CN113392535B - Heat accumulating type electric heating double-layer optimal configuration method - Google Patents

Heat accumulating type electric heating double-layer optimal configuration method Download PDF

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CN113392535B
CN113392535B CN202110722231.3A CN202110722231A CN113392535B CN 113392535 B CN113392535 B CN 113392535B CN 202110722231 A CN202110722231 A CN 202110722231A CN 113392535 B CN113392535 B CN 113392535B
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electric heating
capacity
heat
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CN113392535A (en
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程濛
邢秦浩
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Abb Power Grid Investment China Co ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention provides a heat accumulating type electric heating double-layer optimization configuration method, which relates to the technical field of intelligent power grids, and the method uses the bearing capacity of a power distribution network and the continuous heating load of power failure as constraints to build a double-layer optimization model framework, and comprises an upper-layer planning model and a lower-layer scheduling model; the upper planning model is constructed by taking the minimum annual total cost of a regenerative electric heating system as an optimization target and taking equipment capacity constraint and power failure non-stop heating constraint as constraint conditions; the lower layer scheduling model is constructed by taking the annual operation and maintenance cost of the regenerative electric heating system as a minimum target; the method fully utilizes the heat storage equipment to realize 'power failure without stopping heating' and reduce energy consumption cost on the premise of meeting the safety constraint of a power distribution network.

Description

Heat accumulating type electric heating double-layer optimal configuration method
Technical Field
The invention relates to the technical field of smart power grids, in particular to a heat accumulating type electric heating double-layer optimal configuration method.
Background
Because of the special heat storage capacity, the peak-valley electricity price policy widely proposed by various levels of governments can be utilized to further reduce the heating cost of users, effectively relieve the contradiction between the heating demands of users and the high heating cost, realize peak clipping and valley filling to a certain extent, reduce the running pressure of a power grid, reduce the transformation cost of the power grid and improve the heating reliability. However, the existing heat accumulating type electric heating system has the problems that the investment cost is too high in planning construction, the heating cost of a user is high in operation, energy waste is caused by excessive heating, the comfort level is affected due to unreasonable heating mode, and the like.
Disclosure of Invention
The invention aims to provide a heat accumulating type electric heating double-layer optimal configuration method, which fully utilizes heat accumulating equipment on the premise of meeting the safety constraint of a power distribution network, realizes 'power failure without stopping heating' and reduces energy consumption cost.
The embodiment of the invention provides a heat accumulating type electric heating double-layer optimal configuration method, which comprises the following steps:
constructing a double-layer optimization model framework by taking the bearing capacity of the power distribution network and the uninterrupted power-failure heating load as constraints, wherein the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model;
the upper planning model is constructed by taking the minimum annual total cost of a regenerative electric heating system as an optimization target and taking equipment capacity constraint and power failure non-stop heating constraint as constraint conditions;
the lower layer scheduling model is constructed by taking the annual operation and maintenance cost of the regenerative electric heating system as a minimum target;
solving the capacities of the heat accumulating type electric heating heat pump and the heat accumulating water tank based on the upper planning model and combining the lower scheduling model, and outputting the capacities of all the devices;
and solving the lower scheduling model according to the equipment capacity obtained by the upper planning model to obtain an optimal scheduling scheme.
Optionally, the regenerative electric heating system adopts a unified bus structure to model the regenerative electric heating system, comprises an electric bus and a thermal bus,
electric bus power balancing constraints:
P grid (t)＝P HP (t); wherein: p (P) grid (t) represents the power purchased from the power grid at time t, kW;
thermal bus power balancing constraints:
wherein: h load And (t) represents the thermal load at time t, kW.
Optionally, the load-bearing capacity of the distribution network at each moment is considered according to 60-80% of the upper limit of the active transmission capacity of the distribution line,
P mar (t)＝0.7P N -1.05P bas (t)
wherein: p (P) mar (t) is the carrying capacity of the power distribution network at the moment t, and kW; p (P) N For distribution linesRated transmission power of the road, kW; p (P) bas (t) is a base load value of the power distribution network at the moment t, and kW;
P grid,max ＝minP mar (t) wherein: p (P) grid,max The electric heating system can purchase electric quantity, kW, under the condition of considering the carrying capacity of the power distribution network.
Alternatively, the blackout period maintenance heating load is calculated as follows:
H outage ＝5H load,max
wherein: h outage Maintaining a heating load for a blackout period, kWh; h load,max kWh, which is the maximum annual heat load.
