Distributed resource energy sharing and polymerization optimization method under coupling of electricity market and carbon market

Abstract

The invention belongs to the technical field of power systems, and relates to a distributed resource energy sharing and aggregation optimization method under power-carbon market coupling, which comprises the following steps: 1, establishing a power P2P market transaction model; 2, establishing a carbon emission permit market clearing model; 3, establishing an energy utilization right market clearing model; 4, performing linearization processing on the electric power P2P market transaction model through a KKT condition; 5; comprehensively considering each cost and each cost / income, and establishing a distributed resource energy sharing and aggregation optimization model of the distributed resource main body by taking the lowest cost of the distributed resource main body as an objective function; according to the method, the distributed resource main body electric energy balance constraint, the power purchasing and selling constraint and the distributed renewable energy source and energy storage device constraint are considered, distributed resource main body energy sharing optimization is realized by taking the lowest total cost of the distributed resource main body as an objective function on the premise of maximizing social welfare, and the problem of unstable decision optimization is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and relates to a distributed resource energy sharing and aggregation optimization method under the coupling of electricity and carbon markets. Background Art

[0002] The load in the traditional power system usually refers to equipment that simply consumes electricity, such as incandescent lamps and arc furnaces. However, with the application of distributed resource technology, the characteristics of power loads have changed significantly, gradually showing the characteristics of integrated production and consumption. In this mode, power consumers are not only users of electricity, but may also become producers of electricity, thus having a dual identity. This two-way interactive power load structure requires the power system to have more flexible management and scheduling capabilities to cope with the increasingly complex energy consumption pattern. With the rapid development of energy Internet technology, distributed resource aggregates have become one of the key strategies to resolve the contradiction between power systems and distributed power sources. This aggregate can not only cope with the challenges brought by distributed new energy generation and load uncertainty, but also optimize the operating efficiency and economy of the power system. Through advanced information and communication technology, distributed resource aggregates realize efficient aggregation, coordination and optimization of distributed resources, enabling them to actively participate in the power exchange of the main power grid power market, form profit flows, and bring significant economic benefits to the system. In addition, as an important part of the energy Internet, the development and application of distributed resource aggregates are of great significance to improving the flexibility and reliability of the power system.

[0003] In this context, P2P (peer to peer) power trading, as an emerging market mechanism, has begun to be applied in the power sector. P2P trading enables distributed resources to directly trade power, enhancing the flexibility and autonomy of trading. However, the existing P2P trading system still lacks efficient management and optimization methods, resulting in low resource scheduling efficiency during distributed resource trading, and failing to fully realize the optimal utilization of local resources and maximize the benefits of power sharing.

[0004] At the same time, carbon emission control and green energy utilization have become the focus of global attention. Carbon trading and energy rights trading, as important market mechanisms to promote a low-carbon economy, are gradually penetrating into the electricity market. Carbon trading limits carbon emissions through market-based means and encourages distributed resource entities to reduce their carbon footprint; while energy rights trading promotes efficient energy utilization by limiting and adjusting the total amount of energy consumption. However, there is a lack of effective connection and coordination between the existing electricity market and carbon emissions and energy rights trading, resulting in the failure to achieve synchronous optimization of energy utilization efficiency and carbon emission control in the electricity market.

[0005] Therefore, how to combine electricity trading, carbon emission trading and energy consumption rights trading to promote the optimization of distributed resources of distributed resource entities has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] The present invention provides a distributed resource energy sharing and aggregation optimization method under the coupling of electricity and carbon market, which encourages distributed resource subjects to improve P2P participation to reduce energy costs, improve the efficiency of electricity transactions between distributed resource subjects, and can also achieve carbon emission control and promote the realization of green and low-carbon goals: fully consider the impact of policies and competitive behaviors of market subjects on market transactions in the carbon emission rights market and the energy rights market, consider the characteristics of oligopoly competition structure and the constraints of market transaction prices, establish a carbon emission rights and energy rights market transaction model, consider the production cost and utility function of distributed resource subjects, construct an electricity P2P transaction model, consider the nonlinear problem of the objective function in the electricity P2P transaction model, and linearize it through KKT conditions. Considering the power balance constraints of distributed resource subjects, power purchase and sale constraints, and distributed renewable energy and energy storage device constraints, the distributed resource subject energy sharing optimization is achieved with the lowest total cost of distributed resource subjects as the objective function under the premise of maximizing social welfare.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a distributed resource energy sharing and aggregation optimization method under the coupling of electricity and carbon markets, comprising the following steps:

[0008] Step 1: Establish the electricity P2P market transaction model;

[0009] Step 2: Establish a carbon emission rights market clearing model;

[0010] Step 3: Establish the energy rights market clearing model;

[0011] Step 4: Linearize the electricity P2P market transaction model through KKT conditions and transform it through the Big M method;

[0012] Step 5: Comprehensively consider the electricity purchase cost, P2P transaction cost, energy storage operation cost and the cost / benefit of distributed resource entities participating in the carbon emission rights and energy use rights market, take the lowest cost of distributed resource entities as the objective function, and establish a distributed resource energy sharing and aggregation optimization model for distributed resource entities.

[0013] Preferably, in step 1, the objective function of the power P2P market transaction model is:

[0014]

[0015] In formula (1), a P2P 、b P2P 、c P2Prepresents a non-negative parameter; Represents the total amount of distributed resource subject k participating in power P2P transactions at time t.

[0016] Preferably, the constraints of the objective function of the power P2P market transaction model include:

[0017]

[0018]

[0019] In formula (2) to formula (4), represents the transaction volume of electricity P2P between distributed resource entities k and l at time t; represents the constraint dual variable; They represent the upper and lower limits of electricity P2P transaction volume respectively; Represents the dual variable of the constraint.

[0020] Preferably, in step 2, the carbon emission rights market clearing model includes:

[0021]

[0022] In formula (5) to formula (11), λ C represents the market transaction price of carbon emission rights, α C and β C represents the parameters of the Cournot model, It represents the transaction quota of distributed resource entities in the carbon emission rights market; represents the free initial quota of carbon emission rights, Indicates the carbon emission quota actually used; Φ k represents the carbon quota coefficient of distributed resource subject k, Indicates carbon emission intensity; represents the load of distributed resource subject k at time t; represents the upper limit of carbon emission price; η C The table reflects the proportional coefficient between the unit price of carbon emission quota and the change in demand; ρ C represents the demand ratio, Indicates the maximum total demand of distributed resource entities.

[0023] Preferably, in step 3, the energy rights market clearing model includes:

[0024]

[0025] In formulas (12) to (16), λ ER Represents the market transaction price of energy rights; α ER and β ER represents the parameters of the Cournot model, Represents the transaction quota of distributed resource subject k in the energy rights market; Indicates that energy rights are initially allocated free of charge. represents the energy quota actually used; w represents the conversion relationship between energy quota and carbon emission quota; η ER It represents the proportional coefficient reflecting the change in the unit price of energy quota and demand; ρ ER Indicates the demand ratio.

[0026] Preferably, in step 5, the objective function of the distributed resource energy sharing and aggregation optimization model of the distributed resource subject is:

[0027]

[0028] In formula (24), represents the electricity purchase price of the distributed resource entity, Indicates the amount of electricity purchased by the distributed resource entity; represents the P2P transaction price of electricity between distributed resource entities k and l, It represents the electricity P2P transaction volume between distributed resource entities; Indicates the transmission fee for the unit electricity volume traded between distributed resource entities; c ESS represents the unit operating cost of energy storage, They respectively represent the charging and discharging power of the energy storage device of the distributed resource entity k at time t.

[0029] Preferably, the constraints of the objective function of the distributed resource energy sharing and aggregation optimization model of the distributed resource subject include:

[0030]

[0031] In formula (25) to formula (32), They represent the output of wind power and photovoltaic equipment of the distributed resource entities respectively; It indicates the maximum allowed value of electricity purchased by the distributed resource subject; Respectively represent the maximum predicted output of wind power and photovoltaic equipment; P ch,max , P dis,max Respectively represent the maximum value of the charging and discharging power of the energy storage device; represents the storage capacity of the energy storage device at time t; Indicates the initial storage capacity of the energy storage device, Indicates the storage capacity of the energy storage device at the last moment.

