Power transaction decision optimization method under interaction of power, green certificate and carbon market

Abstract

The invention belongs to the technical field of power systems, and relates to a power transaction decision optimization method under power, green certificate and carbon market interaction, which comprises the following steps: 1, constructing a power transaction decision operation three-layer model framework under power, green certificate and carbon market interaction; 2, establishing a lower-layer power centralized market model; 3, establishing a lower-layer power distributed market model; 4, establishing a lower-layer green evidence market model; 5, establishing a lower-layer carbon market clearing model; 6, establishing a middle-layer power consumer equilibrium decision model; 7, establishing an upper DSO power distribution network operator operation decision model; 8, reconstructing a middle-lower layer model, and constructing a three-layer model iterative solution algorithm; according to the invention, through collaborative transaction decision-making of the power centralized and distributed markets, new energy consumption and traditional energy utilization are balanced, so that social welfare maximization and improvement of multi-subject economic benefits are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and relates to a method for optimizing power trading decisions under the interaction of power, green certificate and carbon markets. Background Art

[0002] The intermittent, random and volatile characteristics of renewable energy generation have significantly increased the difficulty of regulating the power system, posing a huge challenge to the traditional power system operation mode centered on flexible resources such as thermal power. How to achieve stable operation of the system and efficient allocation of resources under the background of large-scale access to renewable energy has become a key issue that needs to be solved urgently.

[0003] In recent years, various environmental rights markets, represented by the green certificate market and the carbon market, have developed rapidly, aiming to achieve low-carbon value pricing of resources through market-based means, and further promote energy conservation, emission reduction and efficient use of new energy. The introduction of these market mechanisms not only provides additional economic incentives for renewable energy power generation, but also optimizes the dynamic allocation of resources through market signals. However, the interaction between various markets and their coupling effects on the operation of centralized and distributed power markets have brought new complexities to the operational decisions of market players.

[0004] In the centralized power market, traditional power generators participate in power trading by declaring electricity volume, while new energy power generators mainly realize the value of environmental rights and interests through the green certificate market. At the same time, the rise of the distributed market provides a more flexible trading form for distributed resource producers and consumers. Peer-to-peer (P2P) trading, as an important mechanism of the distributed market, can effectively utilize the flexibility of distributed resources and realize two-way interaction between the user side and the power grid. However, due to the coupling of the distributed market and the centralized market, as well as the introduction of the environmental rights market, the market price formation mechanism and trading rules are more complicated. When making decisions, market players need to fully consider the linkage effects between various markets and the dynamic trade-offs of their own interests.

[0005] As an important entity that coordinates resources on both the power generation and power consumption sides, the distribution system operator (DSO) plays a key role in the interaction between the centralized and distributed markets and the environmental equity market. DSO is not only responsible for the daily operation of the distribution network, but also needs to coordinate the interaction between the distribution side market and the transmission side market, and optimize the power purchase and sale and equity trading strategies in different market environments to maximize economic benefits and system efficiency.

[0006] Therefore, how to build a scientific and efficient operation strategy under the interaction of electricity, green certificate and carbon markets not only affects the absorption capacity of new energy, but also directly affects the overall economy and sustainability of the power system. Summary of the invention

[0007] The present invention provides an optimization method for power trading decisions under the interaction of power, green certificate and carbon markets, so as to realize the coordinated optimization of power trading decisions under a multi-market environment, and provide an important reference for the efficient coupling and operation of distributed energy and green certificate and carbon markets: fully consider the impact of the policies and trading mechanisms of the green certificate market and carbon market on the costs of market entities, and combine the price fluctuations of the power market, the intermittent nature of renewable energy generation, the dynamic changes of user loads and other multi-dimensional factors to construct a three-layer operation decision model for multi-market interaction, and optimize the operation strategies of DSO and large users in a multi-market environment. Through the coordinated transaction decisions of centralized and distributed power markets, the consumption of new energy and the utilization of traditional energy are balanced to maximize social welfare and improve the economic benefits of multiple entities.

[0008] The technical solution adopted by the present invention to solve the technical problem is: a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, comprising the following steps:

[0009] Step 1: Construct a three-layer model framework for power trading decision-making and operation under the interaction of power, green certificate and carbon market;

[0010] Step 2: Establish a lower-level electricity centralized market model;

[0011] Step 3: Establish the lower-level electricity distribution market model;

[0012] Step 4: Establish the underlying green certificate market model;

[0013] Step 5: Establish a lower carbon market clearing model;

[0014] Step 6: Establish a balanced decision model for middle-level power users;

[0015] Step 7: Establish the upper-level DSO distribution network operator operation decision model;

[0016] Step 8: Reconstruct the middle and lower layer models and build an iterative solution algorithm for the three-layer model.

