A power market energy transaction method considering system loss and voltage regulation

Abstract

A kind of flexible interconnected power distribution system end-to-end energy transaction method based on deep reinforcement learning.Established the model of intelligent soft switch (Soft Open Point, SOP) and system power flow constraint model, then the action behavior of SOP is established as Markov decision process.In addition, a corresponding objective function is set for each distribution network to evaluate its strategy, which includes system loss cost, SOP loss cost and voltage violation.In this strategy, energy trading between distribution networks can be safer.On the basis of transaction, the power balance constraint model is constructed, and another objective function is set for each distribution network to evaluate its trading strategy, which includes energy trading cost between distribution networks and trading cost between distribution network and upper grid.Therefore, the safety and economy of distribution network in energy trading can be guaranteed, and the system loss is reduced, the PV consumption rate and the trading efficiency of distribution network are improved.

Description

TECHNICAL FIELD

[0001] The application relates to a power market energy transaction method considering system loss and voltage regulation BACKGROUND

[0002] With the increasing application of distributed energy in distribution network systems, the intermittence and randomness of the distributed energy will bring problems such as voltage violation and loss cost increase to the system, which will also affect the safety and stability of power market energy transaction. Traditional distribution network energy transaction is mainly one-way, and power is usually transmitted to consumers by generators over a long distance. Peer to peer (P2P) energy transaction encourages multi-directional transaction within the local area. In addition, other participants in the power market can also benefit, thereby reducing operating costs and improving the reliability of the power system. At the same time, as a new type of power electronic device, a smart soft open point (SOP) has gradually replaced the traditional tie switch, bringing great potential to the flexibility of the operation of the distribution network. In an interconnected distribution system, the SOP can be set between the connection areas according to the expectation to realize accurate power flow control. With the development of artificial intelligence, the deep deterministic policy gradient algorithm as a data-driven method reduces the dependence on the physical model of the power system, which can mine the physical model of the system from data and form an optimization decision method capable of processing high-dimensional complex information as input. Therefore, the combination of artificial intelligence technology and the distribution network system has good development prospects. SUMMARY

[0003] In order to overcome the shortcomings of the prior art, the application provides a power market energy transaction method considering system loss and voltage regulation. A SOP model and a system power flow constraint model are established, and then the action behavior of the SOP is established as a Markov decision process. In addition, each distribution network is provided with a corresponding objective function to evaluate its strategy, and the objective function includes system loss cost, SOP loss cost and voltage violation. On the basis of the strategy, the energy transaction between the distribution networks can be safer. In the transaction process, a power balance constraint model is constructed, and each distribution network is also provided with another objective function to evaluate the good and bad of its transaction strategy, and the objective function includes the energy transaction cost between the distribution networks and the transaction cost between the distribution network and the upper grid. Therefore, the application can ensure the safety and economy of the distribution network in the energy transaction process. Finally, the model is solved on an example, and it is shown that the P2P energy transaction model can reduce system loss and improve energy transaction efficiency by adjusting the action of the SOP.

[0004] In order to achieve the above purpose, the technical scheme of the application is as follows:

[0005] 1. A power market energy transaction method considering system loss and voltage regulation, characterized in that it comprises the following steps:

[0006] S1: constructing a smart soft open point (SOP) model;

[0007] S2: constructing a system power flow constraint model;

[0008] S3: constructing a power distribution network peer-to-peer (P2P) energy transaction model, setting the transaction volume between the power distribution networks as a decision variable, and optimizing the power distribution network system P2P energy transaction with the lowest transaction cost as the target;

[0009] S4: constructing a power balance constraint at the time of transaction to ensure the safety of the transaction;

[0010] S5: initializing system parameters, setting a corresponding reward function, and solving the model.

