Bidding method, device and equipment for collaboratively participating in electricity market based on double-layer game

Abstract

The invention relates to the technical field of power dispatching, in particular to a bidding method, device and equipment for collaboratively participating in a power market based on a double-layer game. Specifically, an upper-layer optimization model and a lower-layer optimization model are constructed; the upper-layer optimization model optimizes day-ahead market bidding and energy storage protocol transaction parameters by taking photovoltaic aggregator expected revenue maximization as a target; and the lower-layer optimization model optimizes charging, discharging and energy states based on an upper-layer result by taking energy storage operator interest arbitrage income maximization as a target. And through iterative optimization to Nash equilibrium, a scheduling plan is determined. According to the method, the problems of limited photovoltaic income and imperfect cooperation mechanism are solved, market rules and physical constraints are considered, the method can be expanded to multi-type resource scenes, and the distributed resource market response and income level is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power dispatching, in particular to a bidding method, device and equipment for participating in an electricity market based on double-layer game cooperation. BACKGROUND

[0002] With the deepening of the reform of the electricity market, distributed renewable energy, especially photovoltaic power generation, is accelerating access to the distribution network and becoming an important part of the power system. Compared with centralized power sources, distributed photovoltaic power has the advantages of being clean, low-carbon and locally consumed, but due to its significant intermittency and uncertainty, its participation in the spot market is limited. In addition, the generation period of distributed photovoltaic power is mostly concentrated in the daytime flat period or valley period, when the electricity price is relatively low, making it difficult to support its economy and affecting the enthusiasm of investment and operation.

[0003] As an important flexible resource for regulating renewable energy output, the energy storage system has the ability of energy time shifting and load regulation, can effectively alleviate the volatility and uncontrollability of photovoltaic power generation, improve its response ability to market price signals, and realize the arbitrage logic of "low-price charging and high-price discharging". However, there is still a lack of systematic and market-oriented optimization mechanism between distributed photovoltaic power and energy storage systems, especially in the electricity market, the benefit distribution mode, participation strategy and transaction mechanism between the two are not perfect, and it is difficult to form a stable and efficient bilateral cooperation relationship.

[0004] Therefore, it is urgent to establish a two-stage bidding strategy model that takes into account both the electricity market mechanism and the physical operation constraints. On the one hand, by constructing a bilateral game mechanism between the distributed photovoltaic aggregator and the energy storage, the marginal benefits and game equilibrium of each party are determined; on the other hand, a bidding and dispatch optimization model for the spot market is designed, which combines electricity price fluctuations and output uncertainty to realize the coordinated dispatch of resources under the economic driving. SUMMARY

[0005] Therefore, the main purpose of the present application is to provide a bidding strategy model for the cooperation between the distributed photovoltaic aggregator and the energy storage in the electricity market based on double-layer game, in order to solve the following technical problems: improving the income level of the distributed photovoltaic aggregator in the electricity spot market; optimizing the coordinated dispatch between the distributed photovoltaic power and the energy storage, improving the resource utilization efficiency; establishing a reasonable benefit distribution mode and transaction mechanism to promote the market cooperation between the distributed photovoltaic power and the energy storage.

[0006] The application provides a bidding method for participating in an electricity market based on double-layer game cooperation, comprising: constructing an upper-layer optimization model; the upper-layer optimization model is constructed based on basic information of the distributed photovoltaic aggregator, and has a target function of maximizing expected revenue under different scenarios, and decision variables of the upper-layer optimization model include: a bidding price and corresponding power of the day-ahead electricity market, and an agreement price and agreement power signed by the energy storage operator; constructing a lower-layer optimization model; the lower-layer optimization model is constructed based on basic information of the energy storage operator, has a target function of maximizing arbitrage revenue of the energy storage operator, and takes decision variables optimized and output by the upper-layer optimization model as input; decision variables of the lower-layer optimization model include: charging power and discharging power of each period, and a state-of-charge dynamic trajectory of the energy storage device; substituting the decision variables of the lower-layer optimization model into the upper-layer optimization model to re-optimize; the upper-layer optimization model and the lower-layer optimization model are iteratively optimized until a Nash equilibrium is reached, and a scheduling plan is determined based on the decision variables finally determined by the lower-layer optimization model and the upper-layer optimization model.

