CN114154279B - Opportunity constraint assessment method for distribution network bearing capacity of heat accumulating type electric heating access - Google Patents
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Abstract
The opportunity constraint evaluation method for the distribution network bearing capacity of the heat accumulating type electric heating access comprises the steps of acquiring meteorological data and distribution network data; establishing a heat load demand model, and extracting a typical heat load demand curve by adopting a clustering method; establishing a heat accumulating type electric heating system and an operation control model; establishing a distribution network bearing capacity evaluation model containing heat accumulating type electric heating access, and determining opportunity constraint confidence level according to load regulation capacity; acquiring distribution network basic electricity load probability distribution based on non-parameter nuclear density estimation, extracting a basic load curve of a heat accumulating type electric heating access distribution network according to opportunity constraint confidence level, and carrying out deterministic conversion on an assessment model containing opportunity constraint; and solving the converted evaluation model to obtain the maximum heating area which can be borne by the distribution network. The invention can effectively increase the heat supply area which can be borne by the distribution network, improve the utilization rate of the distribution network and realize reasonable evaluation of the heat accumulating type electric heating access scale under the current distribution network structure and demand response.
Description
Technical Field
The invention relates to the field of power distribution network planning, in particular to an opportunity constraint evaluation method for the carrying capacity of a distribution network containing regenerative electric heating access.
Background
The heat accumulating type electric heating has the capacity of adjusting the power and time transfer, and can actively participate in the regulation of a power grid; at present, research on regenerative electric heating is mainly focused on improving renewable energy consumption and optimizing configuration of equipment parameters, and the proposed regenerative electric heating planning scheme does not consider the bearing potential of a distribution network, and is modeling on the premise of meeting the planning scheme and being convenient to reconstruct and expand, so that the result may deviate from reality, and the feasibility of the planning scheme is affected. Therefore, an assessment of the maximum heating area carried under the current grid structure of the power distribution network is necessary. Meanwhile, regarding calculation of the maximum bearing capacity of the power distribution network, a currently widely adopted method is a deterministic evaluation method based on typical days. The deterministic evaluation method can ensure that the original load is normally powered up to the greatest extent, but is not suitable for the development mode of 'double carbon' target, and the power grid regulation mode is inevitably changed from 'source follow-up' to 'source load interaction'. Researches show that the demand response technology is utilized to reasonably regulate and control the flexible resources at the user side, so that the short-time peak load can be effectively reduced, the power grid bearing capacity is improved, and the running and planning cost of the power grid is reduced. If the positive effect that the user actively participates in the power grid peak shaving under the demand response is ignored, the evaluation result of the load bearing capacity of the distribution network is relatively conservative, and the full utilization of the distribution network resources is restricted to a certain extent.
Disclosure of Invention
The invention aims to provide a method for reasonably evaluating the heat accumulating type electric heating access scale under the current distribution network structure and the demand response, which considers the influence of the demand response on the bearing capacity of a distribution network, can effectively increase the bearable heating area of the distribution network and improve the utilization rate of the distribution network.
The opportunity constraint evaluation method for the distribution network bearing capacity of the heat accumulating type electric heating access comprises the following steps:
step one: acquiring meteorological data and distribution network basic data;
step two: establishing a heat load demand model, and extracting a typical heat load demand curve by adopting a clustering method;
step three: establishing a heat accumulating type electric heating system and an operation control model;
Step four: establishing a distribution network bearing capacity opportunity constraint assessment model containing regenerative electric heating access, and determining opportunity constraint confidence according to load regulation capacity;
Step five: acquiring distribution network basic electricity load probability distribution based on non-parameter nuclear density estimation, extracting a basic load curve of a heat accumulating type electric heating access distribution network according to opportunity constraint confidence, and carrying out deterministic conversion on an evaluation model containing opportunity constraint conditions;
Step six: solving the converted evaluation model to obtain the maximum heating area which can be borne by the distribution network;
further, the meteorological data and the distribution network basic data include:
1) The outdoor ambient temperature and the relative humidity of the heating season are daily;
2) Distribution network equipment parameter information: a distribution network topology; line transmission power limit, main transformer rated capacity of distribution network transformer station and maximum load rate;
3) The power load data of the daily distribution network foundation in the heating season;
In the second step, a heat load demand model is established, a clustering method is adopted to extract a typical heat load demand curve, firstly, the heat load demand is calculated by utilizing the indoor body temperature, and the indoor body temperature is represented by the formula (1-2):
At＝-1.3+0.92×Ta,t+2.2×Pa,t (1)
Wherein: a t is the indoor somatosensory temperature at the moment t; t a,t is the air temperature at time T; p a,t is the actual water vapor; r t is the relative humidity at time t;
The energy consumption characteristics of different building types are different and are usually described by adopting design solar heat indexes. Because the heat consumption of the actual building is greatly influenced by the indoor and outdoor temperature difference, the heat index is corrected based on the indoor body temperature, and the correction formula is as shown in the formula (3) and the formula (4):
Q'k,t＝Qk·Φt (3)
Φt＝(Cn-At)/[Cn-Cw] (4)
Wherein: q k is a solar heat index of the K-th building design, k= … K; q' k,t is the actual heat index of the kth building at the moment t; phi t is the actual heating area heat index correction value at the moment t; c n is the indoor temperature of the design day; c w is the outdoor temperature of the design day;
Thermal load demand model:
Wherein: Representing the heat load demand at node i in period t; a k represents the proportion of the k type building to the total heat supply area; u k,t is a 0-1 variable, i.e., u k,t =0 indicates that the kth class of building has no heat demand during the t period, and u k,t =1 indicates that the kth class of building has heat demand during the t period; x i is the heat accumulating type electric heating area covered by the node i;
further, according to the third step, a regenerative electric heating system and an operation control model are established, which is characterized in that:
1) Heat accumulating type electric heating system model
The system consists of an electric boiler and a heat storage water tank, wherein the electric boiler converts electric energy obtained from a power grid into heat energy, and one part of the converted heat energy is supplied to a heat user, and the other part of the converted heat energy is stored through the heat storage water tank.
