Multi-subject bidding simulation method, system and simulation platform based on energy block transaction

Abstract

The invention relates to a multi-subject bidding simulation method, system and simulation platform based on energy block transaction, and the method comprises the following specific steps: obtaining the power generation amount and operation cost parameters of different types of market participants, establishing four types of market participation subject models including time-sharing blocks, continuous blocks, curve blocks and variable blocks; establishing an electric power spot market clearing model based on energy block transaction based on the electric quantity and quotation of the energy blocks in the four types of market participation main body models; and on the basis of the power spot market clearing model, bidding strategy decision models of the four types of market participants are established, optimal bidding strategies and corresponding market clearing results of the four types of market participants are solved, and development of the power spot market multi-subject bidding simulation system is realized.

Description

Technical Field

[0001] The present invention relates to a multi-agent bidding simulation method, system and simulation platform based on energy block trading, and belongs to the field of power spot market transaction simulation. Background Art

[0002] As the world pays more attention to renewable energy, the energy market is undergoing profound changes. The traditional energy market mainly relies on fossil fuels, while the modern energy market increasingly relies on renewable energy such as solar energy and wind energy. This transformation brings new challenges and opportunities, especially in the trading and dispatch of energy.

[0003] Energy Blocks is a concept that divides energy resources into tradable units. These units can represent a fixed amount of energy within a certain period of time and are used to simplify the energy trading process. Energy blocks are often used for scheduling and trading in the electricity market, allowing market participants to trade and invest in a relatively simple way.

[0004] In the energy market, Multi-Agent Systems (MAS) are used to simulate and optimize the behavior of multiple market participants (such as power generation companies, consumers, traders, etc.). Each subject has independent goals and strategies in the system, and forms market dynamics through interactions and transactions. Multi-agent systems can simulate complex market environments and help analyze the impact of different strategies on the market. Bidding strategies are crucial in the energy market. Market participants need to formulate bidding strategies based on predicted market demand, energy price fluctuations, and their own resource conditions. The simulation method based on energy block trading can help participants test and optimize their bidding strategies under different market conditions and improve their competitiveness in the market.

[0005] In the context of the new power system, different types of market players have different physical and economic characteristics and transaction needs. In order to reduce carbon emissions, it is necessary to give priority to the use and consumption of renewable energy. However, renewable energy has great instability and randomness. This will seriously affect the formulation and implementation of power plans. With the increasing proportion of renewable energy, thermal power and gas units are changing from main power sources to regulating power sources. The power source that bears the basic load needs to operate continuously and smoothly to avoid frequent start and stop. The demand side needs to give priority to ensuring power supply and maintaining price stability. Energy storage has flexible two-way interactive characteristics, fast response speed and strong regulation ability. At this stage, the power spot market transaction simulation platform does not take into account the differentiated physical and economic characteristics and diversified transaction needs of the above-mentioned multi-type market participants, and it is difficult to accurately simulate the market behavior of multi-type market participants in the power spot market, which puts higher technical requirements on the simulation of the new power market.

[0006] Existing technologies such as the Chinese patent with patent number "CN117634929A" disclose a flexibility premium evaluation method that takes into account the flexible regulation characteristics of virtual power plants. The method includes the following steps: S1: Introduce flexible energy blocks and flexible hour trading methods, and establish a parameterized characterization model for flexible energy blocks and flexible hour trading; S2: Establish constraints from the two aspects of power generation and consumption balance and market clearing, and linearize the constraints; S3: Establish a spot market clearing model that adapts to the participation of virtual power plants with the maximization of social welfare as the optimization goal; S4: Introduce adjustment priority parameters in the declaration mechanism, use the spot market clearing model, and based on the priority parameters, perform spot market clearing deductions under multiple scenarios to achieve the evaluation of flexibility premium space.

[0007] The problem with the above-mentioned prior art is that linearization constraints and priority parameters may lead to reduced transparency of market clearing results, especially in the deduction of multiple scenarios, the impact of different priority parameters on the clearing results may be difficult to intuitively explain. Although flexibility is emphasized, the flexible energy blocks and flexible hour trading methods defined therein may be too specific and may not be widely applicable to all electricity market situations. Flexible energy blocks and flexible hour trading methods are introduced, and corresponding parameterized characterization models and linearization constraints are established, which increases the complexity of the model and the difficulty of implementation. Summary of the invention

[0008] In order to solve the above problems existing in the prior art, the present invention proposes a multi-agent bidding simulation method, system and simulation platform based on energy block trading.

[0009] The technical solution of the present invention is as follows:

[0010] On the one hand, the present invention proposes a multi-agent bidding simulation method based on energy block trading, comprising the following specific steps:

[0011] Obtain the power generation and operating cost parameters of different types of market participants, and establish four types of market participant models including time-sharing blocks, continuous blocks, curve blocks, and variable blocks;

[0012] Based on the quantity and quotation of energy blocks in the four types of market participants, a power spot market clearing model based on energy block transactions is established;

[0013] Based on the electricity spot market clearing model, a bidding strategy decision model for four types of market participants is established, and the optimal bidding strategies of the four types of market participants and the corresponding market clearing results are obtained.

