Power market low-carbon strategy optimization method and device under carbon market background and medium

Abstract

The invention discloses an electricity market low-carbon strategy optimization method and device under a carbon market background and a medium, and relates to the field of low-carbon strategy optimization, and the method comprises the steps: constructing an upper-layer power generation enterprise decision model; the upper-layer power generation enterprise decision model comprises an upper-layer objective function and an upper-layer constraint condition; constructing a lower-layer market clearing model; the lower-layer market clearing model comprises an electricity market clearing model, a carbon market clearing model and a low-carbon optimization module model; determining the upper-layer power generation enterprise decision model and the lower-layer market clearing model as a power-carbon multi-market double-layer decision model, and converting the double-layer optimization model into a single-layer equilibrium constraint planning model; and solving the single-layer equilibrium constraint programming model by using an iterative optimal algorithm based on a Markov chain to obtain an optimal power market low-carbon strategy. The total carbon emission amount of the system is reduced.

Description

Technical Field

[0001] The present application relates to the field of low-carbon strategy optimization, and in particular to a method, device and medium for optimizing low-carbon strategy in the electricity market under the background of a carbon market. Background Art

[0002] In order to achieve the "dual carbon" goal and build a new power system with new energy as the main body, it is necessary to coordinate the coordinated development of multiple types of energy and support it with a flexible power market mechanism. In the process of power system transformation, thermal power units, as market players in the power market and carbon power market, play a key role in emission reduction. However, there is currently a lack of market mechanisms for the characteristics of carbon emissions from thermal power units that change with load, and the emission reduction potential of thermal power units participating in the power market has not yet been explored. Therefore, in order to give full play to the synergistic role of thermal power units, it is necessary to consider the carbon emission characteristics of thermal power units and establish an optimization mechanism for power market transactions under the new power system.

[0003] At present, existing research on the coupling mechanism between electricity market and carbon market is mainly based on the assumption of static carbon emission intensity, which deviates from the actual emission characteristics of thermal power units. There are currently few studies based on dynamic carbon emission characteristics, which mainly focus on studying the trading strategies of individual power generation companies and rarely consider the game between multiple strategic power generation companies, resulting in a relatively high total carbon emissions. Summary of the invention

[0004] The purpose of this application is to provide a method, device and medium for optimizing low-carbon strategies in the electricity market under the background of a carbon market, thereby reducing the total carbon emissions of the system.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In the first aspect, the present application provides a method for optimizing low-carbon strategies in the electricity market under the background of a carbon market, including:

[0007] Constructing an upper-level power generation enterprise decision model; the upper-level power generation enterprise decision model includes: an upper-level objective function and an upper-level constraint condition, wherein the upper-level objective function aims to maximize the power generation scale of the power generation enterprise after the power generation enterprise makes decisions in the power market and the carbon market; the power generation enterprise is a thermal power enterprise;

[0008] Construct a lower-level market clearing model; the lower-level market clearing model includes: an electricity market clearing model, a carbon market clearing model and a low-carbon optimization module model; the electricity market clearing model includes: an electricity market objective function and an electricity market constraint condition, the electricity market objective function aims to maximize the grid support capacity under the premise of ensuring the safety of grid operation; the carbon market clearing model aims to maximize the low-carbon transformation capacity of the generator set; the low-carbon optimization module model includes: a low-carbon optimization objective function and a low-carbon optimization constraint condition, the low-carbon optimization objective function aims to minimize the carbon emissions in the secondary market;

[0009] Determine the upper-level power generation enterprise decision-making model and the lower-level market clearing model as a two-level decision-making model for the electricity-carbon multi-market, and transform the two-level optimization model into a single-level equilibrium constraint programming model;

[0010] The single-layer equilibrium constraint programming model is solved by using an iterative optimal algorithm based on Markov chains to obtain the optimal low-carbon strategy for the electricity market.

