Quotation strategy generation method and system based on multivariate subject reputation evaluation

Abstract

The invention discloses a quotation strategy generation method and system based on multivariate subject reputation evaluation. The method comprises the steps that market boundary conditions are acquired, and a market pricing mechanism is determined according to a market transaction mode, a clearing mode, quotation content and a settlement process; according to the output uncertainty of the new energy generator, the multivariate subject reputation is determined, and based on the multivariate subject reputation, a double-layer optimization model for the new energy generator and the energy storage operator to participate in the day-ahead market is established. Comprising an upper-layer Nash game model of a new energy power generator and an energy storage operator taking market income maximization as a target and a lower-layer joint clearing model of an electric energy market and an auxiliary service market taking social welfare maximization as a target; and realizing information interaction between the upper-layer Nash game model and the lower-layer joint clearing model through an interaction decision variable, solving the double-layer optimization model by using an interior point method and a self-adaptive global harmony search algorithm, and generating a quotation strategy of a new energy power generator and an energy storage operator. According to the scheme, the rationality of the transaction is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power market transactions, and in particular relates to a quotation strategy generation method and system based on multi-subject reputation evaluation. Background Art

[0002] In the initial construction stage of the power market, fixed grid-connected electricity prices are usually used to purchase new energy in full, which cannot effectively promote the rational allocation of market resources. In order to promote the formation of a competitive environment, independent system operators (ISOs) allow new energy producers (NEPs) to participate in market quotations. However, the output of new energy power generation is uncertain, volatile and undispatchy. The uncertainty of new energy power generation has become one of the main factors leading to NEP economic losses. In order to cope with the impact of the uncertainty of new energy power generation on the balance of supply and demand in the power market and the fluctuation of stable electricity prices, existing studies have explored different market mechanisms and rules. Among them, existing technologies attempt to develop optimal market quotation strategies for new energy power producers. These strategies take into account the uncertainty of wind power output, electricity prices and loads, and propose the optimal quotation strategy for wind power producers considering risk factors, which effectively controls profit fluctuations. Existing technologies usually attach energy storage to large power generation companies, which are suitable for operators with small scale of energy storage, small assets and lack of market trading experience, and also apply artificial intelligence methods such as neural networks, genetic algorithms, genetic programming and finite state automata, evolutionary computing, Q learning, etc. to construct adaptive and evolutionary quotation strategies. Since the game relationship between new energy power generators and energy storage operators is ignored, energy storage operators are unable to pursue maximum profits and can only obtain benefits through sharing with the entities they depend on.

[0003] The proportion of new energy participating in the market has increased dramatically, and the risks and operating costs of the system have also increased successively, and the risk costs borne by ISO have further increased. Since new energy power generators do not bear risks and have low marginal costs, they will continue to increase installed capacity to increase revenue. The revenue is obtained by new energy power generators, while the risks are borne by ISOs, which is not conducive to the long-term stable operation of the market. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for generating a quotation strategy based on multi-subject reputation evaluation to increase the rationality of the quotation strategy, promote the balance of power grid supply and demand, and improve the economic benefits of new energy power generators.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions.

[0006] The present invention first discloses a quotation strategy generation method based on multi-subject reputation evaluation, which includes the following steps:

[0007] Obtain market boundary conditions and determine the market pricing mechanism based on market trading patterns, clearing methods, quotation content and settlement processes;

[0008] Determine the reputation of multiple entities according to the output uncertainty of new energy power generators, and establish a two-layer optimization model for new energy power generators and energy storage operators to participate in the day-ahead market based on the reputation of multiple entities and the market pricing mechanism. The two-layer optimization model includes an upper-layer Nash game model of new energy power generators and energy storage operators with the goal of maximizing market returns and a lower-layer joint clearing model of the electric energy market and ancillary service market with the goal of maximizing social welfare;

[0009] Information interaction is achieved between the upper Nash game model and the lower joint clearing model through interactive decision variables. The interior point method and adaptive global harmony search algorithm are used to solve the two-layer optimization model to generate the bidding strategies of new energy power generators and energy storage operators.

[0010] The present invention further includes the following preferred embodiments:

[0011] The establishment of a two-tier optimization model for new energy power generators and energy storage operators to participate in the day-ahead market based on the reputation of the multiple entities and the market pricing mechanism further includes:

[0012] The scenario method is used to describe the uncertainty of renewable energy output, taking into account electricity revenue, maintenance cost, electricity purchase cost, electricity sales revenue and penalty cost, and taking the maximization of renewable energy generator revenue as the objective function:

[0013]

[0014] Among them U n , V n are the objective function and decision variables of the new energy power generator respectively; is the fixed unit price in scenario w; is the offset price coefficient in scenario w; T is the total trading period; is the market clearing electricity price of the energy storage operator at time t under scenario w; is the electricity purchase price of the energy storage operator at time t1 under scenario w. The energy storage operator charges at the peak time of renewable energy output, and the renewable energy power generator generates electricity sales benefits; is the electricity selling price of the energy storage operator at time t2 under scenario w. The energy storage operator discharges electricity at the peak load moment, and the new energy power generator incurs electricity purchase costs; is the bid power and actual power output at time t in scenario w; c n is the price coefficient of the maintenance cost of the new energy unit; ρ wis the probability of the generated renewable energy output scenario w; Ω is the total number of generated scenarios; The power purchased and sold to the energy storage operator at time t in scenario w; P n The penalty price for renewable energy output lower than the winning bid; Δt is the quotation time interval;

