Optimization method applied to operation cost of ice storage system

Abstract

The invention relates to an optimization method applied to the operation cost of an ice storage system. Energy consumption mathematical models, including a refrigerating unit energy consumption model, a water pump energy consumption model and an ice storage device energy consumption model, of energy station operation equipment are established according to the actual situation of ice storage system equipment; and according to the established energy consumption model and a local peak-valley electricity price policy, a hourly control strategy of the ice storage system under different electricity prices is determined. And by taking the daily operating cost as an objective function and the hourly load distribution proportion as a decision variable, establishing an operating cost optimization model of the ice storage system by applying a particle swarm algorithm, and solving the operating cost optimization model. Operation regulation and control of the ice storage system can be guided, and a foundation is laid for practical engineering application.

Description

Technical Field

[0001] The present invention belongs to the technical field of heating, ventilation and air conditioning, and particularly relates to an optimization method for the operating cost of an ice storage cooling system. Background Art

[0002] Since the load demand change cycle of the cooling system in summer often has a high overlap with the power grid load change cycle, the operating cost of the cooling system increases. Ice storage air conditioning uses the latent heat of water freezing into ice to work. It refrigerates and stores ice at night and stores it in the ice storage device, and melts the ice for cooling during the day, achieving the effect of "shifting peak and filling valley" for the power consumption of the cooling system. Therefore, many airports adopt ice storage technology to save operating costs. At present, there are various research methods for short-term and day-ahead load forecasting at home and abroad, and the research is quite in-depth. These methods make full use of the information of time series data and can accurately predict the hourly load of buildings. According to the load forecasting results, this chapter optimizes the operating strategy of the ice storage cooling system of the airport terminal energy station to reduce the operating energy consumption and operating cost of the airport energy station. Summary of the Invention

[0003] The present invention aims to overcome at least one defect (shortcoming) of the above-mentioned prior art, and provides an optimization method for the operating cost of an ice storage cooling system, which can guide the operation and regulation of the ice storage cooling system and lay a foundation for practical engineering applications.

[0004] The technical problems of the present invention are solved by the following technical solutions:

[0005] Step 1: Establish an equipment energy consumption model according to the equipment parameters of the ice storage cooling system;

[0006] Step 2: According to the established equipment energy consumption model and the local peak-valley electricity price policy, formulate an hourly control strategy for the ice storage cooling system under different electricity prices;

[0007] Step 3: Establish an operating cost optimization model of the ice storage cooling system according to the established equipment energy consumption model and the hourly control strategy of the ice storage cooling system under different electricity prices;

[0008] Step 4: Solve the established operating cost optimization model of the ice storage cooling system.

[0009] The specific implementation method of the refrigeration unit energy consumption model in the above Step 1 is as follows:

[0010]

[0011] Wherein, P chiller (t) is the power of the refrigeration unit at time t, in kW; Q k (t) is the refrigeration capacity of the kth refrigeration unit at time t, in kW; COP kThe coefficient of performance of the k-th refrigeration unit at time t is (t).

[0012] The specific implementation method of the variable-frequency water pump energy consumption model in step 1 is as follows:

[0013]

[0014] Where: P v is the total energy consumption of the variable-frequency water pump, kW; P v，i is the energy consumption of the i-type variable-frequency water pump during operation, kW; N v，i is the number of operating i-type variable-frequency water pumps; q e，t is the rated flow rate of the i-type variable-frequency water pump, m 3 / h; q i，t is the flow rate of the i-type variable-frequency water pump at time t, m 3 / h.

[0015] The specific implementation method of the non-variable-frequency water pump energy consumption model in step 1 is as follows:

[0016]

[0017] Where: P f is the total power of the non-variable-frequency water pump, kW; P f，i is the power of the i-type non-variable-frequency water pump, kW; N f，i is the number of operating i-type non-variable-frequency water pumps.

[0018] The specific implementation method of ice storage in the ice storage device model in step 1 is as follows:

[0019]

[0020] Where: Q ice (t) is the ice storage capacity of the ice tank at time t, kW; PLR k (t) is the partial load ratio of the k-th dual-condition unit at time t; Q n (k) is the rated ice-making capacity of the k-th dual-condition unit, kW; δ is the conversion rate of the cooling capacity of the dual-condition unit into ice storage capacity, which is obtained by fitting according to actual project data.