[0011] Optionally, the upper layer objective function includes:

[0012]

[0013]

[0014] Among them, f i G is the power generation scale of thermal power company i in the power market and carbon market; T is the number of time periods; is the potential power generation scale of thermal power company i in the power market during period t; is the potential power generation scale of thermal power company i in period t in the carbon market; is the total amount of fuel consumed by the generating unit of thermal power company i during period t; is the power generation of thermal power company i in the power market during period t; is the electricity price in the electricity market during period t; is the carbon emission intensity standard of thermal power company i; DI(·) is the carbon emission intensity function; is the carbon price in the carbon market during period t; a i , b i and c i are the unit fuel coefficients of thermal power company i; ω i and i is the carbon emission coefficient of the units of thermal power company i.

[0015] Optionally, the upper-layer constraint conditions include: expected constraint and power constraint;

[0016] The expected constraints include:

[0017]

[0018] Among them, B E,min It is the expected lower limit of thermal power companies in the electricity market; B is the expected variable of the vth period of thermal power company i in period t; E,max The expected upper limit for thermal power companies in the electricity market; is the expected variable of the v-1th period of thermal power company i in period t;

[0019] The power constraint includes:

[0020]

[0021] Among them, P i g,min is the lower limit of unit output of thermal power company i; P is the power consumption of thermal power company i in the vth period of time t; i g,max is the upper limit of unit output of thermal power company i.

[0022] Optionally, the electricity market objective function includes:

[0023]

[0024] Among them, F E is the value of the electricity market objective function; N is the number of thermal power companies; V is the number of sections; is the power generation of thermal power company i in the vth period of time t.

[0025] Optionally, the power market constraints include: power market unit maximum and minimum output constraints, unit power constraints, power market unit ramp constraints, power market node power balance constraints, power market transmission line capacity constraints and power market node phase angle constraints;

[0026] The maximum and minimum output constraints of the power market units include:

[0027]

[0028] in, and All are Lagrange multipliers;

[0029] The unit power constraints include:

[0030]

[0031] in, and All are Lagrange multipliers;

[0032] The power market unit ramping constraints include:

[0033]

[0034] in, is the power generation of thermal power company i in the vth period of time t+1; is the upper limit of the power increase of the units of thermal power company i; Lower the upper limit of power for the units of thermal power company i; and All are Lagrange multipliers;

[0035] The power market node power balance constraint includes:

[0036]

[0037] Among them, n It is the set of nodes where thermal power enterprises are located; is the lth load in period t; b nm is the line admittance between node n and node m; θ n,t is the phase angle of node n in period t; θ m,t is the phase angle of node m in period t; is the market clearing electricity price at node n;

[0038] The power market transmission line capacity constraints include:

[0039]

[0040] in, is the upper limit of the transmission line capacity between node n and node m during period t; and All are Lagrange multipliers;

[0041] The power market node phase angle constraints include:

[0042]

[0043] in, and are all Lagrange multipliers.

[0044] Optionally, the carbon market clearing model includes: a primary carbon quota allocation mechanism and a secondary carbon quota circulation mechanism;

[0045] The primary carbon quota allocation mechanism includes:

[0046]

[0047] in, Free carbon quotas obtained for power generation company j; is the power generation of power generation enterprise j in the power market during period t;

[0048] The secondary carbon quota circulation mechanism includes:

[0049]

[0050] in, is the potential power generation scale of power generation enterprise j in period t in the carbon market; is the variable carbon quota of power generation company j in period t; is the carbon emission intensity standard of power generation company j.

[0051] Optionally, the low-carbon optimization objective function includes:

[0052]

[0053] Among them, F GR Ψ is the objective function value for low carbon optimization; Ne It is a collection of power generation enterprises, which includes thermal power enterprises and non-thermal power enterprises.

