Operation optimization model construction and operation optimization method and system for electric heating load

Abstract

The invention provides an electric heating load operation optimization model construction and operation optimization method and system, and is applied to the technical field of heat exchange station electric heat compensation equipment control. The method comprises the steps that building thermal inertia of each electric heating load node in a power system is considered, an electric heating load node energy consumption model is constructed according to the energy interaction relation between electric heating equipment of each electric heating load node and the environment, and the electric heating load node energy consumption model is calculated based on the electric heating load node energy consumption model and the electricity price of a power spot trading market. Constructing an operation optimization objective function by taking the minimum daily operation total cost of each electric heating load node participating in the power market transaction as an objective; and on the basis of the power balance relationship and the load adjustment range of each electric heating load node and the energy interaction relationship and the heat energy adjustment range in each electric heating load node, and in combination with the optimal power flow constraint, constructing a constraint condition for the operation optimization objective function to obtain an operation optimization model. The problem of overload of the power distribution network caused by centralized concurrence of electric heating operation is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of control of electric supplementary heating equipment in heat exchange stations, and particularly relates to a method and system for constructing an operation optimization model and optimizing the operation of electric heating loads. Background Art

[0002] In recent years, with the vigorous development of new energy construction in China and the promotion of the clean-up process on both the supply and demand sides, the proportion of wind and light access in the power system has increased significantly, showing the characteristics of "double highs" (high proportion of renewable energy access and high proportion of power electronic equipment application) and "double randomness" (randomness on the supply side and randomness on the demand side). Electric flexible loads play a huge role in the consumption of new energy, hedging the output of renewable energy, and improving the operation stability of the power system, and are one of the important research contents of the current new power system.

[0003] In northern China, clean heating is a major livelihood project. In recent years, power grids and power generation enterprises have made significant investments through supporting power grid construction and electricity price concessions. However, at present, the main regulation means are time-of-use electricity price and demand-side management of orderly power consumption, and there is a problem of overload of the distribution network caused by the concentrated concurrency of electric heating operation. Summary of the Invention

[0004] In order to overcome the problem of overload of the distribution network caused by the concentrated concurrency of electric heating operation, the present invention provides a method and system for constructing an operation optimization model and optimizing the operation of electric heating loads.

[0005] On the one hand, the present invention provides a method for constructing an operation optimization model of electric heating loads, including:

[0006] Considering the building thermal inertia of each electric heating load node in the power system, and constructing an energy consumption model for the electric heating load node according to the energy interaction relationship between the electric heating equipment and the environment at each electric heating load node;

[0007] Based on the energy consumption model of the electric heating load node and the electricity price in the electricity spot trading market, determining the daily operation total cost for each electric heating load node to participate in the electricity market transaction, and constructing an operation optimization objective function with the minimization of the daily operation total cost as the goal;

[0008] Based on the power balance relationship and load regulation range of each electric heating load node, as well as the energy interaction relationship and heat energy regulation range within each electric heating load node, and combining with the optimal power flow constraint, constructing constraint conditions for the operation optimization objective function;

[0009] Based on the operation optimization objective function and the constraint conditions, constructing an operation optimization model of electric heating loads.

[0010] Optionally, considering the building thermal inertia of each electric heating load node in the power system, an energy consumption model for the electric heating load node is constructed according to the energy interaction relationship between the electric heating equipment and the environment at each electric heating load node, including:

[0011] Based on the adjustability of the load, the electric heating load nodes are divided into rigid loads and flexible loads;

[0012] Based on the heat storage equipment and building thermal inertia of the electric heating load node, an equivalent heat storage body of the electric heating load node is constructed. The electric heating equipment of the electric heating load node is regarded as a flexible load, and an energy interaction relationship is constructed among the electric heating equipment, the equivalent heat storage body, and the electric heating demand of the electric heating load node by using the energy interaction relationship between the electric heating equipment and the environment of the electric heating load node, so as to obtain a flexible load energy interaction model of the electric heating load node;

[0013] Based on the power supply and energy consumption balance relationship of the electric heating load node, an energy consumption model of the electric heating load node is constructed by using the rigid load and flexible load energy interaction models of the electric heating load node.

[0014] Optionally, determining the daily operating total cost of each electric heating load node participating in the power market transaction based on the energy consumption model of the electric heating load node and the electricity price in the power spot trading market includes:

[0015] Based on the energy consumption model of the electric heating load node, determine the active power curve of each electric heating load node participating in the power spot market transaction on the day before;

[0016] Based on the active power curve of each electric heating load node participating in the power market transaction on the day before and the real-time clearing electricity price in the power spot trading market on the day before, determine the daily operating total cost of each electric heating load node participating in the power market transaction.

[0017] Optionally, the operation optimization objective function is as follows:

[0018]

[0019] P Li (t) = P ei (t) + P heati (t);

[0020] In the formula, C(t) is the clearing electricity price at time t in the power spot trading market on the day before, P Li (t) is the active power of the electric heating load node i participating in the power market transaction at time t on the day before, Δt is the clearing time interval of the power spot trading market, P ei (t) is the rigid load power of the electric heating load node i at time t on the day before, P heati(t) is the power consumption of the electric heating equipment at node i of the electric heating load at the previous day's time t, T is the optimization period, and S Nh is the set of electric heating load nodes in the power system.

[0021] Optionally, the constraint conditions include at least one of the following:

[0022] Power balance constraints of each electric heating load node, power constraints of electric heating equipment in each electric heating load node, chronological balance constraints of the heat storage quantity of the equivalent heat storage body in each electric heating load node, limit power constraints of the equivalent heat storage body in each electric heating load node, heat storage capacity constraints of the equivalent heat storage body in each electric heating load node, and optimal power flow constraints.

[0023] Optionally, the power balance constraints of each electric heating load node are as follows:

[0024] P heati (t) = P needi (t) + P si (t);

[0025] In the formula, P needi (t) is the electric heating demand of node i of the electric heating load at the previous day's time t, and P si (t) is the charge and discharge power of the equivalent heat storage body in node i of the electric heating load at the previous day's time t;

[0026] The power constraints of the electric heating equipment in each electric heating load node are as follows:

[0027] 0 ≤ P heati (t) ≤ P heati.max ;

[0028] In the formula, P heati.max is the upper limit of the heating power of the electric heating equipment at node i of the electric heating load at the previous day's time t;

[0029] The chronological balance constraints of the heat storage quantity of the equivalent heat storage body in each electric heating load node are as follows:

[0030] E Si (t + 1) = E Si (t) + P si (t) × Δt;

[0031] E Si (1) = E Si (T);

[0032] In the formula, E Si (t + 1) is the heat storage quantity of the equivalent heat storage body in node i of the electric heating load at the previous day's time t + 1, and E Si (t) is the heat storage quantity of the equivalent heat storage body in node i of the electric heating load at the previous day's time t, ESi (1) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the initial moment of the day-ahead optimization period, E Si (T) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the end moment of the day-ahead optimization period;

[0033] The limit power constraints of the equivalent heat storage bodies in each electric heating load node are as follows:

[0034] -P siin.max (t) ≤ P si (t) ≤ P siout.max (t);

[0035] In the formula, P siin.max (t), P siout.max (t) are respectively the maximum heat storage power and the maximum heat release power corresponding to the heat storage equipment and the building thermal inertia in the electric heating load node i at the day-ahead moment t;

[0036] The heat storage capacity constraints of the equivalent heat storage bodies in each electric heating load node are as follows:

[0037] 0 ≤ E Si (t) ≤ E Si.max ;

[0038] In the formula, E Si (t) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the day-ahead moment t, and E Si.max is the maximum heat storage quantity corresponding to the heat storage equipment and the building thermal inertia in the electric heating load node i at the day-ahead moment t.

