CN113392535A - Double-layer optimal configuration method for heat accumulating type electric heating - Google Patents
Double-layer optimal configuration method for heat accumulating type electric heating Download PDFInfo
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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 is characterized in that a double-layer optimization model framework is built by taking the bearing capacity of a power distribution network and the load of power failure non-stop heating as constraints, and the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model; the upper-layer planning model is constructed by taking the minimum annual total cost of the heat accumulating type 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 minimum annual operation and maintenance cost of the heat accumulating type electric heating system as a target; the method is characterized in that a double-layer optimal configuration model of the heat accumulating type electric heating system with the lowest annual value cost of the heat accumulating type electric heating system on the upper layer and the lowest user operation cost on the lower layer is established.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a double-layer optimal configuration method for heat accumulating type electric heating.
Background
Due to the specific heat storage capacity of the heat storage type electric heating, the heating cost of a user can be further reduced by utilizing a peak-valley electricity price policy widely proposed by governments at all levels, the contradiction between the heating requirement of the user and the high heating cost is effectively relieved, meanwhile, peak clipping and valley filling can be realized to a certain degree, the operation pressure of a power grid is reduced, the reconstruction cost of the power grid is reduced, and the heating reliability is improved. However, the existing heat accumulating type electric heating system has the problems of high investment cost, high heating cost of users, energy waste caused by excessive heating, unreasonable heating mode to influence comfort level, power failure and heating stop and the like in planning and construction.
Disclosure of Invention
The invention aims to provide a double-layer optimal configuration method for heat accumulating type electric heating, which makes full use of heat accumulating equipment on the premise of meeting the safety constraint of a power distribution network, realizes 'no power failure and no heating interruption' and reduces energy consumption cost.
The embodiment of the invention provides a double-layer optimal configuration method for heat accumulating type electric heating, which comprises the following steps:
establishing a double-layer optimization model framework by taking the bearing capacity of the power distribution network and the load of power failure non-stop heating as constraints, wherein the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model;
the upper-layer planning model is constructed by taking the minimum annual total cost of the heat accumulating type 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 minimum annual operation and maintenance cost of the heat accumulating type electric heating system as a target;
solving the capacities of the heat storage type electric heating heat pump and the heat storage water tank based on the upper layer planning model and combined with the lower layer scheduling model, and outputting the capacities of all equipment;
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 is modeled by a unified bus type structure, including an electric bus and a thermal bus,
electric bus power balance constraint:
Pgrid(t)＝PHP(t); in the formula: pgrid(t) power, kW, purchased from the grid at time t;
thermal bus power balance constraint:
in the formula: hload(t) represents the thermal load at time t, kW.
Optionally, the bearing capacity of the power distribution network at each moment is considered by 60-80% of the upper limit of the active transmission capacity of the power distribution line,
Pmar(t)＝0.7PN-1.05Pbas(t)
in the formula: pmar(t) the bearing capacity of the power distribution network at the moment t, kW; pNRated transmission power, kW, for the distribution line; pbas(t) is the base load value of the power distribution network at the moment t, kW;
Pgrid,max＝minPmar(t), wherein: pgrid,maxThe electric heating system can purchase electric quantity, kW, under considering the bearing capacity of the power distribution network.
Optionally, the maintenance heating load during the blackout period is calculated as follows:
Houtage＝5Hload,max
in the formula: houtageKeeping heating load at kWh for the power off period; hload,maxThe annual heat load maximum, kWh.
Optionally, the objective function of the upper layer 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, as shown in the following formula:
in the formula:
respectively showing the annual value of the initial investment cost of the heat accumulating type electric heating, the annual operation cost of the equipment and the annual maintenance cost of the equipment.
Optionally, the device capacity constraint is:
QHP,min≤QHP≤QHP,max
QHWT,min≤QHWT≤QHWT,max
in the formula: qHP,max，QHP,minThe upper and lower capacity limits, kW, can be set for the heat pump; qHWT,max，QHWT,minThe heat storage water tank can be provided with an upper and a lower capacity limit, kWh.
