the minimum thermal output of the CHP unit, H CHPmax Indicates the maximum thermal output of the CHP unit.

7. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1, characterized in that: The unit ramp constraint is based on the following formula Among them, r d is the downward climbing rate, r u is the upward climbing rate, ΔT is the unit scheduling time; represents the power output of the i-th CHP unit at time t, Represents the power output of the i-th CHP unit at time t-1.

8. The method for day-ahead scheduling of an electric and thermal integrated system considering multiple thermal inertias according to claim 1 is characterized in that: The grid constraints are based on the following formula are the active power and reactive power injected by node i in period t respectively; G ij 、B ij are the real and imaginary parts of the node admittance matrix respectively; are the node voltage amplitude and voltage phase difference respectively; represents the voltage at node j at time t.

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