[0007] In some embodiments, the target function of the upper-layer optimization model is as follows:

[0008] wherein, is the income obtained by the distributed photovoltaic aggregator in the market at a price of ; represents an agreement revenue of selling electricity to the energy storage after reaching an agreement with the energy storage operator; is a price scenario, used to represent price uncertainty; represents a probability weight of each scenario; represents an expected revenue calculated by weighting under multiple price scenarios.

[0009] In some embodiments, the constraints of the upper-layer optimization model include: a bid upper and lower limit constraint, an agreement price upper and lower limit constraint, an agreement transaction power upper limit, and a distributed photovoltaic aggregator winning power constraint.

[0010] In some embodiments, the target function of the lower-layer optimization model is as follows:

[0011] wherein, and are charging power and discharging power of the energy storage operator j at a t period, respectively; is a market price in a t period scenario ; represents that the energy storage charges energy in a low-price period and discharges energy in a high-price period, and makes arbitrage therefrom; represents the need to pay the transaction price when charging from the distributed photovoltaic aggregator agreement, that is, represents the procurement cost.

[0012] In some embodiments, the energy state dynamic equation of the energy storage and the upper and lower limits of the energy state of the energy storage.

[0013] The application provides a bidding device for participating in the electricity market based on double-layer game cooperation, which comprises: A first construction module is configured to construct an upper-layer optimization model; the upper-layer optimization model is constructed based on basic information of a distributed photovoltaic aggregator, and the maximum expected revenue of the distributed photovoltaic aggregator under different scenarios is taken as an objective function; the decision variables of the upper-layer optimization model include the bidding price and the corresponding power of the day-ahead electricity market, the agreement price and the agreement power signed by the energy storage operator; A second construction module is configured to construct a lower-layer optimization model; the lower-layer optimization model is constructed based on basic information of the energy storage operator, and the maximum arbitrage revenue of the energy storage operator is taken as an objective function; the decision variables of the lower-layer optimization model include the charging power and the discharging power of each period and the state-of-charge dynamic trajectory of the energy storage device; An iterative optimization module is configured to substitute the decision variables of the lower-layer optimization model into the upper-layer optimization model to re-optimize; the upper-layer optimization model and the lower-layer optimization model are iteratively optimized until a Nash equilibrium is reached; and the scheduling plan is determined based on the decision variables finally determined by the lower-layer optimization model and the upper-layer optimization model.

[0014] In some embodiments, the objective function of the upper-layer optimization model is as follows:

[0015] wherein, is the price at which the distributed photovoltaic aggregator trades in the market; is the traded power; is the obtained revenue; represents the agreement revenue of selling electricity to the energy storage after reaching an agreement with the energy storage operator; is the price scenario, which is used to represent the price uncertainty; represents the probability weight of each scenario; represents the expected revenue calculated by weighting under multiple price scenarios; The constraints of the upper-layer optimization model include: the upper and lower limits of the bid, the upper and lower limits of the agreement price, the upper limit of the agreement traded power, and the winning power constraint of the distributed photovoltaic aggregator.

[0016] In some embodiments, the objective function of the lower-layer optimization model is as follows:

[0017] In the formula, and These represent the charging and discharging power of energy storage operator j during time period t, respectively. Scenario for time period t The market electricity price below; This means that energy storage charges energy during periods of low electricity prices and discharges it during periods of high electricity prices, thus profiting from the difference. This indicates the transaction price that energy storage needs to pay when charging from distributed photovoltaic aggregator agreements, which represents the procurement cost; The constraints of the lower-level optimization model include: energy storage charging power constraints, energy storage discharging power constraints, energy storage energy state dynamic equations, and energy storage energy state upper and lower limits.

[0018] This application provides an electronic device, including: A processor, and a memory for storing a processor-executable program; The processor is used to implement the bidding method for collaborative participation in the electricity market based on two-level game theory, as described above, by running the program in the memory.

[0019] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the bidding method for participating in the electricity market based on a two-level game theory, as described above.

