A flexible load control method for distribution network under the access of new energy

Abstract

The present invention discloses a method for flexible load control of distribution network substations under new energy access, comprising the following steps: S1, aggregation modeling of typical flexible loads of users in the substation, wherein the typical flexible loads include electric vehicle charging and discharging models, temperature control load power consumption models and energy storage equipment charging and discharging models; S2, flexible load dispatching of users in the substation for regional load aggregators; S3, joint dispatching of multiple flexible load groups of users in the substation; S4, day-ahead dispatching of distribution network substation loads with multi-agent game; S5, real-time control of substation loads with uncertainty of source, load and storage based on day-ahead dispatching; the present invention realizes active distribution network optimization dispatching based on flexible loads on multiple time scales, implements the use of flexible loads to smooth fluctuations of renewable energy, and solves the problem of difficulty in real-time dispatching caused by uncertainty of source, load and storage.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method for flexible load control in a distribution network under the access of new energy. Background Art

[0003] Under the background of "dual carbon", energy transformation is vigorously promoted. New energy installed capacity and output have repeatedly set new highs. Distributed new energy technologies represented by rooftop photovoltaics are developing rapidly. With the development and vigorous promotion of distributed new energy technologies, the self-generated volatility of new energy power generation leads to imbalance of distribution network source and load, which will become a new problem faced by the stability of the power grid. The scale of smart home appliances in residents' households under the substation continues to expand, and the construction of "new infrastructure" facilities such as 5G base stations and charging piles is developing rapidly, providing new means for load regulation. While the power load in the substation has surged, the flexible load resources have increased. The adjustment capacity of flexible load resources can realize the optimized operation of the substation, alleviate and smooth the fluctuations caused by the access of new energy, and achieve a win-win situation for the distribution network, substation, and users. During peak power consumption, due to excessive load access to the power grid, users' electricity consumption is affected, affecting their production and life. With the vigorous development of "new infrastructure", a large proportion of new energy is connected to the power grid. The promotion and application of new technologies such as home appliance networking, orderly charging, and household energy storage has become a trend. It is urgent to use flexible load resources in the substation to achieve optimized operation of the power grid under new energy access. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the existing defects and provide a method for flexible load control in distribution network substations under new energy access, realize active distribution network optimization scheduling based on flexible loads on multiple time scales, implement the use of flexible loads to smooth the fluctuations of renewable energy, solve the problem of real-time scheduling difficulties caused by source-load-storage uncertainty, and effectively solve the problems in the background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for flexible load control of a distribution network under the access of new energy, comprising the following steps:

[0006] S1. Aggregation modeling of typical flexible loads of users in the substation area; typical flexible loads include electric vehicle charging and discharging models, temperature control load power consumption models and energy storage equipment charging and discharging models;

[0007] Electric vehicle charging and discharging model: When an electric vehicle is connected to charging at time t0, and the initial state of charge is SOC0, the state of charge of the electric vehicle at time t can be calculated by the following formula:

[0008]

[0009] Where: P ratedis the charging power of the electric vehicle; η is the charging efficiency of the electric vehicle; C is the battery capacity of the electric vehicle, and charging stops when the state of charge of the electric vehicle reaches the user's expected value;

[0010] Electricity consumption model of temperature control load: Differential equation of the first-order thermodynamic model of air conditioning load:

[0011]

[0012] Where: Tr(t) is the room temperature, Tout(t) is the outdoor temperature, Qac(t) is the air conditioning cooling capacity, R is the equivalent thermal resistance of the air-conditioning building system, C is the equivalent heat capacity of the air-conditioning building system, and the electrical model of the air-conditioning body is:

[0013] The relationship between electric power Pac(t) and cooling capacity Qrated(t) is as follows:

[0014] Qac(t)=η ac P ac (t) (3)

[0015] Pac(t)=w ac (t)P acn (4)

[0016] Where: P ac (t) is the power consumption of the fixed frequency air conditioner at time t, P acn is the rated power, η ac is the energy efficiency ratio, w ac (t) is the air conditioner operation state. When it is 0, the air conditioner is in a stopped state. When it is 1, the air conditioner is in a running state.

[0017] Energy storage equipment charging and discharging model: The mathematical model is as follows:

[0018] E es (t) = E es (t-Δt)+ηP es-c Δt (5)

[0019]

[0020] The former is the charging energy balance equation, and the latter is the discharging energy balance equation, where: E es (t) is the energy stored in the energy storage device at time t, η is the charge and discharge loss coefficient of the energy storage device, P es-c is the charging power of the energy storage device, P es-f is the charging power of the energy storage device;

[0021] The constraints are as follows:

[0022] SOC min ≤SOC≤SOCmax (7)

[0023] P es-cmin ≤P es-c ≤P es-cmax (8)

[0024] P es-fmin ≤P es-f ≤P es-fmax (9)

[0025] Where: SOC max and SOC min They are the upper and lower limits of the SOC of the energy storage device, P es-cmax and P es-cmin is the upper and lower limits of the energy storage charging power, P es-fmax and P es-fmin are the upper and lower limits of the energy storage discharge power, formula (7) is the SOC constraint, formula (8) is the maximum and minimum constraints of the charging power, and formula (9) is the maximum and minimum constraints of the discharge power;

[0026] S2. Flexible load dispatching for regional load aggregators: With the goal of optimizing regional power consumption, maximizing the benefits of aggregators, and maximizing user satisfaction, the strategy takes full account of the load's own operating constraints, user usage habits, and power balance, and obtains a joint dispatching strategy that is mainly based on temperature-controlled loads and electric vehicles, supplemented by energy storage.