[0054] Optionally, the low-carbon optimization constraint conditions include: low-carbon optimization unit maximum and minimum output constraints, low-carbon optimization unit ramp constraints, power generation scale constraints, low-carbon optimization power balance constraints and power grid security constraints;

[0055] The maximum and minimum output constraints of the low-carbon optimization units include:

[0056]

[0057] in, is the lower limit of the unit output of power generation enterprise j; is the upper limit of the unit output of power generation enterprise j;

[0058] The low-carbon optimization unit ramp constraints include:

[0059]

[0060] in, is the power generation of power generation enterprise j in the vth period of time t+1; is the upper limit of the power increase of the units of power generation enterprise j; Lower the upper limit of power for the units of power generation enterprise j;

[0061] The power generation scale constraints include:

[0062]

[0063] in, The potential power generation scale of power generation enterprise j in the power market after low-carbon optimization; The potential power generation scale of power generation enterprise j in the carbon market after low-carbon optimization; is the total fuel consumption of power generation units of power generation enterprise j after low-carbon optimization;

[0064] The low-carbon optimized power balance constraint includes:

[0065]

[0066] The grid security constraints include:

[0067]

[0068] in, Optimize the power generation of power generation enterprises within the scope of low carbon; The electricity generated by power generators outside the scope of low-carbon optimization; is the lth load in period t participating in low-carbon optimization; θ′ n,t is the phase angle of node n in period t after low carbon optimization; θ' m,t is the phase angle of node m in period t after low carbon optimization; θ′ 1,t is the phase angle of node 1 in period t after low carbon optimization.

[0069] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for optimizing low-carbon strategies in the electricity market under the carbon market background.

[0070] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for optimizing low-carbon strategies in the electricity market under the carbon market background.

[0071] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0072] The present application discloses a method, device and medium for optimizing the low-carbon strategy of the electricity market under the background of the carbon market. First, an upper-level power generation enterprise decision model is constructed; the upper-level power generation enterprise decision model includes: an upper-level objective function and an upper-level constraint condition; secondly, a lower-level market clearing model is constructed; the lower-level market clearing model includes: an electricity market clearing model, a carbon market clearing model and a low-carbon optimization module model; then, the upper-level power generation enterprise decision model and the lower-level market clearing model are determined as a two-level decision model of the electricity-carbon multi-market, and the two-level optimization model is converted into a single-level equilibrium constraint programming model; finally, the single-level equilibrium constraint programming model is solved using an iterative optimal algorithm based on a Markov chain to obtain the optimal low-carbon strategy for the electricity market. The upper-level power generation enterprise decision model in the present application takes into account the strategic decision of the power generation enterprise, and the lower-level market clearing model takes into account the electricity market and the carbon market, and is solved using an iterative optimal algorithm based on a Markov chain. It can effectively reduce the total carbon emissions of the system by optimizing the clearing results without changing the market settlement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0074] Figure 1 A schematic flow chart of a method for optimizing a low-carbon strategy in a power market under a carbon market background provided in an embodiment of the present application;

[0075] Figure 2 This is a schematic diagram of the optimization principle of low-carbon strategies in the electricity market under the background of carbon market;

[0076] Figure 3 It is a flow chart of iterative optimal algorithm based on Markov chain;

[0077] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0079] The purpose of this application is to provide a method, device and medium for optimizing low-carbon strategies in the electricity market under the background of a carbon market, aiming to reduce the total carbon emissions of the system.

[0080] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0081] In an exemplary embodiment, Figure 1 and Figure 2 As shown, the low-carbon strategy optimization method for the electricity market under the carbon market background in this embodiment includes:

[0082] Step 1: Construct a decision-making model for upper-level power generation enterprises.

[0083] Among them, the upper-level power generation enterprise decision-making model includes: upper-level objective function and upper-level constraints. The upper-level objective function aims to maximize the power generation scale of the power generation enterprise after the power generation enterprise makes decisions in the electricity market and carbon market; the power generation enterprise is a thermal power enterprise.

[0084] As an optional implementation method, the units of thermal power companies under the power generation enterprises participate in the electricity market and carbon market as decision-making entities, with the maximization of power generation scale as the decision-making goal. The upper objective function includes:

[0085]

[0086] Among them, f i G is the power generation scale of thermal power company i in the power market and carbon market; T is the number of time periods; is the potential power generation scale of thermal power company i in the power market during period t; is the potential power generation scale of thermal power company i in period t in the carbon market; is the total amount of fuel consumed by the generating unit of thermal power company i during period t; is the power generation of thermal power company i in the power market during period t; is the electricity price in the electricity market during period t; is the carbon emission intensity standard of thermal power company i; DI(·) is the carbon emission intensity function; is the carbon price in the carbon market during period t; a i , b i and c i are the unit fuel coefficients of thermal power company i; ω i and i is the carbon emission coefficient of the units of thermal power company i.