[0039] On the other hand, the present invention also provides an optimized operation method for electric heating loads, including:

[0040] Based on the historical load data of each electric heating load node, the day-ahead weather conditions, and the heat disturbance factors of each electric heating load node, predict the day-ahead rigid load power and the day-ahead electric heating demand of each electric heating load node;

[0041] Based on the day-ahead rigid load power and the day-ahead electric heating demand of each electric heating load node, solve the operation optimization model of the electric heating load constructed according to any one of claims 1-6 to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node;

[0042] Based on the charge and discharge power of the equivalent heat storage body in each electric heating load node, control and adjust the operation power of the electric heating equipment in each electric heating load node.

[0043] Optionally, based on the day-ahead rigid load power and the day-ahead electric heating demand of each electric heating load node, solve the operation optimization model of the electric heating load to obtain the optimized operation plan of each electric heating load node, including:

[0044] The operation optimization model of the electric heating load is simplified by using a DC power flow model, so that the active power of each network node, the active power of each branch, and the phase angle satisfy a linear relationship, and a simplified operation optimization model is obtained;

[0045] Based on the power transfer distribution factor, linear optimization solution is carried out on the simplified operation optimization model to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node;

[0046] Wherein, in the process of the linear optimization solution, the power transfer distribution factor is used to establish a linear relationship between the active power flow of the branch in the power system and the transfer power of each electric heating load node.

[0047] Optionally, the linear relationship between the active power flow of the branch in the power system and the transfer power of each electric heating load node is as follows:

[0048]

[0049] In the formula, P mn is the active power flow of the branch between network nodes m and n; P mn 0 is the basic power flow of the branch between network nodes m and n, ΔP Li is the total active power change of the electricity consumption load of the electric heating load node i, L mn-i is the power transfer distribution factor of the network topology branch mn corresponding to the electric heating load node i, S N is the set of network nodes in the power system, S Nh is the set of electric heating load nodes in the power system.

[0050] On the other hand, the present invention also provides a system for constructing an operation optimization model of an electric heating load, including:

[0051] A node model construction module, which is used to consider the building thermal inertia of each electric heating load node in the power system, and construct an energy consumption model of the electric heating load node according to the energy interaction relationship between the electric heating equipment and the environment of each electric heating load node;

[0052] A target function construction module, which is used to determine the daily operation total cost of each electric heating load node participating in the power market transaction based on the energy consumption model of the electric heating load node and the electricity price in the power spot trading market, and construct an operation optimization target function with the minimization of the daily operation total cost as the goal;

[0053] A constraint condition construction module, which is used to construct constraint conditions for the operation optimization target function based on the power balance relationship and load regulation range of each electric heating load node, the energy interaction relationship and heat energy regulation range in each electric heating load node, and in combination with the optimal power flow constraint;

[0054] An optimization model construction module, configured to construct an operation optimization model of the electric heating load based on the operation optimization objective function and the constraint conditions.

[0055] On the other hand, the present invention further provides an optimized operation system for an electric heating load, including:

[0056] A prediction module, configured to predict the rigid load power and the electric heating demand of each electric heating load node on the day before based on the historical load data of each electric heating load node, the weather conditions on the day before, and the heat disturbance factors of each electric heating load node;

[0057] An optimized operation module, configured to solve the operation optimization model of the electric heating load constructed in any one of the above based on the rigid load power and the electric heating demand of each electric heating load node on the day before, to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node; and control and adjust the operation power of the electric heating equipment of each electric heating load node based on the charge and discharge power of the equivalent heat storage body in each electric heating load node.

[0058] On the other hand, the present invention further provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected through a bus;

[0059] The memory is configured to store one or more programs;

[0060] When the one or more programs are executed by the at least one processor, the operation optimization model construction method or the optimized operation method of the electric heating load described in any one of the above is implemented.

[0061] On the other hand, the present invention further provides a readable storage medium, on which an execution program is stored, and when the execution program is executed, the operation optimization model construction method or the regional power demand prediction method of the electric heating load described in any one of the above is implemented.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] The present invention provides a method and system for constructing an operation optimization model and optimizing the operation of an electric heating load. By considering the building thermal inertia of each electric heating load node in the power system and according to the energy interaction relationship between the electric heating equipment and the environment at each electric heating load node, an energy consumption model for the electric heating load node is constructed. By considering the building thermal inertia for equivalent energy storage, the refined splitting of the power of the electric heating load node is realized, and the conformity between the model and the actual situation is improved. Based on the energy consumption model of the electric heating load node, the flexibility of the electric heating load node, and the elasticity of the electricity price in the electricity spot trading market, an operation optimization model for multiple electric heating loads participating in the day-ahead electricity market trading in the power system is established; through the refined splitting of the electric heating load node, the model can achieve precise operation control of the power of the electric heating load and improve the control accuracy of the electric heating load. At the same time, the model considers the influence of the combined action of multiple electric heating load nodes on the branch power and node voltage in the network topology, so that the problem of overload of the distribution network caused by the concentrated and concurrent operation of the electric heating load can be solved by regulating the power of each electric heating load node. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a schematic flow chart of a method for constructing an operation optimization model of an electric heating load according to an example of the present invention;

[0065] Figure 2 is a schematic diagram of an energy consumption model of an electric heating load according to an example of the present invention;

[0066] Figure 3 is a schematic diagram of adjusting flexible control variables of an electric heating load node according to an example of the present invention;

[0067] Figure 4 is a schematic diagram of the topology of an IEEE-3 standard example according to an example of the present invention;

[0068] Figure 5 is a graph of the operating power of the electric heating load at node 3 before and after optimization at each moment according to an example of the present invention;

[0069] Figure 6 is a schematic diagram of the structure of an electronic device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The following further elaborates in detail on the specific embodiments of the present invention with reference to the accompanying drawings.

[0071] Embodiment 1

[0072] A method for constructing an operation optimization model of an electric heating load provided by the present invention, as shown in Figure 1 shown, includes:

[0073] Step S110: Consider the building thermal inertia of each electric heating load node in the power system, and construct an energy consumption model for the electric heating load node according to the energy interaction relationship between the electric heating equipment and the environment at each electric heating load node;

[0074] Step S120: Based on the energy consumption model of the electric heating load node and the electricity price in the electricity spot trading market, determine the daily operating total cost of each electric heating load node participating in the electricity market transaction. With the minimization of the daily operating total cost as the goal, construct an operation optimization objective function;

[0075] Step S130: Based on the power balance relationship and load regulation range of each electric heating load node, as well as the energy interaction relationship and heat energy regulation range within each electric heating load node, combined with the optimal power flow constraint, construct constraint conditions for the operation optimization objective function;

[0076] Step S140: Based on the operation optimization objective function and the constraint conditions, construct an operation optimization model for the electric heating load.