Optionally, the heating load is maintained when the capacity of the heat storage water tank is larger than the power failure period without power failure, and the heating constraint is maintained when the power failure period is as follows:
Houtage≤QHWT,min
in the formula, QHWT,minThe capacity of the heat storage water tank is larger than HoutageThe heating load is maintained for the blackout period.
Optionally, the objective function of the lower layer scheduling model is:
in the formula
Respectively showing the annual value of the initial investment cost of the heat accumulating type electric heating, the annual maintenance cost of the equipment.
Optionally, the constraints of the lower layer scheduling model include: power balance constraint, equipment operation constraint and upper limit constraint of purchased electric quantity considering the bearing capacity of the power distribution network.
Optionally, the two-layer planning model is solved using AIMMS software.
Compared with the prior art, the invention has the following advantages:
the invention provides a double-layer optimal configuration method for heat accumulating type electric heating, which is characterized in that a double-layer optimal model frame is built by taking the bearing capacity of a power distribution network and the load of power failure non-stop heating as constraints, and the double-layer optimal model frame comprises an upper-layer planning model and a lower-layer scheduling model; the upper-layer planning model is constructed by taking the minimum annual total cost of the heat accumulating type 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 minimum annual operation and maintenance cost of the heat accumulating type electric heating system as a target; solving the capacities of the heat storage type electric heating heat pump and the heat storage water tank based on the upper layer planning model and combined with the lower layer scheduling model, and outputting the capacities of all equipment; 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, and a double-layer optimal configuration model of the heat accumulating type electric heating system with the lowest annual value cost of the heat accumulating type electric heating system on the upper layer and the lowest user operation cost on the lower layer is established by taking the transmission power limit of the power distribution network and the 'power failure non-stop heating' in a fault state as constraints.
Drawings
FIG. 1 is a schematic diagram of a thermal storage electric heating system according to an embodiment of the present invention;
fig. 2 is a schematic structural view of a heat accumulating type electric heating system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a two-layer optimization configuration model according to an embodiment of the present invention;
FIG. 4 is a typical daily heat load level for a heating season according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The embodiment of the invention provides a double-layer optimal configuration method for heat accumulating type electric heating, which comprises the following steps:
establishing a double-layer optimization model framework by taking the bearing capacity of the power distribution network and the load of power failure non-stop heating as constraints, wherein the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model;
the upper-layer planning model is constructed by taking the minimum annual total cost of the heat accumulating type 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 minimum annual operation and maintenance cost of the heat accumulating type electric heating system as a target;
solving the capacities of the heat storage type electric heating heat pump and the heat storage water tank based on the upper layer planning model and combined with the lower layer scheduling model, and outputting the capacities of all equipment;
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 double-layer optimal configuration method for heat accumulating type electric heating, wherein a double-layer optimal model framework is built by taking the bearing capacity of a power distribution network and the load of power failure non-stop heating as constraints, and comprises an upper-layer planning model and a lower-layer scheduling model; the upper-layer planning model is constructed by taking the minimum annual total cost of the heat accumulating type 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 minimum annual operation and maintenance cost of the heat accumulating type electric heating system as a target; solving the capacities of the heat storage type electric heating heat pump and the heat storage water tank based on the upper layer planning model and combined with the lower layer scheduling model, and outputting the capacities of all equipment; 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, and a double-layer optimal configuration model of the heat accumulating type electric heating system with the lowest annual value cost of the heat accumulating type electric heating system on the upper layer and the lowest user operation cost on the lower layer is established by taking the transmission power limit of the power distribution network and the 'power failure non-stop heating' in a fault state as constraints.