[0021] The specific implementation method of ice melting in the ice storage device model in step 1 is as follows:

[0022] Q melt = U * A * ΔT m * 10 -3

[0023] Where: Q melt is the heat exchange amount of ice melting in the ice tank, kW; U is the heat transfer coefficient of ice melting, W / (m 2 ·K); A is the heat transfer contact area of ice melting, m 2; ΔT m is the logarithmic mean temperature difference, in K.

[0024] The specific implementation method of the hourly control strategy of the ice thermal energy storage system at different electricity prices in Step 2 is as follows:

[0025] During the valley electricity price period, the base load chiller supplies cooling, and the dual-mode chiller stores ice; during the flat electricity price period, the dual-mode chiller and the ice melting supply cooling together; during the peak electricity price period, the ice storage supplies cooling first.

[0026] The operation cost optimization model of the ice thermal energy storage system in Step 3 includes: an objective function, decision variables, and constraint conditions. The objective function is: the daily operation cost of the ice thermal energy storage; the decision variables are: the hourly cooling load ratio borne by the chiller and the ice melting; the constraint conditions are that the chiller load rate is between 30% and 90%, the water pump flow rate is between 30% and 100%, and the ice melting volume ≥ 95%.

[0027] The advantages and positive effects of the present invention are:

[0028] The present invention establishes an energy consumption mathematical model of the operation equipment of the energy station according to the actual situation of the equipment of the ice thermal energy storage system, including the energy consumption model of the chiller, the energy consumption model of the water pump, and the energy consumption model of the ice storage device. According to the established energy consumption model and the local peak-valley electricity price policy, the hourly control strategy of the ice thermal energy storage system at different electricity prices is determined. Taking the daily operation cost as the objective function and the hourly load distribution ratio as the decision variable, a particle swarm optimization algorithm is used to establish and solve the operation cost optimization model of the ice thermal energy storage system. It can guide the operation and regulation of the ice thermal energy storage system and lay a foundation for practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present application or the existing technical solutions, the following will briefly introduce the drawings required in the description of the specific embodiments or the existing technical solutions.

[0030] Figure 1 It is the optimization control flow chart of the ice thermal energy storage system of the present invention at different electricity prices. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following will further elaborate on the present invention with reference to the accompanying drawings.

[0032] An optimization method for the operation cost of an ice thermal energy storage system includes the following steps:

[0033] Step 1, the specific calculation method of the refrigeration coefficient of the chiller is:

[0034] Define the calculation method of the part load ratio PLR of the unit as:

[0035]

[0036] Where: Q 0 is the actual refrigerating capacity of the refrigeration unit, in kW; Q cap is the maximum refrigerating capacity of the refrigeration unit under the current working conditions, in kW.

[0037] Q cap The specific calculation method is as follows:

[0038] Q cap = Q e *R cap

[0039] R cap = a 0 + b 0 *T cw，l + c 0 *(T cw，l ) 2 + d 0 *T cond，l + e 0 *(T cond，l ) 2 + f 0 *(T cw，l , T cond，l )

[0040] Where: Q e is the rated refrigerating capacity of the refrigeration unit, in kW; R cap is the maximum refrigerating capacity coefficient of the refrigeration unit; T cw，l is the chilled water outlet temperature of the refrigeration unit, in °C; T cond，l is the cooling water outlet temperature of the refrigeration unit, in °C; a 0 , b 0 , c 0 , d 0 , e 0 , f 0 are the correlation coefficients of each influencing factor.

[0041] In addition, the COP of the refrigeration unit is affected by the temperatures of the chilled water and the cooling water and the part load ratio PLR. Let COP T and COP PLR respectively represent the influences of the chilled water, the cooling water temperatures and the part load ratio on the COP of the refrigeration unit:

[0042] COP T = a 1 + b 1 *T cw，l + c 1 *(T cw，l ) 2 + d 1 *T cond，l + e 1 *(T cond，l) 2 +f 1 *(T cw，l ,T cond，l )

[0043] COP PLR =a 2 +b 2 *PLR + c 2 *(PLR) 2

[0044] Step 2. As shown Figure 1 below, the specific implementation method of the hourly control strategy of the ice storage cooling system under different electricity prices is as follows:

[0045] Judge the peak-valley electricity price. During the valley electricity price period, base load cooling is adopted and the dual-mode refrigeration unit stores ice. During the flat electricity price period, the proportion of the cold load borne by the dual-mode unit and ice melting is allocated. During the peak electricity price period, ice melting for cooling is given priority. If the ice storage volume is not enough, the refrigeration unit is used for cooling.