[0015] The penalty cost is that if the bid power is greater than the actual power, the credit value will be reduced and the penalty will be imposed:

[0016]

[0017] In the formula is the credit value of the new energy power generator in the transaction in the tth period and the wth scenario; is the deviation power of new energy in the tth period and the wth scenario; ξ is the allowable deviation power; P n The penalty amount for renewable energy output lower than the winning bid amount is The electricity purchase price for energy storage operators.

[0018] The upper-level Nash game model meets the upper and lower limit constraints of the quotation:

[0019]

[0020] It is the lower and upper limits of the quotations for new energy power generators.

[0021] The upper-level Nash game model satisfies the energy storage charging and discharging constraints:

[0022]

[0023] Where: is the rated power of the energy storage; is the transaction capacity between the energy storage operator and the new energy generator at time t under scenario w; η c , η f is the charging and discharging efficiency of energy storage; S SOCmin , S SOCmax are the minimum and maximum state of charge of the energy storage.

[0024] Based on the reputation of the multiple entities and the market pricing mechanism, a two-tier optimization model for new energy power generators and energy storage operators to participate in the day-ahead market is established, further including:

[0025] The objective function is to maximize the revenue of energy storage operators:

[0026]

[0027] Among them U b , V bare the objective function and decision variables of the energy storage operator respectively; C r,w , C g,w , C s,w , C o,w They are respectively the compensation income for the capacity and electricity obtained from providing ancillary services, replacement costs and operating costs.

[0028] The lower-level joint clearing model takes maximizing the welfare of the whole society as its objective function:

[0029]

[0030] is the j-th quotation of the traditional power generator under scenario w; M is the number of quotation segments; is the winning bid electricity of the traditional power generator in the jth segment at time t under scenario w; c r The price coefficient for providing reserve capacity to traditional power generators; R t,w The backup capacity provided at time t for the traditional power generator scenario w; c u , c d is the startup and shutdown cost of the traditional power unit; t,w , ν t,w These are the startup and shutdown variables for traditional power units.

[0031] The method of solving the two-layer optimization model by using the interior point method and the adaptive global harmony search algorithm to generate the bidding strategies of the new energy power generator and the energy storage operator further includes:

[0032] Step 3.1: Output according to new energy The distribution function of Ω is used to generate Ω scenarios, and the parameters of each power generator and energy storage operator are given, and l = 0;

[0033] Step 3.2: Set the number of iterations of the upper model k = 0;

[0034] Step 3.3: Use the interior point method to optimize and solve the revenue objective functions of the new energy generator and the energy storage operator, and obtain a new round of strategy combinations, namely:

[0035]

[0036]

[0037] Step 3.4: Determine whether Nash equilibrium is reached. If the optimal strategies of the renewable energy generator and the energy storage operator are the same in two consecutive solutions, that is:

[0038]

[0039] The upper model is solved, and go to step 3.6;

[0040] Step 3.5: Obtain the bidding strategy solved by the interaction between the new energy generator and the energy storage operator Let k=k+1, and return to step 3.3;

[0041] Step 3.6: Combine strategies that meet the Nash equilibrium Input to the lower level joint clearing model;

[0042] Step 3.7: The lower-level joint clearing model is optimized and solved using the adaptive global harmony search algorithm

[0043] Step 3.8: Determine whether the two-layer optimization model converges; the convergence condition is that the difference between the results of the upper and lower layers in two iterations is within the preset error range of ε1, ε2, ε3, that is:

[0044]

[0045] If the convergence conditions are met, the benefits of new energy generators and energy storage operators are calculated and the optimization results are output; if not, Pass it to the upper layer, set l=l+1, and return to step 3.2.

[0046] The present invention also discloses a quotation strategy generation system based on multi-subject reputation evaluation using the above-mentioned quotation strategy generation method based on multi-subject reputation evaluation, comprising:

[0047] The market acquisition module is used to obtain market boundary conditions and determine the market pricing mechanism based on the market transaction model, clearing method, quotation content and settlement process;

[0048] A model building module, for determining the reputation of multiple entities according to the output uncertainty of the new energy power generator, and establishing a two-layer optimization model for the new energy power generator and the energy storage operator to participate in the day-ahead market based on the reputation of the multiple entities and the market pricing mechanism, wherein the two-layer optimization model includes an upper-layer Nash game model of the new energy power generator and the energy storage operator with the goal of maximizing market revenue and a lower-layer joint clearing model of the electric energy market and the ancillary service market with the goal of maximizing social welfare;

[0049] The model solving module is used to realize information interaction between the upper Nash game model and the lower joint clearing model through interactive decision variables, solve the two-layer optimization model using the interior point method and the adaptive global harmony search algorithm, and generate the quotation strategies of the new energy power generators and energy storage operators.