Specifically, in this embodiment, fig. 1 is a schematic structural diagram of a typical thermal storage electric heating system, which is composed of a heat pump, a thermal storage water 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, wherein the heat pump is used as a heating device, and the thermal storage water tank is used as a thermal storage device. The operation modes of the system under normal power supply and fault power failure are respectively as follows:
and (3) normal power supply: the heat pump can meet the heat load demand of the user and simultaneously store heat for the heat storage water tank in the valley electricity price period. The heat storage water tank releases heat in a high electricity price period, basic heat requirements are met under the driving of a heat supply network circulating water pump, and the insufficient part is complemented by a heat pump;
and (4) power failure due to fault: when the power supply of the power distribution network is interrupted due to the fault of the power grid, the storage battery/diesel generator drives the heat supply network circulating water pump, and the heat storage amount in the heat storage water tank is used for maintaining the heating requirement in the power interruption period.
Heat accumulating type electric heating equipment model:
in this embodiment, a Heat Pump (HP) is selected as the heating device, the heat pump is distinguished by heat source types, and includes water (ground) source heat pump, air source heat pump, and the like, the heat pump uses air/water/soil as a heat source, and converts low-grade heat energy in air/water/soil into high-grade heat energy under the driving of electric energy, and the heating power is as follows:
HHP(t)＝PHP(t)/ACOP (1)
in the formula, HHP(t) is the heating power of the heat pump at the moment t, kW; ACOP is the annual comprehensive efficiency ratio of the heat pump; pHPAnd (t) is the electric power consumed by the heat pump at the moment t, kW.
The heat pump output should be less than capacity:
0≤HHP(t)≤QHP (2)
in the formula: qHPIs the heat pump capacity, kW.
A heat storage water tank:
the Hot Water Tank (HWT) is used for storing heat in a period of low electricity price, releasing heat in a period of high electricity price and in the case of power supply interruption, and retaining the original buffer function. In order to embody the applicability of the method, the temperature change of the heat storage water tank is converted into the heat change, and the quality adjustment is carried out corresponding to the specific indoor temperature adjustment mode. The hot water storage tank characteristics can be expressed as a relationship among the stored heat amount, the stored/released heat power, and the heat loss:
in the formula: wHWT(t) is the heat storage capacity of the heat storage water tank at the moment t, 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 the charge and discharge efficiency of the heat storage water tank;
is the self heat release loss rate; Δ t is the simulation time step, which is 1 hour in this example.
The charging and discharging and energy storage of the heat storage water tank are restricted as follows:
0≤WHWT(t)≤QHWT (6)
in the formula:
the maximum energy charging and discharging rate of the heat storage water tank is achieved; qHWTkWh is the hot water storage tank capacity.
The heat accumulation type electric heating system purchases electricity from a power grid, converts the electricity into heat energy through a heat pump, and reasonably dispatches through a heat accumulation water tank to jointly meet heat load. 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 type 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).
Electrical bus power balance constraints
Pgrid(t)＝PHP(t) (7)
In the formula: pgrid(t) power, kW, purchased from the grid at time t;
thermal bus power balance constraint
In the formula: hload(t) represents the thermal load at time t, kWh.
Specifically, the large-scale popularization and application of electric heating can greatly increase the power consumption load of the power distribution network, the load of the power distribution network is easy to be subjected to peak-to-peak conditions, and the pressure of the power grid is greatly increased. In order to guarantee the operation safety of a power grid, the bearing capacity of a power distribution network needs to be researched by combining the time-space distribution characteristic of an electric load, and for an electric heating load access area, the time-space characteristic of the power distribution network can be described by a typical daily load curve; in order to ensure the power supply capacity and flexibility of the power distribution network, the bearing capacity of the power distribution network at each moment is considered 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 according to the embodiment;
Pmar(t)＝0.7PN-1.05Pbas(t) (9)
in the formula: pmar(t) the bearing capacity of the power distribution network at the moment t, kW; pNRated transmission power, kW, for the distribution line; pbas(t) is the base load value of the power distribution network at the moment t, kW;
Pgrid,max＝min Pmar(t) (10)；
in the formula: pgrid,maxThe electric heating system can purchase electric quantity, kW, under considering the bearing capacity of the power distribution network.
