CN-122022880-A - Air source heat pump baseline load optimization control method and system in spot market

CN122022880ACN 122022880 ACN122022880 ACN 122022880ACN-122022880-A

Abstract

The invention discloses a method and a system for optimizing and controlling an air source heat pump baseline load in a spot market, wherein the method and the system comprise the steps of collecting heat pump load related data before each optimizing period, inputting the heat pump load related data into a preset air source heat pump load model, predicting the temperature of air source heat pump effluent, backwater temperature and indoor average temperature in the next period, constructing a baseline load curve optimizing and controlling model in the spot market aiming at minimizing the total running cost of an air source heat pump unit, minimizing the carbon emission of the air source heat pump unit and minimizing the service life loss of the air source heat pump, inputting the temperature of the air source heat pump effluent, backwater temperature and indoor average temperature into the baseline load curve optimizing and controlling model in the spot market, and solving the baseline load curve optimizing and controlling model to obtain a baseline load curve optimizing result. The invention has the outstanding advantages of good stability, high reliability, good peak shaving effect, low cost and the like when running in spot market.

Inventors

CHU LINLIN
ZHANG TAO
DOU ZHENLAN
YI YUE
CHEN YANJUN
ZHENG YURONG
ZHOU JING
MENG FANQIANG
GAO JUN
SUN ZHIPENG

Assignees

国网上海市电力公司

Dates

Publication Date: 20260512
Application Date: 20251224

Claims (12)