The heating power of the electric boiler is shown in the formula (6-7):
Wherein: heating power for an electric boiler t moment of an access node i; /(I) The electric power consumed at the moment of the electric boiler t for the access node i; η HP is the heating efficiency of the electric boiler; /(I)The heat storage power at the moment t of the heat storage water tank of the access node i; /(I)Directly supplying heat power to an electric boiler t of an access node i;
The heat storage water tank is used for storing heat in the valley period and releasing heat in the electricity consumption peak period. The heat storage characteristic can be expressed as a relationship between the heat storage amount and the heat storage/release power:
Wherein: η loss represents the self-heat release loss coefficient of the heat storage tank; the heat storage quantity of the heat storage water tank t at the moment of the node i is stored; The exothermic power of the access node i at the moment of the heat storage water tank t is shown; t 1 and t 2 represent the start time and the end time of the valley period, respectively, that is, the valley period is 22:00 to the next day 8:00; t 1' represents the starting time of the next-day off-peak period; alpha is heat accumulating electric heating permeability, alpha epsilon [0,1], wherein when alpha=1, the heat accumulating water tank covers all heat supplying areas; Δt is the time step;
The heat accumulating type electric heating system obtains electric energy from a power grid side, and provides heat energy for users by coordinating the working state of the heat accumulating water tank through electric-heat conversion of an electric boiler. The operation process needs to satisfy the heat power balance:
2) Heat accumulating type electric heating operation control model
In order to ensure safe and reliable operation of the distribution network, the maximum total operation power of the electric boiler connected to the distribution network should satisfy the following conditions:
Wherein: maximum operating power of an electric boiler connected to the distribution network at the time t; /(I) Maximum transmission power can be increased for the moment t of the distribution network substation;
the distribution network transformer substation can increase transmission power by assigning the load amount which the power grid main transformer allows to increase after considering a certain margin and basic load. The maximum transmission power of the distribution network transformer substation at the moment t can be increased as follows (12):
Wherein: rated capacity for a distribution transformer; delta is the maximum load rate of the distribution transformer; /(I) Rated power factor for the electrical load; /(I)The basic load of the distribution network transformer substation at the moment t in heating season;
Because the heat storage water tank stores heat in the valley period, the power grid is required to provide enough electric quantity to meet the heat requirement of users in the peak period; in order to ensure stable and reliable operation of the heat storage equipment, the operation power of the heat storage water tank is usually limited within the minimum power increment of the heat storage period, and the corresponding heat storage capacity is also limited within the minimum power increment; in practice, however, the amount of power available during the off-peak period is much greater than the minimum available power; if the running power of the heat storage water tank is limited within the minimum bearing capacity of the distribution network, the residual electric quantity in the valley period is not fully utilized; therefore, the available electric quantity in the valley period is regarded as one of key factors influencing the operation of the heat storage water tank, and the specific expression is shown as the following formula (13-14):
Wherein: w i.max is the maximum available low-valley power of the power transmission side line adjacent to the node i; alpha 1 is a margin coefficient; p i,max is the transmission power limit of the branch of the adjacent power supply side of the node i; p base,i,t is the base load of the power supply side branch circuit adjacent to the node i at the moment t; The maximum heat storage capacity of the heat storage water tank connected to the node i is achieved;
Further, in the fourth step, the objective function of the distribution network bearing capacity opportunity constraint evaluation model of the regenerative electric heating access is:
wherein: m is the number of nodes in the evaluation area, i= … m; s is the heating area of the evaluation area.