[0017] Preferably, in step 1, the three-layer models are interconnected through market prices, load demands and electricity trading strategies;

[0018] The decision-making subject of the upper model is DSO, and the DSO distribution network operator operation decision model is established with the optimization goal of minimizing DSO costs; DSO can act as an agent for power users to purchase electricity from the centralized power market, and participate in the green certificate market to purchase green certificates to meet the green certificate assessment indicators. The price of green certificates is determined by the supply and demand relationship in the green certificate market;

[0019] The middle-level model establishes the user's electricity transaction decision problem with the minimum cost of electricity users as the objective function; there are multiple electricity users at different nodes in the distribution network, equipped with wind power, distributed photovoltaic and distributed energy storage power generation equipment. Electricity users can independently decide the output of each power generation equipment and give a strategy for purchasing electricity from DSO; while electricity users realize electricity transactions with DSO, they also participate in the carbon market and the electricity distribution market and carry out market quota transactions;

[0020] The lower-level model takes maximizing social welfare as its objective function to realize the modeling of centralized electricity markets, distributed electricity markets, green certificates and carbon markets; in the centralized electricity market, the objective function is to minimize the cost of power generators to form the centralized electricity market price; in the distributed electricity market, the cost-utility function of electricity users is modeled in the form of a quadratic function to form the P2P trading electricity price; in the green certificate market, the objective function is to maximize the revenue of renewable energy power generators, and the green certificate price is obtained by the Cournot model; the carbon emission rights price is obtained by the Cournot model based on the quota trading volume information.

[0021] Preferably, in step 2, the lower-level centralized electricity market model includes:

[0022] (1)

[0023] (2)

[0024] (3)

[0025] (4)

[0026] (5)

[0027] (6)

[0028] In formula (1) to formula (6), , , represents the cost coefficient of conventional generator set i, Indicates the power generation of conventional units, represents the price of carbon emission rights, represents the carbon quota trading volume of traditional energy power generator j; , , , They represent the sets of conventional generators, centralized new energy generators, power users and nodes connected to node n respectively; represents the power generation of centralized renewable energy unit i at time t, Indicates the amount of electricity traded by electricity users; represents the line admittance, , Represent the phase angles of nodes n and m respectively; represents the power balance constraint dual variable. , Respectively represent the upper and lower limits of the generator set output; , is the dual variable; Indicates the maximum transmission capacity of the line. , represents the dual variable of power flow constraint; , represents the phase angle constraint dual variable; represents a balance node, represents the equilibrium node constraint dual variable.

[0029] Preferably, in step 3, the lower-layer power distribution market model includes:

[0030] (7)

[0031] (8)

[0032] (9)

[0033] (10)

[0034] In formula (7) to formula (10), represents the production cost or utility to the electricity user; , , represents a non-negative parameter; represents the total amount of electricity P2P transactions participated by electricity user k; represents the P2P transaction volume from power user k to l at time t, represents the P2P transaction volume from electricity user l to k at time t; represents the dual variable, represents the set of users connected to user k; represents the P2P transaction price from user k to l at time t, , Respectively represent the lower and upper limits of P2P transactions; , Dual variable representing the volume constraint.

[0035] Preferably, in step 4, the lower green certificate market model includes:

[0036] (11)

[0037] (12)

[0038] (13)

[0039] (14)

[0040] (15)

[0041] (16)

[0042] (17)

[0043] In formula (12) to formula (17), Indicates the transaction price of green certificates. represents the number of green certificates sold by renewable energy generator j, represents the set of renewable energy generating units of power generator j, Represents the output of renewable energy units, represents the dual variable, , represents the positive parameter of the Cournot model; represents the dual variable, Indicates the maximum price of green certificate, represents the dual variable; It represents the proportional coefficient reflecting the change in green certificate unit price and demand. Indicates the assessment ratio; represents the maximum amount of electricity purchased from the centralized electricity market, represents the total maximum electricity purchase demand of DSO, Indicates the minimum time interval.

[0044] Preferably, in step 5, the lower carbon market clearing model includes:

[0045] (18)

[0046] (19)

[0047] (20)

[0048] (twenty one)

[0049] (twenty two)

[0050] (twenty three)

[0051] (twenty four)

[0052] (25)

[0053] In formula (18) to formula (25), represents the price of carbon emission rights, represents the carbon quota trading volume of traditional energy power generator j; , represents the parameters of the Cournot model; , They represent the free initial quota of carbon emission rights and the actually used carbon emission rights quota, represents the set of conventional generators of power generator j; Represents the carbon quota coefficient of the generator set, Indicates carbon emission intensity; represents the upper limit of carbon emission rights price, It represents the proportional coefficient reflecting the change in unit price of carbon emission quota and demand. Indicates the demand ratio; represents the maximum total demand of electricity users, represents the maximum load of power user k at time t; Indicates the minimum time interval, Indicates the power generation of conventional units.

