KR20200076159A - Method for calculating power rate of commercial hvac system, apparatus and method for scheduling load using the same - Google Patents

Abstract

Disclosed are a method for calculating a power distribution rate of a commercial HVAC system, and an apparatus and a method for scheduling a load of an HVAC system using the same. Provided are the method for calculating the power distribution rate of the commercial HVAC system, and the apparatus and the method for scheduling the load of the HVAC system using the same, which are highly precise, highly efficient, and highly reliable since a line network can be efficiently operated by scheduling a load in accordance with price of an input power set on the previous day, and are highly efficient by providing optimal power efficiency information in accordance with a retail price of an input power while the thermal comfort of commercial HVAC system users is ensured.

Description

METHOD FOR CALCULATING POWER RATE OF COMMERCIAL HVAC SYSTEM, APPARATUS AND METHOD FOR SCHEDULING LOAD USING THE SAME}

The present invention relates to a method for calculating the distribution rate of a commercial HVAC system and a load scheduling device and method of the HVAC system using the same, and more specifically, a method for calculating the distribution rate of a commercial HVAC system that induces a demand response and an HVAC using the same It relates to an apparatus and method for load scheduling of a system.

A commercial HVAC system is a thermal air conditioning system that dynamically controls the temperature, ventilation, and humidity in the building to maintain proper conditions, and uses the input power provided by a distribution supplier as a power source.

The problem is that it is difficult or expensive to store power in a centralized power distribution provider. In order to secure the stability and safety of the power supply, a demand response that meets demand and supply is required.

However, the conventional commercial HVAC system is controlled to keep the indoor temperature of the target space constant at a specific value regardless of the retail price of input power. Accordingly, in order to maximize profits, distribution suppliers have devised a time-of-use (TOU) method that has a relatively high distribution rate and require unreasonable payments to commercial HVAC system users.

Such a distribution method of the conventional commercial HVAC system does not also consider distribution network operating conditions and power loss, and thus a reasonable new distribution rate calculation method capable of applying a demand response is required.

An object of the present invention to solve the above problems is to provide a method for calculating the distribution rate of a commercial HVAC system with high efficiency and high reliability.

Another object of the present invention for solving the above problems is to provide a load scheduling device of a high efficiency and high reliability HVAC system.

Another object of the present invention for solving the above problems is to provide a load scheduling method of a high efficiency and high reliability HVAC system.

A load scheduling apparatus according to an embodiment of the present invention for achieving the above object includes a memory and a processor that executes at least one instruction in the memory, wherein the at least one instruction is input power It includes an instruction to schedule the load of the HVAC system based on a distribution pricing model that provides the operational efficiency of a commercial HVAC (Heating, Ventilation, Air Conditioning System) system according to the retail price of the.

In this case, the distribution charge calculation model can be derived using a decision model.

Here, the decision model may include a high-level decision model that calculates the retail price of the input power that guarantees the profits of the distribution supplier.

In addition, the scheduling may be performed for the load in the HVAC system to be used the next day.

The high-level decision model subtracts the wholesale price of the input power from the retail price of the input power on a specific bus in the distribution network and the incremental power loss in the distribution network to supply the distribution You can calculate a company's profit.

In this case, the retail price of the input power may be an amount equal to or less than the retail price of the input power calculated by a time of use (TOU) method from a wholesale price of the input power or more.

In addition, the decision model is a low-level decision model that calculates an optimal operating cost for at least one of the commercial HVAC system user-specific partial input power on a specific bus in a distribution network supplying the input power. It can contain.

Here, the lower-level decision model may calculate an operating cost according to the input power in the approximate section by reflecting a partial linear approximate section of the input power corresponding to a specific room temperature in the thermal response model. .

The thermal reaction model may be modeled by measuring a change in room temperature according to a change in surrounding environment and time in a specific space.

At this time, the ambient environment may include at least one variable of ambient temperature, atmospheric temperature, indoor convective heat gain, indoor radiant heat gain, and cooling rate of the commercial HVAC system.

The distribution rate calculation model may be based on a real-time pricing method.