[0027] Temperature control load: Temperature control is used for temperature load. According to the changes in electricity price and outdoor temperature, the load is transferred from the high electricity price period to the low electricity price period to reduce user costs, or the temperature setting value is adjusted according to the system operation. The load aggregator controls the temperature control load through temperature control means, considers the benefits generated by the control behavior, and gives users a certain subsidy. When the interests of the load aggregator are maximized, the optimization goal is:

[0028] maxG(10)

[0029] Where G represents the interest of the load aggregator;

[0030] Users are often concerned about their own comfort. The comfort of temperature control load is closely related to temperature. Therefore, the user level takes the minimum temperature change as the optimization goal, which is expressed as follows:

[0031]

[0032] In the formula, Indicates the temperature setting value. Indicates the expected value of temperature;

[0033] The aggregate power constraints are:

[0034] P dr,t,min ≤P dr,t ≤P dr,t,max (12)

[0035] Where P dr,t,min and P dr,t,max They represent the minimum and maximum values ​​of the demand-side response respectively;

[0036] Electric vehicles: Centralized V2G technology is used to achieve unified control of decentralized electric vehicles. The mathematical model of double-layer optimization can be described as follows:

[0037]

[0038] Where: F is the objective function of the upper optimization model, x is the decision variable of the upper optimization model, G is the constraint condition of the upper optimization model, and f i is the objective function of the lower optimization model, y i is the decision variable of the lower optimization model, g i It is the constraint condition of the lower optimization model; the time-optimized charging and discharging power is used as the connection variable between the upper-layer time scheduling optimization and the lower-layer space scheduling optimization. When performing real-time space allocation scheduling, the number of charging vehicles is allocated according to the charging and discharging power in each period of the daily plan to achieve a balance between power supply and demand;

[0039] Energy storage equipment: Under the premise of ensuring that the dispatchable capacity of energy storage on the user side and the energy storage capacity on the aggregation side meet the peak load demand of the system, with the goal of maximizing the annual revenue of the aggregator and minimizing the peak-to-valley difference of the electricity consumption curve, the objective function for configuring the energy storage capacity on the aggregation side is:

[0040] maxF net =F1-F2-F3-F4 (15)

[0041]

[0042] The revenue of the aggregator is the revenue from participating in peak load regulation F1. The expenditure of the aggregator is divided into three parts: the first part is the cost of aggregating user-side energy storage F2, the second part is the cost of configuring and maintaining the aggregate-side energy storage F3, and the third part is the charging cost of the aggregate-side energy storage F4.

[0043] S3, joint dispatch of multiple flexible load groups of users in the substation area;

[0044] When typical flexible loads of users in the substation area participate in the grid dispatching, since the number of typical flexible loads in the substation area is limited, the regulation potential provided by the typical flexible loads is also limited. Therefore, the multiple flexible loads in the substation area are jointly regulated to meet the dispatching conditions that cannot be met by a single type of flexible load;

[0045] When considering multiple flexible loads, the loads often show differences in usage time and ambiguity in operating status. This study establishes a universal controllable margin index to make multiple flexible loads comparable. In the process of optimal combination, they can be controlled as a unified resource pool.

[0046] Controllable energy state margin:

[0047] For structurally heterogeneous flexible loads, the controllable energy state margin index is defined as follows:

[0048] ε c,ac / cv / cs (t) = 1-|S ac / cv / cs (t)| (17)

[0049] Among them, S…(t) ranges from [-1,1]; ε c,… (t) The value range is [0,1]. The closer its value is to 1, the greater the adjustability and the higher the satisfaction; the closer its value is to 0, the smaller the adjustability and the lower the satisfaction.

[0050] Controllable power margin:

[0051] ε p (t)=|P s (t)| (18)

[0052] Where P s (t) is the positive and negative response potential of the load at time t. This indicator represents the equivalent adjustable power of the load based on the consideration of the subjective and objective participation capabilities of the load;

[0053] Controllable time margin:

[0054] The controllable time margin is not only determined by the power consumption time set by the load itself, but also depends on the controllable time converted from the load's adjustable energy space in order to meet the user's power demand. The minimum of the above two is the actual controllable time margin of the load.

[0055] ε t (t) = min{t f -t,E cont (t) / P(t)} (19)

[0056] In the formula, t f The power consumption time set for the load; E cont (t) is the adjustable energy space of the load; P(t) is the operating power of the load at time t;

[0057] S4. Establishing the day-ahead dispatching of distribution network loads based on multi-agent game;

[0058] According to the non-cooperative game model, the load aggregators in the substation area are divided into consumer load aggregators and provider load aggregators. Consumer load aggregators offer a purchase price to purchase the excess electricity of other load aggregators. The higher the bid, the more energy they get. Multiple consumer load aggregators form a game situation through bidding.

[0059] The non-cooperative game model of energy trading between load aggregators in the substation area G = {I1; S1…S n ; U}, there are

[0060] (1) Participants: Consumer load aggregators;

[0061] (2) Strategy: Each consumer load aggregator participates in the bidding by changing the power purchase price t, thereby controlling the energy of its own transactions with other load aggregators to minimize its own economic cost. Participants can arbitrarily select values ​​in the strategy set. The set of all feasible power purchase price strategies for participant i is defined as:

[0062]

[0063] in, and They represent the maximum and minimum electricity purchase prices of participant i, is the price at which load aggregators purchase electricity from the distribution network. It is the price at which load aggregators sell electricity to distribution networks. The bid price of each load aggregator is between the purchase and sale prices of distribution networks, and the bidding range is consistent. This definition can ensure that the benefits of energy transactions between load aggregators in the substation and adjacent load aggregators are greater than the benefits of transactions with the distribution network, so that energy flows first within the load aggregators in the substation;

[0064] Benefit function: The benefit function of the participant is defined as:

[0065]

[0066] For participant i, the optimal bidding strategy is:

[0067]

[0068] Where Ci represents the set {C1,...,C i-1 ,C i+1 ,...,C I};

[0069] With the comprehensive goals of optimizing economic benefits, minimizing network losses, maximizing renewable energy consumption, and minimizing voltage deviation, the distribution network dispatching center coordinates the output of distributed power sources and flexible loads in the day ahead to achieve active power balance of the power system. The objective function is as follows:

[0070] Objective function 1: The optimization goal is to minimize the operating cost of the active distribution network, as shown in the formula;

[0071] minC=C pcc +C FL +C MR +C DERG (twenty three)

[0072] Where C is the lowest total operating cost of the distribution network during the entire dispatching cycle; C pcc The cost of purchasing electricity from the upper grid; C FL Compensation cost for flexible load dispatch; C MR is the dispatching cost of distributed power grid; C DERG Penalize costs for abandoning wind and solar renewable energy;

[0073] Objective function 2: In order to reduce the burden on the upper power grid, reduce the standby power and increase the utilization of equipment, the minimum fluctuation of the tie line power is taken as the target optimization:

[0074]

[0075] P in the formula PCC (t) is the exchange power of the tie line at the time, P avgpcc (t) is the average power of the tie line during the dispatch period, and nT24 is the entire dispatch period;

[0076] Objective function 3: Minimize active network loss. When the DG processing of the power system is too large to meet the load consumption of the area, it will also feed back electric energy to the upper power grid. At this time, it will cause long-distance transmission of electricity, resulting in increased losses. Active network loss is also a criterion for measuring power loss. The active loss of the network is equivalent to the minimum sum of the power injected by all nodes in the system, as shown in the following formula:

[0077]

[0078] The formula nbus is the number of nodes, V i is the voltage amplitude of node i, G ij , B ij are the branch conductance and susceptance, θ ij is the phase angle difference between nodes i and j, Γ is the set of nodes connected to j in the system;

[0079] Objective function 4: Minimum node voltage deviation. Distributed power sources are connected to the active distribution network. Due to the volatility of renewable distributed power sources, the power quality of the system is greatly affected. The node voltage should be guaranteed to be within a reasonable fluctuation range during scheduling. The actual node voltage of the grid is compared with the standard voltage. It is expected that when renewable energy is connected, some voltage margin will be left to reduce the possibility of voltage crossing the line. The minimum deviation between the actual voltage and the rated voltage is taken as the optimization target as shown in the formula:

[0080]

[0081] Where V i (t) is the voltage amplitude of the ith node at time t, and nbus is the number of nodes;

[0082] S5. Real-time control of the load in the substation with uncertain source, load and storage based on day-ahead dispatch;

[0083] The day-ahead optimization dispatch is to make a preliminary plan for the operation of flexible loads in the substation, determine the load management strategy within the substation, and use the optimization results of the interconnection line power as the reference value for intraday optimization. The intraday optimization process can correct the day-ahead optimization results and reduce the actual dispatch deviation caused by the prediction error. The intraday real-time optimization dispatch is based on more accurate intraday short-term prediction data to correct the day-ahead results while ensuring the interconnection line power. The real-time optimization updates the prediction information of photovoltaic and wind power generation power and load at intervals of 15 minutes, and optimizes the charging and discharging power of the battery in a rolling manner.

[0084] The short-term forecast period of the intraday plan is 4 hours, the rolling cycle is 15 minutes, and the control period is 15 minutes. The intraday correction strategy includes the following steps:

[0085] 1) At the current time k, based on a certain prediction model, predict the power value of the system within 16 time periods.

[0086] Based on the optimization model, the control instructions at the future k+1, k+2, ..., k+16 moments are obtained;

[0087] 2) Only the [k, k+1] period is controlled, and the state of each unit of the load in the substation obtained at this moment is input as the initial value of the [k+1, k+2] period;

[0088] 3) Repeat the above steps, and so on, to optimize backwards;

[0089] The daily optimization of distribution network load takes into account both economic cost and tie line change value. The objective function is as follows:

[0090]

[0091] Where, F2 is the objective function value of the distribution network intraday optimization, F P is the economic cost of daily operation, P grid1 , P grid2 are the optimized values ​​of the tie line power in the day-ahead and intraday stages respectively, μ is the weight coefficient of the tie line power change, the constraints of intraday scheduling are consistent with those of day-ahead scheduling, and the economically optimal scheduling result is given by the particle swarm optimization algorithm.

[0092] Preferably, in step S1, the electric vehicle is charged in a two-stage constant current-constant voltage charging mode. The power battery is charged at a constant current in the first stage. In the constant current stage, the battery terminal voltage continues to rise until it reaches the cut-off voltage, and then the terminal voltage is kept unchanged. The charging process enters the second stage, i.e., the constant voltage stage. In the constant voltage stage, the charging current continues to decrease until it reaches the cut-off current. At this time, the charging process is terminated. The two-stage constant current-constant voltage charging process can be considered as constant power charging.

[0093] Preferably, in step S1, the equivalent thermodynamic model of the air conditioner uses components such as capacitors, resistors and current sources to simulate the temperature change process in the building, wherein the cooling capacity of the air conditioner is regarded as a current source, the indoor temperature is regarded as a voltage source, the thermal resistance of the building system is regarded as a resistor, and the heat capacity of the building system is equivalent to a capacitor, and then Kirchhoff's law is used to solve the indoor temperature.

[0094] Preferably, in step S4, the constraints include:

[0095] 1) Flexible load constraints:

[0096] Temperature control load constraints:

[0097]

[0098] Energy storage load constraints:

[0099]

[0100] In the formula They represent the maximum charge and discharge capacity of the energy storage in time period t, with discharge taking negative value and charge taking positive value. i (t) represents the energy storage charge state, which is equal to the ratio of the remaining power to the maximum power. During the entire charging and discharging process, the energy storage system is prevented from overcharging and discharging, and its SOC is kept within a certain range. Indicates the minimum and maximum limit values ​​of the charge of the i-th energy storage device. In order to facilitate the next stage of scheduling, the energy storage battery is kept at the end Greater than a set value

[0101] 2) Network operation constraints:

[0102] (1) System power flow constraints:

[0103]

[0104] in: Inject active and reactive power into the tie line of node i at time t, is the output active and reactive power of distributed power sources, For active and reactive loads, is the reactive power of capacitor switching, θ ij is the voltage phase angle difference between nodes i and j, V i (t), V j (t) Voltage amplitude at nodes i and j, G ij , B ij are branch conductance and susceptance respectively, and nbus is the number of branches;

[0105] (2) Upper and lower limits of active and reactive power of tie lines:

[0106]

[0107] 3) Node voltage and line power constraints:

[0108]

[0109] V i (t) represents the actual node voltage value of the i-th node at time t, V i max 、V i min are the upper and lower limits of the node voltage, Maximum allowed line power.

[0110] Compared with the prior art, the beneficial effects of the present invention are as follows: according to the different loads within the user, the characteristic potential and dispatching characteristics of the user that can participate in the grid dispatching are analyzed, and a centralized aggregator model dispatching framework for flexible loads to participate in the dispatching is proposed; considering the game behavior of multiple substations, the uncertainty of source, load and storage is quantified, and under the constraints of grid power balance, node voltage constraints, energy storage constraints, flexible load constraints, active and reactive output, etc., the maximum consumption of new energy and the optimal economic benefits of the grid are taken as optimization goals, and the day-ahead and real-time dispatching strategies are obtained, and the active distribution network optimization dispatching based on flexible loads is realized on multiple time scales, and the use of flexible loads to smooth the fluctuations of renewable energy is implemented, which solves the problem of difficult real-time dispatching caused by the uncertainty of source, load and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 It is a schematic diagram of the process of the present invention;

[0112] Figure 2 This is a schematic diagram of the intraday rolling optimization strategy of the present invention. DETAILED DESCRIPTION

[0113] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "back", "left", "right", etc. indicating directions or positional relationships, they only correspond to the drawings of the present application for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific direction.