[0054] Preferably, in step 6, the middle-level power user balance decision model includes:

[0055] (26)

[0056] (27)

[0057] (28)

[0058] (29)

[0059] (30)

[0060] (31)

[0061] (32)

[0062] (33)

[0063] (34)

[0064] In formula (26) to formula (34), represents the user's cost, Indicates the electricity purchase price for power users, represents the amount of electricity purchased by electricity users from DSO, Indicates the output of wind power equipment. Indicates the output of photovoltaic equipment; It indicates the maximum value of electricity that the power user is allowed to purchase from the DSO. , Respectively represent the maximum predicted output of wind power and photovoltaic equipment, , They represent the maximum value of the charging and discharging power of the energy storage device, Represents a binary variable to judge the buying and selling status, Indicates the electricity price sold to power users; It indicates the transmission fee per unit of electricity traded between users. represents the unit operating cost of energy storage, Indicates the energy storage charging power, represents the energy storage discharge power, represents the storage capacity of the energy storage device at time t, , Respectively represent the energy storage charging and discharging efficiency, Indicates the initial storage capacity of the energy storage device, The storage capacity of the energy storage device at the last moment; represents the P2P transaction price from user k to l at time t, represents the P2P transaction volume from power user k to l at time t, represents the price of energy usage rights, represents the transaction volume of energy quota of user k, represents the carbon emission quota trading volume of user k, represents the load of user k at time t, Indicates the minimum time interval, represents the price of carbon emission rights, represents the set of users connected to user k, Represents the total amount of electricity P2P transactions participated by electricity user k.

[0065] Preferably, in step 7, the upper-level DSO distribution network operator operation decision model includes:

[0066] (35)

[0067] (36)

[0068] (37)

[0069] (38)

[0070] (39)

[0071] (40)

[0072] (41)

[0073] (42)

[0074] In formula (35) to formula (42), represents the total DSO cost, represents the cost of transactions between DSO and electricity users, represents the active power loss cost of the distribution network, represents the cost of DSO purchasing electricity from the centralized electricity market, represents the cost of DSO purchasing electricity from distributed renewable energy sources, represents the DSO gas purchase cost, It represents the cost of DSO purchasing green certificates from the green certificate market. represents the distributed renewable energy grid-connected power at node i at time t, Indicates the maximum value of distributed renewable energy grid power. Indicates the green certificate trading volume of DSO in the green certificate market, Indicates the DSO wind power and photovoltaic output, Indicates green certificate assessment indicators, represents the renewable energy consumption responsibility weight, Indicates load, represents the branch resistance, represents the square of the branch current, Indicates the user's minimum electricity price. Indicates the user's maximum electricity price. Indicates the user's minimum electricity purchase price, Indicates the user's highest electricity purchase price, represents the average electricity purchase price of users, represents the average electricity price of the user, Represents a collection of nodes. Indicates the electricity purchase price for power users, Indicates the electricity price sold to electricity users.

[0075] Preferably, in step 8, the middle and lower layer models are first transformed, and the lower layer models are replaced with corresponding optimality conditions and complementary relaxation conditions and incorporated into the middle layer model to form the middle and lower layer models.

[0076] Preferably, in step 8, in order to balance the mutual influence between the DSO operation decision in the upper model and the power transaction decision of the power user in the middle and lower model, an iterative algorithm is constructed as follows:

[0077] First, the purchase and sale prices and iteration times between DSO and power users are initialized, the equilibrium problem between different power users is solved, the equilibrium solution of the power trading strategy game of all power users is obtained, and the purchase and sale decisions of power users are passed to DSO;

[0078] Then, DSO updates the node net load. Based on the updated node net load, DSO optimizes the upper-level operation decision model, obtains the purchase and sale electricity price, and updates and passes it to the middle and lower-level models;

[0079] The solution loop is iterated alternately until the two market transaction results converge or the maximum number of loops is reached.

[0080] The beneficial effects of the present invention are:

[0081] 1. This invention starts from the perspective of the interaction between electricity, green certificates and carbon markets, takes the optimization of coordinated operation between DSO and large users as the core, comprehensively considers various economic factors such as electricity prices, green certificate market prices, carbon market prices and their dynamic fluctuations, and combines the transaction characteristics of the electricity market to propose a three-layer model of operation decision-making under multi-market interaction. By constructing a distributed energy trading mechanism and game model under a multi-market environment, it accurately depicts the complex interactive relationship between large users and market players, comprehensively considers the cost of purchasing and selling electricity, the cost of energy storage operation, the transaction cost of green certificates and carbon markets and policy incentives, and realizes the optimal allocation of resources and the maximization of operational benefits.