Load scheduling method according to another embodiment of the present invention for achieving the above object is a step of calculating the distribution fee according to the input power using the distribution fee calculation model and commercially to be used the next business day (翌日) according to the calculated distribution fee And scheduling at least one load in the HVAC (Heating, Ventilation, Air Conditioning System) system.

In addition, the load scheduling method may further include generating the distribution rate calculation model using a decision model before calculating the distribution rate according to the input power using the distribution rate calculation model.

The step of generating the distribution price calculation model using the decision model includes calculating a retail price of the input power that guarantees the profit of the distribution supplier using a high-level decision model and a low-level decision model Using, it may include the step of calculating an optimal operating cost for the partial input power for each user of the commercial HVAC system on a specific bus in a distribution network supplying the input power.

In this case, the high-level decision model subtracts the wholesale price of the input power and the incremental power loss in the distribution network from the retail price of the input power on a specific bus in the distribution network. You can calculate profits from distribution suppliers.

Further, the retail price of the input power may be an amount equal to or less than the retail price of the input power calculated by a time-of-use (TOU) method from a wholesale price of the input power or more.

Generating the distribution rate calculation model using the decision model is a thermal reaction prior to the step of calculating the retail price of the input power that guarantees the profit of the distribution supplier, using the high-level decision model And generating a model.

The thermal reaction model may be modeled by measuring a change in room temperature according to a change in surrounding environment and time in a specific space.

In addition, the distribution price calculation model may be based on a real-time pricing method.

In accordance with another embodiment of the present invention for achieving the above object, a method for calculating a distribution fee for input power distributed to a commercial heating, ventilation, air conditioning system (HVAC) system is described above on a specific bus in a distribution network. Calculating a retail price of input power, calculating a partial input power consumption for the retail price of at least one of the commercial HVAC system users, and calculating the retail price of the input power by reflecting the partial input power usage It includes.

A method for calculating the distribution rate of a commercial HVAC system according to an embodiment of the present invention and a load scheduling device and method of the HVAC system utilizing the same, by scheduling the load according to the price of the input power set the previous day, high-precision enabling efficient operation of the wiring network , It is possible to provide a distribution method of a commercial HVAC system with high efficiency and high reliability, and a load scheduling device and method of the HVAC system utilizing the same.

In addition, the distribution rate calculation method of the commercial HVAC system and the load scheduling device and method of the HVAC system utilizing the same ensure the thermal comfort of users of the commercial HVAC system, and at the same time, provide optimal power efficiency information according to the retail price of input power. By providing, it is possible to provide a method for calculating a distribution rate of a high-efficiency commercial HVAC system and a load scheduling apparatus and method of the HVAC system utilizing the same.

1 is a block diagram of a load scheduling apparatus for a commercial HVAC system according to an embodiment of the present invention.
2 is a flowchart of a load scheduling method for a commercial HVAC system according to an embodiment of the present invention.
3 is a flowchart of a step of generating a rate calculation model among load scheduling methods for a commercial HVAC system according to an embodiment of the present invention.
4 is an image of a test experimental model for generating a thermal reaction model according to an experimental example of the present invention.
5 is a graph illustrating a change in room temperature according to an increase in input power of a commercial HVAC system according to an embodiment of the present invention.
6 is a conceptual diagram illustrating profit calculation of a commercial HVAC system according to an embodiment of the present invention.

The present invention can be applied to various changes and can have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. In describing each drawing, similar reference numerals are used for similar components.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from other components. For example, the first component may be referred to as a second component without departing from the scope of the present invention, and similarly, the second component may be referred to as a first component. The term "and/or" includes a combination of a plurality of related described items or any one of a plurality of related described items.

When an element is said to be "connected" or "connected" to another component, it is understood that other components may be directly connected or connected to the other component, but other components may exist in the middle. It should be. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that no other component exists in the middle.

The terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "include" or "have" are intended to indicate the presence of features, numbers, steps, actions, components, parts or combinations thereof described in the specification, but one or more other features. It should be understood that the existence or addition possibilities of fields or numbers, steps, operations, components, parts or combinations thereof are not excluded in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person skilled in the art to which the present invention pertains. Terms, such as those defined in a commonly used dictionary, should be interpreted to have meanings consistent with meanings in the context of related technologies, and should not be interpreted as ideal or excessively formal meanings unless explicitly defined in the present application. Does not.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In order to facilitate the overall understanding in describing the present invention, the same reference numerals are used for the same components in the drawings, and duplicate descriptions for the same components are omitted.

