Wind-solar-storage collaborative day-ahead quotation strategy generation method and device

Abstract

The invention relates to a wind-solar-storage collaborative day-ahead quotation strategy generation method, and the method comprises the steps: constructing a supply-demand coupling bidding model of a new energy power generator and an energy storage operator according to the coupling and opponent disk mutual characteristics of two market subjects, namely, new energy and energy storage, in market transaction; a new energy power generator benefit maximization objective function, an energy storage operator benefit maximization objective function and a market overall welfare objective function are established, then a composite game model of the new energy power generator and the energy storage operator is established, and the new energy power generator and the energy storage operator can obtain an optimal strategy of day-ahead bidding at the same time through model solution based on Nash equilibrium. The strategy simultaneously conforms to Pareto improvement of the whole market.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power spot market, and in particular to a method and device for generating a wind-solar-storage collaborative day-ahead quotation strategy. Background Art

[0002] With the acceleration of the construction of my country's power market, conditions have been provided for various entities on the power generation side to enter the market. Among them, new energy is an important power generation resource, and its proportion in my country's energy system has reached a significant level.

[0003] Currently, there are two main ways for new energy to enter the electricity market. One is to enter the market through VPP virtual power plants and other institutions that have market access conditions and operational capabilities. Those that enter the market in this way are mainly some distributed power sources and stations. The other is some entities with considerable capacity and market access qualifications, institutions that own large-scale new energy stations and supporting facilities, generally referred to as new energy power generators.

[0004] After the market mechanism of full-power bidding and settlement is improved, new energy power generators need to consider the issue of new energy consumption. Energy storage, as an important technical means of regulating the power system, can choose to enter the market by quoting or reporting quantity or by quoting quantity without quoting under the current market mechanism. In particular, energy storage operators represented by large-scale energy storage stations have become important participants in the market.

[0005] Whether from the perspective of power system dispatch or power market transactions, the synergy of energy storage and new energy is an important form of combined business. In the power market, energy storage operators are important counterparties of new energy power generators. On the one hand, new energy power generators need to purchase electricity from energy storage to smooth out output deviations and reduce intraday operating risks; on the other hand, new energy power generators are the main demanders of flexible resources of energy storage operators.

[0006] How new energy power generators and energy storage operators can simultaneously obtain the best strategy for bidding on the day before is a problem that needs to be solved. Summary of the invention

[0007] In view of this, the purpose of the present invention is to provide a method and device for generating a wind, solar and energy storage collaborative day-ahead bidding strategy to solve the problem in the prior art of how new energy power generators and energy storage operators can simultaneously obtain the best strategy for day-ahead bidding.

[0008] According to a first aspect of an embodiment of the present invention, a method for generating a wind-solar-storage collaborative day-ahead bidding strategy is provided, comprising:

[0009] Establish a bidding strategy for new energy power generators and a quotation strategy for energy storage operators, and establish a supply-demand coupling bidding model for new energy power generators and energy storage operators based on the bidding strategy for new energy power generators and the quotation strategy for energy storage operators;

[0010] Establish the first objective function of maximizing the benefits of new energy power generators, the second objective function of maximizing the benefits of energy storage operators, and the third objective function of maximizing the overall welfare of the market;

[0011] A compound game model of new energy power generators and energy storage operators is established based on the supply-demand coupling bidding model, the first objective function, the second objective function and the third objective function of new energy power generators and energy storage operators;

[0012] The composite game model is used to perform the following steps:

[0013] S11, set the iteration steps to h and l, and the initial values ​​of h and l are both 0;

[0014] S12. Calculate the first objective function according to the strategy of the new energy power generator to obtain a first solution, calculate the second objective function according to the strategy of the energy storage operator to obtain a second solution; derive the strategy of the new energy power generator according to the first solution and the second solution and strategies of energy storage operators

[0015] S13. Judgment strategy and strategies Does it meet the Nash equilibrium condition? If so, change the strategy and strategies As a Nash equilibrium solution, execute step S15, if not, execute step S14;

[0016] S14, let the iteration step h = h + 1, change the strategy As a new energy power generation company strategy, As the energy storage operator strategy, the process proceeds to step S12;

[0017] S15, calculate the overall solution of the third objective function as the clearing result based on the Nash equilibrium solution, calculate whether the quadratic norm of the difference between the clearing results of iteration steps l and l-1 is less than a preset threshold, if so, output the strategy and strategies If it is not less than the threshold, set l=l+1, h=0 and go to step S12.

[0018] Preferably, the bidding strategy formula for new energy power generators is established, and the formula is as follows:

[0019]

[0020] Among them, α represents the coefficient related to the operating cost of new energy, representing the fixed component of the price to cover the cost; P t represents the actual output of new energy during period t; P t,bid It represents the winning bid power of the new energy power generator in the day-ahead period in time period t; β represents the coefficient related to the expected output deviation rate in the quotation; Δt represents the relevant timing length when the deviation is settled.