[0032] Preferably, in step 4, when the transformation is performed by the Big M method, the general form of the constraint is:

[0033] 0≤f(x)⊥g(y)≥0 (21)

[0034] In formula (21), f(x) and g(y) are arbitrary functions;

[0035] The relaxation complementarity condition is relaxed to:

[0036] 0≤f(x)≤Mc (22)

[0037] 0≤g(x)≤M(1-c) (23)

[0038] In formula (22) and formula (23), M represents a sufficiently large positive number, and c represents a binary variable.

[0039] The beneficial effects of the present invention are:

[0040] The present invention can fully consider the impact of the carbon emission rights market and the energy use rights market, and consider the carbon quota and energy use rights transactions and constraints of each distributed resource entity, establish an electricity P2P transaction model, a carbon emission and energy use rights market transaction model, and an energy sharing and optimization model that considers the overall benefits of distributed resource entities. It can promote the realization of green and low-carbon goals, improve the efficiency of electricity transactions between distributed resource entities, and solve the problem of unstable decision optimization through a linear processing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a distributed resource energy sharing and aggregation result diagram of the distributed resource energy sharing and aggregation optimization method under the coupling of the electricity-carbon market of the present invention;

[0042] Figure 2 It is a schematic diagram of economic incentives for distributed resource energy sharing transactions of the present invention;

[0043] Figure 3 It is a graph of the trading results of the carbon emission rights and energy use rights quotas of the distributed resource subject of the present invention;

[0044] Figure 4 It is a distributed resource main body cost result diagram of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] refer to Figures 1 to 4 In this embodiment, the distributed resource energy sharing and aggregation optimization method based on the distributed resource subject electricity carbon market coupling specifically includes the following steps:

[0047] Step 1: Establish the electricity P2P market transaction model, including the objective function and constraints:

[0048] Objective function:

[0049]

[0050] In the formula, a P2P , b P2P 、c P2P is a non-negative parameter; is the total amount of distributed resource subject k participating in power P2P transactions at time t.

[0051] Constraints:

[0052]

[0053] In the formula, is the transaction volume of electricity P2P between distributed resource entities k and l at time t; is the constraint dual variable; They are the upper and lower limits of electricity P2P trading volume respectively; is the dual variable of the constraint.

[0054] Step 2: Establishing a carbon emission rights market clearing model includes the following:

[0055]

[0056] In the formula, λ C is the market transaction price of carbon emission rights, α C and β C represents the parameters of the Cournot model, It is the transaction quota for distributed resource entities in the carbon emission rights market; Free initial quota for carbon emission rights, is the carbon emission quota actually used; Φ k is the carbon quota coefficient of distributed resource subject k, is carbon emission intensity; is the load of distributed resource subject k at time t; is the upper limit of carbon emission price; η C The proportional coefficient reflecting the change in unit price of carbon emission quota and demand; ρ C is the demand ratio, It is the maximum total demand of distributed resource entities.

[0057] Step 3: Establish the energy rights market clearing model. It includes the following:

[0058]

[0059]

[0060] In the formula, λ ER is the market transaction price of energy rights; α ER and β ER represents the parameters of the Cournot model, The transaction quota of distributed resource subject k in the energy rights market; Free initial allocation of energy rights. is the energy quota actually used; w is the conversion relationship between energy quota and carbon emission quota, usually 2.54; η ER It is the proportional coefficient reflecting the change in the unit price of energy quota and demand; ER is the demand ratio.

[0061] Step 4: Linearize the power P2P market transaction model through KKT conditions. It includes the following:

[0062]

[0063] For the complementary relaxation constraints of formulas (19)-(20), they can be transformed by the big M method. The general form of the two constraints is:

[0064] 0≤f(x)⊥g(y)≥0 (21)

[0065] Where f(x) and g(y) are arbitrary functions.

[0066] Introducing a sufficiently large positive number M and a binary variable, the above-mentioned relaxed complementarity condition is relaxed to:

[0067] 0≤f(x)≤Mc (22)

[0068] 0≤g(x)≤M(1-c) (23) where M is a sufficiently large positive number and c is a binary variable.

[0069] Step 5: Comprehensively consider the electricity purchase cost, P2P transaction cost, energy storage operation cost and the cost / benefit of distributed resource entities participating in the carbon emission rights and energy use rights market, take the lowest cost of distributed resource entities as the objective function, and establish a distributed resource energy sharing and aggregation optimization model for distributed resource entities, including objective functions and constraints.