1 is a block diagram of a load scheduling apparatus for a commercial HVAC system according to an embodiment of the present invention.

Referring to FIG. 1, the load scheduling device may be linked with a heat-conditioning system (HVAC; Heating, Ventilation, Air Conditioning System, HVAC system) provided in a commercial building to schedule a load of the HVAC system D. However, the load scheduling device is not limited to the above, and may be provided mounted on the HVAC system D.

According to an embodiment, the load scheduling apparatus may set a schedule of the load in the HVAC system D according to the retail price of input power distributed to the HVAC system D.

Here, the retail price of power provided from the distribution supplier S may be calculated using a bi-level decision model.

The bi-level decision model may be a model generated by reflecting both user positions of the distribution supplier and the commercial HVAC system (D) having conflicting interests in calculating distribution rates.

According to an embodiment, the bi-level decision model is a model generated based on the Stackelberg game theory, which is a distribution supplier who wants to maximize profits from distribution power retailers ( S) can include a high-level decision-making step to reflect the purpose and a low-level decision-making step to reflect the user's purpose of using a commercial HVAC system (D) that maximizes power efficiency but minimizes distribution costs. have. The method of calculating the distribution fee of the commercial HVAC system (D) using the bi-level decision model will be described in more detail when the following load scheduling method is described.

The load scheduling device may include a memory 1000 and a processor 5000.

The memory 1000 may include at least one instruction for executing the processor 5000, which will be described later.

According to an embodiment, the at least one command is a command to calculate a retail price of input power distributed to a commercial heating, ventilation, air conditioning system (HVAC) system using a distribution pricing model and retail of the calculated input power It may include an instruction to schedule the load in the commercial heating, ventilation, air conditioning system (HVAC) system to minimize the distribution fee according to the price.

As described above, the processor 5000 may operate according to at least one instruction stored in the memory 1000. As described above, the operation of the processor 5000 will be described in more detail in the following description of the load scheduling method.

In the above, a load scheduling apparatus of a commercial HVAC system according to an embodiment of the present invention has been described. Hereinafter, as described above, the load scheduling method performed by the operation of the processor in the load scheduling apparatus will be described in more detail.

2 is a flowchart of a load scheduling method for a commercial HVAC system according to an embodiment of the present invention.

Referring to FIG. 2, the processor 5000 in the load scheduling apparatus may calculate a retail price of input power distributed to a commercial HVAC system using a distribution price calculation model (S1000). According to an embodiment of the present invention, the distribution price calculation model changes the price of input power in real time according to the size of the load on a specific bus in the distribution network. It may be a model based on a real-time pricing (RTP) method.

Here, the distribution charge calculation model may be generated and provided before calculating the retail price of input power. The method for generating the distribution charge calculation model will be described in more detail in FIG. 3 below.

3 is a flowchart of a step of generating a distribution rate calculation model among load scheduling methods for a commercial HVAC system according to an embodiment of the present invention.

Referring to FIG. 3, the processor 5000 may generate a thermal reaction model (S1000).

More specifically, the thermal response model may be a model for reflecting a heat loss rate of a target space that is changed according to conditions of a surrounding environment and time, when generating a low-level decision model among the decision models to be described later. The thermal reaction model will be described in more detail with reference to FIG. 4 below.

4 is an image of a test experimental model for generating a thermal reaction model according to an experimental example of the present invention.

Referring to FIG. 4, the thermal reaction model may be a model of a change in the indoor temperature of the target space according to a change in surrounding environment and time, as described above.

Measurement of internal heat gain over time in a test space according to an experimental example of the present invention

A test space area of 5.18 m X 3.66 m X 2.44 m and 3.45 m X divided into two zones by a wall including a window divided into a test space of 8.63 m X 3.66 m X 2.44 m where the indoor temperature is kept constant. A climate space area of 3.66m x 2.44m was prepared.