Specifically, according to the notification about the technical measure of ensuring continuous heating in the implementation of power failure, the time of the user power failure does not exceed the requirement of 5 hours. Therefore, within 5 hours, the power supply can be recovered through emergency repair of the power enterprise, the heating duration can be maintained for 5 hours in the power failure period, and the heating load is maintained in the power failure period as follows:
Houtage＝5Hload,max (11)
in the formula: houtageKeeping heating load at kWh for the power off period; hload,maxThe annual heat load maximum, kWh.
In the heat accumulating type electric heating double-layer optimization configuration method considering the time-space characteristics and the bearing capacity of the power distribution network, which is constructed in the embodiment, the upper layer model solves the heat accumulating type electric heating capacity by taking the minimum total system cost as an optimization target, and outputs a planning scheme, namely the capacity of each device; the lower-layer model aims at the minimum annual operation and maintenance cost, and the optimal scheduling scheme for solving the garden comprises the output of each device and the electric quantity purchased. The overall framework of the two-layer model is shown in fig. 3.
Specifically, the upper layer planning model:
an objective function:
the objective function of the upper-layer planning model is the minimization of the annual total cost of the heat accumulating type electric heating system, and the objective function comprises the annual values of heat accumulating type initial investment cost and the like, the operation cost and the maintenance cost, and is shown as the formula.
In the formula:
respectively showing the annual value of the initial investment cost of the heat accumulating type electric heating, the annual operation cost of the equipment and the annual maintenance cost of the equipment.
The equal-year value of the investment cost is the sum of the equal-year values of the investment cost of the heat pump and the heat storage water tank, and the expression is as follows:
in the formula:
respectively representing the annual values of the investment cost of the heat pump and the heat storage water tank, and the like. The annual value calculation of investment cost of each device can adopt the following formula:
in the formula cI,HP，cI,HWTThe unit investment cost of the heat pump and the heat storage water tank is high; qHPIs the heat pump capacity, kW; qHWTkWh is the thermal storage water tank capacity; r is the withdrawal rate, 8% in the text; lHP，lHWTThe expected service life of the heat pump and the heat storage water tank is year.
The annual operation cost of the heat accumulating type electric heating system is mainly annual electricity purchasing cost and is related to the electric power consumed by the heat pump, and the expression is as follows:
in the formula: c. Celec(t) is the electricity price per kWh for the period t.
The annual maintenance cost of the equipment is related to the type and operation condition of each equipment. The heat pump maintenance cost is expressed as the product of unit power maintenance cost and output power, the heat storage water tank maintenance cost is related to capacity, and can be expressed by the following formula:
in the formula: c. CM,HPFor heat pump unit power maintenanceThis, yuan/kW; c. CM,HPThe maintenance cost per unit capacity of the heat pump, yuan/kWh.
Constraint conditions are as follows:
and (3) equipment capacity constraint:
due to investment, space and other limitations, the capacity of the heat accumulating type electric heating system equipment can be set to have upper and lower limits.
QHP,min≤QHP≤QHP,max (18)
QHWT,min≤QHWT≤QHWT,max (19)
In the formula: qHP,max，QHP,minThe upper and lower capacity limits, kW, can be set for the heat pump; qHWT,max，QHWT,minThe heat storage water tank can be provided with an upper and a lower capacity limit, kWh.
Constraint of 'power failure without stop heating':
for providing guarantee for user heating reliability under the large application environment of heat accumulating type electric heating, the user heat demand in the power failure period is considered in the embodiment, and the user heat demand in the power failure period is met and is used as one of the constraint conditions of the heat accumulating type electric heating planning model. In order to realize 'power failure does not stop heating', the capacity of the heat storage water tank is greater than the capacity of the heat storage water tank to maintain the heating load in the power failure period, and the heat supply constraint in the power failure period is as follows:
Houtage≤QHWT,min (20)
lower layer scheduling model
Objective function
The objective function of the lower-layer scheduling model is the minimization of annual operation and maintenance cost of the heat accumulating type electric heating system, and the operation and maintenance cost can be obtained by the formulas (16) - (17).