[0020] The application provides a bidding method for participating in an electricity market based on double-layer game cooperation, which comprises the following steps: firstly, constructing an upper-layer optimization model; the upper-layer optimization model is constructed based on the basic information of a distributed photovoltaic aggregator, and the distributed photovoltaic aggregator is taken as an object, and the maximum expected income is taken as an objective function in different scenarios; the decision variables of the upper-layer optimization model include the bidding price and the corresponding electricity quantity of the day-ahead electricity market, and the contract price and the contract electricity quantity signed by an energy storage operator; constructing a lower-layer optimization model; the lower-layer optimization model is constructed based on the basic information of the energy storage operator, and the maximum arbitrage income of the energy storage operator is taken as an objective function, and the decision variables optimized and output by the upper-layer optimization model are taken as inputs; the decision variables of the lower-layer optimization model include the charging power and the discharging power of each period, and the state dynamic trajectory of the energy storage device; the decision variables of the lower-layer optimization model are substituted into the upper-layer optimization model to re-optimize; the upper-layer optimization model and the lower-layer optimization model are iteratively optimized until a Nash equilibrium is reached, and the scheduling plan is determined based on the decision variables finally determined by the lower-layer optimization model and the upper-layer optimization model. By constructing the double-layer game model, the application realizes the collaborative optimization scheduling between the distributed photovoltaic aggregator and the energy storage operator. Compared with the prior art, the application has the following remarkable advantages: the income level of the distributed photovoltaic aggregator in the electricity spot market is improved, and the market competitiveness is enhanced. The collaborative scheduling between the distributed photovoltaic and the energy storage is optimized, the resource utilization efficiency is improved, and the operation cost is reduced. A reasonable benefit distribution mode and a transaction mechanism are established, the marketization cooperation of the distributed photovoltaic and the energy storage is promoted, and the development of the new energy electricity market is promoted. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:

[0022] Figure 1 FIG. 1 is a flowchart of a bidding method for participating in an electricity market based on double-layer game cooperation provided by an embodiment of the application.

[0023] Figure 2 FIG. 2 is a structural schematic diagram of a bidding device for participating in an electricity market based on double-layer game cooperation provided by an embodiment of the application.

[0024] Figure 3 FIG. 3 is a structural schematic diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0025] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0026] The application discloses a double-layer scheduling method and system for distributed photovoltaic aggregators and energy storage collaborative optimization. The method comprises the following steps: acquiring operation parameter information of distributed photovoltaic aggregators and energy storage operators; in an upper-layer optimization model, taking maximization of expected income of the distributed photovoltaic aggregators as a target, considering price uncertainty, jointly optimizing day-ahead market bidding price, power and agreement price, power of the energy storage operators, and formulating market transaction and agreement interaction strategies thereof; taking the agreement transaction price and power output by the upper-layer optimization model as input, taking maximization of income of each energy storage operator as a target, optimizing charging and discharging power and energy state of the energy storage operators under different price scenarios, and obtaining scheduling results of the energy storage; feeding back the response results of the energy storage to the upper layer, iteratively solving until a convergence condition is met, and finally outputting market bidding strategies of the distributed photovoltaic aggregators, agreement interaction schemes of the energy storage, and scheduling plans of each energy storage device.

[0027] After introducing the basic principle of the application, various non-limiting embodiments of the application will be specifically introduced below with reference to the accompanying drawings.

[0028] Figure 1 is a flowchart of a bidding method based on double-layer game collaborative participation in a power market provided by an embodiment of the application. As shown in the figure, the method comprises the following contents. Figure 1

[0029] In step S110, an upper-layer optimization model is constructed; the upper-layer optimization model is constructed based on basic information of the distributed photovoltaic aggregator, and the target function is maximization of expected income of the distributed photovoltaic aggregator under different scenarios; the decision variables of the upper-layer optimization model include: bidding price and corresponding power of the day-ahead power market; agreement price and agreement power signed by the energy storage operator; First, we construct an upper-layer optimization model. This model is designed based on the basic information of the distributed photovoltaic aggregator, and the purpose is to maximize the expected income of the distributed photovoltaic aggregator under different market scenarios. The income mainly comes from two aspects: one is the income obtained by participating in the power spot market; the other is the income obtained by signing an agreement with the energy storage operator.