[0114] See also Figure 1-2 The present invention provides a technical solution: a method for flexible load control in a distribution network area under the access of new energy, comprising the following steps:

[0115] S1. Aggregation modeling of typical flexible loads of users in the substation area; typical flexible loads include electric vehicle charging and discharging models, temperature control load power consumption models and energy storage equipment charging and discharging models;

[0116] Electric vehicle charging and discharging model: The charging method of electric vehicles is constant current-constant voltage two-stage charging. The power battery is charged with a constant current in the first stage. In the constant current stage, the battery terminal voltage continues to rise until it reaches the cut-off voltage. The terminal voltage remains unchanged and the charging process enters the second stage, the constant voltage stage. In the constant voltage stage, the charging current continues to decrease until it reaches the cut-off current. At this time, the charging process is terminated. The constant current-constant voltage two-stage charging process can be considered as constant power charging. The time when an electric vehicle is connected to charging is t0, and the initial state of charge is SOC0. The state of charge of the electric vehicle at time t can be calculated by the following formula:

[0117]

[0118] Where: P rated is the charging power of the electric vehicle; η is the charging efficiency of the electric vehicle; C is the battery capacity of the electric vehicle, and charging stops when the state of charge of the electric vehicle reaches the user's expected value;

[0119] Electricity consumption model of temperature control load: The equivalent thermodynamic model of air conditioning uses components such as capacitors, resistors and current sources to simulate the temperature change process in the building. The cooling capacity of the air conditioner is regarded as a current source, the indoor temperature is regarded as a voltage source, the thermal resistance of the building system is regarded as a resistor, and the heat capacity of the building system is equivalent to a capacitor. Then, Kirchhoff's law is used to solve the differential equation of the first-order thermodynamic model of indoor temperature and air conditioning load:

[0120]

[0121] Where: Tr(t) is the room temperature, Tout(t) is the outdoor temperature, Qac(t) is the air conditioning cooling capacity, R is the equivalent thermal resistance of the air-conditioning building system, C is the equivalent heat capacity of the air-conditioning building system, and the electrical model of the air-conditioning body is:

[0122] The relationship between electric power Pac(t) and cooling capacity Qrated(t) is as follows:

[0123] Qac(t)=η ac P ac (t) (3)

[0124] Pac(t)=w ac (t)P acn (4)

[0125] Where: P ac (t) is the power consumption of the fixed frequency air conditioner at time t, P acn is the rated power, η ac is the energy efficiency ratio, w ac (t) is the air conditioner operation state. When it is 0, the air conditioner is in a stopped state. When it is 1, the air conditioner is in a running state.

[0126] Energy storage equipment charging and discharging model: The mathematical model is as follows:

[0127] E es (t) = E es (t-Δt)+ηP es-c Δt (5)

[0128]

[0129] The former is the charging energy balance equation, and the latter is the discharging energy balance equation, where: E es (t) is the energy stored in the energy storage device at time t, η is the charge and discharge loss coefficient of the energy storage device, P es-c is the charging power of the energy storage device, P es-f is the charging power of the energy storage device;

[0130] The constraints are as follows:

[0131] SOC min ≤SOC≤SOC max (7)

[0132] P es-cmin ≤P es-c ≤P es-cmax (8)

[0133] P es-fmin ≤P es-f ≤P es-fmax (9)

[0134] Where: SOC max and SOC min They are the upper and lower limits of the SOC of the energy storage device, P es-cmax and P es-cmin is the upper and lower limits of the energy storage charging power, P es-fmax and P es-fmin are the upper and lower limits of the energy storage discharge power, formula (7) is the SOC constraint, formula (8) is the maximum and minimum constraints of the charging power, and formula (9) is the maximum and minimum constraints of the discharge power;

[0135] S2. Flexible load dispatching for regional load aggregators: With the goal of optimizing regional power consumption, maximizing the benefits of aggregators, and maximizing user satisfaction, the strategy takes full account of load operation constraints, user usage habits, and power balance, and obtains a joint dispatching strategy that is mainly based on temperature-controlled loads and electric vehicles, supplemented by energy storage.

[0136] Temperature control load: Temperature control is used for temperature load. According to the changes in electricity price and outdoor temperature, the load is transferred from the high electricity price period to the low electricity price period to reduce user costs, or the temperature setting value is adjusted according to the system operation. The load aggregator controls the temperature control load through temperature control means, considers the benefits generated by the control behavior, and gives users a certain subsidy. When the interests of the load aggregator are maximized, the optimization goal is:

[0137] maxG(10)

[0138] Where G represents the interest of the load aggregator;

[0139] Users are often concerned about their own comfort. The comfort of temperature control load is closely related to temperature. Therefore, the user level takes the minimum temperature change as the optimization goal, which is expressed as follows:

[0140]

[0141] In the formula, Indicates the temperature setting value. Indicates the expected value of temperature;

[0142] The aggregate power constraints are:

[0143] P dr,t,min ≤P dr,t ≤P dr,t,max (12)

[0144] Where P dr,t,min and P dr,t,max They represent the minimum and maximum values ​​of the demand-side response respectively;

[0145] Electric vehicles: Centralized V2G technology is used to achieve unified control of decentralized electric vehicles. The mathematical model of double-layer optimization can be described as follows:

[0146]

[0147] Where: F is the objective function of the upper optimization model, x is the decision variable of the upper optimization model, G is the constraint condition of the upper optimization model, and f i is the objective function of the lower optimization model, y i is the decision variable of the lower optimization model, g i It is the constraint condition of the lower optimization model; the time-optimized charging and discharging power is used as the connection variable between the upper-layer time scheduling optimization and the lower-layer space scheduling optimization. When performing real-time space allocation scheduling, the number of charging vehicles is allocated according to the charging and discharging power in each period of the daily plan to achieve a balance between power supply and demand;

[0148] Energy storage equipment: Under the premise of ensuring that the dispatchable capacity of energy storage on the user side and the energy storage capacity on the aggregation side meet the peak load demand of the system, with the goal of maximizing the annual revenue of the aggregator and minimizing the peak-to-valley difference of the electricity consumption curve, the objective function for configuring the energy storage capacity on the aggregation side is:

[0149] maxF net =F1-F2-F3-F4 (15)

[0150]

[0151] The revenue of the aggregator is the revenue from participating in peak load regulation F1. The expenditure of the aggregator is divided into three parts: the first part is the cost of aggregating user-side energy storage F2, the second part is the cost of configuring and maintaining the aggregate-side energy storage F3, and the third part is the charging cost of the aggregate-side energy storage F4.