[0050] Accordingly, the present application also discloses a terminal, including a processor and a storage medium;

[0051] The storage medium is used to store instructions;

[0052] The processor is used to operate according to the instructions to execute the steps of the aforementioned quotation strategy generation method based on multi-subject reputation evaluation.

[0053] Correspondingly, the present application also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the aforementioned method for generating a quotation strategy based on multi-subject reputation evaluation are implemented.

[0054] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a method and system for generating a quotation strategy based on the reputation evaluation of multiple subjects, which is based on the reputation of multiple subjects and imposes penalties when the contract is not completed, thereby increasing the rationality of the quotation strategy. A two-layer market optimization model for new energy power generators and energy storage operators is constructed, which effectively reduces the penalty costs of new energy power generators, improves the economic benefits of new energy power generators, promotes the balance of supply and demand in the power market and stabilizes electricity price fluctuations, enables energy storage operators and new energy power generators to increase net income, and promotes the balance of supply and demand and orderly operation of the power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of a method for generating a quotation strategy based on the reputation of multiple entities and the balance of market supply and demand in the present invention;

[0056] Figure 2 It is a framework diagram of the double-layer optimization model in the present invention;

[0057] Figure 3 This is a flowchart for solving the double-layer optimization model in the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0059] The embodiments described in this application are only some embodiments of the present invention, not all embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0060] In view of the shortcomings of the prior art, the present invention proposes a method and system for generating a quotation strategy based on the reputation evaluation of multiple subjects, establishes a quotation strategy model for new energy power generators and a quotation strategy model for energy storage operators affected by the state of charge, constructs a market two-layer optimization model for new energy power generators and energy storage operators, obtains the quotation strategy of each subject under the Nash equilibrium, introduces a reputation evaluation mechanism to punish its behavior of not reaching the contracted electricity quantity, and alleviates the excessive risk cost borne by ISO. This trading mechanism effectively reduces the penalty cost of new energy power generators, improves the economic benefits of new energy power generators, promotes the balance of supply and demand in the power market and stabilizes electricity price fluctuations, and at the same time enables energy storage to have sufficient electricity and capacity space.

[0061] Competition in the electricity market is a typical Complex Adaptive System (CAS) problem, involving multiple entities, whose behaviors influence each other and all entities have the willingness to participate in energy storage operators. In this system, both new energy generators and energy storage operators pursue the maximization of their own interests. The purchase volume of new energy generators is subject to the electricity price set by energy storage operators, and the unit price of energy storage operators directly affects the decision-making of new energy generators. Both parties seek a win-win situation through the process of game. See Figure 1 As shown, the quotation strategy generation method based on multi-subject reputation evaluation disclosed in the present invention includes the following steps:

[0062] Step 1: Obtain market boundary conditions and determine the market pricing mechanism based on market trading patterns, clearing methods, quotation content and settlement processes.

[0063] Based on the typical foreign power market mechanism and the power spot market mechanism in trial operation in my country, the market rules for bilateral transactions between energy storage operators and new energy power generators are designed as follows:

[0064] (1) Trading model

[0065] In the centralized trading model, each power generator declares a price to the independent system operator (ISO), and the power generator's transaction volume is determined after unified clearing. Market members must submit quotations before 12:00 on the day before the transaction date. During this period, the energy storage operator announces constraints such as the purchase and sale price, maximum charging and discharging power and capacity. New energy power generators choose suitable energy storage operators for transactions based on their own demand characteristics. The electricity price of the energy storage operator determines the purchase volume of new energy power generators. After the transaction is completed, it is recorded and reported to the ISO. ISO jointly clears the electricity market and ancillary service market based on the quotation content, and announces the market clearing results before 16:00 on the day before the transaction date.

[0066] (2) Market members

[0067] Market members include new energy power generators, energy storage operators, traditional power generators and electricity users.

[0068] (3) Quotation content

[0069] The quotation of new energy power generators includes the unit price of electricity in each period and the charging and discharging power with energy storage operators at each time. Energy storage operators use the remaining capacity to participate in the auxiliary service market and obtain compensation. Traditional power generators can submit multiple quotation curves to ISO, with fixed power in each section and adjustable prices to reflect the difference in marginal costs.

[0070] (4) Clearing method

[0071] A unified marginal price (Market Clearing Price, MCP) is used for joint clearing of the main energy market and ancillary services. The purchase and sale prices of energy storage operators directly affect the purchase volume of new energy power generators. After the transaction is completed, ISO, with the goal of maximizing social welfare, determines the market clearing price, the winning bid volume of the power generator, and the charging and discharging power of the energy storage operator when providing ancillary services based on the power generator's quotation and the remaining capacity of the energy storage operator.

[0072] Step 2: Determine the reputation of multiple entities according to the output uncertainty of new energy power producers. Based on the reputation of multiple entities and the market pricing mechanism, establish a two-layer optimization model for new energy power producers and energy storage operators to participate in the day-ahead market. The two-layer optimization model includes an upper-layer Nash game model of new energy power producers and energy storage operators with the goal of maximizing market returns and a lower-layer joint clearing model of the electric energy market and ancillary service market with the goal of maximizing social welfare.

