KR20110109041A - District heat network optimal supply temperature decision method - Google Patents

Abstract

The present invention relates to a method for determining an optimal supply temperature of a district heating network, which is an important operating variable that has a decisive effect on the energy efficiency of a wide area energy network.
The method for determining the optimal supply temperature for district heating network according to the present invention is based on the neural network model that can predict the supply flow rate under the given operating conditions as the backpropagation method using the supply temperature, air temperature, associated heat and time of the district heating network in the past. Establishing; Determining a maximum allowable supply flow rate of a pump operating according to the number of movable boilers for district heating and heat dissipation of the heat storage tank; Determining the minimum allowable supply temperature using the optimization technique using the neural network model in the allowable supply flow range.

Description

District Heating Network Optimal Supply Temperature Decision Method

The present invention relates to a method for determining an optimal supply temperature of a district heating network, which is an important operating variable that has a decisive effect on the energy efficiency of a wide area energy network.

District Heat Networks include various energy production facilities, such as CHP, heat-only boilers, and local waste incinerators, that produce heat and electricity simultaneously to provide energy to users in a large area. It consists of a long-distance heat pipe network and heat storage tank (heat storage tank) for delivering the produced energy, and the equipment that operates it.

District heating Each branch office maintains the heat of the entire area by keeping the difference of supply and recovery pressure, supply pressure, recovery temperature and supply temperature within the operating range in the area where heat transfer of the local heat pipe network is the weakest to satisfy the local heat demand. Use methods to satisfy supply. The weak point of this heat transfer in the local heat pipe network is called the critical point, and if the heat supply condition is satisfied at the critical point, the heat supply of the entire area is smoothly supplied.

Improving energy efficiency while maintaining heat supply conditions at the critical point is a very important challenge. Economic operation, which means the efficient use of energy in district heating, maximizes profits through optimal electricity and heat production and linkage between branches, increases the heat transfer capacity by maximizing the difference in supply and recovery temperature of each branch, and increases the average temperature in the pipe. This can be achieved by minimizing heat loss.

The optimization of supply and recovery temperatures is the most important factor in economic operation of a single heat source, excluding the optimization of electricity and linkage in wide area energy networks. Minimizing the supply temperature also affects the minimization of heat losses through the heat pipe network and the permissible material of the installation piping, so the method of determining the minimum allowable supply temperature is very important for the operation of district heating. In addition, the increase in supply temperature and recovery temperature difference, that is, the reduction of recovery temperature in the heat pipe network can increase the amount of heat transferred to the heat network, thereby reducing the cost of constructing a new heat pipe when expanding the additional supply area.

Accordingly, a problem to be solved by the present invention is to provide a method for determining an optimal supply temperature of a district heating network, which is an important operation variable that has a decisive effect on the energy efficiency of a wide area energy network.

The method for determining the optimal supply temperature for district heating network according to the present invention is based on the neural network model that can predict the supply flow rate under the given operating conditions as the backpropagation method using the supply temperature, air temperature, associated heat and time of the district heating network in the past. Establishing; Determining a maximum allowable supply flow rate of a pump operating according to the number of movable boilers for district heating and heat dissipation of the heat storage tank; Determining the minimum allowable supply temperature using the optimization technique using the neural network model in the allowable supply flow range.

Preferably, the node input XData of the neural network model is represented by the following equation.

XData = {Ta [k- (d)] Ta [k- (d-1)] Ta [k- (d-2)] Ta [k- (d-3)] ... Ta [k- (d )] Ta (k) Qn [k- (d-1)] Qn [k- (d-2)] Qn [k- (d-3)] ... Qn (k) Ts [k- (d) Ts [k- (d-1)] Ts [k- (d-2)] Ts [k- (d-3)] ... Ts (k) Time24] (where Ta is the past atmospheric temperature, Ts is past supply temperature, Qn is past associated heat, and d is delay time.)

The district heating network optimum supply temperature determination method according to the present invention has the advantage of obtaining the optimum supply temperature to minimize the heat loss from the pipe network while satisfying the local heat requirements.

In addition, when applied in parallel with the optimal control of the district heating network, it is possible to achieve a reduction of the recovery temperature according to the application purpose and to maximize the difference between the supply temperature and the recovery temperature.