Then, lighting and heat sources in the test space area, and a variable speed heat pump (VSHP), which is a type of HVAC system, was arranged to measure the room temperature (T _ht ) in the test space area.

In order to confirm the internal heat gain of the test space area according to the experimental example of the present invention, the experimental results are summarized as a model using an inverse transfer function (ITF).

In order to calculate the indoor temperature for each space and time in a building where a commercial HVAC system is installed, it can be generalized as shown in [Equation 1] below based on the experimental results.

T _ht : room temperature

T _at : Ambient temperature

T _xt : Air temperature

Q _ht : HVAC system cooling rate

Q _ct : Indoor convection gain

Q _xt : Radiant heat gain

a _ht to f _ht : parameters

Here, the variables of the ambient temperature (T _at ), atmospheric temperature (T _xt ), indoor convection gain (Q _ct ), and radiant heat gain (Q _xt ) of [Equation 1] are operational information of the building where the commercial HVAC system is installed. Can be obtained from

At this time, the parameters (a _ht ~ f _ht ) may also vary depending on the structure of the building and the direction in which the building is located. Accordingly, the indoor temperature (T _ht ) measurement data of the target space calculated using the thermal reaction model according to Equation 1 is compared with the measurement data provided by the target building where the commercial HVAC system is installed, and the power distribution will be described later. It can be reflected in the pricing model.

In order to simplify the thermal reaction model according to [Equation 1], the parameter g _ht indicating the environmental condition of the target building can be expressed as [Equation 2] below. Here, g _ht may be b _ht T _at + c _ht T _xt + e _ht Q _ct + f _ht Q _rt .

g _ht : parameters

The room temperature (T _ht ) in the thermal reaction model according to [Equation 2] is as shown in [Equation 3], the cooling rate (Q) for each time (t) provided by the commercial HVAC system (h) (Q) _ht ). At this time, δ _mhk may be the partial input power _disclosed in FIG. 4 to be described later.

Q _ht : Overall commercial HVAC system cooling rate

δ _mhk : Partial input power

In addition, the indoor temperature (T _ht ) for the total input power of a commercial HVAC system may be simplified as shown in [Equation 4] using [Equation 2] and [Equation 3].

5 is a graph illustrating a change in room temperature according to an increase in input power of a commercial HVAC system according to an embodiment of the present invention.

Referring to FIG. 5, a change in room temperature (T _ht ) according to an increase in input power over time of a commercial HVAC system simplified by Equation 4 may be represented by a non-linear curve graph (A).

In addition, based on the curve graph (A), the indoor temperature (T _ht ) change for the partial input power of the commercial HVAC system, based on the segment (m) of the input power, the indoor temperature for the partial input power ( T _ht ) can be represented by a linear graph (B) of partial linear approximation.

More specifically, according to an embodiment, in the partial input power (δ _mht ) of a commercial HVAC device, m may be a power segment, h may be a commercial HVAC system number, and t may be time.

Here, when the partial input power (δ _mht ) represents a value between 0 and δ _{mh, max} , the linear slope (F _mhtj ) of the room temperature (T _ht ) at the partial input power (δ _mht ) is time t=k It can be represented by a constant linear gradient (B) of the room temperature T _ht at time t=j determined by the mth segment power of the commercial HVAC system h at (k≦j).

In other words, the operating range of a commercial HVAC system from 0 to P _{h, max} can be divided into N _L segments. Accordingly, through the partial linearization method, the nonlinear thermal response in the building to the input power of a commercial HVAC system can be approximated as shown in [Equation 5].

N _L : Number of power segments

F _mhtj : linear slope

Referring to FIG. 4 again, as described above, the generalized [Equation 1] is a formula obtained by using a test model according to an experimental example of the present invention, and thus an ITF (Inverse Transfer Function) Model), it can be converted to [Equation 6], which can also be applied to buildings with multiple zones.

In more detail according to an embodiment, the ITF model applied to Equation 1 above may interact with at least one ITF model in different regions. Thus, the extended ITF model can be integrated with the HVAC system model. Accordingly, the extended ITF model is expressed as a set of nonlinear curves for the internal temperature for each zone, and individual curves can be expressed similarly to the curve graph (A) of FIG. 4.