[0046] Step 3. The calculation method of the objective function of the operation cost optimization model of the ice storage cooling system is as follows:

[0047]

[0048] Where: n is the total number of devices; univalent t is the hourly electricity price of the energy station, yuan / kWh.

[0049] Step 3. The calculation method of the decision variables of the operation cost optimization model of the ice storage cooling system is as follows:

[0050] Q sgk (t) = Q(t) * α(t)

[0051] Q r (t) = Q(t) * (1 - α(t))

[0052] Where: Q r (t) is the hourly cooling capacity provided by ice melting, kW; Q sgk (t) is the hourly cooling capacity provided by the dual-mode unit, kW; α(t) is the proportion of the hourly cooling capacity of the dual-mode refrigeration unit.

[0053] The 24-dimensional decision variables for each day are represented as follows:

[0054] x t = [α 1 , α 2 , α 3 …, α 24

[0055] Step 3. The calculation method of the constraint conditions of the operation cost optimization model of the ice storage cooling system is as follows:​

[0056] The partial load ratio limit of the chiller is:

[0057] 30% ≤ PLR ≤ 90%

[0058] The limit on the number of operating chiller units is:

[0059] N jizai ≤ N jizai，max

[0060] N sgk ≤ N sgk，max

[0061] Wherein, N jizai，max is the total number of base load chiller units; N sgk，max is the total number of dual-mode chiller units.

[0062] The constraint condition of the ice storage device is:

[0063] 0 ≤ Q xubing ≤ C

[0064] Wherein, Q xubing is the ice storage capacity of the ice storage tank, kWh; C is the maximum ice storage capacity of the ice storage tank, kWh.

[0065] Q r (t) ≤ Q r，max (t)

[0066] 0.95 * Q xubing ≤ Q r，supply ≤ Q xubing

[0067] Wherein, Q r，supply is the actual ice melting cooling supply of the ice storage device, kWh; Q xubing is the ice storage capacity of the ice storage tank, kWh.

[0068] The constraint condition of the water pump is:

[0069] 0.3 * G 0 ≤ G ≤ G 0

[0070] Wherein, G 0 is the rated flow of the variable frequency water pump, m 3 / h.

[0071] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes, but is not limited to, the embodiments described in the specific embodiments. Any other embodiments obtained by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. An optimization method for the operating cost of an ice storage cooling system, characterized in that: It includes the following steps: Step 1: Establish an equipment energy consumption model according to the equipment parameters of the ice storage cooling system; Step 2: According to the established equipment energy consumption model and the local peak-valley electricity price policy, formulate an hourly control strategy for the ice storage cooling system under different electricity prices; Step 3: Establish an operating cost optimization model for the ice storage cooling system according to the established equipment energy consumption model and the hourly control strategy of the ice storage cooling system under different electricity prices; Step 4: Solve the established operating cost optimization model of the ice storage cooling system.

2. The optimization method for the operating cost of an ice storage cooling system according to claim 1, characterized in that, the equipment energy consumption model in Step 1 includes a refrigeration unit energy consumption model, a water pump energy consumption model, and an ice storage device model.

3. The water pump energy consumption model according to claim 2, characterized in that, it includes a variable-frequency water pump and a non-variable-frequency water pump energy consumption model.

4. The ice storage device model according to claim 2, characterized in that, it includes an ice storage model and an ice melting model.

5. The optimization method for the operating cost of an ice storage cooling system according to claim 1, characterized in that, the hourly control strategy in Step 2 is: During the valley electricity price period, the base-load refrigeration unit supplies cooling, and the dual-condition refrigeration unit stores ice; during the flat electricity price period, the dual-condition refrigeration unit and ice melting supply cooling together; during the peak electricity price period, ice storage supplies cooling first.

6. The optimization method for the operating cost of an ice storage cooling system according to claim 1, characterized in that, the operating cost optimization model of the ice storage cooling system in Step 3 includes: an objective function, decision variables, and constraint conditions.

7. The operating cost optimization model of the ice storage cooling system according to claim 6, characterized in that, the objective function is: the daily operating cost of the ice storage cooling system; the decision variables are: the hourly cooling load ratio borne by the refrigeration unit and ice melting; the constraint conditions are that the refrigeration unit load rate is between 30% and 90%, the water pump flow rate is between 30% and 100%, and the ice melting volume ≥ 95%.

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