Further, according to the distribution network bearing capacity opportunity constraint evaluation model of the heat accumulating type electric heating access in the fourth step, the constraint conditions of the distribution network bearing capacity opportunity constraint evaluation model of the heat accumulating type electric heating access are as follows:
1) Tidal current constraint
Pbase,t+Pt E＝Bθt (16)
Wherein: p base,t is a basic power vector injected by the distribution network at the moment t; p t E is an electric boiler operation power vector of the power distribution network accessed at the moment t; b is a node susceptance matrix; and theta t is a node phase angle vector at the time t.
2) Branch transmission power constraint
The branch transmission power should not exceed the transmission power limit.
0≤Pi,j,t≤α2Pi,j,max (17)
Wherein: p i,j,t and P i,j,max are the transmission power and the limit thereof at time t of the branch ij, and α 2 is the margin coefficient.
3) Node phase angle constraint
θi,j,min≤θi,j,t≤θi,j,max (18)
Wherein: θ i,j,max and θ i,j,min are the upper and lower limits of the phase angle difference of the node ij, respectively.
4) Thermal power balance constraint
The thermal power balance constraint is as in formula (6) -formula (10).
5) Distribution network substation transmission power increasable constraint
The total electric power of the electric boiler connected to the distribution network should be smaller than the maximum electric power of the electric boiler allowed to be connected to the distribution network.
6) Thermal storage tank operating power constraints
In order to fully utilize the low-valley electricity quantity, the low-valley available power operation of the tracking circuit of the heat storage water tank is considered. The specific expression is as follows:
Wherein: And (5) the maximum power of the operation of the heat storage water tank on the node i at the moment t.
7) Heat storage capacity constraint of heat storage water tank
The total heat storage capacity of the heat storage water tank of the access node i in the valley period should be smaller than the allowable maximum heat storage capacity.
Considering that the distribution network base load is a random variable, the inequality constraint can be represented by an opportunity constraint:
Wherein: p r {. Beta is a pre-set chance constraint confidence, e.g., 0.95,1-beta represents load adjustability.
Further, according to the deterministic conversion of the distribution network bearing capacity opportunity constraint evaluation model of the regenerative electric heating access in the fifth step, in order to improve the calculation efficiency, the load reference value of the distribution network access regenerative electric heating is determined by means of distribution network base load probability distribution, and the evaluation model containing opportunity constraint conditions is converted into a deterministic evaluation model:
Wherein: g (·) is all inequality constraints in formula (23); h (·) is an equality constraint; and a reasonable regulation interval is provided for ensuring the safe and reliable operation of the power grid and giving a demand response.
And estimating a distribution network base power utilization load probability density function by adopting the non-parameter core density. If the historical data of the base load of the distribution network is x 1,…,xn,…,xN, the estimation formula of the probability density function is as follows:
wherein: n is the sample volume; h is the bandwidth; k (·) is a kernel function.
The distribution network bearing capacity opportunity constraint assessment model of the heat accumulating type electric heating access is carried out by gurobi optimization software.
Compared with the prior art, the invention has the following technical effects:
1) The invention considers the influence of demand response in the distribution network bearing capacity evaluation model, introduces the opportunity constraint condition of the distribution network for utilizing the low-valley electric quantity constraint and describing the randomness of the distribution network basic electric load, matches the peak load with shorter duration in the distribution network bearing capacity evaluation, utilizes the flexible resource of the user under the demand response, reduces the huge investment and resource waste brought by meeting the small probability load peak in the distribution network planning through peak clipping, and simultaneously can enlarge the distribution network bearing heat accumulating type electric heating scale and accelerate the electric energy substitution process.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a flow chart of an opportunity constraint assessment method for the carrying capacity of a distribution network containing regenerative electric heating access;
FIG. 2 is a block diagram of a regenerative electric heating system;
FIG. 3 is a network topology of a distribution network in a rural area in Hebei province;
FIG. 4 is a schematic diagram of a heat load curve and 5 clusters per building area in a heating season;
FIG. 5 is a graph of a distribution network base electrical load probability distribution function in a heating season;
FIG. 6 is a graph of the base power consumption of a heating season distribution network substation;
FIG. 7 is a schematic diagram of the evaluation results of the carrying capacity of the distribution network in different evaluation modes;
Fig. 8 is a graph of power supply side line power of the corresponding node 4 in different evaluation modes;
fig. 9 is a schematic diagram of thermal storage type electric heating access scale under three different confidence levels of scheme a=0.2;
Fig. 10 is a graph of the increase rate of the load-bearing heating area of the distribution network at three different confidences of the scheme at a=0.2.