Optionally, the objective function of the upper planning model is to minimize the annual total cost of the regenerative electric heating system, including annual values such as initial investment cost of regenerative type, operation cost and maintenance cost, as shown in the following formula:
wherein:the annual values such as initial investment cost and the annual running cost of equipment and annual maintenance cost of equipment of the regenerative electric heating are respectively represented.
Optionally, the device capacity constraint is:
Q HP,min ≤Q HP ≤Q HP,max
Q HWT,min ≤Q HWT ≤Q HWT,max
wherein: q (Q) HP,max ，Q HP,min The upper limit and the lower limit of the capacity can be set for the heat pump, and kW; q (Q) HWT,max ，Q HWT,min The heat storage water tank can be provided with the upper and lower limits of capacity, and kWh.
Optionally, the power failure is not stopped, the capacity of the heat storage water tank is larger than the heating load maintained in the power failure period, and the heating constraint is maintained in the power failure period as follows:
H outage ≤Q HWT,min
in which Q HWT,min The capacity of the heat storage water tank is larger than H outage The heating load is maintained for the blackout period.
Optionally, the objective function of the lower layer scheduling model is:
in the middle ofThe annual values of initial investment cost and the like of the regenerative electric heating and the annual maintenance cost of equipment are respectively represented.
Optionally, the constraints of the underlying scheduling model include: power balance constraints, equipment operation constraints, and upper limit constraints on the amount of electricity purchased taking into account the load carrying capacity of the distribution network.
Optionally, the dual-layer planning model is solved by using AIMMS software.
Compared with the prior art, the invention has the following advantages:
the invention provides a heat accumulating type electric heating double-layer optimization configuration method, which uses the bearing capacity of a power distribution network and the power failure continuous heating load as constraints to build a double-layer optimization model frame, and comprises an upper-layer planning model and a lower-layer scheduling model; the upper planning model is constructed by taking the minimum annual total cost of a regenerative electric heating system as an optimization target and taking equipment capacity constraint and power failure non-stop heating constraint as constraint conditions; the lower layer scheduling model is constructed by taking the annual operation and maintenance cost of the regenerative electric heating system as a minimum target; solving the capacities of the heat accumulating type electric heating heat pump and the heat accumulating water tank based on the upper planning model and combining the lower scheduling model, and outputting the capacities of all the devices; according to the equipment capacity obtained by the upper layer planning model, the lower layer scheduling model is solved to obtain an optimal scheduling scheme, the upper layer is established by taking the limit of transmission power of a power distribution network, the power failure without stopping heating and the like under the fault state as constraints, the annual cost of a heat accumulating type electric heating system and the like is minimum, and the lower layer is established by taking a double-layer optimal configuration model of the heat accumulating type electric heating system with the minimum user operation cost.
Drawings
FIG. 1 is a schematic diagram of a regenerative electric heating system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a regenerative electric heating system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a dual-layer optimization configuration model according to an embodiment of the present invention;
fig. 4 is a graph of a heating Ji Dianxing daily heat load level in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
The embodiment of the invention provides a heat accumulating type electric heating double-layer optimal configuration method, which comprises the following steps:
constructing a double-layer optimization model framework by taking the bearing capacity of the power distribution network and the uninterrupted power-failure heating load as constraints, wherein the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model;
the upper planning model is constructed by taking the minimum annual total cost of a regenerative electric heating system as an optimization target and taking equipment capacity constraint and power failure non-stop heating constraint as constraint conditions;
the lower layer scheduling model is constructed by taking the annual operation and maintenance cost of the regenerative electric heating system as a minimum target;
solving the capacities of the heat accumulating type electric heating heat pump and the heat accumulating water tank based on the upper planning model and combining the lower scheduling model, and outputting the capacities of all the devices;
and solving the lower scheduling model according to the equipment capacity obtained by the upper planning model to obtain an optimal scheduling scheme.
The embodiment provides a heat accumulating type electric heating double-layer optimization configuration method, which uses the bearing capacity of a power distribution network and the power failure continuous heating load as constraints to build a double-layer optimization model frame, wherein the double-layer optimization model frame comprises an upper-layer planning model and a lower-layer scheduling model; the upper planning model is constructed by taking the minimum annual total cost of a regenerative electric heating system as an optimization target and taking equipment capacity constraint and power failure non-stop heating constraint as constraint conditions; the lower layer scheduling model is constructed by taking the annual operation and maintenance cost of the regenerative electric heating system as a minimum target; solving the capacities of the heat accumulating type electric heating heat pump and the heat accumulating water tank based on the upper planning model and combining the lower scheduling model, and outputting the capacities of all the devices; according to the equipment capacity obtained by the upper layer planning model, the lower layer scheduling model is solved to obtain an optimal scheduling scheme, the upper layer is established by taking the limit of transmission power of a power distribution network, the power failure without stopping heating and the like under the fault state as constraints, the annual cost of a heat accumulating type electric heating system and the like is minimum, and the lower layer is established by taking a double-layer optimal configuration model of the heat accumulating type electric heating system with the minimum user operation cost.