[0014] As a preferred implementation, the four types of market participant models are expressed in the form of price constraints and electricity constraints. The specific mathematical model is expressed as follows:

[0015] Time-sharing block:

[0016]

[0017] Among them, the subscript i p Indicates the number of the time-sharing block, I PB represents a set of time-sharing blocks; the subscript t represents the number of the scheduling period, and T represents the set of scheduling periods; Represents time-sharing block i p The reported price for period t, Represents time-sharing block i p The lower bound of acceptable bids reported for period t, Represents time-sharing block i p The acceptable upper bound of the reported t period; Represents time-sharing block i p The reported power consumption during period t, Represents time-sharing block i p The lower bound of the reported power consumption during period t, Represents time-sharing block i p The upper limit of the reported electricity consumption during period t;

[0018] Continue Block:

[0019]

[0020] Among them, the subscript i s Indicates the number of the continuous block, I SB Represents a collection of persistence blocks; Indicates continuous block i s The price of the whole energy block reported, continuous block i s The lower and upper bounds of the acceptable bids for the reported whole energy block are Indicates continuous block i s The total power reported for the entire energy block; The start time and end time of the continuous block respectively. For continuous block i s Average power consumption in period t between the start and end time periods;

[0021] Curve Block:

[0022]

[0023] Among them, the subscript i c Indicates the number of the curve block, I CB Represents a collection of curve blocks; Represents curve block i c The price of the whole energy block reported, the curve block i c The lower and upper bounds of the acceptable bids for the reported whole energy block are Represents curve block i c The reported power consumption during period t; It represents the power generation in period t predicted by the curve block a day ago;

[0024] Variable blocks:

[0025]

[0026] Among them, the subscript i v Indicates the number of the variable block; Represents a variable block i v The lower and upper limits of the acceptable bids for the whole energy block are Represents a variable block i v The total power reported for the entire energy block; are the start and end times of the variable block, For variable block i v The power called in during period t has an upper limit of

[0027] As a preferred implementation, based on the electricity quantity and quotation of energy blocks in the four types of market participant models, a power spot market clearing model based on energy block transactions is established;

[0028] The electricity spot market clearing model aims to minimize the total call cost of market participants, including node power balance constraints, four types of energy block call power constraints, power system flow constraints, general node and balance node voltage phase angle constraints. The specific mathematical model is expressed as:

[0029] The objective function of the electricity spot market clearing model is expressed as:

[0030]

[0031] In the formula, Indicates the calling cost of the time-sharing block, represents the calling cost of the continuation block, represents the calling cost of the curve block, Indicates the calling cost of a mutable block;

[0032] The node power balance constraint of the power system is expressed as:

[0033]

[0034] The calling power constraint of the time-sharing block is expressed as:

[0035]

[0036] The call power constraint of the continuous block is expressed as:

[0037]

[0038] The continuity constraint of continuous block call power is expressed as:

[0039]

[0040] The call power constraint of the curve block is expressed as:

[0041]

[0042] The call power constraint of the variable block is expressed as:

[0043]

[0044] The total call power constraint of the variable block is expressed as:

[0045]

[0046] The power flow constraint of the power system is expressed as:

[0047]

[0048] The general node voltage phase angle constraint of the power system is expressed as:

[0049]

[0050] The voltage phase angle constraint of the balance node of the power system is expressed as:

[0051]

[0052] in, They represent the electricity cleared in the t period of the time-sharing block, continuous block, curve block, and variable block respectively;

[0053] Subscripts m and n represent the bus numbers of the power system, N represents the set of power systems, and ref represents the balancing bus number of the power system;

[0054] (m,n) represents the line mn of the power system, whose set is L; (i p ,n) represents the time-sharing block at bus n, (i s , n) represents the continuous block at busbar n, (i c , n) represents the curve block at the busbar n, (i v , n) represents the variable block at bus n;

[0055] represents the base load at bus n during period t;

[0056] B m-n represents the admittance of line mn;

[0057] θ n,t It represents the voltage phase angle at bus n during period t;

[0058] λ n,t It is the dual variable of the node power balance constraint at bus n during period t, and its physical meaning is the clearing electricity price at bus n during period t;

[0059] Respectively represent the minimum call amount of the time-sharing block, continuous block, and curve block in the t period;

[0060] Indicates the maximum transmission capacity of line mn;

[0061] Represents the dual variable of the inequality constraint.

[0062] As a preferred implementation, the bidding strategy decision model of the four types of market participants is a two-layer optimization model, the upper layer is the bidding decision problem of a single market participant, and the lower layer is the electricity spot market clearing problem, which is specifically expressed as follows:

[0063] Time-sharing block bidding strategy decision model:

[0064]

[0065] Among them, F market is the objective function of the electricity spot market clearing model; C market is the set of constraints for the electricity spot market clearing model;

[0066] Continuous block bidding strategy decision model:

[0067]

[0068] Curve block bidding strategy decision model:

[0069]

[0070] Variable block bidding strategy decision model:

[0071]

[0072] The Lagrangian function of the electricity spot market clearing model is expressed as follows:

[0073]

[0074] Where L is the Lagrangian function of the electricity spot market clearing model;

[0075] Taking partial derivatives of the Lagrangian function of the electricity spot market clearing model, we get the steady-state equation. The derivation process is:

[0076] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the time-sharing block electricity

[0077]

[0078] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the continuous block electricity

[0079]

[0080] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the curve block electricity

[0081]

[0082] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the variable block electricity

[0083]

[0084] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the node voltage phase angle

[0085]

[0086] The equality constraints, steady equations and complementary slack conditions of the electricity spot market clearing model are combined to form the Karush-Kuhn-Tucker conditions for clearing the lower-level electricity spot market.