Constraining
1) Power balance constraint
The heat accumulating type electric heating system needs to meet the power balance constraint of the electric bus and the thermal bus at each moment, and the formulas (7) to (8) are shown.
2) Plant operating constraints
The operation constraints of the heat pump and the heat storage water tank are shown in formulas (1) to (6).
For the heat storage water tank, the heat storage quantity at the beginning and the end of the scheduling period needs to be kept consistent.
WHWT(1)＝WHWT(T) (22)
In the formula: wHWT(1)，WHWTAnd (T) the heat storage amount at the beginning and the end of the dispatching cycle, kWh respectively.
3) Upper limit constraint of purchased electric quantity considering carrying capacity of power distribution network
The access of the electric heating load enables the regional electric load to be increased rapidly, and the requirement of the heat load is guaranteed as much as possible under the condition of avoiding the load of the power distribution network from increasing the peak. The bearing capacity of the power distribution network is considered, the electric quantity which can be purchased by the electric heating system at the peak moment of power utilization is limited, the load pressure of the power distribution network is relieved, and the orderly and reliable power supply and the safe and stable operation of the power distribution network are guaranteed.
The present embodiment uses the AIMMS software to solve the above-described two-layer planning model.
Optionally, the constraints of the lower layer scheduling model include: power balance constraint, equipment operation constraint and upper limit constraint of purchased electric quantity considering the bearing capacity of the power distribution network.
Optionally, the two-layer planning model is solved using AIMMS software.
Indexes of influence of the planning scheme on the power grid are as follows:
1) peak to valley difference of load
The load peak-valley difference delta P reflects the impact of the electrical heating load on the load characteristics of the distribution network. The calculation method is shown as formula (24).
ΔP＝PHP,max-PHP,min (24)
In the formula: pHP,max，PHP,minRespectively the peak valley value of the heat pump power consumption load.
2) Power consumption coincidence rate
In order to represent the influence degree of the heat accumulating type electric heating load access on the load increase of the power distribution network, the definition of the power consumption concurrency rate of the basic load of the power distribution network and the electric heating load is given, so that the adverse influence of the electric heating load on the peak value of the basic load of the power distribution network is described.
ξ＝Ptotal,max/Pbas,max+Pgrid,max (25)
In the formula: ptotal,maxThe maximum value of the total power load of the power distribution network is kW; pbas,maxThe maximum value of the basic electric load of the power distribution network is kW; pgrid,maxThe maximum value of the electric heating load is kW.
Analysis by calculation example:
the heat accumulating type electric heating system with the structure shown in figure 2 is adopted to verify the correctness and the effectiveness of the method.
Fig. 4 is a typical daily heat load level for a heating season for a heating zone. The main parameters of HP and HWT are shown in table 1. The time-of-use electricity price and the electricity consumption peak electricity purchasing amount limit are shown in table 2. The basic electrical load and carrying capacity of the distribution network at each moment is shown in appendix A, A1 and A2.
Tab.1 Parameters of the HP and HWT
TABLE 2 time-of-use electricity price and electricity purchase quantity constraints
Tab.2 Electricity price and constraints
To more clearly demonstrate the effectiveness of the method herein, 4 exemplary scenarios were constructed for comparison as shown in table 3:
1) analyzing an electric heating optimal configuration scheme under normal power supply:
scene 1: the method of our article should meet the different subject needs of only configuring HP and not configuring HWT;
scene 2: the user invests and configures HP and HWT at the same time;
scene 3: a user invests and configures HP and HWT at the same time, and the influence of the peak power supply allowance of power utilization is considered;
2) considering the analysis of the configuration scheme of 'power failure and no power off' as follows:
scene 4: a user invests and configures HP and HWT at the same time, and heat supply is maintained in consideration of the power failure period, so that 'power failure continuous heating' is realized;
TABLE 3 example scenarios
Tab.3 Scenarios of case study
Example results and analysis
The system configuration scheme and the annual cost under each scene are shown in table 4, and under normal power supply, the peak-to-valley difference and the power consumption concurrence rate of scenes 1 to 3 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 concurrence
Tab.5 Peak-valley difference and load coincidence factor
And (3) carrying out economic analysis on the configuration scheme under normal power supply:
as can be seen from table 4, the annual total cost for scenario 2 is reduced by 10.25% compared to scenario 1. Due to the fact that the HWT price is low, the HP capacity can be effectively reduced through the heat storage device in the heat storage type electric heating, and investment cost is reduced. But will increase the system operating and maintenance costs due to heat loss from the HWT and maintenance costs.