[0082] 2. In the model design, the present invention promotes the local consumption of new energy by optimizing P2P transactions and power market transaction strategies, reduces the total operating costs of large users and DSOs, and significantly improves the flexibility and economy of the power system by balancing the energy-saving needs of high-energy-consuming users and system stability goals. Compared with existing methods, the present invention more systematically and completely depicts the collaborative optimization model in a multi-market interaction environment, and can fully reflect the optimization method of power trading decisions under the interaction of power, green certificate and carbon markets. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a three-layer model framework diagram of power trading operation decision of a power trading decision optimization method under the interaction of power, green certificate and carbon market of the present invention;

[0084] Figure 2 This is a comparative analysis diagram of the DSO power centralized market power purchasing strategy of the present invention;

[0085] Figure 3 It is a graph of the power P2P transaction results of the present invention;

[0086] Figure 4 It is a result diagram of user quota transaction situation of the present invention;

[0087] Figure 5 It is an analysis diagram of the electricity purchase and sale price strategy of the power user of the present invention;

[0088] Figure 6 It is the renewable energy consumption responsibility weight impact analysis diagram of the present invention;

[0089] Figure 7 This is an analysis diagram of the impact of the environmental rights market of the present invention on the cost of electricity users. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the relevant technologies in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] refer to Figures 1 to 7 In this embodiment, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market specifically includes the following steps:

[0092] Step 1: Construct a three-layer model framework for power trading decision-making and operation under the interaction of power, green certificate and carbon markets.

[0093] The three-layer model framework of power trading operation decision-making under the interaction of power, green certificate and carbon market is constructed as follows Figure 1As shown in the figure, the three-layer models are interconnected through market prices, load demand and electricity trading strategies. Among them, the decision-making subject of the upper model is DSO, and the DSO operation decision model is established with the optimization goal of minimizing DSO costs. DSO can act as an agent for power users to purchase electricity from the centralized power market, and participate in the green certificate market to purchase green certificates to meet the green certificate assessment indicators. The price of green certificates is determined by the supply and demand relationship in the green certificate market. The middle-level model establishes the user's electricity trading decision problem with the minimum cost of power users as the objective function. There are multiple power users at different nodes in the distribution network, with wind power, distributed photovoltaic and distributed energy storage power generation equipment. Power users can independently decide the output of each power generation equipment and give a power purchase strategy to DSO. While power users and DSO realize power transactions, they also participate in the carbon market and the distributed power market to carry out market quota transactions. The lower-level model takes the maximization of social welfare as the objective function to realize the modeling of centralized power, distributed markets, green certificates and carbon markets. In the centralized electricity market, the objective function is to minimize the cost of power generators to form the centralized electricity market price; in the distributed electricity market, the cost-utility function of electricity users is modeled in the form of a quadratic function to form the P2P trading electricity price; in the green certificate market, the objective function is to maximize the revenue of renewable energy power generators, and the green certificate price is obtained by the Cournot model; the carbon emission rights price is obtained by the Cournot model based on the quota trading volume information.

[0094] Step 2: Establish a lower-level electricity centralized market model.

[0095] (1)

[0096] (2)

[0097] (3)

[0098] (4)

[0099] (5)

[0100] (6)

[0101] in, , , is the cost coefficient of conventional generator set i, is the power generation of conventional units, is the price of carbon emission rights, is the carbon quota trading volume of traditional energy power producer j. , , , They represent the sets of conventional generators, centralized new energy generators, power users and nodes connected to node n respectively; is the power generation of centralized renewable energy unit i at time t, Trading electricity for electricity users; is the line admittance, , are the phase angles of nodes n and m respectively; is the dual variable of the power balance constraint. , They are the upper and lower limits of the generator set output respectively; , is the dual variable. is the maximum transmission capacity of the line, , is the dual variable of the power flow constraint. , is the phase angle constraint dual variable; For the balance node, Constrain the dual variables for equilibrium nodes.

[0102] Step 3: Establish the lower-level power distribution market model. It includes the following:

[0103] (7)

[0104] (8)

[0105] (9)

[0106] (10)

[0107] in, production costs or utility for electricity users; , , is a non-negative parameter; The total amount of electricity P2P transactions participated by electricity user k; is the P2P transaction volume between power users k and l; is the dual variable, is the set of users connected to user k; is the P2P transaction price, , They are the lower and upper limits of P2P transactions respectively. , is the dual variable of the trading volume constraint.

[0108] Step 4: Establish the underlying green certificate market model. It includes the following:

[0109] (11)

[0110] (12)

[0111] (13)

[0112] (14)

[0113] (15)

[0114] (16)

[0115] (17)

[0116] In the formula, is the green certificate transaction price, is the number of green certificates sold by renewable energy generator j, The output of renewable energy units, is the dual variable, , is the positive parameter of the Cournot model; is the dual variable, is the maximum price of green certificate, is the dual variable; To reflect the proportional coefficient between the unit price of green certificates and the change in demand, For the assessment ratio; is the maximum amount of electricity purchased from the centralized electricity market, It is the total maximum electricity purchase demand of DSO.