[0087] As an optional implementation, the upper-layer constraint conditions include: expectation constraint and power constraint.

[0088] Expected constraints, including:

[0089]

[0090] Among them, B E,min It is the expected lower limit of thermal power companies in the electricity market; B is the expected variable of the vth period of thermal power company i in period t; E,max The expected upper limit for thermal power companies in the electricity market; is the expected variable of the v-1th period of thermal power company i in period t.

[0091] Power constraints, including:

[0092]

[0093] Among them, P i g,min is the lower limit of unit output of thermal power company i; P is the power consumption of thermal power company i in the vth period of time t; i g,max is the upper limit of unit output of thermal power company i.

[0094] Step 2: Construct the lower-level market clearing model; the lower-level market clearing model includes: electricity market clearing model, carbon market clearing model and low-carbon optimization module model.

[0095] Among them, the electricity market clearing model includes: electricity market objective function and electricity market constraints. The electricity market objective function aims to maximize the grid support capacity under the premise of ensuring the safety of grid operation; the carbon market clearing model aims to maximize the low-carbon transformation capacity of power generation units; the low-carbon optimization module model includes: low-carbon optimization objective function and low-carbon optimization constraints. The low-carbon optimization objective function aims to minimize carbon emissions in the secondary market.

[0096] As an optional implementation method, in the power market clearing model, the power market operator integrates the unit information of all market players, and dispatches with the objective function of maximizing the support capacity of the power grid under the premise of ensuring the safety of system operation. The power market objective function includes:

[0097]

[0098] Among them, F E is the value of the electricity market objective function; N is the number of thermal power companies; V is the number of sections; is the power generation of thermal power company i in the vth period of time t.

[0099] As an optional implementation method, the power market constraints include: power market unit maximum and minimum output constraints, unit power constraints, power market unit ramp constraints, power market node power balance constraints, power market transmission line capacity constraints and power market node phase constraints.

[0100] The maximum and minimum output constraints of power generation units in the electricity market include:

[0101]

[0102] in, and are all Lagrange multipliers.

[0103] Unit power constraints, including:

[0104]

[0105] in, and are all Lagrange multipliers.

[0106] The ramping constraints of power market units include:

[0107]

[0108] in, is the power generation of thermal power company i in the vth period of time t+1; is the upper limit of the power increase of the units of thermal power company i; Lower the upper limit of power for the units of thermal power company i; and are all Lagrange multipliers.

[0109] Power market node power balance constraints, including:

[0110]

[0111] Among them, n It is the set of nodes where thermal power enterprises are located; is the lth load in period t; b nm is the line admittance between node n and node m; θ n,t is the phase angle of node n in period t; θ m,t is the phase angle of node m in period t; is the market clearing electricity price at node n.

[0112] Transmission line capacity constraints in the electricity market include:

[0113]

[0114] in, is the upper limit of the transmission line capacity between node n and node m during period t; and are all Lagrange multipliers.

[0115] Phase angle constraints at power market nodes include:

[0116]

[0117] in, and are all Lagrange multipliers.

[0118] As an optional implementation method, the carbon market is cleared with the goal of maximizing the capacity of low-carbon transformation. Considering the large scale of the carbon market, it is difficult for several power generation companies to influence the clearing price of the carbon market. This application assumes that power generation companies are price acceptors in the carbon market. Power generation companies adjust the output power according to the real-time price of the carbon market to adjust the number of carbon quotas, thereby participating in carbon market decision-making. The carbon market clearing model includes: a primary carbon quota allocation mechanism and a secondary carbon quota circulation mechanism.

[0119] The primary carbon quota allocation mechanism includes:

[0120]

[0121] in, Free carbon quotas obtained for power generation company j; is the power generation of power generation enterprise j in period t in the power market.

[0122] The secondary carbon quota circulation mechanism includes:

[0123]

[0124] in, is the potential power generation scale of power generation enterprise j in period t in the carbon market; is the variable carbon quota of power generation company j in period t; is the carbon emission intensity standard of power generation company j.

