Regional-level air conditioner load electric power quantity grading optimization method and system considering weather factors and storage medium

Abstract

The invention discloses a regional-level air-conditioning load electric power and electric quantity grading optimization method and system considering weather factors and a storage medium, and the method comprises the steps: building a grading regulation and control frame under unbalanced source load, which comprises a photovoltaic system, an electric energy storage system, an air-conditioning load aggregator and a flexible air-conditioning load cluster; based on the actual output of a photovoltaic system, the state of charge of an electric energy storage system and the adjustable potential of a flexible air conditioner load cluster responding to the excitation of an air conditioner load aggregator, determining a regional-level air conditioner load electric power and electric quantity grading optimization target, and constructing a multi-objective function of the optimization target and constraint conditions of the multi-objective function; and solving the target function to obtain an optimal regulation and control strategy for realizing the hierarchical optimization target of the regional-level air-conditioning load electric power and electric quantity. The method effectively reduces the impact of the air-conditioning load on the power grid, improves the peak regulation capability and stability of the power grid, and provides powerful support for the sustainable development of a power system.

Description

Technical Field

[0001] The present invention relates to the field of air-conditioning load demand response, and particularly to a method, system and storage medium for hierarchical optimization of regional air-conditioning load power and electricity considering weather factors. Background Art

[0002] As an important part of power consumption, air-conditioning energy consumption accounts for up to 30%-45% of the power load peak, and this proportion is increasing year by year. The high proportion of air-conditioning load not only increases the pressure on the power grid, but also brings severe load fluctuations to the power grid due to its special electricity consumption characteristics - large-scale simultaneous start and stop, forming a "peak load", which affects the stable operation of the power grid. The existing dispatching methods for such air-conditioning loads are unable to cope with the high proportion of air-conditioning loads and severe load fluctuations due to factors such as technical limitations, response speed, and cost, and are difficult to meet the requirements of rapid response and filling the load gap, and cannot ensure the stability of the power grid. Especially in the context of the increasing proportion of renewable energy, the problem of source-load imbalance becomes increasingly prominent. Summary of the Invention

[0003] Object of the Invention: The object of the present invention is to provide a method for hierarchical optimization of regional air-conditioning load power and electricity considering weather factors, which can quickly respond to fill the load gap of the power grid and eliminate the new peak load caused by large-scale air-conditioning start and stop.

[0004] Technical Solution: To achieve the above object, a method for hierarchical optimization of regional air-conditioning load power and electricity considering weather factors according to the present invention includes the following steps:

[0005] Build a hierarchical control framework under source-load imbalance, including a photovoltaic system, an electric energy storage system, an air-conditioning load aggregator, and a flexible air-conditioning load cluster;

[0006] Based on the actual output of the photovoltaic system, the state of charge of the electric energy storage system, and the adjustable potential of the flexible air-conditioning load cluster to respond to the incentives of the air-conditioning load aggregator, determine the hierarchical optimization target of regional air-conditioning load power and electricity, and construct the multi-objective function of the optimization target and the constraint conditions of the multi-objective function;

[0007] Solve the objective function to obtain the optimal control strategy for achieving the hierarchical optimization target of regional air-conditioning load power and electricity.

[0008] Among them, the actual output of the photovoltaic system is expressed as:

[0009]

[0010] Where P PV is the actual output of the photovoltaic system; is the rated power of the photovoltaic system; K p∨is the number of photovoltaic panels; S is the actual light intensity, with the unit of kW / m 2 ; is the light intensity under standard conditions, with the unit of 1kW / m 2 ; K t is the power temperature coefficient; is the actual temperature on the surface of the photovoltaic system; T ref is the ambient temperature under standard conditions.

[0011] Among them, the state of charge of the electrical energy storage system is expressed as:

[0012]

[0013] Among them, SOC t is the state of charge of the electrical energy storage system at time t; SOC t-1 is the state of charge of the electrical energy storage system at time t - 1; μ charge and μ discharge are the charging efficiency and discharging efficiency of the electrical energy storage system; and are the charging power and discharging power of the electrical energy storage system at time t; BE is the capacity of the electrical energy storage system.