[0117] Step 5: Establish a lower-level carbon market clearing model. This includes:

[0118] (18)

[0119] (19)

[0120] (20)

[0121] (twenty one)

[0122] (twenty two)

[0123] (twenty three)

[0124] (twenty four)

[0125] (25)

[0126] in, , represents the parameters of the Cournot model; , They represent the free initial quota of carbon emission rights and the actually used carbon emission rights quota respectively; is the carbon quota coefficient of the generator set, is the carbon emission intensity; is the upper limit of carbon emission rights price, To reflect the proportional coefficient between the unit price of carbon emission quota and the change in demand, is the demand ratio; is the maximum total demand of electricity users, is the maximum load of power user k at time t.

[0127] Step 6: Establish a balanced decision model for mid-level power users

[0128] (26)

[0129] (27)

[0130] (28)

[0131] (29)

[0132] (30)

[0133] (31)

[0134] (32)

[0135] (33)

[0136] (34)

[0137] in, Cost to users, The electricity purchase price for power users, The amount of electricity purchased by electricity users from DSO, Powering wind power equipment, Provide power for photovoltaic equipment; The maximum value of electricity that a power user is allowed to purchase from a DSO. , are the maximum predicted outputs of wind power and photovoltaic equipment, , are the maximum values ​​of charging and discharging power of the energy storage device, is a binary variable to determine the buying and selling status, Selling electricity prices to electricity users; It is the transmission fee for the unit electricity traded between users. is the unit operating cost of energy storage, The charging power for energy storage, is the energy storage discharge power, is the storage capacity of the energy storage device at time t, , are the energy storage charging and discharging efficiency, is the initial storage capacity of the energy storage device, It is the storage capacity of the energy storage device at the last moment.

[0138] Step 7: Establish upper-level DSO operation decision model:

[0139] (35)

[0140] (36)

[0141] (37)

[0142] (38)

[0143] (39)

[0144] (40)

[0145] (41)

[0146] (42)

[0147] is the total cost of DSO, is the transaction cost between DSO and electricity users, is the active power loss cost of the distribution network, The cost of DSO purchasing electricity from the centralized electricity market, The cost of DSO purchasing electricity from distributed renewable energy sources, is the gas purchase cost of DSO, The cost of purchasing green certificates from the green certificate market for DSO, is the distributed renewable energy grid-connected power at node i at time t, is the maximum power of distributed renewable energy grid access, is the green certificate trading volume of DSO in the green certificate market, Provide wind and photovoltaic power for DSO, It is the green certificate assessment indicator. is the renewable energy consumption responsibility weight, For load, is the branch resistance, is the square of the branch current, The lowest electricity price for users. The maximum electricity price for users. The lowest electricity price for users. The maximum electricity purchase price for users. is the average electricity purchase price for users, The average electricity price sold to users.

[0148] Step 8: Reconstruct the middle and lower layer models and build an iterative solution algorithm for the three-layer model.

[0149] In order to effectively solve the constructed three-layer optimization model, the middle and lower models can be transformed first. Since the trading model of the power market, green certificate market, carbon market and energy rights market in the lower model is a linear programming problem, its KKT condition is a necessary and sufficient condition to meet the optimality of the model. Therefore, the lower model can be replaced by the corresponding optimality conditions and complementary relaxation conditions and incorporated into the middle model to form the middle and lower model.

[0150] The DSO operation decision in the upper model and the power transaction decision of the power users in the middle and lower models affect each other. On the one hand, the higher the retail side electricity price of the DSO, the higher the cost of the power users, which will encourage users to reduce the load as much as possible and actively participate in the distributed power market transactions instead of purchasing electricity from the DSO; on the other hand, the better the power transaction strategy of the power user, that is, the lower the cost, the lower the amount of electricity purchased by the user from the DSO, which will reduce the revenue of the DSO. In order to balance this trade-off, the present invention constructs an iterative algorithm. First, the purchase and sale electricity price and the number of iterations between the DSO and the power user are initialized, the equilibrium problem between different power users is solved, the game equilibrium solution of the power transaction strategy of all power users is obtained, and the purchase and sale electricity decision of the power user is passed to the DSO. Then, the DSO updates the node net load. Based on the updated node net load, the DSO optimizes the upper-level operation decision model, obtains the purchase and sale electricity price and updates it to the middle and lower models. The above steps are solved alternately and iterated until the results of the two market transactions converge or the maximum number of cycles is reached.

[0151] The following uses the modified IEEE-30 node transmission network and IEEE-33 node distribution network as examples to illustrate the implementation process of this method.