[0152] S3, joint dispatch of multiple flexible load groups of users in the substation area;

[0153] When typical flexible loads of users in the substation area participate in the grid dispatching, since the number of typical flexible loads in the substation area is limited, the regulation potential provided by the typical flexible loads is also limited. Therefore, the multiple flexible loads in the substation area are jointly regulated to meet the dispatching conditions that cannot be met by a single type of flexible load;

[0154] When considering multiple flexible loads, the loads often show differences in usage time and ambiguity in operating status. This study establishes a universal controllable margin index to make multiple flexible loads comparable. In the process of optimal combination, they can be controlled as a unified resource pool.

[0155] Controllable energy state margin:

[0156] For structurally heterogeneous flexible loads, the controllable energy state margin index is defined as follows:

[0157] ε c,ac / cv / cs (t) = 1-|S ac / cv / cs (t)| (17)

[0158] Among them, S...(t) ranges from [-1,1]; ε c,… (t) The value range is [0,1]. The closer its value is to 1, the greater the adjustability and the higher the satisfaction; the closer its value is to 0, the smaller the adjustability and the lower the satisfaction.

[0159] Controllable power margin:

[0160] ε p (t)=|P s (t)| (18)

[0161] Where P s (t) is the positive and negative response potential of the load at time t. This indicator represents the equivalent adjustable power of the load based on the consideration of the subjective and objective participation capabilities of the load;

[0162] Controllable time margin:

[0163] The controllable time margin is not only determined by the power consumption time set by the load itself, but also depends on the controllable time converted from the load's adjustable energy space in order to meet the user's power demand. The minimum of the above two is the actual controllable time margin of the load.

[0164] ε t (t) = min{t f -t,E cont (t) / P(t)} (19)

[0165] Where, t f The power consumption time set for the load; E cont (t) is the adjustable energy space of the load; P(t) is the operating power of the load at time t;

[0166] S4. Establishing the day-ahead dispatching of distribution network loads based on multi-agent game;

[0167] According to the non-cooperative game model, the load aggregators in the substation area are divided into consumer load aggregators and provider load aggregators. Consumer load aggregators offer a purchase price to purchase the excess electricity of other load aggregators. The higher the bid, the more energy they get. Multiple consumer load aggregators form a game situation through bidding.

[0168] The non-cooperative game model of energy trading between load aggregators in the substation area G = {I1; S1…S n; U}, there are

[0169] (1) Participants: Consumer load aggregators;

[0170] (2) Strategy: Each consumer load aggregator participates in the bidding by changing the power purchase price t, thereby controlling the energy of its own transactions with other load aggregators to minimize its own economic cost. Participants can arbitrarily select values ​​in the strategy set. The set of all feasible power purchase price strategies for participant i is defined as:

[0171]

[0172] in, and They represent the maximum and minimum electricity purchase prices of participant i, is the price at which load aggregators purchase electricity from the distribution network. It is the price at which load aggregators sell electricity to distribution networks. The bid price of each load aggregator is between the purchase and sale prices of distribution networks, and the bidding range is consistent. This definition can ensure that the benefits of energy transactions between load aggregators in the substation and adjacent load aggregators are greater than the benefits of transactions with the distribution network, so that energy flows first within the load aggregators in the substation;

[0173] Benefit function: The benefit function of the participant is defined as:

[0174]

[0175] For participant i, the optimal bidding strategy is:

[0176]

[0177] Where C -i represents the set {C1,...,C i-1 ,C i+1 ,...,C I};

[0178] With the comprehensive goals of optimizing economic benefits, minimizing network losses, maximizing renewable energy consumption, and minimizing voltage deviation, the distribution network dispatching center coordinates the output of distributed power sources and flexible loads in the day ahead to achieve active power balance of the power system. The objective function is as follows:

[0179] Objective function 1: The optimization goal is to minimize the operating cost of the active distribution network, as shown in the formula;

[0180] minC=C pcc +C FL +C MR +C DERG (twenty three)

[0181] Where C is the lowest total operating cost of the distribution network during the entire dispatching cycle; C pcc The cost of purchasing electricity from the upper grid; C FL Compensation cost for flexible load dispatch; C MR is the dispatching cost of distributed power grid; C DERG Penalize costs for abandoning wind and solar renewable energy;

[0182] Objective function 2: In order to reduce the burden on the upper power grid, reduce the standby power and increase the utilization of equipment, the minimum fluctuation of the tie line power is taken as the target optimization:

[0183]

[0184] P in the formula PCC (t) is the exchange power of the tie line at the time, P avgpcc (t) is the average power of the tie line during the dispatch period, and nT24 is the entire dispatch period;

[0185] Objective function 3: Minimize active network loss. When the DG processing of the power system is too large to meet the load consumption of the area, it will also feed back electric energy to the upper power grid. At this time, it will cause long-distance transmission of electricity, resulting in increased losses. Active network loss is also a criterion for measuring power loss. The active loss of the network is equivalent to the minimum sum of the power injected by all nodes in the system, as shown in the following formula:

[0186]

[0187] The formula nbus is the number of nodes, V i is the voltage amplitude of node i, G ij , B ij are the branch conductance and susceptance, θ ij is the phase angle difference between nodes i and j, Γ is the set of nodes connected to j in the system;

[0188] Objective function 4: Minimum node voltage deviation. Distributed power sources are connected to the active distribution network. Due to the volatility of renewable distributed power sources, the power quality of the system is greatly affected. The node voltage should be guaranteed to be within a reasonable fluctuation range during scheduling. The actual node voltage of the grid is compared with the standard voltage. It is expected that when renewable energy is connected, some voltage margin will be left to reduce the possibility of voltage crossing the line. The minimum deviation between the actual voltage and the rated voltage is taken as the optimization target as shown in the formula:

[0189]

[0190] Where V i (t) is the voltage amplitude of the ith node at time t, and nbus is the number of nodes;

[0191] The constraints include:

[0192] 1) Flexible load constraints:

[0193] Temperature control load constraints:

[0194]

[0195] Energy storage load constraints:

[0196]

[0197]

[0198] In the formula They represent the maximum charge and discharge capacity of the energy storage in time period t, with discharge taking negative value and charge taking positive value. i (t) represents the energy storage charge state, which is equal to the ratio of the remaining power to the maximum power. During the entire charging and discharging process, the energy storage system is prevented from overcharging and discharging, and its SOC is kept within a certain range. Indicates the minimum and maximum limit values ​​of the charge of the i-th energy storage device. In order to facilitate the next stage of scheduling, the energy storage battery is kept at the end Greater than a set value