[0077] In the present exemplary embodiment, in the energy consumption model of the electric heating load node, the building thermal inertia can be regarded as a kind of energy storage capacity, and an equivalent energy storage body is constructed within each electric heating load node to realize the quantification of the building thermal inertia energy storage capacity. The optimal power flow constraint includes at least one of active power flow balance constraint, reactive power flow balance constraint, branch power constraint, node voltage constraint, etc. The power balance relationship and load regulation range of each electric heating load node can include the power balance relationship of various energy consumption forms and the load regulation range of each energy consumption form in the energy consumption model of the electric heating load of each node. The energy interaction relationship and heat energy regulation range within each electric heating load node can include the energy balance relationship of various energy consumption forms and the heat energy regulation range of each energy consumption form in the energy consumption model of the electric heating load of each node. The energy consumption forms in the energy consumption model of the electric heating load can include non-callable energy and callable energy, and the callable energy can include heating, heat storage, heating, etc. The present invention aims to minimize the operating cost of electric heating loads participating in the day-ahead electricity energy spot market, establish an objective function for the operation optimization model of electric heating loads participating in the day-ahead electricity energy spot market in the power grid, analyze the constraint conditions other than the conventional branch capacity and node voltage, and consider the influence of the combined action of multiple electric heating loads on the branch power and node voltage in the network topology, so as to solve the problem of overload of the distribution network caused by the concentrated concurrency of electric heating operations in cold regions at present.

[0078] In an exemplary embodiment, the step S110 of considering the building thermal inertia of each electric heating load node in the power system and constructing an energy consumption model for the electric heating load node according to the energy interaction relationship between the electric heating equipment and the environment at each electric heating load node includes:

[0079] Based on the adjustability of the load, the electric heating load nodes are divided into rigid loads and flexible loads;

[0080] Based on the heat storage equipment and building thermal inertia of the electric heating load nodes, an equivalent heat storage body of the electric heating load nodes is constructed. The electric heating equipment of the electric heating load nodes is regarded as a flexible load. By using the energy interaction relationship between the electric heating equipment of the electric heating load nodes and the environment, an energy interaction relationship is constructed among the electric heating equipment, the equivalent heat storage body, and the electric heating demand of the electric heating load nodes, and a flexible load energy interaction model of the electric heating load nodes is obtained;

[0081] Based on the power supply and energy consumption balance relationship of the electric heating load nodes, an energy consumption model of the electric heating load nodes is constructed by using the rigid load and flexible load energy interaction models of the electric heating load nodes.

[0082] In the present exemplary embodiment, based on the adjustability of the load, each electric heating load node can be decomposed into an electric heating flexible load and a rigid load. The rigid load refers to the load that must be satisfied for the life and work of users and does not accept the regulation of the power grid. That is, the rigid load is the load for which users do not change their power consumption patterns under the regulation of the power grid. For example, basic living electrical equipment such as lighting in a household. The electric heating flexible load refers to the load that can actively participate in the operation control of the power grid, interact with the power grid in terms of energy, and has the characteristics of flexible adjustment. The flexibility of this load is manifested in that its power consumption can be flexibly adjusted within a certain period of time to adapt to the demand changes of the power grid. For example, an air conditioner. According to the energy usage and interaction relationship of the electric heating load nodes, the flexible load is decomposed into energy storage and electric heating. Considering the thermal inertia of the buildings in each electric heating load node, the building thermal inertia is regarded as a form of energy storage, and an equivalent heat storage body including heat storage equipment and building thermal inertia is constructed. Therefore, based on the power supply and energy consumption balance relationship of the electric heating load nodes, an energy consumption model of the electric heating load nodes as shown in Figure 2 can be constructed. As can be seen from Figure 2 , the load power P L of the electric heating load nodes is divided into the corresponding rigid load P e and the power P heat of the electric heating equipment of the electric heating load nodes. And P heat can be further divided into the charge-discharge power P s to the equivalent heat storage body in the flexible electric heating load and the natural electric heating load demand P need before the regulation of the electric heating load. P s is essentially the heat storage / discharge power of the equivalent heat storage body. When P s is positive, it represents the heat storage state, and when P s is negative, it represents the heat release state. Based on this, the energy consumption model of the electric heating load nodes can be expressed as Equation (1):

[0083]

[0084] In the formula, P Li is the active power of the electric heating load node i participating in the electricity market transaction, P ei is the rigid load power of the electric heating load node i, P heati is the power consumption of the electric heating equipment of the electric heating load node i, P needi is the electric heating demand of the electric heating load node i, P si is the charge and discharge power of the equivalent heat storage body in the electric heating load node i, S Nh is the set of electric heating load nodes in the power system. The rigid load can be predicted according to the load curves of the same type on historical similar days. For example, the daily rigid load curve can be given by predicting the load of the same type on several maximum historical similar days; the natural electric heating load demand before regulation can be given by using the neural network model method based on temperature prediction and indoor personnel activity (thermal disturbance factor) prediction.

[0085] In an exemplary embodiment, determining the daily operating total cost of each electric heating load node participating in the electricity market transaction based on the energy consumption model of the electric heating load node and the electricity price in the electricity spot trading market includes:

[0086] Determining the active power curve of each electric heating load node participating in the electricity spot market transaction on the day before based on the energy consumption model of the electric heating load node;

[0087] Determining the daily operating total cost of each electric heating load node participating in the electricity market transaction based on the active power curve of each electric heating load node participating in the electricity market transaction on the day before and the real-time clearing electricity price in the electricity spot trading market on the day before.

[0088] In this exemplary embodiment, the electric heating flexible load nodes are incorporated into the power grid topology structure, and a mathematical model for minimizing the operating cost of multiple electric heating load nodes (aggregated load) participating in the day-ahead electricity energy spot market is established, that is, by utilizing the flexibility of the electric heating load and the elasticity of the electricity price in the electricity spot market, the total operating cost of multiple electric heating loads (aggregated load) in the power system is minimized on the operating day. The operating optimization objective function is as follows:

[0089]

[0090] P Li (t) = P ei (t) + P heati (t) (3)

[0091] In the formula, C(t) is the clearing electricity price at time t in the day-ahead electricity spot trading market, P Li(t) is the active power of the electric heating load node i participating in the electricity market transaction at the day-ahead time t, P ei (t) is the rigid load power of the electric heating load node i at the day-ahead time t, P heati (t) is the power consumption of the electric heating equipment of the electric heating load node i at the day-ahead time t, Δt is the clearing time interval of the electricity spot market, T is the optimization period, S Nh is the set of electric heating load nodes in the power system.