[0014] Among them, the adjustable potential of the flexible air-conditioning load cluster to respond to the incentive of the air-conditioning load aggregator within a scheduling period is expressed as:

[0015]

[0016] Among them, K bess is the maximum participation capacity rate of flexible air-conditioning users to respond to the incentive of the air-conditioning load aggregator, is the power of the flexible air-conditioning load cluster.

[0017] Among them, the maximum participation capacity rate K bess is expressed as:

[0018]

[0019] Among them, x o is the dead zone of the flexible air-conditioning load cluster users' response, that is, the incentive price for non-response; x 1 is the saturation value of the upper limit function y 1 of the incentive level; x 2 is the saturation value of the lower limit function y 2 of the incentive level; x bess is the optimal incentive price, x max 、x min are the maximum and minimum values of the incentive price,

[0020]

[0021] Among them, y 1 is the upper limit function of the user response participation capacity rate of the flexible air-conditioning load cluster; y 2 is the lower limit function of the user response participation capacity rate of the flexible air-conditioning load cluster.

[0022] Among them, the flexible air-conditioning load cluster is divided into a second-level response air-conditioning load cluster with the number of J and a minute-level response air-conditioning load cluster with the number of V based on the load type, the temperature change parameter RC, and the characteristic temperature difference QR by using the k-means clustering method. The power of the flexible air-conditioning load cluster can be expressed as:

[0023]

[0024] Among them, is the adjustable potential of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster; is the adjustable potential of the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster,

[0025] represents the actual power consumption of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster and the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t;

[0026]

[0027] Among them, S ac (t) is the on-off state of the air conditioner at time t, which is on or off; is the rated power of the air conditioner; η ac is the energy efficiency ratio of the air conditioner at the rated power;

[0028]

[0029] Among them, T max and T min are the maximum and minimum values of the indoor environmental temperature respectively, T set is the temperature set by the user, θ ac is the temperature dead zone of the flexible air-conditioning load; R a is the equivalent thermal resistance of the air-conditioned room, T 0 is the outdoor temperature.

[0030] Among them, the optimization objectives include: minimizing the load gap between the adjustable potential of the flexible air-conditioning load cluster responding to the incentive of the air-conditioning load aggregator and the discharge power P bess of the electrical energy storage system, and maximizing the revenue of the air-conditioning load aggregator;

[0031] The multi-objective function based on the optimization objective is expressed as:

[0032]

[0033] Wherein, is the load gap at time t; is the adjustable potential of the flexible air-conditioning load cluster at time t; P bess,t is the discharge power of the electrical energy storage system at time t; C sub is the subsidy price given by the power grid operator to the air-conditioning load aggregator; C isub is the incentive price given by the air-conditioning load aggregator to the flexible air-conditioning load users.

[0034] Wherein, the constraint conditions of the multi-objective function include power balance constraint, photovoltaic system output constraint, electrical energy storage system charge and discharge constraint, human comfort constraint, and adjustable potential constraint of the flexible air-conditioning load group;

[0035] The power balance constraint is expressed as:

[0036] Wherein, P load,t 、P bess,t 、P pv,t and respectively represent the power of the flexible air-conditioning load cluster, the power of the basic electrical load, the discharge power of the electrical energy storage system, the actual output of the photovoltaic system, and the load gap at time t;

[0037] The photovoltaic system output constraint:

[0038] are respectively the minimum and maximum outputs predicted by the photovoltaic system at time t;

[0039] The electrical energy storage system charge and discharge constraint:

[0040]

[0041] Wherein, and respectively represent the maximum charge and discharge power of the electrical energy storage system; SOC t represents the state of charge of the electrical energy storage system at time t, SOC min and SOC max respectively represent the minimum and maximum SOC t ; SOC 0 represents the state of charge of the electrical energy storage system at the start of regulation, SOC T is the state of charge of the electrical energy storage system after the end of regulation;

[0042] The human comfort constraint is expressed as:

[0043]

[0044] Among them, is the regulated temperature of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster and the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t; is the temperature set by the user of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster and the v-th flexible air-conditioning load user in the minute-level response air-conditioning load cluster at time t; ΔT max is the maximum temperature adjustment amount;

[0045] The adjustable potential constraint of the flexible air-conditioning load group is expressed as:

[0046]

[0047] Among them, P rc is the adjustable potential of the flexible air-conditioning load cluster; is the aggregated power of the flexible air-conditioning load cluster.