Under the scene 1, all heat loads are met by the heat pump, and due to the fact that electric heating is connected, electricity needs to be purchased from a power grid at each moment according to heat load requirements so as to meet all increased electric loads, and therefore the electricity purchasing quantity has large fluctuation along with the change of the heat loads. And scene 2 can reduce the electricity purchasing quantity at the electricity price peak value through energy storage by arranging a heat storage device, reasonably arranges an electricity purchasing plan, and realizes peak clipping and valley filling. As can be seen from Table 5, in scene 2, compared with scene 1, the peak-to-valley difference of the access electric heating load is reduced by 95.45%, and the influence on the load characteristics of the power 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 power distribution network, in scene 1, because the heat pump heat supply load 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 power distribution network, the electric heating load at part of the time exceeds the bearing capacity of the power distribution network, and the capacity expansion needs to be carried out on the power distribution network so as to increase the power supply amount and greatly increase the cost. Although the increase of the heat storage equipment in the scene 2 enables the electric heating system to have certain peak clipping and valley filling capabilities, the electric heating system still exceeds the bearing capacity of the power distribution network at part of time, but the out-of-limit amplitude is smaller than that in the scene 1, and the safe operation of the power distribution network can be guaranteed only under the condition of micro-capacity increase of the power grid. And the power consumption coincidence rate is in higher level under two kinds of scenes, so the joining of electric heating makes the distribution network appear in the condition of basic power consumption peak superposition electric heating load peak easily, brings very big challenge to distribution network safe operation.
Therefore, in order to guarantee the safe operation of the power distribution network and give consideration to the heat load demand of users, the situation 3 considers that the heat accumulating type electric heating load is limited at the time of the peak of the power consumption to guarantee the safe operation of the power distribution network, and the power purchasing constraint is not considered at the time of the valley of the power consumption.
As can be seen from table 4, since there is a limit to the amount of electricity purchased at peak demand time, the HWT is required to store energy to cope with the peak load, so scenario 3 increases the capacity of the HP and HWT devices, and the investment cost increases. But the total load peak-valley difference and the power utilization synchronous rate 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 increase can be effectively realized.
Analysis of the configuration scheme considering 'power failure and no stop heating':
scene 4 further considers the situation of power failure and power failure, and guarantees that the user does not stop heating during power failure.
As can be seen from table 4, scenario 4 further increases the HP and HWT equipment capacity and the investment cost is increased significantly compared to scenario 3. The electric load at the time of power failure can only be guaranteed by the HWT, so that the HWT capacity needs to be greatly increased, and the HP capacity needs to be correspondingly and reasonably increased, so that under the condition that the heat load requirement at the time of valley leveling is guaranteed, enough heat is stored by the HWT to deal with the heat load at the time of power failure. Compared to scenario 3, HP and HWT capacities increased by 28.23% and 106.14%, respectively, and the total cost increased by 18.18%. In order to guarantee that the user does not stop heating in power failure, the large increase of the operation and maintenance cost also indicates that the bearing capacity of the power distribution network is considered, and the huge economic benefit of power distribution network failure is avoided.
Sensitivity analysis
In order to analyze the influence condition of each parameter on the electric heating system, a group of sensitivity simulation analysis is carried out.
The main change parameters of the electric heating system optimization configuration model comprise electricity price, HWT cost, load, electricity purchasing upper limit at the moment of electricity utilization peak and power failure heat maintaining time.