[0087] Among them, the equality constraints of the electricity spot market clearing model include:

[0088] Node power balance constraints of the power system:

[0089]

[0090] Continuous block calls the continuity constraint of power:

[0091]

[0092] The total call power constraint of the variable block:

[0093]

[0094] Balance node voltage phase angle constraint:

[0095]

[0096] The complementary slack conditions include:

[0097] The relaxation relationship between the call power constraint of the time-sharing block and the dual variable is:

[0098]

[0099] The relaxation relationship between the call power constraint of the continuous block and the dual variable is:

[0100]

[0101] The relaxation relationship between the call power constraint of the curve block and the dual variable is:

[0102]

[0103] The relaxed relationship between the call power constraint of the variable block and the dual variable:

[0104]

[0105] The relaxed relationship between the power flow constraints and the dual variables of the power system is:

[0106]

[0107] The general node voltage phase angle constraint of the power system and the relaxation relationship between the dual variables are:

[0108] Binary variable constraints:

[0109]

[0110] Among them, N m represents the set of buses associated with bus m;

[0111]

[0112] represents the binary variable in the complementary slack condition;

[0113] According to the strong duality theorem and the Karush-Kuhn-Tucker condition, the objective function of the upper bidding decision problem of the four types of energy blocks is linearized, and the final form of the bidding strategy decision model of the four types of energy blocks is as follows:

[0114] Time-sharing block bidding strategy decision model:

[0115]

[0116] Among them, j p Indicates that the time-sharing block i p The numbers of all other time-sharing blocks except C market,KKT The Karush-Kuhn-Tucker condition for clearing the underlying electricity spot market;

[0117] Continuous block bidding strategy decision model:

[0118]

[0119] Among them, j s Indicates that except for the continuous block i s The numbers of all other continuation blocks except ;

[0120] Curve block bidding strategy decision model:

[0121]

[0122] Among them, j c Indicates the removal of curve block i c The numbers of all curve blocks except ;

[0123] Variable block bidding strategy decision model:

[0124]

[0125] Among them, j v Indicates that except for the variable block i v The numbers of all other variable blocks except .

[0126] As a preferred implementation method, the optimal bidding strategies of the four types of market participants and the corresponding market clearing results are obtained through an iterative algorithm. The solution process is as follows:

[0127] S1. Set the initial quote vector for all energy blocks Set the convergence accuracy ε and the number of iterations k = 0;

[0128] Where i represents the number of all energy blocks, i∈I=I PB ∩I SB ∩I CB ∩I VB , the superscript (k) indicates the kth iteration, Represents vector p (k) The price of energy block i in the middle;

[0129] S2. Solve the optimal bidding strategy for all energy blocks in turn. When solving the optimal bidding strategy for any energy block i, the bids of all energy blocks j except energy block i are calculated. Fixed, solve the bidding strategy decision model of energy block i to obtain the optimal bidding strategy of energy block i

[0130] Wherein, j represents the number of all energy blocks except energy block i, j∈I\{i};

[0131] S3, determine whether the convergence accuracy is met, if Then update the quotes p of all energy blocks (k+1) , k=k+1, execute step S2; if Then the algorithm converges and executes step S4;

[0132] S4. Output the optimal bidding strategy p for all energy blocks (k+1) .

[0133] On the other hand, the present invention also provides a multi-agent bidding simulation system based on energy block trading, comprising:

[0134] Energy block modeling module, which obtains the power generation and operating cost parameters of different types of market participants, and establishes four types of market participant models including time-sharing block, continuous block, curve block and variable block;

[0135] The market clearing module establishes a power spot market clearing model based on energy block transactions based on the power and quotation of energy blocks in the four types of market participant models;

[0136] The energy block bidding decision module, based on the electricity spot market clearing model, establishes a bidding strategy decision model for four types of market participants, and solves the optimal bidding strategies of the four types of market participants and the corresponding market clearing results.

[0137] In another aspect, the present invention further provides a multi-agent bidding simulation platform based on energy block trading, comprising:

[0138] The energy block modeling sub-platform obtains the power generation and operating cost parameters of different types of market participants, and establishes four types of market participant models including time-sharing blocks, continuous blocks, curve blocks, and variable blocks;

[0139] The market clearing sub-platform establishes a power spot market clearing model based on energy block transactions based on the power and quotation of energy blocks in the four types of market participant models;

[0140] The energy block bidding decision sub-platform, based on the electricity spot market clearing model, establishes a bidding strategy decision model for four types of market participants, and solves the optimal bidding strategies of the four types of market participants and the corresponding market clearing results.

[0141] The present invention has the following beneficial effects:

[0142] The present invention establishes four types of market participant models including time-sharing blocks, continuous blocks, curve blocks and variable blocks. These models can more comprehensively cover different types of power generation enterprises and users, and improve the diversity of market participants. Through these four types of models, market participants can participate in electricity spot market transactions more flexibly and choose appropriate trading methods and strategies according to their own characteristics and needs. Based on the electricity volume and quotation of energy blocks in the four types of market participant models, an electricity spot market clearing model based on energy block transactions is established, which can more accurately reflect the market supply and demand relationship and price signals, and improve the accuracy and efficiency of market clearing. By optimizing the market clearing mechanism and bidding strategy decision-making model, the optimal allocation of power resources can be better achieved, and resource waste and cost losses can be reduced. The four types of market participants trade according to their own optimal bidding strategies, which can maximize their respective economic benefits, thereby improving the economic benefits of the entire power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0143] Figure 1 Flow chart for implementing the present invention.

[0144] Figure 2 This is a diagram of the transaction structure of multiple types of market participants in the new electricity market. DETAILED DESCRIPTION

[0145] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of 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.