[0048] Among them, the method for solving the objective function to obtain the optimal regulation strategy for realizing the hierarchical optimization goal of regional air-conditioning load power and electricity quantity is as follows: using the optimized non-dominated sorting multi-objective genetic algorithm NSGA-II to solve the multi-objective function to obtain the Pareto solution set, and the Pareto solution set contains multiple scheduling measures that meet all constraint conditions; using the fuzzy theory to find the optimal compromise scheduling measure of the multi-objective function in the Pareto solution set, that is, the optimal regulation strategy that takes into account both the minimum load gap and the maximum benefit of the air-conditioning load aggregator.

[0049] A regional air-conditioning load power and electricity quantity hierarchical optimization system considering weather factors according to the present invention includes:

[0050] Hierarchical regulation framework building module: used to build a hierarchical regulation framework under source-load imbalance, including a photovoltaic system, an electric energy storage system, an air-conditioning load aggregator, and a flexible air-conditioning load cluster;

[0051] Hierarchical optimization problem construction module: used to determine the hierarchical optimization goal of regional air-conditioning load power and electricity quantity based on the actual output of the photovoltaic system, the state of charge of the electric energy storage system, and the adjustable potential of the flexible air-conditioning load cluster to respond to the incentives of the air-conditioning load aggregator, and construct the multi-objective function of the optimization goal and the constraint conditions of the multi-objective function;

[0052] Optimal regulation strategy solving module: used to solve the objective function to obtain the optimal regulation strategy for realizing the hierarchical optimization goal of regional air-conditioning load power and electricity quantity.

[0053] A computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device

[0054] Beneficial effects: The present invention has the following advantages: 1. The present invention can quickly respond to load changes in the power grid, supplement the load generated by the increased air-conditioning energy consumption, effectively reduce the impact of air-conditioning load on the power grid, balance power supply and demand, and thus improve the stability of the power grid; 2. The present invention uses a photovoltaic system and an electrical energy storage system as backup power sources for the power grid, which can quickly eliminate peak loads caused by factors such as large-scale simultaneous start-stop of equipment, extreme weather, and equipment failures, and reduce power grid load fluctuations; 3. The present invention fully considers the willingness of air-conditioning users to respond to the incentives of air-conditioning load aggregators and user comfort. At the same time, the regional air-conditioning is divided into second-level and minute-level regulation and control. Through refined management, unnecessary energy consumption is reduced while maximizing the benefits of air-conditioning load aggregators. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic flow chart of the present invention;

[0056] Figure 2 is a comparison chart of the grading regulation and control effects of a flexible air conditioner without energy storage;

[0057] Figure 3 Comparison chart of the grading regulation and control effects of a flexible air conditioner with energy storage. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The technical solutions of the present invention will be described in detail below in conjunction with the embodiments and the drawings.

[0059] As Figure 1 shown, a method for hierarchical optimization of regional air-conditioning load power and electricity considering weather factors includes the following steps:

[0060] S1: Build a hierarchical regulation and control framework under the imbalance between power generation and load, including a photovoltaic system, an electrical energy storage system, an air-conditioning load aggregator, a flexible air-conditioning load cluster, a power dispatching center, and a basic electrical load.

[0061] Among them, the photovoltaic system and the electrical energy storage system are used as power sources to assist the power grid to reduce the air-conditioning load reduction amount. The power dispatching center formulates a hierarchical regulation and control strategy based on the real-time operation status and output data of the photovoltaic system and the electrical energy storage system, and the adjustable load capacity information reported by the air-conditioning load aggregator, and sends the hierarchical regulation and control strategy to the flexible air-conditioning load aggregator to regulate the regional air-conditioning load.

[0062] S2: Calculate the actual output of the photovoltaic system, the state of charge of the electrical energy storage system, and the adjustable potential of the flexible air-conditioning load cluster to respond to the incentives of the air-conditioning load aggregator.