[0021] Preferably, the bidding strategy formula of the energy storage operator is established as follows:

[0022]

[0023] Among them, η t,b,1 , η t,b,2 , η t,b,3 Respectively represent the price level of energy storage purchasing electricity under three charge states; η t,s,1 , η t,s,2 , η t,s,3 Respectively represent the price level of energy storage selling electricity under three states of charge; SOC t Indicates the actual charge level of the energy storage during period t; SOC min Indicates the minimum state of charge allowed for normal operation of energy storage; SOC max It indicates the maximum state of charge allowed for normal operation of the energy storage; SOC1 and SOC2 indicate the interval boundaries for dividing the energy storage charge state.

[0024] Preferably, the supply-demand coupling bidding model of the new energy power generator and the energy storage operator is established, including the bidding strategy formula of the new energy power generator and the quotation strategy formula of the energy storage operator, as well as the following formula:

[0025] Approximate confidence interval of renewable energy output in period t:

[0026] P t ∈[P t,f -μ P -2σ P ,P t,f +μ P +2σ P ]

[0027] Among them, μ P represents the expected output of new energy, σ P Represents the standard deviation of renewable energy output, P t,f It represents the predicted value of the output of new energy;

[0028] The maximum electricity purchase demand and maximum electricity sales supply of new energy in period t are expressed as follows:

[0029]

[0030] Assuming that the threshold and confidence level of market deviation assessment are both 5%, the maximum electricity purchase demand and maximum electricity sales supply of new energy in period t are expressed as:

[0031]

[0032] SOC1 and SOC2 are expressed as:

[0033]

[0034] Among them, Q rated Indicates the rated capacity of the energy storage; index indicates the index function, the time under the query condition; t1 indicates the index to P t,f The specific time returned when the maximum value is taken; L t It indicates the load demand level in period t; t2 indicates the specific time when the maximum load demand level is returned.

[0035] Preferably, the first objective function for maximizing the benefits of new energy power generators is established as follows:

[0036]

[0037] In the above formula, k represents the scene, K represents the maximum number of scenes; f(k) represents the probability of the kth scene occurring; T represents the maximum number of time periods within a day; ρ t (k) represents the market clearing price in period t under scenario k; P t,bid (k) represents the winning bid power of the new energy power generator in the tth period under scenario k; ξ represents the new energy operation and maintenance cost coefficient related to the unit output; P represents the price at which the energy storage operator sells electricity to new energy sources when the load peak occurs in scenario k; t,buy (k) represents the amount of electricity purchased by the new energy generator from the energy storage in period t under scenario k; P represents the electricity purchase price of the energy storage operator to the new energy generator when the new energy output peak occurs in scenario k; t,sell (k) represents the amount of electricity sold by the new energy generator to the energy storage in period t under scenario k; τ dev Represents the penalty coefficient for the output deviation of renewable energy.

[0038] Preferably, the second objective function for maximizing the benefits of the energy storage operator is established as follows:

[0039]

[0040] In the above formula, R aux,Q (k) represents the capacity compensation income obtained by the energy storage operator for providing ancillary services in scenario k; R aux,P(k) represents the electricity compensation income obtained by the energy storage operator for providing auxiliary services in scenario k; C sub (k) represents the capacity replacement cost of charging and discharging provided by the energy storage operator under scenario k; C op (k) represents the operation and maintenance cost of the energy storage operator under scenario k.

[0041] Preferably, the third objective function of the overall market welfare is established as follows:

[0042]

[0043] In the above formula, m represents the quotation segment of the price declared by the traditional unit; M represents the total number of all quotation segments;

[0044] ρ m,g,t (k) represents the price quoted by the traditional unit in the mth period of time t under scenario k; P m,g,t (k) means k scenario, and ρ m,g,t (k) the corresponding winning power; τ spare represents the cost coefficient of traditional units providing spare capacity; Q spare,t (k) represents the reserve capacity provided by the traditional unit in period t under scenario k; τ on and τ off Respectively represent the single cost of startup and shutdown of traditional units; χ on,t (k) is a dummy variable, indicating that in scenario k, when a startup event occurs in the traditional unit during period t, the value is 1, otherwise it is 0; off,t (k) is also a dummy variable, indicating that in scenario k, when a shutdown event occurs in the traditional unit during period t, the value is 1, otherwise it is 0; d v represents the decision variables in the model.