[0152] The IEEE-30 node transmission network has 41 branches, 6 conventional units and 4 new energy units, with a reference voltage of 110kV. The IEEE-33 distribution network has 32 branches, 2 wind turbines and 1 photovoltaic power station.

[0153] Compared with the power purchase and sale strategy of DSO in the centralized power market when the environmental equity market is considered, when the environmental equity market is not included, the power generation unit does not need to pay the carbon trading cost, and the power price in the centralized power market is only affected by the energy cost of the unit, so its power price is lower. The results are as follows: Figure 2 As shown. The price fluctuates between 0:00 and 2:00, and between 0.129 yuan / kWh and 0.123 yuan / kWh during the period from 3:00 to 6:00. In the period from 9:00 to 20:00, the price rises significantly to around 0.143 yuan / kWh due to the peak load. After considering the environmental rights market, the transaction price of the centralized power market reached 0.198 yuan / kWh, 0.124 yuan / kWh, and 0.211 yuan / kWh during the above time periods. Since the load is the lowest from 3:00 to 6:00, the output of the generator set is low, so the cost of its carbon emission rights has little impact on the transaction price during this period.

[0154] Figure 3 The daily P2P electricity transaction volume between 10 power users is given. When the user assumes the role of the buyer in the transaction, the transaction volume is positive. It can be seen that the total daily P2P electricity transaction volume of 10 power users is 1844.27kWh, and the maximum transaction volume occurs between users 5 and 6. The load demand and DERs output of the two users are quite different, and there is a larger space for P2P electricity transactions. The P2P electricity prices in peak, flat and valley periods are roughly 0.37 yuan / kWh, 0.32 yuan / kWh, and 0.27 yuan / kWh, respectively. The P2P electricity transaction prices between power users are consistent, indicating that the market equilibrium has been reached. It can be seen that the P2P transaction electricity price is higher than the electricity selling price of power users and lower than the electricity purchasing price. The price difference between the purchase and sale prices of power users and DSO transactions is the main economic incentive for conducting P2P electricity transactions.

[0155]

[0156] Table 1 shows the transaction volume of DSO participating in the green certificate market. In Table 1, the number of green certificates obtained by wind power and photovoltaic power generation is 101.82MWh, which does not meet the renewable energy consumption responsibility weight standard. Therefore, DSO needs to purchase 1.59MWh of green certificates from the green certificate market. At this time, the market price of green certificates is 252.56 yuan / MWh, and the cost of DSO for purchasing green certificates increases by 401.57 yuan.

[0157] Figure 4The diagram is a schematic diagram of the free initial quota and actual usage of carbon emission rights for each power user. The free initial quota is set at 1.5t. The carbon emission rights quota actually used by the power user is lower than the free initial quota it has, and needs to be purchased from the market. The red shaded area in the figure represents the purchase of carbon emission rights quotas for power users 1 to 5, and the blue shaded area represents the purchase of quotas for users 6 to 10. Since the energy consumption level of power users 6 to 10 is higher than that of users 1 to 5, their purchase of carbon emission rights quotas is also correspondingly higher.

[0158] Compared with the electricity price strategy of power users considering power P2P transactions, Figure 5 The results of the power purchase and sale price strategy without P2P transaction scenario are given. During 1:00 to 7:00 and 22:00 to 24:00, the overall load of power users is at the lowest level of the day, and the wind power and photovoltaic power generation output of some power users is higher than their load demand. Since P2P transaction behavior is not considered, their excess power is no longer sold to other power users with load demand during this period, but is sold to DSO, and the highest amount of electricity sold is from 11:00 to 16:00. During 8:00 to 10:00 and 17:00 to 21:00, all power users have no excess electricity sold to DSO. Therefore, during 11:00 to 16:00, the power user's electricity sales price is the lowest, which is 0.1 yuan / kWh; during 8:00 to 10:00 and 17:00 to 21:00, the electricity sales price is the highest, reaching 0.25 yuan / kWh; during 22:00 to 7:00, the electricity sales price is low, which is 0.2 yuan / kWh. It can be seen that DSO can maximize its own profits by reasonably adjusting the purchase and sale prices of electricity for power users in different purchase and sale periods.

[0159] Figure 6 The impact analysis of the responsibility weight of renewable energy consumption is given. With the increase of the responsibility weight of renewable energy consumption, the market demand for green certificates increases. Although it promotes the effective consumption of distributed new energy, it drives up the price of green certificates and increases the cost of DSO. However, excessively high green certificate prices will increase the cost pressure of the responsible entities for consumption, which is not conducive to their long-term development, and will also suppress the market demand for green certificates, leading to a sluggish green certificate market; too low green certificate prices may weaken the investment incentives of centralized new energy power generators for new energy projects, which is not conducive to the realization of long-term energy structure optimization goals.