In this document, scenario 4 is used as a control group of other parameter change simulation experiments, and the capacity and the annual cost of the electric heating system equipment under the condition that a single factor is increased by 10% and decreased by 10% are recorded, including: HP capacity, HWT capacity and annual investment, operation, maintenance costs, and comparative analysis with scenario 4.
Further, in order to more clearly reflect the sensitivity of the configuration scheme of the electric heating system to a single parameter of the model, the offset phi of the simulation result after the parameter is changed is defined.
φ＝(φ-φ0)/φ0 (25)
In the formula: phi is the equipment capacity and 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 is a0And (4) configuring a scheme and annual cost for the electric heating system under the scene 4.
The following conclusions were drawn from the normalized sensitivity analysis results:
1) the sensitivity of the equipment capacity of the electric heating system to the change of price parameters such as electricity price, equipment investment cost and the like is low; system cost variations are generally affected by corresponding price parameter variations. The electricity price is lower than the unit investment cost of the equipment, so the influence of the change of the electricity price on the capacity of the equipment is lower; under the condition of more system constraint conditions, the optimization space of the system configuration scheme is smaller for ensuring the heating reliability of the system, so the influence of the small-amplitude change of the equipment investment cost on the system configuration scheme is smaller. The price parameter variation has only a certain influence on its corresponding cost item.
2) The capacity and the cost of the electric heating system are most sensitive to the load change, the power failure time is long, and the sensitivity to the upper limit of the purchased electric quantity is the lowest among the capacity, the cost and the cost. The load is a main influence factor of the configuration scheme of the electric heating system, so that the change of the load greatly influences the applicability of the configuration scheme of the system, and in order to avoid the inapplicability of the configuration scheme or the frequent replacement of equipment, the load prediction accuracy is improved at the initial stage of construction, and the factors such as load fluctuation, increase and the like are fully considered, so that the robustness of the configuration scheme of the system is improved. Because the system has lower sensitivity to the upper limit of the electricity purchasing quantity at the peak time, 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 guaranteed, and the influence on the system cost is smaller.
3) HP and HWT capacities are not as sensitive to parameter variations. Generally speaking, the HWT price is low, and the HWT energy storage effect has a great influence on the flexibility of system operation scheduling; the heat pump is expensive and needs to guarantee the supply of heat energy as the only heat energy source of the electric heating system, so the heat pump is greatly influenced by the heat load level. The HWT capacity will be more sensitive to parameter changes.
The invention constructs an electric heating system device and a system model, and provides a heat accumulating type electric heating double-layer optimization configuration method considering the space-time characteristic and the bearing capacity of a power distribution network by considering the bearing capacity model of the space-time characteristic of a power grid and the heating load model constraint in the power failure period, and the conclusion is as follows:
1) the access of an electric heating system causes the load of a distribution network to be greatly increased, and the load capacity of the distribution network is exceeded in serious cases, so that the operation safety of the distribution network is influenced; the heat accumulating type electric heating system can play a certain role in peak clipping and valley filling through the heat accumulating water tank, so that the load peak-valley difference of the power distribution network is reduced, and the energy consumption cost is reduced.
2) The power grid bearing capacity is considered, and when the transmission power constraint of the power distribution network is taken as the limit, the phenomenon that a large number of electric heating loads are superposed at the moment of a power utilization peak can be avoided, the power utilization simultaneous rate is reduced, the operation pressure of the power distribution network at the moment of the load peak is effectively relieved, and the operation safety of the power distribution network is guaranteed.
3) The thermal storage device is used for keeping heating in the power failure period and guaranteeing the heat load requirements of users by taking 'power failure uninterrupted heating' in a fault state as a constraint and fully utilizing the thermal storage device.
4) According to sensitivity analysis, the load is a key factor influencing the optimal configuration scheme of the electric heating system, and the reasonable and accurate prediction of the load can improve the practicability of the optimal configuration scheme.