Accordingly, the thermal reaction model may be expressed as a form in which the room temperature T _ht ^e of each zone is approximated using partial linearization, as shown in [Equation 6]. Here, F _mhtj ^e can reflect the interaction between multiple zones for commercial HVAC system operation.

Therefore, the load scheduling method of the commercial HVAC system according to the embodiment of the present invention can calculate the distribution cost minimized by calculating the distribution rate based on the thermal reaction model reflecting the internal heat gain of the target space by space and time, and the commercial HVAC It is possible to provide a high-precision and high-performance load scheduling method in which thermal comfort of system users is guaranteed.

Referring back to FIG. 3, the processor 5000 may generate a decision model (S1300). Here, the decision model may include a high-level decision model and a low-level decision model.

Generally, in calculating distribution rates, the objectives sought may be different between distribution providers and users of commercial HVAC systems. For example, in the case of distribution suppliers, profit seeking may be a priority, and for users of commercial HVAC systems, it may be aimed at maximum power efficiency at the lowest cost.

Thus, the distribution charge calculation model for load scheduling of the load scheduling device according to the embodiment of the present invention is a step-by-step decision model between a distribution supplier and a user of a commercial HVAC system based on the Stackelberg game theory. It can be calculated by reflecting.

More specifically, according to an embodiment, the processor 5000 may calculate the maximum profit of the distribution provider using a high-level decision model (S1310). The method of calculating the maximum profit of the distribution supplier using the high-level decision model will be described in more detail in FIG. 6 below.

6 is a conceptual diagram illustrating profit calculation of a commercial HVAC system according to an embodiment of the present invention.

Referring to FIG. 6, the profits taken by the distribution supplier are input power purchase costs according to the locational margin price (LMP) purchased in the wholesale market the previous day from the distribution price obtained from the user of the commercial HVAC system, Incremental power loss of power in the distribution network and bus voltage deviation can be defined as the value subtracted.

The following [Equation 7] summarizes a method of calculating the optimal distribution power price (C _t ) for maximizing the profit (J _DV ) of the distribution supplier according to an embodiment of the present invention.

C _t : Distribution power price

J _DV : maximum profit

δ _mht ^v : Partial input power

M _t : wholesale price

L _t : Incremental power loss

N _T , N _B , N _HV , N _L : Wiring network

Here, the distribution power price (C _t ) is the wholesale price (M _t ) as the lower limit, and the power price calculation (TOU, Time Of Use) amount (U _t ) for each time zone in which the price of the input power varies for a specific time period is set as the upper limit. Can be set.

Accordingly, the distribution price calculation model according to an embodiment of the present invention may cause a decrease in profits of a distribution provider in the short term, but as a result, it induces a demand response of users of commercial HVAC systems according to a reduction in power rates, thereby supplying distribution It can lead to mutual satisfaction between business and commercial HVAC system users.

In addition, the distribution price calculation model according to an embodiment of the present invention prevents the indiscriminate pursuit of profits by distribution suppliers, and ensures the flexibility of commercial HVAC system loads, thereby improving the voltage stability of the distribution network and improving the efficiency of the distribution network. Can provide

In order to calculate the incremental power loss of Equation 7, the processor 5000 sums the input power of the entire commercial HVAC system (P _t ^V ) of a specific bus V at a specific time t in the distribution network. ) Can be modified as shown in [Equation 8].

V: Bus

N _H ^V : Number of HVAC systems with RTP calculation method

N _L ^V : Number of power segments

N _C ^V : Number of HVAC systems with TOU calculation method

Thereafter, the processor 5000, when the bus V is included in N _B (V _B N _B ={1, 13, 18, 42, 47, 52, 57, 60, 63, 67, 76, 81, 89 , 97, 101}), the sum (P _t ^V ) of the input power of the entire commercial HVAC system can be expressed in the form of [Equation 9].

The processor 5000 uses the power flow equation for a three-phase network to sum the input power (P _t ^V ) of the entire commercial HVAC system [Equation 10] and [Equation 11] Can be induced. As a result, conversion to a processor (5000) is the incremental power loss (L _t) and a variation of the bus voltage in the power distribution network in accordance with commercial HVAC load (△ V _t) for each of the sensitivity matrix of J _{loss, t} and J _{v, t} Can be calculated.