[0030] ​To achieve this goal, the model needs to determine several key decision variables. Specifically, the bidding price and corresponding power in the day-ahead electricity market need to be decided, as well as the contract price and contract power with the energy storage operator. These decision variables will directly affect the revenue level of the distributed PV aggregator.

[0031] Of course, when making these decisions, some constraints also need to be considered. First, the bidding price cannot be lower than the marginal cost of the distributed PV aggregator, nor can it be higher than the upper limit set by the market, to ensure the rationality of bidding behavior. Second, the contract price also needs to be set within a reasonable range to avoid excessively low or high prices that prevent transactions from proceeding normally. In addition, the contract power cannot exceed the actual demand or capacity of the energy storage operator to ensure the executability of the contract. Finally, the winning power of the distributed PV aggregator cannot exceed its current output capacity and grid connection capacity to ensure that the power it bids is schedulable.

[0032] By considering these objectives and constraints comprehensively, the upper-level optimization model can develop the optimal market trading and contract interaction strategy for the distributed PV aggregator.

[0033] Step S120, constructing a lower-level optimization model; the lower-level optimization model is constructed based on the basic information of the energy storage operator, and the maximum arbitrage profit of the energy storage operator is taken as the objective function, and the decision variables optimized by the upper-level optimization model are taken as the input; the decision variables of the lower-level optimization model include: the charging power and discharging power of each period; the state of charge dynamic trajectory of the energy storage device; Next, we construct the lower-level optimization model. This model is designed based on the basic information of the energy storage operator, with the goal of maximizing the arbitrage profit of the energy storage operator. The energy storage operator earns revenue by charging during low-price periods and discharging during high-price periods, while also considering the cost of charging from the distributed PV aggregator.

[0034] The decision variables of the lower-level optimization model mainly include the charging power, discharging power of each period, and the state of charge dynamic trajectory of the energy storage device. These variables will directly affect the revenue level of the energy storage operator.

[0035] When making these decisions, certain constraints also need to be considered. First, the charging power of the energy storage device cannot exceed its maximum charging power limit to ensure the safe operation of the device. Second, the discharging power also cannot exceed its maximum discharging power limit. In addition, the state of charge of the energy storage device will change dynamically according to the charging and discharging power and needs to be kept within a reasonable range, i.e., cannot be lower than the minimum state of charge and cannot exceed the maximum state of charge. At the same time, the power charged by the energy storage device from the distributed photovoltaic aggregator agreement cannot exceed the range allowed by the agreement transaction power. Finally, the total output of all energy storage operators cannot exceed the market allowed regulation capacity to ensure the stable operation of the market.

[0036] By considering these objectives and constraints comprehensively, the lower-level optimization model can develop the optimal charging and discharging strategy for the energy storage operator.

[0037] Step S130, substitute the decision variables of the lower-level optimization model into the upper-level optimization model to re-optimize; through the iterative optimization of the upper-level optimization model and the lower-level optimization model until the Nash equilibrium is reached, based on the decision variables finally determined by the lower-level optimization model and the upper-level optimization model, determine the dispatch plan.

[0038] Finally, we determine the final dispatch plan through the iterative optimization of the upper-level optimization model and the lower-level optimization model until the Nash equilibrium is reached.

[0039] Before starting the optimization, the relevant parameters need to be initialized, including the operating parameters of the distributed photovoltaic aggregator and the energy storage operator, the market electricity price scenario, etc. These parameters will serve as the basis data for optimization.

[0040] First, the upper-level optimization model calculates the optimal bidding strategy and agreement transaction scheme of the distributed photovoltaic aggregator according to the current market electricity price scenario and the agreement transaction of the energy storage operator. Then, these results are input into the lower-level optimization model. The lower-level optimization model calculates the optimal charging and discharging strategy of the energy storage operator according to the input agreement transaction price and power.

[0041] Next, the charging and discharging strategy of the energy storage operator calculated by the lower-level optimization model is fed back to the upper-level optimization model. The upper-level optimization model recalculates the optimal bidding strategy and agreement transaction scheme of the distributed photovoltaic aggregator according to this feedback information. This process will be repeated until the convergence condition is met, i.e., the Nash equilibrium is reached. Nash equilibrium refers to the situation in a non-cooperative game where each participant has chosen the optimal strategy, provided that the strategies of other participants remain unchanged.