[0063] (1) The actual output of the photovoltaic system is significantly affected by various meteorological factors, especially irradiance and ambient temperature. The relationship expressions between the output of the photovoltaic system and irradiance and ambient temperature are as follows:

[0064]

[0065] Among them, P PV is the actual output of the photovoltaic system; is the rated power of the photovoltaic system; K p∨ is the number of photovoltaic panels; S is the actual light intensity, with the unit of kW / m 2 ; is the light intensity under standard conditions, with the unit of 1kW / m 2 ; K t is the power temperature coefficient; is the actual temperature on the surface of the photovoltaic system; T ref is the ambient temperature under standard conditions.

[0066] (2) The state-of-charge expression of the electrical energy storage system is as follows:

[0067]

[0068] Among them, SOC t is the state-of-charge of the electrical energy storage system at time t; SOC t-1 is the state-of-charge of the electrical energy storage system at time t - 1; μ charge and μ discharge are the charging efficiency and discharging efficiency of the electrical energy storage system; and are the charging power and discharging power of the electrical energy storage system at time t; BE is the capacity of the electrical energy storage system.

[0069] (3) The adjustable potential expression of the flexible air-conditioning load cluster responding to the incentives of the air-conditioning load aggregator within a scheduling period is:

[0070]

[0071] Among them, K bess is the maximum participation capacity rate of flexible air-conditioning users responding to the incentives of the air-conditioning load aggregator, is the power of the flexible air-conditioning load cluster.

[0072] When the incentive level breaks through a certain set threshold, flexible air-conditioning users will start to respond; however, when the incentive level reaches or exceeds the saturation value, flexible air-conditioning users will stop responding based on their own comfort considerations. Therefore, the relationship between the incentive level and the participation capacity rate is expressed as:

[0073]

[0074] Among them, y 1 is the upper limit function of the user response participation capacity rate of the flexible air-conditioning load cluster; y 2 is the lower limit function of the user response participation capacity rate of the flexible air-conditioning load cluster; x o is the dead zone of the user response of the flexible air-conditioning load cluster, that is, the incentive price for non-response; x 1 is the saturation value of the incentive level of the upper limit function y 1 ; x 2 is the saturation value of the incentive level of the lower limit function y 2 ;

[0075] Therefore, the maximum participation capacity rate K bess is expressed as:

[0076]

[0077] Among them, x bess is the optimal incentive value, x max , x min are the maximum and minimum values of the incentive price.

[0078] Using the k-means clustering method, the flexible air-conditioning load cluster is divided into a second-level response air-conditioning load cluster with a quantity of J and a minute-level response air-conditioning load cluster with a quantity of V based on the load type, temperature change parameter RC, and characteristic temperature difference QR. In the flexible air-conditioning load clusters with the same regulation speed, each flexible air-conditioning load shows similar operating cycles and on-duty ratios due to its similar physical characteristics and the same set temperature, and the cluster power consumption shows a trend of fluctuating up and down. When the operating state of the cluster tends to be stable, this fluctuation will also gradually stabilize, and the fluctuation amplitude will be significantly reduced. Therefore, the power of the flexible air-conditioning load cluster can be expressed as:

[0079]

[0080] Among them, is the adjustable potential of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster; is the adjustable potential of the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster,

[0081] represents the actual power consumption of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster and the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t;

[0082]

[0083] Among them, S ac (t) is the on-off state of the air conditioner at time t, which is on or off; is the rated power of the air conditioner; η ac is the energy efficiency ratio of the air conditioner at the rated power;

[0084]

[0085] where, T max and T min are the maximum and minimum values of the indoor environmental temperature respectively, R a is the equivalent thermal resistance of the air-conditioned room, and T 0 is the outdoor temperature.

[0086] When the single flexible air conditioner is in an uncontrolled state, its switching state will be automatically adjusted periodically according to the difference between the indoor temperature and the temperature preset by the user. The relationship between the indoor temperature and the user-set temperature is:

[0087]

[0088] T set is the temperature set by the user, and θ ac is the temperature dead zone of the flexible air conditioner load. In the application scenario of precision air conditioners, it refers to the difference range between the set temperature value and the actual temperature value. Specifically, when the air conditioner is set to a certain specific target temperature, the air conditioning system will not immediately stop or change its working state to exactly reach this set value, but will maintain this temperature within a relatively small temperature range, and this range is called the temperature dead zone.