L _t : Incremental power loss

J _loss,t : Sensitivity matrix of incremental power loss

A: Matrix for converting individual HVAC loads to total HVAC loads on the distribution network bus

P _S,T ^V : Total input power of HVAC system in RTP method sensitive to retail price on time t and bus v

A·P _t ^v : Column vector

△V _t : Deviation of bus voltage

J _v,t : Sensitivity matrix of bus voltage deviation

t _ps : Start time of peak time zone

t _pe : End time of peak time zone

Referring to [Equation 11], the processor 5000 is a distribution network caused by P _t ^v _- on all buses (n NN _B ) belonging to N _B in a peak time zone (t _ps ≤ t ≤ t _pe ). The value of ΔV _t ⁿ of all the buses (n∈N _B ) present in can be maintained within or around ±ΔV _max .

At this time, to the increment power loss (L _t) and a variation of the bus voltage to calculate (△ V _t), the transformation matrix A in the distribution network can be defined as [Equation 12].

More specifically, each element of P _t ^v in [Equation 11] may be rearranged according to a network topology. According to an embodiment, each element of A·P _t ^v is for bus n=V, and the total commercial HVAC system load P _t ^V is represented by 6·N _A components, as in Equation 13 below. Can.

[P _t ^va , P _t ^vb , P _t ^vc ] ^T : 3 phase active power

[Q _t ^va , Q _t ^vb , Q _t ^vc ] ^T : 3 phase reactive power

At this time, when the commercial HVAC system load P _t ^V is assumed to be a three-phase balanced condition, the three-phase active power (P _t ^V ) is P _t ^va =P _t ^vb =P _t ^vc =1/3·P _t ^v _, and three phase reactive powers Q _t ^va , Q _t ^vb , and Q _t ^vcs may be set to zero. Accordingly, according to an embodiment, when the commercial HVAC system is a variable speed drive (VSD), the variable speed drive (VSD) is operated at a unit power factor, thereby improving energy efficiency and efficiency of use of converter capacity.

Referring back to FIG. 3, the processor 5000 may calculate an optimal operating cost of a commercial HVAC system according to partial input power in a specific bus v among distribution networks using a low-level decision model (S1350) ).

More specifically, according to an embodiment, the optimal operating cost of the commercial HVAC system according to the partial input power (δ _mht ^v ) may be expressed as Equation 14 below.

δ _mht ^v : Partial input power

At this time, the total operating cost (J _UC ^v = ∑ _t Ut·∑ _v ∑ _h P ^v _c,ht ) of the commercial HVAC system on a specific bus v in the distribution network is constant, and the optimum partial input power (δ _mht ^v ) It can be represented by adding with Equation 14 above without affecting the value (J _U ^v = J _UV ^v + J _UC ^v ).

Σ δ _mht ^v _-: Input power of commercial HVAC systems

P _h,max ^v- _: Maximum input power of commercial HVAC systems

D _h ^v : Limit of the rate of reduction of the input power

R _h ^v : Limit of increasing speed of input power

△t _unit : Unit time interval (1h)

The processor 5000 applies the thermal reaction model in [Equation 5] to [Equation 15], so that the approximate room temperature T _ht ^v is a value between T _ht,min ^v and T _ht,max ^v . You can keep it.

More specifically, referring to FIG. 5, the processor 5000 sets the boundary conditions given to δ _mht ^v disclosed in [Equation 16] using the binary variable b _mht ^v to complete the partial linear approximation. Can.

According to the boundary conditions according to the embodiment, δ _mht can increase from 0 to δ _mh,max only after increasing from δ _{(m-1) ht} to δ _(m-1)h,max .

In addition, the boundary conditions are the conditions in which the input power (∑ δ _mht ^v ) of commercial HVAC systems should be less than the maximum input power (P _h,max ^v- ) according to [Equation 19], in [Equation 20]. Accordingly, a condition for designating an increase limit (R _h ^v ) of input power and a decrease limit (D _h ^v ) of input power during a unit time interval (t _unit = 1 h) may be included.