[0073] Usually, the quotation strategy of market trading entities is based on their own marginal costs. For example, thermal power units determine their strategies based on the secondary fuel consumption curve. New energy power generators have no fuel consumption costs and their power generation costs are zero. However, since wind turbine blades and photovoltaic panels will be damaged during operation, maintenance costs will be incurred. Therefore, the quotation strategy of new energy power generators is:

[0074]

[0075] Where: is the quotation of the renewable energy power generator at time t; is the winning bid and actual output of the new energy power generator at time t; Δt is the quotation time interval. The first item is the fixed unit price a generated by the maintenance cost n , in order to pursue profits, usually a n Slightly higher than the maintenance cost price coefficient. The latter term is the unit price of the imbalance cost generated when the bid power of new energy is not equal to the actual output, which is determined by the offset price coefficient b nIt is composed of multiplying by the unbalanced power and then dividing by the actual power. When the latter term is positive, it reflects that the new energy power generator still needs the energy storage operator to provide power support, resulting in electricity purchase costs. When the latter term is negative, it reflects that the new energy power generator can sell the surplus electricity to the energy storage operator and generate electricity sales income.

[0076] Energy storage operators participate in market transactions mainly by charging at low prices and discharging at high prices to gain profits. This paper considers the impact of the state of charge (SOC) on the charging and discharging price, denoted as S SOC， The SOC is divided into three intervals, and a bidding strategy for energy storage is proposed.

[0077] (1) In [S SOCmin ,S SOC1 ) is a period of power shortage, when the energy storage capacity is relatively sufficient, and the energy storage operator hopes to purchase more electricity to meet the demand for insufficient output of new energy. At this time, the energy storage operator appropriately increases the purchase and sales prices of electricity to avoid losing "business opportunities" due to its own power shortage.

[0078] (2) In [S SOC1， S SOC2 ] The scope is the parity period, and energy storage operators can not only obtain "business opportunities" for insufficient output of new energy, but also have the space to purchase excess power from new energy. At this time, the purchase price and selling price of energy storage operators are in the parity period.

[0079] (3) In (S SOC2 ,S SOCmax ] During periods of abundant electricity, in order to purchase low-priced excess electricity from new energy sources, energy storage operators appropriately reduce purchase and selling prices.

[0080] The bidding strategy for energy storage is:

[0081]

[0082] Where: Bidding strategies for energy storage operators; These are the three ranges of electricity purchase prices for energy storage operators; These are the three electricity selling prices for energy storage operators.

[0083] The quotation strategy based on the reputation of multiple entities and the balance of market supply and demand includes two parts: a double-layer optimization model and a solution algorithm. Figure 2 The framework diagram of the two-layer optimization model of the present invention. The upper layer is the Nash game of new energy power generators and energy storage operators with the objective function of maximizing their respective profits. The lower layer is the joint clearing model of the electric energy market and the ancillary service market with the objective function of maximizing social welfare. Through the continuous interaction between the upper and lower layers, the optimal bidding strategy of new energy power generators and energy storage operators is obtained.

[0084] Firstly, a two-party game model is established between the upper-level NEP and BESP to maximize market benefits.

[0085] The uncertainty of renewable energy output will increase the bidding risk of renewable energy power generators. If the winning bid is greater than the actual power, the credit value will be reduced and the company will be penalized, resulting in penalty costs:

[0086]

[0087] In the formula is the credit value of the new energy power generator in the transaction in the tth period and the wth scenario; is the winning bid volume of the new energy power generator in the transaction under scenario w during period t; is the actual output of the new energy power generator in the transaction under the scenario w in the t period; is the deviation power of new energy in the t period and w scenario; ξ is the allowable deviation power. n The penalty amount for renewable energy output lower than the winning bid amount is The electricity purchase price for energy storage operators.

[0088] When a deal is reached with the energy storage operator, electricity can be sold for profit when the output is sufficient, and electricity can be purchased to reduce penalty costs when the output is insufficient, thereby reducing the risk cost of the new energy generator. This invention adopts the scenario method to describe the uncertainty of new energy output. Considering the electricity revenue, maintenance cost, electricity purchase cost, electricity sales revenue and penalty cost, the objective function is to maximize the revenue of the new energy generator:

[0089]

[0090] Where: U n , V n are the objective function and decision variables of the new energy power generator respectively; is the fixed unit price in scenario w; is the offset price coefficient in scenario w; T is the total transaction period, and in the specific embodiment of the present invention, T=24; is the market clearing electricity price of the energy storage operator at time t under scenario w; is the electricity purchase price of the energy storage operator at time t1 under scenario w. The energy storage operator charges at the peak time of renewable energy output, and the renewable energy power generator generates electricity sales benefits; is the electricity selling price of the energy storage operator at time t2 under scenario w. The energy storage operator discharges electricity at the peak load moment, and the new energy power generator incurs electricity purchase costs; is the bid power and actual power output at time t in scenario w; c n is the price coefficient of the maintenance cost of the new energy unit; ρw is the probability of the generated renewable energy output scenario w; Ω is the total number of generated scenarios; The power purchased and sold to the energy storage operator at time t in scenario w; P n The penalty price for renewable energy output lower than the winning bid volume.