The flowchart 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 used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The double-layer optimal configuration method for the heat accumulating type electric heating is characterized by comprising the following steps:
establishing a double-layer optimization model framework by taking the bearing capacity of the power distribution network and the load of power failure non-stop heating as constraints, wherein the double-layer optimization model framework comprises an upper-layer planning model and a lower-layer scheduling model;
the upper-layer planning model is constructed by taking the minimum annual total cost of the heat accumulating type 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 minimum annual operation and maintenance cost of the heat accumulating type electric heating system as a target;
solving the capacities of the heat storage type electric heating heat pump and the heat storage water tank based on the upper layer planning model and combined with the lower layer scheduling model, and outputting the capacities of all equipment;
and solving the lower scheduling model according to the equipment capacity obtained by the upper planning model to obtain an optimal scheduling scheme.
2. The double-layer optimal configuration method according to claim 1, wherein the regenerative electric heating system is modeled by a unified bus type structure, comprising an electric bus and a thermal bus,
electric bus power balance constraint:
Pgrid(t)＝PHP(t); in the formula: pgrid(t) power, kW, purchased from the grid at time t;
thermal bus power balance constraint:
in the formula: hload(t) represents the thermal load at time t, kW.
3. The double-layer optimization configuration method according to claim 1, wherein the distribution network load capacity is considered at each moment according to 60-80% of the upper limit of the distribution line active transmission capacity,
Pmar(t)＝0.7PN-1.05Pbas(t)
in the formula: pmar(t) the bearing capacity of the power distribution network at the moment t, kW; pNRated transmission power, kW, for the distribution line; pbas(t) is the base load value of the power distribution network at the moment t, kW;
Pgrid,max＝minPmar(t), wherein: pgrid,maxThe electric heating system can purchase electric quantity, kW, under considering the bearing capacity of the power distribution network.
4. The two-tier optimal configuration method of claim 1, wherein the maintenance heating load during blackout periods is calculated as follows:
Houtage＝5Hload,max
in the formula: houtageKeeping heating load at kWh for the power off period; hload,maxThe annual heat load maximum, kWh.
5. The two-tier optimal configuration method of claim 4,
the objective function of the upper-layer planning model is the minimization of the annual total cost of the heat accumulating type electric heating system, and comprises the annual values of heat accumulating type initial investment cost and the like, the operation cost and the maintenance cost, and the following formula is shown as follows:
in the formula:
respectively showing the annual value of the initial investment cost of the heat accumulating type electric heating, the annual operation cost of the equipment and the annual maintenance cost of the equipment.
6. The two-tier optimal configuration method of claim 5, wherein the device capacity constraints are:
QHP,min≤QHP≤QHP,max
QHWT,min≤QHWT≤QHWT,max
in the formula: qHP,max，QHP,minThe upper and lower capacity limits, kW, can be set for the heat pump; qHWT,max，QHWT,minThe heat storage water tank can be provided with an upper and a lower capacity limit, kWh.
7. The double-deck optimal allocation method according to claim 5, wherein the thermal storage water tank is kept under heating load when the power failure occurs, and the capacity of the thermal storage water tank is larger than that of the thermal storage water tank during the power failure, and the thermal storage water tank is kept under heating constraint during the power failure as follows:
Houtage≤QHWT,min
in the formula, QHWT,minThe capacity of the heat storage water tank is larger than HoutageThe heating load is maintained for the blackout period.
8. The two-tier optimal configuration method of claim 5, wherein the objective function of the lower-tier scheduling model is:
in the formula
Respectively showing the annual value of the initial investment cost of the heat accumulating type electric heating, the annual maintenance cost of the equipment.
9. The two-tier optimal configuration method of claim 8, wherein the constraints of the lower-tier scheduling model include: power balance constraint, equipment operation constraint and upper limit constraint of purchased electric quantity considering the bearing capacity of the power distribution network.
10. The dual-layer optimization configuration method according to any one of claims 1 to 9, wherein the dual-layer planning model is solved using AIMMS software.
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