[0045] According to a second aspect of an embodiment of the present invention, a device for generating a wind-solar-storage collaborative day-ahead quotation strategy is provided, including:

[0046] A main controller, and a memory connected to the main controller;

[0047] a memory in which program instructions are stored;

[0048] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0049] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0050] It can be understood that the method for generating a wind-solar-storage collaborative day-ahead bidding strategy shown in the present invention constructs a supply-demand coupling bidding model for new energy power generators and energy storage operators based on the coupling and counterparty characteristics of the two market players, new energy and energy storage, in market transactions, and establishes an objective function for maximizing the benefits of new energy power generators, an objective function for maximizing the benefits of energy storage operators, and an objective function for the overall welfare of the market, and then establishes a composite game model for new energy power generators and energy storage operators. By solving the model based on Nash equilibrium, both can simultaneously obtain the optimal strategy for day-ahead bidding, and this strategy also conforms to the Pareto improvement of the entire market.

[0051] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0053] Figure 1 The present invention is a schematic diagram of the steps of a method for generating a wind-solar-storage collaborative day-ahead bidding strategy according to an exemplary embodiment. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0055] At present, my country is in the transition stage from the initial stage to the perfection of the power market construction. For the bidding of new energy on the power generation side, the spot pilot rules of many provinces before generally adopt the trading rules of priority consumption or quotation without quotation, while the traditional electricity is pre-cleared in the day ahead by quotation and quotation. The difference in bidding rules between new energy and traditional power sources is actually to promote the level of consumption of new energy.

[0056] Under this differentiated bidding rule, improving the level of new energy consumption and ensuring market fairness and a reasonable risk-return matching mechanism is an important issue in the future construction of the electricity market.

[0057] Judging from the pilot projects in some provinces this year, combined with the mature experience of the electricity market mechanism in developed countries, it is a necessary path for the improvement of the market in the foreseeable future for new energy to participate in the market's full electricity bidding and clearing by quoting prices and reporting quantities.

[0058] In view of the above problems, considering that the market mechanism of full-power bidding and settlement will be improved sooner or later, the issue of new energy consumption needs to be considered. Energy storage, as an important technical means of regulating the power system, can choose to enter the market by quoting or reporting quantity or by quoting quantity without quoting under the current market mechanism. In particular, energy storage operators represented by large-scale energy storage sites have become important participants in the market.

[0059] Whether from the perspective of power system dispatch or power market transactions, the synergy of energy storage and new energy is an important form of combined business. In the power market, energy storage operators are important counterparties of new energy power generators. On the one hand, new energy power generators need to purchase electricity from energy storage to smooth out output deviations and reduce intraday operating risks; on the other hand, new energy power generators are the main demanders of flexible resources of energy storage operators.

[0060] To sum up, from a forward-looking perspective, under the visible trend of full-electricity bidding and settlement, exploring the game between new energy power generators and energy storage operators is an important topic in the electricity spot market and is also something that market participants must consider when formulating trading strategies.

[0061] In one embodiment, Figure 1 is a schematic diagram of the steps of a method for generating a wind-solar-storage collaborative day-ahead bidding strategy according to an exemplary embodiment, see Figure 1 , a method for generating a wind-solar-storage collaborative day-ahead quotation strategy is provided, comprising:

[0062] Step S1: Establish a bidding strategy formula for new energy power generators and a quotation strategy formula for energy storage operators, and establish a supply-demand coupling bidding model for new energy power generators and energy storage operators based on the bidding strategy formula for new energy power generators and the quotation strategy formula for energy storage operators.

[0063] Step S2: Establish a first objective function for maximizing the benefits of new energy power generators, establish a second objective function for maximizing the benefits of energy storage operators, and establish a third objective function for the overall welfare of the market.

[0064] Step S3: establishing a composite game model of the new energy power generator and the energy storage operator according to the supply-demand coupling bidding model of the new energy power generator and the energy storage operator, the first objective function, the second objective function and the third objective function.

[0065] Step S4: Utilize the composite game model to perform the following steps:

[0066] S11, set the iteration steps to h and l, and the initial values ​​of h and l are both 0;

[0067] S12. Calculate the first objective function according to the strategy of the new energy power generator to obtain a first solution, calculate the second objective function according to the strategy of the energy storage operator to obtain a second solution; derive the strategy of the new energy power generator according to the first solution and the second solution and strategies of energy storage operators

[0068] S13. Judgment strategy and strategies Does it meet the Nash equilibrium condition? If so, change the strategy and strategies As a Nash equilibrium solution, execute step S15, if not, execute step S14;

[0069] S14, let the iteration step h = h + 1, change the strategy As a new energy power generation company strategy, As the energy storage operator strategy, the process proceeds to step S12;

[0070] S15, calculate the overall solution of the third objective function as the clearing result based on the Nash equilibrium solution, calculate whether the quadratic norm of the difference between the clearing results of iteration steps l and l-1 is less than a preset threshold, if so, output the strategy and strategies If it is not less than the threshold, set l=l+1, h=0 and go to step S12.