[0070] Objective function:

[0071]

[0072] In the formula, The electricity purchase price for distributed resource entities, Purchase electricity for distributed resource entities; is the P2P transaction price of electricity between distributed resource entities k and l, It is the electricity P2P transaction volume between distributed resource entities; It is the transmission fee for the unit electricity volume traded between distributed resource entities; c ESS is the unit operating cost of energy storage, are respectively the charging and discharging power of the energy storage device of the distributed resource entity k at time t.

[0073] Constraints:

[0074]

[0075]

[0076] in, They are the output of wind power and photovoltaic equipment of distributed resource entities respectively; The maximum permissible value of electricity purchased for distributed resource entities; are the maximum predicted outputs of wind power and photovoltaic equipment respectively; P ch ,max , P dis,max are the maximum values ​​of charging and discharging power of the energy storage device respectively; is the storage capacity of the energy storage device at time t; is the initial storage capacity of the energy storage device, It is the storage capacity of the energy storage device at the last moment.

[0077] The following uses 10 distributed resource entities as examples to illustrate the implementation process of this method.

[0078] Carbon emission rights parameter η C , C They are taken as 0.35 and 0.3 respectively; the initial capacity of energy storage is its minimum value; the energy weight parameter η ER , ER Take 0.3 and 0.324 respectively.

[0079] Figure 1 The daily P2P electricity transaction volume between 10 distributed resource entities is given. When the distributed resource entity assumes the role of buyer in the transaction, the transaction volume is positive. It can be seen that the total daily P2P electricity transaction volume of the 10 distributed resource entities is 1844.27 kWh, and the maximum transaction volume occurs between distributed resource entities 5 and 6. The difference between their own load demand and DERs output is large, and they have a larger P2P electricity transaction space.

[0080] Figure 2The P2P transaction prices of electricity between distributed resource entities are given. The 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 transaction prices of electricity between distributed resource entities are consistent, indicating that the market equilibrium has been reached. It can be seen that the P2P transaction price is higher than the electricity selling price of distributed resource entities and lower than the electricity purchase price. The price difference between the purchase and sale prices of distributed resource entities and DSO transactions is the main economic incentive for conducting P2P electricity transactions.

[0081] Figure 3 The figure is a schematic diagram of the free initial quota and actual usage of carbon emission rights and energy use rights of each distributed resource subject in the carbon emission rights market and energy use rights market. Taking carbon emission rights as an example, the free initial quota is set to 1.5t. The carbon emission rights quota actually used by the distributed resource subject is lower than the free initial quota it owns, and it needs to be purchased from the market. The shaded area on the right side of the figure represents the purchase amount of carbon emission rights quotas of distributed resource subjects 1 to 5, and the shaded area on the left side represents the purchase amount of quotas of distributed resource subjects 6 to 10. Since the energy consumption level of distributed resource subjects 6 to 10 is higher than that of 1 to 5, their purchase amount of carbon emission rights quotas is also correspondingly higher.

[0082] Figure 4 The energy cost results of 10 distributed resource entities when considering and not considering P2P transactions. It can be seen that when not considering P2P transactions, the total cost of 10 distributed resource entities is 5962 yuan, and after considering P2P transactions, the total cost is 5078 yuan, which is reduced by 14.83%. Therefore, it can be seen that the method proposed in the present invention can promote the sharing of distributed resources, improve energy transaction efficiency, reduce energy costs, and achieve the optimized operation of the aggregate.

[0083] In summary, the present invention can fully consider the impact of the carbon emission rights market and the energy use rights market, and consider the carbon quota and energy use rights transactions and constraints of each distributed resource subject, establish an electricity P2P transaction model, a carbon emission and energy use rights market transaction model, and an energy sharing and optimization model that considers the overall benefits of distributed resource subjects, which can promote the realization of green and low-carbon goals, improve the efficiency of power transactions between distributed resource subjects, and solve the problem of unstable decision optimization through a linear processing method. Therefore, the present invention has broad application prospects in the field of power transaction market mechanisms.