[0199] 2) Network operation constraints:

[0200] (1) System power flow constraints:

[0201]

[0202] in: Inject active and reactive power into the tie line of node i at time t, is the output active and reactive power of distributed power sources, For active and reactive loads, is the reactive power of capacitor switching, θ ij is the voltage phase angle difference between nodes i and j, V i (t), V j (t) Voltage amplitude at nodes i and j, G ij , B ij are branch conductance and susceptance respectively, and nbus is the number of branches;

[0203] (2) Upper and lower limits of active and reactive power of tie lines:

[0204]

[0205] 3) Node voltage and line power constraints:

[0206]

[0207]

[0208] V i (t) represents the actual node voltage value of the i-th node at time t, V i max 、V i min are the upper and lower limits of the node voltage, Maximum allowed line power.

[0209] S5. Real-time control of the load in the substation with uncertain source, load and storage based on day-ahead dispatch;

[0210] The day-ahead optimization dispatch is to make a preliminary plan for the operation of flexible loads in the substation, determine the load management strategy within the substation, and use the optimization results of the interconnection line power as the reference value for intraday optimization. The intraday optimization process can correct the day-ahead optimization results and reduce the actual dispatch deviation caused by the prediction error. The intraday real-time optimization dispatch is based on more accurate intraday short-term prediction data to correct the day-ahead results while ensuring the interconnection line power. The real-time optimization updates the prediction information of photovoltaic and wind power generation power and load at intervals of 15 minutes, and optimizes the charging and discharging power of the battery in a rolling manner.

[0211] like Figure 2 As shown, the short-term forecast period of the intraday plan is 4 hours, the rolling cycle is 15 minutes, the control period is 15 minutes, and the intraday correction strategy includes the following steps:

[0212] 1) At the current time k, based on a certain prediction model, predict the power value of the system within 16 time periods.

[0213] Based on the optimization model, the control instructions at the future k+1, k+2, ..., k+16 moments are obtained;

[0214] 2) Only the [k, k+1] period is controlled, and the state of each unit of the load in the substation obtained at this moment is input as the initial value of the [k+1, k+2] period;

[0215] 3) Repeat the above steps, and so on, to optimize backwards;

[0216] The daily optimization of distribution network load takes into account both economic cost and tie line change value. The objective function is as follows:

[0217]

[0218] Where, F2 is the objective function value of the distribution network intraday optimization, F P is the economic cost of daily operation, Pgrid1 , P grid2 are the optimized values ​​of the tie line power in the day-ahead and intraday stages respectively, μ is the weight coefficient of the tie line power change, the constraints of intraday scheduling are consistent with those of day-ahead scheduling, and the economically optimal scheduling result is given by the particle swarm optimization algorithm.

[0219] The present invention solves the problem that the flexible load regulation characteristics are difficult to analyze due to the limitations of many influencing factors such as the type of flexible load, the basic operating conditions and related parameters of the equipment used, and the user's electricity consumption expectations; since the flexible load access locations are scattered and most of them are small and medium-sized loads with small individual capacities, they cannot be effectively aggregated to form economies of scale, and a flexible load aggregation model is established to lay the foundation for flexible loads to participate in scheduling. With the continuous growth of user loads in the substation area and the continuous popularization of various types of flexible loads, a typical flexible load optimization scheduling strategy is proposed with the goals of optimizing substation energy consumption, maximizing the benefits of aggregators, and user comfort, filling the gap in flexible load scheduling strategies; for multiple In order to solve the problem of difficulty in joint dispatching caused by the large scale, great differences and diverse usage time of heterogeneous flexible loads, corresponding joint optimization strategies are proposed, and a multi-station load joint control architecture for new energy access is proposed, which fills the gap in the control architecture at the distribution network-station level, and absorbs renewable energy through the resources of "source" and "load" to ensure the economic, safe and stable operation of the power grid. A distribution network day-ahead dispatching strategy considering the game of multiple stations is proposed, which provides new ideas for the optimized operation of the distribution network, realizes active distribution network optimization dispatching based on flexible loads on multiple time scales, implements the use of flexible loads to smooth the fluctuations of renewable energy, and solves the problem of difficult real-time dispatching caused by the uncertainty of source, load and storage.

[0220] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and it is intended that all changes that fall within the meaning and scope of equivalent elements are included in the content of the present invention.