Specifically, in this embodiment, fig. 1 shows a schematic structural diagram of a typical electric heating system with heat storage, where the system is composed of a heat pump, a heat storage tank, a radiator, a heat pump circulating water pump, a heat supply network circulating water pump, a power storage battery/diesel generator and a heating pipeline, where the heat pump is used as a heating device, and the heat storage tank is a heat storage device. The system has the following operation modes under normal power supply and fault power failure:
normal power supply: the heat pump in the valley electricity price period can store heat for the heat storage water tank while meeting the heat load requirement of a user. The heat storage water tank releases heat in a high electricity price period, the basic heat requirement is met under the drive of the heat supply network circulating water pump, and the deficiency is complemented by the heat pump;
fault power failure: when the power grid fails to cause interruption of power supply of the power distribution network, the storage battery/diesel generator drives the heat supply network circulating water pump, and the heat storage quantity in the heat storage water tank is utilized to maintain heating requirements of the power interruption period.
Heat accumulating electric heating equipment model:
in this embodiment, a Heat Pump (HP) is selected as a heating device, the heat pump is distinguished by heat source types, including water (ground) source heat pump, air source heat pump, and the like, the heat pump uses air/water/soil as a heat source, and low-grade heat energy in the air/water/soil can be converted into high-grade heat energy under the driving of electric energy, and the heating power is shown as the following formula:
H HP (t)＝P HP (t)/ACOP (1)
in the formula H HP (t) is the heat pump heating power at the moment t, kW; ACOP is the annual overall efficiency ratio of the heat pump; p (P) HP And (t) the heat pump consumes electric power, kW, at the moment t.
The heat pump output power should be less than capacity:
0≤H HP (t)≤Q HP (2)
wherein: q (Q) HP Is the heat pump capacity, kW.
Thermal storage water tank:
the heat storage water tank (HWT) is used for storing heat in a period with lower electricity price, releasing heat in a period with higher electricity price and when power supply is interrupted, and retaining the original buffering effect. In order to embody the applicability of the method, the water temperature change of the heat storage water tank is converted into heat change, and the quality adjustment is carried out corresponding to a specific indoor temperature adjustment mode. The characteristics of the thermal storage tank can be expressed as the relationship between the heat storage capacity, the heat storage/release power and the heat loss:
wherein: w (W) HWT (t) is the heat accumulation amount of the heat accumulation water tank at the moment t, and kWh;the heat storage power of the heat storage water tank at the moment t is kW; />The heat-release power of the heat-storage water tank at the moment t is kW; /> Respectively charging and discharging efficiency of the heat storage water tank;is the self-exothermic loss rate; Δt is the simulation time step, taken 1 hour in this example.
The energy charging and discharging and energy storing constraints of the heat storage water tank are as follows:
0≤W HWT (t)≤Q HWT (6)
wherein:the maximum charge and discharge rate of the heat storage water tank is set; q (Q) HWT And the capacity of the heat storage water tank is kWh.
The heat accumulating type electric heating system purchases electricity from a power grid, converts the electricity into heat energy through a heat pump, and reasonably dispatches the heat energy through a heat accumulating water tank to meet heat load together. In order to flexibly describe the operation mode of the heat accumulating type electric heating and the coupling relation of each link, a unified bus structure is adopted to model the heat accumulating type electric heating. As shown in fig. 2, the regenerative electric heating system includes an electric bus and a thermal bus. The power balance constraints to be satisfied are shown in equations (7) - (8).
Electric bus power balance constraint
P grid (t)＝P HP (t) (7)
Wherein: p (P) grid (t) represents the power purchased from the power grid at time t, kW;
thermal bus power balance constraint
Wherein: h load And (t) represents the thermal load at time t, kWh.