[0089] S3: Determine the regional-level air-conditioning load power and electricity quantity grading optimization objectives, and construct the multi-objective function of the optimization objective and the constraint conditions of the multi-objective function.

[0090] (1) The optimization objectives include: minimizing the load gap between the adjustable potential of the flexible air-conditioning load cluster responding to the incentives of the air-conditioning load aggregator and the discharge power P bess of the electric energy storage system, and maximizing the revenue of the air-conditioning load aggregator;

[0091] (2) The multi-objective function based on the optimization objective is expressed as:

[0092]

[0093] where, is the load gap at time t; is the adjustable potential of the flexible air-conditioning load cluster at time t; P bess,t is the discharge power of the electric energy storage system at time t; C sub is the subsidy price given by the grid operator to the air-conditioning load aggregator; C isubIt is the incentive price given by the air-conditioning load aggregator to flexible air-conditioning load users.

[0094] (3) The constraint conditions of the multi-objective function include power balance constraint, photovoltaic system output constraint, charge and discharge constraint of the electrical energy storage system, human comfort constraint, and adjustable potential constraint of the flexible air-conditioning load group;

[0095] The power balance constraint ensures that the power supply and load in the entire power grid are maintained in a balanced state at each moment t, expressed as:

[0096] Among them, P load,t 、P bess,t 、P pv,t and respectively represent the power of the flexible air-conditioning load cluster, the power of the basic electrical load, the discharge power of the electrical energy storage system, the actual output of the photovoltaic system, and the load gap at moment t;

[0097] The output of each photovoltaic system cannot exceed its maximum predicted value. Therefore, the photovoltaic system output constraint is expressed as:

[0098]

[0099] are respectively the minimum and maximum predicted outputs of the photovoltaic system at moment t;

[0100] The charge and discharge constraint of the electrical energy storage system:

[0101]

[0102] Among them, and respectively represent the maximum charge and discharge power of the electrical energy storage system; SOC t represents the state of charge of the electrical energy storage system at moment t, SOC min and SOC max respectively represent the minimum and maximum SOC t ; SOC 0 represents the state of charge of the electrical energy storage system at the beginning, SOC T is the state of charge of the electrical energy storage system after the regulation ends;

[0103] When considering human comfort, in order to ensure the comfortable experience of users, for a single air conditioner, the temperature adjustment amount must be limited within the maximum allowable adjustment range. Therefore, the human comfort constraint is expressed as:

[0104]

[0105] Among them, It is the regulated temperature of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster and the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t; It is the temperature set for the user of the j-th flexible air-conditioning load in the second-level response air-conditioning load cluster and the user of the v-th flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t; ΔT max It is the maximum adjustment amount;

[0106] The adjustable potential constraint of the flexible air-conditioning load group is limited by its theoretically maximum aggregated power, expressed as:

[0107]

[0108] Among them, P rc is the adjustable potential of the flexible air-conditioning load cluster; is the aggregated power of the flexible air-conditioning load cluster.

[0109] S4: Solve the objective function to obtain the optimal regulation strategy for achieving the hierarchical optimization goal of regional air-conditioning load power and electricity.

[0110] Use the optimized non-dominated sorting multi-objective genetic algorithm NSGA-II to solve the multi-objective function to obtain the Pareto solution set, which contains multiple scheduling measures that meet all constraint conditions; use the fuzzy theory to find the optimal compromise scheduling measure in the Pareto solution set, that is, the optimal regulation strategy that takes into account both the minimization of the load gap and the maximization of the revenue of the air-conditioning load aggregator. Specifically:

[0111] By fuzzifying the single-objective function values of each solution to accurately evaluate and determine the fuzzy values of the optimal solutions.

[0112]

[0113] Among them, F x (x) is the x-th frontier solution of the objective function; and are the maximum and minimum values of the objective function frontier solution set respectively.