In addition, it is possible to achieve an increase in local heat supply capacity and, in some cases, maximize waste heat recovery. In addition, if the operating cost of the supply pump of the district heating network is large, it can be used to optimize the heat dissipation and operation cost by optimizing the supply temperature and flow rate.

1 is a diagram illustrating a structure of a neural network model for predicting a supply flow rate satisfying a user's heat demand in a District Heat (DH) network.
Figure 2 is a graph of comparative analysis with the prediction DH flow and actual operation data of district heating sedimentation branch to confirm the reliability of the Neural Network Model of the present invention.
Figure 3 is a Sensibility Analysis graph to give a random change to each Input Node to confirm the reliability of the Neural Network Model of the present invention.
Figure 4 is a graph of the five boiler and heat storage tank (ACC) operation schedule of thermal boilers PLB 1-5 for the Suseo branch.
5 is a graph showing the maximum allowable flow rate of the district heating network according to FIG.
6 is an Optimization Logic Diagram for determining a minimum district heating network supply temperature.
7 to 9 is an analysis graph of the results using the district heating network optimum supply temperature determination method according to the present invention.

Hereinafter, an embodiment of a district heating network supply temperature determination method of the present invention will be described in detail with reference to the accompanying drawings.

The district heating network supply temperature determination method of the present invention comprises the steps of: establishing a neural network model for predicting the supply flow rate in a given operating condition; Determining a maximum allowable supply flow rate according to a boiler and heat storage tank operating state; Determining the minimum allowable supply temperature using the optimization technique using the neural network model in the allowable supply flow range.

Establishing a neural network model to predict supply flow at given operating conditions

1 is a diagram illustrating a structure of a neural network model for estimating a supply flow rate satisfying a user's heat demand in a district heating network.

The supply flow rate of the district heating network of the present invention depends on the supply temperature, the atmospheric temperature, the associated heat quantity and the time of the district heating network in the past, and the neural network uses the backpropagation method.

The neural network model is used as a model for predicting the average district heating network flow rate every hour. The district heating network flow rate is the pressure difference, supply temperature, and temperature at each critical point of the district heating network to satisfy the requirements of the district heat users. Manipulated variable to maintain operating conditions such as supply pressure.

If the time zone to predict DH flow rate is k and the delay time is d time, the node input XData of neural network model is composed as follows. This delay time can be changed according to the application area.

XData = {Ta [k- (d)] Ta [k- (d-1)] Ta [k- (d-2)] Ta [k- (d-3)] ... Ta [k- (d )] Ta (k) Qn [k- (d-1)] Qn [k- (d-2)] Qn [k- (d-3)] ... Qn (k) Ts [k- (d) Ts [k- (d-1)] Ts [k- (d-2)] Ts [k- (d-3)] ... Ts (k) Time24] (where Ta is the past atmospheric temperature, Ts means past supply temperature and Qn means past associated heat.)

For example, if the delay time is 5 hours, the XData is configured as follows.

XData = (Ta (k-5) Ta (k-4) Ta (k-3) Ta (k-2) Ta (k-1) Ta (k) Qn (k-5) Qn (k-4) Qn (k-3) Qn (k-2) Qn (k-1) Qn (k) Ts (k-5) Ts (k-4) Ts (k-3) Ts (k-2) Ts (k-1 ) Ts (k) Time24]

In order to confirm the reliability of the Neural Network Model, data of January 26, 28, 29, and February 2, 2010 of the District Heating Sustainability Office were analyzed based on 24 hours to predict DH Flow and actual operation. Comparison with the data is shown in FIG. 2. As can be seen in Figure 2, in the present invention, the regression of the Neural Network Model using the supply temperature, air temperature, associated heat and time of the district heating network as a control variable can be seen that very high reliability can be obtained with an average of 0.93.

In addition, as shown in FIG. 3, in order to confirm the reliability of the Neural Network Model, Sensibility Analysis was performed by varying each input node by a predetermined value and observing the result. This analysis was carried out to investigate the effect of each input node on the results of the Neural Network Model, and it can be seen that the variation of each operating variable is similar to the field value. Accordingly, it can be seen that the neural network model used in the present invention can use a model that can predict the supply flow rate in a given operating condition.