[0146] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0147] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0148] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0149] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0150] Embodiment 1:

[0151] See also Figure 1-2 The present invention proposes a multi-agent bidding simulation method based on energy block trading, which includes the following specific steps:

[0152] Obtain the power generation and operating cost parameters of different types of market participants, and establish four types of market participant models including time-sharing blocks, continuous blocks, curve blocks, and variable blocks;

[0153] Based on the quantity and quotation of energy blocks in the four types of market participants, a power spot market clearing model based on energy block transactions is established;

[0154] Based on the electricity spot market clearing model, a bidding strategy decision model for four types of market participants is established, and the optimal bidding strategies of the four types of market participants and the corresponding market clearing results are obtained.

[0155] As a preferred implementation, the four types of market participant models are expressed in the form of price constraints and electricity constraints. The specific mathematical model is expressed as follows:

[0156] Time-sharing block:

[0157]

[0158] Among them, the subscript i p Indicates the number of the time-sharing block, I PB represents a set of time-sharing blocks; the subscript t represents the number of the scheduling period, and T represents the set of scheduling periods; Represents time-sharing block i p The reported price for period t, Represents time-sharing block i p The lower bound of acceptable bids reported for period t, Represents time-sharing block i p The acceptable upper bound of the reported t period; Represents time-sharing block i p The reported power consumption during period t, Represents time-sharing block i p The lower bound of the reported power consumption during period t, Represents time-sharing block i p The upper limit of the reported electricity consumption during period t;

[0159] Continue Block:

[0160]

[0161] Among them, the subscript i s Indicates the number of the continuous block, I SB Represents a collection of persistence blocks; Indicates continuous block i s The price of the whole energy block reported, continuous block i s The lower and upper bounds of the acceptable bids for the reported whole energy block are Indicates continuous block i s The total power reported for the entire energy block; The start time and end time of the continuous block respectively. For continuous block i s Average power consumption in period t between the start and end time periods;

[0162] Curve Block:

[0163]

[0164] Among them, the subscript i c Indicates the number of the curve block, I CB Represents a collection of curve blocks; Represents curve block i c The price of the whole energy block reported, the curve block i c The lower and upper bounds of the acceptable bids for the reported whole energy block are Represents curve block i c The reported power consumption during period t; It represents the power generation in period t predicted by the curve block a day ago;

[0165] Variable blocks:

[0166]

[0167] Among them, the subscript i v Indicates the number of the variable block; Represents a variable block i v The lower and upper limits of the acceptable bids for the whole energy block are Represents a variable block i v The total power reported for the entire energy block; are the start and end times of the variable block, For variable block i v The power called in during period t has an upper limit of

[0168] Establishing mathematical models for four types of market participants, namely time-sharing blocks, continuous blocks, curve blocks and variable blocks, can classify and describe the physical and economic characteristics of differentiated market participants, facilitate market operators to organize quotations and clear the market by selecting a single energy block or flexibly combining different energy blocks, thereby adapting to and meeting the diversified trading needs of multiple types of market participants, such as priority absorption, continuous operation, and flexible adjustment, which is conducive to promoting the standardization of trading products in the electricity spot market and enabling market participants to participate in the electricity spot market more simply and conveniently.

[0169] As a preferred implementation, based on the electricity quantity and quotation of energy blocks in the four types of market participant models, a power spot market clearing model based on energy block transactions is established;

[0170] The electricity spot market clearing model aims to minimize the total call cost of market participants, including node power balance constraints, four types of energy block call power constraints, power system flow constraints, general node and balance node voltage phase angle constraints. The specific mathematical model is expressed as:

[0171] The objective function of the electricity spot market clearing model is expressed as:

[0172]

[0173] In the formula, Indicates the calling cost of the time-sharing block, represents the calling cost of the continuation block, represents the calling cost of the curve block, Indicates the calling cost of a mutable block;

[0174] The node power balance constraint of the power system is expressed as:

[0175]

[0176] The calling power constraint of the time-sharing block is expressed as:

[0177]

[0178] The call power constraint of the continuous block is expressed as:

[0179]

[0180] The continuity constraint of continuous block call power is expressed as:

[0181]

[0182] The call power constraint of the curve block is expressed as:

[0183]

[0184] The call power constraint of the variable block is expressed as:

[0185]

[0186] The total call power constraint of the variable block is expressed as:

[0187]

[0188] The power flow constraint of the power system is expressed as:

[0189]

[0190] The general node voltage phase angle constraint of the power system is expressed as:

[0191]

[0192] The voltage phase angle constraint of the balance node of the power system is expressed as:

[0193]

[0194] in, They represent the electricity cleared in the t period of the time-sharing block, continuous block, curve block, and variable block respectively;

[0195] Subscripts m and n represent the bus numbers of the power system, N represents the set of power systems, and ref represents the balancing bus number of the power system;

[0196] (m,n) represents the line mn of the power system, whose set is L; (i p ,n) represents the time-sharing block at bus n, (i s , n) represents the continuous block at busbar n, (i c , n) represents the curve block at the busbar n, (i v , n) represents the variable block at bus n;

[0197] represents the base load at bus n during period t;

[0198] B m-n represents the admittance of line mn;

[0199] θ n,t It represents the voltage phase angle at bus n during period t;

[0200] λ n,t It is the dual variable of the node power balance constraint at bus n during period t, and its physical meaning is the clearing electricity price at bus n during period t;

[0201] Respectively represent the minimum call amount of the time-sharing block, continuous block, and curve block in the t period;

[0202] Indicates the maximum transmission capacity of line mn;

[0203]

[0204] Represents the dual variable of the inequality constraint.