[0125] As an optional implementation method, the low-carbon optimization module reallocates the power of the units without changing the basic dispatch results of the power spot market. The low-carbon optimization objective function includes:

[0126]

[0127] Among them, F GR Ψ is the objective function value for low carbon optimization; Ne It is a collection of power generation enterprises, which includes thermal power enterprises and non-thermal power enterprises.

[0128] As an optional implementation method, the low-carbon optimization constraints include: maximum and minimum output constraints of low-carbon optimization units, ramp constraints of low-carbon optimization units, power generation scale constraints, low-carbon optimization power balance constraints and grid security constraints.

[0129] The maximum and minimum output constraints of low-carbon optimization units include:

[0130]

[0131] in, is the lower limit of the unit output of power generation enterprise j; is the upper limit of the unit output of power generation enterprise j.

[0132] Low-carbon optimized unit ramping constraints, including:

[0133]

[0134] in, is the power generation of power generation enterprise j in the vth period of time t+1; is the upper limit of the power increase of the units of power generation enterprise j; Reduce the upper limit of power for the units of power generation enterprise j.

[0135] Power generation scale constraints, including:

[0136]

[0137] in, The potential power generation scale of power generation enterprise j in the power market after low-carbon optimization; The potential power generation scale of power generation enterprise j in the carbon market after low-carbon optimization; is the total fuel consumption of power generation units of power generation enterprise j after low-carbon optimization.

[0138] Low carbon optimized power balance constraints, including:

[0139]

[0140] Grid security constraints include: low-carbon optimization node power balance constraints, low-carbon optimization transmission line capacity constraints, and low-carbon optimization node phase angle constraints. Specifically:

[0141]

[0142] in, Optimize the power generation of power generation enterprises within the scope of low carbon; The electricity generated by power generators outside the scope of low-carbon optimization; is the lth load in period t participating in low-carbon optimization; θ′ n,tis the phase angle of node n in period t after low carbon optimization; θ' m,t is the phase angle of node m in period t after low carbon optimization; θ′ 1,t is the phase angle of node 1 in period t after low carbon optimization.

[0143] Step 3: Determine the upper-level power generation enterprise decision-making model and the lower-level market clearing model as a two-level decision-making model for the electricity and carbon multi-market, and transform the two-level optimization model into a single-level equilibrium constraint programming model.

[0144] Specifically, the lower market clearing model in the two-layer optimization model is transformed into the corresponding optimality condition through KKT conditions, strong duality theorem and binary decomposition method, and the two-layer optimization model is transformed into a layer equilibrium constraint programming model (Mathematical Problem with Equilibrium Constraints, MPEC).

[0145] The two-level optimization model is a nonlinear model. The decision information of the power generation enterprise in the upper power generation enterprise decision model is transmitted to the lower market clearing model for market clearing. The clearing result in turn affects the optimal decision of the upper power generation enterprise. The upper power generation enterprise decision model and the lower market clearing model are coupled with each other. First, the Karush-Kuhn-Tucker (KKT) theorem is used to transform the lower market clearing model into the optimality condition of the upper power generation enterprise decision model, and the two-level model describing the decision-making behavior of a single subject is transformed into a single-level model. Then, due to the existence of nonlinear complementary relaxation constraints, the big M method is used to linearize the model. Since multiple subjects participate in market decision-making, it is necessary to solve the equilibrium solution between multiple two-level models. It is an equilibrium problem with equilibrium constraints (Equilibrium Program with Equilibrium Constraints, EPEC). The iterative optimal algorithm based on Markov chain is used to solve the market equilibrium, combined with the CPLEX commercial solver for solution.

[0146] The lower-level electricity market clearing is reconstructed and the Lagrangian function is established, which is:

[0147]

[0148] Among them, L is the Lagrangian function constructed based on the underlying model.

[0149] Find the Lagrangian function with respect to The partial derivative of , we can get the gradient condition as:

[0150]

[0151] in, and are all Lagrange multipliers.

[0152] Find the Lagrangian function with respect to θ n,,t The partial derivative of , we can get the gradient condition as:

[0153]

[0154] in, is the market clearing electricity price at node m.