According to an embodiment, when a commercial HVAC system is provided with a variable speed heat pump (VSHP), an increase limit (R _h ^v ) of the input power during the unit time interval (t _unit = 1 h) is It can be determined in a range in which no severe working stress is applied to the heat pump compressor.

In the load scheduling method according to an embodiment of the present invention, the low-level decision model reflects the thermal response model disclosed above to determine the operating cost of the commercial HVAC system for each partial input power, thereby guaranteeing thermal comfort of the commercial HVAC system user It is possible to provide a high-performance and high-efficiency load scheduling method capable of calculating a minimum cost distribution fee within a range.

Thereafter, the processor 5000 may generate a distribution charge calculation model using the generated decision model (S1500).

In other words, the processor 5000 may combine one upper decision model and a plurality of lower decision models to generate a rate calculation model. Accordingly, the load scheduling apparatus according to an embodiment of the present invention can set the operation schedule of at least one HVAC system existing in the distribution network.

More specifically, according to an embodiment, when the processor 5000 refers to [Equation 16] to [Equation 18], the binary variable for partial linear approximation (b _mht ^v ) is b _mht ^v ∈{0, If 1}, it can be relaxed to 0≤b _mht ^v ≤1. At this time, the scheduling by δ _mht ^v may be maintained as before.

Thereafter, the KKT condition is applied to a sub-decision model to generate a pricing model, and as shown in [Equation 21], a Lagrange equation and a complementary slack condition can be derived.

In order to apply the KKT condition, the binary variables b _mht ^v ∈{0, 1} for partial linear approximation in [Equation 16] to [Equation 18] may be relaxed to 0 ≤ b _mht ≤ 1. In this case, referring to FIG. 5, when the time t is k and j, the absolute value of F _mhkj , which is the slope of the partially linearized curve, decreases monotonically as the load of the commercial HVAC system increases. The schedule of δ _mht ^v ) can be kept constant.

Thereafter, the processor 5000 applies the KKT condition along with the relaxed b _mht condition to the low-level decision model, so that the optimal partial input power according to the retail price (C _t ) between the distribution supplier and the user of the commercial HVAC system (∑ _m δ _mht ^v ) can be generated as shown in [Equation 23] to calculate the distribution price calculation model.

η: upper limit of the sum of complementart slackness term

π: positive constant

Referring to FIG. 2 again, the processor 5000 may perform load scheduling during the next business day of the commercial HVAC system based on the distribution charge calculation model (S5000).

In more detail, the distribution rate calculation model of the RTP rate method has a large difference in distribution rates between peak and off-peak time zones formed according to time zones, so the operation of the load in the HVAC system to be used the next day is preliminary. Can be scheduled on.

As described above, a method for calculating a distribution rate of a commercial HVAC system according to an embodiment of the present invention and a load scheduling apparatus and method of the HVAC system using the same have been described.

The method for calculating the distribution fee of the commercial HVAC system and the load scheduling apparatus and method of the HVAC system using the same, according to an embodiment of the present invention, enables efficient operation of the wiring network by scheduling the load according to the price of the input power set the previous day. It is possible to provide a high-precision, high-efficiency and high-reliability commercial HVAC system's distribution rate calculation method and a HVAC system's load scheduling device and method using the same. In addition, by ensuring the thermal comfort of users of commercial HVAC systems, and by providing optimal power efficiency information according to the retail price of input power, a distribution method of a high-efficiency commercial HVAC system is calculated and the load scheduling of the HVAC system using the same Devices and methods can be provided.

The operation of the method according to an embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. The computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer system are stored. The computer-readable recording medium can also be distributed over network coupled computer systems so that the computer readable program or code is stored and executed in a distributed fashion.

In addition, the computer-readable recording medium may include a hardware device specially configured to store and execute program instructions, such as a rom, a ram, and a flash memory. Program instructions may include high-level language code that can be executed by a computer using an interpreter or the like, as well as machine code such as those produced by a compiler.

While some aspects of the invention have been described in the context of an apparatus, it can also represent a description according to a corresponding method, where a block or apparatus corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be represented by features of corresponding blocks or items or corresponding devices. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, programmable computer, or electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such an apparatus.