[0042] When the Nash equilibrium is reached, it means that the strategies of the distributed photovoltaic aggregators and the energy storage operators have reached the optimal balance. At this time, we can output the final scheduling plan, including the market bidding strategy of the distributed photovoltaic aggregators, the protocol interaction scheme with the energy storage, and the scheduling plan of each energy storage device.

[0043] Through the above steps, the application constructs a bidding strategy model of distributed photovoltaic aggregators and energy storage participating in the electricity market based on double-layer game. The model makes the strategies of the distributed photovoltaic aggregators and the energy storage operators reach the optimal balance through iterative optimization, so as to realize the collaborative scheduling of resources driven by economy, and improve the market response ability and comprehensive income level of distributed resources.

[0044] Unlike the traditional centralized control type virtual power plant scheme, the application emphasizes the autonomy and collaboration of distributed photovoltaic aggregators and energy storage, and constructs a distributed optimization framework driven by game on the basis of ensuring system stability. The model explicitly introduces the price fluctuation factor, improves the robustness of the strategy through multi-scenario optimization, so that the distributed photovoltaic aggregators can obtain stable income under uncertain price environment. At the same time, the model considers various physical and market rule constraints including the upper and lower limits of the offer, the protocol power limit, the dynamic operating characteristics of the energy storage, the upper limit of the new energy winning power, etc., and has good engineering landing and market compatibility.

[0045] In addition, the mechanism proposed by the application has strong universality and can be extended to the aggregation and transaction scenarios of wind power, adjustable load, electric vehicles and other types of resources, and can provide a theoretical basis and implementation path for the development of future multi-energy collaboration and multi-agent participating electricity market.

[0046] Specifically, the upper optimization model: distributed photovoltaic aggregator income maximization model; This layer is the leading layer, and the goal is to maximize the income of the distributed photovoltaic aggregator in the face of price uncertainty, including: offering price (price, power) in the day-ahead spot market, negotiating protocol transaction price and power with multiple energy storage operators, considering market offering constraints, power limits, and protocol transaction upper limits. The objective function is as follows:

[0047] Among them, is the income obtained by the distributed photovoltaic aggregator in the market at the price of the transaction power ; represents the protocol income of selling electricity to the energy storage after reaching an agreement with the energy storage operator; is the price scenario, used to represent the price uncertainty; represents the probability weight of each scenario; represents the expected revenue calculated by weighting under multiple price scenarios.

[0048] (1) Upper and lower bound constraints of bidding price The market price of the distributed photovoltaic aggregator cannot be lower than its marginal cost, nor higher than the upper limit specified by the market. Upper and lower bound constraints of bidding price (upper layer) need to be established to control abnormal bidding behavior and maintain rational competition in the market. The upper and lower bound constraints of bidding price (upper layer) are as follows:

[0049] wherein, and are the lower and upper limit values of the bidding price, respectively.

[0050] (2) Upper and lower bound constraints of protocol price The protocol price needs to be controlled within a reasonable range to avoid manipulation or invalid transactions, i.e., the upper layer optimization model needs to satisfy the following upper and lower bound constraints of protocol price (upper layer):

[0051] wherein, and are the lower and upper limit values of the protocol price, respectively.

[0052] (3) Upper limit of protocol transaction capacity (upper layer) The distributed photovoltaic aggregator can sell no more than the demand or capacity of each energy storage operator, preventing the protocol transaction capacity from exceeding the actual charging capacity of the energy storage device.

[0053]

[0054] wherein, is the maximum transaction capacity, determined by the energy storage according to its capacity state and power capacity limit.

[0055] (4) Winning power constraints of distributed photovoltaic aggregator (upper layer) The bidding power of the distributed photovoltaic aggregator is limited by its current output capacity and grid-connected capacity, and it is necessary to ensure that the distributed photovoltaic aggregator's bidding does not exceed the dispatchable capacity or the maximum allowed power of the system. Therefore, the winning power constraints of the distributed photovoltaic aggregator need to be satisfied:

[0056] wherein, is the upper limit of the output capacity of the distributed photovoltaic aggregator.