Particularly, the large-scale popularization and application of electric heating can greatly increase the electric load of the power distribution network, the condition of peak-to-peak load of the power distribution network is easy to occur, and the pressure of the power distribution network is greatly increased. In order to ensure the operation safety of the power grid, the carrying capacity of the power distribution network needs to be researched by combining the space-time distribution characteristics of the electric load, and for an electric heating load access area, the space-time distribution characteristics of the power distribution network can be described by a typical daily load curve; in order to ensure the power supply capacity and the flexibility of the power distribution network, the bearing capacity of the power distribution network considers the bearing capacity of the power distribution network at each moment according to 60-80% of the upper limit of the active transmission capacity of the power distribution line, and the bearing capacity of the power distribution network at each moment is considered according to 70% of the upper limit of the active transmission capacity of the power distribution line;
P mar (t)＝0.7P N -1.05P bas (t) (9)
wherein: p (P) mar (t) is the carrying capacity of the power distribution network at the moment t, and kW; p (P) N Rated transmission power for the distribution line, kW; p (P) bas (t) is a base load value of the power distribution network at the moment t, and kW;
P grid,max ＝min P mar (t) (10)；
wherein: p (P) grid,max The electric heating system can purchase electric quantity, kW, under the condition of considering the carrying capacity of the power distribution network.
Specifically, the power-off duration of the user in the notification of continuous heating guarantee of technical measures about realizing power-off and continuous heating is not longer than 5 hours. Therefore, in 5 hours, the power supply can be restored through the rush repair of the power enterprise, and the power failure period is set to maintain the heating duration to be 5 hours, and the power failure period maintains the heating load to be calculated as follows:
H outage ＝5H load,max (11)
wherein: h outage Maintaining a heating load for a blackout period, kWh; h load,max kWh, which is the maximum annual heat load.
According to the heat accumulating type electric heating double-layer optimal configuration method which is constructed by the embodiment and takes the space-time characteristics and the bearing capacity of the power distribution network into consideration, an upper model solves the heat accumulating type electric heating capacity by taking the total annual total cost of the system as an optimal target, and a planning scheme, namely the capacity of each device, is output; the lower model aims at the minimum annual operation and maintenance cost, and the optimal dispatching scheme for solving the park comprises output and electricity purchasing quantity of each device. The overall framework of the two-layer model is shown in fig. 3.
Specifically, the upper layer planning model:
objective function:
the objective function of the upper planning model is to minimize the annual total cost of the regenerative electric heating system, including annual values such as initial investment cost of regenerative type, operation cost and maintenance cost, as shown in the formula.
Wherein:the annual values such as initial investment cost and the annual running cost of equipment and annual maintenance cost of equipment of the regenerative electric heating are respectively represented.
The investment cost and other annual values are the sum of the investment cost and other annual values of the heat pump and the heat storage water tank, and the expression is as follows:
wherein:the investment cost of the heat pump and the heat storage water tank are respectively represented by the annual value of the investment cost of the heat pump and the heat storage water tank. The annual value calculation of the investment cost of each device and the like can adopt the following formula:
in which c I,HP ，c I,HWT The unit investment cost of the heat pump and the heat storage water tank is respectively calculated;Q HP the capacity of the heat pump is kW; q (Q) HWT kWh is the capacity of the heat storage water tank; r is the discount rate, 8% of which is taken out; l (L) HP ，l HWT The expected service life years of the heat pump and the heat storage water tank are respectively.
The annual operation cost of the regenerative electric heating system is mainly annual electricity purchasing cost, and is related to the consumed electric power of the heat pump, and the expression is as follows:
wherein: c elec And (t) is the electricity price of the period t, yuan/kWh.
The annual maintenance costs of the plant are related to the type and operation of the individual plant. The maintenance cost of the heat pump is expressed as the product of the maintenance cost of unit power and the output power, and the maintenance cost of the heat storage water tank is related to the capacity and can be expressed by the following formula:
wherein: c M,HP Maintenance cost per unit power of the heat pump, yuan/kW; c M,HP Maintenance costs per unit capacity for heat pump, meta/kWh.
Constraint conditions:
device capacity constraints:
because of investment, space and other limitations, there are upper and lower limits on the installable capacity of regenerative electric heating system equipment.
Q HP,min ≤Q HP ≤Q HP,max (18)
Q HWT,min ≤Q HWT ≤Q HWT,max (19)
Wherein: q (Q) HP,max ，Q HP,min The upper limit and the lower limit of the capacity can be set for the heat pump, and kW; q (Q) HWT,max ，Q HWT,min The heat storage water tank can be provided with the upper and lower limits of capacity, and kWh.
"power failure no stop warm" constraint:
in order to provide guarantee for the reliability of user heating in a large heat accumulating type electric heating application environment, the embodiment considers the user heat requirement in the power failure period, and takes the user heat requirement meeting the power failure period as one of constraint conditions of a heat accumulating type electric heating planning model. In order to realize 'power failure without stopping heating', the capacity of the heat storage water tank is larger than the heating load maintained in the power failure period, and the heating constraint maintained in the power failure period is as follows:
H outage ≤Q HWT,min (20)
lower layer scheduling model
Objective function
The objective function of the lower scheduling model is to minimize the annual operation maintenance cost of the regenerative electric heating system, and the operation maintenance cost can be obtained by the formulas (16) - (17).