[0155] The complementary slack constraints are:

[0156]

[0157] Among them, ⊥ represents the complementarity between variables, that is, one of the variables at both ends of ⊥ must be 0 and the other must be non-zero; It is the transmission capacity of the transmission line from node n to node m in the power grid at time t.

[0158] The dual feasibility condition is:

[0159]

[0160] The Big M method is used to convert complementary slack constraints into linear constraints. Taking one of them as an example, the converted linear constraints are as follows:

[0161]

[0162] in, is a 0-1 variable, and M is a sufficiently large constant. The conversion of other complementary slack constraints into linear constraints is similar, and will not be repeated here.

[0163] Step 4: Use the iterative optimal algorithm based on Markov chain to solve the single-layer equilibrium constraint programming model and obtain the optimal low-carbon strategy for the electricity market.

[0164] Specifically, the optimal low-carbon strategy for the electricity market includes: the power generation, start and shutdown instructions, and carbon emissions of each generator set in the power grid.

[0165] The MPEC model can be solved by using MATLAB software in conjunction with CPLEX. There may be no purely strategic equilibrium solution when multiple market players make decisions. Therefore, the optimal decision-making algorithm based on Markov chain is used to solve the equilibrium solutions of multiple MPEC models, that is, to solve EPEC and then obtain the market equilibrium solution while taking into account the maximum support capacity of the power grid and the maximum low-carbon transformation capacity of the generator set. Based on the low-carbon optimization module, the total carbon emissions of the system can be reduced by optimizing the final market results without changing the basic market instructions.

[0166] Step 4 specifically includes:

[0167] When multiple power generation companies participate in the market, each entity competes to maximize its own power generation scale, forming the EPEC model. It is difficult to solve the model directly using optimization methods. The iterative optimal algorithm solves the optimal strategy of a power generation company by fixing the strategies of other power generation companies, and then gradually solves the optimal strategy of each company when the strategies of other companies are fixed. It gradually iterates until the fluctuations of the expected power generation scale of all power generation companies are within the error range. It is considered that each power generation company has made the optimal strategy, and any other strategy will reduce the expected power generation scale, that is, reaching the Nash equilibrium.

[0168] In the iterative optimal algorithm, each round only determines the strategy based on the expected power generation scale, which can easily lead to local optimality and fail to reach the global optimality. Therefore, this application considers the Markov decision process based on the iterative optimal algorithm. In each round of iteration, the power generation company not only updates the strategy based on the performance of the new round of power generation scale, but also searches for strategies as alternatives from the solution space, thereby improving the global search capability. The Markov chain is a mathematical model that describes the state transition of a system. The next state depends only on the current state and has nothing to do with the previous state. In such a case, Figure 3 In the iterative optimal algorithm based on Markov chain shown in FIG, the possible strategy combinations of all power generation enterprises constitute the state space. The strategy set of all power generation enterprises in each round is a state in the iterative process. The probability of moving from one state to another new state is the transition probability, which is usually described by the transition state equation.

[0169] The transfer state equation of the power generation enterprise strategy is:

[0170]

[0171] Among them, T B is the transfer state function; f i G' is the power generation scale of the next round of alternative state; f i G is the power generation scale of the alternative state of the current round; γ is the equilibrium factor. When γ is small, the model converges faster, and when γ is large, it is easier to accept the alternative state.

[0172] The variable ξ randomly selected from uniform distribution is:

[0173] ξ~U(0,1).

[0174] Wherein, U(·) is a mathematical symbol representing uniform distribution.

[0175] When the transfer state function value T BWhen is greater than the random variable ξ, the power generation enterprise accepts the new strategy as the state; otherwise, the current strategy is retained. When the states of all power generation enterprises no longer transfer, or the power generation scale fluctuation of the power generation enterprise is less than the set threshold, the model converges to the optimal solution.