[0091] The constraints are as follows:

[0092] Quote upper and lower limits:

[0093]

[0094] It is the lower and upper limits of the quotations for new energy power generators.

[0095] Energy storage charging and discharging constraints:

[0096]

[0097] Where: is the rated power of the energy storage; is the transaction capacity between the energy storage operator and the new energy generator at time t under scenario w; η c , η f is the charging and discharging efficiency of energy storage; S SOCmin , S SOCmax are the minimum and maximum state of charge of the energy storage.

[0098] In the energy storage operator part of the upper model, the discharge income, compensation income for providing ancillary services, electricity purchase costs, replacement costs, and operating costs are considered, with maximizing the income of the energy storage operator as the objective function:

[0099]

[0100] Where: U b , V b are the objective function and decision variables of the energy storage operator respectively; C r,w , C g,w , C s,w , C o,w They are respectively the compensation income for the capacity and electricity obtained from providing ancillary services, replacement costs and operating costs.

[0101] Then, a day-ahead market clearing model is constructed with the lowest total bids of new energy generators and traditional generators at the lower level.

[0102] The present invention adopts the MCP mechanism to generate a clearing plan. Compared with the PAB mechanism, the MCP enables power generators to quote according to marginal costs, improve market efficiency, and promote market supply and demand stability. The lower-level joint clearing model takes the maximization of social welfare as its objective function:

[0103]

[0104] is the j-th quotation of the traditional power generator under scenario w; M is the number of quotation segments; is the winning bid electricity of the traditional power generator in the jth segment at time t under scenario w; c r The price coefficient for providing reserve capacity to traditional power generators; R t,w The backup capacity provided at time t for the traditional power generator scenario w; c u , c d is the startup and shutdown cost of the traditional power unit; t,w , ν t,w is the startup and shutdown variable of the traditional power unit. When startup or shutdown occurs at time t, u t,w (v t,w ) is 1, otherwise it is 0.

[0105] The constraints include:

[0106] Power balance constraints:

[0107]

[0108] d t is the load demand at time t.

[0109] Constraints on the amount of electricity that can be awarded in new energy bidding:

[0110]

[0111] Charging and discharging constraints of remaining energy storage capacity:

[0112]

[0113] S SOCmin ≤S SOCt,w ≤S SOCmax

[0114]

[0115] Where E t，w is the remaining battery capacity at time t under scenario w; is the transaction capacity between the energy storage operator and the new energy power generator at time t under scenario w; E N is the rated capacity of energy storage.

[0116] Energy storage operators and new energy power generators maximize their respective interests through game-playing, namely, Nash equilibrium:

[0117]

[0118] In the formula They are the optimal bidding strategies for new energy power generators and energy storage operators respectively.

[0119] Step 3: Information interaction is achieved between the upper Nash game model and the lower joint clearing model through interactive decision variables, and the interior point method and adaptive global harmony search algorithm are used to solve the two-layer optimization model to generate the bidding strategies of new energy power generators and energy storage operators.

[0120] As a mature optimization algorithm, the Interior Point Method (IPM) performs well in solving convex problems. In the above model, since equations (1) to (7) are all linear functions, the entire upper model is a convex problem. The IPM method can quickly and accurately solve the bidding strategy of power generators. The lower model involves the start and stop variables of traditional power generating units, so it belongs to a mixed integer model. In order to solve this complexity, the Self-Adaptive Global Harmony Search (SGHS) algorithm is used. The SGHS algorithm is simple in concept, requires fewer parameter adjustments, and has a fast solution speed. It is very suitable for the solution process of the lower-level joint clearing model. The process is as follows Figure 3 The solution steps are as follows:

[0121] Step 3.1: Output according to new energy The distribution function of Ω is used to generate scenarios, and the parameters of each generator and energy storage operator are given, and l=0.

[0122] Step 3.2: Set the number of iterations of the upper model k = 0.

[0123] Step 3.3: Use the interior point method to optimize and solve formulas (1)-(6) and formula (7) respectively to obtain a new round of strategy combinations, namely:

[0124]

[0125] Step 3.4: Determine whether Nash equilibrium is reached. If the optimal strategies of the renewable energy generator and the energy storage operator are the same in two consecutive solutions, that is:

[0126]

[0127] The upper model is solved and go to step 3.6.

[0128] Step 3.5: Obtain the bidding strategy solved by the interaction between the new energy generator and the energy storage operator Let k=k+1 and return to step 3.3.

[0129] Step 3.6: Combine strategies that meet the Nash equilibrium Input to the lower level joint clearing model.