Constraint
1) Power balance constraint
The regenerative electric heating system needs to meet the power balance constraint of the electric bus and the thermal bus at all times, as shown in formulas (7) - (8).
2) Plant operation constraints
The operation constraint of the heat pump and the heat storage water tank is shown in formulas (1) - (6).
For the heat storage water tank, the heat storage quantity needs to be kept consistent at the beginning and the end of a dispatching cycle.
W HWT (1)＝W HWT (T) (22)
Wherein: w (W) HWT (1)，W HWT (T) accumulating heat for the beginning and the end of a scheduling period respectively, and kWh.
3) Electricity purchase quantity upper limit constraint considering power distribution network bearing capacity
The electric heating load is connected to make the area electric load increase, so as to ensure the heat load demand as far as possible under the condition of avoiding the peak-to-peak load of the power distribution network. The load capacity of the distribution network is considered, the power consumption of the electric heating system at the time of peak electricity consumption is limited, the load pressure of the distribution network is relieved, and the orderly and reliable supply of power and the safe and stable operation of the power network are ensured.
The present embodiment uses AIMMS software to solve the above-described two-layer planning model.
Optionally, the constraints of the underlying scheduling model include: power balance constraints, equipment operation constraints, and upper limit constraints on the amount of electricity purchased taking into account the load carrying capacity of the distribution network.
Optionally, the dual-layer planning model is solved by using AIMMS software.
Index of the impact of the planning scheme on the grid:
1) Peak-valley difference of load
The load peak Gu Cha P reflects the impact of the electrical heating load on the load characteristics of the distribution network. The calculation method is shown in the formula (24).
ΔP＝P HP,max -P HP,min (24)
Wherein: p (P) HP,max ，P HP,min And respectively consuming the peak-valley value of the electric load for the heat pump.
2) Power consumption timing rate
To characterize the extent of impact of regenerative electric heating load access on the increase in power distribution network load, a definition of the power distribution network base load and the electric heating load power utilization time rate is given herein, thereby describing the adverse impact of electric heating load on the power distribution network base load peak value.
ξ＝P total,max /P bas,max +P grid,max (25)
Wherein: p (P) total,max The maximum value of the total power load of the power distribution network is kW; p (P) bas,max The maximum value of the basic electric load of the power distribution network is kW; p (P) grid,max Maximum electric heating load, kW.
And (3) carrying out calculation analysis:
the correctness and the effectiveness of the proposed method are verified by adopting a regenerative electric heating system with the structure shown in fig. 2.
Fig. 4 shows a heating load level of Ji Dianxing days for a certain heating area. The main parameters of HP and HWT are shown in Table 1. The time-of-use electricity prices and peak electricity purchase limits are shown in Table 2. The basic electric load and the bearing capacity of the distribution network at each moment are shown in the annex A graph A1 and the annex A2.
Tab.1 Parameters of the HP and HWT
Table 2 time-of-use price and purchase amount constraints
Tab.2 Electricity price and constraints
To more clearly demonstrate the effectiveness of the methods herein, 4 exemplary scenarios were constructed for comparison as shown in table 3:
1) And (3) analyzing an electric heating optimal configuration scheme under normal power supply:
scene 1: the user's method of our article should meet different subject requirements with only HP configuration, no HWT configuration;
scene 2: the user invests in configuration HP and HWT at the same time;
scene 3: the user invests in configuration HP and HWT at the same time, and considers the influence of power supply allowance of the electricity consumption peak;
2) Consider a "power outage no-stop" configuration scheme analysis:
scene 4: the user invests in configuration HP and HWT at the same time, and considers the period of power failure to maintain heating, so as to realize 'power failure without stopping heating';
TABLE 3 example scenarios
Tab.3 Scenarios of case study
Example results and analysis
The system configuration scheme and annual cost in each scene are shown in Table 4, and the peak-valley difference and power consumption time rate of scenes 1-3 under normal power supply are shown in Table 5.
Table 4 configuration scheme and annual cost Tab.4 System planning schemes and annual costs
TABLE 5 peak-to-valley difference and power consumption timing rate
Tab.5 Peak-valley difference and load coincidence factor
Configuration scheme economy analysis under normal power:
as can be seen from table 4, the annual cost of scenario 2 is reduced by 10.25% compared to scenario 1. Because the HWT price is lower, the heat accumulating type electric heating can effectively reduce the HP capacity and reduce the investment cost by arranging heat accumulating equipment. But will increase the system operation and maintenance costs due to heat loss and maintenance costs of the HWT.