[0092] In an exemplary embodiment, corresponding constraint conditions are constructed for the operation optimization objective function, and these constraint conditions can be divided into two categories: one is the traditional optimal power flow constraint, that is, the general constraint, and the general constraint can include: active and reactive power flow balance constraints, branch apparent power capacity constraints, and node voltage upper and lower limit constraints; the other is the constraint determined by the characteristics and working states of various devices of the electric heating nodes, and this type of constraint is constructed based on the energy consumption model of the electric heating load nodes. Specifically, the constraints based on the energy consumption model of the electric heating load nodes can include one or more of the following: power balance constraints of each electric heating load node, power constraints of electric heating equipment in each electric heating load node, chronological balance constraints of the heat storage quantity of the equivalent heat storage body in each electric heating load node, limit power constraints of the equivalent heat storage body in each electric heating load node, and heat storage capacity constraints of the equivalent heat storage body in each electric heating load node.

[0093] (1) General constraint conditions:

[0094] 1a) Active and reactive power flow balance constraints:

[0095]

[0096] In the formula, P Gk (t), Q Gk (t) respectively represent the total active power and reactive power injected by the network node k in the power system at the time t; Q Li (t) is the reactive power of the electric heating load node in the power system; V k (t), V j (t) respectively represent the voltage amplitudes at the network nodes k and j in the power system at the time t; G jk , B jk respectively represent the impedance and admittance values between the network nodes j and k in the power system; θ jk represents the phase angle difference between the network nodes k and j in the power system; k ∈ S N , j ∈ S N , t ∈ T, S N is the set of network nodes in the power grid.

[0097] 1b) Branch apparent power capacity constraints:

[0098] P jk (t) = V k (t)V j (t) × (G jk cosθ jk (t) + B jk sinθ jk (t)) (6)

[0099] Q jk (t) = V k (t)V j (t) × (G jk sinθ jk (t) - B jk cosθ jk (t)) (7)

[0100]

[0101] S jk (t) ≤ S jk.max (9)

[0102] Wherein, P jk (t) and Q jk (t) respectively represent the active power flow and reactive power flow calculated from branch k and branch j in the power system; S jk (t) is the apparent power of branch k and branch j at time t, and S jk.max is the upper limit of the apparent power of branch k and branch j.

[0103] 1c) Node voltage upper and lower limit constraints:

[0104] V i.min ≤ V i (t) ≤ V i.max (10)

[0105] Wherein, V i.min and V i.max respectively represent the upper and lower limit values of the voltage of the electric heating load node i, and V i (t) is the voltage value of the electric heating load node i at time t.

[0106] (2) Constraints based on the energy consumption model of the electric heating load node:

[0107] 2a) Power balance constraints for each electric heating load node, that is, the equality relationship constraints among the control variable P si (t), the disturbance variable P needi (t), and the state variable P heati (t) are as follows:

[0108] Pheati P(t) = needi P(t) + si P(t)(11)

[0109] Wherein, P needi (t) is the electric heating demand of the electric heating load node i at the pre - day time t, and P si (t) is the charge - discharge power of the equivalent heat storage body in the electric heating load node i at the pre - day time t.

[0110] 2b) Power constraint of electric heating equipment in each electric heating load node, that is, the power limit constraint of the state variable electric heating demand P heati (t) for the heating equipment is as follows:

[0111] 0 ≤ P heati (t) ≤ P heati.max (12)

[0112] Wherein, P heati.max is the upper limit of the heating power of the electric heating equipment at the pre - day time t for the electric heating load node i.

[0113] 2c) The time - series balance constraint of the stored heat quantity of the equivalent heat storage body in each electric heating load node can include the time - series power balance constraint and the optimization - period power balance constraint of the equivalent heat storage body. The time - series power balance constraint of the equivalent heat storage body is the equality relationship of the stored - heat state quantities at two adjacent moments during the heat storage / discharge time - series process of the equivalent heat storage body, ignoring its heat loss, as shown in the following formula (13); the optimization - period power balance constraint is the constraint that the stored - heat quantities of the equivalent heat storage body at the beginning and end of the optimization period should be equal, as shown in the following formula (14). Specifically, it is as follows:

[0114] E Si (t + 1) = E Si (t) + P si (t)×Δt (13)

[0115] E Si (1) = E Si (T) (14)

[0116] Wherein, E Si (t + 1) is the stored - heat quantity of the equivalent heat storage body in the electric heating load node i at the pre - day time t + 1, ESi(t) is the stored - heat quantity of the equivalent heat storage body in the electric heating load node i at the pre - day time t, and E Si (1) is the stored - heat quantity of the equivalent heat storage body in the electric heating load node i at the initial moment of the pre - day optimization period, and E Si (T) is the stored - heat quantity of the equivalent heat storage body in the electric heating load node i at the end moment of the pre - day optimization period.

[0117] 2d) The limit power constraint of the equivalent heat storage body in each electric heating load node, that is, the constraint on the limit value of the charging / discharging power of the control variable P si (t) is as follows:

[0118] -P siin.max (t) ≤ P si (t) ≤ P siout.max (t) (15)

[0119] In the formula, P siin.max (t), P siout.max (t) are the maximum charging power and the maximum discharging power of the heat storage device and the building thermal inertia corresponding to the electric heating load node i at the pre-dispatch time t, respectively.

[0120] 2e) The heat storage capacity constraint of the equivalent heat storage body in each electric heating load node, that is, the constraint on the maximum heat storage value of the equivalent heat storage body, that is, the stored heat cannot exceed the maximum value of the corresponding equivalent heat storage in the electric heating load node. Specifically, it is as follows:

[0121] 0 ≤ E Si (t) ≤ E Si.max (16)

[0122] In the formula, E Si (t) is the stored heat of the equivalent heat storage body in the electric heating load node i at the pre-dispatch time t, and E Si.max is the maximum stored heat corresponding to the heat storage device and the building thermal inertia of the electric heating load node i at the pre-dispatch time t. Therefore, the finally constructed operation optimization model of the electric heating load is formulas (2)-(16).

[0123] Regarding the problem of coal-burning pollution in winter heating in northern China, in order to reduce the problem of coal-burning pollution in winter, clean energy is currently vigorously developed to solve this problem. Clean heating in northern China is a major livelihood project. In recent years, from the central government to local governments at all levels, a large amount of supporting funds have been invested, mainly used for subsidizing the procurement of electric heating equipment, and part of it is used for supporting power grid transformation or heating fee subsidies; at the same time, power grids and power generation enterprises have made relatively large investments through supporting power grid construction and electricity price concessions. In recent years, with the vigorous development of new energy construction in China and the promotion of the cleanization process on both the supply and demand sides, the power system shows the characteristics of high proportion of renewable energy access such as wind and light, high proportion of application of power electronic devices, randomness on the supply side and randomness on the demand side. Electric flexible loads play a huge role in the consumption of new energy, hedging the output of renewable energy, and improving the operation stability of the power system, and are one of the important contents of the current research on new power systems.

[0124] The present invention considers that in the power market environment, flexible loads can participate in the interaction through price signals and compensation mechanisms, and uses electric heating loads as a flexible load to participate in the grid interaction, and uses electric heating loads as an important part of the power flexible load. From the optimization perspective of minimizing the operating cost, the heat storage equipment and building thermal inertia in the electric heating load are stored in an equivalent heat storage body, and the model is modeled through the adjustable characteristics of the equivalent heat storage body. By aggregating such flexible load resources, the level of new energy consumption can be significantly improved, the construction of green power systems can be promoted, and the operation efficiency of the power grid can be improved. For example, by using the thermal storage electric heating load to respond in real time through a virtual power plant and participate in auxiliary service market transactions, clean energy can be increased and carbon dioxide emissions can be reduced. Although my country is in the early stages of the development of the power market, by gradually building a complete market rule and electricity price formation mechanism, the reasonable regulation of various flexible loads can be achieved, and the regulation method represented by time-of-use electricity prices and orderly electricity consumption will gradually develop into a regulation method with the guidance of power users and the satisfaction of user participation as the goal.