Detailed Description
The opportunity constraint assessment method of the distribution network bearing capacity of the heat accumulating type electric heating access is shown in a flow chart of fig. 1, and comprises the following steps:
Step one: acquiring meteorological data and distribution network basic data:
1) The outdoor ambient temperature and the relative humidity of the heating season are daily;
2) Distribution network equipment parameter information: a distribution network topology; line transmission power limit, main transformer rated capacity of distribution network transformer station and maximum load rate;
3) The power load data of the daily distribution network foundation in the heating season;
step two: establishing a heat load demand model, and extracting a typical heat load demand curve by adopting a clustering method;
firstly, calculating the heat load demand by using the indoor body temperature, wherein the indoor body temperature is represented by the formula (1-2):
At＝-1.3+0.92×Ta,t+2.2×Pa,t (1)
Wherein: a t is the indoor somatosensory temperature at the moment t; t a,t is the air temperature at time T; p a,t is the actual water vapor; r t is the relative humidity at time t;
The energy consumption characteristics of different building types are different and are usually described by adopting design solar heat indexes. Because the heat consumption of the actual building is greatly influenced by the indoor and outdoor temperature difference, the heat index is corrected based on the indoor body temperature, and the correction formula is as shown in the formula (3) and the formula (4):
Q'k,t＝Qk·Φt (3)
Φt＝(Cn-At)/[Cn-Cw] (4)
Wherein: q k is a solar heat index of the K-th building design, k= … K; q' k,t is the actual heat index of the kth building at the moment t; phi t is the actual heating area heat index correction value at the moment t; c n is the indoor temperature of the design day; c w is the outdoor temperature of the design day;
Thermal load demand model:
Wherein: Representing the heat load demand at node i in period t; a k represents the proportion of the k type building to the total heat supply area; u k,t is a 0-1 variable, i.e., u k,t =0 indicates that the kth class of building has no heat demand during the t period, and u k,t =1 indicates that the kth class of building has heat demand during the t period; x i is the heat accumulating type electric heating area covered by the node i;
step three: establishing a heat accumulating type electric heating system and an operation control model;
1) Heat accumulating type electric heating system model
The system consists of an electric boiler and a heat storage water tank, as shown in figure 2. The electric boiler converts electric energy obtained from the power grid into heat energy, and part of the converted heat energy is supplied to a heat user and the other part of the converted heat energy is stored through the heat storage water tank.
The heating power of the electric boiler is shown in the formula (6-7):
Wherein: heating power for an electric boiler t moment of an access node i; /(I) The electric power consumed at the moment of the electric boiler t for the access node i; η HP is the heating efficiency of the electric boiler; /(I)The heat storage power at the moment t of the heat storage water tank of the access node i; /(I)Directly supplying heat power to an electric boiler t of an access node i;
The heat storage water tank is used for storing heat in the valley period and releasing heat in the electricity consumption peak period. The heat storage characteristic can be expressed as a relationship between the heat storage amount and the heat storage/release power:
Wherein: η loss represents the self-heat release loss coefficient of the heat storage tank; the heat storage quantity of the heat storage water tank t at the moment of the node i is stored; The exothermic power of the access node i at the moment of the heat storage water tank t is shown; t 1 and t 2 represent the start time and the end time of the valley period, respectively, that is, the valley period is 22:00 to the next day 8:00; t 1' represents the starting time of the next-day off-peak period; alpha is heat accumulating electric heating permeability, alpha epsilon [0,1], wherein when alpha=1, the heat accumulating water tank covers all heat supplying areas; Δt is the time step;
The heat accumulating type electric heating system obtains electric energy from a power grid side, and provides heat energy for users by coordinating the working state of the heat accumulating water tank through electric-heat conversion of an electric boiler. The operation process needs to satisfy the heat power balance:
2) Heat accumulating type electric heating operation control model
In order to ensure safe and reliable operation of the distribution network, the maximum total operation power of the electric boiler connected to the distribution network should satisfy the following conditions:
Wherein: maximum operating power of an electric boiler connected to the distribution network at the time t; /(I) Maximum transmission power can be increased for the moment t of the distribution network substation;
the distribution network transformer substation can increase transmission power by assigning the load amount which the power grid main transformer allows to increase after considering a certain margin and basic load. The maximum transmission power of the distribution network transformer substation at the moment t can be increased as follows (12):
Wherein: rated capacity for a distribution transformer; delta is the maximum load rate of the distribution transformer; /(I) Rated power factor for the electrical load; /(I)The basic load of the distribution network transformer substation at the moment t in heating season;
Because the heat storage water tank stores heat in the valley period, the power grid is required to provide enough electric quantity to meet the heat requirement of users in the peak period; in order to ensure stable and reliable operation of the heat storage equipment, the operation power of the heat storage water tank is usually limited within the minimum power increment of the heat storage period, and the corresponding heat storage capacity is also limited within the minimum power increment; in practice, however, the amount of power available during the off-peak period is much greater than the minimum available power; if the running power of the heat storage water tank is limited within the minimum bearing capacity of the distribution network, the residual electric quantity in the valley period is not fully utilized; therefore, the available electric quantity in the valley period is regarded as one of key factors influencing the operation of the heat storage water tank, and the specific expression is shown as the following formula (13-14):
Wherein: w i.max is the maximum available low-valley power of the power transmission side line adjacent to the node i; alpha 1 is a margin coefficient; p i,max is the transmission power limit of the branch of the adjacent power supply side of the node i; p base,i,t is the base load of the power supply side branch circuit adjacent to the node i at the moment t; The maximum heat storage capacity of the heat storage water tank connected to the node i is achieved;
Step four: establishing a distribution network bearing capacity opportunity constraint assessment model containing regenerative electric heating access, and determining opportunity constraint confidence according to load regulation capacity;
When the randomness influence of the basic load of the distribution network is considered in the evaluation of the load bearing capacity of the distribution network containing the heat accumulating type electric heating, if the normal electricity utilization of the basic load of the distribution network is not influenced under any climate condition, the heat supply scale which can be borne by the distribution network is very small, the investment cost of the power grid is high, and the actual utilization rate of resources is low. One reasonable option is to bring the probability of the system operating within the constraints to an acceptable value and to determine the confidence level in combination with the load adjustability under demand response.