Under scenario 1, all heat loads are met by the heat pump, and because the electric heating is connected, electricity needs to be purchased from the power grid correspondingly according to the heat load demands at each moment to meet all the increased electric loads, the electricity purchasing quantity has larger fluctuation along with the change of the heat loads. And the scene 2 is provided with the heat storage equipment, so that the electricity purchasing quantity at the peak value of electricity price can be reduced through energy storage, and the electricity purchasing plan is reasonably arranged, so that peak clipping and valley filling are realized. As can be seen from table 5, compared with the scene 1, the peak-to-valley difference of the electric heating load of the scene 2 is reduced by 95.45%, and the influence on the load characteristic of the distribution network is greatly reduced. The peak-valley difference of the distribution network is obviously reduced under the condition of considering the basic load of the distribution network.
Considering the bearing capacity of the distribution network, in the situation 1, because the heat supply quantity of the heat pump needs to be balanced with the heat load in real time, the electric heating load fluctuates along with the heat load at any time, and under the condition of considering the basic electric load of the distribution network, the electric heating load exceeds the bearing capacity of the distribution network at part of time, and the capacity of the power network needs to be expanded so as to increase the power supply quantity, so that the cost is greatly increased. The heat storage equipment in the scene 2 is added, so that the electric heating system has certain peak clipping and valley filling capacity, but still exceeds the bearing capacity of the power distribution network at partial moments, but the out-of-limit amplitude is smaller than that of the scene 1, and the safe operation of the power distribution network can be ensured only under the condition of micro capacity increment of the power grid. And the electricity consumption time rate is in higher level under two scenes, so that the addition of electric heating makes the distribution network easily appear in the condition that basic electricity consumption peak overlaps electric heating load peak, and great challenges are brought to the safe operation of the distribution network.
Therefore, in order to ensure safe operation of the power distribution network and give consideration to the demand of the user for the heat load, the scenario 3 considers limiting the heat accumulating type electric heating load at the time of electricity consumption peak so as to ensure the operation safety of the power grid, and does not consider electricity purchasing constraint at the time of electricity consumption level valley.
As can be seen from table 4, since there is a limit in the amount of electricity purchased at the peak hours of electricity consumption, the HWT is required to store energy to cope with the peak load, so that scenario 3 increases the capacity of HP and HWT devices and the investment cost increases. But therefore, the peak-valley difference and the power consumption time rate of the total load of the power distribution network are effectively reduced, the safe and reliable power supply of the power distribution network is ensured, and the electric heating load access of the power distribution network under the condition of no capacity increment can be effectively realized.
Consider the "power outage no-stop warm" configuration scheme analysis:
scenario 4 further considers the fault power failure condition, and the user is guaranteed to 'power failure without stopping heating'.
As can be seen from table 4, scenario 4 further increases the HP and HWT device capacity, with a significant increase in capital costs, as compared to scenario 3. The electric load at the time of power failure can only be ensured by the HWT, so that the capacity of the HWT needs to be greatly increased, and the capacity of the HP needs to be reasonably increased correspondingly, so that the heat load at the time of power failure can be dealt with by storing enough heat through the HWT under the condition of ensuring the heat load demand at the time of flat valley. The hp and HWT capacities are increased by 28.23% and 106.14%, respectively, and the total cost is increased by 18.18% compared to scenario 3. And the great increase of operation and maintenance cost is considered to ensure that the user has no power failure and no stop heating, so that the bearing capacity of the power distribution network is also considered, and the huge economic benefit of power distribution network faults is avoided.
Sensitivity analysis
In order to analyze the condition of the electric heating system affected by each parameter, a set of sensitivity simulation analysis is performed.
The main change parameters of the optimal configuration model of the electric heating system comprise electricity price, HWT cost, load, electricity consumption peak time electricity purchasing upper limit and power failure maintenance heating duration.
The control group of the simulation experiment is changed by taking the scene 4 as other parameters, and the capacity and annual cost of the electric heating system under the condition that the single factor is increased by 10% and reduced by 10% are recorded, wherein the control group comprises the following components: HP capacity, HWT capacity, and annual investment, operation, maintenance costs, and is compared with scenario 4.
Further, to more clearly reflect the sensitivity of the electric heating system configuration scheme to a single parameter of the model, an offset phi of the simulation result after changing the parameter is defined herein.