[0205] A power spot market clearing model based on energy block trading is established to describe the selection and calling process of four types of energy blocks, namely time-sharing blocks, continuous blocks, curve blocks and variable blocks, by market operators in the unified clearing market, thereby adapting to and satisfying the differentiated calling of multiple types of market participants in the power spot market.

[0206] As a preferred implementation, the bidding strategy decision model of the four types of market participants is a two-layer optimization model, the upper layer is the bidding decision problem of a single market participant, and the lower layer is the electricity spot market clearing problem, which is specifically expressed as follows:

[0207] Time-sharing block bidding strategy decision model:

[0208]

[0209] Among them, F market is the objective function of the electricity spot market clearing model; C market is the set of constraints for the electricity spot market clearing model;

[0210] Continuous block bidding strategy decision model:

[0211]

[0212] Curve block bidding strategy decision model:

[0213]

[0214] Variable block bidding strategy decision model:

[0215]

[0216] The Lagrangian function of the electricity spot market clearing model is expressed as follows:

[0217]

[0218] Where L is the Lagrangian function of the electricity spot market clearing model;

[0219] Taking partial derivatives of the Lagrangian function of the electricity spot market clearing model, we get the steady-state equation. The derivation process is:

[0220] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the time-sharing block electricity

[0221]

[0222] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the continuous block electricity

[0223]

[0224] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the curve block electricity

[0225]

[0226] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the variable block electricity

[0227]

[0228] According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the node voltage phase angle

[0229]

[0230] The equality constraints, steady equations and complementary slack conditions of the electricity spot market clearing model are combined to form the Karush-Kuhn-Tucker conditions for clearing the lower-level electricity spot market.

[0231] Among them, the equality constraints of the electricity spot market clearing model include:

[0232] Node power balance constraints of the power system:

[0233]

[0234] Continuous block calls the continuity constraint of power:

[0235]

[0236] The total call power constraint of the variable block:

[0237]

[0238] Balance node voltage phase angle constraint:

[0239]

[0240] The complementary slack conditions include:

[0241] The relaxation relationship between the call power constraint of the time-sharing block and the dual variable is:

[0242] The relaxation relationship between the call power constraint of the continuous block and the dual variable is:

[0243] The relaxation relationship between the call power constraint of the curve block and the dual variable is:

[0244] The relaxed relationship between the call power constraint of the variable block and the dual variable:

[0245] The relaxed relationship between the power flow constraints and the dual variables of the power system is:

[0246]

[0247] The general node voltage phase angle constraint of the power system and the relaxation relationship between the dual variables are:

[0248]

[0249] Binary variable constraints:

[0250]

[0251] Among them, N m represents the set of buses associated with bus m;

[0252] represents the binary variable in the complementary slack condition;

[0253] According to the strong duality theorem and the Karush-Kuhn-Tucker condition, the objective function of the upper bidding decision problem of the four types of energy blocks is linearized, and the final form of the bidding strategy decision model of the four types of energy blocks is as follows:

[0254] Time-sharing block bidding strategy decision model:

[0255]

[0256] Among them, j p Indicates that the time-sharing block i p The numbers of all other time-sharing blocks except C market,KKT The Karush-Kuhn-Tucker condition for clearing the underlying electricity spot market;

[0257] Continuous block bidding strategy decision model:

[0258]

[0259] Among them, j s Indicates that except for the continuous block i s The numbers of all other continuation blocks except ;

[0260] Curve block bidding strategy decision model:

[0261]

[0262] Among them, j c Indicates the removal of curve block i c The numbers of all curve blocks except ;

[0263] Variable block bidding strategy decision model:

[0264]

[0265] Among them, j v Indicates that except for the variable block i v The numbers of all other variable blocks except .

[0266] Based on the electricity spot market clearing model, a bidding strategy decision model for four types of market participants, namely time-sharing block, continuous block, curve block and variable block, is established. This can facilitate the description of the differentiated decision-making behaviors of multiple types of market participants under the current electricity spot market trading mechanism, and then analyze the optimal response of different market participants in the market, providing a reference for market decision makers to evaluate the feasibility of the current trading mechanism.

[0267] As a preferred implementation method, the optimal bidding strategies of the four types of market participants and the corresponding market clearing results are obtained through an iterative algorithm. The solution process is as follows:

[0268] S1. Set the initial quote vector for all energy blocks Set the convergence accuracy ε and the number of iterations k = 0;

[0269] Where i represents the number of all energy blocks, i∈I=I PB ∩I SB ∩I CB ∩I VB , the superscript (k) indicates the kth iteration, Represents vector p (k) The price of energy block i in the middle;

[0270] S2. Solve the optimal bidding strategy for all energy blocks in turn. When solving the optimal bidding strategy for any energy block i, the bids of all energy blocks j except energy block i are calculated. Fixed, solve the bidding strategy decision model of energy block i to obtain the optimal bidding strategy of energy block i

[0271] Wherein, j represents the number of all energy blocks except energy block i, j∈I\{i};

[0272] S3, determine whether the convergence accuracy is met, if Then update the quotes p of all energy blocks (k+1) , k=k+1, execute step S2; if Then the algorithm converges and executes step S4;

[0273] S4. Output the optimal bidding strategy p for all energy blocks (k+1) .

[0274] The algorithm for solving the optimal bidding strategy is an iterative algorithm based on the fixed point theory. This method solves the generalized Nash game equilibrium of four types of market participants through distributed iteration. When the algorithm converges, the optimal bidding strategy of different types of market participants under the multi-agent game equilibrium is obtained. The algorithm has low solution complexity and is easy to implement. The iterative process can clearly describe the decision-making optimization process of market participants.