[0130] Step 3.7: Optimize and solve the lower-level joint clearing model using the SGHS algorithm

[0131] Step 3.8: Determine whether the two-layer optimization model converges; in the process of interactive solution of the upper and lower layers, if the strategy combination If there is a change, the market clearing result is Therefore, the convergence condition of the two-layer optimization model is that the difference between the results of the upper and lower layers in two iterations is within the preset error range of ε1, ε2, ε3, that is:

[0132]

[0133] If the convergence condition is met, the benefits of the new energy generator and the energy storage operator are calculated using equations (4) and (8), and the optimization results are output; if not, Pass it to the upper layer, set l=l+1, and return to step 3.2.

[0134] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a method and system for generating a quotation strategy based on the reputation evaluation of multiple subjects, which is based on the reputation of multiple subjects and imposes penalties when the contract is not completed, thereby increasing the rationality of the quotation strategy. A two-layer market optimization model for new energy power generators and energy storage operators is constructed, which effectively reduces the penalty costs of new energy power generators, improves the economic benefits of new energy power generators, promotes the balance of supply and demand in the power market and stabilizes electricity price fluctuations, enables energy storage operators and new energy power generators to increase net income, and promotes the balance of supply and demand and orderly operation of the power market.

[0135] The present invention may be a system, a method and / or a computer program product. The present invention also discloses a quotation strategy generation system based on multiple subject reputation evaluation based on the aforementioned quotation strategy generation method based on multiple subject reputation evaluation, comprising:

[0136] The market acquisition module is used to obtain market boundary conditions and determine the market pricing mechanism based on the market transaction model, clearing method, quotation content and settlement process;

[0137] A model building module, for determining the reputation of multiple entities according to the output uncertainty of the new energy power generator, and establishing a two-layer optimization model for the new energy power generator and the energy storage operator to participate in the day-ahead market based on the reputation of the multiple entities and the market pricing mechanism, wherein the two-layer optimization model includes an upper-layer Nash game model of the new energy power generator and the energy storage operator with the goal of maximizing market revenue and a lower-layer joint clearing model of the electric energy market and the ancillary service market with the goal of maximizing social welfare;

[0138] The model solving module is used to realize information interaction between the upper Nash game model and the lower joint clearing model through interactive decision variables, solve the two-layer optimization model using the interior point method and the adaptive global harmony search algorithm, and generate the quotation strategies of the new energy power generators and energy storage operators.

[0139] Based on the spirit of the present invention, those skilled in the art can easily think that a computer program product can be obtained based on the aforementioned quotation strategy generation method based on multi-subject reputation evaluation. The computer program product may include a computer-readable storage medium, which carries computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure. That is, the present application also includes a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the aforementioned quotation strategy generation method based on multi-subject reputation evaluation.

[0140] Computer readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device. Computer readable storage medium can be, for example, - but not limited to - electrical storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, for example, punch card or groove protrusion structure with instructions stored thereon and any suitable combination of the above. Computer readable storage medium used here is not interpreted as instantaneous signal itself, such as radio wave or other free propagating electromagnetic wave, electromagnetic wave propagated by waveguide or other transmission medium (for example, light pulse by optical fiber cable) or electrical signal transmitted by wire.

[0141] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0142] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages-such as Smalltalk, C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network-including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A quotation strategy generation method based on multi-subject reputation evaluation, characterized in that: The following steps are involved: Obtain market boundary conditions and determine the market pricing mechanism based on market trading patterns, clearing methods, quotation content and settlement processes; Determine the reputation of multiple entities according to the output uncertainty of new energy power generators, and based on the reputation of multiple entities and the market pricing mechanism, establish an upper-level Nash game model of new energy power generators and energy storage operators with the goal of maximizing market revenue when new energy power generators and energy storage operators participate in the day-ahead market, and a lower-level joint clearing model of the electric energy market and ancillary service market with the goal of maximizing social welfare; Information interaction is achieved between the upper Nash game model and the lower joint clearing model through interactive decision variables. The interior point method and adaptive global harmony search algorithm are used to solve the two-layer optimization model to generate the bidding strategies of new energy power generators and energy storage operators.

2. The method for generating a quotation strategy based on multi-subject reputation evaluation according to claim 1, characterized in that: The establishment of a two-tier optimization model for new energy power generators and energy storage operators to participate in the day-ahead market based on the reputation of the multiple entities and the market pricing mechanism further includes: The scenario method is used to describe the uncertainty of renewable energy output, taking into account electricity revenue, maintenance cost, electricity purchase cost, electricity sales revenue and penalty cost, and taking the maximization of renewable energy generator revenue as the objective function: Among them U n , V n are the objective function and decision variables of the new energy power generator respectively; is the fixed unit price in scenario w; is the offset price coefficient in scenario w; T is the total trading period; is the market clearing electricity price of the energy storage operator at time t under scenario w; is the electricity purchase price of the energy storage operator at time t1 under scenario w. The energy storage operator charges at the peak time of renewable energy output, and the renewable energy power generator generates electricity sales benefits; is the electricity selling price of the energy storage operator at time t2 under scenario w. The energy storage operator discharges electricity at the peak load moment, and the new energy power generator incurs electricity purchase costs; is the bid power and actual power output at time t in scenario w; c n is the price coefficient of the maintenance cost of the new energy unit; ρ w is the probability of the generated renewable energy output scenario w; Ω is the total number of generated scenarios; The power purchased and sold to the energy storage operator at time t in scenario w; P n The penalty price for renewable energy output lower than the winning bid; Δt is the quotation time interval; The penalty cost is that if the bid power is greater than the actual power, the credit value will be reduced and the penalty will be imposed: In the formula is the credit value of the new energy power generator in the transaction in the tth period and the wth scenario; is the deviation power of new energy in the tth period and the wth scenario; ξ is the allowable deviation power; P n The penalty amount for renewable energy output lower than the winning bid amount is The electricity purchase price for energy storage operators.