φ＝(φ-φ 0 )/φ 0 (25)
Wherein: phi is the annual cost of the electric heating system under the condition that the single factor is increased by 10 percent or reduced by 10 percent; phi (phi) 0 And (3) configuring a scheme and annual cost for the electric heating system under the scene 4.
From the normalized sensitivity analysis results the following conclusions were drawn:
1) The sensitivity degree of the equipment capacity of the electric heating system to the price parameter changes such as electricity price and equipment investment cost is low; the system cost changes are generally affected by corresponding price parameter changes. The electricity price is smaller than the investment cost of the unit equipment, so that the influence of the electricity price change on the capacity of the equipment is smaller; under the condition of more constraint conditions of the system, the optimization space of the system configuration scheme is smaller in order to ensure the heating reliability of the system, so that the influence of small-amplitude changes of the equipment investment cost on the system configuration scheme is smaller. The price parameter variation only has a certain influence on its corresponding cost term.
2) The capacity and the cost of the electric heating system equipment are most sensitive to load change, the power failure time is inferior, and the sensitivity to the upper limit of the electricity purchasing quantity is the lowest among the three. The load is a main influencing factor of the electric heating system configuration scheme, so that the change of the load greatly influences the applicability of the system configuration scheme, and in order to avoid the inapplicability of the configuration scheme or the frequent replacement of equipment, the load prediction accuracy is improved in the initial stage of construction, the factors such as load fluctuation, growth and the like are fully considered, and the robustness of the system configuration scheme is improved. Because the sensitivity of the system to the upper limit of the electricity purchasing quantity at the peak time is lower, a certain margin can be considered when the electricity purchasing quantity is limited at the peak time, the safety and the power supply reliability of the power system can be fully ensured, and the influence on the system cost is smaller.
3) The HP and HWT capacities are sensitive to parameter variations to different extents. In general, because the HWT is cheaper and the HWT energy storage effect has a greater impact on the system operation scheduling flexibility; the heat pump has higher price, and is used as the only heat energy source of the electric heating system, and the supply of heat energy needs to be ensured, so that the heat pump has larger influence on the heating load level. The HWT capacity will be more sensitive to parameter variations.
The invention builds an electric heating system device and a system model, considers the bearing capacity model of the space-time characteristics of a power grid and the constraint of a heating load model in a power failure period, and provides a heat accumulating type electric heating double-layer optimizing configuration method considering the space-time characteristics and the bearing capacity of a power distribution network, wherein the conclusion is as follows:
1) The access of the electric heating system can cause the load of the distribution network to be greatly increased, and the load capacity of the distribution network can be exceeded when serious, so that the operation safety of the distribution network is affected; the heat accumulating type electric heating can play a role in peak clipping and valley filling to a certain extent through the heat accumulating water tank, so that the peak-valley difference of the load of the power distribution network is reduced, and the energy consumption cost is reduced.
2) When the power grid bearing capacity is considered and the transmission power constraint of the power distribution network is taken as a limit, a large amount of electric heating loads can be prevented from being overlapped at the time of electricity consumption peak, the electricity consumption time rate is reduced, the operation pressure of the power distribution network at the time of load peak is effectively relieved, and the operation safety of the power distribution network is ensured.
3) The power failure without stopping heating in the fault state is used as constraint, and the heat storage equipment can be fully utilized, so that the power failure period is ensured to maintain heating, and the heat load requirement of a user is ensured.
4) The sensitivity analysis shows that the load is a key factor influencing the optimal configuration scheme of the electric heating system, and reasonable and accurate prediction of the load can improve the practicability of the optimal configuration scheme.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (7)

1. The heat accumulating type electric heating double-layer optimal configuration method is characterized by comprising the following steps of:
constructing a double-layer optimization model framework by taking the bearing capacity of the power distribution network and the uninterrupted power-failure heating load as constraints, wherein the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model;
the upper planning model is constructed by taking the minimum annual total cost of a regenerative electric heating system as an optimization target and taking equipment capacity constraint and power failure non-stop heating constraint as constraint conditions;
the lower layer scheduling model is constructed by taking the annual operation and maintenance cost of the regenerative electric heating system as a minimum target;
solving the capacities of the heat accumulating type electric heating heat pump and the heat accumulating water tank based on the upper planning model and combining the lower scheduling model, and outputting the capacities of all the devices;
solving a lower scheduling model according to the equipment capacity obtained by the upper planning model to obtain an optimal scheduling scheme;
the load-bearing capacity of the distribution network at each moment is considered according to 60-80% of the upper limit of the active transmission capacity of the distribution line,
P mar (t)＝0.7P N -1.05P bas (t)
wherein: p (P) mar (t) is the carrying capacity of the power distribution network at the moment t, and kW; p (P) N Rated transmission power for the distribution line, kW; p (P) bas (t) is a base load value of the power distribution network at the moment t, and kW;
P grid,max ＝min P mar (t) wherein: p (P) grid,max The method comprises the steps of (1) considering the available electric quantity kW of an electric heating system under the bearing capacity of a power distribution network;
the maintenance heating load during the outage period is calculated as follows:
H outage ＝5H load,max
wherein: h outage Maintaining a heating load for a blackout period, kWh; h load,max kWh, which is the maximum annual heat load;
the objective function of the upper planning model is to minimize the annual total cost of the regenerative electric heating system, including annual values such as regenerative initial investment cost, operation cost and maintenance cost, and the like, and the objective function is shown in the following formula:
wherein:the annual values such as initial investment cost and the annual running cost of equipment and annual maintenance cost of equipment of the regenerative electric heating are respectively represented.