[0176] This application considers the coupling mechanism of the electricity-carbon market, adopts a two-layer optimization model to describe the decision-making process and market equilibrium results of power generation enterprises participating in the electricity-carbon market, proposes a low-carbon optimization module, reduces the total carbon emissions of the power system without changing the basic scheduling arrangement, and solves the market equilibrium through an iterative optimal algorithm based on Markov chain. The simulation results show that the advantages of this application include:

[0177] (1) The low-carbon optimization module proposed in this application can effectively reduce the carbon emissions of the system without reducing the scale of power generation enterprises by organizing power generation units to redistribute electricity.

[0178] (2) The dynamic carbon emission characteristics of thermal power units are manifested in that the carbon emission intensity decreases as the load of the random units increases. The competent authorities should consider the dynamic carbon emission characteristics when formulating market rules and mechanisms, which will help further reduce the carbon emissions of the system.

[0179] (3) The coupling mechanism between the electricity market and the carbon market and the interaction mechanism between the electricity and carbon markets are taken into account, so that the model can better reflect the actual market operation rules. In addition, the iterative optimal algorithm based on Markov chain improves the efficiency of model solution compared with the traditional analytical method.

[0180] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing a low-carbon strategy for an electricity market in the context of a carbon market.

[0181] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for optimizing a low-carbon strategy for an electricity market under a carbon market background is implemented.

[0182] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements a method for optimizing a low-carbon strategy for an electricity market in a carbon market context.

[0183] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing a low-carbon strategy in the electricity market under the background of a carbon market is implemented.

[0184] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0185] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0186] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0187] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0188] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0189] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing low-carbon strategies in the electricity market under the background of carbon market, characterized in that: The method for optimizing the low-carbon strategy of the electricity market under the carbon market background includes: Constructing an upper-level power generation enterprise decision model; the upper-level power generation enterprise decision model includes: an upper-level objective function and an upper-level constraint condition, wherein the upper-level objective function aims to maximize the power generation scale of the power generation enterprise after the power generation enterprise makes decisions in the power market and the carbon market; the power generation enterprise is a thermal power enterprise; Construct a lower-level market clearing model; the lower-level market clearing model includes: an electricity market clearing model, a carbon market clearing model and a low-carbon optimization module model; the electricity market clearing model includes: an electricity market objective function and an electricity market constraint condition, the electricity market objective function aims to maximize the grid support capacity under the premise of ensuring the safety of grid operation; the carbon market clearing model aims to maximize the low-carbon transformation capacity of the generator set; the low-carbon optimization module model includes: a low-carbon optimization objective function and a low-carbon optimization constraint condition, the low-carbon optimization objective function aims to minimize the carbon emissions in the secondary market; Determine the upper-level power generation enterprise decision-making model and the lower-level market clearing model as a two-level decision-making model for the electricity-carbon multi-market, and transform the two-level optimization model into a single-level equilibrium constraint programming model; The single-layer equilibrium constraint programming model is solved by using an iterative optimal algorithm based on Markov chains to obtain the optimal low-carbon strategy for the electricity market.

2. The method for optimizing low-carbon strategies in the electricity market under the carbon market background according to claim 1 is characterized in that: The upper layer objective function includes: in, is the power generation scale of thermal power company i in the power market and carbon market; T is the number of time periods; is the potential power generation scale of thermal power company i in the power market during period t; is the potential power generation scale of thermal power company i in period t in the carbon market; is the total amount of fuel consumed by the generating unit of thermal power company i during period t; is the power generation of thermal power company i in the power market during period t; is the electricity price in the electricity market during period t; is the carbon emission intensity standard of thermal power company i; DI(·) is the carbon emission intensity function; is the carbon price in the carbon market during period t; a i , b i and c i are the unit fuel coefficients of thermal power company i; ω i and i is the carbon emission coefficient of the unit of thermal power company i.

3. The method for optimizing low-carbon strategy of power market under the carbon market background according to claim 2 is characterized in that: The upper-level constraints include: expected constraints and power constraints; The expected constraints include: Among them, B E,min It is the expected lower limit of thermal power companies in the electricity market; B is the expected variable of the vth period of thermal power company i in period t; E,max It is the expected upper limit of thermal power companies in the electricity market; is the expected variable of the v-1th period of thermal power company i in period t; The power constraint includes: in, is the lower limit of unit output of thermal power company i; is the vth period electricity of thermal power company i in period t; is the upper limit of unit output of thermal power company i.