[0125] The present invention processes the load flexibility of the heat storage equipment and building thermal inertia of the electric heating load node, gives the disturbance variable part and the control variable part of the load, and performs energy storage equivalence on the heat storage equipment and building thermal inertia; analyzes the physical meaning represented by each parameter after the load node decomposition, and takes the limit equivalent heat storage capacity as an important input condition of the optimization model, and its size can reflect the flexibility of the user node. The present invention establishes the objective function of the operation optimization model of the electric heating load in the power grid participating in the day-ahead electric energy spot market, and analyzes the constraints other than the conventional branch capacity and node voltage; the model considers the impact of multiple loads acting together on the branch power and node voltage in the network topology, and solves the problem of overload of the distribution network caused by concentrated concurrency in the current operation of electric heating in the north.

[0126] Example 2

[0127] Based on the same inventive concept, the present invention also provides an optimized operation method of electric heating load, comprising:

[0128] Based on the historical load data of each electric heating load node, the weather conditions the day before and the heat disturbance factors of each electric heating load node, the day before rigid load power and the day before electric heating demand of each electric heating load node are predicted;

[0129] Based on the day-ahead rigid load power and day-ahead electric heating demand of each electric heating load node, the operation optimization model of the electric heating load constructed in Example 1 is solved to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node;

[0130] The operating power of the electric heating equipment at each electric heating load node is regulated based on the charging and discharging power control of the equivalent heat storage body in each electric heating load node.

[0131] In the present exemplary embodiment, the day-ahead rigid load power of each electric heating load node can be predicted through the historical load data of each electric heating load node, such as the historical rigid load curve. For example, the average situation of the rigid load curves of several similar days in the past is used as the day-ahead rigid load power. Through the day-ahead weather conditions of each electric heating load node and the heat disturbance factors of each electric heating load node, the trained neural network prediction model is used to predict the day-ahead electric heating demand. Based on the day-ahead rigid load power and day-ahead electric heating demand of each electric heating load node and the line topology parameters in the power grid (such as reactance value, capacity limit, power reference value, voltage reference value, etc.), the model is solved to obtain the control variable (the charge and discharge power in the node equivalent heat storage body), and based on the control variable, the operating power of the electric heating equipment at each electric heating load node is obtained.

[0132] In some embodiments, based on the day-ahead rigid load power and day-ahead electric heating demand of each electric heating load node, the operation optimization model of the electric heating load is solved to obtain the optimized operation plan for each electric heating load node, including:

[0133] The direct current power flow model is used to simplify the operation optimization model of the electric heating load, so that the active power of each network node, the active power of the branch, and the phase angle satisfy a linear relationship, and a simplified operation optimization model is obtained;

[0134] Based on the power transfer distribution factor, linear optimization solution is carried out on the simplified operation optimization model to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node;

[0135] Wherein, in the process of the linear optimization solution, the power transfer distribution factor is used to establish a linear relationship between the active power flow of the branch in the power system and the transfer power of each electric heating load node.

[0136] In the present exemplary embodiment, by using the direct current power flow model to simplify the operation optimization model of the electric heating load participating in the day-ahead electricity spot market, that is, regarding the power system as a direct current power flow model. The direct current power flow model is simplified by ignoring the reactive power flow, so that the active power of the node, the active power of the branch, and the phase angle satisfy a linear relationship, as shown in Equation (17):

[0137]

[0138] In the formula, P ei (t) represents the rigid load power of the electric heating load node i in the power system at time t; θ k (t) and θ j (t) respectively represent the phase angles corresponding to the network nodes k and j in the power system.

[0139] Therefore, the operation optimization objective function and constraints of the electric heating load are simplified as follows:

[0140] (1) Simplified result of the objective function

[0141] In the simplified operation optimization model of the DC power flow, since only the reactive power part is ignored and the objective function in the original operation optimization model of the electric heating load does not involve reactive power, the objective function remains unchanged, as shown in Equation (2).

[0142] (2) Simplified result of the constraints

[0143] The DC power flow ignores the factors of reactive power and voltage. Therefore, the reactive power flow balance constraint in Equation (5) and the node voltage constraint in Equation (10) in the general constraints of the operation optimization model are no longer retained, and the branch capacity constraint is only the branch active power constraint. Remove Equations (7), (8) and modify Equation (9) to the following equation:

[0144] |P jk (t)|≤P jk.max (18)

[0145] In the formula, P jk.max is the upper limit of the active power of branch k, j.

[0146] And the constraints based on the energy consumption model of the electric heating load nodes, that is, Equations (11)-(16) do not involve reactive power, so they remain unchanged. The simplified result of the finally formed general constraints is as shown in Equation (19). The reactive power flow balance and node voltage constraints are ignored in the simplified result of the general constraints, as follows:

[0147]

[0148] That is to say, the finally obtained simplified operation optimization model is Equations (2)(3), Equations (11)-(16) and Equation (19). After obtaining the simplified operation optimization model, the power transfer distribution factor (DF) can be used to solve the model by the linear optimization algorithm.

[0149] Specifically, through the power transfer distribution factor (DF), an approximate linear relationship between the active power output and the branch active power flow can be established under the DC power flow. Its function is to calculate the change in the branch active power flow caused by the increase in the active power output of the generator. The power transfer distribution factor (DF) of the electric heating load node i in the power system corresponding to branches m, n in the entire network topology is shown in the following formula:

[0150]

[0151] In the formula, L mn-iis the power transfer distribution factor of the network topology branch mn corresponding to the electric heating load node i, B mi and B ni are the elements of the admittance matrix from network nodes m and n in the power system to the electric heating load node i respectively, and X mn is the reactance of the branch mn.

[0152]

[0153] In the formula, represents the admittance between network node m and any other network node k1 except node m in the power system.

[0154]

[0155] In the formula, represents the admittance between network node n and any other network node k2 except node n in the power system.

[0156] When the electric heating load in the power system changes during the optimization process, the active power flows of branches m and n can be calculated by superimposing the transfer powers for each electric heating load node, as shown in the following formula:

[0157]

[0158] In the formula, P mn is the active power flow of the branch between network nodes m and n; P mn 0 is the base power flow of the branch between network nodes m and n, ΔP Li is the total active power change of the electrical load of the electric heating load node i, S N is the set of network nodes in the power system, and S Nh is the set of electric heating load nodes in the power system. Specifically, the simplified optimization model is solved through the built-in algorithms of the Cplex toolkit, such as the simplex method and the interior point method.

[0159] In this example, the model is simplified using the DC power flow, and through the power transfer distribution factor (DF), a linear relationship matrix is directly established between the load node power and the branch power, and the optimal operation strategy for the electric heating load to participate in the electricity spot market trading is given through linear optimization.