[0071] It can be understood that the method for generating a wind-solar-storage collaborative day-ahead bidding strategy shown in this embodiment constructs a supply-demand coupling bidding model for new energy power generators and energy storage operators based on the coupling and counterparty characteristics of the two market players, new energy and energy storage, in market transactions, and establishes an objective function for maximizing the benefits of new energy power generators, an objective function for maximizing the benefits of energy storage operators, and an objective function for the overall welfare of the market, and then establishes a composite game model for new energy power generators and energy storage operators. By solving the model based on Nash equilibrium, both can simultaneously obtain the optimal strategy for day-ahead bidding, which also conforms to the Pareto improvement of the entire market.

[0072] Under the condition of full power bidding, new energy power generators and energy storage operators are the main counterparties. On the one hand, as the main flexibility resource in the electric energy market and ancillary service market, energy storage is the main means for new energy to smooth out output deviations, and therefore is its main demand side; on the other hand, new energy has low power generation costs and is an important source of electricity for energy storage charging. As the main trading parties, exploring the bidding strategy under the game situation of both parties is of great decision-making significance for maximizing their respective benefits.

[0073] First, for new energy parties, unlike the power generation cost function of traditional energy, after the new energy is put into production, its power generation cost can be ignored, and the main operating cost is the operation and maintenance cost of the unit. Considering that new energy power generators face operation and maintenance costs and intraday market deviation settlement during operation, their profit decisions must not only cover their operation and maintenance costs, but also the risk is positively correlated with output deviation. Therefore, in step S1, the bidding strategy formula for new energy power generators is established, and the formula is as follows:

[0074]

[0075] Among them, α represents the coefficient related to the operating cost of new energy, representing the fixed component of the price to cover the cost; P t represents the actual output of new energy during period t; P t,bid It represents the winning bid power of the new energy power generator in the day-ahead period in time period t; β represents the coefficient related to the expected output deviation rate in the quotation; Δt represents the relevant timing length when the deviation is settled.

[0076] In the above formula It represents the variable part of the quotation, and also indicates the valuation of the uncertainty of expected output, that is, risk. t -P t,bid When P is greater than zero, after the new energy power generator has fulfilled the bid output, the remaining electricity can be supplied to the energy storage operator to meet its charging needs; t -P t,bid When it is less than zero, after the new energy power generator has fulfilled its bid, there is still a power supply gap, and it is necessary to purchase electricity from energy storage to fill the gap.

[0077] For energy storage operators, due to the electrical characteristics of energy storage equipment, their bidding strategy is constrained by the electrical state of energy storage. When energy storage needs to be charged, energy storage operators need to purchase electricity within a certain price range in order to control costs; when energy storage needs to be discharged, energy storage operators need to sell electricity within a certain price range in order to increase profits.

[0078] Therefore, the bidding strategy of energy storage operators is actually to determine the optimal purchase / sale price within a certain price range according to the state of charge of energy storage, that is, the charging and discharging state. At the same time, in order to ensure profit margins, similar to market makers in the securities market, under the same state of charge, there is a fixed difference between the price of energy storage selling electricity and the price of electricity purchased. Therefore, the bidding strategy formula of energy storage operators is established, which is expressed as the following piecewise function:

[0079]

[0080] Among them, η t,b,1 , η t,b,2 , η t,b,3respectively represent the price levels of the energy storage purchasing electricity from the grid under three charge states; η t,s,1 、η t,s,2 、η t,s,3 respectively represent the price levels of the energy storage selling electricity to the grid under three charge states; SOC t represents the actual charge level of the energy storage at time t; SOC min represents the minimum charge state allowed for the normal operation of the energy storage; SOC max then represents the maximum charge state allowed for the normal operation of the energy storage; SOC1 and SOC2 represent the interval boundaries for dividing the charge state of the energy storage.

[0081] When SOC min ≤SOC t <SOC1, the energy storage has insufficient electricity and needs to be charged. At this time, the demand level of the energy storage operator for purchasing electricity from the grid is relatively high. At the same time, the electricity of the energy storage is in short supply, and a higher premium is required for supplying electricity to the grid. In this interval, the energy storage will increase both its level of purchasing electricity from the grid and selling electricity to the grid.

[0082] When SOC1≤SOC t <SOC2, the electricity of the energy storage is in a normal supply and demand state. At this time, the energy storage operator will appropriately reduce the prices of its purchasing electricity from the grid and selling electricity to the grid.