[0275] Embodiment 2:

[0276] The present invention also provides a multi-agent bidding simulation system based on energy block trading, comprising:

[0277] Energy block modeling module, which obtains the power generation and operating cost parameters of different types of market participants, and establishes four types of market participant models including time-sharing block, continuous block, curve block and variable block;

[0278] The market clearing module establishes a power spot market clearing model based on energy block transactions based on the power and quotation of energy blocks in the four types of market participant models;

[0279] The energy block bidding decision module, based on the electricity spot market clearing model, establishes a bidding strategy decision model for four types of market participants, and solves the optimal bidding strategies of the four types of market participants and the corresponding market clearing results.

[0280] Embodiment three:

[0281] The present invention also provides a multi-agent bidding simulation platform based on energy block trading, comprising:

[0282] The energy block modeling sub-platform obtains the power generation and operating cost parameters of different types of market participants, and establishes four types of market participant models including time-sharing blocks, continuous blocks, curve blocks, and variable blocks;

[0283] The market clearing sub-platform establishes a power spot market clearing model based on energy block transactions based on the power and quotation of energy blocks in the four types of market participant models;

[0284] The energy block bidding decision sub-platform, based on the electricity spot market clearing model, establishes a bidding strategy decision model for four types of market participants, and solves the optimal bidding strategies of the four types of market participants and the corresponding market clearing results.

[0285] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0286] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0287] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0288] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0289] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-agent bidding simulation method based on energy block trading, characterized in that: The specific steps include: Obtain the power generation and operating cost parameters of different types of market participants, and establish four types of market participant models including time-sharing blocks, continuous blocks, curve blocks, and variable blocks; Based on the quantity and quotation of energy blocks in the four types of market participants, a power spot market clearing model based on energy block transactions is established; Based on the electricity spot market clearing model, a bidding strategy decision model for four types of market participants is established, and the optimal bidding strategies of the four types of market participants and the corresponding market clearing results are obtained.

2. The multi-agent bidding simulation method based on energy block trading according to claim 1 is characterized in that: The four types of market participant models are expressed in the form of price constraints and electricity constraints. The specific mathematical model is expressed as follows: Time-sharing block: Among them, the subscript i p Indicates the number of the time-sharing block, I PB represents a set of time-sharing blocks; the subscript t represents the number of the scheduling period, and T represents the set of scheduling periods; Represents time-sharing block i p The reported price for period t, Represents time-sharing block i p The lower bound of acceptable bids reported for period t, Represents time-sharing block i p The acceptable upper bound of the reported t period; Represents time-sharing block i p The reported power consumption during period t, Represents time-sharing block i p The lower bound of the reported power consumption during period t, Represents time-sharing block i p The upper limit of the reported electricity consumption during period t; Continue Block: Among them, the subscript i s Indicates the number of the continuous block, I SB Represents a collection of persistence blocks; Indicates continuous block i s The price of the whole energy block reported, continuous block i s The lower and upper bounds of the acceptable bids for the reported whole energy block are and Indicates continuous block i s The total power reported for the entire energy block; and The start time and end time of the continuous block respectively. For continuous block i s Average power consumption in period t between the start and end time periods; Curve Block: Among them, the subscript i c Indicates the number of the curve block, I CB Represents a collection of curve blocks; Represents curve block i c The price of the whole energy block reported, the curve block i c The lower and upper bounds of the acceptable bids for the reported whole energy block are and Represents curve block i c The reported power consumption during period t; It represents the power generation in period t predicted by the curve block a day ago; Variable blocks: Among them, the subscript i v Indicates the number of the variable block; Represents a variable block i v The lower and upper limits of the acceptable bids for the whole energy block are and Represents a variable block i v The total power reported for the entire energy block; and are the start and end times of the variable block, For variable block i v The power called in during period t has an upper limit of 3. The multi-agent bidding simulation method based on energy block trading according to claim 2 is characterized in that: Based on the electricity quantity and quotation of energy blocks in the four types of market participant models, a power spot market clearing model based on energy block transactions is established; The electricity spot market clearing model aims to minimize the total call cost of market participants, including node power balance constraints, four types of energy block call power constraints, power system flow constraints, general node and balance node voltage phase angle constraints. The specific mathematical model is expressed as: The objective function of the electricity spot market clearing model is expressed as: In the formula, Indicates the calling cost of the time-sharing block, represents the calling cost of the continuation block, represents the calling cost of the curve block, Indicates the calling cost of a mutable block; The node power balance constraint of the power system is expressed as: The calling power constraint of the time-sharing block is expressed as: The call power constraint of the continuous block is expressed as: The continuity constraint of continuous block call power is expressed as: The call power constraint of the curve block is expressed as: The call power constraint of the variable block is expressed as: The total call power constraint of the variable block is expressed as: The power flow constraint of the power system is expressed as: The general node voltage phase angle constraint of the power system is expressed as: The voltage phase angle constraint of the balance node of the power system is expressed as: in, They represent the electricity cleared in the t period of the time-sharing block, continuous block, curve block, and variable block respectively; Subscripts m and n represent the bus numbers of the power system, N represents the set of power systems, and ref represents the balancing bus number of the power system; (m,n) represents the line mn of the power system, whose set is L; (i p ,n) represents the time-sharing block at bus n, (i s , n) represents the continuous block at busbar n, (i c , n) represents the curve block at the busbar n, (i v , n) represents the variable block at bus n; represents the base load at bus n during period t; B m-n represents the admittance of line mn; θ n,t It represents the voltage phase angle at bus n during period t; λ n,t It is the dual variable of the node power balance constraint at bus n during period t, and its physical meaning is the clearing electricity price at bus n during period t; Respectively represent the minimum call amount of the time-sharing block, continuous block, and curve block in the t period; Indicates the maximum transmission capacity of line mn; Represents the dual variable of the inequality constraint.