Determining maximum allowable flow rate according to boiler and heat storage tank operating condition

The maximum allowable flow rate is determined by the number of operating boilers and the heat dissipation of the heat storage tank to satisfy the heat demand at each branch of district heating. For example, the hydrothermal source consists of five boilers and one heat storage tank, four pumps involved in the local supply flow rate, and three pumps involved in the heat storage. The two boilers and the three boilers each have a maximum allowable flow rate of 600 ton / h and 1750 ton / h for heating per hour, and the heat storage tank has a maximum allowable flow rate of 1000 ton / h for heat radiation operation only. For example, if two boilers with a maximum allowable supply flow rate of 1750 ton / h are operated with heat radiation, the maximum allowable supply flow rate is determined to be 4500 ton / h.

Experimental Example

Figure 4 is the result of five boiler and heat storage tank (ACC) operation schedule of thermal boilers PLB 1-5 for the Suseo branch. Optimal schedule results indicate that PLB 1 and 2 have lower boiler efficiencies than other boilers and do not have a high heat requirement, so only three boilers, PLB 3, 4 and PLB5, are operated. In addition, the operation schedule for the heat radiation of the heat storage tank was presented. Through this boiler and heat storage tank operation schedule, the maximum allowable flow rate of the district heating network can be determined as shown in FIG. 5. Two of the five boilers have a maximum allowable flow rate of 600 ton / h for operation, three to 1750 ton / h and 1000 ton / h for heat dissipation from the heat storage tank. From this, the maximum allowable flow rate is determined by the boiler and heat storage operation schedule.

Determination of allowable minimum supply temperature using optimization method using neural network model in allowable flow rate range

6 is an optimized logic diagram for determining a minimum district heating network supply temperature. As shown, the supply temperature at which the district heating network flow rate due to the influence of the district heating network supply temperature in the past does not exceed the maximum allowable supply flow value within the predicted interval affected by the current supply temperature or If no solution satisfies the limit of allowable flow, the maximum allowable supply temperature can be obtained by an optimized solution.

That is, as shown in FIG. 6, the district heating network supply initially estimated from the district heating network supply temperature (DH supply temperature) and the high limit / low limit (Limit is presented from the site under the condition of safe operation). Find the maximum and minimum temperature and estimated values. From the neural network model obtained above, the estimated district heating network flow rate is calculated within the predicted interval affected by the current supply temperature. From the results, until the difference between the minimum value of the Estimated district heating network supply temperature and the Estimated district heating network supply temperature is smaller than the tolerance (0.01 in this study), the following solution is considered to optimize the district heating network supply temperature. Obtain Since the current supply temperature affects the forecast interval, all optimization solutions are considered within the forecast interval. Estimated District heating network supply temperature, the formula for calculating the estimated maximum value / minimum value, see Figure 6.

In the first case, when the estimated flow rate is lower than the maximum allowable flow rate obtained from boiler and heat storage operation schedules, the maximum value of the Estimated district heating network supply temperature is reduced. Applying the reduced maximum value, the optimum solution is obtained by reestimating the Estimated district heating network supply temperature until the difference between the minimum value of the Estimated district heating network supply temperature and the Estimated district heating network supply temperature is less than the tolerance. At this time, the supply temperature is solved within the range not exceeding the high / low limit of the district heating network supply temperature.

In the second case, the minimum value of the Estimated District Heating Network supply temperature is increased when the estimated flow rate is higher than or equal to the maximum allowable flow rate and the Estimated District Heating Network supply temperature does not deviate from the maximum / minimum value of the Estimated District Heating Network supply temperature. . By applying the increased minimum value, the optimum solution is obtained by reestimating the Estimated district heating network supply temperature until the difference between the minimum value of the Estimated district heating network supply temperature and the Estimated district heating network supply temperature is less than the tolerance. At this time, the supply temperature is solved within the range not exceeding the high / low limit of the district heating network supply temperature.