[0083] When SOC2≤SOC t <SOC max , the electricity of the energy storage is in a relatively surplus state and needs to be discharged as soon as possible. At this time, the supply level of the energy storage for selling electricity to the grid is relatively high, while the demand for purchasing electricity from the grid is at a relatively low level. Therefore, both selling and purchasing electricity will be discounted. The selling and purchasing electricity prices in this interval are the lowest among the three intervals.

[0084] Since SOC min and SOC max depend on the manufacturing process of the equipment, they are rated. What we need is to determine the specific values of SOC1 and SOC2 based on the predicted output situation of new energy, that is, to realize the description of the synergy between the behaviors of the counterparties.

[0085] It should be noted that the established supply-demand coupling bidding model for new energy power generators and energy storage operators includes the bidding strategy of new energy power generators and the quotation strategy of energy storage operators, as well as the following formulas.

[0086] Since the output of new energy is uncertain, probability needs to be used to describe the relationship between its output and the charge state of the energy storage. When the sampling scale is sufficient, it can be approximately considered that the prediction residual of the output of new energy follows a normal distribution. When the confidence level is 5%, the approximate confidence interval P of the output of new energy at time t t is:

[0087] Pt ∈[P t,f -μ P -2σ P ,P t,f +μ P +2σ P ]

[0088] Among them, μ P represents the expected output of new energy, σ P Represents the standard deviation of renewable energy output, P t,f Represents the predicted value of renewable energy output.

[0089] Then, in order to cope with the output deviation of new energy in period t, the maximum power purchase demand and maximum power sales supply in period t are expressed as follows:

[0090]

[0091] Assuming that the threshold and confidence level of market deviation assessment are both 5%, that is, when the deviation between the actual output of renewable energy and the predicted output (i.e. the winning bid output in the intraday stage) is less than 5%, renewable energy power generators do not need to accept deviation assessment. Therefore, it can be approximately considered that P t -P t,f =0, then the above formula can be rewritten as:

[0092]

[0093] Then the load of the time period is introduced into the model, and SOC1 and SOC2 are expressed as:

[0094]

[0095] Among them, Q rated Indicates the rated capacity of the energy storage; index indicates the index function, the time under the query condition; t1 indicates the index to P t,f The specific time returned when the maximum value is taken; L t It indicates the load demand level in period t; t2 indicates the specific time when the maximum load demand level is returned.

[0096] The reason why t1 and t2 are chosen at the time of maximum output and maximum load demand of new energy is to match the supply and demand arrangement of energy storage charging during the peak output period of new energy and discharging during the peak load period.

[0097] Without considering the game between the two parties, the respective goals of new energy power generators and energy storage operators are to maximize benefits.

[0098] For new energy power generators, since their output deviation will face possible deviation assessment penalties, uncertainty needs to be described in their decision-making. The uncertainty of new energy output can be expressed through scenarios. Therefore, the first objective function for maximizing the benefits of new energy power generators is established as follows:

[0099]

[0100] In the above formula, k represents the scenario, K represents the maximum number of scenarios, and the renewable energy output scenario can be generated in various ways, the specific methods are not described here; f(k) represents the probability of the kth scenario occurring; T represents the maximum number of time periods within a day; ρ t (k) represents the market clearing price in period t under scenario k; P t,bid (k) represents the winning bid power of the new energy power generator in the tth period under scenario k; ξ represents the new energy operation and maintenance cost coefficient related to the unit output; represents the price at which the energy storage operator sells electricity to new energy sources when the load peak occurs in scenario k; P t,buy (k) represents the amount of electricity purchased by the new energy generator from the energy storage in period t under scenario k;

[0101] P represents the electricity purchase price of the energy storage operator to the new energy generator when the new energy output peak occurs in scenario k; t,sell (k) represents the amount of electricity sold by the new energy generator to the energy storage in period t under scenario k; τ dev Represents the penalty coefficient for the output deviation of renewable energy.

[0102] The quotation constraints of the first objective function F1 are:

[0103] ζ t,min ≤ζ t ≤ζ t,max

[0104] Among them, t,min and t,max Respectively represent the lower and upper limits of the allowed quotes.

[0105] At the same time, when purchasing and selling electricity from energy storage operators, new energy power generators need to comply with the charging, discharging and charge state constraints of energy storage, specifically:

[0106]

[0107] In the above formula, P es,rated Indicates the rated power of energy storage; Q t (k) represents the change in energy storage capacity caused by the transaction between the new energy generator and the energy storage operator in period t under scenario k; Indicates the charging efficiency coefficient of energy storage; Indicates the discharge efficiency coefficient of energy storage.