4. The multi-agent bidding simulation method based on energy block trading according to claim 3 is characterized in that: The bidding strategy decision model of the four types of market participants is a two-layer optimization model. The upper layer is the bidding decision problem of a single market participant, and the lower layer is the electricity spot market clearing problem. The specific expression is as follows: Time-sharing block bidding strategy decision model: Among them, F market is the objective function of the electricity spot market clearing model; C market is the set of constraints for the electricity spot market clearing model; Continuous block bidding strategy decision model: Curve block bidding strategy decision model: Variable block bidding strategy decision model: The Lagrangian function of the electricity spot market clearing model is expressed as follows: Where L is the Lagrangian function of the electricity spot market clearing model; Taking partial derivatives of the Lagrangian function of the electricity spot market clearing model, we get the steady-state equation. The derivation process is: According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the time-sharing block electricity According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the continuous block electricity According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the curve block electricity According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the variable block electricity According to the Lagrangian function of the electricity spot market clearing model, the derivative function is obtained by taking the partial derivative of the node voltage phase angle The equality constraints, steady equations and complementary slack conditions of the electricity spot market clearing model are combined to form the Karush-Kuhn-Tucker conditions for clearing the lower-level electricity spot market. Among them, the equality constraints of the electricity spot market clearing model include: Node power balance constraints of the power system: Continuous block calls the continuity constraint of power: The total call power constraint of the variable block: Balance node voltage phase angle constraint: The complementary slack conditions include: The relaxation relationship between the call power constraint of the time-sharing block and the dual variable is: The relaxation relationship between the call power constraint of the continuous block and the dual variable is: The relaxation relationship between the call power constraint of the curve block and the dual variable is: The relaxed relationship between the call power constraint of the variable block and the dual variable: The relaxed relationship between the power flow constraints and the dual variables of the power system is: The general node voltage phase angle constraint of the power system and the relaxation relationship between the dual variables are: Binary variable constraints: Among them, N m represents the set of buses associated with bus m; represents the binary variable in the complementary slack condition; According to the strong duality theorem and the Karush-Kuhn-Tucker condition, the objective function of the upper bidding decision problem of the four types of energy blocks is linearized, and the final form of the bidding strategy decision model of the four types of energy blocks is as follows: Time-sharing block bidding strategy decision model: Among them, j p Indicates that the time-sharing block i p The numbers of all other time-sharing blocks except C market,KKT The Karush-Kuhn-Tucker condition for clearing the underlying electricity spot market; Continuous block bidding strategy decision model: Among them, j s Indicates that except for the continuous block i s The numbers of all other continuation blocks except ; Curve block bidding strategy decision model: Among them, j c Indicates the removal of curve block i c The numbers of all curve blocks except ; Variable block bidding strategy decision model: Among them, j v Indicates that except for the variable block i v The numbers of all other variable blocks except .

5. A multi-agent bidding simulation method based on energy block trading according to claim 4, characterized in that: The optimal bidding strategies and corresponding market clearing results of the four types of market participants are obtained through iterative algorithms. The solution process is as follows: S1. Set the initial quote vector for all energy blocks Set the convergence accuracy ε and the number of iterations k = 0; Where i represents the number of all energy blocks, i∈I=I PB ∩I SB ∩I CB ∩I VB , the superscript (k) indicates the kth iteration, Represents vector p (k) The price of energy block i in the middle; S2. Solve the optimal bidding strategy for all energy blocks in turn. When solving the optimal bidding strategy for any energy block i, the bids of all energy blocks j except energy block i are calculated. Fixed, solve the bidding strategy decision model of energy block i to obtain the optimal bidding strategy of energy block i Wherein, j represents the number of all energy blocks except energy block i, j∈I\{i}; S3, determine whether the convergence accuracy is met, if Then update the quotes p of all energy blocks (k+1) , k=k+1, execute step S2; if Then the algorithm converges and executes step S4; S4. Output the optimal bidding strategy p for all energy blocks (k+1) .

6. A multi-agent bidding simulation system based on energy block trading, characterized in that: include: Energy block modeling module, which obtains the power generation and operating cost parameters of different types of market participants, and establishes four types of market participant models including time-sharing block, continuous block, curve block and variable block; The market clearing module establishes a power spot market clearing model based on energy block transactions based on the power and quotation of energy blocks in the four types of market participant models; The energy block bidding decision module, based on the electricity spot market clearing model, establishes a bidding strategy decision model for four types of market participants, and solves the optimal bidding strategies of the four types of market participants and the corresponding market clearing results.