[0114] Perform weighted averaging on the fuzzified objective function solutions and sort the results accordingly to determine the optimal compromise solution of the multi-objective function:

[0115]

[0116] Among them, λ x is the weight coefficient of the objective function; W is the number of frontier solutions corresponding to each objective; Y is the number of optimization objectives.

[0117] Based on the above method, in this embodiment, the source-load imbalance phenomenon that occurs in a certain area during the period from 13:00 to 14:00 in summer is targeted. During this period, the outdoor temperature is stably maintained at 33°C, and the key parameters of the air conditioner follow a uniform distribution or a normal distribution. In order to accurately analyze this phenomenon, the Monte Carlo simulation technique is used to sample each parameter according to its specific probability distribution. Through simulation calculations, specific parameter values are obtained, as shown in Table 1.

[0118] Table 1 Flexible air-conditioning load parameters

[0119]

[0120]

[0121] To mobilize the enthusiasm of air-conditioning users to respond through pre-overexcitation measures, different temperature settings are divided into different incentive levels, and the load reduction rates under different incentive levels are compared, as shown in Table 2. During the peak power load period, the power dispatching center control proposed a demand to reduce the load by 150 kW. In order to respond to this reduction requirement, the air-conditioning load aggregator took temperature control measures to precisely control various types of air conditioners. The details of the number of air conditioners in each temperature range are shown in Table 3:

[0122] Table 2 Incentive level data corresponding to different temperature ranges

[0123]

[0124] Table 3 Number of air conditioners in each temperature range

[0125]

[0126] As can be seen from Table 3, without incentives, the temperatures of various types of air conditioners are mainly distributed between 26 - 28°C, while after the introduction of incentives, the air-conditioning temperatures are mainly distributed between 27 - 28°C. Further analysis shows that in the non-incentive state, the operating cost of the load aggregator is 106.284 yuan; while under the incentive strategy, this cost is reduced to 93.864 yuan. In addition, comparing the load reduction rates, the reduction rate without incentives is 90.24%, while after the introduction of incentives, the reduction rate is increased to 95.78%. Therefore, the incentive-based flexible air-conditioning hierarchical control method not only significantly enhances the adjustment potential of air conditioners, but also realizes the reduction of operating costs.

[0127] Further analysis of the hierarchical optimization effect of flexible air conditioners under source-load imbalance. By setting different control methods, the hierarchical control effects of flexible air conditioners in two scenarios with and without energy storage are compared and analyzed. At the same time, three different control methods are set: second-level control, minute-level control, and second-level + minute-level control that combines the two. The initial power of the load is set to 400 kW. When source-load imbalance occurs, 150 kW of load needs to be reduced. The capacity of the energy storage device is 100 kWh. The specific control effects are as Figure 2 and Figure 3 shown.

[0128] From Figure 2 (a), it can be observed that when the air-conditioning load aggregator implements control, directly controlling the air-conditioning switch can quickly reduce the load. However, after about 5 minutes, as the indoor temperature gradually rises, the previously turned-off air conditioners will turn on again, resulting in new load peaks, and this frequent on-off control will accelerate the wear of the air-conditioning body. In Figure 2 (b), the load aggregator selects the temperature control strategy. Under this strategy, the load decreases in a relatively gentle manner and reaches the required load reduction at about 4 minutes without generating new load peaks. In Figure 2 (c), the load aggregator first performs on-off control on some users, which enables the load to be quickly reduced. Subsequently, in order to prevent new load peaks from appearing, temperature control is carried out again. This combined method combines the advantages of the two control strategies. Figure 2 (d) shows the effect of the second-level + minute-level control method proposed by the present invention. This method can quickly achieve the load reduction target and effectively avoid new load peaks. However, it can also be seen from the figure that the adjustment potential of flexible air conditioners alone may not be sufficient to completely fill the load gap, so an energy storage system is needed as a supplement.