[0160] The regulation process of the present invention can be as follows: after the electricity spot market price curve is released on the day-ahead, the charging and discharging power of the equivalent heat storage body in the electric heating load node is adjusted according to the price change frequency. The actual operation is to form transferable power by adjusting the power of the electric heating equipment at the electric heating load node, and this part of the power directly affects the heat storage value of the corresponding equivalent heat storage body and the power consumption of the corresponding electric heating equipment. Specifically,Figure 3 As shown in the figure, the ordinate in the figure represents power P, and the abscissa represents time. P heat (t1) is the power consumption of the electric heating equipment in the electric heating load node at time t1, and P heat (t2) is the power consumption of the electric heating equipment in the electric heating load node at time t2. When the strategy is optimized by the present invention, the charging and discharging power P s (t1) of the equivalent heat storage body at time t1 is positive, that is, in the section from t1 to t2 in the figure, which means that the optimized operation mode stores heat in the node equivalent heat storage body on the premise of meeting the user's heating demand power P need (t1). Similarly, if the charging and discharging power P s (t2) of the equivalent heat storage body at time t2 is negative after the strategy optimization, that is, in the section from t2 to t3 in the figure, it means that the heat load demand P need (t2) of the electric heating load node needs to be supplemented by releasing the energy stored in the node equivalent heat storage body.

[0161] Experimental verification

[0162] Using the optimized operation model established by the present invention for linear optimization solution, the optimized operation strategy under the constraints of branch power and heat storage capacity can be obtained, and the minimum heating cost and the branch power flow distribution of the network topology can also be obtained; the effectiveness of the present invention is verified by comparing and analyzing the above calculation results before and after optimization.

[0163] Specifically, taking the IEEE-3 standard topology as an example, as Figure 4 shown, node 1 is a power generation node (balanced node), and node 3 is a load node. The generator G and the bus are regarded as a single-machine infinite system, and the topology parameters are set as shown in Table 1.

[0164] Table 1 Branch parameter setting table of IEEE-3 standard example

[0165]

[0166] The power base value in Table 1 is taken as 10 MVA, the voltage base value is taken as 10 kV, and p.u. is the per-unit value unit. The following takes node 3 as an example for illustration. The load node 3 is assigned according to the actual "flexible" electric heating load, and the load data is shown in Table 2. The load node 3 is an electric heating load, which is affected by the indoor and outdoor temperatures and shows a natural change trend of being high at night and low during the day.

[0167] Table 2 Assignment situation table of electric heating data of load node 3

[0168]

[0169]

[0170] In Table 2, the maximum heat storage capacity of load node 3 is taken as 1.907, and the maximum power of the electric heating equipment is taken as 0.54. The specific clearing electricity price is shown in Table 3.

[0171] Clearing Electricity Price of a Certain Day in the Electricity Spot Market of a Certain Electricity Spot Pilot

[0172]

[0173] After the calculation of the optimized operation method of the present invention for load node 3, the results are as Figure 5 shown. It can be seen from Figure 5 that after the operation optimization by the present invention, the load operation power curve shows obvious time-varying characteristics, indicating that by utilizing the flexible characteristics of the electric heating load, the price signal of the electricity spot market can be fully responded to. The optimized power of the electric heating at each moment of load node 3 is shown in Table 4.

[0174] Table 4 Optimized Power Table of Electric Heating at Each Moment of Load Node 3

[0175]

[0176] By comparing Table 2, Table 4 with the clearing electricity price on the operation day, it can be seen that after the optimization of the present invention, the change in the daily operation cost of the electric heating load at node 3 = the power before optimization at time t0 (Table 2) * the electricity price at time t0 (Table 3) * 1 hour - the power after optimization at time t0 (Table 4) * the electricity price at time t0 * 1 hour, where t0 = 1, 2, 3, 4.......24. The sum of the change amounts in each hour of the operation day can be used to represent the total change in the operation cost before and after optimization. When the total change in the operation cost is positive, it means that the cost after optimization is less than the cost before optimization, that is, the effectiveness of the model is verified. Through the simulation calculation of the IEEE-3 standard node, compared with the traditional power flow analysis, the optimized model of the present invention can reduce the operation cost of the electric heating on the user side under the constraints such as the branch power capacity, verifying the effectiveness and engineering practical value of the model.

[0177] By utilizing the flexible characteristics of the electric heating load, the present invention can fully respond to the price signals in the electricity spot market. By analyzing the flexible characteristics of the electric heating load, considering the limit of the transferable capacity of the virtual energy storage of the building envelope structure, the price signals in the electricity spot market, and the relevant theories based on the traditional optimal power flow, the operating cost of the regional electric heating load can be reduced. The optimization model for the participation of electric heating in the electricity spot trading proposed by the present invention can support the sustainable development of clean electric heating in the north through market-based means. A user node model is established. By changing the operating strategy at the clearing node to respond to the electricity price signal, while considering the relevant constraints such as the power and equivalent capacity of the node, as well as the load changes of the node and the branch constraints caused by the redistribution of the power flow, the operating optimization model for the participation of electric heating in the electricity spot trading is constructed.

[0178] Embodiment 3

[0179] Based on the same inventive concept, the present invention also provides a system for constructing an operating optimization model of an electric heating load, including:

[0180] A node model construction module, configured to consider the building thermal inertia of each electric heating load node in the power system, and construct an energy consumption model of the electric heating load node according to the energy interaction relationship between the electric heating equipment and the environment of each electric heating load node;

[0181] A target function construction module, configured to determine the daily total operating cost of each electric heating load node participating in the electricity market trading based on the energy consumption model of the electric heating load node and the electricity price in the electricity spot trading market, and construct an operating optimization target function with the minimization of the daily total operating cost as the goal;

[0182] A constraint condition construction module, configured to construct constraint conditions for the operating optimization target function based on the power balance relationship and load regulation range of each electric heating load node, the energy interaction relationship and heat energy regulation range within each electric heating load node, and in combination with the optimal power flow constraint;

[0183] An optimization model construction module, configured to construct an operating optimization model of the electric heating load based on the operating optimization target function and the constraint conditions.

[0184] In a possible implementation manner, the node model construction module includes:

[0185] A load division sub-module, configured to divide the electric heating load node into a rigid load and a flexible load based on the adjustability of the load;

[0186] A flexible load interaction model construction sub-module, which is used to construct an equivalent heat storage body of the electric heating load node based on the heat storage device and building thermal inertia of the electric heating load node, regard the electric heating equipment of the electric heating load node as a flexible load, and utilize the energy interaction relationship between the electric heating equipment of the electric heating load node and the environment to construct an energy interaction relationship among the electric heating equipment, the equivalent heat storage body and the electric heating demand of the electric heating load node, so as to obtain a flexible load energy interaction model of the electric heating load node;

[0187] A node model construction sub-module, which is used to construct an energy consumption model of the electric heating load node based on the power supply and energy balance relationship of the electric heating load node and by using the rigid load and flexible load energy interaction models of the electric heating load node.