Because the heat accumulating type electric heating mostly adopts central heating by taking a building as a unit, the method has more practical significance in directly establishing an evaluation model aiming at the maximum load bearing heating area of the distribution network.
The objective function of the distribution network bearing capacity opportunity constraint evaluation model of the heat accumulating type electric heating access is as follows:
wherein: m is the number of nodes in the evaluation area, i= … m; s is the heating area of the evaluation area.
The constraint conditions of the distribution network bearing capacity opportunity constraint assessment model of the heat accumulating type electric heating access are as follows:
1) Tidal current constraint
Pbase,t+Pt E＝Bθt (16)
Wherein: p base,t is a basic power vector injected by the distribution network at the moment t; p t E is an electric boiler operation power vector of the power distribution network accessed at the moment t; b is a node susceptance matrix; and theta t is a node phase angle vector at the time t.
2) Branch transmission power constraint
The branch transmission power should not exceed the transmission power limit.
0≤Pi,j,t≤α2Pi,j,max (17)
Wherein: p i,j,t and P i,j,max are the transmission power and the limit thereof at time t of the branch ij, and α 2 is the margin coefficient.
3) Node phase angle constraint
θi,j,min≤θi,j,t≤θi,j,max (18)
Wherein: θ i,j,max and θ i,j,min are the upper and lower limits of the phase angle difference of the node ij, respectively.
4) Thermal power balance constraint
The thermal power balance constraint is as in formula (6) -formula (10).
5) Distribution network substation transmission power increasable constraint
The total electric power of the electric boiler connected to the distribution network should be smaller than the maximum electric power of the electric boiler allowed to be connected to the distribution network.
6) Thermal storage tank operating power constraints
In order to fully utilize the low-valley electricity quantity, the low-valley available power operation of the tracking circuit of the heat storage water tank is considered. The specific expression is as follows:
Wherein: And (5) the maximum power of the operation of the heat storage water tank on the node i at the moment t.
7) Heat storage capacity constraint of heat storage water tank
The total heat storage capacity of the heat storage water tank of the access node i in the valley period should be smaller than the allowable maximum heat storage capacity.
Considering that the distribution network base load is a random variable, the inequality constraint can be represented by an opportunity constraint:
Wherein: p r {. Beta is a pre-set chance constraint confidence, e.g., 0.95,1-beta represents load adjustability.
Step five: acquiring distribution network basic electricity load probability distribution based on non-parameter nuclear density estimation, extracting a basic load curve of a heat accumulating type electric heating access distribution network according to opportunity constraint confidence, and carrying out deterministic conversion on an evaluation model containing opportunity constraint conditions;
In order to improve the calculation efficiency, determining a load reference value of the power distribution network connected with the heat accumulating type electric heating by means of distribution network base load probability distribution, and substituting the load reference value into a model for solving; converting the assessment model containing the opportunity constraint conditions into a deterministic assessment model:
Wherein: g (·) is all inequality constraints in formula (23); h (·) is an equality constraint; and a reasonable regulation interval is provided for ensuring the safe and reliable operation of the power grid and giving a demand response.
And estimating a distribution network base power utilization load probability density function by adopting the non-parameter core density. If the historical data of the base load of the distribution network is x 1,…,xn,…,xN, the estimation formula of the probability density function is as follows:
wherein: n is the sample volume; h is the bandwidth; k (·) is a kernel function.