In the third case, when the estimated flow rate is higher than or equal to the maximum allowable flow rate and the Estimated district heating network supply temperature is lower than or equal to the minimum value of the Estimated district heating network supply temperature, the minimum value of the Estimated district heating network supply temperature is reduced. Using the reduced minimum value, obtain the optimized solution by reestimating the Estimated district heating network supply temperature until the difference between the minimum value of the Estimated district heating network supply temperature and the Estimated district heating network supply temperature is less than the tolerance. At this time, the supply temperature is solved within the range not exceeding the high / low limit of the district heating network supply temperature.

In the fourth case, when the estimated flow rate is higher than or equal to the maximum allowable flow rate and the Estimated district heating network supply temperature is higher than or equal to the maximum value of the Estimated district heating network supply temperature, the minimum value of the Estimated district heating network supply temperature and Estimated district heating If the difference in network supply temperature is less than tolerance, the optimal solution for the Estimated district heating network supply temperature is determined.

In the fifth case, when the predicted flow rate does not yield a solution that meets the conditions within the maximum allowable flow rate, the optimal solution is determined by optimizing the value of the maximum allowable temperature of the district heating network supply temperature given at the site.

The district heating network supply temperature optimization can predict the lowest supply temperature for the next 24 hours from the start of the forecast. Variables that the user must enter in order to predict the minimum supply temperature are entered from the site and are estimated start time, current time, maximum allowable district heating network flow rate for each time zone, and maximum limit of supply for each time zone. temperature, lower limit of supply temperature, hourly delta limit of supply temperature, 24 hours of standby temperature and 24 hours of associated calories do. At this time, the air temperature of the next 24 hours can be provided from the Meteorological Agency, the future 24 hours associated calories include that it can be connected to the local network to provide the associated calories.

For the input data, the DHNetwork supply temperature optimization can calculate the maximum allowable flow rate and the minimum allowable supply temperature that does not violate a given operating condition.

Experimental Example

Before applying the system developed in the field, we applied the district heating network Supply Temperature Decision System developed in this study to the past operation data and analyzed the results. The past operational data applied were December 1, 2009 and February 2, 2010, each based on 24 hours.

7 is a result of December 1, 2009 Data. It can be seen that the optimized DH supply temperature is properly indicated in order to maintain an adequate heat supply in a situation where the atmospheric temperature drops sharply after 18:00. The optimized DH feed temperature showed a similar behavior to the actual operated feed temperature. The estimated DH supply flow rate and the actual operating DH supply flow rate are similar, suggesting that the operation was relatively economical in the field. In addition, the reliability of the present invention can be confirmed.

8 is a result of applying the operation data on February 2, 2010. It can be seen that the optimized DH supply temperature is properly indicated in order to maintain an adequate heat supply in a situation where the outside temperature drops sharply after 22:00. As a result of optimization, the minimum value of the DH supply temperature is maintained at 113 degrees from 1 to 17 hours. Compared with the actual operation, the site supply temperature is kept very high, which increases the heat loss from the pipe network and reduces the thermal efficiency. In the case of applying the present invention, there is proposed an operation of reducing heat loss by minimizing the supply temperature.

In addition, the site application of the district heating network optimal supply temperature system was carried out from February 8 to 11, 2010, and updated the actual operating data every hour to provide the operator with the results. In addition, it was carried out flexibly according to the situation of the site.

As described above, the input data are the DH supply temperature, associated heat, outdoor temperature and time, and the limit conditions are the maximum and minimum values of the DH supply temperature, and the maximum allowable range of the DH supply flow rate. The maximum value of DH supply temperature was 120 ℃ and the minimum value was initially set at 113 ℃ for stable supply. The maximum value of the DH feed flow rate used the results of the boiler and heat storage schedules described above.

9 is the result for the February 8 application. Because of the high outside temperature in winter, the District Heating Network Supply Temperature Decision System decided to keep the minimum value of DH supply temperature at 113 ° C. As shown, as a result of field application of the optimum feed temperature system of the present invention, the feed temperature could be lowered to 113 ° C. while meeting local heat demand. In this case, the flow rate of the district heating network predicted by the optimal supply temperature determination system was compared with the actual operation data. It can be seen that the on-site application of the present invention can lower the average DH supply temperature by 5 ° C.

The district heating network optimum supply temperature determination system developed in the present invention can obtain the optimum supply temperature to minimize the heat loss from the pipe network while satisfying the local heat requirements. The application of the developed system to the district heating district has reduced heat losses by 5.16%.