[0188] In a possible implementation manner, the objective function construction module is specifically used for:

[0189] Based on the energy consumption model of the electric heating load node, determine the active power curve of each electric heating load node participating in the day-ahead electricity spot market transaction;

[0190] Based on the active power curves of each electric heating load node participating in the day-ahead electricity market transaction and the real-time clearing electricity price of the day-ahead electricity spot market, determine the daily operating total cost of each electric heating load node participating in the electricity market transaction.

[0191] In a possible implementation manner, the operation optimization objective function is as follows:

[0192]

[0193] P Li (t) = P ei (t) + P heati (t);

[0194] In the formula, C(t) is the clearing electricity price at time t in the day-ahead electricity spot market, P Li (t) is the active power of the electric heating load node i participating in the electricity market transaction at time t in the day-ahead, Δt is the clearing time interval of the electricity spot market, P ei (t) is the rigid load power of the electric heating load node i at time t in the day-ahead, P heati (t) is the electricity consumption power of the electric heating equipment of the electric heating load node i at time t in the day-ahead, T is the optimization period, S Nh is the set of electric heating load nodes in the power system.

[0195] In a possible implementation manner, the constraint conditions include at least one of the following:

[0196] The power balance constraints of each electric heating load node, the power constraints of the electric heating equipment in each electric heating load node, the time-series balance constraints of the heat storage quantity of the equivalent heat storage body in each electric heating load node, the limit power constraints of the equivalent heat storage body in each electric heating load node, the heat storage capacity constraints of the equivalent heat storage body in each electric heating load node, and the optimal power flow constraints.

[0197] In a possible implementation manner, the power balance constraints of each electric heating load node are as follows:

[0198] P heati (t) = P needi (t) + P si (t);

[0199] Wherein, P needi (t) is the electric heating demand of the electric heating load node i at the pre-dispatch time t, and P si (t) is the charge and discharge power of the equivalent heat storage body in the electric heating load node i at the pre-dispatch time t;

[0200] The power constraints of the electric heating equipment in each electric heating load node are as follows:

[0201] 0 ≤ P heati (t) ≤ P heati.max ;

[0202] Wherein, P heati.max is the upper limit of the heating power of the electric heating equipment of the electric heating load node i at the pre-dispatch time t;

[0203] The time-series balance constraints of the heat storage quantity of the equivalent heat storage body in each electric heating load node are as follows:

[0204] E Si (t + 1) = E Si (t) + P si (t) × Δt;

[0205] E Si (1) = E Si (T);

[0206] Wherein, E Si (t + 1) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the pre-dispatch time t + 1, E Si (t) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the pre-dispatch time t, E Si (1) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the initial moment of the pre-dispatch optimization period, E Si (T) is the heat storage quantity of the equivalent heat storage body in the electric heating load node i at the end moment of the pre-dispatch optimization period;

[0207] The limit power constraints of the equivalent heat storage bodies in each electric heating load node are as follows:

[0208] -P siin.max (t) ≤ P si (t) ≤ P siout.max (t);

[0209] In the formula, P siin.max (t), P siout.max (t) are respectively the maximum heat storage power and the maximum heat release power corresponding to the heat storage equipment and the building thermal inertia of the electric heating load node i at the pre-day time t;

[0210] The heat storage capacity constraints of the equivalent heat storage bodies in each electric heating load node are as follows:

[0211] 0 ≤ E Si (t) ≤ E Si.max ;

[0212] In the formula, E Si (t) is the heat storage amount of the equivalent heat storage body in the electric heating load node i at the pre-day time t, and E Si.max is the maximum heat storage amount corresponding to the heat storage equipment and the building thermal inertia of the electric heating load node i at the pre-day time t.

[0213] Embodiment 4

[0214] Based on the same inventive concept, the present invention also provides an optimized operation system for electric heating loads, including:

[0215] A prediction module, configured to predict the pre-day rigid load power and the pre-day electric heating demand of each electric heating load node based on the historical load data of each electric heating load node, the pre-day weather conditions, and the heat disturbance factors of each electric heating load node;

[0216] An optimized operation module, configured to solve the operation optimization model of the electric heating load constructed in Embodiment 1 based on the pre-day rigid load power and the pre-day electric heating demand of each electric heating load node to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node; control and adjust the operation power of the electric heating equipment of each electric heating load node based on the charge and discharge power of the equivalent heat storage body in each electric heating load node.

[0217] In a possible implementation manner, the optimized operation module includes:

[0218] A model simplification sub-module, configured to simplify the operation optimization model of the electric heating load by using a DC power flow model, so that the active power of each network node, the active power of each branch, and the phase angle satisfy a linear relationship, and obtain a simplified operation optimization model;

[0219] A solution sub-module, configured to perform linear optimization solution on the simplified operation optimization model based on the power transfer distribution factor, so as to obtain the charging and discharging power of the equivalent heat storage body in each electric heating load node;

[0220] Wherein, in the linear optimization solution process, the power transfer distribution factor is used to establish a linear relationship between the active power flow of the branch in the power system and the transfer power of each electric heating load node.

[0221] In a possible implementation manner, the linear relationship between the active power flow of the branch in the power system and the transfer power of each electric heating load node is as follows:

[0222]

[0223] In the formula, P mn is the active power flow of the branch between network nodes m and n; P mn 0 is the base power flow of the branch between network nodes m and n, ΔP Li is the total active power change of the electrical load of the electric heating load node i, L mn-i is the power transfer distribution factor of the network topology branch mn corresponding to the electric heating load node i, S N is the set of network nodes in the power system, S Nh is the set of electric heating load nodes in the power system.

[0224] Embodiment 5

[0225] As Figure 6 shown, the present invention further provides an electronic device, which may be a computer device, a single-chip microcomputer device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor and the transceiver component are connected through a bus; the memory can be used to store an execution program, and the exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and the data can be called and / or modified when the instructions are executed.

[0226] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the method for constructing an operation optimization model of an electric heating load and the method for optimizing the operation of an electric heating load in the above embodiments.

[0227] Embodiment 6

[0228] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically a readable storage medium (Memory) for an electronic device. The readable storage medium for an electronic device is a memory device in the electronic device, used for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device, and of course can also include the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more executable programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory. By loading and executing one or more instructions stored in the storage medium by the processor, the steps of the method for constructing an operation optimization model of an electric heating load and the method for optimizing the operation of an electric heating load in the above embodiments can be implemented.

[0229] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0230] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more flows and / or blocks. Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0231] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means for implementing the functions specified in one or more flows and / or blocks. Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0232] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks. Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the scope of the protection of the pending claims of the application.

Claims

1. A method for constructing an operation optimization model of electric heating load, characterized in that: include: Considering the building thermal inertia of each electric heating load node in the power system, an energy consumption model of the electric heating load node is constructed according to the energy interaction relationship between the electric heating equipment and the environment of each electric heating load node; Based on the energy consumption model of the electric heating load node and the electricity price in the electricity spot trading market, determine the total daily operating cost of each electric heating load node participating in the electricity market transaction, and construct an operation optimization objective function with the goal of minimizing the total daily operating cost; Based on the power balance relationship and load regulation range of each electric heating load node and the energy interaction relationship and thermal energy regulation range within each electric heating load node, combined with the optimal power flow constraint, a constraint condition is constructed for the operation optimization objective function; Based on the operation optimization objective function and the constraint conditions, an operation optimization model of the electric heating load is constructed.