Step six: solving the converted evaluation model to obtain the maximum heating area which can be borne by the distribution network;
the distribution network bearing capacity opportunity constraint evaluation model of the heat accumulating type electric heating access is carried out by gurobi optimization software.
The opportunity constraint evaluation method of the distribution network bearing capacity of the regenerative electric heating access is further described below by using the drawings and the embodiments.
The calculation analysis is performed by combining a rural distribution network in Hebei province, wherein the distribution network is provided with 18 nodes and 17 branches, the network topology structure of the distribution network is shown in fig. 3, and the network parameters are shown in table 1.
Table 1 evaluation of regional distribution network parameters
Considering that living habits of residents in rural areas are relatively fixed, four typical buildings such as schools, residential buildings, hospitals and office buildings are taken as examples to evaluate the maximum bearing capacity of the power distribution network. The heat supply time and the design solar heat index of each building are shown in table 2.
TABLE 2 building Heat load demand time and design solar Heat index
The heating season daily unit building area heat load demand is calculated using the heat load demand model, as shown in fig. 4. The evaluation of the carrying capacity of the distribution network is carried out for the whole heating season, which can cause excessive variables, and in order to improve the calculation efficiency, the k-means clustering method is considered to be adopted to gather 150d into 5 types to represent the whole heating season. And the clustering center is used as a distribution network bearing capacity to evaluate a typical heat load curve, such as a thick solid line in fig. 4. The probability distribution of the collected historical electricity consumption data of the base load of the heating distribution network is estimated by a non-parameter nuclear density method as shown in figure 5. The confidence is 0.95 and the maximum load daily distribution network substation basic electricity load curve is shown in fig. 6.
Under the above calculation conditions, the opportunity constraint evaluation method for the distribution network bearing capacity of the heat accumulating type electric heating access is applied to evaluate the distribution network bearing capacity of the embodiment as follows:
In order to verify the effectiveness of the distribution network bearing capacity opportunity constraint evaluation model considering that the distribution network can utilize the low-valley electric quantity and the heat accumulating type electric heating access, the invention compares the influence of three evaluation modes on the distribution network bearing capacity. The specific steps are as follows:
scheme one: based on the maximum load day, the operation power of the heat storage water tank is not more than the evaluation model of the lower limit of the available power of the line.
Scheme II: based on the maximum load day, the thermal storage tank operating power tracks an assessment model of the available power in the valley period.
Scheme III: the invention provides a distribution network bearing capacity opportunity constraint evaluation model containing regenerative electric heating access.
The electric boiler access node 4 is selected to examine the influence of different evaluation schemes on the load capacity of the distribution network, and the evaluation result is shown in fig. 7.
As can be seen from fig. 7, comparing the second and third schemes, the user side can actively participate in the power grid adjustment due to the excitation of the demand response, the power margin of the distribution network increases, and the supportable heat supply scale increases accordingly. Under different permeability, the heat supply area borne by the distribution network can be improved by more than 5% by considering the evaluation mode of the load adjustable capacity. Comparing the first scheme with the second scheme, taking the permeability of 0.2 as an example, the time-by-time power curves of the power supply side line of the node 4 under three evaluation modes are shown in fig. 8. The utilization rate of the low-valley electricity of the lines in the scheme II and the scheme III reaches 100 percent. Compared with the scheme I, the utilization rate of the electricity of the second low-valley is increased by 12.2%, the heat storage capacity is increased by 20.5%, and the corresponding heat supply scale can be improved by 8000m 2. The method for evaluating the bearing capacity of the distribution network by considering the available low-valley electricity quantity is verified, the power supply margin of the distribution network can be more fully utilized, and the maximum heating scale of the distribution network bearing is more effectively improved. The invention provides a distribution network bearing capacity opportunity constraint evaluation model with heat accumulating type electric heating access, which can better mine the potential of the distribution network to accept heat accumulating type electric heating load under the current grid structure. Compared with the scheme III and the scheme I, under different permeabilities, the heat supply scale borne by the distribution network is averagely improved by 24%, the utilization rate of the corresponding low-valley electricity of the distribution network line can be improved by 12%, and the night heat accumulation capacity can also be improved by 27%.
To study the impact of load adjustability on the proposed assessment model, the following calculations were analyzed: taking permeability of 0.2 as an example, respectively carrying out optimization solution according to the method under the condition that the confidence coefficient value is 0.85-1 to obtain distribution network bearing capacities under different confidence coefficients, as shown in fig. 9; the distribution network with different confidence is calculated to bear the heating scale increase rate, as shown in fig. 10.