[0011] 2. The power market energy transaction method considering system loss and voltage regulation according to claim 1, characterized in that in the step S1, the modeling of the SOP specifically comprises:

[0012] S1-1: The SOP is a continuous power electronic device based on a full-controlled device, and a steady-state model of the SOP is usually developed into a power injection model, which involves power injection at the terminal port of the SOP, wherein the controllable variables are active power transmission and reactive power compensation, so the SOP can be directly introduced into the existing power flow analysis without considering detailed controller design:

[0013]

[0014]

[0015]

[0016] wherein, N T is the number of ports of the SOP; is the active and reactive power injected by the converter at node i at time period t; is the active loss of the converter of the SOP at node i at time period t; is the loss coefficient of the converter of the SOP at node i; is the capacity of the converter of the SOP at node i;

[0017] 3. The power market energy transaction method considering system loss and voltage regulation according to claim 2, characterized in that in the step S2, the system power flow constraint model is constructed, specifically comprising:

[0018] S2-1: The power flow model of distribution network is a power flow equation established from branch power. Compared with the traditional power flow calculation based on node power, the power flow model is more suitable for power flow calculation of radial distribution system:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] wherein Ω b r ij and x ij are the resistance and reactance of branch ij. P ij is the active power of node i flowing to node j on the branch; Q ij is the reactive power of node i flowing to node j on the branch; P i is the sum of active power injected at node i; Q i is the sum of reactive power injected at node i; U i is the voltage value of node i; I ij is the current value of node i flowing to node j on the branch; is the active power injected by the distributed power source at node i; is the active power consumed by the load at node i; is the reactive power injected by the distributed power source at node i; is the reactive power consumed by the load at node i; is the active power of the converter at node i; is the reactive power of the converter at node i;

[0026] 4. The power market energy trading method considering system loss and voltage regulation according to claim 3, characterized in that in the step S3, the distribution network end-to-end energy trading model comprises the following parts:

[0027] S3-1: Assuming that the local P2P price is variable, in order to encourage transactions between distribution networks, the P2P transaction price and the transaction price of the upper grid are set as follows:

[0028]

[0029] wherein Pij(t) represents the price of selling electricity between distribution network i and j at time t, Pji(t) represents the price of buying electricity between distribution network i and j at time t, Pij(t) represents the price of selling electricity between distribution network i and j at time t, Pji(t) represents the price of buying electricity between distribution network i and j at time t,

[0030] S3-2: At the beginning of each time period, distribution network i needs to predict the load demand data and dynamic price with the historical data of renewable energy generation as the state. The state of distribution network i at time t is denoted as s2 i，t = [G i，t , D i，t , p t ], G i，t represents the renewable energy generation of distribution network i at time t, D i，t represents the predicted load power of distribution network i at time t, and p t represents the electricity price between distribution networks at time t. The present application uses x i，j，t (i≠j) to represent the expected transaction amount between distribution networks at time t, and if i=j, it represents the transaction amount between the distribution network and the upper-level network. Before the transaction, each distribution network determines the transaction strategy X i，t according to the state, where X i，t = [x i，j，t ] 1≤j≤N，i≠j If x i，j，t >0, it means that distribution network i wants to buy electricity from distribution network j, and if x i，j，t <0, it means that distribution network i wants to sell electricity to distribution network j. Since the actual transaction amount may be different from the expected transaction amount, the actual transaction amount a i，j，t should be represented as follows:

[0031]

[0032] where N represents the number of distribution networks; x i，j，t (i≠j) represents the expected transaction amount between distribution networks at time t, and if i=j, it represents the transaction amount between the distribution network and the upper-level network at time t. If x i，j，t >0, it means that distribution network i wants to buy electricity from distribution network j at time t, and if x i，j，t <0, it means that distribution network i wants to sell electricity to distribution network j at time t.

[0033] 5. The power market energy transaction method considering system loss and voltage regulation according to claim 4, wherein the power balance constraint at the time of transaction in step S4 comprises the following parts:

[0034] S4-1: The power balance constraint at the time of transaction is as follows:

[0035]

[0036] where p i，j，t represents the actual transmission power between distribution network i and distribution network j at time t; p i，i，t represents the actual transmission power between distribution network i and the upper-level power grid at time t; P G，i，t represents the renewable energy generation of distribution network i at time t; P L，i，t represents the load demand of distribution network i at time t;

[0037] 6. The power market energy transaction method considering system loss and voltage regulation according to claim 5, characterized in that the reward function in step S5 comprises the following parts:

[0038] S5-1: The distribution network sets the corresponding reward function F 1，i，t as follows:

[0039]

[0040]

[0041]

[0042]

[0043] where represents the power loss of all lines in the entire power grid system at time t; represents the loss cost of SOP at time t; represents the voltage violation in the sub-network represented by the agent at time t. λ1 represents the penalty factor of voltage violation; N t is the set of time periods; N n is the set of all nodes in the system; U t，i represents the voltage at node i at time t; U max and U min are the upper and lower limits of the safe operation range of the node voltage, respectively;

[0044] S5-2: The distribution network sets the corresponding reward function r 2，i，t according to the minimum energy transaction cost:

[0045]

[0046]

[0047] r 2，i，t = u 1，i，t + u 2，i，t (19)

[0048] wherein, u 1，i，t represents the trading cost between the distribution network and the upper-level power grid at time period t; u 2，i，t represents the trading cost between the distribution network and the upper-level power grid at time period t.

[0049] The beneficial effects of the present application are:

[0050] 1) The system loss and voltage regulation are considered, and the safety and economy in the energy trading process are improved.

[0051] 2) In the distributed collaborative control, only local information is used to make decisions, the requirement for communication capability is reduced, and the communication cost is reduced.

[0052] 3) The control inaccuracy caused by prediction error is avoided, and the voltage regulation and energy trading of the power grid system can be performed in the case that the prediction information has errors. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a schematic diagram of a 123-node system example of the present application.

[0054] Figure 2 is a schematic diagram of the daily operation curve of photovoltaic and load of the present application.

[0055] Figure 3 is a schematic diagram of the algorithm training process of the present application.

[0056] Figure 4 is a schematic diagram of the SOP active power transmission amount of the present application.

[0057] Figure 5 is a schematic diagram of the SOP reactive power compensation amount of the present application.

[0058] Figure 6 is a schematic diagram of the electricity price of the present application.

[0059] Figure 7 is the trading situation of the distribution network 1 and other distribution networks of the present application.

[0060] Figure 8 is the trading situation of the distribution network 2 and other distribution networks of the present application.

[0061] Figure 9 is the trading situation of the distribution network 4 and other distribution networks of the present application.

[0062] Figure 10 is the trading situation of the distribution network 5 and other distribution networks of the present application.

[0063] Figure 11 is a flowchart of the method of the present application. DETAILED DESCRIPTION

[0064] The application will be further described below with reference to the accompanying drawings.

[0065] With reference to Figures 1-11 A power market energy transaction method considering system loss and voltage regulation, comprising the following steps:

[0066] S1: constructing a smart soft open point (SOP) model;

[0067] S2: constructing a system power flow constraint model;

[0068] S3: constructing a power distribution network peer-to-peer (P2P) energy transaction model, setting the transaction amount between the power distribution networks as a decision variable, taking the minimum transaction cost as the target, and optimizing the P2P energy transaction of the power distribution network system;

[0069] S4: constructing a power balance constraint at the time of transaction to ensure the safety of the transaction;

[0070] S5: initializing system parameters, setting a corresponding reward function, and solving the model.

[0071] 2. The power market energy transaction method considering system loss and voltage regulation according to claim 1, wherein the modeling of the SOP in the step S1 specifically comprises:

[0072] S1-1: the SOP is a continuous power electronic device based on a full-controlled device, and a steady-state model of the SOP is usually developed into a power injection model, which involves power injection of a terminal port of the SOP, wherein the controllable variables are active power transmission and reactive power compensation, so that the SOP can be directly introduced into existing power flow analysis without considering detailed controller design:

[0073]

[0074]

[0075]

[0076] wherein, N T is the number of ports of the SOP; is active and reactive power injected by a converter at node i at time period t; is active loss of the converter of the SOP at node i at time period t; is a loss coefficient of the converter of the SOP at node i; is the capacity of the converter of the SOP at node i;

[0077] 3. The power market energy trading method considering system loss and voltage regulation according to claim 2, wherein in the step S2, a system power flow constraint model is constructed, specifically comprising:

[0078] S2-1: The distribution network power flow model is a power flow equation established from branch power. Compared with the traditional power flow calculation based on node power, the power flow model is more suitable for the power flow calculation of radial distribution systems:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] wherein Ω b is all the conducting branches. r ij and x ij are the resistance and reactance of the branch ij. P ij is the active power of node i flowing to node j on the branch; Q ij is the reactive power of node i flowing to node j on the branch; P i is the sum of the active power injected at node i; Q i is the sum of the reactive power injected at node i; U i is the voltage value of node i; I ij is the current value of node i flowing to node j on the branch; is the active power of the distributed power source injected at node i; is the active power consumed by the load at node i; is the reactive power of the distributed power source injected at node i; is the reactive power consumed by the load at node i; is the active power of the converter at node i; is the reactive power of the converter at node i;

[0086] 4. The power market energy trading method considering system loss and voltage regulation according to claim 3, wherein in the step S3, the distribution network end-to-end energy trading model comprises the following parts:

[0087] S3-1: Assuming that the local P2P price is variable, in order to encourage the transaction between distribution networks, the P2P transaction price and the transaction price of the upper grid are set as follows:

[0088]

[0089] wherein, represents the price of selling electricity between distribution networks at period t, represents the price of buying electricity between distribution networks at period t, represents the price of selling electricity from the distribution network to the upper grid at period t, represents the price of buying electricity from the upper grid to the distribution network at period t;

[0090] S3-2: At the beginning of the energy transaction in each period, the distribution network needs to obtain the historical data of renewable energy generation, predict the load demand data and the dynamic changing price as the state. The state of the distribution network i at period t is denoted as s2 i，t = [G i，t , D i，t , ρ t ], G i，t represents the renewable energy generation of the distribution network i at period t, D i，t represents the predicted load power of the distribution network i at period t, and ρ t represents the transaction price between distribution networks at period t. The present application uses x i，j，t (i≠j) to represent the expected transaction amount between distribution networks at period t, and if i=j, it represents the transaction amount between the distribution network and the upper grid. Before the transaction, each distribution network determines the transaction strategy X i，t according to the state, wherein X i，t = [x i，j，t ] 1≤j≤N，i≠j If x i，j，t >0, it represents that the distribution network i wants to buy electricity from the distribution network j, and if x i，j，t <0, it represents that the distribution network i wants to sell electricity to the distribution network j. Since the actual transaction amount may be different from the expected transaction amount, the actual transaction amount a i，j，t should be represented as follows:

[0091]

[0092] wherein, N represents the number of distribution networks; x i，j，t (i≠j) represents the expected transaction amount between distribution networks at period t, and if i=j, it represents the transaction amount between the distribution network and the upper grid at period t, and if x i，j，t >0, it represents that the distribution network i wants to buy electricity from the distribution network j at period t, and if x i，j，t <0, it represents that the distribution network i wants to sell electricity to the distribution network j at period t;

[0093] 5. The method of claim 4, wherein the power balance constraint at the time of transaction comprises the following parts:

[0094] S4-1: The power balance constraint at the time of transaction is as follows:

[0095]

[0096] wherein p i，j，t represents the actual transmission power between distribution network i and distribution network j at time t; p i，i，t represents the actual transmission power between distribution network i and the upper-level power grid at time t; P G，i，t represents the renewable energy generation of distribution network i at time t; P L，i，t represents the load demand of distribution network i at time t.

[0097] 6. The method of claim 5, wherein the reward function comprises the following parts:

[0098] S5-1: The distribution network sets the corresponding reward function F 1，i，t as follows according to the minimum power loss cost and voltage deviation:

[0099]

[0100]

[0101]

[0102]

[0103] wherein represents the power loss of all lines in the entire power grid system at time t; represents the loss cost of SOP at time t; represents the voltage violation in the sub-network represented by the agent at time t. λ1 represents the penalty factor of voltage violation; N t is the set of time periods; N n is the set of all nodes in the system; U t，i represents the voltage at node i at time t; U max and U min are the upper limit and lower limit of the safe operation range of the node voltage, respectively;

[0104] S5-2: The distribution network sets the corresponding reward function r 2，i，t according to the minimum energy transaction cost:

[0105]

[0106]

[0107] r 2，i，t =u 1，i，t +u 2，i，t (19)

[0108] wherein, u 1，i，t represents the trading cost between the distribution network at t period; u 2，i，t represents the trading cost between the distribution network at t period and the upper grid.