Claims

1. A method for flexible load control in distribution network area under new energy access, characterized by: The following steps are involved: S1. Aggregation modeling of typical flexible loads of users in the substation area; typical flexible loads include electric vehicle charging and discharging models, temperature control load power consumption models and energy storage equipment charging and discharging models; Electric vehicle charging and discharging model: When an electric vehicle is connected to charging at time t0, and the initial state of charge is SOC0, the state of charge of the electric vehicle at time t can be calculated by the following formula: Where: P rated is the charging power of the electric vehicle; η is the charging efficiency of the electric vehicle; C is the battery capacity of the electric vehicle, and charging stops when the state of charge of the electric vehicle reaches the user's expected value; Electricity consumption model of temperature control load: Differential equation of the first-order thermodynamic model of air conditioning load: Where: Tr(t) is the room temperature, Tout(t) is the outdoor temperature, Qac(t) is the air conditioning cooling capacity, R is the equivalent thermal resistance of the air-conditioning building system, C is the equivalent heat capacity of the air-conditioning building system, and the electrical model of the air-conditioning body is: The relationship between electric power Pac(t) and cooling capacity Qrated(t) is as follows: Qac(t)=η ac P ac (t) (3) Pac(t)=w ac (t)P acn (4) Where: P ac (t) is the power consumption of the fixed frequency air conditioner at time t, P acn is the rated power, η ac is the energy efficiency ratio, w ac (t) is the air conditioner operation state. When it is 0, the air conditioner is in a stopped state. When it is 1, the air conditioner is in a running state. Energy storage equipment charging and discharging model: The mathematical model is as follows: E es (t)=E es (t-Δt)+ηP es-c Δt (5) The former is the charging energy balance equation, and the latter is the discharging energy balance equation, where: E es (t) is the energy stored in the energy storage device at time t, η is the charge and discharge loss coefficient of the energy storage device, P es-c is the charging power of the energy storage device, P es-f is the charging power of the energy storage device; The constraints are as follows: SOC min ≤SOC≤SOC max (7) P es-cmin ≤P es-c ≤P es-cmax (8) P es-fmin ≤P es-f ≤P es-fmax (9) Where: SOC max and SOC min They are the upper and lower limits of the SOC of the energy storage device, P es-cmax and P es-cmin is the upper and lower limits of the energy storage charging power, P es-fmax and P es-fmin are the upper and lower limits of the energy storage discharge power, formula (7) is the SOC constraint, formula (8) is the maximum and minimum constraints of the charging power, and formula (9) is the maximum and minimum constraints of the discharge power; S2. Flexible load dispatching for regional load aggregators: With the goal of optimizing regional power consumption, maximizing the benefits of aggregators, and maximizing user satisfaction, the strategy takes full account of load operation constraints, user usage habits, and power balance, and obtains a joint dispatching strategy that is mainly based on temperature-controlled loads and electric vehicles, supplemented by energy storage. Temperature control load: Temperature control is used for temperature load. According to the changes in electricity price and outdoor temperature, the load is transferred from the high electricity price period to the low electricity price period to reduce user costs, or the temperature setting value is adjusted according to the system operation. The load aggregator controls the temperature control load through temperature control means, considers the benefits generated by the control behavior, and gives users a certain subsidy. When the interests of the load aggregator are maximized, the optimization goal is: maxG(10), where G represents the interest of the load aggregator; Users are often concerned about their own comfort. The comfort of temperature control load is closely related to temperature. Therefore, the user level takes the minimum temperature change as the optimization goal, which is expressed as follows: In the formula, Indicates the temperature setting value. Indicates the expected value of temperature; The aggregate power constraints are: P dr,t,min ≤P dr,t ≤P dr,t,max (12) Where P dr,t,min and P dr,t,max They represent the minimum and maximum values ​​of the demand-side response respectively; Electric vehicles: Centralized V2G technology is used to achieve unified control of decentralized electric vehicles. The mathematical model of double-layer optimization can be described as follows: Where: F is the objective function of the upper optimization model, x is the decision variable of the upper optimization model, G is the constraint condition of the upper optimization model, and f i is the objective function of the lower optimization model, y i is the decision variable of the lower optimization model, g i It is the constraint condition of the lower optimization model; the time-optimized charging and discharging power is used as the connection variable between the upper-layer time scheduling optimization and the lower-layer space scheduling optimization. When performing real-time space allocation scheduling, the number of charging vehicles is allocated according to the charging and discharging power in each period of the daily plan to achieve a balance between power supply and demand; Energy storage equipment: Under the premise of ensuring that the dispatchable capacity of energy storage on the user side and the energy storage capacity on the aggregation side meet the peak load demand of the system, with the goal of maximizing the annual revenue of the aggregator and minimizing the peak-to-valley difference of the electricity consumption curve, the objective function for configuring the energy storage capacity on the aggregation side is: <h2 style=";text-align:left;direction:ltr">maxF<h2 style=";text-align:left;direction:ltr"> net <h2 style=";text-align:left;direction:ltr"> =F1-F2-F3-F4 (15) The revenue of the aggregator is the revenue from participating in peak load regulation F1. The expenditure of the aggregator is divided into three parts: the first part is the cost of aggregating user-side energy storage F2, the second part is the cost of configuring and maintaining the aggregate-side energy storage F3, and the third part is the charging cost of the aggregate-side energy storage F4. S3, joint dispatch of multiple flexible load groups of users in the substation area; When typical flexible loads of users in the substation area participate in the grid dispatching, since the number of typical flexible loads in the substation area is limited, the regulation potential provided by the typical flexible loads is also limited. Therefore, the multiple flexible loads in the substation area are jointly regulated to meet the dispatching conditions that cannot be met by a single type of flexible load; When considering multiple flexible loads, the loads often show differences in usage time and ambiguity in operating status. This study establishes a universal controllable margin index to make multiple flexible loads comparable. In the process of optimal combination, they can be controlled as a unified resource pool. Controllable energy state margin: For structurally heterogeneous flexible loads, the controllable energy state margin index is defined as follows: ε c,ac / cv / cs (t)=1-|S ac / cv / cs (t)| (17) Among them, S…(t) ranges from [-1,1]; ε c,… (t) The value range is [0,1]. The closer its value is to 1, the greater the adjustability and the higher the satisfaction; the closer its value is to 0, the smaller the adjustability and the lower the satisfaction. Controllable power margin: ε p (t)=|P s (t)| (18) Where P s (t) is the positive and negative response potential of the load at time t. This indicator represents the equivalent adjustable power of the load based on the consideration of the subjective and objective participation capabilities of the load; Controllable time margin: The controllable time margin is not only determined by the power consumption time set by the load itself, but also depends on the controllable time converted from the load's adjustable energy space in order to meet the user's power demand. The minimum of the above two is the actual controllable time margin of the load. ε t (t)=min{t f -t,E cont (t) / P(t)} (19) Where, t f The power consumption time set for the load; E cont (t) is the adjustable energy space of the load; P(t) is the operating power of the load at time t; S4. Establishing the day-ahead dispatching of distribution network loads based on multi-agent game; According to the non-cooperative game model, the load aggregators in the substation area are divided into consumer load aggregators and provider load aggregators. Consumer load aggregators offer a purchase price to purchase the excess electricity of other load aggregators. The higher the bid, the more energy they get. Multiple consumer load aggregators form a game situation through bidding. The non-cooperative game model of energy trading between load aggregators in the substation area G = {I1; S1…S n ; U}, there are (1) Participants: Consumer load aggregators; (2) Strategy: Each consumer load aggregator participates in the bidding by changing the power purchase price t, thereby controlling the energy of its own transactions with other load aggregators to minimize its own economic cost. Participants can arbitrarily select values ​​in the strategy set. The set of all feasible power purchase price strategies for participant i is defined as: in, and They represent the maximum and minimum electricity purchase prices of participant i, is the price at which load aggregators purchase electricity from the distribution network. It is the price at which load aggregators sell electricity to distribution networks. The bid price of each load aggregator is between the purchase and sale prices of distribution networks, and the bidding range is consistent. This definition can ensure that the benefits of energy transactions between load aggregators in the substation and adjacent load aggregators are greater than the benefits of transactions with the distribution network, so that energy flows first within the load aggregators in the substation; Benefit function: The benefit function of the participant is defined as: For participant i, the optimal bidding strategy is: Where Ci represents the set {C1,...,C i-1 ,C i+1 ,...,C I }; With the comprehensive goals of optimizing economic benefits, minimizing network losses, maximizing renewable energy consumption, and minimizing voltage deviation, the distribution network dispatching center coordinates the output of distributed power sources and flexible loads in the day ahead to achieve active power balance of the power system. The objective function is as follows: Objective function 1: The optimization goal is to minimize the operating cost of the active distribution network, as shown in the formula; minC=C pcc +C FL +C MR +C DERG (23) Where C is the lowest total operating cost of the distribution network during the entire dispatching cycle; C pcc The cost of purchasing electricity from the upper grid; C FL Compensation cost for flexible load dispatch; C MR is the dispatching cost of distributed power grid; C DERG Penalize costs for abandoning wind and solar power; Objective function 2: In order to reduce the burden on the upper power grid, reduce the standby power and increase the utilization of equipment, the minimum fluctuation of the tie line power is taken as the target optimization: P in the formula PCC (t) is the exchange power of the tie line at the time, P avgpcc (t) is the average power of the tie line during the dispatch period, and nT24 is the entire dispatch period; Objective function 3: Minimize active network loss. When the DG processing of the power system is too large to meet the load consumption of the area, it will also feed back electric energy to the upper power grid. At this time, it will cause long-distance transmission of electricity, resulting in increased losses. Active network loss is also a criterion for measuring power loss. The active loss of the network is equivalent to the minimum sum of the power injected by all nodes in the system, as shown in the following formula: The formula nbus is the number of nodes, V i is the voltage amplitude of node i, G ij , B ij are the branch conductance and susceptance, θ ij is the phase angle difference between nodes i and j, Γ is the set of nodes connected to j in the system; Objective function 4: Minimum node voltage deviation. Distributed power sources are connected to the active distribution network. Due to the volatility of renewable distributed power sources, the power quality of the system is greatly affected. The node voltage should be guaranteed to be within a reasonable fluctuation range during scheduling. The actual node voltage of the grid is compared with the standard voltage. It is expected that when renewable energy is connected, some voltage margin will be left to reduce the possibility of voltage crossing the line. The minimum deviation between the actual voltage and the rated voltage is taken as the optimization target as shown in the formula: Where V i (t) is the voltage amplitude of the ith node at time t, and nbus is the number of nodes; S5. Real-time control of the load in the substation with uncertain source, load and storage based on day-ahead dispatch; The day-ahead optimization dispatch is to make a preliminary plan for the operation of flexible loads in the substation, determine the load management strategy within the substation, and use the optimization results of the interconnection line power as the reference value for intraday optimization. The intraday optimization process can correct the day-ahead optimization results and reduce the actual dispatch deviation caused by the prediction error. The intraday real-time optimization dispatch is based on more accurate intraday short-term prediction data to correct the day-ahead results while ensuring the interconnection line power. The real-time optimization updates the prediction information of photovoltaic and wind power generation power and load at intervals of 15 minutes, and optimizes the charging and discharging power of the battery in a rolling manner. The short-term forecast period of the intraday plan is 4 hours, the rolling cycle is 15 minutes, and the control period is 15 minutes. The intraday correction strategy includes the following steps: 1) At the current time k, based on a certain prediction model, predict the power value of the system in 16 time periods. Based on the optimization model, obtain the control instructions for the future k+1, k+2, ..., k+16 times; 2) Only the [k, k+1] period is controlled, and the state of each unit of the load in the substation obtained at this moment is input as the initial value of the [k+1, k+2] period; 3) Repeat the above steps, and so on, to optimize backwards; The daily optimization of distribution network load takes into account both economic cost and tie line change value. The objective function is as follows: Where, F2 is the objective function value of the distribution network intraday optimization, F P is the economic cost of daily operation, P grid1 , P grid2 are the optimized values ​​of the tie line power in the day-ahead and intraday stages respectively, μ is the weight coefficient of the tie line power change, the constraints of intraday scheduling are consistent with those of day-ahead scheduling, and the economically optimal scheduling result is given by the particle swarm optimization algorithm.