[0057] The lower layer optimization model is the profit maximization problem of the energy storage operator. The energy storage operator optimizes its own profit under the given upper layer transaction price and protocol power, and the decision variables include: charging power, discharging power and energy state. The price is regarded as a known input (from the scenario set). The objective function is as follows:

[0058] In the formula, and are the charging power and discharging power of the energy storage operator j at the t period; is the market price of the t period scenario . indicates that the energy storage charges energy at the low price period and discharges energy at the high price period, and makes a profit therefrom; indicates that the energy storage needs to pay the transaction price when charging from the distributed photovoltaic aggregator agreement, representing the purchase cost.

[0059] (1) Energy storage charging power constraint (lower layer)

[0060] In the formula, is the maximum charging power of the energy storage device of the energy storage operator j.

[0061] (2) Energy storage discharging power constraint (lower layer)

[0062] In the formula, is the maximum discharging power of the energy storage device of the energy storage operator j.

[0063] (3) Energy storage energy state dynamic equation (lower layer)

[0064] In the formula, and are the charging efficiency and discharging efficiency of the energy storage device of the energy storage operator j; and are the state of charge expected to be reached by the energy storage operator j at the t period and the t-1 period.

[0065] (4) Energy storage energy state upper and lower limits (lower layer)

[0066] In the formula, and are the lower limit and upper limit values of the energy state.

[0067] (5) Charging power does not exceed protocol transaction volume (lower layer) The traded electricity quantity between the energy storage and the distributed photovoltaic aggregator in the agreement is the upper limit of its actual chargeable quantity, i.e.

[0068] (6) Energy storage power balance constraint (global) Net output of energy storage Subtract the charging quantity from the discharging quantity, i.e.

[0069] (7) Total capacity constraint of energy storage market (global) The total output of multiple energy storage operators cannot exceed the market allowed regulation capacity, i.e. the following capacity constraint needs to be met:

[0070] Wherein, is the market allowed regulation capacity.

[0071] Further, the model result output is as follows: (1) Market bidding data of the distributed photovoltaic aggregator: , the bid price of the distributed photovoltaic aggregator in the tth time period to the power market; , the electricity quantity planned to be sold by the distributed photovoltaic aggregator in the tth time period on the market.

[0072] (2) Agreed electricity quantity and price with energy storage: , the agreed traded electricity price of the distributed photovoltaic aggregator with the jth energy storage device in the tth time period; , the electricity quantity planned to be transferred by the distributed photovoltaic aggregator to the jth energy storage device.

[0073] (3) Dispatching plan of each time period of energy storage: , the charging power of the jth energy storage device in the tth time period; , the discharging power of the energy storage in the tth time period; , the electricity quantity state of the jth energy storage device at the end of the tth time period.

[0074] The device embodiment of the present application can be used to execute the method embodiment of the present application. For details not disclosed in the device embodiment of the present application, please refer to the method embodiment of the present application.

[0075] Figure 2 As shown in the figure, the device includes: Figure 2 ​The first construction module 21 is configured to construct an upper-layer optimization model; the upper-layer optimization model is constructed based on basic information of the distributed photovoltaic aggregator, and the distributed photovoltaic aggregator is taken as an object, and a maximum expected revenue in different scenarios is taken as an objective function; and decision variables of the upper-layer optimization model include a bidding price and corresponding power in a day-ahead electricity market, and a contract price and contract power signed by the energy storage operator; The second construction module 22 is configured to construct a lower-layer optimization model; the lower-layer optimization model is constructed based on basic information of the energy storage operator, and a maximum arbitrage revenue of the energy storage operator is taken as an objective function; and decision variables of the lower-layer optimization model include charging power and discharging power in each period, and a state-of-charge dynamic trajectory of the energy storage device; The iterative optimization module 23 is configured to substitute the decision variables of the lower-layer optimization model into the upper-layer optimization model to re-optimize; the upper-layer optimization model and the lower-layer optimization model are iteratively optimized until a Nash equilibrium is reached; and a scheduling plan is determined based on the decision variables finally determined by the lower-layer optimization model and the upper-layer optimization model.