2. The method of claim 1, wherein the regenerative electric heating system is modeled using a unified bus structure, comprising an electric bus and a thermal bus,
electric bus power balancing constraints:
P grid (t)＝P HP (t); wherein: p (P) grid (t) represents the power purchased from the power grid at time t, kW;
thermal bus power balancing constraints:
wherein: h load And (t) represents the thermal load at time t, kW.
3. The two-layer optimal configuration method according to claim 1, wherein the device capacity constraint is:
Q HP,min ≤Q HP ≤Q HP,max
Q HWT,min ≤Q HWT ≤Q HWT,max
wherein: q (Q) HP,max ，Q HP,min The upper limit and the lower limit of the capacity can be set for the heat pump, and kW; q (Q) HWT,max ，Q HWT,min The heat storage water tank can be provided with the upper and lower limits of capacity, and kWh.
4. The double-layer optimal configuration method according to claim 1, wherein the power failure is continuous, the capacity of the heat storage water tank is larger than the heating load maintained in the power failure period, and the heating constraint maintained in the power failure period is as follows:
H outage ≤Q HWT,min
in which Q HWT,min The capacity of the heat storage water tank is larger than H outage The heating load is maintained for the blackout period.
5. The dual-layer optimal configuration method according to claim 1, wherein the objective function of the lower layer scheduling model is:
in the middle ofThe annual values of initial investment cost and the like of the regenerative electric heating and the annual maintenance cost of equipment are respectively represented.
6. The method of two-layer optimal configuration according to claim 5, wherein the constraint conditions of the lower layer scheduling model include: power balance constraints, equipment operation constraints, and upper limit constraints on the amount of electricity purchased taking into account the load carrying capacity of the distribution network.
7. The method of claim 1-6, wherein AIMMS software is used to solve the bilayer planning model.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016134935A (en) * 2015-01-15 2016-07-25 大阪瓦斯株式会社 Cogeneration system
CN109063992A (en) * 2018-07-18 2018-12-21 国网重庆市电力公司经济技术研究院 Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation
CN109638818A (en) * 2018-12-13 2019-04-16 中国电力科学研究院有限公司 A kind of heat storing type electric heating system capacity configuration optimizing method and device
CN110163450A (en) * 2019-05-31 2019-08-23 国网山东省电力公司经济技术研究院 A kind of distribution network planning bi-level optimal model construction method limited based on operation
CN111125638A (en) * 2019-12-13 2020-05-08 国网山东省电力公司经济技术研究院 Method and device for constructing planning model of active power distribution network and computing equipment
CN112464466A (en) * 2020-11-24 2021-03-09 国网能源研究院有限公司 Optimal scheduling method and device for heat accumulating type electric heating system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016134935A (en) * 2015-01-15 2016-07-25 大阪瓦斯株式会社 Cogeneration system
CN109063992A (en) * 2018-07-18 2018-12-21 国网重庆市电力公司经济技术研究院 Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation
CN109638818A (en) * 2018-12-13 2019-04-16 中国电力科学研究院有限公司 A kind of heat storing type electric heating system capacity configuration optimizing method and device
CN110163450A (en) * 2019-05-31 2019-08-23 国网山东省电力公司经济技术研究院 A kind of distribution network planning bi-level optimal model construction method limited based on operation
CN111125638A (en) * 2019-12-13 2020-05-08 国网山东省电力公司经济技术研究院 Method and device for constructing planning model of active power distribution network and computing equipment
CN112464466A (en) * 2020-11-24 2021-03-09 国网能源研究院有限公司 Optimal scheduling method and device for heat accumulating type electric heating system

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