2. According to a method for flexible load control in distribution network area under new energy access according to claim 1, it is characterized by: In step S1, the electric vehicle is charged in a two-stage constant current-constant voltage charging mode. The power battery is charged at a constant current in the first stage. In the constant current stage, the battery terminal voltage continues to rise until it reaches the cut-off voltage, and then the terminal voltage is kept unchanged. The charging process enters the second stage, i.e., the constant voltage stage. In the constant voltage stage, the charging current continues to decrease until it reaches the cut-off current. At this time, the charging process is terminated. The two-stage constant current-constant voltage charging process can be considered as constant power charging.

3. According to a method for flexible load control in distribution network area under new energy access according to claim 1, it is characterized by: In step S1, the equivalent thermodynamic model of the air conditioner uses components such as capacitors, resistors and current sources to simulate the temperature change process in the building, wherein the cooling capacity of the air conditioner is regarded as a current source, the indoor temperature is regarded as a voltage source, the thermal resistance of the building system is regarded as a resistor, and the heat capacity of the building system is equivalent to a capacitor, and then Kirchhoff's law is used to solve the indoor temperature.

4. According to a method for flexible load control in distribution network area under new energy access according to claim 1, it is characterized in that: In step S4, the constraints include: 1) Flexible load constraints: Temperature control load constraints: Energy storage load constraints: In the formula They represent the maximum charge and discharge capacity of the energy storage in time period t, with discharge taking negative value and charge taking positive value. i (t) represents the energy storage charge state, which is equal to the ratio of the remaining power to the maximum power. During the entire charging and discharging process, the energy storage system is prevented from overcharging and discharging, and its SOC is kept within a certain range. Indicates the minimum and maximum limit values ​​of the charge of the i-th energy storage device. In order to facilitate the next stage of scheduling, the energy storage battery is kept at the end Greater than a set value 2) Network operation constraints: (1) System power flow constraints: in: Inject active and reactive power into the tie line of node i at time t, is the output active and reactive power of distributed power sources, For active and reactive loads, is the reactive power of capacitor switching, θ ij is the voltage phase angle difference between nodes i and j, V i (t), V j (t) Voltage amplitude at nodes i and j, G ij , B ij are branch conductance and susceptance respectively, and nbus is the number of branches; (2) Upper and lower limits of active and reactive power of tie lines: 3) Node voltage and line power constraints: V i (t) represents the actual node voltage value of the i-th node at time t, are the upper and lower limits of the node voltage, Maximum allowed line power.

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