As can be seen from fig. 9, as the confidence decreases, the load adjustability increases, and the maximum heating area carried by the distribution network increases. The method shows that the enthusiasm of the load side to actively participate in the power grid regulation is improved by formulating a reasonable demand response incentive policy, and the distribution network bears a larger heating area under the existing distribution network topology structure. As can be seen from fig. 10, although the load adjustability is positively correlated with the load-carrying heating scale variation of the distribution network, the heating scale growth rate fluctuates significantly. The increase rate of the change of the heat supply scale which can be borne by the distribution network reaches an extreme value when the confidence coefficient is 0.94 and 0.88 respectively, and the method has reference significance for power distribution network planning: in the power distribution network planning, a demand response technology and a distribution network heating area growth rate can be comprehensively considered to select a proper opportunity constraint confidence level so as to formulate an economic and reasonable heat accumulating type electric heating planning scheme.
Claims (3)
1. The opportunity constraint evaluation method for the distribution network bearing capacity of the heat accumulating type electric heating access is characterized by comprising the following steps of:
step one: acquiring meteorological data and distribution network basic data;
step two: establishing a heat load demand model, and extracting a typical heat load demand curve;
step three: establishing a heat accumulating type electric heating system and an operation control model;
Step four: establishing a distribution network bearing capacity opportunity constraint evaluation model containing regenerative electric heating access, and determining opportunity constraint confidence according to load regulation capacity;
step five: acquiring distribution network basic electricity load probability distribution based on non-parameter nuclear density estimation, extracting a basic load curve of a heat accumulating type electric heating access distribution network according to opportunity constraint confidence level, and carrying out deterministic conversion on an opportunity constraint assessment model;
Step six: solving the converted evaluation model to obtain the maximum heating area which can be borne by the distribution network;
in the second step, when the heat load demand model is established, the following steps are adopted:
calculating the heat load demand by using the indoor body temperature, wherein the indoor body temperature is represented by the following formulas (1) and (2):
At＝-1.3+0.92×Ta,t+2.2×Pa,t (1)
Wherein: a t is the indoor somatosensory temperature at the moment t; t a,t is the air temperature at time T; p a,t is the actual water vapor; r t is the relative humidity at time t;
the energy consumption characteristics of different building types are different, and the actual building heat consumption is greatly influenced by indoor and outdoor temperature differences and is described by adopting a design solar heat index, the heat index is corrected based on the indoor body temperature, and the correction formula is shown as the following formula (3) and formula (4):
Q'k,t＝Qk·Φt (3)
Φt＝(Cn-At)/[Cn-Cw] (4)
Wherein: q k is a solar heat index of the K-th building design, k= … K; q' k,t is the actual heat index of the kth building at the moment t; phi t is the actual heating area heat index correction value at the moment t; c n is the indoor temperature of the design day; c w is the outdoor temperature of the design day;
Thermal load demand model:
Wherein: Representing the heat load demand at node i in period t; a k represents the proportion of the k type building to the total heat supply area; u k,t is a 0-1 variable, i.e., u k,t =0 indicates that the kth class of building has no heat demand during the t period, and u k,t =1 indicates that the kth class of building has heat demand during the t period; x i is the heat accumulating type electric heating area covered by the node i;
The heat accumulating type electric heating system and the operation control model established in the step three are as follows:
1) Heat accumulating type electric heating system model
The system consists of an electric boiler and a heat storage water tank, wherein the electric boiler converts electric energy obtained from a power grid into heat energy, one part of the converted heat energy is supplied to a heat user, and the other part of the converted heat energy is stored through the heat storage water tank;
the heating power of the electric boiler is shown in the formula (6-7):
Wherein: heating power for an electric boiler t moment of an access node i; /(I) The electric power consumed at the moment of the electric boiler t for the access node i; η HP is the heating efficiency of the electric boiler; /(I)The heat storage power at the moment t of the heat storage water tank of the access node i; /(I)Directly supplying heat power to an electric boiler t of an access node i;
The heat storage water tank is used for storing heat in a valley period and releasing heat in a peak period of electricity consumption, and the heat storage characteristic can be expressed as the relation between heat storage capacity and heat storage/release power:
Wherein: η loss represents the self-heat release loss coefficient of the heat storage tank; The heat storage quantity of the heat storage water tank t at the moment of the node i is stored; /(I) The exothermic power of the access node i at the moment of the heat storage water tank t is shown; t 1 and t 2 represent the start time and the end time of the valley period, respectively, that is, the valley period is 22:00 to the next day 8:00; t 1' represents the starting time of the next-day off-peak period; alpha is heat accumulating electric heating permeability, alpha epsilon [0,1], wherein when alpha=1, the heat accumulating water tank covers all heat supplying areas; Δt is the time step;
The heat accumulating type electric heating system obtains electric energy from a power grid side, and through electric-to-heat conversion of an electric boiler, heat energy is provided for a user by coordinating the working state of a heat accumulating water tank, and the heat power balance needs to be met in the operation process:
2) Heat accumulating type electric heating operation control model
In order to ensure safe and reliable operation of the distribution network, the maximum total operation power of the electric boiler connected to the distribution network should satisfy the following conditions:
Wherein: maximum operating power of an electric boiler connected to the distribution network at the time t; /(I) Maximum transmission power can be increased for the moment t of the distribution network substation;
the distribution network transformer substation can increase transmission power, namely, the load quantity allowed to be increased after a certain margin and a basic load are considered for the main transformer of the power grid is assigned, and the maximum transmission power at the moment t of the distribution network transformer substation is as shown in the formula (12):
Wherein: rated capacity for a distribution transformer; delta is the maximum load rate of the distribution transformer; /(I) Rated power factor for the electrical load; /(I)The basic load of the distribution network transformer substation at the moment t in heating season;
Because the heat storage water tank stores heat in the valley period, the power grid is required to provide enough electric quantity to meet the heat requirement of users in the peak period; in order to ensure stable and reliable operation of the heat storage equipment, the operation power of the heat storage water tank is usually limited within the minimum power increment of the heat storage period, and the corresponding heat storage capacity is also limited within the minimum power increment; in practice, however, the amount of power available during the off-peak period is much greater than the minimum available power; if the running power of the heat storage water tank is limited within the minimum bearing capacity of the distribution network, the residual electric quantity in the valley period is not fully utilized; therefore, the available electric quantity in the valley period is taken as one of key factors influencing the operation of the heat storage water tank, and the specific expression is shown as the following formula (13-14):
Wherein: w i.max is the maximum available low-valley power of the power transmission side line adjacent to the node i; alpha 1 is a margin coefficient; p i,max is the transmission power limit of the branch of the adjacent power supply side of the node i; p base,i,t is the base load of the power supply side branch circuit adjacent to the node i at the moment t; The maximum heat storage capacity of the heat storage water tank connected to the node i is achieved;
in the fourth step, constraint conditions of the distribution network bearing capacity opportunity constraint evaluation model of the heat accumulating type electric heating access are as follows:
1) Tidal current constraint
Wherein: p base,t is a basic power vector injected by the distribution network at the moment t; The operation power vector of the electric boiler is accessed to the distribution network at the moment t; b is a node susceptance matrix; θ t is the node phase angle vector at time t;
2) Branch transmission power constraint
The branch transmission power should not exceed the transmission power limit:
0≤Pi,j,t≤α2Pi,j,max (17)
Wherein: p i,j,t and P i,j,max are the transmission power and its limit at time t of the branch ij, and α 2 is the margin coefficient:
3) Node phase angle constraint
θi,j,min≤θi,j,t≤θi,j,max (18)
Wherein: θ i,j,max and θ i,j,min are respectively the upper and lower limits of the phase angle difference of the node ij;
4) Thermal power balance constraint
The thermal power balance constraint is as shown in the formula (6) -formula (10);
5) Distribution network substation transmission power increasable constraint
The total electric power of the electric boiler connected into the distribution network is smaller than the maximum electric power of the electric boiler allowed to be connected into the distribution network:
6) Thermal storage tank operating power constraints
In order to fully utilize the low-valley electricity quantity, the low-valley available power operation of the tracking line of the heat storage water tank is considered, and the specific expression is as follows:
Wherein: The maximum power of the operation of the heat storage water tank on the node i at the moment t is set;
7) Heat storage capacity constraint of heat storage water tank
The total heat storage capacity of the heat storage water tank of the access node i in the valley period is smaller than the allowable maximum heat storage capacity:
considering that the distribution network base load is a random variable, the inequality constraint can be represented by an opportunity constraint:
Wherein: p r {. Beta is a preset chance constraint confidence, e.g., 0.95,1-beta represents load adjustability;
in step five, the assessment model containing the opportunity constraints is converted into a deterministic assessment model:
wherein: g (·) is all inequality constraints in formula (23); h (·) is an equality constraint; the control interval is reasonable in demand response for ensuring safe and reliable operation of the power grid;
Estimating a distribution network base power consumption load probability density function by adopting non-parameter nuclear density, and setting the distribution network base load historical data as x 1,…,xn,…,xN, wherein an estimation formula of the probability density function is as follows:
wherein: n is the sample volume; h is the bandwidth; k (·) is a kernel function.
2. The method of claim 1, wherein in step one, the weather data and the distribution network base data include the following categories:
1) The outdoor ambient temperature and the relative humidity of the heating season are daily;
2) Distribution network equipment parameter information: a distribution network topology; line transmission power limit, main transformer rated capacity of distribution network transformer station and maximum load rate;
3) And the power load data of the daily distribution network foundation in the heating season.
3. The method according to claim 1, wherein in step four, the objective function of the distribution network load-bearing capacity opportunity constraint assessment model of the regenerative electric heating access is:
wherein: m is the number of nodes in the evaluation area, i= … m; s is the heating area of the evaluation area.
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