[0108] Similarly, following the idea of ​​scenario analysis, and considering that energy storage also participates in the ancillary service market, the second objective function for maximizing the benefits of energy storage operators is established as follows:

[0109]

[0110] In the above formula, R aux,Q (k) represents the capacity compensation income obtained by the energy storage operator for providing ancillary services in scenario k; R aux,P (k) represents the electricity compensation income obtained by the energy storage operator for providing auxiliary services in scenario k; C sub (k) represents the capacity replacement cost of charging and discharging provided by the energy storage operator under scenario k; C op (k) represents the operation and maintenance cost of the energy storage operator under scenario k.

[0111]

[0112] In the above formula, τ aux,Q It represents the unit price of capacity compensation income obtained by providing ancillary services.

[0113]

[0114] In the above formula, τ aux,P Indicates the unit price of electricity compensation income obtained by providing ancillary services; represents the charging power provided by the energy storage for auxiliary services during period t in scenario k; It represents the discharge power provided by energy storage for ancillary services in period t under scenario k.

[0115]

[0116] τ sub Represents the unit cost of capacity replacement.

[0117]

[0118] Among them, τ op Represents the unit operation and maintenance cost of energy storage; Ω t (k) represents the number of energy storage cycle charge and discharge in time period t under scenario k; Ω max Indicates the maximum number of charge and discharge cycles within the energy storage life.

[0119] For the energy storage benefit objective function, the constraints are:

[0120]

[0121] Finally, after considering the benefit maximization decision-making conditions of energy storage operators and new energy power generators, it is necessary to comprehensively consider the interaction between the two as counterparties under market clearing conditions.

[0122] Assuming that MCP (marginal clearing price) is used as the market equilibrium condition, and in the equilibrium state, maximizing social welfare is the goal pursued by the market, then traditional generators are included in the model, and the third objective function of the overall market welfare is established as follows:

[0123]

[0124] In the above formula, m represents the quotation segment of the price declared by the traditional unit; M represents the total number of all quotation segments;

[0125] ρ m,g,t (k) represents the price quoted by the traditional unit in the mth period of time t under scenario k; P m,g,t (k) means k scenario, and ρ m,g,t (k) the corresponding winning power; τ spare represents the cost coefficient of traditional units providing spare capacity; Q spare,t (k) represents the reserve capacity provided by the traditional unit in period t under scenario k; τ on and τ off Respectively represent the single cost of startup and shutdown of traditional units; χ on,t (k) is a dummy variable, indicating that in scenario k, when a startup event occurs in the traditional unit during period t, the value is 1, otherwise it is 0; off,t (k) is also a dummy variable, indicating that in scenario k, when a shutdown event occurs in the traditional unit during period t, the value is 1, otherwise it is 0; d v represents the decision variables in the model.

[0126]

[0127] The constraints of the market equilibrium model include power balance constraints and new energy bidding power constraints, where the power balance constraints are:

[0128]

[0129] The power constraints for new energy bids are:

[0130] P t (k)-P t,sell (k)≤P t,bid (k)≤P t (k)+P t,buy (k)

[0131] Other constraints include the quotation constraints of the objective function, the charging and discharging and charge state constraints of energy storage, and the constraints on the energy storage benefit objective function. As for the climbing constraints of traditional units and other conditions, they will not be elaborated here.

[0132] Based on the solution of the compound game model of Nash equilibrium, since in the model, the decisions of new energy power generators and energy storage operators are coupled, and as counterparties in the transaction, the quotation of one party will inevitably affect the demand or supply of the other party, we can use Nash equilibrium to pursue the common optimality of the decisions of both parties, and use the market clearing of the above part of MCP as the condition for testing Nash equilibrium.

[0133] The conditions for Nash equilibrium can be expressed as a utility function, specifically:

[0134]

[0135] In the above formula, U ne and U es They represent the utility of the renewable energy generator and the energy storage operator after obtaining the strategies of both parties; S refers to the strategy, that is, the specific value of the decision variable of the decision maker; It represents the optimal bidding strategy adopted by the new energy power generator according to its own objective function; It represents the optimal bidding strategy adopted by the energy storage operator according to its own objective function.

[0136] Since the established composite game model includes the respective benefit functions of renewable energy power generators and energy storage operators as well as their common objective function under market clearing conditions, we can divide the model into two layers. The upper model obtains the optimal strategies of the two players and interacts with them, and the lower model is used to determine whether the solution of the upper model satisfies the Nash equilibrium.

[0137] First, in S11, the iteration step of the upper model is set to h, and the iteration step of the lower model is set to l, and the initial values ​​of h and l are both 0.

[0138] In S12, the IPM algorithm (interior point method) is used to calculate the first objective function F1 and the second objective function F2 respectively to obtain a first solution and a second solution.