[0160] The carbon emission rights of electricity users are in a tightened state, such as Figure 7As shown in the figure, carbon market transactions have caused the cost of electricity users to rise. The cost of low-energy-consuming users 1-5 increased by an average of 24.25%, while the cost of high-energy-consuming users 6-10 increased by an average of 46.70%. This shows that high-energy-consuming electricity users need to bear higher additional costs in the market, and their living space is shrinking, while low-energy-consuming electricity users can gain greater competitiveness in the market, which will prompt high-energy-consuming electricity users to carry out energy-saving transformation to better adapt to the current market environment.

[0161] In summary, the present invention starts from the perspective of the interaction between electricity, green certificates and carbon markets, takes the coordinated operation optimization of DSO and large users as the core, comprehensively considers various economic factors such as electricity prices, green certificate market prices, carbon market prices and their dynamic fluctuations, and combines the transaction characteristics of the electricity market to propose a three-layer model of operation decision-making under multi-market interaction. By constructing a distributed energy trading mechanism and game model under a multi-market environment, the complex interaction relationship between large users and market players is accurately portrayed, and the cost of purchasing and selling electricity, the cost of energy storage operation, the transaction cost of green certificates and carbon markets, and policy incentives are comprehensively considered to achieve the optimal allocation of resources and maximize operational benefits; therefore, the present invention has broad application prospects in the field of electricity market mechanisms.

[0162] It should be emphasized that the above are only preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Any simple modification made to the above embodiments based on the technical essence of the present invention also falls within the protection scope of the present invention. Other equivalent changes and modifications still fall within the scope of the technical solution of the present invention.

Claims

1. A method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: The following steps are involved: Step 1: Construct a three-layer model framework for power trading decision-making and operation under the interaction of power, green certificate and carbon market; Step 2: Establish a lower-level electricity centralized market model; Step 3: Establish the lower-level electricity distribution market model; Step 4: Establish the underlying green certificate market model; Step 5: Establish a lower carbon market clearing model; Step 6: Establish a balanced decision model for middle-level power users; Step 7: Establish the upper-level DSO distribution network operator operation decision model; Step 8: Reconstruct the middle and lower layer models and build an iterative solution algorithm for the three-layer model.

2. According to claim 1, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: In the step 1, the three-layer models are interconnected through market prices, load demands and electricity trading strategies; The decision-making subject of the upper model is DSO, and the DSO distribution network operator operation decision model is established with the optimization goal of minimizing DSO cost; The middle-level model establishes the user's electricity transaction decision problem with the minimum cost of electricity users as the objective function; The lower-level model takes maximizing social welfare as its objective function to realize the modeling of centralized electricity markets, distributed electricity markets, green certificates, and carbon markets.

3. According to claim 1, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: In step 2, the lower-level centralized electricity market model includes: （1） （2） （3） （4） （5） （6） In formula (1) to formula (6), , , represents the cost coefficient of conventional generator set i, Indicates the power generation of conventional units, represents the price of carbon emission rights, represents the carbon quota trading volume of traditional energy power generator j; , , , They represent the sets of conventional generators, centralized new energy generators, power users and nodes connected to node n respectively; represents the power generation of centralized renewable energy unit i at time t, Indicates the amount of electricity traded by electricity users; represents the line admittance, , Represent the phase angles of nodes n and m respectively; represents the dual variable of the power balance constraint; , Respectively represent the upper and lower limits of the generator set output; , is the dual variable; Indicates the maximum transmission capacity of the line. , represents the dual variable of power flow constraint; , represents the phase angle constraint dual variable; represents a balance node, represents the equilibrium node constraint dual variable.

4. According to claim 1, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: In step 3, the lower-layer power distribution market model includes: （7） （8） （9） （10） In formula (7) to formula (10), represents the production cost or utility to the electricity user; , , represents a non-negative parameter; represents the total amount of electricity P2P transactions participated by electricity user k; represents the P2P transaction volume from power user k to l at time t, represents the P2P transaction volume from electricity user l to k at time t; represents the dual variable, represents the set of users connected to user k; represents the P2P transaction price from user k to l at time t, , Respectively represent the lower and upper limits of P2P transactions; , Dual variable representing the volume constraint.

5. According to claim 1, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: In step 4, the lower green certificate market model includes: （11） （12） （13） （14） （15） （16） （17） In formula (12) to formula (17), Indicates the transaction price of green certificates. represents the number of green certificates sold by renewable energy generator j, represents the set of renewable energy generating units of power generator j, Represents the output of renewable energy units, represents the dual variable, , represents the positive parameter of the Cournot model; represents the dual variable, Indicates the maximum price of green certificate, represents the dual variable; It represents the proportional coefficient reflecting the change in green certificate unit price and demand. Indicates the assessment ratio; represents the maximum amount of electricity purchased from the centralized electricity market, represents the total maximum electricity purchase demand of DSO, Indicates the minimum time interval.