4. The method for optimizing low-carbon strategies in the electricity market under the carbon market background according to claim 3 is characterized in that: The electricity market objective function includes: Among them, F E is the value of the electricity market objective function; N is the number of thermal power companies; V is the number of sections; is the power generation of thermal power company i in the vth period of time t.

5. The method for optimizing low-carbon strategy of power market under the carbon market background according to claim 4 is characterized in that: The power market constraints include: power market unit maximum and minimum output constraints, unit power constraints, power market unit ramp constraints, power market node power balance constraints, power market transmission line capacity constraints and power market node phase angle constraints; The maximum and minimum output constraints of the power market units include: in, and All are Lagrange multipliers; The unit power constraints include: in, and All are Lagrange multipliers; The power market unit ramping constraints include: in, is the power generation of thermal power company i in the vth period of time t+1; is the upper limit of the power increase of the units of thermal power company i; Lower the upper limit of power for the units of thermal power company i; and All are Lagrange multipliers; The power market node power balance constraint includes: Among them, n It is the set of nodes where thermal power enterprises are located; is the lth load in period t; b nm is the line admittance between node n and node m; θ n,t is the phase angle of node n in period t; θ m,t is the phase angle of node m in period t; is the market clearing electricity price at node n; The power market transmission line capacity constraints include: in, is the upper limit of the transmission line capacity between node n and node m during period t; and All are Lagrange multipliers; The power market node phase angle constraint includes: in, and are all Lagrange multipliers.

6. The method for optimizing low-carbon strategy of power market under the carbon market background according to claim 5 is characterized in that: The carbon market clearing model includes: a primary carbon quota allocation mechanism and a secondary carbon quota circulation mechanism; The primary carbon quota allocation mechanism includes: in, Free carbon quotas obtained for power generation company j; is the power generation of power generation enterprise j in the power market during period t; The secondary carbon quota circulation mechanism includes: in, is the potential power generation scale of power generation enterprise j in period t in the carbon market; is the variable carbon quota of power generation company j in period t; is the carbon emission intensity standard of power generation company j.

7. The method for optimizing low-carbon strategy of power market under the carbon market background according to claim 6 is characterized in that: The low-carbon optimization objective function includes: Among them, F GR Ψ is the objective function value for low carbon optimization; Ne It is a collection of power generation enterprises, which includes thermal power enterprises and non-thermal power enterprises.

8. The method for optimizing low-carbon strategy of power market under the carbon market background according to claim 7 is characterized in that: The low-carbon optimization constraints include: low-carbon optimization unit maximum and minimum output constraints, low-carbon optimization unit ramp constraints, power generation scale constraints, low-carbon optimization power balance constraints and power grid security constraints; The maximum and minimum output constraints of the low-carbon optimization units include: in, is the lower limit of the unit output of power generation enterprise j; is the upper limit of the unit output of power generation enterprise j; The low-carbon optimization unit ramp constraints include: in, is the power generation of power generation enterprise j in the vth period of time t+1; is the upper limit of the power increase of the units of power generation enterprise j; Lower the upper limit of power for the units of power generation enterprise j; The power generation scale constraints include: in, The potential power generation scale of power generation enterprise j in the power market after low-carbon optimization; The potential power generation scale of power generation enterprise j in the carbon market after low-carbon optimization; is the total fuel consumption of power generation units of power generation enterprise j after low-carbon optimization; The low-carbon optimized power balance constraint includes: The grid security constraints include: in, Optimize the power generation of power generation enterprises within the scope of low carbon; The electricity generated by power generators outside the scope of low-carbon optimization; is the lth load in period t participating in low-carbon optimization; θ' n,t is the phase angle of node n in period t after low carbon optimization; θ' m,t is the phase angle of node m in period t after low carbon optimization; θ' 1,t is the phase angle of node 1 in period t after low carbon optimization.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing low-carbon strategies in the electricity market under the carbon market background as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the low-carbon strategy of the electricity market under the carbon market background described in any one of claims 1 to 8 is implemented.

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