This optimum supply temperature determination system can achieve the reduction of recovery temperature according to the application purpose when applied in parallel with the optimal control of district heating network and can be applied to maximize the difference between supply temperature and recovery temperature.

In this case, an increase in local heat supply capacity and, in some cases, maximize waste heat recovery can be achieved. In addition, if the operating cost of the supply pump of the district heating network is large, it can be used to optimize the heat dissipation and operation cost by optimizing the supply temperature and flow rate.

Claims (3)

Establishing a neural network model for predicting the supply flow rate at a given operating condition as a backpropagation method using the supply temperature, the air temperature, the associated heat quantity and the time of the district heating network in the past;
Determining a maximum allowable supply flow rate of a pump operating according to the number of movable boilers for district heating and heat dissipation of the heat storage tank;
A method of determining an optimal heating temperature for a district heating network, comprising: determining an allowable minimum supply temperature using an optimization technique using the neural network model in an allowable supply flow range. The method of claim 1, wherein the node input XData of the neural network model is represented by the following equation.
XData = {Ta [k- (d)] Ta [k- (d-1)] Ta [k- (d-2)] Ta [k- (d-3)] ... Ta [k- (d )] Ta (k) Qn [k- (d-1)] Qn [k- (d-2)] Qn [k- (d-3)] ... Qn (k) Ts [k- (d) Ts [k- (d-1)] Ts [k- (d-2)] Ts [k- (d-3)] ... Ts (k) Time24] (where Ta is the past atmospheric temperature, Ts is past supply temperature, Qn is past associated heat, and d is delay time.) The method of claim 1, wherein the determination of the allowable minimum supply temperature by using an optimization technique using the neural network model in the allowable supply flow range includes:
Obtaining the initial estimated district heating network supply temperature and estimated maximum / minimum value from the district heating network supply temperature (DH supply temperature) and the high limit / low limit of the supply temperature (Limit is presented from the site under the condition of safe operation). Wow;
Obtaining an estimated district heating network flow rate within the prediction interval affected by the current supply temperature through the Neural Network Model obtained above;
Calculating the optimized value of the district heating network supply temperature by considering the following cases until the difference between the minimum value of the estimated district heating network supply temperature and the estimated temperature of the district heating network supply is smaller than the tolerance. How to determine optimal network supply temperature.
i) When the estimated flow rate is lower than the maximum allowable flow rate obtained from the boiler and heat storage operation schedule, reduce the maximum value of the Estimated district heating network supply temperature and apply the reduced maximum value to the minimum value of the Estimated district heating network supply temperature and Estimated Calculating the Estimated district heating network supply temperature until the difference of the district heating network supply temperature is smaller than the tolerance;
ii) Increase and increase the minimum value of the Estimated district heating network supply temperature when the estimated flow rate is higher than or equal to the maximum permissible flow rate and the Estimated district heating network supply temperature does not exceed the maximum / minimum value of the Estimated district heating network supply temperature. Calculating the estimated temperature of the district heating network supply by applying the minimum value until the minimum difference between the minimum temperature of the estimated district heating network supply temperature and the estimated temperature of the district heating network supply temperature becomes smaller;
iii) When the estimated flow rate is higher than or equal to the maximum allowable flow rate and the Estimated district heating network supply temperature is lower than or equal to the minimum value of the Estimated district heating network supply temperature, decrease the minimum value of the Estimated district heating network supply temperature and reduce the minimum value. Obtaining the estimated temperature of the district heating network supply until the difference between the minimum value of the estimated district heating network supply temperature and the estimated temperature of the district heating network supply is smaller;
iv) when the estimated flow rate is higher than or equal to the maximum allowable flow rate and the estimated temperature of the estimated district heating network supply is higher than or equal to the maximum value of the estimated temperature of the district heating network supply, the minimum value of the estimated temperature of the district heating network supply and the supply of the estimated district heating network If the difference in temperature is smaller, determining an optimized solution of the Estimated district heating network supply temperature;
v) determining the optimum solution of the maximum allowable temperature of the district heating network supply temperature given at the site when the estimated flow rate does not yield a condition within the maximum allowable flow rate in the forecast interval.

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