[0076] In some embodiments, the objective function of the upper-layer optimization model is as follows:

[0077] wherein, is a revenue obtained by the distributed photovoltaic aggregator in the market at a price of a transaction power ; represents a contract revenue obtained by selling electricity to the energy storage after reaching an agreement with the energy storage operator; is a price scenario, and is used to represent price uncertainty; represents a probability weight of each scenario; represents an expected revenue calculated by weighting in multiple price scenarios; The constraints of the upper-layer optimization model include: a bidding upper and lower limit constraint, a contract price upper and lower limit constraint, a contract transaction power upper limit, and a distributed photovoltaic aggregator winning power constraint.

[0078] In some embodiments, the objective function of the lower-layer optimization model is as follows:

[0079] wherein, and are charging power and discharging power of the energy storage operator j in the t period, respectively; is a market price in the t period and in the scenario ; represents that the energy storage charges energy in a low-price period and discharges energy in a high-price period to arbitrage. This indicates the transaction price that energy storage needs to pay when charging from distributed photovoltaic aggregator agreements, which represents the procurement cost; The constraints of the lower-level optimization model include: energy storage charging power constraints, energy storage discharging power constraints, energy storage energy state dynamic equations, and energy storage energy state upper and lower limits.

[0080] Below, for reference Figure 3 This describes an electronic device according to embodiments of the present application. Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0081] like Figure 3 As shown, the electronic device 300 includes one or more processors 310 and memory 320.

[0082] The processor 310 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0083] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may execute the program instructions to implement the bidding method based on two-layer game-theoretic collaborative participation in the electricity market and / or other desired functions described in the various embodiments of this application above. Various contents, such as category correspondence, may also be stored in the computer-readable storage medium.

[0084] In one example, the electronic device 300 may also include an input device 330 and an output device 340, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0085] In addition, the input device 330 may also include, for example, a keyboard, mouse, interface, etc. The output device 340 can output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, speaker, printer, and communication network and its connected remote output devices, etc.

[0086] Of course, for the sake of simplicity, Figure 3Only some of the components of the electronic device related to the present application are shown, and components such as a bus, an input / output interface, and the like are omitted. In addition to this, the electronic device can include any other appropriate components according to the specific application.

[0087] In addition to the methods and devices described above, an embodiment of the present application can also be a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform steps of the bidding method for participating in the electricity market based on the double-layer game according to various embodiments of the present application described in the above "Exemplary Method" section of the specification.

[0088] The computer program product can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0089] In addition, an embodiment of the present application can also be a computer readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps of the bidding method for participating in the electricity market based on the double-layer game according to various embodiments of the present application described in the above "Exemplary Method" section of the specification.

[0090] The computer readable storage medium can be any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0091] The above description is given for illustrative and descriptive purposes. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A bidding method based on double-layer game cooperative participation in power market, characterized in that, The method comprises the following steps: constructing an upper-layer optimization model; the upper-layer optimization model is constructed based on basic information of a distributed photovoltaic aggregator, and the upper-layer optimization model takes maximization of expected benefits of the distributed photovoltaic aggregator in different scenarios as an objective function, and decision variables of the upper-layer optimization model comprise a bidding price and corresponding power of a day-ahead electricity market, and a contract price and contract power signed by an energy storage operator; constructing a lower-layer optimization model; the lower-layer optimization model is constructed based on basic information of the energy storage operator, and the lower-layer optimization model takes maximization of arbitrage benefits of the energy storage operator as an objective function, and decision variables of the lower-layer optimization model are inputted by the decision variables optimized and outputted by the upper-layer optimization model; the decision variables of the lower-layer optimization model comprise charging power and discharging power of each period, and a state-of-charge dynamic trajectory of the energy storage device; the decision variables of the lower-layer optimization model are substituted into the upper-layer optimization model to re-optimize; the upper-layer optimization model and the lower-layer optimization model are iteratively optimized until a Nash equilibrium is reached, and a scheduling plan is determined based on the decision variables finally determined by the lower-layer optimization model and the upper-layer optimization model.

2. The bidding method for participating in the electricity market based on double-layer game cooperation according to claim 1, characterized in that, The objective function of the upper-layer optimization model is as follows: wherein, Pd is the price at which the distributed PV aggregator sells the electricity in the market The amount of electricity traded The revenue obtained; represents the revenue from the agreement with the energy storage operator after selling electricity to the energy storage; is the price scenario, used to represent price uncertainty; represents the probability weight of each scenario; represents the expected revenue calculated by weighting under multiple price scenarios.