[0084] It should be emphasized that the above are only preferred embodiments of the present invention and do not limit the present invention in any form. 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 distributed resource energy sharing and aggregation optimization method under the coupling of electricity and carbon markets, characterized in that: The following steps are involved: Step 1: Establish the electricity P2P market transaction model; Step 2: Establish a carbon emission rights market clearing model; Step 3: Establish the energy rights market clearing model; Step 4: Linearize the electricity P2P market transaction model through KKT conditions and transform it through the Big M method; Step 5: Comprehensively consider the electricity purchase cost, P2P transaction cost, energy storage operation cost and the cost / benefit of distributed resource entities participating in the carbon emission rights and energy use rights market, take the lowest cost of distributed resource entities as the objective function, and establish a distributed resource energy sharing and aggregation optimization model for distributed resource entities.

2. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 1 is characterized in that: In step 1, the objective function of the power P2P market transaction model is: In formula (1), a P2P , b P2P 、c P2P represents a non-negative parameter; Represents the total amount of distributed resource subject k participating in power P2P transactions at time t.

3. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 2 is characterized in that: The constraints of the objective function of the power P2P market transaction model include: In formula (2) to formula (4), represents the transaction volume of electricity P2P between distributed resource entities k and l at time t; represents the constraint dual variable; They represent the upper and lower limits of electricity P2P transaction volume respectively; Represents the dual variable of the constraint.

4. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 1 is characterized in that: In step 2, the carbon emission rights market clearing model includes: In formula (5) to formula (11), λ C represents the market transaction price of carbon emission rights, α C and β C represents the parameters of the Cournot model, It represents the transaction quota of distributed resource entities in the carbon emission rights market; represents the free initial quota of carbon emission rights, Indicates the carbon emission quota actually used; Φ k represents the carbon quota coefficient of distributed resource subject k, Indicates carbon emission intensity; represents the load of distributed resource subject k at time t; represents the upper limit of carbon emission price; η C The table reflects the proportional coefficient between the unit price of carbon emission quota and the change in demand; ρ C represents the demand ratio, Indicates the maximum total demand of distributed resource entities.

5. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 1 is characterized in that: In step 3, the energy usage rights market clearing model includes: In formulas (12) to (16), λ ER Represents the market transaction price of energy rights; α ER and β ER represents the parameters of the Cournot model, Represents the transaction quota of distributed resource subject k in the energy rights market; Indicates that energy rights are initially allocated free of charge. represents the energy quota actually used; w represents the conversion relationship between energy quota and carbon emission quota; η ER It represents the proportional coefficient reflecting the change in the unit price of energy quota and demand; ρ ER Indicates the demand ratio.

6. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 1 is characterized in that: In step 5, the objective function of the distributed resource energy sharing and aggregation optimization model of the distributed resource subject is: In formula (24), represents the electricity purchase price of the distributed resource entity, Indicates the amount of electricity purchased by the distributed resource entity; represents the P2P transaction price of electricity between distributed resource entities k and l, It represents the electricity P2P transaction volume between distributed resource entities; Indicates the transmission fee for the unit electricity volume traded between distributed resource entities; c ESS represents the unit operating cost of energy storage, They respectively represent the charging and discharging power of the energy storage device of the distributed resource entity k at time t.

7. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 6 is characterized in that: The constraints of the objective function of the distributed resource energy sharing and aggregation optimization model of the distributed resource subject include: In formula (25) to formula (32), They represent the output of wind power and photovoltaic equipment of the distributed resource entities respectively; It indicates the maximum allowed value of electricity purchased by the distributed resource subject; Respectively represent the maximum predicted output of wind power and photovoltaic equipment; P ch，max , P dis，max Respectively represent the maximum value of the charging and discharging power of the energy storage device; Represents the storage capacity of the energy storage device at time t; Indicates the initial storage capacity of the energy storage device, Indicates the storage capacity of the energy storage device at the last moment.

8. The distributed resource energy sharing and aggregation optimization method under the electricity-carbon market coupling according to claim 1 is characterized in that: In step 4, when the transformation is performed by the Big M method, the general form of the constraint is: 0≤f(x)⊥g())≥0(21) In formula (21), f(x) and g(y) are arbitrary functions; The relaxation complementarity condition is relaxed to: 0≤f(x)≤Mc (22) 0≤g(x)≤M(1-c) (23) In formula (22) and formula (23), M represents a sufficiently large positive number, and c represents a binary variable.

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