2. The method according to claim 1, characterized in that The energy consumption model of electric heating load nodes is constructed by considering the building thermal inertia of each electric heating load node in the power system and the energy interaction relationship between the electric heating equipment and the environment of each electric heating load node, including: Based on the adjustability of load, the electric heating load nodes are divided into rigid load and flexible load; An equivalent heat storage body of the electric heating load node is constructed based on the heat storage device of the electric heating load node and the thermal inertia of the building, the electric heating device of the electric heating load node is used as a flexible load, and the energy interaction relationship between the electric heating device of the electric heating load node and the environment is utilized to construct an energy interaction relationship between the electric heating device of the electric heating load node, the equivalent heat storage body and the electric heating demand, so as to obtain a flexible load energy interaction model of the electric heating load node; Based on the power supply and energy consumption balance relationship of the electric heating load node, the energy consumption model of the electric heating load node is constructed by utilizing the rigid load and flexible load energy interaction model of the electric heating load node.

3. The method according to claim 2, characterized in that The total daily operating cost of each electric heating load node participating in the electricity market transaction is determined based on the electric heating load node energy consumption model and the electricity price in the electricity spot trading market, including: Based on the electric heating load node energy consumption model, determining the active power curve of each electric heating load node participating in the electricity spot market transaction a day ago; Based on the active power curve of each electric heating load node participating in the electricity market transaction a day ago and the real-time clearing electricity price of the electricity spot trading market a day ago, the total daily operating cost of each electric heating load node participating in the electricity market transaction is determined.

4. The method according to claim 3, characterized in that The operation optimization objective function is as follows: P Li (t)=P ei (t)+P heati (t); Where C(t) is the clearing price of the day-ahead electricity spot market at time t, P Li (t) is the active power of the electric heating load node i participating in the electricity market transaction at time t a day ago, Δt is the clearing time interval of the electricity spot trading market, P ei (t) is the rigid load power of electric heating load node i at time t the day before, P heati (t) is the power consumption of the electric heating equipment of the electric heating load node i at time t the day before, T is the optimization period, S Nh It is a collection of electric heating load nodes in the power system.

5. The method according to claim 4, characterized in that The constraint condition includes at least one of the following: The power balance constraints of each electric heating load node, the power constraints of electric heating equipment in each electric heating load node, the time-series balance constraints of the heat storage amount of the equivalent heat storage body in each electric heating load node, the limit power constraints of the equivalent heat storage body in each electric heating load node, the heat storage capacity constraints of the equivalent heat storage body in each electric heating load node and the optimal power flow constraints.

6. The method according to claim 5, characterized in that The power balance constraints of each electric heating load node are as follows: P heati (t)=P needi (t)+P si (t); Where P needi (t) is the electric heating demand of electric heating load node i at time t the day before, P si (t) is the charge and discharge power of the equivalent heat storage body in the electric heating load node i at time t the day before; The power constraints of electric heating equipment in each electric heating load node are as follows: 0≤P heati (t)≤P heati.max ; Where P heati.max is the upper limit of the heating power of the electric heating equipment at the electric heating load node i at time t the day before; The time series balance constraints of the heat storage of the equivalent heat storage body in each electric heating load node are as follows: Is i (t+1)=It is i (t)+P si (t)×Δt; AND Si (1)=And Si (T); In the formula, Es i (t+1) is the heat storage capacity of the equivalent heat storage body in the electric heating load node i at time t+1 the day before, Es i (t) is the heat storage capacity of the equivalent heat storage body in the electric heating load node i at time t the day before, E Si (1) is the heat storage capacity of the equivalent heat storage body in the electric heating load node i at the initial time of the day-ahead optimization cycle, E Si (T) is the heat storage capacity of the equivalent heat storage body in the electric heating load node i at the end of the day-ahead optimization period; The limit power constraints of the equivalent heat storage in each electric heating load node are as follows: -P siin.max (t)≤P si (t)≤P siout.max (t); Where P siin.max (t), P siout.max (t) are the maximum heat storage power and maximum heat release power corresponding to the thermal inertia of the heat storage equipment and the building at the electric heating load node i at time t the day before; The heat storage capacity constraints of the equivalent heat storage body in each electric heating load node are as follows: 0≤Es i (t)≤E Si.max ; In the formula, Es i (t) is the heat storage capacity of the equivalent heat storage body in the electric heating load node i at time t the day before, E Si.max It is the maximum heat storage capacity corresponding to the thermal inertia of the heat storage equipment and building at the electric heating load node i at time t the day before.

7. A method for optimizing the operation of electric heating load, characterized in that: include: Based on the historical load data of each electric heating load node, the weather conditions the day before and the heat disturbance factors of each electric heating load node, the day before rigid load power and the day before electric heating demand of each electric heating load node are predicted; Based on the day-ahead rigid load power and the day-ahead electric heating demand of each electric heating load node, the operation optimization model of the electric heating load constructed according to any one of claims 1 to 6 is solved to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node; The operating power of the electric heating equipment at each electric heating load node is regulated based on the charging and discharging power control of the equivalent heat storage body in each electric heating load node.

8. The method according to claim 7, characterized in that Based on the day-ahead rigid load power and day-ahead electric heating demand of each electric heating load node, the operation optimization model of the electric heating load is solved to obtain the optimal operation plan of each electric heating load node, including: The DC power flow model is used to simplify the operation optimization model of the electric heating load so that the active power of each network node, the branch active power and the phase angle satisfy a linear relationship, thereby obtaining a simplified operation optimization model; Based on the power transfer distribution factor, the simplified operation optimization model is linearly optimized to obtain the charge and discharge power of the equivalent heat storage body in each electric heating load node; Among them, in the linear optimization solution process, the power transfer distribution factor is used to establish a linear relationship between the branch active power flow of the power system and the transfer power of each electric heating load node.

9. The method according to claim 8, characterized in that The linear relationship between the branch active power flow of the power system and the transfer power of each electric heating load node is as follows: Where P mn is the active power flow of the branch between network nodes m and n; P mn 0 is the basic power flow of the branch between network nodes m and n, ΔP Li is the total active power change of the electric heating load node i, L mn-i is the power transfer distribution factor of the network topology branch mn corresponding to the electric heating load node i, S N is the set of network nodes in the power system, S Nh It is a collection of electric heating load nodes in the power system.

10. An optimized operation system for electric heating load, characterized in that: include: A prediction module is used to predict the day-ahead rigid load power and the day-ahead electric heating demand of each electric heating load node based on the historical load data of each electric heating load node, the day-ahead weather conditions and the heat disturbance factors of each electric heating load node; An optimization operation module is used to solve the operation optimization model of the electric heating load constructed according to any one of claims 1 to 6 based on the day-ahead rigid load power and the day-ahead electric heating demand of each electric heating load node, and obtain the charging and discharging power of the equivalent heat storage body in each electric heating load node; based on the charging and discharging power of the equivalent heat storage body in each electric heating load node, control and adjust the operating power of the electric heating equipment at each electric heating load node.

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