[0139] Since there will be iterations in the future, the strategy of new energy power generators is derived based on the first and second solutions. and strategies of energy storage operators The strategy update process of two players in IPM can be expressed as:

[0140]

[0141] Represents new energy power generators and energy storage operators, whose strategy is to find a strategy combination that maximizes their utility in the l-step of the lower model. Indicates that the utility of the new energy generator in the next step depends on its previous step strategy And the latest strategies to deal with rival energy storage operators Argmax means to find the corresponding variable that maximizes its utility represents the strategy update of the new energy power generator under iteration steps h and l; Represents the strategy update of the energy storage operator under iteration steps h and l.

[0142] In step S13, the strategy is determined and strategies Whether the Nash equilibrium condition is met is determined by the following formula:

[0143]

[0144] If the above conditions are met, the upper model is solved and the strategy and strategies As a Nash equilibrium solution Execute step S15, if not, execute step S14.

[0145] In step S14, let the iteration step h = h + 1, and change the strategy As a new energy power generation company strategy, As the energy storage operator strategy, the process proceeds to step S12.

[0146] In step S15, the SGHS algorithm (harmonic search algorithm fused with genetic algorithm) can be used to solve the Nash equilibrium solution. Substitute the third objective function F3 and obtain the overall solution as the clearing result of this step l. Calculate whether the quadratic norm of the difference between the clearing result of iteration step l and the clearing result of l-1 is less than the preset threshold ∈. If so, it indicates that it is the global optimal solution, and the output strategy and strategies If it is not less than the threshold, set l=l+1, h=0 and go to step S12.

[0147] The technical solution proposed in the present invention aims at the defects of the current new energy bidding rules and the dilemma of new energy consumption, and proactively proposes a bidding mechanism for new energy quantity and quotation based on the trend of market mechanism improvement; at the same time, based on the coupling and counterparty characteristics of the two market players of new energy and energy storage in market transactions, an upper and lower model including the maximization of the benefits of both and the market clearing conditions is constructed. By solving the model based on Nash equilibrium, the two can simultaneously obtain the optimal strategy for day-ahead bidding, and this strategy also meets the Pareto improvement of the entire market.

[0148] According to a second aspect of an embodiment of the present invention, a device for generating a wind-solar-storage collaborative day-ahead quotation strategy is provided, including:

[0149] A main controller, and a memory connected to the main controller;

[0150] a memory in which program instructions are stored;

[0151] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0152] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0153] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0154] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0155] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0156] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0157] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0158] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0159] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0160] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for generating a day-ahead bidding strategy for wind, solar and energy storage collaboration, characterized in that: include: Establish a bidding strategy for new energy power generators and a quotation strategy for energy storage operators, and establish a supply-demand coupling bidding model for new energy power generators and energy storage operators based on the bidding strategy for new energy power generators and the quotation strategy for energy storage operators; Establish the first objective function of maximizing the benefits of new energy power generators, the second objective function of maximizing the benefits of energy storage operators, and the third objective function of maximizing the overall welfare of the market; A compound game model of new energy power generators and energy storage operators is established based on the supply-demand coupling bidding model, the first objective function, the second objective function and the third objective function of new energy power generators and energy storage operators; The composite game model is used to perform the following steps: S11, set the iteration steps to h and l, and the initial values ​​of h and l are both 0; S12. Calculate the first objective function according to the strategy of the new energy power generator to obtain a first solution, calculate the second objective function according to the strategy of the energy storage operator to obtain a second solution; derive the strategy of the new energy power generator according to the first solution and the second solution and strategies of energy storage operators S13. Judgment strategy and strategies Does it meet the Nash equilibrium condition? If so, change the strategy and strategies As a Nash equilibrium solution, execute step S15, if not, execute step S14; S14, let the iteration step h = h + 1, change the strategy As a new energy power generation company strategy, As the energy storage operator strategy, the process proceeds to step S12; S15, calculate the overall solution of the third objective function as the clearing result based on the Nash equilibrium solution, calculate whether the quadratic norm of the difference between the clearing results of iteration steps l and l-1 is less than a preset threshold, if so, output the strategy and strategies If it is not less than the threshold, set l=l+1, h=0 and go to step S12.

2. The method for generating a wind-solar-storage collaborative day-ahead quotation strategy according to claim 1, characterized in that: The bidding strategy formula for new energy power generators is as follows: Among them, α represents the coefficient related to the operating cost of new energy, representing the fixed component of the price to cover the cost; P t represents the actual output of new energy during period t; P t,bid It represents the winning bid power of the new energy power generator in the day-ahead period in time period t; β represents the coefficient related to the expected output deviation rate in the quotation; Δt represents the relevant timing length when the deviation is settled.