[0129] After introducing the energy storage system in the fifth minute of the control, comparing Figure 2 the data, it can be observed that all control methods can effectively approach the load gap. Among them, the error of the second-level control is 31.71%, the error of the minute-level control is 17.15%, and the error of the second-level + minute-level control method is only 5.96%. This data clearly shows that the hierarchical control strategy has better performance in terms of control accuracy. From Figure 3 (a)-(d), it can be seen that under the second-level control method, due to the addition of energy storage, the newly formed load peak is reduced by about 80 kW compared to when there is no energy storage, and the load fluctuations under all control methods are reduced as a whole. This indicates that the introduction of energy storage not only helps to improve the control accuracy but also can effectively suppress the load fluctuations and improve the stability of the system.

Claims

1. A method for optimizing regional air conditioning load power level considering weather factors, characterized in that: The following steps are involved: Build a hierarchical control framework under source-load imbalance, including photovoltaic systems, electric energy storage systems, air conditioning load aggregators, and flexible air conditioning load clusters; Based on the actual output of the photovoltaic system, the charge state of the electric energy storage system, and the adjustable potential of the flexible air-conditioning load cluster in response to the incentives of the air-conditioning load aggregator, the regional air-conditioning load power and electricity hierarchical optimization target is determined, and the multi-objective function of the optimization target and the constraints of the multi-objective function are constructed; The objective function is solved to obtain the optimal control strategy for achieving the regional air-conditioning load power and electricity graded optimization goal.

2. According to the method for optimizing regional air-conditioning load power and quantity according to weather factors in accordance with claim 1, it is characterized in that: The actual output of the photovoltaic system is expressed as: Among them, P PV The actual output of the photovoltaic system; is the rated power of the photovoltaic system; K p∨ is the number of photovoltaic panels; S is the actual light intensity, in kW / m 2 ; is the light intensity under standard conditions, the unit is 1kW / m 2 ; K t is the power temperature coefficient; is the actual temperature of the photovoltaic system surface; T ref is the ambient temperature under standard conditions.

3. According to the method for optimizing regional air-conditioning load power and electricity in accordance with weather factors in claim 1, it is characterized in that: The state of charge of the electric energy storage system is expressed as: Among them, SOC t SOC is the state of charge of the energy storage system at time t; t-1 is the state of charge of the energy storage system at time t-1; μ charge and μ discharge Charging efficiency and discharging efficiency of electric energy storage system; and is the charging power and discharging power of the energy storage system at time t; BE is the capacity of the energy storage system; The adjustable potential of the flexible air-conditioning load cluster in response to the incentive of the air-conditioning load aggregator within a dispatch cycle is expressed as: Among them, K bess The maximum participating capacity rate for flexible air-conditioning users in response to the incentives of air-conditioning load aggregators, is the power of the flexible air conditioning load cluster.

4. According to claim 3, the method for optimizing regional air-conditioning load power and electricity in a hierarchical manner taking weather factors into consideration is characterized in that: The maximum participating capacity rate K bess It is expressed as: Among them, x o is the response dead zone of the flexible air conditioning load cluster users, that is, the incentive price of non-response; x1 is the saturation value of the incentive level of the upper limit function y1; x2 is the saturation value of the incentive level of the lower limit function y2; x bess is the optimal incentive price, x max 、x min are the maximum and minimum incentive prices, Among them, y1 is the upper limit function of the user response participation capacity rate of the flexible air-conditioning load cluster; y2 is the lower limit function of the user response participation capacity rate of the flexible air-conditioning load cluster.

5. According to claim 3, the regional air conditioning load power and electricity classification optimization method considering weather factors is characterized in that: The k-means clustering method is used to divide the flexible air-conditioning load clusters into J second-level response air-conditioning load clusters and V minute-level response air-conditioning load clusters based on load type, temperature change parameter RC and characteristic temperature difference QR. It can be expressed as: in, is the adjustable potential of the jth flexible air-conditioning load in the second-level response air-conditioning load cluster; is the adjustable potential of the vth flexible air-conditioning load in the minute-level response air-conditioning load cluster, It represents the actual power consumption of the jth flexible air-conditioning load in the second-level response air-conditioning load cluster and the vth flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t; Among them, S ac (t) is the switch status of the air conditioner at time t, either on or off; is the rated power of the air conditioner; ac is the energy efficiency ratio of the air conditioner at rated power; Among them, T max and T min are the maximum and minimum values ​​of the indoor ambient temperature, T set is the temperature set by the user, θ ac is the temperature dead zone of the flexible air conditioning load; R a is the equivalent thermal resistance of the air-conditioned room, and T0 is the outdoor temperature.