6. According to claim 1, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: In step 5, the lower carbon market clearing model includes: （18） （19） （20） （21） （22） （23） （24） （25） In formula (18) to formula (25), represents the price of carbon emission rights, represents the carbon quota trading volume of traditional energy power generator j; , represents the parameters of the Cournot model; , They represent the free initial quota of carbon emission rights and the actually used carbon emission rights quota, represents the set of conventional generators of power producer j; Represents the carbon quota coefficient of the generator set, Indicates carbon emission intensity; represents the upper limit of carbon emission rights price, It represents the proportional coefficient reflecting the change in unit price of carbon emission quota and demand. Indicates the demand ratio; represents the maximum total demand of electricity users, represents the maximum load of power user k at time t; Indicates the minimum time interval, Indicates the power generation of conventional units.

7. According to claim 1, a method for optimizing power trading decisions under the interaction of power, green certificate and carbon market, characterized in that: In step 6, the middle-level power user balance decision model includes: （26） （27） （28） （29） （30） （31） （32） （33） （34） In formula (26) to formula (34), represents the user's cost, Indicates the electricity purchase price for power users, represents the amount of electricity purchased by electricity users from DSO, Indicates the output of wind power equipment. Indicates the output of photovoltaic equipment; It indicates the maximum value of electricity that the power user is allowed to purchase from the DSO. , Respectively represent the maximum predicted output of wind power and photovoltaic equipment, , They represent the maximum value of the charging and discharging power of the energy storage device, Represents a binary variable to judge the buying and selling status, Indicates the electricity price sold to power users; It indicates the transmission fee per unit of electricity traded between users. represents the unit operating cost of energy storage, Indicates the energy storage charging power, represents the energy storage discharge power, represents the storage capacity of the energy storage device at time t, , Respectively represent the energy storage charging and discharging efficiency, Indicates the initial storage capacity of the energy storage device, The storage capacity of the energy storage device at the last moment; represents the P2P transaction price from user k to l at time t, represents the P2P transaction volume from power user k to l at time t, represents the price of energy usage rights, represents the transaction volume of energy quota of user k, represents the carbon emission quota trading volume of user k, represents the load of user k at time t, Indicates the minimum time interval, represents the price of carbon emission rights, represents the set of users connected to user k, Represents the total amount of electricity P2P transactions participated by electricity user k.

8. The method for optimizing power trading decisions under the interaction of power, green certificate and carbon market according to claim 1 is characterized in that: In step 7, the upper-level DSO distribution network operator operation decision model includes: （35） （36） （37） （38） （39） （40） （41） （42） In formula (35) to formula (42), represents the total DSO cost, represents the cost of transactions between DSO and electricity users, represents the active power loss cost of the distribution network, represents the cost of DSO purchasing electricity from the centralized electricity market, represents the cost of DSO purchasing electricity from distributed renewable energy sources, represents the DSO gas purchase cost, It represents the cost of DSO purchasing green certificates from the green certificate market. represents the distributed renewable energy grid-connected power at node i at time t, Indicates the maximum value of distributed renewable energy grid power. Indicates the green certificate trading volume of DSO in the green certificate market, Indicates the DSO wind power and photovoltaic output, Indicates green certificate assessment indicators, represents the renewable energy consumption responsibility weight, Indicates load, represents the branch resistance, represents the square of the branch current, Indicates the user's minimum electricity price. Indicates the user's maximum electricity price. Indicates the user's minimum electricity purchase price, Indicates the user's highest electricity purchase price, represents the average electricity purchase price of users, represents the average electricity price of the user, Represents a collection of nodes. Indicates the electricity purchase price for power users, Indicates the electricity price sold to electricity users.

9. The method for optimizing power trading decisions under the interaction of power, green certificate and carbon market according to claim 1 is characterized in that: In step 8, the middle and lower layer models are first transformed, and the lower layer models are replaced with corresponding optimality conditions and complementary relaxation conditions and incorporated into the middle layer model to form the middle and lower layer models.

10. The method for optimizing power trading decisions under the interaction of power, green certificate and carbon market according to claim 1 is characterized in that: In step 8, in order to balance the mutual influence between the DSO operation decision in the upper model and the power transaction decision of the power user in the middle and lower models, an iterative algorithm is constructed. The specific iterative algorithm is as follows: First, the purchase and sale prices and iteration times between DSO and power users are initialized, the equilibrium problem between different power users is solved, the equilibrium solution of the power trading strategy game of all power users is obtained, and the purchase and sale power decisions of power users are passed to DSO; Then, DSO updates the node net load. Based on the updated node net load, DSO optimizes the upper-level operation decision model, obtains the purchase and sale electricity price, and updates and passes it to the middle and lower-level models; The solution loop is iterated alternately until the two market transaction results converge or the maximum number of loops is reached.

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