[0109] In order to make the skilled in the art better understand the present application, the example analysis includes the following constitutes:

[0110] I. Example description and simulation result analysis

[0111] In order to verify the effectiveness of the present application, the system as shown in Figure 1 is used for example analysis. Five distribution networks are interconnected through a five-terminal SOP, and five photovoltaics are connected at nodes 17, 33, 40, 86 and 108. Figure 2 The daily operation curve of photovoltaic and load. The trading electricity price is as shown in Figure 6 . The reference voltage is set to 12.66KV, the voltage amplitude boundary is [0.95, 1.05]p.u., the SOP capacity upper limit is 0.5MVA, and the SOP loss coefficient is 0.02. The training iteration number is 50000 times, the maximum capacity of experience pool is 10000 groups, the soft update parameter tau is 0.01, the sample number of small batch sampling is 32 groups, the learning rate of action network is 0.0001, and the learning rate of evaluation network is 0.001. Other system parameters are shown in Table 1.

[0112] Table 1 System parameters

[0113]

[0114] The simulation program is realized in the Pycharm environment of the computer with Windows10, Intel(R)CoreTM i5 CPU@3.5GHz, 8GB memory. Figure 3 The algorithm training process converges after about 18000 samples. The active power transmission and reactive power compensation of SOP are shown in Figure 4 and Figure 5 . As can be seen from the figure, there is sufficient light between about 400 minutes to 1000 minutes, and its high permeability will have a certain influence on the system power flow, at this time the action amplitude of SOP increases obviously. The trading amount between each distribution network is shown in Figure 7 , Figure 8 , Figure 9 , Figure 10As shown, it can be seen from the figure that the power distribution network will take a transaction action with a larger transaction volume to obtain greater benefits in the case of relatively high electricity price.

[0115] In the specification, the illustrative expressions of the present application are not necessarily directed to the same embodiment or example, and a person skilled in the art can combine and combine different embodiments or examples described in the specification. Moreover, the content described in the embodiments of the specification is only a list of implementation forms of the inventive concept, and the protection scope of the present application should not be regarded as limited to the specific forms stated in the implementation cases, and the protection scope of the present application also includes equivalent technical means that can be thought of by a person skilled in the art according to the inventive concept.

Claims

1. A method for power market energy trading considering system losses and voltage regulation, Comprising, characterized in that, Comprising the following steps: S1: constructing a SOP model, specifically comprising: S1-1: SOP is a continuous power electronic device based on full-control devices, and the steady-state model of SOP is usually developed into a power injection model, which involves the power injection of the terminal port of SOP, wherein the controllable variable is the transmission of active power and the compensation of reactive power, so that SOP can be directly introduced into the existing power flow analysis without considering the detailed controller design: where NT is the number of ports of the SOP; is the active and reactive power injected by the converter at node i at time period t; is the active loss of the converter at node i at time period t by the SOP; is the loss coefficient of the converter at node i by the SOP; is the capacity of the converter at node i by the SOP; S2: constructing a system power flow constraint model, specifically comprising: S2-1: the distribution network power flow model is a power flow equation established from the branch power, compared with the traditional power flow calculation based on node power, the power flow model is more suitable for the power flow calculation of radial distribution system: where Ω b is the sum of all the active power of the branches; r ij and x ij are the resistance and reactance of the branch ij; p ij is the active power of the branch from node i to node j; q ij is the reactive power of the branch from node i to node j; p i is the sum of the active power injected at node i; Qi is the sum of the reactive power injected at node i; Ui is the voltage value at node i; I ij is the current value of the branch from node i to node j; is the active power injected by the distributed power at node i; is the active power consumed by the load at node i; is the reactive power injected by the distributed power at node i; is the reactive power consumed by the load at node i; is the active power of the converter at node i; is the reactive power of the converter at node i; S3: constructing a distribution network end-to-end energy transaction model, setting the transaction amount between distribution networks as a decision variable, taking the minimum transaction cost as the target, and optimizing the P2P energy transaction of the distribution network system; S4: constructing a power balance constraint at the time of transaction to ensure the safety of the transaction; S5: initializing system parameters, setting a corresponding reward function, and solving the model.