3. The bidding method for participating in the electricity market based on double-layer game cooperation according to claim 1, characterized in that, The constraints of the upper-layer optimization model comprise: a bidding upper and lower limit constraint, a contract price upper and lower limit constraint, a contract transaction power upper limit, and a distributed photovoltaic aggregator winning power constraint.

4. The bidding method for participating in the electricity market based on double-layer game cooperation according to claim 1, characterized in that, The objective function of the lower-layer optimization model is as follows: where, and are the charging and discharging power of energy storage operator j at time period t, respectively; is the market price at time period t under scenario ; represents that the energy storage charges at low price period and discharges at high price period to arbitrage; represents that the energy storage needs to pay the transaction price when charging from the distributed photovoltaic aggregator agreement, i.e.: represents the procurement cost.

5. The bidding method for participating in the electricity market based on double-layer game cooperation according to claim 1, characterized in that, The constraints of the lower-layer optimization model comprise an energy storage charging power constraint, an energy storage discharging power constraint, an energy storage energy state dynamic equation, and an energy storage energy state upper and lower limit.

6. A bidding device based on double-layer game cooperative participation in the electricity market, characterized in that, The method comprises the following steps: a first constructing module is configured to construct an upper-layer optimization model; the upper-layer optimization model is constructed based on basic information of a distributed photovoltaic aggregator, and the upper-layer optimization model takes maximization of expected benefits of the distributed photovoltaic aggregator in different scenarios as an objective function, and decision variables of the upper-layer optimization model comprise a bidding price and corresponding power of a day-ahead electricity market, and a contract price and contract power signed by an energy storage operator; a second constructing module is configured to construct a lower-layer optimization model; the lower-layer optimization model is constructed based on basic information of the energy storage operator, and the lower-layer optimization model takes maximization of arbitrage benefits of the energy storage operator as an objective function, and decision variables of the lower-layer optimization model are inputted by the decision variables optimized and outputted by the upper-layer optimization model; the decision variables of the lower-layer optimization model comprise charging power and discharging power of each period, and a state-of-charge dynamic trajectory of the energy storage device; an iterative optimization module is configured to substitute the decision variables of the lower-layer optimization model into the upper-layer optimization model to re-optimize; the upper-layer optimization model and the lower-layer optimization model are iteratively optimized until a Nash equilibrium is reached, and a scheduling plan is determined based on the decision variables finally determined by the lower-layer optimization model and the upper-layer optimization model.

7. The bidding device based on double-layer game collaborative participation in the electricity market according to claim 6, characterized in that, The objective function of the upper-layer optimization model is as follows: wherein, is the price at which the distributed PV aggregator trades in the market is the traded energy volume is the obtained revenue; is the revenue from selling energy to the energy storage operator after the agreement with the energy storage operator; is the price scenario, used to represent price uncertainty; is the probability weight of each scenario; is the expected revenue, calculated as a weighted sum over multiple price scenarios. The constraints of the upper-layer optimization model comprise: a bidding upper and lower limit constraint, a contract price upper and lower limit constraint, a contract transaction power upper limit, and a distributed photovoltaic aggregator winning power constraint. 8.The bidding device based on double-layer game cooperative participation in electricity market of claim 6, wherein, The objective function of the lower-layer optimization model is as follows: where, and are the charging and discharging power of energy storage operator j at time period t, respectively; is the market electricity price at time period t under scenario ; represents that the energy storage charges at low electricity price period and discharges at high electricity price period to arbitrage; represents that the energy storage needs to pay the transaction price when charging from the distributed photovoltaic aggregator agreement, i.e.: represents the procurement cost; The constraints of the lower-layer optimization model comprise an energy storage charging power constraint, an energy storage discharging power constraint, an energy storage energy state dynamic equation, and an energy storage energy state upper and lower limit.

9. An electronic device, comprising: The method comprises the following steps: a processor and a memory for storing programs executable by the processor; The processor is configured to implement the bidding method for participating in the electricity market based on the double-layer game by running a program in the memory.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program, when executed by the processor, causes the processor to implement the bidding method for participating in the electricity market based on the double-layer game.

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