1. The air source heat pump baseline load optimization control method in the spot market is characterized by comprising the following steps: collecting heat pump load related data before each optimization period; Inputting the heat pump load related data into a preset air source heat pump load model, and predicting the temperature of the air source heat pump water outlet, the backwater temperature and the indoor average temperature in the next period; Constructing a spot market baseline load curve optimization control model, constructing a multi-objective optimization function by using the spot market baseline load curve optimization control model and minimizing the total operation cost, carbon emission and life loss of an air source heat pump unit, constructing a dynamic weight distribution mechanism based on a time period electricity price, a new energy power generation probability and the number of adjustable units, respectively determining target weights of the total operation cost, the carbon emission and the life loss of the air source heat pump unit based on the dynamic weight distribution mechanism, and enabling each target weight to meet normalization constraint in real time; And inputting the temperature of the outlet water of the air source heat pump, the backwater temperature and the indoor average temperature as constraint conditions into a baseline load curve optimization control model in the spot market, and solving the baseline load curve optimization control model to obtain a baseline load curve optimization result.
2. The baseline load optimization control method according to claim 1, wherein: the heat pump load related data comprises the number of current air source heat pumps started, the average value of the water outlet temperature, the average value of the backwater temperature and the average value of the indoor average temperature of the air source heat pumps at the preset moment of day, and the outdoor temperature in each period of the optimization period.
3. The baseline load optimization control method according to claim 1, wherein: the air source heat pump load model comprises an air source heat pump main machine sub-model, an air source heat pump circulating water sub-model and a tail end room heat exchanger sub-model; The air source heat pump host submodel is constructed based on the outlet water temperature and the outdoor temperature of the air source heat pump, the electric power of a single air source heat pump and the number of opening air source heat pumps; The air source heat pump circulating water sub-model is constructed based on the heating/cooling total amount of the air source heat pump, the backwater heat capacity of the air source heat pump, the water outlet temperature of the air source heat pump, the backwater temperature of the air source heat pump and the indoor average temperature; The end room heat exchanger sub-model is constructed based on the thermal conductance of the end room, the thermal capacitance of the end room, the indoor average temperature, and the outdoor temperature.
4. A baseline load optimization control method according to claim 1 or 2 or 3, characterized in that: Inputting the heat pump load related data into a preset air source heat pump load model, predicting the temperature of the air source heat pump water outlet, the backwater temperature and the indoor average temperature in the next period of time, and comprising the following steps: The method comprises the steps of taking the current number of open air source heat pumps, the average value of the water outlet temperature, the average value of the backwater temperature and the average value of the indoor average temperature of the air source heat pumps at a preset moment of day, and the outdoor temperature of a first period in an optimization period as the number of open air source heat pumps, the water outlet temperature, the backwater temperature, the indoor average temperature and the outdoor temperature of the air source heat pumps in the first period in the optimization period, and inputting an air source heat pump load model; and simulating the air source heat pump load model, and predicting the outlet water temperature, the backwater temperature and the indoor average temperature of the air source heat pump in the second period of the optimization period.
5. The baseline load optimization control method according to claim 1, wherein: The on-spot market baseline load curve optimization control model comprises a multi-objective optimization function and a baseline load curve optimization constraint.
6. The baseline load optimization control method according to claim 1, wherein: The total running cost of the air source heat pump unit is obtained by calculating based on the electric power, the time period length, the electricity price of a single air source heat pump, the number of the air source heat pump units started when the optimal running cost is only considered without participating in the spot market, the number of the adjustable units reported to the spot market by a load aggregator, the price of the patch electricity used by the spot market, the new energy power generation probability, the running and maintenance cost of the unit running time of the heat pump and the total running time of the heat pump; The carbon emission of the air source heat pump unit is obtained by calculation based on the electric power and electricity price of a single air source heat pump, the number of the air source heat pump units which are started when only the optimal running cost is considered without participating in the spot market, the number of the adjustable units reported to the spot market by a load aggregator, the total refrigerant charge of the heat pump units, the time period leakage rate of the heat pump refrigerant and the global warming potential value of the heat pump refrigerant; The service life loss of the air source heat pump unit is calculated and obtained based on the number of adjustable units reported to the spot market by a load aggregator and the service life loss coefficient of the adjustable capacity of the heat pump.
7. The baseline load optimization control method according to claim 5, wherein: The baseline load curve optimization constraint comprises an air source heat pump load model prediction sub-constraint, a unit start-stop number sub-constraint, an indoor temperature sub-constraint and a dynamic weight sub-constraint; The air source heat pump load model predictor constraint is constructed based on the air source heat pump outlet water temperature, the backwater temperature and the indoor average temperature predicted by the air source heat pump load model; The dynamic weight sub constraint is a normalized constraint of a target weight of the total operation cost of the air source heat pump unit, a target weight of the carbon emission and a target weight of the service life loss.
8. The baseline load optimization control method according to claim 1 or 6, characterized in that: solving the baseline load curve optimization control model to obtain a baseline load curve optimization result, wherein the method comprises the following steps: Setting the number of adjustable units reported to the spot market by a load aggregator in the spot market base line load curve optimization control model and the new energy power generation probability to be zero, and solving the spot market base line load curve optimization control model to obtain the number of air source heat pump units started when only the optimal running cost is considered in each period of time without participating in the spot market; substituting the number of the air source heat pump units started when the optimal running cost is only considered in the spot market in each period and the new energy power generation probability in each period into the baseline load curve optimization control model under the spot market, and solving the baseline load curve optimization control model under the spot market again to obtain the number of the adjustable units reported to the spot market by the load aggregator in each period; And determining a baseline load curve optimization result based on the number of the air source heat pump units started when the optimal running cost is only considered in the spot market without participating in the spot market in each period, the number of the adjustable units reported to the spot market by a load aggregator, and the baseline load of each period corresponding to the electric power of a single air source heat pump.
9. The baseline load optimization control method according to claim 8, wherein: After the baseline load curve optimization result is obtained, the number of the air source heat pump units started in each period in the optimization period is adjusted according to the number of the air source heat pump units started when the optimal operation cost is only considered in the spot market without participation in each period and the number of the adjustable units reported to the spot market by a load aggregator.
10. A baseline load optimization control system using the baseline load optimization control method according to any one of claims 1 to 9, comprising a heat pump load related data acquisition module, an optimization control model input data generation module, a curve optimization control model construction module, and a curve optimization control model solving module: the heat pump load related data acquisition module is used for acquiring heat pump load related data before each optimization period; The optimized control model input data generation module is used for inputting the heat pump load related data into a preset air source heat pump load model and predicting the temperature of the air source heat pump water outlet, the backwater temperature and the indoor average temperature in the next period; The curve optimization control model construction module is used for constructing a baseline load curve optimization control model in the spot market, the baseline load curve optimization control model in the spot market constructs a multi-objective optimization function by minimizing the total operation cost, minimizing the carbon emission and minimizing the service life loss of the air source heat pump unit, a dynamic weight distribution mechanism is constructed based on the time period electricity price, the new energy power generation probability and the number of adjustable units, the target weights of the total operation cost, the carbon emission and the service life loss of the air source heat pump unit are respectively determined based on the dynamic weight distribution mechanism, and the target weights meet the normalization constraint in real time; The curve optimization control model solving module is used for inputting the temperature of the air source heat pump water outlet, the water return temperature and the indoor average temperature as constraint conditions into the baseline load curve optimization control model in the spot market, and solving the baseline load curve optimization control model to obtain a baseline load curve optimization result.
11. A terminal comprises a processor and a storage medium, and is characterized in that: The storage medium is used for storing instructions; The processor is operative in accordance with the instructions to perform the steps of the baseline load optimization control method according to any one of claims 1-9.
12. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the baseline load optimization controlling method according to any one of claims 1-9.