6. The method for optimizing regional air-conditioning load power and quantity according to claim 1, characterized in that: The optimization objectives include: minimizing the adjustable potential of the flexible air conditioning load cluster in response to the incentive of the air conditioning load aggregator within a scheduling cycle The discharge power P of the energy storage system bess load gap between them and maximize the revenue of air-conditioning load aggregators; The multi-objective function based on the optimization goal is expressed as: in, is the load gap at time t; is the adjustable potential of the flexible air conditioning load cluster at time t; P bess,t is the discharge power of the energy storage system at time t; C sub is the subsidy price that the grid operator gives to the air conditioning load aggregator; C isub It is the incentive price that air conditioning load aggregators give to flexible air conditioning load users.

7. The method for optimizing regional air-conditioning load power and quantity according to claim 6, characterized in that: The constraints of the multi-objective function include power balance constraints, photovoltaic system output constraints, electric energy storage system charging and discharging constraints, human comfort constraints and flexible air conditioning load group adjustable potential constraints; The power balance constraint is expressed as: in, P load,t , P bess,t , P pv,t and They represent the power of the flexible air conditioning load cluster, the power of the basic electric load, the discharge power of the electric energy storage system, the actual output of the photovoltaic system, and the load gap at time t respectively; The photovoltaic system output constraints are: are the predicted minimum and maximum outputs of the PV system at time t, respectively; The electric energy storage system charging and discharging constraints: in, and Respectively represent the maximum charging and discharging power of the energy storage system; SOC t Indicates the state of charge of the energy storage system at time t, SOC min and SOC max Represents the minimum and maximum SOC respectively t ; SOC0 represents the state of charge of the energy storage system at the beginning of regulation, SOC T It is the charge state of the electric energy storage system after the regulation is completed; The human comfort constraint is expressed as: in, is the control temperature of the jth flexible air-conditioning load in the second-level response air-conditioning load cluster and the vth flexible air-conditioning load in the minute-level response air-conditioning load cluster at time t; The temperature set for the jth flexible air-conditioning load user in the second-level response air-conditioning load cluster and the vth flexible air-conditioning load user in the minute-level response air-conditioning load cluster at time t; ΔT max is the maximum temperature adjustment; The adjustable potential constraint of the flexible air-conditioning load group is expressed as: Among them, P rc Provides adjustable potential for flexible air conditioning load clusters; Aggregate power for flexible air conditioning load clusters.

8. The method for optimizing regional air-conditioning load power and quantity according to claim 1, characterized in that: The method for solving the objective function to obtain the optimal control strategy for achieving the regional air-conditioning load power and electricity graded optimization goal is: using the optimized non-inferiority sorting multi-objective genetic algorithm NSGA-II to solve the multi-objective function to obtain a Pareto solution set, which includes multiple scheduling measures that meet all constraints; using fuzzy theory to find the optimal compromise scheduling measure of the multi-objective function in the Pareto solution set, that is, the optimal control strategy that takes into account both minimizing the load gap and maximizing the benefits of the air-conditioning load aggregator.

9. A regional air conditioning load power and electricity classification optimization system considering weather factors, characterized in that: include: Hierarchical control frame construction module: used to build hierarchical control frames under source-load imbalance, including photovoltaic systems, electric energy storage systems, air conditioning load aggregators, and flexible air conditioning load clusters; Hierarchical optimization problem construction module: used to determine the hierarchical optimization target of regional air-conditioning load power based on the actual output of the photovoltaic system, the charge state of the electric energy storage system, and the adjustable potential of the flexible air-conditioning load cluster in response to the incentives of the air-conditioning load aggregator, and to construct the multi-objective function of the optimization target and the constraints of the multi-objective function; Optimal control strategy solving module: used to solve the objective function and obtain the optimal control strategy to achieve the regional air-conditioning load power and electricity graded optimization goal.

10. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1 to 8.

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