3. The method for generating a wind-solar-storage collaborative day-ahead quotation strategy according to claim 2 is characterized in that: The bidding strategy formula for energy storage operators is established as follows: Among them, η t,b,1 , η t,b,2 , η t,b,3 Respectively represent the price level of energy storage purchasing electricity under three charge states; η t,s,1 , η t,s,2 , η t,s,3 Respectively represent the price level of energy storage selling electricity under three states of charge; SOC t Indicates the actual charge level of the energy storage during period t; SOC min Indicates the minimum state of charge allowed for normal operation of energy storage; SOC max It indicates the maximum state of charge allowed for normal operation of the energy storage; SOC1 and SOC2 indicate the interval boundaries for dividing the energy storage charge state.

4. The method for generating a wind-solar-storage collaborative day-ahead bidding strategy according to claim 3 is characterized in that: The supply-demand coupling bidding model for new energy power generators and energy storage operators is established, including the bidding strategy of new energy power generators and the bidding strategy of energy storage operators, as well as the following formula: Approximate confidence interval of renewable energy output in period t: P t ∈[P t,f -m P -2s P ,P t,f +m P +2s P ] Among them, μ P represents the expected output of new energy, σ P Represents the standard deviation of renewable energy output, P t,f It represents the predicted value of the output of new energy; The maximum electricity purchase demand and maximum electricity sales supply of new energy in period t are expressed as follows: Assuming that the threshold and confidence level of market deviation assessment are both 5%, the maximum electricity purchase demand and maximum electricity sales supply of new energy in period t are expressed as: SOC1 and SOC2 are expressed as: Among them, Q rated Indicates the rated capacity of the energy storage; index indicates the index function, the time under the query condition; t1 indicates the index to P t,f The specific time returned when the maximum value is taken; L t It indicates the load demand level in period t; t2 indicates the specific time when the maximum load demand level is returned.

5. The method for generating a wind-solar-storage collaborative day-ahead quotation strategy according to claim 4 is characterized in that: The first objective function for maximizing the benefits of new energy power generators is established as follows: In the above formula, k represents the scene, K represents the maximum number of scenes; f(k) represents the probability of the kth scene occurring; T represents the maximum number of time periods within a day; ρ t (k) represents the market clearing price in period t under scenario k; P t,bid (k) represents the winning bid power of the new energy power generator in the tth period under scenario k; ξ represents the new energy operation and maintenance cost coefficient related to the unit output; P represents the price at which the energy storage operator sells electricity to new energy sources when the load peak occurs in scenario k; t,buy (k) represents the amount of electricity purchased by the new energy generator from the energy storage in period t under scenario k; P represents the electricity purchase price of the energy storage operator to the new energy generator when the new energy output peak occurs in scenario k; t,sell (k) represents the amount of electricity sold by the new energy generator to the energy storage in period t under scenario k; τ dev Represents the penalty coefficient for the output deviation of renewable energy.

6. The method for generating a wind-solar-storage collaborative day-ahead bidding strategy according to claim 5 is characterized in that: The second objective function for maximizing the benefits of energy storage operators is established as follows: In the above formula, R aux,Q (k) represents the capacity compensation income obtained by the energy storage operator for providing ancillary services in scenario k; R aux,P (k) represents the electricity compensation income obtained by the energy storage operator for providing auxiliary services in scenario k; C sub (k) represents the capacity replacement cost of charging and discharging provided by the energy storage operator under scenario k; C op (k) represents the operation and maintenance cost of the energy storage operator under scenario k.

7. The method for generating a wind-solar-storage collaborative day-ahead bidding strategy according to claim 6, characterized in that: The third objective function of the established market overall welfare is as follows: In the above formula, m represents the quotation segment of the traditional unit price; M represents the total number of all quotation segments; ρ m,g,t (k) represents the price quoted by the traditional unit in the mth period of time t under scenario k; P m,g,t (k) means k scenario, and ρ m,g,t (k) the corresponding winning power; τ spare represents the cost coefficient of traditional units providing spare capacity; Q spare,t (k) represents the reserve capacity provided by the traditional unit in period t under scenario k; τ on and τ off Respectively represent the single cost of startup and shutdown of traditional units; χ on,t (k) is a dummy variable, indicating that in scenario k, when a startup event occurs in the traditional unit during period t, the value is 1, otherwise it is 0; off,t (k) is also a dummy variable, indicating that in scenario k, when a shutdown event occurs in the traditional unit during period t, the value is 1, otherwise it is 0; d v represents the decision variables in the model.

8. A device for generating a day-ahead quotation strategy for wind, solar and energy storage collaboration, characterized in that: include: A main controller, and a memory connected to the main controller; a memory in which program instructions are stored; The main controller is used to execute program instructions stored in the memory and execute the method according to any one of claims 1 to 7.

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