Description

Air source heat pump baseline load optimization control method and system in spot market Technical Field The invention belongs to the technical field of load optimization control, and particularly relates to a method and a system for optimizing and controlling the baseline load of an air source heat pump in the spot market. Background As a type of load-class regulation capability unit in a virtual power plant, the air source heat pump can reduce the running cost of the unit by participating in the spot market and simultaneously provide the regulation capability of a load side for a power grid. The user baseline load (customer baseline load, CBL) is the basis for calculating the economic compensation of spot market participants, and the manner in which it is controlled directly affects the market participant's interests. The principle of calling the air source heat pump in the market at the present stage is that when new energy sources such as wind power, photovoltaic and the like are generated greatly, the air source heat pump is called to fill valleys and peak shaving, and the traditional user base line load estimation method cannot reserve sufficient peak shaving margin when the new energy sources are generated greatly, so that the market income of users is reduced. Along with the promotion of the spot electric market and the rapid rise of the grid-connected capacity of the new energy, the user load is taken as a market resource, and powerful help can be provided for solving the problem of random fluctuation of the power grid after the new energy is connected by participating in the electric market. As a heating/cooling mode with wide application prospect, the air source heat pump has high heating market occupation ratio of 44.4% in North China and excellent adjustable potential. With the promotion of the reform of the electric power market, the air source heat pump participates in the spot market, so that the load side regulating capability can be effectively utilized, and the absorption and utilization of new energy power generation are promoted. The baseline load profile is an hourly average load profile of the power consumer when not engaged in a paid peak shaving auxiliary service transaction, and when not implementing demand response and orderly power usage. When the current virtual power plant load type regulating capability unit selects to participate in the market in the day-ahead in a manner of no quotation, a 96-point baseline load curve is declared, the current market is participated in clearing, and the market price is accepted. In general, calculating the power consumer baseline load should take the same type of day (weekday or holiday) as the typical day, 5 consecutive days before the virtual power plant participated in the adjustment day, and take the average load of the consumer on the typical day as the consumer baseline load. The rule of the air source heat pump unit for the current spot market is that when the new energy consumption is difficult and the power grid is insufficient in standby the next day in the future, the user side is called to fill the valley and adjust the peak, the base line load curve is optimized according to the predicted generation probability of the new energy in the adjustment day, the dispatching of the spot market can be responded better, the income of participating in the spot market is improved, and the running cost of the unit is reduced. At present, the research on the baseline load at home and abroad mainly focuses on the prediction and estimation methods of the baseline load, and mainly comprises an average method, a regression method, a comparison method and the like. The existing baseline load calculation method of the spot market is an average method, and the average value of the historical load data of the user in the days before the adjustment day is taken as the baseline load. The prior art analyzes the influence of deviation on the whole system and the user according with the calculation of the base line load by different average methods. The regression method is a widely applied method, the baseline load is estimated by fitting the relation between various load influencing factors and load data, the prior art adopts a day-ahead data driving method to estimate the adjustment reserve, and the history data is utilized to more accurately estimate the requirement of the adjustment reserve. The prior art also provides a novel resident accurate prediction user baseline load prediction method adapting to the dynamic time-sharing electricity price aiming at the problem that the electricity consumption behavior of resident users becomes complex and changeable under the dynamic time-sharing electricity price. In addition, the prior art provides a resident user baseline load estimation method based on Latin hypercube sampling and scene reduction, which solves the problem of estimation accuracy reduction when the comparison group is less. The above research