2. The method of claim 1, wherein the system loss and voltage regulation are considered in the power market energy transaction. In the step S3, the distribution network end-to-end energy transaction model comprises the following parts: S3-1: assuming that the local P2P price is variable, in order to encourage the transaction between distribution networks, the P2P transaction price and the transaction price of the upper-level power grid are set as follows: wherein, Pb(t) represents the price of buying electricity from the distribution grid at time period t, Pb(t) represents the price of buying electricity from the distribution grid at time period t, Pb(t) represents the price of buying electricity from the distribution grid at time period t, Pb(t) represents the price of buying electricity from the distribution grid at time period t, S3-2: At the beginning of each time period of energy transaction, the distribution network needs to predict the load demand data and the dynamic changing electricity price as the state with the historical data of renewable energy generation, and the state of the distribution network i in the t time period is denoted as S2 i,t = [G i,t , D i,t , ρ t ], G i,t represents the renewable energy generation of the distribution network i in the t time period, D i,t represents the predicted load power of the distribution network i in the t time period, and ρ t represents the electricity price between the distribution networks in the t time period; the present application uses x i,j,t (i≠j) to represent the expected transaction amount between the distribution networks in the t time period, and if i=j, it represents the transaction amount between the distribution network and the upper-level power grid; before the transaction, each distribution network determines the transaction strategy x i,t according to the state, wherein X i,t = [x i,j,t ] 1≤j≤N,i≠j ; if x i,j,t >0, it represents that the distribution network i wants to buy electricity from the distribution network j, and if x i,j,t <0, it represents that the distribution network i wants to sell electricity to the distribution network j; since the actual transaction amount may be different from the expected transaction amount, the actual transaction amount a i,j,t should be represented as follows: where N denotes the number of distribution networks; x i,j,t (i≠j) denotes the expected transaction volume between distribution network i and j at time period t, and if i=j, it denotes the transaction volume between distribution network i and the upper-level power grid at time period t. If x i,j,t > 0, it denotes that distribution network i wants to buy power from distribution network j at time period t, and if x i,j,t 0, it denotes that distribution network i wants to sell power to distribution network j at time period t.

3. The method of claim 1, wherein the system loss and voltage regulation are considered in the power market energy transaction. The power balance constraint at the time of transaction in the step S4 comprises the following parts: S4-1: the power balance constraint at the time of transaction is as follows: wherein p i,j,t represents the actual transmission power between distribution network i and distribution network j at time t; p i,i,t represents the actual transmission power between distribution network i and the upper-level power grid; P G,i,t represents the renewable energy generation of distribution network i at time t; P L,i,t represents the load demand of distribution network i at time t.

4. The method of claim 1, wherein the system loss and voltage regulation are considered. The reward function in the step S5 comprises the following parts: S5-1: The power distribution network sets the corresponding reward function r according to the power loss cost and the minimum voltage deviation 1,i,t As follows: wherein represents the power loss of all lines in the whole grid system during time period t; represents the loss cost of SOP during time period t; represents the voltage violation in the sub-grid during time period t, and λ1represents the penalty factor of voltage violation; N t is the time period set; N n is the union of all nodes of the system; U t,i represents the voltage at node i over the time period t; U max and U min are the upper and lower bounds of the safe operating range of the node voltage, respectively; S5-2: The power distribution network sets the corresponding reward function r according to the minimum energy transaction cost 2,i,t : r 2,i,t = u 1,i,t + u 2,i,t wherein u 1,i,t denotes the trading cost between the distribution network and the upper-level network at time period t; u 2,i,t denotes the trading cost between the distribution network and the upper-level network at time period t.

Images