In embodiments, a programmable logic device (eg, field programmable gate array) may be used to perform some or all of the functionality of the methods described herein. In embodiments, the field programmable gate array may work with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by some hardware device.

Although described above with reference to the preferred embodiments of the present invention, those skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can.

1000: Processor 5000: Memory
S: Power distribution supplier D: Commercial HVAC system

Claims (20)

Memory; And
A processor that executes at least one instruction in the memory,
The at least one command,
Load scheduling device comprising instructions to schedule the load on the HVAC system based on a distribution pricing model that provides operational efficiency of a commercial heating, ventilation, air conditioning system (HVAC) system at retail price of input power . According to claim 1,
The distribution rate calculation model is a load scheduling device derived using a decision model. According to claim 2,
The decision model above
Load scheduling device comprising a high-level decision model to calculate the retail price of the input power to ensure the profits of the distribution supplier. According to claim 1,
The scheduling is a load scheduling device that is performed on a load in the HVAC system to be used the next day. According to claim 2,
The high-level decision model
A load that calculates the profits of the distribution supplier by subtracting the wholesale price of the input power and the incremental power loss in the distribution network from the retail price of the input power on a specific bus in the distribution network. Scheduling device. The method of claim 5,
The retail price of the input power is
A load scheduling device that is an amount equal to or less than the retail price of the input power calculated by a time of use (TOU) method from the wholesale price of the input power or more. According to claim 2,
The decision model above
And a low-level decision model for calculating an optimum operating cost for at least one commercial HVAC system user-specific partial input power on a specific bus in a distribution network supplying the input power. The method of claim 7,
The lower level decision model
A load scheduling apparatus for calculating an operating cost according to the input power in the approximate section by reflecting a partial linear approximate section of the input power corresponding to a specific room temperature in a thermal reaction model. The method of claim 8,
The thermal reaction model is a load scheduling device that is modeled by measuring changes in room temperature according to changes in surrounding environment and time in a specific space. The method of claim 8,
The ambient environment, load scheduling device including at least one variable of ambient temperature, ambient temperature, indoor convective heat gain, indoor radiant heat gain and the cooling rate of the commercial HVAC system. According to claim 1,
The distribution rate calculation model is a load scheduling device based on a real-time pricing method. The step of making a retail price according to the input power using the distribution pricing model and
And scheduling at least one load in a commercial HVAC (Heating, Ventilation, Air Conditioning System) system to be used the next business day according to the calculated distribution fee. The method of claim 12,
A load scheduling method further comprising the step of generating the distribution price calculation model using a decision model before the step of calculating the distribution rate according to the input power using the distribution rate calculation model. The method of claim 13,
Generating the distribution rate calculation model using the decision model is
Calculating a retail price of the input power that ensures the profits of the distribution supplier, using a high-level decision model; and
Using a low-level decision model, calculating an optimum operating cost for at least one partial input power per user of the commercial HVAC system on a specific bus in a distribution network supplying the input power Load scheduling method. The method of claim 14,
The high-level decision model subtracts the wholesale price of the input power from the retail price of the input power on a specific bus in the distribution network and the incremental power loss in the distribution network to supply the distribution A load scheduling method that calculates a company's profit. The method of claim 15,
A load scheduling method in which the retail price of the input power is an amount less than the retail price of the input power calculated by a time-of-use (TOU) method from a wholesale price of the input power or more. The method of claim 12,
Generating the distribution rate calculation model using the decision model is
And generating a thermal response model prior to calculating the retail price of the input power that ensures the profits of the distribution supplier, using the high-level decision model. The method of claim 17,
The thermal reaction model is a load scheduling method that is modeled by measuring changes in room temperature according to changes in surrounding environment and time in a specific space. The method of claim 12,
The distribution price calculation model is a load scheduling method based on a real-time pricing method. In the method of calculating the distribution fee for the input power to be distributed to a commercial HVAC (Heating, Ventilation, Air Conditioning System) system,
Calculating a retail price of the input power on a specific bus in a distribution network;
Calculating partial input power usage for the retail price of at least one of the commercial HVAC system users; And
And calculating a retail price of the input power by reflecting the partial input power usage.

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