7. A multi-agent bidding simulation system based on energy block trading according to claim 6, characterized in that: In the energy block modeling module, the four types of market participant models are expressed in the form of price constraints and power constraints. The specific mathematical model is expressed as follows: Time-sharing block: Among them, the subscript i p Indicates the number of the time-sharing block, I PB represents a set of time-sharing blocks; the subscript t represents the number of the scheduling period, and T represents the set of scheduling periods; Represents time-sharing block i p The reported price for period t, Represents time-sharing block i p The lower bound of acceptable bids reported for period t, Represents time-sharing block i p The acceptable upper bound of the reported t period; Represents time-sharing block i p The reported power consumption during period t, Represents time-sharing block i p The lower bound of the reported power consumption during period t, Represents time-sharing block i p The upper limit of the reported electricity consumption during period t; Continue Block: Among them, the subscript i s Indicates the number of the continuous block, I SB Represents a collection of persistence blocks; Indicates continuous block i s The price of the whole energy block reported, continuous block i s The lower and upper bounds of the acceptable bids for the reported whole energy block are and Indicates continuous block i s The total power reported for the entire energy block; and The start time and end time of the continuous block respectively. For continuous block i s Average power consumption in period t between the start and end time periods; Curve Block: Among them, the subscript i c Indicates the number of the curve block, I CB Represents a collection of curve blocks; Represents curve block i c The price of the whole energy block reported, the curve block i c The lower and upper bounds of the acceptable bids for the reported whole energy block are and Represents curve block i c The reported power consumption during period t; It represents the power generation in period t predicted by the curve block a day ago; Variable blocks: Among them, the subscript i v Indicates the number of the variable block; Represents a variable block i v The lower and upper limits of the acceptable bids for the whole energy block are and Represents a variable block i v The total power reported for the entire energy block; and are the start and end times of the variable block, For variable block i v The power called in during period t has an upper limit of 8. A multi-agent bidding simulation system based on energy block trading according to claim 7, characterized in that: The market clearing module establishes a power spot market clearing model based on energy block transactions based on the power quantities and quotations of the energy blocks in the four types of market participant models; The electricity spot market clearing model aims to minimize the total call cost of market participants, including node power balance constraints, four types of energy block call power constraints, power system flow constraints, general node and balance node voltage phase angle constraints. The specific mathematical model is expressed as: The objective function of the electricity spot market clearing model is expressed as: In the formula, Indicates the calling cost of the time-sharing block, represents the calling cost of the continuation block, represents the calling cost of the curve block, Indicates the calling cost of a mutable block; The node power balance constraint of the power system is expressed as: The calling power constraint of the time-sharing block is expressed as: The call power constraint of the continuous block is expressed as: The continuity constraint of continuous block call power is expressed as: The call power constraint of the curve block is expressed as: The call power constraint of the variable block is expressed as: The total call power constraint of the variable block is expressed as: The power flow constraint of the power system is expressed as: The general node voltage phase angle constraint of the power system is expressed as: The voltage phase angle constraint of the balance node of the power system is expressed as: in, They represent the electricity cleared in the t period of the time-sharing block, continuous block, curve block, and variable block respectively; Subscripts m and n represent the bus numbers of the power system, N represents the set of power systems, and ref represents the balancing bus number of the power system; (m,n) represents the line mn of the power system, whose set is L; (i p ,n) represents the time-sharing block at bus n, (i s , n) represents the continuous block at busbar n, (i c , n) represents the curve block at the busbar n, (i v , n) represents the variable block at bus n; represents the base load at bus n during period t; B m-n represents the admittance of line mn; θ n,t It represents the voltage phase angle at bus n during period t; λ n,t It is the dual variable of the node power balance constraint at bus n during period t, and its physical meaning is the clearing electricity price at bus n during period t; Respectively represent the minimum call amount of the time-sharing block, continuous block, and curve block in the t period; Indicates the maximum transmission capacity of line mn; Represents the dual variable of the inequality constraint.

9. A multi-agent bidding simulation platform based on energy block trading, characterized in that: include: The energy block modeling sub-platform obtains the power generation and operating cost parameters of different types of market participants, and establishes four types of market participant models including time-sharing blocks, continuous blocks, curve blocks, and variable blocks; The market clearing sub-platform establishes a power spot market clearing model based on energy block transactions based on the power and quotation of energy blocks in the four types of market participant models; The energy block bidding decision sub-platform, based on the electricity spot market clearing model, establishes a bidding strategy decision model for four types of market participants, and solves the optimal bidding strategies of the four types of market participants and the corresponding market clearing results.

10. A multi-agent bidding simulation platform based on energy block trading according to claim 9, characterized in that: The energy block modeling sub-platform, the four types of market participant models are expressed in the form of price constraints and power constraints, and the specific mathematical model is expressed as follows: Time-sharing block: Among them, the subscript i p Indicates the number of the time-sharing block, I PB represents a set of time-sharing blocks; the subscript t represents the number of the scheduling period, and T represents the set of scheduling periods; Represents time-sharing block i p The reported price for period t, Represents time-sharing block i p The lower bound of acceptable bids reported for period t, Represents time-sharing block i p The acceptable upper bound of the reported t period; Represents time-sharing block i p The reported power consumption during period t, Represents time-sharing block i p The lower bound of the reported power consumption during period t, Represents time-sharing block i p The upper limit of the reported electricity consumption during period t; Continue Block: Among them, the subscript i s Indicates the number of the continuous block, I SB Represents a collection of persistence blocks; Indicates continuous block i s The price of the whole energy block reported, continuous block i s The lower and upper bounds of the acceptable bids for the reported whole energy block are and Indicates continuous block i s The total power reported for the entire energy block; and The start time and end time of the continuous block respectively. For continuous block i s Average power consumption in period t between the start and end time periods; Curve Block: Among them, the subscript i c Indicates the number of the curve block, I CB Represents a collection of curve blocks; Represents curve block i c The price of the whole energy block reported, the curve block i c The lower and upper bounds of the acceptable bids for the reported whole energy block are and Represents curve block i c The reported power consumption during period t; It represents the power generation in period t predicted by the curve block a day ago; Variable blocks: Among them, the subscript i v Indicates the number of the variable block; Represents a variable block i v The lower and upper limits of the acceptable bids for the whole energy block are and Represents a variable block i v The total power reported for the entire energy block; and are the start and end times of the variable block, For variable block i v The power called in during period t has an upper limit of

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