3. The method for generating a quotation strategy based on multi-subject reputation evaluation according to claim 2, characterized in that: The upper-level Nash game model meets the upper and lower limit constraints of the quotation: It is the lower and upper limits of the quotations for new energy power generators.

4. The method for generating a quotation strategy based on multi-subject reputation evaluation according to claim 3 is characterized in that: The upper-level Nash game model satisfies the energy storage charging and discharging constraints: Where: is the rated power of the energy storage; is the transaction capacity between the energy storage operator and the new energy generator at time t under scenario w; η c , η f is the charging and discharging efficiency of energy storage; S SOCmin , S SOCmax are the minimum and maximum state of charge of the energy storage.

5. The method for generating a quotation strategy based on multi-subject reputation evaluation according to claim 4 is characterized in that: Based on the reputation of the multiple entities and the market pricing mechanism, a two-tier optimization model for new energy power generators and energy storage operators to participate in the day-ahead market is established, further including: The objective function is to maximize the revenue of energy storage operators: Among them U b , V b are the objective function and decision variables of the energy storage operator respectively; C r,w , C g,w , C s,w , C o,w They are respectively the compensation income for the capacity and electricity obtained from providing ancillary services, replacement costs and operating costs.

6. The method for generating a quotation strategy based on multi-subject reputation evaluation according to claim 5 is characterized in that: The lower-level joint clearing model takes maximizing the welfare of the whole society as its objective function: is the j-th quotation of the traditional power generator under scenario w; M is the number of quotation segments; is the winning bid electricity of the traditional power generator in the jth segment at time t under scenario w; c r The price coefficient for providing reserve capacity to traditional power generators; R t,w The backup capacity provided at time t for the traditional power generator scenario w; c u , c d is the startup and shutdown cost of the traditional power unit; t,w , ν t,w These are the startup and shutdown variables for traditional power units.

7. The method for generating a quotation strategy based on multi-subject reputation evaluation according to claim 6, characterized in that: The method of solving the two-layer optimization model by using the interior point method and the adaptive global harmony search algorithm to generate the bidding strategies of the new energy power generator and the energy storage operator further includes: Step 3.1: Output based on new energy The distribution function of Ω is used to generate Ω scenarios, and the parameters of each power generator and energy storage operator are given, and l = 0; Step 3.2: Set the number of iterations of the upper model k = 0; Step 3.3: Use the interior point method to optimize and solve the revenue objective functions of the new energy generator and the energy storage operator, and obtain a new round of strategy combinations, namely: Step 3.4: Determine whether Nash equilibrium is reached. If the optimal strategies solved by the renewable energy generator and the energy storage operator twice are the same, that is: The upper model is solved, and go to step 3.6; Step 3.5: Obtain the bidding strategy solved by the interaction between the new energy generator and the energy storage operator Let k=k+1, and return to step 3.3; Step 3.6: Combine strategies that meet the Nash equilibrium Input to the lower level joint clearing model; Step 3.7: The lower-level joint clearing model is optimized and solved using the adaptive global harmony search algorithm Step 3.8: Determine whether the two-layer optimization model converges; the convergence condition is that the difference between the results of the upper and lower layers in two iterations is within the preset error range of ε1, ε2, ε3, that is: If the convergence conditions are met, the benefits of new energy generators and energy storage operators are calculated and the optimization results are output; if not, Pass it to the upper layer, set l=l+1, and return to step 3.

2.

8. A quotation strategy generation system based on multi-subject reputation evaluation, characterized in that: include: The market acquisition module is used to obtain market boundary conditions and determine the market pricing mechanism based on the market transaction model, clearing method, quotation content and settlement process; A model building module, for determining the reputation of multiple entities according to the output uncertainty of the new energy power generator, and establishing a two-layer optimization model for the new energy power generator and the energy storage operator to participate in the day-ahead market based on the reputation of the multiple entities and the market pricing mechanism, wherein the two-layer optimization model includes an upper-layer Nash game model of the new energy power generator and the energy storage operator with the goal of maximizing market revenue and a lower-layer joint clearing model of the electric energy market and the ancillary service market with the goal of maximizing social welfare; The model solving module is used to realize information interaction between the upper Nash game model and the lower joint clearing model through interactive decision variables, solve the two-layer optimization model using the interior point method and the adaptive global harmony search algorithm, and generate the quotation strategies of the new energy power generators and energy storage operators.

9. The quotation strategy generation system based on multi-subject reputation evaluation according to claim 8 is characterized in that: The model building module is further used to: The scenario method is used to describe the uncertainty of renewable energy output, taking into account electricity revenue, maintenance cost, electricity purchase cost, electricity sales revenue and penalty cost, and taking the maximization of renewable energy generator revenue as the objective function: Among them U n , V n are the objective function and decision variables of the new energy power generator respectively; is the fixed unit price in scenario w; is the offset price coefficient in scenario w; T is the total trading period; is the market clearing electricity price of the energy storage operator at time t under scenario w; is the electricity purchase price of the energy storage operator at time t1 under scenario w. The energy storage operator charges at the peak time of renewable energy output, and the renewable energy power generator generates electricity sales benefits; is the electricity selling price of the energy storage operator at time t2 under scenario w. The energy storage operator discharges electricity at the peak load moment, and the new energy power generator incurs electricity purchase costs; is the bid power and actual power output at time t in scenario w; c n is the price coefficient of the maintenance cost of the new energy unit; ρ w is the probability of the generated renewable energy output scenario w; Ω is the total number of generated scenarios; The power purchased and sold to the energy storage operator at time t in scenario w; P n The penalty price for renewable energy output lower than the winning bid; Δt is the quotation time interval; The penalty cost is that if the bid power is greater than the actual power, the credit value will be reduced and the penalty will be imposed: In the formula is the credit value of the new energy power generator in the transaction in the tth period and the wth scenario; is the deviation power of new energy in the tth period and the wth scenario; ξ is the allowable deviation power; P n The penalty amount for renewable energy output lower than the winning bid amount is The electricity purchase price for energy storage operators.

10. The quotation strategy generation system based on multi-subject reputation evaluation according to claim 9 is characterized in that: The upper-level Nash game model meets the upper and lower limit constraints of the quotation: It is the lower and upper limits of the quotations for new energy power generators.

11. The quotation strategy generation system based on multi-subject reputation evaluation according to claim 10 is characterized in that: The upper-level Nash game model satisfies the energy storage charging and discharging constraints: Where: is the rated power of the energy storage; is the transaction capacity between the energy storage operator and the new energy generator at time t under scenario w; η c , η f is the charging and discharging efficiency of energy storage; S SOCmin , S SOCmax are the minimum and maximum state of charge of the energy storage.

12. The quotation strategy generation system based on multi-subject reputation evaluation according to claim 11 is characterized in that: The model building module is further used to: The objective function is to maximize the revenue of energy storage operators: Among them U b , V b are the objective function and decision variables of the energy storage operator respectively; C r,w , C g,w , C s,w , C o,w They are respectively the compensation income for the capacity and electricity obtained from providing ancillary services, replacement costs and operating costs.

13. The quotation strategy generation system based on multi-subject reputation evaluation according to claim 12 is characterized in that: The lower-level joint clearing model takes maximizing the welfare of the whole society as its objective function: is the j-th quotation of the traditional power generator under scenario w; M is the number of quotation segments; is the winning bid electricity of the traditional power generator in the jth segment at time t under scenario w; c r The price coefficient for providing reserve capacity to traditional power generators; R t,w The backup capacity provided at time t for the traditional power generator scenario w; c u , c d is the startup and shutdown cost of the traditional power unit; t,w , ν t,w These are the startup and shutdown variables for traditional power units.

14. The quotation strategy generation system based on multi-subject reputation evaluation according to claim 13 is characterized in that: The model solving module is further used for: Step 3.1: Output based on new energy The distribution function of Ω is used to generate Ω scenarios, and the parameters of each power generator and energy storage operator are given, and l = 0; Step 3.2: Set the number of iterations of the upper model k = 0; Step 3.3: Use the interior point method to optimize and solve the revenue objective functions of the new energy generator and the energy storage operator, and obtain a new round of strategy combinations, namely: Step 3.4: Determine whether Nash equilibrium is reached. If the optimal strategies solved by the renewable energy generator and the energy storage operator twice are the same, that is: The upper model is solved, and go to step 3.6; Step 3.5: Obtain the bidding strategy solved by the interaction between the new energy generator and the energy storage operator Let k=k+1, and return to step 3.3; Step 3.6: Combine strategies that meet the Nash equilibrium Input to the lower level joint clearing model; Step 3.7: The lower-level joint clearing model is optimized and solved using the adaptive global harmony search algorithm Step 3.8: Determine whether the two-layer optimization model converges; the convergence condition is that the difference between the results of the upper and lower layers in two iterations is within the preset error range of ε1, ε2, ε3, that is: If the convergence conditions are met, the benefits of new energy generators and energy storage operators are calculated and the optimization results are output; If not satisfied, Pass it to the upper layer, set l=l+1, and return to step 3.

2.

15. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the quotation strategy generation method based on multi-subject reputation evaluation according to any one of claims 1-7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for generating a quotation strategy based on multi-subject reputation evaluation described in any one of claims 1 to 7 are implemented.

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